From 5f70fc3eecce34e50359c30a7430c0fd0e3134e7 Mon Sep 17 00:00:00 2001
From: Grigorii Smirnov-Pinchukov <smirngreg@mail.ru>
Date: Wed, 15 Sep 2021 20:54:12 +0200
Subject: [PATCH 001/313] Fix uneven splitting of loglike evaluations

Fix uneven splitting of loglike evaluations while calculating the initial living points likelihood (#42).
---
 ultranest/integrator.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index d9ee38b3..2d676c48 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1337,12 +1337,8 @@ def _widen_roots(self, nroots):
         if self.log and num_live_points_missing > 0:
             self.logger.info('Sampling %d live points from prior ...', num_live_points_missing)
         if num_live_points_missing > 0:
-            if self.mpi_rank != 0:
-                num_live_points_todo = num_live_points_missing // self.mpi_size
-            else:
-                # rank 0 picks up what the others did not do
-                num_live_points_todo = num_live_points_missing - (num_live_points_missing // self.mpi_size) * (self.mpi_size - 1)
-
+            num_live_points_todo = (num_live_points_missing + self.mpi_size - 1 - self.mpi_rank) // self.mpi_size
+ 
             active_u = np.random.uniform(size=(num_live_points_todo, self.x_dim))
             active_v = self.transform(active_u)
             active_logl = self.loglike(active_v)

From 56ae8a4c0424aebe9e4d5e60d4cb13fe4514aed5 Mon Sep 17 00:00:00 2001
From: Grigorii Smirnov-Pinchukov <smirnov@mpia.de>
Date: Wed, 15 Sep 2021 23:44:27 +0200
Subject: [PATCH 002/313] Isolate todo_points_for_this_process, add test

---
 tests/test_utils.py     | 12 +++++++++++-
 ultranest/integrator.py |  6 +++---
 ultranest/utils.py      | 15 +++++++++++++++
 3 files changed, 29 insertions(+), 4 deletions(-)

diff --git a/tests/test_utils.py b/tests/test_utils.py
index 52a20017..5a1b8c26 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -2,8 +2,9 @@
 import tempfile
 import os
 from ultranest.utils import vectorize, is_affine_transform, normalised_kendall_tau_distance, make_run_dir
+from ultranest.utils import todo_points_for_this_process
 from numpy.testing import assert_allclose
-
+import pytest
 
 def test_vectorize():
 	
@@ -63,3 +64,12 @@ def test_make_log_dirs():
 			pass
 	finally:
 		shutil.rmtree(filepath)
+
+@pytest.mark.parametrize("mpi_size", [1, 4, 10, 100, 1000, 515, 14365, 13512, 86400])
+@pytest.mark.parametrize("num_live_points_missing", [0, 1, 4, 10, 100, 1000, 515, 14365, 13512, 86400])
+def test_todo_points_for_this_process(mpi_size, num_live_points_missing):
+    processes = range(mpi_size)
+    todo = [todo_points_for_this_process(rank, num_live_points_missing, mpi_size) for rank in processes]
+    assert sum(todo) == num_live_points_missing
+    assert max(todo) - min(todo) in {0, 1}
+
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 2d676c48..74298dcd 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -17,7 +17,7 @@
 import numpy as np
 
 from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify as _listify
-from .utils import is_affine_transform, normalised_kendall_tau_distance
+from .utils import is_affine_transform, normalised_kendall_tau_distance, todo_points_for_this_process
 from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
 from .store import HDF5PointStore, TextPointStore, NullPointStore
 from .viz import get_default_viz_callback, nicelogger
@@ -1337,8 +1337,8 @@ def _widen_roots(self, nroots):
         if self.log and num_live_points_missing > 0:
             self.logger.info('Sampling %d live points from prior ...', num_live_points_missing)
         if num_live_points_missing > 0:
-            num_live_points_todo = (num_live_points_missing + self.mpi_size - 1 - self.mpi_rank) // self.mpi_size
- 
+            num_live_points_todo = todo_points_for_this_process(self.mpi_rank, num_live_points_missing, self.mpi_size)
+
             active_u = np.random.uniform(size=(num_live_points_todo, self.x_dim))
             active_v = self.transform(active_u)
             active_logl = self.loglike(active_v)
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 0abbb657..4c900c35 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -441,3 +441,18 @@ def verify_gradient(ndim, transform, loglike, gradient, verbose=False, combinati
             print("expectation was L=", Lexpected, ", given", Lref, grad, eps)
         assert np.allclose(Lprime, Lexpected, atol=0.1 / ndim), \
             (u, uprime, theta, thetaprime, grad, eps * grad / L, L, Lprime, Lexpected)
+
+def todo_points_for_this_process(mpi_rank, num_live_points_missing, mpi_size):
+    """
+    Calculates number of living points to be evaluated by this process.
+
+    Parameters
+    ----------
+    mpi_rank : int
+        process id
+    num_live_points_missing : int
+        number of live points to be splitted
+    mpi_size : int
+        total number of processes
+    """
+    return (num_live_points_missing + mpi_size - 1 - mpi_rank) // mpi_size

From 0190fcce07c49a396492c53ac49e74f3f42a43c4 Mon Sep 17 00:00:00 2001
From: Grigorii Smirnov-Pinchukov <smirnov@mpia.de>
Date: Thu, 16 Sep 2021 00:38:41 +0200
Subject: [PATCH 003/313] Rename todo_points_for_this_process to
 distributed_work_chunk_size

---
 tests/test_utils.py     | 10 +++++-----
 ultranest/integrator.py |  4 ++--
 ultranest/utils.py      | 11 ++++++-----
 3 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/tests/test_utils.py b/tests/test_utils.py
index 5a1b8c26..e721ecd2 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -2,7 +2,7 @@
 import tempfile
 import os
 from ultranest.utils import vectorize, is_affine_transform, normalised_kendall_tau_distance, make_run_dir
-from ultranest.utils import todo_points_for_this_process
+from ultranest.utils import distributed_work_chunk_size
 from numpy.testing import assert_allclose
 import pytest
 
@@ -65,11 +65,11 @@ def test_make_log_dirs():
 	finally:
 		shutil.rmtree(filepath)
 
-@pytest.mark.parametrize("mpi_size", [1, 4, 10, 100, 1000, 515, 14365, 13512, 86400])
-@pytest.mark.parametrize("num_live_points_missing", [0, 1, 4, 10, 100, 1000, 515, 14365, 13512, 86400])
-def test_todo_points_for_this_process(mpi_size, num_live_points_missing):
+@pytest.mark.parametrize("mpi_size", [1, 4, 10, 37, 53, 100, 1000, 513])
+@pytest.mark.parametrize("num_live_points_missing", [0, 1, 4, 10, 17, 31, 100, 1000, 513])
+def test_distributed_work_chunk_size(mpi_size, num_live_points_missing):
     processes = range(mpi_size)
-    todo = [todo_points_for_this_process(rank, num_live_points_missing, mpi_size) for rank in processes]
+    todo = [distributed_work_chunk_size(num_live_points_missing, rank, mpi_size) for rank in processes]
     assert sum(todo) == num_live_points_missing
     assert max(todo) - min(todo) in {0, 1}
 
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 74298dcd..a8c83c64 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -17,7 +17,7 @@
 import numpy as np
 
 from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify as _listify
-from .utils import is_affine_transform, normalised_kendall_tau_distance, todo_points_for_this_process
+from .utils import is_affine_transform, normalised_kendall_tau_distance, distributed_work_chunk_size
 from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
 from .store import HDF5PointStore, TextPointStore, NullPointStore
 from .viz import get_default_viz_callback, nicelogger
@@ -1337,7 +1337,7 @@ def _widen_roots(self, nroots):
         if self.log and num_live_points_missing > 0:
             self.logger.info('Sampling %d live points from prior ...', num_live_points_missing)
         if num_live_points_missing > 0:
-            num_live_points_todo = todo_points_for_this_process(self.mpi_rank, num_live_points_missing, self.mpi_size)
+            num_live_points_todo = distributed_work_chunk_size(num_live_points_missing, self.mpi_rank, self.mpi_size)
 
             active_u = np.random.uniform(size=(num_live_points_todo, self.x_dim))
             active_v = self.transform(active_u)
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 4c900c35..84d38327 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -442,17 +442,18 @@ def verify_gradient(ndim, transform, loglike, gradient, verbose=False, combinati
         assert np.allclose(Lprime, Lexpected, atol=0.1 / ndim), \
             (u, uprime, theta, thetaprime, grad, eps * grad / L, L, Lprime, Lexpected)
 
-def todo_points_for_this_process(mpi_rank, num_live_points_missing, mpi_size):
+def distributed_work_chunk_size(num_total_tasks, mpi_rank, mpi_size):
     """
-    Calculates number of living points to be evaluated by this process.
+    Computes the number of tasks for process number `mpi_rank`, so that
+    `num_total_tasks` tasks are spread uniformly among `mpi_size` processes.
 
     Parameters
     ----------
+    num_total_tasks : int
+        total number of tasks to be split
     mpi_rank : int
         process id
-    num_live_points_missing : int
-        number of live points to be splitted
     mpi_size : int
         total number of processes
     """
-    return (num_live_points_missing + mpi_size - 1 - mpi_rank) // mpi_size
+    return (num_total_tasks + mpi_size - 1 - mpi_rank) // mpi_size

From fd1f68d7ea58987ab4a1f816bbd50aa06c81843b Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 3 Sep 2021 14:56:44 +0200
Subject: [PATCH 004/313] handle discretized priors where posterior may
 collapse into a single value

---
 ultranest/viz.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ultranest/viz.py b/ultranest/viz.py
index 67b3a3f0..78e13873 100644
--- a/ultranest/viz.py
+++ b/ultranest/viz.py
@@ -292,7 +292,8 @@ def __call__(self, points, info, region, transformLayer, region_fresh=False):
         if self.grid is None:
             self.initialize(paramnames, width)
 
-        indices = ((p - plo_rounded) * width / (phi_rounded - plo_rounded).reshape((1, -1))).astype(int)
+        with np.errstate(invalid="ignore"):
+            indices = ((p - plo_rounded) * width / (phi_rounded - plo_rounded).reshape((1, -1))).astype(int)
         indices[indices >= width] = width - 1
         indices[indices < 0] = 0
         ndim = len(plo)

From 324ffe5e66d94259322960bf390f7665a435b982 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 3 Sep 2021 13:15:23 +0200
Subject: [PATCH 005/313] give debug warnings when handling for plateaus and
 linearly dependent situations

---
 ultranest/integrator.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index a8c83c64..f8b69bbc 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2020,6 +2020,8 @@ def _should_node_be_expanded(
             return False
 
         if not live_points_healthy:
+            if self.log:
+                self.logger.debug("not expanding, because live points are linearly dependent")
             return False
 
         # some reasons to stop:
@@ -2034,6 +2036,8 @@ def _should_node_be_expanded(
 
         # in a plateau, only shrink (Fowlie+2020)
         if (Lmin == parallel_values).sum() > 1:
+            if self.log:
+                self.logger.debug("Plateau detected at L=%e, not replacing live point." % Lmin)
             return False
 
         expand_node = False

From b9f2179290b992c652b405961842b93096e9b6d1 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 27 Jun 2021 10:57:02 +0200
Subject: [PATCH 006/313] remove MLFriends radius usage from RobustEllipsoid.
 implement volume function

---
 ultranest/mlfriends.pyx | 51 +++++++++++++++++++----------------------
 1 file changed, 23 insertions(+), 28 deletions(-)

diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index d4065c3f..4db57395 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -750,7 +750,7 @@ class MLFriends(object):
 
     def estimate_volume(self):
         """Estimate the order of magnitude of the volume around a single point
-        given the current transformLayer and
+        given the current transformLayer.
 
         Does not account for:
         * the number of live points
@@ -759,7 +759,8 @@ class MLFriends(object):
 
         Returns
         -------
-        volume (float)
+        volume: float
+            Volume
         """
         r = self.maxradiussq**0.5
         N, ndim = self.u.shape
@@ -1102,13 +1103,7 @@ class RobustEllipsoidRegion(MLFriends):
         # draw from unit cube in prior space
         u = np.random.uniform(size=(nsamples, ndim))
         wmask = self.inside_ellipsoid(u)
-        # check if inside region in transformed space
-        v = self.transformLayer.transform(u[wmask,:])
-        vmask = np.logical_and(
-            v > (self.bbox_lo - self.maxradiussq).reshape((1, -1)),
-            v < (self.bbox_hi + self.maxradiussq).reshape((1, -1))
-        ).all(axis=1)
-        return u[wmask,:][vmask,:]
+        return u[wmask,:]
 
     def sample_from_transformed_boundingbox(self, nsamples=100):
         """Draw uniformly sampled points from MLFriends region.
@@ -1148,13 +1143,7 @@ class RobustEllipsoidRegion(MLFriends):
         #assert self.inside_ellipsoid(w).all()
 
         wmask = np.logical_and(w > 0, w < 1).all(axis=1)
-        v = self.transformLayer.transform(w[wmask,:])
-        vmask = np.logical_and(
-            v > (self.bbox_lo - self.maxradiussq).reshape((1, -1)),
-            v < (self.bbox_hi + self.maxradiussq).reshape((1, -1))
-        ).all(axis=1)
-
-        return w[wmask,:][vmask]
+        return w[wmask,:]
 
     def sample(self, nsamples=100):
         """Draw uniformly sampled points from MLFriends region.
@@ -1196,18 +1185,7 @@ class RobustEllipsoidRegion(MLFriends):
 
         """
         # require points to be inside bounding ellipsoid
-        mask = self.inside_ellipsoid(pts)
-        
-        if mask.any():
-            # additionally require points to be near neighbours
-            v = self.transformLayer.transform(pts[mask,:])
-            vmask = np.logical_and(
-                v > (self.bbox_lo - self.maxradiussq).reshape((1, -1)),
-                v < (self.bbox_hi + self.maxradiussq).reshape((1, -1))
-            ).all(axis=1)
-            mask[mask] = vmask
-
-        return mask
+        return self.inside_ellipsoid(pts)
 
     def compute_enlargement(self, nbootstraps=50, minvol=0., rng=np.random):
         """Return MLFriends radius and ellipsoid enlargement using bootstrapping.
@@ -1257,6 +1235,23 @@ class RobustEllipsoidRegion(MLFriends):
         assert maxf > 0, (maxf, self.u, self.unormed)
         return maxd, maxf
 
+    def estimate_volume(self):
+        """Estimate the volume of the ellipsoid.
+
+        Does not account for the intersection with the unit cube borders.
+
+        Returns
+        -------
+        logvolume: float
+            logarithm of the volume.
+        """
+        ndim = len(self.ellipsoid_cov)
+        sign, logvol = np.linalg.slogdet(self.ellipsoid_cov)
+        if sign > 0:
+            return logvol + ndim * np.log(self.enlarge)
+        else:
+            return -1e300
+
 class WrappingEllipsoid(object):
     """Ellipsoid which safely wraps points."""
 

From 14709e563e1b2d2a516ddd7cdca417663b3faa5d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 16 Sep 2021 08:17:37 +0200
Subject: [PATCH 007/313] speed-up for region sampling by avoiding a repeated
 check

---
 ultranest/integrator.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index f8b69bbc..6ad4c676 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1655,6 +1655,10 @@ def _create_point(self, Lmin, ndraw, active_u, active_values):
             loglikelihoods of current live points
 
         """
+        assert self.region.inside(active_u).any(), \
+            ("None of the live points satisfies the current region!",
+             self.region.maxradiussq, self.region.u, self.region.unormed, active_u)
+
         nit = 0
         while True:
             ib = self.ib
@@ -1681,10 +1685,6 @@ def _create_point(self, Lmin, ndraw, active_u, active_values):
                 # skip if we already know it is not useful
                 ib = 0 if np.isfinite(self.likes[0]) else 1
 
-            assert self.region.inside(active_u).any(), \
-                ("None of the live points satisfies the current region!",
-                 self.region.maxradiussq, self.region.u, self.region.unormed, active_u)
-
             use_stepsampler = self.stepsampler is not None
             while ib >= len(self.samples):
                 ib = 0

From eb211b4eeb0bb1f82bcb75e382544c34f1fa4998 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 16 Sep 2021 08:43:32 +0200
Subject: [PATCH 008/313] =?UTF-8?q?Bump=20version:=203.3.1=20=E2=86=92=203?=
 =?UTF-8?q?.3.2?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 8d530f49..41ececd1 100644
--- a/setup.py
+++ b/setup.py
@@ -61,7 +61,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.3.1',
+    version='3.3.2',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 7d9cf73a..3f639839 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.3.1'
+__version__ = '3.3.2'

From 58bd4fa104f52f32eeb0f51984586526aa4f3608 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 16 Sep 2021 10:21:31 +0200
Subject: [PATCH 009/313] keep minimum number of live points set by number of
 clusters for longer

Previously, when nlive < cluster_num_live_points * nclusters,
the iteration was stopped and the required number of live
points was set from parents of the current live points
until the current Lmin.
On the next iteration, once Lmin was reached, the number
of live points decreased in each iteration.
At some point, the criterion is hit again, and the process repeats.
This creates many iterations, and a slow-down, because each
iteration integrates from the start (although no new live point
evaluations).

This change sets the required number of live points until Lmax,
the current highest likelihood.
This avoids doing too many iterations.

A potential drawback is that Lmax is actually very high, ie.,
well past the point where the clusters cease to exist.
---
 ultranest/integrator.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 6ad4c676..4eb070bc 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2386,7 +2386,7 @@ def run_iter(
 
                         _, cluster_sizes = np.unique(self.region.transformLayer.clusterids, return_counts=True)
                         nclusters = (cluster_sizes > 1).sum()
-                        region_sequence.append((Lmin, nlive, nclusters))
+                        region_sequence.append((Lmin, nlive, nclusters, np.max(active_values)))
 
                         # next_update_interval_ncall = self.ncall + (update_interval_ncall or nlive)
                         next_update_interval_volume = main_iterator.logVolremaining + update_interval_volume_log_fraction
@@ -2554,14 +2554,14 @@ def run_iter(
 
             Lmax = main_iterator.Lmax
             if len(region_sequence) > 0:
-                Lmin, nlive, nclusters = region_sequence[-1]
+                Lmin, nlive, nclusters, Lhi = region_sequence[-1]
                 nnodes_needed = cluster_num_live_points * nclusters
                 if nlive < nnodes_needed:
-                    Llo, Lhi, target_min_num_children_new = self._expand_nodes_before(Lmin, nnodes_needed, update_interval_ncall or nlive)
+                    Llo, _, target_min_num_children_new = self._expand_nodes_before(Lmin, nnodes_needed, update_interval_ncall or nlive)
                     target_min_num_children.update(target_min_num_children_new)
                     # if self.log:
                     #     print_tree(self.root.children[::10])
-                    minimal_widths.append((Llo, Lmin, nnodes_needed))
+                    minimal_widths.append((Llo, Lhi, nnodes_needed))
                     Llo, Lhi = -np.inf, np.inf
                     continue
 

From 1f6160788e5361c70e18ffcbd5bc64ea5c4387d0 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 16 Sep 2021 10:32:24 +0200
Subject: [PATCH 010/313] new feature: terminal visualisation of posteriors

---
 ultranest/integrator.py | 31 +++++++++++++++++++++++++------
 1 file changed, 25 insertions(+), 6 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 4eb070bc..3a88fa01 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2687,7 +2687,7 @@ def store_tree(self):
             dump_tree(os.path.join(self.logs['results'], 'tree.hdf5'),
                       self.root.children, self.pointpile)
 
-    def print_results(self, logZ=True, posterior=True):
+    def print_results(self, logZ=True, posterior=True, use_unicode=True):
         """Give summary of marginal likelihood and parameters."""
         if self.log:
             print()
@@ -2704,12 +2704,31 @@ def print_results(self, logZ=True, posterior=True):
                 sigma = v.std()
                 med = v.mean()
                 if sigma == 0:
-                    i = 3
+                    j = 3
                 else:
-                    i = max(0, int(-np.floor(np.log10(sigma))) + 1)
-                fmt = '%%.%df' % i
-                fmts = '\t'.join(['    %-20s' + fmt + " +- " + fmt])
-                print(fmts % (p, med, sigma))
+                    j = max(0, int(-np.floor(np.log10(sigma))) + 1)
+                fmt = '%%.%df' % j
+                try:
+                    if not use_unicode:
+                        raise UnicodeEncodeError("")
+                    # make fancy terminal visualisation on a best-effort basis
+                    ' ▁▂▃▄▅▆▇██'.encode(sys.stdout.encoding)
+                    H, edges = np.histogram(v, bins=40)
+                    # add a bit of padding, but not outside parameter limits
+                    lo, hi = edges[0], edges[-1]
+                    step = edges[1] - lo
+                    lo = max(self.transform_limits[i,0], lo - 2 * step)
+                    hi = min(self.transform_limits[i,1], hi + 2 * step)
+                    H, edges = np.histogram(v, bins=np.linspace(lo, hi, 40))
+                    lo, hi = edges[0], edges[-1]
+                    
+                    dist = ''.join([' ▁▂▃▄▅▆▇██'[i] for i in np.ceil(H * 7 / H.max()).astype(int)])
+                    print('    %-20s: %-6s│%s│%-6s    %s +- %s' % (p, fmt % lo, dist, fmt % hi, fmt % med, fmt % sigma))
+                except:
+                    fmts = '    %-20s' + fmt + " +- " + fmt
+                    print(fmts % (p, med, sigma))
+            print()
+
 
     def plot(self):
         """Make corner, run and trace plots."""

From 6461f108e4527b36ad461c245b62d919da6429d5 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 16 Sep 2021 16:38:29 +0200
Subject: [PATCH 011/313] remove unused arguments to print_results. Document
 plot().

---
 ultranest/integrator.py | 22 ++++++++++++++++++----
 1 file changed, 18 insertions(+), 4 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 3a88fa01..4e4fc7da 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -876,7 +876,7 @@ def run(
 
         return self.results
 
-    def print_results(self, logZ=True, posterior=True):
+    def print_results(self):
         """Give summary of marginal likelihood and parameters."""
         print()
         print('logZ = %(logz).3f +- %(logzerr).3f' % self.results)
@@ -2687,8 +2687,15 @@ def store_tree(self):
             dump_tree(os.path.join(self.logs['results'], 'tree.hdf5'),
                       self.root.children, self.pointpile)
 
-    def print_results(self, logZ=True, posterior=True, use_unicode=True):
-        """Give summary of marginal likelihood and parameters."""
+    def print_results(self, use_unicode=True):
+        """Give summary of marginal likelihood and parameter posteriors.
+
+        Parameters
+        ----------
+        use_unicode: bool
+            Whether to print a unicode plot of the posterior distributions
+
+        """
         if self.log:
             print()
             print('logZ = %(logz).3f +- %(logzerr).3f' % self.results)
@@ -2731,7 +2738,14 @@ def print_results(self, logZ=True, posterior=True, use_unicode=True):
 
 
     def plot(self):
-        """Make corner, run and trace plots."""
+        """Make corner, run and trace plots.
+
+        calls:
+        
+        * plot_corner()
+        * plot_run()
+        * plot_trace()
+        """
         self.plot_corner()
         self.plot_run()
         self.plot_trace()

From 5c1093220eee6d18febc00cb6a81924b71550550 Mon Sep 17 00:00:00 2001
From: Grigorii Smirnov-Pinchukov <smirngreg@mail.ru>
Date: Fri, 17 Sep 2021 12:02:33 +0200
Subject: [PATCH 012/313] Update integrator.py

Fix crashing when number of processes is larger than number of living points #43
---
 ultranest/integrator.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 4e4fc7da..22195960 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1351,10 +1351,10 @@ def _widen_roots(self, nroots):
                 recv_samples = self.comm.bcast(recv_samples, root=0)
                 recv_samplesv = self.comm.bcast(recv_samplesv, root=0)
                 recv_likes = self.comm.bcast(recv_likes, root=0)
-
-                active_u = np.concatenate(recv_samples, axis=0)
-                active_v = np.concatenate(recv_samplesv, axis=0)
-                active_logl = np.concatenate(recv_likes, axis=0)
+                
+                active_u = np.concatenate([u for u in recv_samples if u.size > 0], axis=0)
+                active_v = np.concatenate([v for v in recv_samplesv if v.size > 0], axis=0)
+                active_logl = np.concatenate([logl for logl in recv_likes if logl.size > 0], axis=0)
 
             assert active_logl.shape == (num_live_points_missing,), (active_logl.shape, num_live_points_missing)
 

From 74d5dd4703ebbf0a78adeb147aa757a71c16dea2 Mon Sep 17 00:00:00 2001
From: Grigorii Smirnov-Pinchukov <smirnov@mpia.de>
Date: Fri, 17 Sep 2021 15:05:43 +0200
Subject: [PATCH 013/313] Stop calling functions on empty sequence in
 _widen_roots #45

---
 ultranest/integrator.py | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 22195960..619d259a 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1338,12 +1338,17 @@ def _widen_roots(self, nroots):
             self.logger.info('Sampling %d live points from prior ...', num_live_points_missing)
         if num_live_points_missing > 0:
             num_live_points_todo = distributed_work_chunk_size(num_live_points_missing, self.mpi_rank, self.mpi_size)
-
-            active_u = np.random.uniform(size=(num_live_points_todo, self.x_dim))
-            active_v = self.transform(active_u)
-            active_logl = self.loglike(active_v)
             self.ncall += num_live_points_missing
 
+            if num_live_points_todo > 0:
+                active_u = np.random.uniform(size=(num_live_points_todo, self.x_dim))
+                active_v = self.transform(active_u)
+                active_logl = self.loglike(active_v)
+            else:
+                active_u = np.empty((0, self.x_dim))
+                active_v = np.empty((0, self.num_params))
+                active_logl = np.empty((0,))
+
             if self.use_mpi:
                 recv_samples = self.comm.gather(active_u, root=0)
                 recv_samplesv = self.comm.gather(active_v, root=0)
@@ -1351,11 +1356,11 @@ def _widen_roots(self, nroots):
                 recv_samples = self.comm.bcast(recv_samples, root=0)
                 recv_samplesv = self.comm.bcast(recv_samplesv, root=0)
                 recv_likes = self.comm.bcast(recv_likes, root=0)
-                
-                active_u = np.concatenate([u for u in recv_samples if u.size > 0], axis=0)
-                active_v = np.concatenate([v for v in recv_samplesv if v.size > 0], axis=0)
-                active_logl = np.concatenate([logl for logl in recv_likes if logl.size > 0], axis=0)
 
+                active_u = np.concatenate(recv_samples, axis=0)
+                active_v = np.concatenate(recv_samplesv, axis=0)
+                active_logl = np.concatenate(recv_likes, axis=0)
+     
             assert active_logl.shape == (num_live_points_missing,), (active_logl.shape, num_live_points_missing)
 
             if self.log_to_pointstore:

From d91bd46b62f44b5d8d0169615f9fa1c3571ef67e Mon Sep 17 00:00:00 2001
From: Grigorii Smirnov-Pinchukov <smirngreg@mail.ru>
Date: Fri, 17 Sep 2021 15:32:00 +0200
Subject: [PATCH 014/313] Remove trailing whitespace

---
 ultranest/integrator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 619d259a..a42befcb 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1360,7 +1360,7 @@ def _widen_roots(self, nroots):
                 active_u = np.concatenate(recv_samples, axis=0)
                 active_v = np.concatenate(recv_samplesv, axis=0)
                 active_logl = np.concatenate(recv_likes, axis=0)
-     
+
             assert active_logl.shape == (num_live_points_missing,), (active_logl.shape, num_live_points_missing)
 
             if self.log_to_pointstore:

From 74952d1afb8ffa51108cd3641a6408fa58b2c843 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 17 Sep 2021 16:22:27 +0200
Subject: [PATCH 015/313] =?UTF-8?q?Bump=20version:=203.3.2=20=E2=86=92=203?=
 =?UTF-8?q?.3.3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 41ececd1..74a7457f 100644
--- a/setup.py
+++ b/setup.py
@@ -61,7 +61,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.3.2',
+    version='3.3.3',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 3f639839..5ec2629d 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.3.2'
+__version__ = '3.3.3'

From ffe71f16560bbab26b807abe84713e7c82bc4d9e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 17 Jan 2022 09:16:31 +0100
Subject: [PATCH 016/313] closes issue #54, allowing class methods as
 likelihoods

---
 tests/test_utils.py | 11 ++++++++++-
 ultranest/utils.py  |  2 +-
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/tests/test_utils.py b/tests/test_utils.py
index e721ecd2..ae7793d6 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -18,6 +18,16 @@ def myfunc(x):
 	assert_allclose(np.array([myfunc(a)]), myvfunc([a]))
 	b = np.array([[1.2, 2.3, 3.4], [1.2, 2.3, 3.4]])
 	assert_allclose(np.array([myfunc(b[0]), myfunc(b[1])]), myvfunc(b))
+	
+	class FuncClass(object):
+		def __call__(self, x):
+			return (x**2).sum()
+		def foo(self, x):
+			return x
+	
+	mycaller = FuncClass()
+	vectorize(mycaller)
+	vectorize(mycaller.foo)
 
 
 def test_is_affine_transform():
@@ -72,4 +82,3 @@ def test_distributed_work_chunk_size(mpi_size, num_live_points_missing):
     todo = [distributed_work_chunk_size(num_live_points_missing, rank, mpi_size) for rank in processes]
     assert sum(todo) == num_live_points_missing
     assert max(todo) - min(todo) in {0, 1}
-
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 84d38327..907d523c 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -130,7 +130,7 @@ def vectorized(args):
         """Vectorized version of function."""
         return np.asarray([function(arg) for arg in args])
 
-    vectorized.__name__ = function.__name__
+    vectorized.__name__ = getattr(function, '__name__', vectorized.__name__)
     return vectorized
 
 

From 7a1e603aaa2975b87631a49a47b97f69241984da Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 17 Jan 2022 10:08:53 +0100
Subject: [PATCH 017/313] [ci] install openblas&lapack

---
 .circleci/config.yml | 2 +-
 ultranest/utils.py   | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 1fb9d00c..6ba565fd 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -13,7 +13,7 @@ jobs:
       - checkout
       
       - run: sudo apt-get update -y
-      - run: sudo apt-get install -y python3-dev python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev libhdf5-dev
+      - run: sudo apt-get install -y python3-dev python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
       - run: sudo pip3 install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 907d523c..7e527785 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -130,6 +130,8 @@ def vectorized(args):
         """Vectorized version of function."""
         return np.asarray([function(arg) for arg in args])
 
+    # give a user-friendly name to the vectorized version of the function
+    # getattr works around methods, which do not have __name__
     vectorized.__name__ = getattr(function, '__name__', vectorized.__name__)
     return vectorized
 

From 3efac65a3c50f2e35f0909b7ff6d91a08b34da09 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 16 Mar 2022 17:00:46 +0100
Subject: [PATCH 018/313] add vectorized, cython-based step sampler

---
 setup.py                    |  18 +-
 tests/test_clustering.py    |   3 +-
 ultranest/hotstart.py       |  72 ++++++++
 ultranest/integrator.py     |  17 +-
 ultranest/mlfriends.pyx     |  89 +++++++++
 ultranest/popstepsampler.py | 315 +++++++++++++++++++++++++++++++
 ultranest/stepfuncs.pyx     | 356 ++++++++++++++++++++++++++++++++++++
 ultranest/utils.py          |  24 +++
 8 files changed, 882 insertions(+), 12 deletions(-)
 create mode 100644 ultranest/popstepsampler.py
 create mode 100644 ultranest/stepfuncs.pyx

diff --git a/setup.py b/setup.py
index 74a7457f..02f0e1fe 100644
--- a/setup.py
+++ b/setup.py
@@ -10,13 +10,19 @@
 from distutils.extension import Extension
 from Cython.Distutils import build_ext
 
-extra_include_dirs = []
+extra_include_dirs = ['.']
 try:
     import numpy
-    extra_include_dirs = [numpy.get_include()]
+    extra_include_dirs += [numpy.get_include()]
 except:
     pass
 
+ext_args = dict(
+    include_dirs=extra_include_dirs,
+    extra_compile_args=['-O3'],
+    extra_link_args=['-O3'],
+)
+
 
 with open('README.rst') as readme_file:
     readme = readme_file.read()
@@ -49,8 +55,12 @@
     ],
     description="Fit and compare complex models reliably and rapidly. Advanced Nested Sampling.",
     install_requires=requirements,
-    ext_modules = [Extension('ultranest.mlfriends', ["ultranest/mlfriends.pyx"], 
-        include_dirs=['.'] + extra_include_dirs)],
+    ext_modules = cythonize([
+        Extension('ultranest.mlfriends', ["ultranest/mlfriends.pyx"], 
+            **ext_args),
+        Extension('ultranest.stepfuncs', ["ultranest/stepfuncs.pyx"], 
+            **ext_args),
+    ]),
     license="GNU General Public License v3",
     long_description=readme + '\n\n' + history,
     include_package_data=True,
diff --git a/tests/test_clustering.py b/tests/test_clustering.py
index 8206b2f3..aa1d93df 100644
--- a/tests/test_clustering.py
+++ b/tests/test_clustering.py
@@ -4,7 +4,7 @@
 import matplotlib.pyplot as plt
 from ultranest.utils import create_logger
 from ultranest import ReactiveNestedSampler
-from ultranest.mlfriends import MLFriends
+from ultranest.mlfriends import MLFriends, AffineLayer
 
 here = os.path.dirname(__file__)
 
@@ -85,6 +85,7 @@ def __init__(self):
         self.log = True
         self.logger = create_logger("mock")
         self.region_class = MLFriends
+        self.transform_layer_class = AffineLayer
 
 
 def test_overclustering_eggbox_txt():
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index fcdd582d..c79581d2 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -167,6 +167,78 @@ def aux_loglikelihood(x):
 
     return aux_loglikelihood, aux_transform
 
+def get_extended_auxiliary_independent_problem(loglike, transform, ctr, err, df=1):
+    """Return a new loglike and transform based on an auxiliary distribution.
+
+    Given a likelihood and prior transform, and information about
+    the (expected) posterior peak, generates a auxiliary
+    likelihood and prior transform that is identical but
+    requires fewer nested sampling iterations.
+
+    This is achieved by deforming the prior space, and undoing that
+    transformation by correction weights in the likelihood.
+
+    The auxiliary distribution used for transformation/weighting is
+    a independent Student-t distribution for each parameter.
+
+    Usage::
+
+        aux_loglikelihood, aux_transform = get_auxiliary_problem(loglike, transform, ctr, invcov, enlargement_factor, df=1)
+        aux_sampler = ReactiveNestedSampler(parameters, aux_loglikelihood, transform=aux_transform, derived_param_names=['logweight'])
+        aux_results = aux_sampler.run()
+        posterior_samples = aux_results['samples'][:,-1]
+
+    Parameters
+    ------------
+    loglike: function
+        original likelihood function
+    transform: function
+        original prior transform function
+    ctr: array
+        Posterior center (in u-space).
+    err: array
+        Standard deviation around the posterior center (in u-space).
+    df: float
+        Number of degrees of freedom of the auxiliary student-t distribution.
+        The default is recommended. For truly gaussian posteriors,
+        the student-t can be made more gaussian (by df>=30) for accelation.
+
+    Returns:
+    ---------
+    aux_loglike: function
+        auxiliary loglikelihood function.
+    aux_transform: function
+        auxiliary transform function.
+        Takes d u-space coordinates, and returns d + 1 p-space parameters.
+        The first d return coordinates are identical to what ``transform`` would return.
+        The final coordinate is the log of the correction weight.
+    """
+    ndim, = np.shape(ctr)
+    assert np.shape(err) == (ndim,)
+    assert df >= 1, ('Degrees of freedom must be above 1', df)
+
+    rv_aux = scipy.stats.t(df, ctr, err)
+    # handle the case where the aux distribution extends beyond the unit cube
+    aux_lo = rv_aux.cdf(0)
+    aux_hi = rv_aux.cdf(1)
+    aux_w = aux_hi - aux_lo
+    weight_ref = rv_aux.logpdf(ctr).sum()
+
+    def aux_transform(u):
+        # get uniform gauss/t distributed values:
+        x = rv_aux.ppf(u * aux_w + aux_lo)
+        weight = -rv_aux.logpdf(x).sum() + weight_ref
+        return np.append(transform(x), weight)
+
+    def aux_loglikelihood(x):
+        x_actual = x[:-1]
+        weight = x[-1]
+        if -1e100 < weight < 1e100:
+            return loglike(x_actual) + weight - weight_ref
+        else:
+            return -1e300
+
+    return aux_loglikelihood, aux_transform
 
 def reuse_samples(
     param_names, loglike, points, logl, logw=None,
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index a42befcb..f5ed6499 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1015,6 +1015,7 @@ def __init__(self,
 
         self.sampler = 'reactive-nested'
         self.x_dim = x_dim
+        self.transform_layer_class = AffineLayer if x_dim > 1 else ScalingLayer
         self.derivedparamnames = derived_param_names
         self.num_bootstraps = int(num_bootstraps)
         num_derived = len(self.derivedparamnames)
@@ -1670,6 +1671,7 @@ def _create_point(self, Lmin, ndraw, active_u, active_values):
             if ib >= len(self.samples) and self.use_point_stack:
                 # root checks the point store
                 next_point = np.zeros((1, 3 + self.x_dim + self.num_params)) * np.nan
+                # print("1", self.mpi_rank, next_point)
 
                 if self.log_to_pointstore:
                     _, stored_point = self.pointstore.pop(Lmin)
@@ -1677,13 +1679,17 @@ def _create_point(self, Lmin, ndraw, active_u, active_values):
                         next_point[0,:] = stored_point
                     else:
                         next_point[0,:] = -np.inf
+                    # print("2", self.mpi_rank, next_point)
                     self.use_point_stack = not self.pointstore.stack_empty
 
                 if self.use_mpi:  # and informs everyone
                     self.use_point_stack = self.comm.bcast(self.use_point_stack, root=0)
+                    # print("3", self.mpi_rank, next_point)
                     next_point = self.comm.bcast(next_point, root=0)
 
                 # unpack
+                if np.ndim(next_point) != 2:
+                    print("XXXX ", self.mpi_rank, next_point, self.use_point_stack)
                 self.likes = next_point[:,1]
                 self.samples = next_point[:,3:3 + self.x_dim]
                 self.samplesv = next_point[:,3 + self.x_dim:3 + self.x_dim + self.num_params]
@@ -1792,10 +1798,7 @@ def _update_region(
         if self.region is None:
             # if self.log:
             #    self.logger.debug("building first region ...")
-            if self.x_dim > 1:
-                self.transformLayer = AffineLayer(wrapped_dims=self.wrapped_axes)
-            else:
-                self.transformLayer = ScalingLayer(wrapped_dims=self.wrapped_axes)
+            self.transformLayer = self.transform_layer_class(wrapped_dims=self.wrapped_axes)
             self.transformLayer.optimize(active_u, active_u, minvol=minvol)
             self.region = self.region_class(active_u, self.transformLayer)
             self.region_nodes = active_node_ids.copy()
@@ -2318,7 +2321,7 @@ def run_iter(
             self.ib = 0
             self.samples = []
             if self.draw_multiple:
-                ndraw = 100
+                ndraw = self.ndraw_min
             else:
                 ndraw = 40
             self.pointstore.reset()
@@ -2485,8 +2488,8 @@ def run_iter(
                                 np.inf if ncall_here == 0 else it_here * 100 / ncall_here,
                                 nlive))
                             sys.stdout.flush()
-                        self.logger.debug('iteration=%d, ncalls=%d, logz=%.2f, remainder_fraction=%.4f%%, Lmin=%.2f, Lmax=%.2f' % (
-                            it, self.ncall, main_iterator.logZ,
+                        self.logger.debug('iteration=%d, ncalls=%d, regioncalls=%d, ndraw=%d, logz=%.2f, remainder_fraction=%.4f%%, Lmin=%.2f, Lmax=%.2f' % (
+                            it, self.ncall, self.ncall_region, ndraw, main_iterator.logZ,
                             100 * main_iterator.remainder_fraction, Lmin, main_iterator.Lmax))
 
                         # if efficiency becomes low, bulk-process larger arrays
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 4db57395..651b305c 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -1252,6 +1252,95 @@ class RobustEllipsoidRegion(MLFriends):
         else:
             return -1e300
 
+class SimpleRegion(RobustEllipsoidRegion):
+    """Axis-aligned ellipsoidal region.
+
+    Defines a region around nested sampling live points for
+
+    1. checking whether a proposed point likely also fulfills the
+       likelihood constraints
+    2. proposing new points.
+
+    Learns geometry of region from existing live points.
+    """
+
+    def create_ellipsoid(self, minvol=0.0):
+        """Create wrapping ellipsoid and store its center and covariance.
+
+        Parameters
+        ----------
+        minvol: float
+            If positive, make sure ellipsoid has at least this volume.
+        """
+        assert self.enlarge is not None
+        # compute enlargement of bounding ellipsoid
+        ctr = np.mean(self.u, axis=0)
+        var = np.var(self.u, axis=0)
+        a = np.diag(1. / var)
+        cov = np.diag(var)
+
+        self.ellipsoid_center = ctr
+        self.ellipsoid_invcov = a
+        self.ellipsoid_cov = cov
+
+        l, v = np.linalg.eigh(a)
+        self.ellipsoid_axlens = 1. / np.sqrt(l)
+        self.ellipsoid_axes = np.dot(v, np.diag(self.ellipsoid_axlens))
+        self.ellipsoid_axes_T = self.ellipsoid_axes.transpose()
+
+        l2, v2 = np.linalg.eigh(cov)
+        self.ellipsoid_inv_axlens = 1. / np.sqrt(l2)
+        self.ellipsoid_inv_axes = np.dot(v2, np.diag(self.ellipsoid_inv_axlens))
+
+
+
+    def compute_enlargement(self, nbootstraps=50, minvol=0., rng=np.random):
+        """Return MLFriends radius and ellipsoid enlargement using bootstrapping.
+
+        The wrapping ellipsoid covariance is determined in each bootstrap round.
+
+        Parameters
+        ----------
+        nbootstraps: int
+            number of bootstrapping rounds
+        minvol: float
+            minimum volume to enforce to wrapping ellipsoid
+        rng:
+            random number generator
+
+        Returns
+        -------
+        max_distance: float
+            square radius of MLFriends algorithm
+        max_radius: float
+            square radius of enclosing ellipsoid.
+        """
+        N, ndim = self.u.shape
+        assert np.isfinite(self.unormed).all(), self.unormed
+        selected = np.empty(N, dtype=bool)
+        maxd = 1e300
+        maxf = 0.0
+
+        for i in range(nbootstraps):
+            idx = rng.randint(N, size=N)
+            selected[:] = False
+            selected[idx] = True
+
+            # compute enlargement of bounding ellipsoid
+            ctr = np.mean(self.u[selected,:], axis=0)
+            var = np.var(self.u[selected,:], axis=0)
+            # compute expansion factor
+            f = np.sum((self.u[~selected,:] - ctr.reshape((1, -1)) / var)**2, axis=0).max()
+            assert np.isfinite(f), (ctr, var, self.unormed, f)
+            if not f > 0:
+                raise np.linalg.LinAlgError("Distances are not positive")
+            maxf = max(maxf, f)
+
+        assert maxd > 0, (maxd, self.u, self.unormed)
+        assert maxf > 0, (maxf, self.u, self.unormed)
+        return maxd, maxf
+
+
 class WrappingEllipsoid(object):
     """Ellipsoid which safely wraps points."""
 
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
new file mode 100644
index 00000000..63b8c4be
--- /dev/null
+++ b/ultranest/popstepsampler.py
@@ -0,0 +1,315 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+import numpy as np
+from ultranest.utils import submasks
+from ultranest.stepfuncs import evolve, step_back, generate_unit_directions
+
+
+class PopulationSliceSampler():
+    def __init__(
+        self, popsize, nsteps, generate_direction, scale=1.0, 
+        scale_adapt_factor=0.9, log=False, logfile=None
+    ):
+        """
+        Vectorized slice/HARM sampler.
+
+        Revert until all previous steps have likelihoods allL above Lmin.
+        Updates currentt, generation and allL, in-place.
+
+        Parameters
+        ----------
+        popsize: int
+            number of walkers to maintain
+        nsteps: int
+            number of steps to take until the found point is accepted as independent.
+        generate_direction: function `(u, region, scale) -> v`
+            function such as `generate_unit_directions`, which
+            generates a random slice direction.
+        scale: float
+            initial guess scale for the length of the slice
+        scale_adapt_factor: float
+            smoothing factor for updating scale.
+            if near 1, scale is barely updating, if near 0,
+            the last slice length is used as a initial guess for the next.
+
+        """
+        self.nsteps = nsteps
+        self.nrejects = 0
+        self.scale = scale
+        self.scale_adapt_factor = scale_adapt_factor
+        self.allu = []
+        self.allL = []
+        self.currentt = []
+        self.currentv = []
+        self.currentp = []
+        self.generation = []
+        self.current_left = []
+        self.current_right = []
+        self.searching_left = []
+        self.searching_right = []
+        self.ringindex = 0
+
+        self.log = log
+        self.logfile = logfile
+
+        self.popsize = popsize
+        self.generate_direction = generate_direction
+
+    def region_changed(self, Ls, region):
+        """notification that the region changed. Currently not used."""
+        # self.scale = region.us.std(axis=1).mean()
+        if self.logfile:
+            self.logfile.write("region-update\t%g\t%g\n" % (self.scale, region.us.std(axis=1).mean()))
+
+    def _setup(self, ndim):
+        """Allocate arrays."""
+        self.allu = np.zeros((self.popsize, self.nsteps + 1, ndim)) + np.nan
+        self.allL = np.zeros((self.popsize, self.nsteps + 1)) + np.nan
+        self.currentt = np.zeros(self.popsize) + np.nan
+        self.currentv = np.zeros((self.popsize, ndim)) + np.nan
+        self.generation = np.zeros(self.popsize, dtype=int) - 1
+        self.current_left = np.zeros(self.popsize)
+        self.current_right = np.zeros(self.popsize)
+        self.searching_left = np.zeros(self.popsize, dtype=bool)
+        self.searching_right = np.zeros(self.popsize, dtype=bool)
+
+    def step_back(self, Lmin):
+        """see `:func:ultranest.stepfuncs.step_back` :func:ultranest.stepfuncs.step_back."""
+        step_back(Lmin, self.allL, self.generation, self.currentt)
+
+    def setup_start(self, us, Ls, starting):
+        """Initialize walker starting points.
+
+        For iteration zero, randomly selects a live point as starting point.
+
+        Parameters
+        ----------
+        us: np.array((nlive, ndim))
+            live points
+        Ls: np.array(nlive)
+            loglikelihoods live points
+        starting: np.array(nwalkers, dtype=bool)
+            which walkers to initialize.
+
+        """
+        if self.log: print("setting up:", starting)
+        nlive = len(us)
+        i = np.random.randint(nlive, size=starting.sum())
+
+        if not starting.all():
+            while starting[self.ringindex]:
+                # if the one we are waiting for is being restarted,
+                # we may as well pick the next one to wait for
+                # because every other one is started from a random point
+                # as well
+                self.shift()
+
+        self.allu[starting,0] = us[i]
+        self.allL[starting,0] = Ls[i]
+        self.generation[starting] = 0
+
+    def __str__(self):
+        s1 = ('G:' + ''.join(['%d' % g if g >= 0 else '_' for g in self.generation]))
+        s2 = ('S:' + ''.join(['S' if not np.isfinite(self.currentt[i]) else 'L' if self.searching_left[i] else 'R' if self.searching_right[i] else 'B'
+            for i in range(self.popsize)]))
+        return s1 + '  ' + s2
+
+    def setup_brackets(self, mask_starting, region):
+        """Pick starting direction and range for slice
+
+        Parameters
+        ----------
+        region: MLFriends object
+            Region
+        mask_starting: np.array(nwalkers, dtype=bool)
+            which walkers to set up.
+
+        """
+        if self.log: print("starting brackets:", mask_starting)
+        i_starting, = np.where(mask_starting)
+        self.current_left[i_starting] = -self.scale
+        self.current_right[i_starting] = self.scale
+        self.searching_left[i_starting] = True
+        self.searching_right[i_starting] = True
+        self.currentt[i_starting] = 0
+        # choose direction for new slice
+        self.currentv[i_starting,:] = self.generate_direction(
+            self.allu[i_starting, self.generation[i_starting]],
+            region)
+
+    def _setup_currentp(self, nparams):
+        if self.log: print("setting currentp")
+        self.currentp = np.zeros((self.popsize, nparams)) + np.nan
+
+    def advance(self, transform, loglike, Lmin):
+        """Advance the walker population
+
+        Parameters
+        ----------
+        transform: function
+            prior transform function
+        loglike: function
+            loglikelihood function
+        Lmin: float
+            current log-likelihood threshold
+
+        """
+        movable = self.generation < self.nsteps
+        all_movable = movable.all()
+        # print("moving ", movable.sum(), self.popsize)
+        if all_movable:
+            i = np.arange(self.popsize)
+            args = [
+                self.allu[i, self.generation],
+                self.allL[i, self.generation],
+                # pass values directly
+                self.currentt,
+                self.currentv,
+                self.current_left,
+                self.current_right,
+                self.searching_left,
+                self.searching_right
+            ]
+            del i
+        else:
+            args = [
+                self.allu[movable, self.generation[movable]],
+                self.allL[movable, self.generation[movable]],
+                # this makes copies
+                self.currentt[movable],
+                self.currentv[movable],
+                self.current_left[movable],
+                self.current_right[movable],
+                self.searching_left[movable],
+                self.searching_right[movable]
+            ]
+        if self.log: print("evolve will advance:", movable)
+
+        (
+            (
+            currentt, currentv,
+            current_left, current_right, searching_left, searching_right),
+            (success, unew, pnew, Lnew),
+            nc
+        ) = evolve(transform, loglike, Lmin, *args, log=self.log)
+
+        if self.log: print("movable", movable.shape, movable.sum(), success.shape)
+        moved = submasks(movable, success)
+        if self.log: print("evolve moved:", moved)
+        self.generation[moved] += 1
+        if len(pnew) > 0:
+            if len(self.currentp) == 0:
+                self._setup_currentp(nparams=pnew.shape[1])
+
+            if self.log: print("currentp", self.currentp[moved,:].shape, pnew.shape)
+            self.currentp[moved,:] = pnew
+
+        # update with what we learned
+        # print(currentu.shape, currentL.shape, success.shape, self.generation[movable])
+        self.allu[moved, self.generation[moved]] = unew
+        self.allL[moved, self.generation[moved]] = Lnew
+        if all_movable:
+            # in this case, the values were directly overwritten
+            pass
+        else:
+            self.currentt[movable] = currentt
+            self.currentv[movable] = currentv
+            self.current_left[movable] = current_left
+            self.current_right[movable] = current_right
+            self.searching_left[movable] = searching_left
+            self.searching_right[movable] = searching_right
+        return nc
+
+    def shift(self):
+        """Update walker from which to pick next."""
+        # this is a ring buffer
+        # shift index forward, wrapping around
+        # this is better than copying memory around when a element is removed
+        self.ringindex = (self.ringindex + 1) % self.popsize
+
+    def __next__(
+        self, region, Lmin, us, Ls, transform, loglike, ndraw=10,
+        plot=False, tregion=None, log=False
+    ):
+        """Sample a new live point.
+
+        Parameters
+        ----------
+        region: MLFriends object
+            Region
+        Lmin: float
+            current log-likelihood threshold
+        us: np.array((nlive, ndim))
+            live points
+        Ls: np.array(nlive)
+            loglikelihoods live points
+        transform: function
+            prior transform function
+        loglike: function
+            loglikelihood function
+        ndraw: int
+            not used
+        plot: bool
+            not used
+        tregion: bool
+            not used
+        log: bool
+            not used
+
+        Returns
+        -------
+        u: np.array(ndim) or None
+            new point coordinates (None if not yet available)
+        p: np.array(nparams) or None
+            new point transformed coordinates (None if not yet available)
+        L: float or None
+            new point likelihood (None if not yet available)
+        nc: int
+
+        """
+        nlive, ndim = us.shape
+        # initialize
+        if len(self.allu) == 0:
+            self._setup(ndim)
+
+        #print(str(self), "(start)")
+        self.step_back(Lmin)
+
+        starting = self.generation < 0
+        if starting.any():
+            self.setup_start(us[Ls > Lmin], Ls[Ls > Lmin], starting)
+        assert (self.generation >= 0).all(), self.generation
+
+        #if self.log: print("generation:", self.generation)
+
+        # find those where bracket is undefined:
+        mask_starting = ~np.isfinite(self.currentt)
+        if mask_starting.any():
+            self.setup_brackets(mask_starting, region)
+
+        if self.log: print(str(self), "(before)")
+        nc = self.advance(transform, loglike, Lmin)
+        if self.log: print(str(self), "(after)")
+
+        # harvest top individual if possible
+        if self.generation[self.ringindex] == self.nsteps:
+            if self.log: print("have a candidate")
+            u, p, L = self.allu[self.ringindex, self.nsteps, :].copy(), self.currentp[self.ringindex, :].copy(), self.allL[self.ringindex, self.nsteps].copy()
+            assert np.isfinite(u).all(), u
+            assert np.isfinite(p).all(), p
+            self.generation[self.ringindex] = -1
+            self.currentt[self.ringindex] = np.nan
+            self.allu[self.ringindex,:,:] = np.nan
+            self.allL[self.ringindex,:] = np.nan
+
+            # adjust guess length
+            newscale = (self.current_right[self.ringindex] - self.current_left[self.ringindex]) / 2
+            self.scale = self.scale * 0.9 + 0.1 * newscale
+
+            self.shift()
+            return u, p, L, nc
+        else:
+            return None, None, None, nc
+
+__dir__ = [PopulationSliceSampler, generate_unit_directions]
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
new file mode 100644
index 00000000..09ca4911
--- /dev/null
+++ b/ultranest/stepfuncs.pyx
@@ -0,0 +1,356 @@
+# cython: language_level=3,annotate=True,profile=True,fast_fail=True,warning_errors=True
+"""Efficient Helper functions for stepsamplers
+"""
+
+import numpy as np
+cimport numpy as np
+from numpy import pi, nan as np_nan
+cimport cython
+from cython.parallel import prange
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cdef _within_unit_cube(
+    np.float_t [:, :] u, 
+    np.uint8_t [:] acceptable, 
+):
+    cdef size_t popsize = u.shape[0]
+    cdef size_t ndim = u.shape[1]
+    cdef np.uint8_t good
+    cdef size_t i, j
+
+    for i in range(popsize):
+        for j in range(ndim):
+            if not 0.0 < u[i,j] < 1.0:
+                acceptable[i] = 0
+                break
+
+
+def within_unit_cube(u):
+    """whether all fields are between 0 and 1, for each row
+
+    Parameters
+    ----------
+    u: np.array((npoints, ndim), dtype=float):
+        points
+
+    Returns
+    ---------
+    within: np.array(npoints, dtype=bool):
+        for each point, whether it is within the unit cube
+    """
+    acceptable = np.ones(u.shape[0], dtype=bool)
+    _within_unit_cube(u, acceptable)
+    return acceptable
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cdef _evolve_prepare(
+    np.ndarray[np.uint8_t, ndim=1] searching_left, 
+    np.ndarray[np.uint8_t, ndim=1] searching_right,
+    np.ndarray[np.uint8_t, ndim=1] search_right,
+    np.ndarray[np.uint8_t, ndim=1] bisecting
+):
+    # define three mutually exclusive states: 
+    # stepping out to the left, to the right, bisecting on the slice
+    cdef size_t n = searching_left.shape[0]
+    cdef size_t i
+    for i in range(n):
+        search_right[i] = not searching_left[i] and searching_right[i]
+        bisecting[i] = not (searching_left[i] or searching_right[i])
+
+def evolve_prepare(searching_left, searching_right):
+    """Get auxiliary slice sampler state selectors.
+
+    Vectorized computation for multiple (`nwalkers`) walkers.
+
+    Parameters
+    ----------
+    searching_left: np.array(nwalkers, dtype=bool)
+        whether stepping out in the negative direction
+    searching_right: np.array(nwalkers, dtype=bool)
+        whether stepping out in the positive direction
+
+    Returns
+    -------
+    search_right: np.array(nwalkers, dtype=bool):
+        if searching right and not left
+    bisecting: np.array(nwalkers, dtype=bool):
+        if not searching right nor left any more
+    """
+    search_right = np.empty_like(searching_left)
+    bisecting = np.empty_like(searching_left)
+    _evolve_prepare(searching_left, searching_right, search_right, bisecting)
+    return search_right, bisecting
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cpdef evolve_update(
+    np.ndarray[np.uint8_t, ndim=1] acceptable, 
+    np.ndarray[np.float_t, ndim=1] Lnew, 
+    np.float_t Lmin, 
+    np.ndarray[np.uint8_t, ndim=1] search_right, 
+    np.ndarray[np.uint8_t, ndim=1] bisecting, 
+    np.float_t[:] currentt,
+    np.float_t[:] current_left,
+    np.float_t[:] current_right,
+    np.uint8_t[:] searching_left,
+    np.uint8_t[:] searching_right,
+    np.uint8_t[:] success
+):
+    """Update the state of each walker.
+
+    This uses the robust logic of slice sampling, 
+    with stepping out by doubling.
+
+    Parameters
+    ----------
+    acceptable: np.array(nwalkers, dtype=bool)
+        whether a likelihood evaluation was made. If false, rejected because out of contour.
+    Lnew: np.array(acceptable.sum(), dtype=bool)
+        likelihood value of proposed point
+    Lmin: float
+        current log-likelihood threshold
+    search_right: np.array(nwalkers, dtype=bool)
+        whether stepping out in the positive direction
+    bisecting: np.array(nwalkers, dtype=bool)
+        whether bisecting. If neither search_right nor bisecting, then 
+    currentt: np.array(nwalkers)
+        proposed coordinate on the slice
+    current_left: np.array(nwalkers)
+        current slice negative end
+    current_right: np.array(nwalkers)
+        current slice positive end
+    searching_left: np.array(nwalkers, dtype=bool)
+        whether stepping out in the negative direction
+    searching_right: np.array(nwalkers, dtype=bool)
+        whether stepping out in the positive direction
+    success: np.array(nwalkers, dtype=bool)
+        whether the walker accepts the point.
+
+    Writes to `currentt`, `current_left`, `current_right`, 
+    `searching_left`, `searching_right`, `success`.
+    """
+    cdef size_t popsize = acceptable.shape[0]
+    cdef size_t j = 0
+    cdef size_t i
+    cdef float my_nan = np_nan
+    
+    for i in range(popsize):
+        if acceptable[i]:
+            if Lnew[j] > Lmin:
+                success[i] = 1
+            j += 1
+
+    for i in prange(popsize, nogil=True):
+        # handle cases based on the result:
+        # 1) step out further, if still accepting
+        if success[i] != 0:
+            if searching_left[i]:
+                current_left[i] *= 2
+            elif search_right[i]:
+                current_right[i] *= 2
+        # 2) done stepping out, if rejected
+        else:
+            if searching_left[i]:
+                searching_left[i] = 0
+            elif search_right[i]:
+                searching_right[i] = 0
+        # bisecting, rejected or not acceptable
+        if bisecting[i]:
+            if currentt[i] < 0:
+                # bisect shrink left:
+                current_left[i] = currentt[i]
+            else:
+                current_right[i] = currentt[i]
+            # bisect accepted: start new slice and new generation there
+            if success[i] != 0:
+                currentt[i] = my_nan
+        else:
+            success[i] = 0
+
+# precompute to avoid slow allocations.
+pnew_empty = np.empty((0,1))
+Lnew_empty = np.empty(0)
+
+def evolve(
+    transform, loglike, Lmin, 
+    currentu, currentL, currentt, currentv,
+    current_left, current_right, searching_left, searching_right,
+    log=False
+):
+    """Evolve each slice sampling walker.
+
+    Parameters
+    ----------
+    transform: function
+        prior transform function
+    loglike: function
+        loglikelihood function
+    Lmin: float
+        current log-likelihood threshold
+    search_right: np.array(nwalkers, dtype=bool)
+        whether stepping out in the positive direction
+    bisecting: np.array(nwalkers, dtype=bool)
+        whether bisecting. If neither search_right nor bisecting, then 
+    currentu: np.array((nwalkers, ndim))
+        slice starting point (where currentt=0)
+    currentL: np.array(nwalkers)
+        current loglikelihood
+    currentt: np.array(nwalkers)
+        proposed coordinate on the slice
+    currentv: np.array((nwalkers, ndim))
+        slice direction vector
+    current_left: np.array(nwalkers)
+        current slice negative end
+    current_right: np.array(nwalkers)
+        current slice positive end
+    searching_left: np.array(nwalkers, dtype=bool)
+        whether stepping out in the negative direction
+    searching_right: np.array(nwalkers, dtype=bool)
+        whether stepping out in the positive direction
+
+    Returns
+    -------
+    currentt: np.array(nwalkers)
+        as above
+    currentv: np.array((nwalkers, ndim))
+        as above
+    current_left: np.array(nwalkers)
+        as above
+    current_right: np.array(nwalkers)
+        as above
+    searching_left: np.array(nwalkers, dtype=bool)
+        as above
+    searching_right: np.array(nwalkers, dtype=bool)
+        as above
+    success: np.array(nwalkers, dtype=bool)
+        whether the walker accepts the point.
+    unew: np.array((success.sum(), ndim))
+        coordinates of accepted points
+    pnew: np.array((success.sum(), nparams))
+        transformed coordinates of accepted points
+    Lnew: np.array(success.sum())
+        log-likelihoods of accepted points
+    nc: int
+        number of points for which the log-likelihood function was called.
+
+    This function writes in-place to 
+    `currentt`, `currentv`, `current_left`, `current_right`, `searching_left`, 
+    `searching_right` and `currentu`, but also returns these.
+    """
+    search_right, bisecting = evolve_prepare(searching_left, searching_right)
+
+    unew = currentu
+    unew[searching_left,:] = currentu[searching_left,:] + currentv[searching_left,:] * current_left[searching_left].reshape((-1,1))
+    unew[search_right,:] = currentu[search_right,:] + currentv[search_right,:] * current_right[search_right].reshape((-1,1))
+    currentt[bisecting] = np.random.uniform(current_left[bisecting], current_right[bisecting])
+    unew[bisecting,:] = currentu[bisecting,:] + currentv[bisecting,:] * currentt[bisecting].reshape((-1,1))
+
+    acceptable = within_unit_cube(unew)
+
+    nc = 0
+    if acceptable.any():
+        pnew = transform(unew[acceptable,:])
+        Lnew = loglike(pnew)
+        nc += len(pnew)
+    else:
+        pnew = pnew_empty
+        Lnew = Lnew_empty
+
+    success = np.zeros_like(searching_left)
+    evolve_update(
+        acceptable, Lnew, Lmin, search_right, bisecting, currentt,
+        current_left, current_right, searching_left, searching_right,
+        success
+    )
+
+    return (
+        (
+        currentt, currentv,
+        current_left, current_right, searching_left, searching_right), 
+        (success, unew[success,:], pnew[success[acceptable],:], Lnew[success[acceptable]]), 
+        nc
+    )
+
+cdef _fill_directions(
+    np.ndarray[np.float_t, ndim=2] v,
+    np.ndarray[np.int_t, ndim=1] indices,
+    float scale
+):
+    cdef size_t nsamples = v.shape[0]
+    cdef size_t ndim = v.shape[0]
+    cdef size_t i
+    for i in range(nsamples):
+        v[i, indices[i]] = scale
+
+def generate_unit_directions(ui, region, scale=1):
+    """randomly chose a axis vector.
+
+    Parameters
+    ----------
+    ui: np.array((npoints, ndim), dtype=float)
+        starting points (not used)
+    region:
+        not used
+    scale: float
+        length of returned vector
+
+    Returns
+    ---------
+    v: np.array((npoints, ndim), dtype=float)
+        Random axis vectors of length `scale`, one for each starting point.
+    """
+    nsamples, ndim = ui.shape
+    v = np.zeros((nsamples, ndim))
+    # choose axis
+    j = np.random.randint(ndim, size=nsamples)
+    _fill_directions(v, j, scale)
+    return v
+
+def step_back(Lmin, allL, generation, currentt, log=False):
+    """Revert walkers which have wandered astray.
+
+    Revert until all previous steps have likelihoods allL above Lmin.
+    Updates currentt, generation and allL, in-place.
+
+    Parameters
+    ----------
+    Lmin: float
+        current loglikelihood threshold
+    allL: np.array((nwalkers, ngenerations))
+        loglikelihoods of the chain. NaN where not evaluated yet.
+    generation: np.array(nwalkers, dtype=int)
+        how many iterations each walker has completed.
+    currentt: np.array(nwalkers)
+        current slice coordinate
+
+    """
+    # step back where step was excluded by Lmin increase
+    # delete from the back until all are good:
+    max_width = generation.max() + 1
+    below_threshold = allL[:,:max_width] < Lmin
+    problematic_parent = np.any(below_threshold, axis=1)
+    if not problematic_parent.any():
+        return
+    parent_i, = np.where(problematic_parent)
+    below_threshold_parent = below_threshold[parent_i,:]
+    # first, all of them (because we already identified them)
+    problematic = np.ones(len(parent_i), dtype=bool)
+    step = 0
+
+    while True:
+        step += 1
+        ii, = np.where(problematic)
+        i = parent_i[problematic]
+        g = generation[i]
+        generation[i] -= 1
+        currentt[i] = np_nan
+        allL[i,g] = np_nan
+        below_threshold_parent[problematic, g] = False
+        if log:
+            print("resetting %d%%" % (problematic.meancount_good_generations() * 100), 'by', step, 'steps', 'to', g)
+
+        problematic = np.any(below_threshold_parent, axis=1)
+        if not problematic.any():
+            break
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 7e527785..b57247aa 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -459,3 +459,27 @@ def distributed_work_chunk_size(num_total_tasks, mpi_rank, mpi_size):
         total number of processes
     """
     return (num_total_tasks + mpi_size - 1 - mpi_rank) // mpi_size
+
+
+def submasks(mask, *masks):
+    """
+    Get indices for an array, so that
+    array[indices] is equivalent to a[mask][mask1][mask2].
+
+    Parameters
+    ----------
+    mask : np.array(dtype=bool)
+        selection of some array
+    masks : list of np.array(dtype=bool)
+        each further mask is a subselection
+
+    Returns
+    -------
+    indices : np.array(dtype=int)
+        indices which select the subselection in the original array
+
+    """
+    indices, = np.where(mask)
+    for othermask in masks:
+        indices = indices[othermask]
+    return indices

From c31d356a93e340d3be1e12ce4b24ebefbfd60354 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 16 Mar 2022 17:34:16 +0100
Subject: [PATCH 019/313] add test for population stepsampler

---
 examples/testfeatures.py      |  7 +++++++
 tests/test_popstepsampling.py | 38 +++++++++++++++++++++++++++++++++++
 2 files changed, 45 insertions(+)
 create mode 100644 tests/test_popstepsampling.py

diff --git a/examples/testfeatures.py b/examples/testfeatures.py
index 13c9e374..319e4949 100644
--- a/examples/testfeatures.py
+++ b/examples/testfeatures.py
@@ -127,6 +127,12 @@ def transform(x):
         resume='resume' if args.resume else 'overwrite',
         wrapped_params=wrapped_params,
     )
+    if args.axis_aligned:
+        transform_layer_class = ScalingLayer
+        region_class = SimpleRegion
+    else:
+        region_class = [MLFriends, RobustEllipsoidRegion](int(args.ellipsoidal))
+    sampler.transform_layer_class = transform_layer_class
     print("MPI:", sampler.mpi_size, sampler.mpi_rank)
     for result in sampler.run_iter(
         update_interval_volume_fraction=args.update_interval_iter_fraction,
@@ -138,6 +144,7 @@ def transform(x):
         cluster_num_live_points=args.cluster_num_live_points,
         min_num_live_points=args.num_live_points,
         max_ncalls=int(args.max_ncalls),
+        region_class=[MLFriends, RobustEllipsoidRegion](int(args.ellipsoidal)),
     ):
         sampler.print_results()
         print(
diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
new file mode 100644
index 00000000..37e73838
--- /dev/null
+++ b/tests/test_popstepsampling.py
@@ -0,0 +1,38 @@
+import numpy as np
+from ultranest import ReactiveNestedSampler
+from ultranest.popstepsampler import PopulationSliceSampler, generate_unit_directions
+
+def loglike_vectorized(z):
+    a = np.array([-0.5 * sum([((xi - 0.7 + i*0.001)/0.1)**2 for i, xi in enumerate(x)]) for x in z])
+    b = np.array([-0.5 * sum([((xi - 0.3 - i*0.001)/0.1)**2 for i, xi in enumerate(x)]) for x in z])
+    return np.logaddexp(a, b)
+
+def loglike(x):
+    a = -0.5 * sum([((xi - 0.7 + i*0.001)/0.1)**2 for i, xi in enumerate(x)])
+    b = -0.5 * sum([((xi - 0.3 - i*0.001)/0.1)**2 for i, xi in enumerate(x)])
+    return np.logaddexp(a, b)
+
+def transform(x):
+    return x # * 10. - 5.
+
+paramnames = ['param%d' % i for i in range(3)]
+
+def test_stepsampler_cubeslice(plot=False):
+    np.random.seed(3)
+    nsteps = np.random.randint(10, 50)
+    popsize = np.random.randint(1, 20)
+    sampler = ReactiveNestedSampler(paramnames, loglike_vectorized, transform=transform, vectorized=True)
+
+    sampler.stepsampler = PopulationSliceSampler(
+        popsize=popsize, nsteps=nsteps, 
+        generate_direction=generate_unit_directions,
+    )
+    r = sampler.run(viz_callback=None, log_interval=50)
+    sampler.print_results()
+    a = (np.abs(r['samples'] - 0.7) < 0.1).all(axis=1)
+    b = (np.abs(r['samples'] - 0.3) < 0.1).all(axis=1)
+    assert a.sum() > 1
+    assert b.sum() > 1
+
+if __name__ == '__main__':
+    test_stepsampler_cubeslice()

From ae21e4799239d24757482a3e2113f3d677390fdf Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 23 Mar 2022 13:12:27 +0100
Subject: [PATCH 020/313] more efficient release test script, test only what is
 needed

---
 Makefile                | 10 +++++++---
 examples/runfeatures.sh | 12 ++++++++++++
 2 files changed, 19 insertions(+), 3 deletions(-)
 create mode 100644 examples/runfeatures.sh

diff --git a/Makefile b/Makefile
index f69de529..41092319 100644
--- a/Makefile
+++ b/Makefile
@@ -87,10 +87,14 @@ docs: ## generate Sphinx HTML documentation, including API docs
 servedocs: docs ## compile the docs watching for changes
 	watchmedo shell-command -p '*.rst' -c '$(MAKE) -C docs html' -R -D .
 
-release: dist ## package and upload a release
+release-test: install
 	rm -rf logs/features-*
+	# grep iterated examples/runfeatures.sh | sed 's,python3,,g' | xargs -rt --max-lines=1 mpiexec -np 3 coverage run --parallel-mode
+	# grep -v iterated examples/runfeatures.sh | sed 's,python3,,g' | xargs -rt --max-lines=1 mpiexec -np 5 coverage run --parallel-mode
 	echo testfeatures/runsettings-*-iterated.json | xargs --max-args=1 mpiexec -np 3 coverage run --parallel-mode examples/testfeatures.py
-	bash -c 'echo $$RANDOM' | xargs mpiexec -np 5 coverage run --parallel-mode examples/testfeatures.py --random --seed
+	grep -- --random examples/runfeatures.sh | xargs mpiexec -np 5 coverage run --parallel-mode examples/testfeatures.py --random --seed
+
+release: release-test ## package and upload a release
 	twine upload -s dist/*.tar.gz
 
 dist: clean ## builds source and wheel package
@@ -99,4 +103,4 @@ dist: clean ## builds source and wheel package
 	ls -l dist
 
 install: clean ## install the package to the active Python's site-packages
-	$(PYTHON) setup.py install
+	$(PYTHON) setup.py install --user
diff --git a/examples/runfeatures.sh b/examples/runfeatures.sh
new file mode 100644
index 00000000..65673b82
--- /dev/null
+++ b/examples/runfeatures.sh
@@ -0,0 +1,12 @@
+python3 examples/testfeatures.py testfeatures/runsettings-7e3ca1f36c-iterated.json
+python3 examples/testfeatures.py --random --seed=25
+python3 examples/testfeatures.py --random --seed=54
+python3 examples/testfeatures.py --random --seed=63
+python3 examples/testfeatures.py --random --seed=67
+python3 examples/testfeatures.py --random --seed=68
+python3 examples/testfeatures.py --random --seed=69
+python3 examples/testfeatures.py --random --seed=81
+python3 examples/testfeatures.py --random --seed=82
+python3 examples/testfeatures.py --random --seed=90
+python3 examples/testfeatures.py --random --seed=94
+python3 examples/testfeatures.py --random --seed=99

From 53e2c8d73c38e96797470f0d903cc39595795248 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 23 Mar 2022 13:14:16 +0100
Subject: [PATCH 021/313] test also alternative region classes for high-d
 problems

---
 examples/testfeatures.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/examples/testfeatures.py b/examples/testfeatures.py
index 319e4949..88cf5ce2 100644
--- a/examples/testfeatures.py
+++ b/examples/testfeatures.py
@@ -12,7 +12,7 @@
 
 
 def get_arg_hash(runargs):
-    return hashlib.md5(str(sorted(runargs.items())).encode()).hexdigest()[:10]
+    return hashlib.md5(str(runargs).encode()).hexdigest()[:10]
 
 
 def main(args):
@@ -120,6 +120,7 @@ def transform(x):
             return x
 
     from ultranest import ReactiveNestedSampler
+    from ultranest.mlfriends import MLFriends, RobustEllipsoidRegion, SimpleRegion, ScalingLayer
     sampler = ReactiveNestedSampler(
         paramnames, loglike,
         transform=transform if args.pass_transform else None,
@@ -127,12 +128,11 @@ def transform(x):
         resume='resume' if args.resume else 'overwrite',
         wrapped_params=wrapped_params,
     )
-    if args.axis_aligned:
-        transform_layer_class = ScalingLayer
+    if hasattr(args, 'axis_aligned') and args.axis_aligned:
+        sampler.transform_layer_class = ScalingLayer
         region_class = SimpleRegion
     else:
-        region_class = [MLFriends, RobustEllipsoidRegion](int(args.ellipsoidal))
-    sampler.transform_layer_class = transform_layer_class
+        region_class = RobustEllipsoidRegion if hasattr(args, 'ellipsoidal') and args.ellipsoidal else MLFriends
     print("MPI:", sampler.mpi_size, sampler.mpi_rank)
     for result in sampler.run_iter(
         update_interval_volume_fraction=args.update_interval_iter_fraction,
@@ -144,7 +144,7 @@ def transform(x):
         cluster_num_live_points=args.cluster_num_live_points,
         min_num_live_points=args.num_live_points,
         max_ncalls=int(args.max_ncalls),
-        region_class=[MLFriends, RobustEllipsoidRegion](int(args.ellipsoidal)),
+        region_class=region_class,
     ):
         sampler.print_results()
         print(
@@ -173,6 +173,7 @@ def transform(x):
 def run_safely(runargs):
     id = get_arg_hash(runargs)
     if os.path.exists('testfeatures/%s.done' % id):
+        print("not rerunning %s" % id)
         return
 
     print("Running %s with options:" % id, runargs)
@@ -261,6 +262,8 @@ def choose(myargs):
             min_ess = choose([0, 4000]),
             max_iters = choose([None, 10000]),
             max_ncalls = choose([10000000., 10000., 100000.]),
+            axis_aligned = choose([False, True]),
+            ellipsoidal = choose([False, True]),
         )
         if not progargs.random:
             key = i

From 94cfb73cd852fd7ae5615bab62e6646e426d09ae Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 23 Mar 2022 13:14:44 +0100
Subject: [PATCH 022/313] implement more direction proposals (not tested yet)

---
 tests/test_popstepsampling.py |   4 +-
 ultranest/popstepsampler.py   |   6 +-
 ultranest/stepfuncs.pyx       | 142 +++++++++++++++++++++++++---------
 3 files changed, 112 insertions(+), 40 deletions(-)

diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 37e73838..f2bf5dbd 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -1,6 +1,6 @@
 import numpy as np
 from ultranest import ReactiveNestedSampler
-from ultranest.popstepsampler import PopulationSliceSampler, generate_unit_directions
+from ultranest.popstepsampler import PopulationSliceSampler, generate_cube_oriented_direction
 
 def loglike_vectorized(z):
     a = np.array([-0.5 * sum([((xi - 0.7 + i*0.001)/0.1)**2 for i, xi in enumerate(x)]) for x in z])
@@ -25,7 +25,7 @@ def test_stepsampler_cubeslice(plot=False):
 
     sampler.stepsampler = PopulationSliceSampler(
         popsize=popsize, nsteps=nsteps, 
-        generate_direction=generate_unit_directions,
+        generate_direction=generate_cube_oriented_direction,
     )
     r = sampler.run(viz_callback=None, log_interval=50)
     sampler.print_results()
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 63b8c4be..a0940521 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -3,7 +3,9 @@
 
 import numpy as np
 from ultranest.utils import submasks
-from ultranest.stepfuncs import evolve, step_back, generate_unit_directions
+from ultranest.stepfuncs import evolve, step_back
+from ultranest.stepfuncs import generate_cube_oriented_direction, \
+   generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
 
 
 class PopulationSliceSampler():
@@ -311,5 +313,3 @@ def __next__(
             return u, p, L, nc
         else:
             return None, None, None, nc
-
-__dir__ = [PopulationSliceSampler, generate_unit_directions]
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 09ca4911..51c8dcc0 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -273,41 +273,6 @@ def evolve(
         nc
     )
 
-cdef _fill_directions(
-    np.ndarray[np.float_t, ndim=2] v,
-    np.ndarray[np.int_t, ndim=1] indices,
-    float scale
-):
-    cdef size_t nsamples = v.shape[0]
-    cdef size_t ndim = v.shape[0]
-    cdef size_t i
-    for i in range(nsamples):
-        v[i, indices[i]] = scale
-
-def generate_unit_directions(ui, region, scale=1):
-    """randomly chose a axis vector.
-
-    Parameters
-    ----------
-    ui: np.array((npoints, ndim), dtype=float)
-        starting points (not used)
-    region:
-        not used
-    scale: float
-        length of returned vector
-
-    Returns
-    ---------
-    v: np.array((npoints, ndim), dtype=float)
-        Random axis vectors of length `scale`, one for each starting point.
-    """
-    nsamples, ndim = ui.shape
-    v = np.zeros((nsamples, ndim))
-    # choose axis
-    j = np.random.randint(ndim, size=nsamples)
-    _fill_directions(v, j, scale)
-    return v
-
 def step_back(Lmin, allL, generation, currentt, log=False):
     """Revert walkers which have wandered astray.
 
@@ -354,3 +319,110 @@ def step_back(Lmin, allL, generation, currentt, log=False):
         problematic = np.any(below_threshold_parent, axis=1)
         if not problematic.any():
             break
+
+
+cdef _fill_directions(
+    np.ndarray[np.float_t, ndim=2] v,
+    np.ndarray[np.int_t, ndim=1] indices,
+    float scale
+):
+    cdef size_t nsamples = v.shape[0]
+    cdef size_t ndim = v.shape[0]
+    cdef size_t i
+    for i in range(nsamples):
+        v[i, indices[i]] = scale
+
+def generate_cube_oriented_direction(ui, region, scale=1):
+    """Draw a unit direction vector in direction of a random unit cube axes.
+
+    Parameters
+    ----------
+    ui: np.array((npoints, ndim), dtype=float)
+        starting points (not used)
+    region:
+        not used
+    scale: float
+        length of returned vector
+
+    Returns
+    ---------
+    v: np.array((npoints, ndim), dtype=float)
+        Random axis vectors of length `scale`, one for each starting point.
+    """
+    nsamples, ndim = ui.shape
+    v = np.zeros((nsamples, ndim))
+    # choose axis
+    j = np.random.randint(ndim, size=nsamples)
+    _fill_directions(v, j, scale)
+    return v
+
+def generate_random_direction(ui, region, scale=1):
+    """Draw uniform direction vector in unit cube space of length `scale`.
+
+    Parameters
+    -----------
+    region: MLFriends object
+        current region (not used)
+    scale: float
+        length of direction vector
+    
+    Returns
+    --------
+    v: array
+        new direction vector
+    """
+    del region
+    nsamples, ndim = ui.shape
+    v = np.random.normal(size=(nsamples, ndim))
+    v *= scale / np.linalg.norm(v, axis=1)
+    return v
+
+
+def generate_region_oriented_direction(ui, region, scale=1):
+    """Draw a random direction vector in direction of one of the `region` axes.
+
+    If given, the vector length is `scale`.
+    If not, the vector length in transformed space is `tscale`.
+
+    Parameters
+    -----------
+    region: MLFriends object
+        current region
+    scale: float
+        length of direction vector in t-space
+
+    Returns
+    --------
+    v: array
+        new direction vector (in u-space)
+    """
+    nsamples, ndim = ui.shape
+    # choose axis in transformed space:
+    j = np.random.randint(ndim, size=nsamples)
+    v = region.transformLayer.axes[j] * scale
+    return v
+
+
+def generate_region_random_direction(ui, region, scale=1):
+    """Draw a direction vector in a random direction of the region.
+
+    The vector length is `scale` (in unit cube space).
+
+    Parameters
+    -----------
+    region: MLFriends object
+        current region
+    scale: float:
+        length of direction vector (in t-space)
+    
+    Returns
+    --------
+    v: array
+        new direction vector
+    """
+    nsamples, ndim = ui.shape
+    # choose axis in transformed space:
+    v1 = np.random.normal(size=(nsamples, ndim))
+    v1 *= scale / np.linalg.norm(v1, axis=1)
+    v = np.einsum('ij,kj->ki', region.transformLayer.axes, v1)
+    return v

From cc6976398c9c1a18fe7a7cec1bde3e1e87fe00bd Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 23 Mar 2022 13:36:31 +0100
Subject: [PATCH 023/313] add test and fix bugs detected by tests

---
 tests/test_popstepsampling.py | 33 +++++++++++++++++++++++++++++++--
 ultranest/stepfuncs.pyx       |  4 ++--
 2 files changed, 33 insertions(+), 4 deletions(-)

diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index f2bf5dbd..9d656636 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -1,6 +1,7 @@
 import numpy as np
 from ultranest import ReactiveNestedSampler
-from ultranest.popstepsampler import PopulationSliceSampler, generate_cube_oriented_direction
+from ultranest.popstepsampler import PopulationSliceSampler, generate_cube_oriented_direction, \
+    generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
 
 def loglike_vectorized(z):
     a = np.array([-0.5 * sum([((xi - 0.7 + i*0.001)/0.1)**2 for i, xi in enumerate(x)]) for x in z])
@@ -34,5 +35,33 @@ def test_stepsampler_cubeslice(plot=False):
     assert a.sum() > 1
     assert b.sum() > 1
 
+from ultranest.mlfriends import update_clusters, AffineLayer, ScalingLayer, MLFriends, RobustEllipsoidRegion, SimpleRegion
+
+def test_direction_proposals():
+    proposals = [generate_cube_oriented_direction, generate_random_direction, 
+        generate_region_oriented_direction, generate_region_random_direction]
+
+    points = np.random.uniform(size=(100, 10))
+    minvol = 1.0
+
+    scale = 1. # np.random.uniform()
+    for layer in AffineLayer, ScalingLayer:
+        transformLayer = layer()
+        transformLayer.optimize(points, points)
+        for region_class in MLFriends, RobustEllipsoidRegion, SimpleRegion:
+            region = region_class(points, transformLayer)
+            r, f = region.compute_enlargement(minvol=minvol, nbootstraps=30)
+            region.maxradiussq = r
+            region.enlarge = f
+            region.create_ellipsoid(minvol=minvol)
+
+            for prop in proposals:
+                print("test of proposal:", prop, "with region:", region_class, "layer:", layer)
+                directions = prop(points, region, scale=scale)
+                norms = np.linalg.norm(directions, axis=1)
+                #print(norms[0], directions[0])
+                assert directions.shape == points.shape, (directions.shape, points.shape)
+                #assert np.allclose(norms, scale), (norms, scale)
+
 if __name__ == '__main__':
-    test_stepsampler_cubeslice()
+    test_direction_proposals()
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 51c8dcc0..82c93a73 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -374,7 +374,7 @@ def generate_random_direction(ui, region, scale=1):
     del region
     nsamples, ndim = ui.shape
     v = np.random.normal(size=(nsamples, ndim))
-    v *= scale / np.linalg.norm(v, axis=1)
+    v *= scale / np.linalg.norm(v, axis=1).reshape((nsamples, 1))
     return v
 
 
@@ -423,6 +423,6 @@ def generate_region_random_direction(ui, region, scale=1):
     nsamples, ndim = ui.shape
     # choose axis in transformed space:
     v1 = np.random.normal(size=(nsamples, ndim))
-    v1 *= scale / np.linalg.norm(v1, axis=1)
+    v1 *= scale / np.linalg.norm(v1, axis=1).reshape((nsamples, 1))
     v = np.einsum('ij,kj->ki', region.transformLayer.axes, v1)
     return v

From 0bd4f1629f04bf86d4674105af9027739b325f29 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 29 Mar 2022 01:57:40 +0200
Subject: [PATCH 024/313] when running out of constraint, revert to earliest
 known good point

MPI runs with a stepsampler can violate detailed balance and be biased.

The stepsampler can end up with some of its trajectory
out of the likelihood constraint, when it is raised.

Reverting only to the last good point retains non-reversible
trajectories.

This patch reverts to the first good point (if any), instead,
ensuring reversibility.

This patch also removes the AHARM step sampler, which was never
completed and is superseded by another branch.
---
 tests/test_stepsampling.py |  73 +----
 ultranest/stepsampler.py   | 549 +------------------------------------
 2 files changed, 11 insertions(+), 611 deletions(-)

diff --git a/tests/test_stepsampling.py b/tests/test_stepsampling.py
index 4d4c6c92..2443de22 100644
--- a/tests/test_stepsampling.py
+++ b/tests/test_stepsampling.py
@@ -1,7 +1,7 @@
 import numpy as np
 from ultranest.mlfriends import ScalingLayer, AffineLayer, MLFriends
 from ultranest import ReactiveNestedSampler
-from ultranest.stepsampler import RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler, SpeedVariableRegionSliceSampler, AHARMSampler, RegionBallSliceSampler
+from ultranest.stepsampler import RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler, SpeedVariableRegionSliceSampler, RegionBallSliceSampler
 from ultranest.stepsampler import generate_region_random_direction, ellipsoid_bracket, crop_bracket_at_unit_cube
 from ultranest.pathsampler import SamplingPathStepSampler
 from numpy.testing import assert_allclose
@@ -297,74 +297,6 @@ def test_crop_bracket(plot=False):
     assert (ucurrent + v * left >= 0).all(), (ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
     assert (ucurrent + v * right >= 0).all(), (ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
 
-def test_aharm_sampler():
-    def loglike(theta):
-        return -0.5 * (((theta - 0.5)/0.01)**2).sum(axis=1)
-    def transform(x):
-        return x
-
-    seed = 1
-    Nlive = 10
-    np.random.seed(seed)
-    us = np.random.uniform(size=(Nlive, 2))
-    Ls = loglike(us)
-    Lmin = Ls.min()
-    transformLayer = ScalingLayer()
-    transformLayer.optimize(us, us)
-    region = MLFriends(us, transformLayer)
-    region.maxradiussq, region.enlarge = region.compute_enlargement()
-    region.create_ellipsoid()
-    assert region.inside(us).all()
-    nsteps = 10
-    sampler = AHARMSampler(nsteps=nsteps, region_filter=True)
-
-    nfunccalls = 0
-    ncalls = 0
-    while True:
-        u, p, L, nc = sampler.__next__(region, Lmin, us, Ls, transform, loglike)
-        nfunccalls += 1
-        ncalls += nc
-        if u is not None:
-            break
-        if nfunccalls > 100 + nsteps:
-            assert False, ('infinite loop?', seed, nsteps, Nlive)
-    print("done in %d function calls, %d likelihood evals" % (nfunccalls, ncalls))
-    
-
-def run_aharm_sampler():
-    for seed in [733] + list(range(10)):
-        print()
-        print("SEED=%d" % seed)
-        print()
-        np.random.seed(seed)
-        nsteps = max(1, int(10**np.random.uniform(0, 3)))
-        Nlive = int(10**np.random.uniform(1.5, 3))
-        print("Nlive=%d nsteps=%d" % (Nlive, nsteps))
-        sampler = AHARMSampler(nsteps, adaptive_nsteps=False, region_filter=False)
-        us = np.random.uniform(0.6, 0.8, size=(4000, 2))
-        Ls = loglike_vectorized(us)
-        i = np.argsort(Ls)[-Nlive:]
-        us = us[i,:]
-        Ls = Ls[i]
-        Lmin = Ls.min()
-        
-        transformLayer = ScalingLayer()
-        transformLayer.optimize(us, us)
-        region = MLFriends(us, transformLayer)
-        region.maxradiussq, region.enlarge = region.compute_enlargement()
-        region.create_ellipsoid()
-        nfunccalls = 0
-        ncalls = 0
-        while True:
-            u, p, L, nc = sampler.__next__(region, Lmin, us, Ls, transform, loglike)
-            nfunccalls += 1
-            ncalls += nc
-            if u is not None:
-                break
-            if nfunccalls > 100 + nsteps:
-                assert False, ('infinite loop?', seed, nsteps, Nlive)
-        print("done in %d function calls, %d likelihood evals" % (nfunccalls, ncalls))
-
 
 if __name__ == '__main__':
     #test_stepsampler_cubemh(plot=True)
@@ -372,6 +304,5 @@ def run_aharm_sampler():
     #test_stepsampler_de(plot=False)
     #test_stepsampler_cubeslice(plot=True)
     #test_stepsampler_regionslice(plot=True)
-    run_aharm_sampler()
-    #test_ellipsoid_bracket()
+    test_ellipsoid_bracket()
     #test_crop_bracket(plot=True)
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 9d635a77..f2197a55 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -286,7 +286,6 @@ def __init__(
         self.scale = 1.0
         self.max_nsteps = max_nsteps
         self.next_scale = self.scale
-        self.last = None, None
         self.nudge = 1.1**(1. / self.nsteps)
         self.nsteps_nudge = 1.01
         self.generate_direction = generate_direction
@@ -369,7 +368,6 @@ def adjust_outside_region(self):
         assert self.scale > 0
         assert self.next_scale > 0
         # reset chain
-        self.last = None, None
         if self.adaptive_nsteps:
             self.logstat.append([-1.0, self.scale, self.nsteps, np.nan, np.nan])
         else:
@@ -393,7 +391,6 @@ def adjust_accept(self, accepted, unew, pnew, Lnew, nc):
         """
         if accepted:
             self.next_scale *= self.nudge
-            self.last = unew, Lnew
             self.history.append((unew.copy(), Lnew.copy()))
         else:
             self.next_scale /= self.nudge**10
@@ -472,7 +469,6 @@ def finalize_chain(self, region=None, Lmin=None, Ls=None):
             self.next_scale = self.scale / self.nudge**10
         # print("updating scale: %g -> %g" % (self.scale, self.next_scale))
         self.scale = self.next_scale
-        self.last = None, None
         self.history = []
         self.nrejects = 0
 
@@ -522,25 +518,15 @@ def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=10, plot=Fals
 
         """
         # find most recent point in history conforming to current Lmin
-        ui, Li = self.last
-        if Li is not None and not Li >= Lmin:
-            print("wandered out of L constraint; resetting", ui[0])
-            del ui, Li
-            ui, Li = None, None
-
-        if Li is None and self.history:
-            # try to resume from a previous point above the current contour
-            for j, (uj, Lj) in enumerate(self.history[::-1]):
-                is_inside = not self.region_filter or (region.inside(uj.reshape((1,-1))) and (tregion is None or tregion.inside(transform(uj.reshape((1, -1))))))
-                if Lj > Lmin and is_inside:
-                    del ui, Li
-                    ui, Li = uj, Lj
-                    self.last = ui, Li
-                    break
-            pass
-
-        # select starting point
-        if Li is None:
+        for j, (uj, Lj) in enumerate(self.history):
+            if not Lj > Lmin:
+                self.history = self.history[:j]
+                # print("wandered out of L constraint; reverting", ui[0])
+                break
+        if len(self.history) > 0:
+            ui, Li = self.history[-1]
+        else:
+            # select starting point
             self.new_chain(region)
             # choose a new random starting point
             # mask = region.inside(us)
@@ -548,7 +534,6 @@ def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=10, plot=Fals
             #    region.maxradiussq, region.u, region.unormed, us)
             i = np.random.randint(len(us))
             self.starti = i
-            del Li, ui
             ui = us[i,:]
             # print("starting at", ui[0])
             # assert np.logical_and(ui > 0, ui < 1).all(), ui
@@ -649,7 +634,6 @@ def new_chain(self, region=None):
         self.axis_index = 0
 
         self.history = []
-        self.last = None, None
         self.nrejects = 0
 
     def adjust_accept(self, accepted, unew, pnew, Lnew, nc):
@@ -676,7 +660,6 @@ def adjust_accept(self, accepted, unew, pnew, Lnew, nc):
                 # start with a new interval next time
                 self.interval = None
 
-                self.last = unew, Lnew
                 self.history.append((unew.copy(), Lnew.copy()))
             else:
                 self.nrejects += 1
@@ -1025,517 +1008,3 @@ def crop_bracket_at_unit_cube(ui, v, left, right, epsilon=1e-6):
 
     assert left <= 0 <= right, (left, right)
     return left, right, cropped_left, cropped_right
-
-def _prepare_steps(
-    nsteps_done, nsteps, directions, ndraw,
-    current_interval, loglike, transform, region, ndim, region_filter, 
-    Lmin, verbose,
-):
-    point_sequence = []
-    point_expectation = []
-    intervals = []
-    nsteps_prepared = 0
-    while nsteps_prepared + nsteps_done < nsteps and len(point_sequence) < ndraw:
-        if verbose:
-            print("loop:", nsteps_prepared, nsteps_done, 'of', nsteps)
-        v = directions[nsteps_done + nsteps_prepared]
-        if verbose:
-            print("direction:", v)
-        if len(point_sequence) == 0:
-            ucurrent, left, right = current_interval
-            assert (ucurrent >= 0).all(), ucurrent
-            assert (ucurrent <= 1).all(), ucurrent
-            assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), (
-                'cannot start from outside ellipsoid!', region.inside_ellipsoid(ucurrent.reshape((1, ndim))))
-            if region_filter:
-                assert region.inside(ucurrent.reshape((1, ndim))), (
-                    'cannot start from outside region!', region.inside(ucurrent.reshape((1, ndim))))
-            assert loglike(transform(ucurrent.reshape((1, ndim)))) >= Lmin, (
-                'cannot start from outside!', loglike(transform(ucurrent.reshape((1, ndim)))), Lmin)
-        else:
-            left, right = None, None
-        assert (ucurrent >= 0).all(), ucurrent
-        assert (ucurrent <= 1).all(), ucurrent
-        if verbose:
-            print("preparing step: %d from %s" % (nsteps_prepared + nsteps_done, ucurrent))
-
-        if left is None or right is None:
-            # in each, find the end points using the expanded ellipsoid
-            assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), ('current point outside ellipsoid!')
-            left, right = ellipsoid_bracket(ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.enlarge)
-            left, right, _, _ = crop_bracket_at_unit_cube(ucurrent, v, left, right)
-            assert (ucurrent + v * left <= 1).all(), (
-                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
-            assert (ucurrent + v * right <= 1).all(), (
-                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
-            assert (ucurrent + v * left >= 0).all(), (
-                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
-            assert (ucurrent + v * right >= 0).all(), (
-                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
-
-            assert left <= 0 <= right, (left, right)
-            if verbose:
-                print("   ellipsoid bracket found:", left, right)
-
-        while True:
-            # sample in each a point until presumed success:
-            assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), ('current point outside ellipsoid!')
-            t = np.random.uniform(left, right)
-            unext = ucurrent + v * t
-            assert (unext >= 0).all(), unext
-            assert (unext <= 1).all(), unext
-            assert region.inside_ellipsoid(unext.reshape((1, ndim))), ('proposal landed outside ellipsoid!', t, left, right)
-
-            # compute distance vector to center
-            d = unext - region.ellipsoid_center
-            # distance in normalised coordates: vector . matrix . vector
-            # where the matrix is the ellipsoid inverse covariance
-            r = np.einsum('j,jk,k->', d, region.ellipsoid_invcov, d)
-            if verbose:
-                print("   proposed slice point", t, r)
-
-            likely_inside = r <= 1
-            if not likely_inside and r <= region.enlarge:
-                # The exception is, when a point is between projected ellipsoid center and current point
-                # then it is also likely inside (if still inside the ellipsoid)
-
-                # project ellipsoid center onto line
-                # region.ellipsoid_center = ucurrent + tc * v
-                tc = np.dot(region.ellipsoid_center - ucurrent, v)
-                # current point is at 0 by definition
-                if 0 < t < tc or tc < t < 0:
-                    if verbose:
-                        print("   proposed point is further inside than current point")
-                    likely_inside = True
-                #    print("   proposed point %.3f is going towards center %.3f" % (t, tc))
-                # else:
-                #    print("   proposed point %.3f is going away from center %.3f" % (t, tc))
-                else:
-                    # another exception is that points very close to the current point
-                    # are very likely also inside
-                    # to find that out, project all live points on the line
-                    tall = np.einsum('ij,j->i', region.u - ucurrent, v)
-                    # find the range and identify a small part of it
-                    epsilon_nearby = 1e-6
-                    if tc < (tall.max() - tall.min()) * epsilon_nearby:
-                        likely_inside = True
-                        if verbose:
-                            print("   proposed point is very nearby")
-
-            if verbose:
-                print("   proposed point %s (%f) is likely %s (r=%f)" % (unext, t, 'inside' if likely_inside else 'outside', r))
-            intervals.append((nsteps_prepared, ucurrent, v, left, right, t))
-            point_sequence.append(unext)
-            point_expectation.append(likely_inside)
-            #   If point radius in ellipsoid is <1, presume that it will be successful
-            if likely_inside:
-                nsteps_prepared += 1
-                ucurrent = unext
-                assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), ('current point outside ellipsoid!')
-                break
-
-            #   Else, presume it will be unsuccessful, and sample another point
-            #   shrink interval
-            if t > 0:
-                right = t
-            else:
-                left = t
-
-    assert len(point_sequence) == len(point_expectation)
-    assert len(point_sequence) == len(intervals)
-    assert nsteps_prepared <= len(point_sequence)
-
-    assert len(point_sequence) > 0, (len(point_sequence), ndraw, nsteps_prepared, nsteps_done, nsteps)
-
-    if verbose:
-        print("proposed sequence:", point_sequence)
-        print("expectations:", point_expectation)
-
-    return np.array(point_sequence, dtype=float), np.array(point_expectation, dtype=bool), intervals, nsteps_prepared
-
-
-def _evaluate_with_filter(
-    region_filter, loglike, transform, Lmin, region, tregion,
-    point_sequence, point_expectation, 
-    verbose
-):
-    truncated = False
-    # region-filter, transform, tregion-filter, and evaluate the likelihood
-    if region_filter:
-        mask_inside = region.inside(point_sequence)
-        # identify first point that was expected to be inside, but was marked outside-of-region
-        i = np.where(np.logical_and(point_expectation, ~mask_inside))[0]
-        if verbose:
-            print("region filter says:", mask_inside, i)
-        if len(i) > 0:
-            imax = i[0] + 1
-            # truncate there
-            point_sequence = point_sequence[:imax]
-            point_expectation = point_expectation[:imax]
-            mask_inside = mask_inside[:imax]
-            truncated |= True
-            del imax
-        if not mask_inside.any():
-            return None
-    else:
-        mask_inside = None
-
-    t_point_sequence = transform(point_sequence)
-    if region_filter and tregion is not None:
-        tmask = tregion.inside(t_point_sequence)
-        # identify first point that was expected to be inside, but was marked outside-of-region
-        i = np.where(np.logical_and(point_expectation, ~tmask))[0]
-        if verbose:
-            print("tregion filter says:", tmask, i)
-        mask_inside[~tmask] = False
-        del tmask
-        if len(i) > 0:
-            imax = i[0] + 1
-            # truncate there
-            point_sequence = point_sequence[:imax]
-            point_expectation = point_expectation[:imax]
-            t_point_sequence = t_point_sequence[:imax]
-            mask_inside = mask_inside[:imax]
-            truncated |= True
-            del imax
-        if not mask_inside.any():
-            return None
-
-    # we expect the last point to be an accept, otherwise we would not terminate the sequence
-    assert point_expectation[-1]
-    if region_filter:
-        # set filtered ones to -np.inf
-        L = np.ones(len(t_point_sequence)) * -np.inf
-        nc = mask_inside.sum()
-        L[mask_inside] = loglike(t_point_sequence[mask_inside,:])
-    else:
-        nc = len(point_sequence)
-        L = loglike(t_point_sequence)
-    Lmask = L > Lmin
-
-    i = np.where(point_expectation != Lmask)[0]
-    if verbose:
-        print("reality:", Lmask)
-        print("difference:", point_expectation == Lmask)
-    return point_sequence, t_point_sequence, L, Lmask, i, nc, truncated
-
-class AHARMSampler(StepSampler):
-    """Accelerated hit-and-run/slice sampler, vectorised.
-
-    Uses region ellipsoid to propose a sequence of points
-    on a randomly drawn line.
-
-    (in development)
-    """
-
-    def __init__(
-        self, nsteps, adaptive_nsteps=False, max_nsteps=1000,
-        region_filter=False, log=False, direction=generate_region_random_direction,
-        orthogonalise=True,
-    ):
-        """Initialise vectorised hit-and-run/slice sampler.
-
-        Parameters
-        -----------
-        nsteps: int
-            number of accepted steps until the sample is considered independent.
-
-        adaptive_nsteps: False, 'proposal-distance', 'move-distance'
-            Select a strategy to adapt the number of steps. The strategies
-            make sure that:
-
-            * 'move-distance' (recommended): distance between
-              start point and final position exceeds the mean distance
-              between pairs of live points.
-            * 'move-distance-midway': distance between
-              start point and position in the middle of the chain
-              exceeds the mean distance between pairs of live points.
-
-        max_nsteps: int
-            Maximum number of steps the adaptive_nsteps can reach.
-
-        region_filter: bool
-            if True, use region to check if a proposed point can be inside
-            before calling likelihood.
-
-        direction: function
-            function that draws slice direction given a point and
-            the current region.
-
-        orthogonalise: bool
-            If true, make subsequent proposed directions orthogonal
-            to each other.
-
-        log: file
-            log file for sampler statistics, such as acceptance rate,
-            proposal scale, number of steps, jump distance and distance
-            between live points
-
-        """
-        self.history = []
-        self.nsteps = nsteps
-        self.nrejects = 0
-        self.max_nsteps = max_nsteps
-        self.last = None, None
-        self.generate_direction = direction
-        adaptive_nsteps_options = [
-            False,
-            'move-distance', 'move-distance-midway',
-        ]
-
-        if adaptive_nsteps not in adaptive_nsteps_options:
-            raise ValueError("adaptive_nsteps must be one of: %s, not '%s'" % (adaptive_nsteps_options, adaptive_nsteps))
-        self.adaptive_nsteps = adaptive_nsteps
-        self.region_filter = region_filter
-        self.log = log
-        self.adaptive_nsteps_needs_mean_pair_distance = False
-        self.nsteps_nudge = 1.01
-        self.orthogonalise = orthogonalise
-
-        self.logstat = []
-        self.logstat_labels = ['rejection_rate', 'steps']
-        if adaptive_nsteps:
-            self.logstat_labels += ['jump-distance', 'reference-distance']
-
-    def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=1024, plot=False, tregion=None, verbose=False):
-        """Get next point.
-
-        Parameters
-        ----------
-        region: MLFriends
-            region.
-        Lmin: float
-            loglikelihood threshold
-        us: array of vectors
-            current live points
-        Ls: array of floats
-            current live point likelihoods
-        transform: function
-            transform function
-        loglike: function
-            loglikelihood function
-        ndraw: int
-            number of draws to attempt simultaneously.
-        plot: bool
-            whether to produce debug plots.
-        tregion: WrappingEllipsoid
-            optional ellipsoid in transformed space for rejecting proposals
-
-        """
-        # find most recent point in history conforming to current Lmin
-        ui, Li = self.last
-        if Li is not None and not Li >= Lmin:
-            print("wandered out of L constraint; resetting", ui[0])
-            ui, Li = None, None
-
-        if ui is not None and not region.inside_ellipsoid(ui.reshape((1, -1))):
-            print("wandered out of ellipsoid; resetting", ui[0])
-            ui, Li = None, None
-
-        if Li is None and self.history:
-            # try to resume from a previous point above the current contour
-            for j, (uj, Lj) in enumerate(self.history[::-1]):
-                if Lj > Lmin and region.inside(uj.reshape((1,-1))) and (tregion is None or tregion.inside(transform(uj.reshape((1, -1))))):
-                    ui, Li = uj, Lj
-                    # print("recovering at point %d/%d " % (j+1, len(self.history)))
-                    self.last = ui, Li
-
-                    # pj = transform(uj.reshape((1, -1)))
-                    # Lj2 = loglike(pj)[0]
-                    # assert Lj2 > Lmin, (Lj2, Lj, uj, pj)
-                    assert region.inside_ellipsoid(ui.reshape((1, -1)))
-
-                    break
-            pass
-
-        # select starting point
-        ndim = us.shape[1]
-        if Li is None:
-            self.directions = None
-
-            self.history = []
-            self.last = None, None
-            self.nrejects = 0
-
-            # choose a new random starting point
-            i = np.random.randint(len(us))
-            self.starti = i
-            ui = us[i,:]
-            assert region.inside_ellipsoid(ui.reshape((1, -1)))
-            assert np.logical_and(ui > 0, ui < 1).all(), ui
-            Li = Ls[i]
-            self.history.append((ui.copy(), Li.copy()))
-            del i
-            print("starting at", ui)
-            # set initially nleft = nsteps
-            self.nsteps_done = 0
-
-            # generate nsteps directions
-            self.directions = []
-            for i in range(self.nsteps):
-                v = self.generate_direction(ui, region)
-                self.directions.append(v)
-            self.directions = np.array(self.directions)
-
-            if verbose:
-                print("directions:", self.directions)
-            if self.orthogonalise:
-                # orthogonalise relative to this previous direction
-                for i in range(self.nsteps // ndim):
-                    # go back only ndim steps, then start fresh
-                    self.directions[i * ndim:(i + 1) * ndim], _ = np.linalg.qr(self.directions[i * ndim:(i + 1) * ndim])
-
-            assert (ui >= 0).all(), ui
-            assert (ui <= 1).all(), ui
-            self.current_interval = ui, None, None
-            if self.region_filter:
-                assert region.inside(ui.reshape((1, ndim))), ('cannot start from outside region!', region.inside(ui.reshape((1, ndim))))
-
-        del ui
-        nc = 0
-        while True:
-            # prepare a sequence of points until nsteps are reached
-            point_sequence, point_expectation, intervals, nsteps_prepared = _prepare_steps(
-                self.nsteps_done, self.nsteps, self.directions, ndraw,
-                self.current_interval, loglike, transform, region, ndim, self.region_filter, 
-                Lmin, verbose
-            )
-            point_sequence, t_point_sequence, L, Lmask, indices_deviating, nc_here, truncated = _evaluate_with_filter(
-                self.region_filter, loglike, transform, Lmin, region, tregion,
-                point_sequence, point_expectation, 
-                verbose
-            )
-            del point_expectation
-            nc += nc_here
-
-            self.nrejects += (~Lmask).sum()
-            #print("calling likelihood with %5d prepared points, accepted:" % (
-            #    len(point_sequence)), '=' * (i[0] + Lmask[i[0]] * 1 if len(i) > 0 else len(Lmask)))
-            # identify first point that was unexpected
-            any_deviating = len(indices_deviating) > 0
-            if any_deviating and nsteps_prepared + self.nsteps_done == self.nsteps:
-                # everything according to prediction.
-                if verbose:
-                    print("everything according to prediction and done")
-                # done, return last point
-                for ui, Li in zip(point_sequence[Lmask], L[Lmask]):
-                    self.history.append((ui, Li))
-                self.finalize_chain(region=region, Lmin=Lmin, Ls=Ls)
-                return point_sequence[-1], t_point_sequence[-1], L[-1], nc
-            elif any_deviating:
-                # everything according to prediction.
-                if verbose:
-                    print("everything according to prediction")
-                # continue from last point
-                for ui, Li in zip(point_sequence[Lmask], L[Lmask]):
-                    self.history.append((ui, Li))
-                self.nsteps_done += nsteps_prepared
-                assert self.nsteps_done == len(self.history), (self.nsteps_done, len(self.history))
-                nsteps_prepared, ucurrent, v, left, right, t = intervals[-1]
-                assert (ucurrent >= 0).all(), ucurrent
-                assert (ucurrent <= 1).all(), ucurrent
-                self.current_interval = ucurrent, None, None
-                if self.region_filter:
-                    assert region.inside(ucurrent.reshape((1, ndim))), ('suggested point outside region!', region.inside(ucurrent.reshape((1, ndim))))
-            else:
-                # point i unexpectedly inside or outside
-                imax = indices_deviating[0]
-                for ui, Li in zip(point_sequence[:imax][Lmask[:imax]], L[:imax][Lmask[:imax]]):
-                    self.history.append((ui, Li))
-                nsteps_prepared, ucurrent, v, left, right, t = intervals[imax]
-                if self.region_filter:
-                    assert region.inside(ucurrent.reshape((1, ndim))), ('suggested point outside region!', region.inside(ucurrent.reshape((1, ndim))))
-                assert (ucurrent >= 0).all(), ucurrent
-                assert (ucurrent <= 1).all(), ucurrent
-                if point_expectation[imax]:
-                    if verbose:
-                        print("following prediction until %d, which was unexpectedly rejected" % imax)
-                    # expected point to lie inside, but rejected
-                    # need to repair interval
-                    self.nsteps_done += nsteps_prepared
-                    assert self.nsteps_done + 1 == len(self.history), (self.nsteps_done, len(self.history))
-                    if t > 0:
-                        right = t
-                    else:
-                        left = t
-                    if verbose:
-                        print("%d steps done, continuing from unexpected outside point" % self.nsteps_done, imax, point_sequence[imax], "interval:", t)
-                    self.current_interval = ucurrent, left, right
-                else:
-                    if verbose:
-                        print("following prediction until %d, which was unexpectedly accepted" % imax)
-                    if imax == len(point_sequence) - 1 and truncated:
-                        assert False
-                    ucurrent = point_sequence[imax]
-                    if self.region_filter:
-                        assert region.inside(ucurrent.reshape((1, ndim))), ('accepted point outside region!', region.inside(ucurrent.reshape((1, ndim))))
-                    # expected point to lie outside, but actually inside
-                    # adopt as point and continue
-                    # print(len(self.history), self.nsteps_done, nsteps_prepared, Lmask[:imax].sum())
-                    self.nsteps_done += nsteps_prepared + 1
-                    self.history.append((ucurrent.copy(), L[imax]))
-                    assert self.nsteps_done + 1 == len(self.history), (self.nsteps_done, len(self.history))
-                    self.current_interval = ucurrent, None, None
-                    if self.nsteps_done == self.nsteps:
-                        # last point was inside, so we are actually done there
-                        self.finalize_chain(region=region, Lmin=Lmin, Ls=Ls)
-                        return point_sequence[-1], t_point_sequence[-1], L[-1], nc
-                    else:
-                        if verbose:
-                            print("%d steps done, continuing from unexpected inside point" % self.nsteps_done, imax, point_sequence[imax])
-
-            # need to exit here to only do one likelihood evaluation
-            # per function call
-            if verbose:
-                print("breaking")
-            break
-
-        # do not have a independent sample yet
-        return None, None, None, nc
-
-    def region_changed(self, Ls, region):
-        assert region.inside_ellipsoid(region.u).all()
-        ui, Li = self.last
-        if ui is not None and not region.inside(ui.reshape((1, -1))):
-            print("wandered out of ellipsoid; resetting", ui[0])
-            self.last = None, None
-
-    def finalize_chain(self, region=None, Lmin=None, Ls=None):
-        """Store chain statistics and adapt proposal."""
-        self.logstat.append([self.nrejects / self.nsteps, self.nsteps])
-        if self.log:
-            ustart, Lstart = self.history[0]
-            ufinal, Lfinal = self.history[-1]
-            # mean_pair_distance = region.compute_mean_pair_distance()
-            mean_pair_distance = np.nan
-            tstart, tfinal = region.transformLayer.transform(np.vstack((ustart, ufinal)))
-            # L index of start and end
-            # Ls_sorted = np.sort(Ls)
-            iLstart = np.sum(Ls > Lstart)
-            iLfinal = np.sum(Ls > Lfinal)
-            # nearest neighbor index of start and end
-            itstart = np.argmin((region.unormed - tstart.reshape((1, -1)))**2)
-            itfinal = np.argmin((region.unormed - tfinal.reshape((1, -1)))**2)
-            np.savetxt(self.log, [_listify(
-                [Lmin], ustart, ufinal, tstart, tfinal,
-                [self.nsteps, region.maxradiussq**0.5, mean_pair_distance,
-                 iLstart, iLfinal, itstart, itfinal])])
-
-        if self.adaptive_nsteps:
-            self.adapt_nsteps(region=region)
-
-        self.last = None, None
-        self.history = []
-        self.nrejects = 0
-
-    def generate_new_interval(self, ui, region):
-        v = self.generate_direction(ui, region)
-        assert region.inside_ellipsoid(ui.reshape((1, -1)))
-        assert (ui > 0).all(), ui
-        assert (ui < 1).all(), ui
-
-        # use region ellipsoid to identify limits
-        # rotate line so that ellipsoid is a sphere
-        left, right = ellipsoid_bracket(ui, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.enlarge)
-        left, right, _, _ = crop_bracket_at_unit_cube(ui, v, left, right)
-        self.interval = (v, left, right, 0)

From b926591fc3080df64ef5d1f1b3d453e17d72f52d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 1 Apr 2022 16:22:15 +0200
Subject: [PATCH 025/313] intermediate commit implementing correlated gaussian
 problem

---
 evaluate/evaluate_sampling.py | 14 +++++-------
 evaluate/problems.py          | 40 ++++++++++++++++++++++++++++++++---
 2 files changed, 42 insertions(+), 12 deletions(-)

diff --git a/evaluate/evaluate_sampling.py b/evaluate/evaluate_sampling.py
index 5e8562db..95eac71c 100644
--- a/evaluate/evaluate_sampling.py
+++ b/evaluate/evaluate_sampling.py
@@ -3,7 +3,7 @@
 from ultranest.mlfriends import ScalingLayer, AffineLayer, MLFriends
 from ultranest.stepsampler import RegionMHSampler, CubeMHSampler
 from ultranest.stepsampler import CubeSliceSampler, RegionSliceSampler, RegionBallSliceSampler, RegionSequentialSliceSampler, SpeedVariableRegionSliceSampler
-from ultranest.stepsampler import AHARMSampler
+#from ultranest.stepsampler import AHARMSampler
 #from ultranest.stepsampler import OtherSamplerProxy, SamplingPathSliceSampler, SamplingPathStepSampler
 #from ultranest.stepsampler import GeodesicSliceSampler, RegionGeodesicSliceSampler
 import tqdm
@@ -29,11 +29,11 @@ def quantify_step(a, b):
     return [stepsize, angular_step, radial_step]
 
 #@mem.cache
-def evaluate_warmed_sampler(problemname, ndim, nlive, nsteps, sampler):
+def evaluate_warmed_sampler(problemname, ndim, nlive, nsteps, sampler, seed=1):
     loglike, grad, volume, warmup = get_problem(problemname, ndim=ndim)
     if hasattr(sampler, 'set_gradient'):
         sampler.set_gradient(grad)
-    np.random.seed(1)
+    np.random.seed(seed)
     def multi_loglike(xs):
         return np.asarray([loglike(x) for x in xs])
     us = np.array([warmup(ndim) for i in range(nlive)])
@@ -107,6 +107,7 @@ def __init__(self):
     
     def __next__(self, region, Lmin, us, Ls, transform, loglike):
         u, father = region.sample(nsamples=self.ndraw)
+        assert u.ndim > 1, (self.ndraw, region, u.shape)
         nu = u.shape[0]
         self.starti = np.random.randint(len(us))
         if nu > 0:
@@ -136,7 +137,7 @@ def main(args):
         #CubeMHSampler(nsteps=16), #CubeMHSampler(nsteps=4), CubeMHSampler(nsteps=1),
         #RegionMHSampler(nsteps=16), #RegionMHSampler(nsteps=4), RegionMHSampler(nsteps=1),
         ##DESampler(nsteps=16), DESampler(nsteps=4), #DESampler(nsteps=1),
-        #CubeSliceSampler(nsteps=2*ndim), CubeSliceSampler(nsteps=ndim), CubeSliceSampler(nsteps=max(1, ndim//2)),
+        CubeSliceSampler(nsteps=2*ndim), #CubeSliceSampler(nsteps=ndim), CubeSliceSampler(nsteps=max(1, ndim//2)),
         #RegionSliceSampler(nsteps=ndim), RegionSliceSampler(nsteps=max(1, ndim//2)),
         #RegionSliceSampler(nsteps=2), RegionSliceSampler(nsteps=4), 
         #RegionSliceSampler(nsteps=ndim), RegionSliceSampler(nsteps=4*ndim),
@@ -145,11 +146,6 @@ def main(args):
 
         #SpeedVariableRegionSliceSampler([Ellipsis]*ndim), SpeedVariableRegionSliceSampler([slice(i, ndim) for i in range(ndim)]),
         #SpeedVariableRegionSliceSampler([Ellipsis]*ndim + [slice(1 + ndim//2, None)]*ndim), 
-        
-        AHARMSampler(nsteps=64),
-        AHARMSampler(nsteps=64, adaptive_nsteps='move-distance'),
-        AHARMSampler(nsteps=64, region_filter=False),
-        AHARMSampler(nsteps=64, orthogonalise=True),
     ]
     if ndim < 14:
         samplers.insert(0, MLFriendsSampler())
diff --git a/evaluate/problems.py b/evaluate/problems.py
index 394f556f..83a9b936 100644
--- a/evaluate/problems.py
+++ b/evaluate/problems.py
@@ -67,6 +67,41 @@ def warmup_asymgauss(ndim):
     return loglike_asymgauss, gradient_asymgauss, volume_asymgauss, warmup_asymgauss
 
 
+def generate_corrgauss_problem(ndim, gamma=0.95):
+    mean = np.zeros(ndim)
+    M = np.ones((ndim, ndim)) * gamma
+    np.fill_diagonal(M, 1)
+    Minv = np.linalg.inv(M)
+    Mdet = np.linalg.det(M)
+    center = np.ones(ndim) * 0.5
+
+    loglike_asymgauss, gradient_asymgauss, volume_asymgauss, warmup_asymgauss = generate_asymgauss_problem(ndim)
+    
+    layer = AffineLayer(center, M, Minv)
+    
+    def warmup_corrgauss(ndim):
+        # the gaussian is defined in our aux coordinate system:
+        y = warmup_asymgauss(ndim)
+        # so transform to these
+        return layer.transform(y)
+    
+    def loglike_corrgauss(x):
+        """  gaussian problem """
+        # transform back to aux coordinate system, where gaussian is nice
+        y = layer.untransform(x)
+        return loglike_asymgauss(y)
+
+    def volume_corrgauss(loglike, ndim):
+        # volume is defined in aux coordinate system
+        # we hope that no intersection with unit cube happens
+        return volume_asymgauss(loglike, ndim)
+
+    def gradient_corrgauss(x):
+        y = layer.untransform(x)
+        return gradient_to_center(y)
+    
+    return loglike_corrgauss, gradient_corrgauss, volume_corrgauss, warmup_corrgauss
+
 
 def loglike_pyramid(x): 
     """ hyper-pyramid problem (squares) """
@@ -187,6 +222,8 @@ def get_problem(problemname, ndim):
         return loglike_gauss, gradient_gauss, volume_gauss, warmup_gauss
     elif problemname == 'asymgauss':
         return generate_asymgauss_problem(ndim)
+    elif problemname == 'corrgauss':
+        return generate_corrgauss_problem(ndim)
     elif problemname == 'pyramid':
         return loglike_pyramid, gradient_pyramid, volume_pyramid, warmup_pyramid
     elif problemname == 'multigauss':
@@ -195,6 +232,3 @@ def get_problem(problemname, ndim):
         return loglike_shell, gradient_shell, volume_shell, warmup_shell
     
     raise Exception("Problem '%s' unknown" % problemname)
-
-
-

From d3141f201c491c8e0435e663212264f937445190 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 1 Apr 2022 16:46:03 +0200
Subject: [PATCH 026/313] fix corrgauss shrinkage problem

---
 evaluate/evaluate_sampling.py |  3 +--
 evaluate/problems.py          | 10 ++++++----
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/evaluate/evaluate_sampling.py b/evaluate/evaluate_sampling.py
index 95eac71c..dca1a831 100644
--- a/evaluate/evaluate_sampling.py
+++ b/evaluate/evaluate_sampling.py
@@ -106,8 +106,7 @@ def __init__(self):
         self.adaptive_nsteps = False
     
     def __next__(self, region, Lmin, us, Ls, transform, loglike):
-        u, father = region.sample(nsamples=self.ndraw)
-        assert u.ndim > 1, (self.ndraw, region, u.shape)
+        u = region.sample(nsamples=self.ndraw)
         nu = u.shape[0]
         self.starti = np.random.randint(len(us))
         if nu > 0:
diff --git a/evaluate/problems.py b/evaluate/problems.py
index 83a9b936..24bb5d3d 100644
--- a/evaluate/problems.py
+++ b/evaluate/problems.py
@@ -73,22 +73,24 @@ def generate_corrgauss_problem(ndim, gamma=0.95):
     np.fill_diagonal(M, 1)
     Minv = np.linalg.inv(M)
     Mdet = np.linalg.det(M)
-    center = np.ones(ndim) * 0.5
+    center = np.zeros(ndim)
 
     loglike_asymgauss, gradient_asymgauss, volume_asymgauss, warmup_asymgauss = generate_asymgauss_problem(ndim)
     
+    from ultranest.mlfriends import AffineLayer
+    
     layer = AffineLayer(center, M, Minv)
     
     def warmup_corrgauss(ndim):
         # the gaussian is defined in our aux coordinate system:
         y = warmup_asymgauss(ndim)
         # so transform to these
-        return layer.transform(y)
+        return layer.transform(y - 0.5) + 0.5
     
     def loglike_corrgauss(x):
         """  gaussian problem """
         # transform back to aux coordinate system, where gaussian is nice
-        y = layer.untransform(x)
+        y = layer.untransform(x - 0.5) + 0.5
         return loglike_asymgauss(y)
 
     def volume_corrgauss(loglike, ndim):
@@ -97,7 +99,7 @@ def volume_corrgauss(loglike, ndim):
         return volume_asymgauss(loglike, ndim)
 
     def gradient_corrgauss(x):
-        y = layer.untransform(x)
+        y = layer.untransform(x - 0.5) + 0.5
         return gradient_to_center(y)
     
     return loglike_corrgauss, gradient_corrgauss, volume_corrgauss, warmup_corrgauss

From b1a68a34f590f5515cce7f4008bd804555febc11 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 10:41:51 +0200
Subject: [PATCH 027/313] add differential evolution and orthogonal proposals
 (WIP)

---
 evaluate/evaluate_sampling.py |  30 +++---
 evaluate/problems.py          |   2 +-
 ultranest/popstepsampler.py   |   7 +-
 ultranest/stepsampler.py      | 169 +++++++++++++++++++++++++++++++---
 4 files changed, 178 insertions(+), 30 deletions(-)

diff --git a/evaluate/evaluate_sampling.py b/evaluate/evaluate_sampling.py
index dca1a831..a193901c 100644
--- a/evaluate/evaluate_sampling.py
+++ b/evaluate/evaluate_sampling.py
@@ -1,6 +1,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from ultranest.mlfriends import ScalingLayer, AffineLayer, MLFriends
+from ultranest.mlfriends import ScalingLayer, AffineLayer, RobustEllipsoidRegion
 from ultranest.stepsampler import RegionMHSampler, CubeMHSampler
 from ultranest.stepsampler import CubeSliceSampler, RegionSliceSampler, RegionBallSliceSampler, RegionSequentialSliceSampler, SpeedVariableRegionSliceSampler
 #from ultranest.stepsampler import AHARMSampler
@@ -8,28 +8,28 @@
 #from ultranest.stepsampler import GeodesicSliceSampler, RegionGeodesicSliceSampler
 import tqdm
 import joblib
-import warnings
+import warnings, traceback
 from problems import transform, get_problem
 
-#mem = joblib.Memory('.', verbose=False)
+mem = joblib.Memory('.', verbose=False)
 
 def quantify_step(a, b):
     # euclidean step distance
-    stepsize = ((a - b)**2).sum()
+    stepsize = np.linalg.norm(a - b)
     # assuming a 
     center = 0.5
     da = a - center
     db = b - center
-    ra = ((da**2).sum())**0.5
-    rb = ((db**2).sum())**0.5
+    ra = np.linalg.norm(da)
+    rb = np.linalg.norm(db)
     # compute angle between vectors da, db
     angular_step = np.arccos(np.dot(da, db) / (ra * rb))
     # compute step in radial direction
     radial_step = np.abs(ra - rb)
     return [stepsize, angular_step, radial_step]
 
-#@mem.cache
-def evaluate_warmed_sampler(problemname, ndim, nlive, nsteps, sampler, seed=1):
+@mem.cache
+def evaluate_warmed_sampler(problemname, ndim, nlive, nsteps, sampler, seed=1, region_class=RobustEllipsoidRegion):
     loglike, grad, volume, warmup = get_problem(problemname, ndim=ndim)
     if hasattr(sampler, 'set_gradient'):
         sampler.set_gradient(grad)
@@ -38,7 +38,7 @@ def multi_loglike(xs):
         return np.asarray([loglike(x) for x in xs])
     us = np.array([warmup(ndim) for i in range(nlive)])
     Ls = np.array([loglike(u) for u in us])
-    vol0 = max((volume(Li, ndim) for Li in Ls))
+    vol0 = volume(Ls.min(), ndim)
     nwarmup = 3 * nlive
     
     if ndim > 1:
@@ -46,7 +46,7 @@ def multi_loglike(xs):
     else:
         transformLayer = ScalingLayer()
     transformLayer.optimize(us, us)
-    region = MLFriends(us, transformLayer)
+    region = region_class(us, transformLayer)
     region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
     region.create_ellipsoid(minvol=vol0)
     assert region.ellipsoid_center is not None
@@ -61,9 +61,9 @@ def multi_loglike(xs):
             with warnings.catch_warnings(), np.errstate(all='raise'):
                 try:
                     nextTransformLayer = transformLayer.create_new(us, region.maxradiussq, minvol=minvol)
-                    nextregion = MLFriends(us, nextTransformLayer)
+                    nextregion = region_class(us, nextTransformLayer)
                     nextregion.maxradiussq, nextregion.enlarge = nextregion.compute_enlargement(nbootstraps=30)
-                    if nextregion.estimate_volume() <= region.estimate_volume():
+                    if isinstance(nextregion, RobustEllipsoidRegion) or nextregion.estimate_volume() <= region.estimate_volume():
                         nextregion.create_ellipsoid(minvol=minvol)
                         region = nextregion
                         transformLayer = region.transformLayer
@@ -203,15 +203,15 @@ def main(args):
             axspeed.plot([lastspeed[1], cdf_expected.mean()], [lastspeed[2], ncalls], '-', color=color)
         lastspeed = [samplername, cdf_expected.mean(), ncalls]
         
-        stepsizesq, angular_step, radial_step = steps.transpose()
-        assert len(stepsizesq) == len(Lsequence), (len(stepsizesq), len(Lsequence))
+        stepsize, angular_step, radial_step = steps.transpose()
+        assert len(stepsize) == len(Lsequence), (len(stepsize), len(Lsequence))
         # here we estimate the volume differently: from the expected shrinkage per iteration
         it = np.arange(len(stepsizesq))
         vol = (1 - 1. / nlive)**it
         assert np.isfinite(vol).all(), vol
         assert (vol > 0).all(), vol
         assert (vol <= 1).all(), vol
-        relstepsize = stepsizesq**0.5 / vol**(1. / ndim)
+        relstepsize = stepsize / vol**(1. / ndim)
         relradial_step = radial_step / vol**(1. / ndim)
         axstep1.hist(relstepsize[np.isfinite(relstepsize)], bins=1000, cumulative=True, density=True, 
             histtype='step',
diff --git a/evaluate/problems.py b/evaluate/problems.py
index 24bb5d3d..88681002 100644
--- a/evaluate/problems.py
+++ b/evaluate/problems.py
@@ -96,7 +96,7 @@ def loglike_corrgauss(x):
     def volume_corrgauss(loglike, ndim):
         # volume is defined in aux coordinate system
         # we hope that no intersection with unit cube happens
-        return volume_asymgauss(loglike, ndim)
+        return volume_asymgauss(loglike, ndim) / Mdet
 
     def gradient_corrgauss(x):
         y = layer.untransform(x - 0.5) + 0.5
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index a0940521..78221e8b 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -58,6 +58,10 @@ def __init__(
         self.popsize = popsize
         self.generate_direction = generate_direction
 
+    def __str__(self):
+        return 'PopulationSliceSampler(popsize=%d, nsteps=%d, generate_direction=%s, scale=%.g)' % (
+                self.popsize, self.nsteps, self.generate_direction, self.scale)
+
     def region_changed(self, Ls, region):
         """notification that the region changed. Currently not used."""
         # self.scale = region.us.std(axis=1).mean()
@@ -111,7 +115,8 @@ def setup_start(self, us, Ls, starting):
         self.allL[starting,0] = Ls[i]
         self.generation[starting] = 0
 
-    def __str__(self):
+    @property
+    def status(self):
         s1 = ('G:' + ''.join(['%d' % g if g >= 0 else '_' for g in self.generation]))
         s2 = ('S:' + ''.join(['S' if not np.isfinite(self.currentt[i]) else 'L' if self.searching_left[i] else 'R' if self.searching_right[i] else 'B'
             for i in range(self.popsize)]))
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index f2197a55..aa09cb01 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -17,6 +17,8 @@ def generate_random_direction(ui, region, scale=1):
 
     Parameters
     -----------
+    ui: array
+        starting point
     region: MLFriends object
         current region (not used)
     scale: float
@@ -38,8 +40,12 @@ def generate_cube_oriented_direction(ui, region, scale=1):
 
     Parameters
     -----------
+    ui: array
+        starting point
     region: MLFriends object
         current region (not used)
+    scale: float
+        factor to multiple the vector
 
     Returns
     --------
@@ -56,18 +62,111 @@ def generate_cube_oriented_direction(ui, region, scale=1):
     return v
 
 
+def generate_cube_oriented_differential_direction(ui, region, scale=1):
+    """Draw a unit direction vector in direction of a random unit cube axes.
+
+    Guess the length from the difference of two points in that axis.
+
+    Parameters
+    -----------
+    ui: array
+        starting point
+    region: MLFriends object
+        current region
+    scale: float
+        factor to multiple the vector
+
+    Returns
+    --------
+    v: array
+        new direction vector
+    """
+    nlive, ndim = region.u.shape
+    v = np.zeros(ndim)
+
+    # choose axis
+    j = np.random.randint(ndim)
+    # choose pair
+    while v[j] == 0:
+        i = np.random.randint(nlive)
+        i2 = np.random.randint(nlive - 1)
+        if i2 >= i:
+            i2 += 1
+        
+        v[j] = (region.u[i,j] - region.u[i2,j]) * scale
+    
+    return v
+
+
+def generate_differential_direction(ui, region, scale=1):
+    """Draw a vector using the difference between two points.
+
+    Parameters
+    -----------
+    ui: array
+        starting point
+    region: MLFriends object
+        current region
+    scale: float
+        factor to multiple the vector
+
+    Returns
+    --------
+    v: array
+        new direction vector
+    """
+    nlive, ndim = region.u.shape
+    # choose pair
+    i = np.random.randint(nlive)
+    i2 = np.random.randint(nlive - 1)
+    if i2 >= i:
+        i2 += 1
+    
+    # use doubling procedure to identify left and right maxima borders
+    v = (region.u[i,:] - region.u[i2,:]) * scale
+    return v
+
+def generate_partial_differential_direction(ui, region, scale=1):
+    """Draw a unit direction vector in direction of a random unit cube axes.
+
+    Parameters
+    -----------
+    ui: array
+        starting point
+    region: MLFriends object
+        current region
+    scale: float
+        factor to multiple the vector
+
+    Returns
+    --------
+    v: array
+        new direction vector
+    """
+    nlive, ndim = region.u.shape
+    # choose pair
+    i = np.random.randint(nlive)
+    i2 = np.random.randint(nlive - 1)
+    if i2 >= i:
+        i2 += 1
+    mask = np.random.uniform(size=ndim) < 0.1
+    # use doubling procedure to identify left and right maxima borders
+    v = np.zeros(ndim)
+    v[mask] = (region.u[i,mask] - region.u[i2,mask]) * scale
+    return v
+
+
 def generate_region_oriented_direction(ui, region, scale=1):
     """Draw a random direction vector in direction of one of the `region` axes.
 
-    If given, the vector length is `scale`.
-    If not, the vector length in transformed space is `tscale`.
-
     Parameters
     -----------
+    ui: array
+        starting point
     region: MLFriends object
         current region
     scale: float
-        length of direction vector in t-space
+        factor to multiple the vector
 
     Returns
     --------
@@ -87,6 +186,8 @@ def generate_region_random_direction(ui, region, scale=1):
 
     Parameters
     -----------
+    ui: array
+        starting point
     region: MLFriends object
         current region
     scale: float:
@@ -99,13 +200,13 @@ def generate_region_random_direction(ui, region, scale=1):
     """
     # choose axis in transformed space:
     v1 = np.random.normal(0, 1, size=len(ui))
-    v1 *= scale / (v1**2).sum()**0.5
+    v1 *= scale / np.linalg.norm(v1)
     v = np.dot(region.transformLayer.axes, v1)
     return v
 
 
-def generate_mixture_random_direction(ui, region, scale=1, uniform_weight=1e-6):
-    """Draw from a mix of a ball proposal and a region-shaped proposal.
+def generate_mixture_random_direction(ui, region, scale=1):
+    """Draw either from a region-direction or a unit axis.
 
     Parameters
     -----------
@@ -121,12 +222,10 @@ def generate_mixture_random_direction(ui, region, scale=1, uniform_weight=1e-6):
     v: array
         new direction vector
     """
-    v1 = generate_random_direction(ui, region)
-    v1 /= (v1**2).sum()**0.5
-    v2 = generate_region_random_direction(ui, region)
-    v2 /= (v2**2).sum()**0.5
-    v = (v1 * uniform_weight + v2 * (1 - uniform_weight))
-    v *= scale * (v2**2).sum()**0.5
+    if np.random.uniform() < 0.5:
+        return generate_differential_direction(ui, region, scale=scale)
+    else:
+        return generate_region_oriented_direction(ui, region, scale=scale)
     return v
 
 
@@ -797,6 +896,50 @@ def RegionSequentialSliceSampler(*args, **kwargs):
     return SliceSampler(*args, **kwargs, generate_direction=SequentialDirectionGenerator())
 
 
+class OrthogonalDirectionGenerator(object):
+    def __init__(self, generate_direction):
+        """Orthogonalizes vectors.
+
+        Parameters
+        -----------
+        generate_direction: function
+            direction proposal to orthogonalize
+        """
+        self.axis_index = 0
+        self.generate_direction = generate_direction
+        self.directions = None
+
+    def __call__(self, ui, region, scale=1):
+        """Iteratively return a orthogonalized vector.
+
+        Parameters
+        -----------
+        ui: array
+            current point (in u-space)
+        region: MLFriends object
+            region to use for transformation
+        scale: float
+            length of direction vector
+
+        Returns
+        --------
+        v: array
+            new direction vector (in u-space)
+        """
+        ndim = len(ui)
+        if self.directions is None or self.axis_index >= ndim:
+            proposed_directions = np.empty((ndim, ndim))
+            for i in range(ndim):
+                proposed_directions[i] = self.generate_direction(ui, region, scale=scale)
+            q, r = np.linalg.qr(proposed_directions)
+            self.directions = np.dot(q, np.diag(np.diag(r)))
+            self.axis_index = 0
+
+        v = self.directions[self.axis_index]
+        self.axis_index += 1
+        return v
+
+
 class SpeedVariableGenerator(object):
     """Propose directions in region, but only some dimensions at a time, completely user-definable.
     """

From 8478e41c76adfa3532d671ceb43aaae9244b058b Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 11:16:55 +0200
Subject: [PATCH 028/313] support gridded/categorical parameters with
 WrappingEllipsoid

---
 tests/test_regionsampling.py | 47 ++++++++++++++++++++++++++++++++++--
 ultranest/integrator.py      |  4 +--
 ultranest/mlfriends.pyx      | 35 ++++++++++++++++++---------
 3 files changed, 71 insertions(+), 15 deletions(-)

diff --git a/tests/test_regionsampling.py b/tests/test_regionsampling.py
index 5a17ec7f..a37e09e3 100644
--- a/tests/test_regionsampling.py
+++ b/tests/test_regionsampling.py
@@ -2,6 +2,7 @@
 import os
 import matplotlib.pyplot as plt
 from ultranest.mlfriends import ScalingLayer, AffineLayer, MLFriends
+from ultranest.mlfriends import RobustEllipsoidRegion, SimpleRegion, WrappingEllipsoid
 from numpy.testing import assert_allclose
 
 here = os.path.dirname(__file__)
@@ -141,6 +142,48 @@ def test_region_mean_distances():
     assert np.isclose(meandist, d / N), (meandist, d, N)
 
 
+def test_ellipsoids():
+    tpoints = np.random.uniform(0.4, 0.6, size=(1000, 1))
+    tregion = WrappingEllipsoid(tpoints)
+    print(tregion.variable_dims)
+    tregion.enlarge = tregion.compute_enlargement(nbootstraps=30)
+    tregion.create_ellipsoid()
+    
+    for umax in 0.6, 0.5:
+        print()
+        print(umax)
+        points = np.random.uniform(0.4, 0.6, size=(1000, 3))
+        points = points[points[:,0] < umax]
+        tpoints = points * 10
+        tpoints[:,0] = np.floor(tpoints[:,0])
+        print(points, tpoints)
+
+        transformLayer = AffineLayer(wrapped_dims=[])
+        transformLayer.optimize(points, points)
+
+        region = MLFriends(points, transformLayer)
+        region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
+        region.create_ellipsoid()
+        assert region.inside(points).all()
+        
+        region = RobustEllipsoidRegion(points, transformLayer)
+        region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
+        region.create_ellipsoid()
+        assert region.inside(points).all()
+        
+        region = SimpleRegion(points, transformLayer)
+        region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
+        region.create_ellipsoid()
+        assert region.inside(points).all()
+        
+        tregion = WrappingEllipsoid(tpoints)
+        print(tregion.variable_dims)
+        tregion.enlarge = tregion.compute_enlargement(nbootstraps=30)
+        tregion.create_ellipsoid()
+        assert tregion.inside(tpoints).all()
+    
+
 if __name__ == '__main__':
-    test_region_sampling_scaling(plot=True)
-    test_region_sampling_affine(plot=True)
+    #test_region_sampling_scaling(plot=True)
+    #test_region_sampling_affine(plot=True)
+    test_ellipsoids()
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index f5ed6499..168bf97b 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1898,10 +1898,10 @@ def _update_region(
                 # verify correctness:
                 nextregion.create_ellipsoid(minvol=minvol)
 
-                # check if live points are numerically colliding or become linearly dependent
+                # check if live points are numerically colliding or linearly dependent
                 self.live_points_healthy = len(active_u) > self.x_dim and \
                     np.all(np.sum(active_u[1:] != active_u[0], axis=0) > self.x_dim) and \
-                    np.linalg.matrix_rank(nextregion.ellipsoid_cov)
+                    np.linalg.matrix_rank(nextregion.ellipsoid_cov) == self.x_dim
 
                 assert (nextregion.u == active_u).all()
                 assert np.allclose(nextregion.unormed, nextregion.transformLayer.transform(active_u))
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 651b305c..c5a87dd2 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -328,11 +328,11 @@ def make_eigvals_positive(
     except np.linalg.LinAlgError as e:
         print(a, targetprod)
         raise e
-    mask = w < 1.e-10
+    mask = w < max(1.e-10, 1e-300**(1. / len(a)))
     if np.any(mask):
         nzprod = np.product(w[~mask])  # product of nonzero eigenvalues
         nzeros = mask.sum()  # number of zero eigenvalues
-        w[mask] = (targetprod / nzprod) ** (1./nzeros)  # adjust zero eigvals
+        w[mask] = (targetprod / nzprod) ** (1. / nzeros)  # adjust zero eigvals
         a = np.dot(np.dot(v, np.diag(w)), np.linalg.inv(v))  # re-form cov
 
     return a
@@ -482,7 +482,7 @@ class ScalingLayer(object):
         self.mean = wrapped_points.mean(axis=0).reshape((1,-1))
         self.std = centered_points.std(axis=0).reshape((1,-1))
         self.axes = np.diag(self.std[0])
-        self.volscale = np.product(self.std)
+        self.logvolscale = np.sum(np.log(self.std))
         self.set_clusterids(clusterids=clusterids, npoints=len(points))
 
     def set_clusterids(self, clusterids=None, npoints=None):
@@ -599,7 +599,7 @@ class AffineLayer(ScalingLayer):
         eigvalmin = eigval.max() * 1e-40
         eigval[eigval < eigvalmin] = eigvalmin
         a = np.linalg.inv(cov)
-        self.volscale = np.linalg.det(a)**-0.5
+        self.logvolscale = np.linalg.slogdet(a)[1] * -0.5
 
         self.T = eigvec * eigval**-0.5
         self.invT = np.linalg.inv(self.T)
@@ -765,7 +765,7 @@ class MLFriends(object):
         r = self.maxradiussq**0.5
         N, ndim = self.u.shape
         # how large is a sphere of size r in untransformed coordinates?
-        return np.log(self.transformLayer.volscale) + np.log(r) * ndim #+ np.log(vol_prefactor(ndim))
+        return self.transformLayer.logvolscale + np.log(r) * ndim #+ np.log(vol_prefactor(ndim))
 
     def set_transformLayer(self, transformLayer):
         """Update transformation layer. Invalidates attribute `maxradius`.
@@ -1353,13 +1353,19 @@ class WrappingEllipsoid(object):
             live points
         """
         self.u = u
+        # allow some parameters to have exactly the same value
+        # this can occur with grid / categorical parameters
+        self.variable_dims = np.std(self.u, axis=0) > 0
+        if self.variable_dims.all():
+            self.variable_dims = Ellipsis
 
     def compute_enlargement(self, nbootstraps=50, rng=np.random):
         """Return ellipsoid enlargement after `nbootstraps` bootstrapping rounds.
 
         The wrapping ellipsoid covariance is determined in each bootstrap round.
         """
-        N, ndim = self.u.shape
+        N = len(self.u)
+        v = self.u[:,self.variable_dims]
         selected = np.empty(N, dtype=bool)
         maxf = 0.0
 
@@ -1367,8 +1373,8 @@ class WrappingEllipsoid(object):
             idx = rng.randint(N, size=N)
             selected[:] = False
             selected[idx] = True
-            ua = self.u[selected,:]
-            ub = self.u[~selected,:]
+            ua = v[selected,:]
+            ub = v[~selected,:]
 
             # compute enlargement of bounding ellipsoid
             ctr, cov = bounding_ellipsoid(ua)
@@ -1380,14 +1386,14 @@ class WrappingEllipsoid(object):
                 raise np.linalg.LinAlgError("Distances are not positive")
             maxf = max(maxf, f)
 
-        assert maxf > 0, (maxf, self.u)
+        assert maxf > 0, (maxf, self.u, self.active_dims)
         return maxf
 
     def create_ellipsoid(self, minvol=0.0):
         """Create wrapping ellipsoid and store its center and covariance."""
         assert self.enlarge is not None
         # compute enlargement of bounding ellipsoid
-        ctr, cov = bounding_ellipsoid(self.u, minvol=minvol)
+        ctr, cov = bounding_ellipsoid(self.u[:,self.variable_dims], minvol=minvol)
         a = np.linalg.inv(cov)
 
         self.ellipsoid_center = ctr
@@ -1413,4 +1419,11 @@ class WrappingEllipsoid(object):
             True if inside wrapping ellipsoid, for each point in `pts`.
 
         """
-        return _inside_ellipsoid(u, self.ellipsoid_center, self.ellipsoid_invcov, self.enlarge)
+        # check the variable subspace with the ellipsoid
+        inside_variable = _inside_ellipsoid(u[:,self.variable_dims], self.ellipsoid_center, self.ellipsoid_invcov, self.enlarge)
+        if self.variable_dims is Ellipsis:
+            return inside_variable
+        else:
+            # the remaining dims must be exactly equal
+            inside_fixed = np.all(self.u[0, ~self.variable_dims] == u[:,~self.variable_dims], axis=1)
+            return np.logical_and(inside_fixed, inside_variable)

From 35dc2aa89027f08bdebb841fede686a393c87792 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 11:16:55 +0200
Subject: [PATCH 029/313] support gridded/categorical parameters with
 WrappingEllipsoid

---
 tests/test_regionsampling.py | 47 ++++++++++++++++++++++++++++++++++--
 ultranest/integrator.py      |  4 +--
 ultranest/mlfriends.pyx      | 35 ++++++++++++++++++---------
 3 files changed, 71 insertions(+), 15 deletions(-)

diff --git a/tests/test_regionsampling.py b/tests/test_regionsampling.py
index 5a17ec7f..a37e09e3 100644
--- a/tests/test_regionsampling.py
+++ b/tests/test_regionsampling.py
@@ -2,6 +2,7 @@
 import os
 import matplotlib.pyplot as plt
 from ultranest.mlfriends import ScalingLayer, AffineLayer, MLFriends
+from ultranest.mlfriends import RobustEllipsoidRegion, SimpleRegion, WrappingEllipsoid
 from numpy.testing import assert_allclose
 
 here = os.path.dirname(__file__)
@@ -141,6 +142,48 @@ def test_region_mean_distances():
     assert np.isclose(meandist, d / N), (meandist, d, N)
 
 
+def test_ellipsoids():
+    tpoints = np.random.uniform(0.4, 0.6, size=(1000, 1))
+    tregion = WrappingEllipsoid(tpoints)
+    print(tregion.variable_dims)
+    tregion.enlarge = tregion.compute_enlargement(nbootstraps=30)
+    tregion.create_ellipsoid()
+    
+    for umax in 0.6, 0.5:
+        print()
+        print(umax)
+        points = np.random.uniform(0.4, 0.6, size=(1000, 3))
+        points = points[points[:,0] < umax]
+        tpoints = points * 10
+        tpoints[:,0] = np.floor(tpoints[:,0])
+        print(points, tpoints)
+
+        transformLayer = AffineLayer(wrapped_dims=[])
+        transformLayer.optimize(points, points)
+
+        region = MLFriends(points, transformLayer)
+        region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
+        region.create_ellipsoid()
+        assert region.inside(points).all()
+        
+        region = RobustEllipsoidRegion(points, transformLayer)
+        region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
+        region.create_ellipsoid()
+        assert region.inside(points).all()
+        
+        region = SimpleRegion(points, transformLayer)
+        region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
+        region.create_ellipsoid()
+        assert region.inside(points).all()
+        
+        tregion = WrappingEllipsoid(tpoints)
+        print(tregion.variable_dims)
+        tregion.enlarge = tregion.compute_enlargement(nbootstraps=30)
+        tregion.create_ellipsoid()
+        assert tregion.inside(tpoints).all()
+    
+
 if __name__ == '__main__':
-    test_region_sampling_scaling(plot=True)
-    test_region_sampling_affine(plot=True)
+    #test_region_sampling_scaling(plot=True)
+    #test_region_sampling_affine(plot=True)
+    test_ellipsoids()
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index f5ed6499..168bf97b 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1898,10 +1898,10 @@ def _update_region(
                 # verify correctness:
                 nextregion.create_ellipsoid(minvol=minvol)
 
-                # check if live points are numerically colliding or become linearly dependent
+                # check if live points are numerically colliding or linearly dependent
                 self.live_points_healthy = len(active_u) > self.x_dim and \
                     np.all(np.sum(active_u[1:] != active_u[0], axis=0) > self.x_dim) and \
-                    np.linalg.matrix_rank(nextregion.ellipsoid_cov)
+                    np.linalg.matrix_rank(nextregion.ellipsoid_cov) == self.x_dim
 
                 assert (nextregion.u == active_u).all()
                 assert np.allclose(nextregion.unormed, nextregion.transformLayer.transform(active_u))
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 651b305c..c5a87dd2 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -328,11 +328,11 @@ def make_eigvals_positive(
     except np.linalg.LinAlgError as e:
         print(a, targetprod)
         raise e
-    mask = w < 1.e-10
+    mask = w < max(1.e-10, 1e-300**(1. / len(a)))
     if np.any(mask):
         nzprod = np.product(w[~mask])  # product of nonzero eigenvalues
         nzeros = mask.sum()  # number of zero eigenvalues
-        w[mask] = (targetprod / nzprod) ** (1./nzeros)  # adjust zero eigvals
+        w[mask] = (targetprod / nzprod) ** (1. / nzeros)  # adjust zero eigvals
         a = np.dot(np.dot(v, np.diag(w)), np.linalg.inv(v))  # re-form cov
 
     return a
@@ -482,7 +482,7 @@ class ScalingLayer(object):
         self.mean = wrapped_points.mean(axis=0).reshape((1,-1))
         self.std = centered_points.std(axis=0).reshape((1,-1))
         self.axes = np.diag(self.std[0])
-        self.volscale = np.product(self.std)
+        self.logvolscale = np.sum(np.log(self.std))
         self.set_clusterids(clusterids=clusterids, npoints=len(points))
 
     def set_clusterids(self, clusterids=None, npoints=None):
@@ -599,7 +599,7 @@ class AffineLayer(ScalingLayer):
         eigvalmin = eigval.max() * 1e-40
         eigval[eigval < eigvalmin] = eigvalmin
         a = np.linalg.inv(cov)
-        self.volscale = np.linalg.det(a)**-0.5
+        self.logvolscale = np.linalg.slogdet(a)[1] * -0.5
 
         self.T = eigvec * eigval**-0.5
         self.invT = np.linalg.inv(self.T)
@@ -765,7 +765,7 @@ class MLFriends(object):
         r = self.maxradiussq**0.5
         N, ndim = self.u.shape
         # how large is a sphere of size r in untransformed coordinates?
-        return np.log(self.transformLayer.volscale) + np.log(r) * ndim #+ np.log(vol_prefactor(ndim))
+        return self.transformLayer.logvolscale + np.log(r) * ndim #+ np.log(vol_prefactor(ndim))
 
     def set_transformLayer(self, transformLayer):
         """Update transformation layer. Invalidates attribute `maxradius`.
@@ -1353,13 +1353,19 @@ class WrappingEllipsoid(object):
             live points
         """
         self.u = u
+        # allow some parameters to have exactly the same value
+        # this can occur with grid / categorical parameters
+        self.variable_dims = np.std(self.u, axis=0) > 0
+        if self.variable_dims.all():
+            self.variable_dims = Ellipsis
 
     def compute_enlargement(self, nbootstraps=50, rng=np.random):
         """Return ellipsoid enlargement after `nbootstraps` bootstrapping rounds.
 
         The wrapping ellipsoid covariance is determined in each bootstrap round.
         """
-        N, ndim = self.u.shape
+        N = len(self.u)
+        v = self.u[:,self.variable_dims]
         selected = np.empty(N, dtype=bool)
         maxf = 0.0
 
@@ -1367,8 +1373,8 @@ class WrappingEllipsoid(object):
             idx = rng.randint(N, size=N)
             selected[:] = False
             selected[idx] = True
-            ua = self.u[selected,:]
-            ub = self.u[~selected,:]
+            ua = v[selected,:]
+            ub = v[~selected,:]
 
             # compute enlargement of bounding ellipsoid
             ctr, cov = bounding_ellipsoid(ua)
@@ -1380,14 +1386,14 @@ class WrappingEllipsoid(object):
                 raise np.linalg.LinAlgError("Distances are not positive")
             maxf = max(maxf, f)
 
-        assert maxf > 0, (maxf, self.u)
+        assert maxf > 0, (maxf, self.u, self.active_dims)
         return maxf
 
     def create_ellipsoid(self, minvol=0.0):
         """Create wrapping ellipsoid and store its center and covariance."""
         assert self.enlarge is not None
         # compute enlargement of bounding ellipsoid
-        ctr, cov = bounding_ellipsoid(self.u, minvol=minvol)
+        ctr, cov = bounding_ellipsoid(self.u[:,self.variable_dims], minvol=minvol)
         a = np.linalg.inv(cov)
 
         self.ellipsoid_center = ctr
@@ -1413,4 +1419,11 @@ class WrappingEllipsoid(object):
             True if inside wrapping ellipsoid, for each point in `pts`.
 
         """
-        return _inside_ellipsoid(u, self.ellipsoid_center, self.ellipsoid_invcov, self.enlarge)
+        # check the variable subspace with the ellipsoid
+        inside_variable = _inside_ellipsoid(u[:,self.variable_dims], self.ellipsoid_center, self.ellipsoid_invcov, self.enlarge)
+        if self.variable_dims is Ellipsis:
+            return inside_variable
+        else:
+            # the remaining dims must be exactly equal
+            inside_fixed = np.all(self.u[0, ~self.variable_dims] == u[:,~self.variable_dims], axis=1)
+            return np.logical_and(inside_fixed, inside_variable)

From f3266e6249235a0bc19c387129a4fd982e8bbf6e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 12:15:31 +0200
Subject: [PATCH 030/313] improve docs for stepsamplers, recommend
 differential-mix

---
 docs/example-sine-highd.ipynb | 378 ++++++++++++++++++++++++++++++----
 docs/performance.rst          |   7 +-
 ultranest/stepsampler.py      |  25 ++-
 3 files changed, 362 insertions(+), 48 deletions(-)

diff --git a/docs/example-sine-highd.ipynb b/docs/example-sine-highd.ipynb
index 2217408c..2894e566 100644
--- a/docs/example-sine-highd.ipynb
+++ b/docs/example-sine-highd.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,9 +89,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -117,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,9 +265,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b82daa9d8824915a182f309ffe8076b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=-4e+01   [-35.7627..-35.7623]*| it/evals=8480/59249 eff=14.4098% N=400    0 0  \n",
+      "[ultranest] Likelihood function evaluations: 59322\n",
+      "[ultranest]   logZ = -52.38 +- 0.1759\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 2137.3, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.06 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.18, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.19 bs:0.18 tail:0.01 total:0.18 required:<0.50\n",
+      "[ultranest] done iterating.\n",
+      "\n",
+      "logZ = -52.389 +- 0.404\n",
+      "  single instance: logZ = -52.389 +- 0.192\n",
+      "  bootstrapped   : logZ = -52.381 +- 0.404\n",
+      "  tail           : logZ = +- 0.010\n",
+      "insert order U test : converged: True correlation: inf iterations\n",
+      "\n",
+      "    B                   : 0.42  │ ▁▁▁▁▁▁▁▁▁▂▂▂▃▃▅▅▅▆▆▇▆▅▅▄▄▂▂▂▁▁▁▁▁▁▁▁▁ │1.63      1.04 +- 0.15\n",
+      "    A1                  : 3.02  │ ▁ ▁▁▁▁▁▁▁▁▂▃▃▃▄▅▆▇▇▇▇▇▇▆▅▄▃▂▂▁▁▁▁▁▁▁▁ │4.76      3.94 +- 0.21\n",
+      "    P1                  : 2.869 │ ▁▁▁▁▁▁▁▁▁▂▃▄▄▅▅▇▇▇▇▇▆▅▅▄▃▂▂▁▁▁▁▁▁▁▁▁▁ │3.296     3.076 +- 0.051\n",
+      "    t1                  : 0.00  │▇▃▁▁                                 ▁▂│1.00      0.16 +- 0.34\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "result1 = sampler1.run(min_num_live_points=400)\n",
     "sampler1.print_results()"
@@ -269,9 +330,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43317fab364a42ad9d476036e4a37d46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Z=-185.2(0.00%) | Like=-175.37..-69.25 [-187.1261..-173.2950] | it/evals=1492/87818 eff=1.4928% N=213 13 3 \r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/user/.local/lib/python3.8/site-packages/ultranest-3.3.3-py3.8-linux-x86_64.egg/ultranest/integrator.py:1633: UserWarning: Sampling from region seems inefficient (0/40 accepted in iteration 2500). To improve efficiency, modify the transformation so that the current live points are ellipsoidal, or use a stepsampler, or set frac_remain to a lower number (e.g., 0.5) to terminate earlier.\n",
+      "  warnings.warn(warning_message)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=-4e+01  [-89.9197..-75.4800] | it/evals=2194/402993 eff=0.4985% N=213     \n",
+      "[ultranest] Likelihood function evaluations: 402993\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/user/.local/lib/python3.8/site-packages/numpy/core/_methods.py:232: RuntimeWarning: overflow encountered in multiply\n",
+      "  x = um.multiply(x, x, out=x)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Reached maximum number of likelihood calls (402993 > 400000)...\n",
+      "[ultranest] done iterating.\n"
+     ]
+    }
+   ],
    "source": [
     "result2 = sampler2.run(min_num_live_points=400, max_ncalls=400000)"
    ]
@@ -280,14 +402,87 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The efficiency is very low. This is not just because of the dimensionality of the problem, but also because of the degeneracies. To make progress, lets use a slice sampler:"
+    "The efficiency is very low. This is not just because of the dimensionality of the problem, but also because of the degeneracies.\n",
+    "\n",
+    "To make progress in high-dimensional or otherwise tricky problems, a step sampler can be used.\n",
+    "\n",
+    "## Step samplers in UltraNest\n",
+    "\n",
+    "To find a replacement live point, step samplers perform a random walk in parameter space. After a number of steps (nsteps), the final point is declared a \"independent\" sample.\n",
+    "\n",
+    "There are several step samplers available. Here we will use a [SliceSampler](https://johannesbuchner.github.io/UltraNest/ultranest.html#ultranest.stepsampler.SliceSampler).\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Widening roots to 400 live points (have 400 already) ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5715d983c988449f89817cc7270464f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=-2e+01   [-19.8295..-19.8288]*| it/evals=6503/659654 eff=1.6785% N=213 \n",
+      "[ultranest] Likelihood function evaluations: 660367\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/user/.local/lib/python3.8/site-packages/numpy/core/_methods.py:232: RuntimeWarning: overflow encountered in multiply\n",
+      "  x = um.multiply(x, x, out=x)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest]   logZ = -45.36 +- 0.2433\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 1522.6, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.12 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy wants 211 minimum live points (dlogz from 0.20 to 0.57, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.33 bs:0.24 tail:0.01 total:0.24 required:<0.50\n",
+      "[ultranest] done iterating.\n",
+      "\n",
+      "logZ = -45.419 +- 0.572\n",
+      "  single instance: logZ = -45.419 +- 0.237\n",
+      "  bootstrapped   : logZ = -45.360 +- 0.572\n",
+      "  tail           : logZ = +- 0.010\n",
+      "insert order U test : converged: True correlation: inf iterations\n",
+      "\n",
+      "    B                   : 0.33  │ ▁▁▁▁▃▅▇▇▇▅▂▁▁▁▁                     ▁ │3.44      1.01 +- 0.16\n",
+      "    A1                  : 0.10  │▁                ▁      ▁▁▁▂▄▅▇▇▄▂▁▁▁▁ │5.31      4.19 +- 0.24\n",
+      "    P1                  : 1.0   │▇▁▁                     ▁            ▁ │87.2      3.1 +- 1.2\n",
+      "    t1                  : 0.000 │▇▄▁▁              ▁▁                 ▁ │0.817     0.020 +- 0.017\n",
+      "    A2                  : 0.21  │ ▁▁▁▁▂▃▄▆▇▇▇▆▄▃▁▁▁▁  ▁          ▁   ▁▁ │4.01      1.22 +- 0.26\n",
+      "    P2                  : 1.000 │ ▁▁▂▇▄▁                             ▁▁ │3.131     1.258 +- 0.052\n",
+      "    t2                  : 0.000 │▁▁▁▁▁▁▂▂▅▇▇▇▅▃▁▁▁▁▁                   ▁│1.000     0.269 +- 0.052\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "import ultranest.stepsampler\n",
     "\n",
@@ -296,13 +491,12 @@
     "\n",
     "nsteps = 2 * len(parameters2)\n",
     "# create step sampler:\n",
-    "sampler2.stepsampler = ultranest.stepsampler.RegionSliceSampler(nsteps=nsteps)\n",
-    "\n",
-    "# alternatively, we can let the sample identify the number of steps needed on the fly:\n",
-    "# This is done by requiring the point to move at least the typical distance\n",
-    "# between live points, on average.\n",
-    "#sampler2.stepsampler = ultranest.stepsampler.RegionSliceSampler(nsteps=400, adaptive_nsteps='move-distance')\n",
-    "\n",
+    "sampler2.stepsampler = ultranest.stepsampler.SliceSampler(\n",
+    "    nsteps=nsteps,\n",
+    "    generate_direction=ultranest.stepsampler.generate_mixture_random_direction,\n",
+    "    # adaptive_nsteps=False,\n",
+    "    # max_nsteps=400\n",
+    ")\n",
     "\n",
     "# run again:\n",
     "result2 = sampler2.run(min_num_live_points=400)\n",
@@ -313,7 +507,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The efficiency is now constant (at 1/nsteps)."
+    "The efficiency is now constant, and proportional to 1/nsteps."
    ]
   },
   {
@@ -327,9 +521,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 698.4x698.4 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from ultranest.plot import cornerplot\n",
     "cornerplot(result1)"
@@ -337,18 +544,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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68MEHdf78+cJt7e3tOnTokI4fP65HHnlEDz30UKGqJpPJKJvNKh6Pr7lyEwAAAAAAqI+G7fmSD1geMbOPS3pU0guVy2QWJJ2U9GNm9gvOudRm9uP7vlpaWrS0tKSWlpZCqJJMJgurC124cEEzMzPyPE979uxRLBbT3NyclpZyiyudOXNGkrRr1y6dOnWq8PPDDz+s2dlZ9fb26vOf/3zp69Nll12m973vfUqn0zp48GAh+PnIRz6iZz3rWXrKU54iSbriiit07tw5dXV1KZ1O69FHH5Xv+8pms4pEIrr88ssLz6ncm7SZtwQAAAAAAFRRw4Yv+cqXFkl7JH1E0uclfUHSonLLTf/GZoMXSYpGo1paWtLg4KCi0ahisZiCIFAymVRbW5s8zyuEMwcOHNDk5KRisZjS6bQikdzb94Qn5GZEXXfddbrvvvt07bXXqqWlRXfeeack6eTJk4XlpYv98A//sObn5/WP//iP+vVf/3UNDQ2pp6dHDz/8sJ73vOcVKlyuu+46ffWrXy0sdZ1IJC7p8eKcKyyHHQqCgJWPAAAAAACos4a9Inc5WUn/IOlFkt4m6QeS7pb0+c30eCmWTqfV3t6uaDSqgYEBeZ4n3/c1MTGh8fFxnT9/XhcvXlRHR4fOnz+vqakpdXR0XDLtSJIOHDigbDar8fFxLSws6Ctf+cqq+45EInrta1+rdDqtD3/4wwqCQNdcc40eeughLS4uFu73Ez/xE8pkMvrEJz4h3/dlZtq/f7+uuOIK7d+/X7FYTJOTk5qZmdHc3JxSqZSCIFAqldK5c+eUSm06owIAAAAAABvUsOFLke9Lermkc86550h6mXMuWa0nj8Vi6unpKQQvoY6ODnmep0wmo46ODvX392vfvn06ePCgYrGYpqamLnmuyy67TJJ0zz336Nvf/rbm5ubW3P+ePXv0Yz/2YxodHdWXv/xlXXvttVpcXNRDDz1UuM+TnvQk3Xjjjfr617+uubm5QsAiSZ2dnUomkzp+/LgWFxf16KOP6tixY5qZmSmEMAAAAAAAoH4aPnxxzj0i6bnOuV/J31TVMXuep3g8vix4icfj2rt377JVj6RcE94gCHTTTTfprrvu0l/91V8te66rr75aV1xxhd7xjnfou9/9bmHZ6pXMz8/rox/9qO688061tLQoHo/r4YcfliR1dXUtu+/g4GAhdJmbm9PZs2cLAVBYkRMGL6dPn9b58+cVBIG6u7sVj8c3/T4BAAAAAICNadieL8Wcc2elXB8Y59xSrffneV4hjOnu7pbneZqcnNT09LTm5ub06le/Wg899JB+7dd+TZdffnkhZGlvb9ef/dmf6U/+5E/0z//8z7r++ut17733rrif//iP/1Bra6tuvvlmPeMZz1AqldJnPvMZ/ciP/IgOHDhQuF8mk9G//uu/6pnPfKYuXLgg3/fV29urnp4ezc7OateuXXr84x+v7u5uRaNRdXV1aWhoSJlMprAKUiqVovcLAAAAAAB10BThS8iVdpmtoTC0iMViymQyam9vV1dXl3p7e3X69Gn9/M//vM6cOaOf+qmf0rvf/W4NDw9Lyk1X+t3f/V1df/31es973rPqPvbt26fXve51isfjCoJAf/d3f6eOjg49//nPX3a/73znO5qbm9NTnvIUJRIJzc7OqqenRzMzM5qdnZUk7d27Vx0dHYrFYoWQpa2tTVIueEkmkwqCQJ7nEcIAaxgaGlpx1bChoSGNjo5u7YAAAAAANDWuwEuEjWolFUKR06dP6/Tp0zp37pwuXLigTCajbDarX/3VX1U0GtXv/M7vaGpqSktLS1paWlIQBHrRi16kd7/73avu68CBA8pkMjp27Jg+/elP69SpU3r2s58tKTclKZvNKpvN6itf+Yo8z9NNN92koaEhDQ4OSpLa2trU39+vwcFBdXR0yMzKBiuxWEyJREJSbglt3/er/bYB28ro6Kicc2W/yq1cBgAAAACraarKl63g+36hWe3AwICmpqZ09uxZXbhwQbt27dLu3bt18eJF9fb26sCBA/rlX/5l3X777XrnO9+pO++8c1mfl6c97Wl68pOfrN/+7d/Wl7/8ZT396U/Xc5/7XJ09e1YPPPCA7r//fv3RH/1R4f7Petaz9Cd/8ieFv7i3t7dLyk1PuvrqqxUEQeG2xcVFPfDAA7r22msv6Q9TKuxrk81m5fv+mr1oAAAAAABA9RC+lIjFYkqlUspms0qlUoUlqMPqkXQ6rSAIFIvFdPXVV6urq0uTk5P6i7/4C91222364Ac/uGy6Qk9Pj97//vfrve99r/74j/9Yd911l1pbW3XFFVfohhtu0Gte8xpdddVVuvrqq7Vv376yUx1+8IMfqK+vT77va3p6Wrt379bo6GhhrJFIRK2trbpw4YJ2796t1tZWBUEg3/eXVcKEY0+n0zThBQAAAABgixC+lPA8TwMDA/J9vxBgdHd3KxKJaPfu3Tpx4oRSqZQOHjyotrY2tbS06IUvfKEWFhb0/ve/X4cOHdLb3va2S57zV3/1V/VzP/dzOnPmjA4dOqTW1laNj49r9+7dq45nYmJCo6Oj+pEf+RHt379fnZ2dhaa7YY+XZDKp2dlZnTp1SldffbV6enqWLTMdBi3FfWwAAAAAAMDWoOdLieKKkXg8rkQiUeir4vt+oV/K3NycstmsIpGIuru79brXvU7Pf/7zdfvtt+vFL36x7rvvvkueOxaL6aqrrlJra2tFY/nOd76jH/3RH1UkEtGLXvQi7d69W+fOnSv0pBkcHFQkElEikVB7e7vm5+c1MTGh48eP68KFC1pYWFBbW1uhkqe0EgYAAAAAANQeV+ElfN+/pCltuPT03NycotGo4vG4HnnkEd17773q6OjQwYMHtWfPHr397W/XW97yFh05ckQ/8iM/ote//vWamJhY9xiCINDhw4d1yy23SJLe8pa3aPfu3YWlo+PxuIaHh7W4uKhsNqv29vZCZc7i4qKmpqbkeZ7a29s1PT2tZDKpqakpmu0CAAAAAFAHTDsqUTw1JwxipNwS0nv37i00t52enlY6ndbs7KyWlpbU2dmptrY2Pec5z9HBgwd1xx136O/+7u/00Y9+VL/0S7+k2267raLpPlNTU/qt3/otff3rX9fLXvYy/cqv/IqmpqY0Pz+vnp4eDQ4OamlpSY8++qhmZmZ0+vTpwupHhw4d0uzsrBYXF9XV1aWenh5Fo1Gl0+nCf5lyBAAAAADA1iJ8KRGuDCQ9FsR0dHTI8zx1dnZKki6//HJ1d3dr9+7dOn/+vM6fPy/f99XZ2alUKqWOjg69/OUv18jIiI4cOaJ3vetd+vCHP6w3vvGNeu5zn6uuri61trYWlq4Nfetb39Ib3/hGXbx4Uf/5P/9nPfOZz9TQ0JAymYw8z9Ps7Kx6e3s1Pz+vXbt2KRaLqb+/X52dnVpYWFBHR4f6+/vV3d2tvr4+RSK5wxu+HprsAgAAAACw9QhfVlEcxEjS/Py8UqmUPM9TV1eXotGoBgcHdf78eXV2dmpxcVG7d+/W/v37NTExoUgkopGRET372c/WJz7xCb35zW/Wm9/8Zkm5QKe7u7vwFYvF9LWvfU1XX321fvZnf7awz4sXL2pgYECSlM1mlU6n1dXVVVgVKR6Py8xkZhofH1csFtPAwAB9XQAAAAAAaBCEL+sQVsKUTuF5whOeoGQyqbGxMbW2tioWiykIAnV3d+vqq69Wf3+/enp69OIXv1hBEGhmZkYPPPCA5ubmZGbyfV/pdFovfvGL9frXv16+78s5p4MHD+rgwYM6e/asenp6NDMzU1jByPd9ZTIZ+b6vvr4+nTx5UidPnlQQBBoZGVFPT0+93iYAAAAAAFCE8GUdiithiitiwj4ws7OzikQiOnDggDo6OhQEgRKJhPbv3y9JetGLXqS2tjadO3dO586d0+LiYmHq0eLiovr6+hSNRjU9PS0z02WXXaZoNKqOjo7CtCLP8+T7vrLZrBYWFjQ/P68gCNTa2qq2tjbNzs4qlUoRvgAAAAAA0CAIX6okHo+ru7tbs7OzymQy2r9/v+bm5goNe/v6+hQEgU6fPq2xsTFJUjKZ1Pj4uHp7e9XX16dEIqGenp7CtKFwulF437CxbltbW2F1o/Pnz6utrU3z8/O66qqrdPbsWXq7AAAAAADQQAhfVhAEgXzfVywWW7N/inOucN+5ubnC7eG0pGw2q7m5OcViMR04cEBtbW1KpVLKZDKKRqMyM6XTaU1MTKitrU2e52nPnj1qaWnR3NycZmZmdP/99ysIAs3Pz6uzs1Pz8/Pq7+/X4OCgxsfHdfHiRXV0dOjKK69kRSMAAAAAABoI4csKipeZXquSJLxvPB7X3r17C+GHmSkWi2lpaUnpdLqwKlEkElE8HtfQ0JCGhoa0uLiolpYWnTp1SjMzM+ro6FA2m9W5c+cUjUbV0tKipaUlnTp1SouLi+ro6JCUmwbleZ7a29u1a9cu9fX1KZPJ1PaNAQAAAAAA60L4soIwQFmriiQIAgVBoHg8Xlh5qNTCwoKy2azOnz+v3bt3a3BwUEEQKJvN6tFHH9WuXbs0MTGhTCajVCqlq666Sul0WsePH5fnebrsssv0pCc9Sel0WrOzs1paWlJnZ+eyqpxYLLauwAgAAAAAAGwN1iNeQdhcd60pR77vF5af9jyvsOxz+OV5nmKxmCKRSKFJbiKRUCQS0ZkzZ3Tx4kWlUikNDAyop6dHLS0thSlJkhSJRDQ9Pa3LL79c/f39Wlpa0kMPPaSLFy9qampKkgrjjMViSiQSTDsCAAAAAKCBUPmySZVWyJTeLxaL6eDBgzp//rwOHDigbDarRCKhY8eOaXx8XGfOnFF7e7v27dtXWOXo0KFDkqRMJlMIc3zfL1S5FK/GBAAAAAAAGgPhyyZVEniE1TGJRKJQSeN5ntra2tTZ2VmocvF9X1JuKlNvb6+6urp0zTXXKJvNKhqNKp1O69ChQ0qn04Wfo9HosqlGa1XqAAAAAACArUX4sgVWqo4Jfw6CQOPj45qdnVUsFissNR32hYnH44VlqQcHB5VIJCSpcPuJEyfU0dGhvXv3FqYfEcIAAAAAANAYCF9qoHSZ6uLqmNJt8Xi80LS3o6Oj0B8mFosplUopmUwqGo2uuI/wcV1dXZJEw10AAAAAABoM4UsNrLbqULltnucpEonI8zxlMhml02l5nqd0Oq2xsTF5nqeBgYHCc4TBS7i89f79+wtVNGHlCwAAAAAAaAyELzVQOs2ouNplpSlI0WhUqVRKvb29ymQyisVihYqXvr6+QgVNMplcFrCUTjGi4gUAAAAAgMZC+FIDpU14S6tdygUk6XRa2WxW09PThZ4vnudpz549hfuUBi4ELQAAAAAAND66sm6BWCymRCKx6nSgsNdLuHx0OWHgQjNdAAAAAACaB5UvW6CSKpWwr0s4PQkAAAAAAGwPTR2+mJk551y9x1EtTCUCAAAAAGD7aer5K2HwYmZW77EAAAAAAACU03SVL2bmSfp9Sb6ko5LudM4tbrcqmFLFKybR8wUAAAAAgObRjFfxX5C0V9INkn5N0v8xs3bnnFupAsbMbjOzI0ePHtXIyIgOHz68leOtinDFpJWa8aJ2Dh8+rJGREUm61syOmNlt9R4TAAAAAKB5WDMVi5hZXNIHJN2qXOXLT0l6taT7JP2ec25ptcePjIy4I0eO1HiUtUHlS/2Z2d3OuZGVtjfz+bUTmZk28vm30cdV8Lyrnl8AAAAAmlfTXMXnq1raJF0l6Y3OuUDSp5SrhHmypP31G13tscw0AAAAAADNqWmu5F3OtHL9Xv7AzF7nnJt3zr1HUoukffUdIQAAAAAAwKUauuFuvrnuCyS1Ouc+JUnOuU+Z2S9I+mszu0HSeUm9kk7XbaAAAAAAAAAraNjwJT/N6NvKhSsjZvajzrnflCTn3P81s1OSnilpt6RXOecerd9oAQAAAAAAylsxfDGzgyttc86dqs1wlnm1pBnn3AvM7NWSXmxmT5P07Xxj3e865/6/LRgHAAAAAADAhq1W+XJUUrTkNpPklOuxUmsXJA2ZWULSs5VrqnuTpNNm9keSXmNmvyRp1jXTkk0AAAAAAGBHWa3h7oskXZR0edHXofx/t8LXJf2apAVJn3TOXSnp8ZLaJXVL+m3nXJLgBQAAAAAANLLVwpd/lfQD59zJ8Eu5Sphf24qBOedSkr7snFuQdI+ZPU7Sa5QLXj7rnKPBLgAAAAAAaHgrTjtyzmUlPcPMOiT9hKRfkPQ0SXdt0djknMuaWVTSGyT9pKSMpJ92zs1s1RgAAAAAAAA2Y7WGuzdK+kVJPy3puKRhSfucc+NbMrI851zazP63pPdKSm/1/gEAAAAAADZjtWlH31Ouv8vNzrkR5Rrb1iX4cM7N5Kc+EbwAAAAAAICmslr4crukGyV92Mz+q7ZmhSMAAAAAAIBtZcXwxTn3O5IOSHqbpB+TtMfMPmNmP7tVgwMAAAAAAGh2q1W+yDmXdc59yjn3YuWmIH1X0lu3ZGQAAAAAAADbwKrhSzHn3Bnn3NslXVHD8QANKQgCaR2/LwAAAAAAhNZ9Memcc7UYCNDIfN+X6HsEAAAAANgA/pIPVCAWi0nSUr3HAQAAAABoPoQvQAU8z5OkoN7jAAAAAAA0H8IXAAAAAACAGiJ8wbYXBIFSqVTYNBcAAAAAgC1F+IJtz/d9JZPJsGkuAAAAAABbKlLvAQC1lm+WW/gvAAAAAABbifAF257neYrH4/UeBgAAAABgh2LaEYBtaXh4WGa24tfQ0FC9hwgAAABgh6DyBQ0nCAL5vq9YLBYu8byp+2FnOnnypJxz9R4GAAAAAFD5gsZTaYNcGukCAAAAAJoBlS9oOJU2yKWRLgAAAACgGVD5groLgkCpVEpBEEh6rEHuWlOJKr0fAAAAAAD1xFUr6m4904dKg5pKtwEAAAAAUC+EL9hS5QKSWCymRCJR0fSh1YIa3/c1MzOjiYkJAhgAAAAAQMMgfMGW2myT3NWCmlgspkgkomw2SxNeAAAAAEDDoOEutlS5JrlhICNJ8Xh81ceHfV5W2jYwMFBYfhoAAAAAgEZA+IItVS48qeaqRaXPHwRBIYyhMS8AAAAAoB64GkXd1XLVos1OcwIAAAAAYLMIX7AhzbLq0Hqa+QIAAAAAUAuEL9iQtVYdWm+1Sa0Cm1pW1QAAAAAAUAl6vmBDVuvTspEeLutpugsAAAAAQDOhHAAbslpFyUaqTaLRqDzPUzQaXfV+jTSlCQAAAACASjRl+GI5rfUeB6onnU4rCAKl0+lV70cDXQAAAABAs2m68MXMPEnflPTCeo8F1VNpY1wa6AIAAAAAmk1ThS/54OXrkv7FOfdPZvY4M0uYma3xuNvM7MjRo0c1MjKiw4cPb82Aa2Q7Tr2pdKpSPRroHj58WCMjI5J0rZkdMbPbtmznAAAAAICmZ865eo+hYmb2i5J+UtKtkv5e0h5JZyT9laSPujVezMjIiDty5Eith1lzqVRKyWRSiURiWzSnDYJAvu8rFos19KpEZna3c25kpe3b5fzaLsxMtfh8q+Hzrnp+AQAAAGhejXulW943JHVL+pSkD0gakXRc0s9Iarh5KLWqUNkuU2/C9ycMk+jjAgAAAADYjpoifMk32PWcc/dLepOkPkl3O+dmJb1B0l5JQ/UcYzm1ag5bj6k3tVC8vPR2CJMAAAAAACgnUu8BrMXMepxzM0UBzFfM7MXOufvM7KmSrlDudUzVeaiXCMOEeoYKjTylp/j9abSxAQAAAABQLQ0dvpjZuyWlzOxdzrmpfABj+eDllZLeJ+lBSbc65ybqO9pLhRUq9VRcXbLWWLY6qGmE9wcAAAAAgFpr6PBF0vPz/501s/c75yaLVjb6kqR+SdH89COUsZ7qm/UENQAAAAAAoDINO9fDzJ4jKZD0eUkvkfQLZtbvnHNm9hJJfyDJW0/wEgTBtlqeuRLr6Q+zXRr5AgAAAADQSBo2fJF0j6QR59yvS/qipJdK+jkz61Guv8u7nHML63nCpaWlLVlRp1arHNVacVCz1mso3d6srxkAAAAAgFpr2GlHzrmpou/famaLkn5eUsY596cbec6WlpYtqeoIp+8EQSDP85qyoexaU5BKtzNlCQAAAACA8ho2fAnlG+w659wfmllG0j9t9Lk8z9uSECQajRaqQFKplKTmCyRKe8WUNuMt3R6+5mg0Wp8BAwAAAADQoBq+HCPf48XLf/9HzrlH6j2mtaTT6ULVSyP3UFltqlBpr5iwsiWctlW6PXzN6XR6614AAAAAAABNoOErXyTJOddUjUSKq0LWW2mzlcs9VzpVKGxUHI/HVwyS1rOqEgAAAAAAO0nDV75stZWqQdbTULbcCkOVPr60wqSWKl3dyPd9pVKpVadtrWdVJaCZDQ0NyczKfg0PD9d7eAAAAAAaUFNUvmyllapBNttQttLHb2UFSRiYrIWqFuAxo6OjK24zs60bCAAAAICmQfhSYqWgofj2jUwNqjTAqDQQ2YiNTmkqHdNWTo0CAAAAAKDZceVcYqXpM8W3rzY1qHh6UfH3jTAtp1pTmrZyahQAAAAAAM2OypcNWK2KpXh6kaRNTVWqtmpNH2IaEgAAAAAAlSN82YDVpgaVCyaKv9/qKTul+6tGCFTLqVEAAAAAAGw3TDtaw3pWOZKWT08qN9Voq6fsMEUIAAAAAID6InxZQyqV0rlz55RKparyfJUs77zewGez+wMAAAAAALVD+LLFKmm8W2lD32rtDwAAAAAA1A49X9YQBhebrRxZT6+XShv6lvZd2Yp+MiwzDQAAAADA+nD1XKJ0qegwaJBUdgnpSp5HWl81y2rVKqtNI9qK/i70kAEAAAAAYH2ofCmRSqU0Pj6uwcFBeZ6nZDJZCGGy2WzhfjMzM0qlUhoYGCgbkpRWqIRhSVtbm86ePatoNFpoyhvuV5Ki0aimpqbU19enSGT54Vmr6mQ9S0BvtIKFZaYBAAAAAFgfwpdVhAFDEATKZrPyPE9BECgWiykSiSibzRYCjNIgI3xsNBpVKpVSLBZTPB7X2NiYTpw4UZjK1NnZqcHBQSUSCUWjUZ08eVIXLlyQJO3Zs2fZeFabciStvgR0adiy1nOthGWmAQAAAABYnx0XvoRByErVHqU9XsJpRolEovB4z/M0MDCgZDKpZDJZCGGCICg8NgwpUqnUspCjr69PQRAsq3wJ95lMJpVOpxWJRNTb23vJ2CqtOilX1bJSJQ4VLAAAAAAA1NaO6vkSBMGa/UqKwxDf9zU+Pq7JycnC7WG/Fc/zlE6ndfbsWU1OTioejysIAp07d07JZLLQw6W0R4vneUokEurp6VFPT48SicSyIGhxcVGtra06ffr0smlOpWMr99rCfZb2ZQkDpOLQhVWQAGBtZna7md1lZh8ys9ZKtpvZLWb2JTP7ipm9bOtHDQAAgEazo668w+CjksqRVCqlaDSqwcFB9ff3F5rhFgcWfX192rVrl9rb2wu3zc7Oanx8XDMzM/J9v1AJE1bGFAcjpY124/G4Lr/8crW1tenChQuamppaNp7VGvymUimdPn1aDz30kDKZzLKgxff9QsUOYQsAlGdmHyj5+QZJ+51zT5d0TNKPr7XdzDok/YakFzjnnumc++SWDB4AAAANbcdNO6qkX0nxFJ1EIlGYOhRWixRP6zl06JB831c0GtXExISkXFgSiUQK/V6y2azGx8fV1dWlgYEBSSoEMsVTgYrDoampKfX29mp6elqjo6Nqb2/X/v37l/WUCStbwtd04cIFJZNJzc3Nad++fYWxlla9AAAqcrOkL+S/v1PSz0n6yBrbxyXNS/qMmfmSXu+cG9ua4QIAAKBR7bjwpRKl/VBKVyoqrkCJx+OF3i7ZbFZdXV3q7+9XIpFQMpnU6Oioenp6dP78+ULvmDCo6e3tXVaJUxzq7NmzR6lUSseOHdOJEyfU3d2trq4uTU9Pq7W1VUtLS2ptbVUkEilU1xw8eLDQdyabzSqVShVWaerp6aHqBcCGmNmwpEeKbnKSTkj6sKT/ISmQdJmkH5N0WNKznXNf3eQ+/07SsyV1ShqT9EfOub9e4b69kt4v6bmSpiT9jnPu74u2p0oe0iHpL5xzv7bGMHZJOpf//qKk0mZc5bYPSrpS0k2SniPpDyT98hr7AQAAwDZH+FKiXLPasOIlXKkoFotp3759yypJYrFYoerE933F43FNTk5qenpaqVRK3d3d8jxPmUxG586d08WLFyXlpi5NTEyor6+v0GOmv7+/sLLSVVddpdbWVqVSKc3Ozqq9vV0LCwuan5/X0tKSDh48qGg0qnQ6Lc/z1NPTs2yaUzabVSQSoeoFQDW8VdKnJbUqF7T8T+VCmRlJ1Z5e878k/bxzbsHMrpH0VTP7d+fc3WXu++eSMsoFHzdK+hcz+75z7j5Jcs4VSh7NLK5cmPOP+Z8PSvrb/OZrzOyr+e+fm39difzP3ZKmS/ZbbvuMpG845zJm9iVJv7PeFw4AAIDth/ClSBAEmpiYKDS6LZ2i1Nvbq8HBQe3evfuSSpLigGZ+fl6S1NraqiAIlMlktLCwoPPnz+u+++7TwMCAstmszp8/r0wmo7Nnz+rEiRPq7+/XQw89pOnpaZlZYVWllpYWpdNpzc/P67LLLlNvb6/uv/9+nTt3rjA9qbhKp3h6VPHqSwCwSaecc/fkv/+umf2GpKdIerOkJ0oakfRX1dhRGJyEP+a/rpC0LHwxs05Jr5D0BOdcStK/mtmnJb02P65Sr5A0Iemu/H5OSbol/1wfcM7dWvTc35T0RuXCmedJ+kbJc5Xb/l1Jv2FmplwQdGJdLxwAAADbEuFLkXCKTnG/luLgIpPJqKOjQ5lMpuzj4/G4BgYGNDExoUgkokcffVQTExNqbW1VR0eHHn74YV28eFFnz54tBDw33HCDfN/XAw88ULitr69Pnufp4MGDmpqa0tLSkgYHB7Vnzx719vYqnU6rt7dX2WxWAwMDywKXYuVuA4DNMrM2Sf9FuWk2DznnLkq6x8x61njcrZL+psymDxaHHkX3/wtJtyo3TejfJX22zGOvkpR1zj1YdNv3Jf2nFYbxs5L+1jnnVhurJDnn7jGzcTO7S9IpSe80sz3K9XF5a7nt+YqXT0r6mnKB0evW2g8AAAC2P8KXIsXVI6XNcMPbw94u4dSkVCq17D6hCxcu6Pjx4zp69Kh6e3u1a9cu7du3T/v27dPU1JTuv/9+LSwsqKWlRZ7n6eGHH9bg4KAuv/xy7dq1S+fPn5eUq8YxMw0MDMjzPE1NTWliYkLt7e267LLLCrcDO9Hw8LBOnjxZdtvQ0NAWj2ZH+BszKw5PPq1cj5dKfVq5CplSpdN5JEnOuV8xs1+T9FTlqlMWytwtLilZcttFSV2ldzSzIeVCmZ9fYX+3lrntt0puGlNu+tVK2+Wc+3PlpkIBAAAAkghflimuFCltthv2gunr61M6nS4ENOfOndP8/LyGh4eVTqcLS0R3dXXp4MGDMjMlEgm1tLSou7tbQ0NDuueee3T27FldvHhR09PTWljIXU8cOHBAz33uc+V5no4dO6ZIJKKxsTHt3bt32VSijo6OwspJYW+Y4hCIMAY7xcmTJ1VBAQOqJ+z5Ekgadc6Vhh6rcs5Na4WgZZXHLCk3leg1kl4v6f+U3CWlx/quhBKSZss83Wsl/atz7pEy2wAAAICaIXxZQemUnVQqpfHxcQ0ODiqRSBSWcO7s7FQQBBofH1cQBFpYWFAqldJ//Md/KJPJqL29XYuLi5qentbp06c1PDysK6+8UrfccosuXryo06dP69ixY/J9X2NjY3rkkUfU19enIAgKDXa7u7uXjSlsoBuGLGGj3nLjBrB1hoaGlGv1UX7b6Ojo1g6o+op7vqzbeqcdlYgo1/Ol1IOSImb2OOfcQ/nbbpB0X5n7/oyk/13ZaHPM7HbllpQelfQ659xiJdvN7FWS/o9zrn89+wMAAMD2RIlEBQ4fvrSq3vd9pVIpJRIJdXd3a3Z2tjAlSZKi0agWFxfV3t6ulpYWTUxM6Lvf/a6++MUv6vjx4/I8TxcvXtSFCxcUiUTU09OjZDKpb3zjG/qnf/onvec979HY2JgOHjyogwcPFpakDhvohqsZSbnqnMHBQQ0ODm5qVaNyr7PWdso+660ZXnMzjFFafZyjo6NyzpX9Wml61A4TTjsq/fr94juZ2YCZvdLM4mbWYmbPk/QqSV8qfULn3JykOyS93cw6zexpkl4q6UMlz3mzpP3Kr3JUCTO7QdJ+59zTJR2T9OOVbDezFkk/Iel0pfsCAADA9kb4UoHDhw8rHo9r7969y6YlJRIJxeNxxeNxdXV1qbW1tbBSUWtrqxYXF/Xggw9qdHRUe/fu1fXXX69nP/vZuu6663T//ffr3nvv1dzcnGKxmG688UaNjIzosssu0+7du/Xd735XHR0dWlhY0IULF+R53rJKl2QyKd/3JeWqXRKJhBKJxKamHO2UIKRZLvKrqRle80pjHB4elpmV/apHX5dmeC8blXNu2jl3T5mvU6V3VW6K0RlJFyS9U9L/45z7dHgHM/ucmf1u/sdfUa4p74SkjyjXELe08uVnJd3hnCs3HWklN0v6Qv77OyU9rcLtr1Iu5AnWsS8AAABsY7aT+iWY2aSkjfz5+VpJR9e4j6fcP7Rb8z9nJbUpdxHRkv85KiktaUlSp3Jl9OHSSZn87ZZ/rsdJeij/nE6X/iPeK3PbZlXyOqutmfY5tNoUgk2cX1uhHu/zejXDGKXajXPV8wtbLx/u3O+c+5SZXSnp7c65V6+2Xbm+Mp9UbjWo7zjnRuowdAAAADSYHdXzZaMXNmZ2ZKv/Ac0+m2+fjXzhXI/3eb2aYYxS84wTlckvHf0PZTa9UtKMHmvm261LmwWX2/4aSR9zzgUr9f8BAADAzsO0o8rUY54B+9xe+6y3ZnjNzTBGqXnGiQo458acc7eU+RqT9E1Jz8nf9XmSvlHy8HLbr5P0M2Z2p6THmVnp6kwAAADYgXbUtCMAANbDzN4h6SZJpyT9nKRe5XrKvLXcdudcpuixVEkBAABAEuELAAAAAABATTHtqAJmdhv7ZJ/NrBleczOMUWqeca5XvV4X+wUAAMBOQPhSmXr8Y5l9bq991lszvOZmGKPUPONcr3q9LvYLAACAbW9HrXbU19fnhoeH1/24WCymkZGRqs3PCoJAnuct+3lxcVGS1NraKs/zqr7PSrDP1d19991Tq61o1NfX5/bt2yfnnMxMkUhk2XGup3q8z+vVDGOUajfO1c6vjX52rUe93n/2W3trfXYBAACg9nZU+DI8PKwjR46s+3EjIyMbelw5qVRKyWRSiURC8XhcUi58SaVSkqR4PC7P86q6z0qxz9WZ2cnVtg8PD+uf//mflclk1NbWpoGBgYYJX+rxPq9XM4xRqt04Vzu/NvrZtR71ev/Zb+2t9dkFAACA2ttR4ctG3XbbxqrEgyCQ7/uKxWKFi/BYLLbsv6Gw2iW830b3uRnsc/MGBgbk+76i0eglgVo91eN9Xq9mGKPUPONcr3q9LvYLAACAnWBHrXY0MjLitvIvjuWqXDZzP9SXmd292rKxxedXKpXSuXPnJEl79+7luGJNq51fW/3Zhe1lrc8uAAAA1B6VLzW0UpVLKKyMiUajq94PzScajaqzs1OxWIzjCgAAAAA7XGM0pNimwqlEvu8rCIJLtvu+r2QyqXQ63RBTU1A9vu9rbm5OnudxXAEAAABgh+OqsMbCgCWVSimVSi0LYWKxmBKJRKE/SLmABs2l+DiGjZQ5rgAAAACwsxG+1FgYsEhSMpmU7/uFbZ7nKR6PK51OX7IN1bGVAUgQBIXjGI/H1d3dXZha1qgIiAAAAACg9uj5UmNhwBIEQWEaUqm1esNg48LKI0k1b3rreZ4SiURh1apw5aNGPq5b+f4AAAAAwE5F+LJFwhBmpW1hb5hoNKp0Or1s2WlsXL2CrXLLjDcigj8AAAAAqD3ClwZR3Bsmm80qlUppYGCgoS/cm8FqoVe1hdOOwu/Hx8c1ODhYmHbWiLby/QEAAACAnYor+wYR9obp6+tTJBJRNptt6F4huFTxtCMAAAAAAEJUvjSI4gqEZugVspp6TblphKk+4TEMlw5vxGO40vvUCO8fAAAAAGxHXGE1oDCIadYL4HAK1VZX7tRrv+U08jFc6X1qpPcPy917770ys7Jfw8PD9R4eAAAAgDVQ+YKqq1cTV5rHVmal94n3r3FlMhk558puM7MtHg0AAACA9Wq8P8vvYEEQKJVKKQiCeg9lUzZS9VGN196o1SaNclzDcUgq+z416vsHAAAAAM2Oq6wGspOnfWzn194or61RxgEAAAAAOw3TjhpIo0772IpGrI362quhWq9ts8dhO7/HAAAAANDICF8aSPGKR40krJiQVLPxNeprr4ZqvbbNHoft/B4DAAAAQCMjfFmnnbgcLxUT61eL84TjAAAAAADNaWekB1W0E/tmNHsj1no0vK3FedIIx6FRmgcDAAAAQDOh8mWd1lt9sBMrZRrNVkybKhYEgYIgUDwe33ZVKlv9XgIAAADAdkD4sk5r9c0oDVu4WK2/rZ6uEx7zSCSy7Y45U58AAAAAYP0oxaiy0ukmsVhMiUTikotVpm9sna2erhONRrWwsKBMJrPtpqc1wtQnAAAAAGg2XEFVWWnYstLF6k7sHbOS7RZEpdNptbe3q62trSkqRLbb+w8AAAAAjYZpR1VW6XK+TN94TCqV0vj4uAYHB5VIJOo9nE0rPrbNUCFSPDUuFovRowgAAAAAqozwpU4qDWnQfJrt2BaHRfQoAgAAAIDq2xbhi5mZc87VexzYmHBaFlVA9VEcFlGRBQAAAADV13TzCszMM7M3mNlPmtmTJck558zMVnnMbWZ25OjRoxoZGdHhw4e3bsBYU6M3cT18+LBGRkYk6VozO2JmtxVv307nV6Mfi+1orfMLAAAAQPOzZioYyQcsX5V0WtJBSXOSvuSce2cljx8ZGXFHjhyp3QDXoXRJajQ+M7vbOTey0vbw/OLYYiNWO79WK+4zMzXT5zi23lqfXQAAAKi9ZrsyPCDponPuNZJeKelOSS80s1+u77DWj9WONqYZVuZpxGPbDO8bAAAAAGxXTRG+5KcaPVvSj0nqN7PnO+fOSvqYpG9JeraZ7arrIPMqvcgtXZK6Gs+5EzRisFGq3LHdyDGs5nFvhvcNAAAAALarhm+4a2aepH+TlJHkJI1I+gcze7pz7l4z+1NJ/ywpIelC3QaaV+lqMetZEYcVaB7TDA1hyx3bjRzDah73ZnjfAAAAAGC7avjwRdKbJU07555nZr8t6e8kPVvSZ83sLZJ2KVfBM1/HMRbU4iKXC+fHNNsyzqGNHMNqHvdmfd8AAAAAYDtohvDlhKSfMbMflfR8PdZod7+k65XrA/PzzrmJ+g3xMbW4yOXCuflt5Bhy3AEAAABge2iG8OVLkm6Q9CeSuiU9XtLlkv5c0jslTTjnluo3PAAAAAAAgJU1fMNd59ykc+53JL1J0gOSYpJ+VNJuSfPNHrwEQaBkMqlkMklD3Sqod3Pi4n3Xeyzr0UxjBQAAAIBm0wyVL6FRSYOSPqrc1KNXOudm6jmgavB9X+Pj45IunWYSBIF831csFpPnNXxO1hDq2Zw4DNLCfadSKY2Pj2twcFCJRGJLx7JeNHUGAAAAgNppmvDFOfeImb1UuQBm2jl3ut5jqoZYLKbBwcHC98W4IF6/ejYn9jyv4uXDGw1NnQEAAACgdpomfJEk59yYpLF6j6Oawgv2crggXr96N6kt3nc8HpfneU1x/Or9vgEAAADAdtZU4ctOwwVxc+P4AQAAAACkJmi4u5PRBLX5cMwAAAAAAKUIX9ZQz4vpsOeL7/tbvm9sTCXHjIAGAAAAAHYWph2todpNb9ezghE9X5pP8TFb6VjTSBkAAAAAdhbClzVUOwBZz4U3PUOaT/ExS6VSZY81oRoAAAAA7CyEL2uodgDChffOsdKxJlQDAAAAgJ2Fni+btN7+HeGF91pTjmqxb2yd0ilHHCsAAAAA2LkIXzapnk1xacjbuEqPDccKAAAAAHYuph1tUj2mEYVVFdFodMP7Xk/j30bVyK+htPFuEASKx+PLjlUjj19q/PEBAAAAQLPgimqTqjmNqFJhFUU6na5436XTXpq1EqP4dTTyayg+L3zfVyqVkud5hWMVBIEmJiY0MzPTMOPfLucIAAAAADQaKl+a0EaqbUpXWWrWxr/Fr6NZXkO5cfq+r2w2q0gk0jDj3y7nCAAAAAA0GsKXJrSR1XJKL6SbdcWd4tfRLK+h3DhLX0cj2C7nCAAAAAA0GsKXHWK7XEjzOmqnEccEAAAAANtBY/zJHQAAAAAAYJsifEHDKW38ivrgOAAAAABAdRC+oOGwyk5j4DgAAAAAQHXQ8wUNh1V2GgPHAQAAAACqY1tUvpiZ1XsMlarHVI5mmz4SNn5tlFWAqi08HtlstqGPy3Y/DgAAAACwVZruqsrMPDP7AzP7RTN7hSQ559xqAYyZ3WZmR44ePaqRkREdPnx4y8ZbGnzUYyoH00c25/DhwxoZGZGka83siJndVrx9vedXeDwmJiZ07tw5pVKpGo4ejW6t8wsAAABA8zPnXL3HULF8wPJ5SeckBZKulfQt59x/C7e7VV7QyMiIO3LkyJaMVcoFLxMTE8pms+rp6VE8HlcQBPJ9X7FYbMsqCuqxz+3IzO52zo2stH2t8ys8DtFoVOl0WtlsVpOTkxocHFQikajJmNE8Vju/VvtoMzM10+c4tt5an10AAACovWbr+ZKQtCDpv0pKS3qepLea2f9wzr1lteClHnzfVzabVSQSKfTNCKdybKVa7JNAZ/3CihdJhSCu+NzYKhw7AAAAANhaTRG+mJkn6ZckdShX8fIq59x7zewLkgYk/ZSZXeGce7ie4yxV3LB0u13klgYJWFtpA9t6BHESxw4AAAAAtlrDhy/5qUb/JukeSRlJZyT9rpkdl/RlSX8t6VWSovUa40rqdXG9FVgJZ/0a5Xzg2AEAAADA1mr48EXSiHJ9XX7dzL4k6bik/ZI+qFzwkpHUL2mmbiPcgRolSMD6cewAAAAAYGs1w1yYlKQXmNnnJd0p6Z3KVbzskTQvaUi5aUiP1m+Ijat4taVmW3K6keTfsy35fWnGY9Ys4wQAAACAemiGypfjkv5F0ksk/blz7iEze6Wkj0t6r3Nupp6Da3TF/T0k0etjg/LLdLds1b6a7ZjRRwYAAAAAVtbw4YtzbtHM3i3pekm/YmZZSbsldUtqqNWNwr/+S7kL0EZosluuv0c1en3stBVz8u/ZUiX3Xe29qeR9q/SYNdIxoI8MAAAAAKys4cMXSXLOPWJmPy/pVyX9L0njkn7OOXexviNbzvd9jY+PS6qsr8ZWXDyXjqNaVQk7rdIhf3wqmlPj+75mZmaUSqU0MDCw7NhW8r5Veswa6RjQRwYAAAAAVtYU4YskOedGzey3Jf3P3I+NFbxIub/6Dw4OFr5fSyNdPFcqDIyi0dziUlQ6XCoWiymVSimbzcr3/WXHtlyFyEZDOKpNAAAAAKA5NE34IuUSFzXwqkae5ymRSFR8/9KL51pUwlT7OZsxMNpqnudpYGBgWdPc8L0vVyGy0fe09LkaaRoSAAAAAOAxq4YvZvaMlbY5575e/eHsLKUXz7UINqr9nFRbVMbzPHmep2QyueaUnGq9pwRjAAAAANCY1qp8+QdJg5Ks5HanLVr5pV7qUUVQi2Cj2s9Jb4/KhBUv8Xh8zfe+Wu8pwRgAAAAANKa1UoUXSTrvnPNKvrZ18CI9VkWQX2J4S4QX4dUMe2rxnFib7/tKpVKFCpitwLEGAAAAgMa01lXa9yW1mNkttR9KY4nFYkokEpdUEYTLSQdBUPZn7Gzh+RCNRgvnD+cIAAAAAOxsa4Uv75YUl/Q5M3vrFoynYaxURVBaEVONCplqXJxzgd8YwvMhnU4rFosVKmCSyaRSqRTHCAAAAAB2oLXClxdLGpL0Q5J+vPbDaXylFTGxWEzxeLzQ42MjqhHg1GOaFC5VfH4UN8ANV8EKjxFhGQAAAADsHGs13F1wzo1JGjOz1q0YUKMrbY66nlVtpMcqVCQVKmuq0SiVZquNofgciEajSiaThca74fbSYIYGxgAAAACwva0VvgQrfI8i6wk+fN/X+Pi4pOVTmzZ7AR4+RxjubOUqTSgvnU5rbm5Oc3NzikQiy84PwjIAAAAA2DnWCl+uMbOl/PdW9L12wopHlVpPeBKLxTQ4OFj4vtrqVVFRj6W5G13psS49Nhs9PrzXAAAAANBc1gpfnrklo9hBPM8r9P+ohXpVVDCN5lKlx7pax4b3GgAAAACay6rhi3Pua1s1EFRHNaYwbQTTaNZWrWPDew0AAAAAzYU5C6iKlZbmrgQr/6xPJe817ykAAAAANA7CF9Qdy2RXH+8pAAAAADQOwpc6yWazGhsbUzabrel+mqECIhaLKZFINP00mqWlpcLS0qW2+jhsl/cUAAAAALYDwpcKVfvieWpqSmfPntXU1FRV9r3SfZqhAmIzU5YayeLiok6cOKFUKnXJtmQyqePHjxca5dbadnlPAQAAAGA74MqsQtUMMYIgUDQa1Z49e9TX17fufZcLWlYaHxUQW8fzPAVBUDYk831fqVRqS0OwZqh6AgAAAICdYK2lppFXzRVmfN+X7/tKJBKKRNY+BKX7LrfU8Erjq9fqRztRS0uLurq6ylabDAwMyPO8isK2amFJagAAAABoDNsifDEzc865Wu6jmiFGcVASBIF831csFltxikjpvssFLYQs9dfS0qK9e/cuOy7Fx3fPnj1bOh6WpAYAAACAxtB0047MzDOzPzCzXzSzV0iSc86Zma3ymNvM7MjRo0c1MjKiw4cPb8lYV5r2UdyPYyPTmejnsbUOHz6skZERSbrWzI6Y2W3F24vPr1tuuUV//dd/XdhWj5474XknifOkCax1fgEAAABoflbjgpGqygcsn5d0TlIg6VpJ33LO/bdw+2oVMCMjI+7IkSNbMlZJSqVSSiaThZ4r5SpcKql8aTTNOOZqMLO7nXMjK21/0pOe5I4cOVLV47uRxxefd1RDNY/Vzq/VPtrMTM30OY6tt9ZnFwAAAGqv2a6cE5IWJf26pNdL+l+S/pOZ/Q8pVwFTx7FdorjZ7UoVEBupYql3I9VmWEGpHhYXFy9Z6WizVUobea9psgwAAAAAjaUpwpf8VKPfknSjpFZJr3LOpSV9QdJ7JT3VzK6o4xDLKr7wjkajhf9uVr3DDy7uy1taWqp6ILaR95ppaQAAAADQWJrl6uyZkm6X9BRJpyX9oZm9UNKCpPcr9zo2n2rUUDqdVhAESqfTa953rcqWeocfXNyXF/bwqWYAw3sNAAAAAM2v4Vc7yvd5OSFpXNILJH1CueqX90n6iKQZSf35/26pcv04VurRsZ4VjoqXCC7XK4aVjRqTmSmbzcr3/UuOT3ET3FgspnQ6veN65gAAAADATtXw4Uu+j8sjZvZR5QKYl0n6jKTnSOqTtEu5aUiPVvJ8qVSqahe9xSFJeLFd7jZpeWASNkQtvU8oDGqi0agmJiaUzWZXvK+0cxvgNhozUyQSURAEymazywIW3/c1Pj4uSers7NTc3JwGBweVSCTqPGoAAAAAQK01fPiSr3xpkbRHuUqXOyX9i6SMpN91zo1X+lxBEKwaeqxXcTXLardV8rhiYVCTSqWUzWYViURWrZhZKfBpJtshQIpEIoXjVjz9KB6PKxqNqrOzs3DM5+bm6jLG7fA+AwAAAECzafjwJV/5kjWzf5D0IknXS7pfkq9cAFMxz/PW7JWynovTctN/KpkSVOm0oeKQxvO8FStmKgl8NqvWF+3bIUAqbbCcTqcVjUaX9e8Jg7TwvxuxmWOxHd5nAAAAAGg2DR++FPm+pD+U9HXn3MvMzDaytPRaF5zVuDgNL47b2to0PT2tvr4+RSLrf6tLQ5qVQpat6AFT64v2rQiQtkLxsQirYJLJpOLxeCH4q/R4rVbpNDMzo1Qqpb6+vnX1j9ku7zMAAAAANJOmCV+cc4+Y2XOdc2fzN3mSlqq9n2pcnIZBxfz8vC5evChJ2rNnz6bHVs9Gu2u9Lxupxih9zHasxCitXlqPlQKvWCymVCqlTCajkydPqr29/ZL7rHQ8tuv7DAAAAACNrKmaPoTBS77qperBi7TxpX2Ll4cOl4I+cOCA9u3bp76+vg09Tz2VjmOt9yUMCnzfr3gfG3lMo1vv+7aa0iXFi1dMGhgYUCQSUSaTked5l4Ri2/G9BQAAAIBm1TSVL8U2Mt1oo4orCCStWN1RWqUQVhesVfFSWqHQKD051juOjVQMbccpMOH7FgRBIRRZKXhZqTql+Pbi977cOTY3N1c23NmO7y0AAAAANKumDF+2UvEFr6QVA4mNXuwW9+8YGBgorGoUfm3lijTFF/3rfT0bmc6yHafAFDfYDatUVnqNpWFK+P6v9NjSYxKGLsWVMdt9GhcAAAAANCPClzWstpx0seKL3fX0P4lGo1pYWCg8JrygTiaTW34BvVL1Dio3NTWlbDZb0cpapedW+P4XN+ctFp4PYTizVmUMAAAAAKAxEL6soTQAKb34LReurOciOJ1Oq729fdnSw5udMlJp+FN6P6aqbE4QBMpms4pEIpdMBSqtZvE8b9XVrFY7bqlUSuPj4xocHFQikSj7eAAAAABA4yB8qcB6+7KsZ+pQuQvuSipeVgtYKg1/Su/HVJXNcc4pkUgsC16KpxKNj49LWl7BUnwMN/v+c/wAAAAAoDERvlSgNKRYq8IgvJAOpw7FYrEVg5KNXjCvFrBUWgFBpUR1hSsclQvq4vG4BgcHJV06zUha3zSh0l4vAAAAAIDGRvhSgfAiN2ymGjakLb7IXm0KT6UX2evpFbNacFJpoEOlRHV5nrdsmekwqAt/jsViSqfTklSoiioO89azH44bAAAAADQPwpcKhBe7qVRqxZWPVpvCU2mFyXoqIbgAbzxmpiAIlvXvKa6CCqcfhVKplBKJxJauaAUAAAAA2HqEL+uw2spHlVairFbdwjSg5uZ5nnp6ei45tsWVU+l0es2Vs9ZTAQUAAAAAaHyEL2sovRAuXfkoVGklymrVLeupZuECvTGVO34r9f0pvW9xc97ilZEAAAAAAM2N8GUNYVgSrlq0UthRbinhcsr1jyntGRPud7VgpTTEIYxpDNlsVlNTU+rt7VU6nV622lXYD2al86i4OW8ikVhWFcPxBQAAAIDmRfiyhvACOAiCQthRrorB9/1LlhIuVXwBXRqepFIpjY+Pa3BwsNAjJNxW7vGlU5Q2unIOquvkyZO6cOGC5ubmlM1mdf78ee3evVt79+5VIpFYdh6Vhmbllh2nGgYAAAAAmh/hyxrCIKW4YqFc0BGLxS5ZSjgUXnBPTk6qtbVV0WhUfX19Ze8b3hauhlNcOVG63+KLcPrF1N/S0pIymYy6urq0a9cu+b6vbDa7rNIllUotW+GoOHRLJBKXBCurVcMAAAAAAJoD4UuF1lq9yPM8JRKJws9hxUI0GtXU1JTOnDmjyclJdXd367LLLlM6nV7WhFeSBgcHC1OWwuqXSldNYvWjxhCJRNTZ2alUKqX5+Xl5nqdsNivf9wvhS+kKR2FVS7npauWqYQAAAAAAzYXwZQNWCzpKp4kkk0nNzs6qs7NT6XRa3d3damtrWxag+L6/7KI8CAJls1lJud4wlewX9dfS0qLBwUFls1mdOnVK09PTymQyisfj2rt37yUhmpSrYOru7i4ENJtpwgwAAAAAaEyEL1UUBIEmJiaUzWaVSCSUSCSUzWY1Nzen/v5+dXd3KxaLFSpkwoa7xdUN4XNcvHhRnuctq5BB4/M8T5OTk1pcXFRbW5s6OzvV2tpaqGYq18Ont7dX09PTy4I2AAAAAMD2sS3CFzMz55yr9zjCHh+RSKQwhSQIAkUikUL/lnQ6rUQisWL/llQqpWw2q66urmW9QdAcgiBQZ2en5ubmtHv3bnV3d1/SnycUngPheULQBgAAAADbU9OFL2bmSfoVSROSRp1z33HOuUYIYMr15yhu2BuGM8VLSpcuOV2NHh8sS1wfS0tLGh8fV39/f6G6qVwfl+J+QFLuHEin0wRtAAAAALBNNdWVuZmZpK9IuknSGyS9zcx+U5JWC17M7DYzO3L06FGNjIzo8OHDWzPgEuGUozAUicfj8n1f586dUyqV2nRoEvaZCXvN+L5fg1ex8xw+fFgjIyOSdK2ZHTGz24q3h+fXsWPH9LKXvUwf/OAHC9POJBWObSiseAkrXSo51uGxLX4ebA9rnV8AAAAAmp81wGydipnZfkl/4Zx7qZntk/QTkl4q6WPOufet9fiRkRF35MiRFbdvNvwIQ49EIlFY5aivr0+RSGTZtuKpJclksrDUcLjCUbklh9ez//CCnsqX6jKzu51zIyttv+aaa9xnPvMZXXHFFYVVjk6ePKnW1lb19vYuW92q+Dxb6dwoVsl90NxWO79WK+wzMzXT5zi23lqfXQAAAKi9prgyNzPPzH5K0m2S9pvZc51zZyV9TNK3JD3bzHZtdj9hRcJaFSMrVSGE00iy2awmJiZ05swZnTx5UkEQLKt6KX6OWCxWWAmn9D7rFT6+tIcM1RJbY2lpSd3d3YUeLidPntT09LQWFxcL08uCIChUPYXBWDQaled5qzbc3ey5AQAAAACon4YPX/JTjb4p6VWSXi7pSZL+ysye4Jw7J+lPJR2UlNjsviq9wF0ppEmn05qdndXo6Kii0ah27dqllpYWTUxMFJ4/XIa6eOpJeLukTYUmpRf1lYZJqA7nnM6ePVsI35xzWlxcVHd396rHIp1OFxrurqT02AIAAAAAmkczNNx9saQx59x/MbNBSV+UlJF0xMz+m6Q2SS2S5je7o3CqzlpTj4qb4pbe3tXVJUmKRCLav3+/vve976mrq6vQVDWbzUp6rNFuNBq9ZOWj0p/LqWSK1ErjrKft3Ay4paVF8XhcU1NT8n1fDzzwgNra2nTs2DENDw+XDfbCVbDqvbLVdj4uAAAAAFBvzXCVNS3piWY2olzI0ibpmZJeJ+lGSU+V9Drn3EQ1drZahUI4VUgqv4qN53nas2eP9u/fr3g8rkcffVRjY2MaHx9ftgx1LBZbVu1QWnHT1tam+fl5tbW1bWicxeNptGqJ7VyNE/be6OvrUyqVknNOZqb29vZC9ZO0vKrJ932lUil5nlfX47SdjwsAAAAA1FszVL58T9LtkmYl9UjaK8mXFEg6J+kNzrnFau1stWqRSipSipeW3rVrlx7/+Mdr9+7dikQiy8KQ4qBlYmJCfX19hYvv6elpXbx4UZ2dndqzZ8+6x9nImnXclQiPuST19PSovb1dQ0NDmp6e1uzsrKRLz6FGeT8aZRwAAAAAsB01fPjinJs3s//rnEub2ZOUm3b0Skm/K+k11QxepMfCk1LrnR7i+77S6bT2798vSYWVasKAJQxgHnnkEV24cKHw3JLU29srSerr61vXOJth6shK7+92YGY6duyYdu/erbm5OV122WWKRqPas2dPYQUs3/eXnUON8n40yjgAAAAAYDtq+PBFkpxzYSfSFj3WdPfFzrn7t2oM4fSQ4gBlNeUqCUpDG9/31draql27dikWi2l8fFyStHfvXu3Zs0dBECyrkijdb7kli8NlqxOJTfcfxjp5nqexsTEFQaC9e/cqGo0qm80WppYlk0mNjo5qeHi4YcMxAAAAAED1NdsV4H2S3ifp+bUKXoqXkS7+fj1L/YahSFjpIOUa7E5MTCiTySx7zt7eXh04cEC+72vXrl3q7OxUW1ubUqlUIUwJe8aUok9HYwnDlsHBQfX392tmZkZHjhwpNOANA7zi45XNZjU2NlZoxAwAAAAA2H6aovIl5Jzzzey/VnuqUbHinhySllWeVDItIwiCQsiysLBQqILwfV9nz57V3Nyc2tvblUwmC885MTGhs2fPqq2tTb29vZqeni5MQ+rv7y8EOeHzh9UupdU1pf1ksLVSqZROnDihdDqtH/3RH9XRo0e1tLSkWCymgwcPKhqNyvO8ZdPJpqamdPbsWUlasb8PAAAAAKC5NVX4Ikm1DF6kXJARVr1spAlpKpXSxYsX5XmestlsIYAJe3/09fVpampKY2Nj6uzs1N69e9XW1qZIJFIIT9ra2gpTVdLp3IyrdDpddhnq4kCIvh31ZWbq6urS3NycvvKVrygej2twcFDXXHNNYZpR6fSxMIhZrb8PAAAAAKC5NV34Umvhkr/JZHLZykWpVOqSRralPVfC+4VLSg8ODioSyb3FyWRSkUikELJ0dnaqq6tLQRBodHS0ENDE4/FC75cwgJEeC4Ci0ahSqVRhalIjN9fdiQYHB/XII48oCAJdeeWVuuGGGxSJRBQEgcbGxnThwgW1tbXp0KFDhfOBihcAAAAA2N4IX8ooDTzKLTFdPL0olUppYGBAvu8rCAItLS1pfn5e3d3d6u3tLYQy4apGAwMD2r9/v2KxmFKplKTHqlZisZg6OzsVi8UKPWOKA5Z0Oq0gCApTk4rHhPqKRCLq6+tTS0uLUqmUuru7C8d9165dGhsb09TUlNrb2+V5noaGhgoBGwEaAAAAAGxfhC9llFa8hP1Wiqcf+b6vTCajmZkZ9fT0FEISSRoYGND09HRhKknYh2V0dFSSlEgkCs8vSZdffnlhn2GAE043Gh8fV2dn5yX9XKLR6LLKGNRfS0uLBgcHNTw8rJmZGe3bt09nzpzR5OSkhoeH1dnZqb6+Ps3NzenChQuFEEYiQAOAncLMbpd0s6RRSa8rnU5dul3SEyW9W9KipEcl/Uytp2ADAIDq48/tqwgrXsJ+K8XVCbFYTG1tberp6VFbW1uheiEej6utrU179uwpTDcJq1t2796t7u7uQmASrmZUHLxEo1FFIhFls1n5vq+5uTmNjo7q+PHjOnHihHzfVzweX9YjBo1hcXFRnucV/nv27FlNTU1pfn5eUm4J8csuu0zd3d2FcK3SFbQAAM3HzD5Q8vMNkvY7554u6ZikH69g+2lJz3LOPUO5QOaltR85AACoth1V+RI20q00sFit4a7neYWpRsU9X0qnCYUBTjwe1/79+wtTicLeLWH1i+/7mpmZKUxdCZeaDislCFka3/z8vLLZrIaGhhQEgZLJpLLZrAYGBtTZ2Vk4htFoVIODgxoYGFBbW9um9lnunAMANKybJX0h//2dkn5O0kdW2+6cK96ekRTUepAAAKD6dtTV2tLSknzfX/U+YaVKGNKsVl1Suj0MWor3EYvFCtOM4vG40um0ksmkpqamFASBuru7C71ewoqXdDot3/c1Ojoqz/N04MABXXHFFcv6xIShDRqHmWlhYaFwLBOJhHbt2qXLL79cvb29isVi8n1f58+flyRlMpmKn7v4vCxW7pwDgGZkZsfN7A/MzDOzg2b282a2ZGa3VOn5e83sk2Y2Z2YnzezVG7mvmbWb2fvzt8+a2T1m9oIKh7FLUjL//UVJvZVuN7MhSc+V9JkK9wUAABrIjqp8aWlpWXGKh3NO0vLmup2dnZJyF8lmtuLzfuxjH1M2m1VLS4uy2awkqbW1VZL02te+dsXH3XjjjXr6059eWBFJygVELS0tSiQSeslLXqLFxUWl02l5nqeWlhZdvHhRc3Nzy8ZXarWxonba2tq0sLCghx9+uNA0WZKy2WwhoIvFYhocHCx8X6lyTZ+Ln2OnTl0Kf2/Luffee1f8XfihH/qhFR936NAhXXvttStu/8d//MdVx9TR0bHiNn43gYq8RNIna/C8f65c5cigpBsl/YuZfd85d9867xtRbirQf5J0StILJX3MzK53zo2a2UFJf5t/nmvM7Kv5758raUZSIv9zt6Tpkv2W3W5mCUkfknQr/V4AAGhOO6ryJVxGejUdHR1KJBKrXkCVWlxclO/7ymazMjNls1ktLi4Wen2s5MSJE/rGN76hhx9+uFC50NLSoqWlJXmep76+Pl24cEGTk5OF5+ro6FAsFitMoQoFQaC5uTkqYuooCALdf//9OnHihKTcylTnzp3T9773PR0/frzQ+2cjwgqq0pBlreosAGgGZjYq6QpJb81/PVHSL1bx+TslvULSW5xzKefcv0r6tKRL/kKy1n2dc3POuT9wzo065wLn3D9LekTSD+e3n3LO3eKcu0XSneH3zrmMpG9Kek5+V8+T9I2S3V+y3cwikv5B0tuccw9U5x0BAABbjSu2Ep7nLevPUSzsr7FawBE22b3rrrt0++23r7ov3/f1ve99T5/+9Kf1l3/5l/r4xz+u6elpZbNZBUGgWCymjo4OdXR0yDlXWMJayjXrLQ535ufnlUwm1wx8UDvhuXPmzJlC/57wtvC88X1f4+PjGh8fL9xWyTQyQpats7CwoKWlpXoPA9hpXijpjKS/lPQTzrl7JB1f60FmdquZuTJfHyi561WSss65B4tu+76kx5d52vXcV2Y2mH9MuQqaZfKva9zM7so/3yfMbI+ZvW2l7ZJeJekpkt5iZl81s59aaz8AAKDx7KhpR5sRBEEhGJGWT/NobW2VmcnM9Pd///f62te+Jt/312ymGj5XuHLR2bNn9bGPfUwveMELNDg4qFgspv7+fvm+r1QqpXQ6XZjC0tPTs6w6J/x+PRU7qK5IJKJ9+/bp4Ycf1qlTp3T27Flls1nt2bNHHR0d8n1fAwMDy6YdJZNJjY6Oanh4WIlEgua5DeDs2bM6e/asOjo6FI/H1dPToyuvvJIpQ0ANOefuN7MFSWPOuTVDlyKfVq5KplTpdJ64HuulErooqavMYyu+r5m1SvqwpA86546VbnfO3Vrmtt8quWlMuWqflbZ/KP8FAACaGOFLhcLgIxKJKBqNLttmZmptbdW5c+f0uc99Tk94whMUj8f1b//2b6s+5xOe8AQNDQ3p9OnTeuCBB9Tf36/p6Wndf//9uv766wuBztLSkrLZrHp7e5VMJgvTnIp7voQVFqiv6667ToODg7pw4YJOnTpVOC7nz59XW1ubpqam1NfXp3Q6rSAINDk5WWiYG4lEyvZ1wdbq7+9XT0+PUqmULl68qMnJSe3bt4/fL6ABOeemdWnQUk5Kj/VSCSUkzW70vmbmKReKZCS9oZLxAgCAnYvwpUJh4BKNRlesSggrWZ7xjGdodHS00IB1JZFIRFdddZWuuuoqTU/n/u3Y3t6ubDYr55zm5uYUjUYLlTWLi4vq7e0tVODMz89zQdhAnHP67ne/q/b2do2OjurBBx9Ud3e3JicntWvXLl24cEGPf/zjCytdhUtR7927VwMDA8ua8qJ+4vG4Hve4x0mSHn30UX3/+99ftbEvgKpaV4mZmd0q6W/KbPpgSdXJg5IiZvY459xD+dtuUPmpQmve13KlcO9XriHvC9fTBNfMblduSelRSa8rfWzpdkkxSV+UdJ2km5xzP6h0XwAAoHHs6PAl7LchqbCEs+/76uvrUzKZ1COPPKK2tjbdcccdetzjHqdMJqP+/n7NzMyotbVV999/v3p6enTvvfcqk8locnKyKuO6cOGCPvOZz2hyclKDg4NaXFxUW1ubrr32Wi0sLKi3t1fz8/MyMx06dEjRaFTHjx9XR0eHrrnmGs3NzWnXrl3KZrOFi/pKls8+fPiwbrvttqq8hkptp30uLCzojjvuUGtrqw4cOKClpSWlUqlCeLZr1y7NzMzouuuuUyaTUTab1ezsbKHHUHhsaqEe7/N6BEGgP/uzP9Mb3vCGZedm2CfH8zwdO3ZMXV1dSqfTknLVaF/84hd1ww03SMr1ULr66qs1Ojqqe+65Ry0tLTp+/LjMTBMTE4pEIkqn0xvqi3TmzBl1dnaqpaVFr3jFK3Tw4EFdc8012rVrl1KplA4cOKDHP/7xmpmZUW9vr3p6ehSJRArhaHEz7NLfwaLPobrONavXOcJ+UWRG0pPN7Drn3P0VPqaiaUfOuTkzu0PS283sF5RbweilyoUc2sB93yvpWknPcc5V/KFiZjdI2u+ce7qZ/Z6kH5f0kTW2f1zSiyS9o9L9AACAxrMjw5fwgi6bzerEiROFC+Gw4uTBBx/UD37wAz300ENKp9P6+Mc/rquvvrqwipHnefJ9v3ARd/DgQUkqrFi0Wc45nThx4pLVccxMnZ2dGhwc1COPPKLOzk4NDQ2pp6dH586dU0dHh6LRqPbt26eRkRF1d3drcHBQAwMDmpub09zcnDzP0+WXX65EorSiensFIfXYZ3t7u9rb22Vm2r17twYGBpRIJLS0tKR4PK5sNqv29nZlMhnF43EFQVCYzub7fk2nGjX6BZ/v+3r/+9+v173udYX3IQgCjY2N6eLFizp58qS+973vycw0Pj6uhYUFHT9+XGfPntVHP/pRtbe3a3JyUnv27NHU1JTOnDmjm2++uVCNtlmZTKZQ/XL06FENDw/ra1/7mqRcmNLf36/LL79c8Xhcra2tespTnqKhoSHt2rVL7e3tOnXqlLLZrDKZjPr6+nTVVVcVfgfDJsySWqsy2A3aaWHETttvk/iwpD+U9CeSnl/JA9Yx7UiSfkXS/5U0Iem8pNcXLzNtZp+TdJdz7v9d7b5mNiTplyQtSBor6gf1S865D68xhpslfSH//Z2Sfk5F4Uu57c65j0iapO8UAADNzXZSOb2ZTUo6qdxfmFskOeUueFrzP2clLUlqlxTN3xaRtEu5Od3KP9ZKvq/Fv4gsPz5X8r3y4zRJQX5ci/mfw9eVVe4fi0H+9sWS+yzmt5W6VtLRGryW1TTTPoecc/0rbTSzKUlh+VNaufc6fJ/D750ufe+9MrdVWz3e5/UqHaOn3O9mRLlzt0259y+Wv93lb7Oi+4XvY0SP/e5U+/ez9PfRKfe5sZTf/4JyPSMWiu5TXNWSlTSv5cfck3TQOddXdoePfXbVUr3OEfZbe6t+dmHrmNnvSrrfOfcpM7tS0tudc6+uZHt+Bad3Mu0IAIDmtKMqXzb6j08zO+KcG6n2eNjn9trnShfOjaAe7/N6NcMYpfqMcysunOv1/rNfbDdmtkfSP5TZ9ErlplaFpafdurRqZ63tAACgSe2o8GUTDrNP9tnkmuE1N8MYpeYZ53rV63WxX2wrzrkxSbeU22Zm35T0Rkl/K+l5kr5Rcpe1tgMAgCa1o6YdAQAA1JOZvUPSTZJOKdfzpVe5njJvLbfdOZcxs88q1/j3pKS/dM59oA5DBwAAm0D4AgAAAAAAUEN1Xdq0WZjZli9NwT631z7rrRleczOMUWqeca5XvV4X+915dsLn/k54jQAAYH0IXypTj3/QsM/ttc96a4bX3AxjlJpnnOtVr9fFfneenfC5vxNeIwAAWIcd1XC3r6/PDQ8Pr/txsVhMIyMjZednBUEgz6s8wwqCQIuLi1pcXNTCwoKy2aw6OjrU0dEhSWppaZHneavus1bY5+ruvvvuqdVWnSk+v9Z7XtRaPd7n9drMGIMg0NLSUuH3J7wtnU4rk8nI8zy1t7cv2yZJra2tknTJY2s1ztWsdn61t7e7vXv3qq+v8gW11nsO1uscYb+1t9q5tdH/L25GM33uN8v+6rXPtf6/CAAAHrOjwpfh4WEdOXJk3Y8bGRkp+7hUKqVkMqlEIqF4PL7qcwRBIN/3FY1G5fu+stmsZmZmND09reHhYfX09CidTisWi8nzvBX3WUvsc3VmdnK17QcPHtR3vvMd+b5f8XmxVerxPq/XZsYY/n6Fvz/hbclkUslkUp7naWBgQJlMpvA7KKlwfEofW6txrma182vv3r06cuRIxeHLej6bQlt5jhQfryc/+cl1OTfr9TtRj/2udm5t9P+Lm9FMn/vNsr967XOt/y8CAIDH7KjwZaNuu618JW8sFpMkRaNRpVKpVS/eUqmUxsfHNTg4qHg8Lt/3NTw8rMsvv7xwn+KLpJX2WUvsc3Oy2ax+8IMf6KqrrlIikSicH42gHu/zem1mjJ7nXRIyeJ6nnp4e9fT0FG5ra2uTJCUSiWX3XU9IVo/3sq+vr2zwUi50kh77TIpGoxXvYytfVxhQFu93pddSK/X6nWiG38Va206f+42yv3rtEwAAVG5HrXY0MjLiavFXoZX+ylx8MZFMJjU6Oqrh4WFFIpGGq4zA2szsbufcyErbr776avfHf/zHuvbaa3XFFVds5dCwDax2fo2MjLiwqqo4nFjps2cjlS9bJQgCpVIpSbnQa63Xgs1b69xq9Ko4NK61/r8IAAAeQ+VLFYQVDqWVDsV/3fU8T52dnYV+LuXuj+bmeZ76+vp04MCBeg8F21Dx50kYTqz0WdLInzG+7yuVSimRSCyrcGnkMQMAAACbRfhSBeWmPEiXXkyEwctK90dza2tr08jIiCIRfq1QfeXCiZU+Sxr5M2alkKWRxwwAAABsVuMsx9IgwpL4cDWUzQgvJhpp1RvUTmtrq9LpdFXOnVLVPC/RnMp9npSeF81wnvC5CAAAgJ2If/2WCEv7w9VQGv150TjC1XVqcYw5f1BO6XnBeQIAAAA0JuZHlKhV3wH6GWx/nufVbJUjzh+UU3pecJ4AAAAAjYnKlxK1LolvhmkB2BjnXM2OLVM1UE7peVH6c1iNlUwm+cwBAAAA6ogruSqoJFBJpVI6d+6cJiYmmBawTWUyGT344IOamJio91CwzVUa4qZSKZ04cULnzp3jMwcAAACoI8KXKlhPn4VYLLbm1BSqY5qTc06e5ykIAo4dqq74c2E9nzkdHR3q6upiKhIAAABQR/R8qYJK+iyEUwGi0ajS6fSqzxdeWIWPKxVefIXLVqMxtLS0aHh4WJIKx4fjhGop/lyotLdLPB7X/v37G+4cbNbPsGYdNwAAAOqPfz1WQSX9OML7pNNpJZNJpVKpFatbotFoIagphxVNGlMQBIrH4+rp6SkEL+s5TlQ8YTXFVXOV9gBa637VOufW+zzN+hnWrONezb333iszK/sVhskAAADYPCpfaiT8C2lY6RJeMIV/qQ4bYUq6pELC933Nzs4WLrZKsaJJY1paWlIymVRPT4+kyo5T8V/S16p4ws4WnidBEGhiYkJ9fX2KRDb3EV6tc67S5yn+XJSa7zNsO372ZjIZOefKbjOzLR4NAADA9kX4UiPhxUjxX4OLQxZJhTAmvG8QBIWeIasJ/5qNxjMxMaHp6WldfvnlSiQSisfjhaqAclMVNjKVBDtPcVibSqV05swZzc3N6dChQ5ua/lKtc67S52n2gHGrP3uZ5gQAALB9EL5UUfE/lKPRqFKplHp7e5XJZBSNRjUxMaFsNispd+ER/iO+tBomHo9r7969XIQ3Gc/z1NHRsWrAUnrhVnzRSqiGlYTnRhAE6u3t1dzcnFpbW+X7fsXnTOmFfDUv7Cs9dwkY16fZwyoAAAA8hvClior/oSzlLnYymYzi8bhSqZSy2awikcglFx7FF1ZhNQx/5Ww+2WxWCwsL2rdv37ILpdUuOAlcUCnP85RMJuV5ng4dOrSsiq4SpRfy9biw53xfH8IqAACA7YPwpYrK/UO59LbVghUuTJpbe3t7YbpR8THmuKIaNlslVe6zqPi/aDx8dgAAAGwflFdUUfHKIqWrjFS6OkktsZpObbW2tqqnp6eqx5hjhtBmP0Ma8TOplvjdAQAAQCPZnv/q3oEqudDYjsukNpONXAxyzIBL8XkHAACAZkP4skXCi4VsNruuC/BKL9grudAIl65mmkHthMcqbJ4crmIlbexikGMGSYVzSsr1FhobGys0796J+LwDAABAs6HnyzpsZnWQ0qWnK22um0qlND4+rsHBwUKTzHKPWal/Q+mY6R9QO8XLAUvSo48+qgsXLui6665TPB5XMplULBZb82KQY4ZSS0tLhZWNJiYmdOLECQVBoH379jX8csRhgCypatOc6FcDAACAZkP4sg6bWR0kvEiIRqNKp9PLLtQrfa7V9r/SRXqtVjRp9Au+evA8b9lf2iORiLLZrHzfVzqd1tjYmPbt26eenp5Vn6fSY8Yx2DlaWlqWBQ7xeLzwc6MvR+z7vsbHxyVVr4HsWs8TBIEmJiYK1UGN+L4AAABgZyF8WYfN/LW1+GKhdFnp1YR/KS63glKtx7yaRr/gq5fi9+LQoUPq6upSX19f4bbi71dS6THjGOwcYRNvSUokErryyiubZtWiWCymwcHBwvdbwfd9ZbNZRSKRhn1fAAAAsLMQvlRoPVUGa913Pc9V+hfe9V5k12raSqNf8NVTNpvV1NSU+vr6tGfPnsLtxd+Hyp0LlR4zjgGk8udL8TkYidT3Yz6sCNtKpctyAwAAAPXW1P8qNTPbqn2tp1nqWvet5LkafZnU7b5M7WZMTU3p7NmzmpqaWvM4bmZFFo7BzlTJOVN8Dm5Uo38GrYbfDQAAADSapq58cc65rdpXpVUGYTPd4p4MG3kuppQ0r3BqUV9f37KGyeX++k/1Ctar+JxZqYqu+BzcKD6DAAAAgOppuj8LmplnZv/LzP67mV23Vfut9C+pvu8rlUot69FQ7rlisZh83y+ENaV/YWaZ1OYViUQ0MDBQuHhdrXKAv9BjvYrPmZWqYCKRiPbs2bPilKNKqlrCz6BoNNq0FTAAAABAo2iqK778NKMjki6X9FOSbi+zvdzjbjOzI0ePHtXIyIgOHz5cszFWEpqEK3HMzMzI9/2yF1BclDeOw4cPa2RkRJKuNbMjZnZb8fZy51e4wsvs7CxNP7Gq1c6v0nPrfe9737IgZKMhbSVTl8LPoHQ6veGpcaivtT67AAAAsHVsC2fubJqZ3SjpPc65p5tZp6RvS3qnpKSkzzjnsqs9fmRkxB05cqT2A11DKpXSzMxMoUJCEksGV8FKUzCqtSSzmd3tnBtZaXvx+RVWFoQXyj09PQ0/daOS94nlrWtntfMrPLdSqZSSyaQSicSmzqdqNhBHdVSzUXup1c4tM1txBq+ZqZn+jYCtt9b/FwEAwGOaredLStKNZvZ/JI1IapX0IuUqYaYk3VXHsV1ipX8sl1uJY7ULKS5+KrNSj4pq9K7IVxqs+eYXH6vwAtn3/cLUjUY+hpW8Tyvdh3O09irpJ1Wp9ayCVqsV07ZCI5yXlY5hrd8/evAAAAA0t6YKX5xzx83sJZKeLOkpkq5zzgVm9iVJPXUdXBkr/WO5+GKmkn+Y84/uyqzUvLYaTW3zUy5aKrnfzMyMUqmUBgYGCsc6rFiQGvcYVvI+rXQfztHaC9/jSCTCe1yhRjgvKx3DWr9/NOcGAABobg0dvuR7uDxVUtQ592VJcs59xcyOSXqLpNeZWSBpSNIP6jfSS631V+owdAmnp0gr/8Ocf3RXZqW/0FfjL/f5935prftFo1EtLCwom81qYmKiEMA0wzGs5H1a6T7N8PqaXRAE8jxP2WxWvu/XJExohEqRamqE87LSMaz1+9fMFUgAAABo4Ia7+eDlLklvkvRBM7vTzA5aboL6OUlvk/RLkl4r6Sedc4/UcbiXKF71SNIlq4UU/zV0rYaZNN/9/9n78/jGsrPA//8cSdYueZO3qq61qzvVSWd3QkKWDplAIHyBGbZJT9YBpvnxHZhAgISQlW8YpkMWmABJqKwkBELmBwyBIRnyy/ZNYJp0Ze90dbqrql2bbckqW9Yu+fre3x+uc+pK1mrLtmQ/79erXi5by72699yre577nOdsXSezunTixrZv+yblcplAIMD6+rrpJPezXm4faaM7Z319nWQySTgcZmRkZMeCCZ0U4h0k/dAud3odenUMCyGEEEKIndXPPaUXAUnHcX4CeCowCXyIm8OLPuY4ztOAH3Uc5+t7s4rNuadpTaVSLC8vk0qlamYp0Xcxg8GgyYIRvbWbnUnbtgkGg8TjccbHx2uCavl8noWFBZPlpJ+/152m/dbZ3s9yuRzZbBbLsnas3cgU94NHjmEhhBBCiMHQz8GXNPA0pdRdjuOk2Bh+dAL4A6XULPBupVTEcZy+vOJ0T9NqWRZra2s1mRAejwePx0M+nyedTsvF8w7Zzc7k+vo65XIZj8dDsVg0+7iZfug0SWd7cHg8HtLpNHNzcySTyR1pN/2QKSK6I8ewEEIIIcRg6OeaL98B/gm4WylVcBznrFLqJ4G3ABHgNx3HKezlCnZCXxAHg0HK5XLNBXKrx0Rv7GadBK/X23D/AqZD2+jxvdzvUkdiMHi9Xk6ePGmyXerbkji45BgWQgghhBgMfXt780ZGy9uAMeC3lFI/yMbwo1HgPsdxLu3l+jVTP5Sk1Z1k/ZjP5yMcDvds6FE/DGc5iHSmi+4YF4tFLMsim82SzWY37Q/JMhCdchzHFMLVsx31S7uR840QQgghhBDt9XPmC47jPKKUeg3wn4C3AqvArzqOU9nbNWuu2bSi7aYb7eWUqP0wvepBp/dBPp8nl8tRKBSIRCJyl1psiWVZzM/PUygUCIVCQP8c23K+EUIIIYQQor2+Dr4AOI4zB7xeKXXvjd9ze7tGzbWaXrrdEJNeDkHZ6eEs+2062p3gHlIWDAYJhUJNpx1vRba1APD5fBw6dIiRkREymQzBYHCvV8nY6vlG2rYQQgghhDhIBuaK13GcXD8HXqB2eun6zkS7ISa9HILifi/bthsOe9nOUIF+KBTb79xDyny+jRhnuVwGbm77TmatkW0tAJRSTE9PY1kWtm2bttQPtnruata2d2sYU7vlyHAqIYQQQgjRS32f+TJI+qGAar1isUgymQRqCzNuZ6hAP37OfhYOh02wRXc09ZAk3bFrtg9kWwu3/dQemn2W3RrGtJtDQYUQQgghhJDgSw91Us+jPtV+p1Pvw+EwU1NTZkiUbds1M6VspRMndUuay+fzDfft5OSk+T/QcF80IttaaDt5rtiLIUDN2vZuBZjaLScYDJLP5/tqiJcQQgghhBhcAzPsaL9wF2LV/3ZyWInH4yEej+Pz+cjn82Y5Wx0qIKn4zekhXnobu4dVuLe3/qefp9uAbFPRSiqVIpPJ7Mi5op+Gt21nCGY356d2yymXy303xEsIIYQQQgwuyXzZZe7Mh0wmYzoAje6+9vJudK/uJu91Kn4/F+nUga5wOIxt21iWBdDwzrm7HTQaFrYX+nnbHnTr6+usrq4Si8V2JCNkvwxn6uX5qdvMFzl+hBBCCCFEKxJ82WW6g60v1C3LaligF1p3JBoNX8rn8+a5zQr+btded9Ly+TzJZJKpqSni8fierEMrehvr9SyVSkSjUaLR6Kb9o9uBtpPbtJOO4V4H1kRrnWaENNrX7fb/Xgf+tO0GMHp5fnJnvnSybbo5XwshhBBCiINHrgJ3SLv0d4/Hw+TkJCMjI+YOa/3sN+Fw2GRS1KsfJlAsFllYWODixYumk78Tejkrk3sb7ZfhTPpz+P1+ADM7TbP9o7Nl4vF4Tztl9duzfrhbo+3cqr2JveXxeIjFYuZc0e0MWfl8noWFhR09N7ht9djezvCnXgc4uj0eujlfCyGEEEKIg0cyX3aI+y5oOBxu2CnQgQxd86N+9ptWd6Pr7/CGw2FisZjp6PQqQLKT3NsI6CjrQn+ufg0Q6M+kt73P58Pj8ZihCzoYs1vrAdQMa9N1afTf3fol+0FsZlkWlmWxvLw8EDNkbeXYhubrvheZW90eD92cr4UQQgghxMEjwZcd4r7Ybtcp0M8NBoOUy2VTM6SbYQIej4fp6Wk8Ho+Z0rjfO9KNOiTtOif9HiBw70u9HwFzx1sHY3ZrPfRP9zCnfg5eicYcx6FSqTA5OUm1Wu16hqzdDlpu5diG5sd3J4GVfg5w9Pt5SwghhBBC7DwJvuwQ98V2o05BfXBFP9ddM6Tbu7h6KJN7SuPdspWU//r6N/uhHkL9vkylUliWRTweZ2Zmxvx9O7azvaQTOJgcx2FoaIhqtbql/bfb+71+eVtZtm7nwWAQ27abFiZvtkwhhBBCCCH6iQRfdkGjTkGn2TDdBlH2qgOynZT/XhbR7adAji6o7PP5ejoMrJNt3c3+aFesWew9pRR+v78vszp2irtOkW3bPa+LtBO6Pf/00/lKCCGEEELsLAm+7JFOahvoTvMgXKBvJ+Xftm0KhULXtVAabZd+mrHHPexot6cN72Z/FItFM901UDM8pd/b3UHh8/mYnJzc9x1697o3Go65V+vS6XbsNuipM+M6eb4QQgghhBhsEnzZI7qDW59x0OjivZ8CCs1sJ+PG4/EQiUQ6nhZXa7Rd+qnug173ZDJJJBIxGSaddqJbvW+7bd3N/giHw0xNTZnf9Ta1bbuvp/U+SDweT8fZSYNwvmimft3rh2PulF4FcrsNeurMuH44XwkhhBBCiJ0lwZc95M440J3lVoUq9+oCvZNgyHbuttcXA+2009Nou/Rr3YdwOEy5XN5WMeT6bdyrDAc93bVeRn1gUOw9HQiD9m18EM4XzezVuvcqkNtt0FP/bLSdBjmDSQghhBBCbCbBlz0UDAaJRCI1Kfa646sLTep0+70MKPS6xki9+g5Lp52efgu02LZtgheangLc3YHaaseyfhvvRIZDfcFgmRmpP3g8HpOd1Gx/6Myq+ja423p5LtgtexHIbff+g5zBJIQQQgghNpPgyx4ql8vA5umH6wtNwt5efPe6xkgjzWZ/GiTr6+ubslrK5TK2bbO8vLztoqH127jbbd7tnfRB3Q/7VbuhXzqTrlAomGF8/RLI6Hf92NZ1zahGQV0hhBBCCDF4Bi74opTyAJ8ALgD/5DjOF278XTmO4+zpynXJ3UnZbqHJnUxR73WNkUb2w11ej8eDZVlks9lNWSO9KBpav4273eb7YRuL5nTtHvfQse3aykxY/RjIGEQejwePx0M2m5VtKoQQQgixDwzirbRPAQ8DSWBEKfVYpdSQ4ziOUko1eoFS6h6l1Nlz584xOzvLmTNndnWFm/F4PASDQVKpFNlslkwmQyqVAjY6Op1OUaxnzVheXiaVSnU9a1An3EMatvPcZo+Fw2Hi8Xhf3i0/c+YMs7OzAHcopc4qpe5xP67b10MPPcSznvUs3v72t5vPqANirfZlN9t2K8/X9DYOBoObXr/V9xTb16p91Z+73v3udzfdR7p2T7PsKr2PLcvqeP/rbJpkMkmxWOz6s7VapmjPfV7cyjHa7twlhBBCCCF2jxqwZBGUUu8G/gT4CFAAosDXgF91HGet1WtnZ2eds2fP7vg6dmNxcZH5+Xmmp6fxeDxUq1X8fn9XM+Lk83kymQyVSoVAIGA6X9vJgqnPpMnn82SzWeLxeNs7sK2e28379Bul1Nccx5lt9vgdd9zhfPCDH2R0dJTDhw+bu9b1n3U72xZubkN3dk03+7nR8gZ5v+wXrdrX7Oys88UvfrGjfdRsX+q/62LN7sez2WzDma26zXxp1rYbLVN0p36/dpPt2KpttUoaVUoxaNcIYne1+14UQgghxE0DM+zoxnCjCPB44I+Av3Ec5+1KqZcA/wl4KnDfHq7iliQSCfPT4/GQSqW6nhGnfmiLbdvbHl5SP0SlmzoOrZ47iPUgOuXz+Th06BDBYLBhAWFtO9vW/byt7ud+nFFLtNfpPmr2vK0MgXPPhNWJZm27F8PuDrr6/SrDCIUQQgghBsvABF8cx7GBnFLqXuDTQPbG3z+ulPo5YGIv12+rfD4fk5OT5g6m+//tuO98umeo2WrNh0Z1Z9yzMLXK3nBrVZ9gP9cuCAQCJBKJmqmgtW62bTv6+fX7eTt1f/bzftkvOt1HzZ5XP5OVWy9mttKFYd1Bl1bL3G3bOT46fe1u1t6SgKkQQgghxGDp6+DLjRouzwSCjuN8HsBxnP+tlHoF8BGl1B8BK8A08MDerWl77otyoOYCvf4OZqedlGZ3Prfake5mPeSua2N6mnD9M5vNmn1vWRbQ3T5upX4/d7pPZN8Npnw+XxPY62UnvxfBt2KxSD6f39aMXjtpO+2+H48tCZgKIYQQQgyWvg2+3Ai8fBm4DjxFKfUgG8OLrjqO8zGl1CLwI0AC+PeO4zy6d2vbnvuiHNjWsBOt13c+ezW06CBz72d3RoplWfh8vh3dXtsdliL6V/0Qs34MoPV7u9rO+smxJYQQQgghtqtvgy/Ai4Ck4zg/pZSaBD4DfAj4WWAZuN9xnM/u5Qp2o9FFeTAYrOlAdXq3uNFwo17o5k6q3HVtzLZtMzMJbOxXXcNH1/XZKdsdliL6l1LKzI4G/dnJ7/d2tZX126lzrRBCCCGEOHj6Lzf8pjTwNKXUXY7jpNgYfnQCeKdS6mnAnyqlYs2ml+43+sLf4/GY/5fLZZLJJAsLCw2niG41/Ws2m93S1K9i5+j9VSwWWVhYYHFxEdu2TRHkcrm816soBpRt2+RyOXPMu88n/W6QpzHv9lwr52YhhBBCCNFMP2e+fAf4J+BupVTBcZyzSqmfBN4ChIHXOI6T28sV3K5wOMzU1BT5fL7hDEfNhhb0411vcXNmGMuySKVSAMTjcdlf4kDrxyFSndrqTGRyrAshhBBCiHp9e9vUcZwi8DZgDPgtpdQPsjGd9Chwn+M4l/Zy/XpB373W//RsIVowGKwZalD/uu3e9R7kO9L9ShdAveWWW/B4PPj9/p5nKch+O3i8Xi8zMzNdBS/c7WQn20y79w6HwzVByEHS7bG7WxlJcg4QQgghhBg8fRt8AXAc5xHgNcD3gLcC/x74VcdxKnu6Yj2kZwgpl8tmyIr7sdXVVVKpFJZlmQyZTi+6212gS4p87+XzeZLJJNevXyeZTJJOp7Ftm0wmw/z8vJnxaDtkvx1M9Z36ZsEV/f9sNsvCwoI5rzRqM73oxLdrj4M0RGpQyDlACCGEEGLw9POwIwAcx5kDXq+UuvfG7wM91KieLs5q2zYej2fT3eFCoUAul6NQKBAKhfD5fDXFXFtpl+4vKfK9l81myeVyjI2NmX2bz+eZm5sjn8/j8XiYnp7e1jJkvx1sugisbltaJpMx01G7/w7N20wvhgRJe9x99du811OPCyGEEEKI3uv74Iu2n4Iu9RfKHo+HfD5PPB6vuXAOh8PEYjFWV1fNVMWJRIJyuWw69q0uuNt1ivp9dpJBs76+XlPrZX193WQqHT16lHK5TCKR2PZyZL8dbDpgoo97PTxRZ8bBRvvTQ95aBUV6ETiR9rj76rf5INfVEUIIIYQ4KAYm+DKImgVH3BfK4XAYy7KwLItMJkMwGMTn85nn2bbN9PQ08XjcpO7r6YsvXbrE0NAQ0PiCWzpFu8txHLLZLJFIhHw+j9frZXl5Gdu2OXXqFIcOHdrrVRT7gDvbQc+iFY1GSSQSpNNpwuEwPp+PbDbL6uoqtm0TjUZNNoyuL9WLKZT1+wSDQRMUlsyL3dcqE+YG2SlCCCGEEHusafBFKfUo4DR6zHGckzu2RvtIJ7MVFYtFlpaWWFpawrZtfD4fk5OTJvCiZ9CJx+M1751Op1lZWWF0dFTS/fuE4zisrq6SyWTwer3EYjFTb+PQoUOMjIzs9SqKfUAHVeuHKrqnNNfnm1KpBGycf9xFb9tlSnQ6jEW/j7tujAR8d1+rTJgbvLu+UkIIIYQQokarzJc/ZKPI7a/szqrsP81S+t3ZK9lsltHRUQKBAB6Ph0QiYS6co9EoMzMzDe9m6uEriUSiZedIagHsHp/Px1Of+lRs2yYYDHL16lWGh4eZmZlhcnKy4Wt6uX9kXx8s9R3u+vNNNBrl5MmT5v/1QxrdP+vpc5A7wNNqaKM782WvSBbOzW2gh5zpIWnA+l6vmxBCCCHEQdcq+PJ+4LeBrziOc0H/USklWS8d0lMNP/roo8zMzJDNZkkkEmZYUTqdZnFxkeHhYUKhEPF4HJ/PRzAYNIUz9XNt2zazHgFmuEC7zoXUAtg9SilGRkYIBoM88MADPPLII4yPj/Pc5z7X1OSo76S69+l294/s6/1NnxO6mfa4PmMOOgvSuYtF1xcEd7/GHQDa6zbXTRbOfgpUuoNO6XTa1Aery4KSOamFEEIIIfZYq+CLH/hR4H8rpX4QUEAA+AxwbBfWbV+4cuUK58+fZ2lpCZ/PRy6XY2pqinA4TDAYZHp6mkQiQbVabTl8oFgsmovqYDDYcaddZiLZPevr65w/f55EIsHQ0BBKKVP3JRqNbuoY2rbN6uoqsVisJ/tHZkDZv9bX11lYWGBqaqphQAU6D7518jxdCFwPhQR2PLDnnr1pK1NTd5OFo6eEb7U9B4U76NSoMLsQQgghhOgPrYIvrwLefOP/OvPFAT6xo2u0zxw5cgSAmZkZFhYWqFarLCws4PP5CAQCjIyM4Pf78fv9wM0imtFotObCub5OjL7IbndxLUV3d4/uPCYSCQ4fPkwsFjNDyXRH0t0x1FNPd9rRbBdMkRlQ9jfdvpq1l04DrVt9XruZk9zruZWgX7FYJJlMmmV122b7KQtnNzUKOsl5XwghhBCi/zQNvjiO8zvA7yil/n+O47xgF9dp37Btm2q1yokTJ/B4PBw5coRLly4RCASwbZtKpUIwGKzprOgOs8/nq7l4dl9MuztFktHQP5RSZtaZarVKqVQyAZhGHUPdie707nSnwRT3MASQrKf9wOPxUKlUCAQCFIvFbc1u1up59YET9/M67cx3E/RzLy8cDjMxMVHTdjtZx63o9tjrZwc16CSEEEIIMWjaTjUtgZetq++ELC8vk8vliEQieDweLMuiXC4D1Ew9rdPH3QEZd0dD7mr2p2q1Sj6fJ5PJEA6HKRQKFAoFM9NMvW73Y6cZC5Lxsv9YlkW1WmV9fb1m//d6aFkv2k43Qx3rl6eHOLmHXO7EOso5VAghhBBC7La2wRfRmUadIHcnRM+AMz09zeTkJB6Pxzxf06/VU003qu0idTz6VyAQ4OjRo1iWhd/vJxaLmX3XSqf7tNMOo9T52X98Ph+33HLLptnNejVltKbPVe4iuzupvq120nb7oX3LeVgIIYQQQnRLrhp7pFgskslkWFxcrJmiVae3F4tFisWimdHI/Vh93Q/9e7lc3lTbRXe2isVizfLdM5OIvaGUwufzkUqluHbtGqFQiNHR0ZpOsWVZLC4u1gRlmu3TreqmjowYDEoppqenTWYINK8P5ZbP51lYWDCFbNvR56N8Pr/l9thNe2527uskCNnqOTt9Puz1MSuEEEIIIfY/yXzpET1caHV1lUKhsClLodXd2mZ3URvVdmn2PjLUZO/pISHT09MEg0FTt8e9T9PpNPPz8wBMT08D/XEnXwyeYrFIPp8nHo/3NNC23fbYD+15p8+H/fAZO3UjACWRWCGEEEKIPSbBly41C5ToWW1s2zbFI+uf320Ng0bDTJoNPRmkzsB+putWZLNZU+fFvW8TiQSA+QlSf0JsTSfHfKeFZRsV2tXZI90OrdmN9txu2M9Onw8H6Zi9kZ3j3ev1EEIIIYQ46CT40qVWd1R18Vw9rKjd87VedBQGqTOwn+mpXuHmtOD10+fqjBchtqOTY77T80Kj81Q/Z9O1Wzc5H95043y0vtfrIYQQQghx0A10KrJSSu32MsPhMPF4vGGgRGe3uAtW1tdk0HeTLcvaNKsRUPOYrlcg9VwGg55aXO/3fD6P3+8nEokwMTHRcXCtk/0tbUJA7fkkm82aelPuxzo5jzQ6rzX6W6ftbqfbZ6vz8CDaye1140aAnCiEEEIIIfbYQGe+OI7jKKU8juPs2oVlqzuqulhlNps1mS/1NRn0HdtsNkuhUCASiQA30+ir1SqVSgXbtpmZmSEajW6a8Uj0p7W1NR566CGOHj1KOp0mEAjg9/sJBAKmUKp7GEezoROdZBz0c1aC2D26HeTzeXK5HHDzHKUf08HApaUlAoEAsLnNuM9rrYZKdtrudrp9DmJmS6uhUnI8CyGEEELsfwOZ+aKU+pRS6jUAjuPYSqmWn0MpdY9S6uy5c+eYnZ3lzJkzO7Zu7juy9ZkwAH6/n1KphM/nI5fLYVmWec7KygrLy8uUy2WuX79uLtbrZzwSu+vMmTPMzs4C3KGUOquUusf9uG5fly5d4ud//ud5//vfz+rqKrlcjnK5bGpuuGdIsW2bVCrF8vIyqVSq5o53J3f199ud/4OsVftqd+7S7SCRSDA1NcXU1FTNsDddd2hubo5r166RyWQIBoMt16e+nbqz8YLBIPF4nGAw2DJTQ9rnZq1mqtO1wtzfFa10minzvve9j6c85SnQ5NwlhBBCCCF2j3IcZ6/XoStKqRngq8AE8HrHcd554+8eNpJhmn6g2dlZ5+zZszu+ju47nPqCOx6PE41GWVxcZH5+nlgsRqFQIBQKMTMzQz6fZ3FxkVAohM/nM5kv4XCYdDpNIpGomWZW7D6l1Nccx5lt9viTn/xk58Mf/jDZbJbl5WWCwSCTk5OcOnWKeDy+qV1kMhkqlQpDQ0P4/X4mJyc7KmzartioGEyt2lcn565m7cK2bbLZrMl8GRsba5ldoTv2Wj6fN5la+jyWz+drzmvSJttrto30tqzfxq3Ub/92zzt8+PA3HMd5SqPnKKWafm0qpRi0awSxu9p9LwohhBDipoHrzTuOs6CU+jgb636P2rhyfAcbgaS+GNden+4PmLvNepabsbExU6AXNi7Mp6enTX0YfXdU330ul8uSjt7nvF4vx48fJ5vNMjExgW3bDA8PA2y6Q60zAoLBIOl02tT+2WpxVCFazZo2MjJCPB6vqS/VjA4CzM3NcfToUZPpUl9M2v1zkNrkXgWK2s1UV7+NW+m0SLvr8b74bhRCCCGEOMgGKviibt6iGwJCwP8NfEApdTfwduATe7l+mjslf2lpCdjonOjhJ3q2G7/fD2x0BvRj7loglmURj8clfX+A+Hw+fD4fhw8fNvtSZw4AZDIZ8vk8k5OTpiM2OTnZtlPs7jC6O16ScSC0dh3yVnVS6ttRsVgkn89TLpcZGRnZ1Mbq36vRsvu1bfZboMi9LTtdn05r3gxibRwhhBBCiP2qf66IO+O98fPjwILjOJ8DUsCTgfG9Wqn68fflctkEVHQdBtjoeOv6Hu7XWJZFMpnEsixTC6RareLz+YhGo0Sj0b7qvIjmgsEglmWxsLCA3+83Hc9gMEg4HMbn81GtVllcXDTZUbqD1Gofu+tFuJ/frI6E2N8a1fxwtwvbtslkMszPz5ti3a3eo74d6eFyOjDYro256xrpddqJttmLWYGkHo0QQgghhNgLfZ35cmMq6WcCQcdxPu84ju5FFIGXKKV+HDgHvBG4sEeruelOqrtwovvOo85m0Z0R/ZqFhQW++93vmjovlmV1VQNE9I9yuczVq1dZXFykVCqRSCQoFAqmwzc5OUkqlWJ1dZVCodDxnelmWQ2dDj8Q+4uuGaSzqBrNnjM3N0c2m6VUKnHixAnzHB1scdd2qW9HPp/PZOh12sYanQc7eV03epG1ItkgQgghhBBiL/Rt8OVG4OXLwHXgKUqp7wL3AFcdx3lIKfUhYNpxnFfv1TrqToyu56I7GbqTMzc3RygU4vDhw0Sj0YbDS8LhMOPj40xOTjI+Pl7TYZHAy+AJh8OcPn2aYDDI2toai4uLNYWSPR4PiUTCzG7Sace0WYdROpIHUzgcNrMQNaoVFA6HOX78OEtLSwwNDdU8RwcwotGoyQCpb0f1Q4a2EiBs955b/dzun0IIIYQQQgyKvg2+AC8Cko7j/JRSahL4DPAh4GeAFeDTjuN8BzZmOtrpYruNOg7t7sKGQiFisVjTzoj+/8jICE94whMIBoN9WSNBdM7j8TA2NobP52NhYcHMXuXuLOpCyz6fr+P93K/1M8Te8Hg8LWsFtSqy2yjA26jmi/vc1kn7axekkawVIYQQQghxkPVzLy4NPE0pdZfjOCk2hh+dAP5AKfVU4PVKqeiNIrw7PpNDo/oFrWoHRKNRZmZmTMelVZ0C3aEol8s1dWHq6fexLGvbdQ9E7+n6Pfl8nnA4zOHDhxkZGcGyLC5dumRqb3Rac6JVTQ5x8LiP+U5rnzSqJ9Tob/Xtq76N1j/eyfLrnyO1VoQQQgghxEHWz5kv3wH+CbhbKVVwHOesUuongbcAUeC1juPkd2tlms0wU38X1v0YQDKZJBKJmMdb3bVtN5RAd4DcHRq5C9w/1tfXSafTNfvGtm2SySQLCwsMDQ3h9/tJJBJdTyktwy0ONtu2a7JGisUiyWQS2Fo2SH0mS7shQ62mltaFduuzYjp5jhBCCCGEEAdF3wZfHMcpKqXeBvxX4LeUUn8KHAFGgfscx6ns5vq4Z/OwLIulpSWmpqaIx+NA4yKW+u+2bROPx80U1Hp4EbDpDrTu3Ojnujsr7sfK5bJ0xPuM4zhUq1UTdKlWqywvLzM8PMzS0hK5XI6VlRVs2+bQoUNt389duBkk0HaQeTwekzWi28TExIQJyvj9fqrV6qbgRrPhQvVDgJoFcCzLIp1O1wQMdYYXYM5ljYYTuQM2/Ta9sxBCCCGEELutb4MvAI7jPKKUeg3wn4C3AqvAr+524EVzdyCaPeYuYgkwPDyMZVl4PB7K5bLJXMnlcsDNO8x6imnLshgZGTHP1TMm1Re+lA5M/7Esi7m5OUZGRhgbG2NpaYnV1VVisRjj4+NbKrLr8XjIZrNS60IY+XyeZDLJ1NQU5XLZzKwVCoWA2nNDJ4GRVtLpNPPz8wBm9qNiscjS0hJATRC4/r3cbVYyt4QQQgghxEHX18EXAMdx5tio73Lvjd9ze7UurTJPms1SpIti6jvEegrWRmn8lmVtKs5aP9xA9K9AIMDMzAxDQ0N4PB5GR0cJhUIkEgmWl5epVquMjo6abKlOSKdVQO15wC2RSAAwNjZmMl/cOgmMNFtesVhkbGysZjn6vaampsz/OwkMSvBQCCGEEEIcdH0ffNH2Muiitco8cWew6OFCgAm8pNNpqtUqfr+/ps5C/ZAi/Td3kUp3TQbRv2zb5tixY5TLZfL5PNlslvX1dTMzjXs4Wqek0yqgdtiR/l0HgROJhAkG96qeSrFYJJPJNC3Y200AcS/I7GBCCCGEEKLfDEzwZa90exFfPzRJDzOyLIu1tTU8Ho8pytpsSBHUDi3o946O2FCpVEilUvh8PizLIpvNUq1WiUQiTE9PmyFEwKbgmxDt1E9TrwN82WyWQqHAxMSEyZzT7WqrtVZ08e/V1VUKhcLABQF3ssaMBHaEEEIIIcRWSPCljXYX8e1mDYGbw5TcP4vFYsezFsnF/uCwLItCoUAsFuP06dMsLy+bIRu6iLK7KPMgdWjF3mp2rtFtzj0NebtaK+3OKTpbq1+HvbVb/51cbykeLIQQQgghtkKCL220u4hvN2tI/TAl/VMPLaqv8QKYgMzU1JSZVlYu9vvf0NAQPp+PSmWjHrTP5zNFSmGjOKnOdnIPIRGiE83ONfo80qgWVbOMlU4y6/p5eFG7c+JOZur0a0BKCCGEEEL0Nwm+tNHuIr7+QryTLBX39NPuego6I0IPI5iZmWmaTSP6UzAYNIE1XWBZqy/YLBlNohvhcNgMZwsGg/h8G6fvZrWoGrUv/TedLaMDvYNmL8+JgzYESwghhBBC9Afp8fVYPp9nYWGhpriqDqrojo4ecqSnEtbPSaVSLCwskEqlKJVK5vXuopf17yX6R6VSYW5uznRsq9VqzX7S+1FPI55Op8lmszXDRYRoRk9Xv7i4SDqdNn/XMyHpqen131KpFJlMpqZ96fNTsVgkEokMbNCvUSHgnSTnXSGEEEIIsV2S+bJNnQwJ0jOH5PP5pnUU9N3oSCRCJBIhGo02fD8ZgtS/fD4fHo+HVCrF8vIypVIJv9+/aT+5M2CKxWJN8WUhWhkbG6NQKJgpoGHjnJBMJoGbQYlmU9dr0WiUkZGRlpkjkpl1k5x3hRBCCCHEdknwZZvqAyn6bqy7U6NnDrEsywxFadYh13dYdUe+3fJE/xgaGiKRSODxeJieniYajTbcT+5hC3oGJBnKIDpRrVYJhUJm2nrYOBdMTU2Z/9f/dJ9H3OendgEVCTjcJOddIYQQQgixXRJ82ab6TnOjTrTH4yGRSJBOpwkGgy3fxz39dCfLE/3D4/EwMjLS1XAI6dSJbjRqL7owbn2mynYL0UrbvEnOu0IIIYQQYrsOdi75FrQb+9/scV1gVf9s9JxOi/VK7YH+VKlUTKZAp/tJ6vmIXtGZKs1qCNW3r3btrdd1VaR9d0a2kxBCCCHE/iTBly616+A0elzX9IhGowSDQRYXF7l27VpNUd5O3rvT54i9YVkWqVTKFFTWhZfdnalWHSvZt6IVdxHdfD6/KZCizzHNMlXq29dutzdp352R7SSEEEIIsT/JsKMutUvFdz+uM1l0hzsej1Mul8nlcmY2I3e2Sydp/jIUoH+FQiFOnjxJOBwmk8mwtLTE+Ph4Te0MoGkdDdm3opV8Ps/q6iqxWAyobUc64BePx5tmqjSqB6ODNrtR8Fnad2dkOwkhhBBC7E8SfKnTbuhPu7H/7totqVQKy7KIx+PE43FzMT0zMwPc7DS5O1Ht6gpI7YH+5fV6TedXDy8rFov4fL5NGQntCvEK0YhuI/WFvTvpsDeqT7Wdgs+dDpNsV4dG1JLtJIQQQgixP0nwpU6vZvhwT/XqrumhZzvSnRW5y7l/rK+vm/07OTmJx+PB7/eTTCaZmpoy+1w6VmIr6mcqalbou5spordz/unkXCkzJgkhhBBCCLFhXwRflFLKcRynF++13WCI7vgEg0Hi8XjNY406IvWdqK3cTRb9IxgM1uwfy7IoFApSPFP0hB7C2KoQrvs8Ew6Ht5TJ18k5RoZJCiGEEEII0bmBC74opTzA+4BV4CLwAcdx1jp9fT6fb9mh2G7Kt+74WJbF9evX8Xq9FItFJicnO+qIyN3kwaU7xuVymdXVVWCjPYVCoZr2JsEzsRXFYpFkMgm0Pk/p80swGDRDH4GGQd5gMEi5XN7UFjs5x3RyrtSZOtLehRBCCCHEQTeIV8KfvvHTAf4d8Ix2L1BK3aOUOvvggw/yrGc9iz/+4z/esZULh8NEo1GWlpZYXl42gZhisdhy6lbdcdcZM+3uJrd7juidM2fOMDs7C3CHUuqsUuoe9+O6fZ0/f57nPOc5fOQjHyEWixGJRLAsi1gsVtNJldlMhFur9qXb1rlz57jrrrv427/9WyYmJjqq7VIul7Esywx5dGdf6TaYTqcbtsVenmOkve+dducuIYQQQgixe1SPRuvsCqVUHPgo8HLHcbJKqW8An3Ac522u5zQdgjQ7O+t88Ytf7Nkd2GYZDPl8nuXlZdbW1jhy5AjVarXtMvP5PNlslng8LtksfUop9TXHcWabPf7EJz7R+fu//3v8fj+Tk5Pk83lT78U9BM2yLNLpNIlEAp9v4JLPxA5p1b70uavdOcJ9TgI2zbZWXxemWeZLL0mm195r1bZajdpVSjFI1whi97X7XhRCCCHETYPW8wsAh4EXA2eA88AcgFLqTuCc4zjrrd6gV4EN92xG+n3dHZqxsTHT2fD7/S3fR79mN6d9Fb3n8/nw+/2MjY2Rz+exLItIJLIpe0DPhFQulyXQJjrmHk7UbPhko9nT9PnE3Q7dQ4bq2+B26k41+rvM3iOEEEIIIcSADTtyHGcJ+CXgEaVUAHgK8C2l1L8Hfh8Y3Ynl6jvH9Wn7ejYj913mbDZrOtWdBFDcr/F4POTz+U3p+Y2WL/qP4zjYts3y8jLJZJLLly9TKBQoFos1+08P6dCdaNmvohM6qFEul5sO42k0XKjVcMdmy2k3TKhYLJLJZEilUg2HM8kQIyGEEEIIIWr1deaLUkoBzwSCjuN8HsBxnLOuxy4AvwU8DbjbcZz0TqyH7mjk83kzhbC7eO52po1u9Jr610uB3cGglMLj8ZisJ3cGk3v/6c6wHmqm/y5EM7Zt18xg5P7p1ossk05nMdLZXXp69U5f24oMURJCCCGEEPtV317d3giufBl4LfBnSqnPKKWO3njMA4wBjwFeBPyk4zjf3ql1CQaDVCoVisUijz76qCliWX832f23dtkq+nG42SFvdodaCuwOBtu2yeVylMtl4vE48Xgcj8dDMBg0w8/cbUL/PRgM7vGai37n8XjMOcA9g1C780v94/V/b/S8TjJlPB4Pk5OTjIyMmPNSLwInkjkjhBBCCCH2q74NvrARVEk6jvMTwFOBSeBDSqlRx3FsoAT8BPA8x3G+18sF13dIyuUygUCAfD7PysoK6XTjBBv9OsuySKVSZDKZmoKXW03P73bYgNg77n2t9/Hy8jK2bZNOp7ly5QoXLlzAsqya2i9CtOM+B7Q6f9i2zfz8PA899BDz8/MtzzvbCXbUn5d6ETiRQLMQQgghhNiv+nnYURp4mlLqLsdxvqSUeibwIPAupdR7gNewMetRqZcLbVRIV3cEJicnWV5eJpFINHyt7nzoAIw7A8ad5QKN0/Ml5X6web1ehoeHsSyrJtg2NjZGtVrFsizm5ubM9NOTk5NSZFl0xV2gGxoP7ykWi6RSKa5du2YyZnS2jB4Op/9td5iQWy/eS4rzCiGEEEKI/aqfgy/fAf4JuFspVXAc56xS6ieBtwBh4Dd7HXiBxoV03R2C6enppq/VBVR1Z1sHXqLRaMMimHrogA62SG2Xwefex0tLSyaoMjk5CcBjH/tYisUiiUTCDDXLZrPS6RQt6WBJJ+eIcDjMyZMnmZycNM+prznkbnPt2l2nQWFpw0IIIYQQQjTXt8EXx3GKSqm3Af8V+C2l1J8CR9iY0eg+x3EqO7Fc93Su7g5HJ1Or6mEk1WqVaDRakwHRaNhQfUeql3ehxe7TRVF9Ph8jIyMAhEIhqtUqqVTK1MiIx+Omzcg+F51YX183bQZoWmdF/x6Px00b1AFAHRz2+/1d1Rrq96CwZAwKIYQQQohB0LfBFwDHcR5RSr0G+E/AW4FV4Fd3KvDivoiv73DU/66f6y6cW98x0un/oVAIn8+3qeNS/3y5czzYdCbL6uoquVyO1dVVhoeHiUQiNbPC6LakO8XSaRTt6MAK1AZAGp2n6mdmq59dS79XuVyuOZc1a4f9HiDs9+CQEEIIIYQQ0OfBFwDHceaA1yul7r3xe26nluW+iK/vcDQKrGSzWTOkqD5TRguFQsRisR2bFlb0l3A4TCqV4pZbbsHj8TA0NGSynhplLUinUXRCKUU+n990zmh0fmo0BbT7OcFgkHK5vOlcBo3bYaMhkv2k34NDQgghhBBCwAAEX7SdDLpo7ov4Rh2OZp0ej8dj7irDzQ5MNBrl8OHDfdlhETvD4/EQiUTw+/2cOHGiYYdVtyX3dL9ScFe04p5quv7v7vOSri+k2119Vov73KR1Erzo5+wSCWILIYQQQohBMDDBl91QfxHfqsPR7g50o+eI/c+d5dJu/0vBXdGNTtuHuy01Cgq3en4zkl0ihBBCCCHE9kjwpY77TnGnHQ7HcVBKEYlEzO/1j7V6XTOtHhP9qdsgii6C2mnxU9E593HY6LFBOy6bfZ5W69PsHNZtkVoJDgohhBBCCLE9Ms6hjs52KRaLpsOx3eEgtm1TKBTMMBMhND1DVrlc3utVObAG4fjUQ9O61ewc5j7PCSGEEEIIIXaeBF/qhMPhhrUVumHbNrlcjlwuh23blEolstkshULBdPL0LEm5XA7Lsrbd+bMsi8XFRSzL2vJ7iN3Xi/Z2EOjjxX2MdNvm9XG5urpa8176+CyVSuZ57uOxH4Izy8vLLCws9Oz9pN0JIYQQQgixu2TYUZ2tptfrIEsoFKJUKrGwsECpVOL48eN4PB6CwSDXrl2jWCxy66234vP5SCaT2LaNz+fD6/WytrbG6Ogo1WqVsbExMpkMIyMjZDIZEokEPl/z3ZVOp5mfnwdgenp6y59fbI+7voYM59i+RlO662LFly5d4vr16xQKBU6cONFwe9cfl9euXWN+fp7p6WnGx8eZmJjA7/dTKpVIJBLYtk06naZarZrp4W3bZmFhAZ/Px4kTJ1oehzsllUrx13/91/z8z/88gUCASqVCKBTaclaetDshhNiflFJvA74fmAN+znGcNddjTwf+O7AGXANeDhwG7ge+e+NpP+M4ztJurrMQQhwUEnxpo77Tpzs7lmWRTqcZGxujWq2aaYPz+TxjY2NmmuGFhQUzhGlhYYF8Ps/o6Chra2v4fD7y+TzDw8Nks1lWVlb47ne/i+M4JBIJHMchHo9jWRarq6vEYjEmJycbdv7GxsYoFArE43EWFxfbBmtE762vr3P+/HkATp48ic/n29JMV93W49gPmn3m+ind3dMjDw0NEQwG8Xq9pFIpJicna167trbGgw8+iNfrZXR01MxeViwWWVhYIBKJUCqVyOfzLC0tcfnyZR7zmMdw4cIFgsEglmWRz+e59dZbmZ+fx+v1EovF9iS4WS6X+dznPscP//APE4vFzN91UKlf2spBbLtCCLFXlFIfcRznla7fnwgcdhznOUqp1wM/Dfyl6yVXgOc7jlNSSv034CeAs8CXHMf56V1cdSGEOJCkd+7SqONQLBZJJpObnmPbNouLi1y8eJHLly/z7Gc/m0qlgmVZPPLII1y8eBHbtllb27jhEI1GuXz5Mo997GO5fv06Dz74oOlEHTt2jCNHjrC+vs7a2hpXrlzB7/cTi8UYGRlhbW2N1dVVkskkpVKJEydOmHXT61qtVgmFQiwsLLC6ugpIBsxectfS6DbDoJ+n9d0pzT6zDrYEg8Gaujj674cOHSKdTmNZFsVi0bzWtm0eeughzp8/b143MjJCOBxmdXWVXC5HOBwmnU4TDAY5e/Ysy8vLnDt3jmg0ytDQEI7jMDc3Z14bjUZNUEbv317UhOrUv/7rv1Iul5mamsLn85nASz+1lYPYdoUQoo98P/BPN/7/GeA/4gq+OI7jHr9aBfR42mcppb4MfBl4vdOqYr0QQogtk+CLi+442LaNx+PB7/eTzWYZHx83WSRzc3Pk83luueUWqtUqDzzwgMl2ePzjH0+pVOKhhx7iO9/5DpZlcfz4cU6ePMl9993H4uIifr+fU6dOkc/nicfjXLt2jXK5zMMPP8wdd9xhhh0Fg0HTefF6veTzeUKhEF6v1wR/kskkU1NTZkhTKBTi8OHDRCIREonEnm3Hg2p9fZ1EIkE8HicajVIulxvW1GiXHeAOOOTz+QORRdBsVh49PCabzZr2Ho/Hax6fnJwkk8mwsLDAsWPHAHjwwQdZX1/H7/dTqVT413/9V44fP87U1BTXr183dZauXLlCqVTCcRwz9Ehn01y8eJFCocD8/DxDQ0PmmJuYmKBUKuHxeDh69CgrKyuMj48Tj8fNPt+J/ZXJZDh27BixWMy8fygUMtut06yT+uf1MltFpqTeTLKBhNhbSqkwoIMO047jlFyPPRb4U+BJwDngvziOc982lvXLwCuBxwN/6c5KqXteAHgP8AJgDLgAvM5xnE938ngLo9z8rKs3Xtto+ceAHwJ+l436j6eAIvB+4CeBv277YYUQQnRNgi83rK+vY9u2CXhks1lyuRxzc3McP36co0ePUiqVOHz4sLnbfO3aNSYmJnj00Ud5+OGHefjhh7Esi2q1SqVSwefz8ZrXvAbbtnEcB8dx+OAHP4hSCqUUT33qUxkfHwc2OpHxeByfz0e5XGZ6epof/uEf5sqVK2QyGfL5PEePHmVoaAiv10symaRQKDA+Ps6jjz7Kfffdx8mTJxkbG2N6etoUI5XhR7unWq2yvLzM2NiYqRfSSLvsAB1wyOfzByaLoJMaJHoIoM5YKRaLFAoFbr31Vr73ve+xuLho3uvixYtks1mTHRKPxymVSly9epXl5WX++I//2GSl1bv11luJxWImCPulL30Jx3GwbZv19XVe97rXkcvlapY7PT3NLbfcQqlUYnp62mSm1He2tztN9dTU1Ka/VSoVgI7bS33762W2itSS2UyygYTYcz/BRmDFf+P/nwBQSg0B/wDcB7wReBXwP5VSJ9wBmi7NsxHQeCEQavE8HxtDgO4CLgMvAj6plHq84zhzrR5nI1vlozfe57RS6os3/v9DQAbQdyiGgeX6BSul4sDHgFe66sFUbjz2N8AzkOCLEELsCOmV36ADKj6fzxTevHbtGqlUygwxWltb4/Tp06ysrDA/P08ymSQYDKKUYnl5mVwuZ+62K6WoVCotpxC+cOEC169fZ3h4mEgkQrlcNkOIgsEgX//613Ech6GhIXOH+9y5c1y8eJHFxUWi0SgnTpzAtm3i8TiJRIJAIGA6YbozKsOPdkcgEOD48eNt7/o3yw6ov0MuWQQ3RaNRYrEYuVwOwByTi4uLJJNJE+wcHx8nGAzyyCOPsLKywrVr13Ach5WVFVNXKZPJtJxiuVwu4zgOXq8Xr9drghuwEVx44IEHTHbMzMwMkUiEQCBgstIWFhbweDzMzMzU1GfRsybpz9NNFoTjOFSr1ZbP6bS91D+vk9dJ9sbWyXEsxJ57GfA3QICNArOfuPH37wNOAD/hOM53lFIlNgIxj2OjDkrXHMf5GwCl1CxwS4vnFYC3uP70D0qpR4GnAnOtHncc56+B591YTn3Nl38BXs1GcOaFwD+7l6uU8rHx+X/HcZzv3fhbzHGc3I2nPIeNQJUQQogdcKCDL7pDEQwGzV1uy7Iol8uUy2UKhQJ+v59kMsnDDz+M3+/nypUrBAIBPB4PoVCIoaEhKpWK6dRVq1WUUqyurpp6Ec2kUilSqRRws3N59OhRfD4f1WqVcDhMJBJhbW2NaDTK+vo6xWLR3D3XWTOhUIgTJ04wMTHBwsIC6XSao0ePMj09XfPZpAO1s4aGhhgZGdny6+vvkEsWwU16WxQKBVP/ZWpqilQqZQpiP+EJT8Dn85FKpZibm+Pq1atcvXqVarVqZjTK5XLm+c3oQMfQ0JA5nn0+H4FAANgY/lMul4lEIjiOw8TEBCsrK4RCIWzb5vLly4yPjzMzM2Pe07ZtlpaWWF1dxePxbNq3N6ax3tJB6T6uO2kv9cvupJ1J9sbWyXEsxN5RSk2zkRHyKjaCL29RSk07jrMIJIFfAx668fRbb/xM1r3HK4EPN3j7P2s2rGgL6zkF3M7NGYe6elxzHOebSqnkjfotl4F33Hj9NPBLwHk2gk5vVEq9EXgvkFVK/S4bw44eZSMLSAghxA440MEX3aHI5/NmyJFt2ywvL5uAx6lTp/g//+f/4PP5sCyLlZUVHMdhdHSUYDBIMplkYWGBXC5HsVhkfn6earXKxYsXOwpwKKWIxWJ4vV4WFjaG6eqaEmfPnuWOO+5gZWUFv9/PHXfcwcjIiBnG5PF4SKVSKKUIBoNcuXLFFB+NxWJMTU2Rz+dNZ69+5iax85oVcW5VXFbukG9wt1e9TYaHh3nwwQdNQVwdJI1EIpw9e5Zjx47xne98h6tXr5LNZs200bZtY9s2lmW1Xe6lS5fMuWBoaIhAIEA8HkfXH/z2t79tjls9xC8UChGPx1FKsb6+TiQSMZ8BYGlpiWq1SiwWIxqN1uxj27Z1ENa7le20G4GRnWybEhQWQuygu4HvsTGER7Ex/fJ/AN7lOM4jwB8CKKWeCbwb+JDjOFfq3uNTwJMbvPemIT1bcWP408fZCOY81M3jjYI/juP8ZoO/LQJvvvHrxxqsRrtaMkIIIXrgQAVfdAdMX+DrIpX6b5FIhKWlJc6dO8fi4iJTU1NUq1Xi8TiPecxjKBQKXLp0CcuyyGazJviSy+UolUoUCgUeeughpqenOXXqFBcvXmy5PqdOnSKRSHDp0iUWFxeZmJhgcXGR9fV1jh07xvnz5836xWIxhoeHOXz4MOvr66RSKU6cOMH999/PxMQEt956K8FgkOnpadbX181del07RA9Nck/XK3Zeo45xu+Ky3djPHVf3TGO6Bsvly5fNsJ/Tp0+ztrbG0NAQuVyOhx9+mK9+9auUSiUuX77MLbfcglLKBF10IKSdeDxOIBCgUCiYmcP8fj8+nw+lFNevX2dkZITV1VUcxyGdTnPkyBHm5uZMNhxs1I3SQc/V1VUikUjDY69YLOqg0JZml3Cfx9znt17ayewNyarpX8eOHWtap+jYsWNtszuF6AMvAx4LlOr+9i79i1Lq14C3s5Hd8v+pfwPHcZbpUaClnlLKw0YwpAr8crePCyGEGCwHKviih+3oC3ydEZLJZFhbWyMcDpNIJDh69Ch+v59oNGrulCulWFxc5OrVq/h8PtPRqVQqlEolqtWqee6/+3f/ju985ztkMhnS6XTT9YlGozzrWc/iWc96Fn/1V38FbHTydCexWq2aDmC5XDbBHp/Px+LiIsFg0MzOsr6+TqVSYWlpiRMnTpggi86E8fv9hMPhljVoRO/pGYuCwaD5W6MhJ1sNoOznjms4HDYFZnXbvfPOO0kmk2YYz8rKCpVKhUgkQiqV4tChQ6RSKVMfRQ836jTwAjA2Nsbp06cBmJ+f59vf/rbJetGzInm9XlZWVkilUkSjUaLRKGtrayZI6h7qp5ddKBRIpVJEIhEOHz68KRgHtE/LaUCfx3Swp5t20A/Bu26yavphfQ+SVsGV7RaPFmKnKaXuZCNj5cXApRt/Pg18+EZh2+8opX6DjQK5Pw98tNEUyzs17EhtHEQfBKaAF7mK33b0eIv3fRsbU07PAT/nfp1S6unAfwfWgGvAyx3HWVNKPY+N4UYe4N2O4/ztVj+XEEKI5g7U1avX6zV3ifP5vLkbrmu2pNNpvF4vhw8f5tChQ2aowcc//nGSyaSZSSgWi5HNZllaWmJhYaHprCnbcenSJarVKktLS1QqFZMFcPnyZYLBICdPnsSyLJaWlnAch2KxyMWLF/nKV77CF77wBfL5vBly4ff7SSQSpNPplsVGz5w50/PP0c5+XqZt2yb41SropQMorYrANhMOhzvKZtqL7dyt+nXUM4DpqaXz+TzVapWZmRkKhYIZWpTL5UilUqytrXHhwgVzrDiOQyAQMJkovaKPoXQ6TT6fJ5PJcO3aNfL5PEoppqamTADm2rVrJiiig7OxWKxmf/Uiq6TTdlDP3fb2qo184AMf6KgAsR6e1a5gcqf26vMOwrG40/bzeX+vlrdXy+xzL2Nj9qFPOo5z340ppP8cWAFefmPmoLcDfwR8C3iiUupJSqn6E7IedlT/7031C1RK+ZRSQTaGkXqVUsEbRW4beS9wB/BjTWZXavf4JkqpJwKHHcd5Dhu1bH667ilXgOc7jvNcNoIzP6GUCgG/DvyI4zg/IIEXIYTYOQcq+KLvEOsORzqdNtPRuovTRiIRYrEYlmVRKBT44he/SDabZWpqilOnTnH06FGmpqYYHR1lZGSEYDDY87uwly5dIhwOEwwGN/0bHR3l0KFDrK+vE41GTYaMnqVleHgYwGS8TE5OUi6XsSwLn8/XtIN2UC4Wd2uZejhJq20OW+84w82Oe7v2NwgX5c3W0bZtLl26xNWrV03tlNHRUaLRqJkG/pZbbiGRSBCLxahWq6ytrVGtVpmcnOTIkSM9zQrKZDI1GWper5e1tTXK5TKVSoWhoSFTHFgHdW3bZnR0lFOnTjE9Pd3z80Wn7aCeu+31ezCi0+Op18vttUE4Fnfafj7v79Xy9mqZ/Uop5QVeAnzGnc3iOI4FfObGYy+88effAL7h+jfrfi/HcZYdx/lmg3+XGyz6DWwMcfot4KU3/v8G13p9Win120qpY8AvAk8CFpVS+Rv/XnLjeS0fb+H7gX+68f/PAM+q+ywLrkBOlY1pq595Yz3/Xin1tzeK8wohhNgBqkGG5b6llFriZuqph40vHR8bRdicGz/Xb/zdw0ZlfIeNCvhXb/zfz8YXlo+NOxvBG8/V/1+78X8PMHTjPWtW48ZPp8Hv9o33rgIxION6jnPj7+tsfEmqG//0/LMe18/yjfXQnxHXY63GX9zB7k8xOEjLPOY4zkSzB+val9Zum++WvdjO3Wq2jvr40senj5vbVB/DPjaOTW48J+Z6/Robx7L/xj/vjed4uTn00n0OcHPq/q/f6zobx/c6UOHmRSxsHH8VNmvXDpq2L6WUw8awpG+1eY/t2Ks20s1ye3k8DcLn7ZVWbavReWunDdJ5f1CWt1fLbPm9KHaXUuq3gQcdx/mfSqlTwP/jOM5/aPC8Y2xMOf1cNrJjfhN4BvAC4Mcdx9lU+0YIIcT2HaiaL1u9QFBKnXUcZ7b9M3tHljl4y+znC9C92M7dGoR1hL1ZT8dxdrzAxl5tf1nu3tqL89Z+Ou/3y/L2apli993ITPlEg4dezMZNu/iN34dpUChYKRVno4jvK2/Ue8kA/+w4TlUp9TngdTux3kIIIQ5Y8GUb9iKXV5a5v5a51wbhMw/COsLgrGe39upzyXIPnoNw3j8In1HsgRvTRj+v0WNKqX8BXg18lI1hVf9c97iPjcDN7ziO870bf74f+PUbBX6fBLSeqlMIIcSWHahhR0IIIYQQQuxXSqm3szGE6DLwH29ktEwDvwScB/4Q+M6Np7/XcZy/Ukr9Z+DfszG89uccx7mw+2suhBD7nwRfhBBCCCGEEEIIIXbQgZrtaKuUUvfIMmWZg2wQPvMgrCMMznp2a68+lyz34DkI5/2D8Bn70V5vA1m+tEEhhGhFgi+d2YsvE1nm/lrmXhuEzzwI6wiDs57d2qvPJcs9eA7Cef8gfMZ+tNfbQJYvhBCiqQNVcDeRSDjHjx/v+nXhcJjZ2VnHtm08ns7iVbZts76+jtfr7fg1jZbZ9Qu3QZbZ2te+9rV0q5lBEomEc/To0a73ezftaqv2Yjt3axDWEXZuPVu1r0Qi4Rw+fNi0q51oM3u1/WW5O69d29rK9+J2DNJ5fyeXt93rhFbL3I3vFZC2JcvfueW3u+YSQohBdKCCL8ePH+fs2bMtn2PbNsVikXA4bC5cZmdn+eIXv0g2myUejxONRtu+PhgMUi6Xa96nG7Ozs23Xtddkma0ppS61evz48eN89atfrWk/jdqTWz6f76pdDVJ76tYgrCPs3Hq2al/Hjh3jf/2v/0UikaBcLps2Ew6Ht9Uu3PZq+8tyd16rttXJ92KvDdJ5fyeX18l5vZtzv15mp98rnWi3fGlbsvydWn67ay4hhBhEByr40olisUg2mwUwFy333HMP4XAYwPzs5vVbcc89u5+5KcvcPo/HU7Pf27WH3WpXe7GduzUI6wh7s56O42DbtgnoAibw0ovzDezd9pflHjz77by/1eXVf1800s0xrpfZ6fdKJ3p5jtkNe318yfLl/CaEEK0cqNmOZmdnnV7cierV67e7LLG7lFJfcxxnttnjjdpXq30sbUW4tWpfs7Ozzuc//3lgowOk24C0C9GJdm1rEDLODir3MQ7s+vHeQeaLtC2xI9pdcwkhxCCSq/U6+k7UVi9sGr3etm3y+Ty2bdc8V99RKhaL21pn0b/cdzbr20A3+3+77VIMvkbtRdqFEPub+xgvFotkMhlSqdSm64ndWL4QQgghtke+TXeYbdukUikymcymTnY4HDZ1G8T+1qjjHAwG8Xg8BIPBPVwzMQhs28ayLHw+n5wveqBZQFyIfm4b4XAYn8+HZVly00YIIYQYQBJ82WH5fJ7V1VU8Hg/hcLjmwq7VHaV+vgAUzdXvM70fg8HgpkBbsVgkl8vJRbRoSylFpVJhbGxM7kD3QD6fZ2FhgXw+v9erIvqIbdssLi5y7dq1HW0bW/1+93g8TE5OMjIyIkFYIYQQYgDJVfwucAdZ6i/63Rdh7v/LkKTBY9t2TUq4znpaXl4mnU5LXQ6xZZZlsbKywpUrVzrusEkAd/DJPtxdOiBeKpUaPt6r/dHs+72T9+/lMCBpX0IIIcTuktmOdpi+SAoGgzUXOe6LHvcdNj2rQC9nK9iv+q3YqMfjqUkJt23bZD0BpFIpJicnze+6bfTrPu637XuQKaXw+Xx4vV6KxWJHs44M2iwlu6mTY68f2r/sw90VDoeZmZkBGm/v7ewP3Z6CwSC2bRONRje1v3bv3+s2Ke1LCCGE2F0SfOmhRhdG+i5VNpslmUwyPj5OLBYDIJPJmMfdF2H69YN0MbQXHZV+vHB0B82y2SylUomjR49SrVapVqukUikSiYSZMng7673T27wft+9BZVkWlmWxtra2qUZQs3awVwHcfghatLPVKX53+7PtxT4chP23UzweD/F4vOnjW9kf+gaLvtni8/mwbZt4PL5p+7Z7/16ek3V2pr7+OMj7XQghhNgtEnzpAX3RUp/Ca1kW5XKZyclJLMsimUxiWZYJvlQqFYaGhvB4PDXZEINoLzrq/ZYdlMvlSCaTRCIRANMO9Dj9VCqFZVmk02nzWDgc3vIF705v837bvgeZ1+sFNtpYPp83bSUcDpNOp7EsC9gcJAgGg5vaV6edrK12xnb77v1OadT+d/s8txdBePdn3M75qZWttoHttJ1etLut7I9isUgymcS2bYaHh2uC792+f32bbPaZOvmsxWKRfD5PNBrddP2is8LqXzsox64QQgjRryT40kKnFxr5fJ5kMsnExATxeBzbtkkmkywtLZkLGXdnaHR0FI/HQyAQMI/pQM2gXtTsRUe937KDisUi9913H09+8pNNhkskEjHBtcnJSYrFIn6/n+XlZdMx3mpnbqe3eb9t34NsaGiIQ4cOsbq6WnNeiUQi5HI5YrGYGdqoO8zZbLYmGKz3ZadtTk9rm8/na4bLtbObd+93UqP2fxACku7PqL/bpqamWmaEdGurbWA7bWc325372iEcDjM1NWWWu53zqvu1uqZYfeAVOvus7gBONpslGo2a65dmr93qOUEIIYQQGyT40kK3F2v6blE+n2diYoLx8XHK5TKJRALbtjlx4gTj4+PE43GKxWJNoCYUClEqlXp+kQs3057159iJC6bd6qj38523YDDIiRMnqFarJqAWCATIZrNmilA9BG11dRXbtpmcnAS21pnrZpv383YTnZmcnKRUKqGUAmBiYsJ0wKampiiXy+Z8pQMxY2NjVKvVTcMa3T+b0ecyXcOo07bW7d37vdLumGg1jLTXy9rr93PbjXP5VtvAdtpOL9pdp9u9/tpBZ5e0e0/92mAw2PZmTLFY3DT9vH4vv99vas01W2+9n+szXdy/19vqOUEIIYQQGyT40oIeB+3OTGl0UeQu3ui+UzgyMmIueqrVKoVCgYmJCcrlMvl83gRZLMsilUoRCoVMoMR9IbTdi2yd9gz9m82w1YvafhKJRIhGo0xOTvLAAw9QKBRQSlEoFEilUpw8edLscz2bRjwe3/Q5dqJjpbdbq5Ry0d/K5TJDQ0NkMhlGRkZM7QidXaU7S8FgkFQqRS6XIxgMmlnWug28urO1Ou2wNmq7jYK/zY7dnR4S5dbuXNLLc02vz1vdvN+NzKctbaT6gvGttnezfd9oP231e6iT17Vbps7s0OfCbo6LTrd7faCnWcZIffYKsCljrdnQL/cy9N/1+ulrB32t0ixDxr1d3L83W+ZWzgn95Pjx41y6dKnhY8eOHWNubm53V0gIIcSBI8GXFvRwET0lpGVZVCoVAoHApk6s+6Iul8vV3JW2LItcLselS5cYGxvjlltuMQEdwMyQo+9WuS/uenHRrtOedSBJr3s/2epFrVs3F/87oVKp8I1vfIMjR46Yuhx+v5/19XVzUXf77bcTjUY5efJk08+xEzUX6lPMAdNm6++6SmCmP+kizl6vl1KphGVZRKNRZmZmNtUlWV1dZWVlhVAoxPLyMqFQiMOHD3d8TmmXLdfsuGr03t0Ef7sZEtXJZ2jVnttlQvQyQ6fXWRfu92v3OW9kXHi3sky9v/L5fNvtXb9P3N9/jbIrus066jSDs5OgWjKZpFAomMBlp9+tne7H+vdsljHSKHsFMENS3cV69edxbxv9uw6MuQOwOvCSz+dZXV3dNDRxUG9ybMelS5dwHKfhYzqjUAghhNhJEnxpw52+n06nqVarBAKBhrUU8vk8qVTKvDaVSlEul1lfXycUChEIBCiVSqYDPDc3x+joKMPDw0QiETMERacdQ3cX7a3u+MXjcXMB3W/ZL/WzLrTSat2bdfx26yKyUqlw/vx5QqEQhw4dwuPx4PV6TYd3dHTUtBmdAeMOhoTDYZNdpX/v1fo3SjGH2u0DyHj+PuU4DouLi1iWRalUolwuA5ihbIA5vvUQN11bKhQKEYlEsCzLPA61HXfdWdM/9XBI2HwnHNh0J939Pvq9NXfNi06GOvXqee2OnXbnwV6eJxu9V7eB4UZDWYC2gZEb22i91XvXr0v97/p7sH6mrQbLqTm36MACsOmmQqtzTSdBvE4yQhoJBoNEIhHGx8c3BT3abZet7kePx0MikSCdTtdsQ30M6vdxZ9XqGzG6FkuzLJpm7aI+myUajdYMTdTbr1E2b6tt2O+BmXbZLUIIIcRekuBLnfoLKd0R0Z0dfVGkp4ysvziJRCLEYjE8Hg+WZbG+vo7X66VSqXDixAlCoRDpdJpUKkWhUCAYDFIqlZiengbYNFwAGmc/NLrga3dR1Ms7ub2kZ11oNPVmNxp9vt38zOvr6yZ76ZZbbsGyLL73ve8RCoVQSrG6umpmt3J3nC5evEgoFGJ4eHhTQK+bO9ydaHRH1v1TxvP3J8uy+M53vkMoFGJqasqcZxq1dZ1pEIvFaupNLCwsUKlUaoa/5fN5MpmMyejTQ5mi0SgTExOmc1YfpKtWqzXTXrfK1tLB3050GvDo5Hm7fb5rlpnRTZZQK80+T7vPeWOZdqv3rl+X+mGKOkBeLpc7Gvajb064h1W6g77NMkFafab6IF6zoZTt2ob+Lvf7/TXP6/Y7tX6WQ/2cZvvbfS3h3iYACwsLJlCq36fRsKL6bederju70b1897Bo93voz5bNZikUCjX15lptw369jtBaZbcIIYQQe+3ABV/apdzWX2y5L2gaTRXpfp9oNMrhw4drLkr0HeTFxUWmp6dNkd2xsTFT4+Xy5cvmwuzy5csEg0FisRiFQgGAWCzW0Ywl7e5O7mTGy3YCA726mGv0+XYzy8fn8xGJREin01y/fp1UKsXq6irVatVc2Pp8PoLBoNl3tm0zNDREqVTi2LFjWJZVsx3qAzW9rttSv30GeTz/fuY4Do7jEAwGTd2o+n2k96XObgNYWloiEomYYHF9QCAYDOLz+ahWqywvL3P77bdTrVbN+wE1y9I/s9kspVLJFA53P95o+Emn54ZeDh3c7Qy/ZsOrmnXguz3vNfs8vfic9eviDuS5Z/Jrl2mkA3mWZeHxeJiZmTH7TK+j3p/u79FOMkzqg3j6de5ARKMsmvr3bpb51WjKdv2cRsN19X6tz0xpNkOUe0hQ/XVIqVQywU/9vFb7W5+n9ed337iob2/1WUL1gX3Lssy1Rif6LXNWCCGEGCQDGXxRSnmAXwP+wXGc73X6uvp6F400yzQATPq1uzOiL6Js2yadTpNIJMydwnw+Ty6XM8ONFhYWiEQilEolc7EzNDRELBbD7/dTKBTMHetgMFgzFGd5eRm/379pbLf7YrjRnbXdsp1U5G4u5nazhku3vF4vx48fN0GS7373u5w4cYKjR48SCARYXFzk9ttvr+mkTUxMsL6+ztraGplMxmRA1XMPzQJ2JO27n7ftQaeDctPT05TLZXK5HIVCoWbYkaZ/z2QyrK6uEggEGBkZYXJy0kxxrocN6b9fuHCBTCbD8vIy8Xic5eVlKpUKExMTNXfQtXB4o4inDgq7H68PAnczXXGj5+7VMIduj4dmw6uaBVmanff24jisXxd3IK/TddUZGbZtEwqFmg4jbbSPdQZWJ0Me3QETPbRpbW0Nj8djam3px9zLhM0Fn7PZrJltsFAobMomcw//aZU16F5f27bNd3kjOtPTPdzo6NGjZmbETvZ5s2Gk9eul379RwV33e7QbftWI3g9CCCGE6Nyg9rB+AHg78HNKqTs7fZG+c9bqIsOdpqsv+ovF4qYx08VisebxdDrN/Pw86XQa27ZZXFxkfn7edJCCwSBXrlxhYWGB4eFhUxfE7/czPDxsAicnT55kfHychx56iNXVVcrlsnlseXnZLK+RcDi86fO5U8B7qf59Gy17J7i3eT9aWFggl8vxr//6r1y4cIGVlRWT0fLoo4/y0EMPATA1NcXU1BQej4eRkRHGxsZIJBJA432mL9h1O4zH46aT26t92+/b9iBzHMfUlNLDJgKBgBlipLk7RKVSycyyViwWa84juhaH7qSOj4+b4zccDlOpVFhYWDDnPsuyTM0Z2DhP6mKl9eqHarrXTbfXbs5LWzm36EC7zhTbim6PB/d3Ryd/79Vyd5Iu6Owu7Fp/XnJ/R05OTjI2Nsb09LQZvlTfPhvtdx3AWF1dNYGSZvQydWahz+fj2LFjjIyMABsBlXQ6TTKZJJlMmgBFq3o1sDnLUGfT1E/bXP98980WvaxGx4Y7iKiDTslkkqWlJcrlsjlO67U6Vhq1rfr1WlxcZGVlZVOQRr93fa2f+mU1+1sqlSKTycDgXkcKIYQQu27gvjTVRkn6i8A88AzgpUqpE21ec49S6uy5c+d43vOexwc+8IGmz3VftAeDQXPR6f6n7+i5OwWJRIJDhw6RSCTI5/MsLi6yvr5R49Dv95uMl7m5Oc6fP082m2VxcZFAIIDf76dcLrO0tGSGAOix8OfPn6darRKPx0kkEqbTrS982l2g79SFfP37dtvB2KqtdsS2E6Q4c+YMs7OzAHcopc4qpe5xP67b19zcHL/7u7/Lpz71KW655RbGx8e5fv068/PzHD16lNHRUdPp1RfCegatI0eOmMKUjfaZ+3O7A4GZTKYmA2E7n7eTbbtTwbyDrFX7cretX/mVX+EDH/gAmUzGTF8+NzdnpqbVU9brIG0kEuH222/H5/OZ6e2j0ag5j7iHCPl8Pk6dOmU6zDoY4/f7WVxcNMHkdDoN1HbKNd02gsFgTXAwHA6b4Se6XefzeRYWFjZNq9vofbdybtHZZclkcsvnvmbHQ6vAznbOt/XbrxeB7E7a1rlz55idneXMmTObXt/sZkSzdXUHMBYXF7l27VpNMEW/dnh4uOE+BpqeX/R217N8uc+FOgNMB6bHxsaIRCJMTEwAkMvlGu6TcDhMLBZjbGysJmNGB3AymQxXrlwx38fN1s29bZoFrHR70uup6ypFIpGWgaFG273T832xeHPms0aZVPXv3ei6otHy8/k8f/Znf8aP/uiPAjym0feiEEIIITZTg1qYTCn1h8A14EXA/WwUFHyP4ziXm71mdnbWOXv2bMv31Z0CgJmZGQAzrlunOI+MjLSsZZDNZllYWDAdpHA4jOM4pNNpwuEwt9xyC47jmBoguVyObDbLyMgIt956K7Ztc+XKFcrlMo888giPe9zjuO2222rWMZPJ4PP5amZIco//rh9f38sU9voCg/0+REUPA3Jvl61QSn3NcZzZZo/fcsstzkte8hISiQRPecpTGBoa4vLlyzzjGc8gkUjw1a9+laWlJe644w5isRiRSIRUKkU+n+fUqVNmyFH9cLdmM1K408l1m+zl521kJ9/7oGvVvp761Kc6f/EXf0Eul2NycpJSqWQei8ViwEYHtlqtkslkGBoawufzEYvFTIAvEAiY2hCWZbG0tMTExITJgNEZKzpIo4Mli4uLJBIJ1tbWOHLkCH6/v+H615+XdKdNd851B04/5i4y6m6/vdDqHLXdc2L9d0S7oq3dvO9Wjq1OltmqbXXyvVi/HPe+bbSu+Xyea9euUSqVOHnypPn+bHQec7+/+3zmrlOihxS5t7v7cb1MqJ1NSbftpaWlhsPe9DYvlUqsrq6aGyjpdJqxsTGWl5epVqv4/X4zrKrRZ250ztbPbzXkTi9ft9H6Yc3126zRdq9/j/rviMXFRXK5HDMzM5vWoX6fuo/fRjWX3HVu9LK8Xu+221YvKaW2VHB3q68TO6fdNZcQQgyigav5ciPzxQtMA38J/G/gnwAFvHe7799s3L5t2zWp+vpOk75z675Y0YV3y+UyxeLGFMOlUsnc+ZqamjLDkEqlEqlUCq/Xa2ZJ0oEU/bwjR45sWkfA3KVzd5jq7zjvRHE8fQdwu7MT7ZZeFfTtZDlPf/rTTQo+QCgU4uLFi4yNjXH77bdj2zZKKZLJJKdPn+b48eOm+KTWqMhuo6nN3YUXO6kx0avPuFPvLVqLxWJMTU2ZrKf66aF1RpyuI6WDHuVyGb/fz/LysjlnlUolM6xCT0OvgzHuDCs965o+1+iOqJu7BofP5zNZAvVFeJeWlgBMTarh4WGq1WpNvYleBYvri7O6bbeGTLPvCL3crZ5vt3pstfs8N84b2z5Ruz9bo7oi9TPv6JsXOhjSbpvXn8/qX1O/3d2FZG375tTo7vdzF/5ttF313xKJBJFIhLGxMTPjoM5oTaVSBIPBmmXqz+xuq/qGjLu+Sjv1dWPc04bD5tpejdqIe33qn+/xeEyh/0af373etn1zdqpGw5gara8QQgghujNwwRdn49aEpZT6BPCjwOOB7wBFYHW779/oor3+jlf93Z/V1VUzDEnfMdPv5ff7KZVKHD16lJWVFdOBvnbtGgsLC4TDYSYnJ8nlcly7ds1c3BQKBcbHx83MJu6ZSvTFkPuOl/viqtWdrl4YtA74bs3OoANnQ0NDZkjZ2toalUrF1GtxHIfz58+bDvTIyAjT09NNL2TdgTb3HVH3hf9uzvAkM13sDcuyOH/+vDmm9T5wd4p0e8jlckBth3h5eZl0Os3x48fx+XwEAgE8Hg+FQoFSqUQkEgGo6dDqgI0uAlpflFO3QZ1FMzU1ZTrPOljj7rDWd5zdHW3d/pt10ptlFmylA7iV81d9R3snzqtb5T5HNJrN78YQEm8vl1l/HnAXEXcH63RQWQcjuqm7or879Xs02u7u7+KpqamaITj65oDeLvPz82a4jzv7JhqNmtpJqVSKlZUVM8NTsVgklUoRCoVM+28WHNLro2/SuIdiNWprjQKNjdpmu5sprYrvNtpXjXR6Q0XO/0IIIcT2DFzwxeVbwO8C/6/jOP+3Uko5bXJG9d2dZhcXze66ulOH3Rc37kAJQCqV4uLFi2acuW3bLCwssLa2ht/vZ2hoiOvXr+PxeAgEAszMzDA+Ps7S0hJXr14FNgrxhUIh06GxbZtSqcT09PSmTnqju1B6hiX3sIRe69UF2E4Midopndw9XltbM3f90+k0q6urOI7D6dOnsSyLq1evMj8/TywWM8MV2k3J697W7qFk+u6q+4K7l0MrujVI+3IQKaWoVqtUq1Wy2WzNzCjuAMjExATDw8OsrKwwNzcH3AzMFItFcx7RwZV0Om2yW3THVHcq9VAMwBRQddPnRX2+WlhYMMMr6odbNOo4uzuNrWZxg5udbHfAB5oHaBoNmWh0THVqu9kyO7mM+mA81AbjbmzL9VbvUV8wttvhpMFgkEgkYpaXSqW4evUqhUKBiYmJTVlP7uU2O28sLy+zsrICwIkTJzad3/T66narM0f07Ef6PVOplKlXNDk5yfT09KYswnQ6zcWLFwkGg1SrVYaGhmqmwQ6FQjX1TvQ2dv+s/5s7E7aRYrHI8vIyyWSSY8eOmRss7ufX79dWw526bdfu19bPUCaEEEKInTGwwRfHcR5VSv2Q4zjzN/7koc0F5vr6uimI10gn6du6k+DuEOvOr23bBINBcrmcCZpEo1ECgQCnTp0ik8mQy+W4cuUKfr+f06dPUywWWVlZIRgMMjk5aTrWy8vLOI5jLr70Xbt4PI5lWWZa6/r1rE/3dj9XF3Tdjl52sju5y90vHflO7h5XKhUuXbpkpu/1er2kUikWFxe5ePEiXq+XkydPcujQIW699VaTTeAexuZO3W+2Hdx3V4GGHS49fr+TqVub6WY/7Ebn9KDTHdylpSUTFIlGo2QyGTOz0dLSEkeOHCGXy+H3+1FKEYlEOHr0KNVqlUQiUdNGbNtmZWXFTEddfy4LhUI1Q+IaiUajVCoVU+Pj1KlTXX0u3XYsy6JcLjM2NtYwI0AfH5ZlmRocbromSCwWY3JysqaGjV7PZpq1dfeQKr0e3Wp3HG1lGc2yJvQNBh0kcN1saFkhW88cpLOg3IGxTs4BemYrHUBIJBLkcjmq1aoZQtvos7U6TyUSCQqFAkNDQ+Z7W3+f6TougCnmrN9fZxnqgGM+n6dYLHLrrbcSCAQYGxujWq3WrEsikTBDi3O5HB6Px2xLPRxYZ8O4h/bWc2eOtQsChsNhkskkKysrRCKRTUNI64JnDbPO6oNm3Xxnus/Z+j11cKzVfu/H72chhBBiUAxs8AVAB15uZL20DLwAeL3elhe2ze666gBL/ZjqRqnow8PDJrshEomYxy3LYnJy0nTGM5kM6XSaQCBAuVxmfHyckydP4vP5zBABx3GIxWImi0UvP5lM1tyRdnPfYbZtm0uXLpm7h+7nbvUCqped7HZ3uXuxjF7p5O6x3o7lcpnV1VVzp962ba5evcrw8DCnT5/mxIkTpg5HNptleXmZUqlksgTcF9+NtkP9dnNf8LsDMbqj2irg2Eo3+2HQhqINGsdxsO2NaaIDgQDDw8Mm8yWfzxMIBCiVSmYmpEAgwPT0tCm0GwwGGRkZ4dFHH8WyrJoOa7VaJRQK1bQngOvXrzM+Pm6KhtcPSXAHnqPRqHmPRsMtWp1v9HN0cd9CoUAoFDLL0O+lz8F+v79pQLFUKpnAty4w3MnMQY3aerOC1t1qdxxt5XzX6DV6G7mHorq+s5qe5HXQQdcUcu+3Ttet/vj3+XxMTU2RTCbNd5I7yKSzSsLhjULMeuiuOzvK5/Nx5MgRc7MCNjJU5ufnmZ6eZmJiwpzjdPDBPZzNtm2q1Spzc3NkMhlTkygajZppqd3LOnToENlslkqlgmVZPPjgg5w+fZqZmRkTlFlaWjLnb/cwq/ohWI2GIzXaX8eOHSMSiZigqH6OrmGjvxPqt717uJXett0G21tl7rTa7/34/SyEEEIMioEOvmjthhtp+uKs1ePuC+9GxfTqL0zrL3gmJiYoFApMT08zPz/P6OioKUJ56dIl4vE4p0+fNtMDLy4umgKZernuDpY7pRogmUxiWRajo6Nt70jr+iONnrvVC6hedrKbpUn3Y0e+k7vHOrg3MTFBpVJhfn6exzzmMdxxxx2Mjo6ysrJCqVQyhU9LpZKZvUPvY/f2aJYK3ig1HTanvDcqxtuNbvaD1ALYWT6fz2SmrK2tcfjwYdPB1MVAp6enzSxplUqF4eFh0ymbnp4mnU6zsLCAz+djeHjYvHelUmmY8VEoFLBtm6WlJaLRqJmKWnPvc4/Hw6FDh2rWWQcEg8Eg6XS64VAh9/vo4U+NMhP0+7l/6ixEuJklcPLkSdMh1x3trXZEG9Xv2Ip2x9FWznfuLBf3UFr9Hg1mFWp67vJ4PGZ2oWaBsWb1ZNzvUZ/x4Q7Owc3vnGw2S6FQqAks6KG79ZaXl1ldXSUUCpkppAETrNDDO69fv86RI0dMBo476KGzbkKhECsrKxSLxU3BF02vczqdNjc99Hf/9evXWVlZMZmkepiVe9sAZihUs+FIbj6fz9wUcT+nUVaNW/253j0cr9Nge7PvEd2m6j9Du88ihBBCiPb2RfCll+rTeaH2TlV9BkI+n6darZJKpUyAY3JyktXVVebm5ohGo9x5552Uy2VWVlYYHR3lSU96kgncLC4uAjcLFhYKBZLJJMPDw4RCIfL5fM3U1nr9Jicna4YRtSve1+yiutsLqE462VtJWd7O2PV+Yds2ly9fJhgMEgwGGR8fN1OXjo+PMzo6ysTEBNFolHQ6zfDwMIFAgHA4bDqKbnrYRH2dhGbqt1v9743uPPe6NobYGY7j1BTytm2bubk5M5uLrsly4sSJmpoUjz76KJlMhlQqRTQa5eTJk2Yf6/cplUqbjlOPx0MkEmF8fNwU4+22HoQ+x1UqFYaGhvD7/S078e4pqhs97u7gu8/PcHPonbsoejdZfY3aeqvzZzfaHUfNhqpA60LDOsul/vXt6oQ00uw5jerJNHuue9iXrk/WKGPPsiwTbNG1V/QsXvX0d2owGGw4DXIoFKJUKtVkh+phaceOHWNycpJwOMyRI0fM3903IurPe3qdLcvi2LFjNZk44+PjZhiee5hVuVyuGb6TyWRYW1sz9WPafac1Kp5eH7hq9Vy9n7YabK/fBu2K78r3ghBCCLF1EnypU5/O2+pCRl/w6NT0dDptZhqZmJjg+PHjZsy7vmDUtVd0J2FsbIxMJsODDz5INps1hQL1nefR0dGaNH4dcKnvkNcXuNTPb3dRvRO6TVl2p/c3es2g8Hq9HD16lEQiYVLVJyYmWF5eplKpmJT+K1eumCl2deHdRnp9h1Fv+/ppq/fL9t/PdHaCzmS5fPkyPp/PBGRXV1fN+cjdYdSZeIC5418/pKFRZoe78+fz+UgmkxSLxa5m+dHBafdQoe0OwWl1fnafJ3vRhnejk9kq2A+b63LUT70MNzOMmj3WC80yberpYV+6LTYK7gI1Mwe16ujrzBAdhHZndkSjUQ4fPmxm5RobGzPDgZaXl4GNQr3uAtClUskM+Wx13ovH45w6daom6KXbv/t4adT+dB0XYNPwuUZB7mZDkro9PrbaXne67QghhBDiJgm+1On0bqf7ImpycrKm6N38/DxHjhzh1KlTXLx4kUKhQLVaNenFehy+zmCZnJykUCiwsrLC6uoqk5OTKKVM4Uh3WnGzoozuYQKd2qnCea0u3nYyvX+veb1ebrnlFgKBAIVCgcc85jGmvsDExAQ+n498Ps/169fx+/1matRmnZlmF9Nb3W+NhiTA/tn+B4HOAKhUKsBG504XBHVn4OkOPWCyXfTfdC2M8+fPc/z4cTOcya1+CGYulyMUCjUcxtOsPdbfjXffyd/qEJxm5+dBCBi26ng3C/a36uS791H983odOKrPtKnPqNPff8ePH284/KW+g99oveuLw+vvSZ0ppb/7dHaLO3tPf7fq7KpSqYTX6zXroIe0BQIBs97NznutskjdmVuNhuwAHDt2jFAoZGrM1H/X1QdPugl27ERgZKfbjhBCCCFukuBLnU4vPOovojyejcKX6XSaZDJJLBbjxIkTnDx5EtgcbLh48SLpdJo777zT3PWrVqtYlkU0GjXZMfrir1FmS/1661kqOlX/GVqluHej24ybXqX377W1tTUWFha4fv0609PTPOUpT2FoaIiJiQkzO0s4HKZUKjE0NES1WqVcLnd9sav3W/0MF+2CMu7lNBoOMOjbfz/THVGdAROJRBgdHWVpaclk1F25cgXAZOAVCgUikQgzMzNmFhrd0V9fX2dubo5yucwTnvCEtsNiPB4PhUKhYT0Jd3vU9Dmx3VC4RsvazuP9rF3Hu5MA7F5kMmrNOv56yCxszDykpzJvFDCqf617vfWU0IB5j7m5OTNkbmJiglgsVlPfpFGB2Wq1ytjYWE3go1wuEwqFaoZ3Ntv2zTJRwuFw00yZ+iGdeqhWfUZPo+3Qzb7bif08yMeUEEIIMWgk+LJF9RdR+ufY2BiRSIQjR47g8Xg2BUosyyKbzTI6Omou1sLhMFNTU4yPj5uLVn3hmMlkzPP0HUb3XWx94RQOh4nFYl3dEatf91Yp7juplxd/ezkNZjQa5fnPf74p+Dk5OWlmpwKIxWJEo1HGx8c31a9wZ7+0+wx6f9XPvrXVIsqtak6I/qCnnff7/Vy7do3Dhw9TrVaJRCJmans9xHF6etpkEpTL5U3ZcuFwmNOnTxMKhRgfH297ztC1YmBz4VV3cU6g6Qwwvbbbx3mny+tkVqetdrz3WrN11d9f+v+NntcoW8a9nXTh6OnpaTPts23bZhin+ztRt2ldxFlPLa3fS2ec6NnA9HrVZ4XqdXJ/r9ZnZ7mzcfSwp0YZgvrmiK6P1Mmw5d1UP6xNzvFCCCHE3pDgyxY1uqOrO7AnTpxo2pHWU2X6fD4zFEUHaXQHStdmKJfLJkPmsY99LMPDw1SrVS5duoRlWZRKpZpsl26Kszb6DM1S3PcyoNGtvZwG0+v1moK6Fy9e5MKFC3i9XhMk0cM7PB6PmVa8USp/s89QX2yxfvat7aSky/Sh/U13HK9cucLCwgK2bdcc+7lczkwNXa1WicfjZlYZXavC3cb8fj+33XZbR8t2B5HrC6+6a3Y0ynTYKe3aa6/PWZ0eH72syTEo5936mwydrHf9dsrn8ywtLTE1NWWGZ2azWTMFdKMhZolEgnQ6vSmw0KhQeaNhU83q7TTLxtHDodxBnXruIFGja4C9Os+6s5MGKeAnhBBC7DcSfOmhRhdW9X9LJBIUCgW8Xi9+vx/AdKKLxSLVatXMlKBrJuiOVyKRIJVKUSgUiMVipoBrrzo9zVLc8/n8pim1+7VjsBNj4ju1vr6Ox+NhfHzcTAkejUaJxWLEYjEikYgpSAmbp/RsV9en0XCjboZ0aO1mxhL9RwfxVlZWKJfLjI6Omlm1PB4PKysr5o5/t7MS1esme6N+6EZ9pt9e1JWC5oW9t7ounR4fvTyOBjUg2sl6N9pOOgiiz4c6g9AduHDvOx1k0e2uVRC60fTJnRTX1zMj6cyXZjc4dOapDt64h125hyrt1Xm2PjtJCCGEEHtDgi891CxzxP3T5/Nx4sSJmjtu7qFD+nd9gXfo0CF8Pp8ZQhCNRikUCsTj8U0dnZ26QK+/EN7OEJdGetlB28u7ekopU0zyCU94Qk2QxN1R0HUH6mf6aDeda7PhRo202qbdzK4h+oO+c6/vvPv9frO/9HS6uuipPk/ojmC3na1usjfatZvtnCdateF2y210Lt7OunR6fPTyOBrUgGgn690o0D88PFzzHeMu1txNvZxG+6DRrErNXu+mZ1tq97ncgRm4OWQXqBmqtFdDPBsFRoUQQgix+yT40kOdjHV3/61+2IjOdHHfNWv0N7jZAd+N8duN1kHu8G42NDRk9oe+0HWntLsvvlsNF2q2TZu1m0ZabdNB7dQddNFolFOnTpn/u4MTeur6+nPHVoJzvWwfezUUrtFnH7R2P6gB0a2sd7PvvkaBlvrl1NdsaaTV61upP066yeSp/79et/3yfSeEEEKI7knwZQ91G6xx/77b47e7vePdjUHrFDWjsxPc6mcCabUNe3l3vdU2HdRO3UFXf/e6PlOqm/3ay9ok7dZ5q+/V6/OCtPv+1mr/tHqsk2DGVvd9p4GSZkN2G71uv3zfCSGEEKJ7EnzZgp2oY9DqPesf22/jt/drp0jXGRgfH6+Zdabb99hKW9uv2/Qgqw+0bKcT1+lr97K2k7Th/cHdhoCetad2NbJ6YScCJdKuhRBCiINLgi9bsBNpw42KqerCtu6CffXDWkR/0vtNF1AOBAJdzUSlSYq6gI32tLCwQKlU4uTJk5tmL9LP6bRj22kHUNqf2C53GwIafs9t9323MryuExIoEUIIIUQvSfBlC7Z7N6zVbDP1xVSLxWJNwT4xGPL5PKurq3g8HgKBwJb3n6SoC9go5qzbUjM7ESiR9ieg9zNFdVI0fCvvW0+Ch0IIIYToJxJ82YLt3g1rNdtMfTHVTmZkEP3J4/EwMTFRM9PFVt5DOg3CcRxCoZCZKasRGSIhdkovix93WjS82/dtRIKHQgghhOgnEnzZA90UQ5XOz2By1+WQoJnYLo/Hw8jISMv2JOcKsVMGtfixHBNCCCGE6CcSfNkDckG4/8k+Fr0m7UnsFTmfCSGEEEJsn9ySF0IIIYQQQgghhNhBEnwRQgghhBBCCCGE2EESfBFCCCGEEEIIIYTYQRJ8EUIIIYQQQgghhNhBEnwRQgghhBBCCCGE2EESfBFCCCGEEEIIIYTYQQMZfFFKeZRSP6qU+rd7vS5CCCGEEEIIIYQQrfj2egW6pZRSwH3AdWBWKfVsx3F+Y49XSwghhBBCCCGEEKKhQcx8+Q9AxnGcHwFeBRxSSn3/jaBMQ0qpe5RSZ8+dO8fs7CxnzpzZtZUVg+/MmTPMzs4C3KGUOquUusf9uLQvsR2t2pe0LbEd0rbETmn3vSiEEEKIzZTjOHu9Dl1RSr0I+APgaTd+3sVGECkFPMdxnLVmr52dnXXOnj27K+sp9h+l1Nccx5lt9ri0L7EdrdqXtC2xHdK2xE7pt7allGIr17VbfZ3YOe2uuYQQYhANYubL/wv8ClAB/tZxnFPA4wA/cHwP10sIIYQQQgghhBBik4Gr+eI4Tl4p9XnHcSyl1DeVUrexkf0SBpb3ePWEEEIIIYQQQgghagxc8AXgRuAlCPwy8LNAFfgPjuNc39s1E0IIIYQQQgghhKg1kMEXAMdxykqpe4H3AmXHcZJ7vU5CCCGEEEIIIYQQ9QY2+ALgOE4GyOzxagghhBBCCCGEEEI0NYgFd4UQQgghhBBCCCEGhgRfhBBCCCGEEEIIIXaQBF+EEEIIIYQQQgghdpAEX4QQQgghhBBCCCF2kARfhBBCCCGEEEIIIXaQBF+EEEIIIYQQQgghdpAEX4QQQgghhBBCCCF2kARfhBBCCCGEEEIIIXaQBF+EEEIIIYQQQgghdlDT4ItS6qhS6kNKqU8ppX7Q9feP7MqaCSGEEEIIIYQQQuwDrTJfPgYUgG8A/8MVgHn6jq+VEEIIIYQQQgghxD7ha/HYjOM4dwEopb4DfFwpdefurJYQQgghhBBCCCHE/tAq82VOKXWvUurpjuP8f4FPAl8ERndlzYQQQgghhBBCCCH2gVbBl1cCh4CfvvH7rwAPAMUdXichhBBCCCGEEEKIfaNp8MVxnHnHcV4OvFkp9XLgS8BPAVd2a+UGjW3b5PN5bNve61URQmxBvx7Dtm333TqJg6NfjwshhBBCiEHStOaLUupJwH8C/gNwATgOHHIcJ7krazZAbNumWCyaC1SAaDS6x2slhOhWsVgkm81i2zYej4dwOIzH0ypBcHesra2Rz+eJx+N7vSriAJHvNiGEEEKI3mnVq/g6cBJ4luM4s0BOAi+N6Q4bQDweJxwO7/EaCSG2IhwOmwBHNpulWJRRluLgku820Y+OHz+OUqrhv2PHju316gkhhBBNtZrt6G1s1H35uFLqI4B3N1ZoEOkL0n65Sy6E2BqPx0M0Gq3JfOkHQ0NDknEgdp18t4l+dOnSJRzH2evVEEIIIbrWqubL64AjwO8APwhMK6X+Xin1it1auUGhO2xycSrE/tBvx7TH4+mbdREHR78dB0IIIYQQg6zlFZXjOJbjOP/TcZz/i40hSPcDb96VNetDWyk62ItChVLsUIjuNTpuenks7eZxWalUqFarO74ccfDYto1lWS3bsnwHCSGEEEJsX8e3sxzHueo4zv8D3LqD69PX9Pj3bupAFItFMpkMqVQK27ZxHKfm304tV4iDrtFx4/5b/bHYbQdzN4/LUqlkUu23cg4Ropn19XXS6bRpy42OA/kOEkIIIYTYvlY1XxpyDvDVvnv8ezevyefzWJZFsVgkEom0fY2eYUKPs9/KcoU46MLhsJmiub6GS6NjyV1ctJP6Krt5XHo8HkZHR3d8OeLg8Xq9jI2Nsby8TDAYbHgcyHeQEEIIIcT2dR18Ocj0+PduKKWYnJw0wZRO1F/8bmW5Qhx0uk5KNputqV0RjUYbZox028HczeMyEAjg9/t3ZVniYPF4PFSrVWzbplwuNzwO5DtICCGEEGL7BjL4opTyAD8N+BzH+Yu9Xp9G3NkrSqmOMl40ucsoRG90cyx1cpzWZ6Xtln6aeUnsP/WzGoXD4T1p50IIIYQQ+9nAXVUppRTwz8B/AP5QKfUPHbzmHqXU2XPnzjE7O8uZM2d2fD23M0ZeZpjoL2fOnGF2dhbgDqXUWaXUPe7H96J9ic70+ljaidoXrdqXbluPPPIIT3/606Vtia500rbOnTvH05/+dP7iL/7CHCdS40W00+57UQghhBCbqUEr4aKU+jHg5x3H+bdKqRngPuCnHMc52+61s7OzztmzbZ/WE/WZL80opbZ8N32v7sIfVEqprzmOM9vs8d1sX2J7dLHaZsdos2NWH3PBYNAM0ejVsdeqfT3+8Y93vva1rzE0NNTodT1Zvti/WrWt2dlZ54tf/GJNW97Jdi72l3Ztaye+E5VSPS82vhPvKban3TWXEEIMokG8mloGnqyUmgVsoAykwQxH2hWdzIyin+P+Qncch0KhUPO3rd5llLuTQtRqd1y6j8n646fRsVn/Wh2sKZfLu3rsFQoF5ufnd2VZ4mCxbZtsNks+nyebzZLJZMhms6a9y3eMOAiOHTuGUqrpv+PHj+/1KgohhNgHBrHmy9eBtwE5YByYAK4ppf4TcEQp9eZez8jU6O3cRXEb1YkoFoskk0nzezgcxnEc0uk06+vr2LZNIBCgWq0SCASIxWIEg0HTaXQcp+GdRtu2KZVKhMNhgsEg+XyeYDBY87hkw4iDyLZtUqkUlmUBzY/LbDaL4zgEAgE8Hg9+v99Mt1utVslms0QiESKRSM0xVCgUyOVyAIRCIfOz/vywE5ko5XKZZDLJkSNHNj3m9Xp7vjxxcOhaR/l8nkwmQzKZZHh4mHg8zsTEBPF4HL/fz+LiIolEoqa9SdaV2C/m5uZaPi5tXQghRC8MXPDFcZySUupDjuOUlVJPAT7PRvHdXwVesltTYbcr5BkKhZiamsK2bdNBS6VS5HI5YrEYoVCIYrFIoVAAGncUGymVSiboA5gZKvRMFN1OlyvEflEsFrEsC5/P1/K4hI3Zg8rlMpZlsby8TCgUwrIsLMuiUqlQLBbxeDzmuNTTVUciEUKhUM1ju8FxHEql0q4tTxwculOZzWZZXV01dZImJiaIRCJ4vV6SyaTJvJqamtrL1RVCCCGEGFgDF3wBcBynfOO/XuAngacA/5fjOA/u1jq0mxnF4/EQi8VYX18HMIGWarVKJBJBKUUgEKBUKhEIBMzrdGZLMBhsmLmiO4/BYJBisUg0Gq3paMpMSeKgqp+xpVEc1uPxmMBnIBDA5/OZTJnh4WECgQDFYhHHcUzABSCdTmNZFsPDw3uSUTY0NMShQ4d2fbniYLBtm9XVVZOBOTo6WvN4IpGo+SmEEEIIIbo36ONSvgu8D/jhrQReOqnb0u37FQqFmvfTw4C8Xi9KKQ4dOmQ6f6VSCdu2qVQq5rnpdJrr169z5coV0yl003fcy+Uy+Xwej8dT0xmUmZLEQdVJ27dtm6WlJVZXV03gUwdkIpEIPp/PvMfS0hKFQoFCocDy8jLLy8v4/f6W711//Nc/vtXzTbVaZXV1tevXCdHO+vo6hUKBSCRCLpfD5/OxtLTEpUuXTA0kGcoqhBBCCLF9A5n5ojmOU1RKvcpxnLWtvL7TITqO45DP583zGo391Z06HTAJhUIUCgWy2SzpdJrPfvazfOlLXyISiXD69GkOHz7MrbfeyuMe9ziTzVIqlbAsi3w+z9raGuFw2KR464wYPeRBMlyEaE0fM4FAgEqlQigUMseYz7dx6rt+/TqACYCm02mGhoZYWVnBsiwKhQJ+v59Lly4RDodZWVlpOuzCPSSw0flkO0MCC4UC9913H095ylO6ep0Q7aytrXHu3DlWVla4fPkyT33qUzly5EjNtNO6ftluD7cTQgghhNhPBjr4ArDVwAt0PkSnk4tPd6cuFAqRy+X467/+a/7xH/+RL3zhC6ysrDA2NkYwGOSTn/ykeZ1SiqNHj3LbbbfxYz/2Y7ziFa9gYmLCPF8X+ARMAEgXApWaLkI0VyqVyGQyVCoVAoGAyTiJx+Om5svo6CiPPvoon/nMZygWixSLRXw+H36/H7/fz7FjxxgeHiYWixGPxxkdHaVQKJggqKaHKNUPA3TbTsB0fX1dZpwRO2J9fZ1Lly5x7tw5yuUyt912G4cOHSKbzRIKhfD5fCbgKMF+IYQQQoitG/jgy3Z0GsBwZ6C0K+R5/vx53vnOd/KJT3yCq1evEolE+PEf/3HuvvtuXvCCFzA0NEQul+ORRx7h4Ycf5sKFCzz88MN885vf5FWvehULCwu87nWvY3x8nEqlQjKZxLZtYrEY0WjULEcI0ZrOePF6vSbTJZvN4vP5cByH1dVVvv3tb/OLv/iLbadxvueee3jXu95FtVo1AVF3ELZUKpHP54nH402HZmwnYOo4TtMpsIXYDp/PR7m8UUYtHA4TCoU4f/48sViMq1evcvToUWKx2B6vpRBCCCHE4DuQwZdG0zG369i0uvhMp9MAnDlzhje84Q34fD6e//zn88Y3vpHjx48zPj4OwIMP3ixL4/V6OX78OM9+9rOBjbuPv/Vbv8W9997LpUuXePWrX82dd95JIpFgaWnJTH/rOA7r6+umMynEQdbsuHUch+vXrzM0NITf72d4eJiVlRVs2yaXyzE3N8c3vvEN3vzmN2NZFh/96EdJJBJUq1WWlpaIRCJUKhUqlQpf+MIXOHPmDP/6r//K29/+dp785CezsrICbAzZ0NkB+udOCAaDVKtVvvWtb216TIYiie0oFArk83lGRkaIRqNcuHCB69evEwwGsSwLv9/P4cOH93o1hRBCCCEG3oHswfd6OmbHcfj93/993vnOd/KiF72Id73rXYyNjQHwwAMPNC2wqetMwEYw5m1vexvhcJiPfvSjrK6u8t73vhe/349t21Sr1Zq6L0KI5vS0036/n7GxMa5evWoK5gaDQb773e/ypje9Cdu2ef/738+tt95qXhsMBrnlllvM789+9rOZnZ3lLW95Cz/zMz/DH/zBH3D06FFGRkaIRCIkEgm8Xq+Zvczv9/e8MKllWVy+fLlnxcGF0CqVChcuXCAWi1GpVPB4PFSrVSYmJigUCqytrTUcaieEEEIIIbpzIIMv3dRe0DM9NLvwtG2b17/+9XzgAx/gxS9+Me9617u2fPfb4/Hwlre8hUgkwnvf+15+7dd+jbe+9a3E43GTeeMe8iAzUAjRWDgcxnEcU2TX5/MRDAaJRqNcvHiR1772tcBGtpo78NLMj/zIj/DYxz6WV7/61fzcz/0cP/7jP85/+S//BY/HY4I6OkDq9XqJx+M9/0zu+k9C9IrjOJTLZdbX11lbW8Pv9+P1ehkaGsLj8VAul83NCim2K4QQQgixdQcy+NJN7QV3lozuyOlaEn6/n1/4hV/gYx/7GL/4i7/I7/zO7zQNhDiOwze/+U2CwSC33347Xq+34fOUUrzmNa8hHA7zzne+E8dxeOtb38rk5KSZVlpPVa3XKxwOSyBGCBelFOFwGKWUyRSbnp7m61//Oi9+8YtRSnHmzBlOnDhBNpvlq1/9KsFg0Ezj7jgOkUiESCSC3+9HKcWxY8f4+Mc/znvf+14++MEPsrS0xJvf/GYOHToEbAwdrFarO/aZpOCu2AmO41AoFCiXy8TjccbGxgiHwwwNDTE+Pk4kEpF6Y0IIIYQQPXAggy+t6EwX3XHT2TE68JLNZvF4PNi2zT/8wz/wsY99jNe85jX8+q//esMpqNfX1/nsZz/LRz7yER555BFgo37MU57yFO68805+/Md/nNtuu23Ta3/5l3+Z6elpfvM3f5Nbb72VV73qVQwPD5uhDaFQyMywks/na6bCFuIg08ew3++nVCoBG8dvOp3mJS95CT6fj/e85z0cP36cfD7PL/zCL5hjs5GJiQk++MEPcvLkSYLBIH/yJ3/Cs5/9bO655x7e//738xu/8RvMzMyYY3gnjsG1tTUefvhhPvWpTzExMcEznvGMhucbIbpVqVS4dOkS1WoVr9fL4cOHWVtbM4EXy7JM4N899FUC/UIIIYQQ3ZHgSx13RkkkEtl0B10HPDweDx/4wAc4ffp0w8BLtVrlf/yP/8E73/lOrl69yrFjx3jjG9/I0NAQ999/P1/72tf40pe+xJ/8yZ8wPj7OM5/5TF7ykpfwjGc8w7zHr/7qUr+fOQABAABJREFUr/Ke97yH73znO6ZQaKFQwLZtlFJ4PB6y2SzRaJR4PE44HG5YTFiIg0Qfwx6Ph3w+j23b+Hw+fvu3f5u5uTm+8IUvEIvFsCyL173udVy8eJF7772XmZkZisUi165dIxQKUSgUKBQKfOQjH+GNb3wjH/3oR03G2t1338073vEOUqkUk5OThMNh89hOHHeVSoWHH36Yt771rQDce++9/Jt/8296vhxx8KysrJgC0rBRgHdycpJMJoPP5yOVSnH06FEAcwMCZAiSEEIIIUS3JPhSp74eTH3NFx3weOCBB7j//vv57//9v9cEXgqFAh//+Md5z3vew/z8PLfddhv33nsvd911l+mc/fAP/zAA3/ve95ifn+df/uVf+MpXvsLnP/95/vEf/5Fjx44BG0Mnfvqnf5o/+IM/YHV1laNHjxIKhahUKjX1atyBlnw+39NiwkIMmmAwaGZv0UGUv//7v+ev/uqv+O3f/m2e/exn861vfYt3vetdfOUrX+H1r389L3zhC83rk8lkTcHdQ4cO8drXvpY///M/5xWveIX5++HDh7l+/TqXL1+uqcvk1qtg6KlTp/iDP/gDHMfh137t1/iTP/kTnvvc5zI0NLTl9xQC4Pjx47zlLW8B4IMf/CBnz55lfX2d0dFREokEsViMYrFILBYzQ49kCJIQQgghRPckNaKOUspkvMDNu+ilUslkvcBGoc5IJMLLX/5y89qLFy/yfd/3fbzhDW/g2LFjfOITn+B973sfz3/+8/F6vVy4cIGlpSXz/JmZGX72Z3+WP/zDP+RTn/oUXq+XV7/61ViWZZ7zsz/7s1iWxac//Wlg4w64OxAUjUZNOng+nycYDJosGCH2O12vwj3ldLlcxrZtM3NLJpPhta99LU996lN505veBMBf/dVf8Zd/+Ze85CUv4ad/+qdbLuNFL3oRP/ADP8C73/1u5ubmzN8PHz7MtWvXyOVyrK6ukk6nWV9fr3mtPn9st17L0NAQ09PTzMzM8Cu/8itcuXKFv/mbv9nWewoBG4GUO++8kzvvvJNf//VfZ21tjS9+8Yuk02kcx+Hy5cssLy+bjM9IJCJZlUIIIYQQWyCZL22Ew2Gq1ar5VywWuX79Op/+9Kd52cteRjgc5kMf+hCWZfGOd7yDXC7Hq1/9am677TZWVlZ48Ytf3PS9n/jEJ/IDP/AD5vdnPetZfOYzn+EVr3gFL3zhC3nNa17D4x73OE6dOsVnPvMZXvnKV1Iul6lUKgSDwZpx972ePluIQVA/TBBu3pVfWFjg+vXrvPKVr0QpxR//8R+TTqf5whe+wNve9jYAPv7xj/Pxj3+85j2PHz/O6dOna/7mOA7r6+u89KUv5cqVK3i9Xg4dOkQymSSbzZJOp/F6veTzeWZmZkwH1Z0p4DhOTU0pfezq7JhWzp07x9Oe9rSav73jHe/gHe94R03gSYhunTt3jtnZ2Zq/Xblyhd/93d/F7/fzjne8g7W1NUZGRhgeHt6jtRRCCCGEGHxy+6oNXVsln8+bTtPf/u3fUi6X+aVf+iXzvL//+7/n0qVLvPSlL+W2227r6L0fffRR5ufnze+PecxjuP3227nvvvvM35VS3H333XzhC1/gW9/6lgmsuLNxCoUCgUCAeDwu6eBi39LZXTr7DDaCGvXtXt+d9/v93HvvvTzyyCO85z3v4ciRIzz00EM1x20jOnPGLRgMcscdd5DJZHjve98LbGS+OI5DLpcjEokQj8exLItHH33U1GaqL07aKBPGFUCS87HoK47jcOLECSzLMkFOIYQQQgixNXKx34bjOKYjlsvlyOVy/Nmf/RnPfe5zedzjHgfAQw89xGc/+1me/exn85SnPAXYqL3yoQ99qOV7FwoFPvnJT/LJT36SixcvAvD85z+fcDjM3/zN35iZWn75l3+Zqakp3vSmNxEIBAiHw6bDqQsgViqVTengjTqrQgyqRoGLZsMgbNvmfe97H3/3d3/Ha17zGp773OeytLTEK1/5yraFQhcXF/nc5z7H17/+dS5dumSWd+jQISYmJnjzm9/MhQsXOHz4MLAxJOiWW27h8OHDxGIx/H4/+XyeQqFggqSaPnbrazZtJ1vt6tWrW36tEK04jsPIyAhra2usr6+boKIQQgghhOieBF/AZI80St/P5/Ncv34d2JjB6F/+5V+Ym5vjF37hF8xz/vZv/5ZEIlFTO+KjH/0oX/va11ou94477uCuu+4il8vxqU99iocffphgMMgP/uAPsrS0xEc/+lEA4vE4b3rTm7jvvvv4x3/8x7Z3/rVe1ZsQYq/pekvRaLRlPSN9LJ8/f563v/3t/MiP/Aj/+T//Z2BjhqClpaW2QdGJiQkOHTpELpfj3LlzfPnLX2ZtbQ2lFI973OPwer28/e1vZ3p6GoD5+XlyuRyO4xAKhRgeHjbHaDweJxAImE6ru06Tpus3Ad6tbJv3ve99W3mZEG2tr6+TSqUIhUJUq1UymQxLS0sSgBFCCCGE2AIJvtBZkCIUCjE+Pm4K8R4/ftw8Vq1WOXLkCIFAwPzN5/MxMTHRcrler5cnP/nJvOQlLwE2Aj0AR44cAWB5edk89/u+7/uAjaK+y8vLLCwsUCgU8Hg8BAIBlpaWagr1QuO77EIMomKxSD6fr5nVy90B1EEXnW3yz//8z6yvr3PPPfeYY/brX/86d911F0984hNbLisSifC4xz2O5z73uYRCIWKxGD7fRnmsYDBogiu6uO76+jrXrl3jypUrFAoFotEoIyMjRCIRIpEIlUplUwZMvRvH6HrTJ7SgzxtC7ITh4WGe8IQnMD4+TqlUolqttmzLQgghhBCiMQm+0DpIEY1GmZiYwOv1mhlUADNtNGwMO1hbW6t53djYWE3wpBW/3w9g3kN3Ft3vqYclWJaF1+utSf++fv06165d4/Lly1iWZTqmje6yCzGI3MdosVgkk8mwuLhILpcztVXcBae/9a1v4fF4zNDAUqnEhQsXeOxjH9vxMhcXFymVSpw8ebJmOvlMJsPo6KgJ1q6trXHp0iUKhQJer5dIJGKGBNq23TI7TbtxjG4pnUCOb7GTvvnNb7KwsMClS5eoVqusra3V3GgQQgghhBCdkat2btaNcHewNKUUwWDQdPD0Xeb64Et91snY2NimgEyr5ft8vprgi9frbRh80WnfgUCAYrGIbduMj48zMjKC1+tlbm6Oa9euyd1wsa+4A4nhcBifz0cul2NhYcEcD/F4nEgkgm3b3HfffZw6dcoEVB966CFs2zbBmHYcx+HChQtEIhGmpqbM33WGzcjICIVCAdgovDs7O8vRo0cZGxvD4/GYYFCpVNrx6Xkl+CJ2UjabZWlpiWKxiN/vJxAImJsQQgghhBCiczLVdAfy+TzLy8sUCgUymQyw0QnTwZG1tTUqlQrnz583r+n04vThhx8GNjpQqVTK/K6UolQqmfRuHexZWVmhWq0SCoVMQCgYDDIxMUE+n6dUKm1atp7K1j29rRCDor4Wk1KKsbExcrkcgUCAarXK0tIS4XCYQqFAPp/n8uXLPP7xjzfH5Be/+EVgI4jpPk6befjhh8nn8xw9epRkMmn+ro/5WCxmMm0OHz6Mx+PB6/WSzWbNc6LRKMFgsGb9GwV4t2sn3lMITdc2GhsbY3x8HJ/PZ9q1tD0hhBBCiM5J8MVF39UGzJ1qPbQnGAyyvr5uOlKBQIChoSEARkZGyOfzNXfVo9Eon/rUpxgeHiYejzdcnt/v58SJE+b9Q6GQ+V0Pc9Lp3Xp56XSaubk5nvGMZzAyMlIz4xFsdApnZmZqZk9xTWW7rVlVhNhrjuOY9ry6usrIyAjhcJh8Ps/S0hKRSIRqtcri4iJ33323yXyZm5sjEomYIUQnT55kaGiIixcvEgwGOXTokFlGNBrl8uXLRKNRXvCCF9QELDOZDN/97neZmJgwQRbbtllcXKRSqZDJZPD5fAwPDzMzM1OTIbcdSimGhoawbRvLsvD5fHg8HqrVqgRUxbb5fD4cx2F9fd0UgNY1jQqFAkop0uk0sPFddfToUQm8CCGEEEJ0Sa7aXUqlEslkkmQyaTJOSqUSy8vLlMtlJiYmGB0dBdrXfBkfHwcwF7Dt1L+Hx+PZNJQpFovh9Xo5efKkGWLh8XgIhUL4fD4KhQKlUsl0zDRdLyMYDMrU02Kg6cBLOBxmdHTUFMKdnJzk2LFjTE5O8sADDwBw+vRp87oHHniAxzzmMZs6jF6vd9MxmsvlWFpa4olPfOKmwEa1WgVgdHTUBGpnZmY4fPgwjuOwtrZGLBZjamqqZY2XrdLBX/fnkOCL2EnXr1/nypUrptbSysqKmQFQCCGEEEJ0TjJfXEKhEFNTU+buci6XIxQKMTY2RrlcNtNtQu1QCL/fTy6XM3ekYSNzRg+JaJWe7TgOjz76KIVCwXTW1tfXa2ZTgZvDHUKhEMeOHTPBGR0kGh8fN3f56wsH63oZ+XxeMmDEQAuHwyZ4eOzYMRYXF7l27RrDw8OcOHECj8fDt7/9bQBuu+02YGPWsO9973v84i/+4qb383q9VKtVc8xns1lyuRzhcNi83i2XywEbwRcd5BkdHcXr9RIIBJiZmeH48eNNM162OgTQcRxz7nH/DST4IravPtAPmO+tkydPEg6HTb2X4eFhc3NBCCGEEEJ0Tq7a6+iU62QyyaOPPmqCG0tLS1y+fJnDhw8D8K1vfcu85mlPexqrq6v83d/9nfmbUoof+7EfM/Uo6rNNHMfh+vXrfOITn+DTn/40fr/fTIH71a9+lUqlwo/92I+Z5/+3//bfWF5e5q677sK2bZLJJEtLSyZTp1KpEIvFiMViTTtjMvW06Fe2bXeclaWzX8rlsjlWL126xPXr1ymVSqZ9Ly0tARuBxng8zkMPPbTpvYaHh1lbW+ORRx7h0Ucf5fr16/j9fp73vOeZQKq2trbG/fffz6lTp3jiE5/IP/zDP3D69GkzvfXQ0BC33HLLpsCLbdvkcjlyuZwJgLaa1r4ZfW7S76+31fOe97yu30sIN6VUzT93YG9ycpJ4PI5lWeYGRf2xIYQQQggh2pPgi0t97RQ9y9H169fNrCqJRILR0VG+8IUvmNc9+clP5jnPeQ7/+I//aArmArzoRS8iHo9TqVRIpVJYloXjOOTzeRYXF7l48SIAP/iDP8hLXvISjh8/zuLiIt/4xjd40pOexA/90A8BcP/99/P2t7+d5z//+Rw7dswEc8LhMFNTU2aIg65Z06wDK1NPi36lAyrtghLFYtFkmIXDYSYmJnjSk57EHXfcwejoKLZtm+Pm61//OrCRmXb33XfzpS99ic9+9rM17xeLxTh06BAjIyNMTExw8uRJTp8+XVMDRrv//vvJ5/P86Z/+KVeuXOG+++7j+7//+/H5fJTL5U3P18djoVAwQVJgSwFQpRQ+nw+fz2eCL+vr6yileMELXtDVewlRz+v1mn9KKfMd4vV6SSaTLCwsmJn/IpHIHq+tEEIIIcRgGtheuFKq57feQqGQqaUyNTVlZjGpVquUSiX8fj+Tk5M87WlP4wtf+ELN0KO7776biYkJ3v/+95taEPo9JyYmsCyLhYUFrl69ysrKCkopTp06xYtf/GJuv/12PB4Pa2trfO5znyMSiZgOJMC9997LxMQEL33pS7l27RrLy8smgBKJRIhEIpRKJXMHfit31YXYS51mZYXDYUZGRpicnAQ2ZhWbmZlhZmaGlZUVMpkMw8PDHD58mK997Wvmda94xSu48847uffee2tmLwJMjZaxsTFTRLve4uIiDz74II997GN51rOexcc+9jE8Hg8veMELGBoaIpFI4DiOyZSD2mCuDpJGo9GeBED1uUd3loXoFcdxamoLLS0tsb6+TrFYJBaLtQzwCyGEEEKI5gYy+KKU+u/Ab3cagFFK3aOUOnvu3DlmZ2c5c+bMpufo8e3hcNikXofDYSKRiLkYtW2bYDDI93//93P58mUuXLjA+vq6uev9ile8gkwmw+/93u9x7do1yuUytm3j9XoZGxsjFArh9/sZHR1lbGyMkZERKpUK5XKZcrnMP//zP5PJZHjOc55jUr/n5+f5p3/6J5797Gdz/PhxpqeniUajWJbF0tKSCbosLCxg27bpwFqWxeLiohnL382wDlHrzJkzzM7OAtyhlDqrlLrH/Xgn7Uu01mlWllKKSCSCUspky5RKJXK5HAsLC5TLZdbX13nCE57A17/+dVPDpVQq8drXvpa1tTXe8IY3YFkW1Wq14T/bts0xWS6XyefzfOlLXyIajfKEJzwBy7L4+Mc/zg/90A+ZGcdGRkZYW1urGY7hDubGYjEikQjFYnHTMdiqfem2pYv5NqoHJUQznbQt2Kj5otul/unxeFBK4fV6TUH4733ve1y8eJFCobBpCnhxsLT7XhRCCCHEZmoQL6CUUt+78d/3AX/sOM5aq+drs7OzztmzZzf93V0Es9Fd5OXlZR5++GEOHz7MxMQEn/3sZ/mJn/gJ/vRP/5Sf//mfZ3l52Tz3y1/+Mj/3cz/H0NAQf/7nf87S0lLTWU/W1tb0xQv33XcfL3vZy3jZy17Gm970JpRSnDhxgt///d/nda97HR/4wAd48pOfTKVSMdNXezweEokE6XSaXC7HzMwMsVgMpRSLi4vMz89z6NAhpqenTa2JeDwuxXa3SCn1NcdxZps93qx9ia1rVaBWPxYIBJibm2NxcZETJ04Qj8f58Ic/zK/+6q/y5S9/mVOnTpnX/OVf/iWvfvWrefnLX85LX/rShsssFovceeed5vff+73f40Mf+hAf+9jHeOYzn8mFCxd44QtfyL333stznvMcjh49amrPzMzMMD093fB92x2DrdrX7bff7vzRH/0RAN/4xjd43etex6te9Sp+5Ed+hBe+8IVttqI46Nq1rXe/+93AxlTqr3zlK0kkEjzrWc8yw/Ke9KQnMTY2RjQaJRKJcPjwYfNdIw62Vm1rp74T3XWJdsteLPOga3fNJYQQg2jgMl+UUi8AbOB/Az8D/IpSqvFYgQ7l83kWFhbI5/ObHnMcB6/Xyx133MHMzAxra2s88YlPZGZmhs9//vObnq9rv4TDYf7tv/233H///S2Xfe3aNd7whjfwH//jf+TYsWP8xm/8Rs2yP/zhD/Oc5zyHu+66i2PHjjE+Pk48Hgc2Zl5Jp9PYts3w8HDNWPxEIsGhQ4dIJBLAxnCNaDSKbduS/SIGRie1YEqlEkNDQwwPDzM5OUksFuO5z30uAP/n//yfmue++MUv5kUvehF/8Rd/wSOPPNJy2d/73vf4/d//fT784Q9z991388xnPhOAj3zkI4yOjvKTP/mTHD9+3BxTU1NTLWeB6VXB609+8pOMjY3xb/7Nv9nW+whR73/+z/9JtVrl9OnTBINBpqameOxjH8vjHvc44vE4IyMj+P1+KdouhBBCCLEFgzhlwTeBWcdxCkqp3wF+FrCVUn/SaQZMJ9bX10mlUti2TbVapVgscuutt5rCts94xjP43Oc+RzKZ3FQn4rbbbuMzn/kML3/5y3n3u9/N/Pw8P/VTP1XzvKtXr/J3f/d3fOUrXwE2OoW/9Eu/VHNR+5nPfIaHH36YX/7lX2Z8fNwMNVhbW2Nubo5qtcpTn/pUgsGgya7RU+FGo9GaO/B6ppRsNmuGeAjR7/TxEAwGyefzBIPBmkBMPp8nGo2aorePPvooJ06c4Pjx40xNTfG5z32Ol73sZeb5Sine/va3c9999/Ff/+t/5dWvfjVPeMITzOMLC/9/9t48TtKrrvd/n9qruraurt7XWTJMJslkGwOEhEVIVDZRkQjKqgl6XViuchUXRPFeEb2Il/u7GpZwQUXkXgS8KIhEdgkOS0gymUxm632pru7q2vfn90f3OamuqV6mp3u6qvv7fr361d31VD3nPE+dc55zvuf7/Xyn+eIXv8hv/uZvcvr0aex2Oy94wQt429veBsDs7Cz/8A//wI/92I/R3d1NW1sbsViMcrlsMo2l0+mGnjpX0u8mJib4jd/4DSzL4tFHH+Xnf/7ncblcWzqXINQyMTFh2veZM2d41rOexcDAAA6Hg1KphNPp5MyZM/j9forFIn19feTzeRHeFQRBEARBuExazvhiWdZ8zd/vUEqVgF8ESsD/3Mo5tdaEXuhVKhVOnTrF4uKi0WnI5XJ4vV5GRkYA+Kmf+ik+//nP8/SnP50PfehD3HzzzavOGY1G+dSnPsXP/uzP8n/+z//hoYce4pd+6ZfI5/P84z/+I9/73vdwOp3cc8893HffffT29q76/Le//W3uvfdejhw5wvHjx4nFYiwsLJDJZFBKMTs7i8/no1gsmt12rf2Sy+U4ePCg8ZDR6OuTXUuh1chms6TTadLpNEtLS+RyOUZGRggGg3i9XpLJJOfOncPlcpm+evfdd/Oxj32Mj33sY6sMMJFIhLe+9a382Z/9Gb/+67/OjTfeSHd3N6OjozzxxHJE40033cQ73vEOXvjCF5r+lUwmecMb3oDD4eAnfuInKJfLZDIZ2tvbWVxcxOPxmMxG3d3dq/rfeuFTm6FYLDI2NsbQ0BC33347L3zhC6/wjgrCMrptwfKzweFwmLbtcrlYWFjg2LFjuN1uvF6vyTQmCIIgCIIgXB4tZ3zRKKWUtcy7lFJF4HNbPZfNZlu1izc/P082m8Vut9PW1kZ7ezu5XI7e3l4ymQyVSoXrrruOT37yk/zyL/8yL33pS/mTP/kTXvnKV646r8fj4b777uPOO+/k/vvv5+1vfzsAoVCIe+65hx/+4R/mh3/4hy+pz0MPPcS9997L8PAw73nPe+jv7wfg8OHD5PN5XC4Xvb29eL1ePB4PqVTKeOSstxspHi9Cq6HDjvx+P8Fg0KR/r8fv99PX10cikcCyLDKZDPfddx+Tk5O8/e1v58CBA9xxxx3m/ddddx3/+3//bz73uc/xyU9+klOnTjE8PMwv/MIvcNttt13SL/P5PG984xs5f/48b3zjG3na057G+Pg4bW1teDwe4vE4Ho+noWGlWq2aVPO6rpeL1+vlpptu4rd+67cu+7OCsB6BQIAbb7wRWM6e5XA4KBaLOBwOlFJMTEzQ29vL8ePHKRQK+P1+0XoRBEEQBEHYAi1rfLEsy1JK2SzLqlqW9SdbOcdaQruRSIS5uTnsdrvxiBkaGiKXyxldmGq1itvt5n3vex9/9md/xpve9Ca+//3v84d/+IeXhAPccsstvPe97+Xs2bPE43Fuv/12XC4XpdKlUVLf/OY3eeMb38jAwABvfetbOXjwIOl0mlKpRLlcpru7G5vNRiAQIJfLcfHiRdra2giFQibbUTgclp1JYU9Q662lDRs641cymSSTyZg+cejQIVKpFJlMhtHRUcrlMvfddx8TExPce++9/P3f/z033HCDObfH4+Gnfuqn+PEf/3FKpZIJ3avXlymXy7z5zW/m5MmT/PzP/zzPeMYzjDHI7/eTTqfJZDJks1k6OzsJBAKr+l82m6VcLovHgNCUKKVMZiPdx+x2O8FgEJfLRTweZ3FxEZvNZtKlC4IgCIIgCJdPyxpfACzLuiLlWL2rDqzyGCkWi0QiEeMpor1KEokE2WyWjo4O/H4/8/PzKKX41V/9VaLRKA888ADf+MY3eO9738utt96KUsqIcbrdbq677jpTRrVaNSmsNd/4xjf4xV/8RYaHh/m93/s9Dh06ZLQsxsfHcbvdFItFhoeHjWs4LO9cRiIRFhYW8Hq9RlvmSkMdBGE3Wav9av2iVCpFIpGgra0Nm81mDI86O5ndbiccDvMHf/AH/O7v/i533303N910E8997nPp7Ozk5ptvxm63mx/dF2v7ZT6f5w//8A/513/9V173utdx9913rwop0vUbGRmhs7PTpJfP5/NmkdrIgHS5aO88ybYhbDd2u51IJEKlUsGyLLxer9mQ8Hq9DA0N0dfXx9LSEsFgULxeBEEQBEEQtkhLG1+ulNpFkWVZZiHl8XhwOBxEIhGKxSJ2u514PM7p06fJ5XIMDg7S1dXFsWPHKBQKnD9/nj/5kz/h5ptv5kMf+hAvetGLOHr0KK95zWu45557GBwcbFi+zqQ0Pj7OF77wBd70pjfxtKc9jbe//e0MDg7icrmIRqP4fD7a29vJ5/N0dnailCISiRiRz9p007ruPp9vlXFJdiuFVmO99uvz+Yz3l8fjoVgsUq1W8fv9uFwujhw5YtK8t7W18Yu/+It87nOfI5fL8Rd/8RcmQ9jzn/987r77bo4dO8bo6CgXLlzg7NmzvPe97+XcuXNMTk4C8LKXvYwXv/jFBINBOjs78Xg8LC4uUi6XSaVShEIh4yWnlFrl4bId4X7ZbJbvfve73HXXXVd0HkGox263Mzc3RyKRQCnFgQMHWFxcxOVyEQwGGR4eZnFxEbvdTnt7u9FIEwRBEARBEC6PfW18qV0UpdNps9CD5V33hYUFoy8RjUY5duwY5XKZXC7H0tIS7e3tRoQQ4J577uGaa67h3LlzfP7zn+ftb387v/3bv83zn/98fu7nfo6Xvexl2O12vve97/Gtb32Lf//3f+ehhx5iamoKWA5Puueee6hUKtjtdgKBAIAREM3n80xMTBAOhymVSqRSKdra2lhYWGBpackIBOuJsQjsCq1MffutVqsm7M/v9+Pz+Uin01y8eBGv18vMzAw9PT3Gw0R7jfX19XHTTTfh9/splUrcddddzM/P8/DDD/PQQw/xqU99alW5fX19HDp0iOHhYX7yJ3+S48ePc9NNN5l+lcvlmJiYwOv1EgqFAMy40NbWtmOGTvE4EHYCy7KoVqsEAgHC4TCRSISxsTFcLheZTIaLFy/S2dmJ2+3m/PnzeDweIpHIbldbEARBEASh5djXxpdaGhkqXC4X8/PzlMtlbDYb/f39LC0tMTExQbVaxeFw0NnZafRW5ubmOH78OD/xEz/Ba1/7Wv7lX/6Fr3/96zz44IO87nWvM6k6i8UiAMPDwxw4cIBnP/vZ3HTTTRw9epQLFy6QSCRIpVJ0dnYSj8eNASiRSFAqlbDZbCa0SAt+6jS3Ou2uXoCKx4vQqtS332w2y+zsrDmWz+dZXFwklUrR3t6Oy+ViaWmJpaUl0uk0Bw8eJJVKkc1myWQylMtlFhcXCYVC3Hnnnbz2ta/FsiwefvhhJicniUQieDwe/H4/k5OTdHZ2cuONN9LV1cXc3Jw5po0+hULBeMcBJtvRToX5ud3ubT+nINhsNtxuN5FIBK/XSzabpaenh66uLtxuNx6Ph7a2NhYXF8lms3R3d4vxRRAEQRAEYQuI8WWF+oWe3+8nkUhw8eJF2tvbjUEjFotRqVTo6emht7eXcrlMuVxmbm6OWCxmQiGSySSDg4O8/vWv5wUveAGPPfYYTz75JG1tbVx77bVcc801OJ1OEyYBMDMzQ39/P7FYjEQiwezsLL29vbhcLnw+H0972tPI5/NG36W3txebzWY8dgKBgDG+CMJew+fz0d3dbf72eDzcfPPNzM/P4/f7yWaztLW1MTs7y8LCAn6/H8uymJ+fp1gs0tXVZfSbgsEgwWCQarXKrbfeyuHDh7HZbITDYdLpNHa7nQMHDuDz+SiXy2SzWZ588kn6+vro6elhaWnJeMYVCgVGRkaMcag+zfR2Xr8gbDfVahWPx4PT6aRQKBAOh+nq6iIcDjMxMYHdbjfHBwYGGB4e3u0qC4IgCIIgtCT7fpW+nihtfZaSZDLJ0tKS2fXzeDxGa0WLfTqdTs6fP088Hsfr9aKUIpvNMjIywstf/nKmp6cBSKVSFAoFent7iUQinDx50oQR6QWfTvfp8/mYnZ0lEAjQ3d1NLpcDWKXtsrS0RLVaJZfLGUFeQdhLaANpNps1/VZrM83MzKCUIpPJoJQiFAqRTCZNprB4PE5XVxft7e2cP3+eb3zjGxw4cIDBwUHy+TxdXV2Uy2XC4TDBYJBcLkehUGB0dNSIbiulyOVyLCwsUCgUaG9vB5ZDFrPZ7IZeZlcqgK37vSBsJ+VymUKhsErUfWBggHK5jNPpRCmF2+2mvb2d66+//pJsfoIgCIIgCMLm2Pcr9PVEPbu6urDZbEQiEbLZrFlk6fAfh8PB8PAwMzMzjI2NEY1GqVQqOBwOent7KRQKVKtV+vv7yWazxGIxTp8+TblcZnh42GRMyuVyxnhjt9tZWlrCbreTy+XMIk0befx+v8nMpBdxXV1d+Hw+pqenmZycxOv10tfXd3VvpCBcBXR/TafTpFIpE/6nQ4NSqZTRP+ro6DCi1aVSiY6ODvL5PIFAgMXFRSYmJgiFQjgcDmO8TKfTuN1uCoUC2WwWm81GqVSiWq0yNDREpVIhn89jWRbhcJiOjg5CoRBdXV0opeju7qZarZJMJi8RJr1SAexKpbJt91EQNE6nk3A4TDweN0b/U6dOMTQ0hN1uJ5lMkkwm6evrMxsCkkVPEARBEATh8tn3xpf1RGkdDgc9PT2k02kWFhbI5XJ0d3djt9vJZrMMDg6aDCexWAyXy2UMKIlEgng8jt/vJxAI4HQ6GR4eplQqkUgkcLlclMtlkw7X5/PhcrkoFAq43W5cLhdOp5NoNEo+nzeivvCUB4BeLPp8PoLBIOVymXg8jsfjuWr3TxCuJrqfejyeVX13fn6evr4+E4aXzWYZGBigr6+PiYkJ04/K5TI33ngjpVKJ2dlZ05eXlpaMxoXdbqdSqTA0NITb7WZqaoqlpSXC4TBtbW1Eo1ECgYDxOtPpdzOZjDEK6XTYtUaWKxHAVkoRjUYl1bSw7Xi9Xvx+P5VKhXA4jGVZVCoVyuUyvb29DA8P09/fTyAQMM+cWuFrQRAEQRAEYXPse+PLZkRpddhPMpkkGAzi8XiIRqMmtMfn8zEwMGBSR6dSKSzLwul0mp1z7cJ9ww03cPHiRdLpNLlcjs7OTpxOJ6Ojo8TjcYaGhhgZGcFms5HJZJifn6erq4uDBw+a3UbLsshkMpdkf9GGINmNFPYqtf21Vlelq6uLbDaLx+PBZrORy+U4evQop06d4oknnsDr9dLR0UEikeDaa681RppEIkGhUKBYLNLW1oZlWTzyyCMkEglyuRyHDx/m8OHD5HI5YxTt6enB4XBQLpfJ5/N4PB5yuRyzs7Mma4wW5l2r7pdLqVQCltMCC8J2opSivb2dSqWC3+/n9OnTlEolk7L9xhtvXCU07ff7CQaDokEk7CgjIyOMjo42PCa6Q4IgCEKrsu+NL2tRq8+glGJwcJBUKkUikSAWi9HZ2QlALBajWCxy8OBBqtWqMZqcPXuWUChEZ2cnlUqFQqHA+fPnqVarOJ1O+vv7yefzJnNRIBBgZmaG+fl5k0Upk8kYI4vNZqNarZLP54HlzEewvKBzuVzGCFObarr+OsQoI7Qq+XyeM2fOcOTIERwOB/Pz86sMoNrwODc3x/nz56lUKpw5c4aFhQXGxsaMCG5/f78xtiQSCdMHPR4PyWQSpRQHDhwgHo8bY4zD4cBms5nz2Gw2otEoCwsLRowXoLOz0xhYdqKvzc/Pb/s5BaFSqbC4uEgymeTUqVN4PB7a29vp6emhWq3y+OOPc/jwYWPY9Pl8oikm7Dijo6Pi6ScIgiDsOfb9DGot40StPkNbWxvFYhGv18vS0hK5XI6hoSGy2awR0O3u7qa3txefz8fMzAyzs7Ok02mcTidPe9rTcDgcxGIxLl68iNPpZGhoiGg0akR2R0ZGcDqdLC0tmV11m81GOp1mZmaGQCBAKBTC5/OZDCs6/l4bZvx+v6mDvq567xhBaEXOnDnDo48+ajxLFhcXAejp6THv0QLZQ0NDdHd3093dTTKZZGRkBLvdbrzMnE6n0Vkql8smNfXi4iJer5cTJ04QiUSMN5rH46FQKFAul4nFYszMzHD69GlGRkZwuVzMzs6aVL26j+2E0VMEd4WdIJfLce7cOebm5iiVSvT29nLbbbfxvOc9j0wmw8zMDKOjoywtLdHX10c+n5dnibDvGB4eRim15rGLFy9e3QoJwi6glHo3cDtwEXiDZVmljY4rpbqBfwBKQAX4Wcuypq9mvQWhmdj3xpe1RDDr9Rl8Ph+9vb0AzM7OGpHParXKzMwM09PTtLW1Gc2Hnp4eLMsyop19fX10dHTgdDoplUpmB10L+BaLRSqVCk6nE5fLRVtbG7FYDMCk/dQ76tlsFrfbbWL1fT6fCX/QnjH6usRFXNgLHDlyBIC+vj7S6TQOhwOXy0WxWGRhYYFIJEKxWFyV7evChQsEg0Gi0ShtbW2kUimSySQOh4PDhw+blNMXL17kwIEDzM7OkslkAIx+UqFQoKuri8OHD5vXv/vd75oyo9Eo09PTxuipuVJx3UZEo9FtOY8g1OJwOLDb7eb51NvbSzqdxuv1EolEjMdLIpHA7/czNDS021UWhKvOesaVtYwygtDKKKU+YlnW62r+vxHotyzrTqXUbwMvBz6+iePzwB2WZVWVUq8Dfh5419W7EkFoLva98WUtEcxafQbLslBKEQgE8Hg8OBwOotGoEctNpVIUi0UApqenSSQSjIyMEAgECAQCdHR0MDU1RSQSIRKJUK1WcblcjI6OsrCwQDqdplKpEI1GqVaruN1uoznR1taGw+FYFcpQX2e9w1674Kt9j4QbCa2Ox+Ph+PHjxlPF6XQyNjaG1+slFovR0dHB5OQkTqfThN4ppfB4PAwNDeH3+5mZmcGyLHK5HGfOnCGVSrGwsIDf7zcLz5mZGZNNbGRkBMuyGBkZIRQKYVkWc3Nz9Pb2YrPZGBoawmazEQwGTciR5krEdddCvA2EncDn8zE0NMTk5CTd3d14PB6mpqZ48MEHOXr0KNFolOPHjzM7O2tE5gVBEIR9x+3Av6z8/Xng9dQYX9Y6bllWbarGAPDYDtdTEJqafT+LulyNBh1+pI0tHo+HUCjE0aNH8fv9JhW0DhfyeDyUy2UT+hMIBMzi0O12MzAwQEdHBwCFQoHBwUFCoRDhcJjDhw8zMjJCKpUyn9fUZp3Q2V101iOPxyM6L8KeJJ/PG+Fbt9tNtVo1Bg6Hw0EoFMLlcjE1NUWpVMLr9RKPx5mfnzc/58+fJ5/PUyqVGBkZ4dixY3i9XvL5PO3t7aYPDQ0NMTAwYIR9dVhTR0cHt912G+FwmGAwyOHDh1eJ/8LljyubQY8TgrCdeDweXvCCF+D3+3E6ncbjxe12myx/s7OzHDhwAJfLtdvVFQRB2HWUUiNKKavmp6qUOquUeqdSyrHynmNKqa8ppVJKqW8rpZ5xhWX+ilLqpFKqoJT6yDrvcyulPqSUGl0p+/tKqR9r8L6fUUo9rpTKKKXOKaXu3KAK7UBy5e8lILLZ40qpm5RSDwG/Anx3g3IEYU+z7z1fNOtpNOjwIZ/Pd8mOtg7zKRaLVKtVent7CYVCOBwOlpaW6O3tZWFhgZGREfx+P+l02gh8AiYzks6GFI/H6ezspFgsEg6HgWWjDCyHPMzPz+Nyucyuf29vrwkrqhUdLZfLgOyWC61Pbd/U/a6np4f5+XmKxSLt7e1EIhHa29uJRqPMzc2RzWbp6uoiGAxSLBa5ePEiyWQSt9tNf38/LpeLfD6PUorOzk7Onj1LPB4nElmeK2jdJC1y7fP5jA5TV1fXKsHRq9HHlFL09fXteDnC/qNcLtPf38/TnvY0YDn1dDAYxO12097eTigUwul0ks1m5XkiCIKwmncAnwWcwF3AHwEXlFJ/A/w/4FvA7wJvAj6tlDpgWdZWBdymWA7X+RHAu877HMA48BxgDHgh8PdKqRssy7oIoJS6C3g3cA/wbaB35fUh4KMr5zmqlPryyt93AwlA7zSFgIW6ctc8blnW94GnK6VeAfwW8IubuWBB2IuI8WWF9TQa6o/pEB+9GKxWqybtrN/vJxwOmx35RCLB3NwcfX19BINBHA4H1WrVhBV5vV5CoRDXXHMNs7OzzM7OGjHRTCZDZ2cnbW1tVKtV5ubmjPiu1+s1KW3rhYLL5TIOh0N0XoQ9QX3/0/1Tp5fW7Vz3ha6uLpORyOFwcO7cOcbGxvD7/Rw9ehSbzWa0Yux2O/F4nHw+bzIbaYNqbfr2bDZLLBYzmcei0agxyug67qSnmc1mw+FwiLaAsO1ks1nOnj3LNddcY/RetLD80tISR44cIRwOy/NEEAThUsZWDAsA/6GU+s/A04GzwAHgxy3LekQplWPZEHMdcHIrBVmW9SkApdQJYGCd92WA36956f8ppS4At7IshAvwTuAPLMv61sr/kzXvf+5KOfWaL98E3sqyceZHgG/UFd3wuFLKZVlWceU9S0B2o2sVhL2MGF9WWE+joZHGSu1i0GazEY/HAcyCbG5ujmKxaBaDOh20XshlMhkmJyfp7e2lq6uLYrHIgQMHCIVCRCIRswAETMrpzs5O+vr6jLioXuw18gyQkCNhr7AZXaZkMsns7KxJKV2bBWlwcNCECfr9fuLxOKlUikgkQqVSwW63093dzcDAAKVSiWAwyPT0NB6Ph3A4bAww3d3dpNNp44FWrVZNGdstrluPFu0WhO3G5XLh9Xqx2WwcPnyYpaUlTp8+jcvlIhAIAMvtT54ngiAIjVFKuYCXsRxq8yQwC7wFOL3ylkMrv2frPvc64IEGp/zftYaPK6xbN3CEFa0VpZQdOAF8Vil1FvAAnwZ+Yz2vHMuyvq+UmlVKfY1lj5o/XTlfD/BLlmW9o9Fx4Cal1J+ynOkoD7xhO65LEFoVMb6sULuQ09QaNeozIZXLZZLJJB6PxyzM9DHtfVIqlXC73dhsNhMmFAqFzO55qVQinU4zPz8PsGrRqGPrtVdNPB7H5/OZUKTa2Pu1PAMEYS/QqG/WovWPdJiQNnLCUx4p7e3tTE1Nkc1maWtro729ncXFRZN9DKBSqZDJZDh79ixzc3OkUimOHDliDKzBYBC/3082mzWZxWoNQjvpGWCz2bDb7Tt2fmH/4nA4yOVypNNpLly4QKFQIJ/PMzw8zIkTJ3C5XOL1IgiC0JgHlFK1xpPPAvdblpUG/hxAKfVM4C+AD1uWNV73+c8CNzc4b31Iz5ZQSjmBv2HZmKMNQd0sh0m9HLiT5RTQnwF+B/ht/dlGxh/Lsn6jwWszLIdfrXX828Czr/BSBGHPIMaXdVgrFMlms5HP55mZmcFms9HT02MWZXoBqHVYstksCwsLJJNJk5o2n88TiUTIZDKUy2VcLhflctnowNRSrVaZmJigUCjQ1ta2St9FsxOZVQSh2dHGUW2g1Dosus8CJBIJ0uk04XCYxcVFZmZmTMpmrYvU0dFBPB6no6ODQCCAzWYjnU6bzGTaWAqrDUG1Y8LVMHhqLwRB2E5KpRLVatUITC8tLTE1NYXf76dYLBKJRMTrRWjIivefNA5hP6M1X6rARcuykrUHlVJvAd7DsnfLJTonlmUtsE2GlnqUUjbgY0CRZaFbjfZu+R+WZU2vvPe/U2d8EQRhZxDjyzqsZ9TQCzj9O5vNGh2XcrlMKBTC7/dTrVY5c+YMlcpyprX5+Xmi0SjZ7HLIo81mY3FxEa/XazxmYrEYnZ2dBINBLly4QDwep729HZ/PRzKZNItBbYTZyDNAEPYi2jjq8/kolUo4nU7TF7RhReu7jI+PMzo6yuzsLAMDAxQKBVwuF+FwmM7OTuO5Fg6HzTkLhQLJZLIp+le5XGZsbGxX6yDsTbS3WE9PDzMzM0xMTJDL5Th37pzRLtMel4JQy8o8RlzyhP1MrebLKpRSv86yQO7PAx+1LMtq8J7XsQNhR2pZIO5DLHu5vNCyrJI+ZlnWolJqAqitzyV1W+O872Y5pfRF4A21513r+ErY0z+w7GFTAX5WG30EYT8iOxbroBddH/zgBy855nA4iEajzM/PG6+VQqFAoVBgcnLS7LLPzs6ilDLCnl/5ylf44he/aD538uRJ5ufnmZubIx6P8+Uvf5nPfe5zvOUtb2FiYoJyuYzb7WZkZIRgMGhS2ur00tvJ/fffv63nkzKbh1a45suto8fjMTvyOv20Frn+9re/zfnz5zlz5gznzp1jenqaubk58vk8Z86c4Utf+hKPPvoo58+f54knnmBhYYHx8XGefPJJEokE2WyWUqlkjKm1+i67cS8rlQonT25Jo2/T7FYbkXJ3F4fDwdjYGA888AAPPPAADz74IOfOnaNYLDI6Osr4+DhTU1PGoLkd7Idxfz9c44q34ZaMLyMjIyil1vwZHh7eUp12u39J+c01vu0WSqkbWPZ4+R/Aw8CNK+mW63dydNhR/c/vNTinQynlYbnP2ZVSHp3WugH/C7gWeMkaOi4PAL+qlOpSSrWzrE/z/za4phuBfsuy7mRZy+blmzw+D9xhWdZzWBbj/fn1yhGEvY5qYIjds5w4ccLaygLmxIkTDRc+MzMzjI+P43a7GRoaIp1Ok8vlyOfzLC0tGSHDyclJpqenKRQKzMzMoJTi4MGDjI+P84Mf/ID+/n5OnDhBsVhkcnKSubk5PvnJT/Lxj3+cgwcP4vP5TPaifD6/Sm9iO93B17rOnaSVylRKfceyrBPrnHdL7etqsBv3+XK53Dqm02kSiYRJ3a77xsmTJ5mYmCCRSDA7O0u1WiUSiZBMJvH7/djtdpM1zOFw0N7ezuDgoMko5HQ6iUQiRp8JIBQKmSxKO3Uv12tfSimrra2NdDq97eVqdquNSLk7z3pt68CBA1ZHRwcLCwtEo1HcbjfRaNToHR0/fpyOjg4OHz68Ssj6Smilcb9VytuNMn/wgx9w4403TliWNbhGfdZ8Jiql2In55y71L3Mtu/2s3UvlbzTn2k2UUiPABeD1lmV9pMHxNwPvbfDR51mW9eUtlvn7rGir1PBOy7J+f+X4PwNfY1nj5SJQAGqt5m+0LOtvVt7rBN4HvIplEdy/B95mWVaeNVBK/RKQsSzro0qpW1m+9l/Z7PGV9/wqMGFZ1j9c5uULwp5hXxlflFIxYHQLH70WeHyNYx6WPYiKLMd8VgHXyo/FspudE/CxPAgWV97vWHmPm+UBsgSoldfLQC/Lqeoslt301MrfauX/p7bit4/1rnOnaKUyhy3L6lzr4BW0r6vBbtzny2UrdXSw3CcUy7tBuq+4V465We43VZb7nVXzuSrLfa+w8ro+Vl05T3nlMzZW97udupdrti+llAVkeCpzwk6wW21Eyt151mtbSZb7gG3lt5vl9l5kuQ+kuHQSf6W00rjfKuXtRpkBYNCyrIaCc7v0TNztZ52Uv33lrzvnEq4uSqm3A6csy/q0Uuowy6mqX7WZ40qpm4C/AsLA3ZZlNetcWRB2nH2l+bLVQVwpdfJqW9+lzNYrs5knCbtxny+XVqgj7E49LctSO13Gbt1/KXd3sSwreLXL3EvjfrOUt1tlrsduPBN3+x5I+c3VBoXLYyVt9N81OPQzQALQz4sQlwoFr3l8RRfn6UqpVwC/RQPxYUHYL+wr48sVsBtBrFLm3ipzt2mFa26FOkLr1PNy2a3rknL3H/th3N8P19iM7PY9kPKFlmUlbfRzGx1TSn0TeCvLui0/Anyj7i0NjyulXJZlFVfeswRsr2ClILQY+yrsSBAEQRAEQRAEQbg8lFLvAZ4BjLGs6VJc8Zb5Jcuy3rHG8duAP2U5dDvPchYkyXYk7FvE+CIIgiAIgiAIgiAIgrCDSKrpTaCUuk/KlDJbmVa45laoI7ROPS+X3bouKXf/sR/G/f1wjc3Ibt8DKX/vtsFmuLbdrsNul98sdRCEK0GML5tjNzq6lLm3ytxtWuGaW6GO0Dr1vFx267qk3P3Hfhj398M1NiO7fQ+k/L1LM1zbbtdht8uH5qiDIGyZfSW4G4lErJGREWy2y7M5+Xw+Tpw4cVXjs6TM5ivzO9/5zvx62Rui0ag1MjJyRXW7XKrVKpVKBbvdvm673o37fLm0Qh1h5+q5XvvaqG1tth2sx27dfyl359lM29qONrRZWmncb5XydqvMKxm3doLdfo7sl/LXGi+2s3xpW81Xh90uf7vqsNF8XhB2FMuy9s3PzTffbFUqFetyufXWWy/7M1eKlNl8ZQInrXXa125cS6VSsVKp1Ibtejfqdrm0Qh0ta+fquV772qjMzbaD9dit+y/l7jybaVvb0YY2SyuN+61S3m6VeSXj1k6w28+R/VL+WuPFdpYvbav56rDb5W9XHTaaz8uP/Ozkz74KO7LZbFva0bvvvvuoVquk02mq1eoO1KxxmVcbKfPqc6Xtymaz4ff7N2zXzXTNa9EKdYTdr2ejNrPZdrAeu3VdUm5zsB1taLPsh3F/N6/xas9Xmond7l/7pfy1xovtKn+l7TbVGmW3v9tmqMNul98sdRCEK2FfZTs6ceKEdfLkyS19Np1Ok0wmCQaD+P3+ba6Z0Aoopb5jWdaJtY5vpX1JuxI067Wv2rYlbUa4XDbbtoS9wdUcI6RtCTtBOp0mEAj8wLKsGxsdl7YlXAkbzecFYSdpKqtyM+Pz+QgGg/h8PvPaVnaXaj+zn3en9jL6ey2Xy6u+30bfd6N2JQiNSCaTJJNJPB7Phm1GxhZBaB422x+3q99ejeeKrqsg1LKZNrzWHKmWlbZb2bmaCoIg7A5ifKmjXC4zMzNDuVy+5Fj9QyWbzZJMJslms5ccr1arZrFU+2Cp/Uz954W9gf5e5+bmmJ6eJplMmp3I6enpVRPWcrnM7Oxsw/ZWiyym9zfVapXZ2Vmmp6eZmZm5ZFypp3ZsadR2Lrc97aX2t5euZbdY7x7uxv3djja+k2z2Wb9Rv90stSEhG51nq+XouiLzyH1NtVolkUgwNTVFuVwmm82SSCSYm5ujWq2umlPrtqbnQ/Pz82v2i5Vwpt3vvIIgCNvMvsp2tBnm5+eZmpoCoKuri2w2i8/nI5vNMjs7Czw1sdG7Svq3noxUq1Wy2SyLi4sUCgUOHjyI3+8nm83i8XiMccbj8QCYh5I+h8fjIZ/P4/P5rkr8/W6j79deuF793fp8PhKJBNPT0+TzeUqlEtFo1Ew+9MR4dHSUxx57jGq1yjXXXLPmeWsmujvqRr6Xvou9hFKKYrFILBbDsiyTYaKnp6fh+z0eD+l0GpfLxdzcnDHu6bazUXuqbwdXq/1dDZrlWlq5r9U+62w226pr0IuvdDpNV1fXVbm2Rt/plX7Pm/1+6t/X6HMej4dkMmme9fXn05/RcwI951jrHl9OHTe6D1u9TzVeNbJA3qdUq1Xm5uaYmpoyG05DQ0PmmfHoo48SDAYZGxsjlUrR2dlJLBajs7OTYDC4aq4rCIKwXxDjSx3RaNT8rp2U+Hw+uru7zd/wlBEGnlp06/+LxSKFQgG32w0sx6/Ozs7S2dlJPp837pZzc3N4vV4cDofZ3Xa73SwsLDA0NITL5WrJyfnl0CyLoe1gamqKubk5IpEIp06d4vz58xw4cACn00k0GsXhcJjdIb/fT0dHB11dXXR0dKx73npD306xl76LvUS5XOYHP/gB8XicgwcP0tfXRyQSWXPhp8ejhYUFyuUyDodjVdvZqD3p8aq7u3tV+IL+3cqGgyvpS+td9+Xek1bqa/XXVtsO6q/B5/OZkAI9zu1Ee6k9p8/nM22+1lih63O51web/37q39foc/l8nkwmQyaTweFwXHK++v5WW+9G93itshux0X3Qr2sDkT6Xvg9rfXe18x9hb6PbYDabpaurC4djeemQzWYpl8t0dXUBMDY2xujoKM985jOZm5sjHo/T29u7at5Ti7QhQRD2I2J8qaFarZLP581uXe2kxWazmUmR9l6onYyk02kmJyex2Wx0dnZis9mw2+0UCgVsNhunT58mlUpRLBax2Wy0tbWZ//UiSu9sh0IhkskkFy9epK2tjd7eXlP2XmSri6FmWwAWi0W+//3vMz09zcjICI899hgzMzN0dnbS09NDqVQil8uxtLSE3+83i+Lrr79+wwnI1Zqk1H4XzXZ/9zPaMNvZ2cng4CAOh4P5+Xk8Hg8XL14kEokYQ23tdxaJRCgWi5cYZ/QOuz53o++53kurtv3VG6bX2/nfjXa0Xpmb7UuXuyC/XGPKZsa9Zrl39dem76E2dNQv3Gu9Rht9vp5yucz8/LwxUK9Vj1r0M9fhcHDgwAFsNhvJZHJVyE1tWY3aZW0IqA690c/+9b6fesNP7fsafc7n89HZ2Uk2m8Xlcl0yf2iErr9etGqv2fp61pdVf536fev1cf0d1Xv36tdrvzt5Lux96ttZuVzm5MmTVCoVbrjhBoLBoDF0AsYTfHR0lFgsxmOPPQZAZ2cnQ0NDpk/DU8bZpaUlXC6X6bu6jWsDjRhlBEHYq4jxpQbtLp1MJs3EQj8Aaicf6XSa6elpAoEAXV1d5PN5qtUqi4uLJJNJcrkckUiEs2fPUigUGB0dZXFxkXw+j81mIxAIkMvlmJmZATAPpkKhQLVaJRAI4HK5WFxcJJPJNDT27CU2miSvRbNNCguFAt/73vewLItAIEA6nWZqaop/+7d/49ixY0xOTuL1enE6neTzeYaGhsjlcmZSvl69L+fatnof6j+XTCYv2Y0VdodcLsfY2BgdHR24XC4ymQw2m41UKmV2GyORCD09PQQCAarVKqlUCo/Hs2riq/uMnlgXi0UmJibM5LnW8Gyz2YyhsP77rw2j0DH80Hjnf72Fd+3ir3axdzlt90q8Fi73HOstyNc6diWeA+sZubaTWqPc/Pz8JWFqa12bvgZtCFlcXOTYsWNmcabrrhdp2oBQfx214b46lK7R+F7fVhYXFymXy+ZZXFvHtULn9IJSGzQBs2FSX8Za30993Wrft9b3qucJ2hutNixLG4t0uGDtvdGf07/Pnz9v5gnaG7fW6NTovjUKPdTXoe9Bd3f3Jd69+jtLp9PGYNtKHlvC1tDtIpPJ0NbWBkCpVKJYLLK0tMTU1BQLCws4HA5SqRTd3d14PB6uu+46Hn/8cdLpNIlEgt7eXuLxOJOTk1x//fWUy2VsNhuZTIbJyUksyzKe33ozamlpiVwux8GDB3f5LgiCIOwM+874sp4Ro9YiPzc3R1tbm3lf7Y5QtVolHo+vmsT5/X6OHj3K5OQkhUKBQqFAPB4nkUhw8OBB7HY7bW1tFItFnE6nse5rI0xbWxvt7e1mwaR3G0KhkFkIw2ojw0baMBstwq/Ggn4rbHZyt5bmzkaf2ykKhQKpVIpAIIDf7zftp1AoYFkWS0tLtLW14ff7TZgZLLfJXC53iZGjdiGgFwqBQIBoNMrCwsKqXeJaNqMV0Ijdvn/C2jgcDjweDz6fj3w+j9frpa2tDY/HY9qBXthmMhnzuVo3b+3NVOvVdO7cOZ544gkGBwcJBAImk9Lc3BxLS0tYlsXs7Cw+n29VW8vn86RSqVUx/npxVrtY04KL+vV6ahd/+v9Gi8T1aNTeL9ebbiNDS+34t1a91lp0Nwop2Sy1ddjJ/llrlKsNU9vMdev66QVUOp0mn8+bRZSmra3NGBBqNy+0N0hPT48J+9XnrA0lymazTE9Pk8vlGBkZAWBgYACbzUY0Gl3ljVNrYNT3S99LLXLe2dlpDN9680TXs5E2S+14XF83fQ/16/VePLpdOxwOotEo8/PzFItFpqamsNlsdHV1GSPWeu0wnU6bvq/bUu0iuZE3TDabpVgsUiqVLvF4K5fLeL1eY0Bq9JyoNf400roTWpe1vMGq1SodHR04nU5yuRwdHR0cPnzY6LpMTEwYz+1sNsuFCxfo6ekhEong8XgoFAqEw2EqlQoPPfQQi4uLeDwejh8/jsfjueRZVds3dH9a+b33dhsFQdj37Cvjy3qx04CZBOkJFDwlhuv1ele93t7ejsPhIBKJmMlJNptlcnKSeDzOwMAAXq+Xzs5OvF4vxWKRJ554gqWlJSqVCh6PB7/fb0KMUqkUqVSKhYUF0uk0NpuNkZERM1HTC4pyuczo6ChOpxOXy7VqcllvlGm0U1jL5UzmN3pv7UNcv3+rhprNTu7qFzuN4v6vptGoUCgQi8VIp9PEYjH8fj9DQ0MMDQ0xMjLCI488wuLiIjabjXw+z8jICC6Xy4SQ6O9SU7swbWtrMwsZHbpUrVaNHkfttW2kFdDou9KTodrJtZ6MyyR796lUljNuzs/P43A4CIfDZnfw+uuvJ5vNEgwGOXv2LKFQyLSzSCRCuVw2C7fZ2VkT8hiPx8nlcpTLZZxOJ/Pz8yaeP5VKAcu7nfF4HFjeGa/VuNI7otls1njo9fT0kM1mSaVSpl3GYjEAs3irxed7SksLaLhI1KzVl9dq71sRD61t67XjS+2ieD0PlO0eb2rrUO+BsJ1l1vb9WoN+rfeb9qpYK3Slo6ODtrY2fD6fuVeRSMSMI3os0cZmfX26rWltiNrzxmIx7HY72WyWSCSCzWbD7XabNqb7QL131/T0NJlMhq6urlXPA7/fb+qm6wTLbXNxcZGlpSXsdjvRaJTDhw+vEsqfm5szGyW9vb3GoKLPoc+bSCQ4deoUx44dY2BgwJSvx+p8Pm+e61NTU2SzWWy2ZfHsjZ59Pp+PQCBwiTGs3tBe/1zUc4raPphOp7l48SJer5dgMLjm87u+TqLTsTdo5BGl5xzlctmESZ8+fRqHw8HTn/50/H6/MczH43Esy2JxcZGbbrqJtrY2kskkY2NjFAoFDh06RDweZ2hoiL6+Pm666SYzhul5aV9fn/Ee1+2tp6fHeM4B9t27Q4IgCDvDvjK+aN2W9RaTtdou2pU6Ho/T0dGBw+EwE6VQKGR2g7LZLEtLS6RSKSzLoqOjg8HBQTweD7FYDKUUIyMj5hzhcJhz585RLpeNi/XU1BThcJgjR46Qy+XMguXChQtcuHCBY8eO4XA4SCaTLC4uEgqFCAaDZnJU+yDVXhU6XGC9iVzt7/UMKOu9t9alW3MlO7TrTe420nKoj/u/mt4c5XIZt9vN0tISNpsNp9NJb28vhw4dIhaLkc/nsSyLRCJBIBAwO60AXq/XXI++RpfLZRYztYtJj8dDW1sbLpeL6elps6Nev0tdrwugqd2J1/erdsdblyOT7ObB5XJxww03YLPZCIfDLCwsmPh4/V0/+uijLC4usrCwwOzsrAk56u7uNoaJVCpFe3s78NRYd/311xONRkkkEmYB3tvbSyAQoFgskkwmyefzPProo2YcSqfTBINBwuGw0evQi0xtiNHGxM7OToBVi2BN7Xir67i0tEQ6nSYcDq+6BzosNJ1OE4lEVnl/rdfeNeuNb43aeu37N+uB0ujYdhkx6z0Q1ivzctD3Xd/HtYzrMzMzpFKpSzTI6kNXqtWqCaEtFArGAKPPAzA0NEQ+nycSiTQ0msOyoXFxcRGXy4XNZmN8fBy32228/2rrVuvRqg0UwKrxTR/3eDzGgKHL0hsZqVSKRCLB/Pw8IyMjps0VCgWy2azxaAFWeQiVy2UTbqzbf+390f0ln8+b76qrq2tVVrJEIrEqBKnR96uf8zpUozYkcL1nYyOjjr7fXq/3klTTjfR9hL1FrfFQtwWPx4PX62Vubs6ETS8uLpJKpVBK0dPTQyKRoL29nXQ6bZ4n3d3dtLe3Mzc3x4EDByiVSliWRS6XIx6Pc+jQITN/np6eJhaL4fP56O/vbziP0+M5UNmVmyMIgrCD7CvjC2w8Oa2fcGv9hEAgQCQSYXR0lHK5TG9vL7Ds8ut0OgkEAni9XkqlEqVSyeywjY2NMTs7y913301HRwelUompqSmmp6fxeDzG7VnvLuvFent7Ox6Ph8nJSaanp1dlwwmFQnR0dJBOp80ELJlMMjU1RVdXF9FodNVOQv2OsH7Y1U+q1jKg1O70wlPChLVeNY0md5ez2Njs7u1GLvz19biaLtLVapVEImG8BzKZDDMzMywsLGC323E4HDidTo4dO0YqlTK7t+3t7asWePp70PfB4XCY7zmbzeJwOOjp6TGhFloUtX4RVvv91ocw1d+vWq0HvaAXmgelFHa7nUqlwuzsLO3t7VQqFZNFxev1GpftkZERent7KRaLnDlzhvHxcXp7e41OTDabJRaLUa1W6ezsJBQK4fP5zEJYf/+6TfX09PDd737XLEB7enpMG9Lt5+DBg0YkvFKpMDAwAGDCS3w+3ypx1EbhcrDs1ZVOp8lms5cYX/QOfrFY5NSpU6b99/X1AZiF91ohmfXj20bpkOvHmq3qvtTWazPeKbUejDpsbL1wj9o+vBVtMJvNdkkWNo0OgdWGidowotryaw18c3NzuN1uHA4HXq/XeOrUbxAARgcNlr+PWCzG8PCwCUEAjKENMMYOvUOun32ZTIbu7m5zXH+nkUiE+fl5ZmZmqFarOBwOow2j24c2LGmDCyw/19PpNH19feRyOZO9sNaDptbTZGFhgaWlJWDZkH7o0CHjAaTvof6t75k2PGazWS5evGjO3dPTs8oboFHYkQ4RrP2+a42TtW1aG3/qPZdstmX9Ob1ZU5sRrVwum7rX99Wr6U0q7By1G2naMwogk8kYLbB8Ps+xY8colUokk0nGx8fNGOp2u+no6CASidDZ2clXvvIVyuUy4XDYzHeCwSBKKR5++GHGx8f5oR/6ITKZjJnD+ny+NfWIVuqz5TTmIyMjjI6ONjw2PDzMxYsXt3pqQRCEK2LfGV82on4BWzuJ00J5OoORnuw4nU5GRkbMJG5iYoJMJsO1117LyMgI5XKZ8fFxFhcX6e/vp1wuE41GzaJHv39pacnsIGs372AwiMPhYG5uDp/Px+LiIh0dHUZLRD84dbhLT0/PKu8FPeHV3hjr7WKtZUBpZJSp3UWsZys7ZdvloVJf9tXctatUKsTjcVwuFx6Ph/PnzxMIBAgEAlxzzTWMjIzQ3d1NPp9nYmKCYrHI8PCw+W60Ma1WO0NPwOGpe1Qul8nn84TD4VWLprXCEvRna3ene3t7L3FRr89QIjQX2ngbCARQShEIBJiZmcHpdBoPKY/HQyqVYnBwkOnpaaanpymVSuZ7bmtrI5PJmPNow4hue7Dcv/UufCQS4fTp00ZTQmtraI8CeMq4kE6naW9vx+VyMTw8TDabJZfLmYV7reeC9virJZvN4nQ6CYfDl3g26HYZjUYZHR01k/v6nfy5ublVC+VaL4368a0+HfJ61C841/PM031wLe+FzYZ46p1lfY9rvVLq9Uf059YLM12P+r6vr1d7Inm9XrPhUH9ubUTQhpdqtWo8SXK5nDEg6fvtcrnMBoE22Pj9fgqFAlNTU1SrVQ4dOmTalfaQ0d5etSlvddiS3rzwer2rvES0gVuL8upz6nFWPxtjsZgxLGrNl4sXL+Lz+cx3UG+kqzX0uVwuAoEAvb29pu/EYjFj4NB10c9Mfa91/QACgQCZTIZUKmUMXvp+13pR9ff3N/TA0lobuiztPaBDp2uf4bUaL40Mlfl8npmZGWMMatQ+t9LOhOahdtyuNe4GAgFmZ2cplUpkMhkTGn327FmUUpw9exbLsgiFQni9XqMdlkqlcLvdpi/6fD5uuukmBgYG+Nd//VdOnz5NLpejr6+P/v5+E6Ja60G2nYyOjmJZVsNjSqltLUsQBOFyEONLHfUT9PoMBaFQyExcfD4fmUyGfD7PxYsXiUajtLe3E4vFiMfjpFIpjh49SjAYJJ1O8+STTzI4OEh/fz+xWMx4GiilzEMiHo8zNTXF7OwsAwMDxGIxwuHwKmOLNvxo3RdY3oXo7+83u3oa/XDTIrAbhVzVTqbW2+nVbqraBf5KJmT6XBvVT5/7cl34t0sTgQ3E30qlEktLSwwPD5NKpXC5XHR2dhIOhxkcHGRgYMAI1+mJsv4MYMJB9HXW/oan7r/2cpqbmyMcDq9KMdwoLEF/Vmtr1Lu114crbXQf1tJ9kN3QnaNarVKpVDhy5Igxso6NjZnwNJvNZsYcrfMSCASMW7fOkqQXdLViitrAq3cfHQ4HU1NTxiCjM8oMDAxQLBZNe9P1qhU37ejoWDUG6dCNpaUlIpEIoVCISCTS0ENDG48cDocpBy71WHG73Xi9XoaHhy/x3NNaBdpIXh+SUmu82MgrrnasqR/f1mrvjbL21JZRX1atEcXj8bCwsEAkEiEYDBqB5UafqzWm6sX9WmGmmxm71vKC9Pl89PX1rSnuXU9bW9sqIV3N7Oys8SCtN+7Xhs7GYjHjdRGLxVhaWiIQCBAKhYyYby6Xw+12m/umswfqMKe5uTlgWU8im82atjwwMGDSs7tcLubm5oznzdGjRykWi0SjUebm5nA6ncRiMcbGxky99cZGKpUyngL1RrJa/bZAIGA2Smq9ewqFAm6321y/Nmrpdqb/rjW8N/qe9D3T9wswHkfaEK/vqfaO0xs+9eeqH/e18bPWCApPifRCYwFtobWoDYnTBj+/309fX5/RFtTebpFIhKmpKZNQ4ujRo7jdbqampkzYs2VZRgtMJ5ZIpVIcOXKExcVFhoeHyeVyJixQj/faqC8IgrAfaGnji1JKWWuZtrdI/SS01qW7fnfIZrNx4MABEwvv9/s5fvw4DoeDXC7H4uIi4+PjwPJi59ChQ0SjUZxOJwBjY2MmLfGTTz5JKBQiFApx5swZ7HY7586dM4KCw8PDdHR0UC6X8Xg8zM7O4nA46Ovru+ShVR/frh9um5k8b/ae1O+WbCW8Ry9g9AJETwAupy6bYTt26lYmxeuKv1Wry6l79c6Pw+Hg+PHjDA8Pc/jwYXPP8vk8Bw8eNDHVsLxIrXU1rz1n/Y67x+Mhl8tht9txuVyr0gPDpdlZ9OR/rUwrW9mVr3+v7IbuLC6XC7fbbQQy4/G4iZ/3er0sLS1RKBQol8t0dXUZ3Reti1IqlXC5XMZ47HK5GB8fp6OjY1WIkQ5zKBaLANjtdqrVKkNDQ4TD4UsWbtoYXD8+1gq16vAQWJ16ul4k1GZbFjzXi8ba4/p/n89n6rGWrkVtuA401p+qX3Q0oj6MqbaMtdr7WovWtUKPtBFFGw70fdeGm7X6rNZ90gZTfb362mu/j82MXfXU3kv93FgvpEkb9Xp7e1eFPOq61IoqJxIJLl68aAxMevzS+kO6rOnpaWA5zDYcDjM5OUk+n6dQKKzSKQkGg6t0TxKJBIuLi4yMjOBwOAgEAqTTaR566CGjcaLD8pLJJOFw2GQN0+c8ePAgS0tL9PT0cPHiRcrlMgsLC2bDRd8brT9UP/bq9xSLRVPHWgN5rYEEnjL01V5HreGwUVZD3WdqPRp139Dt0+/3m5CwRnoya6FDW+upF+mVsb61qW+3elNHz2X0s0Cnmb7zzjtpb29naWnJtINKpUJ3dzd9fX0ma1lfX58Ryk4kErS1tdHR0UEymTThp7UG/EYbRoIgCHuVlja+aMPLThhhNOvtDunjWp1dT3qGhoZIJBJkMhmmp6fp6+vjwIEDOJ1OI9CbyWQIh8OEw2ESiQSVSsXohOjdPR1P29fXZ7KX9Pf3UywWmZycpLe31zy0dNx5/aS2VqSx0eR/KzRauFyJUWStHdvtYiuGoTXOsa74m8fjIRQKrdotevLJJ4lGo5w9e5ZCocBjjz3GyMgI4XCYSCSCw+Hg4MGDRrcCVhtcGmncaN2M+u+y9jtolLJ0g2vb1P1Z673bcY+FtalUKjz22GPY7XYjelgqlfD5fHR0dNDe3k6pVAIwoUW6DYTDYXp6ekwae5/Px+joqPEqqA1xqFaX0+TmcjkjAt3R0WF2/es9SWqNMPrzeoGpqTf86Yl+fSpg/V5tvKldLNa/1ojaY3ontt5IXFuH2t9rsZaRZa3Pr7VoXetc2iMtnU7jdrupVCqXGG4akc/nTXkOh8PcH33vtQ5KZ2enHh8uS7hS30vdhmp1VRoZhBo9E+qvVxtiYrEYhULBiPzqEJnadqLvqxayXVhYwO12G48mff6xsTFuu+0285lqtWq0keLxuBHnPX/+vPEw1RpbWtS51pNLv677ks4qp//W4UXaIKIXqbVGE7/fz8GDB1eFTen+UhvepY2nmUyGnp4eenp6LjHKwVOeTfo+6nLqDVu1hpVaPZj6DFKNjGj1YWyNjD0arTkjY33r0sjbVWt2pdNpjh49yu23304mk8Fut/PQQw+RzWaZmJhgaGiIf/mXf2FycpKuri4OHTpkni9KKSqVCrFYjEgkYjSItBF0YGAAv385C2Ttc0C8qARB2E+0nJ+fUsqmlPp9pdTblFIvUUo5Lcuy1DpBnEqp+5RSJx9//HFOnDjB/fffv6Wy6xcMmtpFgs+3LFx57NgxIpEIvb29HDx4EJfLRTabZXR0lEceeYTx8XH8fj9OpxPLsujr62NwcJDOzk6GhoY4fPgw/f393HnnnSacqXbS53K5Lpk0lkolk02hVgekNs68doLe6Fo2g8/n27KxpLZcfR69OKpftG0Xl7Pj14j7779fT/CfppQ6qZS6r/a4bl+ZTIYf/OAHXLhwgZ6eHpOe3Ol0Eo1GiUajdHZ24vf78Xq9l9RNfz9zc3NMT0+byfBWrmmt76jR9167K7/R/V+r3Cu9x/uZ+++/nxMnTgBcW9++dNs6d+4c73vf+/j+979vsgdFIhGGh4fp7u6mu7ubm266iaNHj9LX10dfX595nzbKXrx4kUcffZRz586Rz+cJhUJmoa/bng6F1KEesCyCqxf7tWNILbWf10YS7QVR2+ZqjTNa26KRYaS+7W5lzNGGS2BL7XW9cMittPdG16ANDj09PUSj0XXFiNc7l/4/Go2uev3+++/nzjvvhAZj12aei5u9743e1+jZk8/nGR4epr+/n4GBAbNRoPVgdDvRXp065FZ7PHV3d5sQWh3i9eijj5o2mc/nTXhNZ2enCanTwtLXXXedCdPr6uoiEoms0nLRrx88eJCDBw+aEApt4KrfzKi/5trr9Pv9Rn+t3kCur6mtrc2E5dX2Ke3Z1NnZecl3Wl+O9vypbYu19aztf436r9ZKmp6eZnZ21vTh+j4OT2nOdHV18f73v3/DcetK51zCzqA9UrSXi0Z7aA8MDDA4OGjSnx87dgyfz0coFGJmZoZgMEgoFOLIkSNce+21RKNR4+miBXV1kopgMGiyQGrPGD021LbTWtZ7JgqCILQ6aoccRnYMpdS/AucAP9C58vebLcsqbOQBc+LECevkyZNbLlvvAm7W3VanYNUZF7T3itPpJJFImHSb09PTFAoFk0ra7XZjt9uNh0T9TlS5XDYCvLWTrtrytMuonmzVeubUv3413T0b3UM9+dNK+c3qfqqU+o5lWSfWOn7NNddYr3zlK+nu7ub6668nHo9jWRY9PT243W6TBUnvCLlcLuLx+CWposvlMrFYzIgub8VbaS1NirXa8OW2bWH7Wa99nThxwvrEJz7B/Pw8TqeTrq4u49kGq3e9i8Uio6OjhEIhI3rrdruZmJgAMLvhBw8evCRNbb3Ic31YRKP36df1Aq/eMFHbtuCpMKD1PClq67RVT73a0Ke1yliPVu4Ttd9TNpslFAp9z7KsWxq9dzPPxa18F7X3r97rslGb0O2o9n7rZ5oOm62th87mpUM4tW5ErSCv1jK6cOECxWKR9vb2VR4mG1EsFhkfHzdC+Y3Cj+vvU+2zrPa6gUv6TjqdbpgFbKO2t9H3UXt8rTlAbf+sTXO9kedLff0CgcC649aVzLmEnaHRnKtRX9Pf86lTp0z4kMvlIplM0t7ezuDgINVqlcHBQUZHR7lw4QIdHR309vauMqzr7Hq9vb2rMpZt1I43eiau17ZqtRQv55iwP9hoPi8IO0lLhR0ppfxAAvjPQBa4B3gV8E6l1G9blnVZrtWXy+WGVmgX9HoxSv2Q0Q83h8PB0tIS8Xgcn8+HZVnG1V4L+9UaKvSuWm2q6dryACNWWVvn2vPsVphIo3J3UvH+auL1ern11ltNdqOBgQFGR0fJZDJmYRAMBk32CYfDYbIO1aJ3TOu/s8vhcsMl9nLY0F4QA9YTXC14GQwGjfdb/ftOnz7N5OQkR44c4dChQ2ZxdfjwYfP91rertcIr640W9eEo+v2141X9LnyjtlVv5GnEleoIbUWcu5ZW7hO13+fKd3FZmi/rnW+zrPXsqT+mcblcJqORpnZnvFZLBpbb0PHjx83ztdbbRH9W/9/d3c3s7OxleysVi0W8Xi/lcnnd8GNNfWhRbdioNgzVh9u1t7ebDHabfT5v9H2s9axv9Ln64xtdY+1nVr6rdW/oVtKfCztLfTuF5f6iDTB6nqq/s6NHj1IoFOjv7yeRSJj2WygUWFpaYnp62hhctJjuzMwMfX19+P3LWfZ06ul63aJWM2wLgiBcKS1jfFkJK3IBR4C3Wpb1B0qpTwNR4CeAfmBsJ+uw1QdFo8kNPBUnrwUvddaDdDpNJpNZtQiv/czl6KSsVefdeuhtdvLXirhcLp7znOeYXVd4KjtEd3f3Jd9lo/awXaK1a03em609XA32ghhwpVKhWCyaReRaZLNZYzAZHBwEdqZ/NWpfm21zm1nErne+zXKlbXqv9ImV+7ejGxON2Kw+D2AMevXim5tpA/XvafSZrRrittIG17ruWoPFWjowG51jK1yOoeZyz7niGbemYU/rD0Hrjr17lUYGUR3+p3WYGj07fL6nBP218d/pdGKz2UwadO0BWZvFqNXnd4IgCNtFyxhfVsKJFpRSvwd8Sik1YVnWh4H/oZR6OdDHDhtftspGk7H6h1IwGFxzp36vGCpq2SuLHMCIKGsOHTrU8LvcqD1cKXvpnl4prezBoLHb7au8Vta6Fp/PRzQaZWho6BKdie2k0Tm3uxxpw9vDSjvYfjGtbaZRP91MG1jPi+tyzrOZc18utUaftbzLWrmdb2TY096erTz27hcaGQI3s1mzlvh/rfB4q7ZvQRCEnaCpjS9KKRvwY4DTsqxPA1iW9Wml1C8AH1RK3QjEgQgwvmsV3SL6AbZWBpH1PiO0Bpfzfcl3uzOs1c9akZ3YxRb2N7VpvXeTvdh29+I11bIZw95eGXv3A1fiBSUIgiBsjqZ9Cq6EGX0L+BXgA0qpP9XHVjxe7gbSQAfwSsuyJnelolugPuPMWhlEBEHYPrYr29duUCqV1s1+JQhbpVKpyLNH2BIr4+ea88jaDGcyxxEEQRCE5vZ8eRWQsCzrx5RSrwJerJR6FvCtFWHd/7As6193t4pbo16DYi+ERQhCs1Pbz/aCDowgbAe1IW2CcDmsGFPW1HzRhj2Z4wiCIAjCMs1sfFkEhpVSQeD5wG3AM4BxpdSfAD+nlHojkFovvXQzUj8REbdNQdh5miHb11ZxOp0yRgg7Qm1WE0G4HDbSfNGGPZnjCIIgCMIyzTzj+irwq0AB+AfLsg4D1wFuIAS8zbKsZKsZXuCpRaBMeAVhd2i1PigLZEEQmo2NNF9k3BIEQRCE1TSt54tlWWml1IOWZZWVUt9XSl0DPJdlw8s/WZaV2NUKCoIgCIIgCIIgCIIgbIKmNb4ArBhePCyL7r4CKAI/K4YXQRAEQRAEQRAEQRBahaY2vgBYlpVXSv0x8L+AvGVZs7tdJ0EQBEEQBEEQBEEQhM3S9MYXgBVPl8QuV0MQBEEQBEEQBEEQBOGyESU0QRAEQRAEQRAEQRCEHUSML4IgCIIgCIIgCIIgCDuIGF8EQRAEQRAEQRAEQRB2EDG+7BLVapV0Ok21Wt3tqgjCvqYV+mK1Wm3q+gmCsP9YGZPWnUc2+9gqCIIgCFcTMb7sEtlslmQySTab3e2qCMK+phX6YqVSaer6CYKw/1gZk+xrHa9Wq00/tgqCIAjC1aQlsh3tRXw+36rfgiDsDq3QF+12e1PXTxCE/cfKmFRZ67jNZiMYDMrYJQiCIAgriPFll7DZbPj9/t2uhiDse1qhL9psNmw2cVQUBKF5WBmT1o0pavaxVRAEQRCuJjKbb0JaQYNCEJqVvdh/RPNF2En2Wn8Rmoe9OB4LgiAIwlYR40sT0goaFILQrOzF/iOaL8JOIbocwk6yF8djQRAEQdgqEnbUhLSCBoUgNCt7sf+I5ouwU4guh7CT7MXxWBAEQRC2ihhfmpBW0KAQhGZlL/Yf0XwRdpK91l+E5mEvjseCIAiCsFVkNr8DSIyzILQWzd5nRfNFuBo0ez8QWg9pU4IgCILwFGJ82QEkxlkQWotm77Oi+SJcDZq9Hwith7QpQRAEQXgKCTvaATYT41ytVslms/h8PgknEISrSKO+1wq6BB6PZ7erIOxxdPv3eDyk02l5PglXTKOxVeY/giAIwn5Fnno7gI5xXm9SIbtBgrA7NOp7m+mzu00+n9/tKgh7HN0P8vm8PJ+EbaHR2CrzH0EQBGG/Ip4vu0Qr7LQLwl6kFfueZDsSriat2EeE1kHalyAIgrBfad5t3j1O/W6QiNIJwqXsRL9oBS8XQdhNGvUReUYJ28V2j8HSNgVBEIRWoSVXH2oZ527XYzsRN1xBuBTpF8uI4K6w20hfFJoVaZuCIAhCq9ByxhellA34JvDCutfV7tToUrayC+Pz+QgGg+KGKwg16H6hBUD3886mCO4Ku4nP58Pv90vac6Hp2Mr8SbxlBEEQhN2gpYwvK4aXrwKfsyzrM0qpa5RSQaWUsizLWudz9ymlTj7++OOcOHGC+++/f0fruZVdGAmFaF7uv/9+Tpw4AXCtUuqkUuq+2uNXu33tJ/aDAOh67Uu3rSeffJLbbrtN2pZwWWymbW123LLZbNhsNtLp9J7sh8LlsZ1t60rZyvxJvGWal43mXIIgCK2MWsdm0XQope4FXgG8DvhboAeYAD4AfGI9AwzAiRMnrJMnT+50NSWN4h5FKfUdy7JOrHX8arWv/ch+6FPrta9bbrnFOnny5J69dmFnWa9tXc64tR/6oXB5bFfbutpIW25+rqRtKaVYa0mw3jFhf7DRfF4QdpJWe+J8AwgBnwY+ApwAzgKvAZomXke8WARhe9nvfUp7HQjCbrLf+6Gwd5C2LAiCIOwGLfXUsSzrFPCbQBT4jmVZKeBXgF5geDfrttNIfLKwl5H2vT6isyHsJtI/hVZC2qsgCILQrDS18UUpZVNKvUIp9Sr9mmVZDwIvtizrB0qpZwL3AA5gfrfqeaVUq1WSySTJZHLNyYLEJwt7mavdvteanDfrpF1r3gjC1aD+mSTPH2ErlEolyuXyVS9X2qsgCILQrDh2uwJrsZK96BvALHC7UupVlmW9GMCyrMeUUj8D/CVwBnidZVlzu1fbKyObzTI7Ows85Qpbj1bxl2xIwl7kardvPTkHVvW3tV7fbfQCOBwO73ZVhH1A/TNJnj/CViiVSszPz9PT03NVy5X2KgiCIDQrTWt8AV4MzFqW9TKlVC/wLaXUDwEnV4R1vwR0Ae6V8KOWoF7kTYcTdHZ2rprk1rOWUUYQ9gI71b61JwuwKr5/rcl5s07a7XY7kUhkt6sh7BN8Ph/d3d3mb3n+CI1Y8WpZcx5pt9txuVxUq9Wrqq0i7VUQBEFoVpo57GgBuFkpdQKoAnkgZlmWpZT6ceB3Wc7W1DKGF7jUHTabzZJOp3E4HASDQRF/E4RtRO/gz87OrnJBX0tssVlFGC3LIpFI7HY1hH2CzWYjGAzKM0lYl/n5eQDneu+Jx+MS/iMIgiAIKzSz58t3gXcDKaAD6AQmlVKvAf4T8GrLsgq7WL8t4fF4SKfTeDweoHl32gWhFan3LKvfwW9VxPNF2Ekk7a6wFaLRKEBpreMOh4O2tjYz3xEEQRCE/U7TzrIsy8oBH7Ys6wnAAzwI/DTwNuBey7Ke3M36bZV8Pk+1WiWfzwNPxdOn0+l1BXcFQdiYbDZLIpFgbm7OuLrX7uA3q6DuRlQqFdk9FnaMWo/M2j7Sqv1FuDo4HA6ANRV1dRvS853tRNqmIAiC0Io0s+cLlmXpJ7Yd+EngFuAllmU9tnu1ujJqPV30bmO1Wt1QcFcQhLXRfcnj8eBwOCiXy2Sz2Uv6UrMK6m5EuVwmnU6L4K6wI9Q+l2r7CNCS/UW4OqwYPtbcxLMsi0KhsCOeL606lguCIAj7m6Y2vtTwGMuZjf7csqwzu12ZK6HWuKK9Xfx+/yWhEeIGLgibp3Yi3tXVtWrnvrb/tGqY33pi3IJwpejnku4zfr9/VXuTtic0YsUbz77WcaWUMYQHg8FtLbtVx3JBEARhf9MSxhfLsrJKqTdZlrVmbHErUGtQ0f/7/f6GAp+yqyMIm6d2Im6z2bDZbKb/aMOFfl0vMtPptBg3BaEG/dxxOBzmuSTPH2EtVjxarLWOW5ZFJpPZkdAgaZuCIAhCK9ISxheAVje8AJe4c6fT6TWzSciujiBsnvqJeK2Bs5ERsxWNmzuhmyAItWj9sbXC9gShlpUxSa11XHseioFbEARBEJZpGePLXqCRQWWtMCPZ1RGErVPr4VIfsrNWaEUz43Q6dWYRQdgxbDYbXV1dqzw0BWEtNvJ8cTqdjIyMAFwSAioIgiAI+xF5El5F9IKw1riiJyO12SZqEUV/Qdg69f0Mlj3OagWuWwXxfBF2GtEaEy6HjTxf7HY7NpuN2dlZ0un01auYIAiCIDQp4vnSJKwVZtSK4RGCIAhC6yHPG+FyWJmvVHa7HoIgCILQKojxZZep3WlsNNkV7RdB2F60J8xm+1QzeANItiPhauDxeEin0zuSGljYe6yMh2u65VarVXw+H729vTJ+CYIgCAIbhB0ppW5USv2bUuoHSqnX17z+xZ2v2v4gnU4zPT1tXHLrw4wahU0IgrDMWmF564XrXW6fWisk8GpSKpXEbV/YMXRfyefzxtgo4a7ClVIsFpmbm5MwNkEQBEFYYSPPlw8BHwcWgXcrpYqWZf0NMLDjNdvj6Alu/eRW3L4FYfM06i/VapW5uTnK5fKq17dKM3iflctl0uk04XB41+og7E10RjD9LNL9RZ5DwpVSrVZZWloyQs5igBEEQRD2OxsZXwKWZf0ZgFLqHPBZpdS3WUfdXtgcetHo9/tXueQ2w0JPEFqFRv0lm81SLpdxOBzb0o+aIfOYw+HY9ToIexObzUYwGDTeYsFg0HgqyHNIuBLcbjehUEhSlwuCIAjCChsZXx5RSv0d8DHLsj6nlPpvwNcB+85XbW9Tu2is3Q1qhoWeILQKjfrLWn2rlfF4PASDwd2uhrBHqU/LLs8hYTuw2+309PRI6nJBEARBWGGjlckbgIPAtQCWZf0x8FvA93e2WnufRroTklZaEK6czWi6SF8ThNXU9hvpH8J2UBvKtlcM4YIgCIJwJWz0NHwXcCPwh0qpdwBYlvVhy7JesOM124c0g7CnIOwHWq2vVSqVlqmr0Pq0Wv8QmhMZtwRBEARhNRsZX14CDAPHgZfvfHX2NhvtJvp8PhNvLwjCzuHxeLDZbC2VUreV6iq0LtVqlWq1it/vl2eRcEXY7fY125B4VwmCIAj7kY2MLwXLsmYsy3oScF6NCu1lZDdREHafarXK/Pw85XKZfD6/29XZFDo7miDsNDrNtP5bFsfCTiDzIUEQBGE/spHxpbrG38IW8Pl8RthQ/9Tu/MhkRBC2j7V2VtfKhtTMO7GlUsmkzhaE7aa27WsPTECeR8K6rIyVa84jy+Uyc3NzDcdU3c48Hk/TjruCIAiCsN1slO3oqFKqsvK3qvkby7Ik49FlYrPZsNlsJJNJIz6XTCYBVrl4i6u3IFw52pgJrMrcslY2pLXe3yy0ipeO0HrUt/367EeC0IgVw9yac0Gl1JppprXAczqdbupxVxAEQRC2k42ML8+7KrXYRzQysOi/Jb2nIGwfaxkz1+pnzWz8dLvddHV17XY1hD1Ko7YvzyNhI1baS2Wt406nk3A4vO6Y2szjriAIgiBsN+saXyzL+srVqsh+oX5CK5NbQdgZLnfx2MyLTafTicOxka1cELZGM7d9oXlZ8RxcN15oo3YlbU8QBEHYT2yk+dISKKXUbtdhPZpZS0IQ9gOt3ge1RpQg7DSt3leE5kPalCAIgiAs03LGF6WUTSn1y0qpX1JKXa+UsluWZTWzAaaRkK5MRgRh59H9TOsKtKp4aKlUMhloBGEnSafTTE9PS3sTtoW9MP4KgiAIwnbRUsaXFQPLvwG3AK8G3gP8ulLKaVmWtc7n7lNKnXz88cc5ceIE999//1Wq8TJa1b82plkyG7UO999/PydOnAC4Vil1Uil1X+3x3W5fwtrUConW98FmYb32pdvW2bNnec5zniNtS7gsNtO2ZNwStsJm2tapU6d41rOexQMPPNC046/QfGw05xIEQWhl1Do2i6ZDKdUH/E/Lsn5CKeUD3gzcBnwe+Kv1DDAAJ06csE6ePLnzFd0EekcelmOia7OuCM2JUuo7lmWdWOt4M7UvofX62Hrt68Ybb7S+853viO6LsCXWa1u141ar9Rlh91mvbd16663W5z73OaLRqIxdwmWz2XFrjc+y1pJgvWPC/mCj+bwg7CStNrNSwHOUUj9vWVYWeD9wAXgZ0LRbKvUhRtVq1Xi8pNNp8X4RhB0gm82ahaR2e2/VMD8JOxJ2ilo9IfHIFLaTarXKzMyMjF2CIAiCsEJLGV8sy5oEfhf470qpl1qWlbQs6y1AOzCyq5Vbh/oJbSuEQghCq6PD/QBmZ2eZnZ1t2UVlrcFWELaTSqVi2pbP58PhcFAul6W9CVeM3niStiQIgiAIyzS1H6hSyga8HHBYlvW3Ky9/GAgB/6CU+h0gCziB2O7UcmO0caXRb3HrFoSdQacwrfV2aVVDp9vtpqura7erIexB7Ha76Rc2m42uri6y2WzL9hWheXA4HBw+fJhoNLrbVREEQRCEpqBpjS8r4rrfAGaB25VSP2tZ1ossy8oB/1UpdRZ4KVAAXmdZ1twuVndd9CJwrf8FQdg5bDab8YBpVZxOp2gmCDuCzWZbtQkgzydhu1BK0dPTs9vVEIRVDA8Ps1aC1OHhYS5evHh1KyQIwr6imWfzLwZmLct6mVKqF/iWUuqHgJMrwrr/x7Ksv1dKqY2EdgVBEARBEARB2N+sZ1xZyygjCIKwXTRzzMsCcLNS6gRQBfJAzLIsSyn1MuC9KxmP9iz1Qr2CIFwZrdqnakVRBWGnaNX+ITQv0qYEQRAE4Sma2fjyXeDdQAroADqBSaXUa4DfBN5vWVZ2L3u9SOYJQdheWrVP1YqiCsJO0ar9Q2hepE0JgiAIwlM0bdiRZVk5pdSHLcvKK6VuAR4Efhp4G/BKy7Ke3N0a7jz1Ar2CIFwZrdqnakVRBWGnaNX+ITQv0qYEQRAE4Sma1vgCYFlWfuVPO/CTwC3ASyzLemz3anX1EOFDQdheWrVP1YuiCsJO0Kr9Q2hepE0JgiAIwlO0ymz+MeAvgR/dL4YXkFhpQdhOWrk/ieaLsJO0ar8QmhsZtwRBEARhNS1hfLEsKwu8ybKsM7tdl52mdoEosdKCsH3o/pROp1tusVkqlUin07tdDWEPUq1WzXOmlQ2UQvOhtaqkXQmCIAjCMk0ddlSLZVml3a7DTqENLT6fzywQQWKlBeFKqe1buh/pxSbQMu7wlUpFFi7CjmCz2QgGg+b5k0gkSKfTdHV1SaibcEXYbDZjeNHG41YZcwVBEARhJ2gZ48teZi2Di8RKC8KVUdu3/H4/fr+farWKzWZrKaOm3W6XhbCwY+jnjM/nI51OUy6XyWaz8vwRrgilFOl0Gr/fbwx8giAIgrCfEeNLEyAGF0HYGRp5j7ViH3M6nS1XZ6H1sNlsdHV1GW8xQbgSar2qxHgsCIIgCGJ8aQpacTEoCK3AXulbku1IuFrslT4jNAfSlgRBEAThKWQ2LwiCIAiCIAiCIAiCsIOI8UUQBEEQBEEQBEEQBGEHEePLVaJRqkVJvygIV4+N+lsz98dSqUS5XN7tagj7gGbuB0JzsdJGNpxH1rYpaV+CIAjCfkaML1cJnXUlm82u+5ogCDvDRv2tmftjqVRifn5+t6sh7AOauR8IzcVKG7Fv5n26TUn7EgRBEPYzIrh7lWiUdaXRa4Ig7Awb9bdm7o9Op5NoNLrb1RD2Ac3cD4TmYqWNVDb5vobzH0EQBEHYT4jx5SrRKIOEZJUQhKvHRv2tmfuj0+nE4ZDhWth5mrkfCM3FSga2DeOH6tuUtC9BEARhvyJhR02ExEILws7Q6n1LayUIwk7Qyn1DaG6kbQmCIAjCU4jxpYmQWGhB2BlavW9VKpWWrbvQ3FSr1ZbuG0LzIm1LEARBEFYjfuxNhMTaC8LO0Op9y263t2zdhebGZrMRDAalfQnbjrQtQRAEQViNeL7sAmuFQOi46JU4akEQBEHYcbQGh4SICIIgCIIg7Byyyt9hGhlastksiUSCubk5yuWyTHgFYYep7XP1fa1RH202jRhdf0HYCarVKnNzcywsLMhzSdg2KpUK09PTpNPpTb1fhyklk0lpe4IgCMKeRIwvO0wjrQmfz4fD4aBcLjM/Py8x0YKww9T2ufq+1qiPNptGTLlcJh6P73Y1hD1KNpulXC5TKpXkuSTsGtlsltnZWWZnZ6XtCYIgCHsS0XzZAarVKtlsFp/P11Brwmaz0dXVRTabxePxkM/nL4mJrj2HhCEJwuXRqP+spfvS6PVm04hxOp309/fvdjWEPYrH48HhcBCNRkkkEkQiEYrFYtO0f6E1sdvt9Pb2rtuOtLEvGo3i8/no7u4GmmfsFQRBEITtRIwvO4DeNYflWHodT1+L1nfR79noHIIgbJ76/pPNZkmn0wSDwUuMmbV9cb3XdhOlFMlkUhYkwo6Qz+epVqskEgmq1SrFYrGp2r/QumzUjubn55mamgKgp6eHYDB4NaolCIIgCLtCS7pUqGWcu12PtfD5fKsU/tfSjyiXy8zMzFAuly95X/05BEFYm/o+5vP58Pv9VKtVqtUqHo8Hm82Gx+PZ0vl2m0qlIothYcfQz6BgMEgul8Plcu1yjYS9Qr2GS/3YGo1G6evrIxqNrnueZhuTBUEQBGErtJzxRSllA74JvHC367IW9VmL1tKP0Ds+8/Pzl7xvo8xHMhERhKeo72M2mw2bzUY6nSabzZqdfe0Bs1G/aTbNl0KhwNmzZ3e7GsIepFqtEovFyGQyTE9Ps7S0xMLCgjxjhCumWq1eouGix9Z0Ok06ncZms9HT04PDsb4jdrONyYIgCIKwFVrK+LJiePkq8DnLsj6jlLpGKRVUSqkNPnefUurk448/zokTJ7j//vuvqB71k1Kt0J9IJBqq9K/lxRKJRAiFQkQiEfM+v99PuVzeUO1fJiJXj/vvv58TJ04AXKuUOqmUuq/2+Ha3L+HyaeRtVq1W8fv9RnspGAxSrVZN9o1isci5c+fI5/OXLDIb9dmdWoyu175025qamuLVr361tC3hsthM2zp9+jQ//dM/zac+9Sna2tqAZePlzMwMiURCnjFCQzbTtp544gle8YpX8E//9E+rdLR0aNF6c5hisciTTz5pDIH6cw6Hg3PnzlEsFnf8GoXdYaM5lyAIQiujLMva7TpsGqXUvcArgNcBfwv0ABPAB4BPWBtczIkTJ6yTJ09ecT3S6TTJZJJgMIjf7yedTjM9PU0mk8HtduNyuRgeHt5wJyedTpNIJHA4HHR1dWGz2Ugmk5w/fx6v10t/f/+aoQa1gqKAiPNeBZRS37Es68Rax7erfQlXhk6bWywWcblcpm8BJBIJzp8/T1dXF7lcjvPnz9Pb20s0GjX9ufY8tf2qvt9vN+u1r0gkYv3lX/4lr3jFK7a9XGHvs17buvHGG61//ud/Zm5ujnQ6TSqVolwuMzIygsfjobOzs6FWkiDA+m3rxIkT1oMPPggsG110cgGbzbZhUoFz587x8MMPEwwGue2224zB5ty5c5w5c4b+/n6uv/56aZd7mI3a1nrzLaUUW1nfbPVzQmux0XxeEHaSVntqfQMIAZ8GPgKcAM4CrwGuijhK7Y66x+MhnU7j8Xjo7u5mZGQEh8NBPB7nwoULJJNJyuXymnovWhBUp7/VO+tOp3NDfYrasCRt/Emn0zt67YLQCtSnzdV9K5lMsrCwwPj4OIuLi7S3t9Pb28vhw4fxeDxMTk6aXVZ9ntqd2d3UYSqVShL+IewINpuNxcVFEokEfX19dHR0kM1mGR8fJ5FIMDs7a0JEpA0Kl0O1WmVycpJHH32UmZkZE26kf3s8HjM+13sW9vf309PTY7IfaXp7e3G73Xg8Hubm5qRNCoIgCC1FSxhfVgR2bZZlnQL+CxAFvmNZVgr4FaAXGL4addGaETabjXw+TzKZJJ/PEwwGCQaDdHZ20t7eTrlcZnp6mtHRURYWFlZNEqrVKqOjo0bh3+Fw4HK5mJubo1wu4/F48Hq95PN5U67E3wtCYxqJ7YbDYQYHB7HZbMYAOj09zSOPPEIqlaJSqeByuYhGo1SrVRYXF3n88cc5ffq0MbbUivTqXdraxcLVpFwuc+HChataprA/qFarxiBZqVSIRqPEYjHGx8fNs04/z8TAL1wONpsNh8OxStAZMDow8/PzxsBdrwWTz+cJhULGiK7HXO19mM/nzdhePzeS+ZIgCILQrDR9qmmlVNiyrESNAebflFIvtizrMaXUM4FDLF/H/NWoT23ccv1regKhd2rS6bTZgbfZbGSzWZP21ul0EggEyGQyeL1eFhYWKJfLOBwOfD4f8Xgcn89ndu7rF5e17rr1oRLihivsJ5LJJBcvXmRkZIRwOGz6hA6hmJmZoaurC6/XS1dXFx0dHRw5csQYOpPJJL29vVSrVTo6OoxHm96N1UZQvTDQ/fBqZx8KBAJXtTxhf+BwOLj++utZWFjg7Nmz+Hw+CoUCHo8Hp9OJ2+02fUAvaiXEVdgs7e3tpFIpPB7PJRpasGygqX89kUiQy+VYXFwkk8lgs9lwuVz4/X4ikQipVIr29nYcDkfDMVkbcuDS+ZIgCIIg7CZNbXxRSr0PSCul3mtZ1vyKAUatGF5+BvhL4AzwOsuy5q5GneqNHbV/+3y+VQYSPVFtb283AoZLS0uUSiVSqRSJRIKxsTECgQDhcJhIJEJvby+xWIxEImEmGMVikVwuR09Pj5lI1E8sALNLKSlphf2A7l/T09PMzMwQjUYJh8PmuMvlIpPJkEwm8Xq9BAIBLMuivb2d6elp3G43hUKBpaUlbDYbhw4dMobOWCxmtC5qjTHaG6ZarVIul1dpGOwk5XKZycnJHS1D2J8opejr6+PixYs8/PDDFItF3G43uVyOVCpFKpWit7fXPFf0s0eeM8JGVCoVTp8+zfz8vBkzu7q6CAaDqzTvtB6MHmszmQzlcpmhoSGq1SqFQsGkP9dCu7FYjFKphN/vx7KsVWHatZtk9cZ5QRAEQdhNmtr4Avzoyu+UUupDlmXFajIbfQnoBDwr4Uc7hmVZVKtVcrkcXq931UJLhyxUKhUKhQJer5dsNsv09DTZbJaFhQXS6TQf/ehH+exnP2vcbxvhdrt51atexY/92I/hdrvx+/0opUgmk7S1tRndCq/XSzAYNGUlEgljdPF6vRsK2QnCXiCdTnP+/HnK5TJtbW1kMhmKxaLZpdcaA3onNZPJAMuT91OnTuF2u3G73SaF86FDh6hWq1QqFTo7O3E6nfj9fhYXF1lYWDCLg+7ubtPHqtUqlmWtWohukHxtS3i9XgqFAg8//PAlx2688cZtL0/YP2SzWb7zne8wOTmJ0+kEwG63k0gkeOihhxgbG+PIkSNcc801dHd3Y7PZ8Hq9WJa1I21d2FtUq1Uj2FwsFk3omt6oWlpaWhVSNDMzQzqdxm6309/fTz6fJx6PMzY2RjQaXaXpNTs7S7lcJhKJsLi4CMDi4iJerxelFIlEwoR9F4tFUqkUnZ2dDedFpVKJgwcPrnkdOmTqcpE+IgiCINTStMYXpdQLgCrwReClyy+pD64YYF4K/Ajw1p02vGhyudyq9M86vKdarTIxMUEmk6FQKHDo0CFjMInH43z84x/nk5/8JKVSiVe84hXccccdOBwOHA6HmQjorEjvf//7eeCBB7h48SK//Mu/bEIi9EQhm82Sz+fp6uoyIQher5dkMmnccw8cOGDqCrI7KewttLeL/tvr9dLW1mba/Pj4OMVikUQiQTQaNQbIqakpE9bX0dFBoVDgscce46tf/Spf/epXqVQqDA4Ocvfdd/PDP/zDhEIhLMsilUqxtLREIBAgFAphs9no6Ogwhhjt+bLTWJZlFheCsJ1Uq1VOnTpFMpnE5/OZxXClUqFcLnP+/HkqlQoOhwO/379hFj9B0OisMcFgkEgkYrxXZmdnAUxqc70plclkqFQq5vPlctkY17X4s1KKTCaDUsq0U1j2spmamiKdThsvYYBQKEShUDBh4alUikAgcIkBxrIscrkcbrdbNq0EQRCEHaOZZ1HfB05YlpVRSr0T+HGgopS6n2V9l/dallW4WpXRHiXJZJJYLEa1WsXhcNDe3o5lWQSDQRPXXCgUeOCBB/i7v/s7FhcXeclLXsKb3vQmhodXawJPT0/T1dVl/v+rv/orPvaxj/Ge97yHRx99lHe+85085znPwWazMTIyQiKRAJY9ZDTa4+Xs2bMsLCzg9Xrp6+sD2JWsLIKwk6TTac6ePUu1WjWZMGw2G11dXSwsLBCJRDh37hyzs7NUKhUWFhbo6Oigvb2deDzO0tISn//85/nCF77A+fPnCQaDvPKVr2R4eJh//Md/5EMf+hB//dd/zY/+6I/yqle9CpvNRqlU4tprryUcDpPL5YBlo+bVFnNcWlq6quUJ+4NiscjZs2fxer0MDQ0xNTXF+Pg4bW1tRCIRQqEQSikKhQKzs7Nmwax/C8JaWJZlDNQej2fVfAeWRc0zmQyzs7PE43EqlQrd3d1Eo1EymQznz59nZmaGw4cPY7fb8Xg8OBwOxsbGaGtro62tjcXFRex2Ox0dHVQqFVKplPGQqVarFItFXC4XZ86cYWZmhsnJSR5//HEuXLjAyMgIP/uzP8vx48fJZDKMj4/T19e35qZVvVex/r+trU08XARBEIRN0bTGF8uy5mv+fodSqgT8PFC0LOvPr0Yd9INVhxpp0Vx4SvxSu2AXi0Xi8Tif//zn+eAHP8j09DQ/+qM/yr333svx48c3VZ5Site85jXcdttt/Of//J/55V/+ZX7xF3+RX/u1XyORSDA/v3xLtCAiYCYg3d3dJtxCdF+EvUZtmE+1WmVhYQGn08nk5CTd3d0mll8L5GojqPZ4CQaD/M3f/A0PPvggCwsLjIyM8F/+y3/h7rvvJpfLcebMGf77f//vTExM8KlPfYovfvGLfOYzn+GWW27hh37oh+jq6iKTyZBOpwkEAnR3dxOLxSgWi2QymTVd2beTeDy+o+cX9ie1RpVKpcL8/DypVIpSqYTD4eDJJ5/k8OHDZnE7PDx8SfpfQWiEw+FgeHjYpIbWaJFdHUI0Pz9vdLUsy8LpdOL1epmamuKJJ57A4/FwzTXXUCgUSKfTKKUoFosMDg4Si8VwOBw4nU4TYjo3tywB+LnPfY4nnnjCZLrT86ZgMEhvby9f//rX+cIXvsCxY8e46667eN7znkd7e7uZY9V7wWjPGcAkT0ilUiilxBgpCIIgbIqmNb5oVgR2Lcuy3qWUKgKfuRrlVqtVZmZmjNhgIBAw2VKy2Szt7e0sLi7icrlIJBL84Ac/4IMf/CBf+cpXeMYznsFHP/pR7rjjji2lhz169Cj/9//+X/7iL/6C//W//hcjIyM8/elPNztIU1NTTExM4HK5OHLkiPEAaGtrMwJzWsCufodGdGCEVkK322KxyNjYGENDQ/T19dHW1kYsFmNsbMyEE83MzPDYY4+hlKK7u9tM1LPZLJ/4xCf49re/zYte9CLuvfdefD6fCVd6y1vewpNPPonL5eKmm27itttu47Of/SwPPfQQH/nIR/irv/orpqamuO+++0xaeC3kW2uU1d4wuVxO+pnQMlQqFeLxOJOTk6vS+ra1tVEoFJiYmMDtdhOJREwKYL/fT29vL3a7fberLzQx1WqVTCZDLBYjl8uZzSttRIenPKi0cf306dN0dXVx4MABHA6H8czSz4HBwUE6OzsBOHv2LGNjY0xOThKNRhkfH+fixYv84z/+I//6r/9KPB7HbrcTDAZNqJEOUU0mk4yMjPDGN76Rz3zmM7zvfe+jUChw9OhRYrEYS0tL9PX14fV6WVxcZHFx0WxsWZZFuVzGsix8Ph/FYpFkMklXV5fpE5ZlGYOS/px+Jsh8TBAEYf/S9MYXy7KslRTTVcuy/uRqlZvNZllaWiIej+P1egGMwK1OIT01NYXdbmd6epq//du/5Stf+Qr33nsv73vf+67YBdXr9fLud7+bD3/4wzz44IOUy2V6enrw+/1MTU1x7tw549KrF3wA4+PjzM3NccMNN1AqlQgEAnR1dTE/P2/iqsUrRmgVdGYv7XWSz+dNP9B6FE6nE4/HQyqVIpvNMj4+zpEjR1haWiKXyzE+Ps74+DgveclLeOCBBwD43ve+h2VZ/Lf/9t84e/Ysv/Zrv8bs7Czf+ta3eP/738/73/9+RkZG+OEf/mETkmS323npS1/KqVOnOHLkCAsLCwwNDRGPx+no6AAwLvTd3d0NBRrXmnSvLERkFi5cdSqVCrFY7JLXU6kU+Xye0dFRYNkz8/jx43R1dRGLxQiHw/IsETYkm83icrmMzh0s67DAssBuoVCgvb2dXC5HLBYjn88zMzODw+Hg6NGj3HLLLRQKBaanp7Hb7USjUZOF68yZM8RiMRN+9O///u+cO3eOYrFo5m1KKUKhEENDQ7S3t3Pw4EEGBgaoVCr87u/+Lv/yL//Cxz72Md72trfx4Q9/GJ/Px/XXX0+lUjGeNAsLC1SrVUKhEIcPHyaXy9HW1obH46FcLpNIJMhms9hsNuMVls1mmZ2dJZPJ0NbWtsrbp1a7TPqQIAjC/qLpjS8AlmXtmLhC7WIIlh+YHo/H7PzF43FmZ2eZmJggHA4Tj8fNbkcsFmNxcZFPf/rT/PM//zP33HMPf/7nf97Q8GJZFt///vfp6emht7d3U3Vrb2+nt7eXiYkJUqkUwWCQZDJJJBLB6/WSz+eZmpqio6ODZDJJMBhkbGyMWCxGR0eHeajbbDbjHSA6MEIrodtrNBo1ukvFYpFYLIZSioMHD+J0Okkmk8bwMj09bfqAx+PB6/UyPT3Ns5/97FXn/uu//mu+9KUv8Z/+03/innvuAeDXfu3XGB8fZ3R0lC996Ut8/OMfJ5fLce211/LpT3+aYrHI8573PL71rW/R3t5uBCDj8TjPfOYzgadEgWvTp+oJt/4NqyfdKzvC4kYgXHV06t61yGazZDIZJicnueWWW+jv7+fAgQNmcSsIa1GpVPB4PPT09NDV1cXc3BzVahWXy0W5XCaXyxktl/7+fgKBANFolEqlwvT0NN/73veIRCLE43HOnDlDKBQilUpRqVRMFqRYLMbp06f58pe/jMfjobOzk8nJSfr6+njHO97Bs5/9bOONcuHCBdrb2039PB4Pb3nLW3jLW97C7//+7/MLv/ALfOpTn2Jubo729nYWFhZYXFzE7/fT1dVFb28vuVyOkydPcujQIYaGhnA4HEb4PRKJmHP7fD6TGa/W8JJMJvH7/QSDQZmPCYIg7ENawviyk6TTaaanpwkEAvj9ftLptHEV1ZlRFhcXWVpaYmZmxoQaud1uJiYm+Md//Ee+/OUv8+IXv5gPfOADDV1IH3nkEf74j/+Yb3/723g8Ht74xjfyC7/wC5uq37XXXksymWR0dNR4uHg8Hm655RazkMtkMiad4y233MKFCxeMzoxe6NW7vQpCK1CrX+RwOJiamjJpn10uF93d3SSTSaanp0mlUrS3t2Oz2ZiYmDDvfeKJJwC44447zHkfeugh/uqv/oq77rqLV73qVavK7O3t5fnPfz5veMMbyOfz/Oqv/iqf/vSnedrTnsbnP/95YrEYoVCIvr4+nvWsZ+HxeMjlcvzHf/wHQ0NDtLW1US6XGR0dxe12mx3fTCaD1+slFApdMule+b+CIFxlNhKOLpVKKKUIBALY7XYGBweN54IgrIcWPY9Go8zPz5PJZMjlcszPzxuPGJ0dslQqMTMzQyQS4aabbuLRRx/l8ccfN4YKPQ8aHh5mcHCQZDLJgw8+yOnTp+np6eGOO+7goYceYmlpid/5nd/h9a9/vcmutBbPe97z+IM/+AN++7d/mz/90z/lAx/4AC996UtJJpNMTEyQSCQolUp0d3eTTqdpb28nmUySSqVIp9NEIhGTCEGn0tbp2nWfqd2M0+O+hBsJgiDsX/al8UXvQHs8HtLptAlX8Hg82Gw2wuEwiUTC7GJoMd1KpWJcYxcWFjh79ixf+9rXOHLkCM9//vP56Ec/uqqchYUFfu/3fm/Va/l8nve97328733v4+jRo7z5zW9u6Cnj9XoZGRnh2muv5SMf+QgjIyNGiG5xcZGpqSkOHDhAsVgkEongdDrp7OxkbGzM7LD09PSQTqeNV4w87IVWRPfXSCRiUo+63W6cTieFQgGlFHa7nRtuuIG2tjbOnz9v3L2LxSLnz5+ns7OT7u5uUqkU586d43d+53cA+OIXv8gXv/jFVeW53e5Vu/qWZeFwOHjiiSew2Ww8/PDDHDp0iL6+PhYWFhgYGKBUKjE1NcXS0hI33nijmfRrA67P52sY+69Z+X/NVXA6nearX/0qN9100yXHdDpXQdgK6XSar33taw2PKaUIBoMEg0E6OjpwuVzEYjEikYhkdxE2RClFLBajXC4zPDyMy+ViaGiIRCLB6OioySD38MMPUywWjXZLJBLh2LFjRrcumUxit9txOp1UKhXGx8d55JFHOH36NNdffz3T09N8/etf50d+5Ef4whe+wLve9S7e9a53XVKfYDDY0HAYDof50pe+REdHB29961v5kz/5Ew4ePIjL5aKjo4N8Ps/c3By9vb0cPXqUoaEhrrvuOpRSuFwuXC4XNpsNj8ezbr+QZAiCIAjCvlyNa9fP+fl5qtWqyVykPUkSiYQJb9ACt1NTU4yNjbG4uEg6nebhhx/mc5/7HLfeeitvfOMbzW4HQC6X4zOf+Qx/8Ad/sG49Tp8+zZ//+Z8zNTW15nuuvfZaI+7p8/no6Oigo6PDpP50u90sLCwY7xedLjQajQLLOyzi3iq0Mrq/FotF49XidruZmZnh3LlzjI2NcfbsWebm5sjlclSrVdxuN/39/Xg8HvL5PM985jPNDut99913WeXrTBZ2u51qtUpvby9jY2M8+eSTnD59mjNnzhj9pVKphMvlwufzEQ6HTRYkm81mvOtqQ48EodkZGBjA7XYbbYvZ2VmjVyEI62FZFtlsllKpZHT0YFnrpVKpmCQB/f39hMNhM685e/YsFy9eZGFhgfHxcc6ePUu1WmVwcJDu7m4qlQrf+973aG9v59FHHyUajfL+97+ft73tbVuqZyAQ4JprruHv//7vSSQSPOtZz2JxcRHAZDzK5/PG48Xn8xlNMa3nojcJtCZg7W8Z7wVBEATNvjS+aINENBolHA7T3d1tjBN+v994vJTLZYrFIrlcjsXFRYrFIoVCgVKpxNe//nWOHDnCZz7zGdxuN7A80fja177GO9/5Tr74xS9yyy23rFuPgwcPMjY2xh/+4R/yiU98wmQCqOXaa68Fll2/c7kcTqcTl8tFoVBgfHwcu91u6h6LxRgdHcVms+FwLDs11e60ay0KmQgIrYBurx6PxxgQ9c/8/DzxeJx0Oo3D4SAWi/HQQw+ZuP+RkRECgQDFYpG5uTme/vSnU6lUeMtb3sLY2Nhl16XWADM3N0coFOLxxx8nHo8zNzfH3NycCU98+OGHWVhYMEYXnV1DZ/vQ+jSC0AocO3aM66+/nuuvv56enh4x5AubRm9ilUolMpkMCwsLxONxwuEwBw8exGaz4XQ6OXjwINdffz1DQ0N4PB5KpRLf+973OHv2LPl8nmg0arxWSqUSX/rSl4wx8K677uL973+/mSttlac97Wm88pWv5P7772doaIjFxUUSiQTRaJT+/n6OHDli0rI7nU7jkROPx42mHmA29mp/y3gvCIKwNkqpdyulvqaU+phSyrmZ40qpEaVUTCn15ZWfzqtf862xL40v2iDhcDhMPLF2RdVxu6lUiosXLzI6Osrp06eZnp7G5XIRCoX4p3/6J7LZLH/5l3+5SmDt3LlzfOITnyCdTvPsZz+bn/7pn163Hj09Pbzyla/EZrPx4IMP8vGPf3zVccuy+M53vgNAX18fd9xxB0eOHKGvr49qtYrT6aStrY3Dhw9z+PBhk7JxLbS+jexaCq2ANlRo0dq5uTl8Ph89PT1Gk6mnp4e+vj6OHz9OZ2cn5XKZZDJJIBDA4XCYSW+xWOTrX/86Dz74IDfffPOW6mOz2fB6veRyORwOB4lEgqWlJdrb2/F6vSYF6eLiIslkErfbTSaTwe12EwwG8Xq94okmtBw33HADT3/607n11ls5evQofX19EjohbAqlFD09PXR2dhoDy+DgIC6Xi7a2NiqVCkopPB4P7e3tdHR0GKO7NlZXKhWWlpbI5/OcP3+eb37zm5w9e9aE+rz2ta/dlpBqpRRveMMbCAQCfPzjH8fhcOB0Ounp6eHgwYPcddddXHfddVx77bVce+21dHV1kUgkKBaLuFwuurq6zHxSG4v0bxnvBUEQllFKfaTu/xuBfsuy7gROAy+/jONfsSzruSs/l6ZtbFL2peZLPTabjc7OTnK5HC6Xi/n5efPb7/cTjUYpl8u0t7dTqVR4xStewdjYGD/+4z++SudlZGSEl7/85Xz5y1/mq1/9Kt/85jfXLffkyZN885vfxOfz8axnPYvnP//55lixWOS+++7jox/9KD/6oz/K8573PPx+vxHWdTqdq7IZ6cVmMBgkEomsmdJWEFqFWnHCubk5E57n9/sJhUKEQiFjSDx06BB+v5/p6Wk8Hg8PP/wwLpeL6667jttvv513vvOdvPrVr+ZnfuZn+Lu/+7st1Ue7znu9XmZnZ4lGowwPDxsdAJ0dQ0/YY7GY6a+wnLq3keaL7quC0Iz09PSYEDr9jBG9F2Gz2Gw2BgYG6Ovrw+FwkE6nTZh0NBqlvb2dyclJHnnkEU6dOsXU1BR+v59AIIDb7aavr49kMklvby9Op5Pu7m4WFhZ45JFHsNvtvPa1r+Xee+/l7rvv3rIQdLVa5YknnuBFL3qR0eYKhULcdddd3HDDDfh8PkZGRrj55pvJZrNmoysSiZhMSHpM1/Oy+t+CIAhCQ24H/mXl788Drwc+vsnjz1JKfQ34GvDbVouIIIrxZQWbzUZbWxuzs7PMzs4Cy54i4XCYZz3rWUxPT+P1eimVSjzjGc/g8OHDvOtd7+IlL3kJL3rRi7j77rtxOBw897nP5TnPeQ6jo6OcPHmSL3/5y2uW2dbWxute9zqOHz++SjNmfn6eD3zgA1y8eJEXv/jFXH/99UZotFAoEA6Hsdvt2O32VYu46elplpaWmJ6epru7m0QiQTqdNhMDveiTXRihFagVJ9QaRtFoFJvNRm9vr0kHn8lkKJVKFItFOjs7SafTJBIJ3G43fr+fF7zgBRw8eJCPfexjdHd38/rXv54HHnhg0/XQei5a3DeXy/H0pz+dF7zgBXi9Xux2O4VCgUKhYLIwTU1NmcxGgBEA1voA9Wmmk8kk7FNPRKG5KRQKJh11KBSSxaSwaQqFggmF1h6AWoy8o6PDtKtSqcTExASpVMq0L7fbTSAQ4MiRI7S1tRGLxZicnGRycpJnP/vZpNNpLly4gN/v5y//8i/50Ic+xHOf+9zLrqMOK69UKthsNpRSvOhFL+JFL3oRhw4dorOzE7vdTm9vL9lslnw+Tz6fp7Ozk97eXtngEgRhR1FKnQX+2rKs31dKHQP+CrgJeBz4NcuyvnWF548AHwLuBuaB37Is628bvM8N/H/AC4AIcG7lvf+8crw+rMIL/H+WZf3qBlVoB6ZX/l5aOfdmjk8Dh4Es8AHgJ4H/u0FZTYEYX+rQbq/FYpF0Ok0+n2d4eJhAIMD8/Dx9fX3Y7XZuuukm/ut//a/8zd/8DZ/85CcZHR3l1a9+NV6vF6UUIyMjjIyMrGt8OXbsGLfeeuuq106dOsUHP/hBLMvirW99K3fddReTk5M4nU7C4TCpVIpIJGIMMHoiU6lUaG9v5+DBgwwODpodpnK5TDabNYYXmTgLrYgOMSqXy8zNzZlMZdoQY7PZmJ+f59ChQ5TLZSYnJ412QC6X4+DBg7z2ta/l0Ucf3dDwUqlUKBQKwLLhRRs+lVJYlsWtt97KPffcw4kTJ8hms4yOjnLx4kWKxSLt7e1Eo1G6u7txOBxmh7S7u5tyuWxCqGqpMYaKGJPQdOiFZ3t7O11dXeL1Imwal8vF4OAgfr+fmZkZcrkcNpuNpaUl4vG4CdlZWFigra2Nvr4+YDn7UH9/P5FIhHPnzjE4OEhHRwczMzNks1kcDgd33XUXZ8+e5ZFHHjHPggcffHDd+liWRbVaxbKsVeFMmjvvvJOf+7mf4+jRoyilqFQqJsW0Ugqfz0dn57KsgN/vl74gCMJVY0Xr5P8B3wJ+F3gT8Gml1AHLsnJXcOr/CRSBbpaNOp9TSj1sWdZjde9zAOPAc4Ax4IXA3yulbrAs66JlWWaBqZTyAzPAJ1f+HwJ0qMhRpdSXV/6+G0gAwZX/Q8BCXbkNj1uWVQAKK+f/FPAMxPjSmmi36kQiQVdXlwkH8Pv9LC4urlLAj0QivPSlL2V2dpavf/3rvOc97+Hee++lt7f3ssu1LIvPf/7zfOYzn6G3t5dXvepV/NiP/RjHjh3jwIEDZDIZ+vv7jZHF5XKZzC7pdNq48nZ3dxuDjK6/eLoIe4X5+Xmmpqbo6enB4XBQLBbJZrOkUimWlpaYnZ0lFAoxMDCA0+nEZrNht9tZWlrC5XJxzTXXcN11112SFr4WbWypx7IsPB4Pv/Ebv2HS7wYCARYXFymXy0SjUTweD21tbUbToFqtmowYuVzO7JrWGkHFKCo0M729vUQiETG8CJeNUorOzk58Ph9dXV04HA7C4TCZTIZgMGgyIJ09exafz8cNN9xANpslEongcrk4efIkp0+fpqenh9tvvx23222M2h6PhzvvvJObbrqJ0dFRzp49y+nTp6lUKmvWJ5VKkUqlLnnd7Xbz9Kc/nXe/+92Uy2U6Ojqw2+3k8/lVBhullMmOKQiCsNMopS4Cw8A7gN8B7MCPW5b1iFIqx7Ih5jrg5BbP3wb8FHC9ZVlp4OtKqc8CrwZ+s/a9lmVlgN+veen/KaUuALcCF+tO/VPAHMvhQFiWNQY8d6XMj1iW9bqaOnwTeCvLxpkfAb5Rd66Gx5VSAcuy9IB+J8ueQC3Bvja+rKWL4vF4cDgcdHZ2Eo/HcTgc2Gw2hoeHSSaTnDp1yqQb7O/v593vfjdf+cpXeO9738sf/dEfcccdd9Df309PTw/vfve76evro7e3l97eXjKZDFNTU0xOTjI1NcW///u/MzU1xYULF3jyySf5mZ/5Gd7whjdw4403UiwW8Xg8RKNR8vn8JfX0+/3Gs8Xlcpk4al03WdQJe4368KO5uTnK5TKhUAiXy4XX6wWW+3BHR4cRTdSpqrPZLAMDAxw5coSvf/3rXLx4kdOnTxONRnE4HFQqFSqVCuVy2fwuFApcd911fOhDH6K3t5dUKmXSW+uU0h0dHYTDYdxut0k1rUOUVkKKVmnYbAWbzdbQKCQIV4I2GDqdTgKBAD09PRw7doxAIEB3dzcDAwN4vd5LjIaCsBFOp5POzk4TrpZOp0kmk0QikVXZGyORCAcOHMCyLC5cuGAyMkYiEZPRcWFhgWq1ys0332y0vvx+P3Nzc7zmNa8hFAoxPz/Pl7/8Zb797W/z+c9/vqGhpZZbbrmF9773vdxxxx1ks1kzfwoGg6at6zrncjlp/4IgXG1eCHwB+BzwZ8CLWBadBTi08nu2/kNKqdcBjdy8/3et4QM4ApQtyzpT89rDLHu3rItSqnvl8/UeMgCvBT66GQ0Wy7K+r5SaXdFuGQP+dOX8PcAvWZb1jkbHgTuUUu9iOezoAsveQC3Bvja+1Ggt4PP5jCEmn8+bbCpaW0I/kMPhsFkARiIRBgYGSKVSHDx4kD/6oz/i5MmTnDp1iu9+97tMTU2RyWTWLN9ut5tsLe3t7bz5zW/m5S9/ObfeeivFYpGJiQnm5uY4fPiwEe2E1UajRnXV1yQTBWGvocOPNNFolPn5eYrFohECDQaDOBwO4vE4Bw8exOFwGLFHHVI4OjpKf38/lmVx+vRpHnroIZOhKBqNGs8Vbfx80YteRDAYpFAomPSi4XCY9vZ2QqEQgUCArq4uisXiKiNprcHlSo2hhw4d2vhNgnCZeL1e+vr68Pl8eDweQqEQPp+Pw4cPMzg4yNGjR4GtGw2F/YvT6WRwcNC0HZvNZkJGM5kMsViMhYUFBgYGiEQieDwePB6PMWJrnb1QKEQ+nzcGkEAggMfjMecPhUIUCgW8Xi+33347z33uc3nzm9/MD37wA+x2O+3t7TgcDmMs19mJdJgTYBIa1OviXanRXBAEYatYlnVKKVUAZizLehL4cwCl1DOBvwA+bFnWeIOPfhZolNqzPqTHDyTrXlsC1nXxWwmB+huWjTmn644Ns2y8+fk1rul1DV77jQavzbDs8bPW8X8G/nm9ejYr+9r4UvtQrTfE6N82m83swlSrVXw+H0eOHOHw4cNmMeXxeAgEAni9Xl72spdxzz334PF4sCzLhC195zvfMQu2G264gSNHjpisKYFAwAiHplIpEonEJQ/6WoNLOp1mdnaW7u5us5irrauI6gr7BW189Pv9DAwMGI+YY8eOkc1mjdi03tWcnZ0lFovhdDo5fvw4lUqF22+/nTe+8Y1897vfxeFwMDg4SDwe58iRI7hcLhYXF4nH42andnR0lEqlQk9PD6FQiHK5TC6Xo1gs0tbWtio0Yzu9z4aGhrblPIJQi8/no7e3l1KpRF9fHwcOHODw4cMMDw+Ty+WYnZ0Vw59wxdSOhfl8nqWlJTPeBoNBMpkM8XickZERE55UrVZN6FI6nWZmZoauri4qlQo+n49isUgmkzHeyS6Xi0AgwNLSEk6nkxMnThjDYiwWM3OmtcKxG43X4kEsCEIzoZR6C/Aelj1bfrHReyzLWuBSQ0sj0jylp6IJAmu6DSqlbMDHWNaJ+ZUGb3k18HXLsi5sovx9yb42vtQ+VNfboVZKYbPZSCaTJq1zLeFwmEOHDjE2NobT6WRkZISuri4jlBsMBhkaGmJxcXHVzr3P58PpdJoFYyKRwGazEYlEcDgcHD58GMBMFrRxSNMobEomCsJ+orbfhsNh83o4HF71v97VBGhvbycej9PR0UE+nyeTyRAOh02oX6FQMGK9DofDTOz1bux1111HpVJhaGiIbDZr+txOGjwdDgfPetazduz8wv7F7/dzyy23MDExwZEjRzhx4gSdnZ0MDQ2RSqUYHBzc7SoKLUq1Wr0k6yIseyxmMhnsdrvxLkyn02Y+k81mzXxIi+/abDauueYa855qtcrExAR+vx/Lsujs7MTv9zM6OkoulzOeM36/n7a2NhwOB16vV+ZIgiC0IgpAKfXrwLtY9ipZM6znMsKOzgAOpdQ1K541ADfSOJQItby7+CGWxXlfaFlWqcHbXgP88UYXVHfed7OcUvoi8Ib68zY6rpQaAf6jpq4/bVlW7HLK3S0kP94KtamY67n//vvx+XwEg0E8Hg+ZTIba9q6U4sCBAyZTksPhwO12c+DAAcLhMDabjQMHDnD06FEOHz5MV1eXKaurqwu73U42myUejwNQLBb5wAc+YEQ9tSdLMBjE5/Ph8/mMASiZTBrvmivl/vvv35bzSJnNRytc81bqWDuRTqfTVKtVEyZY67GWTCZJp9PGU81ms7G4uIjNZsNms+Fw/P/svXmcXFWZ//++te9d3V1dvaW3EEIIEQgGlEVBBxccEQdxXFAYBVGYnxs67jOOOjqOynwVHAZwQ1xRdFxGZURkF8SwCAGyEdJJOt1VXd1de1XXcu/vj845VHW6O91JOt2dft6vV15J6lbde27VOec+5znP83kcWqy6t7eX1atX09PTQzgc1oKRDQ0N9PX1cdddd3HiiSdqB09bWxvBYHBexUh9Ph8nn3zyvJ0fFq6PyHUXFpfLRV9fHyeffDInn3wyK1as0Cl3K1eu1ALuh5PlMO8vh3s8EGpuVdp0ta+3tLQQDof15lE4HKajo0PbPZFIhPb2dlauXKnFzVVfVNGIbrdbV3VU4uq///3v6e7uZu3atXR2duL3+7HZbPrv+Wahf4Plcv0nn3wSwzCm/dPT03PYr7nQ3+1iaMNCX3+xtOEIkwROMwzjJCYiXq5jQpflJMMwTt5XWWgyKu1o8p9/qX3TPhHdnwOfNQzDbxjGmcAFTES2TMV/A8cD509VYckwjDOATvZVOZoN++6r07KslzChZ3PRHI7fY1nWOfv+LAnHC4jzZVZ84xvfwDAM/H4/xWJxSoeH3W6nt7dXGxMKtYOTSCTw+/0Eg0Hsdjs2m03v9MDE4qq1tZXW1lZ8Ph/f+MY36s5f6xxSqRY2m007ZA4Hy8VYXIYT95K450Npo4oMy+fzdf9Wx2KxGLFYTO+aqrEWjUZpb2/H7/fj9/tpb2/XzhxlsDscDoLBIKFQCLvdzi233KINvMlpRvOF0qCZT5abM2K5XXc6bDYbXV1d9Pb26upd893flsO8vxzucTZEo9E6u6hSqfDcc8+RTCYpFot1dhA8P+/a7XaCwWCdY9vn82kR9WAwSENDA263m/Hxcf35b33rW7S2tuJ0Oo+Yw6WWhf4Nlsv1S6USlmVN+2fnzp2H/ZoL/d0uhjYs9PUXSxuOMD8AXgr8ed//Pww8VvNnw+QPWJY1alnW41P82TXF+a8CvExUJ/oREyK3OvLFMIzfGYbxiX1aLu9mohz1kGEY2X1/Lq4516XAz2uqEM2GM4Df7/v37cDkMO+Zjp9pGMZ9hmF8wVhC5RiNWQgRHzUYhjEM9B/ER4+nvoSVDTCneW/tMRsTZcEMwAKqkz4303kmX3Om6xwuDnTN+WApXbPHsqyW6Q4eQv86EizE9zxXDrWNk8fe5LEGsxszDsAJlAFVXqj2fPP1XU7bvwzDSDOh5j5ViOfhYqH6iFx3/pmpbyV4vlpChYln1XyX1VpK8/5Sud5CXXOmvjXVM9EBuJjoYyUOzpaZPNfD/M/Ps0Wuf/iuP9e+Nd8s9He7GNqw0Nc/XG2Y0Z4XjhyGYXwCeNqyrF8YhrEK+KxlWW890HHDMNxMPE/ywDeA31mW9bOFuIe5sqw0Xw52oBmGsdGyrP08i/OJXHPpXXMxT+QL8T3PlaXQRliYdlqWNVkQ7bCzUN+/XHdhsSwrcqSveTTN+4vlegt1zZlYiGfiQn8Hcv0jc/3l2LcWQxsW+vqLpQ3C3NhXNvrHUxx6MxNpVcrGbWB/oeApj1uWNQ6M7zv/z4EXA0vC+SJpR7NjIWLc5JpH1zUXmqVwz0uhjbB02jlXFuq+5LrLj+Uw7y+He1yMLPR3INc/elkM97bQbVjo68PiaIMwByzLGqrRZqn9MwT8CTh331tfBTww6eNTHjcMo7b6zUuA7fN3B4eXZZV2JAiCIAiCIAiCIAjCwmMYxpeZiFzZBbzDsqzSvmiZKy3L+vQ0x89jovJTnom0/HdaljXfKdOHBXG+CIIgCIIgCIIgCIIgzCOSdiQIgiAIgiAIgiAIgjCPiPNlFhiGcYVcU665lFkK97wU2ghLp51zZaHuS667/FgO8/5yuMfFyEJ/B3L9o7cPLoZ7W+g2LPT1F0sbDjfL5XmxXK55IMT5MjsW4oeTax5d11xolsI9L4U2wtJp51xZqPuS6y4/lsO8vxzucTGy0N+BXP/oZTHc20K3YaGvD4ujDYeb5fK8WC7XnJFlVWo6EolYvb29c/6cz+fjlFNOsarVKna7HZttep+VaZqUy2Wq1ar+f6VSwTRNxsfHsdlsuN1u/T673U6lUqFYLGK32/H7/djtdlwuFz09PZbb7cbr9QLoa1erVf1Zp9Opr10ulymXyzidTv26aZqodgPMdA8+n48NGzbMqwhQbXtsNhter5cXvvCFlmVZM36vh5ODvc9HHnkkMVN5Q6fTadntdhwOB3a7HcuycDgcNDY24vP5qFarmKaJ2+3e73ezLAvDMIDnvyOn04ndbp/xN5sOy7KoVCo4HA4MwzjgPU/+XRaCI9H/Dgfz1c6Z+lckErE6Ozv1OK5UKliWpeeZSmVCY8zhmJjSS6USpmlimiYulwubzUapVNJjzjAMDMOo64e19zU+Pk4ul9PnVPOS6ic2mw3DMHRfUfOces1mszHdmFbzo7q+uu5UfdA0zf3OcaC+qo7XjqPa+5zqfmdiqjYcDKo9Xq+37rrqWWBZ1n5z+kznKpfLAPq7Vn/UPFI7hzz22GOjlmU1T3Wug30uTnd/0/1+pmliWRblchmPx8NJJ51kwfPPtcn3ZJomAE6nU/+/WCxSKpVwuVzY7XbK5bKe49S8Oz4+TqFQ0O+pVCqUy2XcbjfHHXec5fF49vuOa9terVbJ5XJ67Kj2qD5f26cm9w3TNCmVSlQqFX2PpmnqttW+Tz3Lx8fHAXC73XXvORim6tOzsQEmj8np+nvtb6Ted6B563D0rbmw0M+RpXx91S/L5bLul2rcqTFkWRalUknbmg6HA6fTSaVSoVKp4HQ6WbNmjWUYBpVKhWq1isPhwGaz6fFls9nweDzAzDap9K3F14aFuL5aP42OjpLL5dR8bxmGgcvlorGxkcbGRtxu96zPeSB7/kj3r4X4XuWa88eB+teycr709vaycePGOX9uw4YNPPzww2SzWQACgcCMxkk2m9ULnx07drBr1y6am5sZHByksbGRcDiMYRgMDw9TKBTYu3cvu3btYuXKlZx++unEYjHe85738OlPf5oTTjiB1atX43A48Pl82Gw20uk0O3bswOv10tnZSSAQACYWYIlEgkgkohdhpmmSz+fx+XwA+t9TtX/Dhg0H9f3Mhdr25PN5zjzzTH7zm99oR0E0Gp33xf/B3qdhGP0zHQ8Gg6xatYre3l6OP/54EokENpuNdevWccopp9Da2qrvUf0+2WyWZDKJw+EgEolQLBZxuVyMjo4SiUSw2Wwz/mbTMTQ0xN69e+no6KCtrU3fc+33P3nRcDDXOZwcif53OJivds7Uv3p7e7n77rv1OI7H44yNjVEoFPD7/Xr8+P1+9uzZw44dOzj++OPJ5XLY7XZKpRK5XA6fz0dvb682pJuamvT8UXtfpVKJ5557jlQqRaFQ4Nhjj6Wjo0P3EzXPhUIhAoEApmkSj8dJpVLYbDaCwSCmaRIKhfRYV38nk0lgYkEbjUZ58YtfPGXfzGazpNNpfQ3FgfqqOl47jtR4q2U2v+N0bTgYVLvOOeecuuuq71I9N8Lh8AGvpT4DE4ZFIpGgVCrhcDgIBAJ4PJ66OcRut8/Ytw5Hf578u6TTaWKxGK2trfr/gUCAbDbLK1/5Su65556659rke8rn88Dzz1vTNNm7dy9DQ0O0tbXh8XjYunUrHR0dlMtlWltbCQQC7Nmzh127drFmzRpcLhfbt2/nkUce4Qtf+AK33nor69at268/1LZdXadQKNDY2Mj27du1s6W7u5tSqaTn6sl9wzRNhoaGSKVSvPnNb+aee+5heHhYt23y2KlUKmzatAmPx8Pq1asJhUKH9BtM1acPZAOoZ5DNZiMQCMzavlHvPxJ9ay4s9HNkqV6/dq7zeDzE43E8Hg+mafLwww/z3HPPEY1GcTqd/OlPfyKZTHLMMcewcuVK3G43AwMDFItFbrjhBr7//e/rDQDlYHe5XHR1dZFMJvH5fLqvzzSXH+iZuNz61mJow5G+vuqXjz32GD//+c/ZvXs39957Ly94wQvw+/2cccYZnH/++bzwhS/UzvLZcCB7/kj3r4X4XeWa88eB+teycr4cLFdccYXe+Uqn09romAqbzaYfKtlslmg0SkNDA16vl76+Pm0cK+OyUqmQTqf1IqpQKNDV1cXb3/52zj//fFwu137GUCAQYOXKlQDaoIKJHeq2trb92lPb1pmM+iuumP/IrNr2+Hw+LrvsMiKRCIlEgkqlQj6fP+RFzoGYr/uMRCJcdtllrFu3js7OTlKpFLFYTBmohMPh/Yxr9fsp40Pde+3veDDfRyQSqftb3XM+nyedTu933pn69JHiSPS/w8FCtbP294lGo3V9p1gs1s0FLpeLvr4+bDYbg4OD+P1+vfjLZDL6PbWfqb0vl8vFcccdV+fQhef7iVp8qc8rR8pUbartc+q4WsQVi0V93cl9sPZctRyor043jiYzm99xujYcDKpdk6+rnhnq95nNtWqfMzDRHyYvYibdu3nINzCLNk33u9R+j+o7CIVCdc+1yfc0ea602Wx0dHQQDofx+Xxks1laWloIh8PaiZPP57HZbKxatYqmpibt/Pb7/YyMjEzpeJncdpvNRnd3NzDRT9UCVL1HtXmqvmGz2WhrayMUCnHllVcSCoXq2qYcUMopqe4hGAwelvl3qj59IBtg8jNoJtRvpBZEC+Won4mFfo4s1etP7gcdHR362LnnnsvmzZsJBAKUy2VaWlrIZDKcfPLJenzEYjFyuRylUoloNEq5XKarq4vR0VHtlDdNs+68cHD2zUKx0L/tYmjDkb6+6pfHHXccxx9/PH19fdjtdv7xH/+Rvr4+2tvbCYfDi3IumgsL8bvKNReOZVVqesOGDdaheL/mGh0wl6gT9f7ZRNccjSyGyIsDYRjGI5ZlbZju+Pr166177rlHGxP5fB6Px7PfDu5CshS+5+XKTP1rLnPXTPPOQswxU/U56YdHlsPVt+bCfP/GS61fLbX2zkRtu+12+xHvW8KRR/3mHo9HO9anip6dfPxQnjkLMW8Ji5NKpcLQ0JCO8m1ra5vSkT5bDmTPS/8SDoUD9S+JfJkDc40OmEvUiXr/oYYdL1UWQ+TFoWK32+t+P3U/i+k3PRq+Z2FmZpp3FmKOmarPST88+pnv33ip9aul1t6ZWKrtFg6e2t98qt9+uuPL2a4VDh8Oh4MVK1YsdDME4bCwdLZaBEEQBEEQBEEQBEEQliDifBEEQRAEQRAEQRAEQZhHxPkiCIIgCIIgCIIgCIIwj4jzRRAEQRAEQRAEQRAEYR4R54sgCIIgCIIgCIIgCMI8Is4XQRAEQRAEQRAEQRCEeUScL4IgCIIgCIIgCIIgCPOIOF8WENM0yWazmKa50E0RhKOepTzeTNNcku0WBOHoZd+cdEA7cinPvYJwsEi/FwRhKsT5soDk83nS6TT5fH6hmyIIRz1LebxVq9Ul2W5BEI5e9s1J9tm8b6nOvYJwsEi/Fw4Xvb29GIYx5Z/e3t6Fbp4wRxwL3YDljM/nq/tbEIT5YymPN7vdviTbLQjC0cu+Oak6y/fJHCYsK6TfC4eL/v5+LMua8phhGEe4NcKhIs6XBcRmsxEIBBa6GYKwLFjK481ms2GzSaCiIAiLh31z0gFzKpby3CsIB4v0e0EQpkKs+UWC5IYKwpFhKY410XwRBGGxMRvNl6U43wrC0YqMR0FYeMT5skiQ3FBBODIsxbEmmi+CICw2ZqP5shTnW0E4WpHxKAgLj6QdLSCmaZLP5/H5fJIbKgiHQO1YOlB6zlIdax6PZ6GbIAiCoNk3J00tRLAP0zQJBAJLbr4VhKORyfbPXGwnQRAODzLSFgjTNBkaGmL37t0MDQ0BEAgEZPIThINgqt0c0zRJp9Mkk0nS6bQOs1V52EtprI2Pj5NOpxe6GYIgCJp9c5J7uuPj4+MMDAwALKn5VhCWCnNNI6rVoclms2SzWW07SUqSIBwZ5Gm4QOTzeTKZDGNjY2QyGQkBFIRDwOfzEQqF6nZX8/k8sViMnTt3EovFlvQYq1QqjIyMLHQzBEEQNPvmpGkjqMvlMmNjY0euQYKwzDjYNCL1OUDbTpKSJAhHBkk7WiB8Ph/t7e20trZis9kkJFcQDoGpqgr4fD5aW1sxTXPJjzGv10tPT89CN0MQBEGzb04qTHfc7Xazdu1aqfgiCPPEwaZR135ORaUt1ZRsQVhqiPNlgbDZbIRCoYVuhiActRxNY8ztduNyuRa6GYIgCJp9c1JpuuNOp5NwOHzE2iMIy42DLWc91eekNLYgHBkk7UgQBEEQBEEQBEEQBGEeEefLAiCiVoKwsCy1MWia5pJpqyAIy4N9c9K0dqTMW4IgzJWlZp8JwlwR58sRxjRN4vE4yWRSRK0EYZ6Z7iG+1ITlyuUy2Wx2oZshCIKg2TcnOac7XqlUiMfjsogShCXCYnB8LDX7TBDmypJ2vhiGYSx0G+ZKPp+nUqlgs9moVCp1JXAFQTi85PN5ksnkfguA2upIi8HYOBDlcplKpbLQzRAEQdDsm5Nm1A7cu3evrqoiCMLiZr4cH3OxsyZXr1wKNpogzIUlLbhrWZa10G2YK7WTSSwWA0TkShDmC5/PRzabpVKpkM/n9TirHXPZbFYvDhbzOCwWiwvdBEEQBM2B5iTDMMjn8+TzeRHeFYQlwHxVPKotbX0gO2vymmgunxWEpcCSi3wxDMNmGMa/G4bxKcMw1s7yM1cYhrHxmWeeYcOGDdx0003z3cxpUZNKIBCgtbWV1tZWKeu2yLnpppvYsGEDwPGGYWw0DOOK2uOLqX8J9dhsNqLRKOFweNpxNnmX5UgzU/9SfWvXrl2cf/750reEOTGbviXzlnAw3HTTTZx//vkAPdP1re3bt/OP//iP/PKXv1y4hgpLDpm3Fg61RlHlpw8Xh2JnHU4b7UD2vCAcCYylFDyyL83oEWAbsBbYaVnW+bXHZ4qG2bBhg7Vx48b5b+g8YJom+Xwen8932CdFYXYYhvGIZVkbpju+lPvXQiP9e+b+dcopp1gbN25ctt+NcGjM1LdmM2/J+BSm41D71nwg/fXoYDH2reXE0TyOlpo9bxgG0y1vZzomLAwH6l9LbTSdBOQsy3oT8GKg1zCMSw3D+DvDMByLJQ1pPvITRYBKOJo5UP9e7jm/IrgrLCRqfGaz2WU9DoWlwWzspeX+TBGEAzF5HMmYEYTDw1LTfMkCJxuGcS2wgQmV/b8FVgIJ4L4FbJvmYPITD+Rhnq88TEFYDEzXv9W4UA99WJ45v9VqVQweYcGYSph6OY5DYXEy2X6ajb0kOhKCMDOTx5EaM6Zp6nF2tEXECMKRYEk5XyzL2m4YxuuA04AXAWstyzINw7gTCC9o42o4GEfJgQwBEeUVjmam699qXAQCgQXVZVlo7Ha7GDnCgmGz2bDZbJimicPhWLbjUFicTLafZmMvyYaWIMzM5HFUWzBEHJeCcPAsaufLPo2X0wGPZVl/BLAs6y7DMDYD/wy80zAME+gBNi1cS+s5GEeJGAKCsD+142I5Ox+cTqcYOcKCImNRWKwcjP0kG1qCMDfUmKmNfBEEYe4sWufLPsfLfcAIcIphGE8BVwC7LcsaNAzjM8C7mUhF+nvLsp5buNYeOmIICML+yLiYQEUeCMJCIWNRWKxI3xSEI4eMN0E4NBazNf8aIGZZ1gXAC4Eo8G2eTy/6nmVZpwJ/a1nWowvTREEQBEEQBEEQBEEQhJlZzM6XBHCqYRhnW5YVZyL9qA/4f4ZhbACuNQzDb1mWlP8RBEEQBEEQBEEQBGHRspidL08CvwfeYhjGBsuyxoELgQbAD/yTZVm5hWygIAiCIAiCIAiCIAjCgVi0zpd9ES3/ATQBHzMM4xVMpB81Ag9ZltW/kO0TBEEQBEEQBEEQBEGYDYtWcBfAsqxthmF8BHgX8DkgBXxgXxSMIAiCIAiCIAiCIAjComdRO18ALMvaCXzSMIwv7vt/ZmFbJAiCIAiCIAiCIAiCMHsWvfNFIU4XQRAEQRAEQRAEQRCWIotW80UQBEEQBEEQBEEQBOFoQJwvgiAIgiAIgiAIgiAI84g4XwRBEARBEARBEARBEOYRcb4IgiAIgiAIgiAIgiDMI+J8EQRBEARBEARBEARBmEfE+SIIgiAIgiAIgiAIgjCPiPNFEARBEARBEARBEARhHhHniyAIgiAIgiAIgiAcYXp7ezEMY9o/PT09C91E4TDiWOgGCIIgCIIgCIIgCMJyo7+/H8uyFroZwhFCIl8EQRAEQRAEQRAEQRDmEXG+HEFM0ySbzWKa5qxeFwRh7sxmPC21MVcul6lUKgvdDGEZUDs2lto4ERYf89V/pG8KwsEj87wgLBzifDmC5PN50uk0+Xx+Vq8LgjB3ZjOeltqYK5fLJBKJhW6GsAyoHRtLbZwIiwvTNOet/0jfFISDR+Z5QVg4RPPlCOLz+er+PtDrgiDMndmMp6U25pxOJ5FIZKGbISwDphobS2WcCIsLm81GKBSal/6z1OZwQVhMyDwvCAuHOF+OIDabjUAgMOvXBUGYO7MZT0ttzDmdThwOma6F+Wfy2FhK40RYfMxX/1lqc7ggLCZknheEhUPSjhaIyTmWknMpCIefoyWvWTRfhCOFShVJp9NLcqwIS5tKpcLQ0JDMd8KCMVtbYSnbFIIgLBxLzvliGIbNMIyfGIbx74ZhvKzmdWOGz1xhGMbGZ555hg0bNnDTTTfNW/tmK/YZj8dJJpM6x3KqnEuZ2BcHN910Exs2bAA43jCMjYZhXFF7/Ej2L2FuqHGVzWaJx+MkEgmee+65RWXYz9S/VN/asmULp5xyivQtYU7Mpm89/fTTet4yTZOhoSG2b9/O4OCgaAAI0zKbvvXMM8+wfv16vva1r83ajkkkEuzdu3dGjSuxjY5uZtu35sveqrXHZ+prh1MrRQpyHBkOZM8LwpHAWGp1xQ3D+F/gcSAB9ANbgG2WZZUNwzCsGW5ow4YN1saNG+e1fdlslnQ6TSgUmjaML5vNkkwmddifel8+n8fn82Gz2WZ9LuHIYRjGI5ZlbZju+JHoX8LsUUaLIp1OMzo6SqlUYsWKFbS1tS1g6/Znpv7V29tr/e53v+P4448/0s0SjgJm6lsnnXSS9cADDxAIBEin02zfvh3TNOno6KCtrU0/jwRhKmbqW+vXr7e+853v4PV66ezsnJUdU6lUSCQSRCKRaVMtxTZaHszUt+bT3jJNU9vjysEyVV+rfd+hzpPT9Wm1XnA4HESjUZmPDxOLzZ43DIODXY8fymeF+eFA/WspigjsAL4H3AzkgADwiGEYH7Asq7yQDYO5iX2qxeFkJ8xcziUIwtTk83my2awWfLTZbESjUUZHR5eceK3H46G1tXWhmyEchdjt9rpnjN/vJxgMiuNFOCx4vV6CweCs7RiHw3FAx7jYRsJ8UquHMlNfO5y6QzMV5Mhms1QqFfL5vDgbBeEoYMk4XwzDsAF+4AXAdcDPLcv6smEYFwPvAl4IPLSATQTmJvZpmiY2m21aA0IE5QTh4Kk1ZmrH0mKLeJkNPp+PcDi80M0QjkJsNpt2sgQCATo7Ow/LTq4g2O32eelPYhsJR4oj1ddmKsgRjUZ1hI0gCEufJWNdWZZlWpaVAb4InAu8aN/rPwCqQMsCNm8/ZiNaqCZbMXIF4fAzeXwtZSHHarUqOd/CvFO7MbBUx4qwuDhUG0eKEwgLwWISHpe1giAcXSzqkWxMcIZhGC9Xr1mW9X/ApcDfGYZxnWEYnwXagE0L1c6pyOfzxGIxYrHYrMW4lFFRqVTEuBCEg2Qq49w0Tfr7+9mzZ8+MQo6LlUKhwNDQ0EI3QzgKsSxrP0fLbERPBWE2HKrTe7Ko6eEUORWE6cjn8wwODrJjx4467ThhdoiTVBCmZ9E6X/ZVL7oP+CjwXcMw/s8wjG7DMGyWZX0PeDVQBiLAmyzLem4Bm7vfROPz+WhtbaW5uZl0Oj0rw0MZFYlEQowLQThIpjLO8/k8TqeTxsZGrfcyUxnqxWY4lMtlSqXSQjdDOAqpVCra0aL6fTgcpqGhgaamJmDxjQdh6TAXR95U/czn82ndLpjQv7LZbHg8HumXwmFjKhve7/dr+2A2nxGeR5ykR46enh4Mw5jyT29v70I3T5iCxaz58hogZlnWGwzDiAK3A98G/h4YBf5iWdYdC9nAWtREA8+H2YZCIYaGhti7dy+FQoG+vr79wgZr1dJrjYtisSj5nYJwEEwlXOfxeHC5XHR0dGCz2bTBNLkaEkyM38njeaExTZNCobDQzRCOQhwOBx0dHUQiEfL5PMlkkvHxcZxOJ6Ojo1pvYDGNB2Hp0NTURCaTweVyaZ276Ziqn03WwigWi5imSbFYBJB+KRwWprPhc7kc+XyeUCi0X99diHnxcFZYmk9EFPvIsXPnzmmPTcQxCIuNxex8SQCnGoZxtmVZ9xiGcTrwNHCNYRjXAx82DONyIDtTeekjxXQTTSQSIZfL4XQ6p1Qqnzx5q+NiSAjCwTGVcN1UBnsgEKjbUYX9x/FiMRycTifBYHChmyEchRiGoUWoVWUN0zQpl8vYbLY6ocfFMh6EpYOK2BsZGcHlcs1o28ylWuRU87YgHCxT9atAIEBDQ8O0lYYWYl5cKo5wEcUWhOlZzM6XJ4HfA28xDCNnWdZGwzAuBP4V8AEf2SfAuyionWhqPdMOh4O+vr5plcprJ2/1udrIl8Xs2RaEhWQuO0CTjaTa0GKbzbZf2PBiMxycTueSK48tLB1qnz0qnaOtrY1SqbRftTBBmAsejwe/318X3Tvd3D3XapFLIQJAWBqoyqMqGvZIl5qeLeIIF4Slz6J9YlmWlQf+A2gCPmYYxiuYKCfdCDxkWVb/QrZvJibnOtZO0JPzQ2tVzNXn4vE4g4ODM4p8Sa6psNyZKad48vioHYP5fB7TNInFYnqMLfb85EKhQH//op3yhCVOrd5YLBZjaGiIeDwuzxfhkFHRhg6HQztJ8vk8o6OjPPfcc3MW4lVzezabXdRztrD0mFwoI5/PaxtB9beFnhOnq3wkawJBWDos5sgXLMvaZhjGR4B3AZ8DUsAHLMsaX8h2HWjHZapddrXgUxO5z+fb7xzq/ZVKhUwms1/+aS1LJfRQEOaLqaLG1HjKZrPEYjFaW1sJhULAxDiMx+MUi0WSySRut3vKcy1GxsfHZZEhzBumaRIIBHC5XAwNDTE+Ps7TTz9NV1cXXV1d8owRDopqtUqlUiEQCOynwZVMJikWi/j9fp32NhOVSoVEIoHH49EpIJPTRgXhYFHCui0tLToKpvZYLBbDNE0aGhqIRqOHJdrqcEZvHcya4HBcXyLQBGHuLPqRYlnWTsuyPgm8ArjIsqzHF7hJB9wlr/VMqwVfMpkE0MZCOp1m+/bterJUn1OGhd/vJ5fL6d35A1UAEITlxlRRY2pMmqZJKpViYGCAZDKpDYRSqcTevXsZGxvD4XDsJ+q4WI2HUqlEPB5f6GYIRyHValVHWo6Ojuooq0KhsN8iRBDmQqlU4r777qNUKtXNrVNVnzsQqmqSEj9VGnkHmrMlIkCAA/cDFeVis9m07a5SMQFaWloIBoNa/+VwMNeI25nuYbo1wUyfORwRv4s9algQFiPTRr4YhuEE3sJEKecfWZY1uO/1qyzLuv4ItU+zmPRdDrRLPrmKSqlUolwuaw0YeH6iV5M9THirE4kEQ0NDtLW10d7ers9xoAoAgrCcmSoKplgsMjw8zMjICCtXriQajeJyuWhoaCCVSukdLph592Yx7OxUq1UxboR5Y2RkRO/6xuNxRkdHaW9vp7W1ddE6JIXFT7lc5umnn8bpdHLuuedq+wcm0pBaW1vrXpsJ5aSJRCKz/gxIlLAwwYH6gcfjqauCWPtv0zQJhUK0tbVNq984W6aqcDrT+WrfP9M9TLcmmCoKWHE4In5rq7Rms1mJgBGEWTDTE+ybwIlAmonKQqfv01n5/4Aj7nxZTBzI8aHyRgFaW1txuVzYbDYtopvP54lEIjrSZXBwkFwuR1tbG5FIBNM08fl8ddEzsgMpCNNTK8IYj8epVCr09vaSyWQYHh4mlUrp0HePx7NfKoUyamrHWq0+wUIb7zabrS5NShAOFyqU3uFwUCwWMQwDy7Lo7OyUxapwyFiWpaOp+vr66qIM52LTOByO/dKTZuMYX+wppcKR4UD9QFVErLUlVB+tLYBxqHPidBVOZ/P+w92XpysUUhv5cyBnijqH0sSBQ7eTFsOGlyDMJzM5X04H1liWZRqG8e/ALwzDOA2QouEzMDlvVE2ukz3X6nXlmKlUKgwNDek85nQ6rdMiJMpFEGaHSi0ql8v09PQQCoWw2Wy6TLMaV7U522rMqjFWa+jUhh0vpPFut9ul1LQwL1iWhcvlolKpYJomfX19mKaJ1+ulWCzKs0c4aAzD4IQTTqChoQHDMIjH43ruPRz9ajaOcbGfBDhwP6h1bKjIdGU/TMfBOAnm6kCpff9c+7JKNWptbZ3xc7WbVoBes8zFmXI4HUOLYcNLEOaTmWaLLPAKwzACwKeAHPA9wHUkGraUqA1NnJw3Cuy30+Pz+YjFYoyOjjI6Oorb7cblcuH1eusWgiqFQvKVBWF2+Hw+XC4XbrdbLxw7OzuJRqMAOgJt27Zt7NmzR2uppNNpPU5V3rQyAGoj1hZqHJqmSbVaXZBrC0c3DoeDlpYWMpkMTz31FCMjIzQ0NGiHzGKo8CEsTQzDwGazYVkW5XJ5znoZB7J/JutcqPdXKhWxm4QDMl3/Uhsuqh9Np2mSz+dJJpNzqgw3V325Q9Gjm0rHZqr7zefzVCoVHA5HXRqRis6f73ZORjQthaOdmSJf3g/8APizZVlvNAzjtcCvgN4j0bClxFRhgaZpTuu5HRkZYWxsjObmZjo6OkgkEjQ1NeFwOMhms6RSKVwul17wTZevKQhCvcZSIBAgGo3qiJXaiDOVj5xIJNi9ezeVSoW1a9fi9Xq10VG7s6Scn7U54OoaR5pyuczAwMARv65w9GOz2QiFQrhcLvbu3UsoFKKzsxO73c5DDz1EU1MTa9askeePcFCMj49js9nw+/2Ew+FZa0NUKhX6+/ux2+14PJ4pK8xMjgRQtljtAlN2zoXpUM6TbDaLx+MhFovh9/ux2WxkMhkymQzBYHC/al0Kn8+nnX2qAtdCM5OmzHQRJZOja+D5NKyFiH6UaDXhaGcm58uTwMuB2w3D6GMi3eg9wKYj0bDFylRhhrWLNPXAVwu/yV7jWpX/5uZmAFwuF5lMBo/Hw9atW8nlcni9XoLBIB6Ph0wmg9frndarLPmRwnLFNE2GhoYYGhrC7/fT2dlZZ2jU7oKq96sUHrvdTiqVorGxEZdr/4A+tVtUmyaoxviRHmflcpnx8fEjek1heVCtVkmn07S1tbFq1Srcbjd+v5+//vWvbN++naamJqLR6KKuBiYsTkzTJBgM0tzcrAXP0+m03lDyeDzs3r2brq6u/ebgRCLB6Ogo1WqVjo4OvbitVCrE43E8Hk9dajbUi3+qiEVBmIraZ3mpVCKTyZBKpejv76e1tRXLsggEAuzYsYM1a9bUbb7URrWrzZ6F7GszifKq12YS+J3K2SFaSYIwfxwo8uXTgAU8u+81E/jhfDdqMTOV51gt0pLJJMPDw7jdbhwOx5Re48mRMSMjI6RSKfL5PLlcDtM06e7upqGhgUgkQj6fx2azkcvlpvWsz6RmLghHM/l8nkxmohBaMBjcT1dJ6Sft3LkTn89HMBhkfHyclpYWxsbG2LVrFw6HQ4+1yburk/PAa9OTjiSWZbF169Yjek1heaAq0qjnlWEYuiqY0+kkn8+zZ88eIpGI7EYKc8ayLPx+P6VSqc7BYpommzdv1hF9fX19dZtIkUiEXC6H3W7XkcAA8XicTZs24fP5dDWuqapASl8VZkJtzih9E5jYCHU6nQwPD9PQ0MDQ0BC7d+/Wjj6v17ufEPliiNKYSZR3rgK/isVwX4JwtDKt88WyrM8AnzEM4w+WZZ17BNu0qJnOG6zCD5UnXeVLTudhVp5q0zT1gq+pqYnR0VE90av3rly5csprCsJyZHJYrSrJPrmChjLiXS4XuVwOt9utd2JbWlpoaWkBYMWKFVQqlSlDhyenIdX+fSTxer2cd955R/y6wtGP3W6nsbERr9fL6Ogo5XIZ0zRZvXo1q1atIpfL6ShMQZgLXq+XDRs21GlJ1FZxVJtFXV1dZLNZBgcHCQaDtLW14XA46OnpIZFI6OqQMDH/RiIRIpEI4XBY7CLhoKh9nqt05NWrV7N9+3Z8Ph+GYRAMBrVtnkwm8fv9i7K/zSTKKxEsgrD4mCnyBQBxvMwelT7k8/kYHh6mtbVVl+2EiR0gtWg0DIOWlhYKhYIOkXU6nYRCIQYGBnTp6ba2tv2iWSanGR1M2UZBWKpMtZNTK4anxoIaI6VSCafTyejoKE1NTVrjJRQKsW7dOgDC4TDFYhGXy8XQ0BCRSAS73V53XcMw6iog1ZafnG/U7q+aSya3SxAOFpvNRnt7O+l0mvHxccbGxohEIvh8PiqVCuFwmHw+L5WPhDnjcDgolUp1FR8nl/Lt6OhgdHQUl8tFoVCgUqlQKBTo6upidHRUO8XVvB4KhVi9erW2o4Ap58WpUO+rtcNqkbn06MeyLEzT1La30nOrVCoMDAxgGAbZbJY1a9ZQKpVYt26dFrxXtnit/bEYUjFnilKRCBZBWHwc0Pki1DM5vE8txBKJBHv37p1SEV05XWqFQX0+n578E4mELvGmImbK5TI7duzA5/MRDoenbYOUohaWE7XVwJThNFlwF+qF9CKRCOl0msHBQfx+P+3t7XWGt0rZCwQCPPfcc4yOjpLL5ejp6dEOFuVEVeUYVVph7TXnk3w+z/33388ZZ5wx79cSlhfVapXh4WEGBgbYtGkTpVKJtrY2HZ3Q2NhIa2urOPeFOVOpVNizZw9DQ0N4vV7a29v1AlbZLUNDQ+zdu5doNEo0GiWTyTA6OkoqlaJSqdDQ0EAgENBzvN/vx+/3Y1kWuVxuSifKTNTaT36///DftLDoKRQKWpg5lUrp1OVsNqs3YR5//HHa2trweDw0NTXVpTXHYjFAHBuCIBwc4nyZI1NVQMlms5RKJfx+v86LV0JwasFWKpX0a2oCTyaTjI+P43Q6danpQqGA2+2mWq1SrVbJZrM4HI66nXYJIxSWKypPOxQKUSwWSafTeje1Ni2itgpBsVhkzZo1eL1empub9bjJZDKk0+k6QV6n04nb7cbpdJJIJDBNU5/D5XKRSqXw+/3TphXOF6Zp4nDIdC0cftTmwNjYGCtWrGBkZAS73c7Y2BjFYlELw4uouzBXHA4HjY2NFItFCoXClO+JRCLA80UKlI7Lrl272LlzJytXrsTv9xMKhfB4PORyuf02rZQzplb4dLroFrGfBLVx09TURKVSIRaLkcvlKJVKWnBc2ehNTU309/djmiY+n49AIEBraysgfUgQhINDrPk5MrkCijIIEokENpsNh8NBPp/XjpZsNsvY2Bjj4+OEw2G9yPN4PBQKBYrFIh6Ph46ODmw2G16vF5fLRUdHB9VqFZ/Pp3fsFXMRzRKEo4law7k24mWyuLUSbEwkErhcLorFIn6/XztKfT4fAwMDjIyM0NLSoqNYwuEwoVBIO1aUg2doaEjnf9tsNvL5vN7BnU3Z1MNBqVSa1/MLyxMV1RUKhahWq3R1dRGPx3X6q6rMt5Cl1oWliWEY9PX11ZV/Vs5sNWc6HA7a2tp0GpJpmjo11O12Mz4+TiaT0c7n2jQRtTGlihWoPgrsF91S65yZHPFSe0yci0c/2WyWvXv3UiwWyeVyZLNZGhsb9YZMJBKhoaGBpqYmHA4HuVxOVxu02WxS2EIQhENCnC9zQGmteDyeupLSxWKRSCSiHSlqR0alIKnolnQ6jd1u1wu5SqVCKpVifHycoaEhQqEQfr9fa8Go3XuYWGzWitYJwnKkNsxXpfIpY2gqh0wmk8E0TW2cKwO+1tD3+/2Ew+G6satS/dLpNC6Xi7a2NiKRCKVSSY95j8ej05Bg/helas4RhMOJYRisXr2axx9/nEKhQKFQIJfL6chMlf4xObpMEGaDcrCovlUsFutSNms17AB27NiBaZr09fXR19enz1E7z6rPK0d4rQNcRSXC8876arVKf38/TqcT2D/daKoqlsLRSz6fJx6Ps3v3bqrVqp7fotEo1WqVpqYmbefXbnYuxr4xWQNSEITFz5J2vhiGYVizVVo7DKgwRLXbMjw8rB/iajfH4/EQjUb1Lo/P52PlypV6p2Z8fJzx8XGdy+z3+9m1axdDQ0PkcjkaGhpoaWnB7/drgU+Vayo734LwPJMV/k3T1PoBypBSx2sNduW0aWlp0REuKhVQGfjpdJpkMqlLVHd0dOBwOHA6nRiGoaPa1O7rkXCKihikMB9YlsXAwAB2u51KpUJra6uueFQsFsnn8+zatUtHkk0uxy4IB0KVhVb/VpEvMDEfj46OEovFaGxsBCYWlOPj47S0tOiFpfqM0tyw2Wy43W4dZaw+p6Icax0siUSCsbExGhsbdaRMbUqSpCItL6LRKMcccwy5XI6xsTHi8Thbtmxh165dhEIhCoWCrrC12J0a4jgUhKXHkna+WJZlGYZhsyzriGwJ1+pIqKgWFZGiUhMA2tragHohz9pzqM96PB76+/sJBoOUy2UqlQojIyOYpklrayt2u518Pq937xW156uNxlHOnyNZhUUQjjSTBXZVP1djRY0DFdWi3jM0NMTw8DCpVIru7m7tsNm1a1edAZ9MJtmxYwfd3d2sXLlSh8rn8/k6g36y82e+71mFPQvC4cSyLIaHh4nFYoRCIYaGhqhUKqxYsYJyuUyhUKBarZLL5XSkl0qTFYQDoaJSalM1JttEsViMRCJBJpOht7eXkZERUqmUTj8yTZNUKoVpmng8HkZGRggGg+RyOdxut45OUJEvtc4Vy7JwuVw0NDTQ1dVFsVjUYuzRaBTDMDAMQ294CUc3ykHX2dnJn/70Jz33jY+PYxgGzc3NDA4OcswxxyyJSD9xHArC0mNJOl8Mw/gVcL9lWV+yLMs8Ug6YWh2JpqYmnepQG+oaiUQwTZN0Ok0mk6GlpYVsNsvAwAAOh4Ouri7y+TxNTU3s3r2bZDJJuVwmnU7j8XgolUoUCgWtJ6NSJpqbm7URolIm4HmvdzKZJJFIaG89iBdcWPpMFVKbzWbZsWMHXq+Xzs5O3c99Ph/t7e20traSz+d1Sp86vmvXLh566CEsy6JUKnHKKaeQy+Uol8skk0nt4FQRa6FQiIaGBizL0k7XTCajjfwjWemgWq0yMDBwRK4lLC8syyIWi1EulykWi+zcuROPx4NlWYTDYWw2G4lEQi96i8UioVBIdA+EA6JsIZjeHrHZbPT09JDL5SgWi5RKJXp6eti9ezcej4exsTG8Xi+5XI5cLkc0GqW5uVlXUlJVlBQqDalUKjEyMoLb7dZiv4lEQj9LSqUS8XhcR0hK6sbRj2maDA4O6iIYmzZt0tHsgUBAR8Mmk0l2797Nscceu9BNPiBScUkQlh5L7iljGEY7sB74rGEYHwJQDhhjmm0LwzCuMAxj4zPPPMOGDRu46aab5nRNy7L0H5WvrKobwUTVFMMwaG1txTAM4vE46XSaUqmEZVnaAInH42zfvp2hoSF2796N0+kkHA7T0dGhyywq1f5YLEapVCKfz+P1enVuczqdJp/P6/aolIl4PE4qlQLQTiHh8HDTTTexYcMGgOMNw9hoGMYVtccPtX8J06P6vBJZVA4Sr9dLMBjUVQtUZIqKRItGozQ0NODz+XTVsGw2S3NzMytWrGDlypXEYjFGR0e1c3Tnzp309/dTLBbp6OggGAwCE+k+agEaj8f1+FMROKZp1s0Rc2Wm/qX6lmVZ/OpXv+KGG27QldDUH0GYjtn0rR07dvDxj3+cP/3pTzgcDsLhMJZlkUgkePjhh/nzn//Mzp07sdlsrFy5Ulc/OoIZv8IiZDZ9a8uWLfzt3/4t3/rWt/bTrKqdM+12O2vXrqW7u1vr56kIBUCnX7vdbgDa29txOp06gqVSqbBz505GR0cpl8sADA8Ps23bNnbv3q2vqewwdc1yuayfDalUqs62mvxHOHLMpm/N1d4yTZN4PM7g4CA7d+5kbGxMp/KXSiXsdjuRSIRVq1bR1tam5znh6OJA9rwgHAmMpfhQMQzji0xE7ZwPfMOyrK8YhmG3LGvGlciGDRusjRs3zvl66uFbm96jQlozmQzxeJyWlhZCoVDdQzybzRKJRPB6vQwPDwPQ2NhILpejo6ODQqHAyMgIlUpF7yKm02kdXhuNRrXjRYXdOhwOncqk2hGPx+nv78eyLE455RRcLldd+0WQ6/BgGMYjlmVtmO74wfYvYXpU31XlIIPBINFotE7culQqUS6Xdf6/EslTqRLwvCaTMq7tdrtOUVKfq1ardHZ20tDQoPO+YUKcsVKpsHv3bpqamrQYr0rBCIfDdelIBxu6PlP/crlc1lVXXcVXvvKV/Y5JCWrhQMzUt9atW2d9+9vf1jovDz30EPl8noaGBgqFAuFwmGg0yvr162lqasJms+H3+7HZbJKmIczYtzZs2GD97//+r7ZxlIYG1JeCVtGFgNbTisViNDc3UyqVcLlcDAwMEIvFOOaYY2hpacFut/PXv/5VO9gzmQwdHR00NzfrdCQV1TA6OqrTRlWUsop4UX08nU7r805zn/Px9QkzcKC+NRd7K51OMzAwQCKRYPfu3WQyGVKpFC6Xi0wmQyQS4fjjj9cab1OlVootffSw2Ox5NQ8ulfMKM3Og/rWkrPYagV0n4AWuAr5pGMZbgC8DP56va9eKWqnoFFVBRVVVgefzLgOBAIVCgdHR0Tqxt1KpRCAQ0IvB/v5+7Ha7dqY4nU6CwSAjIyM0NDToHOdYLKZTk4aHhykWi1QqFbLZLE1NTeRyOZxOpzZUpmu7hCcKSwkVUptOp7UzJBQK1QnelstlnE4nDoejbtd0eHiY0dFRnb6nPqdSKFQE29jYmD53LpfjhS98IcPDwwwMDFAul1m7di35fF5rOoXDYe0QOpIVyHbs2HFEriMsLyqVCs8++ywNDQ2MjIwwODhIMpmkt7cXy7Kw2Wy0tLSQTCapVqu0t7fLwkOYFUovSzmI0+l0nUMdJuwpJaILaAdNMBjUVY4ymQyDg4M6CrK7u5tcLkdTU5M+r5r/PR4PhUKBYDBIOp1m9+7dZLNZ0uk04XAYl8ulnYiFQoHh4WGtw6eEeguFgt74OpR7n7xhJ+NmYSkUCnox+txzzzEyMoLf7ycajeJ0OqlUKtpWnwrlFGxtbZW0S0EQDpol5XwB7EAF+AHwt5Zl3WkYRhw4FTisMYKTRT0ni1oph4Z6oA4NDZFIJOjt7QUmJumuri6y2SxDQ0OMjo4yNjbGnj17CIfDGIbB3r172bZtmxZ8O/HEE3nNa17D+Pg4LpdLGyc2m4329naam5sZHh7GbrdTKpW0YVIqlejr66sr11iLCHIJS51AIMDKlSuB5/uxx+PRKUalUqmufyuhPOWUUSHqgUCA3bt3MzQ0RH9/P48++iibN28mnU6zcuVKjj/+ePbs2cO6deu04zQcDtPW1obD4dDpTerakUikbkdUOWXnw9BWETeCcDhRgqSpVEpruvj9fkzTxOl00tXVRUtLi16MViqVOt0xQZgOwzD0PFmr2VWpVHC5XPh8PizLolKp4PV68fl82mmh0pTGx8dxOp2sWbOG8fFxVqxYQS6XI51O6/k4n8/T29uL3++nv7+fX/ziF9x22238+c9/rmuP0+nUIuyBQICTTjqJl7/85Zx11lmUSiVM06RQKOh0JqUBMh0zRULUpszWltYWFgbl0HO5XDgcDvbu3Ut/fz/ZbFbLBhxzzDFUKhXcbjf5fF5+L0EQ5oVF7XzZp+FyOuCxLOuPlmVV9h3KAxcbhvE64Bngn4FnD/Y6kx+gKjc0lUppYc3JpQvVQk9FvuTzecrlMk1NTYyNjZFKpWhoaMDtdnP33Xfz4IMPcu+9987YDofDwRe+8AWuuuoqAFwuF6VSSSv+K40KlSqxcuVKre+i1PpV+G6tMSCCXMJSx2az7bfTVKu/pKqIKaep6vt2ux2Xy8Wtt97KL3/5S/bs2VOnAXDMMcdwyimnEAgEeOCBB7jlllu45ZZbCAQCnHHGGXi9XgqFAm9605vo7u7WETPqWmqhqpjPKLOdO3ce1vMJAkwskN1uNx6Ph7/85S8MDAxgGAbFYpG+vj7tdNy+fTvhcJjx8XEcDkddvxeEqVBRwUrAXNlXLpdLVxrK5XI6Ldtms5HJZPD7/Xq+V2mhKqqxXC7rlM9sNqurJA0PD/OLX/yCX/7yl2QyGY477ji+8IUv6MjgbDZLJpOhUCjo9PA77riDW2+9FY/Hwyte8Qpe9rKX8da3vlX3+UKhMGM/n2m+r90kqC2tLSwMym5WUVAq6qm/v5/jjjuO8fFxOjs7Offcc6f9vZTYvvyWgiAcCovW+bLP8XIfMAKcYhjGU8AVwB7LsjYbhvFtoM2yrKsP9VqTH6AqJDYYDNZFvUyFz+cjGo2Sz+cZHR1l165ddHZ28thjj3Hbbbdx7733kslkWLVqFZ/73Oc4+eST8Xq9eL1ePB4PPp8Pr9eLZVm8853v5CMf+QjPPvssV1xxBYZh1JVQVLn3wWBQ56g6nU7dFsuytFGi7kUQjlZqI7ry+TzJZFIf83g8WjDvZz/7GZ/85Cfp7OzkzDPP5JRTTuHEE09k/fr1+4UXDw4Ocv/99/PAAw9w33338eSTT/LLX/6Sbdu28da3vpVoNKqdmVMJW89nlJnX6z3s5xQEh8NBqVRicHBQL1CbmpoIBoO0trYSDodJpVLY7Xb9mvRFYa6oFCRVFVKlf5imqSNMTNNk7969DA8Ps3btWr351dTURCKRoFKpaJvMNE0Mw+CnP/0pd955J4899hgej4eLLrqIyy67jDPOOGM/nZZqtVqnkVWpVLj//vu10+bXv/41//RP/8RLX/pSLr/8ct70pjfpaJjaDa3alCKYer6v3fQSW2xhqVQqJJNJcrmcTmHbunUrW7duZffu3bhcLl784hfT0tJCPp+fNtpJNjIFQTgcLFrnC/AaIGZZ1hsMw4gCtwPfBt4IjAG/syzrSYBDLTU9ecFU+7fNZtNiRbWicIAOJy0Wi4TDYRKJBDfccAMbN25ky5Yt+P1+LrroIv7hH/6Bs846a0pDoHaS//Wvf8073vEObrzxRuLxOJ/73OdoaWkBJnKabTabLrno9XoplUp1zpfJOhS1ET3quOQdC0uFA4nbqSpiyiB3OBykUim9WxkMBnn66af5zGc+w7p167jnnnt0BaNyuTzlOdvb27nooot4y1veAsDIyAhXX30111xzDbt27eLLX/6yHl92u70uzQjmd4yp3WFBOJxYlsWf//xnHRGgnimpVIr77ruPXC7HunXrdBl3JbYrCAfCbrfT3t6Oz+cjm82yc+dOvF4voVBIa71ks1ntyM5mswSDQfL5PCMjI5imqRfMSli9XC6TTCZpaWnh6quv5re//S0nnXQS1113HRdffPGsF8emaeJwODjnnHM455xz+H//7//x+OOP8z//8z/cdtttXHzxxWzZsoWrr76aeDxep/MhOnpLB9M0ee6559i+fTvVapV8Ps/DDz/Mb3/7W1wuFxdeeCE///nPSSQS2Gw2/buC/LaCIMwPi9n5kgBONQzjbMuy7jEM43TgaeD/GYZxHfBPhmFcDuQOxfEC+3uz1f9VCoPKdc/lcsTjcQBaWlrw+/26TCHAtddey69//Wte9KIXce2113LBBRfw+OOPk8/n+f3vf7/fdXft2kUkEql77aKLLiKbzfI///M/uFwu/u3f/k3v1Hg8HvL5PA6HA4fDoSNmLMuiUCjgdrvrUjNqDQRAjAVhSXEgAzcej7Np0yb8fj8nnHACPp9P70TabDaeeOIJrrrqKnw+H9dffz2jo6OMjo4C8Mgjj+gUoocffpi9e/dy+umn097eTrlc5rjjjtPX+chHPkK1WuWHP/whhUKBr3/969rBoxYGykGrhLcP9xjz+XysXLmSW2+9db9jF1988WG9lrC8SKfTVKtVxsfHCYfDhMNhnW4H6I2EtrY2stksxWKRlpYWrVUmCDNROxd6vV6tJ2RZ1rTRiytXrqSlpYXh4WGGh4fJZDJkMhmcTidjY2Ok02k+9KEPcdddd3HNNddw6aWX6kiaxx57TF9v27Zt7Nq1i3g8TiwW47nnniOZTJJIJBgdHWX16tW86U1v4kUvehGWZbFu3Tre85738M53vpOPf/zjfPazn+Xxxx/nIx/5CNFolGq1SjabxbIs/H4/Xq9XR+BMh4yRI48SUIYJkd3x8XEsy2J8fJz//d//5dZbbyUSifDpT3+apqYmHn/8cb75zW/S1dXFa1/7WhoaGiS1SBCEeWMxO1+eBH4PvMUwjJxlWRsNw7gQ+FcgAHzUsqzsTCc4VJSyeTQaJRQK6TQhn8+nq62oB/EnP/lJfv3rX/OpT32Kq69+PhOqUCjoBeFkVCWWyVx88cU0Njbywx/+kJGREd761reyatUqQqEQbrcbt9tNS0sL1WqV/v5+3G43gHYQmaapK1JMTpuSB4qwVKg1zCcLYKu8a4/Ho8UXa0PCi8UiV111Fclkkh//+Me0tbXVldsbGxvj8ccf55e//CUDAwMAfOc73+G4447j9NNP5/LLL9cCt4Zh8PGPf5xgMMiNN96I1+vli1/8oo64qW3r5H8fLiqVCkNDQ4yPjx/2cwvLm2q1qsWlVTSm1+vF6XTicrloaGhgz549DA8PEwqFZqWFIQi1KMHZ9vZ2AK3LpbT0VPpRoVAgFouxcuVKxsfHdV9zOp2EQiGi0Si5XI7Pfvaz3HfffVx77bX7OZ9N02RwcJCvfvWrdTp7Ho+HhoYG2traOOmkkwiFQjzwwAN85jOfobu7mwsvvJA1a9bgcDhwuVx85Stfobe3l6985SsMDQ3x61//GpvNRiwWo1gs0tPTA6BFdVUqqjhbFhfKbg+FQvzxj3/k+9//PitXruRjH/uYroT4/ve/n4997GPccMMNvP3tb5/WZhcEQTgcLFrni2VZecMw/gP4PPAxwzBuBLqARuAhy7KO+CqkWCwCE6G0tWHXn/70p/nZz37Gxz/+8TrHSy2WZfHwww+za9cuvYuzd+9evajM5XKsWrWKK6+8Ep/Px4UXXsg555zDlVdeyejoKB/4wAc46aSTcDqdVKtVqtUqmzdv5tlnnyUSidDQ0EC5XGZkZASfz6cjY8LhsG6rRLwIS4naCDQlgA3Q0NBAU1MT+XyeaDTK8PAwNpsNh8PBwMAA27Zt4yc/+QmbNm3ixhtvZN26dfqcmUyGH/3oR9x4440kk0n6+vq4+uqrOfbYY/nTn/7EPffcw80338z3v/99XvrSl/La176Ws88+G7fbzXvf+14CgQDXXHMNmUyGf/3Xf2Xt2rV1i1BJyRCWGkq4NJfLab0wJTgZDAax2+00NjYyMDBAT08PHo9HO/wFYTbUphd5PB7S6TTJZBKXy0WxWNQp3Kq4gNJ+2bp1K9Vqle7ubv08uPzyy3nwwQf5+te/zpvf/Oa664yPj3PzzTdz8803YxgG73nPezjzzDNpaWkhFArx5JNP0tHRod9/6aWXcu+993Lbbbfx1a9+lZ/97Ge8+93v5o1vfCMej4f3vve99Pb28qEPfYgzzzyTH//4x1oXpFqtMjw8zNDQEPF4nGg0yqpVq+qeB9NVvztQSq1w6FiWRbFYxOPxYBgG3/zmN/nud7/LSSedxNVXX13nYGlra+PKK6/kP//zP/nsZz/LJz7xCdF3EQRh3li0zhcAy7K2GYbxEeBdwOeAFPCBI+F4UTs1Kscdnt+Br1arusz0DTfcwK233spHPvIR/umf/mnKc5XLZa699lruu+8+YMITHwwGcTgcNDU10d3djcvl4oEHHuC6667jox/9KACXXHIJLS0tvPWtb+Waa67hk5/8JC6Xi+bmZsbHx7Hb7fT29tLQ0MDY2BjNzc20trbq9ittCkFYytQKYAOUSiU2b95MqVSira2NcDhMc3OzLoP74x//mP/5n//hU5/6FH/zN3+jzzMyMsJ5553HyMgIa9eu5X3vex8nnnii3ql8/etfzwUXXMCWLVvYunUrv/3tb/njH/9IU1MTP/zhD1mxYgXveMc7OPbYY7nyyivx+/3cdNNNZDIZAoGADnufXG1MIQa3sBhJJBL8+te/BiY2FlauXEk0GtXlpN1uN52dncCEU8blcumKR4IwGyanFyUSCZLJJHv27CEYDDI+Pk40GgUmBKCz2SyFQkHPk263m3K5zEc+8hEefPBB/vu//5uLLrqo7hqWZfH617+ehx9+mHPOOYf3v//9tLW1zdguu93Oy172Ms4++2weeughbr/9dj71qU9xww038Nvf/paGhgbOP/98Ojo6ePe7382rXvUq7r77bmw2m05tiUQiRCKRKYszTJc6K5ox84tlWYyOjlIul9m0aROf//znueOOO7j44ot55StfOWVky4te9CJe8YpX8F//9V9cdNFFvPSlL12AlguCsBxY9NaTZVk7gU8ahvHFff/PHInr1u7U1JZsVmGnALt37+ZrX/sab3/727XDZDKlUolrr72WjRs38ra3vY0LLrhACxo+9dRT9Pb26vcGAgH+8Ic/UK1W9WvnnXce//Iv/8LHP/5xLMsiFAoRDAZpbGxkz5499PX1kc/ntVHc3t6OZVmyyBOOGpTTU/07kUgQCAS0A7ShoQGYSPFrbW3Vr1966aV153nqqacYGRnhuuuu0yHikzEMg56eHi644AKuvvpqHnzwQa6++mq+8IUv8F//9V8YhsG73vUuduzYwVe+8hXe8573sGLFClwuF11dXRSLxf10X2pLU9emTgnCYiAYDHLWWWcBcP/992O32ymXy3i9XtxuN4FAQAu5p9NpotGoVDsS5kRtFIHP56O3t5edO3eSy+UYHByktbWVXC5HMpnkueeeIxwOa6eG3W5ndHQUm83GQw89xOte97r9HC8w0XcffvhhPvCBD/CmN71pzu077bTTePe7380f//hHLrvsMq699lr++Z//GYAXvvCF3HLLLZx33nk8+uijnHzyyfh8PpxO54x21nTV7+azKp6ALkm+a9cuLr30UjKZDF/4whd43/vexy9/+ctpPzc2Nobf72fFihVHsLWCICw3lszK3LKszHw7XpSuQqVS0TmiPp+PSqVCLBajUqng8Xi01/x973sf7e3tfO5zn5syzzeXy/HlL3+ZjRs38va3v51HH32Um2++eVrdhq6uLsrl8n5VTU4++WRgYoeyubmZ3t5eRkZGtJOmpaWFvr4+vUhVehhqwScISxnl9FRin9FoVJdoHxkZIZfLMTw8zOjoKCMjI7jdbqrV6n5jcs+ePQCsX7++7vVnn32Wn//85+RyubrX7XY7Z511FldddRX33nsvd911lz52ySWXYJomf/jDH8jn8+zZs4f+/n48Ho+eN5SzJZvN6l3OqcpTz4ZcLscDDzzAZZddxrve9S52794953MIwlR4PB5WrVrFqlWrWLt2rRaJVw6YfD7Pn//8Z7Zt28b27dsZGxsTp74wa9Q8WGuLOBwO1q5dSzAYJBgMYlkWpVKJZ599Fpiwadra2ujo6KCxsVGL9O7du1dHYU3mO9/5Do2NjVxwwQUH3VbDMPibv/kbzj//fH76059SKpX0sdNPPx3DMHj66ad1tTu/368jHnO5XJ2umDqf0iir/S6AuteFw4uy0T/96U8Ti8X41a9+xfvf//4Z9XieeOIJNm7cyNVXX63t50Nhqn4vCIIAS8j5ciRIJBLs3btXl5xTD8eRkRGGhoa08n48HueLX/wimzZt4vrrr59yBz2dTvPGN76RTZs28Y53vIM777yT7du385vf/IYPf/jD7Ny5c7/PKG+7WiQqlGbFE088gcvlYnBwkGeffZaBgQHtbFGL01wuV7fgO9QHiCAsBpQzVFX8ikQitLe309LSQjKZZNeuXfT395NIJHSa0mT27NmD0+nU4e0w4dT40pe+xA9+8AOuuuoqfvOb3+z32Ysvvphjjz2Wf//3f9fj6fjjj2f9+vXceeedZDIZxsfH9XhT80ZtaHkoFCIQCBy0we10Ounq6qKjowPTNLntttvmfA5BmIp4PM61117Ltddey0MPPYRhGAwODlIsFkmn0+zcuZNdu3bhdruJRqNaNFUQDoRlWTzxxBP09/drp4OaF0ulEmvXrqWzs1OLm1erVVwuF319fbS2tuJyuejs7KS5uZnh4WHGx8enTCUaGhriN7/5DW9961tnFEs1TZMtW7bwwx/+kI9+9KPcfvvtU77v9a9/vS61rggGg6xdu5YnnniC1tbWuujFbDbL4OCgvsfpUPcudtn8oQT3v/SlL3Hvvffy9a9/nVNOOWXGz1QqFW6++WZaW1t529vehs1mO2TRXfmtBUGYjkWfdnSksCxLl32ORCJafV/thodCIYaGhsjn8/T393PLLbdw0UUXce6553LHHXfUnSuTyfDZz36WHTt2cNJJJ/GjH/2IcrnMunXrqFarbN26lQ9+8IOceuqpvPSlL9XeeLXLsnHjRlpbW7XAbzAYpK2tjd27d+sqE+VyWUfJOBwOHRYOE44flVbh8Xim1aAQhKWCcobWRpEEAgH27t3Lli1bGB0dxev1Ui6XcblcWgyxdqfr2WefpbW1ldHRUW6//XYGBwfZunUriUSC1atXE4/H+fa3v83PfvYzXvWqV7FmzRr9+TPPPJObb76Zq6++Whvsf//3f8/HP/5xBgYGOOWUUzBNk2Qyid/v10J/gE7ROJRUQJfLRXd3NwCxWIynnnqKb37zm9hsNt75znce/BcrLHt8Ph9r1qwBJkrz5nI5hoaG6OnpYe/evbS3t9Pe3k4kEiEYDJJOp3XflsouwkyUSiV27NhBKBSiq6sLqE+5MQwDm83Gzp07cbvdetGbz+ex2+06GjkWizE0NKQ/l0gk6q5z4403UqlUuOCCC/jWt75Vd6xQKLBnzx79R0Ueu1wunn76ae655x6am5s57bTT9EZaR0cHwWCQH/zgB/T09OB0Omlvb2fDhg385je/wel0YpqmrtCkbMUDRTlIutH8k8/nOeGEE4jFYjQ2NvLlL3+ZL3/5y/q40nap5f/+7/8YGBjg8ssv11GrxWLxkNKD5bcWBGE6xPnC85oMXq9XC9bCxENb7WakUin27NmDYRj813/9F6FQiK985SsYhlG3U55KpfjsZz/LwMAAl112GTfddJM+9te//rXuug8//DDJZJI3vvGNWky0oaGBbDZLMBjU2jAwEf0yMDCAYRh6N2hsbEw/7MPhMG63m0KhULfDPnmxKghLmcnCjcPDw1SrVVauXInf78fn8+lKLM3NzXVjKJFI0NPTQ2trK7feemvdebdu3ar/PT4+zk9+8hM6Ojp4yUteQnt7O+FwmOOPP54//elPbNmyhTVr1vDGN76Rz33uc3znO9/hZS97GePj4+TzeeLxuD6XEutW5bBh6nG4bxxP65VRaUe1PPjgg7P5ygRhRvL5PI8++uh+r//pT3/Cbrfzrne9i/Xr19Pe3o7X6yUSiYjTRZgVLpeLE044Aa/Xq+du5UhXKTo+n0/P221tbcRiMZLJJMFgEJvNpqNd1Oe7u7vrKgpVKhV+8pOfcPbZZ7N27Vp27doFTMzjf/3rX0kmk7ottWlE6t+bNm3Sr9Vqybz85S/n97//va4caRgGp512Gt/97nd56KGHePGLX8z4+Di5XA7DMHSlu5nGhlTQmX/++te/al3GsbExxsbG6o57PB6uvfZa/f9EIsH/9//9f7zsZS/jX//1X/XG56E6TeS3FgRhOpZlKMTkXEwVHlgoFOre5/V6tRJ/uVwmEAjw4IMP8uc//5kvfelLdekLMBHx8ulPf5rBwUEuv/zyA6YG9PT0sGPHDr72ta+xefNmYKK6Uu3iTbFu3TqeeeYZIpEIzc3NrF69mlNPPZXOzk78fj9+v78u9UFRq10jCEudWj0jj8dDX18fJ554Isceeyx2u52xsTEdaTI5fWjPnj3T6gXUsnr1as4991ySySS33norv/vd76hUKrzkJS/B6XTywQ9+EMuyaG9v5/Of/zx/+MMfuP766zEMA8Mw9JirFSX1er11kWiTd0j3jVn7IX9BgnAYsSyLV77ylZxzzjmsWbOGtWvXYhjGrHb5BcEwDDo7O3E4HDqStxYVYQwTdpnb7cbtdlMqlcjlcng8HhwOB42NjTpipXaDDOAPf/gDQ0NDXHLJJXXn3bRpE+l0mmOOOYYXv/jFnH322TO2tdYxA/DKV76SQqHA/fffr1879dRTAbj77rvZvXu3FqRW6Xiy2F5YBgYGphRjnol/+7d/I5fLcfnllzM6OgqIHo8gCPPLspxdJudiTrVYgomFXjQapbW1lUgkQktLC9/4xje44IILePOb37zfea+77jpisRjvfe97+dGPfnRA47StrY33vve9BAIBbr75Zu677z5aWlqIx+P7fXbt2rWUSiUeeughYCIVqaGhQe8OwcQCT6UgqXur1a4RhKOB2vHrcDhobW3Vwo3wfJn4crmsP1MsFkkkErNyvhiGwbp16/iHf/gHTjvtNLZs2cJjjz2Gz+fjjDPO4N5779UVE971rndx7rnncs0115BMJmlubiYYDOpdUNWeQqGA1+vVOhqT88D3OUerCMIiY3BwUJeWNgyDQqEw5WaFIEzFTBtA+XxeawqlUilWr15NX1+fTs9OJBJEIhHK5TJPP/00wH6bXt/73vdob2/nb/7mb/Rre/bsYWRkhDVr1nDMMccQCoUOGK31zDPP1P1//fr1NDc3c+edd+rX1q1bh8fjYdOmTVSrVcbHx/H7/TgcDrGzFgFvfvObD6i7U8tTTz3FLbfcwqte9Sqtpzg5pU0QBOFwsyyfFLXGQG3K0VQPzmKxSCqVIpVKMTIyQqVS4YwzzpjyQb5jxw5OP/10jj32WNLp9AF3WmBiF+clL3kJMGGIPPvsszQ0NOx3/jvuuEPvmj/77LNTCopOvjdBONpQefZqh7F2ERgIBOjr69PVjJSjEiYqFzU1NWkDfja4XC7OOOMMgsEgIyMjAJxwwgkAOlLNMAwuvPBCCoUC27Zt0wLYtdQuVqcbn/s+I6EEwqJDRQSMjY0xODhIpVIhEAhIuWnhoDFNk0wmQ6lUwuv1avF0l8ulixwkEgntqN66dSvbtm3D5/PVpRwBPP3005xzzjla8w4mbCnDMOjo6Jh1myZXKtqyZQvJZJLGxsa6axWLRY455hja2tr2GwNS4WbhsCyLBx54gCuvvHLWn/nlL3+JYRhcddVVHHvssfT19WntR0EQhPliWTpfaqNBlEr95DKzSkDN5XLh9XppaGjgxS9+MWeccQbXX3/9lM6P5uZm0uk0kUiEF77whdx9990HbEssFuMXv/gFvb29tLa2MjQ0xLnnnlvnfNm4cSM//elPOe+88wgGg4yOjvLII4/U7ezDxCIvm83q6keCcLSgjNp0Oq3zuZWotNvtZnh4mHQ6jcPh4PTTT6ejo6NO18XpdPL3f//33HHHHQwODs7p2uPj41pgVIW+Nzc36+N33XWXjpCbKrRepRwpB6/skApLCfUMHBkZYceOHcRiMXnGCLNGRSqqKoxqw2twcFA7sZuamiiXy2zduhW73U53dzfd3d0Ui0UqlQqtra2k02laWlrqzm1ZFqlUqs5BAhMFElT559miRKdhYtPtM5/5DM3NzXWL+S996UsEg0He/e53TymeLhVuFg7DMOZcoeiee+5h/fr1rF69mu7ublpaWuqceIIgCPOBWE/ToHarE4kElmXpB+2ll15Kf38/jz/++H6faW5u1iGLr371q0mlUjNeo1Kp8L3vfQ+Px8Ob3vQm7rzzTlpbWznxxBPr3nfDDTfQ2NjIJZdcQldXl65ytHnzZiqVis6/V4s8iXoRjjamMmptNpvWOqpUKthsNh0V8+pXv5q77767ztHy1re+Fcuy+NGPfjTr61arVUqlkjbqVJSN2h0zTZO77rqL9evX09LSgtvtJpVKkclk9O6naqcsVoWlhmEYrFy5klgsRjwex+FwEAwGJepFmDUq2g/Qc7jH48Fms9HQ0EBDQwPNzc0899xz/PWvf2XHjh2Ew2FWrFhBIBCgWCxSLpfJ5/P7RSUUCgVKpRINDQ11r6vrHcgGq6V24X7TTTexc+dOPvnJT+p01q1bt/LTn/6UCy64QDuSJmsfSeTxwuLxeKbcAJmKTCbDo48+qsX6AV25ShAEYT5Z9i5etQvt9Xp12KkSgQsEApimSTwep1gsYpomK1euBODJJ5/klFNOAdDq+i6Xi0Qiwa5duwiHw7S0tDA8PDzttXfs2EEymeTiiy9m06ZNDA8Pc+GFF5LJZLDb7TrF4o477uDkk09mw4YNVKtVwuEwAwMDBAIBRkZG9MNCDGLhaEUZs8q4qk0ZdLvdhEIhHRkTCAS44oor+Pa3v82tt97KVVddBUyk+L30pS/lxz/+8QGvt3PnTuD5SBelTaDGczgcplKp8OSTT5JIJHjJS15COBzW1RWam5vp7Ozczwi3LOuQSk4LwpEmFArx9NNPU61W6e3tFVFRYU6oaD+VkqkE01VBg2g0SjabZeXKlVq/KxQKkUwmCYfDlMtl7HY7TqeTxsbGuoIEKgrSZrPpfw8PD2NZFna7nVgspqvfzYZisci2bdv48Y9/zPnnn8/JJ59MsVjEbrdz/fXX43a7ed/73ofH4yGXy1EsFmlubsblcuk5vXZ8qGeUzPdHBrfbPWvny4MPPqjLkyu7Ip/Pk8/nZY4TBGFeWfbOl8llD03TZHh4mEqlQjgcxu/3E4lEiMVipNNpGhoaCAQCPPPMM3qnZO3atcCEeNf9999PT08Pfr+fN7zhDdxwww28+c1v3k8k7pFHHuGBBx7gQx/6EK985St585vfzNq1a/nnf/5nDMPQDqGNGzeSSCQ4/fTTgYlFXy6Xo6+vj3K5rHOkK5UKsVhMO2Lk4SEcTdQatT6fj3Q6zfDwMG63m6amJmBiJ0vtUobDYc455xx+8Ytf8G//9m/a8H3f+97HG97wBi6//HJOO+00Hn30UW644Qbe+MY38opXvAKY0Irp7u4G0M6WY489lhe84AU89dRTAHR0dODxeLjvvvsAeMlLXkJDQ4NOR1TRAZO1mw5Ucno62traeMc73gHA9ddfTyqV4qMf/agY9MIh09raytvf/nYAHn74Ye69915gos+fcsop2O12QqEQLS0t9Pb26tRWecYIsyGbze7nmFBOafW6z+ejubmZ9vZ2YrEYTz75JKOjoySTSVasWIHL5WJkZIRVq1bVaeIph0tbWxvhcBiYKBENMDo6SrFY1P8HOO200/B4PDz44IM89thjRKNRPvOZz+ByuXC73Zx44ol8+MMfpqmpieuuu06nM+3Zs4fvf//7vOUtb6Gzs1M7k4rFov4Dz8/pyumi0mVrjwnzh3KivOhFL9Kv/fnPfwZg5cqVtLS0YLfbaWho4KGHHsLtdnPBBRfg9/u1ML5ELQmCMN8se+cLUPeAhIl0IIfDoTUafD4flUqFYDCIx+Nh3bp1/PWvf93vPCokNpFI4Pf7Offcc/nmN7/JE088wbnnnqvft3v3bv70pz9x/PHH86Y3vYnLL7+c8fFxPv/5z++3WLv99tsxDEMr+edyOZ3CMD4+zsjICMFgkNvMIUUAAFmkSURBVEwmQ6VSweVyzTnvVRCWEioKJZ1O09nZidfrJZfL1e2wjoyM8JrXvIaPfOQj3H333doAf8UrXkFPTw/33nsvp512GuvXr+fkk0/mpz/9KXfffTcrV67EMAzsdjutra06zUhFlam0J6X5cuedd3L88cfT0dHBwMAA7e3thMPhaQW8axcdB4NpmqRSKUKhkDhehMNOf3+/fp74fD7K5TKRSAS32013d7eu6iILFGE2VKtVBgcHaW1t1alAyjHh8Xh0VIiKLM5ms2zevJlcLkehUMDtdmOaJuvWrWNkZGS/tCOVVjQ57Qigvb2dv/zlL5RKJa0vs3XrVkqlEsFgkJe+9KX87d/+LS6XS3/mu9/9Ln/5y1+48cYb63RkrrvuOizL4q1vfatOcVVVjtxuN+Pj43VjQqXJKl0yGS9HBo/Ho6NVpzpWyx//+EfOOOMM0um0fl77/f4DVsUSBEE4VMT5wsSDUu2gtLa27rd4UrnJgE49+s1vfkO1WsVut+vzKDG4p59+mu7ubl19ZcuWLaxbt462tjZGRka4/fbbCYfDnHfeeXzhC1/giSee4Mtf/jJ9fX117bIsi9/+9re88IUvZNWqVdrp4vP5CAaDdQs5n8+nxeyKxaLssghHLT6fj97eXvL5PNFoVI8Jr9dLOp3G5XJRKpU444wzaGxs5Oabb9bOF7vdzmWXXca//Mu/sHv3brq6urjyyiu588472bZtG8888wzpdJqHHnoIh8OhFwzK+aIEHJubmykUCtx///287nWvI5PJ6AVFZ2enFuye7IQ52IiBRCLBd77zHe0MmjxXCMLBkslkuOuuuwAYGBigqamJQqFAa2srbrcbt9tNS0uLfr6Ew2Fx/AmzRjlVJjtZVH9S0bo+n49YLEa1WsU0TVatWoXX66W1tZVkMkkqlZrW+TJValFrayumaXLdddcBE2nhfX19nH/++Zxwwgl1thtMzLH/+q//yllnncWb3vQm/frQ0BDf/e53Of/88+nq6mJ8fBy3260X6zAh6F7L5Mge4cjg8Xj0M3IytWn58Xicv/71r7ztbW9jbGyMUCi0XxUtQRCE+UKcL0w8IFtbWwGmFMb0+Xw0NDQQi8XI5XKceeaZ/PCHP+TKK6/kxhtv1O/r7e0lEonwta99jV/96le89rWv5dhjj6W/v5+f/OQn+n0ej4eTTz6Zm2++mdHRUS677DKd8lDLv/3bv/Hwww/zD//wD3pXXe3SBINBAoEA+Xwem81GKBQiEAjUGTTy0BeORmw2G+FwmHA4rNMFs9ksW7ZsweFw0NDQQDqdxufzcdFFF/GNb3yDt7zlLZx33nkAXHLJJfzHf/wHX/3qV3nf+95HT08P5557Lueeey6WZXHnnXdimiZ79uxhYGAAwzBoampi7969PPjgg7zgBS/A4XBw1VVXUSwWefGLX6wXpG63W4swqgofLS0thzwWK5UKQ0NDwIQI6qmnnnpoX6Ig7KNQKPDEE08AE87JkZERuru7tUMzGo1SKpUwTROHwyG7+MKssdlsOBwOSqUS/f39Wp8rFArpFJHayGPTNDnhhBPw+/060njbtm1Uq1UCgQBbtmypO7+KxnrPe97Dxz72Md7whjfUHTv99NOJxWKccMIJ9PX1UalU9itoABMbZtdffz2maXLNNdfo6IfR0VF9zk984hNaf2Z8fByHw1GXqnI4nOzCwaGiqY477jh+9atfsWrVKj1PnXDCCeTzeV3FyLIs3ve+92Gz2TjjjDN0NbfD1YbZOtxED0gQli/L2vlSO/kprYjJVKtVhoeH9Y52KpXikksu4bHHHuOb3/wmwWCQV7/61RiGQSAQ4Jvf/CZ33XUX//u//8t1112H0+lkzZo1OsRWPazvuusumpqauP766znjjDP2u+4Pf/hDvvjFL/K6172ON7zhDZRKJbLZLC6Xi3w+r3dtkskk2WxWRwDYbDbS6bQ8/IWjkskh6zDhMFVjoqGhgTVr1hCJRMhkMrz//e/n4Ycf5l3vehcPPPAAPT09tLS08KEPfYivf/3rfOUrX+E973kPJ5xwAoAex93d3axbt05fd2hoSFcm+/nPf861117LLbfcwtve9jbOOussSqUSbW1tJJNJCoUC4XAYh8NBpVKhUCgc8q6az+djzZo1vOpVrzqk8wjCZDweD8cddxwwIR6fz+dpa2ujWq1SqVR49tlnddqIlEkX5kKlUsGyLHbs2FGXtgZo20vN6S6Xi0wmox0vXq+XPXv2kEwmyeVyvOpVr+InP/kJV155JR0dHQAcd9xx/PrXv+aTn/wkH/3oR/n5z3/O2rVraWpqwu1272dbKU0uhWma/OY3v+G2226jo6ODX/ziF6xevRqAsbExLrjgArZt28att97K+vXrdeRjrZNdNF0WHpXm9bnPfY577rmHbdu2sW7dOux2O4FAoO63GRoa4sEHH+SKK67g5S9/OX19fYdlTlNtgNn1hbm+XxCEo4ejwooyDjJJc6rytQqVNhCPx9m1axcDAwN692/79u1cfPHFvPGNb+RrX/saP/zhD/XnPB4P5513Hl//+te55ppr6Ozs5KmnnuIvf/kLTz31FFu2bGHPnj2cccYZvPvd757S8fKb3/yGL37xi1xwwQV87WtfY9WqVXR2drJq1SpaWlpwuVyMjo5SKpW02K66Byl1KBzNqDGbSCSIxWLEYjEKhQKRSISuri7Wrl2L2+2ms7OTFStW4HQ6ueaaa6hWq7zmNa/R6RXt7e187GMfo6Wlheuuu44f/OAHbNmyhVgsRrlcrrvm8PAwt9xyCw6Hg0svvZTNmzfzsY99jL/7u7/jE5/4BKFQiFwux9DQEPF4XOf/q7F4oF21yZpTgnAkKRaLbNmyhS1btlAul1m5cqVOvS2XyzzzzDPs2rVLO/cFYbZYlqUderVpovl8nmQySTwe15tSKmoxl8sxPDxMIpGgVCoRCoXo6urikksuwTRNvve979VdY/Xq1dx66618/vOf55lnnuGWW27hT3/6036Olsnkcjm+9rWv8dOf/pTTTjuNL3zhC9rxkkqluPDCC9m8eTM33ngj55xzDjabjWAwSDAYZHx8XC+cxd5aeNSztqWlhQ9+8IMUi0Wee+45HRmryGQy7Ny5k9e97nW8//3vp729fb/0s0Ntw2z7gtjqgrB8WXKRL4Zh2IAbgBSwA/imZVnlmT81NbV5uZMn6VwuRywWo7m5me7ubp1Lms/nqVarAPzLv/wLhmHwve99D6/Xy+tf//q6cxx//PGceeaZnHPOOTzzzDMMDw/jdDp50YteRDAYxGaz6XxnxT333MO//Mu/cNppp/Hf//3fNDY2kkwmaWpqwmazYbfbGR0dpb+/n87OTjo6OvQELmGMwtFObclp1eeVzpHX66VUKun8exXSvmrVKm677TauuuoqXvva13LxxRdz4okn0tDQwIc+9CG+973vcf/993PPPffo66jw+GAwSCwWwzAMLrnkEiqVCm9729tYt24dH/rQhzAMA4/Hg9/vx+l0Eo/HaWlpoVgsks1mCYVCGIah55ep/MQ1O2DTDlqlL6DmHkE4XAQCAU488UQMw8DlcuFwOPD7/fo5cuyxx9LY2ChC7sKcMQyDlpYWqtUqa9as0XaJ0qhTDhK18x+JRLQ9VqlUGB8fJxwOU6lUOPbYY7nwwgv58Y9/zLvf/e79ognf8IY3cPbZZ3P55Zfz4IMPsnnzZs4++2xCoRDpdJpUKsXo6CgPP/ywdt6XSiUuvvhizj33XNxuN6VSiR//+Mf853/+J3v27OG//uu/OOussygWi1r/C57XD1GaXiLSujDUPlf9fj+ZTIaXvOQltLa2EovFCAQCutJouVxm27ZtuFwuPvjBD9LU1ITP55tSm+1gmGu0uUSnC8LyZck5X4DfAf2ABfwdsAm4b6YPGIZxBXCFz+djw4YNXHHFFVxxxRV1k99k58ukz1MoFKhUKvj9fl3pZHh4mE996lOUy2VuuukmNmzYwD/8wz/UffaMM86Y1rNerVbrDIh77rmHj33sY6xfv57//M//xO/3U6lUME2TUqmEx+Mhm81qYyQYDNZpu8Tj8TpjZrIzRpwzB8dNN93ETTfdBHC8YRgbgZssy7pJHZ+ufwmHn9oxGwqFyGazpNNpPB4PuVxOO2WU/orD4aCxsZEXvehF3HrrrXz1q1/lRz/6Eb///e+55ppreMc73sE//uM/sm3bNnbv3s3AwAB79uxhaGiIvXv3snfvXiKRCDfccAPt7e289KUvxeVy8f73vx+v10uxWMRms2k9pkwmg2maeqHq8/kOaJh///vfV/3ruMn9S/UtwzDYuHEjf//3f89ll102j9+wcDQx09yl+pbdbufRRx+lvb2d448/nubmZtasWcOqVavo6+vTzzsRchdqmU3f8nq9vPWtb+Xyyy9n3bp1DA0NEYlEcDgcRKNRrcWxdetWAoFA3fxtGAb5fF7bXQ6HgwsvvJDbbruNu+++myuvvHK/NnV3d/PEE09wxx138N73vpdf/OIXdcc9Hg+9vb0cd9xxvPKVr+Ttb387L37xi8nn83zrW99i/fr17Nmzh/Xr1/PpT3+av/mbv9FirLXzuN1ux+fzaXtKnC+Hl9n0LZ/Px6mnnsq73vUubW85HA4GBwf5/Oc/z09+8hPuvvtubrnlFk488UQuuOAC7HY71113HSeffLK2H2KxWF01LuHo5kD2vCAcCYyZnA6LDcMwQsAtwCWWZaUNw3gM+LFlWf9R8x7DmuamNmzYYG3cuHHKc0/+iNJmSafT7NixQ+vCmKZJe3s7NpuNgYEBcrkcDoeDD3/4w9x55518//vf56KLLtLnqdVnmYxyvliWxT333MOFF15IV1cXn/vc52hpaeEFL3gBoVBIP+Dj8Th79+6lra2tTrBOGQHJZFIbNTabTS9Ma8V4a/8vzA3DMB6xLGvDdMdn6l/C/KAcislkkqeffppIJMKqVav0MRUxkslk2Lx5M+Pj4/z5z3/mtttu4y9/+QuvfvWrue666+jp6dHnLJVKWpxPUalU+Lu/+zvuvvturrnmGl7ykpfQ0NCgo2yU+PXOnTvx+Xy6OtlkZjLSZ+pfHo/H6unp4fHHH9/v2OEQCxSObmbqW8Fg0Orq6sIwDILBIM3NzbzwhS/krLPOYtWqVTrqUvRehKmYqW9t2LDBuvvuu/F4PPT39zM2NsaKFStoa2vDsiwsy+KJJ55g27ZtNDY20tnZqdM2DcOgXC7rdDdl71xyySXE43GeeOKJKW0rNXcXCgUeffRRBgYG6O3tpaenh+bm5rrKRKlUihtvvJGvfvWrDA8Pc9ZZZ/GOd7yDCy64gD179uD1euno6JiyBLHYU/PPgfrWX/7yF/1/0zR58MEHuf3227Hb7Tz33HPcdddd2O12zj33XL71rW9x9tln8973vpdXvOIVOiJKnC/Lk8Vmz9dGSC+F8wozc6D+tdQiX9xAJ/Bm4CZgO7ATwDCMdcAzlmUdcly+crx4vV48Hk9dKcSRkRFgYsHT3t5ONpslmUzytre9jVwux6WXXsoPfvAD1q9fzxlnnMGLXvSiKQ2EarXK7373O37/+99z++23Mzg4SG9vL9dccw0Oh0PnRauUonw+T1NTE4DeOVJtUgKk4XC4LqqlNq1qqr8FYSkyOYIrEAhQqVSIRCJEo1EqlQqbN28mHA7T1NSE1+vVY6lQKHDCCSdwwgkn8H//9398+9vf5uSTT+aqq65ixYoVBINB+vr6WL9+vY5esSyLj3/849xxxx189KMf5aSTTtLOzc7OTkZHRykWi0QiEXp7e7Xey+FGjENhPnA4HDrdqLm5mb6+Pk444QS8Xi+maTI8PExra6s4XoQ5Uy6XtdPE6XTS2NhIU1MT2WwWj8dDIpHAbrfj8XhobW3VmirwvPNclZ4ul8t0dXVx6aWXctVVV/HrX/96v1TvWrLZLLt27SKXy/GXv/yF+++/n2KxSKlUYnx8nFQqxU9/+lNSqRSvetWr+OAHP0hHRwfBYBCn00lfXx8wvXNb7KnFRaFQwG63EwqFdGnyaDTKo48+yre+9S1OOOEE3vzmN9Pe3q5teuVQlt9QEIQjyZJyvliWNWwYxpVA0DAMN3AK8M+GYbwJuBS4BEgczLlrHS4q4gUmKqmEw2FdQSgYDOpFnyqFODQ0RDAY5OKLLyYQCLBjxw5+97vfYZomPT09vPOd7+Ttb387LS0tVCoVfvrTn/KVr3yFrVu30tDQwBlnnMF73vMezjrrLCKRCIVCgZaWFjKZDD6fj2KxSKVSIRwO65LYiukU06dKMZIcU+FooLbPK+djIBDQES/K8IKJMTEyMqL1A/L5PE6nk0KhwKte9SrOOeccvvWtb/HlL3+57hoOh4N169ZxyimncOedd9Lf38/FF1/M3/7t3zI6Okq5XCYUCjE6OkpTU5PWnLHb7VPuksKEEyeXyx102p84X4T5QC1+vV4vq1ev5gUveAEul4uRkREKhYIWjZbIF2GulMtl+vv76enp0Robav5Wmi/Nzc10dXUBEw6TXC6nnX2qmICyZ4aHhzn33HNZuXIl//RP/8Spp55KZ2envp5lWdx777184xvf4H/+538olUr7tckwDNxuNx6Ph5e//OVcfPHFnH322QwNDWmbye/361RtZRdO3kQTe2px4XA4KJfL9Pb2snPnTmw2G+FwmFNPPZX3v//9GIZBY2Oj3twE+Q0FQVgYFrXzZV8Vo9MBj2VZfwSwLGtjzbFngY8BpwJvsSzroBwvQJ3DpVZMDdBaK4AW91QpBUNDQ2zevBmv10tvby//+I//SFtbG7t372bTpk384Q9/4NOf/jSf//zntcd99+7drFu3js997nOccsopugSj3W7HsizK5TLZbBa3211XCcDlchGLxYhEIjoyxuVy6ZDcWqSMnXC0Uiu6OzQ0RCqVoqGhQafWwcSCUo0RRS6Xo1Ao0NbWRi6XIxqNEgwGOfXUU0kkEjz77LMYhsGOHTvYuHEjiUSCn//850QiEd73vvdxxRVXkEwmCQQCtLa2YlkWLpeLYrGor6OqpEWj0f0cMDImhcVKIBDA5/Ph9Xq140U993K5HC6Xi1AoJA5AYU7Y7XacTmedXlDt/F0sFvXflUqFeDyuP1s7XxqGQS6XY8+ePRSLRd75znfyH//xH1x44YX8/ve/p1qt8sMf/pBvfetbbNmyhXA4zN/93d9x/vnnc9JJJ+HxeIjFYuTzeYrFIp2dnQQCAVwuF4ODg+zatQvTNPF6vXR3d+vFea1dKHP24mZgYIDh4WEqlQrt7e0cd9xxbN26FYfDwR/+8AdOPfVU0uk0zc3N8lsKC0Jvby/9/f1THqtNfReOfhat82Wfc+U+YAQ4xTCMp4ArLMvata/iUSNwHHAy8BLLsrYcyvUmq9fX6jWoqikKJYJrs9koFAo4HA5CoRDHH388mUyGVCqFaZqcd955XHDBBTz88MPccccd9Pf3Y7PZ+PCHP8xJJ51EsVikWq2SSqXw+/0EAgGq1Sp2u10bw5FIROu6DA0NsWPHDkzT1PmqandmshiihMQKRytqtyqbzZLJZBgbG9OvhUIhSqUSPp+PSqXC6Ogo8Xgch8OB0+kkEono3dRsNquN76GhIXp6evD7/USjUdasWUMgENCaTH19fdjtdl3JyGaz0dzcTCqVwuFwEI/HdeqGOv9kzRe1mD3YqjEyloX5wG6309DQoCv6DQ4O4na7CQQChMNhOjs7cblcC91MYQnicrlwuVx1c55K81Dp0vF4nEwmg9vtJpPJ4Pf7MU2TSqWibRpVYKBarWIYBi94wQv453/+Zz7xiU/Q3t6uHTinnnoqH/jABzj33HM58cQTCYVCuN1uYrEYpmnS1NSEYRhEIhHGx8cplUoUCgWdEqU2whQq9U79kcIFiwsVmeTxeGhsbKSnp4dkMsnevXu1Xo96/quNy1QqRaVSkTlNOOL09/eL/ooALGLnC/AaIGZZ1hsMw4gCtwPfNgzjjZZljRmGUQAuAEqH6ngB9nO41FIb+ZLL5bTIrtfr1SGzNptNl5ktl8s4HA6KxSLJZBK3280b3vAGuru78fv9xGIxdu/eTU9PDx6PR+/qlMtlSqUSfr8fr9db53gxDAOfz6edMpN3j6YqNS3efeFoxufz0d7erkPUVVpEMpnE5/NpjaS9e/eSz+dJpVJ6VzMYDGK329m1axf9/f2Mjo5SrVbrnKIul4toNKqdoXa7nXw+z+joKMlkEpiYN4rFoo5+aW9v15UwYCIMXo1J5cQ92Kox8tAW5gNVEaxQKJBIJDAMg4aGBtxuN8FgkIaGBsLhsDxPhDlTrVbZsWMHLpdLz8emabJ3717i8TjRaFRHJJqmyeDgoO5zLS0tumrd2NiY1tyLRqO4XC5WrFjBRz/6UXbs2MHIyAivf/3rOemkkwgEAlpU1263MzAwwNjYGJlMhmAwSGtrK42NjYyPj+NyubS9pVKNaksPqz9qo0sJq0s0zOJARSZls1lKpRI2m41MJsOePXvIZrOUy2Wi0Shut5twOExbWxuVSoXdu3dzzDHHLHTzBUFYpixm50sCONUwjLMty7rHMIzTgaeB/zQM43rgI0xUPSocjovNVIVELZpsNpvWXPF6vToM1m63Mzg4qHdOLMuipaUFt9vNihUrKJVKxONxtm3bxqpVq0ilUjidTrq6unTZ6LGxMS3eCegd9tqHfCgUYtWqVXg8nimdLEp9X71fEI5mbDbblGkQyrnicrnI5XI0NjYCE84LNc7a29uJx+Nks1m6u7u1ltPY2BgejweXy0VzczNjY2M63z8QCBAIBEgkElpLIBQKUalUaGxs1GKRtbuhuVyuTp+m9u+5UCqVsCzroKNmBGE6DMOgublZP6dUP/P7/ZRKJTKZjK54JAhzoVwu8/jjj+v5WEW8xONxEokE0WiU9vZ2YCK6ZdWqVdrhks/nyeVyelOsVCrR1NSkHShKB8/n87Fjxw6amprweDyMj4+TzWaJx+OEw2HK5TJ2u51169bp1LnaVO1azZipbCgpXLB4qd2EfO655xgaGuK5554jlUrh8XgIhUJkMhm9cdLZ2Ynf76/7zQVBEI40i9n58iTwe+AthmHkLMvaaBjGhcC/Aj7gnw6X4+VATE5JUigFflXi2W6343A4CAQCeielqamJc889l02bNpFOp/H5fPT19dHd3U2pVCIWixEMBnWorRL3VdEs8PwDpjbdYionixgFgvB8BQOXy6XTBfv6+ti6dSuFQkHrtORyOQzDoLW1lVNPPZVdu3aRSCQoFAo0NzcTiURIJBKMj4+TTqfJ5XK0t7fT19eH3+/XEWi1EWeTqR2ThxKNZrfbOe644w7+SxGEaXA4HLS3t+tIgOOOO47u7m49ftSiWRDmimEYeL1ePYfCxFy4cuVK2traiEajOs3HNE3WrFmDy+VidHRUp3M2Nzfr9CWbzUZjYyOpVIrR0VHa29tJpVKsWbNGa3yNjY2RSCSw2Ww0NDRQLpdxOp24XK79nOOTmcqGmjxvS1Tx4sEwDO2ca2lpIRaLsWHDBgYGBggEAgwODmrbvKOjQ0elViqVBW65IAjLmUXrfLEsK28Yxn8Anwc+ZhjGjUAXE1ovD1mWNT4f1zVNk2w2qxdtKh1JOUcymQzwfJWV8fFxnE4nfr+frq4uHRFTLBYpFArYbDY6OjpYtWoVjzzyCENDQzraRYXbqiiX2hxn5egJBAK6TWoBN52TRYwCQah3UqpwdofDwapVq9i+fTuNjY1ks1lsNhurVq3Sud9er5dMJsPo6CiFQoHW1lbC4TDFYpFyuUxzc7PWc1HndzgcmKZJPB7H6/XicDj03FA7Xg+HRkC1Wj1cX5EgaCzLYmBgQEe7jI2N6eozHR0d9PX1SdSLcFA4HA7OOussurq6qFQqeg4Mh8OEw+EpP6NsIaUDo+ZZ5egeGxtj27ZtPPPMM/T29uLz+Whra9ORLM3NzTQ3N+s+Gw6HicViWkR6uuuC2FBLmUAgQG9vL6lUinw+z+bNm9m+fTumadLZ2an/hEIhLSUguj2CICwEi9b5AmBZ1jbDMD4CvAv4HJACPjBfjheYUNiPxWLkcjlgotR0e3s7fr+fQqFALBYDIBgMYpom3d3djI2N4XQ6KZVKjI+P6wVXpVLBZrPhcDgYHBwkEomQzWa1Q0U5XYC6xdtk7ZlsNkssFqO1tVWHzB6MgSBCccJywufz0draqkuaDg4OUqlUKJVKlEolhoeH60qYmqZJY2Mjzc3Nepw0NDQQi8UoFosMDw9rkUhVXcnn85HNZvF6vTp1abImwOHQCFApU4JwuDEMQ2uaVatVcrkcw8PDdHV16f4Mz0eUCcJssSxLC+IqKpUKiUSCSCRSF/USj8cplUq6FLDT6dR9EWDVqlVaQHdgYADLshgfH8cwDEqlkk7HNgyD9vZ2vF6vdr4rra729nZCoZDYQUcJuVxOb0ImEglyuRxjY2Pk83kKhYIWxy8UCvj9flpbWxkfH9eC+yAp+oIgHHkWtfMFwLKsncAnDcP44r7/Z+bzemrBVhv5Upt21NLSQj6fp6mpiWKxSDabpbGxEYfDgcfj0aXsjjnmGC0E9vTTT5NOp3G73bS3t+vIGqUbocpJK+G3WiZH4szEgZwrIhQnLAdqx4FyiCQSCS34WFvxSEW9xGIxmpub6ezsxOfzMTw8rBcBgUCAWCxGJpPRwtdKA0rR3t5OIBDQx6E+hH1yxYy5YhgG0Wj0UL8aQdgPy7JwOp14vV5OPPFE0uk0xx9/vI72HBwc1MKj8twQ5oLdbt8vyiCRSLB3715dtVE5sMfGxhgfHyccDuNwOHS6kHKCR6NRDMMgn8+zdu1avF4vIyMjmKbJ6Ogo4XCYaDRKPp/H7XbrqGXTNOnp6SEQCGjHuthBSx/TNPXvCBMRU0pUWW2Q5HI57HY7Xq9XCzZXKhWt/SPplIIgLASL3vmimG+ni2IqEU/LsnRJO7VwUsrqKie+paWF4eFhYrFYnTDv8PAwmUyGcrlMS0sLlmXpsNdUKkVLS4u+rs/no1Ao6JQjtRtUqVT0TvtM5PN5kskk2WyWaDS63yJPNGGEpc5sordqjWu/308+nyeTyWCaJl6vl3w+T7FY1BFmbrcbQI9jwzB0NTN1DuWQtdls2gGbTCYplUqMjIzQ2tqqRbun0giYXDFjrhiGIbu0wrxQrVapVCpYlqU1X9xuN4XChKRaMBjUUV6CMBdUNbhaZ0ckEgEm5tvBwUE9n6o0UaURo9JFo9EooVAIwzAoFAp1C2yVtt3a2loXSTw+Pq5TQguFgp7Px8bG9PWlPy9tlK2uNjfS6TSxWIyxsTEaGxuJx+P4fD5dqEKJ4ytdRnmeCoKwUCwZ58uRZKoFnippp6oO1UaoKGdJc3MzMJFzrCJVWlpa8Hg8BINBnWs6OjpKKpVi165dOBwOWlpa8Pv9umStOmcsFiOVSuldnwM9LNQOUq0uRS2ycyksdWazazmdk1GNZzWu1G5oY2Mje/bsoVwuMzw8TDQarUv9U85UdT3TNNm1axflcpmGhob9otXm0qbZYrfbtaNWEA4nDodDR25ms1k6Ozt1FZnaEu6CcDBMnvscDgdtbW16HleRv6oijdfrJRwOk81mtQaXSucGtCNQVazp7OzUEYwul4tCoUBTUxO5XE5HDgcCARwOB5VKhWKxKHbQUYJ6TheLRUKhEH6/n5GREfbu3UsulyMQCHDKKafQ29tLS0sL4+Pj+xXOEISjmZ6enmmr+fb09LBz584j2yABEOfLlEy1wKsNnbXb7fq9tYs0h8OhI15yuRzZbJZQKEQ4HMbr9eqQWaX34nA46OzspFQq0drais/no1gs6lzlTCbD+Pi4Nn4PtOtvs9l02K3s6ghHI9M5MSaPjUAggGVZwPNaFUq/wul04vF46Onpwel0YlkWfr+fWCymx+zk8xuGUVfSvbGxUYfIezyeAxrzh+r49Hg8rF27Vt/T5LYJwsHidDrp7OykpaWFk08+mY6ODvx+vyxUhENGbUJNNfepednj8ejqjiqaQVWAVPp4qg+m02mti+d0Ount7QXQ8+LY2Ji23bxeb91mmUpTqtWfEZY26nf3+Xz6Odzc3KwdbX6/n+7ubm2XC8JyYybnitiOC4c4X6Zg8gJPOU0ymYwOY5y82FM7LEBdiLbP59MdXKUvBQIBAoEA4XBYpxWFw2FtYCjdiPb2dn0+2N8pNJUzRqJbhKOZ6fr3VA5TNe5qHSc+n0+XLVWlddUOqzLaPB4PhmFQrVZJJBKEw2GSySSRSESPM6UNU7tomO/7bmhokIelcNjxeDwce+yx2kEZCASIx+M66rKtrU0cMMJBUa1WdWW5yfNk7VyuUkdWrlyptbPsdntdCriyd1SlSIV6P0zoeal0pVQqpUV91aaXaZoS+XKUobQVVQnzYDBIuVymUChQKBT0hmk2mxWtH0EQFgXifJmC2TgwpqpoEovFME1TpwnVlphVDhy1s24YBplMhlQqVVclpVazZbL2zGSnkAjHCcIEakwcbAlJVZUM0A6V/v5+xsbGtIMU0CWmQ6HQlGWl54tKpcL27ds588wz5+0awvLEZrPhdrt55plncLvduFwuMpkMY2NjOhJBni/CwVIrijpdP6qt6DjdPFob2Wuapq48qYR1Ae2w2bp1K4lEAr/fTygUqksZl6jgo4d8Ps/OnTuJx+NEIhE6OjpobW1l586d5HI5UqkU2WxWb7ioSCtBEISFRJwvs6Q2dUHtoNRGuHg8Hq2srzRXgDrhz6lSJpSjR0W7zEWzRQR0BWECNTYm725Njg5ThnutgPVU4e8qPamxsZH29nYGBwdpamrSDpoj7QB1Op309PTM2/mF5YsShA4GgwA6fF8thOX5IhwspmmSy+VoaGg4pH5UO48rO6mlpWXK/unz+XRlOPV+9W+J4Dq68Pl89Pb26oinsbExgsEgvb29pNNpEolEndi9RD4JgrAYWNbOlwNpqEylIwETuzRq91tN6IlEQpdOnGwQeDweMpmJYk1qYefxeHR56dr0prlotkiKkSDUM9khmc1mGRwcJBgMEo1G9RitDVU3DAO/36/zx5VzVY3lSqWC2+1mdHRUV8qY7nrzhd1u16WyBeFwE4lEcDqdFItFBgYG6Orq0hGakuomHCwOh4P29vb9RJunsq2mcqSUSiV2795NY2Ojjhz2eDwMDw9rEfTa6GKYsIs6OjrqIofFTjo6MQxDb6Q8++yzDA4OMjg4SHt7O62trVo4vFgs1m2uCIIgLCTLyvmiFlXKCDjQrrU6rkTjYGKRpVIQVPiiykNWaQiqYlHteVSIbDAYpFKpMD4+TqVS0aUYVdisWggKy4PZlE6ez88fbUzlkCwUCrrkqNvt1qXec7ncftEu6XSadDpNLpfT47C2UoZyssLzjtRa3af5+i2KxSKPPvooJ554IpZl7bfgkH4gHAp2u52xsTE2bdrEwMAAJ5xwAuvWrdPPNOlTS4vFMh9M1m1RTLa91LyttPPUAnnz5s3s3r2bzs5OIpEIqVRK21+Tz6Pma3XvpmkyPDwMMGUbhENjvvvYvufsrE4cj8fZsWMHo6OjZLNZnnvuOU466SRWr15dp8smTrjljeqz+9Zu8lATFoxl5XypVqt16TxT7VrXPlBU2UKPx8PIyAgw4TxRUSz5fF6nK6iqJ/F4nEwmQ2trK8VikUqlQiAQ0BEuPp+P3bt3Y7fbcbvds85BrW2XuvZCG1bC1NRO8GrHRRmDk0u3JpNJNm/ezJo1aw4qskF0f2YmEAiwcuVKnc6nBHWVjovD4dBOWfV+FSrv8/n0+xW1Iru1OgWhUGhefwvlcBkbGyOfz5PL5Whvb9epItOJcau+p16X+UKYjNLMqFar2O12PB6P1k4qFAq6f0/FVAuwxbLwX0zM9juZzXN+Nueay1x0sL+X+tzBMF3EYG27YcJp0tzcrAsSqOensreUo7w2IlHNy16v96DaJsyOqfqYcp7V2jrqvbV9utY2mhyVUrs5Ctj3u3AN6rnocDgolUq0tLToqPKpCmEIy4Na+yedTjM4OIjNZtPrsn19Ysa+JQjzybJyvtjt9in1VmqpXVDl83lSqRRer5eWlhby+TxNTU1156h9+KiqKYVCQUfDlMtlfQ31gHC73XpHMZvN1jlx1INIvR6NRrVaf61RIgvuxUmlUqG/vx+n04nNZiMej+sduUKhgN/vr+t3w8PD9Pf309zcPKPzZToDWXR/pqb2+1JpRur/tZFqkUiEYrGojTT1XjU+DcOoKxtvt9unHXPz+Vu43W4ymQz33Xcf7e3tuFyuGa+t5gs1J0H9fDeblMvJRqssqI9OlLNRhekrR7CKFpvuM8qhPFU/OdLPp/lw+ByqU2LyQnI238lsnvO175lK8HsqTbqZmOxEni017ZjzFz5dFMLkeaypqUk7Xmod32pe3rVrF9lslmAwuF85YeWkkWfj/FD7W1UqFeLxuJ5LMpkMxWKR3t5eQqGQniOUULKyiZSjRv0NTN4crc7UBtUHc7kcjY2NtLS04Pf7KRQKeL1eLRgufWD5YJomQ0NDZDIZnE4n9957L7FYjEgkwktf+lLa29vVhtqMfUsQ5pNl5XxROyezRe2m1C7QlO6DcpIoo6BSqZBMJnG73Xi9Xl0xQgnw5nI5rbZus9n0uWsN2GQySSKRoKmpiV27dpHJZFi1ahV9fX1TGlNHywPlaNkpLZVKPPbYY9qQLZfLPPLII6xevZru7m5CoRDj4+OUSiW9M9PS0kJPTw8tLS0znns64/1wh9IeLb/FTGHtSs+l1pgH6sqhTidsXat/MVmnYD7Dmv1+Px6Ph2QySW9vLx6Ph0qlQrVa1XNRLcqBW+ssVmLhUy2aJ//u2WyWHTt24PV66ezsBJhWyBjqHTNHSx+aipnubbpjc339SKM0jY499ljtfFS/o3JCTkaNr9oKMrW7jWqc1e5Az0VfbbrXJn9G9WF4vuRsNBqd8RqTtR+m67sH60Sa6nPq+5mc+jxV29T7FZMruNWO56kcJ7VVFWf67mvnAvX/yXPmTNS00ZzpfXNh8hyq5iJVXa7WeWyaJt3d3fp7syxrP/2Yo23+WShKpZLeJFJpu/l8HpfLpRe6g4ODwETfqVQqDAwMMDw8zJlnnqk3F1WklMvlIpVK0dnZSaVSqRuTk54tM/atWiedzWajsbGRYrGoIxxU9KqwPKhUKmzbto0nnniCp59+moaGBkZGRohGo7zgBS+gp6enduPqsM1bgjBXlt2sNFMZWmWEtLa26oWZUs1XBpPSfVA75+p4f38/AwMD2liNxWLs3r0br9dLqVTiiSeewDAMVq1ahcPhYHh4mMHBQdra2nQVlfHxcZ555hl6e3upVCoYhlGnM1ErAKqE544G4+Jw7ZQu9GJmbGyMn/3sZ4RCIY455hi2bt3Kli1bsNvtFAoFnE4nyWSSzs5OVq9eraOa1q1bd8D7VuGTlUplSuO9lqkWGbP5PkzT3K+s8lJlprB2ZcwXi8W6HeTJ1Panqb6LI5lDXqlU2Lt3L263m1QqxdjYGCMjI3R3d+tdvkKhQEtLi9anyWQyOvJH3Y/6fX0+H4VCgaamJtLptHb8rly5UhvXXq9XV3CbSchYRf3B8xGAtQvC2TosYGIBXRvxNxvm2t8PZZ6Yaa6a7thcX5+ureozh3t+q1aruFyuA1bXq6W2P6gFvlqE1abcqkWwOqbEVyffRzqdZufOnfT29tYt7pTmWq3TQX221kHY3t6u+33tcXjekVAbDVar46bm1Xw+T6lUYnR0lJUrV+pxc6BNjsn9aaq5R226qN9bLUZVhKtKbVbjt/b7n1zBTV1DOU5qdVJqHbGTo+DU5xVqDPv9fh3Zq6LkGhoatBNr8v0daF6cLbMZh+oeJjugaudxl8tFOp3WzhnR9jg0lM3Z1NREPp/X/eKpp54iFAqxYcMGstksQ0NDerGrnGNKM62trY14PE61WiUcDtPQ0IDH49FjKhaLMTo6Si6XY+3atXWONdXfZ6P5on7r2jFc+7yTykbLg2KxyGOPPcYTTzzB448/zuOPP06xWOSYY46hu7ub3t5eVq9evV/EsCAsFMvK+TJ5Z2cyykhRuy0qamV4eJhKpaKNSpvNRiKRIBwOa0++0+mktbUVu91OMpmkWCzy5JNP4vV6SSQS7Ny5k2QySaVSobGxkccff5yBgQF6e3vp6uri2GOP1aKHoVCI9vZ2Vq1apY0sFfVimib9/f2MjY0B0NbWNqv7PlSjfSYD7FB3uw82XWPyNRda/ySTyfDII49wwgkn4HK5iMfjDA4O8sgjjzA0NERnZyelUkl/P6lUCpiIalA7tkqDZLJeh1o4qzz2YrFIJBKZcoE61SKj9vuY7reqTceZvAO12J18k9s627B29e+p+s5C96dayuUyw8PDhMNhEomEjrArlUoMDg7i8Xjwer3s3LlTp68NDw/T2NioFy3ZbJZUKoXf7ycWi9Hf308+PyEGrpy6asFVKpWACUNcVY+oNWrT6TSxWExH56loGOUYrt1NVw6f2kV0bTpGMpnUbdy5cyfZbBabzabntqkcEFMJJdded7p0jNqon6naU8tU/X+mCIbp5jG1cJwcnTSbee9IpJuqkupPP/00wWAQn89HT09Pnch0OBwmmUzqOWfy+FILtNqUW5WypByB6v+TK5CpHPx0Os3w8PD/396Zh9d1VYf+tzRZoy1Zli3Jjqc4sTHO5JoEAryYx1CGlNA+SEOgEELp19I+hlJoKFBoS0vLS4EwtCGFNm0YwoNA0gRIIAxJCOHRkIG4cRzbsuRJsiRrnmXd9f7YZ58cXV9Nzj333Cut3/fdT1d3Wns6e6299trrhJsM3uHs6+z7zTtxTp06xcDAAMuWLQsdAdGolfb2doaHh1m9evW0owclJSUcO3aMlStXhsnx+/r6ePzxxyktLaWkpITGxsbwyMpcjr3oGPZ3VEvfHPHXhm+PkydPUlVVRWdnJ0NDQ6xZs4ZUyiWIraiomDY2o+Mnevcfv7BcsWJFGJngE4pH+2Y2x/3w8DDDw8OhDvc5pPxCNpMzNVvzYnq7ZboGZ3JAzTSPG/MnPfrR64Ann3yS4eFhWltbKSoqoq+vjy1btrB69WqKi4s5duwYBw8epKWlJYz4XrFiBaOjo4yPj5NKubt/FhcXMzAwwL59+5iYmGBsbIxt27YxNjZGcXExw8PDAHR3d0+b6/11Eszls+bliM7pfn7x123UEWosTiYmJti3bx/33Xcf3/3ud9m3b19oz6xfv57m5mae+9znUldXlzMbduPGjbS1tWV8b8OGDTkpg5H/LCnny1znP6MLFHDJdbu6ujh+/DjFxcXhwvjkyZPh4iR6RvrUqVM89thjdHZ2smnTJqqqqujp6WFycpKioiJOnjwZHicaHx9namoqND5GR0c577zz2L59O+vWrQt3I1taWujs7OTss8+murqazs5OxsbGqKmpCXd8/MKns7MzNEKjiV7nuwifLTw5PSIi3SA703PjURlDQ0MLSgqabgRmMshy6UBQVcbGxmhpaaG3t5f+/n7Gx8dpaWmhv7+foaEhGhsbGRgY4NixY6FzraysjImJCXbs2EFzc3NYr4GBgdDhUlNTEyZoHhgY4PDhw0xMTFBWVhYuiKI7VsuXLz8tvD7TkZPoDnFlZeU0wyXd4M1n5rsgyHSkaKZw/5l2XZOgrKwsPMv++OOPU1VVFY6fY8eOUVpayurVq8MolsrKSnp7e2ltbaWqqoqamprwPH5FRQVdXV2MjY3R09MTzi/+iFJ7eztHjhxhcnKS4uLiMBLBR9+NjIyECzZwDoHx8XFKS0vp6uoK50qf2HhsbIzx8fFQNkw/juGTIQOsW7eOo0ePhjJhegSEX3z7tvC3m/Vt1NPTEy42Z4qk8Edl5uuM9+WMziN9fX1hhI5vh5kcfv7IavoubDSCYaaooOid9aIOoPTPzXU8h1l2kEtKSnj66adDR693SGzatCm8i4g/SjA8PMymTZtO0w/+6BK4IwA9PT1hdGdTUxMVFRVUVFSEi3q/8PJ6p7q6mrVr11JaWhou+kdGRujs7GTZsmXU1dWRSqU4fvz4tGMF/jMlJSVs2rRpmhPQl62zszO8e5mX19vby/DwMKWlpeHYnZqaorGxMcwJ4MdcV1cXVVVVoSOyoaEh1LHeOeLzXHid6xemtbW1lJWVceTIkdDxPjw8HDpC1q1bx8mTJ6mrq2NiYiJ0fgPTnE4+iu3EiRM88cQTbNy4MXR6ersglUoxPj4+zcnn63/8+HEAmpubaWxsDMdqY2Mj/f394bXtx6fXP5luBnCmmyWZfsdf+77PZ/ts9G/0GGn06Jkxf6IOysbGRhobG+nu7mZ8fJz+/n56enrCPIkPPvhgeF0/9dRTjI6OUlxcHOYwrK2tpaenJ5wXent7w7x3LS0toT6ZnJxERKisrKS+vh5Vpba2NtSvIyMj4R2qgvEwZ86Xvr4+wKUH8HagRT8tfkZGRrjlllu49dZbOXToULiWmpqaCiP3duzYwUUXXcTmzZtzNh7a2tpQ1ZzIMgqXJeV8gbkXZqtWrQrD7P1OQHNzc2g4jo+P09zcHHr4W1pa6Ovro6SkhEOHDvHII49QUlLCli1bOO+883j44YeZmJhgzZo1NDc3U1dXR21tLV1dXZxzzjlUVFQwMDDA6OgoBw8e5KKLLmJgYICOjg5SqRT79+9neHg43CmYnJxkfHyc+vp69uzZQ2VlJVu2bOHEiRPs3buXdevWsW7dutAo6e/vD5VrpiMY0cXHTIuN9IiIuViow8Pvvvs+mG1BEiWTQZb+3fnsrs2H+YTAlpSUICIcOHCAVCrF1NRUuIOsqjz55JNhOaampli5cmUYCRVNDOj/njp1KnS8+LJHdy8HBwc5efIkg4ODnH322XR3d4dGtt9JynTMwbeNXyicOHGCVCo1LdQcMu/Yz5U/ICkHRbTt5lOWTI4o31beieWNuXxwQk1MTNDV1UVpaSnLli1DVTlx4gSlpaWhg6S4uDgMAfd3rOno6GBycpLGxkYGBwfDRKqpVCrcbV+xYgXV1dVUVFRQUlJCTU0NTU1N4aK0t7eXw4cPc+LECS688EKqq6tpamoKk5J3dHQwMDBAWVkZNTU14dju7u4OHZCTk5PhcY704xirV68O+8s7eY8dOxYu8qPOQx9BUVFRwfDwcHgt+H7yTg7vdB4aGgrnrfTryzt7Zxoz0c9HncteVqZFY3Ts+EX0bIvVTHOud5r6hT8wzYk622/M5ERilh3k8fFx9uzZw/r167nwwgtpb28PnSDe6eOPERQXF59WZ79g6urqYmJigtHRUWprayktLQ3bpKenB4Cenh4qKirCecZHcg0NDdHU1ER7eztlZWWhY2xycpKKiopwPPl28mN4w4YNoZPHz5PHjx+npaWF8vLy0NEA0NHRQU9PD9u2baOurg5VpaOjg9LS0tB56Z1yDzzwAI2NjTQ0NHDw4EHWrl1LUVER+/btY9WqVWzevJnBwUFKSkpoaGhgeHg4vM2tj/7ymzjj4+P09PRQWlrKypUrGR4eDseGP5rV29vLmjVrwvGeKfoRoL6+nuXLl4eOeCCco73Tqbu7O2xfH5E0MjLC2NgYVVVV05ws0RwqPmLQj7eOjo7w+kwvV7aiaKPXfqb30x2b0SNWvn7t7e2Mjo5OOypmzI6/fqqqqkLHYXV1NeXl5axdu5aysjLuv/9+Tp48GTr3iouLKSoq4qmnnqKiooKtW7eGUW1nnXUW1dXVrFy5kqmpqfB63LhxI8ePH+fkyZNUVFSEiZLB2Uv19fXh8SM/t/rEycHYnDEvRyrlclJ5p8+pU6fCqDljcZJKubybe/fu5fvf/z633347e/fuDd8vKyujsrKSrVu3smvXLrZt28b5559veX+MvMNmqTT8rp8/g1pbW8v3vvc9GhsbQwOnp6eHqqqq0Fjyd0BJpVLh7lV3dzfd3d2MjIxQX1/PS17yEnbv3s0ll1yCiLBq1SpWrlwZJuEdGBhgbGyMPXv28OCDD/L1r3+dZcuWhcbs/v37OXr0KO3t7aGx29PTEy40BgcHUVWqqqrCc40rV66kpqYmvM21X2xC5jsifOUrXwkjg7xB742g2traML+N3xGM7jr7BVnUiRM1GmfipptuorKykjVr1oS5dmb7fvqRgfRImfTvVlZWTssF4GUulLkWML5s4+PjlJWVUVJSwtjYGH19fUxNTVFfX8/69etZv349q1evpqamhpqaGvbt20dXV9e0xbLvF3/8zNdx9erV1NbWsnz5choaGqipqZlWt1WrVtHc3DzttpueG2+8Mfxdv/vrF3Rr1qyhpqZmWhvB9B37mdp3rtcXwpn0iyc6FrzDze8Gw/RxEy0vEDor/PveiRVd7EUj5p5NOc+U8fFxurq66O3tDcdTVVUV7e3tTE1NsWrVKiYmJigtLaWpqYlzzz2X5uZmDh48SFtbG6Ojo6FzZHx8nNHRUdra2mhpaQnngptuuik0Xurq6tixYwcNDQ1hPpmenp5wsbh8+XJqa2tpbGxk8+bNrF27lhUrVjA4OMiyZcvC3/EOnOLiYurq6qbd/tX3yU033RT2nZ8T/SIfmOaAWbNmTbjI8pEMHh/1l0qlWLVqVRgZ4MdldIwMDQ1xww03hL+fafymjxlP9FpMd4h0dnZy4MABOjs7p30+Ov9Gx090bPkydHd3h2NzpnkxWrb08RnF9y2z7CB7veX/NjQ0hE4IX0Y/tiYnJzMen2poaGDdunUsX76c/v5+jh49Go7Turo6Nm7cGD6ampr45je/SWdnZ6gnW1tbefrpp2lvbw/15vDwMCMjI7S1tXH06FE6OjpYvXo1W7ZsCZ0DPmfW5s2bKSsrCx0v3d3dTExMhPNjZ2cnd9xxB+Xl5VRXV7Np0yZEhKmpKQYHBykqKmLz5s00NTVx+PBh9u/fT3t7O319fbS3t3Pw4EFGR0cZGxsLdzV7e3tDfbxx40Y2bdpEeXk5R48eZXR0lK9+9asUFRWFzvXa2lrGx8cpKioK27i8vDy8lr194Z2G/f39YTSr1wO1tbVcfPHFYaLSjo4O2tvb6erqorq6mm984xvT5vGobvXXqB8n3kHpfzfq5PSOq1WrVp02tz7beX6u35hpnPv3onN7ZWUlNTU10+aVJObnKIUg37dxdNyDc462traSSqXCI/Sjo6OhE9bP42VlZbS1tXHixAk6OzvZu3cvXV1dnDhxgp///Oeh3bl8+XIuu+wyXvnKV7Jt2zYaGxtZs2YN69evZ9u2bTQ0NDA4OBhGqnndMh8nytTUFEeOHKGvry+MwPERW+lzdrZIum/zoQxJyE+lUvT19bF//37uvPNOrr32Wm677bZw0xZcjroNGzawe/durrnmGq655hqe//znF4zjJYl2ra+vR0QyPjZu3BiLzCTqmfQ1kwlZSuFRu3bt0ocffnjWz6TvugwNDfHCF76QBx98kMpKlziwo6ODqqqq8Ky1j0SZmJjgqaeeoqamJty9/MUvfsHWrVvZvn07g4ODHDlyJNwt9hEQvb29YYLC0dFRjh07xh133MHdd9/N6Ogora2tlJWVsXLlynDXO6rc/E6sL7ffLW1qappm9Edvpet386O7YLt27cK3z0w7XP68vv/9TDutC9kdi8qcz/d9romZjjdl+m76a5lkzkVgjDyqqjtn+kxtba3u3LmT5uZmli9fTmtrK2eddRYVFRVceumlDA4OhuNkfHwccLkGzjnnHLZv387k5CT9/f1haDiQsZ/SDdJMTqh0du7cyV133TVtoZfeRunnv+fTlrO1+0I5k37JRPSYnM9/kN6Oma5z/355eflp0QtxlDMdEfmVqu7K9N6aNWv05S9/eegIveyyy9i+fXt4JOLss8+ms7OTw4cPs23bNkpKSvj1r3/NQw89RFFREa9+9aupr68Pj1P09PSEi8/169fT29vLFVdcwZ133hmOKz9/DAwM0NLSwsDAQOjUSSfdKRqdd3zk3OTk5LScFL7NL7/8ch555BGA8K5vPpl5dHd/tv7zzPdzAwMD4bw+U1Jgv5DfvHkzjY2N8xrffX19px13Si/X7t27M46faLRDpvwi0TL6xVN0XsiEl7t27doZ567zzz9fb7zxRlasWEFdXR3wzHEgIDwu1t3dHd79Kl2mlxON3vGRFJnmpgsuuIAvfOELbNy4MXTYl5WVcfTo0Wm3pz1w4ACtra2h43/z5s00Nzdz6tQp2traKC0tDXOv+SjPVCoV3nq4urqaJ598kgMHDnDddddx8803s337dmpra0OnTHl5ebjZMjIywqFDh2hpaeGCCy6gsbGRtrY2KioqqKys5PDhw6xevZrGxsZQ5/q8Nd5h1t/fT1FREVdddRX33Xdf6OAYGRkJj4r6iA+/APZRtd656fPB+GTC/pqKXg/t7e1hXf3xo4svvpif/vSn4RE8f2fGTNfUfK+h9DGaridmm7cy2VxzjePZ3s80t2dDv2eTQpCfqR1PnTrF/fffz4EDB1i/fj1NTU1h3kJ/XDqVSnH06NHQGVpXV0dxcXEYLTM0NMT111/Phz/8YSorK8MNIR/dVVpaSnl5OTU1Naxdu5ahoSGOHj1KXV3daccZYXaduHPnTr333ns5fPgw69evZ2JiIrz2ZrJNny1J920+lCEJ+V4XtLe3c88993DTTTfR3NwcHvVXVc4//3ze/OY3s27dOi6++OI5o/RnG1sAy5YtU59eYiFs2LCB1tbWBX8viXadTaaIxHJ8Kt/qGRdzja8l5XwRkS4gcyak2XkOsDfyv9cQqcjzYtzuon/NR0mUAKeAyeB1CV4TQIP3AMqC//29bDcBT0bkFQWfLYr8jpeXTrR86a+nZvk/vZ4zMdPvnwnzlRmVXYprzzOVv1CZng2qOuM9oUWkDziJK1sJrj8ncOX0fZvC9WMprv+Kg8/79/x4iZLeT/616Jibi+cA++b47EJ/M9ucab/MxFzjfa7Pz0S2y+mZcXyJSC9wgmfmjFPB89LgI/568HUowc0pEnx2PPJzRWmf9WzHzTkzXd+ZxuZMpI+lmWQWAVs5fX6daV6bT/9kqx+LgGW4tlvI9TBX+bM1fhbSHmfNMra6gVZO76NMf5lF5nzLA64N9jO/ua4MN76LZ/g8wXsa+W6m39gCHGRu3TFbPWerY3p7+X6OfmemOSlqE6SY3t6QeV7OVE4vs4Rn9KS3G7Ixhv170fLMNm/Nx+Y6k3l5tu/ENT/Pl0KSnz42S4LHOM9Ey0XnfW97+s9O4caZt3PKgLNx19lE8Lmp4DvlEVmTkd+cTa/MZ2yl1wEWNtYXQtJ9mw9lSEq+t/1LcGOsk2fsoRRuzI4x/76fy54/0/XimZJEu5rM+Jh9fC0l58uZIiIPz+bBMpkmM98phDoXQhmhcMq5UJKql8ldeiyFeX8p1DEfSboNTP7iHYP5ULeky5C0/HwpQ7ZZKvpiqcicC8v5Mj+SODBmMheXzKQphDoXQhmhcMq5UJKql8ldeiyFeX8p1DEfSboNTP7iJR/qlnQZkpYP+VGGbLNU9MVSkTkrFvliGIZhGIZhGIZhGIYRIxb5YhiGYRiGYRiGYRiGESPmfDEMwzAMwzAMwzAMw4gRc74YiSMiMvenCl+mMTfWL4ZhxMVSmF+WQh0Nw1ja2DyXHZZKO+ZbPc35YiSOqmquLwwNkh3l2wUZB+IonfuTyZPv/RK0ZXPS5YibpeQQXWpy841ctkOgaxa13aNLMJGfiJQkKLtIRK4UkasTlP8aEXldgvLfJyJbk5CfS/LNlsq1Dsmn+ue7rfhsyVW9loJOhPzTi4kprEIgGJC3AgeBH6jqT4LXJa6ODGR+AhgCfgncq6pTcchKk/luYAL4V1UdjVNeIFOAm4Efq+q/ewdMnBdIUM+/BEZw93y/W1Un45abJEGdHwT+Hrgj4eJkpFD6JSjn/cA/AV+LvJ5X5VwoQb3eCXQCrar6y7ivx0ifHwN6VPW2HM8BScjNaRvnI0E73Aj0Ay3Al1R1Mgdy/xP4map+UlVTIlKkqqkY5eXUdgjk/S0wDHxbVZ/Mtox8RURuAE6KyN+p6qkcyxacfj0BXCoiV6vq5TmW/wvgJLBLRF6kqn+WK/kBLwH+D7BaRG5R1T05lp8TkralAvl/BKSAB4C9qjqVKx0yU/1zqcMKxVZcKEnoxcWuEyMy81IvmvNldv4TeAzoBmpFZDuwP66LPVCkP8ENznOBFwfPD2RTTgaZjwVydgOXAm+KS16Ec4A3AM8PLvp/i3hgNaaL8Qe4elYDLwNeLSLvUdXxQp+8MxFxFnxXVe8QkXNwRuJgntU17/slaMufA3eo6tdEZCfQB7Tl0gDKNpE55wiwHhgWkR+p6vUxKkQB7gbagQ3Ac4JFw3vjdEgkLDenbZzHfB9oAxT4bWAPbiERGyLSBFwEvEJEplT1H72xSXy6Jme2QzC+Hgb2A9uBS4Dfir6/yMfZK4O/gyLy+Vw48yJcDpxQ1dcF4+wXIrJLVR/OkfyrgT5VfZW4yJvLReRS4KEcLcgFt1g8DjwfUBH5oqoeilt2LknalorokAPAc4DXAj8VkU/lyHmdL7Zk3tuKZ0hO9eJi14mQ/3rRnC+z0wLcgovQGMZd8L8KLvY4JrwmoF9Vr40otf9BjM4X4H8Cvar62yJyNnCLiLwWtzP8aFyeUFV9WkS+hbsQrxGRU6p6S/BeHAugatxi+X04r/nv4gyXvxKRD2nM0UUJ8XZgFPg3EbkPaASOAv8iIt/IB0VVQP3yGlxb3isiDwC1uHJ/T0Q+mUflXCjNOOP9zeKOU70BuEJEhlT1xphkLgcmgXcB48BvAh8Vkb9R1Y/EOC6XB/LeDYzlUO5ZuHk9l22cd4jIctw19GeqOiAij+Kc/Q9EPpN1g0hV20Xkqzh75w8CGdcDEuNOXy5thwuAYVX9XRGpwjkA3goMAHdqjqNBcomIvAwXCfBD3HUlIvK5HDpgeoCLRGQXzrk6hrNpiHsnOaAX2BBcWy8FLga+AnSKyIvjbofgWj0U2HLHgFcDRSKSAv5JVQ/HKT+HJG1LNeEiNd8uIpXAe4AXAG8PnF1xy0+6/oVkKy6IJPTiEtCJkOd6cdGf8zoTxJ1hrQHOAz6HC1d6WfD8ucBvxCS6BBcJ8vrgQmvBeUMRkU0Sz7m8HuDFIvIXwJeAlcCf445WnBODvCiTwDKcsfBOEbkHeEW2hQSOrDJcNNGfBpPL7Tgv+sXA2mzLzBMeBFbg6nozsAvnyHsLUJlYqQIKrF8eA7pwc8DXgfOB7+JCrs9KrlhnRjDH/S7wB8BaEXmFqh4H/i/wEPBSEamLQeb7gQuBUuCNqjqG6+9/Bl4QOICzSiD3j3AGZCrHcl8KvBxoEJFXxt3Gec4y3DV9VfD/AaAVQER2iEhxTLtf4MZbJe7o1x+LyK9wi/WskpDtMARcKCKfxTkhSnHO4g/hFmiLmceAXar6Llzdr8T1b67yUjwC/AMwCNQDDcAxEXkH8LHI+IuL+4H/jXMqf0dVt+DGWRmwMWbZPgdICW4xfj/Osf0W4G3AYsrFkbQtJcBlIvJ2VR0BPg8cAl6XI/mJ1r/AbMWFklO9uER0IuS5XjTnSwZUNaWqg7izjS/DhSuhql8FpnAKNqsEnsfDOE/uSRFZAewA9onIm4CPAzUxyHwU+F+4MPwVqrpNVV8YfGR1NuVF5PqIqzuAx1T1X3CTwEtxu9NZRR09uLOiHxORa1V1VFU/BxTjdv8XDYFBVKTufOOfA6uAXwVj+k9wuygbkiwjFFa/qOoR3Ln2SuCJoOx/j/PeNyVauAUSKN+fA28EfgfYidvB2qGq7cBncMdjsn0tvgS3ULkEt0v8cRF5NW7h8GWcPirPpsCgrv8PV8ezcbt1fyEiL8fluPpSTHKLArl/jVuI7AJuFZHzYm7jvEVVu3A5C/aLyDJcnzweOAE/CcThiCoO/n4VaFfVH+Hy7lyEWyxnlSRsB1U9gDuGcAxX3+2qeiUuf0BttuXlE6rararDwfOPAncBf4hzKudC/iguT94+3BzyY+D1uMiEb8cdEaCqQ7i8eePAY+KOg7wJp6d64pQdyNdgB/lW3MLmr4AncHlo+uOWHzf5Ykup6jHgI8CnROS1qjqgqu/FzZkbcyD/SeA6kqt/wdiKCyUBvbjodWLw+3mtF+3YUUBgpL8AKFfVHwOo6j1BmNLNIvI5XIhnI+48Xrbk1qpqny+Cqt4bvF6JU2Lvwp0rvkpVs6LM0mSKqn5HRPbgwrJeiYt+WYnzrGdDXhHOIClR1a9Fwr3acSGDbwWeBm7AKe1syXwVUKqqtwOo6u0i8vvAl0TkAlySupW4heCiwPdtxGj4iYhcrqr/LSIvwC1ASwhCoxMoX0H0S1DO9wJ3BYY1qvpfQVseFpHLcYZHPVBoodWXAx3q8hSswe0KTAAPi8h7cTtMxbhQ2KwQzK8tuHPirwJuw+1E3IiLJOrDKeG+bMkM2IXLf/AuEfkRbldpLfDvOMfLRExyr8OFif+miHwAF933UtwxtY/gDKoistjG+cYMOvXhyHsHce30PFw00rOek9JlRnTNCPAmcUdq9+IWMgefrbxMMiFe22EGeT8Rkadw9bpW3LGPDdmQVwgEdoyq6sdFZAIXlZgTgig6cHOmd2ZfrjlK7qiqp0SkHLcYvhI3p12tqidzIT/gcdwG4f2q+k7fHzmUn3WStKXSbebg5X/FRZ98R0Q+jJvTSnERubHLV9Uf59KWLBRbcaHkWi8uBZ04i8z81YuquuQfuJC+n+EiMY4A9+B2JYuC918OfAp3FGdHFuXegMvEvCpSDv9oBk4BHcC2mGUW4SbxTwOP4u6ytDOLbfsQLkSwE7eY9e+VBDI/HUN//hKXxKoLuD7t/ZcFbXBDNvsz6cdM4yl4fhVugflL4DcSKl/B9AtuoZzCRWrsiLwuuFwhXcCPsnWd5LhuL8QdZ9wVzDNP4aLqrga+iNvFPD8m2Z8BPhi03XtwiRq/DNwUR5/jkhPuD+b09+OOUt4b9O0HgX+JSe5VQbu+CLcbfgC3QEkB/4g7ehRLG+fDg9N16t3A+uC9IpzTsi3QCVvjlMkzevwDwKdirmestsMsdfTz/PuB/8Il5yy4uelZtk1RwvIrg34+NyH5tbiFxZqE5DdHnhcnPR6eZV0Ss6U43Wb+btr7V+Kc+V+OQ4dkkH9X2vux25IUkK14BvXKmV5cCjpxjnrmrV5MvAD58MCFS94WPF+NO8d7L7AyeK02Jrn7gsd1QIMfRMHfIlwW/+05lLkWFzqbtfriskvfHjxvCiaW50XevzDyPCvGEy7s9gfB86txtwV+IYFBgDtelfi4y/F4asA52GoSLF9B9EswkW/CHVG5DxcuuSnyflXwd1nSZT3D+lXgzvluxWWB78ft2l4FfAy30xRHm5bgHDuX4EJcj+PONse2WAjG/GdwUTevDV5bBfw0rnk9kNEAfAJnQO7Dnet+Ds4R00SBL07mUf+ZdGpd8FolLvdP1vTbPGSeF/lstnRNTm2HedSx0bdv0mNgKT7imDsL7eFtjkJ+JGlLMYPNHJFfFC1PAvIbcNGxsdmSFIiteAb1yqleXAo6cZ71zDu9aDlfHN3A80TkMlXtxIUubQL+UUSeB3xRRGqCsKasIM9k6b8Hdy7t90WkQVVVRK4APgvcp1kMXZ1D5muBvwBS6o4kZYvo3QBSuLsBdAXleS3uTkflQZhqtrJs93L6HQBuAX4sIq8BbhSR5dnsz6SZR99+DDe5DiZYzILoF3UcAr6FyyFwCfCHIvLXIvJ24MsiUq3unH3BoafnKfghzvHyEdyttLOeeT5o02hugL8EngR+jQuTj4WgLjfgjlC+U1yOmVfhwrc1RrldqvpBXJ6AfTij6kW4na1RLdA7MyyAmXTqpwKd+u/Avmzqt1lkflpEfgP4kIhUZ1nX5Np2mK2Ou4DPikiVuqScRo6JY+4sNDRY5RQqeWBLZbSZA/mvw13rcSa5nU3+FTg7QWK2JQvCVjwDcq0Xl4JOnE1m3upFc744nsBlzX6jiOwKFlW/gwvjrAQ+oKrZvp/9Y0zP0n8F8DYRqcUNpM8Ei6RsMpfMT6tqthdCM90N4C04Z88XVHUsy2070x0AluEWXR9Ql7CsoI2ENB5j7r5N2llQEP0SnPFOv4PDW3GJUweA96lLdFiw6Ol5Cv4GeIO6BNxx8nggr11dxvsrVLU3ToGBI+3tuHn+E8DvAW/TLOXQmoNWYA3wDdwtMt+aZed2vjKXTn1/DPptJpkrcImx/1xVh7I8v+TadpitjlW4dh3OkizDWIo8RrK21Gw283XA51V1JEY7aTb5HwQ+lwNbsiBsxTMg13pxKejE2WTmrV6Uwhu78SAuS/zf4hxSX8TdPvYtwG/mYtEaJND6PeCfVfUzccvLpUwRKVfVMRHZiXO4fDv4+0ZVfSImmSXqEtGtwx2z2A38KfCCpbD4SWI8zYdC6pdgl2kn7hZ5Nbhb171FVQeSLFc2CXbQrsc5e5/OkcxmdbdcRtxtFHMSBRLstKzAbc7m7E4cItKIc8D0qLtr1pIgCZ26FGQmbasYxlIiIds85zZzPskPylAwtuJCWAr6YqnIfDaY8yWCiGwE3oELc+vHeQUfi1lmmBVe3F0xvhns1C4qmYGs5+Fuv9oK/Jaq/nfM8spxIaL+DgBvUtVfxSkzaZLq24VQKP0iIpuAO1lEd3DIhIiUagLh8ou1PY1nSEinLnqZSdTRMJYS+WBL5dpmzkP5BWErLpSloC+WiswzxZwvGRCRGgDNUX4Mcbexy9Z5u3yWmcQuey1ut3tMVU/kQmbSJNG3C6VQ+iWpKA3DWEzkWqcuFZlJ1NEwlgpJ21JJ2Mz5JD8oQy0FYCueCUtBXywVmQvFnC9GTklql90wng0WpWEYhmEYRi5J2mZOWr5hLEbM+WIYhmEYhmEYhmEYhhEjdrcjwzAMwzAMwzAMwzCMGDHni2EYhmEYhmEYhmEYRoyY88UwDMMwDMMwDMMwDCNGzPliGIZhGIZhGIZhGIYRI+Z8MQzDMAzDMAzDMAzDiBFzvhiGYRiGYRiGYRiGYcSIOV8MwzAMwzAMwzAMwzBixJwvhmEYhmEYhmEYhmEYMWLOF8MwDMMwDMMwDMMwjBgx54thGIZhGIZhGIZhGEaMmPPFMAzDMAzDMAzDMAwjRsz5YhiGYRiGYRiGYRiGESPmfDEMwzAMwzAMwzAMw4gRc74YhmEYhmEYhmEYhmHEiDlfDMMwDMMwDMMwDMMwYsScL4ZhGIZhGIZhGIZhGDFizhfDMAzDMAzDMAzDMIwYMeeLYRiGYRiGYRiGYRhGjJjzxTAMwzAMwzAMwzAMI0bM+WIYhmEYhmEYhmEYhhEj5nwxDMMwDMMwDMMwDMOIEXO+GIjIbhFREXkqeHSJyLdFpDzpshmFi4h8IhhXO4P/S4OxNiIiGxMunlGApM1V+0SkTUQ+LyLFInKRiLSLyO6ky2kUNiLyMRFZKSJVIvJuEWlNukzG4iCDXlwrIj8Ukf0i8oDpRmOhzKIXV4jIt0TkaRF5REQuSrqsRmES0YkvFpFHReSAiNwiIsuSLlshYs4XI0RVt6nqNuBc4DXAKxIuklGgiEgp8DbgB8A7gpd/DHwHqEiqXMbiIJirtgIvAP4A6AZ+CDQmWjBjsfBR4GzgEPC3CZfFWCTMoBf/HnhUVc8BHgc+lkzpjEIng168F0ip6rnA14BPJ1k+o6D5KFCPG0cfArYB5wFvTLJQhYo5X4xM1AMpYH/SBTEKltcAJ4D3AVeLSJWqvhiwnRcjm1QBCrxeVVclXRij8BGRHwVPvwG8CPiTBItjLC5O04vB/98O3u8BbB4zni1eL34LuCV4zcaWcUZEdOI9wH8BPwOmgAFsTJ0RJUkXwMgfROSp4OkG4PtAa3KlMQqcdwA3q+oeETkAvAG4OdkiGYuFYK4S4BTwD7ioKsN41qjqS0VEgVeo6gERuTTpMhmLhtP0oqr+GYCIvB54D3BlguUzCpgMevGTqqoiclnw/weTLJ9RmGTQiSXA3+FOSXw92dIVJuZ8MUKCI0eIyHLgIeAPsTBFY4GIyFm4I2sXiMi7geXA72POFyNL+LnKMAyjEJhJL4rIfwCfBa4CrlTVuxMsplHAZNKLInId7pjIH6vqf+S+VMZiQkTqgLuAGuBSVT2WcJEKEnO+GJkYAyaApqQLYhQkbwN+oKqvARCRRuC4iDwHGE20ZIZhGHOjgCUSNLJJRr2IO952HrBDVTsSLJ+xyBCRa4E/BXap6r6ky2MUNF4n3gEcA35PVceTLVLhYjlfjBB/tyNcrpdjwPUJF8koMESkCLgWuM2/FhiUP+OZBIOGYRj5zK3A3SKyJemCGIXPHHpxF1AH/DSwwSw6wcgWb8Jtst8RjK0fzfUFw5iBW4E9wIuB5wGPB2PKcqKdAaKqSZfBMAzDMAzDMAzDMAxj0WKRL4ZhGIZhGIZhGIZhGDFizhfDMAzDMAzDMAzDMIwYMeeLYRiGYRiGYRiGYRhGjJjzxTAMwzAMwzAMwzAMI0bM+WIYhmEYhmEYhmEYhhEj5nwxDMMwDMMwDMMwDMOIEXO+GIZhGIZhGIZhGIZhxMj/B26WPLP/QJpdAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1152x1152 with 49 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "cornerplot(result2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "59322"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sampler1.ncall"
    ]
@@ -370,9 +601,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PolyCollection at 0x7f53840a7700>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure()\n",
     "plt.title(\"1-sine fit\")\n",
@@ -402,9 +656,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PolyCollection at 0x7f53840a6b20>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure()\n",
     "plt.title(\"2-sine fit\")\n",
@@ -461,9 +738,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1064.3857030391894"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "K = np.exp(result2['logz'] - result1['logz'])\n",
     "K"
@@ -508,7 +796,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/performance.rst b/docs/performance.rst
index 214e826b..25007ef8 100644
--- a/docs/performance.rst
+++ b/docs/performance.rst
@@ -223,9 +223,12 @@ and allows the user to specify the problem dimension and a few sampler parameter
         vectorized=True)
 
     if args.slice:
-        # set up step sampler. Here, we use a slice sampler:
+        # set up step sampler. Here, we use a differential evolution slice sampler:
         import ultranest.stepsampler
-        sampler.stepsampler = ultranest.stepsampler.RegionSliceSampler(nsteps=args.slice_steps)
+        sampler.stepsampler = ultranest.stepsampler.SliceSampler(
+            nsteps=args.slice_steps,
+            generate_direction=ultranest.stepsampler.generate_mixture_random_direction,
+        )
 
     # run sampler, with a few custom arguments:
     sampler.run(dlogz=0.5 + 0.1 * ndim,
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index aa09cb01..a506fe70 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -343,8 +343,25 @@ def __init__(
         nsteps: int
             number of accepted steps until the sample is considered independent.
 
+            To find the right value, run nested sampling several time,
+            always doubling nsteps, until Z is stable.
+
+        generate_direction: function
+            direction proposal function. Available are:
+
+            * :py:func:`generate_cube_oriented_direction` (slice sampling)
+            * :py:func:`generate_region_oriented_direction` (slice sampling on the whitened parameter space)
+            * :py:func:`generate_random_direction` (hit-and-run sampling)
+            * :py:func:`generate_region_random_direction` (hit-and-run sampling on the whitened parameter space)
+            * :py:func:`generate_cube_oriented_differential_direction` (slice sampling with better proposal scale)
+            * :py:func:`generate_differential_direction` (differential evolution slice proposal)
+            * :py:func:`generate_partial_differential_direction` (differential evolution slice proposal on only 10% of the parameters)
+            * :py:func:`generate_mixture_random_direction` (generate_differential_direction and generate_cube_oriented_differential_direction)
+            
+            When in doubt, use :py:func:`generate_mixture_random_direction`.
+
         adaptive_nsteps: False, 'proposal-distance', 'move-distance'
-            Select a strategy to adapt the number of steps. The strategies
+            Strategy to adapt the number of steps. The strategies
             make sure that:
 
             * 'move-distance' (recommended): distance between
@@ -366,6 +383,12 @@ def __init__(
               between chain points exceeds mean distance
               between pairs of live points.
 
+            Adapting can give usable results. However, strictly speaking,
+            detailed balance is not maintained, so the results can be biased.
+            You can use the logstat property to find out the `nsteps` learned 
+            from one run (third column), and use the largest value for `nsteps`
+            of a fresh run.
+
         max_nsteps: int
             Maximum number of steps the adaptive_nsteps can reach.
 

From f3692359bba4236f210a225789fede0876f8e12b Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 12:49:31 +0200
Subject: [PATCH 031/313] =?UTF-8?q?Bump=20version:=203.3.3=20=E2=86=92=203?=
 =?UTF-8?q?.4.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 02f0e1fe..fdc90ee3 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.3.3',
+    version='3.4.0',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 5ec2629d..1e915359 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.3.3'
+__version__ = '3.4.0'

From f9554bf6ba7d7716be037190ee1eb989d1f1868c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 12:55:12 +0200
Subject: [PATCH 032/313] update changelog

---
 HISTORY.rst | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index d1c807f0..d526c53f 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,6 +2,30 @@
 Release Notes
 ==============
 
+3.4.0 (2022-04-05)
+------------------
+
+* add differential evolution proposal for slice sampling, recommend it
+* fix revert of step sampler when run out of constraint, in MPI
+* add SimpleRegion: axis-aligned ellipsoidal for very high-d.
+
+
+3.3.3 (2022-04-05)
+------------------
+
+* pretty marginal posterior plot to stdout
+* avoid non-terminations when logzerr cannot be reached
+
+3.3.0 (2022-04-05)
+------------------
+
+* add RobustEllipsoidRegion: ellipsoidal without MLFriends for high-d.
+* add WrappingEllipsoid: for additional rejection.
+* bug fixes on rank order test
+* add resume-similar
+* modular step samplers
+
+
 3.0.0 (2020-10-03)
 ------------------
 

From 91f18146ed075ed16e4356311e449cd1181c6100 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 13:55:36 +0200
Subject: [PATCH 033/313] [ci] migrate test docker image

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 6ba565fd..c6ea2505 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,7 @@ jobs:
   build:
     
     docker:
-      - image: circleci/python:3.7.0
+      - image: cimg/python:3.7
     
     steps: 
       

From c99415c6d5f33ff4179c257428d981d2d5a3d323 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:03:47 +0200
Subject: [PATCH 034/313] [ci] install pip

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index c6ea2505..36717117 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -13,7 +13,7 @@ jobs:
       - checkout
       
       - run: sudo apt-get update -y
-      - run: sudo apt-get install -y python3-dev python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
+      - run: sudo apt-get install -y python3-dev python3-pip python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
       - run: sudo pip3 install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py

From 174546eb196bb30118266dc6ba39970a6c219d81 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:05:44 +0200
Subject: [PATCH 035/313] [ci] install cython

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 36717117..9eeee7c1 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -13,7 +13,7 @@ jobs:
       - checkout
       
       - run: sudo apt-get update -y
-      - run: sudo apt-get install -y python3-dev python3-pip python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
+      - run: sudo apt-get install -y python3-dev python3-pip cython3 python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
       - run: sudo pip3 install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py

From f9f65542a3148596d6d9b14df20533edb70e184c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:14:03 +0200
Subject: [PATCH 036/313] [ci] install cython with pip as well

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 9eeee7c1..046045c5 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -16,7 +16,7 @@ jobs:
       - run: sudo apt-get install -y python3-dev python3-pip cython3 python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
-      - run: sudo pip3 install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py
+      - run: sudo pip3 install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py cython
       - run: mkdir -p test-reports
 
       - run: python3 setup.py install --user

From c886833bb9bf61dd0d7ec2c97846cc8faf10833b Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:17:50 +0200
Subject: [PATCH 037/313] [ci] use pip belonging to python

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 046045c5..c6ff6b37 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -16,7 +16,7 @@ jobs:
       - run: sudo apt-get install -y python3-dev python3-pip cython3 python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
-      - run: sudo pip3 install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py cython
+      - run: sudo python3 -m pip install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py cython
       - run: mkdir -p test-reports
 
       - run: python3 setup.py install --user

From cb90a531d7fe0e31ef03ab7569444f6d64baa603 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:22:43 +0200
Subject: [PATCH 038/313] [ci] use latest python image

---
 .circleci/config.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index c6ff6b37..ee222ddc 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,17 +6,17 @@ jobs:
   build:
     
     docker:
-      - image: cimg/python:3.7
+      - image: cimg/python:latest
     
     steps: 
       
       - checkout
       
       - run: sudo apt-get update -y
-      - run: sudo apt-get install -y python3-dev python3-pip cython3 python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
+      - run: sudo apt-get install -y python3-dev python3-pip python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
-      - run: sudo python3 -m pip install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py cython
+      - run: sudo pip3 install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py
       - run: mkdir -p test-reports
 
       - run: python3 setup.py install --user

From 1d5f9053d3976ced2c6a16e1e68c5ce996622c7f Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:23:51 +0200
Subject: [PATCH 039/313] [ci] try generic python image

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index ee222ddc..db9277e6 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,7 @@ jobs:
   build:
     
     docker:
-      - image: cimg/python:latest
+      - image: cimg/python
     
     steps: 
       

From 09082432871897d523fd7e931e563e21bac408ed Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:24:34 +0200
Subject: [PATCH 040/313] [ci] try newer python image

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index db9277e6..bfd431da 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,7 @@ jobs:
   build:
     
     docker:
-      - image: cimg/python
+      - image: cimg/python:3.10
     
     steps: 
       

From 990ea1ff4785c5cc1544594894102d918e4e191e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:26:30 +0200
Subject: [PATCH 041/313] [ci] update to python -m pip

---
 .circleci/config.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index bfd431da..8cb8c1bb 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -16,10 +16,10 @@ jobs:
       - run: sudo apt-get install -y python3-dev python3-pip python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
-      - run: sudo pip3 install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py
+      - run: sudo python3 -m pip install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py
       - run: mkdir -p test-reports
 
-      - run: python3 setup.py install --user
+      - run: python3 -m pip install -e .
       - run: for i in examples/test*.py; do python3 $i --help; done
       
       - run: coverage3 run --parallel-mode setup.py test

From 6c802dc834a1eda6db368f041ae6e5368549e280 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:30:08 +0200
Subject: [PATCH 042/313] [ci] remove python3-dev, pip wants to install by
 itself

---
 .circleci/config.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 8cb8c1bb..acccca1d 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -13,7 +13,8 @@ jobs:
       - checkout
       
       - run: sudo apt-get update -y
-      - run: sudo apt-get install -y python3-dev python3-pip python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
+      - run: sudo apt-get install -y python3-pip python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
+      
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
       - run: sudo python3 -m pip install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py

From e8e84693da7d652d6273d2f0762c0a471036bdcc Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:32:31 +0200
Subject: [PATCH 043/313] [ci] use generic image, to avoid python collision

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index acccca1d..bcb165d0 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,7 @@ jobs:
   build:
     
     docker:
-      - image: cimg/python:3.10
+      - image: cimg/base:2021.04
     
     steps: 
       

From 017ebd2df8985c4edc4826d3c17ac820d4f3ed4b Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:35:01 +0200
Subject: [PATCH 044/313] [ci] pip local install

---
 .circleci/config.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index bcb165d0..b283804b 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,7 @@ jobs:
   build:
     
     docker:
-      - image: cimg/base:2021.04
+      - image: cimg/python:3.10
     
     steps: 
       
@@ -17,7 +17,7 @@ jobs:
       
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
-      - run: sudo python3 -m pip install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py
+      - run: python3 -m pip install --user -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py
       - run: mkdir -p test-reports
 
       - run: python3 -m pip install -e .

From aed5d350676216a394ab6b9382306af67eebfeb2 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 14:38:41 +0200
Subject: [PATCH 045/313] [ci] revert to generic image: fastest

---
 .circleci/config.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index b283804b..bcb165d0 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,7 @@ jobs:
   build:
     
     docker:
-      - image: cimg/python:3.10
+      - image: cimg/base:2021.04
     
     steps: 
       
@@ -17,7 +17,7 @@ jobs:
       
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
-      - run: python3 -m pip install --user -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py
+      - run: sudo python3 -m pip install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py
       - run: mkdir -p test-reports
 
       - run: python3 -m pip install -e .

From 8e600ec597eed2debd85c76fedb0cb04a6710301 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 15:13:15 +0200
Subject: [PATCH 046/313] fixed tregion handling of categorical variables; doc
 formatting

---
 docs/example-sine-highd.ipynb | 351 ++++------------------------------
 tests/test_regionsampling.py  |  16 +-
 ultranest/integrator.py       |   2 +-
 ultranest/mlfriends.pyx       |  13 ++
 ultranest/stepsampler.py      |  72 ++++---
 5 files changed, 110 insertions(+), 344 deletions(-)

diff --git a/docs/example-sine-highd.ipynb b/docs/example-sine-highd.ipynb
index 2894e566..80d665e8 100644
--- a/docs/example-sine-highd.ipynb
+++ b/docs/example-sine-highd.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,22 +89,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -130,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -236,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,57 +252,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2b82daa9d8824915a182f309ffe8076b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=-4e+01   [-35.7627..-35.7623]*| it/evals=8480/59249 eff=14.4098% N=400    0 0  \n",
-      "[ultranest] Likelihood function evaluations: 59322\n",
-      "[ultranest]   logZ = -52.38 +- 0.1759\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 2137.3, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.06 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.18, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.19 bs:0.18 tail:0.01 total:0.18 required:<0.50\n",
-      "[ultranest] done iterating.\n",
-      "\n",
-      "logZ = -52.389 +- 0.404\n",
-      "  single instance: logZ = -52.389 +- 0.192\n",
-      "  bootstrapped   : logZ = -52.381 +- 0.404\n",
-      "  tail           : logZ = +- 0.010\n",
-      "insert order U test : converged: True correlation: inf iterations\n",
-      "\n",
-      "    B                   : 0.42  │ ▁▁▁▁▁▁▁▁▁▂▂▂▃▃▅▅▅▆▆▇▆▅▅▄▄▂▂▂▁▁▁▁▁▁▁▁▁ │1.63      1.04 +- 0.15\n",
-      "    A1                  : 3.02  │ ▁ ▁▁▁▁▁▁▁▁▂▃▃▃▄▅▆▇▇▇▇▇▇▆▅▄▃▂▂▁▁▁▁▁▁▁▁ │4.76      3.94 +- 0.21\n",
-      "    P1                  : 2.869 │ ▁▁▁▁▁▁▁▁▁▂▃▄▄▅▅▇▇▇▇▇▆▅▅▄▃▂▂▁▁▁▁▁▁▁▁▁▁ │3.296     3.076 +- 0.051\n",
-      "    t1                  : 0.00  │▇▃▁▁                                 ▁▂│1.00      0.16 +- 0.34\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "result1 = sampler1.run(min_num_live_points=400)\n",
     "sampler1.print_results()"
@@ -330,70 +269,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "43317fab364a42ad9d476036e4a37d46",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Z=-185.2(0.00%) | Like=-175.37..-69.25 [-187.1261..-173.2950] | it/evals=1492/87818 eff=1.4928% N=213 13 3 \r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/user/.local/lib/python3.8/site-packages/ultranest-3.3.3-py3.8-linux-x86_64.egg/ultranest/integrator.py:1633: UserWarning: Sampling from region seems inefficient (0/40 accepted in iteration 2500). To improve efficiency, modify the transformation so that the current live points are ellipsoidal, or use a stepsampler, or set frac_remain to a lower number (e.g., 0.5) to terminate earlier.\n",
-      "  warnings.warn(warning_message)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=-4e+01  [-89.9197..-75.4800] | it/evals=2194/402993 eff=0.4985% N=213     \n",
-      "[ultranest] Likelihood function evaluations: 402993\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/user/.local/lib/python3.8/site-packages/numpy/core/_methods.py:232: RuntimeWarning: overflow encountered in multiply\n",
-      "  x = um.multiply(x, x, out=x)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Reached maximum number of likelihood calls (402993 > 400000)...\n",
-      "[ultranest] done iterating.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "result2 = sampler2.run(min_num_live_points=400, max_ncalls=400000)"
    ]
@@ -415,74 +293,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Widening roots to 400 live points (have 400 already) ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5715d983c988449f89817cc7270464f8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=-2e+01   [-19.8295..-19.8288]*| it/evals=6503/659654 eff=1.6785% N=213 \n",
-      "[ultranest] Likelihood function evaluations: 660367\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/user/.local/lib/python3.8/site-packages/numpy/core/_methods.py:232: RuntimeWarning: overflow encountered in multiply\n",
-      "  x = um.multiply(x, x, out=x)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest]   logZ = -45.36 +- 0.2433\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 1522.6, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.12 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy wants 211 minimum live points (dlogz from 0.20 to 0.57, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.33 bs:0.24 tail:0.01 total:0.24 required:<0.50\n",
-      "[ultranest] done iterating.\n",
-      "\n",
-      "logZ = -45.419 +- 0.572\n",
-      "  single instance: logZ = -45.419 +- 0.237\n",
-      "  bootstrapped   : logZ = -45.360 +- 0.572\n",
-      "  tail           : logZ = +- 0.010\n",
-      "insert order U test : converged: True correlation: inf iterations\n",
-      "\n",
-      "    B                   : 0.33  │ ▁▁▁▁▃▅▇▇▇▅▂▁▁▁▁                     ▁ │3.44      1.01 +- 0.16\n",
-      "    A1                  : 0.10  │▁                ▁      ▁▁▁▂▄▅▇▇▄▂▁▁▁▁ │5.31      4.19 +- 0.24\n",
-      "    P1                  : 1.0   │▇▁▁                     ▁            ▁ │87.2      3.1 +- 1.2\n",
-      "    t1                  : 0.000 │▇▄▁▁              ▁▁                 ▁ │0.817     0.020 +- 0.017\n",
-      "    A2                  : 0.21  │ ▁▁▁▁▂▃▄▆▇▇▇▆▄▃▁▁▁▁  ▁          ▁   ▁▁ │4.01      1.22 +- 0.26\n",
-      "    P2                  : 1.000 │ ▁▁▂▇▄▁                             ▁▁ │3.131     1.258 +- 0.052\n",
-      "    t2                  : 0.000 │▁▁▁▁▁▁▂▂▅▇▇▇▅▃▁▁▁▁▁                   ▁│1.000     0.269 +- 0.052\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import ultranest.stepsampler\n",
     "\n",
@@ -521,22 +334,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 698.4x698.4 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from ultranest.plot import cornerplot\n",
     "cornerplot(result1)"
@@ -544,42 +344,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x1152 with 49 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cornerplot(result2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "59322"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sampler1.ncall"
    ]
@@ -601,32 +377,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PolyCollection at 0x7f53840a7700>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.title(\"1-sine fit\")\n",
@@ -656,32 +409,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PolyCollection at 0x7f53840a6b20>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.title(\"2-sine fit\")\n",
@@ -738,20 +468,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1064.3857030391894"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "K = np.exp(result2['logz'] - result1['logz'])\n",
     "K"
diff --git a/tests/test_regionsampling.py b/tests/test_regionsampling.py
index a37e09e3..634256e2 100644
--- a/tests/test_regionsampling.py
+++ b/tests/test_regionsampling.py
@@ -164,23 +164,31 @@ def test_ellipsoids():
         region = MLFriends(points, transformLayer)
         region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
         region.create_ellipsoid()
-        assert region.inside(points).all()
+        inside = region.inside(points)
+        assert inside.shape == (len(points),), (inside.shape, points.shape)
+        assert inside.all()
         
         region = RobustEllipsoidRegion(points, transformLayer)
         region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
         region.create_ellipsoid()
-        assert region.inside(points).all()
+        inside = region.inside(points)
+        assert inside.shape == (len(points),), (inside.shape, points.shape)
+        assert inside.all()
         
         region = SimpleRegion(points, transformLayer)
         region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
         region.create_ellipsoid()
-        assert region.inside(points).all()
+        inside = region.inside(points)
+        assert inside.shape == (len(points),), (inside.shape, points.shape)
+        assert inside.all()
         
         tregion = WrappingEllipsoid(tpoints)
         print(tregion.variable_dims)
         tregion.enlarge = tregion.compute_enlargement(nbootstraps=30)
         tregion.create_ellipsoid()
-        assert tregion.inside(tpoints).all()
+        inside = tregion.inside(tpoints)
+        assert inside.shape == (len(tpoints),), (inside.shape, tpoints.shape)
+        assert inside.all()
     
 
 if __name__ == '__main__':
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 168bf97b..c1d0558e 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2455,7 +2455,7 @@ def run_iter(
                     # move also the ellipsoid
                     self.region.ellipsoid_center = np.mean(self.region.u, axis=0)
                     if self.tregion:
-                        self.tregion.ellipsoid_center = np.mean(active_p, axis=0)
+                        self.tregion.update_center(np.mean(active_p, axis=0))
 
                     # if we track the cluster assignment, then in the next round
                     # the ids with the same members are likely to have the same id
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index c5a87dd2..4f8a1a9a 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -1404,6 +1404,19 @@ class WrappingEllipsoid(object):
         self.ellipsoid_axlens = 1. / np.sqrt(l)
         self.ellipsoid_axes = np.dot(v, np.diag(self.ellipsoid_axlens))
 
+    def update_center(self, ctr):
+        """Update ellipsoid center, considering fixed dimensions.
+
+        Parameters
+        ----------
+        ctr: vector
+            new center
+
+        """
+        if self.variable_dims is Ellipsis:
+            self.ellipsoid_center = ctr
+        else:
+            self.ellipsoid_center = ctr[self.variable_dims]
 
     def inside(self, u):
         """Check if inside wrapping ellipsoid.
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index a506fe70..256a780f 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -23,7 +23,7 @@ def generate_random_direction(ui, region, scale=1):
         current region (not used)
     scale: float
         length of direction vector
-    
+
     Returns
     --------
     v: array
@@ -92,9 +92,9 @@ def generate_cube_oriented_differential_direction(ui, region, scale=1):
         i2 = np.random.randint(nlive - 1)
         if i2 >= i:
             i2 += 1
-        
+
         v[j] = (region.u[i,j] - region.u[i2,j]) * scale
-    
+
     return v
 
 
@@ -121,11 +121,12 @@ def generate_differential_direction(ui, region, scale=1):
     i2 = np.random.randint(nlive - 1)
     if i2 >= i:
         i2 += 1
-    
+
     # use doubling procedure to identify left and right maxima borders
     v = (region.u[i,:] - region.u[i2,:]) * scale
     return v
 
+
 def generate_partial_differential_direction(ui, region, scale=1):
     """Draw a unit direction vector in direction of a random unit cube axes.
 
@@ -192,7 +193,7 @@ def generate_region_random_direction(ui, region, scale=1):
         current region
     scale: float:
         length of direction vector (in t-space)
-    
+
     Returns
     --------
     v: array
@@ -226,7 +227,6 @@ def generate_mixture_random_direction(ui, region, scale=1):
         return generate_differential_direction(ui, region, scale=scale)
     else:
         return generate_region_oriented_direction(ui, region, scale=scale)
-    return v
 
 
 def _inside_region(region, unew, uold):
@@ -256,7 +256,7 @@ def inside_region(region, unew, uold):
         point to check
     uold: array
         not used
-    
+
     Returns
     --------
     v: array
@@ -265,6 +265,7 @@ def inside_region(region, unew, uold):
     del uold
     return region.inside(unew)
 
+
 def adapt_proposal_total_distances(region, history, mean_pair_distance, ndim):
     # compute mean vector of each proposed jump
     # compute total distance of all jumps
@@ -275,6 +276,7 @@ def adapt_proposal_total_distances(region, history, mean_pair_distance, ndim):
 
     return far_enough, [d2, mean_pair_distance]
 
+
 def adapt_proposal_total_distances_NN(region, history, mean_pair_distance, ndim):
     # compute mean vector of each proposed jump
     # compute total distance of all jumps
@@ -285,6 +287,7 @@ def adapt_proposal_total_distances_NN(region, history, mean_pair_distance, ndim)
 
     return far_enough, [d2, region.maxradiussq**0.5]
 
+
 def adapt_proposal_summed_distances(region, history, mean_pair_distance, ndim):
     # compute sum of distances from each jump
     tproposed = region.transformLayer.transform(np.asarray([u for u, _ in history]))
@@ -293,6 +296,7 @@ def adapt_proposal_summed_distances(region, history, mean_pair_distance, ndim):
 
     return far_enough, [d2, mean_pair_distance]
 
+
 def adapt_proposal_summed_distances_NN(region, history, mean_pair_distance, ndim):
     # compute sum of distances from each jump
     tproposed = region.transformLayer.transform(np.asarray([u for u, _ in history]))
@@ -301,6 +305,7 @@ def adapt_proposal_summed_distances_NN(region, history, mean_pair_distance, ndim
 
     return far_enough, [d2, region.maxradiussq**0.5]
 
+
 def adapt_proposal_move_distances(region, history, mean_pair_distance, ndim):
     # compute distance from start to end
     ustart, _ = history[0]
@@ -311,6 +316,7 @@ def adapt_proposal_move_distances(region, history, mean_pair_distance, ndim):
 
     return far_enough, [d2, region.maxradiussq**0.5]
 
+
 def adapt_proposal_move_distances_midway(region, history, mean_pair_distance, ndim):
     # compute distance from start to end
     ustart, _ = history[0]
@@ -322,6 +328,7 @@ def adapt_proposal_move_distances_midway(region, history, mean_pair_distance, nd
 
     return far_enough, [d2, region.maxradiussq**0.5]
 
+
 class StepSampler(object):
     """Base class for a simple step sampler, staggering around.
 
@@ -347,18 +354,27 @@ def __init__(
             always doubling nsteps, until Z is stable.
 
         generate_direction: function
-            direction proposal function. Available are:
+            direction proposal function.
+
+            Available are:
 
             * :py:func:`generate_cube_oriented_direction` (slice sampling)
             * :py:func:`generate_region_oriented_direction` (slice sampling on the whitened parameter space)
+            * :py:class:`SequentialDirectionGenerator` (sequential slice sampling on the whitened parameter space)
             * :py:func:`generate_random_direction` (hit-and-run sampling)
             * :py:func:`generate_region_random_direction` (hit-and-run sampling on the whitened parameter space)
             * :py:func:`generate_cube_oriented_differential_direction` (slice sampling with better proposal scale)
             * :py:func:`generate_differential_direction` (differential evolution slice proposal)
             * :py:func:`generate_partial_differential_direction` (differential evolution slice proposal on only 10% of the parameters)
             * :py:func:`generate_mixture_random_direction` (generate_differential_direction and generate_cube_oriented_differential_direction)
-            
-            When in doubt, use :py:func:`generate_mixture_random_direction`.
+
+            Additionally, :py:class:`OrthogonalDirectionGenerator` 
+            can be applied to a generate_direction.
+
+            When in doubt, try :py:func:`generate_mixture_random_direction`.
+            It combines efficient moves along the live point distribution,
+            with robustness against collapse to a subspace.
+            :py:func:`generate_cube_oriented_direction` works well too.
 
         adaptive_nsteps: False, 'proposal-distance', 'move-distance'
             Strategy to adapt the number of steps. The strategies
@@ -385,7 +401,7 @@ def __init__(
 
             Adapting can give usable results. However, strictly speaking,
             detailed balance is not maintained, so the results can be biased.
-            You can use the logstat property to find out the `nsteps` learned 
+            You can use the logstat property to find out the `nsteps` learned
             from one run (third column), and use the largest value for `nsteps`
             of a fresh run.
 
@@ -415,7 +431,7 @@ def __init__(
             False: None,
             'move-distance': adapt_proposal_move_distances,
             'move-distance-midway': adapt_proposal_move_distances_midway,
-            'proposal-total-distances': adapt_proposal_total_distances, 
+            'proposal-total-distances': adapt_proposal_total_distances,
             'proposal-total-distances-NN': adapt_proposal_total_distances_NN,
             'proposal-summed-distances': adapt_proposal_summed_distances,
             'proposal-summed-distances-NN': adapt_proposal_summed_distances_NN,
@@ -595,12 +611,12 @@ def finalize_chain(self, region=None, Lmin=None, Ls=None):
         self.nrejects = 0
 
     def new_chain(self, region=None):
-        """Starts a new path, reset statistics."""
+        """Start a new path, reset statistics."""
         self.history = []
         self.nrejects = 0
 
     def region_changed(self, Ls, region):
-        """React to change of region. 
+        """React to change of region.
 
         Parameters
         -----------
@@ -609,7 +625,6 @@ def region_changed(self, Ls, region):
         Ls: array
             loglikelihood values of the live points
         """
-
         if self.adaptive_nsteps_needs_mean_pair_distance:
             self.mean_pair_distance = region.compute_mean_pair_distance()
             # print("region changed. new mean_pair_distance: %g" % self.mean_pair_distance)
@@ -738,10 +753,14 @@ def move(self, ui, region, ndraw=1, plot=False):
         unew = ui.reshape((1, -1)) + jitter
         return unew
 
+
 def CubeMHSampler(*args, **kwargs):
+    """Gaussian Metropolis-Hastings sampler, using unit cube."""
     return MHSampler(*args, **kwargs, generate_direction=generate_random_direction)
 
+
 def RegionMHSampler(*args, **kwargs):
+    """Gaussian Metropolis-Hastings sampler, using region."""
     return MHSampler(*args, **kwargs, generate_direction=generate_region_random_direction)
 
 
@@ -749,7 +768,7 @@ class SliceSampler(StepSampler):
     """Slice sampler, respecting the region."""
 
     def new_chain(self, region=None):
-        """Starts a new path, reset slice."""
+        """Start a new path, reset slice."""
         self.interval = None
         self.found_left = False
         self.found_right = False
@@ -882,7 +901,9 @@ def RegionBallSliceSampler(*args, **kwargs):
 
 class SequentialDirectionGenerator(object):
     def __init__(self):
+        """Sequentially proposes one region axes after the next."""
         self.axis_index = 0
+
     def __call__(self, ui, region, scale=1):
         """Iteratively choose the next axis in t-space.
 
@@ -914,6 +935,7 @@ def __call__(self, ui, region, scale=1):
         v *= scale / (v**2).sum()**0.5
         return v
 
+
 def RegionSequentialSliceSampler(*args, **kwargs):
     """Slice sampler, sequentially iterating region axes."""
     return SliceSampler(*args, **kwargs, generate_direction=SequentialDirectionGenerator())
@@ -921,7 +943,7 @@ def RegionSequentialSliceSampler(*args, **kwargs):
 
 class OrthogonalDirectionGenerator(object):
     def __init__(self, generate_direction):
-        """Orthogonalizes vectors.
+        """Orthogonalizes proposal vectors.
 
         Parameters
         -----------
@@ -1029,7 +1051,7 @@ def __call__(self, ui, region, scale=1):
             new direction vector
         """
         ndim = len(ui)
-        
+
         v = self.generate_direction(ui=ui, region=region, scale=scale)
         j = self.axis_index % self.nsteps
         self.axis_index = j + 1
@@ -1046,9 +1068,9 @@ def SpeedVariableRegionSliceSampler(step_matrix, *args, **kwargs):
 
     Updates only some dimensions at a time, completely user-definable.
     """
-    
-    
-    return SliceSampler(*args, **kwargs, 
+
+    return SliceSampler(
+        *args, **kwargs,
         nsteps=kwargs.pop('nsteps', len(step_matrix)),
         generate_direction=SpeedVariableGenerator(
             step_matrix=step_matrix,
@@ -1058,7 +1080,9 @@ def SpeedVariableRegionSliceSampler(step_matrix, *args, **kwargs):
 
 
 def ellipsoid_bracket(ui, v, ellipsoid_center, ellipsoid_inv_axes, ellipsoid_radius_square):
-    """ For a line from ui in direction v through an ellipsoid
+    """Find line-ellipsoid intersection points.
+
+    For a line from ui in direction v through an ellipsoid
     centered at ellipsoid_center with axes matrix ellipsoid_inv_axes,
     return the lower and upper intersection parameter.
 
@@ -1099,7 +1123,9 @@ def ellipsoid_bracket(ui, v, ellipsoid_center, ellipsoid_inv_axes, ellipsoid_rad
 
 
 def crop_bracket_at_unit_cube(ui, v, left, right, epsilon=1e-6):
-    """A line segment from *ui* in direction *v* from t between *left* <= 0 <= *right*
+    """Find line-cube intersection points.
+
+    A line segment from *ui* in direction *v* from t between *left* <= 0 <= *right*
     will be truncated by the unit cube. Returns the bracket and whether cropping was applied.
 
     Parameters

From 2a96c2fbbe9a8f4df7148cc4658cc393042bfecb Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 15:24:52 +0200
Subject: [PATCH 047/313] use differential evolution proposal also in
 solvecompat

---
 ultranest/hotstart.py    |  2 ++
 ultranest/integrator.py  |  5 ++---
 ultranest/solvecompat.py | 26 ++++++++++++++++++++------
 3 files changed, 24 insertions(+), 9 deletions(-)

diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index c79581d2..27de3611 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -167,6 +167,7 @@ def aux_loglikelihood(x):
 
     return aux_loglikelihood, aux_transform
 
+
 def get_extended_auxiliary_independent_problem(loglike, transform, ctr, err, df=1):
     """Return a new loglike and transform based on an auxiliary distribution.
 
@@ -240,6 +241,7 @@ def aux_loglikelihood(x):
 
     return aux_loglikelihood, aux_transform
 
+
 def reuse_samples(
     param_names, loglike, points, logl, logw=None,
     logz=0.0, logzerr=0.0, upoints=None,
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index c1d0558e..b5e1a063 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2736,7 +2736,7 @@ def print_results(self, use_unicode=True):
                     hi = min(self.transform_limits[i,1], hi + 2 * step)
                     H, edges = np.histogram(v, bins=np.linspace(lo, hi, 40))
                     lo, hi = edges[0], edges[-1]
-                    
+
                     dist = ''.join([' ▁▂▃▄▅▆▇██'[i] for i in np.ceil(H * 7 / H.max()).astype(int)])
                     print('    %-20s: %-6s│%s│%-6s    %s +- %s' % (p, fmt % lo, dist, fmt % hi, fmt % med, fmt % sigma))
                 except:
@@ -2744,12 +2744,11 @@ def print_results(self, use_unicode=True):
                     print(fmts % (p, med, sigma))
             print()
 
-
     def plot(self):
         """Make corner, run and trace plots.
 
         calls:
-        
+
         * plot_corner()
         * plot_run()
         * plot_trace()
diff --git a/ultranest/solvecompat.py b/ultranest/solvecompat.py
index 9d799087..61ee4612 100644
--- a/ultranest/solvecompat.py
+++ b/ultranest/solvecompat.py
@@ -15,7 +15,7 @@
 import string
 
 from .integrator import ReactiveNestedSampler
-from .stepsampler import RegionBallSliceSampler
+from .stepsampler import SliceSampler, generate_mixture_random_direction
 
 
 def pymultinest_solve_compat(
@@ -31,6 +31,14 @@ def pymultinest_solve_compat(
     Disadvantages compared to using ReactiveNestedSampler directly:
     cannot resume easily, cannot plot interactively.
     Limited results.
+
+    It is recommended that you directly use::
+
+        sampler = ReactiveNestedSampler(paramnames, LogLikelihood, transform=Prior)
+        sampler.run()
+
+    following the UltraNest documentation and manuals,
+    as this gives you more control on resuming and sampler options.
     """
     if paramnames is None:
         paramnames = list(string.ascii_lowercase)[:n_dims]
@@ -60,12 +68,18 @@ def pymultinest_solve_compat(
             min_ess=min_ess, frac_remain=frac_remain,
             Lepsilon=Lepsilon, max_ncalls=40000)
 
-        sampler.stepsampler = RegionBallSliceSampler(
-            nsteps=1000, adaptive_nsteps='move-distance',
-            region_filter=kwargs.get('region_filter', True))
+        sampler.stepsampler = SliceSampler(
+            nsteps=1000,
+            generate_direction=generate_mixture_random_direction,
+            adaptive_nsteps='move-distance',
+            region_filter=kwargs.get('region_filter', True)
+            )
     else:
-        sampler.stepsampler = RegionBallSliceSampler(
-            nsteps=speed, adaptive_nsteps=False, region_filter=False)
+        sampler.stepsampler = SliceSampler(
+            generate_direction=generate_mixture_random_direction,
+            nsteps=speed,
+            adaptive_nsteps=False,
+            region_filter=False)
 
     sampler.run(dlogz=evidence_tolerance,
                 max_iters=max_iter if max_iter > 0 else None,

From a51eeef5dabe7e9ada3d20f85eaaa8372ceb215e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 15:30:01 +0200
Subject: [PATCH 048/313] update changelog

---
 HISTORY.rst | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index d526c53f..08bbc8b7 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -10,15 +10,11 @@ Release Notes
 * add SimpleRegion: axis-aligned ellipsoidal for very high-d.
 
 
-3.3.3 (2022-04-05)
+3.3.3 (2021-09-17)
 ------------------
 
 * pretty marginal posterior plot to stdout
 * avoid non-terminations when logzerr cannot be reached
-
-3.3.0 (2022-04-05)
-------------------
-
 * add RobustEllipsoidRegion: ellipsoidal without MLFriends for high-d.
 * add WrappingEllipsoid: for additional rejection.
 * bug fixes on rank order test

From 557f35cdca49a26b5aed56efd55b2b92cc23adb3 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 15:37:04 +0200
Subject: [PATCH 049/313] avoid duplicate argument in
 SpeedVariableRegionSliceSampler

---
 ultranest/stepsampler.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 256a780f..e78a7817 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -1068,13 +1068,13 @@ def SpeedVariableRegionSliceSampler(step_matrix, *args, **kwargs):
 
     Updates only some dimensions at a time, completely user-definable.
     """
-
+    generate_direction = kwargs.pop('generate_direction', generate_region_random_direction)
     return SliceSampler(
         *args, **kwargs,
         nsteps=kwargs.pop('nsteps', len(step_matrix)),
         generate_direction=SpeedVariableGenerator(
             step_matrix=step_matrix,
-            generate_direction=kwargs.pop('generate_direction', generate_region_random_direction)
+            generate_direction=generate_direction
         )
     )
 

From 167ab066c07ea97be088c88f77a70760dd51f61e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 16:24:31 +0200
Subject: [PATCH 050/313] =?UTF-8?q?Bump=20version:=203.4.0=20=E2=86=92=203?=
 =?UTF-8?q?.4.1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index fdc90ee3..9069e44e 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.4.0',
+    version='3.4.1',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 1e915359..6096ab03 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.4.0'
+__version__ = '3.4.1'

From 5591c88ae3a99d1929b947779a3e0727c4d53da8 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 16:49:14 +0200
Subject: [PATCH 051/313] linting cython code a bit

---
 ultranest/mlfriends.pyx | 4 ++--
 ultranest/stepfuncs.pyx | 4 +---
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 4f8a1a9a..06cd5e3d 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -11,7 +11,8 @@ cimport cython
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-cdef count_nearby(np.ndarray[np.float_t, ndim=2] apts,
+cdef count_nearby(
+    np.ndarray[np.float_t, ndim=2] apts,
     np.ndarray[np.float_t, ndim=2] bpts,
     np.float_t radiussq,
     np.ndarray[np.int_t, ndim=1] nnearby
@@ -360,7 +361,6 @@ def bounding_ellipsoid(
     """
     # Function taken from nestle, MIT licensed, (C) kbarbary
 
-    npoints = x.shape[0]
     ndim = x.shape[1]
 
     # Calculate covariance of points
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 82c93a73..07727fcf 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -4,7 +4,7 @@
 
 import numpy as np
 cimport numpy as np
-from numpy import pi, nan as np_nan
+from numpy import nan as np_nan
 cimport cython
 from cython.parallel import prange
 
@@ -16,7 +16,6 @@ cdef _within_unit_cube(
 ):
     cdef size_t popsize = u.shape[0]
     cdef size_t ndim = u.shape[1]
-    cdef np.uint8_t good
     cdef size_t i, j
 
     for i in range(popsize):
@@ -327,7 +326,6 @@ cdef _fill_directions(
     float scale
 ):
     cdef size_t nsamples = v.shape[0]
-    cdef size_t ndim = v.shape[0]
     cdef size_t i
     for i in range(nsamples):
         v[i, indices[i]] = scale

From 16f417aef8043f26c9e7e2b9246539962bc9148b Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Apr 2022 17:25:50 +0200
Subject: [PATCH 052/313] =?UTF-8?q?Bump=20version:=203.4.1=20=E2=86=92=203?=
 =?UTF-8?q?.4.2?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 9069e44e..3be41178 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.4.1',
+    version='3.4.2',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 6096ab03..550ca8f2 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.4.1'
+__version__ = '3.4.2'

From 461913cbca2e3b58569f6768c25e9facaebc36ae Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 6 Apr 2022 08:42:01 +0200
Subject: [PATCH 053/313] avoid problematic plateau in high-d loggamma

---
 Makefile                 |  8 +++----
 examples/testfeatures.py |  2 +-
 ultranest/mlfriends.pyx  | 51 ++++++++++++++++++++++------------------
 3 files changed, 33 insertions(+), 28 deletions(-)

diff --git a/Makefile b/Makefile
index 41092319..c6f4854e 100644
--- a/Makefile
+++ b/Makefile
@@ -89,10 +89,10 @@ servedocs: docs ## compile the docs watching for changes
 
 release-test: install
 	rm -rf logs/features-*
-	# grep iterated examples/runfeatures.sh | sed 's,python3,,g' | xargs -rt --max-lines=1 mpiexec -np 3 coverage run --parallel-mode
-	# grep -v iterated examples/runfeatures.sh | sed 's,python3,,g' | xargs -rt --max-lines=1 mpiexec -np 5 coverage run --parallel-mode
-	echo testfeatures/runsettings-*-iterated.json | xargs --max-args=1 mpiexec -np 3 coverage run --parallel-mode examples/testfeatures.py
-	grep -- --random examples/runfeatures.sh | xargs mpiexec -np 5 coverage run --parallel-mode examples/testfeatures.py --random --seed
+	grep -v iterated examples/runfeatures.sh | sed 's,python3,,g' | OMP_NUM_THREADS=4 xargs -rt --max-lines=1 mpiexec -np 5 coverage run --parallel-mode
+	grep    iterated examples/runfeatures.sh | sed 's,python3,,g' | OMP_NUM_THREADS=4 xargs -rt --max-lines=1 mpiexec -np 3 coverage run --parallel-mode
+	#echo testfeatures/runsettings-*-iterated.json | xargs --max-args=1 mpiexec -np 3 coverage run --parallel-mode examples/testfeatures.py
+	#grep -- --random examples/runfeatures.sh | sed s,python3,,g | xargs -rt --max-lines=1 mpiexec -np 5 coverage run --parallel-mode 
 
 release: release-test ## package and upload a release
 	twine upload -s dist/*.tar.gz
diff --git a/examples/testfeatures.py b/examples/testfeatures.py
index 88cf5ce2..b4a278a0 100644
--- a/examples/testfeatures.py
+++ b/examples/testfeatures.py
@@ -112,7 +112,7 @@ def loglike(theta):
             L2 = np.log(0.5 * rv2a.pdf(theta[:,1]) + 0.5 * rv2b.pdf(theta[:,1]))
             Lrest = np.sum([rv.logpdf(t) for rv, t in zip(rv_rest, theta[:,2:].transpose())], axis=0)
             like = L1 + L2 + Lrest
-            like = np.where(like < -300, -300 - ((np.asarray(theta) - 0.5)**2).sum(), like)
+            like = np.where(like < -1e100, -1e100 - ((np.asarray(theta) - 0.5)**2).sum(), like)
             assert like.shape == (len(theta),), (like.shape, theta.shape)
             return like
 
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 06cd5e3d..a5133d14 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -9,6 +9,7 @@ cimport numpy as np
 from numpy import pi
 cimport cython
 
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 cdef count_nearby(
@@ -35,8 +36,6 @@ cdef count_nearby(
     cdef size_t na = apts.shape[0]
     cdef size_t nb = bpts.shape[0]
     cdef size_t ndim = apts.shape[1]
-    #assert ndim == bpts.shape[1]
-    #assert nnearby.shape[0] == nb
 
     cdef unsigned long i, j
     cdef np.float_t d
@@ -55,7 +54,8 @@ cdef count_nearby(
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def find_nearby(np.ndarray[np.float_t, ndim=2] apts,
+def find_nearby(
+    np.ndarray[np.float_t, ndim=2] apts,
     np.ndarray[np.float_t, ndim=2] bpts,
     np.float_t radiussq,
     np.ndarray[np.int_t, ndim=1] nnearby
@@ -80,8 +80,6 @@ def find_nearby(np.ndarray[np.float_t, ndim=2] apts,
     cdef size_t na = apts.shape[0]
     cdef size_t nb = bpts.shape[0]
     cdef size_t ndim = apts.shape[1]
-    #assert ndim == bpts.shape[1]
-    #assert nnearby.shape[0] == nb
 
     cdef unsigned long i, j
     cdef np.float_t d
@@ -118,8 +116,6 @@ cdef float compute_maxradiussq(np.ndarray[np.float_t, ndim=2] apts, np.ndarray[n
     cdef size_t na = apts.shape[0]
     cdef size_t nb = bpts.shape[0]
     cdef size_t ndim = apts.shape[1]
-    #assert ndim == bpts.shape[1]
-    #assert f.dtype == np.float_t and g.dtype == np.float_t
 
     cdef unsigned long i, j
     cdef np.float_t d
@@ -197,7 +193,6 @@ cdef _update_clusters(
     np.ndarray[np.int_t, ndim=1] clusterids,
 ):
     """same signature as ``update_clusters()``, see there."""
-    #print("clustering with maxradiussq %f..." % maxradiussq)
     assert upoints.shape[0] == tpoints.shape[0], ('different number of points', upoints.shape[0], tpoints.shape[0])
     assert upoints.shape[1] == tpoints.shape[1], ('different dimensionality of points', upoints.shape[1], tpoints.shape[1])
     clusteridxs = np.zeros(len(tpoints), dtype=int)
@@ -221,13 +216,13 @@ cdef _update_clusters(
         idnearby = np.empty(len(nonmembers), dtype=int)
         members = tpoints[clusteridxs == currentclusterid,:]
         find_nearby(members, nonmembers, maxradiussq, idnearby)
-        #print('merging %d into cluster %d of size %d' % (np.count_nonzero(nnearby), currentclusterid, len(members)))
+        # print('merging %d into cluster %d of size %d' % (np.count_nonzero(nnearby), currentclusterid, len(members)))
 
         if (idnearby >= 0).any():
             # place into cluster
             newmembers = nonmembermask
             newmembers[nonmembermask] = idnearby >= 0
-            #print('adding', newmembers.sum())
+            # print('adding', newmembers.sum())
             clusteridxs[newmembers] = currentclusterid
         else:
             # start a new cluster
@@ -242,7 +237,7 @@ cdef _update_clusters(
 
     assert (clusteridxs > 0).all()
     nclusters = len(np.unique(clusteridxs))
-    #assert np.all(np.unique(clusteridxs) == np.arange(nclusters)+1), (np.unique(clusteridxs), nclusters, np.arange(nclusters)+1)
+    # assert np.all(np.unique(clusteridxs) == np.arange(nclusters)+1), (np.unique(clusteridxs), nclusters, np.arange(nclusters)+1)
     if nclusters == 1:
         overlapped_upoints = upoints
     else:
@@ -261,13 +256,14 @@ cdef _update_clusters(
 
     return nclusters, clusteridxs, overlapped_upoints
 
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def update_clusters(
     np.ndarray[np.float_t, ndim=2] upoints,
     np.ndarray[np.float_t, ndim=2] tpoints,
     np.float_t maxradiussq,
-    clusterids = None,
+    clusterids=None,
 ):
     """Clusters `upoints`, so that clusters are distinct if no 
     member pair is within a radius of sqrt(`maxradiussq`).
@@ -338,6 +334,7 @@ def make_eigvals_positive(
 
     return a
 
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def bounding_ellipsoid(
@@ -514,7 +511,7 @@ class ScalingLayer(object):
         uwpoints = self.wrap(upoints)
         tpoints = self.transform(upoints)
         nclusters, clusteridxs, overlapped_uwpoints = update_clusters(uwpoints, tpoints, maxradiussq, self.clusterids)
-        #clusteridxs = track_clusters(clusteridxs, self.clusterids)
+        # clusteridxs = track_clusters(clusteridxs, self.clusterids)
         s = ScalingLayer(nclusters=nclusters, wrapped_dims=self.wrapped_dims, clusterids=clusteridxs)
         s.optimize(upoints, overlapped_uwpoints)
         return s
@@ -536,6 +533,7 @@ class ScalingLayer(object):
             u = w.reshape(ww.shape)
         return u
 
+
 class AffineLayer(ScalingLayer):
     """Affine whitening transformation.
 
@@ -604,7 +602,7 @@ class AffineLayer(ScalingLayer):
         self.T = eigvec * eigval**-0.5
         self.invT = np.linalg.inv(self.T)
         self.axes = self.invT
-        #print('transform used:', self.T, self.invT, 'cov:', cov, 'eigen:', eigval, eigvec)
+        # print('transform used:', self.T, self.invT, 'cov:', cov, 'eigen:', eigval, eigvec)
         self.set_clusterids(clusterids=clusterids, npoints=len(points))
 
     def create_new(self, upoints, maxradiussq, minvol=0.):
@@ -627,7 +625,7 @@ class AffineLayer(ScalingLayer):
         uwpoints = self.wrap(upoints)
         tpoints = self.transform(upoints)
         nclusters, clusteridxs, overlapped_uwpoints = update_clusters(uwpoints, tpoints, maxradiussq, self.clusterids)
-        #clusteridxs = track_clusters(clusteridxs, self.clusterids)
+        # clusteridxs = track_clusters(clusteridxs, self.clusterids)
         s = AffineLayer(nclusters=nclusters, wrapped_dims=self.wrapped_dims, clusterids=clusteridxs)
         s.optimize(upoints, overlapped_uwpoints, minvol=minvol)
         return s
@@ -678,6 +676,7 @@ def vol_prefactor(np.int_t n):
 
     return f
 
+
 def _inside_ellipsoid(
     np.ndarray[np.float_t, ndim=2] points,
     np.ndarray[np.float_t, ndim=1] ellipsoid_center,
@@ -710,6 +709,7 @@ def _inside_ellipsoid(
     # (r <= 1) means inside
     return r <= square_radius
 
+
 class MLFriends(object):
     """MLFriends region.
 
@@ -765,7 +765,7 @@ class MLFriends(object):
         r = self.maxradiussq**0.5
         N, ndim = self.u.shape
         # how large is a sphere of size r in untransformed coordinates?
-        return self.transformLayer.logvolscale + np.log(r) * ndim #+ np.log(vol_prefactor(ndim))
+        return self.transformLayer.logvolscale + np.log(r) * ndim  #+ np.log(vol_prefactor(ndim))
 
     def set_transformLayer(self, transformLayer):
         """Update transformation layer. Invalidates attribute `maxradius`.
@@ -848,7 +848,7 @@ class MLFriends(object):
 
             # compute distances from a to b
             maxd = max(maxd, compute_maxradiussq(
-                self.unormed[selected,:], 
+                self.unormed[selected,:],
                 self.unormed[~selected,:]))
 
             # compute enlargement of bounding ellipsoid
@@ -878,7 +878,7 @@ class MLFriends(object):
         # generate points near random existing points
         idx = np.random.randint(N, size=nsamples)
         v = np.random.normal(size=(nsamples, ndim))
-        v *= (np.random.uniform(size=nsamples)**(1./ndim) / np.linalg.norm(v, axis=1)).reshape((-1, 1))
+        v *= (np.random.uniform(size=nsamples)**(1. / ndim) / np.linalg.norm(v, axis=1)).reshape((-1, 1))
         v = self.unormed[idx,:] + v * self.maxradiussq**0.5
 
         # count how many are around
@@ -978,7 +978,7 @@ class MLFriends(object):
         if len(samples) == 0:
             # no result, choose another method
             self.current_sampling_method = self.sampling_methods[np.random.randint(len(self.sampling_methods))]
-            #print("switching to %s" % self.current_sampling_method)
+            # print("switching to %s" % self.current_sampling_method)
         return samples
 
     def inside(self, pts):
@@ -1085,7 +1085,7 @@ class RobustEllipsoidRegion(MLFriends):
         self.set_transformLayer(transformLayer)
 
         self.sampling_methods = [
-            self.sample_from_transformed_boundingbox,
+            #self.sample_from_transformed_boundingbox,
             self.sample_from_boundingbox,
             self.sample_from_wrapping_ellipsoid
         ]
@@ -1209,6 +1209,8 @@ class RobustEllipsoidRegion(MLFriends):
             square radius of enclosing ellipsoid.
         """
         N, ndim = self.u.shape
+        if N < ndim + 1:
+            raise FloatingPointError('not enough live points to compute covariance')
         assert np.isfinite(self.unormed).all(), self.unormed
         selected = np.empty(N, dtype=bool)
         maxd = 1e300
@@ -1252,6 +1254,7 @@ class RobustEllipsoidRegion(MLFriends):
         else:
             return -1e300
 
+
 class SimpleRegion(RobustEllipsoidRegion):
     """Axis-aligned ellipsoidal region.
 
@@ -1293,7 +1296,6 @@ class SimpleRegion(RobustEllipsoidRegion):
         self.ellipsoid_inv_axes = np.dot(v2, np.diag(self.ellipsoid_inv_axlens))
 
 
-
     def compute_enlargement(self, nbootstraps=50, minvol=0., rng=np.random):
         """Return MLFriends radius and ellipsoid enlargement using bootstrapping.
 
@@ -1316,10 +1318,13 @@ class SimpleRegion(RobustEllipsoidRegion):
             square radius of enclosing ellipsoid.
         """
         N, ndim = self.u.shape
+        assert np.isfinite(self.u).all(), self.u
         assert np.isfinite(self.unormed).all(), self.unormed
         selected = np.empty(N, dtype=bool)
         maxd = 1e300
         maxf = 0.0
+        if N < ndim + 1:
+            raise FloatingPointError('not enough live points to compute variance')
 
         for i in range(nbootstraps):
             idx = rng.randint(N, size=N)
@@ -1330,8 +1335,8 @@ class SimpleRegion(RobustEllipsoidRegion):
             ctr = np.mean(self.u[selected,:], axis=0)
             var = np.var(self.u[selected,:], axis=0)
             # compute expansion factor
-            f = np.sum((self.u[~selected,:] - ctr.reshape((1, -1)) / var)**2, axis=0).max()
-            assert np.isfinite(f), (ctr, var, self.unormed, f)
+            f = np.sum((self.u[~selected,:] - ctr.reshape((1, -1)))**2 / var, axis=0).max()
+            assert np.isfinite(f), (self.u, ctr, var, self.unormed, f)
             if not f > 0:
                 raise np.linalg.LinAlgError("Distances are not positive")
             maxf = max(maxf, f)

From 30f58c1279b5161486605facdd96aeaf1f513458 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 6 Apr 2022 18:25:06 +0200
Subject: [PATCH 054/313] [docs] fix broken bibtex link, thanks to David
 Bogensberger for pointing out

---
 Makefile        | 6 +++---
 docs/issues.rst | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/Makefile b/Makefile
index c6f4854e..c3db0dc0 100644
--- a/Makefile
+++ b/Makefile
@@ -89,12 +89,12 @@ servedocs: docs ## compile the docs watching for changes
 
 release-test: install
 	rm -rf logs/features-*
-	grep -v iterated examples/runfeatures.sh | sed 's,python3,,g' | OMP_NUM_THREADS=4 xargs -rt --max-lines=1 mpiexec -np 5 coverage run --parallel-mode
-	grep    iterated examples/runfeatures.sh | sed 's,python3,,g' | OMP_NUM_THREADS=4 xargs -rt --max-lines=1 mpiexec -np 3 coverage run --parallel-mode
+	grep -v iterated examples/runfeatures.sh | sed 's,python3,mpiexec -np 5 coverage3 run --parallel-mode,g' | OMP_NUM_THREADS=4 bash
+	grep    iterated examples/runfeatures.sh | sed 's,python3,mpiexec -np 3 coverage3 run --parallel-mode,g' | OMP_NUM_THREADS=4 bash
 	#echo testfeatures/runsettings-*-iterated.json | xargs --max-args=1 mpiexec -np 3 coverage run --parallel-mode examples/testfeatures.py
 	#grep -- --random examples/runfeatures.sh | sed s,python3,,g | xargs -rt --max-lines=1 mpiexec -np 5 coverage run --parallel-mode 
 
-release: release-test ## package and upload a release
+release: release-test dist ## package and upload a release
 	twine upload -s dist/*.tar.gz
 
 dist: clean ## builds source and wheel package
diff --git a/docs/issues.rst b/docs/issues.rst
index 2e931b91..374c2d51 100644
--- a/docs/issues.rst
+++ b/docs/issues.rst
@@ -280,7 +280,7 @@ How should I cite UltraNest?
 
 The main algorithm (MLFriends) is described in:
 
-* Buchner, J. (2014): `A statistical test for Nested Sampling algorithms <https://arxiv.org/abs/1407.5459>`_ (`bibtex <https://scholar.googleusercontent.com/scholar.bib?q=info:yTXfZFtI_vYJ:scholar.google.com/&output=citation&scisdr=CgXWBaaNEK7YkjRrxRc:AAGBfm0AAAAAX8Nu3Rcw9Cl4oM-O20oJrdlLRUQB2IhI&scisig=AAGBfm0AAAAAX8Nu3ftd-SBQKMLTBDogxM9U1n9uLKdi&scisf=4&ct=citation&cd=-1&hl=en&scfhb=1>`__)
+* Buchner, J. (2014): `A statistical test for Nested Sampling algorithms <https://arxiv.org/abs/1407.5459>`_ (`bibtex <https://ui.adsabs.harvard.edu/abs/2016S%26C....26..383B/exportcitation>`__)
 * Buchner, J. (2019): `Collaborative Nested Sampling: Big Data versus Complex Physical Models <https://arxiv.org/abs/1707.04476>`_ (`bibtex <https://ui.adsabs.harvard.edu/abs/2019PASP..131j8005B/exportcitation>`__)
 
 The UltraNest software package is presented in:

From d96ba15f6e4ac466bf5860f159817ae15e02f87d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 7 Apr 2022 15:23:07 +0200
Subject: [PATCH 055/313] add pyproject.toml to let pip know about cython

---
 pyproject.toml | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 pyproject.toml

diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..5256ba89
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,2 @@
+[build-system]
+requires = ["cython", "numpy"]

From 46abf66489026cb4cc4799b7c200e1f7a5c08036 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 7 Apr 2022 15:39:42 +0200
Subject: [PATCH 056/313] build requirements: add setuptools (to address #56)
 and numpy versioning

---
 pyproject.toml | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5256ba89..3a772ef6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,2 +1,6 @@
 [build-system]
-requires = ["cython", "numpy"]
+requires = [
+	"setuptools",
+	"cython",
+	"oldest-supported-numpy",
+]

From 9409ee5f792834989ca8eddac33163c770aac92e Mon Sep 17 00:00:00 2001
From: Alexander Harvey Nitz <alex.nitz@gmail.com>
Date: Thu, 7 Apr 2022 15:54:10 +0200
Subject: [PATCH 057/313] less lines changed

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 3a772ef6..ed85ba17 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,7 @@
 [build-system]
 requires = [
 	"setuptools",
+        "wheel",
 	"cython",
 	"oldest-supported-numpy",
 ]

From b5e772c121d9e297e8059d064cb13947b8499fa3 Mon Sep 17 00:00:00 2001
From: Alexander Harvey Nitz <alex.nitz@gmail.com>
Date: Thu, 7 Apr 2022 15:55:12 +0200
Subject: [PATCH 058/313] tabs?

---
 pyproject.toml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index ed85ba17..0aea3803 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [build-system]
 requires = [
-	"setuptools",
+        "setuptools",
         "wheel",
-	"cython",
-	"oldest-supported-numpy",
+        "cython",
+        "oldest-supported-numpy",
 ]

From b2c34f7bf958cdb94e4bd41683a3b891a9d04db1 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 7 Apr 2022 16:05:07 +0200
Subject: [PATCH 059/313] =?UTF-8?q?Bump=20version:=203.4.2=20=E2=86=92=203?=
 =?UTF-8?q?.4.3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 3be41178..3cd06d22 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.4.2',
+    version='3.4.3',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 550ca8f2..dc6c0335 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.4.2'
+__version__ = '3.4.3'

From 0367dca5275950ab37c2b8a7c5b1c8757cd55033 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 7 Apr 2022 16:42:48 +0200
Subject: [PATCH 060/313] do not include .c files in pypi source tarball

This confuses cython compilations
---
 MANIFEST.in | 1 +
 Makefile    | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/MANIFEST.in b/MANIFEST.in
index cd5f1998..07f43779 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -8,5 +8,6 @@ include pip-requirements.txt
 recursive-include tests *
 recursive-exclude * __pycache__
 recursive-exclude * *.py[co]
+recursive-exclude * *.c
 
 recursive-include docs *.rst conf.py Makefile make.bat *.jpg *.png *.gif
diff --git a/Makefile b/Makefile
index c3db0dc0..b3eb6442 100644
--- a/Makefile
+++ b/Makefile
@@ -1,4 +1,4 @@
-.PHONY: clean clean-test clean-pyc clean-build docs help
+.PHONY: clean clean-test clean-pyc clean-build docs servedocs help install release release-test dist 
 .DEFAULT_GOAL := help
 
 define BROWSER_PYSCRIPT

From bdad622cf5a3b0e141bbccb532cd9a8e04b0f8da Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 7 Apr 2022 16:43:28 +0200
Subject: [PATCH 061/313] =?UTF-8?q?Bump=20version:=203.4.3=20=E2=86=92=203?=
 =?UTF-8?q?.4.4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 3cd06d22..4dabf662 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.4.3',
+    version='3.4.4',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index dc6c0335..ccbf54b9 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.4.3'
+__version__ = '3.4.4'

From 26617d2df6a793aa9712c5d2eb5dffe0e4f41c14 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 7 Apr 2022 18:27:00 +0200
Subject: [PATCH 062/313] extend timeout

---
 docs/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/conf.py b/docs/conf.py
index d6586bfc..8f266cd4 100755
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -94,7 +94,7 @@
 autosectionlabel_prefix_document = True
 
 # avoid time-out when running the doc
-nbsphinx_timeout = 45 * 60
+nbsphinx_timeout = 4 * 60 * 60
 
 nbsphinx_execute_arguments = [
     "--InlineBackend.figure_formats={'svg', 'pdf'}",

From ed1533b308eeb28d84079de1a06f2daf08c0a463 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 8 Apr 2022 22:49:33 +0200
Subject: [PATCH 063/313] better printout when transformed parameter limits
 could not be estimated

---
 ultranest/integrator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index b5e1a063..a335340e 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2732,8 +2732,8 @@ def print_results(self, use_unicode=True):
                     # add a bit of padding, but not outside parameter limits
                     lo, hi = edges[0], edges[-1]
                     step = edges[1] - lo
-                    lo = max(self.transform_limits[i,0], lo - 2 * step)
-                    hi = min(self.transform_limits[i,1], hi + 2 * step)
+                    lo = max(min(lo, self.transform_limits[i,0]), lo - 2 * step)
+                    hi = min(max(hi, self.transform_limits[i,1]), hi + 2 * step)
                     H, edges = np.histogram(v, bins=np.linspace(lo, hi, 40))
                     lo, hi = edges[0], edges[-1]
 

From 3602d20375322b9309e061505677733e196fee24 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 8 Apr 2022 23:00:32 +0200
Subject: [PATCH 064/313] =?UTF-8?q?Bump=20version:=203.4.4=20=E2=86=92=203?=
 =?UTF-8?q?.4.5?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 4dabf662..fc3e687a 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.4.4',
+    version='3.4.5',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index ccbf54b9..84b5c40e 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.4.4'
+__version__ = '3.4.5'

From 09cc3994ff8eeaf27270917a061d87cb6d730b5f Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 8 Apr 2022 23:12:32 +0200
Subject: [PATCH 065/313] avoid numpy deprecation warning

---
 ultranest/store.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/store.py b/ultranest/store.py
index 50311515..7bf424f8 100644
--- a/ultranest/store.py
+++ b/ultranest/store.py
@@ -192,7 +192,7 @@ def _load(self):
         """Load from data file."""
         if 'points' not in self.fileobj:
             self.fileobj.create_dataset(
-                'points', dtype=np.float,
+                'points', dtype=float,
                 shape=(0, self.ncols), maxshape=(None, self.ncols))
 
         self.nrows, ncols = self.fileobj['points'].shape

From 687178bf554e3dba55bbb66e2b65c0500b609e70 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 8 Apr 2022 23:50:45 +0200
Subject: [PATCH 066/313] docs: add treatment for stepfuncs module in docs

---
 Makefile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Makefile b/Makefile
index b3eb6442..394cff8f 100644
--- a/Makefile
+++ b/Makefile
@@ -82,6 +82,7 @@ docs: ## generate Sphinx HTML documentation, including API docs
 	$(MAKE) -C docs clean
 	$(MAKE) -C docs html O=-jauto
 	sed --in-place '/href="ultranest\/mlfriends.html"/d' docs/build/html/_modules/index.html
+	sed --in-place '/href="ultranest\/stepfuncs.html"/d' docs/build/html/_modules/index.html
 	$(BROWSER) docs/build/html/index.html
 
 servedocs: docs ## compile the docs watching for changes

From d73a1e4536a4b4e14582f290050a68008e0ff09a Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 29 May 2022 20:39:27 +0200
Subject: [PATCH 067/313] avoid zero vector in
 generate_partial_differential_direction; better __str__

---
 ultranest/stepsampler.py | 37 +++++++++++++++++++++++++++----------
 1 file changed, 27 insertions(+), 10 deletions(-)

diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index e78a7817..71aaa2b2 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -147,13 +147,23 @@ def generate_partial_differential_direction(ui, region, scale=1):
     nlive, ndim = region.u.shape
     # choose pair
     i = np.random.randint(nlive)
-    i2 = np.random.randint(nlive - 1)
-    if i2 >= i:
-        i2 += 1
-    mask = np.random.uniform(size=ndim) < 0.1
+    while True:
+        i2 = np.random.randint(nlive - 1)
+        if i2 >= i:
+            i2 += 1
+
+        v = region.u[i] - region.u[i2]
+
+        mask = np.random.uniform(size=ndim) > 0.1
+        # at least one must be on
+        mask[np.random.randint(ndim)] = True
+        v[mask] = 0
+        if (v != 0).any():
+            # repeat if live points are identical
+            break
     # use doubling procedure to identify left and right maxima borders
-    v = np.zeros(ndim)
-    v[mask] = (region.u[i,mask] - region.u[i2,mask]) * scale
+    #v = np.zeros(ndim)
+    #v[mask] = (region.u[i,mask] - region.u[i2,mask]) * scale
     return v
 
 
@@ -224,8 +234,10 @@ def generate_mixture_random_direction(ui, region, scale=1):
         new direction vector
     """
     if np.random.uniform() < 0.5:
+        # DE proposal
         return generate_differential_direction(ui, region, scale=scale)
     else:
+        # region-oriented random axis proposal
         return generate_region_oriented_direction(ui, region, scale=scale)
 
 
@@ -455,9 +467,9 @@ def __init__(
 
     def __str__(self):
         if not self.adaptive_nsteps:
-            return type(self).__name__ + '(nsteps=%d)' % self.nsteps
+            return type(self).__name__ + '(nsteps=%d, generate_direction=%s)' % (self.nsteps, self.generate_direction)
         else:
-            return type(self).__name__ + '(adaptive_nsteps=%s)' % self.adaptive_nsteps
+            return type(self).__name__ + '(adaptive_nsteps=%s, generate_direction=%s)' % (self.adaptive_nsteps, self.generate_direction)
 
     def plot(self, filename):
         """Plot sampler statistics.
@@ -899,7 +911,7 @@ def RegionBallSliceSampler(*args, **kwargs):
     return SliceSampler(*args, **kwargs, generate_direction=generate_region_random_direction)
 
 
-class SequentialDirectionGenerator(object):
+class SequentialRegionDirectionGenerator(object):
     def __init__(self):
         """Sequentially proposes one region axes after the next."""
         self.axis_index = 0
@@ -935,10 +947,12 @@ def __call__(self, ui, region, scale=1):
         v *= scale / (v**2).sum()**0.5
         return v
 
+    def __str__(self):
+        return type(self).__name__ + '()'
 
 def RegionSequentialSliceSampler(*args, **kwargs):
     """Slice sampler, sequentially iterating region axes."""
-    return SliceSampler(*args, **kwargs, generate_direction=SequentialDirectionGenerator())
+    return SliceSampler(*args, **kwargs, generate_direction=SequentialRegionDirectionGenerator())
 
 
 class OrthogonalDirectionGenerator(object):
@@ -953,6 +967,9 @@ def __init__(self, generate_direction):
         self.axis_index = 0
         self.generate_direction = generate_direction
         self.directions = None
+    
+    def __str__(self):
+        return type(self).__name__ + '(generate_direction=%s)' % self.generate_direction
 
     def __call__(self, ui, region, scale=1):
         """Iteratively return a orthogonalized vector.

From 83b0594b89f56a835b5ff38fef8a65722e92bf0c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 15 Jul 2022 12:35:32 +0200
Subject: [PATCH 068/313] improve docs on output files, conditional priors and
 parallelization

---
 docs/performance.rst | 103 ++++++++++++++++++++++++++++++++-----------
 docs/priors.ipynb    |  84 ++++++++++++++++++++++++++++++-----
 2 files changed, 151 insertions(+), 36 deletions(-)

diff --git a/docs/performance.rst b/docs/performance.rst
index 25007ef8..158abfa4 100644
--- a/docs/performance.rst
+++ b/docs/performance.rst
@@ -1,7 +1,7 @@
 .. _performance:
 
 ====================================
-Tour of the features
+Features
 ====================================
 
 
@@ -110,30 +110,68 @@ and the parameter constraints:
             param2              0.500 +- 0.099
             param3              0.602 +- 0.098
 
-In the folder my_gauss you can find useful files:
-
-* **debug.log**: log file of the run. Include when reporting bugs.
-* **results/points.hdf5**: file storing all sampled points. Used for resuming.
-* **chains/equal_weighted_post.txt**: posterior samples. Each column corresponds to one parameter.
-* **chains/weighted_post.txt**: weighted posterior samples. Weight, -loglikelihood, parameter value (d times). getdist compatible.
-* **chains/weighted_post.paramnames**: Parameter names
-* **info/results.json**: all results (logz, etc.) as a json dictionary
-* **plots/corner.pdf**: corner plot
-* **plots/run.pdf**: diagnostic plot showing integration progress
-* **plots/trace.pdf**: diagnostic plot showing problem structure
-
 Some features worth noting here:
 
-* Key diagnostic plots are included.
-* The program can resume from crashes -- even if run with a different number of live points.
 * UltraNest shows what it is currently exploring. This is especially useful for debugging models.
+* Key diagnostic plots are included in the output folder (see below).
+* The program can resume from crashes -- even if run with a different number of live points.
 
-Lets go to some more advanced usage examples: Integrating a 100-dimensional gaussian.
-For that, we have to make a few modifications.
+Output files
+============
+
+If a `log_dir` directory was specified, you will find these files:
+
+* debug.log: A debug log of the run
+  * Please attach it or the stdout output when you open a `Github issue <https://github.com/JohannesBuchner/UltraNest/issues>`_.
+  * This contains the efficiency and progress of the sampling.
+* info folder: machine-readable summaries of the posterior
+  * **post_summary.csv**: for each parameter: mean, std, median, upper and lower 1 sigma error. Can be read with `pandas.read_csv <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html>`_.
+  * **results.json**: Contains detailed output of the nested sampling run. Can be read with `json.load <https://docs.python.org/3/library/json.html>`_.
+    * paramnames: parameter names
+    * ncall, niter: Number of likelihood calls, nested sampling iterations
+    * maximum_likelihood: highest loglikelihood point found so far
+    * H, Herr: (global) information gain
+    * ess: effective sample size
+    * logz, logzerr: ln(Z) and its uncertainty. logzerr_tail is the remainder integral contribution, logzerr_bs is from bootstrapping
+    * posterior: for each parameter: mean, std, median, upper and lower 1 sigma error, and `information gain <https://arxiv.org/abs/2205.00009>`_.
+    * insertion_order_MWW_test: MWW test results (see Buchner+21 in prep)
+* chains: machine-readable chains
+  * **equal_weighted_post.txt**: equally weighted posterior samples (similar to a Markov chain). Each column corresponds to one parameter.
+    * You can make a corner plot from this.
+  * weighted_post.txt: posterior samples with a weight attached. 
+    * This is made by nested sampling directly, and the above is produced from this. However, carrying the weights around is cumbersome.
+    * getdist compatible. columns are Weight, -loglikelihood, parameter value (d times). 
+  * weighted_post_untransformed.txt: same as above, but in coordinates before the prior transformation.
+  * run.txt: for each iteration, ln(z) and error, ln(volume), number of live points, log-likelihood threshold, posterior point weight (likelihood x volume) and insertion rank of newly sampled point.
+* plots: Visualisations (by plot functions)
+  * corner.pdf: corner/pairs plot of the marginal and conditional parameter posteriors.
+    * Useful for investigating degeneracies and which parameters were learned.
+  * trace.pdf: diagnostic plot showing problem structure
+    * Visualises how each parameter's range was reduced as the nested sampling proceeds.
+    * Color indicates where the bulk of the posterior lies.
+    * Useful to understand the structure of the inference problem, and which parameters are learned first.
+  * run.pdf: diagnostic plot showing integration progress
+    * Visualises how the number of live points, likelihood and posterior weight evolved through the nested sampling run.
+    * Visualises the evidence integration and its uncertainty.
+
+All of the above can be written, but are never read, by ultranest.ReactiveNestedSampler. The only file used to
+read the state of a previous run is:
+
+* results/points.hdf5: file storing all sampled points. Used for resuming.
+  * this is an internal file.
+  * ncalls: number of likelihood calls
+  * points: the columns are: likelihood threshold under which the point was sampled, likelihood of the point, a quality indicator (0 for MLFriends, otherwise the number of steps in the step sampler), u-space (unit cube) coordinates, p-space (transformed parameters) coordinates.
+
+You can safely store additional files and plots in the sub-folders.
 
 Speed ups
 ===========
 
+Lets go to some more advanced usage examples: Integrating a 100-dimensional gaussian.
+For that, we have to make a few modifications to enhance the
+**computational speed**. Enhancing the **algorithmic speed** (number of likelihood evaluations
+needed per iterations) is discussed in the next section.
+
 Implementing a gaussian likelihood can be done in a few ways.
 
 Very slow:
@@ -172,7 +210,7 @@ To use this function, pass ``vectorized=True`` to ReactiveNestedSampler.
 Lets see how this looks like in a full program.
 
 Vectorized full program
-================================
+------------------------
 
 Below is a Python program that implements a gaussian likelihood,
 and allows the user to specify the problem dimension and a few sampler parameters.
@@ -247,6 +285,7 @@ Note that our likelihood is vectorized, and we pass ``vectorized=True``.
 
 A similar program is included in the git repository as *examples/testasymgauss.py*.
 
+
 High-dimensional models
 ========================
 
@@ -380,13 +419,14 @@ The integral is given as::
 This result is close to the analytic value (0) on infinite bounds 
 (the prior boundaries slightly increase the result).
 
-We can test whether the slice sampler is good enough by halving 
-``slice_steps``. The logZ estimate should ideally be consistent.
+We can test whether the slice sampler is good enough by doubling 
+the number of steps, until the ln(Z) estimate is stable.
 
-Using multiple cores
+Parallelisation
 ====================
 
-Depending on your numpy installation, the above may already use multiple CPUs.
+Your likelihood function may already be using multiple cores,
+whether your intended to or not, due to underlying libraries (e.g., numpy).
 You can control this with the OMP_NUM_THREADS environment variable:
 
 .. code-block:: bash
@@ -394,26 +434,39 @@ You can control this with the OMP_NUM_THREADS environment variable:
         # avoid automatic parallelisation
         export OMP_NUM_THREADS=1
 
+If the likelihood is not parallelised, ultranest can parallelize
+its execution to multiple cores.
+
+Using multiple cores
+--------------------
+
 To use multiple processors and cores, scaling UltraNest all the way to 
 large computing clusters, you can parallelise the program with MPI:
 
-No code changes are required. You need to install MPI (for example, OpenMPI) and mpi4py (pip install mpi4py).
-Then run:
+* No code changes are required. 
+* You need to install MPI (for example, OpenMPI) and mpi4py (pip install mpi4py).
+* Then run your script with mpiexec:
 
 .. code-block:: bash
         
         mpiexec -np 4 python3 gauss.py --x_dim=100 --num_live_points=400 --slice  --slice_steps=100
 
 
+This launches four scripts which are started in parallel, and ultranest 
+coordinates them.
+
+Use as many scripts as processors. If memory is a concern, look into shared memory solutions.
+
 More features
 ===================
 
-To find more features such as ...
+To find more features and details such as ...
 
 * Circular/wrapped parameter spaces
 * Model comparison of empirical and physical models
 * Quantifying posterior uncertainty
 * Visualisation and interoperation with getdist, pandas, matplotlib, ...
 * Using in a Jupyter notebook
+* all the step samplers and slice samplers available
 
 ... see the tutorials!
diff --git a/docs/priors.ipynb b/docs/priors.ipynb
index 9ed0cbb3..c9139efc 100644
--- a/docs/priors.ipynb
+++ b/docs/priors.ipynb
@@ -63,7 +63,7 @@
     "\n",
     "We invert the cumulative probability distribution mapping quantiles (0...1) to the corresponding model parameter value.\n",
     "\n",
-    "Lets start with the uniform distribution."
+    "Lets start with the uniform distribution:"
    ]
   },
   {
@@ -130,7 +130,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## The unit hypercube\n",
+    "## Specifying priors\n",
     "\n",
     "Lets specify a prior for UltraNest with multiple parameters:\n",
     "\n",
@@ -166,7 +166,8 @@
     "Some recommendations:\n",
     "\n",
     "* [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html#continuous-distributions) provides many 1-d distributions that can be used like this.\n",
-    "* avoid building scipy.stats objects in the transform, because this is slow -- build them outside first, then only invoke the .ppf method in the transform.\n"
+    "* avoid building scipy.stats objects in the transform, because this is slow -- build them outside first, then only invoke the .ppf method in the transform.\n",
+    "* If you are looking for a distribution that is not implemented yet, try to follow a random number generator recipe (see the Dirichlet prior for an example, below).\n"
    ]
   },
   {
@@ -260,7 +261,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A similar effect can be achieved by defining transforms in sequence (this is a different prior though):"
+    "#### Conditional prior approach\n",
+    "\n",
+    "Another approach is to sample the second parameter conditional on the first parameter, already transformed. This is akin to Gibbs sampling.\n",
+    "\n",
+    "For an example, we have a first parameter with a Gaussian prior, and a second parameter, with a Gaussian prior centred around the first parameter's value. Therefore, its value shifts with the first parameter:"
    ]
   },
   {
@@ -273,22 +278,79 @@
     "gauss2 = scipy.stats.norm(0, 0.1)\n",
     "\n",
     "\n",
-    "def transform_correlated(quantiles):\n",
+    "def transform_correlated_gibbs(quantiles):\n",
     "    parameters = np.empty_like(quantiles)\n",
     "    # first parameter is independent\n",
-    "    parameters[0] = gauss1.ppf(quantiles[0])\n",
+    "    parameters[:,0] = gauss1.ppf(quantiles[:,0])\n",
     "    # second parameter depends on first parameter, here with a shift\n",
-    "    parameters[1] = parameters[0] + gauss2.ppf(quantiles[0])\n",
+    "    parameters[:,1] = parameters[:,0] + gauss2.ppf(quantiles[:,1])\n",
     "    return parameters"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples = transform_correlated_gibbs(np.random.uniform(0, 1, size=(100, 2)))\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.title('Gibbs prior')\n",
+    "plt.plot(samples[:,0], samples[:,1], 'o', mew=1, mfc='w', mec='k')\n",
+    "plt.xlabel('Parameter 1')\n",
+    "plt.ylabel('Parameter 2');\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, we also achieve a correlated prior. However, this is different from the previous example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Complicated constraints and rejection in the likelihood"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In some situations, you may have more constraints than parameters, such as:\n",
+    "\n",
+    "    parameter_1_lower < parameter_1 < parameter_1_upper\n",
+    "    parameter_2_lower < parameter_2 < parameter_2_upper\n",
+    "    parameter_1 + parameter_2 < constant\n",
+    "\n",
+    "In that case, move either the first two or the last constraint into the likelihood function, whichever option is more relaxed (i.e., causes fewer rejections). This is achieved by returning a very low likelihood (e.g., -1e100), when the constraint is not met.\n",
+    "\n",
+    "It is beneficial for the sampler if you can add a slight slope towards the good region of the constraint. e.g., -1e100 * (1 + parameter_1 + parameter_2) or similar. This is because if you use the exact same constant, this is a likelihood plateau, and the live points have to be reduced until the plateau is traversed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hard vs soft priors\n",
+    "\n",
+    "Priors with hard edges (such as uniform/log-uniform priors) are pretty popular. When the data prefer a parameter at the edge or beyond the edge, the result is often unusable, and the fit has to be rerun with wider priors.\n",
+    "\n",
+    "Softer priors allow with low but non-zero prior probability the data to override the prior, and thus a single run can produce insights. A simple example are (log-)normal distributions.\n",
+    "\n",
+    "On the other hand, some models may have a range of validity for phyiscal or computational reasons, in which case hard priors are appropriate."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Non-analytic priors\n",
     "\n",
-    "Sometimes, the prior may not be easily invertable. For example, when it is given as posterior samples from a previous analysis. I\n",
+    "Sometimes, the prior may not be easily invertable. For example, when it is given as posterior samples from a previous analysis. Lets say as a prior, we want a posterior from another experiment that looks like this:\n",
     "\n"
    ]
   },
@@ -308,7 +370,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this case, you can compute the cumulative distribution numerically and invert it:"
+    "In this case, we can compute the cumulative distribution numerically and invert it. Lets try implementing this and sampling from it:"
    ]
   },
   {
@@ -366,7 +428,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lets have a look at the samples:"
+    "Lets have a look at the samples, and whether the three fractions look uniform and sum up to 1:"
    ]
   },
   {
@@ -423,7 +485,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,

From 28701d4191f8554439f75de7f2ad1482e3dc14e4 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 15 Jul 2022 12:46:37 +0200
Subject: [PATCH 069/313] add more docs and compatibility to dychmc

---
 ultranest/dychmc.py | 25 ++++++++++---------------
 1 file changed, 10 insertions(+), 15 deletions(-)

diff --git a/ultranest/dychmc.py b/ultranest/dychmc.py
index fc988e39..6bfb6ac8 100644
--- a/ultranest/dychmc.py
+++ b/ultranest/dychmc.py
@@ -243,7 +243,7 @@ class DynamicCHMCSampler(object):
     Because of this, the number of steps is dynamic.
     """
 
-    def __init__(self, ndim, nsteps, transform, loglike, gradient, adaptive_nsteps=False, delta=0.9, nudge=1.04):
+    def __init__(self, scale, nsteps, adaptive_nsteps=False, delta=0.9, nudge=1.04):
         """Initialise sampler.
 
         Parameters
@@ -261,22 +261,10 @@ def __init__(self, ndim, nsteps, transform, loglike, gradient, adaptive_nsteps=F
             start point and current position exceeds the mean distance
             between pairs of live points.
 
-        transform: function
-            called with unit cube position vector u, returns
-            transformed parameter vector p.
-        loglike: function
-            called with transformed parameters p, returns loglikelihood
-        gradient: function
-            called with unit cube position vector u, returns
-            gradient (dlogL/du, not just dlogL/dp)
-
         """
         self.history = []
         self.nsteps = nsteps
-        self.scale = 0.1 * ndim**0.5
-        self.transform = transform
-        self.loglike = loglike
-        self.gradient = gradient
+        self.scale = scale
         self.nudge = nudge
         self.nsteps_nudge = 1.01
         adaptive_nsteps_options = (False, 'proposal-total-distances-NN', 'proposal-summed-distances-NN',
@@ -298,6 +286,9 @@ def __init__(self, ndim, nsteps, transform, loglike, gradient, adaptive_nsteps=F
             self.logstat_labels += ['jump-distance', 'reference-distance']
         self.logstat_trajectory = []
 
+    def set_gradient(self, gradient):
+        self.gradient = gradient
+
     def __str__(self):
         """Get string representation."""
         if not self.adaptive_nsteps:
@@ -352,6 +343,9 @@ def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=40, plot=Fals
             whether to produce debug plots.
 
         """
+        self.transform = transform
+        self.loglike = loglike
+
         i = np.random.randint(len(Ls))
         #print("starting from live point %d" % i)
         self.starti = i
@@ -485,7 +479,8 @@ def adjust_stepsize(self):
         self.logstat_trajectory = []
 
         if len(self.logstat) % N == 0:
-            print("updating step size: %.4f %.4f %.1f --> %g " % (alphamean, reflectmean, treeheightmean, self.scale))
+            print("updating step size: alpha=%.4f refl=%.4f treeheight=%.1f --> scale=%g " % (
+                alphamean, reflectmean, treeheightmean, self.scale))
 
     def region_changed(self, Ls, region):
         """React to change of region. """

From 19dad6c98d03b856ddcbc9bf0a8dab0917a3c26a Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 15 Jul 2022 13:07:12 +0200
Subject: [PATCH 070/313] module list highlighted the header of this unused
 module; rewrote docs

---
 ultranest/flatnuts.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ultranest/flatnuts.py b/ultranest/flatnuts.py
index cb64eaea..2f02373a 100644
--- a/ultranest/flatnuts.py
+++ b/ultranest/flatnuts.py
@@ -1,8 +1,9 @@
 """
-FLATNUTS
-=========
+FLATNUTS is a implementation of No-U-turn sampler 
+for nested sampling assuming a flat prior space (hyper-cube u-space).
 
-Experimental.
+This is highly experimental. It is similar to NoGUTS and suffers from 
+the same stability problems.
 
 Directional sampling within regions.
 
@@ -767,4 +768,3 @@ def build_tree(self, startstate, j, rwd):
         # additional criterion: start and end velocities must point in opposite directions
         stop = stopa or stopb or angle(xright-xleft, vleft) <= 0 or angle(xright-xleft, vright) <= 0 or angle(vleft, vright) <= 0
         return (ileft, xleft, vleft), (iright, xright, vright), (ileft,iright), stop
-

From 58466d40024d2144c50d743913037d1813862e72 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 15 Jul 2022 20:45:01 +0200
Subject: [PATCH 071/313] fix bug in post_summary.csv which scrambled the
 column order

added test to check for this issue
---
 tests/test_run.py       | 66 +++++++++++++++++++++++++++++++++++++++--
 ultranest/integrator.py |  4 +--
 2 files changed, 65 insertions(+), 5 deletions(-)

diff --git a/tests/test_run.py b/tests/test_run.py
index 6537c5f9..eb9f32c6 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -2,6 +2,8 @@
 import shutil
 import tempfile
 import pytest
+import json
+import pandas
 from numpy.testing import assert_allclose
 
 def test_run():
@@ -278,6 +280,65 @@ def transform(x):
             assert abs(r['ncall'] - ncalls) <= 2 * sampler.mpi_size, (i, r['ncall'], ncalls, r['ncall'] - ncalls)
             assert paramnames == r['paramnames'], 'paramnames should be in results'
 
+            results2 = json.load(open(folder + '/info/results.json'))
+            print('CSV content:')
+            print(open(folder + '/info/post_summary.csv').read())
+            post_summary = pandas.read_csv(folder + '/info/post_summary.csv')
+            print(post_summary, post_summary.columns)
+            for k, v in r.items():
+                if k in results2:
+                    print("checking results[%s] ..." % k)
+                    assert results2[k] == r[k], (k, results2[k], r[k])
+            
+            assert r['paramnames'] == paramnames
+            samples = np.loadtxt(folder + '/chains/equal_weighted_post.txt', skiprows=1)
+            data = np.loadtxt(folder + '/chains/weighted_post.txt', skiprows=1)
+            data_u = np.loadtxt(folder + '/chains/weighted_post_untransformed.txt', skiprows=1)
+            assert (data[:,:2] == data_u[:,:2]).all()
+            
+            assert_allclose(samples.mean(axis=0), r['posterior']['mean'])
+            assert_allclose(np.median(samples, axis=0), r['posterior']['median'])
+            assert_allclose(np.std(samples, axis=0), r['posterior']['stdev'])
+            for k, v in r.items():
+                if k == 'posterior':
+                    for k1, v1 in v.items():
+                        if k1 == 'information_gain_bits':
+                            continue
+                        for param, value in zip(paramnames, v[k1]):
+                            print("checking %s of parameter '%s':" % (k1, param), value)
+                            assert np.isclose(post_summary[param + '_' + k1].values, value), (param, k1, post_summary[param + '_' + k1].values, value)
+                elif k == 'samples':
+                    assert_allclose(samples, r['samples'])
+                elif k == 'paramnames':
+                    assert v == paramnames
+                elif k == 'weighted_samples':
+                    print(k, v.keys())
+                    assert_allclose(data[:,0], v['weights'])
+                    assert_allclose(data[:,1], v['logl'])
+                    assert_allclose(data[:,2:], v['points'])
+                    assert_allclose(data_u[:,2:], v['upoints'])
+                elif k == 'maximum_likelihood':
+                    print(k, v.keys())
+                    assert_allclose(data[-1,1], v['logl'])
+                    assert_allclose(data[-1,2:], v['point'])
+                    assert_allclose(data_u[-1,2:], v['point_untransformed'])
+                    
+                elif k.startswith('logzerr') or '_bs' in k or 'Herr' in k:
+                    print("   skipping", k, np.shape(v))
+                    #assert_allclose(r[k], v, atol=0.5)
+                elif k == 'insertion_order_MWW_test':
+                    print('insertion_order_MWW_test:', r[k], v)
+                    assert r[k] == v, (r[k], v)
+                else:
+                    print("  ", k, np.shape(v))
+                    assert_allclose(r[k], v)
+
+            logw = r['weighted_samples']['logw']
+            v = r['weighted_samples']['points']
+            L = r['weighted_samples']['logl']
+
+            assert results2['niter'] == len(r['samples'])
+
         # the results are not exactly the same, because the sampling adds
         #ncalls = loglike.ncalls
         #sampler = ReactiveNestedSampler(paramnames,
@@ -340,7 +401,6 @@ def transform(x):
 
 def test_reactive_run_warmstart_gauss():
     from ultranest import ReactiveNestedSampler
-    from ultranest import read_file
     center = 0
 
     def loglike(z):
@@ -353,7 +413,6 @@ def transform(x):
         return x * 20000 - 10000
 
     paramnames = ['a']
-    ndim = len(paramnames)
 
     folder = tempfile.mkdtemp()
     np.random.seed(1)
@@ -450,4 +509,5 @@ def transform(x):
     #test_run()
     #test_reactive_run_warmstart_gauss()
     #test_reactive_run_extraparams()
-    test_dlogz_reactive_run()
+    test_reactive_run_resume_eggbox('hdf5')
+    #test_dlogz_reactive_run()
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index a335340e..a7dff8dc 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2671,8 +2671,8 @@ def _update_results(self, main_iterator, saved_logl, saved_nodeids):
 
             np.savetxt(
                 os.path.join(self.logs['info'], 'post_summary.csv'),
-                [np.hstack([results['posterior'][k] for k in ('mean', 'stdev', 'median', 'errlo', 'errup')])],
-                header=', '.join(['"{0}_mean", "{0}_stdev", "{0}_median", "{0}_errlo", "{0}_errup"'.format(k)
+                [[results['posterior'][k][i] for i in range(self.num_params) for k in ('mean', 'stdev', 'median', 'errlo', 'errup')]],
+                header=','.join(['"{0}_mean","{0}_stdev","{0}_median","{0}_errlo","{0}_errup"'.format(k)
                                   for k in self.paramnames + self.derivedparamnames]),
                 delimiter=',', comments='',
             )

From 58581c9c210dfc7c356c8c3b9ce0210583bfd6a0 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 15 Jul 2022 20:52:11 +0200
Subject: [PATCH 072/313] =?UTF-8?q?Bump=20version:=203.4.5=20=E2=86=92=203?=
 =?UTF-8?q?.4.6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index fc3e687a..10de2156 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.4.5',
+    version='3.4.6',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 84b5c40e..03fba2bf 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.4.5'
+__version__ = '3.4.6'

From f8cc209ccda773269396a773f86ef020f54d51f1 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 15 Jul 2022 21:35:11 +0200
Subject: [PATCH 073/313] docs: mention output file documentation

---
 docs/performance.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/performance.rst b/docs/performance.rst
index 158abfa4..5b2aaf5b 100644
--- a/docs/performance.rst
+++ b/docs/performance.rst
@@ -9,6 +9,7 @@ This tutorial demonstrates:
 
 * How to make a program that uses nested sampling
 * How to store and resume runs
+* The meaning of the output files
 * How to use UltraNest in 100 dimensions
 * How to speed up likelihood functions with vectorization
 * How to write a program with UltraNest

From 0e7a3537a8e01e4ef60e2caf3fc1bfff359ad091 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 21 Jul 2022 14:41:35 +0200
Subject: [PATCH 074/313] remove blank lines before semicolons

---
 docs/priors.ipynb | 25 +++++++------------------
 1 file changed, 7 insertions(+), 18 deletions(-)

diff --git a/docs/priors.ipynb b/docs/priors.ipynb
index c9139efc..37b0147b 100644
--- a/docs/priors.ipynb
+++ b/docs/priors.ipynb
@@ -176,7 +176,9 @@
    "source": [
     "## Dependent priors\n",
     "\n",
-    "In some cases, a previous experiment gives informative priors which we want to incorporate, and they may be inter-dependent. For example, consider a two-dimensional gaussian prior distribution:\n"
+    "### Incorporating covariances\n",
+    "\n",
+    "In some cases, a previous experiment gives informative priors which we want to incorporate, and they may be inter-dependent. For example, consider a two-dimensional gaussian prior distribution.\n"
    ]
   },
   {
@@ -254,7 +256,7 @@
     "plt.contourf(X, Y, Z, cmap='magma_r')\n",
     "plt.plot(samples[:,0], samples[:,1], 'o', mew=1, mfc='w', mec='k')\n",
     "plt.xlabel('Parameter 1')\n",
-    "plt.ylabel('Parameter 2');\n"
+    "plt.ylabel('Parameter 2');"
    ]
   },
   {
@@ -299,7 +301,7 @@
     "plt.title('Gibbs prior')\n",
     "plt.plot(samples[:,0], samples[:,1], 'o', mew=1, mfc='w', mec='k')\n",
     "plt.xlabel('Parameter 1')\n",
-    "plt.ylabel('Parameter 2');\n"
+    "plt.ylabel('Parameter 2');"
    ]
   },
   {
@@ -331,19 +333,6 @@
     "It is beneficial for the sampler if you can add a slight slope towards the good region of the constraint. e.g., -1e100 * (1 + parameter_1 + parameter_2) or similar. This is because if you use the exact same constant, this is a likelihood plateau, and the live points have to be reduced until the plateau is traversed."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hard vs soft priors\n",
-    "\n",
-    "Priors with hard edges (such as uniform/log-uniform priors) are pretty popular. When the data prefer a parameter at the edge or beyond the edge, the result is often unusable, and the fit has to be rerun with wider priors.\n",
-    "\n",
-    "Softer priors allow with low but non-zero prior probability the data to override the prior, and thus a single run can produce insights. A simple example are (log-)normal distributions.\n",
-    "\n",
-    "On the other hand, some models may have a range of validity for phyiscal or computational reasons, in which case hard priors are appropriate."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -363,7 +352,7 @@
     "posterior_samples = np.hstack((np.random.uniform(0, 3, 2000), np.random.normal(3, 0.2, 2000)))\n",
     "\n",
     "plt.figure(figsize=(4,2))\n",
-    "plt.hist(posterior_samples, histtype='step', bins=100);\n"
+    "plt.hist(posterior_samples, histtype='step', bins=100);"
    ]
   },
   {
@@ -389,7 +378,7 @@
     "samples = transform_histogram(np.random.uniform(size=1000))\n",
     "plt.figure(figsize=(4,2))\n",
     "plt.hist(posterior_samples, histtype='step', bins=100, density=True);\n",
-    "plt.hist(samples, histtype='step', bins=100, density=True);\n"
+    "plt.hist(samples, histtype='step', bins=100, density=True);"
    ]
   },
   {

From 7f2db4885a3e546d952c4b46356667b4857d68c8 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 19 Aug 2022 11:31:13 +0200
Subject: [PATCH 075/313] [doc] list formatting

also flush point store when viz_callback is turned off
---
 docs/performance.rst    | 17 +++++++++++++++++
 docs/priors.ipynb       |  3 +--
 ultranest/integrator.py |  1 +
 3 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/docs/performance.rst b/docs/performance.rst
index 5b2aaf5b..0a144130 100644
--- a/docs/performance.rst
+++ b/docs/performance.rst
@@ -123,11 +123,15 @@ Output files
 If a `log_dir` directory was specified, you will find these files:
 
 * debug.log: A debug log of the run
+
   * Please attach it or the stdout output when you open a `Github issue <https://github.com/JohannesBuchner/UltraNest/issues>`_.
   * This contains the efficiency and progress of the sampling.
+
 * info folder: machine-readable summaries of the posterior
+
   * **post_summary.csv**: for each parameter: mean, std, median, upper and lower 1 sigma error. Can be read with `pandas.read_csv <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html>`_.
   * **results.json**: Contains detailed output of the nested sampling run. Can be read with `json.load <https://docs.python.org/3/library/json.html>`_.
+
     * paramnames: parameter names
     * ncall, niter: Number of likelihood calls, nested sampling iterations
     * maximum_likelihood: highest loglikelihood point found so far
@@ -136,22 +140,35 @@ If a `log_dir` directory was specified, you will find these files:
     * logz, logzerr: ln(Z) and its uncertainty. logzerr_tail is the remainder integral contribution, logzerr_bs is from bootstrapping
     * posterior: for each parameter: mean, std, median, upper and lower 1 sigma error, and `information gain <https://arxiv.org/abs/2205.00009>`_.
     * insertion_order_MWW_test: MWW test results (see Buchner+21 in prep)
+
 * chains: machine-readable chains
+
   * **equal_weighted_post.txt**: equally weighted posterior samples (similar to a Markov chain). Each column corresponds to one parameter.
+
     * You can make a corner plot from this.
+
   * weighted_post.txt: posterior samples with a weight attached. 
+
     * This is made by nested sampling directly, and the above is produced from this. However, carrying the weights around is cumbersome.
     * getdist compatible. columns are Weight, -loglikelihood, parameter value (d times). 
+
   * weighted_post_untransformed.txt: same as above, but in coordinates before the prior transformation.
   * run.txt: for each iteration, ln(z) and error, ln(volume), number of live points, log-likelihood threshold, posterior point weight (likelihood x volume) and insertion rank of newly sampled point.
+
 * plots: Visualisations (by plot functions)
+
   * corner.pdf: corner/pairs plot of the marginal and conditional parameter posteriors.
+
     * Useful for investigating degeneracies and which parameters were learned.
+
   * trace.pdf: diagnostic plot showing problem structure
+
     * Visualises how each parameter's range was reduced as the nested sampling proceeds.
     * Color indicates where the bulk of the posterior lies.
     * Useful to understand the structure of the inference problem, and which parameters are learned first.
+
   * run.pdf: diagnostic plot showing integration progress
+
     * Visualises how the number of live points, likelihood and posterior weight evolved through the nested sampling run.
     * Visualises the evidence integration and its uncertainty.
 
diff --git a/docs/priors.ipynb b/docs/priors.ipynb
index 37b0147b..e27ea144 100644
--- a/docs/priors.ipynb
+++ b/docs/priors.ipynb
@@ -23,8 +23,7 @@
    "source": [
     "import numpy as np\n",
     "import scipy.stats\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index a7dff8dc..8b12cc51 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2418,6 +2418,7 @@ def run_iter(
                                 region=self.region, transformLayer=self.transformLayer,
                                 region_fresh=region_fresh,
                             )
+                        if self.log:
                             self.pointstore.flush()
 
                     if nlive < cluster_num_live_points * nclusters and improvement_it < max_num_improvement_loops:

From b45b6a67d72181caae9f6ab3e0ab34b29bd8ecfd Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 5 Sep 2022 13:33:22 +0200
Subject: [PATCH 076/313] integrate continuous-box hot-resume feature

This reparametrizes the prior based on expected marginal posteriors,
and reweights the likelihood.
---
 tests/test_hotstart.py  | 104 +++++++++++++++++++++++++++++-
 ultranest/hotstart.py   | 136 ++++++++++++++++++++++++++++++++++++++++
 ultranest/integrator.py |  78 +++++++++++++++++++++--
 3 files changed, 311 insertions(+), 7 deletions(-)

diff --git a/tests/test_hotstart.py b/tests/test_hotstart.py
index 0e9fcef1..f5557656 100644
--- a/tests/test_hotstart.py
+++ b/tests/test_hotstart.py
@@ -3,7 +3,12 @@
 import scipy.stats
 from numpy import log10
 from ultranest import ReactiveNestedSampler
+from ultranest.utils import vectorize
+from ultranest.integrator import resume_from_hot_file
 from ultranest.hotstart import reuse_samples, get_extended_auxiliary_problem
+from ultranest.hotstart import compute_quantile_intervals, get_auxiliary_contbox_parameterization
+import os
+import tempfile
 
 rng_data = np.random.RandomState(42)
 Ndata = 100
@@ -23,6 +28,102 @@ def log_likelihood(params):
     mean, sigma = params
     return scipy.stats.norm.logpdf(y, mean, sigma).sum()
 
+def extended_prior_transform(x):
+    z = np.empty(3)
+    z[0] = x[0] * 2000 - 1000
+    z[1] = 10**(x[1] * 4 - 2)
+    z[2] = 2 * np.sqrt(2 * np.log(2)) * z[1]
+    return z
+
+def extended_log_likelihood(params):
+    mean, sigma, fwhm = params
+    return scipy.stats.norm.logpdf(y, mean, sigma).sum()
+
+def test_contbox_hotstart():
+    rng_samples = np.random.RandomState(43)
+    N = 100000
+    samples = rng_samples.normal(size=(N,2))
+    samples[:,1] = rng_samples.uniform(size=N)
+    weights = (np.ones(N) / N).reshape((-1,1))
+    logl = weights * 0
+
+    steps = [0.1, 0.01]
+    ulos, uhis = compute_quantile_intervals(steps, samples, weights)
+    print("quantiles:", ulos)
+    print("quantiles:", uhis)
+    assert ulos.shape == (2+1, len(steps)), (uhis.shape, ulos.shape)
+    assert uhis.shape == ulos.shape, (uhis.shape, ulos.shape)
+    tol = dict(atol=1e-3, rtol=0.01)
+    for i in 1, 0:
+        for j, q in enumerate(steps):
+            expectation = np.quantile(samples[:,i], q)
+            actual = ulos[j,i]
+            print(i, j, q, expectation, actual)
+            assert np.isclose(expectation, actual, **tol), (i, j, q, expectation, actual)
+            expectation = np.quantile(samples[:,i], 1-q)
+            actual = uhis[j,i]
+            print(i, j, 1-q, expectation, actual)
+            assert np.isclose(expectation, actual, **tol), (i, j, 1-q, expectation, actual)
+
+    aux_param_names, aux_loglike, aux_transform, vectorized = get_auxiliary_contbox_parameterization(
+        parameters, loglike=log_likelihood, transform=prior_transform,
+        vectorized=False, upoints=samples, uweights=weights,
+    )
+    assert aux_param_names == parameters + ['aux_logweight'], (aux_param_names, parameters)
+    p = aux_transform(np.random.uniform(size=3))
+    assert p.shape == (len(aux_param_names),)
+    L = float(aux_loglike(p))
+    print(L)
+    del aux_param_names, aux_loglike, aux_transform
+    
+    aux_param_names, aux_vloglike, aux_vtransform, vectorized = get_auxiliary_contbox_parameterization(
+        parameters, loglike=vectorize(log_likelihood), transform=vectorize(prior_transform), 
+        vectorized=True, upoints=samples, uweights=weights,
+    )
+    print(aux_param_names, parameters)
+    assert aux_param_names == parameters + ['aux_logweight'], (aux_param_names, parameters)
+    p = aux_vtransform(np.random.uniform(size=(11, 3)))
+    assert p.shape == (11, len(aux_param_names)), p.shape
+    L = aux_vloglike(p)
+    assert L.shape == (11,), L.shape
+    print(L)
+    del aux_param_names, aux_vloglike, aux_vtransform
+    
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        tmpfilename = os.path.join(tmpdirname, 'weighted_posterior_samples.txt')
+        print(tmpfilename)
+        np.savetxt(
+            tmpfilename,
+            np.hstack((weights, logl, samples)),
+            header='weight logl mean scatter',
+            fmt='%f'
+        )
+        aux_param_names, aux_loglike, aux_transform, vectorized = resume_from_hot_file(
+            parameters,
+            tmpfilename,
+            extended_log_likelihood,
+            extended_prior_transform,
+            vectorized=False,
+        )
+        assert aux_param_names == parameters + ['aux_logweight'], (aux_param_names, parameters)
+        p = aux_transform(np.random.uniform(size=3))
+        assert p.shape == (len(aux_param_names)+1,)
+        L = float(aux_loglike(p))
+        print(L)
+        aux_param_names, aux_vloglike, aux_vtransform, vectorized = resume_from_hot_file(
+            parameters,
+            tmpfilename,
+            vectorize(extended_log_likelihood),
+            vectorize(extended_prior_transform),
+            vectorized=True,
+        )
+        assert aux_param_names == parameters + ['aux_logweight'], (aux_param_names, parameters)
+        p = aux_vtransform(np.random.uniform(size=(11, 3)))
+        assert p.shape == (11, len(aux_param_names)+1)
+        L = aux_vloglike(p)
+        assert L.shape == (11,)
+        print(L)
+
 def test_hotstart_SLOW():
     np.random.seed(2)
     ctr = np.array([(42.0 + 1000) / 2000, (log10(0.1) + 2) / 4])
@@ -89,4 +190,5 @@ def test_hotstart_SLOW():
     assert 0.5 < (ref_results['posterior']['stdev'][1] / rec_results2['posterior']['stdev'][1]) < 1.5, (ref_results['posterior'], rec_results2['posterior'])
 
 if __name__ == '__main__':
-    test_hotstart()
+    test_hotstart_SLOW()
+    test_contbox_hotstart()
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index 27de3611..edfb16f6 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -242,6 +242,142 @@ def aux_loglikelihood(x):
     return aux_loglikelihood, aux_transform
 
 
+def compute_quantile_intervals(steps, upoints, uweights):
+    """Compute lower and upper axis quantiles.
+    q and 1-q quantiles along each axis of corresponding to steps
+    
+    Parameters
+    ------------
+    steps: array
+        list of quantiles q to compute.
+    upoints: function
+        samples
+    uweights: array
+        sample weights
+
+    Returns:
+    ---------
+    ulo: array
+        list of lower quantiles (at q)
+    uhi: array
+        list of upper quantiles (at 1-q)
+    """
+    ndim = upoints.shape[1]
+    nboxes = len(steps)
+    ulos = np.empty((nboxes+1,ndim))
+    uhis = np.empty((nboxes+1,ndim))
+    for j, pthresh in enumerate(steps):
+        for i, ui in enumerate(upoints.transpose()):
+            order = np.argsort(ui)
+            c = np.cumsum(uweights[order])
+            usel = ui[order][np.logical_and(c >= pthresh, c <= 1 - pthresh)]
+            ulos[j,i] = usel.min()
+            uhis[j,i] = usel.max()
+    ulos[-1] = 0
+    uhis[-1] = 1
+    return ulos, uhis
+
+
+def get_auxiliary_contbox_parameterization(
+    param_names, loglike, transform, upoints, uweights, vectorized=False,
+):
+    """Return a new loglike and transform based on an auxiliary distribution.
+
+    Given a likelihood and prior transform, and information about
+    the (expected) posterior peak, generates a auxiliary
+    likelihood and prior transform that is identical but
+    requires fewer nested sampling iterations.
+
+    This is achieved by deforming the prior space, and undoing that
+    transformation by correction weights in the likelihood.
+
+    The auxiliary distribution used for transformation/weighting is
+    factorized. Each axis considers the ECDF of the auxiliary samples,
+    and segments it into five quantile segments. Within each segment,
+    the parameter edges in u-space are linearly interpolated.
+
+    Usage::
+
+        aux_loglikelihood, aux_transform = get_auxiliary_contbox_parameterization(
+            loglike, transform, auxiliary_usamples)
+        aux_sampler = ReactiveNestedSampler(parameters, aux_loglikelihood, transform=aux_transform, derived_param_names=['logweight'])
+        aux_results = aux_sampler.run()
+        posterior_samples = aux_results['samples'][:,-1]
+
+    Parameters
+    ------------
+    loglike: function
+        original likelihood function
+    transform: function
+        original prior transform function
+    auxiliary_usamples: array
+        Posterior samples (in u-space).
+
+    Returns:
+    ---------
+    aux_loglike: function
+        auxiliary loglikelihood function.
+    aux_transform: function
+        auxiliary transform function.
+        Takes d u-space coordinates, and returns d + 1 p-space parameters.
+        The first d return coordinates are identical to what ``transform`` would return.
+        The final coordinate is the log of the correction weight.
+    """
+    steps = 10**-(1.0 * np.arange(1, 8, 2))
+    nsamples, ndim = upoints.shape
+    assert nsamples > 10
+    ulos, uhis = compute_quantile_intervals(steps, upoints, uweights)
+    nboxes = len(ulos)
+
+    uinterpspace = np.linspace(0, 1, nboxes)
+    
+    aux_param_names = param_names + ['aux_logweight']
+
+    def aux_transform(u):
+        ndim2, = u.shape
+        assert ndim2 == ndim + 1
+        umod = np.empty(ndim)
+        log_aux_volume_factors = 0
+        for i in range(ndim):
+            ulo_here = np.interp(u[-1], uinterpspace, ulos[:,i])
+            uhi_here = np.interp(u[-1], uinterpspace, uhis[:,i])
+            umod[i] = ulo_here + (uhi_here - ulo_here) * u[i]
+            log_aux_volume_factors += np.log(uhi_here - ulo_here)
+        return np.append(transform(umod), log_aux_volume_factors)
+
+    def aux_transform_vectorized(u):
+        nsamples, ndim2 = u.shape
+        assert ndim2 == ndim + 1
+        umod = np.empty((nsamples, ndim2 - 1))
+        log_aux_volume_factors = np.zeros((nsamples, 1))
+        for i in range(ndim):
+            ulo_here = np.interp(u[:,-1], uinterpspace, ulos[:,i])
+            uhi_here = np.interp(u[:,-1], uinterpspace, uhis[:,i])
+            umod[:,i] = ulo_here + (uhi_here - ulo_here) * u[:,i]
+            log_aux_volume_factors[:,0] += np.log(uhi_here - ulo_here)
+        return np.hstack((transform(umod), log_aux_volume_factors))
+
+    def aux_loglikelihood(x):
+        x_actual = x[:-1]
+        logl = loglike(x_actual)
+        aux_logweight = x[-1]
+        # downweight if we are in the auxiliary distribution
+        return logl + aux_logweight
+
+    def aux_loglikelihood_vectorized(x):
+        x_actual = x[:,:-1]
+        logl = loglike(x_actual)
+        aux_logweight = x[:,-1]
+        # downweight if we are in the auxiliary distribution
+        return logl + aux_logweight
+
+    print("vectorized:", vectorized)
+    if vectorized:
+        return aux_param_names, aux_loglikelihood_vectorized, aux_transform_vectorized, vectorized
+    else:
+        return aux_param_names, aux_loglikelihood, aux_transform, vectorized
+
+
 def reuse_samples(
     param_names, loglike, points, logl, logw=None,
     logz=0.0, logzerr=0.0, upoints=None,
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 8b12cc51..c7e256d0 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -24,6 +24,8 @@
 from .ordertest import UniformOrderAccumulator
 from .netiter import PointPile, SingleCounter, MultiCounter, BreadthFirstIterator, TreeNode, count_tree_between, find_nodes_before, logz_sequence
 from .netiter import dump_tree, combine_results
+from .hotstart import get_auxiliary_contbox_parameterization
+
 
 __all__ = ['ReactiveNestedSampler', 'NestedSampler', 'read_file']
 
@@ -913,6 +915,57 @@ def plot(self):
             plt.close()
 
 
+def resume_from_hot_file(
+    param_names,
+    usample_filename,
+    loglike,
+    transform,
+    vectorized=False,
+    derived_param_names=[],
+    min_num_samples=50
+):
+    # load samples
+    try:
+        with open(usample_filename) as f:
+            old_param_names = f.readline().lstrip('#').strip().split()
+            auxiliary_usamples = np.loadtxt(f)
+    except IOError:
+        warnings.warn('not hot-resuming, could not load file "%s"' % usample_filename)
+        return param_names, loglike, transform, vectorized
+
+    ulogl = auxiliary_usamples[:,1]
+    uweights_full = auxiliary_usamples[:,0] * np.exp(ulogl - ulogl.max())
+    mask = uweights_full > 0
+    uweights = uweights_full[mask]
+    uweights /= uweights.sum()
+    upoints = auxiliary_usamples[mask,2:]
+    del auxiliary_usamples
+
+    nsamples = len(upoints)
+    if nsamples < min_num_samples:
+        warnings.warn('not hot-resuming, file "%s" has too few samples (%d)' % (usample_filename, nsamples))
+        return param_names, loglike, transform, vectorized
+
+    # check that the parameter meanings have not changed
+    if old_param_names == ['weight', 'logl'] + param_names:
+        # resuming from a first run
+        pass
+    elif old_param_names == ['weight', 'logl'] + param_names + ['aux_logweight']:
+        # re-resuming from a hot-resumed run
+        # cut off aux_logweight
+        upoints = upoints[:,:-1]
+    else:
+        warnings.warn('not hot-resuming, file "%s" has parameters %s, expected %s.' % (usample_filename, old_param_names, param_names))
+        return param_names, loglike, transform, vectorized
+
+    return get_auxiliary_contbox_parameterization(
+        param_names, loglike=loglike, transform=transform,
+        vectorized=vectorized,
+        upoints=upoints, 
+        uweights=uweights, 
+    )
+
+
 class ReactiveNestedSampler(object):
     """Nested sampler with reactive exploration strategy.
 
@@ -1010,6 +1063,25 @@ def __init__(self,
             is below this normalised Kendall tau distance.
             Values from 0 (highly conservative) to 1 (extremely negligent).
         """
+        assert resume in (True, 'overwrite', 'subfolder', 'resume', 'resume-similar', 'resume-hot'), \
+            "resume should be one of 'overwrite' 'subfolder', 'resume', 'resume-hot' or 'resume-similar'"
+        append_run_num = resume == 'subfolder'
+        resume_similar = resume == 'resume-similar'
+        resume_hot = resume == 'resume-hot'
+        resume = resume in ('resume-similar', 'resume-hot', 'resume', True)
+        
+        if resume_hot:
+            if log_dir is not None:
+                raise ValueError('resume-hot requires setting log_dir to find <log_dir>/chain/weighted_post_untransformed.txt file')
+            param_names, loglike, transform, vectorized = resume_from_hot_file(
+                param_names,
+                os.path.join(log_dir, "chains", "weighted_post_untransformed.txt"),
+                loglike=loglike,
+                transform=transform,
+                vectorized=vectorized,
+                derived_param_names=derived_param_names,
+            )
+        
         self.paramnames = param_names
         x_dim = len(self.paramnames)
 
@@ -1043,12 +1115,6 @@ def __init__(self,
         self.log_to_disk = self.log and log_dir is not None
         self.log_to_pointstore = self.log_to_disk
 
-        assert resume in (True, 'overwrite', 'subfolder', 'resume', 'resume-similar'), \
-            "resume should be one of 'overwrite' 'subfolder', 'resume' or 'resume-similar'"
-        append_run_num = resume == 'subfolder'
-        resume_similar = resume == 'resume-similar'
-        resume = resume in ('resume-similar', 'resume', True)
-
         if self.log and log_dir is not None:
             self.logs = make_run_dir(log_dir, run_num, append_run_num=append_run_num)
             log_dir = self.logs['run_dir']

From 28681b2775dcd9d6d1f55c87e66e8f7cf8d48531 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 5 Sep 2022 15:56:38 +0200
Subject: [PATCH 077/313] finer interpolation binning from last resolved
 quantile to full prior

---
 tests/test_hotstart.py  | 20 ++++++++---
 tests/test_run.py       | 73 +++++++++++++++++++++++++++++++++--------
 ultranest/hotstart.py   | 56 ++++++++++++++++++++++++++++---
 ultranest/integrator.py | 42 ++++++------------------
 4 files changed, 138 insertions(+), 53 deletions(-)

diff --git a/tests/test_hotstart.py b/tests/test_hotstart.py
index f5557656..d6ca9b27 100644
--- a/tests/test_hotstart.py
+++ b/tests/test_hotstart.py
@@ -6,7 +6,7 @@
 from ultranest.utils import vectorize
 from ultranest.integrator import resume_from_hot_file
 from ultranest.hotstart import reuse_samples, get_extended_auxiliary_problem
-from ultranest.hotstart import compute_quantile_intervals, get_auxiliary_contbox_parameterization
+from ultranest.hotstart import compute_quantile_intervals, get_auxiliary_contbox_parameterization, compute_quantile_intervals_refined
 import os
 import tempfile
 
@@ -42,7 +42,7 @@ def extended_log_likelihood(params):
 def test_contbox_hotstart():
     rng_samples = np.random.RandomState(43)
     N = 100000
-    samples = rng_samples.normal(size=(N,2))
+    samples = rng_samples.normal(0.1, 1e-6, size=(N,2))
     samples[:,1] = rng_samples.uniform(size=N)
     weights = (np.ones(N) / N).reshape((-1,1))
     logl = weights * 0
@@ -51,8 +51,20 @@ def test_contbox_hotstart():
     ulos, uhis = compute_quantile_intervals(steps, samples, weights)
     print("quantiles:", ulos)
     print("quantiles:", uhis)
+    ulos2, uhis2, uinterpspace = compute_quantile_intervals_refined(steps, samples, weights)
+    print("refined quantiles:", ulos2)
+    print("refined quantiles:", uhis2)
+    print("interpolation steps:", uinterpspace)
+    assert np.diff(ulos, axis=0).shape == (2,2), ulos
+    assert (np.diff(uinterpspace) > 0).all(), uinterpspace
+    assert (np.diff(ulos, axis=0) < 0).all(), (ulos, uhis)
+    assert (np.diff(uhis, axis=0) > 0).all(), (ulos, uhis)
+    assert (np.diff(ulos2, axis=0) < 0).all(), (ulos2, uhis2)
+    assert (np.diff(uhis2, axis=0) > 0).all(), (ulos2, uhis2)
     assert ulos.shape == (2+1, len(steps)), (uhis.shape, ulos.shape)
     assert uhis.shape == ulos.shape, (uhis.shape, ulos.shape)
+    assert len(uinterpspace) == len(uhis2)
+    assert len(uinterpspace) == len(uhis2)
     tol = dict(atol=1e-3, rtol=0.01)
     for i in 1, 0:
         for j, q in enumerate(steps):
@@ -99,8 +111,8 @@ def test_contbox_hotstart():
             fmt='%f'
         )
         aux_param_names, aux_loglike, aux_transform, vectorized = resume_from_hot_file(
-            parameters,
             tmpfilename,
+            parameters,
             extended_log_likelihood,
             extended_prior_transform,
             vectorized=False,
@@ -111,8 +123,8 @@ def test_contbox_hotstart():
         L = float(aux_loglike(p))
         print(L)
         aux_param_names, aux_vloglike, aux_vtransform, vectorized = resume_from_hot_file(
-            parameters,
             tmpfilename,
+            parameters,
             vectorize(extended_log_likelihood),
             vectorize(extended_prior_transform),
             vectorized=True,
diff --git a/tests/test_run.py b/tests/test_run.py
index eb9f32c6..7f2b845e 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -1,14 +1,16 @@
+import os
 import numpy as np
 import shutil
 import tempfile
 import pytest
 import json
 import pandas
+from ultranest import NestedSampler, ReactiveNestedSampler, read_file
+from ultranest.integrator import resume_from_hot_file
+import ultranest.mlfriends
 from numpy.testing import assert_allclose
 
 def test_run():
-    from ultranest import NestedSampler
-
     def loglike(y):
         z = np.log10(y)
         a = np.array([-0.5 * sum([((xi - 0.83456 + i*0.1)/0.5)**2 for i, xi in enumerate(x)]) for x in z])
@@ -37,9 +39,6 @@ def transform(x):
 
 
 def test_dlogz_reactive_run_SLOW():
-    from ultranest import ReactiveNestedSampler
-    import ultranest.mlfriends
-
     def loglike(y):
         return -0.5 * np.sum(((y - 0.5)/0.001)**2, axis=1)
 
@@ -60,7 +59,6 @@ def loglike(y):
     assert results['logzerr'] < 0.1 * 2
 
 def test_reactive_run():
-    from ultranest import ReactiveNestedSampler
     np.random.seed(1)
     evals = set()
 
@@ -119,7 +117,6 @@ def transform(x):
 
 
 def test_reactive_run_extraparams():
-    from ultranest import ReactiveNestedSampler
     np.random.seed(1)
 
     def loglike(z):
@@ -139,7 +136,6 @@ def transform(x):
     sampler.plot()
 
 def test_return_summary():
-    from ultranest import ReactiveNestedSampler
     sigma = np.array([0.1, 0.01])
     centers = np.array([0.5, 0.75])
     paramnames = ['a', 'b']
@@ -187,7 +183,6 @@ def transform(x):
 
 @pytest.mark.parametrize("dlogz", [2.0, 0.5, 0.1])
 def test_run_resume(dlogz):
-    from ultranest import ReactiveNestedSampler
     sigma = 0.01
     ndim = 1
 
@@ -230,9 +225,6 @@ def myadd(row):
 
 @pytest.mark.parametrize("storage_backend", ['hdf5', 'tsv', 'csv'])
 def test_reactive_run_resume_eggbox(storage_backend):
-    from ultranest import ReactiveNestedSampler
-    from ultranest import read_file
-
     def loglike(z):
         chi = (np.cos(z / 2.)).prod(axis=1)
         loglike.ncalls += len(z)
@@ -400,7 +392,6 @@ def transform(x):
         shutil.rmtree(folder, ignore_errors=True)
 
 def test_reactive_run_warmstart_gauss():
-    from ultranest import ReactiveNestedSampler
     center = 0
 
     def loglike(z):
@@ -501,6 +492,62 @@ def transform(x):
     for name, col in zip(paramnames, result['samples'].transpose()):
         print('%15s : %.3f +- %.3f' % (name, col.mean(), col.std()))
 
+
+def test_run_hotstart_gauss_SLOW():
+    center = None
+    stdev = 0.001
+
+    def loglike(z):
+        chi2 = (((z - center) / stdev)**2).sum(axis=1)
+        loglike.ncalls += len(z)
+        return -0.5 * chi2
+    loglike.ncalls = 0
+
+    def transform(x):
+        return x * 20000 - 10000
+
+    paramnames = ['a']
+
+    folder = tempfile.mkdtemp()
+    np.random.seed(1)
+    ncalls = []
+    try:
+        for i, resume in enumerate(['overwrite', 'resume-hot', 'resume-hot', 'resume-hot']):
+            print()
+            print("====== Running Gauss problem [%d] =====" % (i+1))
+            print()
+            center = [0, 0, stdev, 50 * stdev][i]
+            print("center:", center, "folder:", folder)
+            if i == 0:
+                sampler = ReactiveNestedSampler(paramnames,
+                    loglike, transform=transform,
+                    log_dir=folder, resume=resume, vectorized=True)
+            else:
+                aux_param_names, aux_loglike, aux_transform, vectorized = resume_from_hot_file(
+                    os.path.join(folder, 'chains', 'weighted_post_untransformed.txt'),
+                    paramnames, loglike=loglike, transform=transform, vectorized=True,
+                )
+                sampler = ReactiveNestedSampler(aux_param_names,
+                    aux_loglike, transform=aux_transform, vectorized=True)
+
+            sampler.run(viz_callback=None)
+            sampler.print_results()
+            print("expected posterior:", center, '+-', stdev)
+            print(sampler.results.keys())
+            print(sampler.results['posterior'].keys())
+            print(sampler.results['posterior']['mean'], sampler.results['posterior']['stdev'])
+            print(sampler.results['weighted_samples']['upoints'], sampler.results['weighted_samples']['weights'])
+            assert center - stdev < sampler.results['posterior']['mean'][0] < center + stdev, (center, sampler.results['posterior'])
+            assert stdev * 0.8 < sampler.results['posterior']['stdev'][0] < stdev * 1.2, (center, sampler.results['posterior'])
+            ncalls.append(sampler.ncall)
+    finally:
+        shutil.rmtree(folder, ignore_errors=True)
+    print(ncalls)
+    
+    # make sure hot start is much faster
+    assert ncalls[1] < ncalls[0] - 800, (ncalls)
+    assert ncalls[2] < ncalls[0] - 800, (ncalls)
+
 if __name__ == '__main__':
     #test_run_compat()
     #test_run_resume(dlogz=0.5)
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index edfb16f6..af283d3e 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -277,6 +277,56 @@ def compute_quantile_intervals(steps, upoints, uweights):
     uhis[-1] = 1
     return ulos, uhis
 
+def compute_quantile_intervals_refined(steps, upoints, uweights, logsteps_max=20):
+    """Compute lower and upper axis quantiles.
+    q and 1-q quantiles along each axis of corresponding to steps
+    
+    Parameters
+    ------------
+    steps: array
+        list of quantiles q to compute.
+    upoints: function
+        samples
+    uweights: array
+        sample weights
+
+    Returns:
+    ---------
+    ulo: array
+        list of lower quantiles (at q)
+    uhi: array
+        list of upper quantiles (at 1-q)
+    """
+    nboxes = len(steps)
+    ulos_orig, uhis_orig = compute_quantile_intervals(steps, upoints, uweights)
+    assert len(ulos_orig) == nboxes+1
+    assert len(uhis_orig) == nboxes+1
+
+    smallest_axis_width = np.min(uhis_orig[-2,:] - ulos_orig[-2,:])
+    logsteps = min(logsteps_max, int(np.ceil(-np.log10(max(1e-100, smallest_axis_width)))))
+
+    weights = np.logspace(-logsteps, 0, logsteps+1).reshape((-1, 1))
+    # print("logspace:", weights, logsteps)
+    assert len(weights) == logsteps+1, (weights.shape, logsteps)
+    # print("quantiles:", ulos_orig, uhis_orig)
+    ulos_new = ulos_orig[nboxes-1, :].reshape((1, -1)) * (1 - weights) + 0 * weights
+    uhis_new = uhis_orig[nboxes-1, :].reshape((1, -1)) * (1 - weights) + 1 * weights
+    
+    # print("additional quantiles:", ulos_new, uhis_new)
+    
+    ulos = np.vstack((ulos_orig[:-1,:], ulos_new))
+    uhis = np.vstack((uhis_orig[:-1,:], uhis_new))
+    # print("combined quantiles:", ulos, uhis)
+    assert (ulos[-1,:] == 0).all()
+    assert (uhis[-1,:] == 1).all()
+    
+    uinterpspace = np.ones(nboxes+logsteps+1)
+    uinterpspace[:nboxes+1] = np.linspace(0, 1, nboxes+1)
+    assert 0 < uinterpspace[nboxes-1] < 1, uinterpspace[nboxes]
+    uinterpspace[nboxes:] = np.linspace(uinterpspace[nboxes-1], 1, logsteps+2)[1:]
+    
+    return ulos, uhis, uinterpspace
+
 
 def get_auxiliary_contbox_parameterization(
     param_names, loglike, transform, upoints, uweights, vectorized=False,
@@ -326,10 +376,8 @@ def get_auxiliary_contbox_parameterization(
     steps = 10**-(1.0 * np.arange(1, 8, 2))
     nsamples, ndim = upoints.shape
     assert nsamples > 10
-    ulos, uhis = compute_quantile_intervals(steps, upoints, uweights)
-    nboxes = len(ulos)
-
-    uinterpspace = np.linspace(0, 1, nboxes)
+    ulos, uhis, uinterpspace = compute_quantile_intervals_refined(steps, upoints, uweights)
+    print("boxes:", ulos, uhis, uinterpspace)
     
     aux_param_names = param_names + ['aux_logweight']
 
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index c7e256d0..99ba85a5 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -916,8 +916,8 @@ def plot(self):
 
 
 def resume_from_hot_file(
-    param_names,
     usample_filename,
+    param_names,
     loglike,
     transform,
     vectorized=False,
@@ -943,20 +943,11 @@ def resume_from_hot_file(
 
     nsamples = len(upoints)
     if nsamples < min_num_samples:
-        warnings.warn('not hot-resuming, file "%s" has too few samples (%d)' % (usample_filename, nsamples))
-        return param_names, loglike, transform, vectorized
+        raise ValueError('file "%s" has too few samples (%d) to hot-resume' % (usample_filename, nsamples))
 
     # check that the parameter meanings have not changed
-    if old_param_names == ['weight', 'logl'] + param_names:
-        # resuming from a first run
-        pass
-    elif old_param_names == ['weight', 'logl'] + param_names + ['aux_logweight']:
-        # re-resuming from a hot-resumed run
-        # cut off aux_logweight
-        upoints = upoints[:,:-1]
-    else:
-        warnings.warn('not hot-resuming, file "%s" has parameters %s, expected %s.' % (usample_filename, old_param_names, param_names))
-        return param_names, loglike, transform, vectorized
+    if old_param_names != ['weight', 'logl'] + param_names:
+        raise ValueError('file "%s" has parameters %s, expected %s, cannot hot-resume.' % (usample_filename, old_param_names, param_names))
 
     return get_auxiliary_contbox_parameterization(
         param_names, loglike=loglike, transform=transform,
@@ -1063,25 +1054,6 @@ def __init__(self,
             is below this normalised Kendall tau distance.
             Values from 0 (highly conservative) to 1 (extremely negligent).
         """
-        assert resume in (True, 'overwrite', 'subfolder', 'resume', 'resume-similar', 'resume-hot'), \
-            "resume should be one of 'overwrite' 'subfolder', 'resume', 'resume-hot' or 'resume-similar'"
-        append_run_num = resume == 'subfolder'
-        resume_similar = resume == 'resume-similar'
-        resume_hot = resume == 'resume-hot'
-        resume = resume in ('resume-similar', 'resume-hot', 'resume', True)
-        
-        if resume_hot:
-            if log_dir is not None:
-                raise ValueError('resume-hot requires setting log_dir to find <log_dir>/chain/weighted_post_untransformed.txt file')
-            param_names, loglike, transform, vectorized = resume_from_hot_file(
-                param_names,
-                os.path.join(log_dir, "chains", "weighted_post_untransformed.txt"),
-                loglike=loglike,
-                transform=transform,
-                vectorized=vectorized,
-                derived_param_names=derived_param_names,
-            )
-        
         self.paramnames = param_names
         x_dim = len(self.paramnames)
 
@@ -1115,6 +1087,12 @@ def __init__(self,
         self.log_to_disk = self.log and log_dir is not None
         self.log_to_pointstore = self.log_to_disk
 
+        assert resume in (True, 'overwrite', 'subfolder', 'resume', 'resume-similar'), \
+            "resume should be one of 'overwrite' 'subfolder', 'resume' or 'resume-similar'"
+        append_run_num = resume == 'subfolder'
+        resume_similar = resume == 'resume-similar'
+        resume = resume in ('resume-similar', 'resume', True)
+
         if self.log and log_dir is not None:
             self.logs = make_run_dir(log_dir, run_num, append_run_num=append_run_num)
             log_dir = self.logs['run_dir']

From 862120ed499e4f4c5a43264579128abac0446f73 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 5 Sep 2022 16:05:24 +0200
Subject: [PATCH 078/313] test more extreme hot-resume scenario; remove prints

---
 tests/test_run.py     | 2 +-
 ultranest/hotstart.py | 2 --
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/tests/test_run.py b/tests/test_run.py
index 7f2b845e..f684a076 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -516,7 +516,7 @@ def transform(x):
             print()
             print("====== Running Gauss problem [%d] =====" % (i+1))
             print()
-            center = [0, 0, stdev, 50 * stdev][i]
+            center = [0, 0, stdev, 1][i]
             print("center:", center, "folder:", folder)
             if i == 0:
                 sampler = ReactiveNestedSampler(paramnames,
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index af283d3e..2f13cc35 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -377,7 +377,6 @@ def get_auxiliary_contbox_parameterization(
     nsamples, ndim = upoints.shape
     assert nsamples > 10
     ulos, uhis, uinterpspace = compute_quantile_intervals_refined(steps, upoints, uweights)
-    print("boxes:", ulos, uhis, uinterpspace)
     
     aux_param_names = param_names + ['aux_logweight']
 
@@ -419,7 +418,6 @@ def aux_loglikelihood_vectorized(x):
         # downweight if we are in the auxiliary distribution
         return logl + aux_logweight
 
-    print("vectorized:", vectorized)
     if vectorized:
         return aux_param_names, aux_loglikelihood_vectorized, aux_transform_vectorized, vectorized
     else:

From 7c7d6eaf4ccb8b766df2dddc194ed0ffb2e4fdf3 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 5 Sep 2022 18:20:53 +0200
Subject: [PATCH 079/313] extend changelog, prepare release

---
 HISTORY.rst | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 08bbc8b7..45eda263 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,6 +2,15 @@
 Release Notes
 ==============
 
+3.5.0 (2022-09-05)
+------------------
+
+* add hot-resume: resume from a similar fit (with different data)
+* fix post_summary.csv column order
+* fix build handling for non-pip systems (pyproject.toml)
+* more efficient handling of categorical variables
+
+
 3.4.0 (2022-04-05)
 ------------------
 

From c71032945582547b7beb5e9286ac417b40fb686e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 5 Sep 2022 18:28:51 +0200
Subject: [PATCH 080/313] =?UTF-8?q?Bump=20version:=203.4.6=20=E2=86=92=203?=
 =?UTF-8?q?.5.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 10de2156..16cd43de 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.4.6',
+    version='3.5.0',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 03fba2bf..9fa6e33f 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.4.6'
+__version__ = '3.5.0'

From 3c54ebf7de2290c9e7703e8fa1107e4b70bcde9c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 6 Sep 2022 09:05:21 +0200
Subject: [PATCH 081/313] rename hot start resuming from similar posterior to
 warm start

---
 docs/example-warmstart.ipynb | 554 +++++++++++++++++++----------------
 docs/index.rst               |   1 +
 tests/test_hotstart.py       |   6 +-
 tests/test_run.py            |   6 +-
 ultranest/hotstart.py        |  22 +-
 ultranest/integrator.py      |  44 ++-
 6 files changed, 366 insertions(+), 267 deletions(-)

diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index abc9998f..c4eecf31 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -4,453 +4,508 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tutorial: Warm and hot start for rapid iterations\n",
+    "# Tutorial: warm start from a similar fit\n",
     "\n",
     "In this tutorial you will learn:\n",
     "\n",
     " - How to play with model variations\n",
-    " - Warm start: How UltraNest can resume and reuse an existing run, even if you modify the data/likelihood\n",
-    " - Hot start: How you can make UltraNest skip ahead to the posterior peak\n",
+    " - Warm start feature: How UltraNest can resume and reuse an existing run, even if you modified the data/likelihood\n",
     "\n",
-    "As a simple example, lets say we want to estimate the mean and standard deviation of a sample of points. Over time, more and more points are added."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate some data"
+    "As a simple example, lets say we want to fit a black body."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "from numpy import pi, log\n",
-    "\n",
-    "np.random.seed(1)\n",
-    "Ndata = 200\n",
-    "mean_true = 42.0\n",
-    "sigma_true = 0.1\n",
-    "y = np.random.normal(mean_true, sigma_true, size=Ndata)\n"
+    "import scipy.stats\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Visualise the data\n",
-    "\n",
-    "Lets plot the data first to see what is going on:\n",
-    "\n"
+    "## Black body model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.errorbar(x=np.arange(Ndata), y=y, yerr=sigma_true, marker='x', ls=' ');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will ingest the data in chunks, with more and more information becoming available to us. Here are the chunks. We will first analyse the orange ones:"
+    "parameters = ['Temperature', 'Amplitude']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.errorbar(x=np.arange(Ndata), y=y, yerr=sigma_true, marker='x', ls=' ')\n",
-    "plt.errorbar(x=np.arange(Ndata)[:10], y=y[:10], yerr=sigma_true, marker='x', ls=' ')\n",
-    "ymin, ymax = plt.ylim()\n",
-    "plt.vlines(np.arange(10, Ndata, 20), ymin, ymax, linestyles='--', color='gray')\n",
-    "plt.ylim(ymin, ymax);"
+    "def black_body_model(wavelength, ampl, T):\n",
+    "    with np.errstate(over='ignore'):\n",
+    "        return ampl / wavelength**5 / (np.exp(1/(wavelength*T)) - 1)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Model setup"
+    "## Generate some data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ultranest import ReactiveNestedSampler\n",
-    "\n",
-    "parameters = ['mean', 'scatter']\n",
-    "\n",
-    "def prior_transform(x):\n",
-    "    z = np.empty_like(x)\n",
-    "    z[0] = x[0] * 2000 - 1000\n",
-    "    z[1] = 10**(x[1] * 4 - 2)\n",
-    "    return z\n",
-    "\n",
-    "import scipy.stats\n",
-    "def log_likelihood(params):\n",
-    "    mean, sigma = params\n",
-    "    return scipy.stats.norm(mean, sigma).logpdf(yseen).sum()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adding one new chunk at a time, no warm start"
+    "Ndata = 10\n",
+    "wavelength = np.logspace(1, 2, Ndata)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "reference_results = []\n",
-    "\n",
-    "for i in range(10, Ndata, 20):\n",
-    "    print()\n",
-    "    print(\"Iteration with %d data points\" % i)\n",
-    "    yseen = y[:i]\n",
-    "    sampler_ref = ReactiveNestedSampler(parameters, log_likelihood, prior_transform)\n",
-    "    res_ref = sampler_ref.run(min_num_live_points=400, max_num_improvement_loops=0, viz_callback=None, frac_remain=0.5)\n",
-    "    reference_results.append(res_ref)\n"
+    "np.random.seed(1)\n",
+    "ampl_true = 42.0\n",
+    "T_true = 0.01  # in um^-1\n",
+    "background_true = 1e-9\n",
+    "y_true = black_body_model(wavelength, ampl_true, T_true)\n",
+    "sigma_true = y_true * 0.1\n",
+    "y_obs = np.random.normal(y_true + background_true, sigma_true, size=Ndata)\n",
+    "sigma = y_true * 0.1"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Warm start"
+    "## Visualise the data\n",
+    "\n",
+    "Lets plot the data first to see what is going on:\n",
+    "\n"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "## Adding one data point at a time, with warm start"
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
+    "plt.plot(wavelength, y_true, ':', color='gray')\n",
+    "plt.ylabel('Spectral flux density [Jy]');\n",
+    "plt.xlabel('Wavelength [$\\mu$m]');\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "results = []\n",
-    "\n",
-    "yseen = y[:]\n",
-    "\n",
-    "# delete any existing content:\n",
-    "ReactiveNestedSampler(parameters, log_likelihood, prior_transform,\n",
-    "                      log_dir='warmstartdoc', resume='overwrite')\n",
-    "\n",
-    "for i in range(10, Ndata, 20):\n",
-    "    print()\n",
-    "    print(\"Iteration with %d data points\" % i)\n",
-    "    \n",
-    "    yseen = y[:i]\n",
-    "    sampler = ReactiveNestedSampler(parameters, log_likelihood, prior_transform,\n",
-    "                                    log_dir='warmstartdoc', resume='resume-similar',\n",
-    "                                    warmstart_max_tau=0.5)\n",
-    "    ncall_initial = int(sampler.ncall)\n",
-    "    res = sampler.run(frac_remain=0.5, viz_callback=None)\n",
-    "    results.append((i, res, ncall_initial))\n",
-    "\n"
+    "## Prior"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Likelihood evaluations saved by warm start"
+    "Here we intentionally set very wide priors:\n",
+    "\n",
+    "* a uniform prior on temperature, and \n",
+    "* a very wide log-uniform prior on the normalisation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "ndim = len(parameters)\n",
-    "plt.figure(figsize=(10, 10))\n",
-    "for (i, res, ncall_initial), res_ref in zip(results, reference_results):\n",
-    "    for j in range(ndim):\n",
-    "        plt.subplot(ndim + 2, 1, 1+j)\n",
-    "        plt.ylabel(parameters[j])\n",
-    "        plt.errorbar(x=i, y=res['samples'][:,j].mean(), yerr=res['samples'][:,j].std(), marker='x', color='r')\n",
-    "        plt.errorbar(x=i, y=res_ref['samples'][:,j].mean(), yerr=res_ref['samples'][:,j].std(), marker='x', color='gray')\n",
-    "    \n",
-    "    plt.subplot(ndim + 2, 1, 1+ndim)\n",
-    "    plt.ylabel('$\\log(\\Delta Z)$')\n",
-    "    plt.plot(i, res['logz'] - res_ref['logz'], 'x', color='r')\n",
-    "    plt.subplot(ndim + 2, 1, 1+ndim+1)\n",
-    "    plt.ylabel('Likelihood call fraction')\n",
-    "    plt.plot(i, ((res['ncall'] - ncall_initial) / res_ref['ncall']), 'x', color='r')\n",
-    "    plt.ylim(0, 1)\n",
-    "\n",
-    "plt.subplot(ndim + 2, 1, 1)\n",
-    "plt.hlines(mean_true, 0, i+1, color='k', linestyles=':')\n",
-    "plt.subplot(ndim + 2, 1, 2)\n",
-    "plt.hlines(sigma_true, 0, i+1, color='k', linestyles=':')\n"
+    "def prior_transform(x):\n",
+    "    z = x.copy()\n",
+    "    z[0] = x[0]\n",
+    "    z[1] = 10**(x[1] * 20 - 10)\n",
+    "    return z\n"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "## Take-aways:\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.title(\"Prior predictive checks\")\n",
+    "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
+    "plt.ylim(0, y_obs.max() * 10)\n",
     "\n",
-    "Notice the time saving in the bottom panel by more than half. This benefit is *independent of problem dimension*. The cost savings are higher, the more similar the modified problem is."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hot start"
+    "for i in range(20):\n",
+    "    T, ampl = prior_transform(np.random.uniform(size=len(parameters)))\n",
+    "    y_predicted = black_body_model(wavelength, ampl, T)\n",
+    "    plt.plot(wavelength, y_predicted, '-', color='gray')\n",
+    "plt.ylabel('Spectral flux density [Jy]');\n",
+    "plt.xlabel('Wavelength [$\\mu$m]');\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We may already know roughly what the posterior looks like. If it is roughly gaussian, we can take advantage of this by running UltraNest on an auxiliary distribution.\n",
-    "\n",
-    "The speed-up depends on how the auxiliary distribution is defined. Therefore, this is left to the user, and not automatically derived. The following illustrates how to create a auxiliary distribution and work with it."
+    "# First simple model"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Guess a useful covariance"
+    "def log_likelihood(params):\n",
+    "    T, ampl = params\n",
+    "    y_predicted = black_body_model(wavelength, ampl, T)\n",
+    "    return scipy.stats.norm(y_predicted, sigma).logpdf(y_obs).sum()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75f027c7ce0f42beb5047c4919e1ab8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=2e+02  7 [173.9132..173.9155]*| it/evals=5960/17596 eff=34.6592% N=400    400 \n",
+      "[ultranest] Likelihood function evaluations: 17598\n",
+      "[ultranest] Writing samples and results to disk ...\n",
+      "[ultranest] Writing samples and results to disk ... done\n",
+      "[ultranest]   logZ = 160 +- 0.1753\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 983.4, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.07 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy wants 398 minimum live points (dlogz from 0.14 to 0.51, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.18 bs:0.18 tail:0.41 total:0.44 required:<0.50\n",
+      "[ultranest] done iterating.\n",
+      "\n",
+      "logZ = 160.064 +- 0.655\n",
+      "  single instance: logZ = 160.064 +- 0.184\n",
+      "  bootstrapped   : logZ = 160.026 +- 0.514\n",
+      "  tail           : logZ = +- 0.405\n",
+      "insert order U test : converged: True correlation: inf iterations\n",
+      "\n",
+      "    Temperature         : 0.00940│ ▁▁▁▁▁▁▁▁▁▁▂▂▃▃▅▅▅▅▆▇▇▅▅▄▃▃▂▂▁▁▁▁▁▁▁▁▁ │0.01033    0.00988 +- 0.00011\n",
+      "    Amplitude           : 37.8  │ ▁▁▁▁▁▁▁▁▂▂▄▃▄▅▆▆▇▇▇▅▅▄▃▃▂▂▁▁▁▁▁▁▁▁ ▁▁ │58.5      47.4 +- 2.7\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "# take result from the second-to-last run\n",
-    "ref_result = reference_results[-2];"
+    "from ultranest import ReactiveNestedSampler\n",
+    "\n",
+    "reference_run_folder = 'blackbody-alldata'\n",
+    "sampler_ref = ReactiveNestedSampler(parameters, log_likelihood, prior_transform, log_dir=reference_run_folder, resume='overwrite')\n",
+    "results_ref = sampler_ref.run(frac_remain=0.5)\n",
+    "sampler_ref.print_results()\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Luckily, the posterior here is already very gaussian-like:"
+    "## Plot the fit"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "import corner\n",
-    "corner.corner(ref_result['samples'], show_titles=True);"
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
+    "from ultranest.plot import PredictionBand\n",
+    "band = PredictionBand(wavelength)\n",
+    "for T, ampl in results_ref['samples']:\n",
+    "    band.add(black_body_model(wavelength, ampl, T))\n",
+    "band.line(color='k')\n",
+    "band.shade(color='k', alpha=0.5)\n",
+    "plt.ylabel('Spectral flux density [Jy]');\n",
+    "plt.xlabel('Wavelength [$\\mu$m]');\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Step 1**: Identify the center and covariance (in u-space, i.e., before the prior transformation).\n",
-    "\n",
-    "You can also do this \n",
+    "# Warm Start with Model modification\n",
     "\n",
-    "* by looking at the data\n",
-    "* from posterior samples of a previous nested sampling or MCMC run\n",
-    "* with a minimizer such as [snowline](https://johannesbuchner.github.io/snowline/).\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We demonstrate the second method here:"
+    "Lets say we alter our model slightly. We include a small constant background:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
-    "indices = np.random.choice(len(ref_result['weighted_samples']['weights']), p=ref_result['weighted_samples']['weights'], size=10000)\n",
-    "u_posterior = ref_result['weighted_samples']['upoints'][indices,:]\n",
-    "ctr = u_posterior.mean(axis=0)\n",
-    "cov = np.cov(u_posterior, rowvar=False)\n",
-    "\n",
-    "print(\"center in unit cube coordinates:\", ctr)\n",
-    "print(\"center in physical coordinates:\", prior_transform(ctr))\n",
-    "print(\"covariance:\", cov)\n",
-    "\n",
-    "invcov = np.linalg.inv(cov)\n",
-    "print(\"precision matrix:\", invcov)"
+    "def log_likelihood_with_background(params):\n",
+    "    T, ampl = params\n",
+    "    y_predicted = black_body_model(wavelength, ampl, T) + 1e-9\n",
+    "    return scipy.stats.norm(y_predicted, sigma).logpdf(y_obs).sum()\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let us intentionally show the case where a poor distribution is chosen:"
+    "We have the same parameters, and expect results to only be slightly different. So lets use **warm start**."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Step 2**: Define the auxiliary distribution\n",
-    "\n",
-    "This is always the same, once you have chosen a center and covariance.\n",
-    "Here we use a multivariate Student-t distribution with one degree of freedom.\n",
-    "\n",
-    "This allows heavier-tailed posterior distributions than a Gaussian,\n",
-    "and is more forgiving if we mis-estimated the center or the covariance."
+    "Using the previous reference run output file ..."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ultranest.hotstart import get_extended_auxiliary_problem\n",
-    "\n",
-    "aux_log_likelihood, aux_transform = get_extended_auxiliary_problem(\n",
-    "        log_likelihood, prior_transform, ctr, invcov, \n",
-    "        enlargement_factor=len(parameters)**0.5, df=2)\n"
+    "posterior_upoints_file = reference_run_folder + '/chains/weighted_post_untransformed.txt'"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "#aux_parameters = ['aux_%d' % (i + 1) for i, p in enumerate(parameters)]\n",
-    "aux_sampler = ReactiveNestedSampler(\n",
-    "    parameters, aux_log_likelihood, transform=aux_transform,\n",
-    "    derived_param_names=['aux_logweight'],\n",
-    ")\n",
-    "aux_results = aux_sampler.run(frac_remain=0.5, viz_callback=None)"
+    "We define our accelerated likelihood and prior transform:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from getdist import MCSamples, plots\n",
-    "\n",
-    "aux_dist_samples_full = np.array([aux_transform(np.random.uniform(size=len(parameters))) for i in range(10000)])\n",
-    "aux_dist_samples = aux_dist_samples_full[aux_dist_samples_full[:,-1] > -1e100,:-1]\n",
-    "\n",
-    "samples_o = MCSamples(samples=ref_result['samples'],\n",
-    "                       names=ref_result['paramnames'],\n",
-    "                       label='Cold start',\n",
-    "                       settings=dict(smooth_scale_2D=3), sampler='nested')\n",
-    "samples_a = MCSamples(samples=aux_dist_samples,\n",
-    "                       names=ref_result['paramnames'],\n",
-    "                       label='Auxiliary distribution',\n",
-    "                       settings=dict(smooth_scale_2D=1), sampler='nested')\n",
-    "samples_g = MCSamples(samples=aux_results['samples'][:,:-1],\n",
-    "                       names=aux_results['paramnames'][:-1],\n",
-    "                       label='Hot start',\n",
-    "                       settings=dict(smooth_scale_2D=3), sampler='nested')\n",
+    "from ultranest.integrator import warmstart_from_similar_file\n",
     "\n",
-    "mcsamples = [samples_o, samples_a, samples_g]\n",
-    "\n",
-    "g = plots.get_subplot_plotter(width_inch=8)\n",
-    "g.settings.num_plot_contours = 3\n",
-    "g.triangle_plot(mcsamples, filled=False, contour_colors=plt.cm.Set1.colors,\n",
-    "               param_limits=dict(zip(parameters, [(41.9, 42.1), (0, 0.2)])))\n",
-    "\n"
+    "aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized = warmstart_from_similar_file(\n",
+    "    posterior_upoints_file, parameters, log_likelihood_with_background, prior_transform)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In a good run, most auxiliary weights should be small (<1). If they are not, you may need to increase the enlargement_factor."
+    "Make accelerated run:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.hist(aux_results['samples'][:,-1], bins=40)\n",
-    "plt.xlabel(\"ln(weights)\");"
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd5bbe64da7e46a294ae91464f7034bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=2e+02  8 [169.3705..169.3741]*| it/evals=1560/4757 eff=35.8045% N=400  \n",
+      "[ultranest] Likelihood function evaluations: 4808\n",
+      "[ultranest]   logZ = 166.7 +- 0.04882\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 867.2, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.07 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.41, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.07 bs:0.05 tail:0.41 total:0.41 required:<0.50\n",
+      "[ultranest] done iterating.\n"
+     ]
+    }
+   ],
+   "source": [
+    "sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized=vectorized)\n",
+    "res = sampler.run(frac_remain=0.5)"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "## Speed-up by hot start"
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
+    "from ultranest.plot import PredictionBand\n",
+    "band = PredictionBand(wavelength)\n",
+    "for T, ampl in results_ref['samples']:\n",
+    "    band.add(black_body_model(wavelength, ampl, T))\n",
+    "band.line(color='k')\n",
+    "band.shade(color='k', alpha=0.5)\n",
+    "\n",
+    "band = PredictionBand(wavelength)\n",
+    "for T, ampl, _ in res['samples']:\n",
+    "    band.add(black_body_model(wavelength, ampl, T))\n",
+    "band.line(color='orange')\n",
+    "band.shade(color='orange', alpha=0.5)\n",
+    "plt.plot(wavelength, y_true, ':', color='gray')\n",
+    "plt.ylabel('Spectral flux density [Jy]');\n",
+    "plt.xlabel('Wavelength [$\\mu$m]');\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Assuming we already obtained the covariance and mean for free, what is the additional cost of the hot start?"
+    "## Speed-up"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Speed-up of warm-start: 266%\n"
+     ]
+    }
+   ],
    "source": [
-    "print(\"auxiliary sampler used %(ncall)d likelihood calls\" % aux_results)"
+    "print(\"Speed-up of warm-start: %d%%\" % ((results_ref['ncall'] / res['ncall'] - 1)*100))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Compare this to the full run with the same number of data points."
+    "The cost savings are higher, the more similar the posterior of the modified run is to the original run. This speed-up increases drastically if you have highly informative posteriors.\n",
+    "This benefit is *independent of problem dimension*."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "print(\"Speedup factor of hot start: %.1f\" % (reference_results[-1]['ncall'] / aux_results['ncall']))"
+    "# Starting from existing posterior samples"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This speed-up increases drastically if you have highly informative posteriors."
+    "The hot-starting works by deforming the parameter space. The prior transform function is adjusted, and the adjustment is removed by reweighting the likelihood function, to produce the same posterior.\n",
+    "To make this work, posterior samples from the unit cube space are required. The deformation uses a factorized auxiliary distribution, based on marginal posterior quantiles.\n",
+    "\n",
+    "If you already have posterior samples (or can generate samples from posterior means and standard deviations), and you can untransform to unit cube samples, then you can create an appropriate weighted_post_untransformed.txt file.\n",
+    "\n",
+    "The weighted_post_untransformed.txt file from a hot-started run cannot be used. This is because it has a deformation already applied."
    ]
   },
   {
@@ -459,15 +514,12 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "* Warm start allows accelerated computation based on a different but similar UltraNest run. \n",
-    "* Hot start allows accelerated computation based on already approximately knowing the posterior peak.\n",
-    "\n",
-    "These feature allows you to:\n",
+    "Hot start allows accelerated computation based on already knowing the posterior peak approximately. This allows you to:\n",
     "\n",
     "* vary the data (change the analysis pipeline)\n",
     "* vary model assumptions \n",
     "\n",
-    "**without needing to start the computation from scratch** (potentially costly).\n",
+    "without needing to start the computation from scratch (potentially costly).\n",
     "\n",
     "These features are experimental and feedback is appreciated. It is recommended to do a full, clean run to obtain final, reliable results before publication.\n"
    ]
@@ -475,7 +527,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -489,7 +541,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/index.rst b/docs/index.rst
index 10c6d6dd..8145cca4 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -27,6 +27,7 @@ Welcome to UltraNest's documentation!
    example-line.ipynb
    example-outliers.ipynb
    example-sine-bayesian-workflow.ipynb
+   example-warmstart.ipynb
 
 .. include:: ../README.rst
 
diff --git a/tests/test_hotstart.py b/tests/test_hotstart.py
index d6ca9b27..6d00f05b 100644
--- a/tests/test_hotstart.py
+++ b/tests/test_hotstart.py
@@ -4,7 +4,7 @@
 from numpy import log10
 from ultranest import ReactiveNestedSampler
 from ultranest.utils import vectorize
-from ultranest.integrator import resume_from_hot_file
+from ultranest.integrator import warmstart_from_similar_file
 from ultranest.hotstart import reuse_samples, get_extended_auxiliary_problem
 from ultranest.hotstart import compute_quantile_intervals, get_auxiliary_contbox_parameterization, compute_quantile_intervals_refined
 import os
@@ -110,7 +110,7 @@ def test_contbox_hotstart():
             header='weight logl mean scatter',
             fmt='%f'
         )
-        aux_param_names, aux_loglike, aux_transform, vectorized = resume_from_hot_file(
+        aux_param_names, aux_loglike, aux_transform, vectorized = warmstart_from_similar_file(
             tmpfilename,
             parameters,
             extended_log_likelihood,
@@ -122,7 +122,7 @@ def test_contbox_hotstart():
         assert p.shape == (len(aux_param_names)+1,)
         L = float(aux_loglike(p))
         print(L)
-        aux_param_names, aux_vloglike, aux_vtransform, vectorized = resume_from_hot_file(
+        aux_param_names, aux_vloglike, aux_vtransform, vectorized = warmstart_from_similar_file(
             tmpfilename,
             parameters,
             vectorize(extended_log_likelihood),
diff --git a/tests/test_run.py b/tests/test_run.py
index f684a076..c6c4f1c4 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -6,7 +6,7 @@
 import json
 import pandas
 from ultranest import NestedSampler, ReactiveNestedSampler, read_file
-from ultranest.integrator import resume_from_hot_file
+from ultranest.integrator import warmstart_from_similar_file
 import ultranest.mlfriends
 from numpy.testing import assert_allclose
 
@@ -493,7 +493,7 @@ def transform(x):
         print('%15s : %.3f +- %.3f' % (name, col.mean(), col.std()))
 
 
-def test_run_hotstart_gauss_SLOW():
+def test_run_warmstart_gauss_SLOW():
     center = None
     stdev = 0.001
 
@@ -523,7 +523,7 @@ def transform(x):
                     loglike, transform=transform,
                     log_dir=folder, resume=resume, vectorized=True)
             else:
-                aux_param_names, aux_loglike, aux_transform, vectorized = resume_from_hot_file(
+                aux_param_names, aux_loglike, aux_transform, vectorized = warmstart_from_similar_file(
                     os.path.join(folder, 'chains', 'weighted_post_untransformed.txt'),
                     paramnames, loglike=loglike, transform=transform, vectorized=True,
                 )
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index 2f13cc35..2a9e5d7b 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -338,14 +338,6 @@ def get_auxiliary_contbox_parameterization(
     likelihood and prior transform that is identical but
     requires fewer nested sampling iterations.
 
-    This is achieved by deforming the prior space, and undoing that
-    transformation by correction weights in the likelihood.
-
-    The auxiliary distribution used for transformation/weighting is
-    factorized. Each axis considers the ECDF of the auxiliary samples,
-    and segments it into five quantile segments. Within each segment,
-    the parameter edges in u-space are linearly interpolated.
-
     Usage::
 
         aux_loglikelihood, aux_transform = get_auxiliary_contbox_parameterization(
@@ -354,6 +346,20 @@ def get_auxiliary_contbox_parameterization(
         aux_results = aux_sampler.run()
         posterior_samples = aux_results['samples'][:,-1]
 
+    This is achieved by deforming the prior space, and undoing that
+    transformation by correction weights in the likelihood.
+    A additional parameter, "aux_logweight", is added at the end,
+    which contains the correction weight. You can ignore it.
+
+    The auxiliary distribution used for transformation/weighting is
+    factorized. Each axis considers the ECDF of the auxiliary samples,
+    and segments it into quantile segments. Within each segment,
+    the parameter edges in u-space are linearly interpolated.
+    To see the interpolation quantiles for each axis, use::
+    
+        steps = 10**-(1.0 * np.arange(1, 8, 2))
+        ulos, uhis, uinterpspace = compute_quantile_intervals_refined(steps, upoints, uweights)
+
     Parameters
     ------------
     loglike: function
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 99ba85a5..c9e4c291 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -27,7 +27,7 @@
 from .hotstart import get_auxiliary_contbox_parameterization
 
 
-__all__ = ['ReactiveNestedSampler', 'NestedSampler', 'read_file']
+__all__ = ['ReactiveNestedSampler', 'NestedSampler', 'read_file', 'warmstart_from_similar_file']
 
 
 def _get_cumsum_range(pi, dp):
@@ -915,7 +915,7 @@ def plot(self):
             plt.close()
 
 
-def resume_from_hot_file(
+def warmstart_from_similar_file(
     usample_filename,
     param_names,
     loglike,
@@ -924,6 +924,46 @@ def resume_from_hot_file(
     derived_param_names=[],
     min_num_samples=50
 ):
+    """Warmstart from a previous run.
+
+    
+
+    Parameters
+    ------------
+    usample_filename: str
+        'directory/chains/weighted_post_untransformed.txt'
+        contains posteriors in u-space (untransformed) of a previous run.
+        Columns are weight, logl, param1, param2, ...
+    min_num_samples: int
+        minimum number of samples in the usample_filename file required.
+        Too few samples will give a poor approximation.
+
+    The remaining parameters have the same meaning as in :class:ReactiveNestedSampler.
+
+    Returns:
+    ---------
+    aux_param_names: list
+        new parameter list
+    aux_loglikelihood: function
+        new loglikelihood function
+    aux_transform: function
+        new prior transform function
+    vectorized: bool
+        whether the new functions are vectorized
+
+    Usage::
+
+        aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized = warmstart_from_similar_file(
+            'model1/chains/weighted_post_untransformed.txt', parameters, log_likelihood_with_background, prior_transform)
+
+        aux_sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, transform=aux_prior_transform,vectorized=vectorized)
+        aux_sampler.run()
+        posterior_samples = aux_results['samples'][:,-1]
+
+    See :py:func:`ultranest.hotstart.get_auxiliary_contbox_parameterization`
+    for more information.
+
+    """
     # load samples
     try:
         with open(usample_filename) as f:

From e3f038ed6db8f4601bc968741228e388323aaf6a Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 6 Sep 2022 09:28:29 +0200
Subject: [PATCH 082/313] module name doc

---
 ultranest/hotstart.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index 2a9e5d7b..ef2e5847 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -1,4 +1,4 @@
-"""Hot start helper functions."""
+"""Warm start and hot start helper functions."""
 
 import numpy as np
 import scipy.stats

From 5648d278a3039b0a09e6cee3a5432a827de2f151 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 6 Sep 2022 09:28:53 +0200
Subject: [PATCH 083/313] =?UTF-8?q?Bump=20version:=203.5.0=20=E2=86=92=203?=
 =?UTF-8?q?.5.1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 16cd43de..66a41db7 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.5.0',
+    version='3.5.1',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 9fa6e33f..18189a1b 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.5.0'
+__version__ = '3.5.1'

From 5611971375bfbe5afbc02f9b28e37c7448919b77 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 6 Sep 2022 11:37:19 +0200
Subject: [PATCH 084/313] update docs

---
 docs/example-warmstart.ipynb | 341 +++++++++++++++++++++++++++++++----
 1 file changed, 305 insertions(+), 36 deletions(-)

diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index c4eecf31..e5d0dc8a 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -218,7 +218,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "75f027c7ce0f42beb5047c4919e1ab8e",
+       "model_id": "120158f83d1347fe83a30de54ea58b7b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -233,25 +233,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ultranest] Explored until L=2e+02  7 [173.9132..173.9155]*| it/evals=5960/17596 eff=34.6592% N=400    400 \n",
-      "[ultranest] Likelihood function evaluations: 17598\n",
+      "[ultranest] Explored until L=2e+02  7 [173.8606..173.8613]*| it/evals=6000/22771 eff=26.8204% N=400    400  400 400 \n",
+      "[ultranest] Likelihood function evaluations: 22781\n",
       "[ultranest] Writing samples and results to disk ...\n",
       "[ultranest] Writing samples and results to disk ... done\n",
-      "[ultranest]   logZ = 160 +- 0.1753\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 983.4, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.07 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy wants 398 minimum live points (dlogz from 0.14 to 0.51, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.18 bs:0.18 tail:0.41 total:0.44 required:<0.50\n",
+      "[ultranest]   logZ = 159.9 +- 0.124\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 981.6, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.07 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.42, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.18 bs:0.12 tail:0.41 total:0.42 required:<0.50\n",
       "[ultranest] done iterating.\n",
       "\n",
-      "logZ = 160.064 +- 0.655\n",
-      "  single instance: logZ = 160.064 +- 0.184\n",
-      "  bootstrapped   : logZ = 160.026 +- 0.514\n",
+      "logZ = 159.914 +- 0.447\n",
+      "  single instance: logZ = 159.914 +- 0.185\n",
+      "  bootstrapped   : logZ = 159.917 +- 0.189\n",
       "  tail           : logZ = +- 0.405\n",
       "insert order U test : converged: True correlation: inf iterations\n",
       "\n",
-      "    Temperature         : 0.00940│ ▁▁▁▁▁▁▁▁▁▁▂▂▃▃▅▅▅▅▆▇▇▅▅▄▃▃▂▂▁▁▁▁▁▁▁▁▁ │0.01033    0.00988 +- 0.00011\n",
-      "    Amplitude           : 37.8  │ ▁▁▁▁▁▁▁▁▂▂▄▃▄▅▆▆▇▇▇▅▅▄▃▃▂▂▁▁▁▁▁▁▁▁ ▁▁ │58.5      47.4 +- 2.7\n",
+      "    Temperature         : 0.00945│ ▁▁▁▁▁▁▁▂▂▂▄▄▅▆▆▆▇▇▆▇▆▅▅▃▃▂▁▁▁▁▁▁▁▁▁ ▁ │0.01038    0.00989 +- 0.00012\n",
+      "    Amplitude           : 37.3  │ ▁▁▁▁▁▁▁▂▂▂▃▄▄▅▇▇▇▇▆▆▇▅▆▅▃▃▂▁▁▁▁▁▁▁▁▁▁ │58.1      47.3 +- 2.7\n",
       "\n"
      ]
     }
@@ -274,12 +274,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -314,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -340,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -356,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -388,7 +388,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd5bbe64da7e46a294ae91464f7034bf",
+       "model_id": "ac84b43200444359bfde94e2d8a087dc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -403,13 +403,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ultranest] Explored until L=2e+02  8 [169.3705..169.3741]*| it/evals=1560/4757 eff=35.8045% N=400  \n",
-      "[ultranest] Likelihood function evaluations: 4808\n",
-      "[ultranest]   logZ = 166.7 +- 0.04882\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 867.2, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.07 nat, need <0.50 nat)\n",
+      "[ultranest] Explored until L=2e+02  6 [169.4148..169.4161]*| it/evals=1626/5461 eff=32.1280% N=400 00 \n",
+      "[ultranest] Likelihood function evaluations: 5484\n",
+      "[ultranest]   logZ = 166.7 +- 0.05516\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 485.6, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.11 nat, need <0.50 nat)\n",
       "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.41, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.07 bs:0.05 tail:0.41 total:0.41 required:<0.50\n",
+      "[ultranest]   logZ error budget: single: 0.08 bs:0.06 tail:0.41 total:0.41 required:<0.50\n",
       "[ultranest] done iterating.\n"
      ]
     }
@@ -421,12 +421,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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Kdnwx914Zwz9XlpKbm4dSil69ejFlyhRWr15Neno6Y8eOxWAwkJGRwfPPP09OTg65ubld9G0JIYQQPYv0eHUzt0TVkVi9C/NfZmE4/DHXXHMNc68fzrTkjxgQeQC7z0KbL5pTocvlctHW1kbRiCL6FfZD+V1QuZTiE0fY39wHZ8YcyLwBZY44+0W1P9A7dlJufiGz+h0ktGEVuuVYx/PKks2Y3BYmD4mmqqqq4/lx48ZRWFjIqlWrKC4uBiAsLIzIyEgef/xx2traOtX2hzbVof5yoON4aFPdl/jmhBBCiO5Pglc3s3fvXpzbVuCpK+Peu27i21O9jI3biEH5A8OK/HsdLrvdjtvtZvy48WRnZ3OiuIKqPa9D404u7+3igSvDCYvLO/vFtAZnDfg9gAZXQ+B5pSDjWhzmLF7+xMiR8pPBSRkxhqdx+8g20uONHZPqlVJcf/31JCQk8Prrr9Pc3AxAfHw8zc3NPPvssx1rgZ3LQ2OSmZgRwcSMCPT3+/PQmOSv9B0KIYQQ3ZUEr25Ea83evXvxlO4lND2fB0YdIzHESpM3Fpc/7IzzWltbMRqNTJw4kcSYULwlr/POvhjWN4yDXvdhzJhGSEjI2S/mc0J7CVhyIDIPLFngawevPfC6IQRT1g24fSE4araeDGeAyUJoaDg/vDYUj7MNuz1wfmhoKLNnz8br9bJo0SK83kAPWkZGBlu2bGHlypVd8ZUJIYQQPYoEr27EZrPR3NyMu/QA0b0KcfrCaPNFcWpYEQKhq7m5mejoaCZOuJwmWzX6yN8wtR/ijkHHuGliNoSfYw0trcFRDa56yL8bCn8ChhAwhEGfB8BZ3THsaI6I5+5xdoaEr4Oqd+lYXTUshRhTMz+enUN1dVVHyEpMTOTGG2+kqqqKZcuWobXuWFx14cKFHDlypKu+OiGEEKJHkODVjZSWlmK1WtFeN9G9C3HrM3us/H4/TU1NpKenM66okOqj63h2Vz/2OsdC7wdJzh2O2WQ8y6cDXgfYiyGqDwz9LaROAsNp5yeMhKybwF7WEbJUdB9InsSJqlbe+cT275XtI7LoFXGcedcNpKysrOOOxn79+jFhwgR27drF9u3bAQgJCSE+Pp7HH3+8Yy0wIYQQ4lIkwasb2b9/P1arFYDIXoWc3tPl9XppamqioE9v+qX5MRUvINe4k5l9S+g/ZAKEJpzlUwmEKHsleJug13wo/GHHwqmfkXk9JIwCe/m/n0u6nFoGUtEA9ubKwHPKCGFpTM46xviiXlRUVHScPmnSJHr37s17773X8XxMTAxOp5N//OMfHT1kQgghxKVGglc3obVmz5492Gw2QlNzMFqiO15zu920trYyenA2NY0e/rljII7wAaiC/2BY/xxMJnX2D/a2Q3sxxA6EIb+D5PGgzvGzKwP0ugciMgIT7wGUYvTw/tzfaxGWmlfBc3KyvSkCgymEeWPbSU2K7QiNBoOBm266iZiYGBYtWtRxV2N6ejoHDhzgnXfe+VrflRBCCNFTSfDqJhobG7HZbFRWVhLVuxC/Dvw0drsdj8vOtIGKdMdy+lv2c1mug5DcGwMLnp6N9oOjArxtUPBt6PtdCI3vXDGm8MD5ygjuwB2KyhSGOfcW/F43q7ecoL41sIgqocmEeKz86NZMPB53R8gKDw9n1qxZOBwOFi9ejM/n65jv9fbbb7Nnz56v/F0JIYQQPZUEr26itLSU+vp6PB4P0b37o1G0tbWRENZGS+ggNlckQNxw0gbfyoTBSRjP9ct52gJ3LMaNgKEPQ+JlZ2wP1ClhSdD3++BpCtwBCRCWQlvijWy39eHwkaP/PjciizjXTn589+XU1dXh8QTugExNTeW6666jrKyMDz74AACTyURycjJPPvlkRw+ZEEIIcamQ4NVNHDx48Iz5XZH+JopSKhiVdJBos52I5IGQcS0Yw87+IdofmJvld0G/HwTuUjzb9kCdEd0Het0NjkrwB3q4olP78a2hmxgb+ho0ney1UgYIS6OPYR1zbp16xmT7wYMHM2rUKDZv3tzRyxUZGQnAk08+2bHHoxBCCHEpkODVTezevRur1UpmciSF0S1807eKEKMHkiZwzeWFjOkfd+4P8LQE5nIljQncsRg/7Mv3cn2epAmQfjU4/n2nY2T2RIjIoaH4Y3YeDQxFYgoHFcKUzENMGDuSsrKyjo+YNm0aOTk5LF26lJqawLyxlJQUiouLWbx4sWymLYQQ4pLRZcFLKfWQUuqQUmq7Umrsp177rlKqQilVqZT6XVfV0FO0tLRQU1NDVVUVI/uEc4VxK63eaGrj7oSUKWA4x5aa2hdY/gE/9P8p5N977rlfX5ZSkD0LYgcF5oxBYO5X9i1saJrAqoMhOJ2OwPNhSRic1cybZCYzM6MjZBmNRm655RbCw8N57bXXcDgcHfO93n//fbZu3Xr+6hVCCCG6sS4JXkqpK4B5wHDgV8BCpQLdL0qpSODPwE3AZcCPlFKFXVFHT1FaWkpjYyMul4uignAmRW5gWNonFOZ+QS+XuwnaSyFlEgz5v8Cdi1+il+uRbTbWlLef8dya8nYe2WY780SDCXp/E0ITwXVyXpYpkukj4rkv+2nCat789+KqEVmENG7ih3eNxmAw0NLSAgSGF2fNmkVLSwtvvPEGfr8fo9FIamoq//znP8/Y+1EIIYS4WHVVj9c0YJPW2g6sAbKAgpOvuYAmwAJEAD6gsYvq6BEOHz5MbW0tAKP7htHbVM52//Czv8HvBXtpYPHTAb+AvDlgsnzp645MCWfW8goaXYH5W2vK25m1vIKRKeGfPdkcBX2/B9rbsZxESHQGcdljoO0YBw7sx+0lMN8rPI24hrf4zwdn09DQgMvlAiAzM5MZM2Zw/PhxPvroIwAiIiIICQnh8ccfx+FwfOk2CCGEED1JVwWveKAS4GT4agQSTj72AI8Cq4B9wHNAbRfV0SPs3LmT+vp60hLDMUYk88fiH7HXPfTzT3Y1gKMcUqfD4P+FmH5f+bqTsywsmpHJwQYXxS1uZi2vYNGMTCZnnSXERaRDn2+Dqw58JyfFx43AFjqe148MYPOBkz1lxnAwhtPLu5y5d86ioqKiY5Ps4cOHM2zYMNavX8+hQ4cASEpKoqamhpdeeukrt0UIIYToCboqeDUCGQBKqQggDmg4+bgI+BmQD6QBU4BbTn+zUmq+UmrbwYMHKSoqYsGCBV1UZvC1t7dTWVlJZWUlI/tEEGtqJCu8lGMhfc880e8NLBFhjoCB/w25twUmtH9Nk7MspFlMlLV6eXBw3NlD1ylxgyD3jkD4035QisRel3NX3jLGGZ8JBEMIDEs6rUzKqWDKlMkddzoqpZgxYwbp6em89dZb2GyBsJaVlcW6detkSyEhhBA9zoIFCygqKgIoVEptU0rNP9u5XRW8VgKjlVLhwCSgHDh88rVowA+0AXbAC8Se/mat9QKtdVFhYSHbtm1j/vyz1t/jlZaW0tTUhNPpZESfCAosR4iLr8ZnMP/7JJctMLE9YyYM+g1E9T5v119T3k51u5fsKBNP7Wn8zJyvz5V2JaRM/veejgYzef3HYzAoXMVvUlUfWMeLiEyUdT13XplD7969Oybbm0wmZs2ahclk4rXXXsPlcrEpvBBv3nBqa2txOp0dtX1mvpkQQgjRzcyfP59t27YBHNRaF2mtz9pj1CXBS2u9GngB2AX8L3A78JxS6rda6w+BvwG7gWPAJ8DzXVFHT3D06NGOQFIx8HY+8o5iux4ReNHvYU2Vn0eqx8CghyD7JjCGnrdrn5rTVRgfSl50CItmZDJrecUXhy+lIPfOwGbbjpOT4kNiIetm3ikfyysb/bg9+uR8rwzM5S/z7XuuJzQ0lMbGwHS+mJgYbrnlFurr61myZAlp3gbeiZ2I1xxOZWUly4/Wn32+mRBCCNFDddlyElrrX2ut+2qth2utN2qt52qtf3Hytf/SWmeePO7RWl+yq2ju2rWL+vp6UuLC6BvSyC1Nf+VDxzjwe1lTY2TWiVsZOeRaiMw979feWutg0YxM4kKNwL/nfG2t7cQkd2MIFPwHmC3/Hl6M7MUVfdu5OeVVQpo3nzwvDIwWYmtf4gff+Satra0dPVp5eXlMnTqVgwcPUrX2LW6xb6IhJJ5GQwS3fdF8MyGEEKIHkgVUg8jhcFBSUkJVVRVFBeFcF76a/2f4B2u9hRQ7Lcwqvo1F1+UzOedrrD5/Dj8pSvxMsJmcZeEnRYmd+4CQ2MAK+T47eO0AxGcVkZcaBjUrsdVVBlaZCE0Al5U81nHvPfdQWVmJzxe4k3LMmDEMGDCADz/8EP/hT4jSDtpDYsir2kKGo/I8tlYIIYQIPgleQVReXk5zczN2u52RfSzkRZRyV/x7pIXYKXPH8OCQhO7f42PJgT7fBGc1+D2BYciMG6jxFfDUhjS2Hz+5z2N4Jlg3Mba3hxkzZpwx2X7mzJkkJSWxaGcJrYQS42/nSPJwfvXCux1LUQghhBAXAwleQXTs2LGO+V3jBliodaWx3nQ11e5wssOcPLW3qXOT3YMtYSRk3/LvyfbGUFIKrmBy4loG+F4P3JF5cr6XKn6RW2dcxsCBA6msDPRohYSEMG7uD3Df9jCGxQ8R7WpgluMT3o69nIffWBPkxgkhhBDnjwSvIDq1fldiTAj+8Cz+q/rnzK+fT2GYjbxoc+cnu3cHGddBwuiT2xeBCk9i/OAUwj3H0dUrsLsI3BhgisR0/Cm+ee+dxMTEUF9fD0BrXA5Tyt/F+8lbNLzxZ/K8Vm5u28jSXSfO2PdRCCGE6MkkeAWJ2+3m+PHjVFVVUtQngoGRe4iOrmLRgE3EmVxgDPtyk92DTRmg1z0QkQnOk+vhxvSHxLG8fTCbF9c58PqA0HhwNxJtfZPvf++7OBwO7HY741yHmZAWSszUu2jf/j6HDh2iDw2Mat/PM888g9frDWrzhBBCiPNBgleQnJrf1dbWzuh+EaSGWflZzCtcEVsGBnPg4EtOdg82U3hgWyFlBHdz4LmUKxiQaGVYxFqMrpNLT4RnQv0nZIUc44EHHqC6urojWMVMnYM5vTfLli3D4XCQlJRESUkJq1evDlKjhBBCiPNHgleQnDhxomN+15gBsextHYw/si+4GwL7IvZUYYmB8OVpAp8TlIGCgaO5LPEgqnwRXpc9MAE/PBOKX6KoXwI33XTTvyfbG00kzvoJ7e3tvP/++wCkp6ezaNGijv0shRBCiJ5KgleQnFq/Kz7KjC8si7drb8QYUwD4wdjN72T8ItF9AsOOjkrw+wIbeGfPprY9gsc+gOI6f2C+lzkajjzOzKunUFRUREVFBQAhGX0YP348u3fv5ujRo4SGhmIymXjuuec69nwUQggheiIJXkHg9Xo5fPgw1dWVjOhjYVzsem5PeZaUxJM9Xcaw4BZ4PiSNh/QZYC8N3OkYnk5s5mWkh5QT2bYlcE5IHLibMZa+wL333E1ycjIed2C7ocsvv5ykpCTeffddnE4nqamp7Nu3j40bNwaxUUIIIcTXI8ErCCorK2lubqalpY3LB4YRHWInLCwUg7cNIvMDc6R6OqUg+1aIGxrYZxIITR7KbQOOkGR/H1oOBhZXDc+E+i1YWjbxve99D601Pp8Pk8nE9ddfT2trKx988AFKKdLT03nppZc6th0SQgghehoJXkFw4sQJqqurARhRmMJq2xR8UYXgbYH4oiBXdx4ZTNB7PoQmgssaeC7tagjPYPXORpZsdaFREJ4Fpa+QamknIyMDj9uDx+MhIyODMWPGsGPHDk6cOEF4eDher5eXX34ZrXVw2yaEEEJ8BRK8gmD37t3U19cTYzFhDk/gk6YxRMXnBl6M7hvU2s47cyT0+z5oL3haA2EsexYmg8ZoP4b2ugL7Pppj4cjjWCLCSExK7FhcddKkSSQkJLB06VLcbjcZGRls2bKFnTt3BrVZQgghxFchwesC8/l8HDx4kNqaKob3jmB8/AbmpP6d+NhwUGaIyAp2iedfeBoUfAdcNvC5wRzN5UPTuS7xDQzVSwJzwEJiwdMGjmoS4uPJzs7GarViNpuZOXMmTU1NrFq1CqUUSUlJPPvss7S1tQW7ZUIIIcSXIsHrAquurqaxsZHGphauHBpGmNGFNyQFg6cFYgcFeoQuRrEDIe9OcJSD9qOi8iB1Ks31Vby8tolmOxCeAd42lKeJ+++/H7vdjtvtJjs7m8suu4ytW7dSWlpKVFQU7e3tvPHGG8FulRBCCPGlSPC6wEpKSjrW7xrQL5dFVbPwRg4Anx3ihwe5ui6WOhVSpkD7yTsdE8bgsRRS12qk0VYVmJBvCAVnHVlxHm655RYqKyvRWnPFFVcQGxvLO++80zH/68MPP+TQoUPBbpUQQgjRaRK8LrA9e/ZQb7MRGW4k1mLG6k4kISkdUBDVO9jldS2lIPeOwDw2RyBoJfaazHcKXiG3/SVwNwXOUSY4+g+mT51Ifn4+VquVkJAQZs6cSUNDA2vWrMFoNBIXF8czzzyD0+kMdsuEEEKITpHgdQH5/X727dtHbW0VI3pHMDruE65NeI3YSHNgjlNoUrBL7HrGECj4VmDSvasejCGYcm8F7Wfvnu1ktZYHhluddZiq3+X+++/H5XLhcrnIy8tjxIgRfPLJJ1RUVBAbG4vVauXdd98NdquEEEKITpHgdQHV1tZSX19PfUMz144MwWzw4TZnYPA0QcKIQG/PpSAkFvr9AHwO8NohNAGdcSP7GrIYZt8J2h+Y71X1HumRbcyaNatjyHHatGlERUWxZMkSvF4vmZmZvPvuu5SUlAS7VUIIIcQXkuB1AZWWlnbsN5jfewBPlT6Ijh4A+CBmUHCLu9As2dDnAXBWg9+DiunLTQOr+V7mY1xrXBbo9QqJg2N/Z+qksfTr14/a2lpCQ0O57rrrsNlsrF27FpPJRGRkJM888wwejyfYrRJCCCHOSYLXBbR3715stjosYQZyYlqJMTWQnJQcmGgemR/s8i68hCLIvgXsZaA1DzseYKV7DPPVP5j5fhYPlY8FVxPGqre455578Hq9OJ1OevfuzdChQ9mwYQNVVVUkJiZSVlbGqlWrgt0iIYQQ4pzOGryUUpO/6LiQhfZ0Wmv27t2LtbaGywoiGBazi1GRa4i2aLDkBOY8BcFDm+pYW2lnbaUd9ZcDPLSp7sIWkHEdJI4Fexm/7nWQq4eM5nD7UEbYy5kftwciMqB6Falh9dxxxx1UVVWhtebKK6/EYrGwZMkSfD4fGRkZvP766x07AgghhBDd0bkWjVoBVABnm3iUDlwEuzlfGDabDZvNRp2tkf++OQWNA3doJgZPK6RdGbS6HhqTzENjkoN2fZQB8ueBsyZwhKUyYkA2GSELSHfngZoW2HLo2AImjv0ftmzZwokTJ0hLS+Paa6/l1VdfZf369UyaNAmz2cxzzz3HT3/6UwwG6cwVQgjR/Zzrf52Oa617aa3zP+8ATlyoIi8GpaWlHb0xqbmD+P3xn2KM6R94Mbpfx3lB74EKBlN4YGV7ZQJ3M4aYAtLT0sC2EVtdJX5TNHjbMJQv4p6770ZrjcPhoG/fvgwaNIj169dTW1tLSkoKBw8e5OOPPw52i4QQQojPda7gNQRAKfWuUuompZT5814XnbNv3z7qbXWEmhUD40oZFrWF5KQYMBgDE81PemhMMvr7/TuOoPZGXUhhiYE9HT1N4HNC6nRsuhd/35jK5qPewF2OdetIMlVx5513Ul1djdaaq666ivDwcJYsWYLWmrS0NF5++WUaGhqC3SIhhBDiM84avLTWp24RWwv8EqhSSv1FKTX4U6+LL6C1Zs+ePdjqarh8QAT9oo6SF7Kf6FA/xAwEw6cz7SUqqjf0uhcclaCMJORP4oqE1Qw2vx8YkgxNhuNPM/6yQQwZMoTq6moiIiKYMWMG1dXVbNy4kfDwcPx+Py+//DJa62C3SAghhDjDF06E0Vr/QWs9ApgIFAE7lVIblFKjury6i0RjYyM1NTXUWBu4aVwMjZ44PKFZGLQD4kcEu7zuJWkcpF0FjgqUJZMxBSFY2rehm/bjM0aCz4mhbCHz5s5FKYXdbqd///4UFhby0UcfYbVaSU9PZ+vWrezYsSPYrRFCCCHO8IXBSyk1XCn1J2AVkEyg9+ttQHYo7qRT63dpDXGZQ3i85DuExPcFDUT1CnZ53YtSgSUmwtPBaYXkCfjDMnhtaygrd7sDQ462T0jgBPPmzaOmpgatNTNmzCAkJIR33nkHrTXJyck8++yztLa2BrtFQgghRIfO3Pq1HkgEbtNaF2itH9Za/wFY17WlXTwOHjxIg62GEJNiQtIOpsS9R2qCBUKiISw12OV1P8ZQKHgQ/A7wezFk3UiC2UacZ0/g9bBUOPEvRg8roKioiMrKSiIjI7nqqquoqKhg8+bNREZG4nA4WLx4cXDbIoQQQpymM8ErXWs9R2t9RtDSWn+ji2q66OzevRubtZarhoeRHl5LjKohKsQLcZfQNkFfVkQm5N4FjgoIiWfaYBOjI5ZBwxYwRYDfhyp5kbvuvAOz2Ux7ezuDBg2ioKCADz/8kPr6ejIyMvjoo484ePBgsFsjhBBCAOdeQNWvlPIBDUopn1LKq5Q6oZSacQHr6/Gam5upqKigsqaemePSONreG3doDgblhbjBwS6ve0uZGJgD56wKhNSoPhSfOMqKnQ50WDo07CDOe5C77767Yyuma665BqPRyNKlS1FKER8fz9NPP43D4QhyY4QQQohz93hNAiafdkwDXgD+3PVlXTzKysqoq6tDa4hIHsTi6llEJ+QG5ndditsEfRnKAPl3gyEMvK2QPpMKVzZHq9w43H4IS4PiFxg5KJfRo0dTWVlJdHQ006dPp7S0lK1btxIbG0tDQwNLly4NdmuEEEKIcy4nse5TxxrgCSDtwpXX8x0+fJhGaxVmE8xMf5+r4t8gOT40MJRmjg52ed1fSExgM22XDYxhjBuUzDez/kZE00eBhVdRqOLnuOMbtxMWFkZbWxtDhw6lV69erFq1isbGRjIyMli2bBnFxcXBbo0QQohLXKf3VVFKPQDsAVZ3XTkXn507d2K11XLTqDAsJifa6yQy1AcJI4NdWs8ROwAyrgFHBYaYfoTED8Bv3cjOI/Xo0FRo2keMcyf33nvvybtHNddddx1KKZYuXYrRaCQqKopnnnkGj0eWnxNCCBE855rjdfunnjpGYCmJ28/yuviU9vZ2SktLqahuYPqYfD5pHI0nPBejQZ2xTZDohMwbAyv8u+og7SqOOIfxzv5EjlZ7A0tPlLzC0L6pTJgwgcrKSmJiYpg2bRrFxcXs3LmTxMREysvLWblyZbBbIoQQ4hJ2rh6v3ymlJimlJiulJgM+oAQYc/Lx7y5EgT1ZaWkpVmsdfr8mMjGPdQ0TSErKCMxdsuQGu7yexRgCvR8AnwvQ9O0/hHmZz1Kgl4MxDAxm1PFnuH32rURERNDS0sKIESPIzc1l5cqVtLS0kJGRwRtvvEFVVVWwWyOEEOISda7gpYF/Ac+c5fB3eXU93NGjR2m0VhIRCjdlvse18QtJjDFBdGEgSIgvJyId8uaAowoVkUlOdjY07aKl9iheUzK0HiWqfTP3338/Vqu1Y8jR7/fz7rvvYjabCQ0N5bnnnsPn8wW7NUIIIS5B55pcn6e1zj/XcSEL7Yl27dqFzVrHbeNCMSo/La4QIsOQbYK+juTLA/PjHJWQPJF2Ux5PfZLFmgOewKr2ZYsY1CuOyZMnU1FRQXx8PFOmTOHo0aPs2bOH5ORkDh06xPr164PdEiGEEJegTk+uF1+Ow+Hg+PHjlFfXM2HUIN6tvRZ/eA5GgxGi+gS7vJ5LKcifCyYLeNqw5M1gYsJ6RoS+F9hs3BCGOv40s2+9iejoaJqbmxk1ahRZWVmsWLGCtrY20tPTeeWVV6ivrw92a4QQQlxiJHh1kfLycmzWWrw+TVJCNGWOLFJTkwKrrofLihxfizka+jwI7nowxzK6MIZ4zy5o2IY/NBnaSrA0r2P+/Pk0NDTg9/uZOXMmXq+X5cuXExoaCsCLL76I1jq4bRFCCHFJ6cwm2bcopcwXopiLydGjR2myVhAfCTNSP2Ry1FskRhohflhgcr34emL6Qeb14CiHuCKI7MV7e+DtLa7AGmnlb9I/O4KpU6dSUVFBYmIikyZN4tChQ+zfv5+0tDR27NjB1q1bg90SIYQQl5DOJICHgGql1F+VUkO7tpyLx65du7DZ6rhjQghKQb0rCkuEGeKGBru0i0fmTIjMA7cVMq7HYnIS6T6MHwOYouDYAm658Tri4+NpampizJgxpKen895772G320lJSeH555+npaUl2C0RQghxifjC4KW1Hkhgy6B24A2l1E6l1H1KKWOXV9dDud1ujh49SmllA8OKxvJ8xRywZGI0GCCyV7DLu3gYzIElJvxeUCYuH5zAlXFvYbCug9AEcFQR3rCab37zmzQ2NqK15vrrr8fpdPLee+9hsVhwOp0sWrRIhhyFEEJcEJ0d81KACQgFwoDvAO91VVE9XXl5OfXWGtxeP/kJDsw4yUyNh/BUCIkNdnkXl/DUwGR7Z3VgUdrYIdRVHGL59jZ0WCZUvkNBmoGrrrqKiooKkpOTmThxIvv37+fgwYNkZGSwbt06Dhw4EOyWCCGEuAR0Zo7XPmADkADM1loXAsOAoV1bWs914sQJmm3l5CTCxKTNDArbQHykAeJlm6AukTQOksYElphIu5pyTx/2VxppbPeDORaO/oMbZ15FcnIyDQ0NjBs3jtTUVJYtW4bL5SIhIYFnnnkGh8MR7JYIIYS4yHWmx+sxIF1rfa/WeoNSKk9r7QcGdXFtPdauXbuwWuu4fXwYWoPVGYUlIhxi+ge7tIuTUpB7F5ijwOdg+MA+fDvnceJbV0BIHLhthFnfY/78+TQ3N3cMOTocDt5//31iYmJobGzk7bffDnZLhBBCXOTOtVdjjFIqD/gxkKCUylNK9QPWA2ita8/1wUqph5RSh5RS25VSYz/1Wi+l1EdKqcNKqWeVUpbz0JZuwev1cujQIUoqG+gzdAqPlXyXkMhUjEYTROYGu7yLlzkS+vwHeBpREemEpwxHN+xk/9EqvOZMqF5Br0Q3M2fOpKKigtTUVMaNG8fu3bs5cuQIGRkZrFixghMnTgS7JUIIIS5i5+rx+h5wHOgFnDj59z5gxxd9qFLqCmAeMBz4FbBQKaVOO2Ux8D9AIYGthyZ8hdq7pYqKCuqtVbg8foYnlZAfdoislBiIKgjsKSi6TnQfyLoJHBWQNIlKPZjX92Wwq9QL5gQ49g+uvWoy6enp1NfXc/nll5OUlMS7776L1+slOjqap59+GrfbHeyWCCGEuEida8ug/6e1NgCrtdaGk4dJaz2zE587DdiktbYDa4AsoABAKZVPYJjyG8BhwA18+DXb0W0UFxfTYitnUBYMjT1AhvEQcVFGiC8KdmmXhvRrAjsDeGxkFozjzoyXGWF6M7DoqqeF0JolzL//flpbWzuGHNva2li5ciUJCQlUVlby/vvvB7sVQgghLlLnGmq86eSfC5VSd59+dOJz44FKgJPhq5HA5HyAVAJ3SO4ALgcmAWd8plJqvlJq28GDBykqKmLBggVfpk1BtXv3bqzWWm4dH4vTF4bNGYUlMhKiC4Jd2qXBYILe80H7wRRJr/x8VNsRnNbduEyZUPshubGt3HjjjVRUVJCRkcGYMWPYuXMnx48fJyMjg7feeovKyspgt0QIIUQPsWDBAoqKigAKlVLblFLzz3buuYYaT/Vs3QncddpxZydqaAQyAJRSEUAc0HDyteaT/76ita4m0Ns1+PQ3a60XaK2LCgsL2bZtG/Pnn7X+bsXn87F//35OlDeS3m8yj5V8B0t0IkazBSIygl3epSMsGXrdA84aiB+JJ7wPC7bksmK3B0KT4dg/mTFtAtnZ2VitViZNmkRCQgJLly5Fa01oaCjPPvssPp8v2C0RQgjRA8yfP59t27YBHNRaF2mtz9pjdK6hxnkn/50CXE8giP0RuLYTNawERiulwgn0aJUTGFYEOATUAtcopUzAOGB3Jz6z26uurqbRWoHX52NK6hZGWD4mK9kCsUNlm6ALLWEUJE8AZyXm7GsZHbeVEWErAptr++yYKxcx//77sdvtaK2ZOXMmzc3NrF69muTkZI4cOcK6deuC3QohhBAXmc6s4/Uw8H3gz8AzwFNf9B6t9WrgBWAX8L/A7cBzSqnfaq19BOZ3/Qw4SmDI8dmvVn73UlJSQrOtjNG9IddSSSQ1xEaFB/ZnFBeWUpD7jcByEtrHZQPTyFS7wLoOHZYB1o1kWazccsstVFZWkpWVxahRo9i6dStlZWWkp6fzyiuvYLPZgt0SIYQQF5HOdMNcA/wDmArkAkM688Fa619rrftqrYdrrTdqredqrX9x8rUPtdYDtdZ5Wut7tNaer9qA7mTPnj3YrHXMHJ9BpSMNq92CxRIBkfnBLu3SZLJAn2+Bpxmi+kLMIDYe9vDqRjc6NAVOPMP0yaPIz8/HarUyZcoU4uLiWLJkCUajEaUUL7zwgmwnJIQQ4rzpTPDyElip/iAQ0bXl9Fx+v589e3ZzrLyRmNwJvFZ9GwnxsRgtqRASH+zyLl1RvSD7FrBXQNrVmE1GzK5yvNoMfg+m8oXcd++9OJ1OAK677joaGxv58MMPSUtLY9euXWzZsiXIjRBCCHGx6Ezw2gAsB54DFpz8W3xKbW0tzdYyDPiYlfkuoyJXk5lsgYSRgWEvETxpV0FMP/A0UTQwn1tSXsFsWwlh6VC/hYzQcmbPnk1lZSW5ubmMGDGCTz75hIqKClJSUnj++edpbm7+4usIIYQQX+ALg5fW+ttAnNZ6EfBtrfUvu76snqe0tJQmWxmT+kO0uQ3ttRMdFQnRsk1Q0BlM0Ot+UAoVngSJY2ipO8KK7U34QtKg+DmmThhGnz59qK2tZdq0acTExPDOO+8QGhqK2+3mtddekyFHIYQQX1tnJtffAHyolCoFNp/8V3zK3r17sVnruHJsX3a3DKbBEY4l0iLzu7qLsETodS84ayFpElW+/uyoiKSmWYHWGEtf5L5778Hr9QJw7bXXYrPZ+Oijj0hPT+fjjz9m//79QW6EEEKInq4zQ42PELiT8fS1vMRptNbs3bObY2VNRKQOZ33DeFITLZhi+4IpPNjliVPiiyBlMjir6TdgGN/Ne4IM+xIITYXG3aQajvKNb3yDyspKevXqxdChQ9m4cSM1NTUkJiby9NNPY7fbg90KIYQQPVhnglcj8KLWet2po6uL6mlsNhtNtceJDPUyN+dVRllWkpYYCfEjg12aOJ1SkHMbhCaCMhOZMQpaD3Oi+DguczoUv8TEUYUMGDCA2tpapk+fTmRkJEuWLMFisdDc3Mzbb78d7FYIIYTowToTvI4Dq5RS/08p9Rul1G+6uqieprS0lCZrGVcMBIPStLmMREVHB/YMFN2LKRwKvgXeVogdQaNpIC/t6c3GIxoMBowlz3H3vDkdq9Zfe+211NXVsX79ejIzM3n//fc5duxYkBshhBCip+pM8HICx4BMAptdZ3VpRT3Qvn37sFprGTuqiDX1k2j3hBAZHQ8R8lV1S5F5kDMbnJXE5V/B7RmvMyHsNQhJhuaDJPv3c9ddd1FZWUmfPn0YNGgQ69evx2azERMTwzPPPIPb7Q52K4QQQvRAnbmr8R7gr8Aa4GcnH4vT7N+7myNlTcQmZnCsvRep8eGYEoaBwRjs0sTZpE6D2IHgs9OnoB8mVym+uo04jBlQupDxI/IZOnQo1dXVXHXVVYSHh7NkyRJiYmKorq5mxYoVwW6BEEKIHqgzdzU+AHwA/A6Yr5SS5SRO43A4qK86QFqMj1mZSxgR+gEpidEQJ9sEdWsGI/S6D5QRIvPQ0QN5fmcub20HbQjFcOIZ5s25E3VyDbYZM2ZQXV3Nxo0bycjI4K233qKioiLIjRBCCNHTdGao8QdAf6AFeBi4pUsr6mFqamporq9g6sDAY5vTQnR0FET1Dm5h4ouFxkPv+eCsQ6VPZ2jcQYaHrUaZY6HtBAnubcyZM4eamhoKCwvp378/a9eupampifDwcP71r391zAUTQgghOqMzwcsEuAENhANxXVpRD/LQpjrylzh4sz6PwhHTeK36VrzaiCUuO3DnnOj+4oZC2lRw1zN8YG/6hW2F2lWBjbTLXmfskCyKioqoqqri6quvJiQkhCVLlpCQkMCxY8f46KOPgt0CIYQQPUhngtc/gL0EJtdvB17o0op6kIfGJNPP1EJ48UZyE/z4/ZAaF4IpeZRsE9RTKAXZsyAsFczRkDCKvaUOXvrYh99gQR1fwF13zMZkMqGU4uqrr6ayspLNmzeTkZHBq6++itVqDXYrhBBC9BCdmVz/CDAH+B/gu1rrX3V5VT2IveoYw5MamZ6yml7GrSQmxgUmbYuewxgGfR4Erx0SJ2AwR+Fz1uP0R4C9gjj7Ru6++25qa2sZMGAAffv2Zc2aNbS1tWEwGHjhhRfw+/3BboUQQoge4KzBSyn136cO4HICw4wjTz4WgN/vp/3INqYONODXJ+d3RUXLNkE9kSUbcr8BrloGDBjE3Ixniah/N7CRdsUSRhYmMHr0aKqqqrjmmmswmUy88847pKSksHv3bjZv3hzsFgghhOgBztXj1efkMR34FjAYeACYeAHq6hHq6+txl+wjdfBNvFg5B5PSWJIHgMkS7NLEV5E6BeKGgPajUqbgajzBql2NuIlGHVvAHbfdTGhoKEoppk+fTllZGdu2bSM1NZUXXniB5ubmYLdACCFEN3fW4KW1nqO1ngN4gSFa61sJ3N0oqeKk2tpaQuuPMjSxlDijleS4UMypY4NdlviqlAF63QPGEIgZSI0azKayJIobQsBZS0zrR9x7773U1dUxaNAgevXqxapVq3C5XHg8Hl555RUZchRCCHFOnZlcn09gv0YAM5DddeX0LCXFxYxOqGB03FYS9BHi4+Mhum+wyxJfR0gc9P4muOvJKRjFd/OfpK9ncWDIsXIZw3pZGD9+PFVVVVx33XUopVi6dClpaWls2rSJl19+WcKXEEKIs+pM8NoI7FRKPUFg9fr3u7aknuOTDauYPDCCNm8ITe5womLiIUJyaY8XNxjSrwJfGzHZE8BeRnXZftp1Aur4Am6/9XoiIiIwGAxMmzaN4uJidu3aRU5ODh988AELFy6U8CWEEOJzdSZ4zSGwpATAkwTmeQng8L7thOVdyz8rvkmY0Ysl/TIwmIJdljgfsm6G8HQIS8MRMYTndg9g9SEzuBqIalrJfffdh9VqZdiwYeTm5rJy5Ura2trIyclhxYoVLFq0CK11sFshhBCim+nMchJurfXjWutva62f0lrL7sCA3W6ntqqMCUk7CLM0ER8dSkjKmGCXJc4XYygUPAjaSXjGJG7OWMa0yFcgLAWqP2BwjpFJkyZRVVXFzJkz0VqzaNEiPB4Pubm5LFu2jMWLF0v4EkIIcYbO9HiJz1FbW0u0sZ5BsUcoNccSn5Ao2wRdbCIyIfcu8NRT0G8g4b5KdO0anIZE1LF/Mvvma4iKisJoNHLzzTdTU1PDiy++iNvtJjc3l6VLl/LGG29I+BJCCNHhSwcvpZS5KwrpaR7d0YDqPZJGTxwH3blExqWzxmrhkW22YJcmzqeUiRA/AkwRED+SN/dn8fInIfjdbVisS5l///3YbDZ69+7N7Nmzqa2t7QhfOTk5LFmyhLfeekvClxBCCKATwUsp9ZRSKuTk370JTLa/5Kmynawp+j0/KP9fqn0pbIu4llnvVTIyJTzYpYnzSRkg/24whEHCKPrGVTIobCMqJB7q1tE/zcPUqVOpqKigoKCA2bNnU1dXxwsvvNARvt566y3eeecdCV9CCCE61eOVAWxUSv0HsBl4r2tL6hnsW9/mV/b/4q3IEZQbM7hzzwAWzchkcpYsc3bRCYkJzPfyNDOwsD+XRW9A1bwHIUlw/GluuX4a8fHxNDU10adPH2677TasVisvvPACLpeLnJwcXn/9dZYuXSrhSwghLnGdCV43AX7gMeAJ2asxsFVQWfFh/iNzFZNNm6ghmQcHRUvoupjF9IeMawEfJE+iuKadFzYqPG4XEbVvMf/++2lsbMTpdNK7d29uv/126uvrO8JXdnY2ixcvZtmyZRK+hBDiEtaZ4PUx4AFuA+5WSv2ta0vq/urr64kyNvGW60ZWeieQYWzkqf121pS3B7s00ZUybwjs6RjdBx2aTFu7kzZvJNg+oW9iCw888ADV1dXY7XZ69erF7bffTkNDA88//3xH+HrttddYsWKFhC8hhLhEdSZ4rQcmaq0XA8ORleupqalB9ZvAN+3/zXjfTrIjPCyakcms5RUSvi5mxhDo/QD4veQXDOOB3KeJa3wLQpPgxL8YM6If3/3ud6mrq6O9vZ38/HzuuOMOmpqaeP7553E6nWRnZ7Nw4UI++OCDYLdGCCFEEHQmeC0HJiilJgODgD91bUnd38H9e3DHJfL3sIdoJgxTSGCYcdGMTLbWOoJdnuhKEemQdxf47BjSrsTXXs76vQ3YnRqOP8uIoYP44Q9/SH19Pa2treTm5nLHHXfQ3NzcEb4yMzN56aWXWL16dbBbI4QQ4gLrTPB65uTxLPAB8HyXVtQDbP74A34S/k+mGD7AYwzFYA7cyTg5y8JPihKDXJ3ocsmXQ8JlEJpEvbmIj0qz2F8bAY274cgTDB5QwI9//GOam5tpbm4mJyeHO++8k9bWVp577rmO8PXcc8/x4YcfBrs1QgghLqDOrFyff/LIBfoCW7q8qm7uyIFdmBOHUe3KwGQ0giEk2CWJC0kpyJ8L5kiSs4fyH73+xUjDKxCeCU174eCfKOydxc9+9jPa29tpamoiOzubO++8k7a2Np577jkcDgeZmZk8++yzfPTRR8FukRBCiAvkyy6gagMu6X1x7HY74f46VjZeT7GzDwZTGChjsMsSF5o5Cvo8CH4H8TnjwVWH7cRattdmQdtxOPgHemcn8Ytf/AK3201DQwNZWVncdddd2O32M8LXM888w7p164LdIiGEEBdAZxZQLVNKlSqlSoEa4JKemFJbW0tSqJUf5P6JVNNxDCGyhMQlK6Zf4E5HUygkXc7m8ljW7HfjMKSBvQr2/5bc1Eh+/vOf4/f7sVqtZGZmctddd+FwOHj++ec7wtfTTz/Nhg0bgt0iIYQQXawzPV53AnedPPpqred0bUndW/GJ4/RK8hKmWsHvxRQSHeySRDBlXgeR+RDTn6sHebg3cwHh1S9DaCLa3QL7f0tWoplf/vKXmM1m6urqyMjIYM6cOTidTp577jnsdjvp6en84x//YNOmTcFukRBCiC501uCllPqNUuo3wBWnHfeefO6StWn9StLzhrKlYSDmkLCOifXiEmUwdywxYYgbSlzeJGgv45Ot23ljTwI+rwv2/S9p0R5+8YtfEB4eTk1NDenp6cyZMwe3231G+Pr73//O5s2bg90qIYQQXeRcPV5Z5zguWft3fUJ9+GgO24cQEx0VWNtJXNrCUyB/HjirILoQcm7D77Hjby1FKRNggP3/R3J4C7/4xS+IiYmhqqqKtLQ05syZg8fj4bnnnqO9vZ3U1FSefPJJtm7dGuxWCSGE6ALnCl4+rfU9QLnW+p7TjwtVXHfj9/vxNB3hwZx/MCR8PWFxeXz5+xPERSlpLGTPAnsZRGQxdkRfbk1/HUPJv3C6vdi9Ftj/OxKMNfz85z8nKSmJyspKUlNTmTt3Lj6f74zw9cQTT7B9+/Zgt0oIIcR5dq7UMFkptQj4rlLq+dOPC1Vcd2Oz2UiPaMKgNKAJTRoS7JJEd6FUYL5X72+CsxpC4lD5d6O14vWN7Ty/KRy/MQ4OPkqs7zg//elPycjIoKKiguTkZObOnYvf7+f555+nra2NlJQUHn/8cXbt2hXslgkhhDiPzhW8ZgO7COzTePxTxyWpsqKC9Kx8PqibgsNj5omWyayttLO20o76ywEe2lQX7BJFsCWPh34/AncTGMyoXvcyIXknl0e9g8FVEdhe6PBfiXbu4cc//jF5eXmUl5eTlJTEvHnz0Fp3hK/k5GT+8pe/sHv37mC3SgghxHmivmizXqXU1Vrr9y5QPWcoKirS27ZtC8alP9c///4XDFXLaTb3Z0zUSkbf9x4qMifYZYnuqK0EDj4K+MEYCaUvg6Oa4ojbMUdlkxleAbl34YgdzxN/+xsHDhwgOzub+vp6nn/+efx+P3PmzCEyMhKbzcYPf/hDBg0aFOxWCSGEOAel1HatddG5zunMyvVBCV3d0bZNa7g56wOujHyR6OhoVHhasEsS3VVkLgz6FZiiwNMEOXPQEXmsPBTF+7vd6LBMKHmR8IYP+O53vsOQIUMoKSkhISGBefPmYTQaeeGFF2htbSUhIYE///nP7N+/P9itEkII8TXJzPAvob1mN7EW8KsQQuN6yx2N4tzCkmHgLyEiC1w1qJzbuaPfNmYl/hNl/QjCs6HsdUJr3uJbDz7AqFGjKCkpIS4ujrlz554RvuLj4/njH//IwYMHg90qIYQQX8O51vHKO9txIQvsLux2O2mxBhZX30qzP56w5KHBLkn0BOZo6P9jiB8GjjIic68mKrkf2vYJyz4pZ0tNDlQtJ6TiFebfdw8TJkzoCF/z5s3DbDbzwgsv0NLSQlxcHI8++iiHDx8OdquEEEJ8Refq8ToOHOOzE+uPXYC6up3q6mqSYkOocqbj9kJ0+rBglyR6CmMY9PkWpE0FeymkTMWfNIXWdg/NtUcCm2vXfoSp+BnunnMHV1xxBcXFxcTExDBv3jxCQ0N58cUXaWlpITY2lj/84Q8cOXIk2K0SQgjxFZw1eGmtDVpr48l/Ow5gXGc+WCn1kFLqkFJqu1Jq7FnOeVsptf4r1n5B7d29neEJh7kp9inCzX6ikvsGuyTRTTy0qQ71lwMdx+fe3WowQe5dkDMLHOUYk0Yya0QrU6Nfh5KXaCMVf/1WjMf/xl2338LVV19NSUkJUVFRzJs3j7CwMF588UWam5uJjo7mD3/4A8eOXZL/H0gIIXq0zmySPUMptUIptVoptRb4wsn2SqkrgHnAcOBXwEKllPrUOdcDk79S1UGw5eOVDM+FRlc4UdExqPDUYJckuomHxiQzMSOCiRkR6O/356ExyZ9/olKQcW1giyFnNYbofqjsW3DbbTy71sOyw+nQdADDkb9w+y3XccMNN1BaWorFYmHevHlERETw0ksv0dzcTGRkJI888gjHj1+yq7sIIUSP1JnJ9Q8BOwAfsBNY3In3TAM2aa3twBoC2wwVnHpRKRUJ/A545EvWGzS2kq0sbvgmld5+hMYVBHowhPgqksdB4Y/B2wzhaYTkzaIoZitDDW+BMRzailEHH+Gma6cwa9YsysrKCA8PZ+7cuVgslo7wZbFY+P3vf09xcXGwWySEEKKTOhO8IrTWvwBqtNbfB0Z24j3xQCXAyfDVCCSc9vr/A54BKj7vzUqp+UqpbQcPHqSoqIgFCxZ04pJdx+fzEWu0YTG2YzBAWIrM7xJfU+xAGPBL0D4wWxgzYgBZYeVQ/BwHa8Oxt1hRBx7m2mmjueuuuygvLycsLIy5c+cSFRXFSy+9RFNTExERETz88MOUlJQEu0VCCHHJWrBgAUVFRQCFSqltSqn5Zzu3M8GrVik1BwhTSj0IRHbiPY1ABoBSKgKIAxpOPh4MXAH89Wxv1lov0FoXFRYWsm3bNubPP2v9F0R9fT0ZETXclvoSFm0lJn1oUOsRF4nT1/rCD3l30+6P4a3d0aw5EgGeFtT+/+PKCYO49957qaysJCQkhLlz5xIdHc3LL79MU1MTYWFh/P73v6e0tDTYLRJCiEvS/PnzObng+0GtdZHW+qw9Rp0JXt8hsG3QP4D7gT914j0rgdFKqXBgElAOnLoHvj+BILaXwHDjCKXUWUNYd3D0yCH6pfmpajFjMIYQndIv2CWJi8Wptb4sOeBvx9LnNubmv8O0sCfBZQWfB/b9L5NG5vHAAw9QXV2NyWRi3rx5xMTEdISvkJAQfv/731NeXh7sFgkhhDiHzgSvf2qtF2qtV2uth2utn/qiN2itVwMvENjr8X+B24HnlFK/1Vq/qrXO1loXAj8Htmutv/c12tDlPlm/gpLoe1nfdCWRUTGosLNMnhbiqzBHQ+GPIG4YuG1k9L+KEEsKvrK3eWObgYqmUNj3f4wdmMR3vvMdamtrUUoxb9484uLieOWVV2hsbMRkMvHwww9TUfG5I/hCCCG6gc4Er71Kqe8qpWLVSZ35YK31r7XWfU+GtY1a67kn54qdfs7zWusJX6nyC6j8wEfkWCqIMLRiju8HBmOwSxIXG2MY9HkwsNaXswayZ9MeNoiqJoWtrhIMEXDgYYp6h/GDH/yA+vp6/H4/c+fOJT4+noULF9LY2IjBYODhhx+msrIy2C0SQgjxOToTvO4E/gLUE7iz0duVBXVHYc4jXJX0PrnhJwiXifWiq3Ss9TUbnNVE503jgcEbGKoWQf3HOHQMHHyUIZlufvSjH9HU1ITP52Pu3LkkJCR0hC+Ahx9+mOrq6iA3SAghxKd1Jnj1B3KBvNP+vWS0t7eTbmnD6we7J4SYjCHBLkl0I49ss7GmvP2M59aUt/PINttX+8DT1/py1WFOnQSJ46ivKeHx1WHsrk2Bw4/TP9HGz372M9ra2nC73cyZM4ekpCReffVVGhoa8Pv9PPzww9TU1Hz9RgohhDhvzhm8Tg4rrtBalxGYIF8HrL0QhXUXZaWlmLKu4smSB1FGM7Gp/YNdkuhGRqaEM2t5BY0uHxAIXbOWVzAyJfzrfXDyuMC8L18LxI8gOmMkhZZ95LiXQ0giHPsnfSwn+PnPfobL5cLpdDJnzhySk5N57bXXaGhowOv18vDDD1NbW3seWiqEEOJ8ONcm2b8iMLTYVyl1aoixHWi6MKV1D1s2rWZI/HFS1UEiIuNQYUnBLkl0I5OzLCyakcnBBhfFLW5mLa9g0YxMJmdZvv6Hxw6Egf8F2o85JpfrhhuJ9e6Hslc52JCCv/gl8sy7+cXPf47f76e9vZ05c+aQmprKokWLqK+vx+128/DDD1NX9znbGAkhhLjgztXj9RcCw4onTv6bB+RorS+pSU5HdyxnXMo+ckz7MMcXBoaChDjN5CwLaRYTZa1eHhwcd35C1ymWHBj034E7H8MSIfs2ShrDWbQ9it21yVD+Flm+9fzi5z/FaDTS0tLCXXfdRVpaGosXL8Zms+FyuXj44YexWq3nry4hhBBfybk2yW7RWpcCQ4CCk8ON2Ressm7C0HIQly8Uc0go4SnDg12O6IbWlLdT3e4lO8rEU3saPzPn62s7fa0vUyi5/SdyW8brDPUsCGwxVP0+6Y73+eXPf0p4eDjNzc3cddddZGRk8Prrr2O1WnE4HDz88MPYbF9x7pkQQojzojOT6x8DZp38+yml1P90YT3dis/nIzoujYdP/JxGTywxGYOCXZLoZk7N6SqMDyUvOoRFMzKZtbzi/IevjrW+hoP20nfQOJRSOI4t4q19ydgrNpDc/Aa/+Nl/Eh0dTUNDA3fccQdZWVm88cYbWK1W7HY7v//976mvrz+/tQkhhOi0zgSv0VrrU3v2jAKu78J6uhWr1UpeRDEjwlcTavAQlzYg2CWJbmZrrYNFMzKJCw2s7XZqztfWWsf5v1jHWl9Xgq8dcudQ683mSK0JWxvQsJ0E68v8/CffJzExEZvNxh133EF2djZvvvkmtbW1tLa28sgjj9DQ0HD+6xNCCPGFOhO8wpRSWSf/jgdCu7CebmXrJ2sZl11Lin8H4VHxGMISvvhN4pLyk6LEz8zpmpxl4SdFiV1zQYMJcu+AnNvA20xu/4l8r/BVslueB1cjnsZDxFY/w0//81ukp6dTW1vL7bffTk5ODm+//Ta1tbU0NTXxyCOPdKz5JYQQ4sLpTPD6PYHV63cCxznH5tYXm8Nb30WHpeHwhWOOHyAT60X3oBRkXBPo/fK2EJYzEyx5HD+6n7+uTaWmuoro8qf48ffuIy8vryN85ebmdoSvxsZGHnnkEZqamoLdGiGEuKR8YfDSWv8TuAz4P6BIa/1kl1fVTTgbDrOg/Js0m/oSkSYT60U3kzQWCn8MfgekXUV8QgLZoceI9ewDh5XIksf4z2/PoW/fvlRXV3PbbbeRn5/PkiVLqK6upr6+nj/84Q80NzcHuyVCCHHJ+MLgpZRKI9Dr9WtgmFKqd5dX1U3E+Y5yU8piLIZGYtNlYr3ohmIHBNb6UgbiMoYxa0AxYU3r8ddvoaTGQfixP/K9+bcyePBgqqqqmD17Nr169WLp0qVUV1djtVp59NFHaWlpCXZLhBDiktCZocZ/AbsIzO0qBZ7qyoK6i/b2dnrFNxPtOUSowUO83NEouitLDgz6FYTGQexgSJrEJyXhPL81kdomL2FHH+U/5l3DyJEjqaioYNasWfTp04d3332XyspKampq+OMf/0hra+sXXuqhTXWovxzoOB7aJAuzCiHEl9GZ4FWotf4N4NFar+cS2atx356dxKXkU9KSiCkiEUNITLBLEuLswpJgwM8hMg8ic7msXzw3pb5BSttS0D5CjjzKN78xmfHjx1NRUcEtt9xCQUEBy5cvp7KykqqqKv74xz/S1tZ2zss8NCaZiRkRTMyIQH+/Pw+NSb5ADRRCiItDZ4LXCaXU94FQpdSDwCWx8dvuj1/ng9bbOeYdSUjCQJlYL7o/czQU/ifED8cUHs+gwr7gqKL++BpWHY7GcOiP3HvTSKZMmUJFRQU333wzffv25b333qO8vJyKigr+9Kc/0d5+ntcgE0II0aEzwes+Amt3pQJzgPnnPv3i0Fb2EfdmPk1e6BEsaSOCXY4QnWMMgz7fCqz1ZQqH7NkcakplZ5mRVqcR49G/Mueaflx11VWUl5dz44030q9fP95//33Ky8spKyvjz3/+M3a7PdgtEUKIi1Jn7mo8AdwATAKu0Vrv7+KauoUobzEpITWEmnzEpsvCqaIHMRj/vdaXMjBuaD4P5v2LmLqXwe9HH36S26ekc/3111NRUcENN9xA//79WblyJaWlpRQXF/OXv/zlM+HrkW22z6zIv6a8nUe2yTZEQgjRWZ25q/FWoApYA1QqpeZ2eVVB5vP5iIpPZUtdL1AGEmRivTiHhzbVsbbSztpKe/eZcH76Wl8KIvOvA0MIW/cc5V9b03Edfpabx0Rw6y23UFFRwXXXXceAAQNYtWoVJSUlHDt2jMceewyH498r8I9MCWfW8goaXT7g39sljUwJD1YrhRCix1Fa63OfoNQh4D+01quVUuOBl7TWuReiuKKiIr1t27YLcakzHD92lPdffQSvz8fIxCOM+Y+PL3gNQpw3Tfvh8F/B7+XwoX3sbczlpuFeDCEWdMb1rDwYyUsvv0x6ejrvvfcee/fuZdKkSeTl5dG3b1++973vERYWBgTC1vS3SkmzmLB7NYtmZH5m5X4hhLhUKaW2a62LznVOZ+Z4GQn0dgF8Alz0kz92rFvMvTkvku9fhSlBhhlFD3dqrS+Thb79BnJL/mYMVW/hamuk/shypve1cc/dc6muruaqq65i8ODBfPTRR5w4cYJDhw7x+OOP43Q6gcB2SGkWE2WtXh4cHCehSwghvqTOBK+NwJNKqeuAvwNblVKTlVKTu7a04Gk4+h6hRhfRoR6i0mVivbgIWLIDa32Fp0DKZIgq4N29YTy3NRV32UomZxfzwPx7qa2t5corr2TIkCGsXbuWEydOsH//fv72t7/hcrlYU95OdbuX7CgTT+1p/MycLyGEEOdm6sQ5E07+e+WnntNAr/NeUTfgdzXzSeMoMNlkYr24eIQlwYBfwOHHQGum5B2lxnaAELsF6jRjkxyEfGs+Tzz1T6644gqUUqxbtw6/3w/AD558lcXhoyiMDyUu1MivRycxa3mFDDcKIcSX0Jm7GvO11vlAf6D3qcda64sydAGYLKmstk1BKQOJmYODXY4Q5485Cgp/BAmXEZfSi8KcWGjcRklpOXv3HaQo6mN++L0HaGxsZMqUKQwfPpyPP/6Yo0ePsrmqjdnOLcSGBNa0m5xlYdGMTLbWOs59TSGEEB3O2uOllBoGvAyMA64AngM8SqkbtdYfXZDqgqCxoYErkz6gpGkLhA/EFBYV7JKEOL+ModDnAQiJheoVYIpg085kGn2h9E/Zz+AYJz/+/jd59K8LuPzyy1FKsXHjRsZoTUvv3lTRi/T0dCAQvqS3SwghOu9cQ41/Bd4B2oHHgT8BB4FHgXPO2O/Jtq1/k2mJcLDOT2y8LCMhLlIGI+R+A0LioHQhtwxtxFn1LkZrFFr56Rfh5hc/epDf/3kB48aNQynFpk2b8Pv9tPQObKhts0WRmJgY5IYIIUTPcq6hxmyt9c8IDDEmAo9qrRee/PuiVbnvPZbVzcBjSiEm86LNl0KcXOtrBvT5FmZLIlE5V6AddazYYWfZdjv59lf5xX/eh9vtZvTo0Vx22WVs3rwZx8qnaW1r5Sc/+QkvvfQSNpssoCqEEJ11ruDlUUolAbcCO7TWLUqpNDo3Ib/HcrdUsqd1COawSJlYLy4NSWOg8CcQlggZN2DGjtlxDOzl5La+zH/98G78fj9FRUVkjLuG1g1vUrv4jzybfAN/OuCSACaEEF/CWRdQVUr9FHgICAEeAHYDC4HFJ3vCulwwFlBd+ctQ+qZ4KHX1Zsx3dmAOi7yg1xciaNrL4MAfwFmLrlqB0h5a4q8nNDKBppS7+O1jC/F4PBw6dIiPP/4Yj8dDYWEhY8eOBUBrzeTJk7nqqqtkCFIIcUnqzAKq51y5/uRK9S6t9Val1FBgDPAPrbX/vFZ6Fhc6eHm9Xir+ZqaiKQRv3GVM+u76C3ZtIboFpxUO/QnaTuCv/pC/H5tFtCWMO0e1YEuax2+feAOHw0FUVBSffPIJW7ZsweVyUVBQwNixYzEajfj9fqZMmcLVV19NQkJCsFskhBAXzNcOXsF2oYPX9g3vcmTTK/gb95Pbfxzj7njygl1biG7D0wqHH4emPRw+Vkq4r4rsvP4QnkZjyhx+9/flNDU1kZaWhsvlYvPmzYG5Xw4H+fn5jBs3jpCQEPx+P1OnTmX69OkSwIQQlwQJXl/S4r/MocEZQVj7XkZMvYeBE++9YNcWolvxueDY02D9GOo3Q3sx+9StpCRGE9p7Nn97dRuHDh0iIiKCpKQkPB4P27ZtY9OmTbS3t5OTk8PYsWMJDw9Hay0BTAhxSZDg9SUt/q8+3Nr/GB+W5tD3ltfI6DPqgl1biG7H74PSV6FqGZ76vTyxdxpZ0e3cMtyOzp/D8bZs3l32Hrt27cJsNpOamorf72fHjh1s2LCB1tZWMjIyGDduHBZLYK0vCWBCiIuZBK8vadV/R1CQ7KDE2YfR39lJSJgsDCkucVpD1XtQ8jJN1hLC27YRGteXdstwwqMSMWTOpMKVzoqVH7FhwwaMRiMpKSkopdi1axcbNmygqamJ1NRUxo0bR3R0NADTpk1j+vTpxMfHB7mBQghx/kjw+pJe+O3N+N0tZMW6uOL76y7YdYXo9qyb4OjfofUo1G/ipep7cRtiuWdUHZgiIWMGNkN/Vq3dwqpVq/D7/aSkpGAymdi7dy/r16+noaGBpKQkxo4dS1xcHEopCWBCiIuKBK8vofzYTnYufYhqWzuF/fpy+V1/uyDXFaLHaD4Ih/4MbaUcLG3E43YxOKEUHTucTdb+DEjzEJN3OS0Rl7F28yGWLVuGy+UiKSmJsLAw9u/fz/r167FarcTHxzN27FgSExMxGAwdQ5ASwIQQPZkEry/w0KY6frM5sOjj5Npn+XDQo7yxO54+Vz/K4El3d9l1heix2svg4B/BZwd3K9R/Ql2jg7+XPcC1efsYnmdEGy2oxJE44iayaU8Nby9ZQnNzMwkJCURFRXHo0CHWrVtHTU0NMTExjBkzhuTkZEwmkwQwIUSPJsGrEyYtLgHgh4euZ2avPaw4ls2A2xaTVXBZl15XiB7LaYNj/wgMOxpCwO+nqWY/ke3bMRm87PVMY1vjIGYNrceS2BtPytVsO2rnrbffoba2lpiYGGJjYzl+/Djr1q2joqKCqKgoRo8eTVpaGiaTiWnTpnHllVdKABNC9CgSvM7hkW02RqaE85tPrADMOfAf7A4ZhdPczmPf/jWhYRFdcl0hLgpaQ3sp1KwG24bAY2M4NO9lX0k7u5oGcEf+h6i4gdR4soiPj8eYPZP9VaG8uWQZxcXFWCwWEhMTKSkpYd26dZSWlhIREcGoUaPIzMwkJCSkowcsLi4u2C0WQogvJMHrHNaUtzNreQXpFhNxoUauKfkl/8/4Pf5b/ZOf/OCfXXJNIS5K7iawbgjc/ehtA2NEIJTVb8bvauSx0u+REunm9uH1EJqATp/B0eYU3lm+hn379hEaGkpKSgrl5eWsX7+e48ePExYWxsiRI8nOziYsLEwCmBCiR5Dg9QXWlLcz/a1SskLdtDjb+Hbdz5lQMJQpcx/vsmsKcdHyuaFpD1S+C+0lgWFITwvlZcUoZyWZ4XW4oobyftVoxvX2EJ8/kQpvAe9+sJktW7ZgNBpJTU2ltraW9evXc/jwYUJDQxkxYgQ5OTlERER0DEFKABNCdEcSvDoh55kjlLV6+a+Ipyg8/DL9r/0TQyfP69JrCnFR0zoQvKpXgW0TKEADjTsprW7mlcrbuKvPGjJT4/CYUjCljMJmHsF76w+wdm1gGZeUlBQaGhpYv349Bw4cwGw2M2zYMPLz87FYLFx55ZVMmzZNApgQoluR4PUFTvV4jW1fy9bQUfyw9RHuu/0/ySkY2WXXFOKS4m6Eug1Q/R5420GZcTUdJ7R5M/jsrG6ayaG2vnxzTA2m+P40R13O6q1VvL/yA9xuN8nJybS1tfHxxx+zd+9eDAYDQ4cOJT8/n6ioKKZPny4BTAjRbUjwOofT53j9+PjNHA0fz1/Nd/DadQVM7y3bmQhxXvnc0Ljr5DBkKSgTuGo5VGylsjWSK1K3QMwADrYPJDM9CWP6lazf387SZStoa2sjMTERt9vNxx9/zO7duwEYPHgwvXv3Jjo6munTp3PllVcSGxsb1GYKIS5tErzO4fS7GhdUFVBp8/FJ7L0YJ/6WnxQldsk1hbjkaQ1tJ6DmA7BtDjznbYfGHbiaK3i0+EcMSyxjxoBWsGThTr6KLSeMvPnO+zQ0NHQEq40bN7Jjxw78fj8DBgygT58+xMXFcdVVVzFt2jQJYEKIoJDg1QlXL9zJa+5JvLfTReyQ+Uy/+7EuvZ4Q4iRX/cm7IVeAzwF+Hw21xZja9hFtasJqHMaKmonMGOgiNn8Se+rieWPpR5SXlxMVFUVISAibNm1i+/bteDweCgsL6du3LwkJCR1DkBLAhBAXUlCDl1LqIeA2oB34jtZ642mvjQceA6KATcD9WmvXpz/jQqxcv/GDZxin3ZxoryN06A0s+Ma0LrueEOJz+FyBYciKpeCoAL8GeynHymy8VzuFe3q9jSU+hxZTH0JTR1Pq6sXryzdz+PBhwsPDsVgsbNmyhS1btuB2uykoKKBfv34kJSVJABNCXFBBC15KqSuAZ4D+wGTgSSBXn7yYUqoUeABYCWwD/qq1fu7Tn3MherxWP3E1UYYGDpc2M/7ul8jrd87vSwjRVbSG1mOBYcj6rYAf7bChGreBq46F1XfS6EvmwTEVkFBEhR7KW6sPsGPHTsxmMzExMWzbto3NmzfjdDrJz8+nsLCQ1NRUrr76aq644goJYEKILtWZ4GXqomtPAzZpre1KqTVAFlAAHFZKRQEbgY+11j6lVEsX1vGFIl37yQgtpzG6gJSswmCVIYRQCqL7BA6nDazrUdUrISwRvG2M0cW0te1CVRxEN+3lRMMEbh+exG3TbmXZpkrWf/wJvXr1oqioiF27drFp0yZOnDhBdnY2lZWVLF++nBkzZjB16lRiYmKC3VohxCWqqwJPPFAJcDJ8NQIJJx+3ArcrpYxKqd8SCGRvnv5mpdR8YH5ERARFRUXMnz+f+fPnd0mhZqNif00Yruh0IiyWLrmGEOJLCkuErBsh/Wpo2AGVS8nNjwSvE9pCabKW8nFpEhGerYzM28y8Qf25Yew1rN7Vzvur15OVlcXgwYM5cOAAGzZsYMWKFWRkZFBWViYBTAhx3i1YsIAFCxYAFCqltgELtNYLPu/crhpq/D2QrbW+XSkVAbQB/bXWh06+Hgu8S2CO1w1a6+LP+5yuHmr0+7z87n9+gdl+lIF9s5lx71+77FpCiK9B+wPDkNXvB4KY3429uRZzy3bM/iYOOC5jbf0EvjGyFXP65aw/YuTt5WtxOp3ExsZy5MgRNmzYQHNzM6mpqfTv35/c3FwJYEKI8yqYQ40rgaeVUuHAJKAcOHza60sI9IjdpbV2d1ENX0ijiLSEYWg4RFLODcEqQwjxRZQBogsCh9MKdeuIMK+E2DRwWgkpbyDOUElU7TsYHJsZaBjKZd8cxQFbAouWbiApKYm5c+dSUlLC+vXr+fDDD0lKSuL48eMsW7aMa6+9liuuuEICmBCiy3XlXY2/4d93NX4b+CaBsPUUUAYUA6fuZHxCa/23T3/GhZhcf2LXckpWfIec6xfRq3BEl15LCHEeeR3QsD2wKKujGjxN0HII3XKEJ0q/Q3Soh7lFlXgTL2d/Qw4Ll22jurqGqKgoqqqqWL9+PTabjfj4ePr3709BQQHXXHONBDAhxFcm63h1woldyzn03i+Z+J31WCIju/RaQoguoP3QciQwDNm4E7xtWK023M3FZISW4QnNZknNtYwZEEd7+CBeXbGf4ydKiYiIwGazsX79empra4mNjaV///707duXa6+9lqlTpxIdHR3s1gkhepBgDjX2GFqZsZvzJXQJ0VMpA8T0CxyOWrCuIynkA0hOA3sO1upqShojGFnyNgVpq/nplUVUmaayZF0ldrud6667jtbWVj7++GM2btzI3r17OXDgAEuXLmXmzJlcccUVEsCEEOfNJd/jVVZWxo7t27nhxhu79DpCiAvIa4f6bVD1Ljhq8LbXYGzZh3JV81HDVHa0jOA/xlfTHHU5y7a0su6T3SilcDqdbNiwgbKyMiwWC4WFhQwYMIDrr79eApgQ4gvJUKMQ4tKm/dByKLAtUeNucNsoqWqlpN7IpIR1EJnHXud4ojKGs73EyPIPd6C1xuVy8cknn3DixAnCwsIoLCxk0KBB3HDDDRLAhBBnJcFLCCFOcdRA7UdQ+2Fgn8j2EjyNh/jT8e9QGFPCzP512BOu5OPiGN54fyculxufz8eWLVs4cuQIoaGh9OvXj0GDBnHTTTdJABNCfIYELyGE+DSvHWxbTg5DVtLWUINuPUIUVdT7s3mj5mauHhZChTuXhcv30tLajs/nY/v27Rw8eBCz2Uzfvn0ZPHgwt956K1OmTJEAJoQAZHK9EEJ8likCUidB8gRoOURk1XvQlAX2StprmtBeO3HVz5MVl0bBDeMo9haxeNUxDCNHMnz4cHbv3s3+/fs5dOgQO3fuZOjQocyePZspU6YQFRUV7NYJIbo56fESQgh7FdSthZoPwVEFbceg9SiLqm+l0pXNd8Yep8Qwidc+rORIcQ1er5cDBw6wZ88elFL07t2boUOHcvvtt0sAE+ISJkONQgjxZXjaoP7kMGRbGbV1Vuobm+hv2QOhyaxqupaE1D58fKCd7fvL8Xg8HD58mF27duH3++nVqxfDhg3jzjvvZPLkyRLAhLjEyFCjEEJ8GeZISJ0CyROh5QApVctJadwDbeG01pezoyqRiZ7lfHuQk/oxU3l3dwhms4mCggKOHz/Ojh07OH78ONu2bWP48OHMmTNHApgQ4gzS4yWEEOdirwzcCVmzBk9LGbQdxewq41D7AN6zzuDm4XZ21KaydO1xHE4XxcXF7NixA7fbTXZ2NkVFRcydO5chQ4aQlpZGSEhIsFskhOgiMtQohBDni6f15N2QS6H1OGXVDWytSufG1DcxRGZxXE/kUGtvlq4vpqXNSWlpKdu3b8flcpGamkpycjIJCQkMGjSI0aNH07t3bzIyMkhJScFoNAa7dUKI80CClxBCnG9+LzTvh8plgb0hW4+gm4/w95K7MRs184YdZUf7eP5zWxoV3nBK9u6AXSug5jjkD4fsgYRtfIWEhAQSEhJITk5m6NChjBkzhl69epGenk5iYiJKqWC3VAjxJUnwEkKIrqI12CtODkN+SEt9Ce2N1aSZjuJV0fy69if8TV1NqKsV5XYwrHYTa7KvZfCu5/Ec2EB1dTV1dXWc+u/gsLAwEhISSExMJDU1lVGjRjFq1Chyc3PJyMggOjpawpgQ3ZwELyGEuBA8LWD7BCrfhaZ9WK11LCy+gsSEch5w/pxcQwMlvmTucKzCd3w/Xr8BrTVaa5xOJ01NTdTW1lJVVYXVau0IY+Hh4R09Y1lZWYwZM4YxY8aQmZlJRkYGERERQW64EOJ0EryEEOJC8nuheR9UvovfuhnVcoj/qp3Bb+3f5HLnAZbn/ZCI8HAOtA/gSGsu8dERHCx3sv9EPe0uhVIKj8eDw+GgubmZuro6qqurPxPGEhMTiY+Pp1evXowbN45Ro0aRmZkpk/eFCDIJXkIIEQxag72cNfu3ctWmPow372GXpw+vJ/8Pk9Vq1ttGsrlpFP+Z9yjKHMnq+qkUt2dzQ2EZ5c5stpeFcKS0EVuzF5QBt9vdEcasVivV1dXYbLbPhLHExET69u3LhAkTGDlypEzeF+ICk+AlhBBBsqa8nVnLK0iPUMQZ2vl13j5m7ejPot5LmBx5GO2sR3mawNPCjtpUatqjmJG8HIBXq26n3pvENwtXYDems9k6gMrmMIorG6ms9+LHiNvtpq2tjdbW1jPC2CkREREdk/f79+/P5MmTGTZsGBkZGTJ5X4guIsFLCCGC5JFtNvbbnLxwqKXjuTn9ohkQb+Qn/V3grIX2Umg9BvYy8LSDpxnc9ZQ1mHG4PPQN3QF+J38vfYAYczO3Zy/Fb47jrYppRIQZCTFqthfDiRonHn8ITqeT1tZWWltbsdls1NTUfCaMJSYmkpKSwpAhQ7jiiisYPHgw6enpxMTESBgT4muS4CWEED2B1uBpAkdtIJC1HYe2E4HFW312vI5GnI42IrGCu4GFJdNJC61iUsJaNCYeOfEjhsafYHi6jeLWFDaVxHKiqo1WlxG320NTUxOtra3U19dTU1NDfX19x6UtFgsJCQlkZGQwbNgwpk6dysCBA0lPT8disQTvOxGiB5LgJYQQPZnfCy4rOOsCIaztGLQVg9MWCGruBnzOFjZVZ5NmOk6v8MPYfeH84cRPmZr8EZclH6OZDFaUj8DrtnOg1EmjMxSHy0tDQwOtra00NDRQU1NDQ0NDx2UjIyNJSEjoWHl/6tSp9O/fn7S0NEJDQ4P3fQjRzUnwEkKIi5HXHghjp4Yr245DWwm4G/E5m6ht8hKpbETrCqxtJp6puJcbUt6mb9Qxar15vFYxkzEpR2m0Kz45EUJdu4WmNjf19fW0trbS2NhITU0NjY2NHZeMjIwkMTGRvLw8Ro8ezRVXXEG/fv1ISUnBZJJtf4UACV5CCHHpONtwZXs52tWIdjdg8DRQ12Lgo+pBXJHwAQkh9Rxs68ei6tuYk7cES5iB3dYM9tTEUVrnpr4NbLb6jp6xurq6M8JYVFQUiYmJFBQUMHr0aKZMmUJhYSEJCQkYDIbgfRdCBIkELyGEuNT5veCyBcLYqeHK1mJwVIK7keZWO0froxgSuR2zr56NjaP5wDadH/d6lJCQUHa0DGdvQz4RnhJ2VYZR3Wqhts5Gc3MzjY2N1NXV0dTU1HG5qKgokpOT6devH+PGjWPSpEkUFhbK5H1xSZDgJYQQ4vN93nBl6zG0o46mVidxhkAw21qby47mIczP+gdKwXu2aznU2o8bMldx2BbFrpokTtSHU1pppbGxsWMV/paWf9/NGRUVRVpaGv3792fChAlMnjyZgoICmbwvLjoSvIQQQnTeqeFKZx04ak6GseOBXjJXA7gbOFQXSV17CJfHrgY0r1R+gyZvLPflv0aTN4E1VYOpbjZyrNLOkUoHDQ2BMGa1Ws8IY9HR0WRkZDBo0CDGjx/PpEmTKCgokMn7okeT4CWEEOLrO3240lEFrUeh5Si0l9DQ7MLucJBpPgKeJp4r+wbhBgez01/DpyJ4seJ2wlQ7Rvtx9lRHs7cylIqqWhobG7FarbS2tnZcJjo6muzsbIYOHcr48eOZPHky+fn5Mnlf9BgSvIQQQnQdryMQxpx10F4SWAy2+QA+ex1Gbz246nm3dDhJ5ipGxW5Ga/j9iZ/RP+oIQ6J3ccQayfb6fuw50cKhE7U0NTVRV1dHe3t7xyViYmLIzc1l+PDhTJgwgUmTJpGTk/OlJu8/ss3GyJRwJmf9e2hzTXk7W2sd/KQo8Xx+I+ISJ8FLCCHEhfWZ4coT0HIImg/idzWyryaSeEM5maYjtHvMPFr8E65MXEFR3B7qXIksqZoObcXsOtbCtmMuGpsCm4Xb7faOS8TFxZGXl8fIkSMZN24cU6ZMIT09/ayT909t37RoRiaTsyyfeSzE+SLBSwghRPfw6eHKliPQtI+2xgqM3kbCfVXUt2kWlV/H1MQP6GM5RpUrnWfL72Zc9IfYGhrYVhbJ/poo9h0uoba2FofDAYBSiri4OPr06UNRURETJkxgypQpJCUldVx+7oqKz2zf9PxVmRf8axAXNwleQgghurdPD1e2HILGXeCso6HFyZbqTEbHfEysqZ4Drf1ZXDOLeZnPY9R2ttZkcqApl+27D7P1UAM2WwNOpxMIhLGEhAQKCgq47LLLmDBhAt+z9qPCAf89KpH/NyY5qM0WFycJXkIIIXqeTw9Xth6Hpl04bEepaNTkhRzE5K1na30h71ln8JP83xNi9PJx/Rh2Ng0krH4VW4752HLMT0llPW1tbdD7Mpj7R9j2DuqyG0hb+ShpbWUkJCSQmJhIYmIiSUlJpKSkkJqaSlpaGhkZGSQlJcnkftFpEryEEEJcPD49XNl8AE/dTsyuYnBa2VuXwN6mfG5PewWlYHnd1exuGUp6zFa+7fsfxla/Sbi1hO1tFqpGzCdx6f/gO7KFxsZG/H7/WS8bGhqKxWIhKiqKmJgY4uPjOwJbUlISycnJpKamkp6eTnp6OklJSVgsFlkw9hIkwUsIIcTF79PDlQ07oGkPZXUubG2aDyhgpHEn1dY+tHqjeCDn76xxX8aC+juZ6NnPnPTnsHtMLLNeh9tnoC8f0GQ3cMI3HLvDS2PlbuqafLhDMqlvdnL4WBl1zW6UIQSPx4PX6/1MSUajkYiICCIjI88IawkJCR1hLSUlhbS0tI6wFh8fj9FovPDfnzhvOhO8pP9UCCFEz2YKh8jcwJF4GeTMAq3J9jST7axluKMGmofirtuOu60cDOOZ7HOQF/ombq+fiMgkIvxu8t02tF8zIaEF/F5erYolRLm5eVQdAE+VziLO3Mht6YcB+FvJPcSZ65kcvYRWl4lVzTcS5qvD0rYFWytYzUU0N1qpKD2KrbmUVl8GB3c3UFzRgM8PBoPhMz1tYWFhHWEtLi7ujN6108NaWloaSUlJJCQkEBERIb1rPYgELyGEEBcfpSAkNnBE94WUiYQUQIjWoL3gc5Lrc4DP2XFMOPXY0wqeZm53N4HTCu5UcDdwe2wl+OxgGgF+J+PTjxBhaCPVEkWq380BfzspIa2MLmhC4efPxQMY1ecgV1+zE4DfHbuV4TE7mJ70Pm6fgUdO/JwBEVspMG6gsd3AFs8thNj347Tux9rSjismm9qKw5Rsr2Bbix9TZBrl1Y20tTvOaKrJZMJisRAdHd0R1uLj488Ia6fmrZ0Ka7GxsdK7FiQSvIQQQlw6lAJlBoMZzFFf6q2xANrfEdSG+JxwKqz5ndzgc4KnDbyt4KrnB4OsaGdf8CSBy8ZdYXuIMDjAPBCz18mE1D1khlnJCzfj9fo5WqEYmu5h2PB2HL4wHjkxkdkjHYyJK6XdG8GjxfO5Kmk5QyzbqHNE81LdAww0fUCk6wC1bSZKTWPx1G2jvu4gDQ0hOLz9WLfpOFV1TbQ4TXgNkTS3tOLz+VBKERERQVRUFLGxsR1hLSEhgeTkZJKTk0lLSyM1NZXExMSOYdLw8PCu+FW6THdcPFeClxBCCNFZygCmiMDRmdNP+ztTa/B7wO9EeR1M9P+7ty3E5+BurwO814OrnnBnLb/sZwVXP/DEE+Kyc5t5K8mhBkJNvYiP8DPMc5j+0e1khhpodJl4rTKRK3sb6GPxUOVM5J/l1/GLaxbSL7KJSmcKT5ffz01JC0k2HudEazoft99Arnsp7pbjWF1NNIXFU1/8DqWHGzngi0JFZHDsRAl1jU5anUaUwYjWuiOsnT4UeuqO0FO9a6eHtdjY2C+108D5NDIl/KyL5waLTK4XQgghegK/D04La3xqqBRvW+CuT0ctfnsdba31hPlqCNFttDl9HG2IJS+ilChjI3XtFjbYhnF5wsckmus40ZbNW7U3clfGi6SG1nSsmfZA9lOkhNayr3Ugb9Tcwq3xT6Hc9Rxu68Mx31ji6l+nqbmVBl86rog+VB7eSH2LCwcxeFUMR4sraGr3E2EJ3BF6qnft9HlrqamppKSknBHWEhISCAsLOy9f26mw9eDgOJ7a09ilOxbI5HohhBDiYmEwgsECpi8ODQYg+rTHkVozzO/uCGxpPie3nBbeevsc/NhpBVcGOGrp1VrN/PR9xIcMQOscksxGpqjt5MSGE6riUC2htDT4uKmPxmLUbG1K4z3r5Tw7aRNhRtjQMJBV9dNY2+u3hBjcrKobyifNE7k27BFa7D6OuQZjJRR9/Gkq9mgOm3LxmJI4fnArje3gUtF4iKCpzUt0zL/nrSUlJZ3Ru/bpsBYTE/OZ3rW1Fe3YHD7+Z7Ot43Ewt4qS4CWEEEJc7JQCY2jgIOYLTw8F0k57nOL3knKqZ83vpJ/XQT+/E3zfAp+Ty5w2Rjqq4P+3d/9BVlZ1HMffnw0Q2BVQJJ0RRJpAwfilGyKiIEL2R6ONVmaNiulYYzVmP2YadRRtmtJmGqeaLDRTc0QbSyszY0EwDDXxBwLuIgYIrD9QQYNdlmV3v/3xnM07tMvy6969z/J5zezc+zznec5z9n73Xr6cc+5zGr8NTW8w9sj3Ofro5VRUjqelpZHhra209V7JSUMGQlszfbb2Z9V/qrhsLFQIHts8mhXbxvGrzz4HwKObz6B2+xi+M+InbG96j8ff+TRvNg9lbPOdvLct2FQ/iQ0bB/LK+hq2NkBz3xFs39mL9eteo5kqelcdQ0WfARw+YCD/HnIyTLwQ+vQDiXUfNBflJd5bTrzMzMxszyp6QUUV9K7q9JDC+WwDKOhxi2Bk205GFvSwTW5tYnLrDmiZDc1bOWf7Js7athFaLiOaNjOxH3yscRWtVWPo228HI9p2MGjnm0w9agC07aJm81FsaR7MxZOzS9xffxrbW6u48rg1wDbuqz+Ppta+HD3oZWY3ncd3G+6n7zsfMOPqn/GFxzaxaGP39Xp5jpeZmZmVr7aW3eazpectO2DXVmh4ncYtG2hueJvDeYO25gbWvFtJa8su/hwTqO71Codta2Pk0IEce+4DRf1Wo+d4mZmZWb5V9IKKw/d4+4/+6QfgI8CY9HxstEHrzuxLCclZwyo9x8vMzMzsoFNFtrIB5XP/saLdWEPSHEl1kp6XNGW3silpf52kOcVqg5mZmVk5KUqPl6SzgdlkvX1nAfMkHR8RoWxBqXnA14AngVck/SMinihGW8zMzMzKRbF6vGYBT0dEI7AIGAaMSmUnpO3FqfzZdLyZmZlZj1asxOtIoB4gJVdbgcEFZVsion2Vz00FZQBIulLSstraWqqrq5k7d26RmmlmZmZ2YObOnUt1dTXAaEnLJF3Z2bFFuZ2EpFuA4yLiIkn9ge3AmIiokzQaWAVURsQOSQ8C6yLi+7vX49tJmJmZWV7sze0kitXjNR+YLKkfMB3YCKxOZXVpe3oqPxWoKVI7zMzMzMpGUSbXR8RCSfcCLwENwEXA3ZLqI+JaSRcBvwAqgXsiYmEx2mFmZmZWTnznejMzM7ODoDuHGq2M+MsJ+ecY5p9jmG+OX/6VSwydeB0CyuWPzfafY5h/jmG+OX75Vy4xLOuhRknvAK93dzt6gNFAbXc3wg6IY5h/jmG+OX75V4oYDo+IIXs6oKwTLzs4JC3raszZyptjmH+OYb45fvlXLjH0UOOhoTz6V+1AOIb55xjmm+OXf2URQ/d4mZmZmZWIe7zMzMzMSsSJVw8k6QeS6iS9KunLkkZKWpq2b5fkuOeApKGStkm63DHMlxSzFyW9JOkKxy9fJFVJelRSffosneYY5kP63HxR0o1pu8O4SZqTYvu8pCmlbKP/cHoYSTOAS4HxZCsGzAXuB34HnAhMBGZ3V/tsn/wcaEvP78IxzAVJ44HvAVOAL6afe3H88uQSYBgwnOy9dyt+D5Y9SVcBLwInFez+v7hJOpssficDNwDzJKlU7XTi1fP0B34RETuBLWn7FOCJiGgDngRmdWP7bC9IOhcI4AWgL3A6jmFeXAC8CywAHgJ+S7YmreOXH28Dvcnee5VkS9/5PVjmIuKX6VYO/wSQ1J+O4zYLeDoiGoFFZEn2qFK104lXDxMRj0bErZKOA34P3AEIqE+HbAIGd1f7rGuSqoBbgG+lXYNwDPPkGGAkcDFwHXAfjl/ezCdLtjYD1wI34Rjm0SA6jtuR7ftS8rWVEsbTiVcPJGkmsBxYClxD1nNybCoeStYTZuXrRrLF4zek7fdxDPPkA+CZiFgbEX8iix04fnnyI2At2T/Q5wOPpP2OYb68T8efnVvb96VesSMoYTydePUwksYADwOXR8TVEdFA1u06I00qnAbUdGcbrUsjga9IqgUmAdfjGObJYqBa0kclVQM7gJU4fnkygCxuO8kS6UpgGY5hrqTerI4+O+cDkyX1A6YDG4HVpWqX7+PVw0i6nmx4Y33B7q+SDV0dBSwErkrj3VbmJC0iG6paAtyDY1j20iTdHwOfB1rI3o/LcfxyQ9JQsrl5J5IlX9eTzbd0DHMgfW4ujoibJI2ig7hJuonsiy8NwDciYmnJ2ufEy8zMzKw0PNRoZmZmViJOvMzMzMxKxImXmZmZWYk48TIzMzMrESdeZmZmZiXixMvMzMysRJx4mZmZmZWIEy8zKxpJL0i6Jj0/QlJruskvkvpLapY05QCvcamkJQejvQV13pja22XdkqZJ2iWpNi0/sj/X65POb5Z09v612szywImXmRVTDXBmej6DbBmWmWn7NLK7Rj/bDe3qyhyydfr21lsRMToiGiUtlnQCgKTBklZ2dXJENEfEaD5czNfMeignXmZWTDXAGWkZnZnAbcCk1DPUvm5apaR5klZJWiHpOkmPS7oBQNKZkuolVaSfn0paJ6lO0vmFF+uoPPVaPS3pr5LWS7o5HdtH0l3p2CWSVkq6XNKCVN184HhgiKSawnO78HFgTXo+DliR2vAvSX+TtFnSLZL+IGmNpIck9T6QF9nM8qNXdzfAzHq0p8gWGB4DzALOT49TyRanvRsYBawFvgR8CvgLcAlwA3Az8DngvrS+2mzgdOAkYBjwHPDNgutd0kn5J8gWH68CaiXdRrZOWzVwAjACeBkgImZKitSWqcCxqa3/OzcitnT0y0oaDtQXrOE3rr1eYDQwHBgPPEHW4/cy2SLM44Dnu3w1zSz3nHiZWdFERJOkp4DZZInLCmAB8BlgElmy1QcYCjxAtiBxb+AR4HZJ44ELgHNSlZ8k61FqT1Iad7tkZ+XLI+ItAEm9gCPIkrElEdEMrJa0upNfo6NzO0y8gAl8mGgBnAI8SLZA7/KI2CJpQ3ptnkl1vgf07aQ+M+thPNRoZsW2APg6UBMRkbavAF6NiE1kPVL9I+JC4OF0zi6yhOU24M2IaJ8ntRJYBZwMnArcsdu1OiuPDtq1CpiahhyHAicWlLUBh+3h3M6MJyVRkkYC55Elm3uqZ1/qN7Occ+JlZsVWA/RLjwBL0+Pf0+MfgVGSasmGFTcA15ANQ04H7i2o606gFqgDFgEbd7tWV+WF7iJLvuqAX6fz2pOgx8gSxOP35hcsMAGokLScbKi0Frh0H+swsx5M2X9AzcwOLZImkA15/pAsMdwAXBART+5jPdPI5qANk/QaMDEitu1nm9YBV0TEwv0538zKn+d4mdmhai3ZpP9nyeZg/WZfk64Cx6Skadf+JF2S+gDLySbym1kP5h4vMzMzsxLxHC8zMzOzEnHiZWZmZlYiTrzMzMzMSsSJl5mZmVmJOPEyMzMzKxEnXmZmZmYl4sTLzMzMrESceJmZmZmVyH8B65oYi91SxFYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -466,14 +466,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Speed-up of warm-start: 266%\n"
+      "Speed-up of warm-start: 315%\n"
      ]
     }
    ],
@@ -489,6 +489,27 @@
     "This benefit is *independent of problem dimension*."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How it works & Limitations\n",
+    "\n",
+    "Warm-starting works by deforming the parameter space. The prior transform function is adjusted, and the adjustment is removed by reweighting the likelihood function, to produce the same posterior.\n",
+    "To make this work, posterior samples from the unit cube space are required. The deformation uses a factorized auxiliary distribution, based on marginal posterior quantiles.\n",
+    "\n",
+    "The weighted_post_untransformed.txt file from a hot-started run cannot be used. This is because it has a deformation already applied.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read the full documentation at\n",
+    "* [warmstart_from_similar_file](https://johannesbuchner.github.io/UltraNest/ultranest.html#ultranest.integrator.warmstart_from_similar_file ) and\n",
+    "* the underlying [get_auxiliary_contbox_parameterization](https://johannesbuchner.github.io/UltraNest/ultranest.html#ultranest.hotstart.get_auxiliary_contbox_parameterization) function"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -500,12 +521,260 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The hot-starting works by deforming the parameter space. The prior transform function is adjusted, and the adjustment is removed by reweighting the likelihood function, to produce the same posterior.\n",
-    "To make this work, posterior samples from the unit cube space are required. The deformation uses a factorized auxiliary distribution, based on marginal posterior quantiles.\n",
     "\n",
-    "If you already have posterior samples (or can generate samples from posterior means and standard deviations), and you can untransform to unit cube samples, then you can create an appropriate weighted_post_untransformed.txt file.\n",
+    "If you already have posterior samples, then you can create an appropriate weighted_post_untransformed.txt file.\n",
+    "However, the inverse of the prior transformation has to be applied.\n",
+    "\n",
+    "In some cases, this is easy to do analytically, e.g., for uniform priors it is just a scaling.\n",
+    "\n",
+    "The following code works for arbitrary, factorized priors (as in the blackbody example in this notebook), for an arbitrary number of parameters.\n",
+    "\n",
+    "Lets start with our posterior samples. These could be obtained posterior samples from MCMC, or generated from the parameter errors quoted in a paper. Here we take it from the reference run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "posterior_samples = results_ref['samples']\n",
+    "\n",
+    "plt.scatter(posterior_samples[:,0], posterior_samples[:,1]);\n",
+    "plt.xlabel('%s (p-space)' % parameters[0])\n",
+    "plt.ylabel('%s (p-space)' % parameters[1]);\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets have a look how our unit-cube prior transform works:\n",
     "\n",
-    "The weighted_post_untransformed.txt file from a hot-started run cannot be used. This is because it has a deformation already applied."
+    "The first parameter has a uniform prior, the other a log-uniform prior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uguess = np.linspace(1e-6, 1-1e-6, 40000)\n",
+    "pguess = np.array([prior_transform(ui * np.ones(len(parameters))) for ui in uguess])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(2, 1, 1)\n",
+    "plt.plot(uguess, pguess[:,0])\n",
+    "plt.xlabel('u-space (%s)' % parameters[0])\n",
+    "plt.ylabel('p-space (%s)' % parameters[0]);\n",
+    "plt.subplot(2, 1, 2)\n",
+    "plt.plot(uguess, pguess[:,1])\n",
+    "plt.xlabel('u-space (%s)' % parameters[1])\n",
+    "plt.ylabel('p-space (%s)' % parameters[1]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we convert the posterior samples to u-space, by finding the unit-cube value by optimization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.optimize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 6410/6410 [00:01<00:00, 4262.71it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "nparams = len(parameters)\n",
+    "u = np.ones(nparams) * 0.5\n",
+    "stdevs = posterior_samples.std(axis=0)\n",
+    "\n",
+    "def minfunc(ui, i, u, pi):\n",
+    "    u[i] = ui\n",
+    "    p = prior_transform(u)\n",
+    "    return (p[i] - pi)**2\n",
+    "\n",
+    "usamples = np.empty((len(posterior_samples), nparams))\n",
+    "for j, sample in enumerate(tqdm.tqdm(posterior_samples)):\n",
+    "    for i, param in enumerate(parameters):\n",
+    "        ui0 = np.interp(sample[i], pguess[:,i], uguess)\n",
+    "        result = scipy.optimize.minimize_scalar(\n",
+    "            minfunc, \n",
+    "            args=(i, u, sample[i]), \n",
+    "            method='brent',\n",
+    "            bounds=(0,1),\n",
+    "            bracket=(ui0 - 1e-4, ui0, ui0 + 1e-4),\n",
+    "            tol=0.001 * stdevs[i],\n",
+    "        )\n",
+    "        usamples[j,i] = result.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets see whether our untransformed (u-space) posterior samples are correct:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weights = results_ref['weighted_samples']['weights']\n",
+    "i = np.random.choice(len(weights), p=weights, size=1000)\n",
+    "plt.scatter(results_ref['weighted_samples']['upoints'][i,0], results_ref['weighted_samples']['upoints'][i,1], \n",
+    "            color='gray', label='reference run');\n",
+    "\n",
+    "plt.scatter(usamples[:,0], usamples[:,1], label='modified run, usamples reconstructed', marker='x', alpha=0.5)\n",
+    "plt.xlabel('u-space (%s)' % parameters[0])\n",
+    "plt.ylabel('u-space (%s)' % parameters[1])\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This looks like great agreement! We successfully untransformed the posterior samples to u-space."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Writing a run file for warm start"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We write a weighted_post_untransformed.txt file based on our untransformed posterior samples. Since these are equally weighted, the first two columns are constants."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weights = np.ones((len(usamples), 1)) / len(usamples)\n",
+    "logl = np.zeros(len(usamples)).reshape((-1, 1))\n",
+    "\n",
+    "np.savetxt(\n",
+    "    'custom-weighted_post_untransformed.txt',\n",
+    "    np.hstack((weights, logl, usamples)),\n",
+    "    header=' '.join(['weight', 'logl'] + parameters),\n",
+    "    fmt='%f'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# weight logl Temperature Amplitude\r\n",
+      "0.000156 0.000000 0.009827 0.584641\r\n",
+      "0.000156 0.000000 0.009769 0.585080\r\n",
+      "0.000156 0.000000 0.009959 0.581489\r\n",
+      "0.000156 0.000000 0.009796 0.584856\r\n",
+      "0.000156 0.000000 0.009792 0.584156\r\n",
+      "0.000156 0.000000 0.009872 0.583356\r\n",
+      "0.000156 0.000000 0.009916 0.582792\r\n",
+      "0.000156 0.000000 0.009762 0.584928\r\n",
+      "0.000156 0.000000 0.010057 0.582260\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head custom-weighted_post_untransformed.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now point the warmstart_from_similar_file function at this file."
    ]
   },
   {
@@ -514,7 +783,7 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "Hot start allows accelerated computation based on already knowing the posterior peak approximately. This allows you to:\n",
+    "Warm start allows accelerated computation based on already knowing the posterior peak approximately. This allows you to:\n",
     "\n",
     "* vary the data (change the analysis pipeline)\n",
     "* vary model assumptions \n",

From 241e28e54c226270005036ed7cd3dc1ea0edc98a Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 6 Sep 2022 12:28:02 +0200
Subject: [PATCH 085/313] include pyx files in release

---
 MANIFEST.in                  |   3 +
 Makefile                     |   1 +
 docs/example-warmstart.ipynb | 308 ++++++-----------------------------
 3 files changed, 54 insertions(+), 258 deletions(-)

diff --git a/MANIFEST.in b/MANIFEST.in
index 07f43779..d6c81dd0 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -5,6 +5,9 @@ include README.rst
 include requirements_dev.txt
 include pip-requirements.txt
 
+recursive-include * *.pyx
+recursive-include * *.pxd
+
 recursive-include tests *
 recursive-exclude * __pycache__
 recursive-exclude * *.py[co]
diff --git a/Makefile b/Makefile
index 394cff8f..8101839e 100644
--- a/Makefile
+++ b/Makefile
@@ -43,6 +43,7 @@ clean-build: ## remove build artifacts
 clean-pyc: ## remove Python file artifacts
 	find . -name '*.pyc' -exec rm -f {} +
 	find . -name '*.pyo' -exec rm -f {} +
+	find . -name '*.pyx.py' -exec rm -f {} +
 	find . -name '*~' -exec rm -f {} +
 	find . -name '__pycache__' -exec rm -fr {} +
 	find . -name '*.so' -exec rm -f {} +
diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index e5d0dc8a..bd04005d 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,22 +98,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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AGdDa8+19Aa2EC6xevZqamhqmT5/udZSYkpSUxKxZs8jLy/M6ioiISLvaKqp2OedGtLbTzIrDkCcqHTt2jOrqaq9jxKTRo0cHv3bOaQ4rERGJWG0VVWMBzOwV4CngZedc/Sf3C1x//fVeR4hpzjn++Mc/kpqaysyZM72OIyIiclatTqlwWgG1EvhnYL+ZPWJmYz6xXySszIykpCSNWRMRkYjW7jxVzrmfOOc+A1wJ+IGNZrbazCaFPV0UKCws5LnnnsPLmem7gmuuuYapU6d6HUNERKRV7U6pYGYTgNuBW4BTBFqtGoEXgNywposCSUlJpKSkaKxPmLX8+e7bt48jR45oPjAREYk4HZmnahWBAuoW59xfWl40s/FhSxVFpkyZ4nWELmX16tUcPHiQUaNG4fP5vI4jIiIS1JGiqq9z7uNPvuicuzUMeaJKU1MTcXFa6aczXXfddfh8PhVUIiIScVqtCMysycwagWNm1mhmDWa228xmdWK+iFZYWMijjz5KU1OT11G6jLS0NJKTk3HOcfToUa/jiIiIBLXVUjX1E9s+AoPVfwYsCVegaJKTk4NzTq1VHnj99dd57733uO+++7SEjYiIRIRWi6rTx0+1MLMtwLc7cmIz+wpwL4EZ2R9zzj15viEj1YgRIxgxotX5USWMJkyYQHZ2NikpKV5HERERATowpUILM/t7YDOwrAPvHQt8B5hC4KnBW8wsphZvrq6upq6uzusYXVZ2djYTJkzAzDSdhYiIRIS2xlR98RMv7SQwncIXW9l/uhuBI8CbwPPA4865hguLGlneffdd5s+fr8LKY7t37+app57SfRAREc+11VL1YzObamYFZlZAYG6qPcDk5u0ft3HsRcBQ4EsECrFfm1lmy04zu9vMirZt24bf72fhwoUX/I10tiFDhnDVVVdplm+PJSQk0NDQwKlTp7yOIiIiMWrhwoX4/X6AfDMrMrO7z/Y+a63rxMw+AtrsV3HODW7l2J8Alzjn5jRvnwIKnHPrTn+f3+93RUVF7X0vIm3SQssiItIZzGy9c87f2v62BqoPuoDrrgBuNbPeQH8Cg9V3XsD5IkpFRQVVVVX07t1bP8wjgJnR0NBAUVEREydO1BxWIiLiiXDNBbAE+F/gbeB3wF3OueNhulan27x5M7/4xS+oqqryOoo027NnD6+99ho7d8ZM7S4iIlGm1e6/zhCt3X8nT56krKxM0ylEmIMHD3LRRRd5HUNERGJUe91/7bZUmdkXzCwhtLGiW/fu3VVQRaCWgqqiokLTLIiISKfrSPffPOCAmf2XmY0Lb5zId+LECYqLi/UIf4QqLy/n0UcfZePGjV5HERGRLqbdoso5NwooACqBF8xso5l91cy65Gjg7du38/vf/57q6mqvo8hZZGdnM2nSJAYPPuuDqSIiImHT0VnOrfm9SUAycB8wF5gZplwRy+/3k5eXR48ePbyOImdhZkyfPt3rGCIi0gV1ZEzVVmA10BO42TmXD4wHxoU3WmTy+Xz07dvX6xjSjtraWl588UW2b9/udRQREekiOjKm6udAX+fcV5xzq81skHOuCRgd5mwR58SJE/zlL3+hoqLC6yjSjvj4eA4fPszx4zEzk4eIiES4Vrv/zKwHkEVgYeTXzawXge6/N4Fc59yhzokYOcrKyigsLGTkyJFeR5F2+Hw+vvrVrxIXF66p2ERERM7U1piqfyDw5B/A7ubPTQQm9uySRo0axcUXX0xycrLXUaQDWgqqffv2YWbqthURkbBq9dd459wPnXNxwDLnXFzzR7xzbnYn5os4KSkpWpomijQ1NfH888+zbNkyr6OIiEiMa6v77wbn3B+AZ8zsy6fvc849HfZkEebjjz9m+fLlXHHFFWRnZ3sdRzooLi6Om2++mYyMDK+jiIhIjGur+2828Afg9k+87oAuV1QdP36cXbt2MWXKFK+jyDlqmWndOUdNTQ0pKSkeJxIRkVjUobX/zCydQDF1JbDcOReSmS+jbe2/lj8rdf9Fp+eee46qqiruuusu3UMRETln7a391+7kn2b27wRmU+8PXA8sBe4KVcBooh/E0W3EiBHU19d7HUNERGJUR2ZUvw6YDrwDDATWhjNQJJm3tpyH3jlCt6Zqvli1mr7jP8e868Z5HUvO0+jRXW5qNRER6UQdKaoaCMygvg1IDW+cyDJvcm9WlFWRVlPL5IZ0rpnQx+tIEgLbt29n586dXHfddV5HERGRGNKRomo1gbmpvggspAvOU1WZnMmXb/py+2+UqHD48GHKysqoqanRnGMiIhIy7RZVzrl7zeyfnHMnzewvzrmDnREsonRgML9EjylTpjB58mR8Pp/XUUREJIZ0ZEHlvwGWm9le4J3mz11GfEMNV3z4HFu3bvU6ioRIXFwcPp+PhoYGLbgsIiIh05GF0eYDjwNfOu2jy/C5Rg6n9ycrK8vrKBJia9as4dlnn+Xo0aNeRxERkRjQkTFVx4HfOOfqwh0mEtUmpLG97xStGxeDLrvsMvLy8ujZs6fXUUREJAZ0pKjaBbxpZisITACKc+7BcIaKJPGNtTT4kryOIWGQmJjIoEGDAKirqyMxMdHjRCIiEs060v1XA+wEcoG85o8uob6+nsu3P0feEY2nimU7d+7kZz/7GYcPH/Y6ioiIRLGOPP33d2Y2FhgDvOacKw9/rMjQ1NTE7t4TOJF2kddRJIxycnIYOnSoWqpEROSCdGSZmr8HfgjUAQPMzDnn/jXsySJAUlISpb1GeR1DwiwtLY0bbrjB6xgiIhLlOtL99y1gBHAS+HfgC2FNFEE+/vhj4pq0VlxXUVVVxZ/+9CdOnjzpdRQREYlCHSmq4gm0UjkgBcgMa6IIMb/oCD/+/TLG7X0j+FphaSXzi454mErCqba2luLiYkpKSryOIiIiUagjT/8tALYAGcB64NfhDBQpJvZJ4ca64YzKyCGOQEE1d0kZi2fleh1NwiQzM5MHHnhAS9eIiMh5MdeBJVjM7EpgIrDVObc0VBf3+/2uqKgoVKcLucLSSq5+cS85afFUNTgWz8qlIC/N61jSCfbt20dWVhYpKSleRxERkQhhZuudc/7W9rfaUmVmPzjLyxPNbKJz7kchSRfBKioqGJlUTU6qj5KKBn4wqZcKqi6ioqKCp556ikmTJjFz5kyv44iISJRoq/tvaPPnwcDFwFvAFGAbEPNF1aZNm3hyxWYOpE+lf3o8j28+TkFumgqrLiA9PZ0vfOELDB482OsoIiISRVodqO6cu8M5dwfQAIx1zt1E4CnALlFVHO85lD9lTiW/ZxKDuieyeFYuc5eUUVha6XU06QT5+fkkJSXhnKOxsdHrOCIiEgU68vTfYALr/wEkAP3DFydybKv08cLsAWQm+QAoyEtj8axc1h2q9jiZdJaGhgYWLVrEypUrvY4iIiJRoCNP/60BNjav/Xcl8FpYE0WAhoYGZnc7Ql7WmSvyFOSp+68riY+Pp0+fPmRlZXkdRUREokBHWqruIDCtAsD/AH8fvjiR4fDhwzz33HPs2bPH6yjisWuvvZZx48Z5HUNERKJAR9b+qwMe7YQsESM7O5uvfvWrgRaK4kNex5EIsH37dnbu3MmsWbMwM6/jiIhIBOpI91+XEx8fT79+/byOIRHkwIED7N+/n/r6ei28LCIiZ3XORZWZJTjnYnpBvPfff58ePXqQm6vZ0yXgyiuv5PLLLychIYGmpibMTC1WIiJyhnbHVJnZ42aW2Pz1EAID12PakiVLWL9+vdcxJIKYGQkJCTjneOmll3j11VfpyGoEIiLSdXSkpaofsMbMngZ+CPx3eCN575vf/CYNDQ1ex5AI1aNHDxITE9VSJSIiZ+hIUXUDgdapnwP/v3PuwfBG8l5qaqrXESRCmRnTp08Pbp86dYq0tDQVWCIi0qEpFd4C6oFbgC+bWUy3VO3atYt169apa0faVVlZycKFC1m2bJnXUUREJAJ0pKhaBVzpnPs9MIEYn1G9uLiYVatWqeVB2pWamspnPvMZRo8e7XUUERGJANZei4yZFXzyNedcYSgu7vf7XVFRUShOFTLOOaqrq0lNTWXe2nIeeudIcN+Dk3oxb3JvD9NJJNu7dy/9+/dXQS4iEqPMbL1zzt/q/g4UVbubv4wDcoH9zrmQtFZFYlElcj5KSkp4+umnuf7665kwYYLXcUREJAzaK6ra7f5zzg1u/hgIDAPeDWG+iHLs2DGWLl3K8ePH23+zyGny8vKYM2cOY8eO9TqKiIh4pCNjqk53BJgcjiCR4OjRo6xfv576+pie21TCwMwYN24cPp+Puro63n33XT3sICLSxbQ7pYKZlQAtPx16A78PayIPDR06lO9973saEyMXZNOmTSxdupTc3Fz69u3rdRwREekkHZmn6vbTvt7jnCsJV5hIEBd3ro13Imfy+/3069dPBZWISBfTalFlZg+18jqxOgHoiy++yLBhwxgxYoTXUSSKmVmwoCotLeXDDz9k2rRpagEVEYlxbTXL5LXx0S4zyzWzCjP7ygWn7AR1dXUcOHCAiooKr6NIhJm3thx7pDj4MW9teYeP3b59O8XFxdTU1IQxoYiIRIJWp1Qwsyecc18zs4fOp2XKzF4EpgHfds798mzv0ZQKEi2m/n4PACtuGnhOxznnqKmpISUlJThwXS1WIiLRqb0pFdoaU1VgZouBGWY28PQdzrk727nobAKD2zecQ1aRmGNmwYLqjTfewMy46qqrVFiJiMSgtrr/bgbeI7Du365PfLTKzLoBDwMPtPGeu82saNu2bfj9fhYuXHiOsUNv+fLlvPrqq17HkBjW0NCg6TpERKLQwoUL8fv9APlmVmRmd5/tfR2ZUf1a59yfO3phM/sJcNQ59+9mVgj8bzR0/73++utUV1czZ84cr6NIBDrf7r/Tnd79V1tbS2JiolqsRESiyIV0/wFwLgVVs6HAHDO7k8Diy8PNbJdzbsU5nqdTzZw50+sIEuNaCqjq6mqeeuopRowYQUHBp5bWFBGRKNWRearOiXPub1q+Pq2lakWoryMSrZKTkxkyZAiDBw/2OoqIiIRQW/NUDWptn3Puo46c3DkXFb+GFxcX89Zbb3HLLbfQvXt3r+NIjDMzrr766uB2eXk52dnZ6goUEYlybQ1U3wXs5NOD1Hd2Qq5OlZCQQGpqKmlpaV5HkS5m//79LFiwgA0b9KCsiEi0a7Wlyjl31oLLzC4LXxxvDB06lKFDh3odQ7qgnJwcpk+fzqhRo7yOIiIiF6jdhe7MbJaZLTWzZWa2EjjXgesRr70nIKXrml90hMLSyjNeKyytZH7RkZCc38yYMmUKSUlJNDY2snXrVv19FBGJUh1ZPXgegUk8G4GNwO/DGaizVVVV8fDDD7N582avo0gEmtgnhblLyjhe2wgECqq5S8qY2Ccl5Nd67733eOGFFygrKwv5uUVEJPw68vRfqnPun8zs1865B8xsY9hTdaLGxkZGjx5NVlaW11EkAhXkpbF4Vi5Xv7iXnLR45i4pY/GsXAryQj/+bsKECWRkZJCX16HlNUVEJMJ0pKXqkJndASSb2T1AtzBn6lTp6elcd9115Obmeh1FIlRBXho5afGUVDRwz5jMsBRUEOgKvPjiiwE4cuQIa9asCct1REQkPDpSVN1HYKmaBcDXgP8Ma6JOpmVDpD2FpZUcqGygf3o8j28+/qkxVuGwYcMG1qxZQ2Vl+K8lIiKh0ZHuvyecc5c3fz0hnGG88Ktf/YqMjAxuuukmr6NIBGoZQ5WflURmko8HL8sOaxdgixkzZjBp0iRN8yEiEkU60lK1xczuN7MMaxb2VJ1o3LhxDB8+3OsYEqHWHapm8axcMpN8wF/HWK07VB3W65oZPXr0AGDt2rWsXLkyrNcTEZEL15GWqtuBVOBngAEO8IUzVGeaOHGi1xEkgn3X3wuAh94+HHytIC8trK1Up3POcejQIerr63HOadZ1EZEI1pGiakTYU3ikurqauLg4kpKSvI4iclZmxuzZs4MFVWNjIz5fzPxOIyISU9rs/mvu6lvqnCsBSoFyIGb6IdauXcv8+fNpbGz0OopIq+Li4vD5fNTV1bFo0SLeeustryOJiMhZtLWg8r8QmPjTmdnpVUfMzJJ5ySWXkJ6ert/8JSrEx8fTs2dPzakmIhKh2ur+ewRYBLwJTG9+rck5FzPTPefm5mp+KokacXFxzJkzJ7h98uRJunfv7mEiERE5Xavdf865k865vcBY4JLmLsD+nZYszOrr6zl48KC6/iQqlZeX89hjj7Fhw4aQnnfe2nLskeLgx7y15SE9v4hILOvIlAo/B+Y2f/24mf0ojHk6zf79+1mwYAG7d+/2OorIOevVqxeXXnopl1xySUjPO29yb67sl8qV/VJxD4xg3uTeIT2/iEgs60hRdZlz7u7mrycBc9p6c7TIzs7mxhtvVPefRKW4uDiuuuoqunXrhnNOvxyIiESAjhRVyWbWssJrFhAT8w+kpqYyatQoUlJSvI4ickE2bdrEb37zG/bs2eN1FBGRLq0j81Q9TGBW9Y+A4cD/F95IneOjjz4iKysrOGu1SGvmrS1n5b4qAOyRYh6c1CuiusXGjBlDXFwcAwYMuKDzzC86wsQ+Z/6SUVhaybpD1cFJUEVEpHXtFlXOuSfMbCUwBtjmnHs//LHCyznHM888w/jx47n22mu9jiMRbt7k3hFVRH1SXFwcY8aMAQJPBO7atYvx48ef83km9klh7pIy+qbFk5nkC657uHiWushFRDqi3aLKzHIItFYNAR42s1rn3M6wJwuzu+66SzOpS8xZu3YtGzZsYOjQoXTr1u2cjm1Z1/DqF/eSkxbfKQtHi4jEko50/z0FvA2MBPYCjwMzwhkq3MyMvn37eh1DJOSuuuoqxo8ff84FVYuCvDRy0uIpqWjgB5N6qaASETkHHRmonu+cewiod86tAgaFOVPYlZSUsHNn1De2iXyKz+ejd+9AV+WWLVt4++23z+n4wtJKDlQ20D89nsc3H6ewtDIcMUVEYlJHiqrdZvYAkGRm9wCHwhsp/NauXcvSpUu9jiESNs45PvjgAz744AOampo6dEzLGKr8rCQGdU9k8axc5i4pU2ElItJBHen++yrwS+Ai4I7m7ag2Z84cTp065XUMkbAxM2644QYaGxuJi4vDOUdgffTWrTtUzeJZuTz09mHgr2Os1h2qVjegiEgHdOTpv91m9jfAUGC3c+5Y2FOFWXJyMsnJyV7HEAkrn8+Hz+ejsbGR559/nkGDBnHppZe2+v6WaRNaiioIFFYqqEREOqbd7j8zuwnYDxQC+8zszrCnCqOjR4/yzjvvUFVV5XUUERERiSEdGVP1I2C2cy6dwFN/D4U3Unjt3buXpUuXUldX53UUkU7h8/mYO3dusJWqpqbG40QiIrGpI0WVj0ArFQSmVojqJp7x48fz7W9/WzOpS5fSMp7q2LFjPPbYY2zcuPGs72uZPX7lvirskWLmrS3vzJgiIlGtIwPV1wD/Y2avElhMeZ2ZFQA45wrbPDICmRnp6elexxDxRI8ePRg2bBj9+/c/6/5Inz1eRCSSdaSo+mzz55mfeM0BF4c8URg553j99dcZMWIEeXl57R8gEmN8Ph/XX399cPvgwYNcdNFFHiYSEYkd7Xb/OecGO+cGAyOAIS3bzrmoKqgATp06xfr16ykvV5eGyPvvv8+CBQvYvXu311FERGJCq0WVmY03s2IzyzSzLwBHgKNmNrWzwoVaeno63/ve9xg3bpzXUUQ8N2zYMGbOnMnAgQO9jiIiEhPaaqn6L+BPQCXwKPCfwDeAn3ZCrrAxM3w+n9cxRDwXHx/P5MmTiYuLo6amhm3btnkdSUQkqrVVVPV3zv1fAt1+vYCfOueeaf46Kq1atYq1a9d6HUMk4qxatYoXXniBjz/+2OsoIiJRq62B6vVmlg3cBGxwzp00s5x2jolo+/btIyEhwesYIhGnoKCAYcOGaaoREZEL0FaB9CRQAiQCf29mlwLPAP/bGcHC4ZZbbsE553UMkYgTHx8fnGZh9+7dbN68mWuvvZakpCSPk4mIRI9Wiyrn3MNmthqodc6tM7NxBMZTLeiscOHQ3qKyIl3d3r17OXz4MImJiQDU1dUFvxYRkdaZly03fr/fFRUVheXc89aW89A7R4LbDw78mBF1pcyZM0e/fYu0wzmHmdHY2Mhjjz3GmDFjKCgo8DqWiIinzGy9c87f2v6OLFMTleZN7s2V/VK5sl8q7oERzO6fxLFjx/Qbt0gHtLToNjU1MWbMGAYMGAAEWq327dvnZTQRkYgVtYPOz9WECROYMGGC1zFEokpCQsIZLVQbNmzgtdde45577qF3by1nIyJyupgsquYXHWFin5QzXissrWTdoWq+64/aGSFEPDd+/HjS0tKCBdV7771Hjx49GDRokMfJRES8F5PdfxP7pDB3SRnHaxsBePmDcma/sIN+Tcc9TiYS3ZKSkhg9ejQQGHe1evVqwjUuUkQk2sRkUVWQl8biWblsO1bLRyfr+HLhUe7vsZeC3DSvo4nEDDPj61//Otdeey0AFRUVPP300xw4cMDjZCIi3ojJ7j8IFFY5afGUVDTwg0m9+OHkOV5HEok58fHxdOvWDYATJ05QWVlJcnIyALW1tSQmJmoaExHpMmKypQoCY6gOVDbQPz2exzcfp7C00utIIjEtLy+Pb37zm2RmZgKwdOlSnnzySU24KyJdRkwWVYWllcxdUkZ+VhKDuidyp9vE3760W4WVSJid3io1ZMgQRo0aFXxt9+7dNDY2ehVNRCTsYrKoWneomsWzcslM8oFzXDWwO/NHNrLuULXX0US6jJEjRzJ58mQADh8+zG9+8xvefvttj1OJiIRPzM6oDjD193sAWHHTwLBdQ0Ta55xjx44d5OXlkZKSQllZGfv372fChAnEx8fs0E4RiTFdd0b1teWs3FfFW2WnsJ+9z7y15V5HEumyzIxLLrmElJTA/HHFxcX85S9/0XgrEYkpYWupMrMfATcRKNwecs799pPvCXdLFQQGy27fvp37779fTyGJRAjnHKdOnSI9PR3nHM888wzDhw/XqgciEtHaa6kKS7u7mU0D7gSGAqOAv5jZi865qnBcry0DBgwgJSVFBZVIBDEz0tPTgcDUC6f/ctfU1ERdXV1wagYRkWgRrsEMqcBjzrlaMztGoLXKk67G/Px88vPzvbi0iHRAcnIyt912W7CwKi4u5pVXXuErX/kK2dnZHqcTEem4sBQ6zrlXnHPzzaw/sBj4jXPuVMt+M7vbzIq2bduG3+9n4cKF4YhBQ0MDNTU1YTm3iIRWS2ty7969GTduHL16BdbpLC0t5dSpU20dKiISVgsXLsTv9wPkm1mRmd19tveFc0zVVcDvgV8D33bOfWqCmnCPqdq5cye//e1v+bu/+zvy8vLCdh0RCQ/nHI8++igZGRnccccdXscRkS7OqzFVI4AXgTudc38IxzU6Iisri+nTp6sLQSRKmRm33XYbdXV1ANTV1VFYWMjkyZPp3r27x+lERM4UrnFONxAo2P7VzLY1f/QN07ValZWVxRVXXKEBryJRrGfPnuTk5ACBrsB3332Xjz/+GEBTMohIRInpyT9FJPZUVlaSlpYGwIoVKzhx4gSzZ88mLi5mp90TkQjRZSf/FJHY1FJQna6loNKAdhHxktaHEJGoNXXq1ODXFRUV/PznP2fmzJlMnDjRu1Ai0mWppUpEYkJCQgJTpkxhyJAhAJw4cYKysjKPU4lIV6KiSkRiQnJyMgUFBWRmZgKwevVqFi1aRHV1tcfJRKSrUPefiMSkGTNmMGLEiOAizitWrGDgwIEMHDjQ22AiErPUUiUiMSkxMZFBgwYBUFNTw4YNG9izZ09wv6ZjEJFQU0uViMS85ORk7r//fpqamgDYu3cvr732Gl/4whfIysryOJ2IxAq1VIlIlxAfH09iYiIA9fX1xMfHk56eDgSeHGwpuEREzpdaqkSkyxkyZEjwKUHnHM8++yxpaWnceuutHicTkWimokpEurwpU6YEJxBtamqiuLiY/Px8fD6fx8lEJJqo+09EujQzY+TIkeTn5wOwc+dOXnjhBXbs2OFxMhGJNiqqREROM3ToUL70pS8xbNgwALZs2cI777yjMVci0i4VVSIipzEzBg8ejJkBsGPHDrZu3RrcPn0qhnlry7FHioMf89aWe5JZRCKDxlSJiLThhhtuoKamBjOjrq6OJ554goKCAl6p6s2VuWlcWVYFwIqbBlJYWsn8oiN819/L49Qi4gW1VImItCM5ORmA6upqMjIySE9PZ2KfFOa+WsrJqhoACksrmbukjIl9UryMKiIeUkuViEgH9ejRg9tuuw2APOD/9j3CD3alkV1vzF1SxtNTM7m8T4K3IUXEM2qpEhE5T7dPGsolvgpKTjVyz5hMqjatYMGCBcH9u3btYt++fR4mFJHOpKJKROQ8Fdd1o5he9E+P5/HNx2kYOIFp06YF97/xxhusWLEiuL1y5UqKi4s9SCoinUFFlYjIeWgZQ5WflcSg7oksnpXLP2xspLz7wOB7brvtNq655hog8NTg5s2b2bt3b3D7V7/6FRs2bAi+v6GhoVO/BxEJLRVVIiLnYd2hahbPyiUzKTDrekFeGotn5bLuUHXwPenp6fTs2RMITNVw3333MXPmTCCw/mBiYmJw1vaamhp+/OMfU1RUBEBjYyOlpaXU19d35rclIhdARZWIyHn4rr8XK8sqWbmvipX7qrBHillZVtnudAotRVRiYiK33norY8eOBQLL41xxxRX069cPgEOHDvHUU08FZ3Y/efIk7777LqdOnQrjdyUiF8JOn8ius/n9ftfyW5mIiPxVTU0Ne/bsIS8vj7S0NN5//32ef/55vv71r3PRRRexd+9e3nvvPaZPn063bt28jivSJZjZeuecv7X9aqkSEYlAycnJDB8+nLS0NABGjBjBAw88QO/evQH4+OOP2bFjB4mJiQC8++67/OIXv6Curg6AqqoqjdES6WSap0pEJAqYGT169AhujxkzhtGjRweXz0lLSyM7OztYZC1fvpxt27bxf/7P/8HMKCsrw+fzkZOT40l+ka5ARZWISJRqKagARo4cyciRI8/YzsnJCb5n+fLl1NTUcPfddwOwbt06UlNTzzhGRC6MiioRkRg0aNAgBg0aFNyePXs21dV/fTJx/fr1ZGdnB4uq559/nv79+3PppZcCgYHzcXEaISJyLvQvRkSkC8jIyDij6+/rX/86119/PRCYM6umpiY4BqupqYmf/OQnrFmzJrj/4MGDNDY2dn5wkSiiokpEpAsys+D4KzPj9ttvZ8qUKUBgDq0JEyZw0UUXAYFB8QsWLAhOVFpTU8OGDRuoqKjwJrxIhFJRJSIiZ0hKSmLGjBkMHjwYgJSUFG688UaGDh0KwP79+3n55Zc5fPgwAOXl5bz66qucOHHCq8giEUFFlYiItCkpKYlRo0aRkZEBBMZr3XvvveTl5QFw7NgxtmzZEnz/+++/z5NPPhmcqLSuro6mpqawZJtfdITC0sozXissrWR+0ZGwXE+kLSqqRETknJgZPXv2JCEhAYDhw4fzj//4j8EpH3w+H0lJSaSmpgKwevVqHn744eCYrfLycsrLy0OSZWKfFOYuKQsWVi1rMk7skxKS84ucCz39JyIiF+z06R2GDx/O8OHDg9uDBg0iISGB+PjAj5xVq1ZRUlLCt771LQC2bt2KmZ3X9A4FeWnMGpDGtBf2Bl+7Y3h3CvLSzvdbETlvKqpERCSsBg4cyMCBA4PbBQUFnDx5Mri9bt06fD5fsKj685//TGZmJpdddhkQePrw9KLtkxZdk8uKfR9SUtHADyb14oeTe4fnGxFph4oqERHpVFlZWWRlZQW377zzTmpqaoLbx48fDy48DfDf//3fjBo1iqlTpwKBMVwZGRnBebQKSys5UNlA//R4Ht98nILcNLVUiSc0pkpERDwVFxcXHH8FcOuttzJz5kwAGhsbufjii8nOzgYCg94fffRRVq1aBcCbeyv4/Isf4WuopaSigSPVjXz+jyWfGrwu0hlUVImISMTy+Xxce+21Z4y3mjNnTnDM1srdR7jp5F94d0Y87oERHLq1F3fbe7z+wX4ATpw4wbJlyzh27BgA1dXVlJaWBheeFgklFVUiIhI1EhMTGTduHH369AHgoSsH8PDXbmTIkCFAYMD8pJ7GP4xOBwJdiatXrw5O71BWVsZTTz0VfPrwww8/ZP78+Rw6dAiA0tJSXnrppeDEpseOHeP9998PFmH19fXU19d33jcsUUVFlYiIRK24uDj69OlDSkpgCoXs7GxuueWW4GzwgwYN4gc/+EFwTq1+/fpx66230qtXLwDS09MZOXIkaWmBMVinTp1iz549OOcA2LVrF88//zy1tbUAbNy4kX/7t3+jsjLQvbhlyxYWLVoULLpKSkpYu3ZtcF6uiooKjhw5EjyfxDYVVSIiEtPMLPj0YGpqKkOHDiU5ORmAnJwcrrvuOrp16wZAfn4+DzzwAN27dwdgzJgx3HPPPcGiKy8vj+nTpweLOOccTU1NwTm7duzYwZtvvhm83ttvv80vfvGLYJbCwkIWLFgQ3N68eTPLli0Lbh84cIC9e/86PURjY6MKsiiiokpERKQVSUlJ9O7dO/ikYU5ODldccUVwe8yYMXz5y18OFlHTpk3jO9/5TnB7zJgx3HDDDcHtrKysYKsZBJb8+fDDD4Pbq1ev5k9/+lNw+w9/+MMZRVhhYSGvvfZacPuDDz5g+/btwe2TJ09SXV0dsu8/0kXajPqaUkFERCREzCzYCgbQp0+f4PgvgLFjxzJ27Njg9jXXXHPG8dOnTz9jeon8/HyqqqqC29XV1WcMsl+zZg3x8fEMGzYMgMWLF5OcnMztt98OwHPPPUdGRgZXX301ECjaMjIyggP/S0tL6datG5mZmRf8vXuhZUb9xbNyKchLC86ov3hWrid5VFSJiIhEiE8WN6NGjTpje9asWWds33bbbcHlfwA++9nPnjHHV48ePYJdmwDvvfceubm5waJq8eLFDB06lNmzZwPws5/9jFGjRjFjxgwAXnrpJS6++GJGjx4NwKZNm8jJyaF378AEq1VVVSQnJwdb7jpbQV4ai2flMndJGfeMyeTxzceDBZYX1P0nIiISpZKSkoLjvQCGDRsWfBISAi1hl19+eXD7m9/8ZrCAArjpppuCM9cDjB49mtzcQCuPc479+/cHn4RsbGzkpZdeYtu2bUDgScif/OQnrF69GgjMIfbkk09SXFwc3L9y5UoOHjwYPP7AgQNntMSFwsqySo5UN/Kjd45wpLqRlWXezVGmokpERKQLOX3Jn/79+wdbnQCuuuoq8vPzg+/7xje+wZQpU4DAk5b3338/fr8/uP+aa67h4osvBqChoeGMVqtTp06xYsWKYFF14sQJFi5cGBxDduTIEf7jP/6DHTt2BPe//PLLwekuqqqq2L59e7D7s6mpKfhU5ek++riuze3OpKJKRERE2mVmZGZmBlvG4uPjmTRpEn379gUCT1befvvtwYlZMzMz+f73vx/sOuzWrRs333wzAwYMCB4/dOhQ0tMDc4pVVlby4YcfBouogwcP8uyzz3L48GEAdu/ezY9+9CPKysqAwPQV//z0H3n1owqW3zgA98AIlt84gCV7Kz2bUd+8fFTT7/e7oqIiz64vIiIikamuro4jR47Qs2dPkpKSOHr0KFu2bMHv99OtWzd2797Nd/78AXdN+wzX5//1YYDC0krWHarmu/5eIc9kZuudc/5W96uoEhEREWlfe0WVuv9EREREQkBFlYiIiEgIhK2oMrN5ZvaBma03synhuo6IiIhIJAjL5J9mNh24CxgBFADPmNlApwWMREREJEaFq6VqBrDWOVcFFAJ5wCVhupaIiIiI58JVVGUB+wCaC6vjQM+WnWZ2t5kVbdu2Db/fz8KFC8MUQ0REROTCLFy4sGXS03wzKzKzu8/2vrBMqWBmDwP9nXNfNLNU4BQwwjn3wenv05QKIiIiEi28mlLhdeAyM0sBpgKlwPYwXUtERETEc2EZqO6cW2ZmvwbeAyqBL2qQuoiIiMSysE2p4Jx70Dk3zDk3wTm3JlzXETQmLcrp/kU/3cPop3sY/SLhHmryzxgQCX+R5Pzp/kU/3cPop3sY/SLhHnq69p+ZHQb2ehYgduQD27wOIedN9y/66R5GP93D6NcZ93CAcy67tZ2eFlUSGmZW1NbTCBLZdP+in+5h9NM9jH6RcA/V/RcbvG/zlAuh+xf9dA+jn+5h9PP8HqqlSkRERCQE1FIlIiIiEgIqqqKQmf3IzD4wsw/N7DYzG2pma5q3Hzcz3dcIZ2a5ZlZhZl/R/Ys+zfdto5m9Z2Zf1T2MLmbWzcxeMbN9zf+XXql7GB2a/+/caGYPNm+f9b6Z2bzme7vezKZ0Vj79pYkyZjYNuBMYC3yRQB/y74DfAMOB8cBdXuWTDnsUaGr++il0/6KGmY0FvgNMAW5p/vg1uofR5A4gDxhA4N/ffPTvMOKZ2TeAjcDI017+1H0zs+kE7t8E4F+AZ8zMOiOjiqrokwo85pyrBY41b38GWO6cawJWAjM8zCftMLPZgAM2AMnA5ej+RZMbgSPAm8DzwNPAJHQPo8khIIHAv780Ait/6N9hhHPO/U/zdAarAZrXFj7bfZsBrHXOVQGFBAroSzojo4qqKOOce8U5N9/M+gOLgScAA/Y1v6UM6OlVPmmbmXUDHgYeaH4pA92/aHMRMBT4EvDPwP+iexhtXidQSJUD/wQ8hO5hNMrg7Pctq+W15sLqOJ10P1VURSEzuwrYBKwBvkWg1aNf8+5cAi1YEpkeBBY550qat0+g+xdtPgbeds7tds79kcD9A93DaPJjYDeBH743AC81v657GF1OcPb/P4+3vNbcmpVJJ91PFVVRxsxGAC8CX3HO/YNzrpJAU+i05gF6VwJveJlR2jQU+Dsz2wZcCnwf3b9oswLwm1lvM/MD1cBWdA+jSXcC962WQJGcBhShexhVmluhzvb/5+vAZWaWAkwFSoHtnZFJ81RFGTP7PoEuhz2nvfx1Al1KvYBlwDea+5clgplZIYGuo1XAInT/okLzgNd/B24CGgj8e9yE7mHUMLNcAmPhhhMorL5PYIyj7mEUaP6/c4Vz7iEzu4Sz3Dcze4jAQySVwL3OuTWdkk1FlYiIiMiFU/efiIiISAioqBIREREJARVVIiIiIiGgokpEREQkBFRUiYiIiISAiioRERGREFBRJSIiIhICKqpE5LyY2QYz+1bz15lm1tg8OS1mlmpmdWY25QKvcaeZrQpF3tPO+WBz3nbPbWZXmlm9mW1rXu7ifK6X2Hx8nZlNP7/UIhINVFSJyPl6A/hc89fTCCz7cVXz9mQCMxm/40Gu9swjsOZbRx10zuU756rMbIWZDQMws55mtrW9g51zdc65fP666KuIxCgVVSJyvt4APtu8bMtVwCPApc0tOi1rcKWZ2TNm9r6ZbTGzfzazpWb2LwBm9jkz22dmcc0f/2lmH5nZB2Z2w+kXO9v+5tamtWb2qpntMbMfNr830cyean7vKjPbamZfMbM3m0/3OjAQyDazN04/th1DgB3NX48BtjRneNfM/mxm5Wb2sJm9YGY7zOx5M0u4kD9kEYke8V4HEJGo9RaBhWhHADOAG5o/X0FgEdNfAZcAu4FbgZnAy8AdwL8APwS+APxv81pddwGXAyOBPGAdcN9p17ujlf2jCCxU3Q3YZmaPEFjzyw8MAwYBmwGcc1eZmWvOcgWBleynnn6sc+6sq9mb2QBg32nrwY1pOS+QDwwAxgLLCbTUbSawWO8YYH27f5oiEvVUVInIeXHO1ZjZW8BdBIqSLcCbwOeBSwkUUolALvAsgYVrE4CXgMfNbCxwI3B18yknEmgJailAqj5xydb2b3LOHQQws3ggk0Chtco5VwdsN7PWVqg/27FnLaqAcfy1iAL4DPAcgYVcNznnjplZSfOfzdvN5zwKJLdyPhGJMer+E5EL8SbwTeANF1id/U3gq8CHzrkyAi1Jqc65m4EXm4+pJ1CMPAIccM61jEvaCrwPTAAmAU984lqt7T/bqvDvA1c0dwPmAsNP29cEJLVxbGvG0lwgmdlQYA6BQrKt82jFepEuREWViFyIN4CU5s8Aa5o/v9b8+Q/AJWa2jUBXXwnwLQJdg1OBX592rieBbcAHQCFQ+olrtbf/dE8RKKw+ABY0H9dS4CwhUPwN7Mg3eJpxQJyZbSLQfbkNuPMczyEiMcwCv1yKiMQOMxtHoBvyXwkUfSXAjc65led4nisJjPnKM7OdwHjnXMV5ZvoI+Kpzbtn5HC8ikU9jqkQkFu0mMID+HQJjnn55rgXVaS5qLojqz6egMrNEYBOBQfEiEsPUUiUiIiISAhpTJSIiIhICKqpEREREQkBFlYiIiEgIqKgSERERCQEVVSIiIiIhoKJKREREJARUVImIiIiEgIoqERERkRD4f2KqxO3hmsUEAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -141,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,22 +141,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.title(\"Prior predictive checks\")\n",
@@ -193,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,57 +179,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "120158f83d1347fe83a30de54ea58b7b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=2e+02  7 [173.8606..173.8613]*| it/evals=6000/22771 eff=26.8204% N=400    400  400 400 \n",
-      "[ultranest] Likelihood function evaluations: 22781\n",
-      "[ultranest] Writing samples and results to disk ...\n",
-      "[ultranest] Writing samples and results to disk ... done\n",
-      "[ultranest]   logZ = 159.9 +- 0.124\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 981.6, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.07 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.42, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.18 bs:0.12 tail:0.41 total:0.42 required:<0.50\n",
-      "[ultranest] done iterating.\n",
-      "\n",
-      "logZ = 159.914 +- 0.447\n",
-      "  single instance: logZ = 159.914 +- 0.185\n",
-      "  bootstrapped   : logZ = 159.917 +- 0.189\n",
-      "  tail           : logZ = +- 0.405\n",
-      "insert order U test : converged: True correlation: inf iterations\n",
-      "\n",
-      "    Temperature         : 0.00945│ ▁▁▁▁▁▁▁▂▂▂▄▄▅▆▆▆▇▇▆▇▆▅▅▃▃▂▁▁▁▁▁▁▁▁▁ ▁ │0.01038    0.00989 +- 0.00012\n",
-      "    Amplitude           : 37.3  │ ▁▁▁▁▁▁▁▂▂▂▃▄▄▅▇▇▇▇▆▆▇▅▆▅▃▃▂▁▁▁▁▁▁▁▁▁▁ │58.1      47.3 +- 2.7\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from ultranest import ReactiveNestedSampler\n",
     "\n",
@@ -274,22 +200,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -314,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -340,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -356,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,45 +288,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ac84b43200444359bfde94e2d8a087dc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=2e+02  6 [169.4148..169.4161]*| it/evals=1626/5461 eff=32.1280% N=400 00 \n",
-      "[ultranest] Likelihood function evaluations: 5484\n",
-      "[ultranest]   logZ = 166.7 +- 0.05516\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 485.6, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.11 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.41, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.08 bs:0.06 tail:0.41 total:0.41 required:<0.50\n",
-      "[ultranest] done iterating.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized=vectorized)\n",
     "res = sampler.run(frac_remain=0.5)"
@@ -421,22 +298,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -466,17 +330,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Speed-up of warm-start: 315%\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"Speed-up of warm-start: %d%%\" % ((results_ref['ncall'] / res['ncall'] - 1)*100))"
    ]
@@ -534,22 +390,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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iT6tqJux8WjaE20XkH0WkEWc3sA/4Fo76aH6QMHANzWtFpAH4OFDajJ+HUlb8heJPYCdAg8CbBQgDSCaYK4iWyXWxDcpp2SDC1GaFCIMkEgQaRi2Tlg3hWzgqoVeBHuBq4Kc4O4UnRQTgJ+5rE46a6CbgHuA1YBdwbUp9G6Kc1bjWLWoZSn89e8Ouoquq+SdObzIvhXzX9xwZRNZ0VlUUdRDFFAgyDOMksQWCiJyqqrGc7lX1VeAS3+HJIc3b3J89wPvj9mc0E2a4FmDQVXnEVQW9fH1rXjVOUiQZRZ10n7260oZhFE+oykhEJojIn4jIUyLyJvCqiBwRkadF5IsiEqjfN/KTdECVp9+vBnLLfYZRiDCI2pXkVp4zYWAYpRNlQ/g1cBbwZ8B/AiYC5+C4kLa5540iiBNQFTYRVrvaBk5mZQ0TDoXsDHpPQKPPNrB45kR0dTtnT29k/Y7eoc/KrfeQj6Qjx8uR58ow0iZqSfoeVX0DQESuAP4YuF9VtwBbRGRaOTo4FolTByBMPZN08rskbBBReLWgD62M5/0T1J8+He7pBME1Hbx03KdNbogs3en3+vIix4Gidhp+jzUv6h2sFKgxusjrdioiXweuAn4PWAPMUdUbU+9ZBEm4nVYzYW6qHp5rZbGFcipB7oRerKtsrktpoffIFQoNazsDgwXrgJlTGwqurRDmJDBragMvLp9XUD8NI03yuZ3GUVovBc4H/lVV/05EdiTWO2ME+YSBx2gSBnDSU6kUTr+zu2h7yZZ9x/J+/iAMTeyFrPIt66oxVogTh6BAE4CIvB2YkmaHap180bZejqDRJAySIjdfUjmIm83Wsq4aY4U4AuGbwLPAHKALSDULaa3iGSXD8h6Bo3Z55Ko5BRlPxxqefaFcaTv2Hu7PazC2rKvGWCHvEkZV7xWRX+CojXaqalf63aoNvHrAew73I5A3arnnyCArt/YUXRN5LLHzmrmBhuWkaR4veQ3GcZwEDGM0EMeofAHwCVX9MxFZB/y1qu4tS+9CGAtG5aBcSnGol/DsqbVCofmNimVSgzCxoY4Db40sKGQGY2M0kkQuo78EnnF/fw1Ym0THap2gegxxKKcwaBrnePZUW+xDXGFQqlrpaL8GCgMwg7ExNokjEOao6g8BVPUbgKWTTIBiJ5R6KW2ia5lclzcjqK5uR1e3D8UOxI0hqDbSVCeZwdgYi8QRCK+4KSzeLSL/DTiQdqdqgXwTSkNI5s6Oc5vYec3cgoVCe3Mjurp9yG3TS3ftJ+x4NZBvpxJ3N9Pe3FhS+U6/wdiilI2xQpz//uuBPwS2AZ903xslcvmcKfjnfO/9jAn1TBs3/KyXt2fdohZWbu3huUPxVr9emoed18wddvzl61tHTP7+aGCPcrp6RpFPVSRrOiPbeDuf3uMDBbvthqVJL7Q2t2FUMwUVyBGROmC1qn4nvS7lZ7QblYMMygLcML+JhS2TAgv3XHDaBB596VhRNoSo+sX+RHO5QmH6uugJtpYIUrNFBRHGMTp7XmbmmWSUi3xG5TheRl8F/gfg6Sj2qmo6BXNjMtoFQlSqA6DoWglRBE1oYVlHWybXcbRv0IRBDu3NjcN2WfkiynNTmQdRzop9huGRROqK/wJcgZPC4ic4eY2MEqhEqgO/z357c2NoUrty1FYYbfgN1PkiyvPZiIK8zLzIaBMIRqWIY0OoU9WHgXGq+k84NZGNIvCMj2F7sjqB5gn1qXx2UGZQo3CW3L8bWROcHC+XfYf7I1NqW/4joxqJs0N4VkS+CRwXkR8CpkgogjiBaAMKvzs+wLg64cRgjUefVSGFJOcbJDql9plTGwJVg+bOalSSODuEzwH/BHwDeBn4Ypwbi8gBEekWkS4R2Swic0XkFyKyS0TWuwbq3PZ17vFdbru5YfceLeS6Iy57qCdWIFqfwtTG4V4tpbhIGvFob25MJT9SmGrJ8h8Z1Uic5cgx4D3AfJzkdk/lu0BEzgIOq2przrHHgXuB7wG/AD4D3J1z2bXAeUArsALYAHwoRv+qEv+OoBDvoIPHlf0rhnuorNzaQ/bZXgbUcUH1Csp7x4KwNBfxaJlcN2Qwrl/TSZIWlLDxt/xHRjUSRyDcAfwBsAW4AViIUz0tigXACRF5CHgb8HX3us+q6qCIPAZcwnCBcAnwqHt+K/D3IjJRVUdlnudiU1PASbWB3y1x46UtQ/e+Y0fv0LFPPxisqzZhEI9cI3rS5vT6kABDcISCCQCjmoijMroYOE9VVwIZ4AMxrukHfgR8FGelfz+OJ97L7vmXgBm+a5p95+uA6bkNRKRDRLZ3dXWRyWTIZrMxulIZ4hgHx9XJiHrBntogKODp2od6uO7hkUFQYXNOvWDqpphMX9dZcgGfILydnGFUkmw2SyaTAWgTke0i0hHULk4cwtPAR1T1dRF5B/AjVY2tyhGR6cBB922rqj4nIn8DzFLVP8pp90PgBVX9ioi0ATuBKap61H/P0RCHEBZrUC8wqAypCCBYbRB2fRBTGoU3+0Z+j15k82irrjaW8OIXglR+xdRvNoxSSCIO4SDwKxH5Jc7u4DcishFAVZeFfOjtQAtODMOVwD5gD7BIRJ4HLgTu9F32MPBZ19h8MbAtSBiMFm5ZeGrswKMgtUEh7odH+pQV85tCJxx/lHIaK2EjmM6DfSOC2AY02gPJMCpFHIHwuPsC+PeY9/0WjgH5VaAH+CNgP7AR+FMce8Q9IrIA2AQsBu7BsT0857YNFDajhVKNhmFuiWFt1y1qiT25tDc3WhxCguQbzzBPo+yzvSYQjKoijspoGjATGACuA7KqWtEajqNBZVQqcQvojKsT7v5w4ekOgiKXTUjEJzcVSFgKkELvYxhpk0Quox8Bv8RR4wwC41X1skR7WSC1IBBguJdRXYgL6Yzxwv4VbYl8nqmSkqe9uZHnDvVFenzFtSlYMjyjVJKwIZwDXAN8QVXfKSI7EuudEUmuW2JdyGR98HhyvqUtk+ssj1HCeAblqER4YTaFXEN0HY6bnle/Lai2s2GUShy302PAV4BtIvJB4M10u2QEEZbSoNRUB15uHlnTSc+RwRFusHEqrBnhnH5nN+sWtbBiflNkTAIMtzV4QsTbWQxyUhh4eMnwDCMp4giE9cDHcGopXwvcnmqPjEDSSHUQ5I7apyeL6uRWWDOhUBw9RwaHhEL/qvbIccxVK+XLpuphyfCMJMkrEFR1g6q+x/lVl6vq98vQL8PH0tYmsktOC63cFZeVW3toWOvsCMJiE7bsO8aS+3eX3mkDOCkUPMJ2Ct7xlVt7YkeZWzI8I0kK+WvahGNPMCpEKakOVm7t4Y4dvaGpt/14QiGs0ppRGLm2mY5zmwJtCh3nNuW1N+RiyfCMpClEIOTRgBrVhueVUmwFNotuTpZcL66J9XDMZxSIKwg8lrVPM4OykShxbAgeP0mtF0bi5OZCKgVPdXTOpoqGnow5/MKgGDZ2vsHm7t7Sb2QYLrEEgoh8GDgkIh9NuT9GAmzu7o1dfyEfW/YdY9JtnRa0VoWYl5GRNHkFgoj8T+CHwCKcdBN/n3qvjKLxdgZJpr5OYjVrpIN5GRlJEmeH8F+Ad6nq5cA84BOp9sgoiVLqMBijD/MyMpIkjkA4yvA6yq+l1BcjAeKsGPMFSBnJ0TQuvXubl5GRNHEEwvPAYyLyt8CjwOsi8g0R+UaqPTOKImzFWC9w32Ut6Op2+le1m8tYmeg9kb9NXO67rKXoOJTc+t6zN+wyY7QRSJz95iH3NR14xj02E2K7tBtlJG4dhlK/vEZxopo92psb6T0+YLmQUqK9ubHoOBR/5lzLg2SEESoQROT7wG2qel3AufcDN6bZMaM44tZhqA/Jnlov0L/KSa8Qlf30xKrhKRjO2fS8CYOECEpF3nmwb+j7aJlcN5RSJA5BdiXPQ8kEgpFL1A7hfwNrRWQm8BucQjenAe8E/gP4WvrdM4ohaiXpBauFeSENqiMICrEz+Gsr5FJonQVd3V7zabh73oweLy8VRlyhEBaLYh5Khp9QgaCq/wz8s4i046SsmAXsBjpVtatM/TMSJE7RHe9MIW6rURN+ofEL09fVtjCAeHaHuLuxzd29CMEqQvNQMvzk/YtQ1U7A/kvHAEm5pC6eOTGB3gSTpBG2FgiqfLfzmrlD72/e9nqgMBAwDyVjBIWkrjBGOUmoCBbPnGgJ76oEWTMygrzzYN+wNCNh37kCyx7qYeXWnjS7aIwyUtszisgZQBfw34DVOafeAfxYVa/PaTsNeB14wT30L6r65bT6VqucObWh4NxGdcCAm8Pfq+Dl2RjilH00yk+uATqKsEptRu0SJ3XFaSLyExF5VkQ+LSLvinnv23AKPfWrapuqtgHvB3rdc7lkgCe8diYM0iGoyE4cNnf3jqjg5U0mK7f20N7cGHl9vvNGZYlbjMcY+8RRGd0N/BswHtiDU0EtEhG5AmdX+ozv1K3AZlX112VeAEwVkcdFZJuInB9y3w4R2d7V1UUmkyGbzcbovuHhL7IzY0I9M8bLUKDTuABZMYijhw6bNNbv6GXnNXNDJ/2J9YUblo3ykmTeK6M6yWazZDIZgDYR2S4iHUHtRDX6r0FEXlTV2SKyU1XPEZHfqGroLkFEpgBPAZcCG4H7VHWDiCwE7gNaVfW475obgEk45Tn/ErhUVQOFAkAmk9Ht27dH9tsonLo1naEGyKi/khXzg1VHQSU6jcoRFnsC4d+hMbYQkadVNRN2Ps4O4QURWQ2MF5EV5M9l9DVgo6ru9R3/IvB9vzAAUNU7VPU7qnoCJ7OqFfCtAGFuiGdObYiMSwjbPZgwqC6idgKmNjIgnkC4HrgSxxh8DRC41chhLnCdiHQB7wW+JSKLgY8DDwddICIPiMhaEWlw29nyvwIE2Ri8BGod5zaFXmcqh9GPfYcGxBAIqvpbVb1YVaeo6gWqujNP+0+o6jzXiPwk8OfAPhyV0FClcRFZICJdItIC3AS8D2f38RFgefGPZBSL38aQm0Bt3aKWyIR4xSRLa5lsXs/VQr04XmQNazuRNZ00rO00l9QaJNSGICKDBKuOVVUrGuJoNoTKEFUAPiiBXpTro67Ony/JKB9hKUbMtjC2KMWG8C6cgjj3AF8H5gNfwTEUGzWElzr5jh29TK4PbhNUzjEqotlWn5XFswnVizPpP3co2BPMbAu1RVQuoxcAROQDqvpZ9/BOEbFq6zWEP//RkYhymv6o2EeumhPqaeTtNOpwXFuN8nL2dMdNuPNgX+iuDxzbgheQOKBYQOIYJ47qZ5yILAUeAj4JWIrEGqKQ/Eeel5KXUdVLvx3mtho1ERnpUkhsSO73ZNHNY5s4AuELwCbgFOBV4NOp9sioKgrJf3TLwlMDi7EkScvkOqu7UAWs39Fru4YxSBwvowdV9VTgVFVtUdWtZeiXUSUUkiJ5aWtTYhlVgyi0MIyRLkFpTIzRTd7/dhHZi7vjF3EsUao6K91uGdVCUEnOIGaMd/42kiy6EpZZtWmcpcmuRtbv6OVHuw5z8K2B0Ep9RnUTxxH808DVwDIcj6PHUu2RUVXkxiaEUQesvfg0IHxHUXhKvfBI50MrLZC9Wjnw1gCKoyq8+sEeZE0nszfsKipOxSg/cQrk/Dzn7aPmZVR75Jbk3Nzdy6pHX+PAW4670YzxwtqLT8YfhO0oLBC29vC+8z2H++l45BUA2zFUOXFURt/IedsKRDgeGmMd7x/a8yKaMq4+8Pyyh3oSSYeQG7hmxXlGL16cigmE6iaOymhmzuvXwOWp9sioajwvoj2H+4dUAx2PvDJMJbC0tYnBmMJgXJ3ErpewZd8xZE0nS+7fXXjHjUQpJu1IkvYlIx3ifKudqnqd+/omcEXanTKqlyAvoqAo5bjeSScGldeOFuZGumXfMRqLMUoYibB45kRevr6V+y5rKajgUiEea0ZlCP2GROQi4GJghYhMdQ+Px8l2uibtjhnVhRdsFhZX4F/9BdkSwgLUPHtEIfSpxSSUk/bmRnZeM3fYMb/6sHm8gNRx4K2BEd+1lzXXqG6iRPbv4aiJGt2f4GQZ+HzanTKqC3+wWRD+1Z9/siimnnM+cmMSLEleuoRFNuc6HOTij1Y3F9TRQZyKaStVdV2Z+hMLy3ZaXmZv2BU5mQdlOi3mPoXiRcjuOnTcivGUAS9DrT8/lRn7Rw/5sp1Gpb++V1WvFpGf49vpq+qFyXazMEwglJew0prg1EyIu/qLs9Pwkt3VC7x9kqmERgvVJBRsdxJOPoEQpTL6sfvzrmS7ZIw2wtQ9s6Y28OLyebHv4/1TrvrZKxw4HiwUZgbc02ozVz9xv5+0J+ugXFoWAxGfKC+jZhG5FqgPeBk1RFRpzUJZ2trE/hVtoeeDXBMfuWrOkLrCGL3EcVkulbhecEYwUTuEq0OOK04KC6NGCDIQl7qymxWy64hyTawXq/07momarJNavYfFOlgMRDyiCuQs8n4XkSacCmovqOrBODcWkTOALmA1MBn4C2C/e/o6VX0ip20d8F1gsdtmmapaiowqIsybpFiC3FLz7To6zm2KXUPBEuCVl8UzJ3LOpueHeSP5XVXDJuU9h/upW9OZyEIjTL1pMRDxyBuYJiKfBF4GHgVeFpFlMe99GyeLYS0AblLVNvf1hK/ttcB5OKkxNgMbYn6GMUrJTZonODuGfJ5K6xa1sGJ++HmPxTMnmjAoM68c6R/hmtp5sI9zNjnrupVbeyLzWXkqpGsf6ilJhZSkerMWiROp/C3gClWdAlwCfCNPe0TkCpzv+Bn30ALgYyKyU0TuEpEJvksuAR5V1UFgK7BQREYU5BWRDhHZ3tXVRSaTIZvNxui+Ua0sbW3ixeXzGFzdzovL58VaGa5b1DJUDziIeiGvt4vZI5InLE6h82AfK7f2xN7Z9anjdFAsxSw0aoFsNksmkwFoE5HtItIR1C5OHMLzwNmqOigiDcAOVQ39jxKRKcBTwKXARuA+4ELgdmAf8DBwl6quzbnmYeCfVfV2Nyr6d8DpqhpYccPcTmubfBOMrm6PDFQLEwgW3JYOxdh+TGinQz630zg7hF8A60Tk48AdwFMicrGIXBzS/mvARlXd6x1Q1WtU9UlVfQVnB+D/tg8Bp7u/n4Gzu+iN0TejBlm3qCW0voK3e1g8c8QGM/J4XJrG2WRVKMU4AjSs7bRaChUgjkD4A+DDwFpgkft+A+HxCXOB60SkC3gvcLuI9IvIAhFpBj4I+Jf3DwMXucbli4Ftqnq04KcxaoYbQmwJHec6xx+5as6Iyd8Lnlq5tWdowmlY2xm79GPTOCvOUy48IZKGa6oRTl6VUUk3F/kZjsqoAfhz9+c/ATfiGJE34XgWvQqsY7iX0a6w+5rKyABHdeQVehdgcqNwpE8jvVXC1E1RifKCajmbeqm8FBoEaQRTdOqKnBssx5nAhwzBqnp2Yj0sAhMIRi5BKTHC8is1rO0sSIUxsR6OfnHkruD0O7strUYA7c2NoQZmj3qBs6c30nWwL3YlPQEGTVVXMknYEP4MxyB8I/BF92UYVUMh0amF6rOPDRBYkOfl61uLKhIz1sknDHR1O/2r2tl5zVzqCqhpYXEE5SHOKB8C7lFVC/UzqpJColOL8XjZsu8YS+7fPcKdNVeNZDuGwon7PVgcQfmIs8TZhONZdLeIbBARCxozysbm7l5mb9hFXYTHSdjqMei4Z3QulHzJ22zHkJ/FMyey5P7dyJrOvDYYz1vM4gjKS5wdwk04UcovpdsVwxhO3MyVhaTBWLeoBWDIGG35kcqD5/EVJyvqivlNQ9+TUV7iCITfqepn0u6IYfiJmwyt0OR76xa1DJtwkvIYMpXRcPzxGnF2BR3nmjCoJHEEwg4R2YSzS1AAVbVsp0bqFGIbKCX5XpxdQlhAm9VqCKYY9Vn/KvMiqjRxBIInrpfmHDOBYKROuTJX5suiGlYNbCwIg6j4i1LoOTJosRqjkDhi/KPAPwCNODmJzHJmlIVyZa5ct6iFySFln2ZNbQhNlpeUMGgal8htiqJajOGlphQxkiF0qSUi7wY6gP8K/BaYDbSoqpUeMspCGoV5wjg6EHw87cIqnp69EqtpTxB57rOVXNFXSz3mWidq7/0r4EFgoap2icgLJgyMcpN0YZ4wKlFYpdJJ8qolL1N7cyMQrILLzT+V6xlmxud0iNor/jXwbuD7InIjVkvZGMNEqafixEKMNvxqqunrKrM78Kqqhdljtuw7xul3drN+R++Q4X9AYf2O3thJCY34RJXQ/KqI/AXwMeCzwDtE5J+BH6vqpnJ10DDKQZh6CgiNhVg8c2LRdgRvVVwpek84KqKWyXUc7Rssa4U5f2lNiLbHhBm9s8/22i4hYWJnOxWRFmA58BlVPSvVXuXBktsZ5WL2hl2BqiQv+2YxnkZBE2KteeT4x6DY5y+H2m1zd29Z7FjlIF9yu9gKUrd62Tfdl2HUBPliIR65ak7sDKph7qtxyJ34xoLwyJcELy7e2KdlV4gbLT9WqLy/mWFUMXHyJKUtDHI5/c7uku9RjUS5nUa5xaZtVygkk+5YwASCYUQQZGwWnJWiZ2CuD0njXC/Oyl5Xt48QBv6qbXEZS+kxztn0/NDvQRXuwBEUL1/fyor5TUPjXC+EllDNPtubaB8LiZYfC1iSccOIINfYvOdwPwJDRV089cFFZwQblwfUsUH4dc7+qm35dhiVMEA3jSN1Q3PnwT7O2fT8kC0hbAflt9NEjVfSiQor4Y5cSWyHYBh5WNraxIvL5zFrasOICl9H+5Xf9PYNW8HmElQTuJBVbJABuhyUy+uo82DfUDrs3B2DR6FG+7DdWrGUK1q+WkhNIIjIGSJyWESWi0i7iGwTkV0i8oCITPe1nSYix0Wky33dmla/DKNYotQH6xa10L+qnVkBK0e/zjnuKrZeGCEMqiHNRFp4O4ZcCvXgGlAStSMsbW0iu+Q0Zk1tQBj79RnS3PfcBngKzzuA+1R1vRvLsAr4ek7bDPCEql6UYn8MoyTiqA+S1DkPaLBHUaNA3xit4eDtGErBU8cl5XFUrmj5aiCV5YaIXIGjan3GPfQC8JD7+yFGCqIFwFQRedzdSZyfRr8MoxTiqA8Kqd5WLKUKA13dPqZ3GpC8cblWSPyvQkSmAH8DrPaOqepnVPUFEfk8cCVwr++yXmAzsBh4HLg75N4dIrK9q6uLTCZDNptNuvuGEUoc9UEcoZGUnruxhPu8fH1rxXMppUmpxuWxlq4km82SyWQA2kRku4h0BLWLHakcFxH5NnBAVf9aRH4G3IcjAH4A/D7wSVV9OuL684Ffqur4sDYWqWxUM/kiW/1eRsWSO6Gfs+n5goK9PGN1EkFuYYLl9Du7K+YmWy/FF9zxB6OBI9THgu0gX6RyGgLhn4B2YAA4E/gdcALYA3xUVQ8HXPMATortLwFfBS5V1YVhn2ECwRjtpDURF3Lf9ubGRCKGozyhkhIKuro9dkS416fnDvUVFcWcL13JaCafQEhcZaSqn1DVearaBjyJYzw+BZgDPOl6Ef2ViCxwf28BbgLeB7wGfAQnZ5JhjFlmjC9Nb5REQZmk0kd4huAgt9EkCvB4cRgd5zblbVsvJwVdsVHMtRaMlkuq0RWqerH76/dCmrS5P3uA96fZF8OoKqQOZxNdOLlpMPzqqUJX/UmW0PQHmiVVYtQTOHGE4ICGC7q42VFrLRgtl7H/hIZRhRx8qzBhEKSuCEq8NqlBmFgPx2Le/uXrWxNNludNxqUIA13dHnh9qcIlTN3kF6rvampk7+H+YUGIYzkYLRcTCIZRAcJWoXVAQ51wYnC4QTNoMgpLvDYr5N5+0kyJUcrkPX1dZyqR0kHeXUFC1T92AixrnzbqDcpxGNvOyIZRpYS5p266rIW7PxwvMrYUXXelUmLEIY4w8EauEBfeIBvEqkdfGyFU/SiODWIsuJ/mw3YIhlEBwiq0ecfjrEbDdhmTGoQjEZPcfZe1pLrazaeCSsK7SYEV8x3PoTjeR8LIyOXN3b0cKEB1N9ZrIYDtEAyjYnhJ8wZXt/Pi8nkFTzJBu4x6iBQGwIhc/kknhIti8cyJ7LxmbiLqKi8aOY730Q3zR7YppqbBWK6FALZDMIyqIU6pRn+bZe3T+OnuN4fe74uhLvKrlDrObUokUC4OW/YdG9pBlJpiOzfXU8vkOl47OjhipyA4wiDIu6hYN9Kx7H5qAsEwqoA4pRqD2mzsfGOYjSGOx5DffdKbLOMIhUIn8TCPIUg2xXau62zcQLQwldvkejhlUrhhfiy7n5rKyDCqgDilGuO0yaf+CfNYWreoZai6WxSHVrYXHBSXRCxCIcQNRAsz7H/vkhZeXD6P+y5rqalaCGA7BMOoCuJ4DMVpE6X+mRWihvJTL8E++56w8Vc2C9sBJBFNXQrf29E7TJ3mf/a4hv18aryxhO0QDKMKiJM2O06bdYtaRtQfXjG/ifsuc9QnVz/Yk9d9MsxIe9EZwRN8UD3k3GjqSjGIo1ZTgivXQX7Dfu75Wxaeys3bXh8zGVCDSDy5XTmw5HbGWCNOhs1is3AWc13Qqr/YjJ9JpbBIgmIT1I2VDKhlT25nGEbhxKm1UGw5xzi2Bz+/6R0ZJ+C/ZuXWHhrWOvWQG9Z2hursg3YQuRTq9bp45sSi1VHFegjFGcOxUEPBbAiGUSXEKdVYTDnHYiKa813jr+ngGXIhuHSlpz5aubWH7LO9w9JSP/bSkcBAtSiXUSguhXhcDyG/e2+Yx5E3HnG8xEYDtkMwjDFOIWU9vVVumCLZuyasRGW+0pXrFrXQv8rxZvIK2AQJg/bmRgZXt0e6jhYTUPfmiYG8K3hvcs+1P4R9lDcexezCqhETCIYxxrl8zpRYx3MnwiByXS7DUkUMaGGqkzAB8tyh/Kkt4kQo+zlwXCONzBA8uSsjVVu54zFWaiiYQDCMMc5Pd78Z63jQROjht1eErc7rYMTqOmzihWjBkg/Po6pYwlbwYZO4Qqj9ppBdWDVjAsEwxjhxV69h7QRGuGSGrc4n1lOQ6iRMsAjEMliXKhSCnjlsEq8XQuMRwoLcRlsQmwkEwxjjxF29FrLKDYt3OBKSPHTP4f7AXUKYYFEYUQJT1gQLiKC+LJ45cdhqPqxkadCzBU3uXj+8Xc/VD/YgOSqxYj3Aqo3U9jMicgbQBawGfg5sxKmtvAX4vKoO5rStA74LLAb2A8tUdWSBVsMwCuaWhacG+tD7V69B7YRwG8S6RS0jjL6eB1EQSXndeALi3q43ONKnnDm1gcvnTOGMKQ3sPdzPGVMamDd9/DDX2U+dPY27nu2lL6dvjULgCt4foVwXELntvfV7E402AeAnzR3CbTjBggB3A/cCrcD5wGd8ba8FznPPbwY2pNgvw6g60vRhj7t6XdraxLL2acOMpwps7Hwjdn+idP9BqqN8XklRvNl30kC8fkfvMLuF//2GX7/BoK9vIiN3Ad73cPWDzg7k3staRlwX57lGK6kIBBG5Audv6RlgArAQ2OruCh4DLvFdcgnwqHt+K7BQRCqbCMUwykSQm2OUIbYY4tZe+OnuN0e4nBYy4c3KY0T16+zjGI+T4MSg4tdmnRgcGVgW9D00h6ibconrTVTtwWuJCwQRmQL8DY6qCKAJZ+f5svv+JWCG77Jm3/k6YHrAvTtEZHtXVxeZTIZsNpts5w2jAlSTD3up7pNh+ncPv86+nMV5gsh9rrDvAamLfCaI501UDsEfRjabJZPJALSJyHYR6Qhql8YO4WvARlXd677vxdktnO6+PwM46LvmkO+8utcNQ1Wzqpppa2tj+/btdHQEPpNhjCqqyYe9VPdJTz0VZMQNsluEGZVbJpfH3yX3ucLG++BbA0MqNwiOR7h8zpS8K/9KCv6Ojg7c/G9dqppR1cDVdBqjPhe4TkS6gPcCfw5sAxa5xuMLgf/ru+Zh4CL3/MXANlU9mkLfDKPqqCYf9iTcJ5e2NrF/RdsIz59l7dNGqKrCPIR6jyevS2r0zeT+52qeUB943ZlTG4ZUbrq6nXsvaxlmj1nWPo2NnW/kXflXk+API3GBoKqfUNV5qtoGPIkjEJYD1wDPAf8G3CMiC0SkS0RagHuAf3fPX+22N4yaoJp82JNyn9zc3cvGzjeGuY6GGaf96Sx+09sXGiBXLDPGC/dc2hL6XJu7e/nd8ZE+s+PqRn4PfnvMT3e/GWvlX02CPwxLf20YVUCcesqjidkbdgWmwIiTfrpuTWdoLqVc6mGEoTiIOGmqw/o7Y7ywf0Vb5P3D+ivAYE4FumpIoZ0v/XX1iCbDqGHGgg97LqWoR6Kyi+bSNKGeKY3C3sP9NI8XDvc5nkN+JjbkV4SE2g9iqK7C+utf+Y+GCmwmEAzDSJy4k2QQQQFyQRx8a4D9Nwxfgd+87fWh7KTe1QfeGsgbFBenv2G7uLiBf97nV5MA8GOpKwzDCKVYv/lS7CJ+O0aYa2rQCvzF5fOYNbWh4FiKfP2Nchn1AvryGdBHA7ZDMAwjkFKKvpSqHsldSYfp3sOESzHqqnz9zecyGmRAX9gyadQJBTMqG4YRSCmG4aQpxOieRr+jDMdh6qZKjFM+zKhsGEZRVJPffJjuPUhQBOn0G8XJfVS3prMoY26UjaGaxqlUzIZgGEYg1e43H6bXB4bZIGaMF0SEA28NFJ0yIsrGUO3jVAgmEAzDCKRSAXNxDdlRev3c4LEp4+pHuKMWmjIiKmCvmgILS2X0iTDDMMpCJfzmCzFkl1oJrlCVTpjaqhzjVK7ARRMIhmGEUm6/+Xyr/lzixjqUEhMRlzTHqRRvr0IxlZFhGFVDIav5uKqaQlQ61VivoJxZUk0gGIZRNRRioC2kElycdpWsVxBFOb2YTGVkGEbVUEgaCIivqonTrhB1VTkph8rLw3YIhmFUDUml3y6Gao0nKKcXk+0QDMOoKiqVAK6cK/FCKKe3lwkEwzAMCldXlZNyCUlTGRmGYVBZdVW1YDsEwzAMl2qvV5A2qewQxOG7IrJPRB4XkVVu/WTvdUhE7vRdM01Ejue0uTWNvhmGYRjBpLVD+ATwceDdwAeBu1T1VHAmfuDfgNt812SAJ1T1opT6ZBiGYUSQlg1hF3A18AZwLvAfOeduBTar6g7fNQuAqe6OYpuInJ9S3wzDMIwAUhEIqrpTVR8D7gK+BawBEJGFwIeBbwZc1gtsBhYDjwN3+xuISIeIbO/q6iKTyZDNZtPofkUZi8+UFDY20dj4hFPrY5PNZslkMgBtIrJdRDqC2qVSMU1E3gn0q+peEWkDngHeCfwd8FtVvTnP9ecDv1TV8UHnx3LFtEwmw1h9tlKxsYnGxiccGxuHSlVM+2Pg4yJyCaBAPfB7OHaFy4MuEJEHgN8CX3LbhX57Tz/99H4R2ZN0p6uENhHpqnQnqhQbm2hsfMKxsXGYFXUyLYHw98B7cCb4Y8Cf4AiGSUC310hEFgCbcNRENwH3AK/h2CCuDbu5qr4tpX5XHBHZHiXBaxkbm2hsfMKxsYlHKgJBVX8H/OeAU+Jr9xTQ5r7tAd6fRn9GGbWt7IzGxiYaG59wbGxikIoNwTAMwxh9WOoKwzAMAzCBYBiGYbiYQEgJEfm6iHSLyNMi8gHfuQ+4x7tF5OvusToRWS8iu0TkFyIy1z3+KRH5jYi8KCL/S0TEPX6ziDwrIv8qIleW/QFLIM2xEZHTReTnItIjIs+IyDkVeMSSKHR83ONtIrJXRJZFtQ0by9FCymPTICLfc4/9WkSWlOu5qgZVtVfCLxyvqRdxvKo+CuzhpL1G3Pcfcc+/CCwClgNP4AjpzwM/B5qBo8AFwKnAy8DHgCuAx3DceS8C/neln7mKxuZW4AH3XuuAH1b6mcswPv8TJxvAALCs0LGs9DNX0dhcB/zSPX8V8GKln7ncL9shpMMlOHmZjgI/A2YC89xzZ7vvH3XP/6vb/hL32CCwFVgInAMMquoTqvo68DTwIeCTOJPhduB/4Ux8o4W0x+Y1YDwwAZjI8LQpo4GCx0dVv6KOK/benPvEHksRmViG50qCtMdGge+pIzUOUoPZoGvugctEM86KFVU9KiKHgBk55w6q6jH3/UvuuWbg/+Ucq8OJ46gTkQ/huOVeALwCvANoAZYAnwJ+ICJnuH/I1U7aY/Md4IvAAaCP0efKXMz4hN0n7lhOx4kXqnZSHRtVvQdARM4F7mR0LbQSwXYI6XAIOB1ARCbh/MMdzDnXnLMqO8M9N3SNe0xx8jtdg/OHuRF4HngVJ2ngFlV9FfgBjnA4JdUnSo60x+Yu4MfANOCr7u+jiWLGJ+w+hYzlaCDtsUFEPg38ArhTVf8q8SeockwgpMPDwPvdP7iLgH3Ac+65bvf9Re759wH/173mIhGpAy4GtuGscL8ELMVJ+fF24CfAo8AfiMhU4EKcFfL+cjxYAqQ9Nr8HHFXVPuAw0FSWp0qOYsYniNhj6apNRgOpjo2ILMZZYFyqqn+T2lNUM5U2YozVF/ANnD/WZ4AP4Kxi/8o99wH3+HPAN9xjdcAdOCvdJ4B57vHrgGeBLuCz7rFxONlgdwO/Bi6s9PNW0dici2MY3OeOzeJKP2/a45Nz3W5cw2mhYzlaXimPzV3A79y/py6gq9LPW+6XRSobhmEYgKmMDMMwDBcTCIZhGAZgAsEwDMNwMYFgGIZhACYQDMMwDBcTCEbFEJFHRKRLRHrdV5eIPFLpfvkRkStF5OKE7zlBRH4rIs1J3jcpRGSziFxV6X4Y5cXcTo2KIyL3AKhqaNnUSuL27yVV/YsE7/kF4DxV/WxS90wSEXkvjl/+u9UmiZrBdghG1eGmaP6OiOx2UxH/oXv8Z+7K9VkR2SMiXxCRJ0XkeRG53m2zW0Q2uWmxd4nIp9zjZ7nX7xWRh9w02bNE5Iib8vgJETlHRLaKSKeIbBeRj4nI53HKwa4QkS+LyD0i8k33nheKyL6cz82KyHMi8s6g/vtYBtzv3mO3iPwfEXlBRLaIyMyAMTlLRB510zL/UkQucft/1H3eF9yxONdt/wU3vXOniDwgIjPc9M7fFSdd+K9E5KthY6OqT+Kkhhh16cON4jGBYFQj13Ayo+mVwD+IyDvccwrMx0nfcT3wQeDbwI051x8HWoGV7rXTgQ3AvwCzcCJ0v+u2nQR0q+oFwLuBTarajpP24EZV/S5OSoz1qnprnn5Px4mU/lBE/xERAdqB37iHZgPfVtV34iTo+3bAvT+Kk6xtMfAXwGnu8YnAVvfah4E73fvPBf7YHat3A5cCnwHe6372ZTiZTt8RMTYvYAKhprBsp0Y1sgB4F05Ka3BSfbe5vz+qqioiewFV1RPu7xNyrn9AVftFZAuOAJnr3vNMHCHSiCM0PDa6P18HPidOYZS3k///w7+g+oHbn7D+v+q+PwVHEPXgJCbsVdVH3XP/AnzNvccm99hPcPL6vx14CKcOxtqcz/1JzrV/ipPPfz/w15zMcdUInA/8Qp3cRUdxhAzuZwWNzSvAnDxjYIwhbIdgVCO/BnYCv4+TeOxOnJz14Ezw+H7367ivFJFGnFV6A85K99fA36pqG04OpNzkZW+4P7+NI3A+DXRyUiAM4tRYACcnTqv7+4W+z/XuE9V/cCbpNzm5ym+Sk9W5LsHJofOUqra5r6/i7DT6VPU84Gu+/n8y91qcXcr/AK4F/gTn/7zB7dOFIjJORM521W7TIsbmNJxCMkaNYDsEoxq5CzgPJyvlfpyiJUcdTUgsGoEdwGRgharuF5HlQFZE/hSnQtaKkM/9UxHpAJ4C5ovIBcAWYJ2IvApsBn4qIr/FUa/E7r930t3hdAHvxFmN7weuF5Hvub//ccA9/x/wKRHpxMni+pWcc+8Vkf/u3utaYBdO1bhf4eT6fxpYBbwHZ5fQiZPu+b+r6hsRY3OW29aoEczLyBhTiMhunMynWyrdlyjcyfds4PvAfao6wpAc4x6zcCbwBlUdSLh/7wPuUNXzk7yvUd2YysgwKsN6nIp31cqNwDcr3QmjvNgOwTAMwwBsh2AYhmG4mEAwDMMwABMIhmEYhosJBMMwDAMwgWAYhmG4/H+XMxYy1l9jaAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "posterior_samples = results_ref['samples']\n",
     "\n",
@@ -569,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -579,22 +422,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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E7WDnG6tarWiK0tzuVrV6y/RSvnn2ZKrD5cyfaKtaGTOQRMl/naouSFkkxgxBVfn7/i5v7L6FJ7e30ROHicX5XFrlVrW6eE6ICbaqlTFDSpT87Ypbk3adPXEe39bmlVJoYcNBd7L2hMoiPnNaBdXhchbZqlbGDFui5L9DRGaq6tbDN4jIDOAjqnpjcKGZXLWztYcVTW7N2pWbWol2xynOFy6YVcanTnUJf/ZYO1lrzEgkSv5/AZ4UkR3AemA7MA2YizsfcHvw4ZlcEFflhd0dfcM59bvcydqZoQLee9w4qsMhLphVRukYO1lrTLIMmvxV9bsi8iPcKl4Lcat3vQTcA6xS1fbUhGiyUUtXjIe8Va2WN0bZ2eYKpS2aVsLXFk+iOlzOiZW2qpUxQUl4ha+qdgErvH/GjMiGA119i5w8tq2NrpgyrjCPS6pCVIdDXDInxKRSK5RmTCrY/zQTmO6Y8tT2tr7KmOu8Va2Om1DIx06eSE04xOJppYyxQmnGpJwlf5NUze3eqlYbozywKcpBb1Wr82aUcsOJE6gOl3PUeFvVyph0GzL5i0g5cDNwLPAD4FlV3Rt0YCYzqCp/a+7sG855Zodb1WpqaQH/csxYqsMh3ja7jPJCm3tvzGjip+dfB7Thkn8BcAdweZBBmdGtrTvOI16htOWNLWyN9gAQmVLMTYvcqlanTLZCacaMZn6S/yLgKOBlVf2ziHwz4JjMKLT5UHdf7/6RLa10xJTQmDwumlPGzeEQl1WVM9VWtTImY/j539oMXAHkicilQGewIZnRIBZXntnZzrKNLSxvivJys3vbjxo3hg+dMIGauSHOmV5KkRVKMyYj+Un+1wO/BaqAnwBXBRmQSZ/9Ha5Q2rLGKPc1RdnXEaMgD86ZXsrt50yhJhxi3gQrlGZMNhgy+avqGhE5FhgH7FPVePBhmVRQVRr2dbGs0ZVSeGp7GzGFypJ8t2ZtuJyL5pQxzla1Mibr+Jntcy1wuapeISIviMinVPWRFMRmAtDRE+exrW19Cb/xkCuUdvKkYj5/eiU14RCnT7FCacZkOz/DPh8EPuDd/hSwFLDkn0G2R7v7yig8uDlKW49SUiC8bXYZnz/drWo1s9wKpRmTS/wk/ynAOgBVfURE/ifYkMxIxVWp39XR17t/frcrlDanfAzvWzCemnCI82aVUWIna43JWX6S/2rgtyLyOK7I24uBRmSOyKHOGCs3t7K8sYUVTVF2t8XIE1g8rYSvv2Uy1eEQCyusUJoxxvGT/D8EfA5YAqwFvhZoRMa31/d39q1Z+/i2VrrjMKHIFUqrCZdz8ZwyKkps7r0x5h/5yQydwHJgJTAGuBs379+kWFdMeWJbq7eqVZTXD7hCaQsrivj3UyuoDoc4a1opBXay1hgzBD/J/07gn4BuXPJ/JsB4zGF2t/WwojHK8qYWHtjUSktXnKJ84fyZpXz8lIlUV4WoGmeF0owxw+Mn+Z8EzABuA76Cq+1jAqKqvLjHW9VqYwvP7epAgellBVw9byzV4XIunF1Gma1qZYwZAT/JvxvIByap6nYRmRtwTDmntTvurWrlZudsb3WrWp0xtYRbzppETbickybZyVpjTPL4Sf5/AW4BmkRkJ/BysCHlhsaDXd7YfQurtrbRGVPGFuZx8Ry3qtWlVSEm26pWxpiA+Cnv8GWvpn8X8DjwdOBRZaGeuLJ6e1vfydpX97lCafMmFPKRk9wiJ2dPL6XQVrUyxqSAn/IOM4BvAScCDbie/56A48oKe9t7uH9TK8s2tnD/pigHOuOMyYNzZ5bxwePHUx0OccyEonSHaYzJQX5n++zEDf0sAX4JnBVgTBlLVXllb2ffcM7TO9qJK0wuzecdR7lVrZbMLmOsFUozxqSZn+Q/V1WXeLd/IyLrgwwo07T3xHm0b1WrKJtbXKG00yYX86UzKqkJl3PaFFvVyhgzuvhJ/g0ico6qPiEiFwDPBR3UaLe1pbuvd//wllbae5SyMcKS2SH+40xXKG16yAqlGWNGLz/JfwGwSkQOABOAZhHZBKCqcwKMbdSIxZW/7mzvW7P2JW9Vq/DYMVx3/ARqwiHOnWGrWhljMoef5P++oIMYjQ50xFi5+Y1VrZrbY+QLnD29lG+ePZmaueUcZ6taGWMylJ/kfwCYDQjwZeArqvrnIINKB1Xltf1vrGr1xDa3qlVFcT6XVoWoCYe4aE6ICcV2stYYk/n8JP//Br4PfBo30+czQFYk/86eOI9va+sbztlw0J2sPbGyiM9FKqkOhzhzqq1qZYzJPn6SfwWuouctqvptb1nHjLWztYcVTS0s2xjlwc2tRLvjFOcLF84u49OnVXBZVTmzx9rJWmNMdvOT/A8BvwEeEJHrge1+DiwiS4F3Aa3AR1V1db9ti4HvAWXAr1R16fDC9i+uyvO7O1i2sYXlTVHqd7lVrWaVF/De48ZRMzfE+TPLKLVCacaYHOIn+X8Cl8S/BVyNW9glIRG5EHeieAFwPnCPiFSpqoo7Q3oPcD3wGPCqiDye7EXhn9zWxh2vHmB5Ywu72mIIcNa0Em5d7Fa1OqHSCqUZY3LXkN1dVX1OVT8FvENVv62qL/g47hLgaVVtAx4FZgHzvG3HevdXeduf9fbvIyK1IlLf0NBAJBKhrq5uGE1y/rqrnd+/fojzZpZx98XT2f2heTx1VZgvnFHJiZOKLfEbY7JOXV0dkUgEYL6I1ItI7WD7iqr6OqiIrFXVhT73rQMOqeqnvft7gber6mpvyOfPqlrpbftPoFxV/yHISCSi9fX1vuI7XFt3nDF5whgrlGaMyTEiskZVI4n2Gc5A93Cy6H7cAjCISCnu4rB9/bZNFJES7/7MftuSpnRMniV+Y4wZxHCS/xeGse9KYJGX4M8DtgCvedvWeffP87afCTw4jGP7diTDRZks19oL1uZcYW1OviGTv4iMFZFbgQ+KyLdEpGKo56jqw8DPgReBr+JOFN8pIreqG2e6Gviat/0ub/+ky7U/mFxrL1ibc4W1OfmGHPMXkd8DE4H7gAuBuKpeGmhUb7z2HmDTCA4xH7cGQa7ItfaCtTlXWJuHZ46qTkq0g5/kvwE42pummQdsUNXwEQaUUiJSP9RJj2ySa+0Fa3OusDYnn58x/+1A76Ltx+DG7DNFrn1XzLX2grU5V1ibk8xPz/854ARgLbAQWA/sBVDVc4MMzhhjTDD8XOH7vcCjMMYYk1K+L/IyxhiTPbKimpmILBWRdSKyxruCuP+2xd7j67xic1lhiDafLSLPi8jrIvJzESlKV5zJlKjN/fa5V0SeSHVsQRnifT5KRFaJyGsicoeIlKUrzmQZor0fE5GtIrJNRG5LV4xBEJGZIvKCiNw0wLZgcpiqZvQ/3PTTJqAUqMZNDe39RiPe/Uu97U3ABemOOcg2e9t725wPvAC8L90xB91mb58rgIPAE+mON0Xv8/PePnnAT4FL0h1zUO0FQkAMOANXPaAbmJ/umJPU7g8De4Au4KbDtgWWw7Kh5z+iInIZatA2i0g5sBp4UlVjuJLcfs7tjHaJ3mdEJATcBnwzPeEFItH7PBc3EePduKvnu4CkVsZNg0TvcSduVcEyXBKM4UrFZDxV/aG6OflPDbA5sByWDcl/IrANwPvl7MctQNO7bZ+qtnv3t/bblskGbbOqtqjq1UCbd2X2POAP6Qo0iRK9zwC34Hq/W1MfWmAStXkq7kP9eeCtuDIq7099iEmV6O+6G7gdeAh4BbgT2JWWKFMrsByWEck/0XgYby4iNwv3y/p0v20TReQhEVkPvBPXE850iQrnISLjcWslVAOLVTXphfPSYNA2i8iJuCGD76YtumAkep8Pej9/qao7cL3+E1MeYXIleo8jwOdx1xxNAy4A/iU9YaZUYIUwR33yF5EP48atBysn3VtE7kpcjwDe+I+xDmjD/bJOwH1iTg4u2pRJVDgP4E+4HtTpqtqY+vACkajNC3CJ4mXc0M9pIpINHwRDFUjcBVSLSAHwFuCldASZRInaOxaIA1Hc/+keYHzqQ0y54AphpvtkxzBOijyKdzIEOA2ox50I+TXwddwfyfPAMtyFaLd6++4FXve2L8eNKaa9PUn4fdzcr82LgbuAW3HjgwpsxNUFaQA+ku54g2zzYftcQ5ac8B2qzbje7ytAI/AzYEy64w24vV/FDXts9dpbmO54k9z2/jmuf7sXe7+P14Cbk/V6GTPPX0QexZ30uFlEGnFDO/cC/w+3cMwnvP3uAFDV93v3u4BTVfUVEXk78J+qOm+AlzDGmJwx6od9Dicik4AqXC/3FdzXw2MTPKVvHJGAFo4xxphMk4lTAJuBncC/q+pyEbkCN+1rMCtxX48fwC0mH8jCMcYYk0kyruevbpzqKuA2EXkN+CBu7utg/h041ZvtI7gTgsYYk9MyZszfGGNM8mRcz384RKQ23TGkUq61F6zNucLanHxZnfyBXPuDybX2QpLb7BXQ+qOINIjIdhHp8m43iMj0ZL7WCNQCiMhJIjKiq3pFZLyIbPAuqhrN7G87yUb1sE9lZaVWVVUd8fMbGhqYP39+8gIa5XKtvWBtzhXW5uFZs2ZNsw6xhm9gs31EZCbwF+BeVb35sG2LcYvElAG/UtWlAx2jqqqK+vr6I44hEomM6PmZJtfaC9bmXGFtHh4R2TTUPoEM+yQqySAiAtwDfAk4FXifiFwQRBy1tbn1TTHX2gvW5lxhbU6+QId9+l+V2++x44BXgTJVbReRXwMbVfULhz8/Eolorn3aG2PMSInIGlWNJNrHd89fRJJVEG3IEqUiUisi9Q0NDUQiEerqAl3E3hhjRpWL/7CJr/11z7CfV1dXRyQSAZgvIvWJZgwNOuYvIsXADbjFIuYDpSLSjqsydydQp6qdw46uX4lS7wNgJq4wVR9VrQPqrOdvjMlFq3e0s6Bi+Kuv1tbWUltbi4g0jKTn/wpwFPA54HigBDeG/1nch8Ergz81oeBKlBpjTIbr6IkT7Y4zqSQ/0NdJNNvnNFU9CCAil+O+AfxeVR8GHhaRccN5IRG5C9imqjeKyNXA93Gzfe7yjmmMMTmvud2VKqssCbb02qBH75f4lwL/jFtMoV1EPqKqH+vdnoiqnt/v9jX9bq/GzfQxxhjTT2/yD7rn7+eE73uAs4Coqn4bV0LZGGNMAPa09wBQOQqSv+ItlyYiU4BQkAEZY0wuS/uwTz9fwa2NWoRbEvCLgUZkjDE5rLkjNcM+QyZ/Vb1bRFYDpwBrVbUh0IiMMSaH7WnvQYAJRWlK/iLy0wEevlREUNVrA4zJGGNyVnN7jIqSfPLzJNDXSTTmv837NxU3M2cfrvdfkeA5xhhjRqC5PUZlcbC9fkg81fPLACLyDHCuqh4SkULgmcCjMsaYHLWnvSfwmT7gb7bPLNzatwBTcN8EjDHGBKC5PcakgGf6gL/k/3vgdRFZBtQDvwg2JGOMyV2723sCn+kD/mb7fExEVgLHAT9Q1fsCj8oYY3JQT1zZ0xZjWlnwPf8hX0FE+q/CtUhEFqnqTQHGZIwxOWlPew8KTB0NyR835g9uiCgC7AouHGOMyV07W11ph6mloyD5q+oHem+LSBmwMtCIjDEmR/Ul/9HQ8xeRcL+7xwHHBBeOMcbkrp1trrTDqOj5Axtwxd0E2AjYeL8xxgSgt+c/ZTT0/IF5qrq+985h3wSMMcYkyc62HsYV5lFS4Ht59SOWqLbPONxi6/eJyBJcz78IeAi37q4xxpgk2tnak5Lxfkh8kdfHcUM+R+GGezbg1u19PgVxGWNMztnZ1pOS8X5IkPxV9RZVzQMeVtU871+Bql6eksiMMSbH7GxLXc8/0bDPlar6B+AeEXl//22qekfgkRljTI7Z2drD1DlpTv7A5cAfgPce9rgClvyNMSaJ2rrjHOqKMyVFwz6JXuX9IiLAhSmJxBhjctjWaDcAM8vTn/xjuF5+f+I9FnzJOWOMySFbo26O/8zQmJS8XqLkX5WSCIwxxrClxfX8Z5WnJvknmu2zWVU3474BfB1YAdyO9fqNMSbpepP/zFD65/n3uhP3AXALcAj4ZZABGWNMLtoadcs3Fqfg6l7wV95hrqou8W7/RkTWJ9zbGGPMsG1p6WZWisb7wV/Pv0FEzgEQkQuA54INyRhjcs+WaDezUjTTB/z1/BcAq0TkADABaBaRTQCqOifA2IwxJmdsbenmnOmlKXs9P8n/fUEHYYwxuay1O87+znjKZvqAv+S/FbgCKO59QFVvCywiY4zJMame5gn+kv/d3s/NuAu8Dr/wyxhjzAhs7k3+KZrmCf6S/3hVXRh4JMYYk6M2HOgC4KjxhSl7TT/Jf5mIfAZ4Fq/Xr6pPBBqVMcbkkI2HuinKF6alqJwz+Ev+xwFvA/Z69xWwWT7GGJMkGw92MXfcGPJEUvaafpL/yUCFqnYEHIsxxuSkDQe6mDsudUM+4O8irweAxdJP0EEZY0yuUFU2Huoelcn/PbhF23twNX56/BxYRJaKyDoRWSMiiw/b9l8isl1EGrx/s4cduTHGZIHm9hgtXXGOGpe6aZ7gb9hn/mH3q4Z6gohciLs4bAFwPm4pyCpV7Z0mejrwblVd5TtSY4zJQhsPupk+o67n75V1bsat6PVr4FEfx10CPK2qbd7+s4B5ACKSD5wCXOt9M/imDSUZY3LVxoNujv/cFPf8EyZ/EYmIyP8AO4EfAr8HjvZx3InANgDvA2A/UOFtqwD+CNwE1ADX4NYL7v+6tSJS39DQQCQSoa6uzn+LjDEmg7y2vxMhOT3/uro6IpEIwHwRqReR2sH2lTdGYg7bIPICMAn4X+AXwJ9Uda6fAETkG8BsVb1aREqBKLBAVdcNsO+fgGcGKhkRiUS0vr7ez0saY0xGumr5Vup3t7Ph/cck7ZgiskZVI4n2SdTzr8QN92wDdjC8sg4rgUUiUgKcB2wBXvOCiohIVETmiMhM4DTAMrwxJic17O9k/sSilL9uohO+c4BLgOuA24C4iLwDuF9V2xMdVFUfFpGfAy8CrcDVwJ0isk1VbxSR23FXDPcAP1XVB0feFGOMySyxuPL3/V1cPCeU8tceNPmrahy3bu8KEZmKm73zDVyhtyEjVdWbcOP6vVb327YUWHokARtjTLZoPNRNZ0yZPzG1M30gwbCPiNzqDcugqjtV9euqOg+oEZEZInJryqI0xpgs1LCvE4D5E0bXsM9O4EkR2QGsB7YD04C5wGzg9uDDM8aY7NWX/EfTmL+q/reI/BhX1G0h7hzAS8A9wKqhxv2NMcYktnZvJ1NLCxhfnJ/y1054ha+qduGN+6cmHGOMyR0v7OnglMnFQ+8YAD+1fYwxxiRZR0+cV/d2csokS/7GGJMzXtnbSUwZvT1/ESn3qnAuF5HLRKRiqOcYY4xJ7IXdbomU0dzzrwPGAcfizhHcEWhExhiTA17Y08G4wryUF3Tr5Sf5LwI+CHSq6p/xqnMaY4w5cs/v7uDkScWkq6ixn+TfDFwB5InIpUBnsCEZY0x2a++J8/zudhZNK0lbDH4Wc7ke+C1uEZefAFcFGZAxxmS7+l3tdMfhLdNL0xbDkMlfVdeIyLG4cf99Xs0fY4wxR+ip7e4a2bPS2PP3M9vnWuB3qtoMrBGRC4IPyxhjstfqHW0cO6GQyhI/gy/B8DPm/0HgC97tT2HVOI0x5ojFVVm9vT2tQz7gL/lPAdYBqOojuOJuxhhjjsCLezrY2xHjvJnpTf5+vnOsBn4rIo/jiry9GGhExhiTxR5oagVgyezUL+DSn5+e/4eAV4El3s9rA43IGGOy2AObopxUWcTUsvSN94O/5N8JLMet4vUAbiUvY4wxwxTtirN6R1talm08nJ+PnjuBfwK6gTHAMwHGY4wxWev+TVG643BJVfqTv5+e/0nADNwiLsfgFl03xhgzTL/9+yEmleRzzoz0nuwFf8m/G8gHJqnqdtwyjsYYY4ahrTvO8qYWrjx6LAV56ann05+f5P8X4BagSUR2Ak2BRmSMMVnovqYord3KO48pT3cogL/yDl8WkXKgC3gceDrwqIwxJsv8dO0BZoQKOHdmWbpDAfyVd5gB/A+wBvi/QPrPVBhjTAbZdKiL+5uifGDh+FEx5AP+hn3uBGK4oZ8DwC8DjMcYY7LOT145AMC1C8enNY7+/Ez1nKuqS7zbvxGR9UEGZIwx2aSlK8YPXtrH2+eGmDO2MN3h9PHT828QkXMAvIqezwUbkjHGZI8f/20/+zvjfPGMSekO5U389PwXAKtE5AAwAWgWkU0AqjonwNiMMSajHeyM8a01e3nb7DLOmJq+2v0D8ZP83xd0EMYYk42+8uwemttjfP0tk9Mdyj/wM+xzABiLW8nrP4Hxqvq4qj4eZGDGGJPJ/rang+++uI8PLBzPaVNGV68f/CX//waKgS/iZvp8JtCIjDEmw3X0xHn3/duoKM7ntlHY6wd/yb8CWAmMVdVv48b9jTHGDEBVueGRHazd28mdF81gUml6SzcPxk/yPwT8BnhARK4HtgcbkjHGZK5bnm3mzlcPctOZlaOieudg/CT/TwBrcfX8S4DPBRmQMcZkIlXlxqd2sfSZPVwzfxw3LRpdUzsP56e2z3PAcyJygzfsY4wxpp/9HTGufXA7f9zQwodOmMAPzp+KyOgo4zAYPz3/Xv8WWBTGGJOBVJX/XXeQ4+/ewF8aW/jWOZP50QVTyR8l9XsSGc6ZiNHfGmOMSYG27ji/+vtBvvPCPl5u7uS0ycXc+/ZZnD7KLuRKZDjJ/wvDObCILAXeBbQCH1XV1f22LQa+B5QBv1LVpcM5tjHGpFJclXX7uli9o40VjVHu3xSlvUc5sbKIn188nfccN468UT7Mc7ghk7+IjAU+D5woImcDX1fVvUM850LclcELgPOBe0SkSlVV3EDYPcD1wGPAqyLyuKo+MrKmGGPM8Kgq3XGIdsfZ295Dc3uMvR0xmttjbGrpZsPBLtYf6OLVvZ0c7IoDML2sgA8sHM87jxnLW2eUjvqx/cH46fnfAUwE7gMuBH4BXDrEc5YAT6tqm4g8CswC5gGvAcd691eparuIPOvtn9Tk/4f1h7jz1QMJ91Ed+jg+dknicYbeK6XxJO21fLRrtL0Xvl4r8V6p/dtJ1nGS9Dfo67VSe5yumNLeE6cjpnT0/Rz8r1OAWeVjOHr8GN593DjOnFrCoqklzJtQmLEJvz8/yf9k4Giv1347sMHHcyYC2wC8D4D9uIvFerftU9V27/7WftsAEJFaoLa0tJRIJEJtbS21tbU+XvYNLV1xtrYMvda8n/fQz9uctOP4eq2h90ppzEk7TpLa5WN7UmIWGfI44iPi1P7tJOs4mfc3WJQvFOfnUVwgFOcLxQV53k+hrCCPipJ8KovzqSwpoKIkn+llBRQXDGdOTPrV1dVRV1cHMF9E6oE6Va0baF8Z6pNeRJ4A3qeqG0TkWOA7qpqw5y8i3wBmq+rVIlIKRIEFqrpORObjrhso83r+vwYaVfXzhx8nEolofX390C02xhjTR0TWqGok0T5+PtaKgbUisgZ4CZglIo+JyGMJnrMSWCQiJcB5wBbckA/AOu/+ed72M4EHfcRhjDEmSfwk/+8BtcB3vZ/fBH7q/RuQqj4M/Bx4EfgqcDVwp4jcqu6rxtXA17ztd3n7J5339Sdn5Fp7wdqcK6zNyTfksE86jXTYJxKJkEvDRrnWXrA25wpr8/D4GfYZ1clfRPYAm0ZwiPlAQ5LCyQS51l6wNucKa/PwzFHVhMWFRnXyHykRqR/q0y+b5Fp7wdqcK6zNyZdZ85iGL9cGCnOtvWBtzhXW5iTL6p6/McaYgWV7z98YY8wAsiL5i8hSEVknImu8onH9ty32Hl/nFZvLCkO0+WwReV5EXheRn4tIUbriTKZEbe63z73ehYlZYYj3+SgRWSUir4nIHSJSlq44k2WI9n5MRLaKyDYRuS1dMQZBRGaKyAsictMA24LJYaqa0f9w9YaagFKgGjc7qHc4S7z7l3rbm4AL0h1zkG32tve2OR94AXeFdtrjDrLN3j5XAAeBJ9Idb4re5+e9ffJw191cku6Yg2ovEAJiwBnADKAbmJ/umJPU7g8De4Au4KbDtgWWw7Kh599XRA7oX0QO3lxErg3oLSKX6QZts4iUA6uBJ1U1hluDeXSuID08id5nRCQE3Ia7CDFbJHqf5wInAO/GXT3fRZKLI6ZBove4EziAKwNfivsg2J+GGJNOVX+oblrmUwNsDiyHZUPyf1MROdwfhO8ichlq0DaraouqXg20icituP88f0hXoEmU6H0GuAXX+92a+tACk6jNU3Ef6s8Db8WVUXl/6kNMqkR/193A7cBDwCvAncCutESZWoHlsGxI/vtxXwPxishNAPb12zbRqyEEMLPftkyWqM2IyHjcWgnVwGJVzeo2i8iJuCGD76YtumAkep8Pej9/qao7cL3+E1MeYXIleo8juHVF5gLTgAuAf0lPmCkVWA7LhuSfi0XkErUZ4E+4HtTpqtqY+vACkajNC3CJ4mXc0M9pIpINHwRD/W3vAqpFpAB4C67wYiZL1N6xQBxXIbgN6AHGpz7ElAsuh6X7ZEeSTpjcjPsjeR5YDNwF3OptW+w9/hpwc7pjDbrNuPFBBTbiLg1vAD6S7niDfp/77XMNWXLCd6g243q/rwCNwM+AMemON+D2fhU37LHVa29huuNNctsfxTvhm4ocZhd5GWNMDsqGYR9jjDHDZMnfGGNykCV/Y4zJQZb8jTEmB1nyN8aYHGTJ3xgfvMJa873bF4mIisgnk3Tsm0TkbhGZLiINIpIvIieJyLCu2BWRLSJy7iDbPigi30tGvCY7WPI3ZggiUgPsV9XeJfU+CDzg/UwaVd2uqvPV1WQ6GfhAEg//C+BKEZmRxGOaDGbJ36SViFzTvwSz16M+aoD9vuiVvG0QkZ+KSKFXxvheEVkrIk0i8nVv3xkiskxEXhWRF0XkWu/xxd4x1ovIchE51nv8syKy0SuB/eEBwrwG+L237yTgMlwdnQm9ZYe93vtKEXlcRPZ48a4UkQ0i8iNvnwHj7dfGOV77z+SNK5V/N9jvSETmisjTXuy/Aoq97aUicqf3Gi+JyNnqasM8ArzjyN4pk20s+ZtM8SngR7iSvo24wmYAVbjSBhHgehG5BDgeeFRVF3jP+ZyICPBr3FWTR+MukX+7iJwH/BtwKu5KyptF5JTDXnshsN67fQ3woLp6Ov/Lm3v/c4GLgM/irlR9P64U7/Ui0luMa6B4D1cPfAFYo6qJ6td8C2hQ1bnAj4FK7/HPAuXAMcAngN+JSDHuqu+FCY5nckg2lPo1WcJL0L2378IlenBF264CbgC+BDyDq+0CLskf8J7zNK642cO4XvM9QBEwBpcYZ+KqQqKq3/Ge8xlcjZinveN1e8d4oV9oc4Dt3u3rcD3+RqAQGC8iH/e2PaOqHSKyGdiqqtu8pAter3yQeI/odwQch/uQQVVXiche7/HTgdOAv3n3872278DVhjHGev4m7RqBo73iZH0nK1X1Gm/8ez7QjEv8N+J6zvOAf/Z2vVBEJniVTCO4WkZfArapK239BK6TsxdXCG2JOD8RkS/gauPsBhbhEvGPceP5h8c4TUTOAaYAM1U1rKozcItrvLs37H7P0QEeGyzegcRxH1yD/o6AtbhvL0UicjpvlPp9hTeqfL4N+B6u1z8NtzCIMZb8Tdo9iUuATcB7cYt0vImqduGS+C9wPfIm4Lfe5m24IZxXgN8Ay7z9akTkFdwQUCnuw+Iq4Iu4AlllwA9V9T7gHlwvud69nO48LIRXcUM61wF/VldbvtfvGN6J34HiHcizQJWI/I7Bf0efwRXyWw98mTcS+624D7z1uLUc/qaqceAory3GWGE3k7lE5A7c8Mp/BPw6VwIfUtWLR3iclMQ7yGuXAK8DkQE+3EwOsp6/MUP7IzCpd55/hnov8AdL/KaX9fyNMSYHWc/fGGNykCV/Y4zJQZb8jTEmB1nyN8aYHGTJ3xhjcpAlf2OMyUH/H1nlEWXyb6G+AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.subplot(2, 1, 1)\n",
     "plt.plot(uguess, pguess[:,0])\n",
@@ -615,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -624,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -633,17 +463,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████| 6410/6410 [00:01<00:00, 4262.71it/s]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "nparams = len(parameters)\n",
     "u = np.ones(nparams) * 0.5\n",
@@ -678,22 +500,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "weights = results_ref['weighted_samples']['weights']\n",
     "i = np.random.choice(len(weights), p=weights, size=1000)\n",
@@ -729,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -746,26 +555,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# weight logl Temperature Amplitude\r\n",
-      "0.000156 0.000000 0.009827 0.584641\r\n",
-      "0.000156 0.000000 0.009769 0.585080\r\n",
-      "0.000156 0.000000 0.009959 0.581489\r\n",
-      "0.000156 0.000000 0.009796 0.584856\r\n",
-      "0.000156 0.000000 0.009792 0.584156\r\n",
-      "0.000156 0.000000 0.009872 0.583356\r\n",
-      "0.000156 0.000000 0.009916 0.582792\r\n",
-      "0.000156 0.000000 0.009762 0.584928\r\n",
-      "0.000156 0.000000 0.010057 0.582260\r\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "!head custom-weighted_post_untransformed.txt"
    ]

From 769d2e65a83258ee30e3de096dc4ed7378ee77ed Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 6 Sep 2022 12:37:07 +0200
Subject: [PATCH 086/313] =?UTF-8?q?Bump=20version:=203.5.1=20=E2=86=92=203?=
 =?UTF-8?q?.5.2?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 66a41db7..43eff2cf 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.5.1',
+    version='3.5.2',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 18189a1b..d0f58b70 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.5.1'
+__version__ = '3.5.2'

From e774f5e1f7443b6cafd854a82d1f83c140a6cdd2 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 6 Sep 2022 14:07:44 +0200
Subject: [PATCH 087/313] [docs] satisfy deprecation warnings

---
 docs/conf.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 8f266cd4..f6640066 100755
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -60,7 +60,7 @@
 
 # General information about the project.
 project = u'UltraNest'
-copyright = u"2014-2020, Johannes Buchner"
+copyright = u"2014-2022, Johannes Buchner"
 author = u"Johannes Buchner"
 
 # The version info for the project you're documenting, acts as replacement
@@ -77,7 +77,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = 'en'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -118,12 +118,13 @@
 #
 html_theme = "sphinx_rtd_theme"
 
+html_baseurl = 'https://johannesbuchner.github.io/UltraNest/'
+
 # Theme options are theme-specific and customize the look and feel of a
 # theme further.  For a list of options available for each theme, see the
 # documentation.
 #
 html_theme_options = {
-    'canonical_url': 'https://johannesbuchner.github.io/UltraNest/',
     'style_external_links': True,
     # 'vcs_pageview_mode': 'edit',
     'style_nav_header_background': '#2980B9',

From 50501eb4098cd5352672f523111d71207fe4bffe Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 6 Sep 2022 15:12:02 +0200
Subject: [PATCH 088/313] typos and doc improvements

---
 docs/example-warmstart.ipynb | 24 ++++++++++++++----------
 ultranest/hotstart.py        | 24 +++++++++++-------------
 ultranest/integrator.py      | 11 ++++-------
 3 files changed, 29 insertions(+), 30 deletions(-)

diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index bd04005d..579e2c54 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tutorial: warm start from a similar fit\n",
+    "# warm starting\n",
     "\n",
     "In this tutorial you will learn:\n",
     "\n",
@@ -57,7 +57,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Generate some data"
+    "### Generate some data"
    ]
   },
   {
@@ -90,7 +90,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Visualise the data\n",
+    "### Visualise the data\n",
     "\n",
     "Lets plot the data first to see what is going on:\n",
     "\n"
@@ -113,7 +113,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Prior"
+    "### Prior"
    ]
   },
   {
@@ -162,7 +162,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# First simple model"
+    "## First simple model\n",
+    "\n",
+    "Here is a typical gaussian likelihood with our black body function:"
    ]
   },
   {
@@ -195,7 +197,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plot the fit"
+    "### Plot the fit"
    ]
   },
   {
@@ -220,7 +222,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Warm Start with Model modification\n",
+    "## Warm starting a modified model\n",
     "\n",
     "Lets say we alter our model slightly. We include a small constant background:"
    ]
@@ -241,7 +243,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have the same parameters, and expect results to only be slightly different. So lets use **warm start**."
+    "We have the same parameters, and expect results to be only mildly different. So lets use **warm starting**."
    ]
   },
   {
@@ -325,7 +327,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Speed-up"
+    "### Speed-up"
    ]
   },
   {
@@ -370,7 +372,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Starting from existing posterior samples"
+    "## Warm starting from posterior samples"
    ]
   },
   {
@@ -383,6 +385,8 @@
     "\n",
     "In some cases, this is easy to do analytically, e.g., for uniform priors it is just a scaling.\n",
     "\n",
+    "### When the transform cannot be inverted easily\n",
+    "\n",
     "The following code works for arbitrary, factorized priors (as in the blackbody example in this notebook), for an arbitrary number of parameters.\n",
     "\n",
     "Lets start with our posterior samples. These could be obtained posterior samples from MCMC, or generated from the parameter errors quoted in a paper. Here we take it from the reference run:"
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index ef2e5847..c836eb6a 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -244,23 +244,22 @@ def aux_loglikelihood(x):
 
 def compute_quantile_intervals(steps, upoints, uweights):
     """Compute lower and upper axis quantiles.
-    q and 1-q quantiles along each axis of corresponding to steps
-    
+
     Parameters
     ------------
     steps: array
         list of quantiles q to compute.
-    upoints: function
-        samples
+    upoints: array
+        samples, with dimensions (N, d)
     uweights: array
         sample weights
 
     Returns:
     ---------
     ulo: array
-        list of lower quantiles (at q)
+        list of lower quantiles (at q), one entry for each dimension d.
     uhi: array
-        list of upper quantiles (at 1-q)
+        list of upper quantiles (at 1-q), one entry for each dimension d.
     """
     ndim = upoints.shape[1]
     nboxes = len(steps)
@@ -279,23 +278,22 @@ def compute_quantile_intervals(steps, upoints, uweights):
 
 def compute_quantile_intervals_refined(steps, upoints, uweights, logsteps_max=20):
     """Compute lower and upper axis quantiles.
-    q and 1-q quantiles along each axis of corresponding to steps
     
     Parameters
     ------------
     steps: array
-        list of quantiles q to compute.
-    upoints: function
-        samples
+        list of quantiles q to compute, with dimensions 
+    upoints: array
+        samples, with dimensions (N, d)
     uweights: array
-        sample weights
+        sample weights. N entries.
 
     Returns:
     ---------
     ulo: array
-        list of lower quantiles (at q)
+        list of lower quantiles (at q), of shape (M, d), one entry per quantile and dimension d.
     uhi: array
-        list of upper quantiles (at 1-q)
+        list of upper quantiles (at 1-q), of shape (M, d), one entry per quantile and dimension d.
     """
     nboxes = len(steps)
     ulos_orig, uhis_orig = compute_quantile_intervals(steps, upoints, uweights)
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index c9e4c291..5a35e457 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -921,13 +921,10 @@ def warmstart_from_similar_file(
     loglike,
     transform,
     vectorized=False,
-    derived_param_names=[],
     min_num_samples=50
 ):
     """Warmstart from a previous run.
 
-    
-
     Parameters
     ------------
     usample_filename: str
@@ -992,8 +989,8 @@ def warmstart_from_similar_file(
     return get_auxiliary_contbox_parameterization(
         param_names, loglike=loglike, transform=transform,
         vectorized=vectorized,
-        upoints=upoints, 
-        uweights=uweights, 
+        upoints=upoints,
+        uweights=uweights,
     )
 
 
@@ -2758,7 +2755,7 @@ def _update_results(self, main_iterator, saved_logl, saved_nodeids):
                 os.path.join(self.logs['info'], 'post_summary.csv'),
                 [[results['posterior'][k][i] for i in range(self.num_params) for k in ('mean', 'stdev', 'median', 'errlo', 'errup')]],
                 header=','.join(['"{0}_mean","{0}_stdev","{0}_median","{0}_errlo","{0}_errup"'.format(k)
-                                  for k in self.paramnames + self.derivedparamnames]),
+                                 for k in self.paramnames + self.derivedparamnames]),
                 delimiter=',', comments='',
             )
 
@@ -2824,7 +2821,7 @@ def print_results(self, use_unicode=True):
 
                     dist = ''.join([' ▁▂▃▄▅▆▇██'[i] for i in np.ceil(H * 7 / H.max()).astype(int)])
                     print('    %-20s: %-6s│%s│%-6s    %s +- %s' % (p, fmt % lo, dist, fmt % hi, fmt % med, fmt % sigma))
-                except:
+                except Exception:
                     fmts = '    %-20s' + fmt + " +- " + fmt
                     print(fmts % (p, med, sigma))
             print()

From 6f1a6efca53dfa49ddc2d4f1fbfa1334bc7323f1 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 6 Sep 2022 15:29:43 +0200
Subject: [PATCH 089/313] =?UTF-8?q?Bump=20version:=203.5.2=20=E2=86=92=203?=
 =?UTF-8?q?.5.3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 43eff2cf..a6183638 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.5.2',
+    version='3.5.3',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index d0f58b70..103a606b 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.5.2'
+__version__ = '3.5.3'

From 86e4b637d5370d0170447d2e4958a3afc2898ffd Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 8 Sep 2022 21:50:53 +0200
Subject: [PATCH 090/313] add a noop logger to avoid lastresort logger noise

---
 ultranest/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/utils.py b/ultranest/utils.py
index b57247aa..7cf1a47c 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -52,7 +52,7 @@ def create_logger(module_name, log_dir=None, level=logging.INFO):
         formatter = logging.Formatter('[{}] %(message)s'.format(module_name))
         handler.setFormatter(formatter)
         logger.addHandler(handler)
-
+        logger.addHandler(logging.NullHandler())
     return logger
 
 

From 8e3065fdd684287216bb4ea05f18bd4831b297a4 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 8 Sep 2022 22:01:48 +0200
Subject: [PATCH 091/313] more informative livepoint error

---
 ultranest/integrator.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 5a35e457..b2f9434b 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1744,7 +1744,14 @@ def _create_point(self, Lmin, ndraw, active_u, active_values):
         """
         assert self.region.inside(active_u).any(), \
             ("None of the live points satisfies the current region!",
-             self.region.maxradiussq, self.region.u, self.region.unormed, active_u)
+             self.region.maxradiussq, self.region.u, self.region.unormed, active_u,
+             getattr(self.region, 'bbox_lo'),
+             getattr(self.region, 'bbox_hi'),
+             getattr(self.region, 'ellipsoid_cov'),
+             getattr(self.region, 'ellipsoid_center'),
+             getattr(self.region, 'ellipsoid_invcov'),
+             getattr(self.region, 'ellipsoid_cov'),
+             )
 
         nit = 0
         while True:

From 864a13dcc184587b824ba87aaaa029ececabf56a Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 8 Sep 2022 23:21:33 +0200
Subject: [PATCH 092/313] add a notebook on debugging techniques

---
 docs/debugging.ipynb         | 839 +++++++++++++++++++++++++++++++++++
 docs/example-warmstart.ipynb |   4 +-
 docs/index.rst               |   1 +
 3 files changed, 842 insertions(+), 2 deletions(-)
 create mode 100644 docs/debugging.ipynb

diff --git a/docs/debugging.ipynb b/docs/debugging.ipynb
new file mode 100644
index 00000000..92aedc3c
--- /dev/null
+++ b/docs/debugging.ipynb
@@ -0,0 +1,839 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Debugging techniques\n",
+    "\n",
+    "In this tutorial you will learn:\n",
+    "\n",
+    " - How to find issues in your model\n",
+    " - How to debug a interrupted run\n",
+    " - How to determine causes of slow-down\n",
+    " - How to debug MPI parallelisation\n",
+    " - How to check step sampler correctness\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This tutorial allows you to make sure your code is good, independent of ultranest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets start with the sine example from the [\"Higher-dimensional fitting\" tutorial](https://johannesbuchner.github.io/UltraNest/example-sine-highd.html):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import scipy.stats\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ultranest\n",
+    "import corner\n",
+    "\n",
+    "from numpy import sin, pi\n",
+    "\n",
+    "def sine_model1(t, B, A1, P1, t1):\n",
+    "    return A1 * sin((t / P1 + t1) * 2 * pi) + B\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "n_data = 50\n",
+    "\n",
+    "# time of observations\n",
+    "t = np.random.uniform(0, 5, size=n_data)\n",
+    "# measurement values\n",
+    "yerr = 1.0\n",
+    "y = np.random.normal(sine_model1(t, B=1.0, A1=4.2, P1=3, t1=0), yerr)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Probabilistic model implementation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = ['B', 'A1', 'P1', 't1']\n",
+    "ndim = len(parameters)\n",
+    "\n",
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = cube[0] * 20 - 10\n",
+    "    params[1] = 10**(cube[1] * 3 - 1)\n",
+    "    params[2] = 10**(cube[1] * 2)\n",
+    "    params[3] = cube[3]\n",
+    "    return params\n",
+    "\n",
+    "def log_likelihood(params):\n",
+    "    B, A1, P1, t1 = params\n",
+    "    y_model = sine_model1(t, B=B, A1=A1, P1=P1, t1=t1).tolist()\n",
+    "    return scipy.stats.norm(y_model, yerr).logpdf(y).sum()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Finding model bugs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have made a happy little mistake in the implementation above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding prior transform bugs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To find it, lets sample from the prior:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p = [prior_transform(np.random.uniform(size=ndim)) for i in range(1000)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 698.4x698.4 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corner.corner(np.array(p), titles=parameters, show_titles=True, plot_density=False, quiet=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See the issue? A1 and P1 are perfectly correlated!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is the bug pointed out, and the corrected version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior_transform(cube):\n",
+    "    params = cube.copy()\n",
+    "    params[0] = cube[0] * 20 - 10\n",
+    "    params[1] = 10**(cube[1] * 3 - 1)\n",
+    "    params[2] = 10**(cube[1] * 2)\n",
+    "    #      ^              ^ \n",
+    "    #                     |\n",
+    "    #                  Mistake\n",
+    "    # correct version:\n",
+    "    params[2] = 10**(cube[2] * 2)\n",
+    "    params[3] = cube[3]\n",
+    "    return params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding likelihood function bugs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Draw uniformly from the prior and plot the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "params = prior_transform(np.random.uniform(size=ndim))\n",
+    "plt.plot(t, sine_model1(t, *params), 'x ');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Repeat this a few times and you have prior predictive checks!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Also have a look at a few randomly drawn likelihood values. If you see values repeated, or infinites, it is not a good sign."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-1961.2412862297433,\n",
+       " -994.4041211269235,\n",
+       " -2339.297354798619,\n",
+       " -599.6818945970597,\n",
+       " -1576.6457304537498,\n",
+       " -145507.65589077197,\n",
+       " -307.52989804045745,\n",
+       " -291.9437786218507,\n",
+       " -1333.029330033732,\n",
+       " -1882.6914521041922]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[log_likelihood(pi) for pi in p[:10]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How can I make the inference go faster?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are two categories of slowdowns:\n",
+    "\n",
+    "1. Computational slow-downs: Your model is implemented so it is is slow to evaluate.\n",
+    "2. Algorithmic slow-downs: Your model is difficult and requires many model evaluations.\n",
+    "\n",
+    "Lets find out which one is blocking you most:\n",
+    "\n",
+    "### Measuring implementation speed\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets measure the speed of our prior transform and model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.31 µs ± 153 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "u = np.random.uniform(size=ndim)\n",
+    "%timeit prior_transform(u)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "901 µs ± 11.9 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "p = prior_transform(u)\n",
+    "%timeit log_likelihood(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we see that the prior transform is very quick: one evaluation per microsecond. But the likelihood is much slower, with one  evaluation per ms. That means for a million samples, we already have to wait 15 minutes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We should speed it up (see [\"Higher-dimensional fitting\" tutorial](https://johannesbuchner.github.io/UltraNest/example-sine-highd.html) for a faster implementation)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Measuring algorithmic speed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the ultranest output, look at the sampling efficiency.\n",
+    "\n",
+    "If it is a few percent to 100%, the inference is very fast algorithmically, and you should focus on the model computation speed (see above). Switching to a step sampler will not lead to improvements.\n",
+    "\n",
+    "If the efficiency is very low (say, 0.1% or lower), the proposal is inefficient. Use a step sampler (see [\"Higher-dimensional fitting\" tutorial](https://johannesbuchner.github.io/UltraNest/example-sine-highd.html))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Looking inside a interrupted run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets run ultranest for 30 seconds and interrupt it, and see the parameter space it is tackling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import signal\n",
+    "def timeout_handler(signum, frame):\n",
+    "    raise KeyboardInterrupt()\n",
+    "old_handler = signal.signal(signal.SIGALRM, timeout_handler) \n",
+    "signal.alarm(30);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee9e27ffafa64d8fba719bdf44538f37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Z=-293.5(0.00%) | Like=-286.66..-128.48 [-312.3024..-284.5850] | it/evals=1332/7292 eff=19.3268% N=400 0 0  \r"
+     ]
+    }
+   ],
+   "source": [
+    "import ultranest\n",
+    "\n",
+    "sampler = ultranest.ReactiveNestedSampler(parameters, log_likelihood, prior_transform,\n",
+    "                                          wrapped_params=[False, False, False, True])\n",
+    "\n",
+    "try:\n",
+    "    sampler.run()\n",
+    "except KeyboardInterrupt:\n",
+    "    print(\"run interrupted!\")\n",
+    "\n",
+    "signal.signal(signal.SIGALRM, old_handler);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampler.logger.handlers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Looking inside the current parameter space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nested sampling is at this likelihood threshold:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "sampler.Lmin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets see the distribution of live points in the parameter space:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "live_points_p = sampler.transform(sampler.region.u)\n",
+    "corner.corner(live_points_p, titles=sampler.paramnames, show_titles=True, quiet=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This does not look trivial, for example P1-t1 plot has two arms. It is not a ellipsoidal contour (which would be the easiest shape).\n",
+    "\n",
+    "However, this plot also includes the prior deformation. What ultranest operates on, primarily, is the unit cube. Lets look at the live points distribution in the un-transformed prior space:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corner.corner(sampler.region.u, show_titles=True, quiet=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, you see relatively complicated shapes. This means that the parameters have a complicated relationship with the observables.\n",
+    "\n",
+    "You can help ultranest by reparametrizing the parameters, or adding derived parameters, which are more ellipsoidal, and better behaved."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets see what models correspond to the current live points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(t, y, 'o ', ms=14, color='k')\n",
+    "\n",
+    "for params in live_points_p:\n",
+    "    plt.plot(t, sine_model1(t, *params), '. ', color=plt.cm.viridis(params[2]/10))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here you see two groups of curves, which are highlighted by color-coding by period.\n",
+    "\n",
+    "Some models (blue) that have the period as in the data (black circles),\n",
+    "and some (yellow) just put a straight line through the data.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This explains the two arms in the distribution plots above as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reparametrizing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The sine curve model has the time shift t1 and the period as parameters. Likely, the data will constrain, for example, when the peak occurs, for example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So you could add a derived parameter that specifies the time of the first peak. If that is closer to the data, the sampler can take advantage of it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also see that when A1 is large, B can take a wider range of values, giving a funnel shape:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(live_points_p[:,0], live_points_p[:,1])\n",
+    "plt.ylabel('Amplitude (A1)')\n",
+    "plt.xlabel('Background level (B)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This funnel is even clearer in unit cube space:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(sampler.region.u[:,0], sampler.region.u[:,1])\n",
+    "plt.ylabel('Amplitude (A1), untransformed')\n",
+    "plt.xlabel('Background level (B), untransformed');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A different parameterization would be to define a background fraction. Instead of background & amplitude being free parameters, you would have this reparametrized model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters_reparametrized = ['Bfrac', 'A1', 'P1', 't1']\n",
+    "\n",
+    "def prior_transform_reparametrized(cube):\n",
+    "    params = cube.copy()\n",
+    "    # amplitude:\n",
+    "    params[1] = 10**(cube[1] * 3 - 1)\n",
+    "    # background is scaled by params[1]\n",
+    "    params[0] = cube[0] * params[1]\n",
+    "    \n",
+    "    # rest is unchanged\n",
+    "    params[2] = 10**(cube[1] * 2)\n",
+    "    params[3] = cube[3]\n",
+    "    return params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is only a toy example to give you ideas how to investigate the geometries the sampler is currently exploring."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parallelisation issues"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you have any MPI issues, test your MPI first in isolation, by running this command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mpiexec -np 4 python3 -c 'from mpi4py import MPI; print(MPI.COMM_WORLD.Get_rank(), MPI.COMM_WORLD.Get_size())'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If it gives an output like the above, your MPI is working. If the last column is 1, your cores are not communicating."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you get an error, fix it first."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are seeing slower runtimes with MPI than without, see here: https://johannesbuchner.github.io/UltraNest/performance.html#parallelisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some MPI implementations have bugs, and you can switch to another MPI implementation. Your computing cluster admins may also help you with MPI troubles."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Debugging step sampler quality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run with nsteps=1, 2, 4, 8, 16, 32, 64 ... steps and look where the log(Z) value stabilizes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Noisy debug logs in jupyter notebooks\n",
+    "\n",
+    "Sometimes you see lots of \"DEBUG:\" statements when ultranest runs in jupyter notebooks. Why does this happen?\n",
+    "\n",
+    "It is because a library (such as corner) started to make logging outputs when no logger was defined. So python automatically installs a logger which jupyter \"helpfully\" prints to the screen.\n",
+    "\n",
+    "You can see the installed logger here and its handlers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "root_logger = logging.getLogger()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_logger.handlers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If some library, like corner, emits a warning ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "logging.warning(\"Oh no, something happened\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A root logging handler is now installed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_logger.handlers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can silence it later with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_logger.handlers[0].setLevel(logging.WARNING)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But the proper way is to set up logging at the top of your notebooks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging, sys\n",
+    "handler = logging.StreamHandler(sys.stderr)\n",
+    "handler.setLevel(logging.WARNING)\n",
+    "logging.getLogger().addHandler(handler)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find out more about python logging at: https://docs.python.org/3/howto/logging.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index 579e2c54..1c5f6faf 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -162,7 +162,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## First simple model\n",
+    "### First simple model\n",
     "\n",
     "Here is a typical gaussian likelihood with our black body function:"
    ]
@@ -327,7 +327,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Speed-up"
+    "## Speed-up"
    ]
   },
   {
diff --git a/docs/index.rst b/docs/index.rst
index 8145cca4..53a7cc4f 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -28,6 +28,7 @@ Welcome to UltraNest's documentation!
    example-outliers.ipynb
    example-sine-bayesian-workflow.ipynb
    example-warmstart.ipynb
+   debugging.ipynb
 
 .. include:: ../README.rst
 

From 4caddcb9d9e527fd3fc906c914142c3a88a80406 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 9 Sep 2022 00:09:15 +0200
Subject: [PATCH 093/313] improve doc on silencing output; avoid corner warning

---
 docs/example-warmstart.ipynb |  2 +-
 docs/issues.rst              | 21 ++++++++++++++++-----
 docs/using-ultranest.ipynb   |  4 ++--
 ultranest/plot.py            |  2 +-
 4 files changed, 20 insertions(+), 9 deletions(-)

diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index 1c5f6faf..f9ac7fc5 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# warm starting\n",
+    "# Warm starting\n",
     "\n",
     "In this tutorial you will learn:\n",
     "\n",
diff --git a/docs/issues.rst b/docs/issues.rst
index 374c2d51..7183dc3b 100644
--- a/docs/issues.rst
+++ b/docs/issues.rst
@@ -16,21 +16,32 @@ Opening a github issue is preferred, because then other people can find the ques
 How do I suppress the output?
 -----------------------------
 
-To suppress the logging to stdout, you can configure your own logger::
+To suppress the live point visualisations, set ``viz_callback=False`` in ``sampler.run()``.
+
+To suppress the status line, set ``show_status=False`` in `sampler.run()``.
+
+See the documentation of `:py:meth:ultranest.ReactiveNestedSampler.run()`.
+
+To suppress the logging to stderr, set up a logging handler::
 
     import logging
     logger = logging.getLogger("ultranest")
     handler = logging.StreamHandler(sys.stdout)
     handler.setLevel(logging.WARNING)
-    formatter = logging.Formatter('[{}] [%(levelname)s] %(message)s'.format(module_name))
+    formatter = logging.Formatter('[ultranest] [%(levelname)s] %(message)s')
     handler.setFormatter(formatter)
     logger.addHandler(handler)
+    logger.setLevel(logging.WARNING)
 
-You may want to alter the above to log to a file only. See the logging python module docs.
+You may want to alter the above to log to a file instead. See the `logging python module<https://docs.python.org/3/library/logging.html>`_ docs.
 
-To suppress the live point visualisations, set ``viz_callback=False`` in ``sampler.run()``.
+To completely turn off logging, you can use::
+
+    import logging
+    logger = logging.getLogger("ultranest")
+    logger.addHandler(logging.NullHandler())
+    logger.setLevel(logging.WARNING)
 
-To suppress the status line, set ``show_status=False`` in ``sampler.run()``.
 
 How should I choose the number of live points?
 -----------------------------------------------
diff --git a/docs/using-ultranest.ipynb b/docs/using-ultranest.ipynb
index c9c1f242..08908472 100644
--- a/docs/using-ultranest.ipynb
+++ b/docs/using-ultranest.ipynb
@@ -249,7 +249,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -263,7 +263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/ultranest/plot.py b/ultranest/plot.py
index 09af78a1..ab0bf6b8 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -58,7 +58,7 @@ def cornerplot(results, logger=None):
     oldfunc = logging.warning
     logging.warning = lambda *args, **kwargs: None
     corner.corner(data[mask,:], weights=weights[mask],
-                  labels=paramnames, show_titles=True)
+                  labels=paramnames, show_titles=True, quiet=True)
     logging.warning = oldfunc
 
 

From d0509921032839bdecc7de9534e6e7267c372abf Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 9 Sep 2022 00:24:32 +0200
Subject: [PATCH 094/313] closes #38 and #52

---
 docs/example-sine-modelcomparison.ipynb | 6 +++---
 ultranest/integrator.py                 | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/example-sine-modelcomparison.ipynb b/docs/example-sine-modelcomparison.ipynb
index 75494638..c0b63ecf 100644
--- a/docs/example-sine-modelcomparison.ipynb
+++ b/docs/example-sine-modelcomparison.ipynb
@@ -126,7 +126,7 @@
     "    params[0] = cube[0] * 20 - 10\n",
     "    # let amplitude go from 0.1 to 100\n",
     "    params[1] = 10**(cube[1] * 3 - 1)\n",
-    "    # let period go from 0.3 to 30\n",
+    "    # let period go from 1 to 100\n",
     "    params[2] = 10**(cube[2] * 2)\n",
     "    # let time go from 0 to 1\n",
     "    params[3] = cube[3]\n",
@@ -479,7 +479,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -493,7 +493,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index b2f9434b..38c2e985 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2284,7 +2284,7 @@ def run(
 
     def run_iter(
             self,
-            update_interval_volume_fraction=0.2,
+            update_interval_volume_fraction=0.8,
             update_interval_ncall=None,
             log_interval=None,
             dlogz=0.5,

From bef34cfc52b800fecbfe0d3138cf2d116b8167f2 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 9 Sep 2022 02:16:10 +0200
Subject: [PATCH 095/313] [docs] config format specs changed (fixed error:
 dict-traits is deprecated in traitlets 5.0)

---
 docs/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/conf.py b/docs/conf.py
index f6640066..ccdb0764 100755
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -98,7 +98,7 @@
 
 nbsphinx_execute_arguments = [
     "--InlineBackend.figure_formats={'svg', 'pdf'}",
-    "--InlineBackend.rc={'figure.dpi': 96}",
+    "--InlineBackend.rc=figure.dpi=96",
 ]
 
 autodoc_member_order = 'bysource'

From 0e0f9a096e42c7194084ba4abf3bc995839b029a Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 9 Sep 2022 02:50:08 +0200
Subject: [PATCH 096/313] [docs] replace KeyboardInterrupt with Timeout, to
 avoid nbsphinx stopping

---
 docs/debugging.ipynb | 237 ++++++++++++++++++-------------------------
 1 file changed, 97 insertions(+), 140 deletions(-)

diff --git a/docs/debugging.ipynb b/docs/debugging.ipynb
index 92aedc3c..5c0ebbd2 100644
--- a/docs/debugging.ipynb
+++ b/docs/debugging.ipynb
@@ -297,7 +297,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6.31 µs ± 153 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
+      "6.66 µs ± 209 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
      ]
     }
    ],
@@ -315,7 +315,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "901 µs ± 11.9 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "937 µs ± 29.8 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -378,14 +378,14 @@
    "source": [
     "import signal\n",
     "def timeout_handler(signum, frame):\n",
-    "    raise KeyboardInterrupt()\n",
+    "    raise TimeoutError()\n",
     "old_handler = signal.signal(signal.SIGALRM, timeout_handler) \n",
     "signal.alarm(30);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "scrolled": false
    },
@@ -400,7 +400,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee9e27ffafa64d8fba719bdf44538f37",
+       "model_id": "146540ae46bc408db966066c74cffd7f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -415,7 +415,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Z=-293.5(0.00%) | Like=-286.66..-128.48 [-312.3024..-284.5850] | it/evals=1332/7292 eff=19.3268% N=400 0 0  \r"
+      "run interrupted!| Like=-258.90..-89.46 [-260.4596..-249.1400] | it/evals=1995/18885 eff=10.7925% N=400 0 0  \n"
      ]
     }
    ],
@@ -427,21 +427,12 @@
     "\n",
     "try:\n",
     "    sampler.run()\n",
-    "except KeyboardInterrupt:\n",
+    "except TimeoutError:\n",
     "    print(\"run interrupted!\")\n",
     "\n",
     "signal.signal(signal.SIGALRM, old_handler);"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sampler.logger.handlers"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -458,11 +449,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-258.733283170889"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sampler.Lmin"
    ]
@@ -476,9 +478,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 698.4x698.4 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "live_points_p = sampler.transform(sampler.region.u)\n",
     "corner.corner(live_points_p, titles=sampler.paramnames, show_titles=True, quiet=True);"
@@ -495,9 +510,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 698.4x698.4 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "corner.corner(sampler.region.u, show_titles=True, quiet=True);"
    ]
@@ -520,9 +548,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.plot(t, y, 'o ', ms=14, color='k')\n",
     "\n",
@@ -577,9 +618,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.scatter(live_points_p[:,0], live_points_p[:,1])\n",
     "plt.ylabel('Amplitude (A1)')\n",
@@ -595,9 +649,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.scatter(sampler.region.u[:,0], sampler.region.u[:,1])\n",
     "plt.ylabel('Amplitude (A1), untransformed')\n",
@@ -613,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -655,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -703,116 +770,6 @@
    "source": [
     "Run with nsteps=1, 2, 4, 8, 16, 32, 64 ... steps and look where the log(Z) value stabilizes."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Noisy debug logs in jupyter notebooks\n",
-    "\n",
-    "Sometimes you see lots of \"DEBUG:\" statements when ultranest runs in jupyter notebooks. Why does this happen?\n",
-    "\n",
-    "It is because a library (such as corner) started to make logging outputs when no logger was defined. So python automatically installs a logger which jupyter \"helpfully\" prints to the screen.\n",
-    "\n",
-    "You can see the installed logger here and its handlers:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "root_logger = logging.getLogger()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "root_logger.handlers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If some library, like corner, emits a warning ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "logging.warning(\"Oh no, something happened\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A root logging handler is now installed:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "root_logger.handlers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can silence it later with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "root_logger.handlers[0].setLevel(logging.WARNING)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But the proper way is to set up logging at the top of your notebooks:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging, sys\n",
-    "handler = logging.StreamHandler(sys.stderr)\n",
-    "handler.setLevel(logging.WARNING)\n",
-    "logging.getLogger().addHandler(handler)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Find out more about python logging at: https://docs.python.org/3/howto/logging.html"
-   ]
   }
  ],
  "metadata": {

From a9883bac5021fc0f23beb7a062b2847e52f17166 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 9 Sep 2022 09:26:37 +0200
Subject: [PATCH 097/313] [docs] mpiexec output does not show in notebook for
 some reason

---
 docs/debugging.ipynb | 54 ++++++++++++++++++++++----------------------
 1 file changed, 27 insertions(+), 27 deletions(-)

diff --git a/docs/debugging.ipynb b/docs/debugging.ipynb
index 5c0ebbd2..6d5d7d25 100644
--- a/docs/debugging.ipynb
+++ b/docs/debugging.ipynb
@@ -297,7 +297,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6.66 µs ± 209 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
+      "6.53 µs ± 256 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
      ]
     }
    ],
@@ -315,7 +315,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "937 µs ± 29.8 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "897 µs ± 6.6 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -400,7 +400,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "146540ae46bc408db966066c74cffd7f",
+       "model_id": "780e0d4d65014955b438d2dd9a7769ed",
        "version_major": 2,
        "version_minor": 0
       },
@@ -415,7 +415,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "run interrupted!| Like=-258.90..-89.46 [-260.4596..-249.1400] | it/evals=1995/18885 eff=10.7925% N=400 0 0  \n"
+      "run interrupted!| Like=-255.66..-89.46 [-260.4596..-249.1400] | it/evals=2060/20807 eff=10.0946% N=400 0 0  \n"
      ]
     }
    ],
@@ -449,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
@@ -457,10 +457,10 @@
     {
      "data": {
       "text/plain": [
-       "-258.733283170889"
+       "-255.4088047726119"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -478,12 +478,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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YYR7HfGyMucN1aYq7bDcDe4BHgTHWWtfs2PuBH4A/KU4d8xMwMYD6+k2QXNJl7Eq03IDiCSSbAUdJy+T+FAeEIiISBhGZoGGMsaEeuF+ZyRLp6elEcoJIpMuPhjpUpfxonqBRmxljXgIetdb+4mVfR2Aq0JDiGbibgOnASUAicKK1dnN11VVEpDaJSDcusMsYU+kmvA0bNvjt3qtE1187Y8xvlS0/hCJdfjTUoSrlq2+9mhljmuN9HdvzrLX/VHS+tfZn4DhjzGBgHPAOEGetbWuMSQceo3iso4iIhFhEgj1rbZNIlOtijFkeyZahSJcfDXWIdPlSOSUB3UnBnGuMqWf/l8h5H8Vj/AzFCZahuAt3vypXUkREvIpUy16kRXrQXKTLh8jXIdLlSwiVJEjuCLQ1xkyleJziVdbae4DOxphJgIPilDSXADuBi40xnwP1gLERqbiISC0QkTF7IiIiIlI9YibPnoiIiIiUVyuDPWNMRPN5Rbr8aKhDpMsPlUi8j0jdu9pWrohIrKiVwR6RT94a6fIh8nWIdPmhEon3Eal7V9vKFRGJCRGZoHHAAQfYqixh5o/T6cThcBAfH09cnPdYNjk5mfT09IgNVox0+dFQh6qUv2LFil2+ZnSH89nyJhL3MVKfXW0o19+zJSJSU0Uk2EtLSwt5Ql/XRBOn00lubi5JSUmlgj3PHHw1OaFwrNShikmVfeZoDMez5U8k7mOkPrvaUG4w+T9FRKJdzKVeiYuLIyUlxe8xo0dHtlco0uVHQx0iXX6oROJ9ROre1bZyRURiRURSr6Snp9twtez5UonVNSTK+VsuLRzPltQeWopPRGJRbZ2gISIiIlIrKNgTERERiWEK9kRERERimII9ERERkRgWM7NxNQFDREREpDy17ImIiIjEMAV7IiIiIjFMwZ6IiIhIDFOwJyIiIhLDFOxJrZGWloYxxuuWlpYW6eqJiIiERcRm4/pa3kyzaiVcNmzYoOdORERqHbXsiYiIiMQwBXsiIiIiMUzBnoiIiEgMi0iw53Q6cTqdkShaREREpFaJSLDncDjIzc2NRNEiIiIitUpEgr34+HiSkpIiUbSIiIhIrRKR1CtxcXHExWm4oDe+UoO4KEWIiIiIVEbE8uwpaBEREREJPzWv1RBOp5Ps7GxNbAmTNm3aaHUNERGJSRFr2ZPKyc3NJSMjA4D69etHuDaxZ/369T73qRVaRERqMgV7NYRrQktycnKEayIiIiI1SdDBnjEmDngQ2AqstdZ+ELJaSTlxcXGkpKSolUlEREQqJagxe6Y44vgUaAkcDww3xrQO4LzRxpjlv/32G+np6UybNi2Y4qWWmjZtGunp6QDtjDHLjTGjI10nERGRaGcqSvXh9SRjDgGmWGv7G2OaAt8CY6y17wdyfnp6ul2+fHmly60NIpF6JdgyA3l2gj3X3/s0xqyw1qZ72+fv2TLGBFTnUJ0nNY+/Z0tEpKaqdMueMaYhUAC0NMb0sdbuADYCu0r2p4WygrWNrxmhrk1ERESkMioV7BljZgA3WWs3A+OBwpKWvXbAn8aYi4AHjTGpoa+qRBulgxEREYl+lZ2g0RfIM8ZkAs9bazONMU2AP4AbgPOAwdbajBDXU6KQZzqYlJSUCNdGREREvAk42DPG9AGygc+As4A4Y8xLgBPoBhwEnGGt/S301ZRo5EoHo3WORUREoldlWvZ+Brpaa7ONMfcCgwAH8ARwJbBcgV7N5HQ6yc3NJSkpqVJrFrvSwQRTXk5OTqXLExERkcoLONiz1u72+PkeY0wBcDmQb619OhyVk+pR3d2xOTk56v4VERGpJpVOqmyMMbbYRGNMIRBQuhWJXtXdHetaBSQxMdFrShNrrVr8REREQqTSwZ611hpj4qy1Tmvt5HBUSqpXfHx8UOvtBpsKxlv3r2dXslLMiIiIhE5QzSfWWuXakJBydSXn5uZGuioiIiIxJei1cUVCSTN7RUREwkPBnkQFz65dLU0mIiISOhoFLyIiIhLDFOyJiIiIxDAFe1IjaB1eERGR4ERszJ6vcVlKuyHeeCZ+FhERkcBpgoZEhL+g3ts+VyJm139FREQkMAr2pEaIi4sLKvGziIhIbacxeyIiIiIxTMGeiIiISAxTsCciIiISw6Im2FNqDQmFVatWYYzxurVp0ybS1RMREal2UTNBwzO1hmvZLKfTSW5uLklJScTFFcelvmZxVrTEVjDnORwO8vLySpUf6HXDIZBlxEJ9fyKhKsulFRQUaLk1ERERDxEL9soGF66UGklJSe7XvAWA1SkvLy+i5YuIiIhUVdS07MXFxZULqFyBn2cAWJ0SExMjWn6oeWspFRERkdgWNcGeN94CwNpUfqhFuqU0FErGdCpSFRERCVBUB3viXbAtdJFuKQ2F3NxcgPhI10NERKSmCLqFxBgTZ4zpbYw5NZQVkoq5WuhKAp+AuVoqa3IXbkmg6oh0PURERGqKoFr2jDFxwLdALtDeGPMTMMpauzWUlQtGVcal1ZQxbbHQQhesks9F+XlEREQCFGxEcymwy1rbE+gAdASmGWP8rlJvjBltjFn+22+/kZ6ezrRp04Is3rdgW728nWutrXDzJdjzAhHqFrpw1dXfNZ1OZ6nfHQ4HWVlZOBwOn2VOmzaNrl27ArQzxiw3xowOunIiIiK1hAnmy9wYMwCYAFxhrf3RGPM4cDUww1p7TUXnp6en2+XLl1e63EA4nU5ycnJITk4OqmXP81zltSsWbF0rCoY9r5udnU1GRgapqamkpKT4LdMYs8Jam+5jnw11nj1jjN/3kpaWxoYNG7zua9OmDevXrw9pfSR8/D1bIiI1VbATNJYDa4DHjTGfAl2Ay4EeoapYsOLi4qhfv361nxsIz27i+HjNMfBUk7umN2zY4DMYjKagXkREaqeg+gGttduB24A3gUbALGAXcKAxpk7oqhdbqtLFHOtiYfKIiIhINAo69Yq1diPwrDHmHOBioCVwqbW2MER1izk1ufVKREREaqZQ5Nn7GFgGFFlrd4TgejEr1pI0i4iISPSrcp+ZtTbXWrs1lgM9p9NJdna2a/WGmFYd79U1EaY23E8REZFIi5kVNCoz87OsigbRB7PMWE2ajeup7HsNx/vwLCM52Xu2nmi9PyIiIjVNzAR7FQkmYbIr4HAFJMnJyQEHITUpWPGsazDvNZDrenKVkZSUVKPuk4iISE1Ua4K9YFrnXMKdkiWaVMd71dhFERGR6lNrgr1omAnrmbRZefZERESkOtSapGbRkMctJyeHzMxMcnJyIlaHaFKbJr6IiIhESq1p2YsGnuPhapJgxjsGoipd6yIiIhKYmG7Zi7aWo7i4OJKTk2tc2pFwrfyRlJREamqqkkyLiIiEUUy37AWS4gOqd+asqyvXGON1IkQkUrZUlLYm2PGO/q7rdDoxxrg/l7LHapauiIhIaMRMsOctOIhkig9f5aWkpJQKcqKdMSYsM3SNMQroREREqkHMBHveuIKUilrLqlNtSuNSkXCNBQy1Nm3a+A1M27RpU421ERERqZyYDvYkutWUCRrr16+PdBVERESCFr3NKWEQbRM2agtf910TNERERMKvVgV74ZpVKv75uu/RkPtQREQk1tWqbtxoWEWjsmrKuDZ/auJ9FxERiRU1M3oIUmVbkpxOJ1lZWRV2++7bt48HHniA33//PRTVLCUSrZHBdHevXbuW+++/n927d5fbpxY8ERGRyKn1377WWp9bTk4OGRkZ5OTkeN1fWFjId999R+fOnRk/fjzp6em88sorFBUVUVhY6PfagdYnMTGR1NRUEhMTK5xV7K+8ypTpGWA6nU6f1yssLKSoqIi5c+fSqVMn7r77bjp27Mjnn39ORkYGBQUFQdVHREREQqdWBHuunG6+Nl9cgZa37kdrLc899xzdu3ensLCQefPm0alTJ0aOHMkVV1wRdEtc2brFx8eTkpJCfHx82PLSlS3Tc+KEvzLz8/MZM2YMF1xwAW3btuXtt9+mbt269OnTh8mTJ5Obm+vznsfFxQX1mYiIiEjl1IpgL1hxcXEkJSW5W7hc9u3bx+DBgxkzZgx9+/blhx9+YNCgQXz22WfcfvvtzJw5kxNPPDHobt1Izxr21e3qWa+1a9fSo0cPnn32WW644QaWLl3KGWecwffff89ZZ53FxIkTGTZsmNduXREREak+CvYq4G3M3MCBA3nnnXd46KGHmD9/Po0bNwYgISGB+++/nw8//JBt27Zx3HHH8e+//4akzOrmLeB01WvPnj0cd9xx/Pzzz8ydO5dHH32UunXrArDffvsxZ84cHn74YT766CNOPfVUv9cUERGR8FKwVwFvueCOOuoonE4nJ554otdJB127dqVu3bqkpaUFtVqGq8wNGzYwc+bMiIxvcwV2eXl55erVsGFDzj77bJxOJ88++yzbtm0rde5vv/3GK6+8gjGGwYMHl7umUt+IiIhUn6CDPWNMnDFmuDFmaCgrFG28dWlOmjSJVq1acemll3oNXMaOHcuOHTuYNGkShYWFQZW5b98+TjnlFC677DJuuOGGagn4rLW8/vrrvPPOO+7ALjExsVS9XOMHp02bxosvvsgPP/xAeno6CxcuBOCVV17h+OOPZ9euXXz00Ufceuut7vOVRFlERCQCKprB6WMWpQG+Ah4H5gCHVub8Ll262GjhdDp9bg6Hw+e+BQsWWMCOHTvWFhYWurf58+dbwF511VV227ZttqioyOv5/uqTlZVl09PTbUpKih0+fLgF7LXXXmsdDkdQ76WoqMhmZmb6PN/pdNqNGzfa008/3QI2Pj7efvXVVxXeg4KCArty5Urbvn17a4yxJ510kgVsjx497Pr1621BQYHf+xssYLn1/WwGfd1wiLb6iH/+ni1t2rRpq6lbcCdBV+CZkp8XAx+UbM0rOG80sDw5Odl26dLFTp061UZasMGe0+m0l156qTXG2C+++MIWFBTY7du32xYtWtijjjrKbt++3Weg5y/QycnJseedd541xtg333zT5uTk2Ouvv94dQPqrk699mZmZdsuWLTYjI8PrOdOmTbP77befTU5OtpMmTbKHHHKIbdWqlV23bp3Ny8vzG+wVFhbavXv32hEjRlhjjL3rrrtsXl6eLSwstPn5+dbhcPjcKmvq1Km2S5cuFsgGlgOjbflnLNhHISyirT7in4I9bdq0xeIW3Enwf8CfwCfA3UAXYCXwcSDnR1PLXlXs3bvXtm7d2h5xxBE2OzvbjhgxwsbHx9svvvgi6Jar8ePHW8Def//9Njc31+bm5pYK+FwtfJUJIh0Oh83IyCgXfHq25p188sn2999/t4WFhXbZsmW2bt26tn///nbPnj0B1z0jI6PU7/4CZbXsSTRSsKdNm7ZY3II7qXiZtSeAv4HzS15rDCyrqHXP2tgJ9jy7c3v27GkBe8stt7iDqsqaO3euBeywYcNsTk6OO9irTMDnr66erXkzZsxwt+Y98cQTNj8/v1R39JNPPmkB++CDD1bp/ijYi676iH8K9rRp0xaLW/AnwsHAZ8DHwCDgMuBHILWic2Mp2HM6nfbyyy+3gO3QoUOpbs/KWL16tU1KSrLdunWze/fuLRXoeQZ8N954owXsfffdF3Sw5xoH2L17d/vXX3+5u2M9t4KCAnvOOefY+Ph4u3LlyirdHwV7UlMo2NOmTVssbkHPxrXWrisJ8FYBE4ChwCXW2oxgr1lTPfLII1x55ZX85z//ceebq6zx48dTt25dXn/9derVq+f1GGMMbdu2df8MweWu27p1Kw0aNOCjjz7i0EMP9VnWAw88gMPh4Mcff6zkuyHouomIiEhoVSnPnrV2A3Ab0AM421r7cygqVdOkpqby3HPPceSRR3rdv3z5cn766Ses9Z4+ZeXKlbz99tuMGTOGZs2a+Szn22+/5brrruO0007j9ttvByqXu87pdJKVlcXtt99OZmYmM2bM8Ht8nTp1KrymP8qrJyIiEnlVTqpsi2VYa/eFokI1mSuYcrVkOZ1O7rnnHrp27Urnzp05/PDDueOOO8oFfvfddx/77bcfY8aM8XntrVu3MnToUFq3bs1rr71GfHw8UD53nbWWOXPm0LFjR1544YVS5eTk5JCZmUnXrl3p3r07Dz/8cKmkyaGmvHoiIiKRpxU0/Cjb5+1wOMjKysLhcGCtxel0ltrvCqays7PZsmUL5513Hvfddx8XXXQRzz33HAcffDCTJ092B37jxo1jzpw5vP3229xwww2kpqaWu6a1lry8PIYOHUpmZiZvv/02DRs2dO8zxpCcnIwxhp9//pmePXsydOhQNm/ezFVXXcXFF19MRkYG+fn5JCUl0aBBA4wxXHvttWzbto3p06fjdDp9boDXOllrKSoqKnU/ym7GGK9r7IqIiEg1isRAwZoyQcNXvrrMzEyvee1cyYt/++0326lTJ2uMsQ8++GCpmbWbNm2yzz77rO3bt6+Nj4+3gN1vv/3s7t27rcPh8JqbzzUB5PXXX/c60WHv3r32xhtvtPHx8bZRo0Z2ypQpNisry95+++0WsMcee6z9+++/S9UzIyPDdu/e3bZo0cJmZWV5zYP3999/W8A+//zzfvP3ue5HKCdh+IMmaEiY+Hu2tGnTpq2mbhEptKYGe65gzhWQeUt/8t1339kWLVrY+vXr27feesvrrNrs7GzrcDjs9u3b7fTp0+2nn37qDrDKBnsvvPCCBextt91WrjyHw2Ffe+0126JFC2uMsZdccondsmWLzc/Pd29vvPGGTUlJsc2bN7dffvllqfMXLVpkAfvkk0/6DfZmzJjhNZArLCwsdT8U7JUXbfUR/xTsadOmLRY39a9Vgrd1cj198skn9OjRg3r16rF48WL69+/v93oHHHAAl1xyCX369PG6f/ny5e4JGffdd1+pff/++y99+vRh2LBhHHTQQXz77bc8//zzHHDAAaWOO+uss/jqq69ISUmhd+/evPbaa+59PXv2pHv37kyaNInMzMxy5TscDuB/M3/Lquh+iIiISOTpWzqEvv76a/Ly8rjooos46qijqny9+fPn43A4eOSRR8jPzy+1b+3atSxdupQLL7yQb7/9lmOPPdbnddq3b8/XX39Nx44dufPOO7G2eNKGK73Kjh07GDhwYKmALzc3l6uuuqpUuhcRERGpeRTshdA999zD4MGDeeCBB3jyySerfD1rLfHx8bRp04bk5ORS+7p06UL37t1ZunQphYWFFV6rYcOGjBgxgo0bN7J+/Xr36yeddBKvvfYa3377rTvgy83NZdCgQSxevJgZM2Zw/PHHV/m9iIiISGQo2AuhhIQEXn31VQYPHswdd9zBE088EZLr1q9fv1xXqTGGe+65xz2jNhA9e/YEYOnSpaVeP++880oFfJ6B3ogRI8pdR8mSQyMtLQ1jjNctLS0t0tUTEZEYkRDpCsQaV8DncDi44447AMrlz8vLy/MZKDmdTurXr1/qNVe3q+uc3NxckpKS6NGjhztf3mWXXYYxxuf4OWst7dq144ADDmDp0qVcfPHFpco877zzABg2bBhOp5PnnnuOESNG4HQ6y43ZcyVLBkhJSQngrog3GzZscH+2ZfkaJykiIlJZCvb8qOgL19f+OnXqMGfOHC666CLuuOMOEhISuPnmm937PQO9vLw8rLXuxMNxcXHlruv6PS4uzp3Lz5XD7p577qFPnz68+OKLXHPNNT7rZK0lLi6Onj178vnnn5e6bnx8PMYYBg8eTOPGjcnMzGTgwIHk5uaSmJiIMQan0+kOMl11TUpKUlAiIiIS5dSNGyaeXbq33norzzzzTLljCgsL6devH8cffzx79uwJ6LplV6VwzagNdDWMHj16lBu356lPnz6cddZZ5Ofnk5GR4b6m59JnmoUrIiJSc+jbOoxcAd9ZZ53FjTfeyMcff1xq/yOPPMJPP/3EunXrGDVqVLkZt1DcCrhq1Sr3vrKBljGG8ePHs23bNl588cUK6+Qat7dkyRK/x7mCysTExFK/a+kzERGRmkXBXpglJCTw9NNPY60tNZHiv//9L5MnT2bw4ME8+eSTLFy4kOOPP56vvvrKfUyTJk1wOBwcc8wx1K9fn/bt23Peeedx99138/rrr7NgwQKuvfZahg8fDsCyZcsqrE/79u1p1aoV9957L2vXrvV5XNmgUq15IiIiNZPxNUA8nNLT0+3y5curvdzq5HlfR4wYwZw5c/juu+/o3Lkze/fupUePHmzfvp0ffviBxo0b8+mnn3LjjTeyYcMGRo0axeTJk2nUqBGrVq1i9erVrF69ml9//ZVff/2VNWvWuMf9JSYmMmDAAM477zwGDBjgc8KEa8wewE8//US/fv1ISUlhyZIlHHzwwX7H+vkblxeJMXvGmBXW2nQf+2wknmlfjDF+J2EEs0/Cx9+zJSJSY0Vi2Y6aslxaVbiWC3v++ectYO+++273a3fccYd7rdusrCz3tmPHDnvjjTfahIQEe8ABB9iXX3651BJprp9zcnLsTz/9ZD/++GObkZFRajk3h8NhCwoK7IoVK2xOTo7PZdhWrFhhGzVqZFu1amX/+usvn8udeVsSLtzLoVWEGFkuLdh9Ej7+ni1t2rRpq6mbWvbCJD8/n2+//ZZTTjmF3r178/bbbxMfH8/KlSs58cQTGThwoNfEy9nZ2ezcuZObb76Z5cuXc+yxxzJkyBBOP/10GjRoQMOGDX2W+euvvzJv3jxeffVVtm7dypFHHskLL7xA27ZtcTgc7L///qWO//nnn+nfvz/JycksWbKEQw45pNw1rbV+u27VsuefWvZqFrXsiUgsUrAXJuvXr+f4448nOTmZb775hv3335/CwkJOOOEEtm3bxuLFi2nUqFG58zIyMmjcuDFOp5PZs2fz7LPPsn79euLj4znxxBM5//zzOeOMM9xr4DqdThYtWsSMGTP4+OOPcTgc9OzZk169evH000+TnZ3Nvffey/Dhw2ncuHG58n7++WdOP/10d5eut4Av2tKrKNiTcFGwJyKxSMFeGOTn59OjRw9++eUXvvzyS4488kgAHnjgAe6//36mT59O//79vZ7rCvZcrLX88ssvvPfee7zzzjts2LCB+Ph4evToQadOnXjrrbdYv349BxxwAOeffz4XX3wxBx98MADbt2/n+uuvZ8mSJZx66qnMmDGDJk2alCvzp59+on///qSkpLBo0SKaN29OcnJyqRm/0UTBnoSLgj0RiUUK9sJg9OjRTJ8+nTlz5nDOOecAxbNvjz/+eM4//3wefvhhEhK857MuG+x5ys/PZ+PGjcyfP5+3336btWvXcvLJJ3PJJZdw5plnkp2d7U6V4uJ0Opk+fTr3338/jRo1YsaMGfTt27fUMdZaVq9e7Z608cYbb3DUUUe5V/JQsBc8BXs1i4I9EYlFCvZC7JdffqFDhw7ceOONPPzww+7Xhw8fzqeffsqvv/5KYWFhUMFeQUGBe8yetZaMjAz2228/9/5///23XLDn8v3333PWWWcRFxfH9u3bSU5Odu+z1lK3bl2+//57unXrxhlnnMH8+fPVshcCCvZqFgV7IhKLlDQtxOLj4wHo2LFjqdcbNmyItdbrOL1gGGNKBXr+bNu2jTvuuAOHw8H9999fKtDz5Er6fMkllyifnoiISIzQ2rghduCBBwLFAZan9u3bs2/fPrZu3eoOCKvDTz/9xIgRI8jKyuKtt95iwIABXo/7+eefmThxIsOGDWPQoEHVVr9Y16ZNG58to23atKnm2oiISG0UdPONKdbHGHNWCOtT46WmppKcnOw12AP47bffqq0u77zzDoMGDaJevXq8//77PgO9goICRo0aRePGjb2mg5HgrV+/3mfeI1/rE4uIiIRSUC17xpg44AtgPXCqMeZ4a+1toaxYTWWMoUWLFmzduhWHw+F+/YgjjgBg9erVtGnTxmfrXkZGhs/1Z3fv3k1eXp7PsrOzs2natCnWWh577DEee+wxjj32WGbMmOHuRvZm8uTJrFy5kvnz5/scLygiIiI1U7Ate0MAp7X2ImAC0NoYc3xFJxljRhtjlv/222+kp6czbdq0IIuPbgceeCDbt2+nTp067u2ggw7igAMO4Pfff6dx48Y+t+bNm7P//vt73VJSUkpds+zWsGFDDjjgAN566y0ee+wxRowYweLFi/m///s/mjZt6vWc1atX8/DDDzNs2DDOOussjDHltmgxbdo00tPTAdoZY5YbY0ZHuk7h4ur+DWZLS0uLdPVFRCSKBDUb1xhzGvAkkA48A5wIxAM7gO7W2kJ/58fybFyAIUOG8OOPP/Lrr7+Wer1Pnz4UFBQwf/58ny17ubm5PlfJ2LZtm89WP5ft27dz0kkn0bt3b9599133RAun01luBnBBQQHdunXjn3/+YfXq1TWmVa8mzcaNBM3kDZ5m44pILAq2Ze8r4DqgEHjLWnsEcBRQF6j1o85btGhRbsweFI/bC/eYvauvvpqioiKeeOIJvzNqrbXcdNNNrFy5kueff77GBHoiIiJSOUEFe9baLGCJtTYf+MkYcxgwFEgG9oSwfjVSUlIS+fn55V4/8MAD2bdvHwUFBVUu459//mH69OlkZmaWev2iiy7CGMNpp53G77//7vVcay1jx47lueee48Ybb9TsWxERkRgW9Gxca22RMaYecC3wCXATMMxauztUlYs1ru7Zffv2Vek6P/zwAwMHDuSBBx7grLPOYu3ate5911xzDZ9//jk5OTkMGTKE3NzccuePGzeOp59+mhtuuIFHHnmkSnURERGR6FalzLklLXuTgN5AL2vtjyGpVYzaf//9Adi7d29Q51trmT17NkOGDCElJYXJkyfz77//cuaZZ7Jo0SL3cccddxwvv/wyq1ev5qabbip1jby8PJ544gkGDx7MI488ElUTMERERCT0qpxU2Vq7F9hb5ZrEIM/UK4B7xYtt27Zx0EEHeT1n8+bN7jVpPRUUFHDttdeyYsUKDjvsMM4++2wyMzMZNmwYb775JpdccgkDBgxg4sSJxMXF0bJlS0aNGsX06dNp27Ytp556Ku3bt2fVqlUUFRXRt29f8vLySElJCf0bFxERkaihFTTCqOyMW9dSaQ6Hw93KV9b27dvLnbd9+3ZuvvlmVq1aRefOnenSpQsZGRlkZGQAMGDAAL744gs+/PBDcnJyeOCBB0hJSeHqq69m+fLlTJgwgXbt2nHUUUfx44/Fja8nnHACKSkpWhZNREQkxumbvhoF0437008/MXToUP7++2+6d+9O165dywVoCQkJ9OrViz59+vDFF18wfPhwNmzYQJ06dXj44YeB4nF6hYWFrFixgkaNGtG+fXsFeiIiIrWAvu3DxOl04nQ6S71W2WBvwYIFXH755aSkpDB79mxat27t81hjDMceeywvvPACe/bsYdiwYfz9998cdNBBjB8/nv/+979MmjSJFStW0KVLF43VExERqSUU7IXBMcccg8PhYP78+aVed43Zy8rKqvAaK1eu5K677uLoo4/m1Vdf5dBDDw2o7K5duzJgwACysrLcwWbdunUBKCwsZOXKlRx/fIWLnYiIiEiMULAXBueddx5t27bl/vvvL9W6V7duXRISEsjOzvZ7/tatW7nxxhtp3rw5jz/+OKmpqQGXvW3bNubOncuAAQM4/PDD2bt3Lw888ABt27blyCOPxFpLv379gn5vIiIiUrMo2AuD+Ph47rzzTlavXl2uda9+/frk5OT4PDcnJ4cxY8ZQUFDAU0895XPpNF+eeeYZjDFce+21AEyePJl9+/Zx7733snTpUurXr89xxx1X6fckIiIiNZNm44bJ+eefzwMPPMD999/PWWed5Z4MkZKSwp49e9i9u3zuaafTycUXX8zmzZs56qijePLJJ0vtT0hI8Jsq5d9//+Wjjz5i1KhRNG3alEWLFvHRRx9xxRVXcPjhh3PXXXfRs2dP6tSpE9L3KiIiItFLwV6Y1KtXj/HjxzN8+HDee+89zjvvPKA42CsoKCA5ObncOQ899BCbN28G4Jdffim3//jjj+fMM8/0Wp61lueff54mTZowefJkCgsLmTx5MscccwyPPfYYW7ZsYc2aNe4WPxEREakd1I0bRkOGDOGII47gvvvuc4/d89WNO2/ePKZMmRJ0WatXr+bPP//krrvuIjU1lbFjx7J7925efPFF6tSpw5IlSwA0Xk9ERKSWUbAXRvHx8YwfP55ffvmFt956Cyhu2Ss7G3fVqlXccsstQc+SdTgcvPfeezRr1oxLL72UJUuW8PrrrzNu3DiOPvpooDiNS4sWLWjXrl3V3pTUSmlpaRhjvG5paWmRrp7EGGPM8caYb40xnxtj5hhj6hhj4owxLxljvjTGfGWMaVfmnP2MMd8bY7KMMUeVvBZvjHnNGLPEGDPTGKPeLKmVFOyF2ZAhQzjyyCO56aab2LdvH0cffTTfffcdH3zwgfuY77//noKCAh588MGgytixYwc7duzgtNNOo06dOsyZM4dGjRpx8803A/Dkk08yf/58hgwZovx6EpQNGzZgrfW6bdiwIdLVkxrOGLO0zEsbgN7W2h7AWmAQ0BGoZ609GRgH3FjmnBxgADDP47WzgbXW2l7A78A5Ia+8SA2gYC/M4uPjmTFjBlu3bmXs2LFMnjyZLl26cP311/Pdd98BuGfH/vTTT0GV4Zrs0aJFC6A4eDzuuONITEzkySef5LbbbuPcc89l0qRJIXhHUpOphU5qAmvtVmttbsmvRYAT2Aw4TPH/se4P7CpzTqG1dmeZSx0K/Fzy84/AyWGrtEgUU7BXDY499lhuvvlmZs2axeeff86sWbNo2bIlo0aN4s8//6R9+/Y0adKEzz//PKjr//vvvwAccMAB7Nu3j99//53jjjuuVKD38ssvaxZuLdGmTRufAR3gs4UO8HlemzZtIvmWai1jTBtjjPXYHMaYv4wxE0q6KI0xppUx5hJjTJExpkcIyz7cGJNnjHnVzzFpxpiPjDF7jDH/GGOmlO0qNcYMMcb8ZozJNsb8bYwJOOAyxhwMnA58QHFwl09xC90zwHMBXOJXoHfJz30pDhJFah2NX6gmEyZM4IMPPmD06NEsWLCAV199lUGDBjFs2DDeffddunfvzuLFiyu8zt9//13utbVr15KQkMCePXtYvnw51lo2bdrEjBkzFOjVQuvXr6/W86Ra3A28B9QB+gEPAuuAvcA7YSrzWeCHCo55DtgBtAAaAp8BVwNPAxhj+gGTgAuA70uOo2Rfa+CVkl87enTljrDWbjTGpAIvA8OttQXGmNOAOGttW2NMOvBYyXX9+QDoaYxZDKwGtlf0pkVikYK9MCk7Ni4pKYlZs2Zx/PHH8+ijj/Liiy/y0Ucf0bNnT8aNG8eIESN466236Nq1q88VM/bff386dOhQ7vWvvvqKJk2a0KZNG1asWIExxh3ozZkzR4GeSM230Vq7suTn5caYm4DjgNspHsuWDrwYqsKMMUMoDiS/AQ7zc+jBwBRrbR7wjzHmE+BIj/33AvdZa5eV/L7FtcNauxHoWVLeUmttT4/yE4A5wARr7R+ulwFXgtJdwH4VvQ9b3GR9U8k1JwAV/x+1SAxSN241OvbYY7nllluYNWsWn3zyCZ06deL6669nyZIldO3aFWOM12TLFdm1axeNGzcG4LvvvnOP3bvvvvsU6InEkJJZqecDjYG/rLUZJUHgmgrOG1mmO9i1zfJybCpwHyVBUgWeAoYYY5KNMQdR3OX6Scl14ikOQpsYY9YYYzaXdPMmBXDdCykOZu82xiw1xlxAcathK2PM58DrwH3GmObGmHs96v4RcAow3Rhzccn+pcaYRUCBtfaLAMoWiTkK9qrZhAkTaN++PVdccQX79u1jwIABOBwOVqxYQefOnYMK9nbv3s0BBxyAtZZly5ZRv359GjZsyP/93/+F4R2IhI4mjATsJWOMBQqAN4B3gWmVOP89ilsAy253ezn2fmCGtXZTANf9nOKWvAyKJ1As53/dys0o7nY+j+KJER2BTsBdZS/i2apX8vtsa+0B1tqeJdsb1toia+0F1toe1tpu1tpvrLX/WGvv8Tivv7X2QGvt8dbal0r297TW9rHWBpfuQCQGKNirZomJiUyZMoXNmzfzwQcf0LVrV1JSUvj+++856aSTyMzMrNT1CgsLycnJoWHDhmRlZbFr1y4KCgro1KmTe4k2kWillC4Bu5viYOloYD9r7VnW2uxAT7bW7rHWrvSylQrojDEdKZ7I8ERF1zTGxAELgLeBFOAAiidAuKb9u2bTPmOt3Wat3QU8DvQPtN4iEhqKBiLANbOxsLCQ+Ph46tWrh8PhICkpyT0rMlCu4+Pi4kqdW7du3dBVWKQK/LXeaZZvwDaWBGerrLWV+z9CKtWN2xNIAzYaY/4BbgbONcb86OWyjYBWFI/Zy7fW7gZmURLMWWv3UNzaV7k/anhPquzlmImmOMHyvJJu5DRjzM6SbtulxpgmlS1XJFbVymBv2rTK9H6E3pw5cyJaPkT+HkS6/FCJxPuI1L3zVa6/VC8VpXvxNwO4ousG2/3rL/iMYYF2406jODeda/8LwIfAqWUvWNJStw64yhiTYIxpCIwEVnocNgu4zhjT1BizPzCG4hmyFfGWVNnNFK+QcWhJguWFwCUluz736Potm3NPpNZSsBcBr7/+ekTLh8jfg0iXHyoK9opTtvgK5ioK6PxxXbdLly5+r1/Z7l9/XcexKtBuXGttTsk4t3+stf8AWUCeZ+BkjPnYGHNHya/nAKcBOymeJFJE6ZUt7qc4fcufwG/AT8DEAOrrLamyp5OBj0t+/hg4qeTnE0ta+x40MR69i1SGicQfOGPMTor/zy1S2lH8hyeay4+jONWAofiPXQJQj+IB2rbktXDXIZyqUn4ba63XLpoIPFuRuI+R+uxqQ7k+ny2pfiVJld8ATrLWFni8fgfwq7X2nZIWxbnAGRT/ncwBpgMfW2vfqv5ai0SfiOTZi/QfU2PMcmttem0tPxrqEK7yq/vZisR9jNRnV9vKlfAyxjSn9Dq2LudZa/8xZZIqlzlmD//Ls7cf8K+1Np/iFTYwxrwNdAMU7IlQe5MqR7oPMdLlQ+TrEOnyQyUS7yNS9662lSthVNJNfJK3fT6SKnv6ChhHcTB4KvC1MSbVWptRsv9kIttzIRJVItKNKyIi4osxZjjF6V9+KXnpeYpz+l3lyqtnjHmI4mBxBzCC4jVwJ1DcjbsOuMRaW9XhLiIxQcGeiIiISAyrlbNxjTGja3P50VCHSJcfKpF4H5G6d7Wt3EjTsxV7ZYpESq0M9oBI/yOPdPkQ+TpEuvxQicT7iNS9q23lRpqerdgrUyQiIjJB44ADDrCRXPcyOTmZ9PT0iPVfR6p8p9OJw+EgPj6+Rt+DFStW7PI167a6n61I3MdwlPnnn3/idDr9rqfsKvfnn3+mYcOGtG7dulqW5KvOe6xnKzJ/F2rDe/X3bImEXWWSlYZq69Kli3U6nV636tClS5eQXKds3YuKimxmZqYtKiqyDofD53sMVfmVratn/cp+BsFe099W0T3wd64/wHLr59mqTuEqz9d9KSoqsp06dbIOhyOk5fXr188ef/zxfj9L12fWqFEje8UVV9iioqIKP69gnw/P63q7x4GcF4za8GxFW5mRKre6y/T3bGnTFu6tVnbjjh4dntb73NxcMjIyyM3N9brf6XSSnZ3NZZddFpbyKxIXF0dKSgpxcXFcfvnlEamDS6TuQaiF61nyJTc3lyFDhpCTk1Ot5QKlnpmEhIRqadWD6r/H0SIS7ztS97o2vVeRSIjIbNz09HT7ww8/eN1Xk1a4KXvvnE4nubm5JCUleV1rMzs7m4yMDFJTU6lfv351VrVcXcsK5r47HA73+/X2xW+t9Xndsvs8711cXJzf+hhjVlgfSXbT09Pt8uXLK/tWoo6vz8t1n5KTk0MabJ122mn88ccfrFy5kgYNGnitT2FhIbNmzeL666/niiuu4Omnn3bv9/c5e1P28/7888+5+OKL+fTTTznwwAMrfA7C8TyXnBfzz5ZEhr9nSyTcamXLXrh4tpx5k5SURGpqKklJSdVcs/CoqCUzUteKZRU9Y8G65ppr2LRpEwMGDCAzM7PUvoKCAqZOncrhhx/OVVddRZcuXarcMuz5eVtrGTduHBs2bODee+/VcyAiEmIK9qpRuL6oIyWUwWusBcI1zRlnnMF//vMfvv32W3fA5xnkXX311Rx00EF88sknfP3113To0KFK5Xl+3p9++inLli3j8MMP54033mDnzp16DkREQigiUYfT6cTpdEaiaPHC6XSSlZVV6c8k2ODV6XSSk5NTqrxYC4RronPPPZdZs2bx7bff0rNnT3dL3kEHHcTHH3/M119/zSmnnBKSoRZxcXEkJydjjOHee++lVatWfPbZZ9StW5cnnngioOfANQZWf0tERPyLyDera6xXKFU0EyXYemZlZeFwOEJ63Wjj6lLLycmplvdYtgsvVu9rTZObm0ufPn2YNWsWq1atKtWS169fP6D8vzOHwxF0wGWtZcGCBSxbtoxx48bRqlUrrrzySl599VX++uuvCp+DYLr+w/F3QkQk2kUk2HPleXNNYvDcoklFXybe6u/aXIPLfW3VzV9dgu1C9XdNf/cgOTnZXV603J9oU9G9DQfX5zJs2DB2797NN998w6mnnkpcXJzPzzIvL8/9PwrBvI/77ruPVq1acckll2CM4ZZbbqFu3bo8+OCDFU3ScdfX298SERH5n4gEe64vj2hXW8aRVXcXqrpso1NcXBz169cnLi6O1NTUgIIm17+R5OTkSpfnGqt3xx13ULduXQCaN2/ubt1bs2ZNwPUVERHf9FfSDwUlIv5V5d/Igw8+SPPmzRk1alSp12+55RYAJk2aFJI6iojUdjEXxWjQtkjN0LJlS/755x+mT5/ufs1ay6RJk3A4HLRs2bJa61PyNyPm/iaKiERkbdxwco2zA0hJSYlwbcQb12xcX8mYpXaYNWsW2dnZXHfddQBcffXVjB07lqeeeoobbriBu+++u1rrUzI2N75aCxURqQYxF+y5xtfF+ji7miwnJ0cBuVC3bl3mzp3L4MGDue6665g/fz6LFy/mhhtu4PHHH6/2iRYlfzMcvvavWrXKZ53atGnD+vXrw1MxEZEqirlgzzWGKBQCScUQ6i+kcC0D5e+6/pY1CwfXYP7aHpBX5TMJx+cViWekbt26vP766wwZMoT33nsvYoEe4Gpl9jn+o6CgwOc90gxgEYlmMRPsheOPbSz9AS+7Fqmn6k5X4ZpFKcX8fTbRIhzPiOt6SUlJzJs3jxUrVnDccceF9VmMpX/TIiKBis5vFgk5rT0bvfTZQJ06dejWrZuCMRGRMIiZlj3xT2MZo5c+GxERCSe17EWZcKWOUc7A6FXRZ6N0QiIiUhX65g9AdX7ZqktPyqquZ0JBpYhIbFI3bgCqM3efuvSkrOp6JpSjUkQkNtX6YM8zlULZWZGudBPVGYCFMnWMxIbqeiZ8PeeBzBbWxAoRkehV64M9T2VbNlzpJuLj46stVUi4vjT1ZRx9qjuPXkV8Pedq8RMRqdkU7HlQF6pIeUqCLSJSsynY86AuVJHy9O9CRKRm02xcERERkRgWsmDPaFCYiIiISNQJqhvXGBMHXA1sBzZYa7+31lpjjLH+VlMXERERkWpV6Za9kha8xcBxwLXABGPMzQAVBXrGmNHGmOW//fYb6enpTJs2LZg6h5S11u9W3WU6nc6g9lWlvpG6Bw6Hg6ysLBwOR0BlTps2jfT0dIB2xpjlxpjRrn3R+GxVxPVeN23axODBg1m6dGlA9z3Yz8vzGG/3vqLnq7pV53Pp79kSEanpTGX/aBpjWgNTrLVnGmMOBM4DBgFzrbVTA7lGenq6Xb58eaUrGw7+3r8rv1hycnJIlxmr6AvZV4+4v30uwfSm+8s1GOw1AykzOzubjIwMUlNTy00AqCAtyQprbbq3fdH0bFXEWsvmzZvp3bs3a9asISkpiQ8++IBevXoBvu+Br+enoufV8zxv997hcJCXl+czn151j9So6G9TOOrj79ny13FhjIlIQCw1h79nSyTcAo5gjDFxxpjeQF/gAGPMqdbarcCbwLdAH2NMw/BUMzJc+cVycnIiXZVqU53LtSUlJZGamlprU3q4Ar3t27fz7rvvcsghhzBw4ECWLFkS1PUq87x6u/d5eXlaqk9EJAYFFOyVjNH7FrgPGAWkA68bYzpYa7cBTwGtgNRwVTQSXF+Irjxj1a2oqIjdu3dXeFwo1zStzgDMldIjlK2mNYVnoLdgwQLOOOMMFi1aVKWArzLPq7d7n5iYWKuDbxGRWBXot+xtwL/W2pOA94AbgE+Bj40xFwPDgHggLxyVjJS4uDjq168fsWDk5ptv5vDDD2fXrl1+jwtla1xtDsCq0+23386aNWv4+OOP6datGwBNmzZl0aJFNG7cmBEjRlT6mlX97PTZi4jEpkD/qq8F0owxJwKnAWMpbsk7EDgKOB64zFq7Iyy1rIU2b97MCy+8wN69e3n88cdxOp3k5OR4bb2r7d2hNVH//v0B2LZtW6nXCwoK2LFjh3u/FAtl67WISG0TaLC3GJgPPA4cRHGANwpYCjwKDLXW/jccFYxl/r7AHn74YZxOJz179mTKlCls2rTJZ+udWmRqngsuuIC2bdty3333lfr8XZ/7HXfcEcHaRZ/qHEsqIhJrAooOrLU7rbV3UNyd+weQBJwMNAJyrbWO8FUxdvn6Atu8eTMvvvgiF198MVOmTCE7O5sXXnhBrXcxJD4+nvHjx7Nq1Srmz58P/O9zHzVqFG3atIlwDaOLWq9FRIJX2aagDUAz4A3gJuBia+2+kNeqlvD2BWatdbfujBs3jnbt2jF48GCeffZZsrOz/bbeRSJfngRv8ODB7tY9h8NR6nOvSr7FWKTWaxGR4FXqL6e1dh1wFnAz0M9a+3MY6lStjDF+t3CWGR8fT/369YmPj3e/tnXrVner3sEHH4wxhvHjx5Odnc2TTz4ZlvpG8h5UZ5nRJiEhwd26N2XKFHerXlpaWkV5BoO6dxWdFxcXF1WfiZ4REZHQqPT/Jltr/7HWrrLWbg5HhWo7b2O22rdvzwUXXMCUKVMqnJnrjQa3Ry/X2L0bb7wRp9PJmDFjKjxHn6eIiFSG+kSiSGZmJi+++CKnnHIKaWlppfZddtllZGVlMX369EpftzYmh64pXGP3AIYPH07btm19HusK8nJycjRZQUREApYQ6QrI/6SkpHD66afz7rvv8uyzz3LNNdcAxQP3r7zySho0aMDJJ59c6eu6xgRGKjm0+HfBBRewb98+zj//fL9j0lxBe/369TVZQUREAqZgL4rExcXxxhtvMHjwYK677joABg0a5F5p4Z133uGEE04I6ropKSka5xSl4uPjueqqqyo8zhXcJSYmEh8fH+5qiYhIjFA3bpSpW7cuc+fO5cwzz+S6666ja9eu7iW1evXqpdmItVgoZ6Tm5eXx6aefatyfiEgtUCsiB2stDoeDrKwsHA5HyFJV+Ep94Sor2C9SV8B31llnkZeXx4IFC+jWrVuFqVXCkXZD6VzCx9/z42u1lFCUl5OTwxlnnMGpp57K6NGjKSwsdP/bqGxd9RyIiES/WtON6xrvBMVj46qrrPr161fqXFdXa7169Xj77bfJzc0NeKydUlLULL4+q7y8PL/PT1U+49zcXAYNGsSiRYs444wzmDFjBkVFRTzwwAM+yxMRkZqt1gR7rvFO1TGoPVQTIowxUTGpwul0kpubS1JSkrqRq0G4JtR4BnozZsxg5MiR3H333UycOBEgqJneIiIS/WpNsOca71SdZYWrla26g6/qbBWV8D0/I0aMcAd6F198MQD33Xcf1loefPBBDjzwQB588EG/11DgLyJS89SaYC+WVHfwVZ2tohI+iYmJWGvJzs4u9brr90A+XwX+IiI1j4K9Gqi6g6/qbBWV8JkxYwaZmZnutD5XX301Y8eO5amnnuKGG27gzjvvrPAaCvxFRGoeBXs1kIIvccnNzcXpdAb0PLhmebvyOM6fP5/Fixdzww038PjjjwfUbaxnT0Sk5tGgGz8qSjfhdDqD2qdUFQLBpzNx7d+0aRNHH3007dq14++//w7o2fLM41jZQE9ERGqmWtGyF64vsupOdRKJL2QFAeFTlXu7efNm98oqderUoVevXixZsoRDDjmkwvLq1avHvHnzWLFiBccdd1xA9dBzICJSc6llrwKuxecDTXDrOl6tdxIunoHeggUL+Oyzz8jOzqZXr16sXbs2oGvUqVOHbt26KYgTEakFFOxVwDX7MDc3NyzHi1TGrl27SgV63bp1o1OnTqUCvq1bt0a6miIiEkUU7FUgKSmJ1NTUgGcfVvZ4kcpYt24da9asYcyYMXTr1s39eqdOnbjtttvYtGkTv//+ewRrKCIi0SakwZ6JwT6hyi4+H+6EylK7paen0717d2bMmEFeXp779cLCQqZOnUrnzp3p1atXBGsoIiLRJuhgzxgTZ4y51hhzvjHmWABrrY3FgE8kWhhjuOeee9i6dSsvvvii+/VXX32VtWvXcs899+h/NEREpJSggr2SgG4JcBxwLTDBGHMzFAd8gVyjNqYkqexkj+oSbAqQcF03XPWJRsE8Ez169KB79+48/PDD5ObmUlBQwMSJE+ncuTP9+/ev9s9SaYZERKJbsC17LYA91trhwIXAJ8Dpxpgr/J1kjBltjFn+22+/0bVrV6ZNmxZk8dXDlVrF1xYXF1epfbV98kZVg91p06bRtWtXgHbGmOXGmNGufZ7PVnp6etQ/W0C5Z6Ls8+JLXFycu3VvxowZvPbaa+5WPa1XG5xp06aRnp4OXp4tEZGazlTm/7yNMXHAcCANOAO4x1r7oTGmBXANcAQw2lq719910tPT7Q8//OCrjIDrU9M4nU5ycnJITk6Oqi/lip6BYD+TstfNzs4mIyOD1NRUv+MaK6pPXFzcCmtturd96enpdvny5UHVNxKCeSZcLWa9evVizZo1JCYm0rBhQ1z/pvx9XqH6LMvuC0eZkWCM8flsGWN8dlwYY9SKKX75e7ZEwi3gpMolXbdfAN+VnLcWeNEY08Na+6cx5ingPSAV2BuGutZ4cXFx1K9fP9LViBitq1pesM+Ea+xenz59AJg/f74CDhER8aoyK2ikAz9Za28yxiwENgPNgE+NMfdSHOTFA3l+riE1gNPpJDc3l6SkpJC2QIZiXdWSLuDoaRaNoJ49e9KvXz+ysrI488wzgeL7k5eXF/LPzlO4ng8REQmPygR7mcCpxphPgK+Bd4FDgROBThQHfpdZa3eEvJZSrVzjyICoW/S+ZLxjfKTrEQ2MMbz//vululHz8vLC/tlF8/MhIiLlVSbYWwN8CJwJzLDW/miMGQS8DjwA7LLWBj3N1NVaEG3j2WqjaO5uLamTI9L1iBZ169Yt9XtiYiIQ3s+u7PNRHa2JIiISvID/Mltri4CnKR6rd0lJoHcecACQX5VAD/7XWpCTk1OVy0QN1yD6nJwcLrnkEsaNG8fOnTvDmqoiVOkxKptIujqV1Klac9eEK+2I65hNmzYxePBgli5dWuXnwN9nV1FKm0DLLFuGqzWxts4yFxGJdpVp2cNau84YcxnFM28nAHuAS6y1+ypbcNkZesnJyaX+Gwtyc3MZNGgQixYtAuCZZ57h2muvZezYsTRp0iTCtfufilJ9VOW61XleTbR582Z69+7NmjVr+OCDD/jggw8qXAEjEjNf/V3X899ubfrsRERqiko33VhrNwC3AT2As621P4ekIiWzEqOxNSkYnoHezJkz+eWXXzjzzDOZPHkyhxxyCOPGjWPXrl2RrqZEkCvQ2759O++++y6HHHIIAwcOZMmSJZGuWqXE2r9dEZFYE9RfZ1ssI5gWvdqgbKA3cuRI2rVrx2uvvcYvv/zCGWecweTJkzn44IMZN24cu3fvjnSVJQScTif79gX2T8Iz0FuwYAFnnHEGixYtqrEBn4iIRC/9r3gYzJgxg4ULFzJ27FhGjhxZap8r6Pvyyy+Jj49n0qRJXHnllRGqqYTSzTffzEEHHRRQoDZ+/HjWrFnD1KlT6datGwBNmzblgw8+oKioiEsvvTTc1RURkVpCwV4YXHDBBRx55JE899xz5b749+zZw4QJE+jfvz8ZGRmcc845TJw4MSz18FyezFoblevy1lRll37bvHkzzz33HPn5+QG1zI0ZM4ZGjRpx2223sXbtWqC4Rfjyyy+nqKiIcePG6bMSEZGQULAXBk2aNGHRokUcfPDB7i/+PXv2cM8993DwwQdz//3307dvX3766Sfmzp3LQQcdFJYvds91V2v7uryh5rqfeXl5OJ1OHnjgAZxOJ998801AXbHHHHMM7733HtnZ2fTq1YvVq1e7u/6ff/55Bg4cGPaZ6VVdq1hERGqGSs3GlcA1bdqUxYsX07t3bwYOHEidOnXcLXlXX301PXv2JC4uzr1eLIQ+Qa23fHnRmDuvJnLdx8TERNasWcNLL73EqFGj6Nq1K4sWLaJPnz4MHDiQDz/80Ofs2m7duvHee+9x5plncswxx2CtZebMmQwfPtydczKclBxZRKR2UMteFVSUs8wV8HXp0oW+ffuyYsUKXn75ZXegB8VBQ2pqqjt4qEoetLLHGmNKpcMINv9aReU5HA6ysrJwOBwhyxkY7Vy55owxPPXUUzidTsaNG4e1liZNmrBw4UIOOeQQBgwYwOLFi73eV2MMJ5xwAp999hlHHXUUs2bNYuTIkcTFxbk/N3+5/Xzd90CVffZERCQ2qWUvTFy565o1a8aXX37p87j4+Hjq168fsjIrqk84xHoLkb/7tnXrVmbMmMGoUaNIS0tzv96sWTMWLlxI3759GThwoNf8ea7PpHPnzqxcubLS9arsfS/7PkL57ImISPRSy55UWW1uIXr44YdxOp3ccccd5fY1bdo0rOlUavN9FxGRwCnYkyqL5uXVwikzM5MXX3yRU045hTZt2ng9pmnTpsyYMYOCggLOOOOMgPPwORwOvvjiC6699lrS0tKYNGlSuWNq630XEZHK0bdECIVzdqNmTkaflJQUTj/9dD766COeffbZcvvXr1/P6NGjOemkk4iPj+eKK66gQYMGPq/nGeC1atWKnj17MnPmTIwxTJw4USuuiIhIUBTshVA405u4rl1ROg4FhdUnLi6ON954gzPPPJPrrrvOHfC5gry2bdvyyiuvcMUVV7BmzRoee+wxr61wubm5jB07lpYtW9KjRw9mzJjBiSeeyJw5c9ixYwcffvgh2dnZPP7449X9FkVEJAZogkYIeUt1EuprV5SOI1STJZxOJ7m5uSQlJREfHx/0dWJd3bp1mTt3LoMHD+a6667j448/5tNPPyUuLo4LL7yQcePG8X//93+lzvG8twUFBZx55pksWrSIs88+m8GDBzNgwIBSn1379u254IILmDJlCmPHjqVx48bV/TZFRKQGi5mWvaqkLAn0mt5SXXgKxRgqX/V3pVHxd21rbalB+97SdfhL5eG5ebZS+roPVWk9DMfnFS4V1dUV8A0aNIiFCxdyxRVX8Oeff/Lcc89xxBFHlLue697u3LnTHejNnDmTefPmMXjwYFJSUsp9Jnfeeae7dS+QdDihvq816fMSEZHS1LJXCWVbzcKRzqQq13PVp7Itet7eh79WSs/7UJXUHZ4tXDV1koHr3tWrV4/58+eTlZXld1weFLfO5ubmMnToUHegV3YN5bKfyZFHHlmqda9JkyYB1S8W7rGIiFSN/vpXQnWnuojk+DvPpMFlheo+1OQl3Lx9NsaYCgM9gPz8fL+Bni933XVXpcfu1eR7LCIioRFzwV44A6TqTnUR6i/qQO7N008/7TXNh6dQ3YeanCeuKp/NsGHDKh3owf/G7j3zzDOsXbs2oHNCdY818UdEpOaKuWAvlloyQh0M+bo31lqys7PZuHEjt956K3fddRdr1qwJSZn+1OQ8cVX5bAoLC0lKSqJHjx6VPve+++6jXr169OzZM6CAL1T3OJb+XYmI1DY171u2AjW5taisqn5Rl22N8XVvXF/kDz74IE6nkzp16jBx4sQq1z+WVeWzefbZZ4mPj+eyyy6rdEvZYYcdxsKFC8nOzg444AuFWPp3JSJS24Qs2DPhWni1kiLVWrRr166om5VYtjXG171JSkoiMzOTl156iVGjRnHllVfy6quvVkvrXm3UunVrHn30URYvXszUqVMrfX7Hjh1ZtGhRtQZ8NbkVVkSktgvqL7cxJs4Yc60x5nxjzLEA1lpb1YCvovQOoUqhEup0FLNmzaJp06ZcfPHFFBYWhi0dhbe6l00H46kyrTFPP/00TqeTcePGccstt1CnTh0eeOCBqAtgI8HXfc/OzsbhcAR1zcsvv5y+ffty6623sm7duoDS4XjuO+aYY9wtfEcffTS33347O3fu9HueUq+IiNRSgQRYZf6oG+BzYHbJfz8Cbg7w3NHA8uTkZNulSxc7depU68npdFa4BSMc13Rdd+bMmdYYY9PS0ixgR44caYuKiqp0XX/lld0yMzPtli1bbGZmZtBlrl+/3tatW9defvnl7uuOGTPGxsfH299//z3E7yJ4U6dOtV26dLFANrAcGG0DfLaqoqL7Hqz169fbBg0a2N69e7ufGdfmcDh8Pq+e+/7880974YUXWmOMTUlJsbfddpvdsWNHWJ71cPwbihb+ni37v2fM5/n+9olYay2w3Fby+1abtlBtlT8BDgTe8/j5emARcEWg1+jSpYv1puwXSFFRkc3MzCz1RRiMcH1RuQK9vn372uzsbHvnnXdawF566aW2qKgo6Ov64q3uZe9RMEaPHm0TEhLsunXr3NfdunWrTUxMtEOHDg3xu6g6f380fT1bVVHRfa/KdV944QUL2GeffTaoYM+1rV69OqCgL5T3wPM+OByOoK8dTfw9Wwr2pCoU7GmL5GasDawLxhgTB4wFfgceBG631n5kjGkBXAMcUfJ/w3srulZ6erpdvny511ZGT9nZ2WRkZJCamupOFBxMT3FF7zGYa86dO5chQ4bQp08f3n33XfeKFXfffTcTJ07kmmuuYcqUKZW+rj+e72PLli288sorXHvttaVyu1X2vWzdupWDDz6YkSNHlhs/NnbsWJ5++mn+/PNPDj300KpVPoSMMSustene9vl6tqoiHM+P67rWWvr168dXX33FsGHDvC6HZ4xh6NChdOvWzX2erzJ//fVXJk6cyOuvv05ycjITJkzgpptuCkldvfH8N1qVBNvRwt+zZYyxvu6DMabC50RqN3/Plki4VWYFjV7AZOAmYB3wijGmp7X2F2PMk8D7QCqwN1SVC+das1W1bNkyrLWcfvrp7voZYxgwYACTJ0/mm2++wel0hmVA++bNm+nduzdr1qzho48+4qOPPgooma83O3bsoKCgoNz6rQDt2rXD6XSydevWqAr2YkleXh4FBQUUFBTwn//8x+vqJ7m5ucyYMYMPPviAXr16+b1eu3bteO211+jTpw+XXXYZb7zxRqlgL9QCXbNZREQiKJDmP4rH6aUBW4HFwB3AAooDu0uAG4HvgaaBXC/QbtxoHrOXn59vzzzzTAvYZ555xjqdTvvNN9/YBg0a2MMOO8z+8MMPVRrP5et9bNy40R522GG2QYMGdsKECTY+Pt6eeOKJdt++fUG9F6fTabt3724PPPBAm5OT474n+fn59pBDDrGdO3eOujFZREE3bii6RrOzs23fvn2tMcZ269bNxsfH2z///LNcV+0///xjjzzySJuUlGQXLVpUYRfv119/7X4ON27cqDF7leDv2ULduFIF/p4tbdrCvVXuYHgcuJXiMXo3AWuBh4DXgaMDvU6kgj1vYwCr8kWVl5fnDvhuvPFG9xfs+vXrwzKOyTPQ++abb6zT6bRvvPFGqYAvGIsWLbKAffrpp933ZMaMGRaw7777bkjfQyjEQrCXk5PjDvRmzZrlHiM5cuRIr+PyPAO+hQsX+qzLV1995X4Of//995A96wr2FOxJ1SjY0xbJLbCDilv2EoD/AMcBHUta+dYDbYD4yhQajmDP3zmuL86yM1dD8QXoGfB5tqRU1PricDjsu+++a7t162bT09Ptm2++aYuKinyet379eneg9/XXX7uv4XA47Ouvv27j4+PtCSec4G7hq8y9KyoqcrfuZWdn27y8PHerXjgmmlRVOII9z/t8zDHH2FNOOcV+9dVX7v9J8LzfnptnMFX2fyZ8fZaeLXozZ850X8s1A/qPP/4oF6R5C/jK1qVsoFf2Wff3TFbl35eCPatgTyqkYE9bJLfKHQyDgHuBt4HPgHeA/StbaKRaXxwOR1ha3PLz8+2zzz5rt2zZUmF9HA6HnT9/vu3cubMF7KGHHmrbtm1rAduhQwc7d+7ccl/03lr0ym5lW/gq+0Xu2brn2aoXjV/k4Qr2PO9zkyZNLGD79etnv/zyywr/R8Lb/0x4C67KBnqe+7Zu3WqTkpLcrXvebN++vVSXrutcz67bTZs2eX3Wq/I/U7WFgj0JFwV72iK5Ve5gOBhYBTxX8rsJptCalB4jVPVxOBz2nXfesZ06dXIHeTNnzrQFBQW2sLDQvvrqq6WCPldLn2cA8vXXX/v9ona18PkK+PzV1eFwuFv3XK16rmAl2oQj2CsbUGdmZtrJkyeXCvpcLX2+gr2KWvY8A71Zs2Z5DQZvvPFG99g9X8qO4Ss7Rs8Xb3VUsFeagj0JFwV72iK5Vf4EONDj50p137q26gr2QpH4NhT1+eyzz0oFeTNmzLAFBQXl6ltYWGhnz57tDvoaN25sAXcAUlE3nMPh8NvCV1FdXa17nq160RgAhDrY27Jli8+WU29B348//ug12Nu+fbt96aWX7MyZM+3MmTPtjBkz3D/PnDnT9unTxx3o+Wr582zd88Xp/F+XbmJiok1JSSk1hMDfeb6GMkTrZ13dFOxJuCjY0xbJLfgTg2zVs7b2tewdfvjhtmHDhu6WvIrG87la+lyBlysACSTYczrLd+lWlPjW8/x+/frZ448/vlRZ0SbUwd4111xjExMTfXaRu7pEJ0+ebFNTU2337t3dz5XrPrlaBl2fmbctPj7eHej5+zwvu+wym5SU5LO+ruM2bdpk69atW2oWb0XBnlr2/FOwJ+GiYE9bJLfK5NkrxVob9RlEXYu3R1qvXr149dVXGTx4MAkJCVR06+Lj4xk6dCgXXXQRgDuZbqDOP/98AIYOHUr//v1588033WX6S3xrjOH99993Pxw5OTkkJSURHx9fqfJrmiVLltCzZ0+/9zklJYWbb76Zzz77jKysLDIyMoDi/HKuvIfbt29nwYIFHH744UD55McNGjSgcePGFdbngAMOoKioqMLjPv30UwoKCgD46KOPuP766ys8J1r+TYiISPUJfcZfKcXpdHLGGWeQk5PDxx9/XG3lnn/++fznP/9h2bJlnH/++RhjAkp8W7duXerVq0dubi4ZGRnk5uZWQ20jZ8eOHfz666/06NGjUuelpqaSlJRULtDr168faWlpXrdAAr1AFRYWMnHiRLp06cLJJ5/MpEmTyMvLC9n1RUQkdijYC7Pc3FyOPvpomjRpwrx584DAu85dXL87nU6/xzudzlLbueeey2uvvcayZcs477zzyMrK8nle2deSkpLcAY2/8qy15Ofn88ILLzBo0CD+/vtvr++hrEDfe7h9/vnnAHTv3r3c/XNtDoejXN2Sk5PZsmWLO9D76KOPSE9Pp6ioqNRW0ecVzHu31jJ79mzWrl3L3XffzT333MO2bduYPn16le5dtHwmIiISWjET7Blj/G6Rqk9ycjKNGjXi7LPP5v333yc3N5e4uLgK6+tZZ9fvgZ7nuZ1//vm89tprfPfdd/Tv35/MzEyfdfXcXN19/u5dQUEBU6dO5fDDD+eqq67iww8/pFevXqxduzYs9zQcli5dSv369enSpYvPe1j2vkPx2sR9+vRhx44dfPTRRxx33HH89NNPPPHEE6VaQ/19Nr4+z4oUFRXx4IMP0qVLFwYOHEivXr3o3r07Dz/8MPn5+T7Pq8wzJyIisSNmgr1oFRcXR/369Rk8eHC1dOVaa/nmm2+49dZb+fPPPwHcAd+yZct8BnyV4QryjjjiCK666ioOOuggPvnkE3744Qeys7ODCvicTifZ2dk4nc4q1a2yli5dykknnUSdOnUCPmfnzp3uFr0PP/yQ4447jmXLltG3b19uu+02unbtyrJly8JW51dffZW1a9dyzz33uIM0z9a9UIvUZyMiIiESiVkh4ZiNG+0KCwttkyZN7AUXXBDQjFqn0+mexVnR7E2n02n37t1rn3zySXvUUUe5z2vevLn99ddf3Ss9+ErLUtHsYNfP+fn59vnnn7etWrWygO3WrZv95JNPSh3z448/2kaNGtlWrVr5zRVXtpyyKUH8IUSzcbdv324B+/DDD/tcIaPsKhn9+vUrlQ4nPz/ffvHFF+48d6+88opt3bq1NcbYMWPG2Ozs7IDured2++232zp16nitc0FBgT3kkENsly5dSp3vypPYokULm52dHfA98OSrnpFMYVTd/D1baDauVIG/Z0ubtnBvatmrJgkJCZx99tl88MEHOByOgM5p0KBBwNcfM2YMY8aMITExkalTp/Ltt9+Sn5/PjTfe6D7Gc9LGqFGjsLZy47CmTp3KVVddxaZNm3jmmWf46quvOOWUU0p1/3Xs2JGXX36ZTZs2ceuttwZ8bc8xgtXliy++AKjU5IykpCQaNGjAggUL3LN3L7nkElJTU/n0008ZMmQIP/30EyNGjODJJ5/k8ccfr1SdcnNz+eqrr3zeh2+++Ya1a9fSo0ePcl39PXv2ZNu2bXz44YeVKrMirs8mkAk+IiIShSIRYdbGlj1rrXsZsr/++iug1p4tW7bY9957L6AWOFdevrlz57pbpA466CB74YUXlmudeuihhyxg//Of/wTcsldUVGQ3b95sx4wZY5OSkmxcXJwdPny4/eOPP0odX3bFD1/8tW5WV8veNddcY+vXr+/OfRhIy97GjRvt33//Xaq187zzzrNNmza1OTk5tqCgwBYUFNj77rvPAvb7778PuGUvOzvb9uvXzxpj7EsvveS1zvn5+e61mJ955hn3uTNnzrTGGNu3b1+blZUV8D3wVJXPJFb4e7ZQy55Ugb9nS5u2cG8RKbS2BnvLli2zgH3nnXcq3bVX0b7CwkLbtm1b26FDB1tYWGi3bt1qAfvoo4+WC1gKCgrsscceaxs3bmy3bdsWUJmeXXlbt261N954ozvoGzFihP3jjz+8ruHrSzQEe0ceeaQ97bTTrLU24GCv7Jafn2/nzJljAbtgwQJbUFBgd+3aZRs1amT79+/v9z163nfPQM+VeNmXvLy8UgGfZ6AXbBeutQr2rPX/bCnYk6pQsKctkltECq2twd6+ffssYCdOnBjyYM/pdNrZs2e7W/fef/99C9jFixd7DVhWr15t69WrZ88555xS+3yt7+pt5YUtW7aUCvqaNGlSbskxXyId7HmO17O2dLD34osv2u+++y7gYG/v3r02OTnZjh49ulSrnq8VOVzbrl277BdffGFfeOEFe/LJJ1tjjH3hhRcCuneeAZ9noFeVoEzBnv9nS8GeVIWCPW2R3CJSaG0N9qy1tlWrVnbYsGF+W5F87SssLPTbApWbm+tu3bv77rutMcb++++/tqioyOu5ru7c1157zf2FXnaiRCDBp6ul7//+7//KBTi+RCLY87z+3LlzLWC//fZbd/CUn59v//jjD2uMsS1btrQ7duyw+fn5Ni8vz+c9z87Otrm5ufbcc8+1TZs2tVu3bnW36uXk5JQKCmfOnGmvu+4626dPH9uiRYtSS6mlpqbaqVOnlguy/d33vLw8e9FFF9lzzz231ESQYCnY8/9sKdiTqlCwpy2SW0QKrc3B3mmnnWY7deoUlpY9h8PhHrvXuHFj27ZtW7/nenbn/vPPP9Za614H1tc6utEgFMHe1VdfbevXr2/z8/Pd96KwsNCOGzfOGmNsXFycHTVqlC0sLLQFBQU+73lhYaF7pjPgnq27bNmyUmsy33777RawKSkptmvXrvbiiy+2kyZNsvPmzbNr164td78VeEWGgj0JFwV72iK5RaTQ2hzsjR071iYmJtrCwsKwBHuusXuAHTp0aIXnenbn1pQgIhTBnmu8nmfgm5OTY1u0aGH79+9vb731VgvYDz/8MKBgLyMjwyYnJ1vADhgwwBYVFbmDvd9//93WqVPHDh8+POAg2lu3uYK98FOwJ+GiYE9bJDelXqlm7du3Jy8vjw0bNoTl+vHx8YwfPx6Azp07V3h8u3btuPfee3n77berde3eSPr2229ZvXo13bt3L/X67Nmz2bZtG5dffjnjx4+nffv2XHfddQFdMzk5mQEDBgC477/LXXfdRXJyMo888ghxcYH9k6staxOLiEj4KdirZg0bNgQgOzs7bGVccMEFPP/884waNSqg48eOHUuTJk145ZVXwlanaLFs2TJOO+00DjvsMC655BL36z/99BM33HAD3bt35/TTTycxMZGBAweyadOmgK/9wAMPMHv2bNLT00u9vnz5cgYMGECzZs0CvlYk8g6KiEhsUrBXQ/3zzz888sgjXoPG+Ph4rrjiCvbff/+ArpWQkMA555zDBx98QE5OTqirGjWWLVvGqaeeSrNmzVi8eDFNmzYFipc/O//882nSpAlz5swhPj4eoNJrxR566KFceOGFpV4rLCxk48aNHHrooZW6lmtt4kBbAkVERHwJ6TeJ0Urq1SI7O5uBAwdy2223MXz48IBX5PDn/PPPJzs7O2a7cssGei1btgSgqKiIIUOGsH37dt588013ABgqGzduxOl0csghh4T0uiIiIoEKOtgzxsQZY641xpxvjDkWikcoK+ALjK9BlE6n0+e+oqIiCgoKGDp0KD///DMXXXQR77zzDrfeeisOh4OioqKgrut0OunevTtNmjThzTffLLe/su8hkDKrct3K3ttvv/2WU045hWbNmvHZZ5/RokULHA4HDoeDm2++mSVLlvDMM89wzDHHUFRU5N6cTidA0PfVWsvatWsBvAZ74bh30aaqn6WIiFRdUMFeSUC3BDgOuBaYYIy5GYoDPj/njTbGLP/tt99IT09n2rRpwRQfE4wxXre4uDi/+2699Vbef/99nnjiCV566SWuueYannjiCaZNm1bhub72GWNKrd0brV2506ZNo2vXrgDtjDHLjTGjXfs8n62uXbu6ny3PFr3PPvuMVq1aud/za6+9xtNPP811113HiBEjSEhIKLW5ulATEhL83rf4+Hifm79gzx9/n5X+fyr0pk2b5hprWe7ZEhGp8YKZwgscBLxb8vOBwPXAIuCKQM6vzalX5s2bZwG7cuXKSqdXefrppy1gr7/+endqj7y8PNu/f38bHx9vP/zwwyrl7/vss88sYN98882A0nxUlAsu2PQhFV2XClKvuI775ptvbIMGDexhhx1mN27c6E6TUlRUZL///nubmJhou3fvbnNzc21eXp4tLCwstd122222Tp06flOvVJQO55ZbbrF169b1mnKlojQ6/u5hTVHRZxlt/D1bKPWKVIG/Z0ubtnBvlWrZK+m6HQxcCjQzxvSz1m4F3gS+BfoYYxpWOQKtBSo7zm7ZsmWMGTOGgQMH8sgjj7hfT0hI4LXXXuOoo45iyJAh/P3330HXqUePHjRp0oT//Oc/QV/DUyjThzidTrKzs91dq778/vvvnHjiiZx44onurlvPMXqua7kmZLz++uvUqVOnyvXzZd26dbRs2TLoiRY1PQVLoJ+biIiET8DfQCVdt18DQ4DBwLHATGPMUdbabcCTQCsgNQz1jBkdOnTAGMPcuXMrdd7PP/+M0+nkiSeecM8WdWnQoAHz588nPz+fxx9/POi6JSQkMHr0aObPn88nn3wS9HVcQpk+JNCgJy4ujvr161O/fn369+9fLtCD4hmyO3fu5LTTTvM7ISMxMZHCwkKKioqCrvdRRx3F2rVrg76fNT0FS00PVkVEYkFlmhsGAP9Ya88BegOfA78CPxhjrgSGAglAXshrGUOOOOIILrjgAqZMmcKuXbsCPq9Ro0aA7/x8rVu35oILLuCVV15h3759QdfvzjvvpH379owePbpK14HicWehSh8SaNBzxBFHsGDBAhYsWMDrr79eLtADqFevHgMGDODdd9/1G8i5ciLu3bs36HrfdttttG/fnssvvzyo+1nTU7DU9GBVRCQWVOYb5F+gkzGmC1AHaAacBVwGdASOBy611u4IcR1jzl133UV2dnalWuEaN24MwL///uvzmGuuuYbs7GxmzZoVdN0SExOZOXMmW7du5aabbvJ7bHV20YU66DnvvPPYsWMHX3zxhc9jXHkK9+zZE3Q5iYmJzJo1K6D76eJ0OsnJyYmJrs+aHqyKiMSCyvwF/hGYBGRS3FV7IFAAFAHbgOHW2v+GvIYxqF27dgwePJgpU6awc+fOUoMofaXccAUeu3fvxul0et06derECSecwLPPPovD4Qjout62rl27cvPNNzNz5ky/efc8u+iCGTAaLr7Kczgc7nt16qmnkpyczLx583A6ne7uWs8tNbV4RMKuXbt8XrOilDae93PGjBmccsopfPHFF37Prei+BlJmdd9zERGJXgEHe9baPGCmtfZPIBH4jOLxe+OB96y1wQ9sqmWMMYwfP57s7GyeeOKJgFKkuFr29uzZ4zfVx3XXXcfff//NJ598UqnUK2W3CRMm0K5dO6644gqfLVueXXSu86y15OTkYK0NOn1IVdOOBJImpUGDBgwYMID58+djraVOnTrl7qXrnmdkZASd0sbl/vvv55FHHmHlypX06NGDU089lW+++abc8a6ArX79+qXuq7fr1oTJD0ohIyISeZXqW7HW5pf8GA+cC9wHDLbW/hTqisW69u3bV2rsnmew588555zDgQceyDPPPFOl+nl2P44ZM8brMd666GrSgPzzzjuPnTt3+uzKDcWYPZc6depw00038ffff7uDvu7du3Pqqafy9ddfu4/Lzc0lKyuLuLi4Crs+a9K9FhGRyAl2IM2vwPPA6dbaX0NYn1ol0LF7RUVFrFy5EmOM3zF7UBxUXHnllXz66af8/vvvVarfsccey80338wrr7zCZ599FtA53gbkR2MLlNPppEWLFgDMmzfP6zGhGLNXVkpKSrmg7+STT+bUU09l9+7d7vuXmJhY4bUqmvwQjfc9VrVp08Zn62VaWlqkqycitVxQwZ61NgcYU9KlK0Fq3749vXv39pmW47fffmPIkCE0bdqUk08+mbi4OA499NAKr3v++ecDxbn5quq4444DYPXq1QEdXxNa+1avXs2hhx5K9+7dAUhOTvZ6nCtnoWvsXiilpKQwduxYfvnlF5o1a8bChQtZt25dpSY0VHRstN33WLZ+/Xqf4yQ3bNgQ6eqJSC2XEOyJ1trCUFaktmrdujW//lq+cTQ/P5/zzz+fLVu2cPbZZ3P66afTp08fd2uTP/Xq1QtJ3fbs2cO1115Lhw4duPrqq4O+jqvlKRrSb2zYsIHTTz8dp9PJzJkz6devHwceeCCFheUf5+nTp5OamsqZZ54Zlrrk5uYydOhQduzYwcyZM13LdQHFrXJ5eXkkJSUFPZM1mu67iIhETtDBnoRG8+bN2bFjB06ns9SX+qRJk/j111/54IMP6N+/P0C1d8fdeOON7Nixgw8++IC6desGfR1XC1SkuRIpZ2dns3TpUjp06ODz2H///Ze33nqLUaNGhaXuubm5DBo0iMWLFzNz5kxGjhxZan9eXh4ZGRkAQZcfLfddREQiS8FehDVr1gyHw8Hu3btp0qQJRUVF/Pbbbzz44IMMHjyYfv36uVud8vPz/S7t5XQ6SUxMdKfY8Ey3UTaYLHte2X3vv/8+r7zyCnfeeSedOnXymrbDNeM22nira2ZmJqeddhobN27kjTfeoHnz5uzcudO93+FwuBNXA8yePZv8/HxGjRpFUVERCQne/6lU9r5CxYEeFLfOusbulX0//sqM1s9EREQiR8FeBHh+GTdv3hyAHTt20LRpU6y1XHPNNaSkpPDYY4+VOtaV5sMXzzQgrnJcP/s7t+y+PXv2cNVVV9GhQwfuuusu9+tOp5Pc3Fx312IgKVQioWy5+fn5nHPOOfzyyy/MmjXLPQ7RU1xcnDuQttYyc+ZMjj32WLp06eI3gKrMfYXiQO+ss85yB3oXX3yxz3Nd76XsNcpe19vnItHDNXnD3/7169dXX4VEpNZRsBdhrmBv+/btHHXUUcyYMYOvv/6a6dOn06xZs4jUydV9+/7775fqvnUN+Ifguxarm8PhYPjw4SxevJinn36aU045pcJzvv76a3777TemT58e0rq4WvQWLVrks0XP89hA73VN/Fxqk4oCOQXnIhJuCvaqgcPhICcnh4KCAveWn59PQUEBW7ZsAWDbtm189913jBs3jl69ejFixIiw1ee7777jt99+c//u2XK1efNmXnnlFe666y46d+5cqguxJgz4dzgc7rx4e/fu5f7772fevHk8+uijDB48OKBrTJ8+nbi4OFq2bMnff/9N06ZN2W+//apUr7y8PAYNGsTChQuZMGECw4YN83t8YmIi27dvZ926dWzatKnUjNqyLY3p6ekcfPDBUf25iG/+Wv78tfqlpaX5nOmr1kIR/4wxE4HuwHZgREmWkbLHXAg8ba1tYow5Hnic4pXDtpacU2MmqppILKGUnp5uly9fXu3lRsKff/7Jaaedxrp16/weV79+fbKysqhfvz7ff/89hx12WLljCgoKfI4dg+IgoF69euzevZtmzZoxYsQIZs6c6d7n+kJp1qxZqfFqZaWnp/PVV19Rt27dCseARaJVwhizwlqb7mNfuQf6tttu46GHHmLHDt/LNltradKkCQDdunWj7POZnJxM8+bNadasGQ0aNHC/77L3p3Hjxpx77rn079+funXruvd99913HH/88e7j4uPjadmyJWlpaaSlpdGyZUt27drF+vXrWb9+PRs2bCAvLy+g+5GUlMQHH3xAr169XPcgoPOkvIqerer+e+laVSWU+yQy/D1bEl7GmKXW2p4evx8F3GWtHWKMuRJIsNZOKXNOHPAmcLC1trMx5kBgj7U2tyRQ/Mla6z1JaxRSy14Y/fe//6Vv375kZ2dzyCGHsHfvXvbu3VtuVm2zZs3cqVV69uxZ5e7bxo0bc8sttzBp0iQGDx7Maaed5t7ncDjYuXMn119/vXtljLIBy0EHHeR3Ikg0a9myJWPHjgWKJzmceOKJHH300ZW6xsKFC1mzZg3bt293bzt27HD/vG/fPp/n/vjjj/znP/9hv/3249xzz2XYsGF0796d4447jrVr17JmzZpSAd369etZuHAhW7dupXHjxqSlpdGhQwcGDhzoDgTbtGlD/fr13WV4fl7Z2dkMGTKEgQMHlgr4RETEp5MB18LvHwOTgClljhkKzANuArDWbvXYVwTUqGz1CvbC5Pvvv+fUU08lLy+PvLw8mjZtSrdu3WjTpg2tW7d2/7d169Y0aNDAfZ63fG/BuOeee3j//fcZPXo0q1atcicGdgUqBx98sDuzfyzN4GzWrJnP5d0CVb9+fTp27Oj+3VpLfHy812PL3ruioiIWLVrEnDlzmDt3LjNnzuTAAw/kggsu4IILLqB79+707dvXfbznOreeM2z9tcqULXPRokX06dPHHfD17t270u9ZolNFXbwioWaM+Qt4leLlUFsC/YBpQB9r7echuH4jYAZwCrALGGet/U8F5xwOrALmWWsvqsq1SuwPbCv5eR/QyHOnMSYeGAycRUmw57HvYOB0YGIA5UQNBXtV4OsL+YsvvmDAgAHuxLgPPvggV155pXv/t99+S7169dwtRZ78Ddrv168fXbt29bl/v/3246yzznL//vzzz9OrVy/GjBnDc889R1JSknvpr0CSM9dEq1at4pBDDvG6r3Xr1uTklBuWAUCHDh1KBd2eDjjgAJ8tZtu2bXMvu+ZSv359Lr/8ci6++GI2b97M3LlzmTJlCk888QR16tShbdu2dOjQgfbt29OxY0c6dOjgnqjj4nQ6fbaulg0+mzRpwsKFC+nbt6/fFr5AgvpYCfpjhcbdSQSdCbwThus+S/G4t2ZAR+BDY8xKa62/ZZqeBX6ozLWMMa2BV0qO62iMWVry8whgD+AaiL0fUHYd0ouAudZap+ffRGNMKvAyMNxaW1DxW40eCvZCbMGCBZx99tkYY8jLy+Opp55i+PDhAZ+/efNmn/t+/fVX2rdv73N/2TQdXbt2ZezYsTz66KOcffbZDBo0yB3sNWrUqFSKFn/XrEmcTidZWVle933xxRc+z8vNzfWakgWKx9f5Smidl5fnc1+dOnU499xzOffcc/n3339ZuHAhq1at4pdffuHzzz9nzpw57mObNm3KwIEDGT58ON26dQMq97k0a9asXAufunRFpDKMMeuANGACsJTiACodeDFE108BzgWOstZmAV8ZY94DhgO3+zhnCLAX+AY4zON1v9ey1m4EepYcW3bM3lfAOIoDt1OBr8sU2x7oZIy5CDjcGPM0MBaYA0yw1v5RhdsQEcGtwyRevfPOO5x55pnExcVRUFDAtGnTKhXoVeSPP/7gv//9L0VFRQGfc8cdd9CuXTuuueYa9u3bF/Mtew6Hg6ysLLKyssjMzHS/5927d/s9LycnJ6wrlDRq1IjBgwdz//33M3/+fP766y/Wrl3LJ598wqOPPkrPnj15/fXX6dWrFx06dGDy5Mls2rSpUmU0bdqUhQsXcsghhzBw4ECWLFkSpncjIjGqP7AJeAG4zFq7ElhT0UnGmJHGGOtlm1Xm0CMAh7X2T4/XVgJH+rhuKsXdyTd52V2pa3my1q4CNhhjvqQ42JtpjGlujLm3ZP9t1tpTrLWnAX9Za68HLgSOA+42xiw1xlxQUTnRRC17IfLaa68xcuRIEhMTKSoq4uWXX3Yvcxaob7/91u/+7OxsZs+eTXJyMp06daJr164cdNBBfs9JTEzkhRdeoFevXtx0003069cPiN1gz1pLfn6++/e4uDji4+OpU6eO39mt69atY9u2bbRq1YrWrVvTsmVLn+P0QqVRo0b07NmTnj17AsWrfLz99tvMnj2bCRMmcO+999KnTx9GjhzJOeecE9Cax02bNlULnwSdzgWU0qU2s9b+ZozJB/6x1v5diVPfo7gVsKyy3aP1KR4j52kf4H0MDdwPzLDWbvLyPAd8Lc9WPY/XxpV5KRu4x8tx6SX/nQ3M9lHPqKeWvRDYuXMnF198MU2bNsXpdPLee+9VOtBbu3ZtqdUqvOnUqROXXXYZRxxxBN999x1PPfUU//5b9t9SeV27duWGG25gxowZ/P138b/fquaNi1YJCQk0btzYve2///6kpqaWms3qTcuWLTnwwAPZsGEDixYt4ueff66eCnto0KABI0eOZOHChfzyyy/ceeed/PXXXwwfPpwHHngg4Ou4Ar60tDQuvPBC9+tOp5Ps7OxqX2NZqt/69evdyyWW3eB/Qz68bYDPc30FgVIsLS3N732NVdbaPdbalV62st0TWUBqmddSgcyy1zTGdAT6Ak/4KDbga4mCvZDYuXMnDoeDFi1acMQRRwS0SkNZru5Vf4wxtG3blmHDhnHBBReUa8XyxzWxw5Wc11++vtpov/32o2fPngwdOpS6detSUBDZsbeHHHIIEyZM4K+//qJhw4buFTIC1bRp01JjNKF4fGFGRkapBM1S+/gLBK21flvuXC2Gld1cM/+rk7/AK1z12bBhg98guwapVHRaiW7cP4GEktm1LscA3iZn9KR4/OBGY8w/wM3AucaYH4O4lrc6TzTGfGmMmWeMSfZ4/XhjzLfGmM+NMXOMMXVKXk8zxuws6cJdaoxpEkg50aJWBnvTpk0L6fUSExMrdfzHH39c8UEV8EzTEYzZsyPbGh3qzyBUKru2bLjHxcXFxZX7rINdxi0xMZHU1NSgV9qI1GcWrc9KuEXifVdUZkWBYrCtieHYwH8Lpa/36i9IrGiLkXQ4e4FjjTHtKnGOqxu37Ha350HW2mzgbeA+Y0yKMeZEYBDeu0enAYd6XOsF4EOKx9hV9lqlmOKkyodaa08GFgKXeOzeAPS21vYA1pZc0+Vza23Pks33ygRRSMFeCFQ28Prkk09CWn4wXn311YiWHytf4JGYBPHii8FNjIuLiyMlJSXo/1FQsFe9ojHYC1ZFQWKXLl2CCiKDbaH09179tc5Vtcwa4jWKlxF7PNATKtGNC3A1kATsoHh261XWI+2KMeZjY8wd1toca+0/ro3ibtu8MkGW32v5UTap8kke72WrtdbV/VE2efKJJa2BD5oa1jcfkeXSjDE7KY6eI6Ud8FuFR0Wm/DiKH66y38gJJVsBxQ9g2WMrOxArmu9BRdpYa702oXt5tuKAeMBR8nPZqcyu/XGAK6N12Xvp7/5G4j5G6rOrDeVW5tkKNz1bsVWmz2dLqpcx5g7gV2vtO8aYhhTn1DulzDEHA28AJ1lrC4wx9Sj+Ds4BpgMfW2vfquaqBy0iA7ci/cAbY5bbCK5RGOnyo6EO4Sq/up+tSNzHSH12ta3csvRsxVa50fJcSXgYY5pTvNxZWedRQVJl4yV5srU2H8gv2f820A1QsBflIt0vFOnyIfJ1iHT5oRKJ9xGpe1fbyo00PVuxV6ZUk5Ku35O87TN+kiobYxLwkjzZGJNqrXXNlDuZyPaMVVpEunFFREREIsUY8xDFweAOipdQawBcRXES6SeAX0oOfd5a+4Yx5gyKVxbJAdYBl1hrA1/hIMIU7ImIiIjEsFo5G9cYM7o2lx8NdYh0+aESifcRqXtX28qNND1bsVdmNKkt97y6y4zW56pWBntApD+MSJcPka9DpMsPlUi8j0jdu9pWbqTp2Yq9MqNJbbnn1V1mVD5XEZmgccABB9hIZFR3SU5OJj09Paz919baUsl5t23bxtatW+ncuTP16tXj4IMPtuvXr6dFixY0aPC/pfwyMjJ8rm4RFxdHUlISf/zxBy1btiy1vu26desoLCwsldRzzZo1pKam0rRpUzIzM9m2bRvt27cnKSmpWu6BP1Upf8WKFbt8zYzcb7/9bPPmzb2eZ60lMTGRgoKCcvewqKjI5333ty8xMZGjjz661PtYt24dTqeTQw89FIBVq1aRlpZG48aN3cf8+eefOJ1O2rZtW+6aW7ZsYceOHXTu3NlrmcHeO1cuMM+ks5URqWemOsv192zVq1fPpqSklHs9IyODuLg4Wrdu7V6OsHPnzlVeIisS97s2fMaRKtPfsxWJ78TacM8jUWYk3qO/Z8slImP20tPT7fLly6u9XI/yCUX5/tYYdTqdpZLXTpw4kbvvvpv8/Hw6dOjAuHHjGDVqFHfffXepheoXLFhAkybeP7OkpCSOOeYYTjzxRB577DHOP/98975hw4axY8cOXnjhBfdrgwYNok+fPlx//fV8/vnnTJgwgf/+97906NAhZPcgWFUp3xizwlfKhLZt29qpU6d6PS8/P5/27duzadOmcvdw9+7dpYIxT/72nXrqqSxYsKDUa8OGDSMnJ4f58+fjcDg4+OCDmTVrFhdffLH7mFNOOYXMzEyWLl1a7prjx4/nySef9LlkW3p6Ot9//73XfeA7yXdmZiaZmZk0aNAAb0GLv3Nd5UbimanOcv09W40aNbLelkL89NNPSUpK4rnnnuOss84CoKCggDp16lSpLpG437XhM45Umf6erUh8J9aGex6JMiPxHv09Wy61sht39OjQtrJWdoH5gQMHhrT8YIT6HtS08kPl3HPPrfYyL7vsslK/B/r8JSUl0aBBg6CXS4vUZxYrz0plReJ916bPuLY+Vy615Z5Xd5nR+lwp2AuB3NxcMjMz/S4wX1hY6P7ZM9jLzs4OqswdO3aUe23Xrl2sXLkSay1FRUUUFf1vVnh+fn6pYyP9QEay/A0b/C+CcO+99wa8pJ2vYK9si7m3FnSHw+Hzuk6nk8zMTK/7yt67QJ4/qPpyabUpEKgM12cb6l6S2vDFGMlyo/25Crfacs8V7BWrlcFeqFXUYpKdnc2sWbPo3Lkz8fHxABx44IG0atWK5557jl9++cXred60aNGCY445hsmTJ/PUU0+5v2AGDBhAXl4eY8aMYeTIkdxxxx3k5OTQqVMndu3axfTp02ndurV7HFltVFBQwKOPPsrw4cNp1qwZxx9/fLlj1q1bx4wZM7jrrrvIy8sLqpykpCRycnIAaNq0KQkJCfz555+ljunYsSM//vije4yXpz59+gBw+umn+wz4ypZXlRY7qRrXmE7P/6ELNqAWEQkH/UUKgYpaTCZOnMjmzZt56qmn3IO269aty2OPPUbjxo2544472Lx5c0BlJSQk8Oabb3LWWWfx2GOP8dZbxau1DB06lFmzZnHbbbeRmprKDz/8wNChQzn22GMZP348WVlZvP/++yQnJ4fmTdcwf/zxB9dffz1PP/00Z599Np999hktW7Ysd9yHH34IFLeczpkzJ6iyGjVqxO7du4HiwbrHHntsubF5Y8eOpW7dujz00EPlzu/ZsyevvPIKy5YtCyjgq2qLnQTPWktubi6JiYmlWlb1WYhINNFfpDD75ptvePTRRxk5ciQnnHBCqX1NmjRh0qRJGGO44447yMrKCuiaiYmJPPHEE3Tq1IkHHniAPXv2AFCvXj1OO+00pkyZwltvvcWll17KpEmT+OOPP7jrrrs4+uijQ/7+ol1BQQEzZ87k6quvJjMzk1mzZvH4449TWFjotdvtww8/pHPnzhx33HE8++yzQbXuNW7cmD179rjH0PXs2ZMffvih1OfbvHlzRo8ezZw5c7y27p177rnMmTMn4IBPIqOoqAiHw0FSUpI72IuLi6vyTFwRkVBSsBcmW7duZcWKFQwePJiDDjqIW265hS1btrBlyxYyMzPdaTBatGjBvffey7Zt25gwYQJ79uxh06ZNXre//vqLl19+mZdffpnZs2dzwgknsHfvXkaNGsXLL7/MqlWr3Nfdf//9mT17NkuWLOGyyy6ja9euAU8gqcmsteTl5ZGXl8eqVau44oormD17Nr179+bpp5+mYcOGjBw5ki5dujBlyhT32MadO3fyzTffsHr1ak488UT37ObnnnuOnTt3kpOT43Xbs2cPu3fvLrUlJSVRVFTE+vXrcTqddO/eHYfDwZdffklRURGZmZkUFRVx/fXXU6dOHR566CGcTmepLS8vjzPPPNPdwte2bVueeOIJ9u3bR15eXrnjPTcJj8LCQrZv315qc7XIFxQUuIM911ANEZFoEZE8e7HCX1dNbm4ut99+O7t37+btt98mNTXVva9Tp04cdNBB7t+7d+9OSkoKo0ePpnHjxlxyySVer/nee++Vao2qU6cOnTp1YsWKFRxyyCEcccQR9OjRA4A333yTl156ieHDh/Poo4+SlZVFTk4O9evXr+rbjmpr1qzhzDPPBIq/nFu0aMHrr79OkyZNePjhh/nss89o1KgRnTt35sknn2TIkCEceeSR7N27lw8++ACAM888k2bNmtGlSxdee+01LrjgApo1a+azzLJd461atQL+N2D/hBNOICEhgc8//5yTTjrJ3UrXunVrrrzySp555hnGjx/PYYcdVu7a5557Ls2bN+eee+5h7NixPPLII9x8881ceeWVJCYmVureqGuxavbbbz9OPfXUUq+tXbuW77//nhNPPNEd7PnKySgiEin66x8mL7/8MosXL2b8+PE+u0+//vprjj32WPr27cuwYcO48cYb+fTTT/nss898XtfpdPL333/z7rvv8sILL3DYYYeRmprKokWL3LNv//zzT0aNGsUJJ5zA1KlTSU5OpkGDBrVivF6TJk249tprufbaaxk/fjzPP/88M2fOpE+fPixfvpx7772X33//nXfffZeGDRty+eWXu+/bp59+yjHHHEPz5s0xxnD11Vezc+dO5s+fX6k6uHLyucbtpaSkcOyxx/L555+Xm0xxyy23UKdOHSZOnOjzeieeeCILFy5kwYIFHHroodx0000cdthhTJkyJehJJBIaGRkZADRo0EAteyIStRTshcF3333H5MmTGTBgACNGjCi331rLFVdcQffu3dmyZQtLly7l6quvZtKkSXTs2JFZs2axevXqcuf99ddfzJw5k/fff58dO3ZgreXzzz+nZ8+e7Nmzh++++46ioiKuuuoq6taty9y5c6lXr16tGsDvdDrJysoiKyuLb775hrPOOosffviBu+66i+XLl7snsDRp0oSnnnqKH3/8kfvvv58NGzbw+++/069fP/e1unbtSocOHZg5c6Y7dc2SJUt49tln/aZvca3K4Qr2oLj19ocffiA7O7vUZ9G8eXOuvPJKXn31VX799Ve/761Hjx4sXLiQTz75hMMPP5wbbriBww47jPfffz/o+yVV42qldQV7CQkJVU6mLCISarH/7R8BP/74Iw6Hg6FDh3odqJ2fn88777xDQkICn3zyCUceeSRff/018fHxXHfddTRv3pxnn33Wnb7DZevWre5B/q4cbZmZme5cfXv27GHEiBEsXbqURx99lBYtWoT5nUafPXv2MG/ePObNm8fvv//OXXfdxc8//8xNN91Ualk6gHPOOYdhw4YxadIkJk+eTJ06dZg9ezY///wzv/zyC9deey2rVq1yj6EDmDt3LnfddRcdO3akd+/ezJgxg40bN7qvmZeXx0MPPYQxxn3/N2/ezLx583wG3LfccgsNGzakf//+rF27tsL32L17d5YsWcJVV13Ftm3b+PDDDyuV1FtCw1rLf//7X/bbbz8SExP5+++/SUxMrBUt6CJSsyjYC4MRI0bQuHFjfC3blZiYyCeffEJqaiq9evVi9erV3HTTTUDx+K+rr76aPXv2MHv27FLn9ejRg0suuYRTTz2VQw89lObNm3P00UezaNEiWrVqRUFBAW+88QYPPfSQz3F/sa5Dhw6sXbuWtWvX8ssvv3DTTTeVGi9Z1vTp07n//vv5/vvv3d2vI0eO5MILL2TlypXccMMNvPvuu+y3334AnHbaaUDxUnTGGB555BFOOukkBg4cyAsvvMCll17K119/zaOPPkrHjh3ZvHkzffr0Yfv27Xz88cdelylr3rw5n376KdnZ2fTu3TuggO+ll17ihRdeoE+fPjzwwAMBJVWW0Fq1ahWbN2+mX79+OJ1OFi1aRGpqqteUPiIikaRgLwxSUlK48sor+eKLL3yukdepUyc+/fRTjDG0adOm1Lqphx12GGeccQZLlizh559/LnVeamoq7dq1o1+/fhxxxBF8++23tGrVinr16vH777/z0EMPceutt4bx3cWWuLg4brnlFqZOnYrT6eTff/+lc+fOXH/99SxYsIDLLrusVIDWr18/kpOTadSoEYsWLWLhwoXccccdGGN48MEH+eqrr3j00Uc5//zz2bZtmzvQ++STT+jWrZvPerieB1fAt27dOp/Hzp49m8suu4zevXvz7rvv0qhRIyVVrmZF/9/eWUdHcbVx+JlsXAmQIAUS3Iq7e3EpUKBIcS9a3FpokeJWXAoUigV3Le4OxT1ACXHi2d37/bHsfNlkNwkhmwQyzzlzIDs7c+/M3J35zXtfUas5ePAgWbNmpVSpUnh7e8v+e4rYU0jPeHp6IkmS0cXT0zO1u5duUcLGzMT333/PkiVLmDNnDmvWrDFYFxgYCOiqKxw4cACtVsu7d+8ACAoKws7OjqZNm3L58mV+//13eeovdl44rVYrC71Hjx5Rs2bNdC/0hBAGlQxiEhUVZXKqs2TJkmzatIlx48Zx5swZMmXKhEajQavVEhwcbFCerkaNGuzcuZNhw4bh5ORE79696d27Ny9evCAoKIhixYrx+vVr2rVrh5+fX4JCT49e8H3zzTfUr1+fgwcPkjt3boPvrFu3jj59+shCTy/wjFkMFZKXmNP5Fy5cwN/fn/bt2xMVFcXdu3eRJInAwEBF7Cmka54/f26ydKCSfzL1UMSemciXLx/Dhg1j9OjRPH/+3GhpLtBN4cWkVq1auLu7A1C2bFnWr19vUEPVxsZG/r+zszNXrlzBy8uLKVOmMHLkyHQRhBEfKpXK5LStVqs1OH8xKVGiBBYWFvzzzz/MmDGD8ePH8/jxYzZu3Eju3LkNblLNmjVj//793Lx5kypVqshpdPQPeW9vbzp27Iifnx+HDh0yKfSMXasyZcpw5MgR6tatS4MGDTh27Bh58uQBdFO3eqG3e/duxZKXwgQFBcnpee7du0fVqlWZMWMGkiSxZ88eSpcuzZUrVxSxp6CgkOZQxJ4Z6dWrF7Nnz2by5MnyQ+Jj8PT0ZOzYsfLfQgjZd8zX15eBAwfi5eWlTN0mIxYWFowcOZLKlSvToUMHqlSpwoQJE+jYsaMs+GrXro2dnR179+6lSpUqBtvH9NFLrEUvNjEtfLVr1+bYsWOcPHnSYOpWEXopjz6qHaBq1aqMHTsWSZIICgri+vXr9OjRQxF7CgoKaRJF7JkRBwcHhg4dyujRozl37pxJ615iOX/+PPv27ePEiRPcunULQBF6SWTfvn0GU7NCCFnMSZJEnTp1uHLlCp07d2bs2LGcO3eO33//XfaNq1OnDvv372fSpEnyPl6/fi0LvX379iVJ6OmJKfgqV66Mr6+vIvRSmTx58rBx48Y4n589exaNRkP+/PmB/yfVVlBQUEgrKGLPzOite7/99ht79+79pH317duX//77j2rVqtG2bVvq1q1LuXLlkqmn6YewsDBatmxp0rcPIEOGDKxcuZI9e/Ywbtw4Zs6cye3bt1m8eDFWVlbcu3ePgIAA/P398fDwAGDTpk08evSIjRs3xqmDnBRKlSrFgQMHaNiwIXXr1mX79u2K0EuDXLt2DSsrKzJmzAgQb7UVBQUFhdQgfTt4pQB6697Ro0c5e/bsJ+0rR44clC5dmoMHDzJ69GhF6CURe3t71q1bh0qlomDBgpw7d4779+/z+PFjHj9+zKVLl8ibNy+tWrVi2LBh9OjRg40bNxIREUGLFi1o2rQpgYGBrFu3TvavBF1Qjq2tLfv27Uu2vhYqVIgLFy6wdetWReilUYKDg3FxcZF9az+2jJ2CgoKCuVHEXgrQq1cv3Nzc4i2JlRgKFizInTt3TEY6KSSe7777jvXr1/Po0SOGDRuGg4MDnp6eeHp6Urp0aU6dOsWPP/7IvHnzaN26NdmzZ2f//v3UrFmTihUrsn//fqpVq2awT301DP1+kwM7Ozvc3d2VRL1pmPfv3+Pk5ERUVBQA1tbWqdwjBQUFBUOUadwUIKbv3tmzZ3FxcTEZNavP1WWMHDlyEBwcjLe3txwBGpv0Ho0rhJAfurF59+6dQSmrypUrs2DBAgYMGECLFi04dOgQTk5OaLVaoqOjmTVrFtWrV6d79+40bNiQWbNmsXz5coN9qtVquZQawMCBA1myZAm//vory5Yt+2RrXMygAIXURaPRGP19BgQEYGdnp4g9BQWFNEv6VgZmxMbGxmAZMGAAbm5uTJ06FTc3NzJlymR0cXZ2xs7OzuhStGhRAO7evYuFhYXRJb0jSRLW1tZGF30ARkBAAAsWLODly5c0adKEBQsWcO3aNZo2bUpoaCiRkZGEhIQQGRlJq1atuHr1Kvnz56dnz57Mnj2bLFmykDNnTnLmzEn27NkNzn/27Nnp1asXf//9d7yJkRPC1PVVrnPqYWlpafQ3GxERgaurq+wDqtTGVVBQSGsoT40UwsHBgREjRnD48GEuXbqUpH3oo/3u3r2bnF1LVxw8eJC6desyY8YMvvvuO54/fy4LvvPnz9O4cWPUarVBRYo8efJw5swZBgwYwNy5cyldujRbt241maB56NChWFlZMXXqVEJCQpSatV8479+/x9nZWbHsKSgopFkUsZeC9O3bFzc3N+bOnZuk7TNnzkzmzJm5c+eOwedarZbQ0FBFVMSDn58fgwYNomfPnri7u7NgwQLCwsJo27atLPjWr1/P+fPnadq0KVqt1sCCZmNjw/z589m5cycajYbvvvuOEiVKsG3btjg+lFmzZpWtezdu3CAsLCylD1chBQkJCTHw2bO0VLxjFBQU0haK2EtB9Na9U6dOJdm6V7x4cY4dO2ZQ9D48PJz3798bfKZgyLhx49i9ezcdO3Zk165dNG/enLVr1/L69Ws5cEYftHH+/HkKFy7MggUL4pzTZs2acfv2bdavX8/bt29p3749p0+fjtPe0KFDsbCwwMvLSwmu+MIJCwvDysoKjUajTLErKCikSZQ7UwrTt29fMmXKlCjrXmBgINevXzf4bOTIkTx9+pSff/5Z/szOzs5g2lEhLn379iVDhgwcO3aM169fA3D8+HEAWrduLX/vu+++49ixY+TPn5/BgweTP3/+OKJPpVIRHR0tJzo2lgIna9asODk5ER0drQiAL5y8efPy4MEDXFxc0Gq1hISEpHaXFBQUFAxQnkIpjIODA3369EnQuqfVaunRowfNmzfnyJEj8ud169aVEzWfO3cO+H/EpiIqTFO8eHHWrVsnT90eOHCABQsW0KJFC7755huD71atWpXjx49z9OhRWfTlzZuX+fPnEx4ezpo1a+jatSt16tTBy8tLyauWzilevDi3b9+Wkyr7+vqmco8UFBQUDEmyOpAkyUKSpMGSJPWTJMnuw2dSQtspQKdOnRK07v35559cvHiRTJkyMWjQIJ48eSKv+/3338mZMyfdunVTpm5j8fLlS4YMGcKQIUOYOHEi79+/l9cVLVqUDRs2EBYWRq9evXB1dWXixIkm91WzZk0D0Tdo0CA8PT1lobdr1y7FmqpAiRIlCA8PJyIiAlDEnoKCQtojSWLvg6i7BlQHfgOWA4gPnuqmRJ8kSb0kSbp89+5dypYty7Jly5LW688ArVZrdFGr1Wi1Wnr27MmpU6e4cOECarVaXi5fvsyqVauYPHkyRYsWZdCgQQghaNeuHQcOHCAoKAghBHPnzuXBgweMGjWKgIAA3r59S0BAQGoftllZtmwZZcuWBSgsSdJlSZJ66dfpx9a7d+9YtmwZf/75J9OmTaNhw4Y8fPiQN2/e4O/vT6FChVi3bh1FixZl+vTpODk5oVaref/+PUFBQUaXYsWKcejQIQ4ePEjRokVp1qwZW7ZswcLCgtDQUKKioowun5L8OvaYef/+vTx2lECc5CcxY+v+/ftUq1aNmTNn8ujRI3lxdXUF4O3bt4Aun6OCgp74xpaCQkohJeWBJElSbWCCEKKmJEl5gHXANMAbuCGEiPdpVLZsWXH58uWk9PezwdQDOTQ0lMDAQCRJonTp0pQoUYI9e/bI6+fNm8fSpUt5/fo1I0aMwMXFhUePHrF06VKKFy/O0aNH5enawYMHs3btWnbu3EnhwoVxcHAgW7ZsKXJ8qYkkSVeEEGWNrStevLjQ1yDes2cPAwYMoHTp0qxZswY7OzuT5yc4ONhkfrTo6GicnZ2NrouKijKZaiN79uy0b9+eBQsWJHhMsYk5fkJDQ+UqDfoEy8qUvXmIb2wVKVJErF+/Ps7nGo2GatWq0aZNG9auXcuaNWv44YcfzN5Xhc+L+MbWl/RMlCTJ5ItufOsUkk58Y0tPUp8Y/kB1SZJGAyuAjMBIYDGQL4n7TBfY2dnh4OBApkyZGDp0KEeOHJF97wDOnj3LkydPaN68OS4uLgDky5ePpk2bcv36dWbPni1/d9KkSXz11Vf89NNPWFhYYGNjk+LHk5bR58+7evUqnTt3/mwd55UAnLSNvsby/fv3AWUaV0FBIe2RJLEnhLgOtAI8ABchRGEhRFVAAO7xbZvesbCwwM7ODgsLC7lm7vDhw3n//j2hoaHs27ePHDlyxInwrFatGgUKFGDKlCncu3cPAGdnZ+bPn8/Dhw/ZvHmzYu0xQpMmTZg5cyaXLl2K1z8vufH29iYsLCxZrokSgJP2yZMnj1zZRhF7CgoKaY1EPT0kHZUlSaql/0wIsR2YBXhIklRfkqTvgUzAM7P09AvEwcGBhQsXcu3aNZo3b45Go6FAgQJ4e3tz5swZ+XtarZaDBw/y4MEDKlSoQM6cOeV1+ulFvRVQwZCIiAi8vLyQJInq1aunSJve3t7Ur18fKysrunTpkiJtKqQekZGRnDp1imLFiqHVasmePXtqd0lBQUHBgARTvX8ItjgJ+AGlJUn6F+gFvEQn7NYBU4FooJ0Qwttsvf0Cad68OevWraNTp060aNFCzvm2fft2AMqXL8/ff//NzZs3qVy5Mtu2bTOYrt2xYwdWVlY0atQoVfqflomIiKBbt26cOXOGmTNn0rRpU7O3qRd6Pj4+7Nmzh1KlSpm9TYXUZffu3fj6+jJo0CDOnDmjXHMFBYU0R2Lq+jQC3gohWkuS5AYcBFYCrYUQQZIkzUHnr2cvhAg0X1e/XFq2bAnoUrK8efOGLl26sHnzZrZv387Jkyfx9/enSZMmNGnSxEDoCSHYuXMnNWvWVCx7HwgICGDLli0AbNu2jbNnzzJz5ky+++47NBoNoDtv//zzD5UqVfroHHnPnj3j5MmT8t8ajQaVSiXvd/r06bLQq1ChQjIdlUJaRa1Ws2bNGr7++mu0Wi2SJFG8ePHU7paCgoKCAYkRez5AOUmSqgshTkqSVAm4A8yWJGk10Bb4SRF6icdYpG6LFi34888/6dy5M3/++acczffo0SO6dOlCkSJF0Gq1REdHy9tcvXqVly9fMnz4cKKjoxNMy5EefL68vb356aefAJ3j/PTp0/n222/ltCVCCIYPH87ChQvp3LkzS5cuBXQRr6Zqmmo0GpydnQkICKBWrVq8efPGZPsuLi6y0EtMmpT0cE2+FIQQBr8/gAMHDvDq1SsGDRrEuXPnyJcvH05OTqnUQwUFBQXjJEbs3QEOAd9LkhQqhLgiSVJLYAI6H715Qogoc3bycyS+h7ipqMr27dtjYWHBDz/8wOHDhzl+/DiWlpZybdXYaT4OHjyIlZUVTZs2xdHRMXkP4DOlaNGieHl5AeDk5ESmTJnkdUIIRo0axcKFCylcuDBr1qyhXbt2NGjQgAwZMsS7XxsbG0aOHImPjw9Hjhwhb968Rr+XKVMm+WH/KfnwFBGY9rCzs6NYsWLy31qtlq5du1KkSBH69evHokWLKF++fCr2UEFBQcE4CT5RhBBhwHR06VVGSZJUDyiDTujtF0I8Mm8X0xfNmzdnxYoVXLhwgRYtWshTj7ERQuDl5UWdOnVQqVRy9n7QPYRCQ0PTZfJdGxsbPD098fT0jCP0hg8fzrx58xg8eDBXr16lSJEi9OzZk6CgoAT3u3v3btauXcuYMWOoU6eO3EbsRbHqpB/27t3Lv//+y7BhwwgODubZs2eKv56CgkKaJDGWPYQQDyVJGgH0BCYBQcAQxaKXvGi1WgIDA6lVqxYrVqygR48elC1bVk4ErNVqZYtPdHQ0L168YNy4cTg6Ohr4noWHh8tlwvRJeNMzMadu+/fvz+zZs5EkidWrV1OpUiX69u3LwoULTVrTXr16Re/evSlWrBjjxo1Llj5ptVrCw8OVlCqfKdHR0UybNg1PT09at24t169WxJ6CgkJaJFFiD0AI8RwYJ0nSNHSVN94ntI3CxxEeHo5arcbS0pK2bdvi6OjI4sWL0Vef0/ucaTQabG1tadWqFd9++608zau35OmniZUkvIZCr0+fPsybN08+n+XLl+fXX39l7NixWFlZMWfOnDjC69WrVzRr1oyQkBD2799vslrGx6IX5JIkKVPwnwmBgYGyi8C+ffu4du0aa9euxcfHhx9//JFcuXJRpUqVVO6lgoKCQlwSLfb0CCE+zzIEnwExKyVYWFjQtGlTg3QhUVFRWFpaEhERga2trUmLkD4Jb3onptAbMGAA06dPlyNn9YwePZrw8HB+++03AAPBpxd6796948CBA5QoUSLZ+qYX4nqhrpD2efr0KZ06dZL/HjZsGI0bN+abb74hKCiIs2fPKsJdQUEhTfLRYk/BfMSsrhHfdxSBYJrw8HBu374NwKpVq1i0aJEs9PQWvZhIksSkSZOIiIhg5syZgE7wvXnzRhZ6Xl5eVK5cOVn7qVTF+PwoXLgwf/31FwC2trZ4enrSuXNnrl69yqZNm5SUKwpmwdPTk+fPn5tc7+HhwbNnz1KuQ2YivuP8Uo4xNVHE3mdEQqk8lALTcPfuXcqW/X896JhCL77zN2rUKABmzpxJREQEly9floVe7NJ1CukTW1tbChUqJP89depUtm7dyqRJk2jcuHEq9kzhS+b58+fx3tuNvcR+jsR3nF/KMaYmithLBZKSlgXAysrqk/adHsiTJw/Tp08HwNXVlVq1ahn4PJrC1dWV6dOnY2try2+//YaTkxOHDh2iYsWKSe5Ler8WXxqSJMm/wW3btvHbb7/xww8/MHbsWOVhpKCgkKZRxJ7CF4WrqyutWrVK0rb6Kd1ChQrx9ddfJ6uPnsLnz9WrVw0CdCpXrszSpUsVoaegoJDmUcSegkIMJEmiQ4cOqd0NhTRI9uzZ6dOnD6Cb0u3atatB+UIFBQWFtIoi9hQUFBQSQbZs2ZItz6KCgoJCSqI4FSkofMaEhobKCbQVFBQUFBSMoYi9z5T0XBItKXyJ58vb25uSJUvi6enJ33//rURjpwBf2hhSUFBIHyjTuGkMYw8SfWktGxsbLC11lyylSqJFRkaaXBcVFRVvRYn41se3TqvVJrsvVFhYGEFBQYSGhpIxY0ajkbLJHT2bkCiIWf7OGKbWBQYGGiR8zps3L+3bt2fDhg3MnDmT/PnzJ6m9+NYnta9fEvpyhiEhIWTOnDnOMad0oIaputl6YicQTwwJvTAk9RjNtd+01KbysqWQlvny79BfAHphFxkZiYWFhZyQ19nZWU7Mq1/SMlqtlrCwsFSxjNjb22NpaYlarTY4j5/LuYtJTKHn5eXF4cOH+eWXXzh8+DCVKlVSrHxmwsLCQh5DERERSJJksKQW3t7etGvXjhMnTqRaH5KCVqslJCREsZQqKKQAn88TLh0Ts4yaHgsLCxwdHVNFpCRVtEVERBASEkJERISZemYaCwsLMmfOjIuLS5qpGZyUqWVvb28DoVeuXDksLS0ZNGgQJ06cIHfu3HTs2JHWrVvz9u1bM/Y+feLm5kaGDBnSzBjy9vambt26bN26lSZNmnD8+HGztGMOYRYWFsb79+8JCwtLtn0qfJl4eHjEebnSL56enqndvc8CRex9BqS10lp60RbfFK8xbG1tcXR0xNbWFvi/aLx06VKihcmHh02STkRaPI/6h92+fft4+vRpvN/39vamVq1aJit7FCxYkAMHDjBt2jT279/P119/zbZt25Ldz+xL9H9MLGlpDL1+/Zq6devy9u1btm/fTp48eWjWrFmyW/i0Wi3v3r0jKCjok4VZTNFob2+Pk5OTUv5RIUGePXuGEMLoEl8pOYX/k/p3LIXPDr1o+1i/On1dX/2DMiwsjFu3blGnTh26deuWqH2Eh4cDfLwzUhpEfx7HjRtH06ZNyZcvHxUqVGDmzJlxhJ9e6L19+zbeEm6WlpYMHz6cXbt24e/vz+jRo3n//r3+vMl8imDTuxXE3qdCyuLl5cWjR4+YPn06TZs2ZfPmzYSHh7NgwYJkbScsLAy1Wo2lpeUnC7OY1rzUmp1Izy8rCukXRewpfDSxRVtsPmaad9myZURFRXH06FHOnj2b4Pc/TJ/F75n+mSBJEuPHj2fBggX07duX33//HYCRI0eSL18+ypUrx4wZMzh//rws9A4dOpRgrV5vb2/69++Pk5MTq1evjuMCAJ8m2Iy5FSikPJ07dyZXrlzMmTOH8PBwpk6dikqlYsyYMcnajr29PS4uLri5uX2yMEsL1rzw8HCCg4OV6WOFdIUSjZvOUKvV/PXXX9y5c4eAgAD8/f0JCAgwWKpUqcLmzZtxcnKSt9NqtURERGBraxvnhh97nX6a18bGRp6yjf398PBwAgMD2bJlC+3atePo0aNMnjyZvXv3xtv/D22n+it5aGgo9+7d486dO9y5c4d///2XO3fuYGVlxfz586lfv77JbfVieOzYsSxcuJCBAwcye/ZsJEli2LBhPH36FC8vL7Zs2cKIESMADGr1BgYGmtz3q1ev+Pbbb3n79i0HDhwwqO0bU3zrhVpMwaa/LnZ2dglG3pozAjw9IoRgxIgRbNiwgaZNm/Ldd99Ro0YNOfreGM7OzixbtowGDRrQpEkTTpw4wZAhQyhVqlSy9k1vgUtr+0oq+jGf3IIzsb8fBYXUIMliT5IkC+B34C2wVwhx98PnklBCAVOF+Hzo/v33X27dusXkyZN58OABNjY2uLi44OzsjKWlJRkzZiRLlixYWVmxf/9+qlSpwqRJk8iYMSMVKlSQBRz8/yYZHR0N6N6Uw8LCiI6Olm90NjY2SJJk1LqnF3pTpkxBq9UyfPhwChYsyMSJEzl8+DDly5eXC86nFPoI3fDwcIYMGcK9e/fkdTHTjgghePv2Lc+fP5cjXq2trcmXLx8lS5bk33//pUGDBnz//ff89ttv5MqVK05b79+/Z+jQofz555/07t2bn3/+meDgYHl9xowZad++Pb169eL58+fs37+fypUrU6hQIQIDA3n//r1RsfXq1SuaN2+Or68v+/bto3z58gbnP+ZxGBNs4eHhhIaGIoSI8yBMKPWKKWKOyY0bN7Jr1y4GDBhApUqVEpVi50t/aIaHh6PRaBg2bBhLly6lfPnyrFu3jqVLl5I5c2ZatmxJ69atqV69ehzhp9FoqFu3Ll27dmX16tUUK1aMH3/8kfDw8HiFTHy3Z61WazKyWKPREBERYdKqL4QwuW18+4WUTVujH/um2ozv/MR3jHqLISS/kFRQ+GRMOT3GtwAScAyYCEwF6gDugEq/3sR2vYDL9vb2okyZMmLp0qVCIfmIiIgwujx//lw0adJEACJr1qxizpw54ubNm+LWrVvi1q1bYtOmTeL48ePy8vPPPwsLCwtRrFgxsX37diGEEBqNRrx//15oNJo47YWFhQl/f38RFhZm0G5YWJjQaDRxlujoaHHp0iVhbW0tOnfuLAICAoS3t7fInDmzqFWrlvD39ze63eLFi0WZMmUEEApcBnqJZBpbERERIiAgQNSuXVtIkiRq1qwp6tSpI+rUqSNq1aol/79OnTriu+++ExMmTBAbN24UZ86cEe/evRMBAQEiICBAvHnzRgwePFhYWFiI7Nmzi3379hkcg1qtFn369BGA6N27t/D395e3jbm8fPnS6OcBAQFGz8/z589Fvnz5hJOTkzhz5ozR8xfz2hkjOjpaBAcHi+jo6I/eNr7zGhERIZYtWyYkSRIqlUoAok6dOuLYsWMm+/kpbSaFpUuXmm1sxUdoaKjo3bu3AMSPP/4oQkNDha+vr9iwYYNo1aqVsLe3F4Bwc3MTffv2Fa9evRJqtVqo1WoRHR0ttFqt8Pf3Fz169BDXrl0T79+/F2q1Wmi1WpNtarVak4tGozG5Ljg4WLx69UoEBwd/9LbxrYuvr6lBUs9PdHS0wfnXL0uWLDE5tvRLmTJlTPZH95g2TULrU5r4+pPUdUltL70AXBYJ6baEvmB0I7AFFgIOwHngNHASGKYXfPEt8Q1shaQTW+SFhISIWbNmCWdnZ2FpaSl69OghLly4IIu8mGLv6NGjYv369WLKlClizZo1BoIvODg43vY+VuxpNBrRvXt3YWlpKW7cuCGLmEmTJgkgjkCKvcQ3sJM6tmIKveXLlxscR3BwsEkh7ePjY1SQHTlyRBQoUEAAolu3bsLf31+o1WoxcOBAAYiBAwcKX19fk4LuY8ReTKF36tSpJIsncwivmEKvdu3a4tWrV2LatGnC3d1dAKJu3bri5MmTqS729JhjbJlCq9WK/v37y0Lv6dOnYvfu3SIkJESEhYWJkJAQERwcLDZv3izatGkjrK2tRaZMmcT69etFdHS0LPY+VkB9ipgJDg6OI2bUarX8kpCexV5Cx5jUsaWIvaS3l14wi9j7YNXLDDwCdgOjAUdgHHAU8ExoH4rYMw8xRcixY8dEsWLF5Afqtm3bZHF39OhRsWDBAjF48GDRtGlTkTt3bmFjYyMAAQgbGxsxe/ZsWfBVrVrVqODTt+Xv7y9evHgh/P39DfoQGhpq1FL0/PlzA6ueftFb92rWrJmiYi8sLMyk0PsYsefv7y+eP38u//369WsxcuRIYWFhIXLkyCHatm0rCz21Wi38/Pw+WezFtuiZssp9itjTP+STIr5iCr2AgAD5vPn5+YmpU6fKoq9OnTpGhWpKk1JiT6vVikGDBslC78GDByJv3rwCEEWLFhXr16+XrUT65datW6JcuXICEN9++614+fJlHEGhVqvl7eJrOznFTEIWv/Qo9mJeB0Xsffq6pLaXXjCbZU+3bzoCamBhjM8uAlUT2lYRe+YhIiJC+Pr6ytNCOXLkEBs3bhTh4eHizJkzYurUqaJixYpCkiRZ2Lm7u4vixYuL1q1bi2HDholZs2aJnDlziixZsojjMaZ0R48ebbS9+Cx7fn5+8kMg5gN81qxZAhD9+vUzmAK9ffu2+PrrrwUgXr9+bVRwREdHJ/sDedSoUSaF3seIvR49eggnJydx+vRpA1F27tw5UbhwYQOhp9FokkXsde7cWQBi/fr18nlKbrGnf5i/f//+o85raGioUKlUImvWrMLPzy/OuQsLCxPBwcFixowZwt3dXUiSFEfwpTQpJfYmTpxoMHXbsWNHAYhOnTrJv80jR44YiD21Wi0iIiLE1KlThY2NjbC2thbt27cX//zzjyw03r9/n+C1Sm6xF9uyZ0zopDexF/M6fGliz8PDQx6jxhYPDw+T2ypiL+kkcN6TR+x9sOZVBmrF+MwSGIMuDcakDxa+f4EcCe1PEXvm4cCBA8LT01NIkiQGDBggfH19xbFjx0TXrl2Fg4ODAMRXX30l+vXrJ9atWyfOnDlj1GevcuXKwsPDQ/47f/78om3btnHaMyWAErLs+fr6ihYtWghAlCxZUpw5c0YsWLBAODk5CXt7ezF9+nSTgiM4ODhZH8harVZ4eHiIBg0ayP0+f/688Pb2/iixd/LkSVlEe3p6iidPnhiIstDQUHHy5ElZ6CWX2Lt69arImDGjyJUrl3j48GGas+z99NNPAhBdu3aNd5o/ICBAuLu7i7p1637xYm/z5s0CED/88IMIDQ0VYWFh4p9//pGt61ZWVmLYsGEiMDAwjtjTL3fv3hX9+/cXLi4uAhCFChUShw8fThXLXuz1xoROehN7X7Jl71PaU8Re0kng3CUo9hIMdZN0oUcngRHAGkmSDkiSlEMIoRZCTAHaA0U+LO2EEN4J7VMheQkJCaF///40aNAAlUrFgQMHyJIlCxUqVKB27dps3ryZ2rVrs2rVKvbt20ffvn0pWbIkzs7ORvf37NkzgxI0bm5uvHv37qP7JUmS0WoDLi4u/PHHH6xevRpvb2+qVq3KgAEDKFasGKdPn6ZHjx5x9mWu3G6XLl3i+fPntGrVCoAbN25QtWpVmjVrJkcbJ4QQgrFjx2JpaYkkSbx8+ZKePXsaFKq3tbWlSpUqyR51WKJECQ4fPkxISAh16tThyZMnybp/+LSqEZMmTeKnn35i9erV9O/f32TuRWdnZ4YPH86RI0c4c+bMp3Y5zXLlyhU6d+5M5cqVWbZsmTweypcvz5o1a2jbti2XL19m0qRJ8aZdyZ8/P3PnzuXVq1esWrUKIQT169dn7ty58ebANIU2Gcuh2dnZ4ezs/MXmYdQmIilzWqq0oqAAiUuq3Ah4K4RoAZRBF3X7pyRJGT6s9xJCtAY6CyFumqWXCiY5fvw4xYsXZ/HixQwYMICjR48yY8YMxo0bR/bs2Vm+fDnPnz9n4sSJlCtXLsGbT0REBG/evCF37tzyZ6bEnlqtxtfXF7Va/VF91qdqqV+/PufOnaNLly5Mnz6d3bt3G7QbE3PdPDdv3oyVlRVNmzYlKiqKnj17YmFhwdWrV5k9e3ac7x87doyDBw8afLZv3z5OnTpFdHQ0jo6OWFlZyXkDU4KSJUvKgq9evXrcvn07zVQHUKlU/Pbbb4waNSpBwde7d2/c3d2ZNGlSmul/crJnzx5q166Nm5sb27dvj5N2plmzZqxevZr8+fMnep/29vZ06dKFixcv0qJFC4YNG0anTp0+OmFwWFgYQUFB+Pr6otVqP6nKRHy/1U/Zb2oSs9/6FCvxJST/XI9T4cslMXn2fIBykiRVF0KclCSpEnAHmClJ0hrgO0mSRgohlNpJZiZmMt3w8HDGjx/PypUryZMnD3v37mX16tWULl2agIAAqlWrRoECBTh//jznz58nR44cJoWUn5+f/P+nT58ihCBDhgz4+PggSRJubm6cP38etVptcAP38/PD398fjUaDq6sroLvJRUZGYmNjgxDCaL48Gxsb7Ozs5H+NiaqUQAjB1q1bqVevHg8ePGD16tXcvHlT7uOkSZPw8PAgf/78lClTBiEEffr0Qa1W8/jxYyRJ4vTp0/z0009YWlri5ubGmDFjGDt2LCqVirlz51K2bFlatmxptP3g4GCT1ht9UmpjhIaGxvksd+7cbN++nRYtWtC0aVN27dpF0aJFDb6j1WqxtLQ0mfw19vU1tq0p4hPhkiTx888/AzBt2jQA/vjjjziWU0tLS4YMGcLo0aM5cuQINWrU+GysQ7qZFONEREQwbdo0Jk2aRKlSpdi8eTMZMmQgKiqKqKgokzklo6OjUalMVwaMjIzE2toaACsrK9atW0fx4sWZOHEit2/fZseOHUaLxAsRN1ecvb09oaGhqNVqWcTEzhlnbLvEngP9tjFz0SUmMXfsfcYcu5IkJdif5MrtF7Pf+kTxtra2Bv3TxshFqS8LJ4RIswnIPT09TdaV9fDw4NmzZynbIQWzkhgzyR3gEPC9JEllhBCRQEsgA5AJmK8IvZQlPDyc9u3bs2rVKvr27cupU6dwdnZm9+7dBAUFUbx4cZ48ecKpU6d49uwZPj4+PHnyBFtbW6NLpkyZKFKkCEWKFJHfRGvWrCl/5ubmhq+vbxyRkTFjRtzc3MiUKRPW1tZYW1vLYk/7IWGuhYVFnMXS0pJs2bKRMWNGMmTIEGdxdXU1up1+SS70U7ht2rTh/v37rFmzBmtra7JlyyY/aKdMmUJYWBg2NjbcvHmTFy9e8Pr1a+7fv4+NjQ1eXl68ffsWtVpNx44dcXNzY9CgQYSHh+Po6EjPnj35999/jR6Hg4MDjo6ORpdMmTLh7OxsdNEnhI29FC9enB07dhASEkKzZs149uyZ0XNnqlRafOc8qefdxsZGrqQyZcoUxo0bx+rVqxk4cCBWVlZx2ujVqxfu7u7MmjXri5gCCw4Opl27dkyaNIkOHTpw/Phxg0Tbtra22NnZYWdnh6+vLydOnDD4LL7rIUkSERERXLp0ic2bNxMYGMiIESPw8vLi+fPnlCtXjqNHj8bpk7Gxo1KpcHd3J0OGDHLb+qlY/Xf0bZpa4luvX2dvb4+zszP29vYG6xNLYqxqn4qx/uv7bWdnh0qlwsHBAZVKZfL4Y5aF+9hjTCn0ieGNLaZEoMLnS4J3UyFEGDAdyAiMkiSpHrrp3EzAPiHEI/N2USEmeqF34sQJ/vjjD6ZMmcKlS5do2LAhQgiKFy/O3bt3efPmDQ8ePJBrzj5+/JiIiIgE9//48WMsLS0NHkhubm5oNJo4FTpUKhWZM2c2sPjY2tri6OhoUCYtrU5p6KdwGzVqxLRp07C0tEStVvPLL7/Qtm1boqKiePDgARs3bgRgy5Yt8rHu378fHx8fvLy8UKlUFCtWjNKlSwNQtGhROnbsSEhICCqVijZt2hAQEJAix1SsWDEDHz5jU7qpVdtWkiQmTZrEuHHjWLFihdEpXQcHB4YOHcqRI0c4d+5civYvubl//z4VK1Zk7969zJw5k5UrV5o850eOHKFUqVI0atSInj17Gq2Goxd2S5cupU+fPlStWhV3d3dq1KhBly5dqFq1Krdv36ZBgwacPHmSLFmyUL9+faZNm2bgP2qKmNOv5nKb0JdLS+p+U8sf8GP7/anHqaCQ3CRqJAohHqIL0LiPLvK2LTBECBFlxr4pxOL69es0btxYFnrff/89u3btonXr1uTIkYNChQpx8+ZNLC0tqV69OnXr1qVQoUJERERw9uxZfvzxR7nkWWyCgoJYv349u3fvxsPDw2BqST9F6+/vn2AfLSws4jiIm7IkpSahoaFs3LiRevXqsWjRIh4/fkxUVBQdOnSgQIECdOnSBQ8PD2xtbfnzzz/x9vZmz5491KpVi2LFinH06FGmTZsmWzF/+OEHg7f3hg0bUqNGDUJDQ3nx4gWjR49OsWMrWbIkhw4d4s2bN3To0MGoBc/YgzwlRHlMwWfKh69Tp06oVCoWLFhgtn6YG29vbypVqoSvry979+5lwIABSJLEjz/+SPPmzXn//r383Vu3btGgQQNcXV3p27cvq1aton///gb7u337NlmzZqVq1aoMHDiQnTt3kjlzZgYPHsyGDRvYtm0b4eHh1K5dGz8/P/Lly8e5c+do0aIFY8aMYd68eSl9CszCp4rQ5AxEScl9pzQeHh4mrbQeHh6p3T2FJJDo2rhCiOfAOEmSpqErh/Y+oW0Ukgd/f3/Gjh3L0qVLcXNzY82aNbJfVrdu3ShTpgyDBg2iU6dOODg4UKlSJdnfK3/+/OTLl4+LFy/KvnR6hBA8ePCAQ4cOcfnyZSIjIylatChDhw41aP/u3btYWVnx1VdfJan/+rfwtOR/NXbsWF69esXEiRPp3bs31tbW5MiRg44dOwK6erfNmzdn/vz5ABw+fJicOXPy+PFjAgMDKV26NIcOHcLe3p7s2bOTM2dOg/1LkiQL87x586botIgQgrVr1xIdHU29evUSfd5j1j82p5+RXvCp1WoDHz59beJOnTqh1Wpp3ry52fpgbqZNm0ZISAi3b9+W/eZu3rzJ8uXLAWjevDk7d+7E0dGRXLlyUbhwYZ48ecK1a9eQJIm6desa7M/Dw4NmzZrJ1uhu3brRr18/smbNCuiuea5cuXj9+jVOTk4AODk5sWXLFjJmzMjTp08/+hhM+XamVRLTX70vHSCfp+RC/1IL4OjomKz7TmkUf70vj4/+BQshQhShlzJotVpWrFhBgQIFWLZsGb179+bixYs0btyYJUuWGAi9Hj16YG9vbyD09ISHh+Pj40Pt2rWxsrIiNDSUAwcOMGLECH7++WeuXLlCs2bN2LRpExs3bqR8+fIG2584cYLy5csnWQCkpTQEarWasWPHMm/ePPr06cPChQuxsrJCrVYzcuRI2eEdkANa7O3t+eeff/juu+948uQJ/v7+VK9enTt37hAdHR1H6OnRW/pS0l9HCMHQoUOZP38+gwYNYsaMGYk+77a2tik2vasP2ogZpRsaGkqrVq04fvw4y5Yto23btmbvhznw9vZmxYoVdOnShQIFCsifT548GRcXFxYtWsT58+dlC5+LiwtHjx4lT548XLhwgVWrVtGuXTuDfTo5ObF27VquXbtGixYtmDVrFsWKFWPChAn4+vpy5MgRLl68yIgRIwzGsN4fLymkhH9ccpKY/sb0pUtu9O4R5ti3gsKnkmjLnkLKEh0dTYcOHdiyZQtVq1bljz/+IFeuXLx9+5bu3btz9OhRGjRoQKdOnejRoweenp5kz57daATn8+fPkSSJOnXqEBoayvDhw/H39ydPnjz07NmTMmXKUKVKFaP9CA0N5fLly4wcOdLch2x2Xr16xffff8+pU6fo0aMHmTNn5vr164Bu6jDmgxmQpyuyZcvGP//8w8yZM+nbty9CCPnNPSoqihw5cpi135cvX8bDwwM3N7d4vyeEYMyYMSxdupRBgwYxa9asjxKaelGeEMll8Ykdpbt//37evn3LsmXL6NSpU5L3m9pMnToVrVbLmDFj5M9u3rzJjh07GDduHN27dydDhgx06tSJ5s2bs3//ftzd3Tl9+jTPnj2jRIkSJvdduHBh1q1bx5gxY/j111+ZNWsWixcvxtXVlRw5cvDDDz8Y3S46OtogWjQxJNUin1oWwfj6q9VqCQsLw97e3mxWN72fnjE+TO2m/tuuQrpFEXtmIiG/DVM3wdu3bxMdHc2oUaM4dOgQQ4YMoWvXrkiSRNu2bTl58iTR0dFUq1YNS0tLOnfujLOzM+XLl+fGjRv4+vrG6cfTp0/Jli0brq6urFq1ioCAAEaPHs3XX38N6ASdqbxc58+fR6PRUL169TjH9LEPj5TC2Lk/cOAAnTp1IiIignnz5lGwYEEaN26Mra0tnp6eVKhQwejUhaOjI5Ik8eLFC/z8/GjcuDFhYWFcu3YNCwsL+Rw8fPgwzrb6VA36CLeoKEMX1/gsEO/evSM0NJStW7cyYsQIvv76a7Zt24alpSURERG4u7sbfF+f2DmpQg/iH7NqtVoOTgkLCyMkJAQhhGzFSOo4iCn4ZsyYYSD09PneTJEaY89UehF9AMSaNWtYsmQJPXr0IGfOnGg0GkJCQpg4cSLOzs50796d0NBQGjRowPLly+nZsycNGzZk586dODk5UbhwYXmcREdHm0zLUqhQIZYtW8aoUaP4/fff8fLy4o8//kClUqHRaNBqtQZ9jY6OJjQ0FEdHR102/XhS7MS0SMdMuxLfdvrvJCW9SnzEl84l9v0ndn+NpUKJ2SdTv4/EpJD52O0+3F+TZmJVUEgGFLGXxogp9IYNG0bnzp2JiIhg1qxZHDp0CGdnZypWrEhwcDBnzpzBwcGBsmXL4u/vL/vtxMTb2xuNRkPx4sX56quvOHr0KAMHDpQfsKATJaYeKhs2bMDKyorKlSsDGLyxfw6+PPro2qlTp1KwYEEWL16Mu7s7bdu2xdLSkujoaGbNmoVarTZaUSRPnjyyED558iTr169HCEHTpk3Jli0br169omLFimTIkCHOti9fvgT+n8oh5vQa6AJfYn+mJyQkhG3btjFixAjy5s3LrVu3WL58OT/++CMODg4G7emnbvVCb86cOUYfSAldq/jy6FlaWsrb69NJfOr1j2mFnjp1KuPHjzeYAvucHN0lSWLNmjX07NmTOnXqMHv2bPka3L17lz179jB69GgyZ84sb/Pdd98B0LNnT1q0aCELPj1WVlby+Hj16hWrV6+mb9++ZMqUCdBdz5IlS/L3338TEhJiYFWKLUqsrKzklD0JHUd834lpIYt97WOmHYH/j5NPIb7t9alOErM+5pj9lD7Fd37i+y18OCcJh0QrKJiJtP2kTmcYE3r379/n+++/Z+PGjXh4eFChQgUePnzI1atXcXFxMeqjF5Nnz57h4OBA1qxZ6d+/Pzly5GDChAmJ7tPp06dlf720GFUbH69evaJu3bpMnTqV7t27s3PnTsLCwmjUqBF3794lMjKS/v37U6xYMZP78PT05L///iNTpkycPHlSzg136dIlnJ2dcXJywsXFJdn7vnXrVoYNG0bVqlXZt28fjRs3Zs6cOdy/f9/gezF99Pr165cki97HYM4pus/Z12nNmjX06NGDOnXqsH37doOpxOnTp+Pi4kK/fv3ibNeiRQvWrVtn4MMXG29vb2rVqsWECRMoVaoUZ8+ejfOdhKYm9eP2U9FbyOKr0GGutCPmquqREnxo9/N5e1FIFTw9Pc0WBa2IvTREv379DITeyZMnad++PQEBASxZsoQ8efJw5swZXr16RYECBahcuXK8Qs/Hx4eAgAA8PDy4evUqd+7cYerUqYwZM4Yff/yRY8eOxVvq7Pr161y/fp3q1asDqZefLSk8fvyY0qVLc/XqVdauXcuyZcu4fv06LVu2JDo6GhcXFwoVKsTAgQPj3Y+npychISGUL1+ekydPAnDnzh3CwsKIiooiS5YsJsWVfjou9vRtQhw5coSRI0dSpEgRVq5cia2tLb/99htWVlYGKVwiIyPp27evLPTmzp1r9mCQ1BL8aTVXI+jK5ZkSenfv3mX37t306NHDqPUXoFWrVixYsIAzZ87I0eB6IiMjqVOnDm/fvmXFihVYW1tTo0YNdu3alai+CSESlWMvJvGda3MGOCREfAEYpvqclseNgkJs4kt0/akR0orYSyNERkby119/0apVKzp37gzAn3/+ibu7O15eXlSpUgUfHx/CwsIoX748BQsWjPct9fXr11y6dAlHR0fev3/PzZs3adu2LbNmzWLlypVs2LCBRo0akTt3boYNG8aJEydk4RcZGcnkyZOpW7cu7u7utG7dGkj9t+OPwdfXF19fX2bPnk2HDh0AuHjxIlFRUSxZsoSAgAC6du1qchpVjz7dTObMmXn58iVarZZs2bLh4uJCYGAgjx8/5sqVK3G2i4qKkoXakydPZMGcGBwdHcmYMSN37tyhe/funDx5krFjxxIWFkb27NkBnZitVq0ay5cvZ+TIkcyfPz/JUZcfQ2oJ/rRgVTblQ6j3X+zWrVuc82JnZ4erqyubN282mv4kMDCQSZMmMWrUKIA4kd0WFhZoNBpcXFz45ptvyJ07N1qtNtHX+tChQwQFBcUb9BHz+MLCwggNDTUpqlIzWXB8CZVNCcHPLaJYQcFcpP2ndjrh6tWrREREUK1aNUAnVq5cuULjxo1lHx1/f3+sra3jOOfH5uXLl1y5cgUXFxfs7Ox4+fIlBQoU4Ny5czx48ICtW7fy4MEDNmzYQM2aNdm6dSstWrSgcOHCDB48mFq1ajFz5ky+++47jh49SvHixc1+/MlN6dKlcXBw4MaNG/Jn+vOod6SOz0ct9jZ660hoaCju7u6sWbMGPz8/7O3tWbhwIa9fv5a3EUKwbNkynj59SnR0NN988w0jRoxIdN8rVqzIiRMnGDduHHfv3qVjx44cPHiQsWPHMn/+fPbu3UvZsmV5/Pgx27ZtY8qUKSmW3iW1BH9asCprNBqjoqFVq1YULFhQjsKNiaenJzt37iQkJITGjRvLgi8gIIDJkydTokQJJk+eTJ06dbh8+TILFy402N7KyopNmzbh4+ND4cKFOXbsGKtWraJx48YJ9lcIwcSJE8mVK5fJKN2YxAysSI0qFQkR39gzJQRTq+KGgnGUZM2phyL2UpD4phROnz4NIL+BHz16FK1WS/369QHdjdvPzw83N7d4H+wvX77k+vXrZMyYESEE7969I0+ePDx//pyIiAgOHjxIw4YNyZw5My1btmTdunX8+++/LF++nAoVKrBlyxYCAgLYuHEjixcvJmvWrJ+FJS82VlZWVKlShRMnTsif6YVbYGBgovejd6jXl6/S+1Q1aNCAX375Rb6es2bNkv2Y9u/fz+nTp7GxscHDw4M1a9agUqkICgpKdLv29vb06tWL06dPM3nyZLZv306XLl345Zdf6Nu3L4UKFeLKlStmTTyclqbA0oJVWaVSGRUNKpWKcePGcevWLXbs2BFnffHixdmzZ48s+H7++We+/vprpk2bRs2aNbl8+TIbN2406TtapkwZ5syZQ2RkJKtWrZIt/wlx6NAhzp8/z5gxY2QLdnzXVC+MHBwc4j3XabFShKnx8Snj5lPHf1r6/aQVnj17ZrZpSoX4UaJx4yExP9L4boix0aes0Gq1cbK3nz59mvz58/Ps2TOeP3+Ol5cX2bJlIygoiCtXruDt7U1UVBQWFha8ePHCaJvv3r3j9evXZMyYkYiICCIiIsibNy9Pnz7F0dGRrVu3UrRo0Tjb2dvb07JlS9laYGVllSirV1qnZs2ajBkzBh8fH9zd3eWIY32UbEhICH5+fgA8ffrU6DELIVCpVISGhgI6i4xeAA4ZMoRdu3Zx9epVXr9+zcKFC6lbty5//fUXDg4OREdHs2XLFlxdXVmyZAk//fQT27dv55tvvpHbN3Wew8LC5ILxnTp14tmzZ7Rs2ZJbt27Ro0cPFixYkOAUdGwSGs+xU1nErAgQn28opE4alJRGH91pLMVG69at+e233/j1119p0aKFwfkQQlC0aFF27txJ8+bNmT17Ns2aNWPEiBEUKlRITocSG7VaLe+nZ8+edOzYETs7O9ndIjo62uR0rkajka16nTt3lvevD7CQJClOWhR9hL2pY9RH+MZMYxI7Ajg+ktv6bKo9rVZLREQE1tbW8d7HEuqPsRQyH5OWJTlT0CgofCqf/xM9jWLs4acP/4/94BRCcObMGZo2bYqtrS1BQUHcu3ePli1bypYEfRRmgQIFjDpHv3z5ktevX1O1alVev37Nu3fvGDRoEPPmzaNs2bLs2LFDdqyO3beEkvV+buiPr1atWoBOSLdu3Zo8efIAuock6B5Uemufo6OjyYjGjBkzylY7/UNEz9atW2nUqBEvXrzg6tWr3Lp1CycnJ4KCgli9ejWlS5fm0aNHjB49Go1GQ79+/bh9+7Y8xW6Kr776Snbo9/Lyolu3blhYWLBjxw6zWfMsLCwMxoY+VUdqRcmmRQFpSiBYW1szfvx4OnbsyM6dO2nVqpW8Tv9iV7lyZS5fvkxYWBiFCxcG/v8yYYzYqUViXwdLS0uT2x49epTz58+zZMkSg/uN/h5kKi1KfOdcP90Wc1yY230godQqxoiIiCA4OBhnZ2eTKaUS0+bHppCJnZYlOVPQKCh8KmnvbvoFY2pK4f79+/j5+cm57C5evIgQgooVK8rfuX37NnZ2diaF3vXr13Fzc5OFXosWLZgzZw4NGzbkyJEjZMmSJdUcq1OLMmXK4ODgwD///AMkbRoXdPnw9JaM2KkxHB0d+euvv1CpVGTIkAFLS0uCgoIYPHgwLVq0QKvV0r17d6ytrdm2bRuvX7/mp59+SlS7kZGRDBw4kNatW1OoUCGuXbuWovViU9MZPy2SUJLntm3bUrBgQSZNmmTyex4eHrLQMxdCCCZNmkSuXLno0qWLwbrkmA5P6+NCPx1ta2v7Sfv51ONMC+fJnKk80grx+QFKkiTXpk7vpM1fazpDn9KjatWqCCE4d+4c2bNnl8twqdVq/v33X1xdXeNs+/r1a9lHLywsjHfv3tGsWTPWrVtHjx492LZtW7qdQrCysqJq1aocP34c0Dmdq1QqAgICPmo/rq6u+Pj4ALqp8tjkzZuXFStWEBgYSHh4OHXq1GHcuHEALF68mJMnTzJ37ly+/fZbhg8fzsqVKzl8+LC8fVRUFEuXLjXYt7+/P1WqVGHBggUMHTqUU6dOKTetVMZUgIYelUrF8OHDTfrupRSHDx/mwoULBr56etKDH1la8O9MK5gzlUdaIT4/QCEEz58/T+0upgmUX0Mq8/z5c37++WcKFixIgQIFOHToEHfv3qVWrVqy6f/OnTtERkYaFXtv3rzB2tqaggULEhoaysiRI7l16xaenp4sWbLki/C9Syp+fn7cuXNHnsrZvn07Go2GO3fuACT6zbZYsWK8evUKS0tLevXqxfr16+N855tvvmHq1KlUqlSJFStWyNNrt2/fRpIkypYtC0CFChUA5D4AzJ49m1GjRhlUNXnw4AFXrlzhl19+YdasWR/tn6eQ/JgK0ACdL+fPP//M0KFDgf+XyksN9C8mly5diiPq9H5k8SVFVlBQ+PJIFrEnKQ4JH8WLFy9YvHgxjRo1onDhwkRERODl5cXhw4dZs2YNZcqUMUitcP78eezs7MiYMWOcfTk6OhIVFUWGDBnIkCEDGzZsoGfPnjx79oyjR4+m5GGlCcLDw7l16xa3bt3ihx9+wMfHh1WrVnH37l3GjBmDp6cnV69epV+/fpQvXz5R+2zcuDF//vkntra2REVF0b17d8aPHx/nQdqnTx/27dtnkDx30qRJZM6cma5du/Lu3Tu5Yoe+msKtW7eYNWsWzs7ObN68mSdPngBQtmxZ3NzcuHv3rsl+RUZGMmrUKEaPHv1FW2rSCrF9GuH/Ii9Pnjz8+uuv1K1bl2vXrsWZPk1JOnTowJgxY1i5ciW9e/c2GBv6Kc7PuVpJYkkPVkwFhcSSLGYfIYSQJEkSCYVjfWYktjTUq1ev+PnnnxFCkD17drJly0bWrFnl/7u7u3P9+nX27NnD3r175dxv+fLlo2/fvnTt2hVJkmjXrh05c+bkxx9/lNtTq9VcunSJMmXKGO2D3vk7JCSEQoUKcf78eXLkyEG+fPno3Lkzly5dkhPxpgf+/fdfg7yAf/zxB4UKFaJChQpYWlri7e1NzZo1GT58+Eftt127dlSoUIHOnTtz8eJFZsyYwZMnT5g9e3a8TuBubm4sWrSI7777jjJlyuDj48OePXuwtrYmMDCQfv36kTFjRnbu3CnnN1y0aBGWlpa0atWKtWvXyrVIY/L48WPatm0rJ3T29fVl6dKlHzV1lRylz/T7SG/TZgEBAcydO5d58+YRHBzMt99+y4QJEyhevDhbtmwhIiJCtuKmNJIk8csvvyBJEpMnTwaQx4Z+ijOx7+cfM0bMWUovKegDNcD80bD6YzcWAKegkBZIstj7YM1bDRwXQqxJbcGnf3szdcMx9QPU508zRkhICJGRkWg0GiwsLLC1tTXYz71797h69SpDhw4lNDQUZ2dnfH19Tb5JqlQqKlSoQO/evalWrRq5cuUCdAEWvXv3xsLCgvr16xvUP33y5AkhISFkyZKFJ0+exAku0Pf/2bNn5M2bFzc3N2bMmMGMGTNo3749TZo0YfXq1VhbWxtNu/Kl4enpycSJEwFdQEblypXp06cPt2/fxt7eHldXV5o0acL+/fsNtnv79q0crRubW7duyQL9m2++ITIykhs3buDl5cXVq1fZtm2bQYF7PWq1Gjs7O5o2bUqrVq3w8vJi1KhRFC1alMjISMaOHcvt27cZPXo0HTt2pHLlymzevJm2bdvi4eFBq1atWLJkCXv37pWjO7VaLYsXL2bs2LFIksSKFSu4efMm8+fPJyQkhGnTpiV6ejpmahUHBwfUajWRkZFGH9Yx04DERJ9OSJKkBOuzpgXis/J8WGfySX316lU5slWtViOE4Ntvv2Xs2LHkzJkTjUZD165dWbt2LQAtW7ZkwoQJZM2a1WT95KioKJNpbdRqdbzT9xqNxqRo02q18gvolClTePLkCb/88guVK1dGCGHSveNT0ofoU7IIIYx+11RfPyadSWLXgS5dkD5QI3YbQgiTzwS9r5ex54hWq0WSJDm9i/6ZYOo8xXweKSikJp9i2csPfAdU/CDy/vwg+CzQGfvi/IIlSeoF9LK3t6ds2bL06tWLXr16fUIX4hL7AfYp2NnZYW1tLScRjZmbSgjBxo0b+f3338mePTsrVqwgX758aDQaAgICeP36NSqViv/++w8fHx88PDyoV68erq6unD9/HltbWzQaDWq1mvHjx+Pj48Ps2bN59eqVQSqUY8eOYWNjQ7ly5Xjz5o0cUapHCMGDBw+wsrKiSJEiFC9enPnz5yOEYPLkyQwdOpTJkycb+IN9rixbtoxly5YBFJYk6TKwTAixDP4/tuzs7JgzZw6dO3emWbNmbNy4kXXr1pEtWzb8/Pzo3LkzkiTFcbTPnj07zZo1A3T+Vg4ODrLf3fjx4w0ejjVr1sTe3p5z587x9OlTmjZtytatWylYsKDBPvUpXkBnYaxbt65cuu3GjRv8+eefNGzYkIMHD/L48WM5DceqVauYMmUK1atXx83Nja1bt9KqVSsiIyMZPHgwy5Yto2TJkvzxxx9kzpxZjuKeP38+AOvXrzf6IIv9WcwUGhYWFkRGRpr87RibwgTDVB6fMzHGViFTY8vS0pJMmTJRokQJypYtS8uWLeUk6E+fPqVnz578888/DBgwACsrKxYtWsT+/fvp27cv48aNM3o/ip1eJSaWlpbyuLt58ya//PILw4cPp1KlSvK28WFhYcHEiRPJli0bv/76KzVq1KBu3bqMHz9ertQTm09JH2Jra0toaCi2trYflWpEX6btY61isfsam5jn72MxJd70bca2GurFXGxRFx4ezqJFi9i4cSMYuW8lB/poVFPrFBQApE8xxEmStAbwBUoDq4QQ6yRJshBCxOskUbZsWXH58uUkt2sMc1j2tFotNjY2cfYZGRnJjz/+yKpVq6hWrRrTpk3D2dnZYNvo6Ghy585tdL96sefv78/cuXM5deoUo0eP5ptvvuHgwYOy2NNoNPzyyy8UKlSIDh064OXlFUfsgS6a18rKijZt2vD9999Tr149GjVqxMSJE5k/fz7Lly9n/PjxTJo0Kf6T+JkgSdIVIURZY+tKlSol9NG39+/fp3bt2jg6OuLj40P79u0pU6aM0X06OjrSpk0bHj9+TN26dalZsyarVq1CkqQ4Yk/PvXv3OHToEJIkYWdnx/r16w1q4EZHR8cZF6Cz5lStWpVXr17RsWNH5s2bR61atTh+/DjVq1fnzJkz7N69m/r169OvXz/++usvzp49S7du3bhy5Qo9evRg1KhRXLt2jR9//BGVSsWmTZvYsmUL8+fPp0ePHh89pQs6S5KpabjYCZdj87lMXSVk2bOysromhChtbH3ZsmXFhQsX4nz+/PlzGjVqxOPHj5k5c6Ys6F++fMmkSZPYsWMH2bNn57fffuP77783OFdRUVEmrXdCCKytrQkODqZMmTI8fvwYCwsLhgwZwqRJk+JNdB37eoWGhrJ06VJmzpyJj48P9erV4+eff5ZfFGKSVBfs9+/f8/79e5ycnIxaeU3tN/Z2McVfQoIuvnWfYjE0ZdnTb2fsOWNsnzG/Z2lpafK+Fd8z0VSSa4XE8Tmdv6T2Nb5nop5PvUNHAzbAX0BfSZIOAPU+cZ+fxKeWxwkLC4vzQIi5z7dv31KrVi1WrVpFz549WbBggdEHekLs2rWLH374gXPnztG7d2+5qkJMHj58SFhYWIJFzJ2dneW3zAwZMtC8eXP27NmDr68v/fv3p2rVqkydOpUzZ858dD8/V8LDw2VfSB8fHwYPHpxgjd+QkBA6duzI+/fv2bFjB15eXvF+v1ChQnTr1g0nJyfCw8Np2bIlf//9d4J9+/3337l58ya9evVi6dKlVKtWjTlz5uDp6Ym3tzcqlYqlS5cCusoMoaGhVKxYUa6FO378eFatWkW7du2ws7MjLCyMtm3b0qZNGwYOHMiKFSvo3bv3R980TP12TP0uvjQ+HPdHHeSlS5eoXLkyb968YdOmTbLQA8iZMyfLly9n165dZMuWjW7dulGtWjVu3ryZ6P1rtVp69erFs2fP2Lt3Lz169GDWrFmUKlWKs2fPJno/Dg4ODB06lIcPHzJ9+nRu3LhB1apVqV+//kf1Jz7s7e1xcnLC1tb2o8qp6bfTWxH108EJRQybo2ybfqwD8T5HEvucUdLAKKQVkjQCJUnSe6TvBK4LIVYC9kAdwLhzymdAREQEISEhREREmPzOtm3buHDhAp06dZKtKh9LVFQUixYtIjQ0lNGjR9OuXTuj33vw4AGWlpZxpgdjo1KpiI6Olm96xYoVIyoqigcPHqBSqejVqxdqtVr2JUoPHD9+nLt37+Li4kLJkiXlvHfxcfr0ae7du8eSJUsoV64cAwcO5NSpU/Fu4+HhwfTp09FoNLi6ujJo0KA4iZdjs2vXLqysrLh//z4RERE4Ojry/v17MmTIwNOnT3F2dubixYsAci6s8PBwtmzZQvPmzYmIiGDmzJnY2NiwefNmxo4dy+vXr9m1axc//fQT5cuXZ+XKlfj6+ibuZCVAeHg4ISEh8eaYS49s376d2rVrY2dnx549ewysujEpX748p0+fZsCAAVy+fJkZM2Ykav/BwcG0bt2azZs389tvv9GwYUOWLFnCoUOHiIiIoEaNGgwfPvyjroutrS1lypShUaNGgC4n35QpU4BPF0/6JMIRERGJEmuxt9MLotjizxTmSCOj36cy1hW+NBIl9iQdlSVJqgUghNA7I/0H9JMk6RTwAOgFnDNLT1MAW1tbHB0d5czrQog4ofvdu3enadOmrFu3Tu+H8dFYW1szffp0MmfOzLRp0zh06JDR77179w43N7d4oz01Gg2vXr0iW7ZsWFhYyAl68+bNS/ny5QkKCmLs2LFkzpz5i/DbSyz6+qFZsmTBysoqUaJcP+0eFRXF+vXr8fDwoG3btnh7e5vc5sWLF/z000/kyZOHKVOmEB0dLQs1U2zatIlixYqxbds2vv76a/bv30+9evW4ceMGrq6uhIeHM3PmTNasWUOvXr0oX748GTNmpFu3bjx58gRbW1vmzp1LZGQkffv2Zfbs2eTKlYsffviBbdu2cfHiRcaMGZNsZfDs7OxwdHRM907mAQEB7Nixgx07djBx4kTatGlD8eLFOXv2bIIvZBcvXuTPP/8kb968TJ06NcG27t+/T7Vq1di9ezezZ89mxIgR8rq6dety/vx5OnTowJw5cyhTpky8Vj6NRsOJEycYOHAgHh4e1KlTh40bN9KqVSv+/vtvVq1aBSTeopYQiRVrpkhs5YmkppGJz1Kt32d6H+sKXx4Jir0PUbcngRHAGkmS9kuSlPNDIMZV4B/gkhCijRBitRDipVl7bEYsLCwMnIT1b6gx3/Ksra3ZvHkzTZs2ZerUqfFO2/3333/MmDGDsmXL0q1bNznZKUDx4sVZvnw5hQsXZvbs2Qbr9Pj6+hqN8ozJmzdviI6OliN7169fz8uXLxkxYgSSJDFq1CjevHnD1KlT01UKFj1fffVVojOo58uXD0dHR7n03K5du/Dw8GDPnj28fBl3WL9+/Zq1a9fK323UqBGWlpacPn063nby5MnD8ePHadeuHbdv38bDw4PMmTPj6upKZGQkS5cu5dmzZ3Tv3p3atWtz7NgxDh06RGhoKLVr1+bFixc0adKEBQsWcP36dfz8/Fi0aBFv375lzJgxVK9enV9++SUpp8sosX8X6ZUnT57QunVrWrduza+//kqrVq04cuQI7u7u8W53+fJlmjRpgru7O4cOHZIr45hi7969VK1aFT8/Pw4dOsTgwYPj+IK5u7uzaNEiDhw4QEREBNWrV8fOzk5e9EED+hKLderUYdWqVVSuXJn169fj4+PDpk2baNy4sfxy+6kiTU9KlQlLajvxWe+Usa7wpZKYUKVGwFshRGtJktyAg8AqoLUQIkiSpL+EEFcBEhOcAbo3K1OpHD4l31d860xFZcXneK5Pt6JSqeIEcqxbt47mzZszZcoUhBDyVKwQgmvXrrFhwwaOHz+OWq2mUqVK7Nu3jxMnTjB9+nSyZs2KRqPBycmJkSNH0qVLF5YuXcqYMWP477//eP36NVqtFl9fX9zc3OS0H+Hh4fj5+Rn04/Hjx3L0m5+fH3v27KFatWpUrFiRBQsWcPr0aQYPHpyg39+XgkajMZiKd3Nzw8fHBx8fH/z9/U1e6+joaPz8/ChUqBCXL1+WxfeqVato3LgxO3fupEmTJvKD+s2bN+zatQtHR0dWr16NlZUVoaGhFCtWjH/++Ye+ffsihJD9OR8+fMiYMWP4/vvvadGiBTY2NgwfPpxy5crxyy+/8P79exwcHFiyZAlPnz5lwoQJ1K5dGy8vL2xsbChRogQHDhygQYMGtGnThs2bN9O4cWMyZsyIRqMhX758NG/eHHt7e9avX290vCc0PRffbyGhbVP64aivU2sqoMQc/cmfPz+LFi0CdC99+fLlk4MLgoKCjKZXuXz5Mu3btydLlizs37+fbNmyodFoDI4j5v+nTp3KpEmTKFWqFBs3biRfvnwIIeIcqz4oqG7duty4cYOlS5calAGMjo6W07pYWlpSvHhxGjdujKOjoxz1HRISIrsc6EWTPkLbmL9nzACExAYoJIb4fEsTCgxKSnumImc/FY1GQ2RkZJwUXQoKaYHEiD0foJwkSdWFECclSaoE3AFmS5K0CmgjSdIdICoxQg/+X2MyORNdJvXHZWNjk6RtbW1t2blzJ506dWLq1KlkzpwZW1tblixZwq1bt8iQIQM//vgjffr0ka1L3bp1o0ePHrRo0YJ58+aRJUsWQJdW4ffff2fChAk4OztjYWFBUFAQWq2WrFmzypUzihUrZiDa3r59y9mzZ2ncuDG1a9dm27ZtsmXo9u3brFixgm7dujFx4sTPPjVGYlGpVAZT8frUA76+vtSqVctkcXR9frkSJUqwfv16rKyssLS0JEeOHOzdu5du3bpx8OBBNm3ahI2NDa1btyZnzpysWrXKIOq6SpUqLF68GK1Wi4ODgxwxOWHCBHbv3s3u3bspVqwYP//8M82bN6dkyZI0a9aMX3/9lT59+nDv3j1Z6MVMhAtQpkwZDh06xDfffEP79u05duwY3333HQA9e/bkwYMH7Nu3L0HLkSlMpVcB0mTZvaSkWfqUh7CdnZ3JXJWOjo5xIuX1U61ZsmTh6NGjRq+LtbU1KpWKO3fu8OOPP/LPP//QsWNHli1bJo9VtVrNy5cv5Wugj3RVqVRIkkSGDBkYOXKkwX4Tk84kZuqdjxVNxlKTJBQ5a4r4tokvNU1S96u/RySlP/Gti4yMTLEkzgoKH0ti7nx3gEPA95IklRFCRAIt0QViZAYWCCEiPyaZcnw1JtMa8fl3xJzS/emnn+jfvz8qlYolS5bw8uVL5syZQ8GCBXF0dKRo0aKcOXOG33//nf3791OqVCk2bdqEEILhw4eTLVs2RowYIb/l6pMnxyy9FZvz58+jUqkoV64cr1+/5uzZs/Tv3x9bW1u6du1KxYoVWbhwYYpMqaRV9FPXiS36XaxYMSIiInj06JH8WaZMmeQp3bZt29K6dWt56jZr1qwG21esWBGNRkPMNArXr19n+/btjBs3jvXr1xMVFUXr1q0pXbo0Xl5eeHp6snr1au7duydP3Xp5eeHu7h7nd1KqVCkOHDggT+k+efKEv/76i1WrVsnpe9ILdnZ2ODk5pcl7yfnz5+Wp2wMHDpgU4O/fv2fYsGGULFmSGzdusGzZMtauXWtwTH5+fgQGBqJWqxP90paYKc5PmW41t2/b51jqTPH3U0jLJPgrF0KEAdOBjMAoSZLqAWWATMB+IcSj+LY32mg8FoS0RkIRunrBN2fOHE6fPs3ly5fp2bOn0ZuypaUlI0aM4MKFC+TNm5fOnTvTrl07QkNDmTRpEhcvXpSrZyQk9qKiorh8+TJff/01jo6O7NixAzs7O8aPH8+oUaPQarVs2LAh3lxcXyLh4eHcuXNH9rHTi73E+u3p07PcunXL4HO9uPP09CRz5szs2rWLr776Ks72ZcuWxdLSknPn/h+nNGnSJFxcXPjpp59o3749d+7ckUVfmzZtKF26NKNHj5aF3s6dO3FwcDCZsqFUqVKyD1+tWrXo168f1atXT1cBOJB201rEFHqmfPSEEGzatImiRYsya9YsunTpwoMHD+jZs2cc61GmTJnInj07OXPmTDPHau5zr7ccxpcZIa2RVsejggIkMhpXCPEQXYDGfWAS0BYYIoSIMmPfkpWkvinGjtA1hrW1NQMHDqRSpUqJmnIoVKgQx48f55dffmHnzp0MGTKEDh06kC1bNu7cuQMgR8Tpo0g1Gg3v3r3j6NGjLFu2jJ9//pmwsDAqVarEq1evePz4MQ0bNiQ0NJTNmzczePDgdJk9/f79+1StWlVOtWJpaYkkSbx79y5R2+fJk0cO0ohN5syZcXZ25u3bt6xYsSKO/yTo3u6zZcvGv//+C0BQUBDbt2+nR48esnBXqVS0b9+eW7dusW7dOqKiopg+fbos9BJjGdALPv1U3bp165I81fo5WlHSMvp6x/EFY/zxxx/yb/78+fMsX77cZDCWpaUlWbJkSZNT6eZCbyWL775rjjx7CgpfKom+ewghngPjJEmahq7yRvzJxNIYSS2jpo/OAuLUQ/wULC0t5fQg33//PYcPH+bNmzdUrVoVgIIFC3Lx4kX27duHSqXi9evXcvmtrFmzUq5cOYoUKUL+/Pl58uQJoLNihYaGAqSLOrjGiFkb18XFhTlz5mBjY0P79u0TlXfOwsKCMmXKYKxSwrFjx7hw4QKlSpVi3rx5LF++nPbt29O/f3/Zr/LkyZO8fPmSfv36AbqE15kzZzYqNvWir23btpw+fZoKFSpgbW1NaGhoonxJS5UqxbVr19BoNEn20wPD34aTk1OS96Ogw9XVFUdHR5PX5NGjR4wePZoGDRqwc+fOeOvfpiX09z9jATHJjd5KFp93UEy/wc+hLrOCQmry0a+KQogQc3QkuTAVoZccEVj6KV3gkwMebty4wbRp02jbti0NGjSgbNmy5MuXTw7AyJgxI6VKleLq1atkypSJIkWKkCFDBrn8l4JxMmTIINe43bhxIwcPHuS3334jX758iU4yXLFiRX7//XeD1DdCCH7//Xdy5MjBgQMHePLkCTNmzGDFihWsX7+eLl260KtXL+bMmcNXX30lB05IkkTNmjX5559/TEYrqlQqatSoAejKWumLySfmOn+KyNOTmN9GfJGvCoaUKlWKFStWoFar41jjtFotPXv2xNramqVLlyYpKXtKETvIIznrjicH+vGaXoLPFBQ+hS9uXsDUDUn/pvgp6KcU4ptaSAxRUVH07NmTTJkyMXv2bJYvX879+/fx8vLixIkT8veqVatGhQoVZL87f39/ReglgLe3t5yAdvPmzVSoUIE+ffp81D70heYvXLhA48aNAZ1V79KlS8yZMwdra2sKFSrEypUr6datG8uWLWPx4sWsXLmSyMhIpk6damCtqVmzJlu3buXZs2cm6yXr0T/AUtLXMjG/jbT2oE/LlC5dmvDwcO7du8fXX39tsG7RokWcOnWKlStXGvX5TEvokywDclJtfcqXtIA+wORL4NatWyZdgNKjO05ax9PT06QfuIeHR6IDAlOSVBF7+rxRsYkvH56ehPKA2djYIITAxsZG/szYG3Zi9mnse8nxFjlo0CBu3rzJ5MmTuXbtGj///DNly5Ylc+bMBAQExBGT+unekJAQHjx4EGd/b968AXRJnPXHbOwcpweLTEBAAJs3bwZ002mjRo3ixYsXgC4JsqmHQ3h4uDyF6erqip2dHYcPH6Z48eJotVqmTZtGjhw5aNeuncF59fDwYO7cufTv358FCxbw7t07vv32W9RqtZwLTl9G69ixY3Tt2lXe1lSuSTs7uyT7IcW3nan2YmJqvbHfVcw2k+P3ldZRq9UmfT9j5lTUC7wLFy7IefKsrKyIjo7m999/p1atWvzwww/ydqamKpOat06/bXzrTO035jr9vU7/76e8MCe1P+mFqKioj65lrZB6PH/+3OT1SqtjOVXEniRJyf4A0O/P2NuepaUlFhYWHz0VZQ7ryvXr11m7di116tShcuXKzJs3j9DQUPr27YtWq6VFixYUKFDA6LZbt26VHygx0T9os2bNKh+XOc7x50DBggXZtGmT0XU5cuQwWUXk9evXBue2dOnS3Lx5E2dnZ86ePcvly5dZvHhxnGoJ+rGUPXt2eSpWj14UFSlShMyZM3PixAkDsRdfVHpC6xLC2Fj/lCh4S0vLNGVFSY2xrVKpTEbHq9Vquaxh4cKFcXR05ObNm3Tp0gXQ9Xffvn28efOGJUuWyNO38T3gk5q3LjGYysMXs02VSmXgw2muvsR3nGn1wWmMz6mvCumPNKMG4stnl1zop6JSq8h1dHQ0Xbp0wdnZmQEDBnDz5k127txJ8+bNyZMnT6r0ScE4ZcuW5dGjR/j6+rJo0SJy5colP7g/FkmSqFGjBidOnEixt/eUHOtKNK8hFhYWlChRgmvXrhl8vnz5cr766isaNmyYSj37P8lVBzexKGNEQSF1STNiLzw8nJCQEIOH06feIGJvn9pJWBcuXMiNGzcYOnQoLi4uzJs3j6xZs9KzZ8+P3tfly5eZPHmy7FOjR5kKSB7KlSsH6KypN27cYMSIEVhbWzNixAg6der00furUaMGL168MFpj1xyYGuvJ/dDVarX4+/sTFBSUai9RaZF8+fLJaZRAV1f34MGDdO/eHUtLS6PXQa1W8/btW9ltIzmJ3V7sOrjmTmMSXz1aBQUF85NmxJ6dnZ3sBKznU60T+u31iTlTO+nlmjVrqFixIlWrVkWr1fLs2TPq1Knz0eLz0qVLbNy4EX9/f968eWNQrzIoKAjA6HRveiAyMpIHDx7w4MEDXr16leT9fP3112TPnp2TJ08C/w+cOHHiBBs2bODs2bMftT99pQ399TE3psZ6clv8wsPDZZ/YtOK4by6EEGg0GjQaTbyi6PXr13h5eVGtWjX5s1mzZmFlZUWvXr0A4+LHz8+P//77z2j+xk8ldnuxq2eY29KnVJdQUEhd0ozY0wc/xE6X8imWOP32nxo9mxw8fPiQGzdu0KZNG0BXJkmr1Rotnh4fly5dYtOmTWTLlg3QiQf9OdNqtbIDuZubWzL2/vPh8ePHtGrVilatWtGwYUOjyZETg0ql4rvvvuPWrVs4OzvLUdL68ztp0qRP7mtqTG0lt3Vb/xDPnDnzF+8jevv2bfLkyUOePHkoVKgQBw4cMPq9cePGER0dzYwZMwDw8fFh9erVdOrUSfYZNSZ+MmXKRNasWePU2E0OEhJbsS19yU1qv2grKKR30vQv71NvEGnpBrNlyxYAWrduDfzfwhNf7dvYXL9+nU2bNpEvXz769u0r70d/fBqNRs4lZyob/5eOq6sr7dq1o23btjg4OLBo0aIk76tly5ZYWVmRIUMGA7Hn6urK4cOHP9q6BzoLi17cRUREpLgPaXL/Joy9pH2pZMmShaFDhzJ06FA8PDzkKjYxUavVbNu2jS5dush+uIsWLSIyMpLhw4fL3zN2HcxZKSOh6/4pdXIVFBTSPl9cnr20QmRkpMHfmzZtomLFiri7u3PkyBE5hUpkZKRBTp7Xr18b9eu6ePEiO3fuJFu2bJQrV46HDx9iY2PD48eP5ci+58+fky9fPiD9ir2AgAA2btwo/33u3DmuXLkiJ6g2JdBevHjBgwcPePfuHW/fvsXd3Z2sWbPi6uqKt7c3Wq2WO3fuEBYWxpAhQ1i2bBmTJk1iy5YtJh+QGo1GjtDU+1KGhobKUZDW1tap6kOq8HFYW1vj6ekJQKNGjZg7dy6DBw+mcePGuLi40LJlSx4/fkxUVBQlS5ZEo9Hw/v17li1bxrfffmsyyl5BQUHB3KSa2DP2gPyUt8r4tk3tt9VHjx5x8+ZNpk+fDkCuXLnk3G8FCxaUHyCgq+0ae9pZP3Xr7u5Oq1at5Dd/Z2dnoqOjcXV1BXRTj76+vlhbW+Pi4pIuUwF8/fXX7Ny5EyEEDRs25MmTJ/z111906tSJVatWGT0nISEhrF692uCzoKAgHj58aPCZftouT548jBgxguHDh3Pt2jU5CXNstFqtQSoc0CUk1lvCrK2tk93FwFy/g7T8+0op9NPuoLPylShRgqNHj1KiRAk0Gg12dnbyi1vx4sWxs7Nj8eLFBAYGMnLkSKNjz5zpTJKyzlykx3uRgkJaIn3cpVMZLy8vAL799lv5s8DAQCDhaVy90MuXLx+VK1c2mOJxcnLi/fv3BgEa+hJf6fXmamNjg6enJ7lz52bChAloNBqOHDnCmTNnDL4nhODNmzfs37+fVatWJbhflUrF8ePHAZ0/ZN++fXFzc2Py5Mkf1T+l3NiXQ8OGDZEkiX379smf3bt3D9C9xEVGRjJ37lxq1qxJ+fLlU6ubCgoKCorYSwl27txJhQoVyJkzp/yZXuzFF6Bx9OhRNm7ciEqlokyZMoSHhxukVrG3tyckJMRA7L179y7dTuECXLlyBZVKhUqlolOnTuTJkwdLS0vZWR50vnLLli1j06ZN3L9/nyJFiiS4X41Gw7lz5wBwd3fHwcGBESNGcOTIEfnz+EipKFyFlMPFxYUaNWpw+/Zt2aJ3//59smbNSoYMGdi8eTOvX7+Wy/cpKCgopBbJIvak9GpGSiS2trYEBAQYCDV9ahR9QEVsNBqNbI3SarVs3LiRgwcPsmTJEry8vDh16hTPnj3D1dVVzueVOXNmLl26ROHChc18RJ8P/v7+aLVag0oA9+/fJzw8XBbJ//77b6L2FRISQu7cueVyWIm17p0/f56hQ4eSL18+2adS4ctAfz31+S7fvn0rR9zeu3cPS0tL6tWrl2r9U1BI73h4eMhVWmIvMV2oUgpPT0+T/TFnHeRkEXvig4pRRJ9xunTpwoMHDzh9+rT8WcmSJQFMpga5cuUKQUFBqFQqChYsyJAhQyhdujQFChQgKiqK69evExoaSqlSpbh27RqFChVCkiT+++8/6tevnwJHlTYpU6aMnAutc+fOBAUFYWtry9SpU+Xv6MWxh4cHLVu2pFSpUgnuV6VSyVG4+vqgDg4ODB06NF7r3vnz52nQoAFZsmRh9+7dSjDGF4a3tzeAbLUPCwszuMbmLHmmoKCQMM+ePZNrUMdenj9/nuL90dfVNbbEDNZMbpIs9iRJspAk6XdJksZLklRPkiQLoZRvMErr1q1xcXFh5cqV8meenp64uLgYFXt6P7McOXJQq1Yt7t27h52dHblz56ZOnTp8//339OvXjx49evDkyRMAKleuLA/cb775JkWOKy1y8+ZNcubMSc6cOVmzZg1CCKZMmUKOHDkAneXFx8cHKysr3r17R65cuQyS35rCzs6OQ4cOkTdvXoPPe/XqZdK6d/HiRQOhp1j1vjy8vb1xcnKSfW/Dw8PNlqtOQUFBIakkSex9sOAdAzIDpYD+QPrM4psI7O3tad++Pdu3b8fPzw8/Pz/evXtHoUKFuHr1qixAfHx8ePjwIWvWrMHPz4/MmTMTFRUFwPr16wkODiYyMpLIyEjUajX+/v7cu3eP4sWLY2Njw9OnT/n666/56quvUvmIUw+VSoWLiwsuLi7Y2tqSP39+SpQowZkzZ7hy5QrHjh3DwsKCLFmyEBoayunTp7l48WKC+929ezfFixdHq9WiVqt5//49arUaGxsbhgwZwpEjRzh79ixarRatVsv58+dp2rQpWbJk4ejRoxQoUEAJzPjMiY6O5u3btwbL8+fPcXNzw9fXV67vbWdnh1arld02lHqwCgoKqU1Snz5ZgSAhRHchREugBGAwd2hsSleSpF6SJF2+e/cuZcuWZdmyZUlsPu1jY2NjsPTt25fIyEg2bdpEkSJFKFKkCHXq1MHX1xcXFxf5sxs3bvDgwQNsbW158+YNFy9exNHRkZcvX2Jvb8/o0aMZPXo0o0aN4tWrV2TKlIm//vqLQYMG8e7duy96CnfZsmWULVsWoLAkSZclSeqlX6cfWxEREdy9e5e7d+9iaWnJsGHDAJ219NWrV7x79w4nJyc5N+F///1HQEAAFStWRKPRoFar6d27N6BLKSJJElu2bKF69epyP2KWHLOxseHHH3/Ezc2NqVOnYmNjw7Vr12Shd/z4cXLlyoWFhYXRRSFtkJixFRQUxN69e9FoNNSvX5+qVasSEBBA1apVqVmzJhYWFrJlTz92IP2kplEwTnxjS+HLIz4fQXP75cVHUu9ClkAFSZJaffj7MeANIEmSp6kpXSHEMiFE2cKFC3P58mW5TmR6oFixYlSsWJFly5bJb/zlypUDdOlV9AQHBxMdHU3GjBnx9fUlIiICGxsbtFqtnJsPdJam8+fPM2TIEJycnDh37hxRUVFftNjr1asXly9fBrgrhCgrhJDfFvRjK1++fOzbt499+/axbds2g0S2+kLvGTJkkPPb6esmg676QefOnVm6dCmSJKHValm+fDktWrQw6EfskmMODg4MHz6cw4cPM2/ePHnq9ujRo/L0sULaJjFjK1euXMyfP59GjRoBuvyZAPnz55f3o0zjKsQmvrGl8OURn4+guf3y4iNJYk8I8RLoAPhLkuQCfA3clySpPfAb4BTf9umV3r17c+/ePS5cuABA3rx5cXV1lcWeRqMhICAAW1tboqOj0Wg0qFQqQkJCsLW15dGjRwghePv2LWPGjKFkyZJ8//33AJw4cQJbW9tE+Z99yVhYWGBnZ4ednZ1cvUJPcHAw1tbWcq47a2trWexptVqaNm3K+vXrkSQJIQTTpk2ja9euBvvYs2cP27dvj1N6qk+fPri5uTF06FBF6KUT9FVwYoo9fXUU0P2eFRQUFNICiRJ7ko7KkiTV0n8mhDgqhDgORAG3gIHAWGC6EEJJKmaENm3akDNnTkaMGMGDBw+QJInq1atz+PBh3r59S1RUFNHR0djZ2REVFSXXs4yOjsbBwUH2E7tx4wbBwcGMHDlSFjSRkZFYWFgQEhKSykeZdhFCEBUVxcuXLwkICJDPsRCCR48ecejQIdlq9+233xrUMgX4888/adGiBW3atIlTc9fBwYHp06dTtWpVReilE3x8fHBycpJzZUZFRREcHIyLiwvnzp1j6dKlicrhqKCgoGBuEhR7H3zvTgIjgDWSJB2QJClnjHWuQE2gM9BSCHHTfN39vLG3t+fo0aNYWVnRo0cPHjx4QJ8+fdBoNCxcuBA7OzscHR0JDg7GwcEBrVYr/z8yMhInJyesrKzIlSsXAH5+fvK+u3fvTnh4OLNnz06tw0vzfPXVV7i7uxMeHs5///2HjY0NWbNm5e3bt/j7+2NpacmBAwewtLSUc6Xp+fPPP+nRowd16tShadOmDBgwII7g++GHHzhx4gQ5cuSQS2spzvlfLrHdkp8/fy4HZjRq1IgsWbKwa9euVOqdgoKCwv9JjGWvEfBWCNECKAO4A6skScrwwS/vHdAUqC2EuG+2nn4h5M+fn1WrVsmCLywsjA4dOrBz507u37+Ps7MzGo0GIYRBPdXIyEjZgqB38Iw591+gQAGaNm3KggULTCZqTu9YWFiQKVMm8ubNS5YsWciZMyeBgYEEBAQAsHHjRqpWrRpnuzVr1shCb8eOHWzevNmk4NOjD+IICwsz6zEppB56q7AefRqkZcuWyVP56TkyXkFBIe2QGLHnA5STJKm6EOIdUAnIDcyUJKkaMAs4IYRIXBmCdII+RYqxJXv27AaCr1atWjg7OzNz5kwsLCxQqVT4+/vLQQRarVaeyn358iW+vr64ublx584dXr58KS9t2rQhNDSU6dOnEx4eTkBAAOHh4XK76QG1Wi2nsYm96K0u+gTJoaGh+Pj4ADoB3bx5c9kSJ4RAq9WyevVqevbsSdWqVVm0aBH+/v68e/eOOXPmUK9ePQYNGsTChQvllCv6xcbGBgcHB2xtbeOs0y96C+PYsWP56quvWLBggcE4MYWp/enTwsS3XiHpRERE8ODBA3kJDAxErVbz4MEDHj9+LIs9X19f+vfvrwg9BQWFNINlIr5zBzgEfC9JUqgQ4ookSS2BCUAmYL4QItycnfxc0Wq1REREYGtra+DMX6RIEUqVKsWZM2eoWbMmEydOZMKECfz00080b96c//77jwsXLtC6dWvCwsJwdHRk8+bNlCpVSi6z5unpyZs3b+S/AUqUKEHr1q1ZvHgxvXr1wsbGBiBdRQfa2tqa9JPatWuX7Ex/5MgRmjVrBsDIkSP5+eefDa6RJEmsXbtWFnorV67ExsaGefPmERkZybBhw1i8eDF9+/Zl0KBBWFhY0K9fP3l7faBIeHi4HBASk4CAAGbNmsUff/xBcHAwefLkYeDAgWi1Wvr06ZPcp0UhGbC2tjaob63/7ek/O3PmDNbW1kRFRVGsWDE5vY+CgoJCapOgZU8IEQZMBzICoyRJqoduOjcTsE8I8ci8Xfx8iYiIICQkxCC9R0zy5cvHqlWrePLkCRqNhnz58nHq1Cm5tu3Lly8pUKCAPC2bJUsWedtcuXIZpGLRM2bMGEJDQ1m6dCmOjo6ydVBBZ23dtWsXnTt3pkWLFmi1Wjp06MAvv/wS57uHDh2Sp25XrFiBjY0NEydOZNasWSxcuJBRo0ZhaWnJ4sWL5SndOXPmGFjPIiIi5Hx8egICAvjll1/IkycPU6dOpXbt2ly8eJHr16/TpEkTBg8ezJIlS1LidCh8IiqVymAa9/Hjx7i6ugJQtGjR1OqWgoKCQhwSY9lDCPFQkqQRQE9gEhAEDBFCRJmzc587eqF169YtAMqWLRvnbf+bb76hfv36TJ06lTlz5tClSxdevnxJrly5+Pfff6lQoQK+vr5YW1vLPnugE3v+/v68f/8eJ6f/Z7opXLgwrVu3ZsmSJQwePDhdWfVMce3aNTZu3Mg///xDWFgYFhYWaLVa6tatK+fUi50W8uHDh9StW5cdO3bg5+fHpEmTWLVqFd27d8fe3p4FCxYA8Pvvv7N582batGnDsGHD+O+//2Sxrp9WtbKyQpIkHj16JFvyvv32W0aOHCnXSAbYsGED7du3Z/DgwVhYWDBgwIAkHa9+itiYRVEh+Yg9bp48eYKNjQ2urq4GL2YKCgqfH56eniZr56ZWYuRPIVFiD0AI8RwYJ0nSNEASQrw3X7e+DCwsLPD396devXpERUWRKVMm6tatS/369WnSpAlubroKc1WqVOHgwYNIkoSFhQVPnz7F0dGRFy9eEBkZSXR0NEIIgoL+n9FGX2f16tWr1KhRw6DdPn36sGXLFk6cOEGrVq1I70ydOpW7d+/Kf2u1WrJly8bixYuxtIz7E3B3d6dMmTJs374dOzs7goKCWLlyJQ0aNGDChAkAvHr1io0bNzJkyBBy5MjB5s2badeuHTNnzoy3L99++y3jx4+nRIkScfzyrK2t2bBhAyVKlGDatGlJFnv64BDQBfcomAc7Ozuio6MJDg7G2dmZd+/eYWFhQe7cueNE6iooKHxePH/+PI4R4HMm0WJPjxBCSeSWCPT+ejNmzECrRQqByQAAF8xJREFU1bJgwQLOnTvH4cOH2bRpE5IkUbZsWapWrcqCBQto1KgRixYtQqVSkSdPHo4cOUKpUqWws7OjYsWKPH78mK1bt9K5c2esrKyoVKkS7u7u/P3333HEnl4UKg7iOpYtW8bevXvZuXMnd+/eRZIk/vvvP2rWrMn+/fsNqmwAXL58GVtbW/mB7eLiQvHixXnz5o382Zs3b8iXLx9Zs2YFdELNy8uLly9fylO5Wq3WwLJmb2+Pu7t7vH29e/cuT58+ZezYsQkelykLnr6yh/5fBfNQpkwZNmzYwPnz5/nmm2/Inj073t7e6SYYSkFB4fNBmeMxExERETx8+JDVq1fTuXNnevbsyapVq3j+/Dlnzpxh4sSJqFQq5s6di6enJ9myZePChQvUqlWL8+fP4+rqipOTExs2bMDa2pp69erx6tUr5syZA4CVlRVt2rThzJkzchSgHn0ZJ731L72TIUMGOnTowObNm7l06RIDBw7EwcGB169fU6VKFc6cOWPwfTs7uziWmcaNG3Pjxg1evHjBu3fvuHDhAo0bNzb4niRJ5MqVC09PT6NLQkIPYMqUKbi4uCTKqhezRm9M9Mm4lSlc8+Lp6Un27Nnl8ePh4SH7aSooKCikJT7asqeQOIKCgpg/fz5arZYuXbrw5s0bed2zZ88oUKAABQoUICQkhAsXLrB8+XKaNGnC6dOnCQkJwdPTk1OnTiGEYMOGDeTOnZvChQuzbt06rK2tKVasGFmyZEGlUjFjxgxat26Ns7MzefPm5dGjR2TIkAFXV9d0l24jICCArVu3Gl0XGBgoVxhp0KABBw4c4P3799SrV0/2czS1zzp16jB16lR27tyJvb09Wq2W2rVrExgYiKOjo9Ht1Gq1yXWgCxjRR0wD3Lhxg507dzJixAgcHR1NXju9xVCx4KUsb9++Ze7cuQafWVpacuvWLaZPn06lSpUIDQ1VRLaCgkKaQxF7ZsLHx4ctW7bQpk2bOM6c4eHhsgjw8/NjzZo1FC5cmDx58rBnzx7AMGHy+/fvuXnzJuXLlydHjhxs2LCBoUOHkjFjRkqVKsXFixdp0KABlpaW2NjY8PTpU/Lly5cuI3E1Go2Bb2NMzp49K0+7AlSuXJkLFy4QFBTE5s2bKVq0KAMGDIhj1cuUKRMeHh6UKlWKo0ePYm9vT4ECBahWrRqhoaHx+mfF9+C3sbExEHu///47Li4uDB8+3OBzY/vULzGDcxJqT+HTCAkJ4eTJk0bXnT9/nnbt2qFWqxXLnoKCQppDeTKYiUWLFqHVavnxxx/jrBNC8PDhQ+bPn8+YMWNwcHCgS5curFq1Kt59hoaG8sMPPyCEYN26dajVaqpWrUpUVBSXLl2Sv/fo0SNlCjcehBBoNBpsbGyoXLmyHDn5888/M2zYMJMF7Js3b87Vq1c5c+YMzZs3T1Yn/OvXr7Njxw4GDRpExowZk22/CimH/qVOrVYTFaUkKlBQUEg7KGLPDHh7e/P333/Tpk0bcuTIIX+uVqvZtm0bkydPZvz48Vy7do1vvvmGSZMmsXHjxgQdux8+fIitrS1t27blxYsX7N27l5w5c+Lp6cnp06fRarVERUXx/Plz8uXLl+7rs/r5+fHo0SOuXr3KP//8w8OHDzlx4gQHDhxg37593LlzB5VKRbly5ShWrBgAq1at4vvvvyc0NDTO/lq0aAHoxGLz5s2Tta+//vorLi4uDBo0yOR30vv1TOvEnE5XrHsKCgppCWUa1wysWrUKtVodx6q3bds2hg0bho2NDV27dqVGjRrY2try7Nkzrl27hqenp8H0bWyioqI4f/48WbJkwc7OjpMnT1K7dm1cXV159uwZr169YtasWWi1WgoXLpyuU3C8ffuWv//+2+g6CwsLrK2tefLkCZkzZyZLlixUrVqVwoULs3nzZg4fPsyKFSviCC8PDw/KlClDaGioySodScHHx4cdO3YwevRoMmTIYPJ7Ma9n7OlbhdTn8ePH8v/DwsLIlClTKvZGQUFB4f8oYs8MPH36lKxZsxpY9UCXQLlmzZr8888/3Lt3j2rVqgG6BMlt27Zl27Zt8e43Q4YMXLt2jTdv3pApUyaaNWvG9u3buXHjBpUqVeL06dOcOXOGtm3b0rJlSzmHXHp04Hd3d6dJkyYEBAQQHBxMcHAwb9++JSoqSraAZsmShcyZMwNw+/ZtTp8+DegSU7dt29bofteuXYtGo0nWKdywsDAA8ubNS2hoqMnrpQRkpG2yZ88u/z8+0a6goKCQ0ihizwy8fv3aaJqNDBky8OeffzJ06FB27NjB06dPGTRoELlz5+bbb7+lUqVKDB482OR+AwMDUalUtG3blty5c7Np0yaePn1Kw4YNefr0Kffu3WPkyJH89ttvsqN+erPo6ZEkKY7f4pkzZ8iSJQtRUVFERUXJQTJ37tyR09fUrFmTtWvXmrScxXygJzfR0dGy5c6YoNOnVFFIm+hTHllZWcUbha2goKCQ0ig+e2bg9evXJsslWVhY0KhRI8aPH09UVBQTJkxg+/btBAYGGkSKGiNPnjz07NmT58+fM336dF6+fEnr1q25desW9+/fp1WrVkyZMkWJyIwHSZKwsbHByckJrVbLlStXZKHXsWNHNm/enGpTpFZWVjg5OSmWu8+Uy5cv4+joiLu7u1JBQ0FBIU2hWPbMwJs3b8ifPz8vX740uj44OJg8efIwceJEVq5cyaZNm9iyZQvFixePd78hISFMnz4dSZKoVKkSxYsXZ+PGjYSGhtKxY0fKli0bb5BHehARQgjUarXJdfryN1FRUVy8eJHAwEBAV7d4ypQpREZGxjmHYWFhJmsMa7VakwETxoI8YvfHysrKYHv9NYpdfUMh7XPlyhUcHR1l1wAFBQWFtIIi9pKZyMhI/Pz8+Oqrr3B2djb6nZo1a5I7d25AZ026f/8+f//9N5s3bwZ0070tWrSgdevWuLq6snDhQrZs2UJgYCD9+vVjyJAhHD58mFGjRmFjY8OhQ4dkoacv02Zra5suxULGjBlp164doMu55+fnx7t37/Dz86NKlSoEBwfj6+vLzZs3CQoKQqVSsWLFCho3boyVlZXRfTo4OODi4mJ0nbOzc7znOb51Qgg5Xx4g10bWb5cer19apnDhwqxfvx7QCfnmzZuTK1cuVq5cSUhICDVq1CBnzpxyzWsFBQWFtIIi9pIZfaWMxJTG0lOwYEF++eUXxo8fz/bt29m9ezd///03f/75J5IkYWVlRceOHenevTt37tyhVq1avHjxgsqVK7N+/Xo8PT3lfUVERMhVIkxZo75k7t+/T9WqVY2ukyRJFsGRkZHY29vj5eVFzZo1CQ4OTuGeKnzOrF69Gn9/f+bNm4ckSdy9exfQvWAolj0FBYW0hiL2khm92EtK2gWVSkW1atWoX78+gYGB7N69Gx8fH9q3b0+2bNlo0aIFV69epWLFiixfvpx69erF8Q3SV81Ij9Uz9KhUKoA4yZGFELi6upIrVy5y587NiBEjKFy48Efte/jw4SxYsMDk+pIlS3L+/PmP77RCmufu3buUL18e0OXMbNSoEUWLFgV0Lxmgc7VQxJ6CgkJaQxF7yYw+c/6niq0MGTLQqVMng8+ePHlCp06dWLNmjVEH8PQ+hQs6i2qHDh0AXd3Sr776ily5cpErVy7c3d0/yuJqjJs3b+Lm5sYPP/wA6ASk/lqcOHGCy5cvf9oBKKRZ9OmOQOdb+d1338nr9Olz1Gp1vKXuFBQU0g4eHh4mg6lilzn93EmXimDZsmWp2r6Xl1eSt3VxcTE5OPVTuBEREQnuJ7XPgbnaz549OxMmTGDChAmMGTOGzp07U6tWLfLmzZts1k4PDw8mT57M5MmTDf5fo0aNZNl/QqTWtUtv7cbG3d2dAQMGMGDAAHr06GHSjzO5SI3jTk/XOK2Mq9QivZzz+Np89uyZHLgXe4mvwEFS20tNFLGXCiSUPDmp2Nra4ujomChRk9rnILXbTy5WrFiR7PtMqCxaenogp2a7qU1aezB+ae2m13GlJ72c85RuM62OK0mfiiJFG5Wkd8DzFG/4/xQG7qZQWxaACtAA+qd3YeB+jL9Tg5Q8B8ndvocQwmjIYyqMrYSOw4KPv87GxszHtGku0kO7iRlbFoAVEI15f8Opcb7TwzVOrTbT0n0L0sc5T402U+MYTY4tPaki9lIbSZIuCyHKptf200IfUrv95CI1jiO1zl16aze1UcbWl9dmWiK9nPOUbjOtjqt0OY0LpLadNbXbh9TvQ2q3n1ykxnGk1rlLb+2mNsrY+vLaTEukl3Oe0m2myXGVLi17CgoKCgoKCgrphfRq2VNQUFBQUFBQSBcoYk9BQUFBQUFB4QtGEXsKCgoKCgrpEMlU0tYvrE2FdCb2JB25UqndypIk1UrptmP0wUKSpKGSJBVMxfZrSpJULTXaTy4+XMs6kiS1SOF2LSRJ6iRJUvsUbrO2JEn1U6pNhbikpwdyejrWtID44LSfkudACCHS8zlPLdJbubS9QIQkSeOFEHdSosEPg/ok4AeUliTpDtBVCPFfSrQfg5rATMBNkqS/Uur4QScagHOAGsgkSdIyIcTslGo/ufhwHCeBZ0B9SZIqCSFGpkC7+jF0EcgmSdIFIcRjM7epv2bhQBFJkq6hG7evzdlurPanAK+BJ0KIPSnRbmrz4bj7AW+B50KIi/qHozBTNF1qtJla7abWsaYlPpyDqUAIcB44KoQwa87XD/ew1cBxIcSaFBxfv6O71nuFEHf1fTHz+BoIRAGrhRDhaWVspTex9wJoDnwvSdJ2IcSVFGizEfBWCNFakiQ34CCwVpKkVkKI9ynQvv6H9gR4BVQCxAfB9Swl2gdGAv5CiIaSJI0AMkiSlNfcgsUMtAO0QoiOkiT1B6p+EHznzNxuWeCaEGKoJEnHgHkfXox7mPGloTvgK4RoLElSFuAmsEySpDZCiDAztQnI4/UQ8B/gAVSRJOmmEOKFOdtNbT4c93F096lcQKgkSceEEDPN+HBK8TZTq90PbR4DXqZUm2mNGOfgMZAfKIPut/3WzE3nB74DKn4QP39+EHwW6Ix9yXr+PxznEeAUkAnILkmSH+AnhNCYQ4B9aPMaunNbE6gMdIxpPU3NcZaupnGB6+gsBeWB9pIkFZUkqYCZ2/QBykmSVF0I8Q6d2MoDLDFzuzJCxzNgC7AHqAj0kSRpqiRJOVOgC08AT0mSqgB1gQ7AcUmS9qZA28mJP+AuSZIjOgFWBvhLkqRzkiRZmbHd9+gsiQeAf4CfgZzo3pTNxWt0x1paCPEWWI/u2s0wY5t6cgNRQoiOwCB057pECrSb2mQHAoUQnYDvgQNAQ0mSepuxzWxAUAq3qW83IIXbzQkEp8KxpiWyorve3YUQLdH9rgzcNMwxxSqEeABsRTe71lmSpE4x1plDANkA/wLTgVrAxA/tD5EkSWWmNmuhG9Mt0d2zckuS1FSSpFKSJFmk9gtFuhB7MQbvSWA/0Akojs7KZu4f+h10VorvJUkqI4SIBFoCzpIk2Zm5bUD2M7NE90M/ie4B2hnoQsqMgWPAdmA2ujfqAuhuMG6SJGVLgfaTi9PAAHRlsryEEAWArwFrdBYoc/EI3U0yP3D3g0W6NuAqSVJWM7V5+UO7syVJGoNO2PZEdxM1G5IkZUA3BZJDkqQ6QggfdNYf3w/rPc3Zfmog6Xwj26CzpmaRJKneh+nyLeim0ut8OC/J3eYwoDS6F7FG5m4zRrud0Y2lHJIkNU6hY62N7mUlsyRJ9VPiWNMolkAFSZJaffj7MeANut+WmUVJNLr7x19A3w8vr/WSu5EPz3tHoAGwEdj54f+HgIboRL858AeqS5I0GlgBZEQ3q7UYyGemNhNNuhB7MQavD1AdkND5j2UAQs3cdhi6t4uMwChJkuqhu8E6mbPdWH0QQgg1sAlojO4t5xZwAQhKgfbfCSHGoBv499EdeyV0IsmsU4LJiRAiBJ3PSSRwTZKkfEB7wB4IMGO7amA+OgtpN0mSmgPfYsbz98GaNxLdAzEjOiuiL7rpELNYMSVJWgn8JITwBsYD0ZIkuaOrNflAkqSOwBRJkpzN0X5q8OHBdAadi0AbdLMOqyRJ+loI8QaYi+7hlNzHXAvdfSk/8BSda4lZ2/xwrCfRvWhnRDeeV0iSVOBDu/OSu13p/76nk4Cu6CwuGyVJKmauNtMyQoiX6GZW/CVJckH3snpf0gV+/YYZnksx7hc7getCiJXo7pl1AJfkbu/D884X+AWduPtKCBEihNAfX47kbvNDu9eBVuhe/F2EEIWFEFUBAbibo82PIV347H14W9ECbuisTPvQWWkGonNANytCiIcffNV6orvpBAGDhBBmbzsWN9H9oE8JIfqlgg/BcyALOtGZC/heCGF2sZmcCCHUkiTZAD+i80GJAjoIIfzM3O5TSZJ6AP3R3cQCgG5CiGAztvkC+EOSpJborMA5gO5CiGgzNVkXXQDVe2CxEOK9pPNzvY/OGt0aaGPOY04FGgP/CSFafhC2m9CNqUuSJA1BJ+gtgYjkavCD6HqMzieyKTrfJlvgtCRJQ9E9gFXJ2eYH9L6nP0mSdASdRSkLcEiSpInoBFdytxvTX3g4sAGdP9V+SZLGoXvhN8expgk+XOtKgI0Q4jiAEOLoh3V26F76BwJNSKb7cew2Y9wv/gMmfrDsPkAntJPF39nYcaKz6uUCfpUkyR/ds94RXYCdWdoUQmyXJOk2cE7SZTHIiM5nMFna/BS++HJpeqEnSVJTdG8W09D5A/2SSv1xRHfeUyQ4w0j72T9MYfDBd0GTwu1nRSe6Az5YcD5LPkz7OAORH6xgKdWuhO7tVEopofzhoeAKqD9Mq5qjjTrAAuAwUA7YBfwJaNE51L8EmooPEXVfCpIkVUYnQFqhexgeRjdl3hKohk54TRVC3DRD27M/tFkf3Qtwf3RiMzcwJbnblCSpELrr+gTdrMJOdA/8KsBCdMJvcnK2K0lSW3QvRz3QvWjnAt6h81uejc6ql6xtphViWFL90M0m/Qv0FEK8/LAuG7oXcD+ghhDivhnavAP0QhccKPHB51cIMfRT24qnzX/RBa/pp6fbonsxDwdmJMe1NtFmL3T3KUt0VvMa6Kau+wohrn5qm5+MEOKLX9BF4EYC7WN9bpHafUvFcyKldh+URVn0C7q3X4cP/5+I7o1/OLobZ1egWGr30UzHbQv0RefHWhgIRGdpaosuEMfSDG1KH87rBqACUBJdQM4zdFNQKjMdqyUwB51V8bsPn2VE50vlbo77MboXyynoxOU9dD5jBdHN8GQ117GmhQWd1XhrjPNwFd3LRIYPn1mh82UrkgJtunz4rHSM7ybL9TbR5pEYx2mZnO0l8jhzobPKZ0jtcaBfvnifPUmSSqF7W+0phNjwIVjBAkCYObdQWkZ8GJEKCmkBIYSfECL0w/9/Rhc13hPoJ4RYLYS4laodNBNCiAhgldBFK9qie2C0Q+ezuEvo/DWTu00h/u/D2wiYgM4Ccx1dtKpZrP3CuO9payAzOgt5st+PRVx/YTt0FtOMQLi5jjWNYCwTRG5gpqRLbj8LOCGE+DcF2pwt6bIxdJYkyeaDC1FyXW9jbXry/+OcLUmSXTKPr/iOsyq6F1WEEIHJ2OYn8cWLPXT5g2oLIdbqfdTSs8hTUEjLfJgeQQgxGV1E2+7U7ZH5EbqAH9BZ9Fqhm25sI4S4Zuamb6KbLv5PCFEP+FYIYbZAI9D5nqKbUr2Fbnq1HTrfU3O7JMT0F/4J6JICbaY2pjJBZEBnSZ8vkt9v3FSbLuhE/QIhRGQyGxvS2nFmAuYJIaKSuc1P4ov32YtJKgQkKCgofCQxAqrSFZIk2aPzaZr3wdKXEm2mig9vKvmefhH+wh+DJEn50QXlWQDL0AVZ/QDUN5cYUdo0X5ufQroSewoKCgppGUmSrIT5op3ja1d5Ef5CkSTJA51LRB10mSBGCV2aEKXNz7DNpKKIPQUFBQUFhS+c1MgEobSZdlDEnoKCgoKCgoLCF0x6CNBQUFBQUFBQUEi3KGJPQUFBQUFBQeELRhF7CgoKCgoKCgpfMIrYU1BQUFBQUFD4glHEnoKCgoKCgoLCF4wi9hQUFBQUFBQUvmAUsaegoKCgoKCg8AXzP5dvhG6BhZqzAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 698.4x698.4 with 16 Axes>"
       ]
@@ -510,12 +510,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 698.4x698.4 with 16 Axes>"
       ]
@@ -548,12 +548,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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mPvTxtbaPnWzjDz75PRwlsYXBFz79Tl5++WUW3vVBEAppC5CK3KJ59BogbYEy4NL53QBULNhMMj7IxdPP4sY7+cjHP4nyV7lSKboHR7In6YmWGSvvOX/5HI42tvH5bz4TJLrUL5i6rGum8j7a2MY/f/NpHCk5eKKFxTXpY6/UH93Y2Midd95Jb28vJSu3UVFsIqRACMH5PsH9999PSUkJO3bsYOnSpZOeZyoa4USYyOj6VcRVqbz3NxxlUVUHq+sucvzcPPY3HGXt8ltQyYOQ3A2haxGh9cH+KYv7stomU0yy6ZZXRxvbLqvw9/iu48QNXQDcQ/LYruPE43EMD53+LvV60MRm+bbfAdui2nE5uePLXDq/m5GRDvLLF6FMSCQS9I/EdTq8B5hQXJyjx+T72TesnLnPe85fPoep6m6vXzUfDIESuk7xeJ73Iy8cJ+l6GK4iaSkeeSFdEnZtfTUfu+9mfnbgNLdvWDIjq7uxsZEtW7YwODiIUoqavAoMT/vRBYJoXgXDw8OMjIywZcsW9uzZM6kCn4pG+FrGVam8t66OsG3DD7AMD1eaDCY/gUoeRPW+D0gCISj+f4ECTy3rgtomK2r5wD1bp5xk03FHDx5vCVwaScfl4PEWlKEDksoQQSp8JBLh0vHnqL3x7ShLIFxFcqQfq1wzUYiEKFm8kepVt1NUtQIhQa5yaXzu3xkaiSMNEWRq5kbC/Ouf38+BEy1sWDl/xlY36KDSh37rVna+2Mgt1y+ds7pfg5gqEaeppSudYYmkqaUry/Lu6xslNJguwNPXNxpsO9rYxj9+42c4KA4evsDi6qk76Ugp2bZtW6C4AQzDAhQCgUL5MiilGBwcZNu2bfz4qRfYf+Yim8bFq6ajEb5WcVUq77rKOMODEiEUhiGpK45DcjeOGsPFwcIjlNwNvvIev6ybTnGncOZiN/tPtlCQG7lMeQ8mEilDGqW0vKS+AvmcnnzCFCypr2Dr1q0caRtFhrQGViGBZ/uRer/GZW71YnKtMkydkImBRfmiG/nat36AXboguGbjhS4++O6bZqW0Uzh28iJf2P500KqqvraU1cvnzfo8c/jlxVSJODt3nw7KLihffvOda4PtpTFdwTK1PSUDfPPRPSTRij2J4puP7uEflr5l0nHs2LGD3t7eQHEDdDXvIX9jLcqPVnY17wm2KaUYMWI88PkH8RS6BPOH0vGq2bptXivt1K5K5W2HrwNho3BA2Njh63CcRgZVyhpw6Grt5qVTX2DT8mtYu+yWKWlGE+GHzx7hb7/1FCh4uUEXh3rbzemsyIb2LpJ5YHgCaSoa2rs439nnPwCaVvXisbN87GMf40NffAhI87gNy0JKD2GYKOkRMfMxXX3e1MMRCuVxsXEPdWUp5S24ddOSK/7MDh3NXjIfOtoyp7xfg5gsEeeWa5ew98C5QIHfcm32XFu6qAJIE0FSMsCFS/0AgdWckifDZz/7WYaGhrL+NzLcQSImMT0Dz5SMDHdkbTcKq3ETHoYncEOKfRnW9XQ0wky8ltqpXZXeo/PJQr7StYHHB+r5StcGzicLcWUPqeEqJXhs/2H+7fEkv/OFvRw+tZO19dX81uu3zHg59fS+02SaIqnyrCncvn4J0jZwIyBtg9vXL6FzUFf5S03wzsERtm3bhu06+jRKIQA5NorhqOBHjuiXjgiOVXSe3UtX827aDz+NN9THe16/kbfcceUd5dddMx8ZMUjmCWTEYN01cz7v1yKONrbx/36ym6ONbVn/r68rg5BfaC0ktJyB/tE4Tr5BosDAyTfoH01nUdaVFgIEVnNKngwvv/zyZf/LrV6MDBu4uSYybJBbvThruxwexoorhFQYI5KiyOXFr2bSanYiIsKvKl6R8hZCvF8IcVAIcUgI8b9erUGd7Huas8lcnhmu51wyl5N9T2OHr0NhozDwpEnvcIz7r9/L4soO9p08qn3iw1/WQc0ZYGldmfZfo/3YS8dNZtMFe1hixpX+7cKbX7caLwzSBi+sZcMwWLRqE9L3h0sDcsvqsB0TKwG2Y4JpZp17dKSLznO7yS1ZQNWyG7HD+Xz/sf0cbWzjoScO88efepCHnjg8q8/sdEcP8TB4lkE8rOU5/PLh2Mk2vvWD3Rw72Tb9zuNwtLGNP/jbB9n+4Av8wd8+mKXAH3vmOGJUYjgKMSp57JnjWcfmF0ZJ5hi4MUEyxyC/MO02uX7NQqwRieEqrBHJ9WsWTjmOePzy9PnhtjMoz0NJD+V5DLedydqeUzwPL2IgbYEXETSeS2eHHm5u4wP/8gO+9NMX+cC//IDDzZN/NikigmlM35Dllx1X7DYRQqwF/gRYDywA/lUI8R9KKfeVDmpZeABLSDwlMIViWXiA88lCvtu1idpQJwP9ufzu63dimR6uZzI49tsZwUwLFX0bIvrWLEbKeOTkRFA6hgJCy5l4Zk9jmkWitPz5P387QBB1T7lZrLwIjIwEPm4hDN182D+9yrEhmbYcjOpyYhULyC9ZhGGYCEMHmf7zx3t44Xm9Ath74BwA9961lplArxx0QAgUT+87HVQrnMMvB6bjYk+HAw0tJHzGlXI8DjSk2SZ9fXrVmJqTKTmFvrE4hgFSCQwh6Mso2XqypRMsgZkAZQlOtnTy5inGEYlEGBsby/rf6KXznPnpv5FbvZjhtjOMXjqftT3omemn9qsMe2eiKqOTrbBfS0WsXonlfR/QDTwF/Dfwb6+G4gZYXHgHHy07zVsKWvlo2WkWF97BycGTNCdzeWZ4IYWFw9iWh2koQpZkQdlJHDXGmIrjqDEY+x6q931TWuFF0QhKCJ9BIiiKZivvW7csxa//jvJl0H7xL37kviz/+LtvX68nnb/vYGdz8DdA0hshUShwcgSJIgMZMsirWszgpSak1NaI9Fx6uoeDYxTw7EuNM/7MhEwNVhPHZ1Nsfw5XByai+s0GQ2MJpN9ExBOKobF0P3BppednppzCxqU1WKYu52CZRnZQUOl0eRkWOm1+Gv/F1q1bJ/z/6KXzdB782WWKG6D3zD5QUnt17HQ5CkjXRUlZ0+ProozHmsXV/ObdW36lFTe8MuVdCSwB3gP8b+CbQoii1EYhxANCiH0NDQ1s2rSJ7du3z/jEIrSexaUf5u7SBSwu/TAitJ5lUYN4Z5jOw2Wc6KjAVQJPgaOgy6thUI0yqhwGVZxhGScpxzQnfBKcburEGlWYSbBGFaebsov41C8oxQqZYAiskEn9gtJJz3XfjWv4xLtvZ+uKBbQ8+32Gus+SjIEXFiRjoKRE2gI3JpCWtn680WGGe87T8PRXaDn6BA1Pf4V119QC6WdjUUbQaDq0t/djJHX6sZHU8hx+eXDsZBuXugYxDQPDELOuuQ3QeL5L/+EbEoEMFBXnkMwzcCOCZJ5BUXF2ow8zCXafhzkm9e9ketsbb1qFbZkIoZkgb7xp1ZTj+NjHPkZubu6sxi5Ge/id2xbxe2+9ga/8cXYxqbX11XzlQ/fxu/dcn8VC+VXF9u3b2bRpE8AKIcQ+IcQDE+33StgmA8DLSqlmoFkIoYDFwF4ApdR2YPumTZvUvn37ZnVilTwIQ/8HSEJyH8peyujFszQ/Vo/jGXQdKkfdZ7Cyso2zyRLeVDZETAmEUCgFCVwSuLR3KRZOMod6+4cxPKUTZHw5E/sbWpE+1Ukpxf6G1ikpfPfduIb7blzDl//wPmIVC2CNi/BMkB7eyDCiUOm2agLMhCRk6YdnuOc8wz3aEhl0kyRzDQxXIS3BoJuc9HrjcfOWJXznx3uD+7l5y5UzV+bwP4tMd4lpGrzpzjW8/pZVs3KZQLqjEwBCcNvm9By4+8aVPPzccVxLF1W7O6M7PMCjzx0nKSTCg6SQPPrc8eD61yyp5kufeMeM8w+2bdtGSUkJIyMjWXTBySCEoKSkhC2bNnPwVNuElv2UFUB/xfDAAw/wwAMPIIRoUEptmmy/V6K8dwLvFkKUA7Vog/LMlEfMFMndaP+1BBxI7mbfuXmsqHqB9Qs6OHi+kuNNNXTlhjGFonOwgL7+ao6fn0d+dIyhsSirF7RiGedYOElspbggRyfaCF0Yvrgg2xLZuMLvmI2HYRps9FNyj55uY39DKxtX1GRN4h8/fYRn9jRSvngrY73tRPsVQihspUiYYUBgKO3VkAYMdjYBkFdUS0HJIgZ7mhFKoSyBZwEIxAwmfgq//y5dH/zZPae5ecuSQJ7D1Y9MdwlIKsryZ624Ad5yW3ZHp5QMWgH/25/dP+HcBZ14Jg2BMnWTYWVku1WkAV5I6Hrh08AwDHbs2JGVYTkVotEoFXWreP9ffUcH9z2PnO6Z1z55reKVKO9HgW8DLwMu8BtKqb5XZVSha4EQ4PddCl3LTau7edfaR/wgpcFjpxbx8skqbl7Ywu6OCn766DuQnjZthVDYpscH7y/mdZNc4o03reLhXX7LMfvypaDyfYcq4++jp9v44N9nVDf707dzzZJqfvz0Ef7+a08CULf5Pobbm/xysAKESTRalH1x0yBcUonwFGuu+0DAB182r4ywMHCkxDYM3njj1MvT8fj9d900I6X90BOHePaF09x8wxLuvWvdrK4xh1cXgbvENAB5Re6STLzltjVZSjsTp9u62dvcSn5B5DLlvXRxOd5uP5xpC5YuTjcIPtzUxgf+OT3vv/JHb2ftNLVDli5dyp49e9i2bRu9vb0MDQ2RV7yA/NJ6BrubGeo9T05OTsBM6UhEKAtZ6KWp4NzAzGufvFZxxcpb6dfpx/2fVxUitJ7h3P+NG38CK3IXeaH15Bj/gGV6WIYCJVFxk1oxxo93rcaIlSBlqnuCQimDpAcvnlO8Z5JrXLOkmv/v3ipeOH6OG1ZdbokcaGjFk+nOHwcaWkGRVTgq5Up5Zk86sCiEwC4qBVL1kQVJ4oQ9bekLQzcvLly4FsLVyIiNDAuMhMEzu/bzpf/9Dg40tLBhxcStql4pHnriEP/4Rf2i2XvoHMCcAv8FIctdYly5uyQTD+04HJRIuHdbmqn0w2eP8Df/+TNQ8NJESWmtnX7zERHIb/W3PfLyCZKu9sclXY9HXj4xrfIGrcCbm5t58skn+bt//ArJ2CaEMFHKQ/Y+z7GDu5BSMjo6ivKfF5+whUJk1T75zg8ep3tYzMht849PPccTp85w17LFfPSOycy3y/Hgy0f42aHT3L5uCfdvvfqZWldlhuVA/CA/PvQVTl8oZ0ntV3jLpqXsP1vN7fUmKA9PGdy97hSmULjS4CvPvB5Za7KmtoP9Z6s52lqJMGB+weTtmn7w9CN8+emLSMvm6NMXKSt6hPtuuzvYvmFFTVaRqORAK9/59rdxi7YE1L5vfOnvqQw9wM2bFrP7aDqCPtzVjL1oA4YL0oLhM2fJqS0maM1jQf/ZwxSVLydeYup/58Lekw38y9L3/1yUdgrPvnD6MnlOef9ikFk/B6XdJac7uvnywy9x+6Yls6Z6PrTjMJ/98pOgYM8RPR9TCvxnB89kJaX97OCZLOUdMJUy5FQ515/89GfI4iqkpQPiP/npE2wocWfk0jAMg7vuuovO4UK2/+dzKAWGMBlKRrXS9q/Z27SP0hXXgjJQSHqbdJxMKYW0i/irr+7EtEPYlsm//vn9kyrwf3zqOb7+1F7sYfh6y16AGSnwB18+wt9/TWdc792vP7urXYFflcr7saNP8MX/fAOuZ2KZHuHwE6yoeS9/8K2jrKttpbJgmDdvaMAytY/w9Ws7+ODtBxC+Mn+4YSlPnFjIipzJGSI/3HMaJ0//7fnyfbelt69ZXM1H/r9beHjXYV547Lv88Xd3MTw8TG7pAfIqFjF0qYnh7vMceOFJFl77TmLlK4JjY7mVCEeiLAPhSHJiZZq+J/RkNB1ForuDRGkNeQJSDQIvXjpPLBYjHo8TiUTYunUrH/vYx15Vv9/NNyxh96FzmmcktTyHXwwKciOBvpRK0dTRw8OPnswq2TAbBb7zRX8FKNJySnnfvn6xtrh9BX77+uwMx7xQCIVC2rpmvTMyzMKFC+nt7aX6pvdg+VEtT8HY+ZFZuzR6jQTpslcwFPayfOF5i1YSHjX88RnkLVrJSJf+DPLK6/GiFsmoIhl3pyyV/JPnj5PTrv8ODWl5Jsr7Ry8fQ5oE9/+jl49d9cr7qowEPHEwF8czAYHjmTxxMBdPHee6G16iQziEyi8BBp4UICxGnKh2lRkQMiVvXX2Sf3nbDk52TM7WCEXy9R8+rSqQfRxuauPvfvg0+7o7Ca+5GRkrAbRiLiyqJ5armw8PDw8TKtA+ylSIJ5xTCI6LkfDAcUmODQTnFf718isWMdbpp+76k3i0swUzp5yqFbdi5pTzzDPPcP/991NfX09j4/Sc7xll5wkBltDlFy0R3P8c/ucxMBwPPn5lwvMnzmVbx+NKNkyHRUvKs3ITFi1J+63fdvMafmPbJmrLC/mNbZuyrW5gx/5TJAsFbq4gWSj4wTP7aGlpYXh4GApy9JiEn15fkMPw8DAXLlxgy5YtM5qbRwe6Gao1GCsTDNYKvJpsGmzRgjVBYhvKl32YK+sZrTRIFApGKwTPd0+e8h4ZS5WdzZang5ljkiwENweShVq+2nFVKm+htKJUGfKJ7scgLJE50K0sHr9UyFMtVey4VMi+9nk4nonnv9oNAbYpWbVgcNJrLC6MZiW1LPbTgaWUPP744/z2X36GhPCw40DEpGjlJsrrrmXx+vsoKlvK4vX3UV53LQB9bQ3gKkRSgasY6DhFe+NzjI300t74HO0Nz+pEHBSeoZBIBi81kVM0D9NPWTZHJUVVy1l+x+8wb/3rWX7H75BbumDGD0nKf/rv//k8H/7k9ydV4DtfOj2lPIf/OaxfNR8zZOp8gKjBwEgc4SrMMYXwFLfPslBZtDhKssjAyREki0yixekU98PNbXxr10HODvfzrV0HL0sxHxFuloKWUTuwjOXQSNazIof8Gj8Z5VylnDorbGVpGU5UMFYscKMCpyVbAfedPwKp/B/hyz5CC2t1DW8/qe5Iz+SNlVcv0UaVGidPh8XlJf6965/F5SUzOu6PH3qMzZ//N/74ocdmtP+riavSbZJnx0gU+m95JcizYzSO1PLDnVV4GBhKYgCeMLCQbKl32fXDe3jTikbesvIkpqFwPJPlC++Z9BpbV53m0V1h4iGTSNJj69tPZ3X+qLj1PVTE9WSNCBjNKye/RrNGUtXVSmquofPcbkY6zlFZuRGEwJJQXlxKcsE6MKAq/zYSw72MjHYRKanS7Ed/Epo5eWDq2uCYgnBpNYRtPfEsg5L6jQx3n896SJqbmyd0oUzXKSWFW65bwp7D57LkOfbJ/zyOnWzj0eeOkwiDq/R8MuM6aUwAlmuwpGpyt99ESGVJKuEhxmVJ/nTvCUYjOujo4vHTvSeyeNNvvnY121/aC34D494T6ZKt1pgi1ilxIwIrrpBjaXeHUore3l6efPJJ7rrrrknHFu9P6LwKHyGVndGcGO4lmaNpu0poOYXhMw0ULd+kXx4CXrekbtLrvO/OzTx9+iyelJiGwfvu3Dz5B5aBN69byQ/3Hsf1JJZp8OZ1K6c95o8feoyfnDgJEPz+p3vfMKPrvRq4KpX3hdFBZDRbzm+pwDXbAYGH8HNRBEkEbUNJWrvLOPRcBT9tXMzrF5/mSHM1771rcqqdOa+A33zr47x4ZjHXLz7DUM51WbzUlXYINwJuTGCNKkoI0Rt2tOL1w+JOWJ+rZuWdIFJVRcCI1OgXuAPKgppNdyPycgJrwIybVK24GQkoGbifMUxb7+CH3K1oXjDe6R6SqQrxZ+LebWu52NHPsy+f5uatS0CpOfbJ/zCOnWzjDz71PcYMCZ7CTNXQyYCUatat7EwXIgMS15VYCRGUIQboHvbLKfuTNJB91OUVEO1SeBEw44rurozCZlEbMwmmo48lamcdOzQ0xGc+85kplffpXl8Zp+Z2RbZFXLzqWt0iDX2N4lXX0ndMZ0gnG5sxlupcFaEEW8snL3WsDPByBa7UrsGZdt2RNiSiEhyFZ0ukPf0xu5rPoYx0EaNdzecA+O6hIwHb5dfW/fz85lel26QvEQ+WT8qXe4aS+h8ZdKbUr+GE1AkEluBgTxX/eXo5uQvaefbIw5Ne40RTMU9fWM3quj6evrCaL/77oayEgnkVLQwtMBgrFwwtMJhX0YI1v4qheYLheYKheQJrfhUAZjgGpJdqrgJrFD/1HkwrCp5W5sIFz4JwKB+h9IMrPDA9UElHR4SUAk/hjmXXRE49JBNh9fJqPv/pd/C/3n3jlAWNjp1s43sP7ediez/fe2g/D+84lrV9PBtlDq8+Hn3uOGPCQyQVppNW3CnvrIAramV38FgLruvpHq5+H9MUysMx3+2hf8r9OZvCM3sasRKK8CBYCSief02wrbNpj5/zoLOEO5v2MB4TlYHNhDPmp/76z5epsu3GuDOAZ6PLSthaTqFgyRqUoeuxKAOeOjp5LuCPD5/A9V04rpT8+PCJKceVwj8/+ZyutQHgKC1Pg9riAsw42INgxrX83UNH+MQTT/Hc+XN84omneP2//wffPXRk2nNdCa5Ky/va+TU81NAQtDW7dn4NraOD0ENG6mzaQTaciOB5RmAVn+up4FxPBTctbJr0Gi1njvDVex/GNiWOZ/DR/76BpzKi34PRXIShUBgYQjIYzUX1JXGrRPCmVV06IOolRrHCBYHlLTzdlDKQXYnlEVAHZdhgdLQb6SRJVQJUKJTy8GKp96nAMEOXjXuqh2T18uppOcLf+ckeXE8XAHI9SZLsWmJz7JOfP3oGRjHTNaMy6ryDbRncfds1vOHWVbNuZdcnsxkdfTJ9kXwrRKxT4UbAims5E4sWl/HysXOBch0avhhsG+vvYLRIYrkmruUx1p/dSAF0T9apUFdSyIkz7ciQrr2TGMzO5xs6e5L8JWsCt8nQvpPBtpGei4RDy9GvD8HS+WVMhp6hkSzKY8/QyKT7ZqK124+P+SuDQJ4CfX2jWP5tGwktP3hEG0NGUq8Cmtt7+YtHnwJ41a3wq9LyHhtpRiR8SzWh5YLcCJ6pJ6UEndPppX77/osgwqltmGOdkwcd1hZ1YpsSy1DYpmRNQXYQZO2iOJYhEUhMQ7J2UZxwnsoK6oTz9AV7Bk6TKITB+YJEIfSPtEBQul4Hn6wEGJ62aswxSbionJ6z+3WNY6VQnocRiWaNP3deNp0Lpn5Ijja28c0fXV6IPxMXLmU/NI5S3HHrCvIKotxx6wrqF5Tx7Qdf5tjJi5OcYQ6vFKX5MfwyN0B62i5YUMK//vWv8Se/c+cV9SA90d3N0DxBvFSvDE90dwcB+O/88FHMuCLSq33r3/nhozz++ONBoLEn7JDI1y38EvkCtzxtmefMX4xQhnZrKIOc+ZfPy3A4POXY9p48j5VUhEbASipyqxYHzCuAWGkN6SwdX/Zh5vhj8fcfcid/Bi72Dmbphou90ythgHtWLyfTINTy1BgciGeNa3AgTtgwsYe0JW6NgpnQv//f7gMzGsdscFVa3q39g6yr6GNTTTv7Wqto7S9ingm1sQEMFwaTIYYcX9EpcOMSYaI7Y5sEb955sfLLzp1KPHjqBbh3gwGmxPUMnnwhu/7Chd58NkU6WLPwEkfOVnChN5/cAoM+5V9UQW6BfvdFr93AaET/7eYKKKqldf8B8heuIN5yhuqSNbhe2sIyHSCe1O6SuERYAuVKXGcQszA/GH9i6PKGCpM9JEcb2/jDv8rodv+X90+Y7DOvtpjzZ3sChWFZBk/u0lbOk7tO8sxzp1BSYVsmn/ubd8y1Uvs54A03r+LRnx3DcbQrQZra/rjvzRtfUePo0lAELyLwooCCUDwZcLXLV91NZXU6ANrRdi6Lq93XO4obEShLu/VCdjreEsspIzyi57eZNIjlXG75TlYGNoWhPr++tz+3o3Yu+fn5gavSDueBoWuoCImW0dTaiLK1OY6+L2MKI79/LI5QPqlAKfrHJk/Uy8RH79Jc8KcaznDHisWBPBVils2wkwwe7Jhls7SghIO0pXK90w2Xfw4lmq9Ky/uta3v5g+t3Ew05/MH1u3nr2l6GBk7T3pfPxcF8hhIRPyyt9/dQCAmGFAiH4K27uCCbS9rY2MjChQu5//77Oavy+OA37+bfnt3MB795NwNFC7L2zTXhC7/+KL97016+8OuPkmvCaDJMpEdhDyoiPYrRpFakwvA5oSlLwjTJuX4zXnUu9pZ19I2eJ2WJ6z0UyZ5OCkrrdV0TywDDZKz9PMaIiz2kMEZcWg89etlnM9lDksrWk1LhOJPXgr5u3UKSeQI3Csk8gZfwsrjBrlR4SpH0+2DO4dXH6mXVfOGv3smmTXU4eQIn34CIpgr+6/ef420f/zr/+v3pfa7j0X62j9yLkkiPIvei5Nkndgdc7ZxoGdpFB6DIiZZl0VATl4a0v3sUwoNgj6SNmYJybWmLcXIKubm5fPzjU1fJyAmHMBIKwwEjociNhNmzZw+1tbXk5eURKihGhkHZChmGUEExeXl51NbW8tEP/BahfjBHIdQPK+ZdbpSlcPdabUHr2JXw5Znho3e9jsc//JszUtwAJQUxMIXWoqagpCDGyoryQHFD+ql/77UbZjyOmeKqVN5lsS4++MjdfHHPZn7vkbspi3XRnxzKWlbV5vezseYi9SU9RCKWfjH7b3VDCoSCZ842B+dsbGxky5YtwWQO50R4yanm3xo38JJTTXhcJ51bl3ZimRLLVFim5NalnUT7h1lb2cFvbzrI2soOon4ZWXvYn+ipYIxDlntF1c5jtFgwuEAwVKN5roNDF0l4o3gxQ7uCYgbCDmEnDExHYScMcqPZEfmpHpL8gmiKhotUivyC6IT7NTZdQiiQtv6MHM/L2q78cUsB+fkTn2MOrxzShIqaQsywFTQZ+NnLJ/n6k3s519vP15/cO2sFPjw4hhWHaJ/CioNBmqtt5ReRWW9Hy2mu9vM/ezLtylEKbzRdIlkkpa5wqRRCKUQybUamyrneeeedU46tojhPX98nHFQU5wW1Tx588EEiFaWpEwIQqSjlwQcfpLm5mWfOtuNGFcpUuFHFd3cfnvQ6oWGFkVC+ha4IDatJ932lsEMpoy0tN7R1onw6MDJIGGVZyexonzPBVam8f3JyGUnPRCoD1zP5ycllzCsYQQhdvkYIxby8QcJ4/NrqYyyrusi8ogFKc0cCqpFQEE/qYJyUkm3btmWxSTqc7KyxDie7JOyLJxfgegauJ3A9gxdPLmDzohG++P6HeeCOvXzx/Q+zeZEOhoQHJeEe7UsM+5Z5VlcbwyJRYeDFBE6eYKjOoGDVBkoWrs9ipRTNW5E1ppKadMR/uofk5IVOXL+/phvW8kRobu9BeYAHyoP+oTjBU5vxYwjBwPDMlpxzmB2OnGnjd//xv/nRrqOat7ymHpmQNLR1ZX3/j+5umNV5+wa1ayJwiYXTc9qM5ej0b0u/OMxYeptSiv6uczqY5Gk6nhfvCXzSlrQRHtrn7WkZ9JzMz89nx44d05ZvGEgmSBTCWBkkCrUM6donq1M+bv/5XF1aw1133YVhGDT39ml3qCHARMuT4Id7jyMjaBdMRMszxeceeY43fvYbfO6Rmb0079uwOosidN+G1Zzp7kWFdMasCqFJFK7iczufnfE4Zoqr0ud9picHpRwECqngeLvNNRXD/PubH+JQRxXrKtv5wovXcri9kpfOz+fWJc2cGC7GkSbhIY+cUQ+rwGU0VgjAjh076O3tzaqlMOaEddVZ/39jTrYv+VBzmN//6j1sqG/nQHMVJ9rg/bd0BNY4SFYv9H3Shq6jEB7WyzUlIHaJILKfKExRG1N+O0VOdR3hTjdICVaA6df9TllHY96wf9j0D4lw/WYPfiNk4U5scTS3dqe/dQMSZFvelmFoH+QrLE06h8mR2eFcSMn5jj6SobRTLTUhCme58snJCzM8nHYIOzL98pVeEjc3HYSUI9mlI4ycUoxgzgje9Vu/z/f/6Sy9vb0kRwexc/KCZyU5OkheXh7FxcUzrm0SNyRjFWmmVjyR7QQudG1iFxVOLtjDUFibJloLW+jy/v6xwp4i5T3HgDEC1gg5M7NPP/fIc3x11z6Egq/u0kWxPnL31O6Td25cw74LF9l15hw3La7jnRvX8K29+/1B+sFPUyvypp7eKc91JbgqLe+PXf80RD2ShR5EPT688VEW51zAiEm8Ig8jJllT38aaa1pZOK+bhktlJJSBa0mkUAwUmQz3hZlvacv4s5/9LEND2Zxps18Ra4NQH8TatJwJIaM0nKzgOzvW0XCyAiGjvNxcnWWNv9ysg0tl1QPcu/UE//y/HuberSfILR4DT2l3iqcwUhlpKZ4tYCUFmCZuFEZLtA9ahUySefitqiBUUY5pmtTW1rJnz54pH5LldRWYLhiewnS1PBGSbopUrB8Ax/C5tZb+/fb7Nk3LFZ/DK0Nmh3PlKJpTDKCMlztATX7+JGeYGNesqkFI37UhFQO954JtA11NWdcIZB85sXKEp33SwpMMDriBS6Mg6ujkBQ9wFQVRJ3BpzLTOth01s1YVdjS7dsi57n7MJER69Ur0XHd/sM0SRuZCFktMrrZqSwrJZI1oeXr8cO9xhP+YCjUzi/17B4/w0OGTDPTHeejwSb538AhLFuVkuXBT7+Oygtm1hZsJrkrL24oNkKzycJQOPReWS9pdi/fueQNJafg1vRWuMgjlSq6J9XJG6uJU8WrAD2DmNWmrciJudLL9IlZdPV6epvAlz2VT41zPhSg4ucJXwi57ThXyB1+5hw2L2jnQVMWes3rp+cbrG/nAFu2Hu3ZZK0V7Bvn3h7boqoKuBGVg94GXo5N0rBFQjiQZdknOt/W4lSB8ySM0KhBSP0QhFeXTn/40f/ZnfzbtsvTk+UtZCUwnz1+asMP3LRsW89jLDcHkys+N0NM/gtR1wNjf2MJtqxenl4NzeNWxZnE1f/L2m/nSg8/THUoQODqUdmsooYgMSMqjs7O89x05h2fj14dXFJalOfsdx3dSuGC1Nl6VouP4zqxjo3kVPvdcj6X5Qnfg0hgLVfF3X3tSd54yBB/9nd/mrltnx1nOt8K6t0pq3lnZK92k4yIthQwJjKQi6aTzD2JjBoMGAUkhNjb5s7CktIR9Z9uCtOUlpTOrUWIJEaxckVqeDtt37cGM+ysmT7F91x5+75517Dx7kkR3FOmYpJbWq+e/+qytq1J5P3axHs9QGJaH58KjrQs501VKAoERljiuAGmgMHAkdFthnR2VWqoI7TrIr3cAgm4dmRhRfSTWpHPT3bPZfjQ3GmWsSk+SeLkg2h6lqCDJvpFKDuytQoYVRTX6mNctvgCkV2rX17XywjV1bJrfzr6WKhoaynEjJnY/YIKbBxYenmmkrREUMiwgrkjmCuxRRdTKm5HiDpCiMU6hed9+xzp2vHASKRSGEiytLmXPJT+RwVWcOdLBmcPtWLbJF/7qna+IujaHiXHs5EU+/7WnGYpJzVYQAmkqvJBmWoBgOMekfuXMG1ADDMQTuH41PGmBaaXdJCVlKzEd/WIwlJZTJVcBLNPW/m70DErEnWBb/3AcTIE09ePSfwWxkN7+EXL6067E3sLs5JmELRkr9J8HJYiMpN0qqt8jd0ThxnRsSOVku/oysaK6HCupKYeG1PJMUFtWSEebXzLA1PJ06B/x3Zroz6x/ZJjDp/tJnM3VdEUDZFhhxAzees30tVJmi1fkNhFC1AghhoQQ73+1BgQQEQor18OMeFi5HrbnYOJh57mYEQ8718UOuVhhB8v2WOQOYhgSM+RihFzMsItpeSyw23j88ccn5EaHV9dDqi6BobScgWTMDJgXSoCbG2Lt0i5UzCNR7qFiHovnaaX9+H5NnUqtlE5dLOWr9z3MB7fu5av3PcyG8g7uW3mc//v+h3nTjSeQFkSEh6ETLIP1oOEqhhYYxP2UfLsgNmPFvXx+OdaoxEgqrFHJ8vkTT9rHnj2uK9c52i9+7nxPOk6pNFNFAY7j8djOmQd75jBzPPr8CQbzFMrUL1lpKpw8fMVN4Fo4fqlr8pNMADviBxJ92cvIni1btBmBwFACgaBsUXbBJuloZZ1yHoat9LwryI3g+Rxs19DybCHGFNYYRPvAGtNyJkaFl+VWGRVpBb16cRVWXLtUrLiWJ0NfXAfgDVefqm8Cw20iFOSmEuTGyVMgVuEE1Evhy/qZ1k+UkAJrzCDUb+j/v8p4pT7vLwCvOv28S1oYQiIEGELSb8UoLR/EEMr/bhUiIjEjHiLq4boGdq6LFfWwYx5WxCVWkKCoOMH9998/YQPURTmXCNkeZtglZHssyrmUtd2x9cRP8TUjxVFOX6pgsE4xUqMYrFOc7dFW6e7mSj795E28eK6GTz95E8mEiWX4NEND8u6bj/CJe5/j+gWtfPL2XXxkw0soBeExSfSSxBpSRC9JlC2yJ3Bs5jSnoYExTAVWEkyl5ZmgwA+K+S7C4G/FzFOLp0LDwfN8b/tOGg6en37n1wi8UDqA7YbBjapABoKX+fCFmWUHphD2jeXUrDFGMkofGDoQrvzfjGswbIRs3Bh4YYEbS5f5AHj+5NksJtLzJ8/OalwAVRUFWWNLySnEYnaWIROLpQOWciCBn3kDQml5HFKZpN/40pfAVShDoVzFN770paxM0slQFvFXKWqcPAXetn4DVuUowpZYlaO8bf0GVlWWB5a4jHk45Q6JsMNPDsysxspscMVuEyHEvegxvup5n1HXI2RIHAm2IYm6HjHl6tKpIkV+9znUCi4URNOTy+eCe0pgFSV1MfkJcO3yQc4LD8s/5trlgzyVsT00ajG6wEEkDVRIIi4YtBq5iEIwkwIZUnQmiwHo9PI4cqGKb/ctwxoS3KTauG/jKUBnb5bljfifmR7v+153hMbzFew8VUu8VC8VvajAHpBp14cCOzLzr6cgL4LrR7aFp+WJ8IabV/HTp4/iSYVpCKoq8mm80AUKPFNgooL6EoWlryzI0nDwPH/2W1/DSbrYIYu/+/r7WbF+wSs659WEY6faOHishfWr58/KvZQbCevM4LDCi0H6ram/99CgIjSksCOTuwcmgjEqsTwv8BuTSCtoKT10LpkCU8spCCFw4wNY0XKUqcdTU1EYbD9yrh1pgrJBOFqeLdrjwyQKdZcaaWs5E/nRCIPdieARzi9Nz9+mc136pWOC8ARN57JXJJmlnMvvfC+6yoQ+0yWZM6OuPzm54fSbJSVPg3luGU6XjnvJrhzmuWX0jcXxcj28Qg+vKO2H6pRDU5/sCnBFlrcQIhf4DPDhKfZ5QAixr6GhgU2bNrF9+/YZn3+NeYmvr3ySP1xwmK+tfJI15iVy+uA/Vu3gQwsO8btFRzH8hAFDKZbST8jw9OQXCgOJbUhynMnXKvuHFvoDHSfrsfOmJef45KbnuWHFWT656Xm21ZwknLQIdZtYgwahbhOG9du8V0RIVEq8HEWiUvJyvILf+497+OJLm/i9/7iH3af1Mk+plHGluGPdSdyYydrqDt6/+SBrqzsQiKwMzvrQzJX3i4fP4oUF0jLwwoIXD09sHTWf78KRmpXjSEnTxR79wFr6x8sVOmGowOKNr7syP13K2n7qJwdxkqmsT5cje2dvsV2tOHaqjQ998vt89b+e50Of/D7HTk3RvWgcdj97kpxzQ0g72+I2HJ0ZGe3WAevTPZPzmSfC4tpSrDEID2oXRWK4M+BqS+VlXSslp2iof/3htyGkAKkQUvCx998RnLd/LI6ydR6Dsmeecp6JuOnhRiBZIHAjWs5EoitBaERgJgShEUGiK4PyGAYvopW+F4FMVu/45LtQie8u9J/rUEn5jBqa7G5pyTwskKfCt146iJfjkJyfwMtx+NZLB+m3xkguTOIVe2SuVmT+zFfR27dvZ9OmTQArhBD7hBAPTLTflVrenwT+n1LqgpgkKquU2g5s37Rpk9q3b9+sTr5p3iVW5A+wPr8bgOg8h5HSQTYVdbKluBPPE2wdaGf/cDkbcrtoPFjOb284wt7BSgrMOANuhM0FHew5fXnARwjNxZYtIUi1nZRomfRknlc/wD+1bSApDQ6NlfFA3UnqXDjmeJAjYcQgP6q5m/F8/x3ov2XHygXPxMp4pr8MqqDzgoncB7+58RgohVSC/efK2Vh1ic+/7adBZcPf/8o9mElYv6yNg6eqaTo3efW08TjZ3g2kaooLX74c//HQbpSV/s46ugf1alXoz6Guqpg33LiKjctqWDODDuHjkWltm5aBaRngSWzbYs3mhdOf4JcEB4+lmwc7jjer2tvxeJy+tXm+lUsQLAn1a6WNqb/F7hm6vlL4wPtu5nc+8V94frDub//iPfzuA4/Q29uLM9RHqKgkuJYz1JfF1T7eNoY1lkoJlJy52BX0iTQ8iHX6PL1BcGcXRwWgNxlP+/R9OROj8SReRFvmXkTLKbi2oYtN+UtX19bP20TJd87oAOGiPFKrV2d0AEhnkk7W0CQhPNJO0pQ8NZoTXTh1fiJgvqS5tQurW5DlBfB/SiIzX8U+8MADPPDAAwghGpRSmybb70qV9xLgzUKI9wG1wHIhRJNSaucVni8L+Tn6A0m5GfJzXCKR9LLDMBSbCrUidz1BzboRqvOG2ZDfHVi3SkHFkiE+OO7coVCIUChEwYjHG/rbyakdZeRCjPaRwqzJ/OWTnyEpDaTPaGlWuWxc2MBRZ37wxdxa3crLQNXwGK3lNkZIIpMGoX6BK8zgNd4Uzef/XLqWR1+sY2tJO4dOVMEFh9evbsY2pE99lNy97hRv3HIay5S4dxp8+CuTdwIaj7ifbKPGyeMxlEhmJQalfKEpeXltBYajqZZXgiN7zwbWNp7k9W/fTHl1EWs2L/yVcpnkF0TGlSOYeRBvwcYaGs6fDSaq4SjCfQp7GJ1k5buuyvKn97tm4kxbD27E0AaCECSsGM3NzTz55JP81ZeewZESJQRCKQoLCvjnBx/kzjvvxDAMPv7Fr6bnhYJv/mgPb71lLQA1sTw6ZF+wrSaWN+U4JoKS6CxJf6KpcfPLQRHuz5Az4j05ts3YWNIv9KRlmCT5rv0Ckap5CE+gTMVY+4X0GKZoaNI7NowXASEFylD0jk3sbs1EMt+PKfj3lMx3icZDGWl2iiVmL6o3RL589UtNXJHyVkq9JfW3EOIZ4NuvluIGeKqpjvetOx6wN55qqmNJTQ8LyQ6gSQmGUKjoxJpm2Lv8AxNC8OCDD3L84rf40Bv8ovLr4F8e28L7MybzXYPneKqlJvC731V9jmc7ajA8ifRrfKtCPYlyoknsmH7dmpZLifAYM6MYtkQ6Bsq3OA4OVHCwvwLCcE3uIAlxEdfTXHbXM7DCEsvylbmQbFh1ed3kyVBgh7mUNxLUDC+wJ/bZVZbmMTykl6QCMC2BSAL+Q/3ECycxpMKyTL70iXdM2qV7MqzZvBA7ZOE4LrZtccebN/xKKe0U+kYTuva6oxC2oG906nrWmcixUv5VPzh3SdcikaYf0pF6U139zFdeAA/tPIpSKlhdPrTzKG+5bQ133XUXn/nvU7gjSZ+VKgiXV2cpsL6R0SCGhPDl1HgNrSbUOHk2KFA2fSQDy79AZbeqscckCiMwIuyx9DMdzrVJegLD059ROFcfO1Hy3WhXKxX9YCYVXkjLmZis6098xEHGJCoiEXGD+IjDdDB7Dbx8GXwwZq/BDav7ucl7nl2dC7m18gz3LDyO61l87VgpMLOCVzPFK+Z5K6VufTUGkolOEcXxBJahcKWgU0Tp762kNJFkZCRMXk6chdV9WAa4EppGC5gXHQ2UvVK6L+D3OpYAe7POnUwmueuuu6g9/JdA2kp/w8bTrFib/kJrzBG+vupJ9g5Wsjm/g+iIpEaNZgVS5ys9wUcW+DPefzDcGo+YlcBVAiuqCHUZJIozKg8qGM3J52fxhezYs4DNJR3s7amksNvlznVNgHaj7G2t4Hdm+Jm9684N/O3XduDFDOwBybvePnEVs8yuLQpIJrwsWrhKUQVdj0efOz5r5b1i/QL+7uvv58jes79y1nYmNq6owQ5buKaHZZlsXFEz/UE+jhxoIXdI4kYF1pjuWylNbVikJrEAep2Z+ZZTZY5PnDqNsHQauwBOnDrN448/zrZt2xh1XKwUNVXAaMjNOnYkGcewTW0de5BMxoNje3pGcGK63LE1rOjpmT0Lae2Cap5vPIe0FYYjWDuuXHHYkcQtEbwgwo4MxnahswMiaSrfhc4OHn/8cV566aXLrlNYtZzwoL5JK64orFpOv99OLYWJkvYsW6AKXETcQBW4WH3TByyNIQuzTbtMjEEDY8iiNtrHp35wK45n0mAspHllJXcsb+CuxbOLX8wEV2WSzjuWnMQw0D++vOfUPH7/S2/BcQ1sS/IPv/8I1yy6hCcN/FaAWAYZS1nB9cWXB5FSnG9XGRy5UMGBc9VsqGvDCmd3lGkaKuTuyubAFfNIRz2vK7/IlqKLgUIP+d9HTkLqTzL18pBC8yeFruW7tbaNsQLBC4NVKCkgYVAoEwwZMU7057BvoBKUomwgyW89fA+batvYd6GaoZ5ZLLUMgZdj4EbR2QHjqGApDPbFdYU439IWUmTVQBcqVYUYugdHJzzHdFixfsGvrNJO4Zol1XzxT9/O/oZWNq6omdVLriA3TFfXEFZcf+Yy5WFTCmloVgcKBnun93lnMi0W3/EhrDDpxLNRN2Ba1N3xYRwlA9dHLGxnHbvkvr+AsJ/kZoGHCI5deM8fMzgvnUCTPzDrj4uGtksYnsCQ+hoNbeOouTkW0k3Xv06EzKAWecUN76SoMh087+s4y/33//2EyXdFuXVkxn6Kcus4N26fiRqayBBYbaHg5SYLLtvlMhiWwBqwYEBfzbAEF3uX43jNSGUgPcHDR9eyo2E1f/Pu2ulPOEtclcp7YDBKRXgMkDjSoH8gQseFQhzXQCoDx4MjZ6pYs1hPgN6+HLxiA4MUNxxMIVkUvpyek6qH/fmnt9JwugrHM7FNjxVL2vnauvR+RwbKmH9hJFDuRwbKKChOcmNea6DQn+/SKa9vLTnNPzVtgQIXBizeWN/Ig84iHKUwhaK0fIjv9y3FsHT0wgx5zHe7cByLpmgYT/eEoDI0yh67lN0dpQgb1uXP/G39pUdfZLRSW//JAi2/+Y7LU5jrq4q5NDAUKGsLgUwq/EoEKCUQ/qwonaXP9bUG4SmspERkdEWfEUxBMk8ELi5rTCHRCiQzmByaJkErxbRIBez6eprIX7A5WFYNnmtieHiYkZERivp6iYn8wC0StkTWscKRKNvUwVKp5dSx+WoMhBVkAveJK8g4sQ1dd0VXiwA7+97yCmP09I4ErpuxxBAtLS0opagLFQFpxR4NFU1KATZMi3iRh7INhCMxey9XcRMl7XmD+D5/7XLyZkCxf/3yVh5rrMTDwETy+qUd7Gkv0Ml/XorSbJBwBd863MMbVk9/ztngqlTeT55bxA8bl3Nn/Vmeal5Ijp3EyFHYlsTxwDYla5e0aSVtKKryRjjWUsHhC5UURuMMjEXYUNfGoa5yYH9w3sx62CVWgoRrAYKEKyixst/GTn+IDz76pkC533n9Kb6bWEqO6XLofBXrFrTzX+1LgZ3cufAcBTGXHWfr2XZNM4VmnP/etwoRBTEG++tSdbn1g+kBFDjU2/2EZDGOMrCFpDAyAkMFpPpmmmXT+91S6PUSrK3qYHNNO3tbqzhzZuJaCqajMEd0QpBwFG6OrutteASsE4Umei+vvQJaAbrJ8cHjLaxfNf+XvrjVZFzuYycv8pG/+H7Quehzfz3zrkOtg0NIW2g+dkLihkT2k+i7T7pHJ7e8J2JaiEgEK66wRhVuTCAiOoiqlMIigpvJ9ugczDoW00i/OAyh0ylTx44Jkqn3kwJjdJYvK2BhUSGHW7RyFkrLmSirzKenb9RPFhOMJNKBSCPXrwWu79KXJ0Yi3yPsWLqOCgaJ/Ms/w4kamsQsm6GEA0IHi2PW9O3j83Mv4Rn6O/cwyc+9xNGRMOHlFvH2GE7cwvPLAXS7rz7P+6pU3qsrO7hlfguWIdlU1c7OlvlUREe4re4CR89UsmZxO8oWfOPZ9Wyoa2M0HuIT372TpGvq97NQhEyPd11/KDjn+HrYN688y64TC3E9E8v0uHllNge54Vw5jl9T3PG0TL7DH/7snkCh1yzXNbO/e245S71hIl1glcHTAwvwRmwYNpBCUpZM0BSClF/FEpJFyT4OeZW4tkChk4oikSR23MMzPEwJt5afm/Fntr6ihX95a7qh8oe+OTFT5aYbl7Kr6TwyZGAkJRHTIOnKtOIOQeol8+Lh5gmt96lw7GQbH/5kWqn9slYnPHaqjcd3HueRnx3DkxLbMvmXT78jUOCHjrXosq5S4bgeh461zFh554bD9LtJjIRfWyfoOe2bnb7Sku7klJ+JmBbFsQXkXvCCpb+ILQhcBsl4P/nxkuAaw2Ig61gplB6Gv12KDAU9kiDSFcMLK8wE5MrsioAzQU/PSMZ9cpnf/NxIP4kihT0ETh5YsXTzAplrgp9kqYQvTwJhhyHdmUzLGZisoUlpJIfOoVH/xaUoLci5bJ/xePjU8iApEKV4+NRyoqUG/W1hvByJVyL1ShjFQO/0L4PZ4qpU3ssLe7jYkc/gaIT8WJzlhT040mTx4i7WL+ng8PkKPvgfGVbx6tM4nomefgqlDFwPRuPpDtnj62F3ywhfeN/DHD5fydoFHZwYKs4aw+aFF2ltK8L1wDI9Ni+8yNlLRZzNUOilrkNeXh7fPnYN0TP6Wrub5pNfN4BtSFwJliG5PdbC62ub+EHnYsrtUd4/7wQDnSGMHtinSoIA6CJ7gJdzKwNLvLd/5m6Le9Y2EDK12yhkSu5ZO3Eh/z2tbayf3447bGBVSNqdFdjJc/QP55A0DOLSDibjqZbZ1dYA3Y4tU6kdPN7yqijvhkMXOLKnmTVb6lmx7tX3H2YilYCTTLpBAG08l3vd6vkQMXEMSVgaWp4hfuPNW/jM9h1+mry4TGkLR/c8vPv1qyY9x0RMi5C0s+h+IZlWGGosgcrTCVxmQuENZfuLvfgohl0QjMGLp+MdTr6BIRShYXBDMHYFibddqVIL/r12jSu9sLawnCOHzmqmTSe0xdMUP/zPA58GnJW7Pw6uGiJESfC9uSr9GU3V0KR7bBRlKP8Fo+gemz7eMxIP+52B9Ic+Eg8z2m2QrMggAfhxgisIE0yLq1J5D/eHWTyvjzqzH8czOHOxiHODRZAUDIxFONFazrLqLjbUdXDwXCUJ18Y2PRwXJAJDSCzTY0WttoyFEPzTP/1TVmrs2opLrCjtZUNdO640sLuzXRS/feN+7lhyNvB5L6zo5ZMP34ppSJQnMA2JHfZ48MEH+dwzL9NFiNT7PjRocE19G4mRMOGcBE8Mz+funhaMM1GMXIVbaPPc2HzyGj2+cvfTHBgpY0NOFz9qXISr0pZ4Z97MA5b5ocSUcgr9Z59mUVEfr9t2luf2LmS+9RJ/+p7nAfjJyyv4+x/dFDzAEXv202P9qvnYlhlY3gW5Eb71g92vyIXScOjC5Wn2PycFfuxUG1//7os6Acf/30Rcbs8WxItNHBeUZeJN1SDgMgi8iF8uNBOetmwNqZfut665vEt7ChMxJqTnAHZwVi1rREorGcszA5M0Es5usWckXCxLokyB8BReIh3AFyEbN1fg+oo3mZh8RTAZynNyaBnuD+ZWeU62ZVubn8/+MHgRAzMukRfTzQssYQddgAxXy5NBlhUw5rhYYwZuVCJtHXmcrqFJnzmC4aY1bp85PaNG2pJkDJStEI7AGJU45sRJOnJs8hfOleKqVN7xpI1tSkxToZDEkza1xX3MLx1kodHPmtpLvPP6o5hC4UqDXSdq+Y2b9jM4FiGetHA8k4jlcLhD+2yVUnznO9/h/e9PFz+syR/EMrSlahmSmvzsCMXelmpuW3yeNbU6KPr0mQW8bsk5DKnIDzkMJm1uWHKeu+66i4ZL3+Jbz20g9TBeW9vKU4eW4ngGtimxVgzx12f87MJuePp8HQsWXaL2UpKmf6tk7fIOvnVyMwUrBghVp6mIy2Mz775xvqOAstoi+p0whXaC8x0Th8u31J/l3XcfBWDz6ja+//hKjpyqIBJ2WVjUy0fv3aVfhoaLHb13xtdPYfXyaj7/6Xdw8HgLBbkR/u/Xn3nFLpQje5q1FSwVyaTLkT3NPxflnbK4E8lUUTINaYIIG1lc7v2Nrbiedjd5UrK/sZW1M8xIfXrfaQIzMsPiNhztFvDCBkZS8U/bn+Qbn/uNCc8xEdPCsEIkLRHEMww3vfL0YjaZ5Ye9WLYCjBh54EqcXINQv9RyCsMJRDRKUE/bm31VjVjI0vfnB8Zj40o/nBrqI5mv3SFeyMScXwl+Ynay2EBm2CJueOLr5+XloUyDsbIUbdfE7lUz6vojTO1rSgVFtTw1koUEJQ5USJEMwXJ7mGMqvWI2hwXmqMHi3OmTfmaLq1J550YTOJ6B8gs75UYTDCYjQaU+Kf3JboBpeNyy6hz2BB92YU46WDHeUjl0oYobFmm/uisNDl2o4o6M7O0LnYXIRecxDfA8LS8q7+Uf738y2Oe5M3qp7AGupTnU0oCukRySrq43nnQhPJby0ek3u1KwVAyRc02SXT9ZxsHOKgxHsWbdGO+NnKDJK2SR2U84PvO39RkK+NSuLboFmoJfj5yacL/bt+oOKim9cdcNpynIS1toKxd3IYTSnYJO/gz4rRmPIYXVy6tZvbyab/1g96viQskvjKL871xJxcjQGN/bvvNVd6GkUt7BN5gMkCEBlsC2rSwu98alNShbl0gVUsszR0ZCAiAcbVEqQ2Akk4Q7HRJFNs1dk7ONIpEIY2PjgnFCkSxK+4ND3WlWiBIqoCOm5Ey4OQbDCy0wYLTCIPdshuUdDeta8z5G49m02pngYkc/yQKFZ+sG3Rc7+rO2N7XrloIp5RkuTq8MZAwYS2c/ygm8idFolAcffJA/fv4Q3piFFde1w22pV8ep5LvJUOiE6VaaCC98eTqosM9L9s1sFVa8pfYs7wr3saN9IaXEGSDG7avP4oSLpj3fbHFVKu9TPaX80xOvY11dO4fOVXHPpgbKc0ZwpSawSiWwjPTSzfAnYkohpX7nRdNKaTy381RHKTcsaglcU6c6SrkjY3v7xXyd/YhW7u0X83nz2pNZ11ldpd0yR9ur+Ku3/IwbF1/g+TO1fO3Zjf4U0Jxpp8+ECGQukwfOxmguLGSs1AoCMSf7S3imbwFWr8kzxR5vKZpYAU+EvYMVqJgKsjr3DmYzRVIJD5sWZ6fNR6OpAkX6nizfby6Ex9DIlfG8jx7fyYFjR6gorWXN4i5W17dyrLnmintiDvaP+X5c/eX+8P+9gJLqVXehZKa8A6iwgRUyueemVdx948osLvezh5uIR3VAylOKZw83zdjyPtPbg2eClQAj6b/0cRGexBpK4sQMou1jUDX547l161aeeeaZrP85Of7+/pcZyIBMJjBisXTQL5n9PMQjcTBy9bGG0rIPL9fIOm8yOnOjIoUx20MamuMtDS1nYiCZ8CNW/qUyuggZcd0aLbXVmCB3aevWrdx11138+SNHMPq15R1SYAvjsmzKiRDpHSHcZyND+juJyOndJltzO3hhqMpXOlo+0V/OO2vPMWJ1UBSN02dFqM/v43vn63nftGecHa4q5Z1SMI8dXcqhC1UculANKMywx1s2nuClphoG4xHyI3HmlQwyEI9QEIlTGI5Tlpe2QqTvZ3K9tLUwntt527KzmIbS1rtS3LYsm22yvLorsygYy6u7ONpezk2LW4JMzqPt5dy6GP7XdXu5bolOCLpnzWmiZpKvPrOFwliC/tEw57qKCEXBydEpvvaw4qIoolQM65eH0PZGsj1GbFAvdUPdJgdrZm6lLqnupEnXF8WMeCwpTHePz0zG+NpPbuG+FaeCe2gfzmFB4VAgg56LhoCLl2bP8z56fCdf/s5DrF54iZ8ePsdvv3k/q+s6QBzELHknMHvLe83mhYTCOuVeCAPpSZQiy4Vy4mgLhw+cZ+2GBay85speEpkp71iwec0Cbt+wmKGBscu43E8dOZ3lhnjqyGn+8G0zS3+OYmHFdWal8ECZitEqGwywSkJYo4qx6hhFnZNbuB/72MfYu3dvFt9ZKg+BnWarqLSCdIWDbYE0dJs9z8g+dyhh40o0R1lqOQW96hGBZhVq9m6TRBSUA4YjkLYiMc6wlZ7ECJJrILPgne3bECmr3B5nU2QySKRrauMjZaWrmTFjRNjBdGxMJy1PhzVeH/tba3AKFPaAYM3SPvrDm/jA9yp1Ml7q3EKxuuaV18Yfj6tGeWcqmF/7m/dnhXJUWLL3UhU/Ob4Cx9ONDrSv0cA2Pe6tO8nbNzfQPpTHy801FISTHGqt4Ob6s6R43uO5nfOLdPzXk4BIyynULuzDMJWf5amoXdjHyy01XF9/MXC1HO0p51Zg7QLtF09ZrzcsbeF1y1p0gSnP4BP/fRuF0ST3bmygezCH/3xxLQuqekgok1NtZbghsJKKPDtJnAipZVjhJMWlJoJnCoQXHIrnd2kZn8jxw4blHO2oYNuiZnY01fPuNUezxp7598YNFya+2BS4cO5H/MPv7Aju/dnDdXheJfk5cRbFfoQIrZ/1OVesX8ADn3gTzz95nIKiHHb+5KCeH1KRV5LDiaMtfPz3v4XjeNi2yWf+9T1XpMCzUt6VIFca/PO//4ykpQj/wOBfP5nmcq+dV0nT6X7w65OsnVc55bkzMdoxqlPVgWQeOLk6ldgaU+RlUP3c8skDc9u2baOkpISRkZGA8pe0HCwrEtQAca20AjI9A2kB6IQsW1lBDRQAbIuikx5OnsAeUiTyMlSD42FKA2UqhAd2RiLRTKGSktCwf1wc3NzsoGeKfRisejKuIca9wzLl8QwSQwhEMq09RGhmY61dHKa9nyBxqnbx9G6TZxrrCfcbhPv1Q/dMYz01tV1IWZj2nSOQCtzIq96z5upQ3uMVzPuuO4gjLdZXdXCwvZL3XXeQva3zMnjXKZeEpgS2x/N493ffjlTahXJ9XQvR3AQnurTrYCJu59f2r6U7HOWO6nM81VZHaWKMDy1Ibz8yUM6qim5SWZ5HBsopyx1FkO7mU5arTYAx1yYWSitahSBkeUEnoP/9tmcpCKUepC5uXnmO5y/O4xsvbCBRpiesB0TDSd688AS3r2zmZyfqKSyd+dta9oQhhyA1WvaEJ0zk6G/J4XtD5Xy7Zxn2CMzPGWBpSV+W5Z1CTtHMiy2lsKjiMLblYRra9XLb+mYMoXA8k6bzF1gyO9o4ACeOtvD0Y99h5fI2jh6vQkYKwPEgZDIwnODwgfM4Ttq3fvjA+WmV90SWeirl/bGdx3n88SM8u/8MY8W6FF4CxaPPnwiUd64Rwh5JMyByjdAUV8vGyKiDF4JknsCLaOVijSki3TKL6ieHJ3/gDcNgx44dWc9NyIxi+gtQEzBM7XoQQmBGotoU8N/MdiyH/Pz84NhEvl5j2qOKRJ5BIl8Fx1qOgRmHlGUgQrN3mxhuKiCo57uWM+9HkswzMVylP1PhpV8uqRBBaufAyLicQRKxbMaSyeA6kRkk2wAk83KIFPUyEgqRk0ySzCue9hg734X+9IDsfJeBGH6WqB9b8L9Qt2xiAsErwS9ceU+kYLbMa2frOx8KrEipNHf58MUq1lVf4sBFbeVsmNfBobYK8nMSXFPVwcZ5HfSPhSmKxjncXkFRYd+k3M7DnZV84Z4nsA3JprIO/uDhbL9YT1eUvx/bGij33GGHs4Mx3ry0kVSHnGNn9cuhYbCYG2NtgQLsjUeYZ6cVr+3XVw1WggpMF+aVDrJ3oBqlBIZQrFzQyZ/dvAshYMuiVna2zdx6XJg7gNtVi4hIVNxgYdnAhIkcw1ETp9rBCEmcAoPPHdtMy0gB2xY1c6KrlPesO4pteCgEXYOzd5vEHRdDpF0vwndNgUf7hY4rUt6tTT/jb/7ycSxL4rpH+PGDi1m0pJ8Xds2jIPdN1C2txLbT9MS1GxZMeb6pLPVn95/h8b0nGYpKZCTb13umK83+OdPTg+Hh1+pQnOnpmfH9uCHJWIkRcIGzLG7SSiqaN7X1t3TpUvbs2cO2bdvo7e3VfmErzVIzkwRMC8Oy8CQIqS1vico6VrgSGbYYyxUYDgjXC47FNpAClKWtXmXMXnlbrq7GmaqYbY1LQMov8xhsNcDUrJu8Gpfa2lp6e3sh7pEsTit2u9eblEFSXVNAU2NX8BlW18xMaXY2XaJjfg4YMCRDlDRdmvaYgfmSZL+HNWzg5koG5kvq7HIO1SawBlIrFYFX4HHzwokLxb0S/MKV90QKpnMkRlXeaNAMvnM4RsIz+ep9DyOEGre8Fxy7VMLysl5sUwYT1/EMvnlwzaTczjfUN3F2MJ8hJ0KeHecN9U1Z290RmxvmXaSzP5cbCi6y/9I8inNH+ZcnruPm5ed49mQdxb7lvbaoM+tYMwFkMK0GB8PESrKrHnb05rKpsp1Hzy7xqYEety44z6FLlezvqGJjRTsV5uTp0Smk4gSnRopZn9/NteXt7L5UxamRYv7zbz5+WSJHZ6WBFdOrBMPySJbCdy8s59s9y7BGBDvP1rF5Xht7L1aztfoCt26cdghZqChOt3yTMu2CMQzon7g/xLTYuPo5bFsHUm1bcv+7dTeUDZs72Xf4MVZe85d8+ctJSO6C0E3MWzH1S+/wgfPEUbgxE89VgaX+r9/dxdef2ae1X6oLu4lfCQ/6h9PfR9/omPZu+FUY+6ZIZR8PFfNrvwqBNSqzLG5pgLQEXkgQzZvet7x06dKgZvcnvnkAMJE+AwbSTItrf+9fEErhRfz5aYisYz/62BGS+ZBS/eaICI5d90ef0wXPfJVoJmevvGVM4SVJ1zYZZ73HL1q6rooATEhcDAVj+9MfHyY04OntnkLFjEkZJB3D3bhREJ5CmVqeCS55Ib1qFYDhy9Ogs7GQ0KCeJ/agSWdjIYPFlYS6LuqXnM/8CrmK5oaDsOny5KBXgl+48p4oU+zhpqX89rpDgbJ7uGkpJbHRoAFxEJEWellSnBMPsgtTFl/IlCwv72LPnj0TcjtjeXEWFwxgiT5cZXB+ID9re1nZMDfVtWAbHo40udBXQG10iFtvOochJBvr23imoQ6A7uEcdjRXUpM/ROtgHrfX6uBnavz5OYn0eH02TDTkcLEnn6/d81P2tlWzubqNJ44s4nsXVuFIA9uQ3FZ0js9N4SLOjBP87fZ7+F83HcQ2JM4qg3/ftZ5vHDly2TFGWPqpz/4SOEfiRpMYIYlbYLCns4y9TeWYY4KhvjAfum/Kr+8yxC0V3HtKgZsmuC7UL5uc+jYVivKbUePc/6nPcsXSF5CD/0B/2+McPVzGNWsfp2peDkb+n0x6vtyyHOIlIRS6QUtumU4Y+eGu1OclkKbCCwlkWH9WXkSRV5Jeiahhl3CfDNgbKnfm9Dnhe9CsUZllcUufmqiEDmyP9c+sAJRhaEbFf5zqpfGI7i+pTFi6sS5gWjiGJF4mQAgcpaBLZh37yRdOM+il88pz8qPBsVYSvBDBh27NbFjZiFuoEKjUFxfPVj3SFRAmCABLVwRj+8yjpxgZTPrZoZCTF56UQTLkJJGWreOulpZnAmPIgFRGvvLlaWD1GLq3pyUQrsLqMbgomlARG6/QQzkCYStkv0lrd/+MxjEb/MKV90SZYqW52Q17S3NHKI9e7v9NKcew6WbJqd8Ry5uUlF9oO9h+ko6tJIV2dnS5PH8Ey9C+W4VHef4IfcNhhJD6f1LS54fMHzq9hA9t1RkF186DRxrrecOS5mD8IZ8WFYwP2DS/gxzhsa7iEusrL6EU/L/4GpaXdrGlqp297VU0dU/ODR0fJ7hzWTMh/35ChuTOZc0THrcpp5M9lJHSGLU5A7RKvUwwbUlx3TALY0P0jEYZapi+vsN42AIazxYRjXkoTzG/aiiwvMurrzDiLn2FNI4RA5AbO0/Dvif584/ehOsYWLbkb//pSVbdNrny3t1wCB2nFkgUuxsO8aY3pt+S0tT1NdLOVf1nfoYbo7N9EAxwQwIzqbQ8Qxi2Th4I9Wf4uP3rKpH2C2fWWZ8Jmhr85h3+xAtkwMshTZ1KyRlQSRmkhqN8ObUtVYow5cedKEAyHTy/XrN/fsYl+uRGbPqlG2zPjaR91QOjCZyyVHaowB2ZPBYTTiiMIfR44wpbzGyszrBFpM3AiyrMMYEzPIMP31A6DwBt5RtJRb8p8RbHg89ZAV6lw5mmV9bMeyK8IuUthPhr4H701/5ppdR3ZnuOiTLFKiIjl8mF49K9UwrBk9A/FqUkGtdK1bf4PAntvZNXH3NdY0pZeQLTn6+m0PIQdtb/htAT7C3LNR87paxvWnBB+3v9VcKYX/wq0+d9vi+futLsDMrfvW4fC4oGAzbLM40T+24nihMMxLPbcI2XU/js2ufYOVLNk70LuLP4PD/tXEjrsK7aBop+EebgWBjblGxY1TnhOabC6VNFXLuqI2CbHGiqJCfiEncsKspmruAy4boS05yYEQOSo4ci3HbHOa5/XTsvPlfF0UMRVt02+fmkeRLTKEEppesQmZq/v229w3cPK1zf2k5fRLP23Z4zwTmSSLyonjNuVKBmkTKe68KYp7AyUqa9kMCJieDmDFdhFM88CAogEx5CisA3LGV6TLn9gr4YBMqxP1s5OT1JKDKCgLfTl2GxWipIIhJSgXUFPm8LjD4ZMGGMvGwK36jrYY2q4PqjsfRSS0ZEFi1TRiZXrHJUnzd4IY7OjCpoSQ8zYWImtRKx5PRMLxUWqJgHuRKGDZQwGI2ZOjAQcB71vvG82TN0ph3zlR4ohLgNeB+6n+VqYJcQ4kdKqVlldkyUKTY8GiLumkGFvOHREIfPV1Ff1I9tSjyfQ2micDyD587WMr9gEBtPu8UkJD2T4+3lvGWS6/YMxUj66euOZ9AzlB2cW1zch1ToDEup5Xr6OHyhgoMXqllf28a18zS3Oz+c5If7V/BMQz23rmjmrmtOZ51LKgPG0f763AhloeyXVFXeMGFfyZvCY2XlxP66ieIElplt3VvmxJOvLGeUd+ad4deqzgT77h+uIGVlKs2nwZWwqHz2bo6kFyZkexgCDMNjff0lDENnbD59tI6aJbM+JZ7S9ZInguvB9a9rYZ7Po92w+RIXW6deMdx37ctsXFDAs0fquXlNM3WVmiaaX3wQJ2ddeseMDMiQ8giJNHXSiCk8L/2MGrGZK7Rk2yh5pJWKGxKMlZrjLG2B09FPY2PjpKvH8VAowhlZ2ImMjuVWn0tMGTj5AntQYfVnf55e2L+80nfkZcRK7TGJZ5q6iYfKblE2UxhJF3sofYPeuOYnjgmm8JcGQuFk6Fwr5uK6IZSh64FbscldVFbIw3PNwLVqhaZXwgDkyawiU+TN4B6LHGStE0wC44KNjUtWSoC/2LBn8DKYLV6J5R0D/lUplRBC9JJd3HLGmChT7AfHVvLNI+vYPK+dvReryDGSNF8q5oW2+UG9aiEUm+e1c7C9khX5XfzeT9/I2spL9I1FKIrG2dtSTa05uaVXn9fHfx9eQW7YYThhc01JtpXZcL6UeNzm0IVK1tV2cPZSAbiCzz1zQ1DN8CO3vsDadfBfu67hGy/rJs8vN82nYzDG1vo2Dl+oZG1tByMYvG7BxUBZelIwlrR55txC3n3NMe2nlgbPnqvlnmVNwX7Hu0pZOMHYJ4oTVOZnu5pS8niIcQbAOyq0Nflk7wKWx3r4TseKwOd+V+n5ST+/yVBUNhJQ3QSabSKEztysLrqymsYJBCEmdpuMuhYV1dpeCBpPV09tP4x5Bg0DJbzzziM807iAijI9rv2tvtPTP5HwhG7GrASeMjHD6QdwY207L5+tCZTExtr2Gd9PLD/M8KCLABIxgZNvpC3LzBRhTLZs2TJp3GY8hK/900Zf+stOlgrixfo6XkRgj7OeZSTb5xjIgGsY5F3wcGKaSjhWOvskHZkUYIEKaR62TGZPxFDIxTXTxbpCobSC9oTwXyb+i0WOm8QZWDy/h329lRhSdyVaVTwzFlAiauOECFYGieT0FEOzIo4jzGCFbVbEye1TjLTnYIQkygVhgUwa2ImZx0RmiitW3kqph4GHhRC1wPeBbymlgve+EOIB4IFYLMamTZuCdvbjMVGm2ImL5fQT4XC7pgQWEsfts+lwcjncUQkKrBHFkZYqhKcYNsOIYsWLw/NpOl+CEgIrqcivnTxY8aXnNnPgdG3wrGxYcoHtGXk8//7SRnoGclFK1wcvKRhmdUkXSddCIUi68HLzfN4B/Pfua1J3DSj++8XVfPuFjbiewDIVf3ffE+y7WElhJMGoYxOxHGpyhugcjfGBh+5mfdUlDrZXsLaqnf/z7A3cuvA8z5xdwIrKiUuyThQnaB3Io7JwJFBqrQMTu4xaLuVRW5nOqBwZM3ln5RneWamV+O0lrUGbt2XhvqCP4VR1ITKxpFJb65kujhTmV/TP6Bzj4TomapKVRM9YFFDUFQyn730oh0VTnG8oEebP7noBgK0LW9lxoh6AFYXdjCmLTTVt7Gut5tjZSk2b8zS9riAzL3tIIVwPL2RgJiUMzczyllKycuFB9hxeTSJmZNUiyQzaKAE55QkGBwfZtm0bzc3N038HpsCJ6QYb1ojv2/MxVqzIdD2MFWeP13A0TS/lGjEyfNLSNhkrVpgujBUL5KwqKGp4IYUb9u81RyDGRaCdhPBd8jq5xUmkr+F6IYSn0slHTO5OakwUMrowvSRq7Cyc0fgKvDijVg6e34KuYGz6/qHuuOxNV5nERpMQMfCS/ueX1Emr4cGZr1a2b9/O9u3bAVYIIfYB25VS28fv90p93ncADwLfBP4oc5t/se2bNm1S+/btm/QcE2WKjYyEtG/Zj+KOqBC2lMQu6WIzVlwnOCgFGIIzo8UYQzqN1jAUhgInKjg9NjnRvrG1HCn18UoqGlvLs7YPJ8NIJYIMqeFkGE8a/vOlc6c8v9tI2HYZSoRJWQ3DXhilNO/I8RTfeHE99647xRcOLKcsf5Rf33qYA03V9A5FuXflKQ61V3LvylOYrqJ6/jAH2yu5a3Ez8UksjIniBE+cW8QjzUu4c9FZnmpaiGlI4LnL9vuXB29k25ZGtq5q5eXjNezYs5R/+oPHAmW7Pq87aPN2vjs/6GM4VUW2TDje5A92YoapyuMxPBKmKJqc0OdtuxCNZgeb8+2pU5s31bZlnSMlFyQT/Mf9P8bwYydJx+Riby4hS7HzxEIG+9O+hEVLutmVnK8TMJTJoiUzo6Tt2LGD004dyVxDZ1ZmDER4CjOudIEqV2EWGiil6O3t5cknn5y2RocISYaq04G9vO6MxDFHkLl8Uc44y3dAkSgSQRmA0EBauZtxrdQ9Oy3PFp6pOzallLMc12NVGn4r1Qw5haLQGIM96RhOfsnktMxeM+TTMDPkGWDRvC4GjkVx8wXWoGLR6ulr2ee2K/rrRTp43q6oCMfp6sjHMQSGVJTlj/D6+iZOWyUzGgcQGLpCiAal1KbJ9nslPu+VwI+A9ymlfnil55koU0wa2udveJpDKy1wQtoCtkfAiQISIv3gRHS2Vizr2dH79dmT18MeGbT1ZEaB6csZiEmHMUJBUkFMOpwbLcANp7mq50YLAMjNT9DbmhMkMVj5HolkuszOyfYyjrf66dNtsOvkQm5cdhbTcjnz5Eo2LGrjsSdXUlQ4wnvfcIA18zuIWkl27V7CzVsuH/tEcQI76vKjl9fwk70rMSKSd2y9nCYIEC2L83++eXsg33pTIw8eW8FTzfXcUd/M/avTTRxGRkJBH8OZLt/jroUiEZAKUi4UgM6RHKqmPHpixEKTK+Ow7RKx9JI0pYxT8qTnC+sVWWpcKfne1zX4maF67JGQxyLfH/6emw7z7LF0AHkkZPGltz3EiqoeGtpL2Hl2IgfX5fjUpz6FvfotJAsut7jNhF/vROhZV5jU9z00NMRnPvOZaZW3UeyCMAPr2ihOfw6mKzTBw+8UY7rZyrM0z6W7z8YL6eSe0rz0sYankCIdwDVm27MTdJ1wN62clZl9fRHXpQJSwVYrw+sXU0kGiQTuoJiafEVteYqMGlZYMxzrxaECvKiBcMCLCi4OFUx7TGWTZGwwSrJaEWoTVHaNIesMcs74E0gJEuTyk/1rWHtzy4zGMRu8Esv7bf7x/0cI8X/8/92ulLq8Zfs0GJ8pZnU7hDwbz6dhJU0HmWsR6/EDJhlfbFgQ9ObLDPAqBSI5uRWozGyrJ5B9DA6FMUmXsBx0wnSrKCqjuM/5fv0Fr118kTP9xYHyuHfzSX60fyXKNRCWJMdyGBg2Ax+kRFGYE6dLRjnQX8HBlypRIUX+WJy+/8phw6I2DjRV0z9J0fmJ4gTRUQ+zx8RQIEZMoqOXuxnWrl3L86M1jFYStJvaOVzLQ48uwxqFPSdraOkvIC+SYENVB2catbWglJrx8v3cYAGdRNk7UMnmgg7W5XWnH7rxRSpmCMeb3GJXCvoGo8RK0263vsEok/OMdL34iJ0Ivq940iYHyB1nwY93QV+7LP0A3ruygVU1mi103ZI28qPTm6ONjY3s3r2be1+3jUV5vWxc2E5OKME18y8xMBwix3LpHYpSnDfGzkMLCUVcfuwfO5GrbDxycBhQ/gpQaTmFyrxhWuL5Qdy8Mi+7vvR1G07zowMrEY7AzVNctyEddI/G4gw72j2F0PJsYcjs9PfQSLYbITomiQsDN0c/35GMoOhQ3CJIORe+PAkKvDiJoVjQIKHAmxl/omM4D5XrofIkYsigY3iqGaSxamkHF84tJTqQks9yoKVKN3BWGbpHCU4cnXntm5nilfi8/wb4m1drIJnZXp/62iEdVU5qCyRiGHh+VbOUIoAMq87/noO3emq7mlx5u2GJ4ZqB1eOGxxXKCYEY1VQy4eqawrb0cMJ+qzUDIo5+En64byWE0td96fR8tv9/D3OgrZIN1R188pGbGRiOBHwOgKGEzcLCfl726lAmCE+wtq6dFw/Vc/hiJRjwrtcdmnDsE8UJ9jfM989tIJC+nEZubi6f+cxn+PjeQySKDOJF+v9eX4S882nr5FusxYvpiP87lx8L/j/j5XtY8hvHtgWt3P584R4GvAib8zuySgbMBn1umErS/nwvxSiTcLi7HIYMKouHA2ZQQ2sZtddMfr6GllKuW54OIDe0lHLjYrjUn8OC8smD3MmkRYrHsrBMP7EpxZ6SJ0OK3gnw7hsPc+/GU0GewUTYsuIil4YifNSXx5c0nggb69rIHSihICfBwEiYZQXpYN271h7hyXOL2Di/nf0tVdxZl51RfNwvkIYFGFpOISfXodeNBjebY03tlpoIMdslqUy8iGbvxOzsF7kZchlamO70k3M6vX04ZjNSpoIa3Wpk8mCi1xkmpzfVkAI8d+oSAymUFAzTXm0HVMXytunvsbGv1HfP6EE39pUyOBjG9F2rqZCxApKJK3MZToVfeJJOJlIZVX/5zaOYiQz/l20GCQZZjIPUbws8hF8XWWn1ZSiYovqZtAWJAoU9LHByFfZo9r6eMhhcKYIvM/+sEbwkUpPY9UuhCWVgjugWVl4Y2pIFfObhGym24jx5YDEtAwWYQgXFeNwoPNW8iFUFuvEBSneLL43Fefeth9h5ciG3LD/LtQsnXmpNFCdoHcnT9ZCFLhHQOpK2HDLru/zXmcd5QczHSOoXVH63B5jBJLMHFG5M+/aP9WXHAWayfH+it46E1BH4hDT5q+ZrUQhsIfnaih1X5DZJJTmlFGVqxW0aMOSGKMiPI5XAUEr/zp/Gwvff2YahXwAp1l5DWykLygcvS/ZK/T5ysZybJ+9MNiV27NhBj1//ZNvqpqyM4Ml+q4xGv+NLGk+E2uIBPrXt2YD++vV964JtgyrMv//aT4Mcgq++tP6y47ctbOKayk6OdpTTmpFxXJY/QkQlKbGT9Dgh8sTslXdlfR8dTmWgnCvtvqzt4Xlx7KEQoUFBMl8Rnpe27gtio/SV5ZByllRPwUb2QgJrTEAczayJziy4Wlk4RLsoDvRMZeH0zKhzlwohmi3HErouUOp5SumoqH0VsU1+nhBxF2mnmxSIuEuiPIRwVbo2AunONcr35aWCHH4ZKJwpCuiIBCSrIVmkl5ih7LmEG1EEmTaGwo0oQkMC01SYSYUXSheFN8Ykobi+uJkE15a09hbT6p8rbEqsjOh9aAQSpR7HB0ux/a9ZIdjXUUlXRwGOZ/Dg7ms42lXGDTdePvaJ4gT3X3uETTWXOHyhirW17exrrWAnXFZ5rbc5n8XFfVyKxahwR4kTIZnBOTZcCHdLvBjE3Msn3HTL9zOXisAgw0o2EAKS0uTHnYu5dsWUh0+IUVdP08wXd0rB3bzgAu1judimpiTaQlFRNPVSOSeWzFKUOTGtFoxI9rnH/5YZftrusRi14TRrp3ssNqWr5lOf+lSwUop7JvlT7Js6Z1N7UfC/NWumr+hVV9iPbXpYfiGwusL+YNv6mo50RrEhWV/TkXXs1gUtfOCmA0Fy1Vd2pQsplReNcLphIS1+XsSiFWenHct4JLFZV9bOtRW69k6yPzuQGBqBm+Ql1i9v4+CZai4YGc0YylzWF3awtbSDl7srGVCTx7JCIY/EqKUDo0oRmiHPW12yYSFaeShfngZR4TKsQqRUdFS4WELhkOaZeyGQNlhTuP6uFFel8kZKlGWk3R8JieGglWmgA1WgrD1DosIGZlJ/jNLWlclCU9C37DFB4WlwcsEeBjEu+p53IclYVUS3j1K+XBYhp0sFBWfi+foY01fMqbetMV5WaRn/f9aYgQyBOax8twmcbyvClPrVn3AFB85N3rhgfJxgY1UnG+ra2VTfjpSgPDFh5bVeL4zTEMEGegkjDUkoY0UTGlaEhrUi7OHylN7plu8t50txSm0MW6IkWDEvOPeF7sIpj50UIUXHaIyopdOnC0JOcM4kIiiPkMJ4eTziPoc30+cNsLR8aobBvMK0S6VnNEZt4VBwjp7R2IScfEj7ulMYdmzKmZwxkTpneXHaLXb06NFpE3aEUlkZwCLjbRdVDknXCJRzVGVbzxU5Y1imbjMIkoqMFoI4AsczkEqXYGbcszITvHn+Kd615XiQ0/Bfe1ZlbX/TojP85usPBuP7xuPplcHNVRf4k/X7sIXEWWLwDwcnJWBwQ90Fdh5ZDFLnGNxQN7Oa9IneEKuNEXJrRhlujZHonZ6lkluYYGFeD939eZQWDtE1lMuaVRc52VDNsBNixA35y0TFxlXnZjSO2eCqVN4yxw7KMChfDiW1fRoodMcPqkswTQPDA88G5Rdf92wwR6dQ3paBGpFB8FPEsoNwthGi6qlhkiUhQj1JjIIYTtJfCSBQIl2gJxmD6EB6ieRGtSURvH3DYIylZdDnsRI6KUB4KYWu/PH77IPk1NzQzDjBkye/i4wpDnZUsb6ynZ+dXMiDD/7Z5ZXXciXxJNhxzdSxlSIpDAxX85nNpEjXk55gdTzd8j3HcAi1xJBRBR44FW7Qmm1kbHKLacpzCofK2MTWdE8iyrPHFjJ/y+Hgwd9xdBHLlk1+vnY3RtwzA0XS7urs2lhsandAQSz94qryl9Up/Vg1yTI709edQkRkf6/j3TMpFOemFejo6Oi0AeN5BcNZWcHzCtLK//DZKlbVdAfP1eGzVWy8Ln1sx1AOntSMLk8KOobSWapb6y/wwpkFuJ7O3N1aP/smHW9bc5JIRvbw29aczNp+w4bzWS+PGzacD7bdWNVOSOg6Q0iPG6smT4j6+G3Ps3llK09dquWOigvcVnluRuNbtqCHT71pZzCHPvXTW6Y9ZkXlJUrtBLffuI+fHaunNGeEwvnDlDlD3FlzicHRMNfM66AoNkan+VqxvH0/s8qQbQWOSzoVVWZYto7CzSyfmWKQhCb/wGKjMKwUpqPwbEHOON0wWmpi2TkYgFtq4xYYxByTBK4/LkHM0B9fskS7VuxRcGIQL/D56ElwQ6BMvZIwx/S9ODHIlYLRkIIx/6EVetzWaNoSVzOgqKbiBJ/9xB6+07Y6+ICqnVE+8duX+6bvXnGaUxfLSI7YhHIcNi1qZffRWhrbysi344y0R9OxhgkSZsd3JBqPZRXdXGwoRPlJFkbSRkYV1pjgoiic/oYmQLGt35IT8bwLDIfTJyr4o5ffyNolHRw+U0kob2rL+6XuavYOVHJXzTl2tNbhOAZvAxr7i8gv68Q2vFT1Uf39S3CkyenhAlKdQVMvXJ95p7sYTYBUKYNMdA9FqC4avuxexic2jZ+90wWMhx0bV+qAtSsNhp300r+malDnQBhgKEVNVXZg9sb68yDS8bcb69PKc2FdD//3PY9w+Hwlaxd0YERn7/PO7Dk7kdzs5bOEXpASB4NmL591qbHnDAQ14k2h5clwXubxtz0bSBoGe3pKWVTeQ/mke6dxU/15bNNvIoLHTRn3Pxnq8wb54D17ANiy/CJffHgL+WaCj7zzUWxTl4jwFLieyb4LVxHb5OcJQ2jTwbMFpqMQpoGTSTVRfgU2n1ctU62OgkiPfgK2rpq8Ma0z6mILbQHbcYWjsh94kSCrwptIQHVdEc2N6aV1da32SeZ06S8pkQ/WGIQHwYsJPN/QNJIGbkzg5mQ8nXaIYtOh05JBoNNwFHZcBHSu5BQFeMYjgYFSIiApJyZQvFJKvBGDo6fT7pib687xkXtfYt/FaorMOJ/7wQ3IhIERlly3rIXHM46fqCPRePR1R6gz+3FyDAaHQwwmIpi+Ii8quTK2yZnOIixbsfdiNZv9ejL72qrZVN1GgR1n25rTfO6/buLEuQpsU/KRd+2a8nxVaowPrD+AbUg2l7Xzld3av7u3q5rHWxdxV805TvSVMOyG6UuEKQon2NNVycaCS9zou56/s/8abqq7wJHzVaxZ0M6uc7X8yQQejYlKGRTmXv4yykTqf+64hKfpAsYNrWVsmtdOiuzQ0FpGqqumbbp4SieOeEpgj3MthSIeJy6Wc+hCFetq2wlF0m+jgWSUG+e3snFBO64UPN9WM+H1p8L+rgoWRIewDYUjBefH8ri1Pr19V/c8vt26gi0lHeztqWR+ZJC3+duqI9mlH6ojk8+j3f1VJKWBxMCRWt48g/GV5Q5nNREpyx2e9pjrfOpoalzXLWshmusELwGl0PEHJUk60/vQZ4urUnkXRCIMD48FVMHc/AgjiSSu0AEXT2irVBmQRYUO/BSKmoI8/vWPpihGbRlIpU0rJXXvu6zNSqBUKvQJljK41DecTjFWSsvohAJDQGhMZ48pk6yWU0jhp4+lzatR1yU/movdOYgX0rzrZJFi1BZpZT6LRjajgyEoJnjyRwcvN9t37NhB06VU0WL9Wnr2ZD3nnQJuX3SWnzUt5L5bj3G6rZSbljdzsGFecOxkHYnGY6wnSs/ZgiDebheDGwNrFIpyZ99WDeD7x1fyaEc9KldiHN1IuFdgDAu25yrevfUQ2+rO8rv/34vsPFrPLdc0s6C6f8rzXV/VmrWEv75Kh5ZL3QS/u/4QtiG5tqyN/z63LCj7qzzBmVOlcI8+x48blvG9netQUiAMRbhijImK0E4U4BVCcaC9kv0Xq7ih9jy1hUMMJmzGnBANXSUURxM8c3YB1eYwcHTa86VQlzeIZeqSxSCpy0tb10OOra1doa3eoXHK5NuHruGFQ/VB3Z4b1jWzznctX+rKRVUJpFIoJbjUNfvypgfaqrh2U7ra5I+alnJrxvZ1uZ20JXMRKExDsi43XWtIjCvrOl7ORHlijJAh/QYnkvLEzJpkOMLEk2CZutiZIyZftacaoJxsn8+GJe3By/aFpvlct+ICAn815v/2lKC1f3re+GxxVSrvdUvn8Xz3mUAXr1s6j0PNbQz3DWIPu4xUh/EyXCJmQvtXNT1Ftxjr6BriS998mt9778S1Qa1cCyeRXv5Z4eyPoiYvj/buXp8vqqgqLeTimP/G97+txJg+3vMDpKBLhwoHAvNHaQaHkmiOq9Lp+3khm0jI1iU8PZ21KaSFF1a6r6FSWJMk6UyEsARnUK9CjKQiPIG7/LOf/SyLb1vrS/oeFpT3sjqvm//auZbbVjbTq8KsqO3k8VNLGBpO+7cn60g0HjVlA1w8WxxEJ6K9CtGrX2q9xlQci8mxq3ce3pIECE3ti+yzkGVgdxr86MBKXGXw3WNrcDyDF/bP49cSR7hm8pgWhbE4foE8UFoGqI0MEfaXu6ZQvGtRQ/AAuitM/vd/pTNTS2SCVhnT5ROklifCRKUMvrl3Dd8/tRopBP/35WsnOEpXGn/b0obLtkwVMM7PHcuyHvMzfOaLKvuzyhQvquzPOtZLmEGPWNfTcgqFOaOBm8MyJIXjfYwzQEVkVPu0DT8gGsk+x9bcS/z60rQf/ExbmmkTtT0eaV7I3qFKNud18MaFk7Ndrq++yNer+oP6PJViZqu9C5fy2TC/g1SLwwuXJp6rmQ1Q/ubf38TfPPQ67lzezJMn6yksHONsRzFffWoLaxd0MDQW4r2vO0RJ7uhlZa5fDVyVyruuvIhdFimeO3XlRby4p5G8pkGQMFZi48XSBZ5NwyDU4+LkCJ2G65e13PFcw6TKu7gkl9a2vqDJaXFJtjWxpKKES2d6dVEiX3aHBO39/dril1BRqI9ZurqKhhNtmI5W5OGCEN6Yk/bdG+D5VrRCB5NWV1UhbcWZwrQ/NIzBqPJIKf2K8MxN73DfGKPFuZpLrrQ8Hi+//DJ9i+5mfW0bCccmbDvEbJe/++nNAOxums97bjhETiTJB2/Yw7df1j4CIcSMK9udaSvBKfSzWw2FNUygyJfXTF8vYiIkQj69x4+2DW/2l/wSCg+aPNK8hITrNwp2BY80L2Eq586+9ir+9sUbg4Jg2xY2sXwddBMm4ZqEfAUe+FkNUEju2JCu572lrpWiUIKNC9vYf7aaJdUT39tEpQyeP7sA6d9MqsN45u8UdXR0gpjNVAHjNlfPx9SCLyUD9I2GoTjtkukbzT7PXavO8HJDOih516r0vdaXD2SdNyXPBocHS7lPCd2KTQkOD5by6xnbKwpGsgorVhSkld13ji3nH4fX4yiDH4/W0TcS5n2TELFCEZf1Vro+T487MwOota2Yzz90PbesOcvOIwuxJihBPL4BytbKVn7cu5I//8mdXLfkAlsrT/PFZ7Zy7GIlB8/PQ6HYdaaO37zpAIXGla06p8JVqbxf2NekW0vZujDVC/uaiHYlcXIsnEIbkWpemnIfGwZK6roQMoOtEY9PHrgK+Q3r9GQWvpyG9BN8VIa8ac0CfnqunxRZYNOaBQBULyhkbzzNm51nhom3uIHv2o34Z0qZPbbidHcPbopHnSpMZAhC/SDDYCTgrhtnVscZoMwcQh1VOMUh7N4kZbWX++y0FSg5dCE989v6UxaG/ty+86K2zG3T445Np4L9ZlpTOhSXuoSApZXfO7YeormtlNetOMuT+6eq9Tc5SnocLlZa2QWH/b/N8iTxnjAyx0Pleohhk/jg1A/srrZaDrRVs+9iDaaQRMIOvw48fa6eH59YwZuWnuYtK09iGRJD+MtoadAylrbGckoSfOmunwYJMdv3rZ/wWhOVMqiv6qHlQj6p2aXG/U5xSk/0X17MaKqA8bMX5/OGxU1BIs6zF+cHfuPGjjKundceMFEaO8rISiGwJV9670/Zf66ajXVtXHLShaBsvaxMh2uM2Qcs76g/h3+T2bIPM+JmsW7MSPrZPTBcgaO0H9tVWn7fJNcx/CbMWfIMcOPqc1ghyeGWKu7YcAY3OU4fTNAApWWgiB0H9HOx48BSNs5rp7B4lLdUnuD2Vc30DEcpyR3jZ8frCYeSTF2ZZva4KpX3xbERZEh/eCokuDg2Qn5NPi1hVz+0imw3yYjHWGZnjpRfeWTyb662uoizrb1BELS2uihru7IETszQRXlMgbIEb7xhJT/ecxw56mHETN54w0oAdp09519Xn2sIBxESSE9hmIJQyCDhecFKAiWwYxb0pV5C/sObkEF7Ks+CI00zrxFdt8GkNZkDApKVNnUbsq09KSW2bSPDBrgqSHC6NJjj60F/DEo3Y3A8OHhWK/lIZOKuPBMhN+LgxbX2kUpw4kwF6xd38PTuxfT0z76tGkCecHGGowhbggQr6gWB64WxIcbMUfqr8vz4hcO89qmz40LhuP9VKKQvA6wu7OJr5///9t47Po68vv9/fmZmi8qqWbYlWZaLZLlK7uX6HdcLV7hCAoEQAqYESIBAEn4QIEDyDSEFAgRMAgRCPTiucv18vdhyk7ssF1myJMu2etkyM5/fH5/Z2V1ZWq1k3VnG83w89JA+2t3Zmd2Z97w/78/7/XqvpL6thEf2VrO+pJWeniAFOWG2HS1j4YJER/FFJafxJYUBFo3SOGMkKYM5s7swu+ciTAFRJXUa171Ra85qNjg3K/U4xlow3lDWyvuev5U1xe3UnS7hzlkJfZKO/mxipo50Ys4d/amzurfNaUJoUDv7JLaEpUmOp47AchbfTDtejzs+yul12/Rp0qac1GyXJ3ZXUjGnh+0DM1iV08Hxpnzuc9ZFq/VuNttlbhy7Wuse9X2sqA+SUj6tDHS5AQZMH3//m+vcmP8/3PlMyuMjNUD55TAp6F++UcM/v+sJ5halHtuGqhb+7/U0eg0TZEoa70HTdMRd1Mk8aJrEfJrT3Vl9UJotnTgxmBELnDBKfNqJAF9g9MO7ZMV8Xt58wBHKEVyyYn7K44UF2WAILENdVoUF2ayYW8amT95LXWMLa6rKWTFXGbc5oQK6zrS7XkVJMJeWYDcxTeK3BTNys2np6kvceIC8nCClRUW01x92PW07vrvOzedQe+bt1jvCc5HaQPzeQEd4rvtYPE5nWRa+mEk06FPdPgTYUYnSi1Kfq3CKkiTQcUZNu8dKD0zmjB1MWdTd2l/G1l1lIOGG+SP31RyL/iIN2xIqvcgG29bc3PFj3QVUVXagaTnYaGi6Tags/SJVuCGLecFuIn6dQNQi3KDSguwB+OzaV3ls50IaDszk4L54epdAEzYzZiYuytJgvzL+Ut0ESoMjZyfEpQySjfeGsmbqO2ayuvAkIT3KkumneerwfO4/sBS0RDVDciFNJgvG041BbptxmGdb5nFb+WGmG4m48j21+3n5eDl9QwFCwQj31KbG06XmFPYIJz0yyfHc31VEeajfTdXb31XEnLSf8Nk0HstnUXm36103HstnaVIayPHBfL56cA1RW8Ov2bzXTtx4bpt7hPXBE2zpLWFdXjvTw6OHIExntpvI2Mls/7YemZUS8996ZBbJn/RIWUPrFxznaEcR8Yt6/YLj5Pmj1B+fyfZjZeRnhekZCrJqbitvWzR6nH6iTEnjTdRGT3KabWwKi7IZjAy434qwQbOUgXDLluOPoQxY3szRV8Ufemgr+HRsp7r1oYe2cse1iRLkW65cymMv7MU0LQxD55YrVUXYirllrtGOU0gAvT8R7hgUUQZzletiYlNSFOJEVx/JOlnvWLeUZ3Y1pnjahoVbngsQ0DL/etoGhEqfdHLE2wbUmw2P0xXk9NOmFbr7akRASBthCaQundxuFd7xh2IZpQcmU1nSSceBEPqQxMpSFaygtFs6rHGkzyQxJ9DDYULu5yItTWmpS8BvI/t1jHxJTEoMIZH96Qsigj7JyeMF2I6mzNzZKoar+eBbj11G1HQ+g/gNDRtDtykIJYxhuNuHVpKIhIW7R/bw4lIGC5OqhvJFjP+5NpELDHBpRQuXzz3OS80VFAYjbGkt5fUWleo6XOJgNB7evYgXD8wDKdi5s5wrFx3lOic2UprfS09/Fk2tRcyd00tpfqp3uK+7kNrCLtfo7esuZKVzmpfn97o57ZZU4/GyctEpdvQWuwZ45aLUNYJWI4uomUjxazUSBV2HWou5avlRVuWfxrLhhSPzmLN8+DsodN+wXPkxHO945sj8oi58uuXG/OcXpTYiGSnL57KqY1yz8BhBv0k4ahC14FevLmNXSym1FSdp7QpRkBPmu8+s49blB5k9AWmIdExJ452tGwwYJtIPIgo5ukHZzAJOHB1IVEVIiKcM+KISW1fetpDOBFSCnuZEb2vtSXiISDVOoqa6jO984T6272tm1ZLZ1FSPXqp+3fIqXm1oAschKFuQT0tbvxtGCRVmgR9iARXpubJiDvduqOWbj72CWxkh1b5oUdxefcurMk/s7+kNY2aLlPFIcbp2kY+VCyCwfMrrzm12PjAgXGhjBTSkDmX5R2nNID0wGTOSRfYZW2VFDsK0nD5K8vrpJMjg4OiLbeloO1FA0NCxsiTCglixKrk3hOSu2Qdo7CpmsCcAPkksJsiOpXe3ymd08sbALKL5kmCPoHyGWjTe2VKGqUSvUUZbYugWb195gJtWHKI5Sexra+MsVlSddFPftjbOYsM1I79fdXU1gUDAzRQZCAfclL7k4pzr5h3junnHsKSSwf1xXS2vARUVFRk1w7CESuWL33ytpJS67+1YwS8eX4e0QWjwxzdv4dNJ6dqn+vOwCrrc0Mip/kR8v8/2saNrBq+fKWXDtDa3TG08HBiaxl83Xu541jV8Y9bLJFdhXFfSxFMnZmNKG11Iritpch+rPzqTy5c1IbCxLI36ozO5bpT3qT86k2tqjqHrEtMS1B+dScko1bbJmSM/+eFVKTH/Y035KY1IRsoaWj6rg2AwITAWDmu83DCXb733MXyO8JgtwbQ0vvnkJSPswbkxJY13Vm6Anvh8JwBZRoCOjl5llG114vg6o/gHTIy+GKIgSDSYJOrt/B7sG316den6Kp589SDxzI5L158tF1dTXZbWaMe59xLlsT+zq5HrlldRNbuYP/m/+4nZNj5do6Wrh6jjYUvghbYmfr21nqXlM3n5cJPrUVZMK6Dp6BnsoEo9vKxm3pjvHSfb8NGD5VqCbMM3YpwuUdDkHLo/ZYi/F6L5Ei0KzQPlGaUHJtN50ufkTKhKv67mHDplLj7DZnbNxHpYdpwJoReIRGdvU2m4ByI2Da0zORwIYRXp7qzlUHdB2u3typlO7+oIUoWc2RVR8qezZpxmZ0MZAglCUjuvjY9cvZXlc9oxLY1XtyZupnvapvPJ/7qV2jnt1DeVYAdHXxwHuPTSS92FS9MUmJaGwHIzWiBhxOOFHdfPPUptbS07duzI6Du4YXEDhSLGyvKT7GiZydqkqfrOQ7NV+TsC25bsPJQqGdwV9ROzdUBJBnRFE3UCzSfz+cKxy92QxlfmvswlS8bcnRSea69IKZ55rr2CZNGAOXKQHy97yk3xy0oqSr2y5ihH2gsZCPvJCUa5smb0EMQbu2dz+ZLjSCfl743ds7nhprOfN3xGuqK6nfLiXpZXnMS0oCAwlNKIxO/3n5Wm6ffbZ42vWnIMv5Ew6LoGmrC5vja1KflkMCWNt5FtQC/u2WxkGwQtHS0q3TS97JNDGP0mCOibratGcXGc1/l8o0+fv/CZ2+FfHmbL1iOsWztfjc+Bey+pdY34zmOt+HsFUoBfCjoMJ94Zv7FIeGp/I+sXlvPK4Sa1+KoDGkQLlBm1goIXDx3lHVeOrSYHMK0kRPfhMPF4/7SS0IhxOlu3kahO4BLAtumeK7CzBNqQJPukhW2ovy05/jDH0tknOdOWS8xyFgRtgUQjZkF2bPz5wQB+0yYc13EFxJCGLyywpcaWgXIC2RGIrzdL6O9Ov8C6u3MmslR1/ZZSjQG2tFYQnmEjQxbSErzuL6Zl12WUt/dgdhuI04mpfPu+IAWBGLuZzuAJSXck/XsmL1yagwYvHy/nUNc0DN1mXdkJFhefQReJ7BbT0nhl7xy+/vUPZHzzXJjXw/W3vISh2dxc28Dx04mZQl5RmBUVbaya08b2plKyi1I9STto89iJ+dR1zmRN0UnsYMIwbW2dmWJ4t7bO5L6M9ijB5cUn+P3JCnfR8fLiEymPTy/qY1ookkjxIzFL0zT49sOXUDP3JLuPzeQDN20d9X2uqj3GZ759MzWV7ew+XMI7r60/6zkjzUjjjbJt2xET1eIL+KoRyUgpmmFTI9tvuzffsKlROb3zrOcB5OdcJKmCJYUhWnp73Yu1pDBEltA5fKYT4Sy0DZVl4+8xMXMMXEX7YeXxsWj66fPymgp6esIsrxm9jH4i1DW2YEdsdAlS2FTmF3G6o5Xk2eYNi6uonlGMFtSICRufTKgoxgOpHX2ZJ/bHpE0sR7hNWmPSHjFOF9NNgn2aullYqpM4PmUcrJCgN0dghlA51PvOZN781uHYUB4b732dl/bPZcGMMzz83FJiFvh0mx4zs36Cwymd3UektQjLJ0DamCGRSBMdgmC2TbBNhVX0IUGQ9BeK3iagBNdT19vU+dM2lEO0NpySkniMLI5ZWZADs5MakYuZBtu1mcgYiCKotNN3KU/WYP/pjhp2RUuIWrp74S+f2c5loVb6zwQoyA2z59hMlixoH1fIqmswh/lajyvu1DWYyO758PqtLM7vcqdY+3tSs6vy9Bg/aV7GqsKT/LZ1Ae+dm2jE8baKZh46WoWlWei2Go+XtcVt/HDa027Me7ZIdSryhvchTRq3nsnjGx983A1RPbdzPqsYmekFA3zs7tcYCPu5asXIHvpIM9KhiI+kTm8MRRLBciklQgiCwWBK+GRYMy50DcxBDUbweexo5rPXTDnXBsRfAv4IGAA+LqV8dTJ2CoHyRl3VH9h7pD3pQYmZ7cMO+EBCsDdC2OesPErwnYng745Qsr5w1Ld4+Imd/Ns3nwKgbtsxAG6/acWk7P6aqnL8hk7MtPAZOrcvW8yOR1uJ+pWg1u1LFnHf2lp+vbWesFMKaWGzuGIGjWc6XcN017plGb+nHLSQhsQyEuOR4nQFhyTJazi9c4QqR4jnmkvw9QksQ0JBIOPmt3FODWTzL09dAVLw+vHZTCvpJ1ezkUJy7Mzo30c6Fs0+SVNTEUZM3bk1E2yfRIvBgrLTnB7MR48IV0MlYKU/rfNsE+qCWIUWepdOnlCr4z49otw8dw0i6beAUyJxAZ7Mz8EMWshcG9GvcTKcPg0yWYN9e2cpZlZ8UVSiRSWHds3geO8MfIbFzWsO8mc3bqPZyh1XyOroQB4rZRu2rZpSHB3IIx5prQz1JIyTM05m0PLzw3VPKtlVqfH7tkTILm9aBP+pGDEp8AlJ3rTxe5H+7Agr9Qgr81QGVe8wv6ovolNkJLzYvohOfC6zorJN6YXoABYrKkdPoT3TH+SSJSdcg/ravllnPWekGWlx3kDa8dDQEIFAwE3jBNh+qJRLFic6Mm0/VMrR3Xnc944D6I6+vGWrWdTLr1ZQPXIpwIQ5lwbE1wLvA5YA1wC/EELMlcm3swlyqj01vn2qfdjqtq2m5HEPSYZ85BzuxcwxMPpNfINqSSVfjn7iv/hyg3tdxseTZbxXzC3jBx+5200pfGTrPsQQBJwMtqe2HuRd65fz1H6nis0xEkd7VQmztEDosKC8eNT3GE7X0BC+PpWfLkxJF0MjVvcZpg5Gwi75um0iMxMhiXibGt0WxCKZN7+N45MBpJ2wEqeTFvlmTc8wb2sYuw9WYGXhZsQAaKZAGpL+aAAjIDB6QNgCqUmM/PR5yLFCSX8AfH0GQ6WSrIizjmIJt2SeYb8F4GtJnNpWFlhVYfeDtBrHlvyMa7D/yX//hl476NYp5LSDEVbCXfNWnMRXGuHrJ1ZTlGW6RTaZ0GEXcvRUIX1hP6FglA47cbN8rqWaa2Yfco3z5pYF3Ja0YLl+Wht7+wvZ1lfC6tBJ1k9LGMgdfdMxpQp/WdJmR990LhvHfgHkDvNSc4ddmq3dueRndaNrSpK2tTuXeCO2wvx+NC3h3Rbmj5yWCZCbHU07hpH1YbRhjVuGj9W+q6yfeLil7sgsugezuHRRM68emE1jexFt7TnU/es8lladpKcvSH4ozN7GmYT8YwtdjZdz8byvB16TUg4KITYDs4Fq4GD6l42NFpP4+m0VezUlml9y+fJ5/H7LAUB5h46wtrpwAjrWzCx8XRGMIWctXMBAcHTjXVU5g63bjrnXZ1VlJsKRmZOcUvjw1n0pj1m2pK6xhRsWV/Fic2LBsmcwjGmAMNS//u3Jl/nJBzOLLgZKAvT3RsBSn0+gJDBidV8sG0cbXWEMgX1GYmeri8POTswdY1kqzpdJ89s4c4tKOH46EfIC3O0VZk1MWW2wL5vK4jMUBiJKKEpAoV/9bcf8zMrv5GRbCfGjmlMxctzR3Z0hMKdJzJAynqJb/b8vkEWsz3IbSQhHtExoYMc0oklNqosKTbqFcO/+RYWZtbmqrq5mTiDMoT6dWIkEaTOwWqL3CM4U+ziglyNOlYOEmt70eu7DKYhF+JP77ySSJwn0Cj5+yU73sS19s/j26wspzA7TNRhkXV5/XGMLgKahafzFwcvcRcnvLHzFzQbZUNDOptYaN169oSC1C08mxDNrksfJ+GwN3Yk765rEZyee4B9m+P1pJiPFBQNpxzCy3kz7mVwKcjrdfWw/c3aacTQaZdeuXW4DlJbTBfSHAxSFwuw8Vkp3fxZFeUP092tIJ4ts1oxerl57hANNZ1fLnivnYryLgBMAjgHvAtw9FEJsBDZmZ2ezZs0aNm7cyMaNGzPacPXcGbSd6kO3VMJW9dwZDEZjKQuWIj7/Q4n2yWwfsVwfkekmRr+JGfJxyy2jz1P6o1EVIzZVcUx/NMM62glw+9ol/G7LXkxLXYx+Q08p8nlqfyM3LK7iuy+obivxc/x4T89ImxuR6b4cOoK9KeM/HaG6LzrNwA4rlT8zG7A1cs4AZ1QZf//sxB7ofcogZdL8Nk4iupWURoHqPjSvvCjj7SRTs/AoW/fOodnS0B2BpGZbteS6YvUx9jcUOzZU3c1bT6Zv+hDqtwm26ljZEn1QEOpX28wPR+g1/VjxlnXDwifZ0xI3n9pZczgcOe5+WbWz5mR8PNNm9bGryJeYOQIxRyjc9d8lzLfHZyQfOV5B34qoqrKVavw+57EFRZ38sqcMOZCHAN5ddDzltY93zElZlHy8Yw5XOBkl87NNfrj0abb2lLA2v535WRPrx5i8NDWcmYV9o47DEZ3sYKIjUziiM5obMMPJxY8/d0bo7EXykWakJdNSZWdLRpAvDgQCKQ1QWjr+h/ddtxOA9dUt/PiZFSyYeZo1727DZyRy+AH6YpmnyW7atIlNmzYBLBZC1AGbpJSbhj/vXKLoXcAsACFENmq933V5pJSbpJRrFi9eTF1dXcaGG6B8lrrI45Vm5bOKOHS0wymtFWpVWKjybsuA7Fw/VkB1jDZDPmJFAfyaQVXZ6Hc7WxNIQ8PK0pCGhq2NcEZNEivmlvGjv7iX+y6p4d5Lavifj97jGu7q6cVsKCunenoxb69ZhBVQbdysALy9ZlHG73G8yfnonSvjeFOnu0gmkq4WMWQRyxUMzRDEcgWGcw4L1NQ9u1Xi6wOjVyKcdM1Mmt/GaQ/3JnLw46jMO145MLFsE036ktpwaZhOJVzM0ogNZTF7ejcBw0ITNgHDYvb07rTbq5xuUnBIkndIUHBIUjldGaOigSgFezT8px3HIH4ITiXvrL7E57jtWIRYnw8rrBPr87HtWOY3uG1iuuq7Ktz0fhj+W8CWyPi8tWN6tvoinW0f0xMrZ33Sj+ZIKmvCpm9Yp4/V+W34NRsdG59mszo/ETbpkQtYGTrNxvI9rAydpkcuGNd+ZUIoK6pmfrY6dUJZCWfKjrcRFKnjkYiaespz4+NkRqoYbmzPBxKnbXw80uviDVBuWdtAffNMfvzySuqbZ3LL2gYK88MEfFbKYiZAUV7m5/7GjRupq6sD2C+lXDOS4YZz87yfAv5bCJEFXA00MwkhE4Dt+5sT7caccZbP2dW4N6SpDvBCwMqF5byw7bDzmFRKflGbnbubWbb47AULgJuvWsqjLzoVlD6dm69aOuLzJouRKjN3Hmvlg//1W6Kmhd/Q+cFHlP74UwcauWFRFZ++8YqRNjUiRaEsersjxAO0RaGsERsVD505Qa5d4SoemgGJERWunfIPgH9AbSPsU0Z7POXx4WwbhHNTsAEjEYbpOT1BWUx/kSMAhet5W47n7TeC3H5VC++/Ygc7m0pZMaeNPi19bn7A6EazQvj71NpJwOgGoLb6BB0v5ZPdphZEw9Md2YIABE/BussT4S+zpRtZqWGZqtLTbOnO+HAGnVLb4e3Pzvo9guFJx0xTqgxbFQ1ippm4gVb4B1J0riv8qd/Fytwe5V07edZFSTf8Un+DOq0cr7TU3zCu/cqE0105TC8aQNdVE4rTXdmUOTF5/7DO68PHycRMncNtxew4XMrKyjYqpned9ZyR9GZ++3INz9eHuXLpMV7cO5czvUHgFffxkSqNG08W8de/vhnT0jF0i2/c9zgBpTVx1ndZnD/Bcz8NEzbeUspnhRA/AXaisk3+eDIWKwGMoA5CTbWRanzZkkqOPOp8EU7pkuomL5gzsxDD0NywhGYqwaG+NBoI46mgfLOoa2whalrYUhIzLeoaW/j0jVeMy2jHqZxdzOGhbnfRrnK2WuxMblTc0dFBpPMUocI5jrAXRHNs7IDA3w9SA99g4h5oWGLc5fFZYThdpnpzhppsSMrP1gbHF8ONs6s1l9KZpyjIitI95EfEID8UpbfHT2SgkJ5wAWvn7aKm4iQxS+OZo+krSPoLJcvnt1M7v536IyX0F6r9u6S2h4aDp2g7UoTeJ8nqVDc1ywdWtoaen5hqF9td9OyaRqxA4usWFGenTxVMZn6Oye4+1aQZJMKQSFO528kx9tjJ8V2ef1bbxD+8sJBokSTQKfizqxJaMqeGslNCH7u7UrV8Bq0aVuY/x8qQygbZ35OQUo6ShY+elHHmUmVxsoHBYeMEOw+Wcs26IyBsLEuw82ApZY6Wk09PNSvDx8k0thbxmf+5hZip4zMs/uXPf8/aYfV3yWmbcZOVFTR58PWlPPBaDZqwuWFVwg8dTVfmga1LiFlKijhm6TywdQm31Ta4TR3i1bOmBb0DWUx2O4ZzShWUUn4R+OIk7YuLHiUR0BFq3B+OYeu4YlVCgmYrV+CVusP4ey2ksNUCp60KZBqa0utHZ1pB+WYxPKVwTVU5O4+1niV8lQnTA9lIDZWNIQXTk7TA43G6J598kk/+QKVHiriIV/cQ0YpcokWgD0l8ST01oyKSUfecZGYG8oke7lXT84jA329hBQV6WI6Y/5oJQz0xmkqnqXMiC7UdARRA/8AQQ0er+EljiPWlbbzRVsp0fSZ3XDn69op8uXzpQw8mms0+fycA331kBRXRLq69bic9fQHyQxF6+gLkTo9Rd6yUV3bPd7vllCw6zd6jxRhDgnAhlMzLXESs3J7B0f19RArBNiRWNmhhVPNnC6QBgQFBbu74LvfOwkL+58bH2dlcwoq17ezIWu0+trggm+UhpXNtSTBlahqqaakbU9y7jo8BfEYFxNpTx+NFn4M09yf6dRqpawQL5nYRX/8VQo3j9A0FyEtq/tw3FHBrsoazp6mEmOmE1Uw1Ht4GbaQZaSAQZvm8NmrntlN/rISAozQ5kq5MXA/l4NHpKds9eHQ6s/N7uGRRM0JYSQVXOo+8sZAPj7MqdSymZJFOT69zh45Pt3sHVVMEobxDJGpK7kz2IwNRrLClDkbDFYC66pLJj81NJsNTCoGzwiiZGvDb1y7ht6/uJuaX+KJqnIymadx888184emDWIO2u54gsgL4z0jMXEBCXzn4hgSxLMngseZxl8d3dPdjxGe1msAKOg2iswVzaiaW0ZM1zdELUIse6p/O321mNtaZGIetEnZ2loKUVOrpF9Q2TOtI6VS+YZpquTWLHv71M79Xi01JuiOWDe+zdP7umcRNrNPOpzqvk4KyCN39ATrts2Oko7Fz9wDBTkHWaZVPb2YJJZAWv6E6M06taHzrMB0dl/JE4XEWLenkiaHpiI5L3cfWlZQQG9CQ0kaisa4kVTcnL7kefdg4gJ2iZhIYoVHBWHQN+CkIpI6nJdm+ebMthK1i8j5hM292Iq30+e2LuPmSegxNYtqC57cv4q5RpOGXV6rFwpgJPsNi+Sg54ckz0s7OTq5Y3ML627e4qYpvHCojFApRVFSUoiuTrIfy7g9/kPaekBunqgj10HIyj//3yytZOq+Dw+1FFORE2H6klBMdeXz4z8b9saVlShrvipJCjpxUFVDCGWeHAk4OsjqNJEKlDCKYUZLPqWZnWmfDtFkh3nffJdx53fLzdASZkxwL/+9ntpwVRsnUeD/dcJiI49lGDDWuWXD2ayPYCE0t/NpCInQDY0CFS2wdhkqdxslS8Ol3vTPjJgxx4rrU8Ruv7RPYPvW/S2rmjmtbcW5dt4JNr2zFNyiJOV63b0D9vW7uHPbWN0GZVIbdhoGW9CmJTx8q5KYaFfQ3LY2nDxXyDuCS2nZ8ho3uTHnjBtzQAWGzsizhfYohjaZoAY2nNXyazdKhzLsE+UOD2F1BTEMSzdPchcu4ERAmYEhyRPqUx+GcOKRRf3IFr5QM0NeeQ+1MDZzObS+dXMSml29l5YyT7OiYycbLF3FV0v1mRqgAGcPNc58RKkg8KLsSBRHCGY+T8OBJkire1TiJk90LKMnbmjKe5WTglBR38dffvpVl89vZc6SEd994dsl7nGXzTvHNDz/mxrwXzx39e0nOHFlX9YmUVMV1Va3cf//9XH/99a7zMlwPZfHcDvJPR8gqijLU6aesuIcFJY189cG7eXxfNSIm3ejAumWt4/7MxmJKGu9Zc6Zh7T2CkCqrZNacaby06wgpqybOgoomBHPmFrPnUCtyyEIYGpevmM+CWZkXuEwVRgqjZMpjO1QOfPzzeWzHAT516wixc01AQLq+lD4IGMLNiMs+ITFDAi0MR0+Mf5Fl1bLZbNlxLCXbJL7w/NwrDXzytjTxjFF4Y/deCkaRAp+3XrK7TlB4IOGx2sH0HuvOIzP52PdvY3VlG9sOl7K/T80I9nQVELM0JJbbcUYTSmXPtDS2bi3lfY7Ikd9vqQwYR7fF78+8AOmW2kZKLj/BUF8hmoDFM06zv6OYgbCfnoEgBVkR6o6X0hEryHibAOsXZzPQ3cuavFbquspYvzgRp3qu6QQxW+Ufm1LjuaYTXJV0X872z0OaKlUVocZxLHxoSfKHFr5xp6mFcoNpxy/tyuXeKxKnzUu7cvkjRw1w5uwYH3z7FnYeKuODb99CaOboC7mG8LNs7klq5p10zET66Hw8c8RsTY2j65pMKUwbSQ9l/bIW1lZvcZ+ztaGMI6dLiOVq2BL0bPir61+isyeLy5Zk3lglU6ak8a5raAEtkQFR19CCiK+cx4VknKGmCRaVT+fpMJgxiRiyeOzRnTz91B7+7Z/eydJRsk2mIsPDKOOJeRf5g7TT534+Rf6RT1q/LYiKpH6Jbi5zIi/b6Ff/G4+2SpwBwySSJ9Cd5hL+/kTGXevJzhSN5ExpPjgAaCTmXYndfnrzEcyght6X2J5ZkN6Q5hdZ7Dlcwp7jKnRQVBnjiSeeYGfrND76w7ezel4r3YNBCrLDdA8GyM+NsP1wKftPJeb5vR1BbFstvthS0NuR+RLeghmnuamiBb/e5KaSXTq3xb2BWlLFST/3+AhyeGm4fNFTvHvBY2jOPjXpJqC2cducFj67+Cl8mlIN3Dd4Q8prTZGDnhQcMUUO8WRCE53kxEKT0fOsRyM3VIsMH3ONc24oVXBt0bxjQOI0jI8BTvctpqTiOe6e10GnBe2n30bqcmuCxrYZVJYcT6T8tc2gOgNl5dYzIWYV97mvaz0ToiLp8htJD2X5/PaUfV4+v53H9lQ7WaYatrR54dA8Nl5dRyh4kQhTBXT9rPFdN6zin370tOqNaII+aKlmw6bJlq1HsGIWMn7NSojFLHbWN19QxhtGTinMhNUzSjhyoAMrC/QhWL1k5DPW5zeIRqNuzNv2obrdxw25kfBay2eNtiw0OkdPnSGWB1FNIGwVytAjaq3CHoikaCRnGpKRkXioLOl/zu/BfgM/EpOEQfcPpfe8DZmFtE2MqMT0C7pPDXDvvfey8E8/R/dQHnuaS7A1lS5oZgmk8xkVJsXSh1rzCEVtYtkC36BkyD9yt/GR0E0fhm6nxNWlTDQ8jkvCXjHncMbbBCiTW9CFdBb+JGUy4RWumHYKEY03VrZYMS01nGDG9qKDq91hxva6BlvLvhfZuwccJUot+95x7RdAV/8hCgw3UYzu/kNMSzq9CouVKYrbxvgYwJALKNWfQwClOpxOk2f+0OtLsc3ZZOkmQ5aBZuTxmdFr9Vw271xMW6efq2qP8UL9XEqLovxpUueykfRQfLqdss8+3eammkae2V9FzFJtALccKWfn8VK+fOezo95wJsrkS11NAnpU4qoDSokelcyfOx09YCDQwNCUgFJYopnQ3NIJMel64xKwbUkoP32l3R8St166lBxLJ7sbciydWy8dOW89ryCIr09l8Pj6QPoElgFSE8QrkuNx6x3HToy4jdFoaGjgeOMebD9IQ2L7IZojiBQKInmCvjNN9Pf3c/z4cdatW0dDQ2b5wpahk9ykN264JWBbghkzctwx4I5Ho6/ZJLvTwt9vk91pQXcW/f39xEwLqalK02i+kuc1Q0p50QzBkJZwKqKahh4WBDtBDwui45hJNJycRszSsFJbmGI747gk7At75ma8TYDTQzFslHG0nXGcI10d6vNycsCPdHWkvDZq685rVZpt1E4ca1jq7IkKjpmCPVFBWI4v/xyguc92900642Sm56hxvHA6PgbQInVqAVcDKYUaj0J5kU7ziUL8hkXziULKizLb15WLp/Pk1mo+s+lmntxazcrFqZkk6SQikitGywt7+O57H2HtvBZX1960dPa1Th/19RNlSnresaipSuENtXgTi5psO9CC5ZzdElzdE4CKsiJajp7BERVUKXMBjZ7BszUM/lCpqS7jO//ffWzf38yqxaPnrc/RQ5w0erF9IC1VJm9loWLgFpCke9ITy3yqF48J+q5xtFjiC5ZZat0CCbrjasU1kjOWmzWAmBjR8xYCBk2NWBD0mMrJHjTTby9XxuhPCsNkxVRanM+2COeD7Vc/ri/v/Ir4TWzbRtM0YoU2A0ENI6yMvT8r8wyMzQfnsqO5iHWlbYT0CIvKTnOgtZjBaIDugQAF2arh8bFI5p2UAPb0V3BG6yJfgx4b2vor3F6Tp2IaA1HcxwZjGskNZnrMFo5Ehfv4NFoocB7r7r+fHgk9VmKcn/vOce1bUfZ09kYPEtKgz1bjZIJaIdJyZiLOOE7TcZPqMh2JjWlqNB03WTFKY5r87AG+/tEX3TTQ53dltsaybH6ET7zjVZ6vn8/VtUdYNj91vWkkPZStR8pYV9nq3ny3HiljX9NMTnbnclnlcbYdm4UtbQzdoqas46zXnytT0nivWF7BzpPOarRPjfsHw0g7cfkuqS5l8MwQV1xWzZXrqqjbepRYzMJCIgMahk9n5dLZo7zDHyaZ5K03dnYqvW5UyMToU4uWtg+EJbH8KtwhNSgaR9f4eExwTiRILJ6yAInEXSRabmIBTUqZsdzs9Jl5nGzvVTnQjuqhsNVNOuBTjaftINhZqYvZo1FeXcCBk71uPvsAKoQQ0Z2UyWFG211nicTc/Y1oAcwsGytL+VcRLXMJAWEVsfdINvsPliAkifUHB1sHMweuXzRKPtwozCjYSFf0E/RqFpatM6MgIUlRkHM3nfa/0mepwyrKuTvltflZN3A88gP6LDUdn5+ViInn+2aghZU3rznj8VJeWE6oH3olzDEgP3fYYryvCmHWpYzd4yqK8bf/dhPLFpxkz6GZ/PFtR0d9nxXVnfh0G91JA11RnVnGzp4jAf79wcswTZ0dR0qprGinNkmKZyQ9lK88eAUbFrZx7eIjPLt/Pq8fLOXdq3fw+PbVxCwNw7C5Zd1BbljWyInWgoz2YzxMybBJdl5Q6XEIgdAE2XlBGo6dcsXztYjkcH0b7Se6efB32wD4iw9fy4LFpcgs3bneZNr3+EPk/tfq+dCmB7j/tdFTqc6Yqv0XQhUyWdlgaxLbUP0hs85IfIOSrDOS3N7MT494TFDoOrnNkuBpSVa7TBg/CZadKrgfl5sdi8iQKtCyAwLpNFm2feq3ZgjsASVa5uuz0aISeyCWdnutUT99ZRpd1Tp9ZRo9/gIAojLphkNiv/3dktxmCa1D7v7m5SnVubjRjY8zwZZ+VZAjU28ywlLrOVZA4u+VXDV/3ihbGJm1s68nz/8tTvXdR57/W6ydnchLX1bxIYq0T2NEaijSPs2yig+lvLag4DPUFHyQuYE51BR8kIKCz7iP5Yc+SI3fYK4BNX6D/NAHx7VfACLrLvJ1P7MNQb7uR2Tdddbj4Ed9+KmPt/Zfzu62mfzf0yvY3TaT1v7LR32fktJ1CCGdjjiSktJ1Ge3f43XTiZk6EkHM1Hm8LnVmMJJERG3JUR7cvoSP/+w2Hty+hNqSo9z/XDUVuV2snN3GshntnGgu4Dv3X8L+5lEaaZ4DU9LzXr2wHF1X5e66prF6YTmD3UNs3XZE1WrYEmkruxCLWTzx7F5+/8o+IlHTdfhM0+bx5/eybOH5q6B8K7n/tXq+9NCzALx8pAlI9NZMJi83SOfgYFLcWLieeNSvpAUCzrqMT2Qe24zHBAU6Rhh8YSeJxZaYWWrB73T32elSmcjNao7Kj7QdRz6m8v+R4PNrlAZzGGxOlF6XFqaPebcN9DFYrrYZLRQYpwsAMGIaFiSMdo/E3xs/FokdEbz22ms88cQT9G57idiKWleVsnfbSzzxRHlGmTSDQ1Hns1Kfv0Q6FcMghcTfD0jBgaaTabczEj65lK6ufBYUnJ1mqgz2h85+kUNBwWdSjHYc4V9JfvHPyI++Af71CH8GK4AjbIOin8Io20j3+Cu75xKVx8AvsKTGK7vncvddjIjQQhxtK6J3IEBeToSqhZlVqWoYBHwWMVPDZ9how0zjSHootXP7eOqAdHWCauf2Ud9YzqmWaUo0Lyte85D09yQyJY334ebT2AMWvpjE8kkON58m3BXB33e2N23bks6+AaIx0415xznTM/liMFOVB7Y6baucqf4DW/eMaLyvW1nFr17f7cyBheqTnpQqGMkHK6jWGvrGIQUbjwkOdbaSlV+ZELrqVz8AuZGzvdNM5GbnTCvgVN8AVkBlriBU5aYehpKiEDKqBLbMXIHRL93QymgIvwZJzZptQ+2tP+ZHb1Y3G2MwcQNygiac6TtMOBzm3nvvpb+/nxmtt5C3oJa+Q/Ucfun33PvYAxll0shsDbok0vmyrIB6P9tI3ffDHeMr0tl5vJX3/zBRofvD99/NiorJcV6EfyVMwGiPZxujPX7VhgW8sbvJDYldtWH0bJM9xxbxl/90J2ZUw/DbfPNLi6jJoCHVuqqTXFezhV1HS6md10bfUKrHPpIeyg83r+EdK/bxtmVHeW7PPH64eQ1vX/46v9x5FdaQniSuJ4naF0mq4K8f20awS62O+JzxmjllJF+SUom5oelq+i8lrpCV1NSPdhGFToIpWbpqPBJ3rFjC/Vv3YGsSTQgunVvBK41N7l1P+gQxJ4l3SGReeBKPCcb6OhmorsQ3qBT5jGgidBzKOzttMxO5WWlAeDogBLH4ihYQk2BmCZrPdNFTpbse0NGu7vQbjEh15scFs2Jqg7olMEzVFzNutEX8DwmBmaUA9Pf3k10yh5lLr0LoOllLr6L/0F7625vcbuNbtmwZ1YDftW4pP3ipDj0qsfwqDKjHBO6BOTeVps4xjmMYW46mCp1tOdoyacb7fHLHDcs50dHNC1sauWpdFXfcsHzU5z75UhRrQC1GWzGNJ1+KZmS8y8sWU5r7e5bO7cC0NNr6F6c8PpIeyjWLj/K3d78MwPqFLYjfSkKyiR9/5Lccap/GzsZStzz+2lWTb7ynZMxbi8Z9ncT4puuW4fPpCAGGoWH4dTSfhs9vUDAtF1tXxtvWAaHSC4tC6afPf0jkZwddHWc0ZzwCDz2yk6wWi0CnTVaLRVksSE15CbquEYingDkLfkYw83t7PCaYvXipMrI5GjHH0Y7fVCLRvlFfl47O8BDxPpKJMm0BAtp6eolIZ5VNCNBQ4zT4BqUbl89tlhgDKlNkMNbtGmuh1ruUlrdzACEjEQctWLIGYRgIoSEMg4Ila9SxJmXS2PbIGShX11Zi56iuRsHTkkC8h0Z8odW5qQyModEynHXzVIWuLgQ+Q2fdvMwrdKcCew608tPfvsGeA6ml5LsbWvnlUztoPt3NL5/awe6G0UvNhZV6qmTqfwyZLbS057GnYSYt7XkMmS1nPSeuh1JRUUEoFOKapUd5+LVFfOr7t/Dwa4u4ZulRDrTmUTWzi5uXN/J3d7/Eh27cwvc+/DCrqzJf0M6UKel5X766kl8eOu1e9JevrmTp4ln8xz//ETvrm1lROxspYOfuZlbUzObFrY04MrrqC7NVYc9Nb3tzNbqnEkVFOXAE180tKhr5xvXarqMYEfBFlFF9qqGRrjwLDLCsuPutfi+emXluajwmiKO7LgDbLxgqkhhRMP0QG9Y9PlO52aJgENUQ3omLCdy4dGBAEh6239JKP+MqEAE6h0zXwzYjKuRzZvAQWdOnY0SUpK27bun8jsUS2QaiIKQKjzSpOjsVJGKrY2XSPLRjH5YGuadkSiVxfP8tHWIhqC0bn8TDiooyfvj+u9lytIV188ovKK97z4FWPvH5X2KaNoah8a2v/hHLFqn9f/zFvcRi6guJxSwef3HvqFlVN127lCee2q10+g2dm67NzAY8VZfHh6/rdVMMv/dMHsvOjjqm6KH85sVHeO3gXAC2NJRzycJj3HaVhS2fddu+6ZoK7e451MeimrO3dy5MSeMdygqo/oEShKbGAEsXz3IrJh96ehfbDrQQKsii8UgHrjqHlJSXFvL5v7yZZYsurOrKc+H2NUt4YOc+YpbTsX7NyPqTlyyfxxOv7E/EpIsDEFWLfXZAYAyowiefKfjTG4aLaY5OPCYoYzGELwBORNf2C2J+FffzJ2nCjqaRPBL94Sj+bonUlSclNeHWAGQH/cigQezUIHZAoEUkeXnptWdLFhTQuWXATRXsk0rONTDkR7cSRfjxvyyfyuVufekpdxt+shC2xDcAZpYaJ5OucbMxCMaAVA6HKmtUJ7st0UyV9hjsg0/dPH5d9xUVZReU0Y7zxOa9mFFL3UyjFk9s3usab2E506F4MlCam/PSxbP4+Eeu5cVXGrjysuqMK6z9uWv42KZ2VlW2sv1wGatq14z63Lgeyk+fetX5jzpTOnqn0dplsfNwKXuOzaQgJ0z3QJCVlW2Ew5MfBZiSxntFzWy0oE4UGz8aK2pUvvaeAyfYubuZ3nCEnz+4FQls3XmMtcvnOF+s+lKv2lB1URluUBftjz9wz5he15f+4haOneniwPFTLKqYzs031fClh591H5chjZgtQdex/CNuYkTiMcG3f/OnqsGxBsKUjkiUMuSxaLxP4Nkayek42d5LdpJcdjQkEVJg+STT5+YjT/bRMyTR+sHWJdn56U/raEjQVa0WUqO5EHNErwqKqlSTj7gjDyBgaAZYAZESZMzyFZDdIbENgb9PYvoKznqf0TJplhXPINQkMYdFtoRUSpn+IfAZ469ivKCRiRtm8hhU16vfP7uHmLTxCS1t16s9+0/wre8/i2la7NzTzPy500ftppXMQH+EvUdnsq9xJlKHhfPHjlFvWF7G4bY+t7Jow/IyXqxv5rsPvX1Y/23J+sqDFE9A1ycdUzLmLQ1BLFfHCmrEcnWkIdhz4ASf/Pyv+e+fvcwvHqpLzJ6B1o4eNKfPoG4LQqPEe//QWVFRxsar1qX1vH7zcj27WzqIapLdLR3oEbizehFFeoAlxdPj0Qgs22br0bPjfumorq7mssWLGZoBsVwYKhGYflvpefttBvbVEwqFqKioSLugN5yAcx3FL2x/H/j6JcEuKDKCDPVE8IVBj0l8YRjqSX/hyS4LqQksn1CFPkPK2FtD/egxdR7ZOkRzYLAYhK2hmVC0OJGBYBAgGtKIZQuiIQ2Ds2Oao2XS/H7Lfswc4UgckzBcSaEa27bZfmB8n/+FzE1vW+quafl8qSHPZYtm8Z//8E4+9M7L+c9/eGdax+yJzSrEIqWTRrx5b0bv331qACMs0WMSIyzpPjV2ptqe4/lYusDWBJYu2HLQT3O77nydwnFZBFIKtjVO495772X+/PkZy0KMxYQ9byHEV4B7UTeAL0spfzYpewRs39eMZduORonN9n3N6FFJxLawdOlUpSXCn1etX8Dv2re7UqpxT93jbH7x7HaVV+ykMH3/kdfoPjmA5YejrWH0Uh0B+HSdtRNY8CoJFWAcV2prxgBgaURCqkvMnMIQPximkZwJ2UE/PX2RlGya+Pe/q/4Y3bEhfPjc/3UP9Y2oXhjvgNKwYx+5WUXulvqH1JZjPV3YeeXYhgr3SIFKTXSEschOlNzJvCzH4RJIIZF5Z+vojJZJc/h0UrNop+eqZoJPE9i2U/YjBKsWXVgLjufCskWz+ObX3umuYw030MsWzcpoNi2NYTIKSUJraUmkZLszrrFobe9RzV909T3u2X8E04yRGwq56YRORQJdfRb9/f0ZZSNlyoSMtxDibcCfAguAZcCLQojfSSkn1h58GKuWzMZn6O6iw6olszly7BQxv5tqgB5T5Q06givXVnHl2qpRv3iPBL0DERUWECqLoqdvSDUhjoAZgNlmDrNnF3F9bRUrJxA7rWt0xKzihklXi39Sh6rrbx2zFH4kIqUGAwKMiMDWJFnduPHqDqJEtUHyRb77v0Ft8Cz1wuQOKJV3fQ49Kbc9u1Adp1ZUhJmduGrjN7j4bz2YMNDSJyCKO2WWvrOv9tEyaeaUF9F14ETC4zYBCTHbyfwWYEubQydPU1t14cWvJ0qmBjodN12jGotbERM9YHDTNZktWObPiFfMpo7TkRP0Qx/u92gODtHbdZRA4XQl34C6xqQm6Grdr7Y/Xl2fNEz0ldnAt6WUEaDT2U7KtoQQG4UQdfv372fNmjVs2jRi9/oRqVlQxrc/dy8b77mMb3/uXmoWlNE9EFYTERuUNKVEi4GMSZ7cvJdli2bxJ/du8Az3GGjmMCm7mE3AURkM9EH7yR627mjiX3+0md2Hxt/9Y5oRRJigh6WbARRPgWtozbzbTDLtA/3YfkE0T2DmCAamQyRf/TZzwRzsY7AYInkqzGEO9qWoFz755JOsW7eO5uZm+vv7ETEr5TOIj/0zZqRIBySqUJ3Uva6EyqLZfpJAr40xKAn02pjtqdWQ6TJpbluzGC2qjLYWVZWV8YpRSDh9z+44NKHP6w+Rh5/cxae+/BsefnJX2ufVLCjj21+8jw++50q+/cX7RuwmNRKLZk9XDc0t1dh80eyxM606+wYdyQ4BNvhy8hA+f0K+wadmcOig+5Nu/EnZSCOxadMm1qxZA7BYCFEnhNg40vPG9LyFEAacVfHxhJTyUSFEBfBr4KdSyv7kJ0gpNwGb1qxZI+vqRpdwHJWk4giAgpygMtY4dwkrqSjn4qnFOWfysoN0dQyqb9QCzek6E58uaiaEQ5IoNo++vi/jkz9OvubDP6C+G11CLAekoQLpheMQb0rGisZvOOqXbQiihlpAjPX2YhhZSrY1RyAsiWGoC0VKSU9PD7feeiu2bbtTWSsyiJadhe1TrTGtSDzbRoNIwmjHawcEAqlJ7FCiBYHeG0ULqTi7BPS+qPvYWJk0B491IGynHJ7UEKB7mAKuXTm1e7C+VTz85C7+5XvK0G3deQyA228cvVCnZkHZuM/b/s4hdEDaqsFLf+fQmK+5pGYej7+x3zVA3S37yZk9Vz0Yn3k6j/n9qZ58umykjRs3snHjRoQQ+6WUo6a9ZOJ5fxEID/v5ohDiOmAX8CrwkQy2kzG7G1r5+NfuZ9P9r/Dxr93P7oZWevvCbndptedqsUkaguqqmZP59n/Q/PENq9FsZbQ0G9YsrSCaAwMz1AKdGcD1lMeTbRJnSDi6zI4qeKBX4u9TQlerZ00sBFBo+5WH6niqevwnDMRsdD1AXLAMiRonYVlWilCZle9ncIYgGhIMzhBY+epALRnF8iutEssP0q+8J+mIYAVzprnbiOlK/EoOG2eSSdMxNJDy2pQYrYCcXD/vefsa7r5shETji5DnXz+UdjwZrKiZjd9noGki43WzL33sFm5evxhraIDTDXUc2fJLuo45onDx883xykeKoWei65OOMT1vKeUXgC8k/08IsQR4A/hTKeUD57QHI7B9XzPRmOmuGG/f18zqpbPRNSVWpTldtiUCTQh6+i4e3e5z5R1X1nK8u4dndh/iupoFzJqZz+aOZpBK19uIgL9fIrI0bls/cq54OlYvms3u5vZE6XnERo9qaBGbnu7xLYnEFxgH2trRCgtcJX8pUmcKdo6BEQZVxSExs9Kf1iKUg29APV+PSGynElcODiFD2aAPM6jOqLdxj/s/M1sjXCgcmVow0UbsNj4S03NysHWV1SIkbgMCnOPqs2L8/MVdXL28iuXzLp6Y92hcvWGB63HHx+l46OldPL/lEFevW8Ad14/uoSezbNEs/v2r94173eye65bzw29+g+4zqutR67bfEwjkUTR/Jfh00JzuQ8Gzaw8y0fVJx0SzTd7hvPZrQoivOf+7Vko5KS2SC3KCSBtAYks1PnKsAytiOW2UJIZfx5YSn6GzcpmXXZIpO4+38uO6HcQsix/X7WBhyXR8fTbGIJjZYPs1pC7xD0j06NjbixM3tL/62W8RhYvUzEhKrGwn4palU1mVuQ508gLjrKvfT64vX2US2BIrW622Cgn26S40YUBeViJTYHCMHTctMPTE800V87b7wxBKGGtbV7rmekzJ5Q62JrogG/kFDJYm3KncQCnfzTCT5rb1S3gkXjVoJ24UuiaIBNR7mpZF3aEWz3iTCJE8//ohrt6wIG3I5KGnd/H/fvA0wpa8vusYQMYGHCGQmkjIFIzBngMn+Mjf/5KyVTczK2az9/nvAlBasAwrKjB9uOETYwRTm4muTzomZLyllF8FvnpO75yGvu4hjIiNrYFmS/q6h9ixpzmhOwFUz5nBFZcsYOWy2ReN7Otk8OCOfUQtZayilkV/7xD+buU9+rshmm8TyRdEpOSRLfvGbO4AqYY2v/pKSvMXgS1VzBjURSElxzszW7BsaGhIEQDqPdlIXmGF24RatyS2T6DFJJYdxGztQITykXGD3pq+a8nQkcPkVC92jfTQEeU1+SwN34BS+ROWSsM2BiVGWHn7MxZfQdeeN1I35lycZSUlGWfS1FaV8f3P3Mun//MhunqG4r0qiEmlKqgJMHSdNQsunlTBsbj9xuVpjXacR5/brdooAiB59LndGRnvPQdb+fjf/wozZmH4dJVPPoZd+e4vXsIUqNaMPo3ZtbfQe+KgkogYplPT19p41usz0fVJx5Qs0snLC6rcVwuw1fiqS1OnSrddv4z33L3eM9zjZLhPMdAfwcoS2H6BlSUwBsDfq2LKVga1TnFDG8/kKJq5RIUCpIqpAyBVCtyDv39izAKFeDu1uOEGyA1Mxxiw0SISLWyjWWCEJZoFAT0bKzKIv8fGNyDx99juAuRoZBv5+Polminx9UuyjXwAomdO4RuErC4I9EKwS+IfUOehbkIgkliw1Lri0oMqlFMgx7dAcLjlNIU5WQknT6jWa5oQrF84h00fv9vzupPYc+AE/3f/6+w5kL6v6vR8FQITw8Zj8cTzTnEPTnHP82MX9+w71p7yXtnFs4nYA8TyDeyACqlpEYmv10IOa6OWqa5POqZkeXzDkQ6Se5w2HOngrz+i2jK98Oohrrp0AbffuOL87NwFzpIZM1ICuvlGgM7gkKsL4o9BoFtNb5bOSB/mGMnQugjVTk2LKm9S2BI5EBszvzXeTi15e9nBaUooylK+srBttU1L0ta0HS07C90Ew+nFEQv3j7htl4Af/wCIAfX8aEAZ3q6TB5hWtkwVVtjSVaSLh1fMnq7E4cUsstqUl65HIDAn83L23z1fzz/9+Bl3276ATkSz0fwCQ9f58C0bPMOdRLy6Ol6E9+9fvW/UmPS77lzHS3VHsLHR0HjXnZl10uns7k87Hgn7rFNeYGTnqnNUCISUCE0gAzpaIJjyvEx1fdIxJT3vzngTBcctiY9vv3EF//rlez3DfQ4cPNaBMaAMjjEAfXYMMySw/WCGBGa2Mig6gr6u9OlSIxna9oMvqj/iOdQSNMsJZ/T0ps1vhUQ7tWTCQnnS8Xex/GrDsaCk8+Q+rOgQEK+sE854dGL93aqwRj1djQEKcxmYqdIbdROEWyWnGBxIhGN6Th1G+gSaLZA+waplFWnfM5mHXtyTkmkSNi1MA65cNt/zuEdg5+5mYqaFbSud8p27m0d97ot1h7EESCGwhBpnhLPG5v5k0E/6betUNCD+Xfa17Sc7Z4aTs5+osJQCsovUzWa8uj7pmJLGu6jAmeo4H0B8vOego/d7cFLWRS9OhBMGcGRPYz7b/T+Ala0RKRCQo7FqcfqF4JEM7akjW2jb/RxDvac43VCH3hNDH7TRu2OcPrp9zL6VI6VPDQx1MDRNYAYFQ9MEQyU6A2U6kRkGhQtXk11cruLrmsrNzi5OjRXruq56ojoM9XYQyYVoriCSq8YAg8cbwbQwBuO3AfWaWA70zhFY+WqBSQhBMC9PORfOT18088yB6c75PDxVMBw1PcM9AitqVMV1Jml8z29RaYTx7y4+HgsphNsjF9sZj8GXPnYrS6pK0H0aS6pKeOiHn6doRiUiBiKG061KqWH2d5+YkK5POqak8b7p6qX4NR1NCvyazk1XL2XPwVb+8ou/5ge/eJm//OKvPQM+Qd6+bgl+J9PCb+jctWZZImgnQAYF4UJB/0wxZsx7JEObPXMOheuvhrnTyV25CgyhClIkxM1UuvzWcPjstM+ug9vAsjCDYMfPWOfikoags6le7bvTjKKzqd55iiA/P5/HHnuMvLw814CfOVIHqKwOkM5YpUmGWgVGJMnjFjA0XcPMFmTPqXIvwLvuvTuljkyO40p6zy1r0bWEcZBO0sF1K6pGecXFTTyN7wPvvjxtyATg6mHecHw8FoXFqZ1D3HEaHnyunj1NJ4kJyZ6mk+xrCXPLdZegWY6DFI0bccmcebO4//77OXLkyKQYbpiiMe9li2bxra++k517mlmxTOVc/vS3b6RMnXbsafYWKyfA8vll/OAT91B3qIU1C8pZPr+M8uJ8ntrbSDRmUnfshAolWDYP1+1Lq1A4kqGdvuoaYtN0QECOIIyG1MEIa5QtvJqDr/8kbX5rvJ1aMsJWKoJCkxjSJixtNKFjGhZdB5XhDeda6JaOpVsM9reflXO9ZcsWbrjhBjo7O/EXlaDHVNWksAXBohK0oU4qN9ylOuPgxM5zIFyo4itaFII23O+kA+4+3s4LDScwoxaGX+ft48iJlzrYORp21Eb4BEvnzuSuS5Z6RTlpyFT35KN/ciWAm+cdH4/FzVct4dEX92BFLPSAzs1Xjf19/vLJbYmbtlDjWzYsSamUVWs1gr/+xMe58W2T+/1OSeMNZ39ZK5epqVN80cLL7Z44y+eXsXx+wigvLC6mryzMnuNOd3fH+9Bi6bczkqE18vPUH44FjBSqypNoHmhlagE0XX7rhg0b2Lx5c8r/8qZXIjQdIdSVEuyTIGx8UqJFIWt+FdKvYWka0pbUXHkDX/6zu1NyrpM7oPz9TzcjJWiWKvaqXH01//AfX+afnzlM5OggUqoGDJGQQI848gESVi5d6qYDLp9bxn9//B62Nrawtqqc5XMzdyTqDrVgContV51Wrqmt9Az3JPLRP7kyY6Mdp2ZBGZ9+7zVs3nKIa9YtyKi8vq8/gspdVumiff0RVi6dTcBvuA3RQWUQ9faMXW4/Xqas8R7OsoVlfPPL97FjT7OX2z2J7DrSyge++RtMy0JHI1tomLqN3x69G0+ckQxt1+43mDF/DikqS04ic1gfdF83GvF2av39idV+3QhgG8LppCPQ1IoUAkHx3NX0ndmP39QREYnIMvj+//vSiMY03gGlP7uUf/7vZxwVQsFnP/Qubryilu+++kNOzxpSMrAIlaoqccItSpI4meVzy8ZltOOsWVCuVDMty8vnniLsOXCCb3//OWKmxd4dzVSWF4/p6YeyA5zqG0gZ11SX8a0v3MvjL+zlsef3Ytm2cjaXTr6zecEYb1AG3DPa50794VbqGlpYU13OI1tU6zQAE5trls6nZk4pa6rLqa1M/1mPZGg7975B3rwacioqMQcHEGWF4LSwipw6OWZ+a7yd2sDAgJvFMm3BamKhxPxUH7DVOqEFgUCIrMK5BE45q029kmOHTqU1qlWzizFzBVrYxg5qVM1WvSJN03Y9bfVOqcpn6+ZPjpFdPq+MTR+/OxG68hYpzzs79wzLaNnTPKbx/qMbV/GPP3mWuBbxH924ClBhseLyfD75gbfR2z3EyqWzMyp2Gy8XlPH2OHfqD7ey8Ru/cb2+S1bMSXl8WkEO7785s9zYkQxt6dpbKC5ahNYNtlFApMtGBgQiYtOzfxvFY+S3xtupJVdYasEst1ITKbFyEobcn53L8rWX09DRjfQJREzy6LPpq+r+94mt+M9Y2IbAGLD43ye28m8fvoOg35fwtGV8KRL0IYkRkRSI1HDP7kOtbN/XzKols8etYrd8XplntKcQK4aFZVdkEJa987rltLZ3u/H1O69bTv3hVj78b791t/O9T91NzRhO0ESZktkm4KUFvlk8+prytCUQsyw0i5Tsk8XlM/ifJ7ew68jYn3vc0CZnchTPWYVvUK20+wbBN6TU/2wdsvNzMspvjS8wVlRUEAqF6GtzNEVSGwMCML+2loEAxEIaZlAQC2mckemFyppPdBHN1zBzBNF8jeYTqvjmXW9bmWS4ldH2d9n4+5V2fLeZWGjdfaiVj/3j/Xz/N6/wsX+8f0La5x5Th2WLZvHvX3EyWr6SPqMlzp6Drfz64W20nujm1w9vY89BNaONmia2VB58XcOb18puSnre8bTA+N3rm1++zwuXvElMy8txs08KcoJ8/bcvuJ/7po/fnbKwORLDMzns/n7ILXAzNoyIKmTxofHeT3854zSp5AXGr377UWJdFtKvFiTNkOYa2JnT8mnsTm0r1iPTr7TGpJ3y/Pj47ivUouHPHquj/Winqx9v+5SK4P72RBfk7fsS02zTVMqX4/W+Pd4c4o3KJ9RVS479lDg/f3ALpqXOHdOy+fmDW1izYT4iItFNiRTwk1+/RtPR03z5o7eMbz8yYEp63juGxZ927Bm9ospjfNx2SWqe922XLGH5/DL+/MZ1dA+E1ecupatqlwlxQ3v//fdTpKmeXsm1iXFVvmlZ+ePa1/gCY9mGteiWwBiU+CKAZYMtkbZN/1CEoOWcxo5Bd8ejEA2bKc93x8DCGcVUFxUhYkla3dkatk9w7apEHna8VZ+uCbdVn8f5J7lR+Sc//+sxtVCSX/eJL/yK7/3qZT7xhV9l9LrjJzpTKq2On+jkUGMH/j4bY0jiG5TEemM88+w+vvjd35/DUY3MlDTe8bTAeEWVlxY4edRWlvH9T9/DR++6jO9/+p6URcl4FoSuiXFnQcQN7Xf/9e/wObnSmhDoMZVyaERgcVnmkrDJtMUG6J2jGif0zhHE8lTmiRkSHB/sJSot/D02hiNMFZVW2u3dtH4RWkwteGoxNYbEjO/l15UCnBkQRPI1imeG+Nx7rnU9c1CpZffcvJKS0gLuuXml53VPEcZTSp/ME8/vZciwsXwwZNgZCVNVlDkNqWVi3NWpFu+T6t4AeH3H0XEcRWZMybCJlxb45lJbWTZiJsny+cOyIMYImYyE1CCaJ4j4JP4+0CIJYaeGoyfHevmI5OPnVPYQZraSaM1rUtkrCLArJSE9SHvZkKNJLigLpC8N/cS9V3KqZ4BXdx/l0pp5fOJelRO8Y49qAuKig/QJrqyZl2K4AR54vp6fPFEHEn7yRB2zZhTwjqu9XO3zzYqa2ei6hpQWuq5l1BEHVGs9txuGjI/T86471/H8jkZsp8n2u+5cx+9fVkZ/uPTBhpXzxn0sYzElPW9QBtyTfH3riYdQJmK4AR7Zso+e6ZJwoSCcWnHMrqa2CW0zLy/LbfRqDOBquiMhZ0jHQCPrlBpnnQJjjNN615FWntnVSE80yjO7Gt3F2dzCoCtvAWD71Mzvtg1n57tvrjvk3kCQzthjCuA0j3aaSI/Yf2wEbr5yKT6fCif6fDo3Xzl21/nndzZiBgW2T+nu/OiBp3js/v/CEiZSqNChFCA0uHuSqyvhHI23EKJcCNEnhPjzydohjwsbMwtXrMnMFUSzVV/MaDacMSfWru6uDctUT0zpdNEhYWBvv3wZ+flZIMDfDwjUOA3bGlpSYvvbnIyAzliESJFGLFcQLdJYu2Ium4aFluJcs2ZBijLhNWu8ZsFTgR17m7GkWiy0pGTH3szCJjXVZbzzxlWUTy/gnTeuyigv+8nXDqg/nMyn57YfYXvddlQbV+XFCyGwbZt3/dlfjqllP17O1fP+TzIST/SYyjzwYj1/8R8P8MCL9ee8rdvXJhZERVAjPF0QniYITxfcsnrxhLZ5z6W1fHjVahbZBdy5cBFCdy4MXXD1iioqZxYhdbACIHVB5cyitNtbXZ0a219drWL7BdlBJSUa1LAFZBs6L2xr5OP/8lt+tzn1s6mcUww5OrYPyNHV2OO8s3LpsPWyDCsbH3qmnp8/uJUTrd38/MGtPPTM2NdC2Yx8tKhEH7DRopKh3tPkT69U+jiOQbdtG9u2OH5oO+vWrZtUAz7hmLcQ4naU47F90vbG4y3ngRfr+dr/PQvA6/uaANWkeKKsqCjjxx+4hy1HW1g3r5wXtxzmmT2HuG7ZAj5xzxUT2uaeg608+LsdxEyLzUe6VTNuTWADP3toK2s3zMN+2en+45dUV85Mu73l88t492XLeWHLIa5at8ANEfX0hxFSIh0VxBe2HXZd/Phnc9c16rOpO9SCJSS2XyA06fWbnCIsW1TGf3z5PnbsbWbl0tksW5TZd7L5jYazxndcl/46KAvlsCee+m+B2d1Jz6nD2LblhPVsTh7byqmmbfR3NiGEGLMZyXgYcwtCCEMIERj2kwv8M/BXaV63UQhRt3//ftasWcOmTZvOeWc9Jp9ntzemHU8EPay68ehh+MQ9V/Dwl94/YcMNKqc6bJnENIkpUhsknOru59X6owS7JL4BSbBL8mp9+pX9B5/dxS9/vYXWY1388tdbePDZXYDyvIWl+qYmCoKkGzZ9LimufS6ZOR5vLssWOetlGRpugGvWV6cdj8Qrb8S1wxWF0xfS39nE0Z0P0dt+iCM7HuTojgfo71Q3finlmM1IADZt2sSaNWsAFgsh6oQQG0d6Xiae9xeBzw/73zeA/5VSHhejiJZLKTcBm9asWSPr6uoyeBuP88G1q6pcrzI+Phd2N7Ty8X+83y30+av3XENP/xCrFk9c3+Fw2xnM5BaRpvKMpabSSrftU3HNeFZLZ3v6FlbPv3Eoea2R5984xJ3XLqfhmNMpRwgnU10mGskKWDBvuruN5fPK+OxdV/HctkO8bfUCz+u+wIl72ZvfaOCa9dVjet0A/UO9CHJcR8K0o4SK5jC/5nY0TSe/eD65+WWcat5On2PA481I0jWr3rhxIxs3bkQIsV9KuWa0541pvKWUXwC+kPw/IcSDwB1CiD8FKoBFQojDUsrnx9qex9QiHiJ5dnsj166qOqeQCcD2/Ul5tjGLb/z4WaSU+Ayd//zcvRMy4NsPOsVC8dJ1nwqZCAE5oSC3X7OMf9nXlljEvGZZ2u1VVc5ga90x9/lVlSr//HTPQFKBnXC78wgAQ5CTl0hBrG9s5Rs/2YxlWmzf28KC0mJqqzwDfiFzx3W1GRntOG0t2ymdfYWbdXTq5C7yiuejaTpCUydPydwNzKhYw95XNrkGPF0zkvEwoZi3lPLO+N9CiM3A/3mG+8LlHVfWukY7WXFwLFXBkVi1OCHwE19plxJipsX2/c0TMt75OUHa4w1hhVDBPl1gCI1Vi8uRGphFOkRsCGjMS/KQR+JIVzfhQuHEyNUYoLA4Byugmj9ITXXpUe8JfsNICY089so+rKjSiLGiFo+9ss8z3hc4ew60jitWHo4NEM0TaKbANiSmHWXw9BGktN3WSkLT0DDIK57vGu90zUjGwzkX6Ugpr5mMHfE4/4ykiDZeA15TXcZ/fu5etu9vJj83i//46WZ3e2P1xByNpYvK2HfqtGtUNSHAklg+NX7stX2YGsgsVdn52Gv70uapnznTj5UlsLISY1DSAQ9t3UfUVBWamhAYusbt65fw9vVLUkIjIkmuPHnscWGy50Arf5Wkp/QfX75vTAOem1eKNASWASDIyp3BgGjE8gs0iWpOjQRNEIsOuq9L14xkPEzJCkuP80PdsPznuoaWCXnfNdVlroddObuY7fubzynmfcvlS3hoyz5ipoUugbCylLZp8dir+1wLmlk5Btxx2TL2/agN2wDNhDvuUWGW5fPKUkS6ugfCo+ptL5w7I+3Y48Jix95hekp7m8c03kVlC7GjEqkLhCXJn1FJtOskQtdA07AsiSYFNjZGdo77unTNSMaDZ7w9XNZUD+vwUn3uGRTJhnyiLJ9fxvc/dQ/bGlrYd6iNF+sOu48JC269fAkPv7IP07QwRqmITCZexr657hDXrFmQUtaeqc52T3/YDcFrmhp7XLjE88PdNosZ5IdXlE2j6dQgwum4FO4/Q+/Jw9hLLTQEaAJbqjzv3pPqnB2rGcl4EFK+ufM9L9vkwuJcY95vFrZt89RTT/FP/7GJgeL1CE1H2hY5p9/g7/5qIyVVNWw/dOIt2+/6xlb+4uu/cW8Y3/nsPV7M+wJnvDHvXQ0tfOArvwChIW2LA09+l4HTTUyvXE/h3FoGT5/AjoXpPXmY/jMqz7uioiLjPG8hxLZ02Sae8faY8jQ0NHD99dfT2dlJf38/OcVzyCuppLf9MAOnm8jNzWXatGlup/i3ivrGVrYfaGHVonLPcF+kPPbcG3zsb/6B00376D91jJzpc1h4w4fRNB3btjj41PcYOKUMd15eHlu2bMn4HB3LeHthE49JZzKNWkNDQ0pLNAA0sHXhlpj19/czMDDAunXrMr44JmOGUVtV5hnti5xb37aeBT/7d2644QZEKETuzEo0zXCyTAR5MyvRwp0UFRVNunPhed4ek0p9Yysf+sdfYdkSXRN8/3PvnLCBs22b+fPnc/z4cddw58yYw4JbP+KGTQ499l8MdDQpb7y0imz6adj5atpp6XiyauobW9l2sIXVCz3v2mN0bNvm6aef5ivf+QWRohr3/8ZgO++/41L+/I/vGHdJvOd5e7ylfPs3L2HaEgGYtuTbv3mJTX/7zglt66mnnqKzsxMpJcXV6ymcsxzLjiDiRRBAbmkV2LhTVWnb/M/PH+KDf3LXqNvNJKumvrGVx17dx+9e3I20JLqhselv7vMMuMeIxJuRnLAL+a8HXkFaEs0GO7uUn7zQzPr17ZN+7kxZPW+PC5OW0z1AQnuk4cQpPvrNB/jtS+NXLPz6179OX18fxdXrmXvJveSXVVNUXgOWjbQtpG3R39ZIXkmlW9UmNI3/vT99y6k1w1QFh2fV1De28qF/vp/fPlePjEmwwYra/OTxreM+Bo+LizXV5fj8BpqWkC6OxSy2H5j8RsSe5+0xqdy0YTH/+/hWhFRC9H2RGK/vbeKNvaq6bHhHmnTEy4gL5ywHQKCKHiLdp4gN9NLVtIuBjiawoUzaIAVS2uzf9mLa7dZWlvG9T909Ysz7d8/X898PvY4ZtVJaWUmgubUr4333uDiJn1s/eXwrL9YdVg2spSQ/lL6700TwjLfHpBJXD9y84xAx26b1VK8SkRJKP2U8xjscVrnTXU27yC+rdtsaZ+VOJzs0g9CMeQx1tSM1iGUJdAmWEETt9N3jYeRWcL97vp5/+tEz7qwhbrTj44rSwoz33ePipbayjCWVpbyw6wjSlghd0DU4+XUAXtjEY9L5xD1X8LuvvZ8bVlejm06jXxMWzRpfw4JgUHkrpxve4Nhr99PT2kBX024ldq/paJpB3sxKcmdVgU/DDurg08iryCDbpLGVHz+yhfrGVvd/D724x1UbBFKMuKFrvOeWtWdv49HUbXh41De2crKjF8Ono/k0DJ8xKQVvw/E8b483jbxgMCX0kBcc39Rxw4YNbN68GVCeu61DLBoBLaEZEY0NMHSqHWI2GmBhM39aTtrt1je28hf//Bs32+Q7f6MKbIoL1OuS868EcNXqSt5zy9qUBad4kU4sZuHzeUU6Horkc0szNO68ahm3XbLkTSkc8zxvjzeN/NyEsZbDxpnw2c9+VhXgLFzPnMvUgmVx5RpiPrAMQcwv0XNyETb4whI9Cr4wvPddf5J2u9v3txCNma5s7fb9ajHpvbesxTC0FK0UTcDSeaVnGebtB1qIxZyMFfPNWZDyuPDYvr/F1UiRpk1Zft6bVvHrGW+PN42evrDb5V1zxuPhhhtuYNq0aRTMTyxYAug2CCnRLYE10E/ezHjfQPVj5JWm3W5+KOg2ykleTKqtKuP7f3cfd19Ti9/JRvH5DFYtPnvKu2pROT6fk7Fi6Kxa5HXS8YBVixOZTJqm0X6m900Lq3lhE483jVWLy/H7DFf/YyQjmA5N03jqqae49o8+BKWJBUukRAgNadv4Ajn0th2mVNoIBH6fzurFFWm3e6CpA1vDFdE/0NSR8nhJUR5//Z5r6OkLs2rxyMU5tVVlfOez93jl8R4p1FaV8Z2/uYffv7yPR17ay4Mv7OaxV/a5obnJxDPeHm8a8RN5+/6WUY3gWFRXV/PsL7/PbX/2SbSiOfS1NzFzyRXgVFj2tR0mOzsbdAG6hjQymEwKUuecTphktFh4uuPzjLbHcGqryti+vwXLtrFtJ6y2v8Ur0vG4sKitKuN9b193TidudXU1B156hG9+7A4WhoZoeu5HtO14kqbnfsS6ZfP5wF99Dt3wAapMedvB9PHnWy9dgs/QEYDP0Ln1UiUhmxyvjF9wHh4TITl8MpFZZyZM2PMWQvw58DGU3/JtKeV/T9peeXgMI15+PFLj1vrGVl448BtXh3z1wvQXSm1VGd//zL1naZbEL7iJhnk8POJMxqxzLCYkTCWEWA78ClgJzAG+DdwkpTSHP9cTpvJ4K5gsAan6xtY39YLz8MiUN0uY6m7gNPAMkA98cSTD7eHxVjFZ8Wcvju1xoTBmzFsIYQghAsk/QDmwAHgP8P8BPxFCFA573UYhRN3+/ftZs2YNmzZtelMOwMPDw+MPiU2bNrFmzRqAxUKIOiHExpGeN2bYRAjxFeDzw/79DaBaSnmH85x+4Bop5Vmya17YxMPDw2P8jBU2GdPzllJ+QUopkn+A54E1QogZQog1qEXLxknbaw+PcVJ/uJUfPr6F+sOezojHxcFEY96/B/4PeB0wgfdJKT29TI/zwng644x7216fSo8pyoSMt1Sxlr9xfjw8UnirO9Bn0hlnInjiUx5TGa/C0mNSeTO94NGId8aJ53lnIr85VkpgfWMrP3jwNaJREwmu+JRnvD2mCp7x9phUhnvBj762b9K98OGefbrOOCO+fowy+LjHHY0pw60JPPEpjymHZ7w9JpVkL1jXNB5+ZR+WbU+aFz6aZz9SZ5zRGKkMPtl4x+VepQRNCNYureCDd17ied0eUwrPeHtMKslecPuZXn730p5JiUXHve32M73nHN9OVwZf39hK+5ledF1D2DaGoXuG22NK4hlvj0kn7gXXH27l0df2jysWPRLJ3rauaRi6hmXbE97maLoTyQuUuq5xx1U13HLZEs9we0xJPOPt8aYx3lj0aCTH0YW0ufOyZZRMyzunbY5UBp/cHUfYNiXT8jzD7TFl8Yy3x5vKeGLRozE8m+TN6gkY747jhlO8BUqPKcyEVAXHg1ce7zEZvFW5415RjsdUYazyeM94e3h4eExBzlnbxMPDw8Nj6uEZb48LgvrGVn78yJZJ68Rd39jKjx+dvO15eLzVeAuWHlOe+sZWPvJP97sLliN1dh9PBxxPs8TjDwHPeHtMeX7/8j5ilgUSYpbF1//3WSS4pe3AuLq+J6cEepolHhcqnvH2mPqI1N+WlMqQx0y3w3u6cvfheCmBHn8IeMbbY8pzy2VLeOSlvZimhaYJLFuCABvIDwWpLC8eV9f32qoyvvPZe7yUQI8LGs94e0x5aqvK+K+/vZftBxy9lOfrXdGonv4wtVVlfOrdV/Pc1kO8be2CjIyx12jY40JnQsZbCJEL/BJYCfQBH5JSvjCZO+bhkUzc2NY3tvLYK/tSQh71ja3828+fJxaz2HnoBJXlxZ5h9viDZ6Ke93uB2cAc4FPA14H1k7VTHh6jMVLI48ePbvEWID0uOiZqvE8CPiAI5ACnJm2PPDzGYHjIw1uA9LgYGbM8XghhAPqwf/uB54ClKCN+3fCwiRBiI7AxOzt79eLFi9m4cSMbN26cvD338EjC0yTx+ENh06ZNbNq0iW3btg0C+4FNUspNw5+XifH+CvD5Yf/+HlAE/ClwPfAToERKGRn+ek/bxMPDw2P8nLO2iZTyC1JKkfyDCpUMARGgxxl7mSseHh4ebxETNbifA34EHEcZ8PdKKQcmba88PDw8PNIyIeMtpWxBhUs8PDw8PM4Dnqqgh4eHxwXIlDDemzadtZB6UeEdv3f8FzPe8U/s+D3jPQXwjt87/osZ7/gndvxvehs0IcQpoGmMpy1G5TNerHjH7x2/d/wXL6Md/xwp5fTRXvSmG+9MEELUpctn/EPHO37v+L3j945/vK+bEmET4OKeN3nH7x3/xY13/BNgSnjeHh4eHh7jY6p43h4eHh4e48Az3h4eHh4XIOfVeAshviSEOCCE2CaEuPR87sv5QAhRLoTYIYT44vnel/OBEOIrzvffIIR49/nen7caofiOEKJZCPGSEOLW871P5wPnOugTQvz5+d6XtxohxBnnGtgvhPjZeF573sSkhBDXAu8DlgDXAL8QQsyVF0kQXgjxUeDLQD7w4Pndm7ceIcTbUKqUC4BlwItCiN9JKQfP7569pdwJvB1YDlyO0guacT536Dzxn6iWpBcVQohKoE9KuWgirz+fnvf1wGvOxboZ1Zmn+jzuz1uKlPK7Tg7nK+d7X84T2cC3HRnhTtS5eLGF8RqA96CUOWu4CJuaCCFuBySw/Xzvy3lgLRAVQjwphNguhLhuPC8+nzKuRcAJACnloBCiC5h2HvfH4y1ESvko8KgQogL4NfBTKWX/ed6ttxQp5V4AIcSPULPQi6pbidML95+BG4H/Pc+7cz4wUef+l4APoaIPJVJKK5MXn09PpwuYBSCEyAYKUR6Yx0WC42nsAl4FPnKed+ctRwgxXwhRIaX8M1T48FtCiNLzvV9vIV8E/ldKefx878j5QEr5Gynl56WUJvBzoJhxhM3Op/F+CtgghMgCrgaagYPncX883kKEEEuA3wF/LqX8y0y9jT8w3gXcL4TIQ4UOdCB0fnfpLWUB8H4hxH5gHfBVIcTV53WP3kKEEN8WQjwghPABd6Bs4MlMX3/ewiZSymeFED8BdgIDwB9fLIuVHgC8A3X+fU0I8TXnf9dKKVvP4z691XwLWA0cRnWm+qSUsuH87tJbh5TyzvjfQojNwP9JKZ8/bzv01vNV4KdAO9AK/JGUMuOFW6/C0sPDw+MC5GJb3ffw8PD4g8Az3h4eHh4XIJ7x9vDw8LgA8Yy3h4eHxwWIZ7w9PDw8LkA84+3h4eFxAeIZbw8PD48LkP8fysDjZn2dyoMAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -618,12 +618,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -649,12 +649,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ck+dj97MOgn/GJOHmc7q5Y+ueyFBFoOyonVEl9knAxTwV1nzjnkaqdUhOnSAFcx4IfqjcHBOngff5C2vxExJQ9mcfZ34DTHvPARfNvzgvW1W1DPdV5Zjmb8SRpmWHNdlKNOlwLHwSxRpzoLlu2z1Sco6w5pv0VASlWnOYoPnIzpis2aSni2qSsKK08rjmKTMmCVMntsdq6+Wcq9nLK9eDTDR/VW0r+quJ4DeMJJK07GKbcLmadLvAkuOnODmZizXmKEdwlOabFsJY4OOYJMyY3H7oPF950yyeuWJerJM56X6rsaFH2c/jnhh27tdYf0mchp/WiMXIFpfaPi/Ha8xyKvArYLmqbsl7YIaRRFQy0ahGt5AsNy5/VOG+HftYNv8o7ti6J/EJI0nQJtWMSTO/uNSbiWsaEyfIw9m6SfNVDq4Jb+GFLW7hC8YUdQ0je1xm9Z+BHwMfAd6K13lrcZ6DMgwXXAtyBfusuOd3Tk5i8ITRHVv3JC4c1cSMZ5GA5NqQfs3gMCvW7ygwE41qYShnpZSzsAYLW1KiWadfoC/A4vLzw+Wbdpyq/h////8lIg8n7m0YDcqLIy7VyA+zbfcIy+4citRG2yW5SUga5WrtcSQtgFFCP4xrpmza9aFwAdpzUCMX2WBhi1v4Zods/xbZkz8uwn+fiCxS1XtEZDFe20XDaCqiTA0BceYGKH+7K65ae6WsGRzm0juHUvMFsrCnFy9Acc7sYGFLWvhatbxyPXAR/h8Dvi9eZsgY8M58h2QY2ZMk5EbVPbonzEVrh1ixfkds8bi0onZ5CroV63c4JYpF1QuqdiFKW9jyXvgMN2JDPUXkfFX9dxF5NbAVeAWwRVWHazU4C/U0qiUcepkXUclL9a4lL9dvTt0nKbS0Verej1dcQj2TNP8viogCq4BL8JSjU0UEVV2f3TANIx+iBHAehMs2BNpsW4Qpae+IctHaoUN+hEoibcKLWWCuqiRyZ8bkdlYuOvZQ1m+llTON5iVJ878Wr/3iHOCp0Ftaq3r+pvkb5RI2X0QJ4DwpjlRxpQ14b0RXrGJcFrNAY0+KbAoLfkhOgps9rcPJNJOl2cionqqSvFT1Wr+mz2pVPSn0Z41cjIakOHmoloI/aFpSCWN4XbGuvHsocb8kp3VAoLGvXHRsZFloONyAJa2chIBTYbu4pK1adlczyselts8XReQtIvJWEfmFiLw191EZRgW4CMdKSMv0FbJZaPofHE5839VvsX33CEt7uvjKm2bFZgGHTVXnnTg1cp/iW4qrSmrtEpsTF+G/EpgMXA18DS/ZyzAajjzKAARlGZIat2S13Iwqic1k4hquFHPCtA6uvNvzLSQtGMF83bF1j/MYo+bY2iU2Jy7CfwawDjhSVf8JODrfIRlGZZTbpSqNcOz5E8vnMntaR1mCvpLLJplNXJ4uOjuEl3dN4IZNpT15i5k+2SvTVY6QVihZnLKq3W/UFhfh/zzwDbzSzpcDyYZJw6gTcUXLVp/TXdDMxJUpHYU/j3LDRat5Iogym6R1Cwv8Dnc9+aLTNZ7fP8qaweFEm38UxYuTtUtsTlyE/1XAQ8CngSmY2cdoUJL6BzwbU+IgiZ37Rnn3uh0cc8OAU9x81hRr5NctnBkrkCvxOxxUL1Ftz0Etae3Y2SFcvqDLyWdg7RKbE5d6/u3Am4GXBNtU9ebUE3uhou8EXgDer6r3ht77R/+95/xN56jq9uJzWKinkRUuXbYalRmT5FAWcV6LUNAj4Nn9WhKqGRcK6tKVzKgP1SZ5BXwTeDVeli94T7OJwl9EzsRLDJuPVwH0dhGZo4dXmtOAC1X1HofrG0bVXLdwJu9et4MDY9lHA7XhhWvmxc79yqV3etbWcttOunJgTJk6sYNnrihtmpJFBVKj8XAx+ywATlLVJf7fmQ7HnA3cp6p7gfXA8cBcOPQkcSqwXEQGReQzUtxR2jAyZmlPFxMkn8D/CWV0k6/U+XxQvZDKKPt6Vj+esJkp3GJyz4HRkpwBs+k3Py5f2zuAc0WkTXwcjpmOnxXsLwC78KKG8P/9N+Aa4C3AMuBt4YNFpE9ENg4MDNDb20t/f7/b3RhGAi+4lfIvm/1lqP3V5AME2nexff3yBV2RDtcrIrYnES7yFk7a2rlfUdWCbmJm029M+vv76e3tBZgnIhtFpC9uXxeb/x6gE8/cIzj08BWRTwMnqOoFItIJ7AHmq+pgxL7fBX6mqp8qfs9s/kaW1MNpG0dSGekk4gquxZVXCG9Pulz4vNX0+TUag6xs/sUenTkOx6wDviwiU4BFwJPAw/6geoF7gFcBo8AfAF9wOKdhVMWMye3OnbzyptIngLiCa1HloYsXhCRfQXhBsaSt1sBF+M8ELgMm+PufRyjyJwpVvUtEbgV+jRftcwFwi4g8paofE5HPAv8FjAA3qep/Vn4LhuHGykXHctHa6tNUwsXO9hwYje2UlRdpQjiqg9e23SOxPQtmT+soWDjMwdsauNj8PwccifcEcCTwfZcTq+o1qvpKVX2Nqt6rqstU9WP+e9eq6ktV9ThVvabi0RsGhc7JuNII4GnHMyYnWiydeGL5XMaums8Ty+eycvGssuzqaUxsS7fVJwnhwF4ftSAFdtswUY5bS9pqDVyE/5GqeiEwqKp/CpyS85gMw5lyK0quXHRs1cI6fO5wglO1zJ7WwVfeNIuF3Z1MiVmj0oRwWnE79a+T5Li1pK3WwOUbu0tEzgemicibORy1Yxh1J6miZJSwCrcQDDdEKYe+H+4oOFfY3l5sZ39y94hTDoDAIaFeXLM/MNe4NGtJMwm5Om2tl+74x0Xz/xvgRLz6PmuA23IdkWGUgYtzstgsBIdNG5U4XpPKFQdF4AKzkGsUqOItSFGLWfBqj0NT3iSTkJlujDAumv+VqvoO///fyHMwhlEuac7J4u5XgVloSnt685Wk6CDXyJdyniwCp2wcO/eNctHaITYM7Y3t+nXeiVO5cdNwiWO3uHuXYbho/h0i8rb03Qyj9qQ5J+PMQi4ROjv3jcZm5LpGvvSd0uW0H3jmnekODukbNg1zzA0DJQ7uNYPDrN78XIHgF+CKBV08c/krTfAbBbgI/1OA74jIThHZJiLb8h6UYbiS5pysNjY9SmsP2hsmRRYFrFrSzRULupx+aAqgY04O6Z37tcTBHWcycmnW4hoxZYwfXNSXy3IfhWFUQZJzMs4sNHWCMKbufXcD8004Vj4QvMEY4ljY3clNv3nOqajczv3Kbed2s+zOIWdzUeCDiFvotu0eYc3gcGLz9SjTGCTfl9HcuCgkn1LVHwd/wD/kPSjDyIrrFs4kSpHeP6Ism3+Uc4jmmBLZyculV+3VG54uu5ro6nO6S2rsJxFEF8WRFP5qPXhbk1jhLyJXich2oDcw94jIfwPH1m54hlE9Ucr9QfXMIU8sn8tt50Y7T8Oc4Gf1RlFsAgpMKHL9ZjpWbi67BPO7/CzkNxw3xfmYNkl2GCcJcyvn0JokqT3fBn4FrAbe5W8bAzblPSjDyIok7TUQbkt7ulhxz+9iI3sCG38b8a0ZA1PJhqG9rN783CFNupJQ0jE8Tf1FR5NU+DpJR8QJcyvn0JrEav6qul1VfwS8Dk/gP4lXptmSvIymIUl7DQu3pMzfQKCmxezvHVH6Hxx29iOknSvrikFRzdfByjm0Ki42//cCTwNbgMf8fw2jKUhqTh4Wbkt7ulg2/6hDoZ3tAkdUUAaomnr9tSCq/IWVc2hNXOr5bwXOUtXHajOkw1g9fyONuDr24fejyiVcvqCrIFEqar/xjNXmH99kVc//IeD5bIZkGNnhEqIYruUTt0AE77eK4Aeatpm9kR0uwn86sEVEHiDIQ1F9Y66jMgwHXIu6uRQpc41smdgmTGiDF8pcKNoFJrdL2cdVwqS29NaSAomx/8b4x8XmfyPwfuDLwE3+n2HUnSxDFF0iW9qA5b93FHveN6+spumdHcKi46bURPCDW0/hoJCc0bq4CP/2iD/DqDtxAju83bVsQVTESzFjwOrNz7FmcNg5DLJdYNn8o7j7yRed9q8lFsff2rgI/4v9v2XAZ/GeAgyj7qSFKJbT6CWIeIkr5BYQmJWuWzgzNQO3s0NYfU43d2zdU3bYpssPs1osjr+1Sf2OqeoS/28R8ArgmdxHZRgOpIUoupQtCD8ZXL3hafpO6Uo16QQas0jhnu14pZOLx1KJc9W1D4ALbVCyUFkcv5G69EvhN/xYYEF+wzGM8khy5qb5BKKihVZvfo4lx0/h7idfjNXWT5jWEVmvJ8gPHrtq/qFteVTHjGvEHocCN5/TnRrxZLQWLk+Xo8CI//dzzOFrNAlpPoG4J4Mtwwf56rndkc3eA405bmHZuW+0QOBn7VQNchTK6Rl8wrSOkg5jJvgNF+E/B6+N44nAS1T16lxHZBgZkeYTSHoyWNrjNUC57dzuSLNSkr08LPCzdKrOmNzOV8/tZtWSbicHNZh5x4gnVX1Q1e21GIhhZE1Us/awzd+loFlxklhw7HULZ3KRX32zmKDK5/bdI7RV0CA+jhdHDnsClvZ0sWFoLzdsGo7dP9zwPS0T2mg9ahFUYBh1Y2lPV0mz9iDq57wTp6YWNIuLGAKYMSla8w6qgCrZ1vopdlYndegSOGTeKSfqyWgdchP+InKtiAyKyP0ickbMPt8RkZ/kNQbDgHjb/h1b96QWNEuKGFq5eFbJ4lGuM7ZcwmYk14ql1qzFiKLsQF8RuQU4AHxQVV+I2edM4BJgPrAYuF1E5mioipyI/LH/nvUHMHIlzbafZP5IOxYK6wblXTOnTQ6XZYi7XnHFUmvWYkRRieb/FPB54JUJ+5wN3Keqe4H1wPHAoRKCIjIV+BTwmaiDRaRPRDYODAzQ29tLf39/BcM0DA+XTOBKjw3MSkGnr7QksWoZVbj0ziGOufHhyM5dQTRQeEGr5v6N5qK/v5/e3l6AeSKyUUT64vZNLel8aEeRmarq9JwoIv3A86r6V/7rncBbVfVe//U/4i0izwCXqeofRZ3HSjobWRBVrrmzQ5xq1qcd2wiloANT0+wYR2419280Jy4lnZN6+E4WkQ+KyC9EZDfw3yLygm/Df7+ITEo47y7gZf55OoGjgWf91wuAM4GVZd6PYVRENc1K0o5dsb7+PQDSrm7NWowokp77fgOsBT4KPA7sAGYBJwN/7r//iphj1wFfFpEpwCK8FpAP++/Nx1sMHgSOArpEZKWqrqjqTgwjAZeyzgFRYZHhxidBSYhGq4kf1c8goJz7N1qDWLOPiBylqs/FHpj+/ieAdwIvAO/Dawf5lKp+LLTPMszsY9SRYkF/3olTCxqwQ+3NPNVGDFmXLsPF7ONs868HJvyNPIlr8Rj1iwgEat4a/+wMIoaEwvpCRutRVRtHEXmcGAVEVU+ucmyGUXei4t/jVKEgLNIlPLKNyqtyBhFDSclhaU8GFsVjuJD0LfkqcBVWv98Yp5QT5x4I1OmT29m5bzR2v84OYdn8oxLLLiThkhWc9LbV8jFcSRL+fw+8V1VvrdVgDKOWuCZlBQJ1zeAwz++PF/zhUMtvPLI7cZFwIXgCKP43ar8xxWr2GGURG+qpqi8CrwpvE4/zcx+VYdSA6xbOjG3c0i6UhEVeveFpDkYI3xmThNvO7Qbg4rVDHHPDAPtGqm/HMqZw27ndHDfV09HingjGFCvVbJRNonFQVXcCiMhs4N3+39HA1PyHZhj5srSnK7YyZyBQw8TW8N+vBY7jnfuzCaKYPrndKbLIbPxGJSQleXWIyNtF5E5gEPgQ8EHADIrGuCGqYQt4greYOCEblIrOHB1LPa/Z+I1KSVIZngK2A18BLgR+oarfrMmoDKNWaIx5xt8ezgOYPrmdCUKJ6SfLss1hnnV4grBMXaNSkoT/Y3jZvK/AL9VgGOONOAH77H4tyQPYuW+UiW3CEW3KC9X5cp1IawTTLqWZvIbhSpLD9wy8kssAdwGz/Fo/s2syMsPIiaA8Q9v1m2P3mT5JIvMADowp+6r35RYQ9yNMe6LI64nDaA0SSzqr6mZV/RCe5n8pcD7eE4FhNCXFXa1i5ae0xTp4sxS6Vyzo4la/T3AlzLnpEevIZVREksP3ayLyOgBVPaCqX1fVs4BXiMhrReRrNRulYWRElDYfxbP7RhMdvOUye1oHVyzoOnRsu3iCf9WSbpb2dPHE8rmxYadAbLN2a8loVEqS5v91YKWI7BCRn4jIv4jIj4GfADcC/1KTERpGhrhm9QYJU1E9fvtO6YoVxlEEETmrlnQzsmI+etV8RlbMZ9WS7pJrRhHkGsQ9HVhLRqMSkmz+31PVP8Srvf854Bd4NfjPVtVTVfW7NRqjYWSGS0x8IKzj6uCvWtJdsD2ukTt4Gn5aRE5SiejwWJKeDqwlo1EuVtXTaCnSSjLPmCSsXFxe+OS0Lw6wJyr112fqBGFSmxdBVFyCIWk8QUvG8BNC3CJhZZyNMFVV9TSM8Ui46XrQAzcsdl8MhXBGNXUpXhSuvHsoUfAD7Dmo7PH/v233CBetHeLyu3YwqS05G1iBO7buKdh23cKZkS0ZLdHLKBfT/I2WJUmLjhKyEwSOnNR+yBl83olTK67eWQ5aVGbCZVEyWptMmrmIyCxgFfBy4NPAz1R1S2ajTMCEv5Enbddvjgz1FNwrfuZNu8DICmvMYpRHVQ3cQ3wF+DUwCdgG3FD90Ayj/sQ5f9uEhhD8YIlcRn64CP95qvoJ4KCq/gQ4MecxGUZNiArlhMYSuJUmfxlGGi7C/3ERuQqYJCJXAL/Ld0iGURuKQzkrSd7KEwFz5Bq54aJWvAf4MvBS4F3AZbmOyDBqyNKerkPO0qRaP/VAscJtRn6kav6q+piqLlbVqar6OlV9qBYDM4xaE+cDmDFJSsxDApx5/JSCBLCkZK9KCJt8wsXorJ6PkQWxmr+IjBFd90pV1QyRxrgjLoZ+5eJZAKnhlWkJZOXQIYdNPsXnDer5gD0ZGJWTpPm/HJgL3AxcCywA/hpY7XJiEblWRAZF5H4ROaPovfNFZIuIPCQinxYRF9+DYeRKXDmHwDT0xPK5fDXUq7dYA8/ShxBeP6KK0Vk9H6NaXOL8N6vq/NDrR1X1FSnHnAncBMzH6wmwCpijqioi7cD/AH3AD/BKRL9HVe8oPo/F+RuNRJRm39khsbV71gwOc/Haofiy0SkE5447h1DaZ9gwILs4/4kislREjhGRywGXAOizgftUdS+wHjge7ykCYALwfuC7wBy8hvDPOJzTMOpKnAa+Yv2OyP2X9nSx5Pgpke9N8Es6JxFo93G+CGvcblSDi/B/H/BPwNPAx4G/cDhmOl4PYPwFYBcww3+9T1XXAAuBzcC9wIPhg0WkT0Q2DgwM0NvbS39/v+PtGEZ+xFXO3Om3fCxmzeAw9+3YF3nMQfXq9qQtANt3j8SWlrYwUKOY/v5+ent7AeaJyEYR6Yvb17m2j4gco6pOGrqIfBo4QVUvEJFOYA8wX1UHRWQaMFdV7xeRI4BvAveo6qeLz2NmH6ORiKsFBNFVNZP2D2hP6dM7Y5IwdWI723aPHNp3ttXzMVLIxOwjIttFZBtwv4hs8/+fxjrgdBGZAiwCngQe9t87Gvi5iJyOZ0KaABzpcE7DqCtJmnbUU4FLjf0kwT9BYPfBw6UmRrWwvr9hVIOL2eci4GJgGV7kz4/SDlDVu4Bb8WoC/V/gAuAWEfmkqm4HrgK+hbco7AI+U8HYDaOmLO3pYsbk9sj3ouzvldjk24VDkUZHTmrnwJhF+Rj5kPrtVNUfh17eIyKPupxYVa8Brgltujf03ueBz7sO0jAahZWLjnWup19JyecxPRzBE5dxbF27jCxIFf4i8onQyx5gNG5fwxjvhJvBpNXTL27E4oICcv1mZk/rYPrkdnbuK/25WZSPkQUuZp/jQ3+/Ac7LdUSG0eC4JHxBdRr6tt0jPL9/lIltFuVj5IOLCrFZVT8bvPArfF6f14AMoxlwKblQbUOYgwozJsLUiR3WtcvInKTaPovwsnOv8MMzwWvo0ocJf6PFSSq5EAjnqFpB5fLsfuWZK6wxu5E9SZr/kXimngn+vwBjuCV5Gca4Js6kE94eLAIXrR2q+Dpm3zfyIvabparfA74nIhtVdVUNx2QYDU+cSadYWC/t6WLF+h3s3F++9m/2fSNPYh2+IvJV/7/vFJEfhf9qNDbDaFiiSi4IXnhnMSsXz2JCTIXP2dM6uGJB16Ha/UEl0HBFUbB6/kb2JD1TftP/98u1GIhhNBNLe7rYMLSXGzcNH6q4qcDqzc+xsLuzwCmbFB66ZnD40Pa4sg1Wz9/Ig9jaPiJyadxBqnpzbiMKYbV9jEYmrnZPVJ2fKFxLRFd7HaP1cKntk6T5XxyzXfHKPBhGSxPn9N22e4Q5Nz2SGpbpEjGUdB3L9DWqIcnhuyT4v4h04XX2elxVn63BuAyj4UmK43cxzbgKdVfnsmGUg0tVz7fj1ea/B3hKRJblPSjDaAainL5h9o4oy+4cOuScLXbaTo9p+F4s1K2ev5EHLm0cB4G/UNW7ROT1wG2qOqcWgzObv9HoBA7bpEzezg5h2fyjWL35uQIzz8Q2QVU5GPoJCrDk+ClsGT5Y4BwGt3pChgFuNn8X4f8o8EpVHRORDmBTuKdvnpjwN5qFtMYtcU1bpk4QXjioiX1+k/oEG0YUWfXwvRdYJSJvBW4EfiEii0VkcRaDNIxmZ83gMHsOJitRcU1b9qQIfrAa/kY+uHiM/sj/901F2xQ4OfMRGUYTERWuGUVau8Y0LLLHyBqXZi4n1WIghtGMRIVrFtPZIbxu1mTufvLFAi2/s0OY0o5T6QeL7DGyxiXaZ7mIPCAiDwd/tRiYYTQDSRp50I5x2fyjuG/HvgLBL8Cy+UexcvGsxIihAIvsMbLGRZ34KPAPwHZINU8aRksRF4Mfzr6dc9MjJU8Hitfpa9USryFMWsSQYWSNi8N3F3Czqt6pqutUdV3egzKMZsElBj8tmSvoDDY7wbRjDl8ja1w0/1vxInx+ha/5q+ryXEdlGE1CXNE28DT+7btHaItx9kYlc8XV/jeHr5E1LsL/w3jZvb/NdyiG0Zws7ekqiMG/8u6hgmqfUYI/KkN3aU8XK+75nTVtN2qCyzfqeVW9JO+BGMZ4YM3gcIHgj2LGJGHl4uikrZWLjo2s9GkOXyNrXIT/JhG5FU/7D8w+VtXTMCK4esPTqVERUye2x2brJtX+N4wscRH+3f6/S0PbUoW/iFwLvBN4AXi/qt4beu/1wOeAacB9wHtUdb/jmA2jYXGxzaftU2xGMow8cIn2OR+4Ba+R+xtdjhGRM4FLgNcAHwduF5FwSMQa4GqgBzgFuKCcQRtGo+Jimzf7vdEIJPXw/X0R+SJeOef3A/OAblV1qelzNnCfqu4F1gPHA3P9807Dqxf0U1UdBZ7H7QnEMBqetDLPwuFmL9aH16gnSVr8r4ATgYWqehqwW1Vdg42n4y0a+AvALmCG/3q3ql4A7BWRT+ItCt8OHywifSKycWBggN7eXvr7+8u6KcOoF0t7uug/axazp3UgwBEdUvAjC/wBQbMXWwCMLOnv76e3txdgnohsFJG+uH2Tevh+ElgGPI1n4/9LVZ3tMgAR+TRwgqpeICKdwB5gvqoO+u93AT/As/n/iapujTqPlXQ2mhmXom/Wh9fIg6pKOqvqx4ATgGvxzDgvFZHvici7HK69DjhdRKYAi4AngXBNoO/iPRmcFif4DaPZcSn6ZslbRr1ItLX7NvnvAt8VkW5gOXANXtZv0nF3+eGhv8aL9rkAuEVEngJuAN4AbAUe8P3AX1DVL1Z3K4ZRf4LOXtt3jzgVwjLnr1EvUjt51RMz+xjNhGtt/wDr0GXkRVadvAzDcMDFzBPEAc2YJEzpaOPitUMW+WPUBXvmNIyMSCrJLFBQ9M17Qhg9dFzfD3cA2FOAUTNM+BtGRsS1amwXGFkx/9DrqPr+QZ9eE/5GrTCzj2FkRFyP3uLtafX9DaMWmOZvGFUQRPckmXyKm7TEdf+yyB+jlti3zTAqxCW6R/BKPoRDQKdPEia2CQfGtGC/806cmv+gDcPHzD6GUSEu0T3Bu30/3ME2P/Z/535ldExL9lu9+TmL+jFqhgl/w6gQFxv97GkdkYtEaa+uw05fw6gFJvwNo0LSbPRBB65yHLnm9DVqhQl/w6iQqPLNwavZ0zoOZe+W48g1p69RK+ybZhgV4tpy8bqFM0scwxPbBFXlYMgaZL16jVpiwt8wqsCl5WLcIhG1zZK8jFphhd0MwzDGGVbYzTAMw4jEhL9h1IA1g8PMuekR2q7fbFU8jYbAbP6GkTPFmcBWxdNoBEzzN4yciUrysoQuo96Y8DeMnLEqnkYjYsLfMHImLnHLErqMemLC3zByJioT2BK6jHpjwt8wcmZpTxf9Z81i9rQOhMLSD4ZRL+y50zBqgEsmsGHUEtP8DcMwWhAT/oZhGC1IbsJfRK4VkUERuV9Ezih6r1NEPiEiW/O6vmE0GpblazQSudj8ReRM4BJgPrAYuF1E5qiqisgxwENAJ/BMHtc3jEbDsnyNRiMvzf9s4D5V3QusB44H5gKo6jOqeizwvriDRaRPRDYODAzQ29tLf39/TsM0jNpgWb5GLejv76e3txdgnohsFJG+uH1zKeksIv3A86r6V/7rncBbVfXe0D7LgGtV9cS481hJZ2O80Hb9ZqJ+aQKMXTW/1sMxxjn1LOm8C3iZP4hO4Gjg2ZyuZRgNj2X5Go1GXsJ/HXC6iEwBFgFPAg/ndC3DaHgsy9doNHJRO1T1LhG5Ffg18AJwAXCLiDylqh/L45qG0ci49vs1jFphbRwNwzDGGdbG0TAMw4jEhL9hGEYLYsLfMAyjBTHhnzOWoFaKzUkhNh+l2JyUkvWcmPDPGfsSl2JzUojNRyk2J6VkPScNHe0jIv8DbKv3OKpkHjBQ70E0GDYnhdh8lGJzUko5czJbVV+StENDC//xgIhsTAu5ajVsTgqx+SjF5qSUrOfEzD75Y8+vpdicFGLzUYrNSSmZzolp/oZhGC2Iaf6GYRgtiAl/wzCMFsSEf0aktK18vYj8UkQeFZFbRWRSvcZZS5LmJLTPd0TkJ7UeW71I+Z6cLCL3iMjDInKziBxRr3HWipT5+ICI/FZEnhKRT9VrjLVGRI4TkV+JyDUR753hz9WgiFxb1YVU1f6q/APOBJ7Aa015Pl54qoTe3wa8GWgHfgVcUu8x13tO/H3+GHgO+Em9x9sIcwL80t+nDbgJOLfeY67XfABTgVHgD/F6gxwE5tV7zDWYkyuB/wEOANcUvSchWdLpz92SSq9lmn82xLatFJFpwL3AT1V1FHienEppNxixcwIgIlOBTwGfqc/w6kLS9+Qk4BTgQrzeFweAu+s0zlqR9B3ZDwwDR+AJulG8JlHjGlVdpV58/oaIt1+JN0f3+HP2X3hzWBEm/LNhOvAUgP+h7AJm+K93q+oFwF4R+STel/vb9RpoDYmdE5+/xdNuf1v7odWNpDl5KZ5S8EvgDXhNkC6t/RBrStLv5iDwWeCHwG+AW4Df1WWUjcN04FlVfdF//VsKf1NlYcI/GxLbVopIF/AjvEfbM1S1FVpaxs6JiCzAe+RfWbfR1Yek78lz/r9fU9UdeFr/gpqPsLYkfUd6gf8NnATMApYAb6/PMBuGXcB0v0MiwHFU0R7XhH82pLWt/C6ehnOaqm6t/fDqQtKczMf7oT+IZ/r5AxFphYUgaU4G8TTb80WkA1gIPFCPQdaQpPk4EhgD9gB7gRGgq/ZDbCgG8eZokT9nrwX+s9KTtYLtOXc0oW0lcAPeY/xW4AERAfiCqn6xTsOtCUlzol4rz68DiMgy4DJVXVG3wdaItDkRkQuBzwF/h2cDv7lug60BDvPxRQ4vgOuA1fUZaX0RkdVAMCcXAF/A84WsVtW7Kj6v70U2DMMwWggz+xiGYbQgJvwNwzBaEBP+hmEYLYgJf8MwjBbEhL9hGEYLYsK/RRCRN4qIisiA//e4iNxUbpE5EZntn6c9r7GWg4i0++OZXbR9q4icmdE1VEROjth+v4jM89/f5s/rEyLyA79I2xoR+fMsxhC65goROTHLc4bO3Ssij4nITXmcP+XaT4rIG2t93VbGhH+LoarzVHUe8BpgKXBOnYfUlIjIW4Bdqhr0VH2XP68n4qXh/wVeBvM14id3ZMRVeFmvefAm4Fequjyn8xsNhAn/1mUGXgblIyJypIjcLiIPiciDInI1gK/V/tR/SrhLRBaGT+BroQ+JyMtE5BQR+YWvOd4WPB2IyHoRWS0im0XkDSLy1+KVtt4iXnnrqf5TyZOh824VkTNF5BoR+Xe/zPFWEbnSf7/XH+ejQGKynIh0isgtvkb+gHjltU8XkT1+SQFE5G4Rea+IvEREvisi20XkPhGZn3DqZcC3IrZ34mWn/kZVf46XyfyqhPGtF5Hl/v+XiV/e2p+/6/15ultEjvGTfV4G3Cwii/w56RevBPSCmM9wvf+Et8nf/noRmSQiX/O3bRKRD4nIecAHgbNF5O9E5KUi8j3/sxoUkUv8890sIt/wj1smIntF5Ev+nH1PRD7jf9b3isjL/WM+4n+HHg19hif5c/y4iHwdmJz0ORrZY8K/xRDf7INXWuE/8MrCzgUeB34P+CsgqCP+JeA7qnoS8GXgT0Kn+ghe4bE3qOpTeEXavgW8HNhSdNmX45XmneQf9zqgBzgB+GjKkOcBbwPeA/ytr0X/M/CvqvoK4L6U4z8CTANegac1fxMvo/S3wFtE5CV4afLfAP7Bn485eP1Sv5Zw3lcV3eet/rxuwxNka/3tj5Mg/FPY4o97AnChqi7DKxNyqare4+9zNF410IlEf4bgZYOeildm5EN41SHfDlwM/DnegrUWL3P0e6r6f4D/Bzznz/HbgH4RCSpu9gKLgXuAKXhz1wOchVd4LFg0/1hEFgHvw3vSPAP4hIicijfXA/5360bgmArnyKgQE/4tRsjscyyeELgcrzjUcXglF5biCRvwBMZd/nG3q+qHQ6dahvf0MOK//j3gLvVSxr9XdNlvqeoe4NXAz1T1GVUdwRM4p0YMM/y9vFdVnwce9a/XhidoAuGaViH1NLw6OZuAVXg9FY7DqxL5DuBPgTtUdZe/758BD+EVFXuZxPtEZgNDodeB2eeleEX8Vvnbd+CZglwo/j2u9edzC/CSmGNuV9UDxH+GAD/0y4k/CrxEVTcBK/DMUuvwFpDia78a+AGAqj6Ct7AEheZ+oKo7Q/uu96ty7gR+5m/bjrcInoZXk+c+4Md4dfkXEPoM/YUsfD6jBpjwb1324dWMnwW8H+hU1XcA/waeIxVPCJ7tv75GRG4MHX8qXmOaz/uvH8KrvAilNcaDipUP4BXyOka84mXn4mnhTwAzReRo8Ryrx4WOjao/8hDwp/4Y35xyn7/hcIXMs/zxPg58Fc/fcSmHa8b8Bk/bfxWeRnydqu6POe9WvLkrwF/U9obem4X3NBDHVqBHRNqAPyo+XcT+Y3hPUAHB3MZ9hiXnEZFXA6/Hs/G/zj92XtF1HgDe4u8/F8/P8GDRNaPGqUX//gZ4Gjgd7zO4EbgT7zN8q2+COo0qShMblWHCv8UImX0exTMhfBZPe57rb387ntb2QWA5npB9FO+x/e9DpzoAXAG8TbyIliuAC0XkIaAkMgZAVdf517sPr3rjb4G/V9VteKaDzXjmgB0pt/EePMH1GPBW4MWEfT+JV8Bwi3+fm1R1zDdV/dQfa/AU8Zd4i9pjeAX5fp5w3s0UOl5v9ef2MTxB9wF/+8nAZhE5zX+/u+g8XwLe6Z9vasL1AtbiFT97Q9H2uM8wigFgN3A/nunvRv/6Yf4S6BKRLcD3gctV9WHKRFX/A7gd78lro7dJ/xv4MF5jki3Ax0leII0csMJuRiaIyGV4lQf/Q0TegSfUcwlJbARE5M+A96pqbLSUiLwWuFFVTxWRyXgC+n+p6gu1GqdhxGHC38gE8WK0/xYv0mUCsKKacrONju94vh9YGgr3LN5nDZ6/49siMhPP3v5QLcdpGHGY8DcMw2hBzOZvGIbRgpjwNwzDaEFM+BuGYbQgJvwNwzBaEBP+hmEYLcj/BzFhWX8Jn1uQAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -680,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -722,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -733,14 +733,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If it gives an output like the above, your MPI is working. If the last column is 1, your cores are not communicating."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you get an error, fix it first."
+    "This should give something like:\n",
+    "\n",
+    "     3 4\n",
+    "     1 4\n",
+    "     0 4\n",
+    "     2 4\n",
+    "\n",
+    "With the first column randomly. If it gives an output like the above, your MPI is working. If the last column is 1, your cores are not communicating. If you get an error, fix it first."
    ]
   },
   {

From 3131a2fa104dce137657ddf09a2d3e630f98345d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 9 Sep 2022 10:04:16 +0200
Subject: [PATCH 098/313] [doc] docstring syntax

---
 ultranest/hotstart.py   | 55 +++++++++++++++++++++--------------------
 ultranest/integrator.py | 35 +++++++++++++-------------
 2 files changed, 45 insertions(+), 45 deletions(-)

diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index c836eb6a..3f10e77c 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -47,7 +47,7 @@ def get_auxiliary_problem(loglike, transform, ctr, invcov, enlargement_factor, d
         The default is recommended. For truly gaussian posteriors,
         the student-t can be made more gaussian (by df>=30) for accelation.
 
-    Returns:
+    Returns
     ---------
     aux_loglike: function
         auxiliary loglikelihood function.
@@ -122,7 +122,7 @@ def get_extended_auxiliary_problem(loglike, transform, ctr, invcov, enlargement_
         The default is recommended. For truly gaussian posteriors,
         the student-t can be made more gaussian (by df>=30) for accelation.
 
-    Returns:
+    Returns
     ---------
     aux_loglike: function
         auxiliary loglikelihood function. Takes d + 1 parameters (see below).
@@ -204,7 +204,7 @@ def get_extended_auxiliary_independent_problem(loglike, transform, ctr, err, df=
         The default is recommended. For truly gaussian posteriors,
         the student-t can be made more gaussian (by df>=30) for accelation.
 
-    Returns:
+    Returns
     ---------
     aux_loglike: function
         auxiliary loglikelihood function.
@@ -254,7 +254,7 @@ def compute_quantile_intervals(steps, upoints, uweights):
     uweights: array
         sample weights
 
-    Returns:
+    Returns
     ---------
     ulo: array
         list of lower quantiles (at q), one entry for each dimension d.
@@ -263,8 +263,8 @@ def compute_quantile_intervals(steps, upoints, uweights):
     """
     ndim = upoints.shape[1]
     nboxes = len(steps)
-    ulos = np.empty((nboxes+1,ndim))
-    uhis = np.empty((nboxes+1,ndim))
+    ulos = np.empty((nboxes + 1, ndim))
+    uhis = np.empty((nboxes + 1, ndim))
     for j, pthresh in enumerate(steps):
         for i, ui in enumerate(upoints.transpose()):
             order = np.argsort(ui)
@@ -276,19 +276,20 @@ def compute_quantile_intervals(steps, upoints, uweights):
     uhis[-1] = 1
     return ulos, uhis
 
+
 def compute_quantile_intervals_refined(steps, upoints, uweights, logsteps_max=20):
     """Compute lower and upper axis quantiles.
-    
+
     Parameters
     ------------
     steps: array
-        list of quantiles q to compute, with dimensions 
+        list of quantiles q to compute, with dimensions
     upoints: array
         samples, with dimensions (N, d)
     uweights: array
         sample weights. N entries.
 
-    Returns:
+    Returns
     ---------
     ulo: array
         list of lower quantiles (at q), of shape (M, d), one entry per quantile and dimension d.
@@ -297,32 +298,32 @@ def compute_quantile_intervals_refined(steps, upoints, uweights, logsteps_max=20
     """
     nboxes = len(steps)
     ulos_orig, uhis_orig = compute_quantile_intervals(steps, upoints, uweights)
-    assert len(ulos_orig) == nboxes+1
-    assert len(uhis_orig) == nboxes+1
+    assert len(ulos_orig) == nboxes + 1
+    assert len(uhis_orig) == nboxes + 1
 
     smallest_axis_width = np.min(uhis_orig[-2,:] - ulos_orig[-2,:])
     logsteps = min(logsteps_max, int(np.ceil(-np.log10(max(1e-100, smallest_axis_width)))))
 
-    weights = np.logspace(-logsteps, 0, logsteps+1).reshape((-1, 1))
+    weights = np.logspace(-logsteps, 0, logsteps + 1).reshape((-1, 1))
     # print("logspace:", weights, logsteps)
-    assert len(weights) == logsteps+1, (weights.shape, logsteps)
+    assert len(weights) == logsteps + 1, (weights.shape, logsteps)
     # print("quantiles:", ulos_orig, uhis_orig)
-    ulos_new = ulos_orig[nboxes-1, :].reshape((1, -1)) * (1 - weights) + 0 * weights
-    uhis_new = uhis_orig[nboxes-1, :].reshape((1, -1)) * (1 - weights) + 1 * weights
-    
+    ulos_new = ulos_orig[nboxes - 1, :].reshape((1, -1)) * (1 - weights) + 0 * weights
+    uhis_new = uhis_orig[nboxes - 1, :].reshape((1, -1)) * (1 - weights) + 1 * weights
+
     # print("additional quantiles:", ulos_new, uhis_new)
-    
+
     ulos = np.vstack((ulos_orig[:-1,:], ulos_new))
     uhis = np.vstack((uhis_orig[:-1,:], uhis_new))
     # print("combined quantiles:", ulos, uhis)
     assert (ulos[-1,:] == 0).all()
     assert (uhis[-1,:] == 1).all()
-    
-    uinterpspace = np.ones(nboxes+logsteps+1)
-    uinterpspace[:nboxes+1] = np.linspace(0, 1, nboxes+1)
-    assert 0 < uinterpspace[nboxes-1] < 1, uinterpspace[nboxes]
-    uinterpspace[nboxes:] = np.linspace(uinterpspace[nboxes-1], 1, logsteps+2)[1:]
-    
+
+    uinterpspace = np.ones(nboxes + logsteps + 1)
+    uinterpspace[:nboxes + 1] = np.linspace(0, 1, nboxes + 1)
+    assert 0 < uinterpspace[nboxes - 1] < 1, uinterpspace[nboxes]
+    uinterpspace[nboxes:] = np.linspace(uinterpspace[nboxes - 1], 1, logsteps + 2)[1:]
+
     return ulos, uhis, uinterpspace
 
 
@@ -354,7 +355,7 @@ def get_auxiliary_contbox_parameterization(
     and segments it into quantile segments. Within each segment,
     the parameter edges in u-space are linearly interpolated.
     To see the interpolation quantiles for each axis, use::
-    
+
         steps = 10**-(1.0 * np.arange(1, 8, 2))
         ulos, uhis, uinterpspace = compute_quantile_intervals_refined(steps, upoints, uweights)
 
@@ -367,7 +368,7 @@ def get_auxiliary_contbox_parameterization(
     auxiliary_usamples: array
         Posterior samples (in u-space).
 
-    Returns:
+    Returns
     ---------
     aux_loglike: function
         auxiliary loglikelihood function.
@@ -381,7 +382,7 @@ def get_auxiliary_contbox_parameterization(
     nsamples, ndim = upoints.shape
     assert nsamples > 10
     ulos, uhis, uinterpspace = compute_quantile_intervals_refined(steps, upoints, uweights)
-    
+
     aux_param_names = param_names + ['aux_logweight']
 
     def aux_transform(u):
@@ -462,7 +463,7 @@ def reuse_samples(
     log_weight_threshold: float
         Lowest log-weight to consider
 
-    Returns:
+    Returns
     ---------
     results: dict
         All information of the run. Important keys:
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 38c2e985..6b375039 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -40,7 +40,7 @@ def _get_cumsum_range(pi, dp):
     dp: float
         Quantile (between 0 and 0.5).
 
-    Returns:
+    Returns
     ---------
     index_lo: int
         Index of the item corresponding to quantile ``dp``.
@@ -65,7 +65,7 @@ def _sequentialize_width_sequence(minimal_widths, min_width):
     min_width: int
         Minimum width everywhere.
 
-    Returns:
+    Returns
     ---------
     Lsequence: list of (L, width)
         A sequence of L points and the expected tree width at and above it.
@@ -925,6 +925,18 @@ def warmstart_from_similar_file(
 ):
     """Warmstart from a previous run.
 
+    Usage::
+
+        aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized = warmstart_from_similar_file(
+            'model1/chains/weighted_post_untransformed.txt', parameters, log_likelihood_with_background, prior_transform)
+
+        aux_sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, transform=aux_prior_transform,vectorized=vectorized)
+        aux_sampler.run()
+        posterior_samples = aux_results['samples'][:,-1]
+
+    See :py:func:`ultranest.hotstart.get_auxiliary_contbox_parameterization`
+    for more information.
+
     Parameters
     ------------
     usample_filename: str
@@ -934,10 +946,10 @@ def warmstart_from_similar_file(
     min_num_samples: int
         minimum number of samples in the usample_filename file required.
         Too few samples will give a poor approximation.
+    otherparameters: ...
+        The remaining parameters have the same meaning as in :class:ReactiveNestedSampler.
 
-    The remaining parameters have the same meaning as in :class:ReactiveNestedSampler.
-
-    Returns:
+    Returns
     ---------
     aux_param_names: list
         new parameter list
@@ -947,19 +959,6 @@ def warmstart_from_similar_file(
         new prior transform function
     vectorized: bool
         whether the new functions are vectorized
-
-    Usage::
-
-        aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized = warmstart_from_similar_file(
-            'model1/chains/weighted_post_untransformed.txt', parameters, log_likelihood_with_background, prior_transform)
-
-        aux_sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, transform=aux_prior_transform,vectorized=vectorized)
-        aux_sampler.run()
-        posterior_samples = aux_results['samples'][:,-1]
-
-    See :py:func:`ultranest.hotstart.get_auxiliary_contbox_parameterization`
-    for more information.
-
     """
     # load samples
     try:

From 6808142a1691596aadafd96ea953ba3bf6331166 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 12 Sep 2022 00:12:26 +0200
Subject: [PATCH 099/313] document missing parameters (thanks to pystrict3)

---
 ultranest/hotstart.py    | 43 ++++++++++++++++++++++++-----------
 ultranest/integrator.py  | 49 ++++++++++++++++++++++++++++++++--------
 ultranest/stepsampler.py | 10 ++++----
 ultranest/utils.py       |  2 ++
 4 files changed, 77 insertions(+), 27 deletions(-)

diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index 3f10e77c..c8d1832d 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -78,8 +78,8 @@ def aux_loglikelihood(u):
         if not (x > 0).all() or not (x < 1).all():
             return -1e300
         # undo the effect of the auxiliary distribution
-        loglike = rv_auxiliary1d.logpdf(coords).sum()
-        return loglike(transform(x)) - loglike
+        loglike_total = rv_auxiliary1d.logpdf(coords).sum()
+        return loglike(transform(x)) - loglike_total
 
     def aux_aftertransform(u):
         return transform(aux_rotator(rv_auxiliary1d.ppf(u)))
@@ -288,13 +288,17 @@ def compute_quantile_intervals_refined(steps, upoints, uweights, logsteps_max=20
         samples, with dimensions (N, d)
     uweights: array
         sample weights. N entries.
+    logsteps_max: int
+        number of intermediate steps to inject between largest quantiles interval and full unit cube
 
     Returns
     ---------
     ulo: array
-        list of lower quantiles (at q), of shape (M, d), one entry per quantile and dimension d.
+        list of lower quantiles (at `q`), of shape (M, d), one entry per quantile and dimension d.
     uhi: array
-        list of upper quantiles (at 1-q), of shape (M, d), one entry per quantile and dimension d.
+        list of upper quantiles (at 1-`q`), of shape (M, d), one entry per quantile and dimension d.
+    uinterpspace: array
+        list of steps (length of `steps` plus `logsteps_max` long)
     """
     nboxes = len(steps)
     ulos_orig, uhis_orig = compute_quantile_intervals(steps, upoints, uweights)
@@ -337,14 +341,6 @@ def get_auxiliary_contbox_parameterization(
     likelihood and prior transform that is identical but
     requires fewer nested sampling iterations.
 
-    Usage::
-
-        aux_loglikelihood, aux_transform = get_auxiliary_contbox_parameterization(
-            loglike, transform, auxiliary_usamples)
-        aux_sampler = ReactiveNestedSampler(parameters, aux_loglikelihood, transform=aux_transform, derived_param_names=['logweight'])
-        aux_results = aux_sampler.run()
-        posterior_samples = aux_results['samples'][:,-1]
-
     This is achieved by deforming the prior space, and undoing that
     transformation by correction weights in the likelihood.
     A additional parameter, "aux_logweight", is added at the end,
@@ -361,15 +357,23 @@ def get_auxiliary_contbox_parameterization(
 
     Parameters
     ------------
+    param_names: list
+        parameter names
     loglike: function
         original likelihood function
     transform: function
         original prior transform function
-    auxiliary_usamples: array
+    upoints: array
         Posterior samples (in u-space).
+    uweights: array
+        Weights of samples (needs to sum of 1)
+    vectorized: bool
+        whether the loglike & transform functions are vectorized
 
     Returns
     ---------
+    aux_param_names: list
+        new parameter names (`param_names`) plus additional 'aux_logweight'
     aux_loglike: function
         auxiliary loglikelihood function.
     aux_transform: function
@@ -377,6 +381,19 @@ def get_auxiliary_contbox_parameterization(
         Takes d u-space coordinates, and returns d + 1 p-space parameters.
         The first d return coordinates are identical to what ``transform`` would return.
         The final coordinate is the log of the correction weight.
+      vectorized: bool
+        whether the returned functions are vectorized
+
+    Usage
+    ------
+    ::
+
+        aux_loglikelihood, aux_transform = get_auxiliary_contbox_parameterization(
+            loglike, transform, auxiliary_usamples)
+        aux_sampler = ReactiveNestedSampler(parameters, aux_loglikelihood, transform=aux_transform, derived_param_names=['logweight'])
+        aux_results = aux_sampler.run()
+        posterior_samples = aux_results['samples'][:,-1]
+
     """
     steps = 10**-(1.0 * np.arange(1, 8, 2))
     nsamples, ndim = upoints.shape
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 6b375039..20367e76 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -18,9 +18,9 @@
 
 from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify as _listify
 from .utils import is_affine_transform, normalised_kendall_tau_distance, distributed_work_chunk_size
-from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
+from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid
 from .store import HDF5PointStore, TextPointStore, NullPointStore
-from .viz import get_default_viz_callback, nicelogger
+from .viz import get_default_viz_callback
 from .ordertest import UniformOrderAccumulator
 from .netiter import PointPile, SingleCounter, MultiCounter, BreadthFirstIterator, TreeNode, count_tree_between, find_nodes_before, logz_sequence
 from .netiter import dump_tree, combine_results
@@ -438,6 +438,9 @@ def __init__(self,
         vectorized: bool
             If true, loglike and transform function can receive arrays
             of points.
+        run_num: int
+            unique run number. If None, will be automatically incremented.
+        
 
         """
         self.paramnames = param_names
@@ -937,6 +940,8 @@ def warmstart_from_similar_file(
     See :py:func:`ultranest.hotstart.get_auxiliary_contbox_parameterization`
     for more information.
 
+    The remaining parameters have the same meaning as in :py:class:`ReactiveNestedSampler`.
+
     Parameters
     ------------
     usample_filename: str
@@ -946,8 +951,13 @@ def warmstart_from_similar_file(
     min_num_samples: int
         minimum number of samples in the usample_filename file required.
         Too few samples will give a poor approximation.
-    otherparameters: ...
-        The remaining parameters have the same meaning as in :class:ReactiveNestedSampler.
+
+    Other Parameters
+    -----------------
+    param_names: list
+    loglike: function
+    transform: function
+    vectorized: bool
 
     Returns
     ---------
@@ -1051,6 +1061,10 @@ def __init__(self,
             are updated until the live point order differs.
             Otherwise, behaves like resume.
 
+        run_num: int or None
+            If resume=='subfolder', this is the subfolder number.
+            Automatically increments if set to None.
+
         wrapped_params: list of bools
             indicating whether this parameter wraps around (circular parameter).
 
@@ -1471,11 +1485,14 @@ def _widen_roots(self, nroots):
     def _adaptive_strategy_advice(self, Lmin, parallel_values, main_iterator, minimal_widths, frac_remain, Lepsilon):
         """Check if integration is done.
 
+        Returns range where more sampling is needed
+
         Returns
         --------
-        Llo, Lhi: floats
-            range where more sampling is needed
-            if done, both are nan
+        Llo: float
+            lower log-likelihood bound, nan if done
+        Lhi: float
+            lower log-likelihood bound, nan if done
 
         Parameters
         -----------
@@ -1489,6 +1506,8 @@ def _adaptive_strategy_advice(self, Lmin, parallel_values, main_iterator, minima
             current width required
         frac_remain: float
             maximum fraction of integral in remainder for termination
+        Lepsilon: float
+            loglikelihood accuracy threshold
 
         """
         Ls = parallel_values.copy()
@@ -1737,7 +1756,7 @@ def _create_point(self, Lmin, ndraw, active_u, active_values):
             number of points to try to sample at once
         active_u: array of floats
             current live points
-        active_values
+        active_values: array
             loglikelihoods of current live points
 
         """
@@ -2100,8 +2119,14 @@ def _should_node_be_expanded(
             maximum number of likelihood function calls allowed
         max_iters: int
             maximum number of nested sampling iteration allowed
-        Llo, Lhi, minimal_widths_sequence, target_min_num_children:
-            Current strategy parameters
+        Llo: float
+            lower loglikelihood bound for the strategy
+        Lhi: float
+            upper loglikelihood bound for the strategy
+        minimal_widths_sequence: list
+            list of likelihood intervals with minimum number of live points
+        target_min_num_children:
+            minimum number of live points currently targeted
         live_points_healthy: bool
             indicates whether the live points have become
             linearly dependent (covariance not full rank)
@@ -2310,6 +2335,10 @@ def run_iter(
                 print('lnZ = %(logz).2f +- %(logzerr).2f' % result)
 
         Parameters as described in run() method.
+
+        Yields
+        ------
+        results: dict
         """
         # frac_remain=1  means 1:1 -> dlogz=log(0.5)
         # frac_remain=0.1 means 1:10 -> dlogz=log(0.1)
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 71aaa2b2..d4890102 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -223,8 +223,8 @@ def generate_mixture_random_direction(ui, region, scale=1):
     -----------
     region: MLFriends
         region
-    uniform_weight: float
-        sets the weight for the equal-axis ball contribution
+    ui: array
+        vector of starting point
     scale: float
         length of the vector.
 
@@ -756,8 +756,10 @@ def move(self, ui, region, ndraw=1, plot=False):
             current point
         ndraw: int
             number of points to draw.
-
-        All other parameters are ignored.
+        region:
+            ignored
+        plot:
+            ignored
         """
         # propose in that direction
         direction = self.generate_direction(ui, region, scale=self.scale)
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 7cf1a47c..8ea524d5 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -154,6 +154,8 @@ def resample_equal(samples, weights, rstate=None):
         Shape is (N, ...), with N the number of samples.
     weights : `~numpy.ndarray`
         Weight of each sample. Shape is (N,).
+    rstate : `~numpy.random.RandomState`
+        random number generator. If not provided, numpy.random is used.
 
     Returns
     -------

From 08ab73c2c1e1e83e3b1ac0dfb1d970b735a623f3 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 12 Sep 2022 11:42:50 +0200
Subject: [PATCH 100/313] =?UTF-8?q?Bump=20version:=203.5.3=20=E2=86=92=203?=
 =?UTF-8?q?.5.4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index a6183638..5ad486d6 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.5.3',
+    version='3.5.4',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 103a606b..f4b86270 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.5.3'
+__version__ = '3.5.4'

From a4ace9e59da95c9409010578372c121a9ca36742 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 13 Sep 2022 16:48:10 +0200
Subject: [PATCH 101/313] [doc] correct notebook to avoid likelihood plateau

---
 docs/example-sine-highd.ipynb | 349 ++++++++++++++++++++++++++++++----
 1 file changed, 307 insertions(+), 42 deletions(-)

diff --git a/docs/example-sine-highd.ipynb b/docs/example-sine-highd.ipynb
index 80d665e8..a673d24d 100644
--- a/docs/example-sine-highd.ipynb
+++ b/docs/example-sine-highd.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,9 +89,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -117,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,8 +216,12 @@
     "    # avoid unnecessary multiple solutions:\n",
     "    #    force ordering by period from large to small\n",
     "    if P1 < P2:\n",
-    "        return -1e300\n",
-    "    \n",
+    "        # instead of returning a very low number:\n",
+    "        ## return -1e300\n",
+    "        # which would give a likelihood plateau causing some loss of live points\n",
+    "        # we give a slope towards the \"good\" parameter space:\n",
+    "        return -1e300 * abs(P1 - P2)\n",
+    "\n",
     "    # compute for each x point, where it should lie in y\n",
     "    y_model = sine_model2(t, B=B, A1=A1, P1=P1, t1=t1, A2=A2, P2=P2, t2=t2)\n",
     "    # compute likelihood\n",
@@ -223,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,9 +269,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36c8a23fc9314d4382ddc2543cf55e32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=-4e+01   [-35.7755..-35.7752]*| it/evals=8440/59678 eff=14.2380% N=400    0 0  \n",
+      "[ultranest] Likelihood function evaluations: 59697\n",
+      "[ultranest]   logZ = -52.21 +- 0.1439\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 2209.0, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.05 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.14, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.19 bs:0.14 tail:0.01 total:0.14 required:<0.50\n",
+      "[ultranest] done iterating.\n",
+      "\n",
+      "logZ = -52.252 +- 0.301\n",
+      "  single instance: logZ = -52.252 +- 0.190\n",
+      "  bootstrapped   : logZ = -52.214 +- 0.301\n",
+      "  tail           : logZ = +- 0.010\n",
+      "insert order U test : converged: True correlation: inf iterations\n",
+      "\n",
+      "    B                   : 0.36  │ ▁  ▁▁▁▁▁▁▁▁▂▂▃▄▅▆▆▇▇▇▇▆▆▅▄▄▂▁▁▁▁▁▁▁ ▁ │1.64      1.04 +- 0.15\n",
+      "    A1                  : 3.07  │ ▁▁▁▁▁▁▁▁▂▂▂▃▃▄▅▇▇▇▇▇▇▆▅▅▄▃▂▂▂▁▁▁▁▁▁▁▁ │4.80      3.94 +- 0.22\n",
+      "    P1                  : 2.878 │ ▁▁▁▁▁▁▁▁▂▃▃▄▄▅▇▇▇▇▇▆▅▅▄▄▃▃▂▂▁▁▁▁▁▁▁▁▁ │3.294     3.074 +- 0.055\n",
+      "    t1                  : 0.00  │▇▄▁                                  ▁▂│1.00      0.18 +- 0.36\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "result1 = sampler1.run(min_num_live_points=400)\n",
     "sampler1.print_results()"
@@ -269,9 +334,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bddc773e7224f198f77288b819f15c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Z=-184.4(0.00%) | Like=-174.71..-46.75 [-184.3100..-169.5248] | it/evals=2752/159174 eff=1.7333% N=400   \r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/user/.local/lib/python3.10/site-packages/ultranest-3.5.4-py3.10-linux-x86_64.egg/ultranest/integrator.py:1732: UserWarning: Sampling from region seems inefficient (0/40 accepted in iteration 2500). To improve efficiency, modify the transformation so that the current live points are ellipsoidal, or use a stepsampler, or set frac_remain to a lower number (e.g., 0.5) to terminate earlier.\n",
+      "  warnings.warn(warning_message)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=-4e+01  2 [-124.0658..-104.8495] | it/evals=3564/400459 eff=0.8909% N=400 \n",
+      "[ultranest] Likelihood function evaluations: 400459\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: overflow encountered in multiply\n",
+      "  x = um.multiply(x, x, out=x)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Reached maximum number of likelihood calls (400459 > 400000)...\n",
+      "[ultranest] done iterating.\n"
+     ]
+    }
+   ],
    "source": [
     "result2 = sampler2.run(min_num_live_points=400, max_ncalls=400000)"
    ]
@@ -293,9 +419,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Widening roots to 400 live points (have 400 already) ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f2ec77fc6ac474c92c5fad0ad5251f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=-2e+01   [-19.8059..-19.8052]*| it/evals=12427/929849 eff=1.6740% N=400   \n",
+      "[ultranest] Likelihood function evaluations: 930279\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: overflow encountered in multiply\n",
+      "  x = um.multiply(x, x, out=x)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest]   logZ = -46.08 +- 0.2089\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 2942.8, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.07 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy wants 398 minimum live points (dlogz from 0.18 to 0.58, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.24 bs:0.21 tail:0.01 total:0.21 required:<0.50\n",
+      "[ultranest] done iterating.\n",
+      "\n",
+      "logZ = -46.147 +- 0.580\n",
+      "  single instance: logZ = -46.147 +- 0.241\n",
+      "  bootstrapped   : logZ = -46.079 +- 0.580\n",
+      "  tail           : logZ = +- 0.010\n",
+      "insert order U test : converged: True correlation: inf iterations\n",
+      "\n",
+      "    B                   : 0.41  │ ▁▁▁▁▁▁▁▁▁▂▃▃▄▄▆▆▆▇▇▇▇▇▅▄▄▃▂▂▁▁▁▁▁▁▁▁▁ │1.62      1.01 +- 0.15\n",
+      "    A1                  : 0.10  │▁▁▁                     ▁▁▁▂▄▆▇▇▄▂▁▁▁▁ │5.31      4.19 +- 0.24\n",
+      "    P1                  : 1.00  │▁▇▁ ▁             ▁      ▁           ▁ │77.45     3.07 +- 0.82\n",
+      "    t1                  : 0.00  │▇▃▁▁ ▁ ▁  ▁▁        ▁   ▁         ▁▁▁▁▃│1.00      0.22 +- 0.40\n",
+      "    A2                  : 0.10  │▁▁▁▁▁▁▃▄▇▇▇▆▄▂▁▁▁▁ ▁           ▁▁ ▁▁ ▁ │4.50      1.21 +- 0.25\n",
+      "    P2                  : 1.000 │ ▁▁▃▇▂▁▁              ▁           ▁▁▁▁ │3.264     1.254 +- 0.059\n",
+      "    t2                  : 0.000 │▁▁▁▁▁▁▁▁▁▂▃▄▅▆▇▇▅▄▂▂▁▁▁▁▁▁  ▁        ▁ │0.718     0.262 +- 0.053\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "import ultranest.stepsampler\n",
     "\n",
@@ -334,9 +525,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 698.4x698.4 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from ultranest.plot import cornerplot\n",
     "cornerplot(result1)"
@@ -344,18 +548,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x1152 with 49 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "cornerplot(result2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "59697"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sampler1.ncall"
    ]
@@ -377,9 +605,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure()\n",
     "plt.title(\"1-sine fit\")\n",
@@ -403,15 +644,28 @@
     "# add 1 sigma quantile\n",
     "band.shade(color='k', alpha=0.3)\n",
     "# add wider quantile (0.01 .. 0.99)\n",
-    "band.shade(q=0.49, color='gray', alpha=0.2)\n",
+    "band.shade(q=0.49, color='gray', alpha=0.2);\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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b0bX0hTJdcxGYeitgaJFbrVYaGxtJJFStm2JoUnLHqXBOfX09AE8//TROp5NLLrkEwEy9PLrRCWB67sFgkFAoRCwWG7APQGlpKc0F/4+f9PwX/8h+jA9d/nHWbWnm0KFDZGdn09DQQDJpPK82m02laJ4kyuCPc6SUNNlX4i/4/5DQ/7IQLP0k4eKrzP3CxVfRNf1edGFHAglHOf5Z96Xtk6qUbG5uVlNjxZCkQn9CCHRdZ8+ePSQSCTZs2MD555+P1+slmUwSi8WYP3/+gOMjkQiRSASfz0dxcTGFhYXYbDaCweCA9EohBPPmzSOZTJJIJLj44otxOBw8/fTTpuRCav3JbrcTDoeH/PFQfDDK4I9jDC8pir3pT2R1PomA/lcSd8vjOJr/RCwWIxDo5emnn+b36wVNsSpakjN53rWWessKs8glhdPpJBKJ0NXVNeg1FYqenh7Tu29rayMcDvPqq6+i6zoXX3wxYChnzpo1a0Bnq3A4jM1mY9KkSRQWFpKVlUV2djbl5eVUVFSQTCYHhBU9Hg9z5syhu7sbr9fLqlWreOONN+jq6iI7O5twOIyUhoOiaRq9vb2j8LcwMVEGf5zS19dHJBpFCMir/9GANnCaHsa++078/h5i8Tgej4eioiLsNhtC0zh06BAvv/wyr7zyyoAviNvtpru7W3lKigHouiQWi5mLtfv27cNut/PCCy9w5plnUlxcbIYEZ8yYcdSxRl59WVnZoLLGLpeLiooKdF1Pk1lInctmsxGLxbjsssvQdZ2///3vWK2GZEPqR8LhcNDT06NmqCdIZhr8o6WHx1GBUkqZsKmpCU3TEEIbMgPHK7qw2+1YLZa0/GRNCHJzcykpKSEYDPLCCy/Q3Nxsfp6aKh9Z5q5QgBG/T+XdBwIB2tra2L59O4FAgEsvvRQwvPs5c+aYBVm+g/fj8K/HFdhIxauVWHbdNeT57XY7xcXFhMPhNKNts9mYN28e3d3dFBUVcdZZZ/HCCy8QCoWwWiyEQiGklGiahpRSZZydIJln8AeTHt60elwY/UQiQXNzM11dXXg8HkR/mttQGTgh8cH9Q7OyssjKyuK1114zF9/A8JSCwaD64ihMJJBIHm5O3tDQgKZpPPvss1RWVjJ37lzTu588ebJ5XO/kf+XdxfsIfTR4TFIlXq+XwsJCQqF0RdjKykocDgfRaJQrrrjCDCVpmkYymaSjowMwfjS6u7uVs3ICZJ7BH0p6ePvtYzOefmKxGA0NDUQiEcPYi8M5zW2l3yAh07N04tLO5qZiRGAPBXI3y9uv5eVf3sil3z3I7b9u4d133zX3dTgcFBQUsGHDBtrb29O2p0rcFQqp60gpsVgsplRxc3MzdXV1XHrppQgh6OnpYfr06aZ3D0bcPicn55gLrwBycnLwer1paZZWq5V58+bR09PDtGnTmDp1Kv/4xz8Mz95iMaWTbTYb0WhUpRifAJln8IeSHh4jSWIpJX19fRw6ZFz/6Gmyw7+estpvcig518jQkdDa5+TnL8ZYnL+LQp9EE1CZp/Mf1wS54vQkj77cw3e+8x2+//3v09raChhfkpycHN544w1TftZqtaLrOn6/f0zuXTG+SCST5qyyo6ODaDTKCy+8gNfr5cblbfxT8hr+OfcrXNJ1Kb6D9xvHJBJomkZeXt5xXUsIQXFxMUKItHh+ZWUldrudWCzGhz70IVZMasQXf5dSyz4uCt2IqPs9YBRu9fT0DM+NZxCZZ/CHkh4eA0niZDJJa2srTU1NOBwOcyoNxjR5XdnL/Gfbf/Cr+C/ZaLuZn/xD4Ps8lH0pxlVnOfA40s/nsul852OSnT+ZwbXXXsu+ffu4+eabef311wHjx8RisbBp0ybzS+Z0Ounq6hqwiKbILPQDv8XaU4MzsJGi9UuI7fslsViMLVu2cN555/Ge8xqaEjNoF3NpWtlIYMpNgJGCWVRUZBZpHQ8Wi4WCgoI02Q+r1cqcOXPw+/18crnk518Atz2JALIs3RQdvB1X6xNKRfMEyTyDP5j0sMVtbD+SHWtGTjO//9yWP1gpeamU0vb/TvvCJBIJtm3bxrZt2ygsLCSZTPLDH/6Qr/xSsnwm/OhHP6LYN3iGjUYCr8vClVdeyQ9/+EOmTp3Kww8/zLPPPgsYU+muri727dtn7N+/CKZS3TKYg48iNq9GkzEEYI02cnr8J7hanyCZTHL++ecDRhaO64iwTSwWw+124/F4TvjSPp8Pm82WVgw4adIkhBCcof1pgFNjIYrvwL0IIdA0TenrHCeZZ/CPlh52TzLeT7kufb8R0sxPJBK0lXyZkG8Jkayz0rwlMOKhr7/+OgcOHKC4uJiWlhZuv/12duzYwUOfFfz9FkFZWdmQPUP1IwRQCwoKuO2221i8eDG//OUvWbduHQD5+fns2rXLNPIpL1/F8jOU7bcjkulpvzYR5yOTNzFz5kzKy8uNbBmrFVt/NpiURvpmQUFB2nrT8SKEoKCgIC0eb7fbmTZtGh4GFw1MZa2pFM3jJ/MMPhjGvWCpYcw/WjvQ2I8A8Xiczs5ODh06RCAQwNKfcnkkPT09vPTSS/j9foqKitiyZQvf/va3CYfD/O/t5/CVD0ksQvJPyWvoYgoJBi7kBvT0zB273c7Xv/515s2bx89//nN27dqF1WrF4XDw9ttvp6W6pWL7isxCDrF+VZadZNWqVYCRoul2e0gJpEWjUbKystIWb08Uj8czwMufPHkyAT130P1Tzk4qe0dlmh07mWnwjwMpjVh7qpFzd3c3LS0ttLa20t7ejt/vJxQKEYlECIfDRCIRotGoUQJe8y0zJGR73I7YeSd2u72/OUS6V1RfX8+LL76Ipmnk5OTw+OOPc//991NeXs6///u/c8bsdEPeGi3iH72fICGNwpSwVsQuz9eJkE0sHqetrc2Mb1qtVv7lX/6F4uJiHnzwQfx+P9nZ2TQ2NtLS0gIYPwxdXV0q1S0Dka6KQbfXd8GyZctIJBLYbDZzjUlKSTKZPO6F2qEQQlBYWJjm5WdlZbHLeh3xo7LTEth5LXCpmd1jt9vV4u1xoAz+IEgpCYfDRGMxIpEwBw8epKGhgcbGRjo7O03jHwgESL71HdxPeXA+4cL1pJvQhpupr6+nsbGRuuwbCPeHbhrPayAy49YBjUiklOzevZv169eTk5ODpmncf//9/PnPf2bFihXccccdRghG+zhtzKEpMYMHO35MY96NVJ5zC8mcamLZy+he8RbTCpOUaHupsO3nq/lfY2b0d+aXwePx8PWvf51QKMTPfvYzALKzs9m2bRu6rmO1WonH40qNMAPpm/atAcJ7wSj8ZodRXNXT08PMmTPN0E3Kuz8yyeBkcbvduFyutOpv5+zP8ULAcGp0Cc29djaL1bwbW0xDQwNgZJ+lnCzFB6Pax2AY3Wg0SjAYJBqNEolESCaTVOg6mqa9b35xZOatRIM1AHSe/icAjtxb04zF2MHinLrU2bBhAw0NDVxQ9BqO5if46AOwrwVu/exCFl7yJfO4SCRCTMawOxxcfPHF+Hw+4yR1h88bmHJT2npAdiCAZ/Nm2tqMLkKTJk3ik5/8JL/+9a959dVXOe+882hpaaGhoYGqqipTjfB48qkVpzZSSjq8HyI+4wfk7fs30GN0Rjx87X+DzL74GtObr6qqgn0Axvvc3MHDLSdKKpZfX19v/pAUFhaySZxDBzXc+WgbP3/ez09/uoCsLDv79+9n+vTp5uJtb2+v2ZBFMTQZ7eFLDM2a+vp66uvr6enpIRaLYbfb8Xg8aELjeJo6HA/xRNwMD5WUlPDojhks/q5GW8DCt27/DosuvQ0hhPGF7OggFouRnZ1NdlbWYWP/Afh8PlasWEFVVRWtra1IKbnkkkuYMWMGv/3tbwkEAuTk5LBr1y6jhZzNRigUUqluGUTKuYmWXk3IvZDGxHTOvq+cV2rLmDlzJoFAgJKSEjMTR9d1cnJyhtW7T5HqmpXy8jVNY8aMGSQTCf5pRTbJZJLXX38dp9NJMBg0BQAdDgd+v1+lFh8DE9/g71gzaHplsl+vOxXD9ng8uFwu8hsfouLVSsrWlePwr8cSqR/qzCdEPB7n7bffpqe7GyEE+fn5PPXUU9x3331MKrLx3F1TOO200wAjo6e1tZXS0lIuuugi7HYHx/sDZLVaqa6uZvLkybS1taFpGjfeeCOhUIjHHnsMp9NJX18fzc3NCCEQQqjF2wyir6/PTAmOxaLUt8fZt28f5513HkIIwuEw06ZN699bIjFSe0eK3NzcNIejsrISCcytcjJ16lReeeUVwIjdp4oVU0kHKhz5wUz8kM6CNdC2zvjzhetIJpN0trXhjUbRxMBwTWDKTdh71g/7MHRdp66ujp07dxKPx1mUZ6ezN8GPfvEjtmzZwvLly3n0C524HRr7MTTJA4EAixYtMqeuJ4oQgjPOOINIJEJnZydVVVVcdNFFPP/881x++eWml19WVmamuuXk5JzUNRXjH13XzUYnKYP59GZDdiS1WGuxWMxQia5LrBbLoEqYw4XL5cLhcJBIJLBarXg8Hqw2O4lkgvPOO49HHnmEgwcPUllZSW1tLfPmzcNms2Gz2fD7/QPkmhXpjLqHL4SoEEIEhBCfH+1rRyIR6uvrjfZtmjYqBk1KnUgkzN///ndqampwu90UFhby/NYAK289wPbt2/nMZz7DV7/6VdwO45+ju7ubRCLBhRdeyIwZMxBCpEstrCvHd/B+pJT9WTXyA7NrLBYL1dXVWPqVBz/2sY9hs9l4/PHHcbvd+P1+Ojo6sFgsJBIJ1fQ8A4hGo2Zabnd3N7qUPLm+lxkzZlBUVERvby9Tp07FarUa1a19b2HrWT+iCrOiX+n1yEVYl8uFnkxy9tlnY7VaeeWVV7BarSSTSVMbSoUjj42xCOk8BIx6pUQikaS+vh4hRL9XPzLGXkpJIBCgvr6e1157jc7OTgJ9fTgcDkpKSojFYvz0pz/lM/c3UJht5Z577uGyyy4z4vVgattfcMEFaQtjgSk30bSykUPn1PHu4n20Fn/JnMLq/d5ZKBQaIDt7JC6Xi+XLl9Pb24vX6+Wyyy5j/fr1HDx4EJfLZQquWSwWVXmbAfT19ZlZY42NjextiLK7PsrZZ58NGOHHyspKXK1PkL33ZrMSd6QVZj0eDxaLxYzJ2+w2NE3D4XBQXV3NG2+8QSKRwOPx8N577wGY4UiVk//+jGpIRwhxJcZa6dbRvG4yqROLx3C5XAPSIt8fia4bBjyRSJiNG5LJpJm9oGkankiEpJ40DXzqc4/Hg9VlQwA2YeOFF17gD3/4A319fXzjI/ncdFUhbzgmAUaZejxpjHHF0hUDps2xWMycYpeUlOB2uw29/FqjYnjatGnEYjH6+vro6elBSonD4RigcZKfn8+8efPYtWsXH/7wh3n++ed55rH/5LWbWyEGrIPeSf9CW/GXKSgoOCGNFMX4J+WY2O12pJTU1taye1MITcDSpUtN1dbc3Fx8G+4d0IDHVJgdgaJFTdPIzc2ls7MTt9uNQOByufC3+Vm5ciUbNmxgy5YtLFmyhNbWVsLhsBkK6u7uJjs7W4Ujh2DUDL4Qwgv8ALgY+NUQ+6wGVrvdbqqrq1m9ejWrV68+qev29fWhxYx4/WDG3nfwfnx1D5jvm/JvJBjsQ8Ta8YkO7CQo3bSM9eEreDe2eNDQSWlWwFjsjPeRk5OTZiRFEjbtDfGtX3+L2tpaZs+ezQ++vJDL8/+MRicfTn6FjdGP8E5wEVllWTgcTqJHGftgMIjT6aS4uBin0znowyyEwOFw4HA4yM3NJRgMmou0qXZ1KWbMmEFtbS1CCK644goee+wxXqqdwqxyC7vLf8rcKXOR/c2njzUjSHFqEY1G0fvTjlO1JU9t6OXc0zzk5OTQ3t7O/PnzEUIM2YBnJBVmfT4fnZ2d5mw1FdefP38+ubm5rFu3jrPOOgtN02hpaWHKlClYLBazADLTUovXrl3L2rVrAeYIIWqAtVLKtUfvN5ohnTuAX0kph3xKpJRrpZTVc+bMoaamZliM/eHOUYeN5JHxcF/dA8RspbSL03i46yH+tHceItpGtmjFKhIIYaj0XeB9jDPz91JUVDTgZbfZsFmteL1e09hLKdm1axf/dO8hrvheHb29vXzta1/jf75zAZflP4mFBALw0MEK+2/42OlhHI70MnVd1wkGg/h8PsrLy3G5XMfkuWiahs/no6qqCk3TBhSlWK1WTj/9dHp7e7nwwgtxOBysfbYLq9XK/v37SSaT2Gy2gWGdHWtGTlBOMaocGfpIFRXWtcW5ankWUkp0XaeiwqjATdhLBz/JCCrMWq1WcnJyzGfXYrFSVFREOBxmxYoVbNu2je7u7rSwDmAu3mYaq1evpqamBmC3lLJ6MGMPo2vwZwCfE0LsBpYAdwshVo7UxaLRKM3NzYaRPCpen4qH7z1zD3+wPEZb0E08HiM/P5/i4mKyLJ1oIt2TtxJjgXzsfa+p6zq1tbU8/vjj3HTTTdx1113srItw+z8V8sADD7B8+XIW8gespCtdWkWM4pYfp21LVb0WFhZSXFx8nKEoA5vNRnl5+aBGv7i4mJKSEhKJBCtXruTJ9b20+40G021tbeYi2JH6JiMlKKcYXVLqqA6Hw3xmt2/fjsMmuGyxj76+PkpKSvolQKCj/F+Rx6IwO8xkZWWlzainT59OMBhkxYoVSCl54403zP7MKdVMm81GX1+fWrwdglEL6UgpP5r6sxDiZeC3Usp1I3GteDxOU1MTNptt0Bh0Kmb51ltvGRohPqOIJGVUNRIDjgFwY0x9e3p68Pv99Pb24vf7ebOtjd31Ud7cdyPBYBAhBHPnzuXOG2Zx/ZzXsIh2QvwbO/RrcL+PAmDSaXhMsViMZDJJZWXl+4tT7Vhj9OUFw+Oed8cAI2y1WikrK6O+vp54PG6uDQghmD9/Pv/4xz+4+OKLef7553jkhW7mXOZh//79lJaWIoQgFAqRlZU19BgUpxyp58vhcNDe3k40GmXTpk1cuMhLlttCqCPE6aefDhi1IJR/HEpKYOPnjdag7kmGsR9h0cGU7pSUOkJoFBcXY7Vayc/PZ9q0abz66qt8+MMfxmq10tjYyOzZs83K276+vmGvBp4IjEkevpRy1UidW9d1mpubzUXLo4nH42zZsoX6+nry8/MNA3hEgV5fXx9BiwWvfWDV3qEOyfVfvz5t2yeXw7f/CSrzoa3Pwd+bLiRaejULs3exWK7F2v/j4aGDxXItEd2NSxuYSSAtXhx+I/9/8ptTSMy5Heu0u9//ZhesOSYv22azmUZf0zTzRzAnJ4cpU6bQ0NDAh0738qsXunngo3ZaW1sJBoPY7Xb8fr8y+BOJHWtw7LyT6f1vg45rqa0tpKenh48tK0dyuDEJGD8ORUVFiKzr4L2fGwdduG7UhlvU8hNsvRsBqHp9EheVXMs/Ws5mxYoVPPLII9TV1VFaWsr+/fuZNWsWQgiz562qJRnIhKu07erqIhaLDeoZBwIBXnzxRZqbmykuLja93d5Qkp/9rZNbbrmFG2+8kdU/TxI8SospHNf4495FXHvttXzxi1/klltu4akff4JffdnOpALQBJT4onx61qsszN7FAvnYwNANMTRNQ9eOitVrLoKFH0VipGZKdxXWnDnD+vfidDopLS0lHA6nTZNnzZpFPB5n9aV5dAaSbNiwAU3TaGhoMIWp1PR44iDn30E46ywi2UtpWFHPi+3nsm3bNjwuK9ee3kgxu/nn3K+Q1/Cg+ZyM5QKo9Yy7ObjsAPXnHqJpZSOxWd8ikUiwfPlyLBYLr776Kna73SwqBMyevKrydiATrtI2HA6n6Xy4Wp/A3rsFZIyyzcuZzMdoL7gIMLyXZ555htV/3U9vSGfmzJl8/OMf5xMzN9BHDCcdaCQIUcAO+zVUrTiHI5epLkz+D7ZBjPoC+diQoRs7ffTMepCcPTeBjJF0lBPKWYW37fF+c4+R/bCpf8F6GKfNXq+X3Nxcent7zfisz+dj8uTJVOfYmV5q56WXXmLZsmXs37+fmTNnGsMJhcjOzh62cSjGjlgsBlIihEZXVxehUIjNmzdTvXg5f3F9mZaWFlauXGnIFUci+Hw+rNaxMxNCCHJycswUzVTzc4vFwhlnnMHrr7/Otddei8PhoLa21pyZWCwW/H5/xmXrfBATzsM/klTBiOgvGPFpXaxw/oYq/XWampr49re/zR/+8AeWznbz/N2T+d73vsc3PlrEFXOaKHIYGju9lPG05WEOaecMOP9QRt1NJ31DNG9IOsoIF19FLOtMYtnLqD/jNVzdL6HpR1W2pvKch5m8vDw0TUtbjJ0+fTpSSq5blcPevXtpa2sjHA7T1dVlClMpJgZH6iQ1Njby7rvvEgqFWL58OfF4HKfTSX6+0WBE1/Vx8UPv9XrNqnIhhCnqtmLFCvx+Pzt27MDn83Ho0CFzNmq32wkGg0pQ7SgmtMH3HRhYMGIlxszwL7ntttvo7u7mlltu4Tc3VbJwiosq/XUWy7VmyqSFBFk0U6W/Puj5h2ozGEjm8I7tU+hiYOgmMPXW/ncSXTcKt6yx5sFvYATynC0WC8XFxUQiEXPKnpubi81m4+rlhuf00ksvYbPZaGhowGq1GkVhKqxzypPKzjGSEw4nLvh8PubNm4ff72fq1KlmJymLxTLoOthoY7PZ8Hq9popmaWkpUkoWLlyI1+vltddeM8M4ra2twGE5clV5m86ENvhDFYzk2vuYMmUK9957r5mNAAwadxdIFsjHiMfjZtOT7u5uWltbeS1wKXGZXiSVFE5ic9ZQee4t+Gf/ECnsSCDhKMc/6z5CRR8zJGl1HYvFQkVFBWKofOYRynN2u91kZWWlpWq63W5yPYIzzzyT119/HbfbzcGDB00PKRQKjchYFKNHNBrt//cUxONx+vr6eOutt1i2bBlWq5VEIkF5ebm573ha9MzJyTGfRZfLZfR17p+ZbN68mVAoNCAnP60b1o41qoaECW7wI9rgDRHagg6+853vmFPXFEOGaGQHkUgEh8NBfn4+U6ZMobq6mqLF36Cl6i70I4x67+wfEiv7/wDSQjdtyzYRLPwowWAQr9eL0+HAbrcbGTML7zHymo9khPOc8/PzTQkIV+sTeCM7Kbe9x9+/uJvL5/Wye/duEokEXV1d2O12pa0zAejr60vrWrV7925isRjLly8n0h+vz87ONsMn40l50ul0punrTJ8+nUgkwnnnnUc8HmfDhg14PB7a2trMsJXVaiUajRqOjaohASawwe/s7GRdz4dIHNUTMxLXOOD7vJmaWKW/Tj7vUih3I+Xg3kzSWc5ll13GypUrOeuss5g/fz6TJ0+mpKQEy7RPEz/CqIeLrxr0HLFYzCykKioqSi+kmnIdLFmLKejmnmS8H8E8Z5vNRm5uLtaGPx5e5xCQ5wjwiy+As+UJ7HY79fX1WK1WIpFIehGW4pTiyGIriSQSjbJ161by8/PNmPi0adMQwvD+vV7viMogHy+pxdtUWCc/Px+n00lFRQVlZWW8+uqrCCGwWq1m+0MwamtSRVmKCWrwY7EYGzdupNm+is3aaoIxC7oOLQEHW21fpN6yAiA9Zi9AEzpHK+VItCPi7sePlJKknjTDN0MWg0y5DmxZYMuGj9aOeFELGNPkvPofDVjncDvghjPexWq1UldXZ3pV8Xd/BR0bjGKvEZTIVQw/R2rnJOJxugIJdu7cyfLly41UYV2ntNSQUEgkEuOy9sLj8ZjaOpqmMX36dHPxds+ePbS2tpKVlcW7776bpsHj9/uHVJDNNCakwX/nnXeIRqN4PB629cxl0tfgtNscPO9aaxp7gPn67weJ2RtynhKQwk7CNXVIr/39SCQS/QtGEofdQUVFhZkKOV6wWq1DLhhX5sFbb71FMpmkq6uLnJ5ncGz/qlFpCSMukasYXo4O5/ytxshgOfvsswmFQuTl5eH1etF1HSHEuHtWwYjJO51OM4GgoqKCRCLB2WefjRCC1157bUBOfurHTOXkG0w4g9/Z2cmBAwfIy8tD13UeeughwjGdX3y9PC3joK+vD88QMXuAWPYyYllnotuPrzFyLBYz08GKi4txOJ1YLKPTbOWEcFcOurmxR+O1117D4XBQX19P7qGBM4GRSh1VDC9HhnOSySSRaJSnNgQoLy9n0qRJBINBpkyZAhjPr9frPSHtptHgyLCOz+ejqKgIl8vFaaedxiuvvIKu6zidTvbv328eY7PZDi/eZjjj81/1JNi9e7epFf+3v/2N3bt3c+/1JcwoO2zsU1O8+FAqgOLYGzTruo4udXQ9STAYNGUMJk2aRFZW1gi1WRk+xMJ/R2rp3lxc2vnj3kXs3LmTRCJBXV3dmEjkKoaHVKNyTTOKrZo642zcG2b58uWA8YNQUlICQDKZHFwSe8caI5TX9sqYZrl4PB7Tawc4L+9lPu/6At86byft7e0Ed/6UrKwsGhsbza5tdrudUCiE/gFd4TKBCWfwUwJhjY2N/OEPf6C6uppPnHu4eCQ1tT3vvPMITv8W+lHGTtdcJJwD0yF1XTdb/4VCIUKhEMFgkHg8jkXTsNvtTJo0ifLycvOhPC4OPgrxXoj7Rzc+PuU65JL/Tss02pt9E52+i5FSmmGduK1k8ONHUCJXMTz09vaaSQqJ/b9mhu8Qid/AL654geLIi+Tn5xttBPtj/IMK9i1YY2S3pF5jlOWSao6SSilOzPk2jYkZLD9zJh6Ph0fWJc3vXionH4xFX1WENQENPhheyk9+8hOcTidf+MIX0mKX4XCYc889F6/XS7j4Kvyz7kvLle+ecS8Jax66njRfqV6ZKZ35kpISqqqqmD59OlOmTDHTK4+UdDguDj7aL6WQklYY3fi4NvXTJHKqifjOom3ZJmwzPktxcTGlpaVs2rQJu93OLut1A34cR0MiV3Fy6LpOIBDA4XBga3ycBZGHyHHraP19Hs6x/YrFhUZry2g0SnZ29rgN56Tw+Xzouo6UEqvVitPhwGqRnHvuuWzatIlAIIDP52P//v1mcWGqgUqm+/jj+1/2BFm3bh3vvfceN9xwAzk5OYBhSru6uli6dGlaBkIqVz6adRYHF7xMb94VWK1WHIkWXIFNuAKbmL5pBlMCv6KiooLCwkK8Xi92u/3E4/I71qRPj2v+2YiHH8kox8etFguy/+vg8/nIz8/n9NNPZ9euXQBsbJ1Gz8z7zJnAaKSOKk6eVFhDCEHWwXuxifSKaZuIM9lv9MoYb7n3Q3F05a3T6UTqOqtWrSKRSJiFg11dXWbs3mKxmI1dMpkJZ/DD4TB/+ctfmDt3LsuWLTO3x2Mx5s+fT1lZWdr+xkNgVB9WVFQwefJkbDYbmm/KyE1hj54ex4coahrF+LimGS0gUxkQ06ZNY+7cuei6zvbt24nFYjTZVxL1LiKRu3zUUkcVJ8eR4Rx7onXQfayxZhKJhOHojAMphWMhJyfHrAuxWq1YrVYKCwuZOnUqL7/8MlJKbDYbtbW15jFCiIyvJZlwBv9vf/sbgUCA6667zvTAE4kEdrud2bNnp+2baiFosRgP+rG2EDxmdqw5toWuUZZWGApbv24OQElJCZWVlRQWFrJx40asVistLS0ITSPRX6GrGN8kk0n6+vqw2+3EYjECyZzB93OUEYvFyM3NHb/ZZEfhdDqx2Wz9Blzgcrnp6+tj1apVHDp0iAMHDpCTk0Ntba3pxGj9cfzUM56JTCiD39TUxLPPPsuSJUuYNm0aYBh7Y6rqS3uYE4kE4XCYoqIibHYbI/KcH+tC1xhIKwyGpllMESqHw0FZWRmLFi1ix44daJpGXV2dkXUkpRJTOwUIhUKmwmRraytP1y8b0OdB11z0TrkVKSUej2dsBnoCCCHIy8szF2/tDiPEunTpUux2Oy+//LIpxdDc3Nwvk74VV2ATlqenZWz9yIQy+G1tbZSWlnLVVYcLpTo7O/F4PGmtDnVdJxqNUlFRYQhEncxFd6w5+XS1MZBWGAwhSMuAmDJlCvPmzSOZTLJr1640udlUbFgxfunp6TETCQ4ePMivX03y//5XENetSAlBCvDPug9/7uV4vd4x1b0/EQ7XC0g0oVFZWUkikWDp0qW88cYbpj5QbN8jaTLplkgDMkOLBieUwV+0aBF33nknRUVFAKaCntN5OLtESkkoFKKwsHB4qgmHK10t8C5pWTqBd09+bCdASnscoKCggClTppCbm8vGjRvNGL8QIk1XXTH+iMfjRCIRbDYb4XCYlpYWNm3axJ7wGXRrM2hMTGfv7OcIF19FMpkcF7r3x4umaWRnZ5sLsVOnTiUWi7Fq1SrC4TAbN27E7XazSDw+oGhQjERSxI41416Rc0IZfDisg52qLry8cjPO3g04/OspW1eO8917yc3NHX8P+DhR87PZbLhcLmKxGDabjaqqKhYuXMi2bdsQQhCNRs3m5mbGw4414/5BzzSOlFJoaWlh//79dHd3c+655xphHowf9JSUwqC596cAWVlZZqplSh5i8uTJlJaW8vLLLwPg07oHPVYOd1LEOPkOvx8TzuCn8Pv9lJWVoc/7Lk0rG2la2ciBpe8RnXkbBQUFQy9O7VgzLioKx5KcnBwzRl9VVcVpp51GPB7nwIEDxOJxcwZg6umfAg96JiGlpKenx8y4OXDgAFu3bsXtdnPGGWeQTCZxOp2mfPCpkHs/FHa7HU3TkNL44Zo1axa9vb2sWrWKPXv20NjYSHCIRkXSVTHKox17Ts1/5WMgEokwd+5c830ymUTXdQoLC98/E2GcVBSOJalsJSmlKZ/rdrvZsmULAPF4AiGE6iY0TklJWVssFgKBAC0tLWzdupVly5Zht9vRpTR/DKSUg0spnEJYrVZTNqG8vBxN0zjnnHOwWq08//zzvK19ckCjIl1z0V72LxlXfTvqBl8IcZcQYo8QYp8QYkRWJf1+/wAp4nA4THFx8bjS+B6vpCqKo9EoVquVqqoq5s2bx9atW5ESYrEodrtd6YyPU/r6+swkhaamJnbu3Ek0GuXcc881KsaFMFMarVbriVeIjwcOPoqlezOuwCaK1i8hu/tpUwhu+fLlrFu3jt3h01mfvIGEtKZ1nwvkX5FxjX1G1eALIc4HPgssBD4JrBVCDGtbeSklkUgkLec+tVp/KlQRjgk71gwIY2VlZZkx+srKSubOnUsgEGDbwRjRaBRN08iqe+Bw3L7tFeirHcObUICRgdbb24vdbkdKybvvvsuWLVsoKioywx1GEoMgFouNqzaGx02/JInQowjAGm0ke+/NLMjaSSwW49JLLyUajbJu3TqaHefTEp9En/tMs1GR0+mkq6sro1KMR9vDdwMPSymjQFf/9Yd1DJFIhMLCQtO7l1KSTCaHjtvvWJPxMfvBwlhOp9NUJSwoKGDu3LlYLBae3xpElxK/3093xddJ5J9zOH7vnTzWd5LxpJrTp5QxW1paeOeddzj33HPNSlOH47BHfyrl3g9g++0DJEk0PUxp20Pk5+dTWFjIrFmzePbZZ9H7e0gf2ZtZ0wzZ8vb29owpJBxVgy+lfFpKeZ8Qogr4I/AbKaWZ3yeEWC2EqNm9ezfV1dWsXbv2uK/hdruZP3+++T4SiZCbmzt0KEfF7AdFCGE2OrfZbEYf39m5vLC1kwrbfuZuO42CpoczLgY63unu7jbz6Wtra9m2bRtSSs455xwikQgejwer1YqURnHdKR3iHCLLxhJtYvbs2fT19XHZZZfR3t5OTU0NFouFeCxGd/fhrB2n00kwGDzlw5Nr166luroaYI4QokYIsXqw/cYihn8hsB14E/jSkZ9JKddKKavnzJlDTU0Nq1cPOub35ZxzzjG1vVNiSeMuBfMUIdUBCYywTsXs89jTBN+v+3d+q/+G4LRvoieTZIhzNO6JxWKEQiFTSqG2tpbNmzczY8YMSktLCQQCTJ8+HRDoUprCgqcsQ0iPJB1lFBcX43Q6WbBgAQUFBfz9738HDK9+7969afu7XC7a2tpO6dDO6tWrqampAdgtpayWUg7qLY92DH8u8CTweSnl16WUw+4eHlktmEo5O6W9mDHE4XBgtVrNkNinFncAYN/5LT6lfRr3/vsA0GVmKxCOF44USmttbaW+vp6GhgYz917X9X7xQOMX2u0e1uWz0WcQSRJdc+GffAsWi8X08i+++GJ2797NztoIFquV+vr6NI8+JRyYCaGd0fbwrwKswD1CiN39r7IPOuhESMXuT3kvZgxJhXVisRgul4tgxY3MrXLw561uHuz4MQe8nwFAV2GdMSeZTKZJKezbt4+33noLi8XCsmXLCAaD5OfnU9L2Mxz+DbgDm7D8wXpqr1mlJEm0foVP9yRC8/6TnpzLAKPnra7rnHfeeTgcDn7+XBcCw5E52stPhXb8fv8o38ToMtox/LullC4p5ZwjXkP0zjs5Ur05T5mUsx1rxuXiscfjMcM6kyZN4qJFbjbuNRrCNDU1IYRGMqk8/LEm5bFqmkZPTw+tra2sX7+eJUuW4PP5CAaDTJ8+ncCUm9i/5F2CH+mbGGtWU66DgqVG0sBHa3HMusFsjuJyuZg0aRK6rnPuuefyxJu9tPUkyM7O5uDBgwNSMlOhnRNueH7wUejYYHyHR7Nr3XEwYQuvEonEqeXdj9PF4yPDOoWFhVywyIMujerNxsZGhDBCOhN9Kjye0XWdrq4us5iqrq6OnTt3EgwGOf/8881+tiUlJWYbw2HRkRqH2Gw2QzCtXwJ52rRpRCIRLr/8chJJyc/+1ommaTgcDvbs2ZN2bGp7a2vr8TdKSXWt0/urz0e5a92xMiENvq7rRuuzU1QfZDwhhCA7O9ucMZ0x1UWez8KOHTuIxWIkEkkssQ7o3DiuPZuJTErF1GKxmBIYGzdupLi4mNNOO43e3l6zsc+pLqVwLGRnZ5uNTnJzc8nLyyMrK4uPLsvikRe6CQQCZGdnU1dXZ3bESpEqSGttbT0+J2aQFNHR7lp3LEzIf/VoNHpqF5SMMzwej6mr7nK7WDnPzfbt240PIy3Yo7WIce7ZTAh2rBkgUielpLu72wxdtrS00NzczJ49ezj//PPRNI1oNMqkSZMAwxma6AWIRzZHEUIwe/ZsAoEAX7syn1BU8ve//91s1r5r164Bht3tNpqpdHV1HftFhxJiG8WudcfChDT4p1ozh/FOqkl7MpnEbrezcoGbQCBAe3s7zkQzgqOmv+PQs5kQDCJSFw6HzVoJKSV79uwxc87PO+88IpEIXq+XvLw8EokENpvt1FnXOkGObo5SUlKC0+lkVoWDy6p9PPfcc4RCIbKzs2lqaqK9vX3AOdxuN52dnccuvTBOutZ9EBPO4CcSCZxO54R/qEeTI7N1rFYrq+a7EUKwe/duNIboETrOPJuJiJSSzs5OM+24u7ubtrY23njjDc4880xycnLo7e1l1qxZCDEBpBSOg1RzlFSF7cVl6ykRe/jeRwMEg0Fqnvmh+Vxv2bJlQA6+EAK3201ra+uxLeKOk651H8SEM/iappGVlTXWw5hwpLJ1BILiPDdTpkyhMvna0AeMM89mIhKJRIhEIqZz89577/H222/T19fHJZdcYi7WlpeXm8dkysw31Rwl5eXLeXfwcNdD7Jn8W5YsWcL/PH2Anp4e3G434XB4QJpm6hwOh4OmpqYP7vA2SIroWHSt+yAmnMF3uVynvNzreMThcJjt5BwOO//vYh93X9k2eC/gcejZnNLsWDNApO5o7z4YDFJXV8crr7xCVVUVc+bMobe3l0mTJuFwOIjH42ZsO1M4UgDQ4XAwY8YMenp6uOaaa4jH4zzxxBOA0Thlz549g8orWK1WrFYrDQ0NHywHflSK6Hgz9jABDX5RUVFGPdSjhRCiX2pBYrPZuHHJQTyOgftJYRmXns0pzZGx+36RunA4bMoogJEme+DAAQ4dOsQll1xididLSQXHYrGMkxix2+04nU4zXDN16lR0XaekpIQLL7yQF198kebmZiwWC06nk82bNw+qDZVa92hqajrle0BMOIOvGDm8Xi8SiaZZyLENsZgldWXsRxgpob293cy7j0aj7N+/nzfffBOv18vZZ59NKBQiJyeHnJwcM/c+U8I5R5KTk2Pm5Hs8HiZNmkRPTw9XXXUVNpuN3/3ud4CRytnV1cU777wz6HmsVuuxh3fGMcrgK44ZV+sTOPu2YfevR4rBHx3dWT7odsXwkUwmzJ7DYKhidnR0UFNTw6pVq3A4HAQCAWbPnm16+llZWRM6934oPB6PuXgLMHPmTGKxGFlZWXzsYx9j8+bNbNu2DTB6/O7Zs4e2trZBz5VqFtPY2Hi4vefR9NUerpYfRxXzKTLvCVCcGAcfRdv8RTQZQwAa+gCVTIlGV+VNYzK8CceONek59/3NZXQpicfjZqVsPB5nz549vPHGG2iaxiWXXEI8Hsdut1NaWmoco+sTN5Fhx5r3lSTRNI2cnBzTQGdlZTFp0iT8fj+XX345ZWVlPPLII8RiMSwWCzk5OWzYsIFwOIzv4P2UrSs3X76D92Oz2cyY/qCevncy2LLHbX/nDzT4QgiV36gYtJJQCEgkjRBDQlqJOybTk3O5OYVWnARH59x7JyOBeCwOCNNbr6+vp7u7m1deeYVzzjmH/Px8enp6mDVrFlartb/hicMM/0w4jkGSJLV4myqwmjVrFtFoFIvFwvXXX09raytPP/00YBRtCSHYsGEDXRVfI5q9jGj2MppWNhKYYjgzNpsNm81GfX39ievujBHH4uG3CCEeEkKcPuKjUYxfhsir1zRY/F2NlsRkoloOwCkd4xzP6MkkWqgOV2Cj6XU6372XmpoaYrEYV1xxBclkEiklVVVGWmyq6jyTsdlsZjMfMOL1qVj+ggULWLp0KU8++SQNDQ2AEffv7u5m8+bNpKSkjyYV029oaEhrqDLeORaDfzEQA/4qhNgmhPiqECL3gw5STDCGyKvvDcOWAzqdvUmz4rOvr2/QfRUnjq5LYrEYSVcVuiWLaPYyXip5kXWdK3nhhRc488wzqaiooKenh2nTpuFyuUw5jExcrD2anJwc88cQYM6cOUSjUZLJJNdffz1Op5Of/OQnpgZPYWEhTU1NBAJ9vJ/Rd7vdtLe309XVdUoICH6gwZdSbpZS3gRcCvQBPwZqhRBfev8jFROKQZtNOOkIGA03XtkZIdpfiZsS85rw7FgzQNtmJEgmk8RiUSzxThyBrYhkL7beLST2/5pt27YRCAS44oor0HWdRCLBtGnTAMzF2lRTlEzG4XDg8/lML9/n8zF9+nR6enrIycnhxhtv5MCBAzz55JPmMUVFRUQiYfqCwSGNeaoit6Ojg46OjiF+GsYPxxLD/1chxFbgdeAdYDlwIfC9ER6bYjxxRCWhBOL2MvyzfkhloY3SHHhxex+yv7l5KjNkwjOIts1wI6XRvUqLtmOLHESkFs1ljFXu32Fv/hPz589n9uzZ9Pb2UllZaRYeJpPJibtYewLk5eWlefmzZs0yfySXLFnCihUrePLJJ9m/fz9gGHOb3U44FGLnzp1DGv1UymtPTw+xWGxcG/1jCelcDTwMlEkpV0spN0gpNwN3juzQFOOO/kpCUXQejdVvEMi/Es1i4UMLLKx7O0gyKWlra0PTNBXWGQYkEIvHCAaD2GINA0TqbFqc714Z5ZOf/CRSSiKRCDNnzgSY+Iu1J4Ddbk/z8t1uN3PmzDFj8Ndffz25ubk8/PDDZoGVAGx2O3v27GHr1q1DzlxToTM9mUTqOvo4De8cS0jnbCnl/0opg0dtf3jkhqUY76Q08jUhuHSRRm9IZ+uBGPX19djtdvr6+k6JmOZ4RUpJPBZHC9UxY/NMhBw886mqwKgg7e3tpaSkhNxcY3lNSYQPTiqWn2L69OnY7XYikQhut5uvfOUrtLe38x//8R9mPF9ghHcOHjzI+vXrh0xK8B28H1dgE1oygNb+KtGa28bdd0Dl4StOCJfLhbfjL2h6kE8siVP7Y4j21PMZ62fxvPdDdF3PjLDOCKDrOq2trSSSCXRXFXHX9CHDBH16LlJKwuEwp512mnm8xWKZ8Lr3J4LT6cTlcpmpwzabjTPOOMNshDJnzhxWr17Nzp07+d///V/TYGuaRnFxMR0dHfzjH/8YtDgrMOUmmlY2Es1eRiR7KYeyP0dDQ8O4SlNWBl9xQjia/0xR7bcRSISASQVwZbXg2cCnec/zaQBCodAHnEVxNMlkkpaWFvr6+rBoFrRYB9bwAQbz0yMJjV3W6wgEApSUlJCXl2dsj0TIzc3NyMraYyE/Pz9NDrm0tJTS0lLT6J933nl87GMf46WXXuK/nu4acKzT6WTdunXs2LHDnAUcjcAI8SSTSQ4dOjRuZrzqiVCcEGL77Wh6etGJ2y5Z5vorDQ0N2O32QdUHFUMTiURobGwkHA7jdhsZUdbIoYENZjAK3jbon6NOGLo5c+fOBQzvXgihFGPfB5fLhcfjMWegQggWLVpEIpEwfwg+/vGPs2zZMu56rI2/bEzXjXI6nRQVFbFv3z5eeeWV933O7Xa7Kbx23G0TR4BRN/hCiDVCiD1CiC1CiOWjfX3FMDFEIVaWpae/ubkgHo8PaCyhGEgikaC9vZ36+np0XU9vMD5E7N6iQavrQvx+P6WlpeTn5wOHY/dWq3U0hn7Kkp+fTyKRMA2w1+tl4cKFdHZ2AkYI50tf+hKLZ7j40n818sYbb6Qdb7FYKC4uJhwO89xzz7Fv3z6klLhan8DeuwW7fz1F65fgan0Cq9WK1+slEAjQ0tIypt+JUX0qhBAXANcDc4FVwO+FEJPlWP/sKY4fd5XRv/YoWgJ2dF2np6cHl8tFJBJRctVDIKU0W0WCkTWSWmRNGY6hSGJF13UikQjz5s0zzyelzDgZ5BMhlZcfCoVwOp0ATJkyhcbGRrq7u8nNzWW6dRNvfDeORcChjod4btt2chZ9Oe082dnZeDwetm/fjqv1SRbLteYCuzXaSPbemwEIF1+Fx+MhHA5z6NAhCgsLxyRldrQ9/IuA9VLKEPAyUAnMHOUxKIaDhfeASDfkkbjg1t8nSSaTtLa2YrVaj70n6KnKCagjxmIx/H4/tbW1tLa24nA4cLlcacY+e+/NZs790SR1CIoiuru7mTJliimdEIlElHd/HOTm5qbl5QshOPPMM5FSUhp9icVyLTYtgSZgciF8auarHHr1XlN5M4XVaqW4uJi58d+i6ekZPJoexnfgXvO90+nE4XDQ0tJCS0vLqC/ojrbBzwMaAfqNfjeQn/pQCLFaCFGze/duqqurWbt27SgPT3HMTLkOlj6C7C/EitlK+e27K/n1q0mampqoq6vDarUSDocndtXtMaojxuNxent7qa2tpa6ujvb2diwWiynfeyS+A/cOWB8BowgrGDP0i7JkE5/yfZvFhe/2fybRdV1598fB0dW3YMyyzjrrLBaJx7GSbow9Dvj4zG3cc889dHR0pH0mhMCrDa6pY4k2pqluZtf9Bx6Ph1AoZC7onixr166luroaYI4QokYIsXqw/Ubb4HcD5QBCCDeQC5jL4FLKtVLK6jlz5lBTU8Pq1YOOWTFemHIdMu8swr4ltC3bRF/BldhsNnbu3EkwGCTYX5Keqdk6sViMvr4+GhsbqaurMypm+6sy3W73kJ64Jdo05DldNoHAUCrNsnRTXPcdXK1PmLF7FT47PvLz89F1Pc1rLykpwWfpGXT/SQWwf/9+brnllgFx/dBh3zWNpKN8gOqmEML09pubm0/a6K9evZqamhqA3VLKainloN7yaBv854GlQggXsBKoBwZ2D1acMmiaQBMCXdeZPHkys2bN4q233kIIQUdHBzabbeKGdQ4+Ch0bIO43/n/wUcDw5ltbW6mrqzMX6dxuNx6P5wPDLVJKopaiIT/XRPpyVypkkEwmM14V80Sw2WwUFBQMkDlOOsoG3T8kCvjBD35AWVkZDz30EA8++KBprHeIa0iQriafkHbaSr8x5PVT7RWbm5tHRWV2VA2+lPJF4NfANuBu4JNqwfYUYseaQZtNWCxWYrEY5eXlzJo1y8wjr6urw2azEQqFTu2wzo41A0XSDj4Km1aD3h8O0KPITavp3fEzamtrCQaDuN1u3G632Xf2gwiHw2zYsIGXuy4kIdOP0SUMGtDHmBHk5uYq7/4EycrKMhu9pwhMvRVdc6Xtl8DODnENJSUlrFmzhk984hNs3LiRf/3Xf+WVV16hTpzNZrGaJFYkEKSAV6Of5sm3XMRiQxchWiwW7HY79fX1Zi3ASDHqqztSyjuAO0b7uophYMGagTHqF1ZisRh+Q15eHnPnzuXPf/4ze/fuxePxmItSkUjk1JXpXbAG2tYZf76w//9PTR7YECYZwrXvLtzLrjguSQMpJQ0NDWzduhUpJXl5H2ZDMovpPT+jKFsSSViw2zQEEgsDC30S9lLl3Z8EmqZRWFhIQ0MDVqsVIQTh4qsAyN5zE0KP0Sfz2Gm5lkPaOYBhpK+66irOPPNMfvGLX/DTn/6UdevW8bnPfY5pZTMAeNlyB3jAZ43i9/uNntD9ktVHY7PZsFgstLW1YbfbzTqMYb/XETmrIqMQmoYQAofDwbRp0ygpKWHbtm0IIejs7MRqtU48MbUh6hCsseZjNvZSSlpaWnjhhRfYsGEDHo/HrAL9xn9sovSfJS/szcFhE1iIg0wOaCupa06is+9QmTknicvlIicnJ229KVx8FfGsMwlnLeFP8R+xtWv2gOMmTZrEnXfeyerVq6mvr+fWW2/l+39sIxI7vCbgcDjMPhE1NTVD5uFrmoamaSOap68MvuKkERiFK/F4nMrKSubMmcM777wDYIqpBQKBAelspzLSXTno9qFiv0cSiUQ4cOAAf/vb33jttddIJpOUlJTgcDjo6OjgnnvuYevWrfzl1iwumu3HQgIBWIQEYahoSiDhKKd9yr/jnvuFYb23TCU/P9+sHTkSi2Zh1apV5OXl0draOiA8qWka559/Pg888ABnn302//l/nVzwrYPs3r3b3EcIYYZtXn755TGrQlcGXzEseL1eEokERUVFzJ07l3g8Tl1dHU1NTWau86nW/3ModF2nZ9LN6JozfbvmIjD11kGPkVLS0dHB5s2beeaZZ3jrrbdwOByUlJSYoa5NmzZxyy23UFtby1e/+lUumx9CHCWbJvr/G8teyoH5L+Gdt1pp5gwTKYE0YIBGjt1u55xzzmH27Nm0tbUNmj+flZXFl7/8Zf5wSyXRhOTOO+/kl7/8ZVraZ2FhIfF4nH/84x80NzeP7A0NgnpSFMNCSnc9OzubGTNm4HQ62b59O7qu093dPWHCOqmisg7vh/DP+iFS2Ps9bguaHiZ391cpW1eO7+D9gLEQu2/fPp577jlefvllWlpaKCgooKioyPw76+3t5Wc/+xkPPPAAxcXF/OAHP2D58uVog8TrAQQSh38D5Z0/P3XXRcYpNpuNkpISIpHIAN0bi8XCvHnzWL58OX6/f8jss5ULvLxy71QuueQSnn32WW699VZ2HDzs7GRlZZGVlcXrr7/OwYMHR/R+jkYF/hTDgtVqxeVykUwmmTJlCrNmzWLbtm1cddVVNDU1kZ+fT19fH0VFRaesRruUkqamJmKxmFEm77kKd9PvsPXtIu49zdyvY9HjdHd3897mzdTV1bHU8yyfc/0d+icEO7maXXyceDzOs88+yxNPGHn0V155JZ/4xCfMeLyOddBFWl3YiflOx7nk3gGfKU4et9tNQUGBqatzNBUVFfh8PrZs2WL+gB+9huJxalx//fVUV1fzk5/8hMvuaOaez5RQdbHxucPhoKCggM2bNxOPx5kxY8aofC+UwVcMG1lZWbS1tVFeXs7s2bPZvn07fr+fQ4cOsWDBArMrU5o42FiwYw3sPKJh27w7PrA9oZQQjcWIx+PvO/54PM66devo6OjA6XRSWFjIQe2zTE7WAkbmRiKRYMObr/PHP/6RtrY2Tj/9dD71qU9RXl6edi5/Mp8crS0t917XXMRtpdjtdjTVq3bEyM3NJRaLoetJNG3g33N2djYrV65k//79vP3221itVnJzcwcY7Xnz5vGDH/yAxx7+Bjc/0sJFDf/DZz/7WaxWK1arlcLCQrZt20YwGGThwoUjfl/K4CuGjZQIVSo9E+Dtt99m6dKlpphab2/v2Bv8wdIs3wcpJbF4DKnr5j2m0GLtiGQAu389Ojb6ErmEY2FKSkoGnKcnmOT/Xv4/nnvuObq6uqioqOC2224b9Ive0dFBLCeXhNOLLVZvqGYKO62T7qLQ/6SK248wQggj3i40dDl4soGmacycOZPy8nJ27dpFXV2d8Wwf1XfG5/Pxm3+r5N//0M7DT/+DlpYWbrrpJpxOp6nDs3//fhKJBHPmzBnR+1JPjeLE2bEmrRDLtuceLBYLNpuNadOmUVlZyVtvvYWmabS0tOBwOAgEAqdUEVZqsTWZHOjpuVqf6G9OIo0sGuLkWds5zbM9bb+mpiZufaSFRV99l9///veUlpbyzW9+k/vuu2+AsU91uyooKCAnOxvpLCJpLzLkFGSM0oP/hiXSMMJ3rYD+giiHUQD3ft3bPB4PS5Ys4cILLyQrK6t/ZnD4R6JKf50ibT8PfrKTjv/xMt/3Nvfee6+ZAppaLK6rq+Ott94asqnKcKA8fMWJc1QhlgC87e0EAgEmTZrE3Llzef755wGora1l9mwjjzkUCp0yDTp6e3vp6ekhaxCP2vve9wc0JxFIFsjHqJNns2vXLjMjx24VXLU8i9Mv/xaTJk0a9FqRSISenh5mzJjBvHnzEG//GICks5KEo4KmuY9SVVWFdd2Fw3+jikHRhMDpcGC1WgmFQmmqpkeTm5vLueeeS9ambPr6+mhpaeH03HdYbP2luRaT7+zjkS9a+dzavdxzzz3cdttteL1ehBAUFRXR2NhIbW2t2d9g2O9nRM6qyFg8Hg+6rlNQUMD8+fORUppiaoFAAJvNNuLl48NFNBqlra2tPwSV/iUPBAJYhxA5c8sOvvWtb3H33Xfz3nvvcfXVV7Plwen8+P+VDWrsdV2ns7OTSCTCihUrWLRo0VGLgIYSZklJiSqwGgOEEJSXl+Pz+UxBwKHIqn0AX3grpZZ9fL3wG5xl+c0A1U27JcFDn/dRV1fHXXfdhd/vN69jtVpHtF5FGXzFsOJwOJBS4vV6mTFjBnl5eWzevBmLxUJLSws2m41IJDLuG5ynQis2mw1P+1NpXYwS+3/Fiy++SJ/MG/TYug7jx+LGG2/k4Ycf5uMf/zhF2YMb6r6+PnOh+6KLLjLzwH0H78fhX9//2oAz0Txi5faKIdixxgxZao9ZKGr9CXl5eQSDwSHDkqlG5qmXXQwuiJZjD/DNb36T5uZm7rzzzlFzgpTBVwwrFosFt9tNMplk0qRJzJ8/nx07dmCz2aitrQWMmOV4V9Ds6OggFouR3f10WjMSa7SR0vo7OM27nZ2Wa/uj94cJx+DN4GX86Ec/4oILLhhUOC3VESy1rnH++eezePHitAXhwJSbaDyvgX3Ve0nknY2WNXWkb1lxNAvWGD0O+l9iwZ0UFBRQWlpKLBY7JqdlSNVN8lm4cCG33XYbHR0d/PCHP5x4apmKzMDn8xGPxykpKWH+/PnE43H27NlDb28vgUAAh8OB3+8ft4u3gUDAzCoarBmJTcSptj7BAX0pv1nvpq7DULPsjWhEraVYpn1m0Cya1AJwe3s7JSUlnH/++axatWrIeG0oFCI3N1eFccYZPp+PqqoqbDYbwWDwfRdZB1PdjEsb2/kEAHPmzOFrX/saBw4c4KGHHhrx74Qy+IphJ+Wp5ubmMmPGDLxerxnWaWpqQtM0pJQEg8ExHulAYrEYra2t5uLcUM1I3LKDO+64g88+HGTNExbeDc4k4pxFTMsZsG8oFCIWj5NIJpk1axaXXXYZixcvJj8/f9AFwNTfjc/no6CgIP3DHWsGlahWjC42m43y8nJKSkrQdX1Iwx8uvgr/rPvMiuyEo5yd7q+zqW2GuX91dTU33HADW7Zs4c9//vP7rhGcLMp1UAw7KalXIQQVFRUsWLCArVu3csMNN3DgwAFmzpyJw+Ggq6sLn883bipvpZS0tbVhsViw9Bc1JR1lWKONA/at7xS0txziT1+Hq5ckgX0EKSBIoblPb2+v6aVneX3Y7XazPmEokskk4XCYnJwcCgoKBv7dDCZRrRgThBD4fD68Xi9+v5+enh6CwSCO/qyeFOFioyIboPP0P1EMVBceoKamhsLCQqxWKx/60Idob2+no6NDLdoqTi2EEGY+cmVlJaeddhqhUIh9+/YRDAbp6enBarUSj8dHJW55rHR3dxMOh02NG4D2sn8hflQzkmAUfvRCLnd//34Syx6jjTn4KcNJD4Xs5vL4P5PV/TQej4eVK1dywQUX4HA4EeL9v26xWIxIJEJpaSlFRUWquOoUQQhBTk4OkyZNorS01JyhDabHk2Lq1KmcccYZtLe3mwb+2muv5Z/+6Z9MZ2MkUE+UYkRIpWfm5+cze/Zs3G4369evx2q10tBgFA5ZrdaB2Qk71gzsLjUKhEIhOjo60qqAI5EIf3snh9einza6GEmobYd7X5rCmVc/QFGR0YrQgZ8smk0ZY6/WyYey/8Qls7soLCzE3fZkWpaPq/WJtGvrum4W4VRVVZ0yNQqKdIQQeL1eqqqqqKysxO12G+G8QZQ1AaZPn87s2bNpb283jx/p2a4y+IoRweFwoGkadrud4uJiTj/9dGpqanC73Rw8eBBd17Hb7fT19aXHPhesgaLzjNe1cmTCF6letG2vwFOTSez/NS0tLTidTtOrTiaTbN68mVgsRrvnItb8NRftU/DpP53D3MvuScuo8dI2QMbYIiNkHbwXV+sTA7J8svfebBr9aDRKOBwmLy+PqqqqtNkFoGL2pyCpBuUlJSVUVlYihCCpJ4GB3v5pp51GYWEh3d3dozI2ZfAVI0LK24nH42Z6ZigUYteuXcRiMTo7O02PZlSbQRzdizZUh1bzRbwdf0mLu77zzju0traSl5fHX//6V+5+rJ2PLTP0zo8MtYRCITQ5eJaGJdo0aJZPqvF4MBjEbrczadIk8vLyBg/hHJUaqOL3pxZOp5PKykpsVhtJXR+wsGuxWFiyZAmASstUnNqkmqIUFhYyY8YMfD4f69evx+FwmDn5TqeT7u7u0euGtf32Ab1oNT1MXv2PzPfNzc3s3r2bgoIC1q1bx6OPPspHlmbx8JfK0oxyT08PsVgMqQ3epDzpKBsyy8cSbaKiooLy8vJjbnKuODXRNA2bzYrD4SDWr7h6JC6Xi7POOouenp4R/x4og68YMVJhD5/PR15eHmeccQZbtmzB4XBQX19PLBZD0zSSyeToNUcZohdtyjAHg0E2btxIbm4u77zzDj//+c+ZP38+//WlMqwWI76aqsL1er1ccMEFJJ1VyKO+SqnuV0O2PHRXjr1qqGJUsWgaFRUVJBKJAUa/uLiYuXPnmjILI4Uy+IoRw2Kx4HK5iMfjTJ48mQULFhCJRMxOWKkWb06nk87OztHx8t1Vg25OOsrQdZ1NmzZhsVjo6OjggQceoLy8nH/5l3/BZjWMfarj1bRp0zjvvPNwu93o9kISrqlpudb+WfcRKvoYnRU3DWiFiMWNWPjvI3yjivGI0+k0jf7R4Z3Zs2dTUlIyos7PqBp8IcRdQog9Qoh9QojrRvPairEhlZ5ZXFzM1KlTyc3N5bXXXsPn8/Huu+8ipcRisZBIJIb3Qd+xZvBsn4X3IC3pmjQpb3zfvn10dHRgt9u5//77sdvt3HzzzaaGjZSS1tZW5s+fz6JFi9LCO7q9kFjWmcSyl9G2bBO9eVcYmTeTr0Wv/hlo/Yux7kmwZC1MUY9/puJwOCgrKyMSiaQ5ORaLhbPPPttUlR0JRq3wSghxPvBZYAYwD3hVCPGklDL0/kcqTmVSFatZWVn4fD6WLVvGc889Rzwep7e3F7/fT05Ojunle73e4fFChmpyMuU6IpEIjm1fRsgYSUc5gam30mhbydtvv0h+fj4PPvggra2tfOc73zErXaWUxONxzjjjDGbMmGGeLiVyliJuLzeLbyoqKoywTfFnofaRgWNRZCwul4uCggK6urrSRPGcTueIiuSNpofvBh6WUkaBrv5rq5DSBMdms2G1Wkkmk0yePJnTTz+dZDLJG2+8kSaolvLyRzpjJxqN0mBdQSzrDNMb9+d+mA0bNuDz+fj73//O5s2bue6668zuQ8lkkng8jtfnSzP2cFgdsXb5QUK+JehuIwe7slLF6BXvT3Z2NkKI0UtYYIQMrhDCKoRwHPkCnpVS3ieEqAL+CPxGStl31HGrhRA1u3fvprq6mrVr147E8BSjjM/nIxaLmRWkU6dO5dVXXyUnJ4cDBw6YhSkul4uOjo4R0xJJJBI0NTVht9sR/SqXUkq2b99ONBqltraW3//+9yxdupTLLrsMMBZo29ra8Hg8uJwDDbiUknA4jKZpOOwOHHY7Tqdz3MhFKMYBO9YMWkthsVjIy8sblnTMtWvXUl1dDTBHCFEjhFg92H4j5WHfAUSOet0hhLgQ2A68CXzp6IOklGullNVz5syhpqaG1asHHbPiFMPj8QCGR+PxeFi+fDm1tbU0NDQgpTQrb1Mx8aMzGIYDKSUtLS2AMetI0dDQwMGDB7HZbDz88MOUlpbyxS9+0TTYbW1tzJgxY9BpdjKZJBQK4fF4KC8vx2JRE1bFUexYAzvvPPx+3h1ptRRZWVlomnbSXv7q1aupqakB2C2lrJZSDuotj8gTKqX8jpRSHPkCfg88CXxeSvl1KeX41MZVDDupqlspJVOnTmXevHlYLBZeeeUVsrOzeeedd8wH3ul0kkgkh10mtrOzc4BOTlJPUlNTQ15eHv/93/9NIBDga1/7mplO2tHRQVlZWX/f2XSPPaWHXlpaSklJyYjqnyhOYT6gctxisZCbmztqmlKj6ZJchbFIfI8QYnf/a4gkZcVEQgiBx+Mxwzput5vq6mpeeeUVAMLhsJmi6W57EmdwG1rn68inJhmVsSeJ3+8fsDgmkQR6e7Farbz00kts3bqV6667jsmTJwOG0qXL5WLx4sVk1/2H2X2qbF05rnd/gJSSqqoqvF7vSY9PkdkMl5d/LIxalo6U8m7g7tG6nmJ84fP5CAQCZGVlkZWVxapVq9i4cSNvvPEGS5cuZdeuXUyzbCR7381osl9sKnQIuWm14VufYBpjKm/e7XanxdVDwSDxRIKenh4effRRzjjjDC655BLAKHGPxWJceOGF2O12AlNuIjDlJsD4cbLb7VSUlKSFhhSKEyXl5Xd2do74tVTQUTEqpMIkUkqmTZtGaWkpVVVVPPvss7hcLvx+P5799wzQnRHJEHL7t07omrquE4vFcLlcaTnzLS0thoph0sKDDz6Iz+cz4/aJ/h+B5cuXD1CtTIWEysrKlLFXDCs+nw8p5Yg2PwFl8BWjhKZpaWEdgIsvvphDhw6xZ88esrKysMdbBj84VH9cC7lSSvr6+ohGo0bXqiPi66FQiI0bN2K1Wrn9Vy00Nzfzz//8z2RlZQFG3H7evHlmM/EUKWNfWlqq4vWKYcdqtZKdnX1MfXJPBmXwFaNGdnY2iUQCl8tFcXExCxcuxOPx8Oyzz+J2uwnouYMel7CXUldXd0wia4lEgpaWFpqamhBCS2s6ous6mzdvRgjBE28GeOxVPx/72MeYN28eYDRAKSkpGVDpGIvFsFgs72/sd6xRMsaKkyInJ2fEZ46qxaFi1Ejpzeu6zvTp02ltbeX888/n6aefprW1le15/8RS+T/YxGFvXtdc9E27DafTSUdHB36/n/z8fDweT1qYJlW01dXVZUozH50Lv2vXLtra2tB1nW//bzNLZ7m4+uqrAcwy9+rq6rTjUkVXVVVV7+/Zq9aDipPE3l/DMZJevvLwFaOGpml4vV5isRiFhYXY7XY+9KEPYbFY+Mtf/kKTbSUvh64liS1NhCxcfJUZEtI0jZaWFg4ePEhzczMdHR00Nzdz8OBBOjs7cTgcOJ1OXK1PpHWZCu76Obt37yY7O5sf//jHOG0aP/3nciwWC8lkku7ubs4666y06thUUVVJScnAxiTHw441yvtXHBOlpaXk5OSM2PmVh68YVXw+H729vTidTqZPn87evXtZuXIl69at4+qrr0bPuZjm6Ovk5OTQu+QvA463Wq1YrVaklGa3KE3T0rJwjuwyBUaXqclt91Bd8Hnu+s0B6urq+OtNsCh/P4uS17AxdAm5s75mri2kSDUgP+mWg8r7VxwjI90bQXn4ilHlyLBOZWUlyWSSK6+8El3Xeeqpp9A0DYvVSm8gMGQvUDBy+1NTYLvdnhaGGazLlE3EmRt/lOeff57LLruM4OLH+IPlMdb2/Tf7HNdy2mmnpe0fiUTweDymeJpCMRFQBl8xqhwZ1vH5fJSUlOB0Ojn//PN58cUXaWlpwaJpSF1n69atJ5SmNlSXqRx7gKlTp3LttdcCmN2HlixZktbeMBaLIYSgqKhIaeIoJhTK4CtGnaysLFM6YebMmYRCIa6++mqsViuPPfYYYOjd1NfXs3fv3uM+/1Bdpho6BV/72tfMkFBnZyfV1dVpIZtkMkkikaCsrCztR0ChOCEOPgodG4z1m6cmD0vl+MmgDL5i1HE6naZkcmFhIV6vF5fLxYc/bMgUv7ozCEBBQQE7duzg0KHB2xIORWvJ14nL9FhoMAqv911GSUkJYGjrTJ48mYqKCnMfXdcJh8OUlpae3CKtQgGGcd+0GvT+rJtQnfF+DI2+MviKUSfVECUVOkn18vzIRz5CSUkJ//Y/zYSiOlarlYKCAjZs2MC+ffs+MLwjpeTgwYM8+ZaL12KfJokVKaG2HR7ZtQzLtE8DxmKs1Wpl4cKFaSGbcDhMYWGhqe6pUJwU22+H5FH9nZIhY/sYoQy+YkzweDymAS8vL2e573k+bfkMv/98C3VtcX7wWB1ghHYKCwvZvn0769evH7RBSqrY6qWXXqKmpoacnBza3Rfx0LpCtE/Bp/90DgXVXwOMkE1vby/Lli1L8+LD4TBer3dEU+IUGUZoiJnpUNtHARWkVIwJKclkXTc8+ciMW2l4by9z59q48ZIWfvZsN575m1m8eDFWq5WSkhI6Ojp47rnnyM/PJycnB4vFgt/vp729HV3X8Xg8ZsjmzTff5OFfNLNyvocvHKFv397ezrx589Kyb6LRKDabTS3SKoYXd5URxhls+xihPHzFmCCESNMOqaysRBMCXUq+c00RC6c4+dnPfkZd3eEvTG5uLkVFRcRiMRoaGqitraWvr4/8/HyKi4tNqeIXXniBhx56iCUzXfzvNyrMxdeuri5KS0vTpBMSiQRSSqWRoxh+Ft4DlqMa51jcxvYxQhl8xZjh8/lMbRybzYbH4yURj+Owafzi6+U4HA7uvvvuNKMvhMDtdpOTk0Nubq7R9LxfYkFKyVNPPcUvfvELFi1axO9vqcLjND7r6+vDarWmSSfoum42MVHql4phZ8p1sGQtaP2hQ/ck4/0JSn0PB8rgK8YMu92O2+02C6ycToeZvVNVaOe73/0uNpuN733ve2zfvv19zxUMBvnJT37CY489xtlnn81NN92E23G4ZWIoFGL58uVpMs2hUIiCggLVbFwxcky5DgqWGh2vPlo7psYeVAxfMcbk5ubS2NjYX1IuyHP0Yo8b3a++XvgNZt9xCZ+/722+//3vs2LFCq688sq0VEq/38+rr77KM888Q29vL1dffTVXX3214fUnQWLE7ZcuXUpu7mE1zlAoRF5enlqkVWQUyuArxhSXy4XD4TD17qV7Mk/LtdTV1RkLq8Xw/e/HePzxx3n22Wd59dVXKS4uprCwEL/fbzZCnz9/Ptdccw3Tpk1LO388FuO0005j0qRJ5rZIJILX6yU/P18t0ioyCmXwFWOKEIKCggIaGxvNbfPmzaO5uZlQKITb7cZut3PddddxxRVX8Nprr7F37166urooLCxk6dKlVFdXpxn0FLF4HKfTmaaTk0gkACgsLFTGXpFxKIOvGHNcLhdOpxMpdYTQsNvtLF26lJdeegmbzWYuqGZlZXH55Zdz+eWXf+A529vbsefZ8Xp9xPoNezKZJBqNUlFRoWQTFBmJWrRVjDlCCPLz89H1w5W0+fn5LF68mI6ODtMrPxZ0XaelpYXi4mKysrJML/5IbXu1SKvIVEbd4AshKoQQASHE50f72orxi8vlwmLR0OXhFoaTJ0/mzDPPpL29/X2lklPE43FaW1uZPn06F5eux9m7AYd/PWXrynG+ey95eXknr22vUJzCjMW89iHg/RuTKjIOIQQ2m41INIqU0vTMp02bht1uZ+PGjTgcDrKzswfE3hOJBH6/HyklZ511FlVVVfSJ0+mb+m+AIZvgcrkozc8f9ftSKMYTo2rwhRBXYmTKbR3N6ypOAQ4+ita1CZcexbZ+CX3TbiNcfBVgVOFmZ2ezY8cOmpub0TTNLLbSdR1N05g2bRrTp0/H7U6vbHTvv4+yhh8f3jDvDtV9SpGxjIjBF0JYgaPr1G3AD4CLgV8NcdxqYLXb7aa6uprVq1ezevXqkRiiYjxxhIysAGyxJrL33gxgGv2srCzOOeccAoEAPT09BIOGhHJeXh55eXmDLsImk0k6yr5CVmSr8QNx4brRuiOFYlRZu3Yta9euBZgjhKgB1kop1x69nziRjkIfhBDiLuDbR23+EdAppbxXCPEy8Fsp5f8Mdnx1dbWsqakZ9nEpxilPTR5UZCrhKKdt2aYTOmUikSAajVJWVoZnfX9WjzL4irHghZXG/0fh+RNCbJFSVg/1+Ygs2kopvyOlFEe+gBnA54QQu4ElwN1CiJUjcX3FKcYQcrFDtSr8IOLxONFolPLycqVtrxhbdqwxul21vQK/E8b7MWTUYvhSyo+m/nyEh79utK6vGMcMISObdJSZC67HSiQSAYy4f0o3R6EYMxasGVdrRmOShy+lXDVUOEeRgQwhI6ud/n0cDgfhcPiYThMKhbDZbMrYKxRDoAqvFGPPEDKy2tRPm/1lg8Gg2fj8aJLJJH19fXi9XsrLy5XUsUIxBKq+XDE+mHIdvPdz489HLG5ZLBbKysoIBAJ0dnYSjUbNtEwpJclkEk3TKC0txev1Kn0cheJ9UAZfMe7RNI3s7Gx8Ph+RSIRQKEQikUDTNFwuFx6Px8zLVygUQ6MMvuKUQdM03G73gOIqhUJxbCi3SKFQKDIEZfAVCoUiQ1AGXzGxOfgodGwwCl+emmy8VygyFGXwFROXIzR6AKO4a9NqZfQVGYsy+IqJy/bbIRlK35YMGdsVigxEGXzFxGUIjZ4htysUExxl8BUTF3fV8W1XKCY4yuArxgc71gy/quAQGj0svOfkz61QnIKowivF+GAkVAWnXGf8f+PnjYVb9yTD2Ke2KxQZhjL4ionNEBo9CkUmokI6CoVCkSEog69QKBQZgjL4CoVCkSEog69QKBQZgjL4CoVCkSEog69QKBQZgjL4CoVCkSEog69QKBQZwqgafCHE54UQbwkhtgkhvjCa11YoFIpMZ9QMvhBiIfBNYDlwDXCNEEJV+ioUCsUoMZoe/tVAB/AC8Cfgp1LKxCheX5GJ7Fgz/KJsCsUpyogYfCGEVQjhOPIFVAAzgE8DtwO/FkLkHnXcaiFEze7du6murmbt2rUjMTxFJrFgDVwrD7+GW6BNoRgHrF27lurqaoA5QogaIcTqwfYTUsphv7gQ4i7g20dt/hEwU0r5kf59+oBVUsrNRx9fXV0ta2pqhn1cCoVCMZERQmyRUlYP9fmIePhSyu9IKcWRL2AdUC2EKBJCVAMC2D8S11coFArFQEZz0fRvwG+BDUACuF5K2T2K11coFIqMZtQMvjRiR7f0vxQKhUIxyqjCK4VCocgQlMFXKBSKDGHCGfxMTOVU95wZqHvODEbynpXBnwCoe84M1D1nBiN5zyOSh3+yCCHagboTPHwOsHsYh3MqoO45M1D3nBmczD1PklIWDvXhuDT4J4MQoub9Cg8mIuqeMwN1z5nBSN7zhAvpAJk3B1T3nCmoe84MRuyeJ5yHr1AoFIrBmYgevkKhUCgGYUIZfCHEGiHEHiHEFiHE8rEez2gghKjobypzx1iPZbQQQtzV/++8Twhx3ViPZyQRBv8lhKgXQrwmhLh8rMc0WvQ/2wEhxOfHeiyjgRCis/+53i2EeHQkrjFhGpAIIS4ArgfmAquA3wshJssJHLMSQnwZuBPIBp4a29GMDkKI84HPYkhtzwNeFUI8KaUMje3IRoyPAlcAC4FzgEeAorEc0CjyEKCP9SBGAyHENCAgpZw9kteZSB7+RcD6/i/+y0AlMHNshzSySCl/0p+C9cZYj2UUcQMPSymjQBfGMzyRnuOj2YfRQ8IPzAfax3Y4o4MQ4kpAAlvHeiyjxGIgJoR4TgixVQhx4UhcZMJ4+EAe0AggpQwJIbqB/LEdkmK4kVI+DTwthKgC/gj8RkrZN8bDGjGklLsAhBCPYMxgB21sMZEQQniBHwAXA78a4+GMFgmM53kN8P8wIhQlUsrkcF5kInlG3UA5gBDCDeRieICKCUa/97MdeBP40hgPZ0QRQkwVQlRJKW/ACFc+KIQoHetxjTB3AL+SUh4a64GMFlLKP0kpv93f9vV3QAEjELqbSAb/eWCpEMIFrATqgb1jOiLFsCOEmAs8CXxeSvn14faAxiHXAo8LIbIwQhwWwDe2QxpxZgCfE0LsBpYAdwshVo7piEYYIcTDQognhBA24CMY9qt1uK8zYUI6UsoXhRC/BrYBQeCTE3nBNoO5CuO5vUcIcU//tguklE1jOKaR5EHgTOA9IAz8i5Ry39gOaWSRUn409WchxMvAb6WU68ZsQKPD3cBvgBagCbhGSjnsC9aq8EqhUCgyhIkU0lEoFArF+6AMvkKhUGQIyuArFApFhqAMvkKhUGQIyuArFApFhqAMvkKhUGQIyuArFApFhqAMvkJxDAghviKEOCiEyBJCPCSEeFYIIcZ6XArF8aAKrxSKY0QI8SdgMobGyelSys6xHZFCcXwoD1+hOHbuw5A5eFwZe8WpiPLwFYpjQAjhBDYA/wBuBC6XUmZSHwLFBGDCiKcpFCPM/UCLlPKbQog9GHrli6SUSoJbccqgPHyFQqHIEFQMX6FQKDIEZfAVCoUiQ1AGX6FQKDIEZfAVCoUiQ1AGX6FQKDIEZfAVCoUiQ1AGX6FQKDIEZfAVCoUiQ/j/Af81w3aUz7y4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure()\n",
     "plt.title(\"2-sine fit\")\n",
@@ -431,7 +685,7 @@
     "# add 1 sigma quantile\n",
     "band.shade(color='k', alpha=0.3)\n",
     "# add wider quantile (0.01 .. 0.99)\n",
-    "band.shade(q=0.49, color='gray', alpha=0.2)"
+    "band.shade(q=0.49, color='gray', alpha=0.2);"
    ]
   },
   {
@@ -468,9 +722,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "448.08936178184297"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "K = np.exp(result2['logz'] - result1['logz'])\n",
     "K"
@@ -481,7 +746,7 @@
    "metadata": {},
    "source": [
     "This tells us, assuming both models are equally probable a-priori, that \n",
-    "the 2-sine model is 150 times more probable to be the true model than the 1-sine model."
+    "the 2-sine model is >100 times more probable to be the true model than the 1-sine model."
    ]
   },
   {
@@ -501,7 +766,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -515,7 +780,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

From 399fb8e80339ac8be7f78d582b165882248984b5 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 13 Sep 2022 16:50:01 +0200
Subject: [PATCH 102/313] add sanity check to hotstart

---
 docs/debugging.ipynb  | 257 +++++++-----------------------------------
 ultranest/hotstart.py |   3 +
 2 files changed, 46 insertions(+), 214 deletions(-)

diff --git a/docs/debugging.ipynb b/docs/debugging.ipynb
index 6d5d7d25..f3cb3880 100644
--- a/docs/debugging.ipynb
+++ b/docs/debugging.ipynb
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,22 +126,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 698.4x698.4 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "corner.corner(np.array(p), titles=parameters, show_titles=True, plot_density=False, quiet=True);"
    ]
@@ -162,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,22 +183,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "params = prior_transform(np.random.uniform(size=ndim))\n",
     "plt.plot(t, sine_model1(t, *params), 'x ');"
@@ -233,29 +207,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-1961.2412862297433,\n",
-       " -994.4041211269235,\n",
-       " -2339.297354798619,\n",
-       " -599.6818945970597,\n",
-       " -1576.6457304537498,\n",
-       " -145507.65589077197,\n",
-       " -307.52989804045745,\n",
-       " -291.9437786218507,\n",
-       " -1333.029330033732,\n",
-       " -1882.6914521041922]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "[log_likelihood(pi) for pi in p[:10]]"
    ]
@@ -290,17 +244,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6.53 µs ± 256 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "u = np.random.uniform(size=ndim)\n",
     "%timeit prior_transform(u)"
@@ -308,17 +254,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "897 µs ± 6.6 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "p = prior_transform(u)\n",
     "%timeit log_likelihood(p)"
@@ -372,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,40 +323,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "780e0d4d65014955b438d2dd9a7769ed",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "run interrupted!| Like=-255.66..-89.46 [-260.4596..-249.1400] | it/evals=2060/20807 eff=10.0946% N=400 0 0  \n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import ultranest\n",
     "\n",
@@ -449,22 +356,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-255.4088047726119"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sampler.Lmin"
    ]
@@ -478,22 +372,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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YYR7HfGyMucN1aYq7bDcDe4BHgTHWWtfs2PuBH4A/KU4d8xMwMYD6+k2QXNJl7Eq03IDiCSSbAUdJy+T+FAeEIiISBhGZoGGMsaEeuF+ZyRLp6elEcoJIpMuPhjpUpfxonqBRmxljXgIetdb+4mVfR2Aq0JDiGbibgOnASUAicKK1dnN11VVEpDaJSDcusMsYU+kmvA0bNvjt3qtE1187Y8xvlS0/hCJdfjTUoSrlq2+9mhljmuN9HdvzrLX/VHS+tfZn4DhjzGBgHPAOEGetbWuMSQceo3iso4iIhFhEgj1rbZNIlOtijFkeyZahSJcfDXWIdPlSOSUB3UnBnGuMqWf/l8h5H8Vj/AzFCZahuAt3vypXUkREvIpUy16kRXrQXKTLh8jXIdLlSwiVJEjuCLQ1xkyleJziVdbae4DOxphJgIPilDSXADuBi40xnwP1gLERqbiISC0QkTF7IiIiIlI9YibPnoiIiIiUVyuDPWNMRPN5Rbr8aKhDpMsPlUi8j0jdu9pWrohIrKiVwR6RT94a6fIh8nWIdPmhEon3Eal7V9vKFRGJCRGZoHHAAQfYqixh5o/T6cThcBAfH09cnPdYNjk5mfT09IgNVox0+dFQh6qUv2LFil2+ZnSH89nyJhL3MVKfXW0o19+zJSJSU0Uk2EtLSwt5Ql/XRBOn00lubi5JSUmlgj3PHHw1OaFwrNShikmVfeZoDMez5U8k7mOkPrvaUG4w+T9FRKJdzKVeiYuLIyUlxe8xo0dHtlco0uVHQx0iXX6oROJ9ROre1bZyRURiRURSr6Snp9twtez5UonVNSTK+VsuLRzPltQeWopPRGJRbZ2gISIiIlIrKNgTERERiWEK9kRERERimII9ERERkRgWM7NxNQFDREREpDy17ImIiIjEMAV7IiIiIjFMwZ6IiIhIDFOwJyIiIhLDFOxJrZGWloYxxuuWlpYW6eqJiIiERcRm4/pa3kyzaiVcNmzYoOdORERqHbXsiYiIiMQwBXsiIiIiMUzBnoiIiEgMi0iw53Q6cTqdkShaREREpFaJSLDncDjIzc2NRNEiIiIitUpEgr34+HiSkpIiUbSIiIhIrRKR1CtxcXHExWm4oDe+UoO4KEWIiIiIVEbE8uwpaBEREREJPzWv1RBOp5Ps7GxNbAmTNm3aaHUNERGJSRFr2ZPKyc3NJSMjA4D69etHuDaxZ/369T73qRVaRERqMgV7NYRrQktycnKEayIiIiI1SdDBnjEmDngQ2AqstdZ+ELJaSTlxcXGkpKSolUlEREQqJagxe6Y44vgUaAkcDww3xrQO4LzRxpjlv/32G+np6UybNi2Y4qWWmjZtGunp6QDtjDHLjTGjI10nERGRaGcqSvXh9SRjDgGmWGv7G2OaAt8CY6y17wdyfnp6ul2+fHmly60NIpF6JdgyA3l2gj3X3/s0xqyw1qZ72+fv2TLGBFTnUJ0nNY+/Z0tEpKaqdMueMaYhUAC0NMb0sdbuADYCu0r2p4WygrWNrxmhrk1ERESkMioV7BljZgA3WWs3A+OBwpKWvXbAn8aYi4AHjTGpoa+qRBulgxEREYl+lZ2g0RfIM8ZkAs9bazONMU2AP4AbgPOAwdbajBDXU6KQZzqYlJSUCNdGREREvAk42DPG9AGygc+As4A4Y8xLgBPoBhwEnGGt/S301ZRo5EoHo3WORUREoldlWvZ+Brpaa7ONMfcCgwAH8ARwJbBcgV7N5HQ6yc3NJSkpqVJrFrvSwQRTXk5OTqXLExERkcoLONiz1u72+PkeY0wBcDmQb619OhyVk+pR3d2xOTk56v4VERGpJpVOqmyMMbbYRGNMIRBQuhWJXtXdHetaBSQxMdFrShNrrVr8REREQqTSwZ611hpj4qy1Tmvt5HBUSqpXfHx8UOvtBpsKxlv3r2dXslLMiIiIhE5QzSfWWuXakJBydSXn5uZGuioiIiIxJei1cUVCSTN7RUREwkPBnkQFz65dLU0mIiISOhoFLyIiIhLDFOyJiIiIxDAFe1IjaB1eERGR4ERszJ6vcVlKuyHeeCZ+FhERkcBpgoZEhL+g3ts+VyJm139FREQkMAr2pEaIi4sLKvGziIhIbacxeyIiIiIxTMGeiIiISAxTsCciIiISw6Im2FNqDQmFVatWYYzxurVp0ybS1RMREal2UTNBwzO1hmvZLKfTSW5uLklJScTFFcelvmZxVrTEVjDnORwO8vLySpUf6HXDIZBlxEJ9fyKhKsulFRQUaLk1ERERDxEL9soGF66UGklJSe7XvAWA1SkvLy+i5YuIiIhUVdS07MXFxZULqFyBn2cAWJ0SExMjWn6oeWspFRERkdgWNcGeN94CwNpUfqhFuqU0FErGdCpSFRERCVBUB3viXbAtdJFuKQ2F3NxcgPhI10NERKSmCLqFxBgTZ4zpbYw5NZQVkoq5WuhKAp+AuVoqa3IXbkmg6oh0PURERGqKoFr2jDFxwLdALtDeGPMTMMpauzWUlQtGVcal1ZQxbbHQQhesks9F+XlEREQCFGxEcymwy1rbE+gAdASmGWP8rlJvjBltjFn+22+/kZ6ezrRp04Is3rdgW728nWutrXDzJdjzAhHqFrpw1dXfNZ1OZ6nfHQ4HWVlZOBwOn2VOmzaNrl27ArQzxiw3xowOunIiIiK1hAnmy9wYMwCYAFxhrf3RGPM4cDUww1p7TUXnp6en2+XLl1e63EA4nU5ycnJITk4OqmXP81zltSsWbF0rCoY9r5udnU1GRgapqamkpKT4LdMYs8Jam+5jnw11nj1jjN/3kpaWxoYNG7zua9OmDevXrw9pfSR8/D1bIiI1VbATNJYDa4DHjTGfAl2Ay4EeoapYsOLi4qhfv361nxsIz27i+HjNMfBUk7umN2zY4DMYjKagXkREaqeg+gGttduB24A3gUbALGAXcKAxpk7oqhdbqtLFHOtiYfKIiIhINAo69Yq1diPwrDHmHOBioCVwqbW2MER1izk1ufVKREREaqZQ5Nn7GFgGFFlrd4TgejEr1pI0i4iISPSrcp+ZtTbXWrs1lgM9p9NJdna2a/WGmFYd79U1EaY23E8REZFIi5kVNCoz87OsigbRB7PMWE2ajeup7HsNx/vwLCM52Xu2nmi9PyIiIjVNzAR7FQkmYbIr4HAFJMnJyQEHITUpWPGsazDvNZDrenKVkZSUVKPuk4iISE1Ua4K9YFrnXMKdkiWaVMd71dhFERGR6lNrgr1omAnrmbRZefZERESkOtSapGbRkMctJyeHzMxMcnJyIlaHaFKbJr6IiIhESq1p2YsGnuPhapJgxjsGoipd6yIiIhKYmG7Zi7aWo7i4OJKTk2tc2pFwrfyRlJREamqqkkyLiIiEUUy37AWS4gOqd+asqyvXGON1IkQkUrZUlLYm2PGO/q7rdDoxxrg/l7LHapauiIhIaMRMsOctOIhkig9f5aWkpJQKcqKdMSYsM3SNMQroREREqkHMBHveuIKUilrLqlNtSuNSkXCNBQy1Nm3a+A1M27RpU421ERERqZyYDvYkutWUCRrr16+PdBVERESCFr3NKWEQbRM2agtf910TNERERMKvVgV74ZpVKv75uu/RkPtQREQk1tWqbtxoWEWjsmrKuDZ/auJ9FxERiRU1M3oIUmVbkpxOJ1lZWRV2++7bt48HHniA33//PRTVLCUSrZHBdHevXbuW+++/n927d5fbpxY8ERGRyKn1377WWp9bTk4OGRkZ5OTkeN1fWFjId999R+fOnRk/fjzp6em88sorFBUVUVhY6PfagdYnMTGR1NRUEhMTK5xV7K+8ypTpGWA6nU6f1yssLKSoqIi5c+fSqVMn7r77bjp27Mjnn39ORkYGBQUFQdVHREREQqdWBHuunG6+Nl9cgZa37kdrLc899xzdu3ensLCQefPm0alTJ0aOHMkVV1wRdEtc2brFx8eTkpJCfHx82PLSlS3Tc+KEvzLz8/MZM2YMF1xwAW3btuXtt9+mbt269OnTh8mTJ5Obm+vznsfFxQX1mYiIiEjl1IpgL1hxcXEkJSW5W7hc9u3bx+DBgxkzZgx9+/blhx9+YNCgQXz22WfcfvvtzJw5kxNPPDHobt1Izxr21e3qWa+1a9fSo0cPnn32WW644QaWLl3KGWecwffff89ZZ53FxIkTGTZsmNduXREREak+CvYq4G3M3MCBA3nnnXd46KGHmD9/Po0bNwYgISGB+++/nw8//JBt27Zx3HHH8e+//4akzOrmLeB01WvPnj0cd9xx/Pzzz8ydO5dHH32UunXrArDffvsxZ84cHn74YT766CNOPfVUv9cUERGR8FKwVwFvueCOOuoonE4nJ554otdJB127dqVu3bqkpaUFtVqGq8wNGzYwc+bMiIxvcwV2eXl55erVsGFDzj77bJxOJ88++yzbtm0rde5vv/3GK6+8gjGGwYMHl7umUt+IiIhUn6CDPWNMnDFmuDFmaCgrFG28dWlOmjSJVq1acemll3oNXMaOHcuOHTuYNGkShYWFQZW5b98+TjnlFC677DJuuOGGagn4rLW8/vrrvPPOO+7ALjExsVS9XOMHp02bxosvvsgPP/xAeno6CxcuBOCVV17h+OOPZ9euXXz00Ufceuut7vOVRFlERCQCKprB6WMWpQG+Ah4H5gCHVub8Ll262GjhdDp9bg6Hw+e+BQsWWMCOHTvWFhYWurf58+dbwF511VV227ZttqioyOv5/uqTlZVl09PTbUpKih0+fLgF7LXXXmsdDkdQ76WoqMhmZmb6PN/pdNqNGzfa008/3QI2Pj7efvXVVxXeg4KCArty5Urbvn17a4yxJ510kgVsjx497Pr1621BQYHf+xssYLn1/WwGfd1wiLb6iH/+ni1t2rRpq6lbcCdBV+CZkp8XAx+UbM0rOG80sDw5Odl26dLFTp061UZasMGe0+m0l156qTXG2C+++MIWFBTY7du32xYtWtijjjrKbt++3Weg5y/QycnJseedd541xtg333zT5uTk2Ouvv94dQPqrk699mZmZdsuWLTYjI8PrOdOmTbP77befTU5OtpMmTbKHHHKIbdWqlV23bp3Ny8vzG+wVFhbavXv32hEjRlhjjL3rrrtsXl6eLSwstPn5+dbhcPjcKmvq1Km2S5cuFsgGlgOjbflnLNhHISyirT7in4I9bdq0xeIW3Enwf8CfwCfA3UAXYCXwcSDnR1PLXlXs3bvXtm7d2h5xxBE2OzvbjhgxwsbHx9svvvgi6Jar8ePHW8Def//9Njc31+bm5pYK+FwtfJUJIh0Oh83IyCgXfHq25p188sn2999/t4WFhXbZsmW2bt26tn///nbPnj0B1z0jI6PU7/4CZbXsSTRSsKdNm7ZY3II7qXiZtSeAv4HzS15rDCyrqHXP2tgJ9jy7c3v27GkBe8stt7iDqsqaO3euBeywYcNsTk6OO9irTMDnr66erXkzZsxwt+Y98cQTNj8/v1R39JNPPmkB++CDD1bp/ijYi676iH8K9rRp0xaLW/AnwsHAZ8DHwCDgMuBHILWic2Mp2HM6nfbyyy+3gO3QoUOpbs/KWL16tU1KSrLdunWze/fuLRXoeQZ8N954owXsfffdF3Sw5xoH2L17d/vXX3+5u2M9t4KCAnvOOefY+Ph4u3LlyirdHwV7UlMo2NOmTVssbkHPxrXWrisJ8FYBE4ChwCXW2oxgr1lTPfLII1x55ZX85z//ceebq6zx48dTt25dXn/9derVq+f1GGMMbdu2df8MweWu27p1Kw0aNOCjjz7i0EMP9VnWAw88gMPh4Mcff6zkuyHouomIiEhoVSnPnrV2A3Ab0AM421r7cygqVdOkpqby3HPPceSRR3rdv3z5cn766Ses9Z4+ZeXKlbz99tuMGTOGZs2a+Szn22+/5brrruO0007j9ttvByqXu87pdJKVlcXtt99OZmYmM2bM8Ht8nTp1KrymP8qrJyIiEnlVTqpsi2VYa/eFokI1mSuYcrVkOZ1O7rnnHrp27Urnzp05/PDDueOOO8oFfvfddx/77bcfY8aM8XntrVu3MnToUFq3bs1rr71GfHw8UD53nbWWOXPm0LFjR1544YVS5eTk5JCZmUnXrl3p3r07Dz/8cKmkyaGmvHoiIiKRpxU0/Cjb5+1wOMjKysLhcGCtxel0ltrvCqays7PZsmUL5513Hvfddx8XXXQRzz33HAcffDCTJ092B37jxo1jzpw5vP3229xwww2kpqaWu6a1lry8PIYOHUpmZiZvv/02DRs2dO8zxpCcnIwxhp9//pmePXsydOhQNm/ezFVXXcXFF19MRkYG+fn5JCUl0aBBA4wxXHvttWzbto3p06fjdDp9boDXOllrKSoqKnU/ym7GGK9r7IqIiEg1isRAwZoyQcNXvrrMzEyvee1cyYt/++0326lTJ2uMsQ8++GCpmbWbNm2yzz77rO3bt6+Nj4+3gN1vv/3s7t27rcPh8JqbzzUB5PXXX/c60WHv3r32xhtvtPHx8bZRo0Z2ypQpNisry95+++0WsMcee6z9+++/S9UzIyPDdu/e3bZo0cJmZWV5zYP3999/W8A+//zzfvP3ue5HKCdh+IMmaEiY+Hu2tGnTpq2mbhEptKYGe65gzhWQeUt/8t1339kWLVrY+vXr27feesvrrNrs7GzrcDjs9u3b7fTp0+2nn37qDrDKBnsvvPCCBextt91WrjyHw2Ffe+0126JFC2uMsZdccondsmWLzc/Pd29vvPGGTUlJsc2bN7dffvllqfMXLVpkAfvkk0/6DfZmzJjhNZArLCwsdT8U7JUXbfUR/xTsadOmLRY39a9Vgrd1cj198skn9OjRg3r16rF48WL69+/v93oHHHAAl1xyCX369PG6f/ny5e4JGffdd1+pff/++y99+vRh2LBhHHTQQXz77bc8//zzHHDAAaWOO+uss/jqq69ISUmhd+/evPbaa+59PXv2pHv37kyaNInMzMxy5TscDuB/M3/Lquh+iIiISOTpWzqEvv76a/Ly8rjooos46qijqny9+fPn43A4eOSRR8jPzy+1b+3atSxdupQLL7yQb7/9lmOPPdbnddq3b8/XX39Nx44dufPOO7G2eNKGK73Kjh07GDhwYKmALzc3l6uuuqpUuhcRERGpeRTshdA999zD4MGDeeCBB3jyySerfD1rLfHx8bRp04bk5ORS+7p06UL37t1ZunQphYWFFV6rYcOGjBgxgo0bN7J+/Xr36yeddBKvvfYa3377rTvgy83NZdCgQSxevJgZM2Zw/PHHV/m9iIiISGQo2AuhhIQEXn31VQYPHswdd9zBE088EZLr1q9fv1xXqTGGe+65xz2jNhA9e/YEYOnSpaVeP++880oFfJ6B3ogRI8pdR8mSQyMtLQ1jjNctLS0t0tUTEZEYkRDpCsQaV8DncDi44447AMrlz8vLy/MZKDmdTurXr1/qNVe3q+uc3NxckpKS6NGjhztf3mWXXYYxxuf4OWst7dq144ADDmDp0qVcfPHFpco877zzABg2bBhOp5PnnnuOESNG4HQ6y43ZcyVLBkhJSQngrog3GzZscH+2ZfkaJykiIlJZCvb8qOgL19f+OnXqMGfOHC666CLuuOMOEhISuPnmm937PQO9vLw8rLXuxMNxcXHlruv6PS4uzp3Lz5XD7p577qFPnz68+OKLXHPNNT7rZK0lLi6Onj178vnnn5e6bnx8PMYYBg8eTOPGjcnMzGTgwIHk5uaSmJiIMQan0+kOMl11TUpKUlAiIiIS5dSNGyaeXbq33norzzzzTLljCgsL6devH8cffzx79uwJ6LplV6VwzagNdDWMHj16lBu356lPnz6cddZZ5Ofnk5GR4b6m59JnmoUrIiJSc+jbOoxcAd9ZZ53FjTfeyMcff1xq/yOPPMJPP/3EunXrGDVqVLkZt1DcCrhq1Sr3vrKBljGG8ePHs23bNl588cUK6+Qat7dkyRK/x7mCysTExFK/a+kzERGRmkXBXpglJCTw9NNPY60tNZHiv//9L5MnT2bw4ME8+eSTLFy4kOOPP56vvvrKfUyTJk1wOBwcc8wx1K9fn/bt23Peeedx99138/rrr7NgwQKuvfZahg8fDsCyZcsqrE/79u1p1aoV9957L2vXrvV5XNmgUq15IiIiNZPxNUA8nNLT0+3y5curvdzq5HlfR4wYwZw5c/juu+/o3Lkze/fupUePHmzfvp0ffviBxo0b8+mnn3LjjTeyYcMGRo0axeTJk2nUqBGrVq1i9erVrF69ml9//ZVff/2VNWvWuMf9JSYmMmDAAM477zwGDBjgc8KEa8wewE8//US/fv1ISUlhyZIlHHzwwX7H+vkblxeJMXvGmBXW2nQf+2wknmlfjDF+J2EEs0/Cx9+zJSJSY0Vi2Y6aslxaVbiWC3v++ectYO+++273a3fccYd7rdusrCz3tmPHDnvjjTfahIQEe8ABB9iXX3651BJprp9zcnLsTz/9ZD/++GObkZFRajk3h8NhCwoK7IoVK2xOTo7PZdhWrFhhGzVqZFu1amX/+usvn8udeVsSLtzLoVWEGFkuLdh9Ej7+ni1t2rRpq6mbWvbCJD8/n2+//ZZTTjmF3r178/bbbxMfH8/KlSs58cQTGThwoNfEy9nZ2ezcuZObb76Z5cuXc+yxxzJkyBBOP/10GjRoQMOGDX2W+euvvzJv3jxeffVVtm7dypFHHskLL7xA27ZtcTgc7L///qWO//nnn+nfvz/JycksWbKEQw45pNw1rbV+u27VsuefWvZqFrXsiUgsUrAXJuvXr+f4448nOTmZb775hv3335/CwkJOOOEEtm3bxuLFi2nUqFG58zIyMmjcuDFOp5PZs2fz7LPPsn79euLj4znxxBM5//zzOeOMM9xr4DqdThYtWsSMGTP4+OOPcTgc9OzZk169evH000+TnZ3Nvffey/Dhw2ncuHG58n7++WdOP/10d5eut4Av2tKrKNiTcFGwJyKxSMFeGOTn59OjRw9++eUXvvzyS4488kgAHnjgAe6//36mT59O//79vZ7rCvZcrLX88ssvvPfee7zzzjts2LCB+Ph4evToQadOnXjrrbdYv349BxxwAOeffz4XX3wxBx98MADbt2/n+uuvZ8mSJZx66qnMmDGDJk2alCvzp59+on///qSkpLBo0SKaN29OcnJyqRm/0UTBnoSLgj0RiUUK9sJg9OjRTJ8+nTlz5nDOOecAxbNvjz/+eM4//3wefvhhEhK857MuG+x5ys/PZ+PGjcyfP5+3336btWvXcvLJJ3PJJZdw5plnkp2d7U6V4uJ0Opk+fTr3338/jRo1YsaMGfTt27fUMdZaVq9e7Z608cYbb3DUUUe5V/JQsBc8BXs1i4I9EYlFCvZC7JdffqFDhw7ceOONPPzww+7Xhw8fzqeffsqvv/5KYWFhUMFeQUGBe8yetZaMjAz2228/9/5///23XLDn8v3333PWWWcRFxfH9u3bSU5Odu+z1lK3bl2+//57unXrxhlnnMH8+fPVshcCCvZqFgV7IhKLlDQtxOLj4wHo2LFjqdcbNmyItdbrOL1gGGNKBXr+bNu2jTvuuAOHw8H9999fKtDz5Er6fMkllyifnoiISIzQ2rghduCBBwLFAZan9u3bs2/fPrZu3eoOCKvDTz/9xIgRI8jKyuKtt95iwIABXo/7+eefmThxIsOGDWPQoEHVVr9Y16ZNG58to23atKnm2oiISG0UdPONKdbHGHNWCOtT46WmppKcnOw12AP47bffqq0u77zzDoMGDaJevXq8//77PgO9goICRo0aRePGjb2mg5HgrV+/3mfeI1/rE4uIiIRSUC17xpg44AtgPXCqMeZ4a+1toaxYTWWMoUWLFmzduhWHw+F+/YgjjgBg9erVtGnTxmfrXkZGhs/1Z3fv3k1eXp7PsrOzs2natCnWWh577DEee+wxjj32WGbMmOHuRvZm8uTJrFy5kvnz5/scLygiIiI1U7Ate0MAp7X2ImAC0NoYc3xFJxljRhtjlv/222+kp6czbdq0IIuPbgceeCDbt2+nTp067u2ggw7igAMO4Pfff6dx48Y+t+bNm7P//vt73VJSUkpds+zWsGFDDjjgAN566y0ee+wxRowYweLFi/m///s/mjZt6vWc1atX8/DDDzNs2DDOOussjDHltmgxbdo00tPTAdoZY5YbY0ZHuk7h4ur+DWZLS0uLdPVFRCSKBDUb1xhzGvAkkA48A5wIxAM7gO7W2kJ/58fybFyAIUOG8OOPP/Lrr7+Wer1Pnz4UFBQwf/58ny17ubm5PlfJ2LZtm89WP5ft27dz0kkn0bt3b9599133RAun01luBnBBQQHdunXjn3/+YfXq1TWmVa8mzcaNBM3kDZ5m44pILAq2Ze8r4DqgEHjLWnsEcBRQF6j1o85btGhRbsweFI/bC/eYvauvvpqioiKeeOIJvzNqrbXcdNNNrFy5kueff77GBHoiIiJSOUEFe9baLGCJtTYf+MkYcxgwFEgG9oSwfjVSUlIS+fn55V4/8MAD2bdvHwUFBVUu459//mH69OlkZmaWev2iiy7CGMNpp53G77//7vVcay1jx47lueee48Ybb9TsWxERkRgW9Gxca22RMaYecC3wCXATMMxauztUlYs1ru7Zffv2Vek6P/zwAwMHDuSBBx7grLPOYu3ate5911xzDZ9//jk5OTkMGTKE3NzccuePGzeOp59+mhtuuIFHHnmkSnURERGR6FalzLklLXuTgN5AL2vtjyGpVYzaf//9Adi7d29Q51trmT17NkOGDCElJYXJkyfz77//cuaZZ7Jo0SL3cccddxwvv/wyq1ev5qabbip1jby8PJ544gkGDx7MI488ElUTMERERCT0qpxU2Vq7F9hb5ZrEIM/UK4B7xYtt27Zx0EEHeT1n8+bN7jVpPRUUFHDttdeyYsUKDjvsMM4++2wyMzMZNmwYb775JpdccgkDBgxg4sSJxMXF0bJlS0aNGsX06dNp27Ytp556Ku3bt2fVqlUUFRXRt29f8vLySElJCf0bFxERkaihFTTCqOyMW9dSaQ6Hw93KV9b27dvLnbd9+3ZuvvlmVq1aRefOnenSpQsZGRlkZGQAMGDAAL744gs+/PBDcnJyeOCBB0hJSeHqq69m+fLlTJgwgXbt2nHUUUfx44/Fja8nnHACKSkpWhZNREQkxumbvhoF0437008/MXToUP7++2+6d+9O165dywVoCQkJ9OrViz59+vDFF18wfPhwNmzYQJ06dXj44YeB4nF6hYWFrFixgkaNGtG+fXsFeiIiIrWAvu3DxOl04nQ6S71W2WBvwYIFXH755aSkpDB79mxat27t81hjDMceeywvvPACe/bsYdiwYfz9998cdNBBjB8/nv/+979MmjSJFStW0KVLF43VExERqSUU7IXBMcccg8PhYP78+aVed43Zy8rKqvAaK1eu5K677uLoo4/m1Vdf5dBDDw2o7K5duzJgwACysrLcwWbdunUBKCwsZOXKlRx/fIWLnYiIiEiMULAXBueddx5t27bl/vvvL9W6V7duXRISEsjOzvZ7/tatW7nxxhtp3rw5jz/+OKmpqQGXvW3bNubOncuAAQM4/PDD2bt3Lw888ABt27blyCOPxFpLv379gn5vIiIiUrMo2AuD+Ph47rzzTlavXl2uda9+/frk5OT4PDcnJ4cxY8ZQUFDAU0895XPpNF+eeeYZjDFce+21AEyePJl9+/Zx7733snTpUurXr89xxx1X6fckIiIiNZNm44bJ+eefzwMPPMD999/PWWed5Z4MkZKSwp49e9i9u3zuaafTycUXX8zmzZs56qijePLJJ0vtT0hI8Jsq5d9//+Wjjz5i1KhRNG3alEWLFvHRRx9xxRVXcPjhh3PXXXfRs2dP6tSpE9L3KiIiItFLwV6Y1KtXj/HjxzN8+HDee+89zjvvPKA42CsoKCA5ObncOQ899BCbN28G4Jdffim3//jjj+fMM8/0Wp61lueff54mTZowefJkCgsLmTx5MscccwyPPfYYW7ZsYc2aNe4WPxEREakd1I0bRkOGDOGII47gvvvuc4/d89WNO2/ePKZMmRJ0WatXr+bPP//krrvuIjU1lbFjx7J7925efPFF6tSpw5IlSwA0Xk9ERKSWUbAXRvHx8YwfP55ffvmFt956Cyhu2Ss7G3fVqlXccsstQc+SdTgcvPfeezRr1oxLL72UJUuW8PrrrzNu3DiOPvpooDiNS4sWLWjXrl3V3pTUSmlpaRhjvG5paWmRrp7EGGPM8caYb40xnxtj5hhj6hhj4owxLxljvjTGfGWMaVfmnP2MMd8bY7KMMUeVvBZvjHnNGLPEGDPTGKPeLKmVFOyF2ZAhQzjyyCO56aab2LdvH0cffTTfffcdH3zwgfuY77//noKCAh588MGgytixYwc7duzgtNNOo06dOsyZM4dGjRpx8803A/Dkk08yf/58hgwZovx6EpQNGzZgrfW6bdiwIdLVkxrOGLO0zEsbgN7W2h7AWmAQ0BGoZ609GRgH3FjmnBxgADDP47WzgbXW2l7A78A5Ia+8SA2gYC/M4uPjmTFjBlu3bmXs2LFMnjyZLl26cP311/Pdd98BuGfH/vTTT0GV4Zrs0aJFC6A4eDzuuONITEzkySef5LbbbuPcc89l0qRJIXhHUpOphU5qAmvtVmttbsmvRYAT2Aw4TPH/se4P7CpzTqG1dmeZSx0K/Fzy84/AyWGrtEgUU7BXDY499lhuvvlmZs2axeeff86sWbNo2bIlo0aN4s8//6R9+/Y0adKEzz//PKjr//vvvwAccMAB7Nu3j99//53jjjuuVKD38ssvaxZuLdGmTRufAR3gs4UO8HlemzZtIvmWai1jTBtjjPXYHMaYv4wxE0q6KI0xppUx5hJjTJExpkcIyz7cGJNnjHnVzzFpxpiPjDF7jDH/GGOmlO0qNcYMMcb8ZozJNsb8bYwJOOAyxhwMnA58QHFwl09xC90zwHMBXOJXoHfJz30pDhJFah2NX6gmEyZM4IMPPmD06NEsWLCAV199lUGDBjFs2DDeffddunfvzuLFiyu8zt9//13utbVr15KQkMCePXtYvnw51lo2bdrEjBkzFOjVQuvXr6/W86Ra3A28B9QB+gEPAuuAvcA7YSrzWeCHCo55DtgBtAAaAp8BVwNPAxhj+gGTgAuA70uOo2Rfa+CVkl87enTljrDWbjTGpAIvA8OttQXGmNOAOGttW2NMOvBYyXX9+QDoaYxZDKwGtlf0pkVikYK9MCk7Ni4pKYlZs2Zx/PHH8+ijj/Liiy/y0Ucf0bNnT8aNG8eIESN466236Nq1q88VM/bff386dOhQ7vWvvvqKJk2a0KZNG1asWIExxh3ozZkzR4GeSM230Vq7suTn5caYm4DjgNspHsuWDrwYqsKMMUMoDiS/AQ7zc+jBwBRrbR7wjzHmE+BIj/33AvdZa5eV/L7FtcNauxHoWVLeUmttT4/yE4A5wARr7R+ulwFXgtJdwH4VvQ9b3GR9U8k1JwAV/x+1SAxSN241OvbYY7nllluYNWsWn3zyCZ06deL6669nyZIldO3aFWOM12TLFdm1axeNGzcG4LvvvnOP3bvvvvsU6InEkJJZqecDjYG/rLUZJUHgmgrOG1mmO9i1zfJybCpwHyVBUgWeAoYYY5KNMQdR3OX6Scl14ikOQpsYY9YYYzaXdPMmBXDdCykOZu82xiw1xlxAcathK2PM58DrwH3GmObGmHs96v4RcAow3Rhzccn+pcaYRUCBtfaLAMoWiTkK9qrZhAkTaN++PVdccQX79u1jwIABOBwOVqxYQefOnYMK9nbv3s0BBxyAtZZly5ZRv359GjZsyP/93/+F4R2IhI4mjATsJWOMBQqAN4B3gWmVOP89ilsAy253ezn2fmCGtXZTANf9nOKWvAyKJ1As53/dys0o7nY+j+KJER2BTsBdZS/i2apX8vtsa+0B1tqeJdsb1toia+0F1toe1tpu1tpvrLX/WGvv8Tivv7X2QGvt8dbal0r297TW9rHWBpfuQCQGKNirZomJiUyZMoXNmzfzwQcf0LVrV1JSUvj+++856aSTyMzMrNT1CgsLycnJoWHDhmRlZbFr1y4KCgro1KmTe4k2kWillC4Bu5viYOloYD9r7VnW2uxAT7bW7rHWrvSylQrojDEdKZ7I8ERF1zTGxAELgLeBFOAAiidAuKb9u2bTPmOt3Wat3QU8DvQPtN4iEhqKBiLANbOxsLCQ+Ph46tWrh8PhICkpyT0rMlCu4+Pi4kqdW7du3dBVWKQK/LXeaZZvwDaWBGerrLWV+z9CKtWN2xNIAzYaY/4BbgbONcb86OWyjYBWFI/Zy7fW7gZmURLMWWv3UNzaV7k/anhPquzlmImmOMHyvJJu5DRjzM6SbtulxpgmlS1XJFbVymBv2rTK9H6E3pw5cyJaPkT+HkS6/FCJxPuI1L3zVa6/VC8VpXvxNwO4ousG2/3rL/iMYYF2406jODeda/8LwIfAqWUvWNJStw64yhiTYIxpCIwEVnocNgu4zhjT1BizPzCG4hmyFfGWVNnNFK+QcWhJguWFwCUluz736Potm3NPpNZSsBcBr7/+ekTLh8jfg0iXHyoK9opTtvgK5ioK6PxxXbdLly5+r1/Z7l9/XcexKtBuXGttTsk4t3+stf8AWUCeZ+BkjPnYGHNHya/nAKcBOymeJFJE6ZUt7qc4fcufwG/AT8DEAOrrLamyp5OBj0t+/hg4qeTnE0ta+x40MR69i1SGicQfOGPMTor/zy1S2lH8hyeay4+jONWAofiPXQJQj+IB2rbktXDXIZyqUn4ba63XLpoIPFuRuI+R+uxqQ7k+ny2pfiVJld8ATrLWFni8fgfwq7X2nZIWxbnAGRT/ncwBpgMfW2vfqv5ai0SfiOTZi/QfU2PMcmttem0tPxrqEK7yq/vZisR9jNRnV9vKlfAyxjSn9Dq2LudZa/8xZZIqlzlmD//Ls7cf8K+1Np/iFTYwxrwNdAMU7IlQe5MqR7oPMdLlQ+TrEOnyQyUS7yNS9662lSthVNJNfJK3fT6SKnv6ChhHcTB4KvC1MSbVWptRsv9kIttzIRJVItKNKyIi4osxZjjF6V9+KXnpeYpz+l3lyqtnjHmI4mBxBzCC4jVwJ1DcjbsOuMRaW9XhLiIxQcGeiIiISAyrlbNxjTGja3P50VCHSJcfKpF4H5G6d7Wt3EjTsxV7ZYpESq0M9oBI/yOPdPkQ+TpEuvxQicT7iNS9q23lRpqerdgrUyQiIjJB44ADDrCRXPcyOTmZ9PT0iPVfR6p8p9OJw+EgPj6+Rt+DFStW7PI167a6n61I3MdwlPnnn3/idDr9rqfsKvfnn3+mYcOGtG7dulqW5KvOe6xnKzJ/F2rDe/X3bImEXWWSlYZq69Kli3U6nV636tClS5eQXKds3YuKimxmZqYtKiqyDofD53sMVfmVratn/cp+BsFe099W0T3wd64/wHLr59mqTuEqz9d9KSoqsp06dbIOhyOk5fXr188ef/zxfj9L12fWqFEje8UVV9iioqIKP69gnw/P63q7x4GcF4za8GxFW5mRKre6y/T3bGnTFu6tVnbjjh4dntb73NxcMjIyyM3N9brf6XSSnZ3NZZddFpbyKxIXF0dKSgpxcXFcfvnlEamDS6TuQaiF61nyJTc3lyFDhpCTk1Ot5QKlnpmEhIRqadWD6r/H0SIS7ztS97o2vVeRSIjIbNz09HT7ww8/eN1Xk1a4KXvvnE4nubm5JCUleV1rMzs7m4yMDFJTU6lfv351VrVcXcsK5r47HA73+/X2xW+t9Xndsvs8711cXJzf+hhjVlgfSXbT09Pt8uXLK/tWoo6vz8t1n5KTk0MabJ122mn88ccfrFy5kgYNGnitT2FhIbNmzeL666/niiuu4Omnn3bv9/c5e1P28/7888+5+OKL+fTTTznwwAMrfA7C8TyXnBfzz5ZEhr9nSyTcamXLXrh4tpx5k5SURGpqKklJSdVcs/CoqCUzUteKZRU9Y8G65ppr2LRpEwMGDCAzM7PUvoKCAqZOncrhhx/OVVddRZcuXarcMuz5eVtrGTduHBs2bODee+/VcyAiEmIK9qpRuL6oIyWUwWusBcI1zRlnnMF//vMfvv32W3fA5xnkXX311Rx00EF88sknfP3113To0KFK5Xl+3p9++inLli3j8MMP54033mDnzp16DkREQigiUYfT6cTpdEaiaPHC6XSSlZVV6c8k2ODV6XSSk5NTqrxYC4RronPPPZdZs2bx7bff0rNnT3dL3kEHHcTHH3/M119/zSmnnBKSoRZxcXEkJydjjOHee++lVatWfPbZZ9StW5cnnngioOfANQZWf0tERPyLyDera6xXKFU0EyXYemZlZeFwOEJ63Wjj6lLLycmplvdYtgsvVu9rTZObm0ufPn2YNWsWq1atKtWS169fP6D8vzOHwxF0wGWtZcGCBSxbtoxx48bRqlUrrrzySl599VX++uuvCp+DYLr+w/F3QkQk2kUk2HPleXNNYvDcoklFXybe6u/aXIPLfW3VzV9dgu1C9XdNf/cgOTnZXV603J9oU9G9DQfX5zJs2DB2797NN998w6mnnkpcXJzPzzIvL8/9PwrBvI/77ruPVq1acckll2CM4ZZbbqFu3bo8+OCDFU3ScdfX298SERH5n4gEe64vj2hXW8aRVXcXqrpso1NcXBz169cnLi6O1NTUgIIm17+R5OTkSpfnGqt3xx13ULduXQCaN2/ubt1bs2ZNwPUVERHf9FfSDwUlIv5V5d/Igw8+SPPmzRk1alSp12+55RYAJk2aFJI6iojUdjEXxWjQtkjN0LJlS/755x+mT5/ufs1ay6RJk3A4HLRs2bJa61PyNyPm/iaKiERkbdxwco2zA0hJSYlwbcQb12xcX8mYpXaYNWsW2dnZXHfddQBcffXVjB07lqeeeoobbriBu+++u1rrUzI2N75aCxURqQYxF+y5xtfF+ji7miwnJ0cBuVC3bl3mzp3L4MGDue6665g/fz6LFy/mhhtu4PHHH6/2iRYlfzMcvvavWrXKZ53atGnD+vXrw1MxEZEqirlgzzWGKBQCScUQ6i+kcC0D5e+6/pY1CwfXYP7aHpBX5TMJx+cViWekbt26vP766wwZMoT33nsvYoEe4Gpl9jn+o6CgwOc90gxgEYlmMRPsheOPbSz9AS+7Fqmn6k5X4ZpFKcX8fTbRIhzPiOt6SUlJzJs3jxUrVnDccceF9VmMpX/TIiKBis5vFgk5rT0bvfTZQJ06dejWrZuCMRGRMIiZlj3xT2MZo5c+GxERCSe17EWZcKWOUc7A6FXRZ6N0QiIiUhX65g9AdX7ZqktPyqquZ0JBpYhIbFI3bgCqM3efuvSkrOp6JpSjUkQkNtX6YM8zlULZWZGudBPVGYCFMnWMxIbqeiZ8PeeBzBbWxAoRkehV64M9T2VbNlzpJuLj46stVUi4vjT1ZRx9qjuPXkV8Pedq8RMRqdkU7HlQF6pIeUqCLSJSsynY86AuVJHy9O9CRKRm02xcERERkRgWsmDPaFCYiIiISNQJqhvXGBMHXA1sBzZYa7+31lpjjLH+VlMXERERkWpV6Za9kha8xcBxwLXABGPMzQAVBXrGmNHGmOW//fYb6enpTJs2LZg6h5S11u9W3WU6nc6g9lWlvpG6Bw6Hg6ysLBwOR0BlTps2jfT0dIB2xpjlxpjRrn3R+GxVxPVeN23axODBg1m6dGlA9z3Yz8vzGG/3vqLnq7pV53Pp79kSEanpTGX/aBpjWgNTrLVnGmMOBM4DBgFzrbVTA7lGenq6Xb58eaUrGw7+3r8rv1hycnJIlxmr6AvZV4+4v30uwfSm+8s1GOw1AykzOzubjIwMUlNTy00AqCAtyQprbbq3fdH0bFXEWsvmzZvp3bs3a9asISkpiQ8++IBevXoBvu+Br+enoufV8zxv997hcJCXl+czn151j9So6G9TOOrj79ny13FhjIlIQCw1h79nSyTcAo5gjDFxxpjeQF/gAGPMqdbarcCbwLdAH2NMw/BUMzJc+cVycnIiXZVqU53LtSUlJZGamlprU3q4Ar3t27fz7rvvcsghhzBw4ECWLFkS1PUq87x6u/d5eXlaqk9EJAYFFOyVjNH7FrgPGAWkA68bYzpYa7cBTwGtgNRwVTQSXF+Irjxj1a2oqIjdu3dXeFwo1zStzgDMldIjlK2mNYVnoLdgwQLOOOMMFi1aVKWArzLPq7d7n5iYWKuDbxGRWBXot+xtwL/W2pOA94AbgE+Bj40xFwPDgHggLxyVjJS4uDjq168fsWDk5ptv5vDDD2fXrl1+jwtla1xtDsCq0+23386aNWv4+OOP6datGwBNmzZl0aJFNG7cmBEjRlT6mlX97PTZi4jEpkD/qq8F0owxJwKnAWMpbsk7EDgKOB64zFq7Iyy1rIU2b97MCy+8wN69e3n88cdxOp3k5OR4bb2r7d2hNVH//v0B2LZtW6nXCwoK2LFjh3u/FAtl67WISG0TaLC3GJgPPA4cRHGANwpYCjwKDLXW/jccFYxl/r7AHn74YZxOJz179mTKlCls2rTJZ+udWmRqngsuuIC2bdty3333lfr8XZ/7HXfcEcHaRZ/qHEsqIhJrAooOrLU7rbV3UNyd+weQBJwMNAJyrbWO8FUxdvn6Atu8eTMvvvgiF198MVOmTCE7O5sXXnhBrXcxJD4+nvHjx7Nq1Srmz58P/O9zHzVqFG3atIlwDaOLWq9FRIJX2aagDUAz4A3gJuBia+2+kNeqlvD2BWatdbfujBs3jnbt2jF48GCeffZZsrOz/bbeRSJfngRv8ODB7tY9h8NR6nOvSr7FWKTWaxGR4FXqL6e1dh1wFnAz0M9a+3MY6lStjDF+t3CWGR8fT/369YmPj3e/tnXrVner3sEHH4wxhvHjx5Odnc2TTz4ZlvpG8h5UZ5nRJiEhwd26N2XKFHerXlpaWkV5BoO6dxWdFxcXF1WfiZ4REZHQqPT/Jltr/7HWrrLWbg5HhWo7b2O22rdvzwUXXMCUKVMqnJnrjQa3Ry/X2L0bb7wRp9PJmDFjKjxHn6eIiFSG+kSiSGZmJi+++CKnnHIKaWlppfZddtllZGVlMX369EpftzYmh64pXGP3AIYPH07btm19HusK8nJycjRZQUREApYQ6QrI/6SkpHD66afz7rvv8uyzz3LNNdcAxQP3r7zySho0aMDJJ59c6eu6xgRGKjm0+HfBBRewb98+zj//fL9j0lxBe/369TVZQUREAqZgL4rExcXxxhtvMHjwYK677joABg0a5F5p4Z133uGEE04I6ropKSka5xSl4uPjueqqqyo8zhXcJSYmEh8fH+5qiYhIjFA3bpSpW7cuc+fO5cwzz+S6666ja9eu7iW1evXqpdmItVgoZ6Tm5eXx6aefatyfiEgtUCsiB2stDoeDrKwsHA5HyFJV+Ep94Sor2C9SV8B31llnkZeXx4IFC+jWrVuFqVXCkXZD6VzCx9/z42u1lFCUl5OTwxlnnMGpp57K6NGjKSwsdP/bqGxd9RyIiES/WtON6xrvBMVj46qrrPr161fqXFdXa7169Xj77bfJzc0NeKydUlLULL4+q7y8PL/PT1U+49zcXAYNGsSiRYs444wzmDFjBkVFRTzwwAM+yxMRkZqt1gR7rvFO1TGoPVQTIowxUTGpwul0kpubS1JSkrqRq0G4JtR4BnozZsxg5MiR3H333UycOBEgqJneIiIS/WpNsOca71SdZYWrla26g6/qbBWV8D0/I0aMcAd6F198MQD33Xcf1loefPBBDjzwQB588EG/11DgLyJS89SaYC+WVHfwVZ2tohI+iYmJWGvJzs4u9brr90A+XwX+IiI1j4K9Gqi6g6/qbBWV8JkxYwaZmZnutD5XX301Y8eO5amnnuKGG27gzjvvrPAaCvxFRGoeBXs1kIIvccnNzcXpdAb0PLhmebvyOM6fP5/Fixdzww038PjjjwfUbaxnT0Sk5tGgGz8qSjfhdDqD2qdUFQLBpzNx7d+0aRNHH3007dq14++//w7o2fLM41jZQE9ERGqmWtGyF64vsupOdRKJL2QFAeFTlXu7efNm98oqderUoVevXixZsoRDDjmkwvLq1avHvHnzWLFiBccdd1xA9dBzICJSc6llrwKuxecDTXDrOl6tdxIunoHeggUL+Oyzz8jOzqZXr16sXbs2oGvUqVOHbt26KYgTEakFFOxVwDX7MDc3NyzHi1TGrl27SgV63bp1o1OnTqUCvq1bt0a6miIiEkUU7FUgKSmJ1NTUgGcfVvZ4kcpYt24da9asYcyYMXTr1s39eqdOnbjtttvYtGkTv//+ewRrKCIi0SakwZ6JwT6hyi4+H+6EylK7paen0717d2bMmEFeXp779cLCQqZOnUrnzp3p1atXBGsoIiLRJuhgzxgTZ4y51hhzvjHmWABrrY3FgE8kWhhjuOeee9i6dSsvvvii+/VXX32VtWvXcs899+h/NEREpJSggr2SgG4JcBxwLTDBGHMzFAd8gVyjNqYkqexkj+oSbAqQcF03XPWJRsE8Ez169KB79+48/PDD5ObmUlBQwMSJE+ncuTP9+/ev9s9SaYZERKJbsC17LYA91trhwIXAJ8Dpxpgr/J1kjBltjFn+22+/0bVrV6ZNmxZk8dXDlVrF1xYXF1epfbV98kZVg91p06bRtWtXgHbGmOXGmNGufZ7PVnp6etQ/W0C5Z6Ls8+JLXFycu3VvxowZvPbaa+5WPa1XG5xp06aRnp4OXp4tEZGazlTm/7yNMXHAcCANOAO4x1r7oTGmBXANcAQw2lq719910tPT7Q8//OCrjIDrU9M4nU5ycnJITk6Oqi/lip6BYD+TstfNzs4mIyOD1NRUv+MaK6pPXFzcCmtturd96enpdvny5UHVNxKCeSZcLWa9evVizZo1JCYm0rBhQ1z/pvx9XqH6LMvuC0eZkWCM8flsGWN8dlwYY9SKKX75e7ZEwi3gpMolXbdfAN+VnLcWeNEY08Na+6cx5ingPSAV2BuGutZ4cXFx1K9fP9LViBitq1pesM+Ea+xenz59AJg/f74CDhER8aoyK2ikAz9Za28yxiwENgPNgE+NMfdSHOTFA3l+riE1gNPpJDc3l6SkpJC2QIZiXdWSLuDoaRaNoJ49e9KvXz+ysrI488wzgeL7k5eXF/LPzlO4ng8REQmPygR7mcCpxphPgK+Bd4FDgROBThQHfpdZa3eEvJZSrVzjyICoW/S+ZLxjfKTrEQ2MMbz//vululHz8vLC/tlF8/MhIiLlVSbYWwN8CJwJzLDW/miMGQS8DjwA7LLWBj3N1NVaEG3j2WqjaO5uLamTI9L1iBZ169Yt9XtiYiIQ3s+u7PNRHa2JIiISvID/Mltri4CnKR6rd0lJoHcecACQX5VAD/7XWpCTk1OVy0QN1yD6nJwcLrnkEsaNG8fOnTvDmqoiVOkxKptIujqV1Klac9eEK+2I65hNmzYxePBgli5dWuXnwN9nV1FKm0DLLFuGqzWxts4yFxGJdpVp2cNau84YcxnFM28nAHuAS6y1+ypbcNkZesnJyaX+Gwtyc3MZNGgQixYtAuCZZ57h2muvZezYsTRp0iTCtfufilJ9VOW61XleTbR582Z69+7NmjVr+OCDD/jggw8qXAEjEjNf/V3X899ubfrsRERqiko33VhrNwC3AT2As621P4ekIiWzEqOxNSkYnoHezJkz+eWXXzjzzDOZPHkyhxxyCOPGjWPXrl2RrqZEkCvQ2759O++++y6HHHIIAwcOZMmSJZGuWqXE2r9dEZFYE9RfZ1ssI5gWvdqgbKA3cuRI2rVrx2uvvcYvv/zCGWecweTJkzn44IMZN24cu3fvjnSVJQScTif79gX2T8Iz0FuwYAFnnHEGixYtqrEBn4iIRC/9r3gYzJgxg4ULFzJ27FhGjhxZap8r6Pvyyy+Jj49n0qRJXHnllRGqqYTSzTffzEEHHRRQoDZ+/HjWrFnD1KlT6datGwBNmzblgw8+oKioiEsvvTTc1RURkVpCwV4YXHDBBRx55JE899xz5b749+zZw4QJE+jfvz8ZGRmcc845TJw4MSz18FyezFoblevy1lRll37bvHkzzz33HPn5+QG1zI0ZM4ZGjRpx2223sXbtWqC4Rfjyyy+nqKiIcePG6bMSEZGQULAXBk2aNGHRokUcfPDB7i/+PXv2cM8993DwwQdz//3307dvX3766Sfmzp3LQQcdFJYvds91V2v7uryh5rqfeXl5OJ1OHnjgAZxOJ998801AXbHHHHMM7733HtnZ2fTq1YvVq1e7u/6ff/55Bg4cGPaZ6VVdq1hERGqGSs3GlcA1bdqUxYsX07t3bwYOHEidOnXcLXlXX301PXv2JC4uzr1eLIQ+Qa23fHnRmDuvJnLdx8TERNasWcNLL73EqFGj6Nq1K4sWLaJPnz4MHDiQDz/80Ofs2m7duvHee+9x5plncswxx2CtZebMmQwfPtydczKclBxZRKR2UMteFVSUs8wV8HXp0oW+ffuyYsUKXn75ZXegB8VBQ2pqqjt4qEoetLLHGmNKpcMINv9aReU5HA6ysrJwOBwhyxkY7Vy55owxPPXUUzidTsaNG4e1liZNmrBw4UIOOeQQBgwYwOLFi73eV2MMJ5xwAp999hlHHXUUs2bNYuTIkcTFxbk/N3+5/Xzd90CVffZERCQ2qWUvTFy565o1a8aXX37p87j4+Hjq168fsjIrqk84xHoLkb/7tnXrVmbMmMGoUaNIS0tzv96sWTMWLlxI3759GThwoNf8ea7PpHPnzqxcubLS9arsfS/7PkL57ImISPRSy55UWW1uIXr44YdxOp3ccccd5fY1bdo0rOlUavN9FxGRwCnYkyqL5uXVwikzM5MXX3yRU045hTZt2ng9pmnTpsyYMYOCggLOOOOMgPPwORwOvvjiC6699lrS0tKYNGlSuWNq630XEZHK0bdECIVzdqNmTkaflJQUTj/9dD766COeffbZcvvXr1/P6NGjOemkk4iPj+eKK66gQYMGPq/nGeC1atWKnj17MnPmTIwxTJw4USuuiIhIUBTshVA405u4rl1ROg4FhdUnLi6ON954gzPPPJPrrrvOHfC5gry2bdvyyiuvcMUVV7BmzRoee+wxr61wubm5jB07lpYtW9KjRw9mzJjBiSeeyJw5c9ixYwcffvgh2dnZPP7449X9FkVEJAZogkYIeUt1EuprV5SOI1STJZxOJ7m5uSQlJREfHx/0dWJd3bp1mTt3LoMHD+a6667j448/5tNPPyUuLo4LL7yQcePG8X//93+lzvG8twUFBZx55pksWrSIs88+m8GDBzNgwIBSn1379u254IILmDJlCmPHjqVx48bV/TZFRKQGi5mWvaqkLAn0mt5SXXgKxRgqX/V3pVHxd21rbalB+97SdfhL5eG5ebZS+roPVWk9DMfnFS4V1dUV8A0aNIiFCxdyxRVX8Oeff/Lcc89xxBFHlLue697u3LnTHejNnDmTefPmMXjwYFJSUsp9Jnfeeae7dS+QdDihvq816fMSEZHS1LJXCWVbzcKRzqQq13PVp7Itet7eh79WSs/7UJXUHZ4tXDV1koHr3tWrV4/58+eTlZXld1weFLfO5ubmMnToUHegV3YN5bKfyZFHHlmqda9JkyYB1S8W7rGIiFSN/vpXQnWnuojk+DvPpMFlheo+1OQl3Lx9NsaYCgM9gPz8fL+Bni933XVXpcfu1eR7LCIioRFzwV44A6TqTnUR6i/qQO7N008/7TXNh6dQ3YeanCeuKp/NsGHDKh3owf/G7j3zzDOsXbs2oHNCdY818UdEpOaKuWAvlloyQh0M+bo31lqys7PZuHEjt956K3fddRdr1qwJSZn+1OQ8cVX5bAoLC0lKSqJHjx6VPve+++6jXr169OzZM6CAL1T3OJb+XYmI1DY171u2AjW5taisqn5Rl22N8XVvXF/kDz74IE6nkzp16jBx4sQq1z+WVeWzefbZZ4mPj+eyyy6rdEvZYYcdxsKFC8nOzg444AuFWPp3JSJS24Qs2DPhWni1kiLVWrRr166om5VYtjXG171JSkoiMzOTl156iVGjRnHllVfy6quvVkvrXm3UunVrHn30URYvXszUqVMrfX7Hjh1ZtGhRtQZ8NbkVVkSktgvqL7cxJs4Yc60x5nxjzLEA1lpb1YCvovQOoUqhEup0FLNmzaJp06ZcfPHFFBYWhi0dhbe6l00H46kyrTFPP/00TqeTcePGccstt1CnTh0eeOCBqAtgI8HXfc/OzsbhcAR1zcsvv5y+ffty6623sm7duoDS4XjuO+aYY9wtfEcffTS33347O3fu9HueUq+IiNRSgQRYZf6oG+BzYHbJfz8Cbg7w3NHA8uTkZNulSxc7depU68npdFa4BSMc13Rdd+bMmdYYY9PS0ixgR44caYuKiqp0XX/lld0yMzPtli1bbGZmZtBlrl+/3tatW9defvnl7uuOGTPGxsfH299//z3E7yJ4U6dOtV26dLFANrAcGG0DfLaqoqL7Hqz169fbBg0a2N69e7ufGdfmcDh8Pq+e+/7880974YUXWmOMTUlJsbfddpvdsWNHWJ71cPwbihb+ni37v2fM5/n+9olYay2w3Fby+1abtlBtlT8BDgTe8/j5emARcEWg1+jSpYv1puwXSFFRkc3MzCz1RRiMcH1RuQK9vn372uzsbHvnnXdawF566aW2qKgo6Ov64q3uZe9RMEaPHm0TEhLsunXr3NfdunWrTUxMtEOHDg3xu6g6f380fT1bVVHRfa/KdV944QUL2GeffTaoYM+1rV69OqCgL5T3wPM+OByOoK8dTfw9Wwr2pCoU7GmL5GasDawLxhgTB4wFfgceBG631n5kjGkBXAMcUfJ/w3srulZ6erpdvny511ZGT9nZ2WRkZJCamupOFBxMT3FF7zGYa86dO5chQ4bQp08f3n33XfeKFXfffTcTJ07kmmuuYcqUKZW+rj+e72PLli288sorXHvttaVyu1X2vWzdupWDDz6YkSNHlhs/NnbsWJ5++mn+/PNPDj300KpVPoSMMSustene9vl6tqoiHM+P67rWWvr168dXX33FsGHDvC6HZ4xh6NChdOvWzX2erzJ//fVXJk6cyOuvv05ycjITJkzgpptuCkldvfH8N1qVBNvRwt+zZYyxvu6DMabC50RqN3/Plki4VWYFjV7AZOAmYB3wijGmp7X2F2PMk8D7QCqwN1SVC+das1W1bNkyrLWcfvrp7voZYxgwYACTJ0/mm2++wel0hmVA++bNm+nduzdr1qzho48+4qOPPgooma83O3bsoKCgoNz6rQDt2rXD6XSydevWqAr2YkleXh4FBQUUFBTwn//8x+vqJ7m5ucyYMYMPPviAXr16+b1eu3bteO211+jTpw+XXXYZb7zxRqlgL9QCXbNZREQiKJDmP4rH6aUBW4HFwB3AAooDu0uAG4HvgaaBXC/QbtxoHrOXn59vzzzzTAvYZ555xjqdTvvNN9/YBg0a2MMOO8z+8MMPVRrP5et9bNy40R522GG2QYMGdsKECTY+Pt6eeOKJdt++fUG9F6fTabt3724PPPBAm5OT474n+fn59pBDDrGdO3eOujFZREE3bii6RrOzs23fvn2tMcZ269bNxsfH2z///LNcV+0///xjjzzySJuUlGQXLVpUYRfv119/7X4ON27cqDF7leDv2ULduFIF/p4tbdrCvVXuYHgcuJXiMXo3AWuBh4DXgaMDvU6kgj1vYwCr8kWVl5fnDvhuvPFG9xfs+vXrwzKOyTPQ++abb6zT6bRvvPFGqYAvGIsWLbKAffrpp933ZMaMGRaw7777bkjfQyjEQrCXk5PjDvRmzZrlHiM5cuRIr+PyPAO+hQsX+qzLV1995X4Of//995A96wr2FOxJ1SjY0xbJLbCDilv2EoD/AMcBHUta+dYDbYD4yhQajmDP3zmuL86yM1dD8QXoGfB5tqRU1PricDjsu+++a7t162bT09Ptm2++aYuKinyet379eneg9/XXX7uv4XA47Ouvv27j4+PtCSec4G7hq8y9KyoqcrfuZWdn27y8PHerXjgmmlRVOII9z/t8zDHH2FNOOcV+9dVX7v9J8LzfnptnMFX2fyZ8fZaeLXozZ850X8s1A/qPP/4oF6R5C/jK1qVsoFf2Wff3TFbl35eCPatgTyqkYE9bJLfKHQyDgHuBt4HPgHeA/StbaKRaXxwOR1ha3PLz8+2zzz5rt2zZUmF9HA6HnT9/vu3cubMF7KGHHmrbtm1rAduhQwc7d+7ccl/03lr0ym5lW/gq+0Xu2brn2aoXjV/k4Qr2PO9zkyZNLGD79etnv/zyywr/R8Lb/0x4C67KBnqe+7Zu3WqTkpLcrXvebN++vVSXrutcz67bTZs2eX3Wq/I/U7WFgj0JFwV72iK5Ve5gOBhYBTxX8rsJptCalB4jVPVxOBz2nXfesZ06dXIHeTNnzrQFBQW2sLDQvvrqq6WCPldLn2cA8vXXX/v9ona18PkK+PzV1eFwuFv3XK16rmAl2oQj2CsbUGdmZtrJkyeXCvpcLX2+gr2KWvY8A71Zs2Z5DQZvvPFG99g9X8qO4Ss7Rs8Xb3VUsFeagj0JFwV72iK5Vf4EONDj50p137q26gr2QpH4NhT1+eyzz0oFeTNmzLAFBQXl6ltYWGhnz57tDvoaN25sAXcAUlE3nMPh8NvCV1FdXa17nq160RgAhDrY27Jli8+WU29B348//ug12Nu+fbt96aWX7MyZM+3MmTPtjBkz3D/PnDnT9unTxx3o+Wr582zd88Xp/F+XbmJiok1JSSk1hMDfeb6GMkTrZ13dFOxJuCjY0xbJLfgTg2zVs7b2tewdfvjhtmHDhu6WvIrG87la+lyBlysACSTYczrLd+lWlPjW8/x+/frZ448/vlRZ0SbUwd4111xjExMTfXaRu7pEJ0+ebFNTU2337t3dz5XrPrlaBl2fmbctPj7eHej5+zwvu+wym5SU5LO+ruM2bdpk69atW2oWb0XBnlr2/FOwJ+GiYE9bJLfK5NkrxVob9RlEXYu3R1qvXr149dVXGTx4MAkJCVR06+Lj4xk6dCgXXXQRgDuZbqDOP/98AIYOHUr//v1588033WX6S3xrjOH99993Pxw5OTkkJSURHx9fqfJrmiVLltCzZ0+/9zklJYWbb76Zzz77jKysLDIyMoDi/HKuvIfbt29nwYIFHH744UD55McNGjSgcePGFdbngAMOoKioqMLjPv30UwoKCgD46KOPuP766ys8J1r+TYiISPUJfcZfKcXpdHLGGWeQk5PDxx9/XG3lnn/++fznP/9h2bJlnH/++RhjAkp8W7duXerVq0dubi4ZGRnk5uZWQ20jZ8eOHfz666/06NGjUuelpqaSlJRULtDr168faWlpXrdAAr1AFRYWMnHiRLp06cLJJ5/MpEmTyMvLC9n1RUQkdijYC7Pc3FyOPvpomjRpwrx584DAu85dXL87nU6/xzudzlLbueeey2uvvcayZcs477zzyMrK8nle2deSkpLcAY2/8qy15Ofn88ILLzBo0CD+/vtvr++hrEDfe7h9/vnnAHTv3r3c/XNtDoejXN2Sk5PZsmWLO9D76KOPSE9Pp6ioqNRW0ecVzHu31jJ79mzWrl3L3XffzT333MO2bduYPn16le5dtHwmIiISWjET7Blj/G6Rqk9ycjKNGjXi7LPP5v333yc3N5e4uLgK6+tZZ9fvgZ7nuZ1//vm89tprfPfdd/Tv35/MzEyfdfXcXN19/u5dQUEBU6dO5fDDD+eqq67iww8/pFevXqxduzYs9zQcli5dSv369enSpYvPe1j2vkPx2sR9+vRhx44dfPTRRxx33HH89NNPPPHEE6VaQ/19Nr4+z4oUFRXx4IMP0qVLFwYOHEivXr3o3r07Dz/8MPn5+T7Pq8wzJyIisSNmgr1oFRcXR/369Rk8eHC1dOVaa/nmm2+49dZb+fPPPwHcAd+yZct8BnyV4QryjjjiCK666ioOOuggPvnkE3744Qeys7ODCvicTifZ2dk4nc4q1a2yli5dykknnUSdOnUCPmfnzp3uFr0PP/yQ4447jmXLltG3b19uu+02unbtyrJly8JW51dffZW1a9dyzz33uIM0z9a9UIvUZyMiIiESiVkh4ZiNG+0KCwttkyZN7AUXXBDQjFqn0+mexVnR7E2n02n37t1rn3zySXvUUUe5z2vevLn99ddf3Ss9+ErLUtHsYNfP+fn59vnnn7etWrWygO3WrZv95JNPSh3z448/2kaNGtlWrVr5zRVXtpyyKUH8IUSzcbdv324B+/DDD/tcIaPsKhn9+vUrlQ4nPz/ffvHFF+48d6+88opt3bq1NcbYMWPG2Ozs7IDured2++232zp16nitc0FBgT3kkENsly5dSp3vypPYokULm52dHfA98OSrnpFMYVTd/D1baDauVIG/Z0ubtnBvatmrJgkJCZx99tl88MEHOByOgM5p0KBBwNcfM2YMY8aMITExkalTp/Ltt9+Sn5/PjTfe6D7Gc9LGqFGjsLZy47CmTp3KVVddxaZNm3jmmWf46quvOOWUU0p1/3Xs2JGXX36ZTZs2ceuttwZ8bc8xgtXliy++AKjU5IykpCQaNGjAggUL3LN3L7nkElJTU/n0008ZMmQIP/30EyNGjODJJ5/k8ccfr1SdcnNz+eqrr3zeh2+++Ya1a9fSo0ePcl39PXv2ZNu2bXz44YeVKrMirs8mkAk+IiIShSIRYdbGlj1rrXsZsr/++iug1p4tW7bY9957L6AWOFdevrlz57pbpA466CB74YUXlmudeuihhyxg//Of/wTcsldUVGQ3b95sx4wZY5OSkmxcXJwdPny4/eOPP0odX3bFD1/8tW5WV8veNddcY+vXr+/OfRhIy97GjRvt33//Xaq187zzzrNNmza1OTk5tqCgwBYUFNj77rvPAvb7778PuGUvOzvb9uvXzxpj7EsvveS1zvn5+e61mJ955hn3uTNnzrTGGNu3b1+blZUV8D3wVJXPJFb4e7ZQy55Ugb9nS5u2cG8RKbS2BnvLli2zgH3nnXcq3bVX0b7CwkLbtm1b26FDB1tYWGi3bt1qAfvoo4+WC1gKCgrsscceaxs3bmy3bdsWUJmeXXlbt261N954ozvoGzFihP3jjz+8ruHrSzQEe0ceeaQ97bTTrLU24GCv7Jafn2/nzJljAbtgwQJbUFBgd+3aZRs1amT79+/v9z163nfPQM+VeNmXvLy8UgGfZ6AXbBeutQr2rPX/bCnYk6pQsKctkltECq2twd6+ffssYCdOnBjyYM/pdNrZs2e7W/fef/99C9jFixd7DVhWr15t69WrZ88555xS+3yt7+pt5YUtW7aUCvqaNGlSbskxXyId7HmO17O2dLD34osv2u+++y7gYG/v3r02OTnZjh49ulSrnq8VOVzbrl277BdffGFfeOEFe/LJJ1tjjH3hhRcCuneeAZ9noFeVoEzBnv9nS8GeVIWCPW2R3CJSaG0N9qy1tlWrVnbYsGF+W5F87SssLPTbApWbm+tu3bv77rutMcb++++/tqioyOu5ru7c1157zf2FXnaiRCDBp6ul7//+7//KBTi+RCLY87z+3LlzLWC//fZbd/CUn59v//jjD2uMsS1btrQ7duyw+fn5Ni8vz+c9z87Otrm5ufbcc8+1TZs2tVu3bnW36uXk5JQKCmfOnGmvu+4626dPH9uiRYtSS6mlpqbaqVOnlguy/d33vLw8e9FFF9lzzz231ESQYCnY8/9sKdiTqlCwpy2SW0QKrc3B3mmnnWY7deoUlpY9h8PhHrvXuHFj27ZtW7/nenbn/vPPP9Za614H1tc6utEgFMHe1VdfbevXr2/z8/Pd96KwsNCOGzfOGmNsXFycHTVqlC0sLLQFBQU+73lhYaF7pjPgnq27bNmyUmsy33777RawKSkptmvXrvbiiy+2kyZNsvPmzbNr164td78VeEWGgj0JFwV72iK5RaTQ2hzsjR071iYmJtrCwsKwBHuusXuAHTp0aIXnenbn1pQgIhTBnmu8nmfgm5OTY1u0aGH79+9vb731VgvYDz/8MKBgLyMjwyYnJ1vADhgwwBYVFbmDvd9//93WqVPHDh8+POAg2lu3uYK98FOwJ+GiYE9bJDelXqlm7du3Jy8vjw0bNoTl+vHx8YwfPx6Azp07V3h8u3btuPfee3n77berde3eSPr2229ZvXo13bt3L/X67Nmz2bZtG5dffjnjx4+nffv2XHfddQFdMzk5mQEDBgC477/LXXfdRXJyMo888ghxcYH9k6staxOLiEj4KdirZg0bNgQgOzs7bGVccMEFPP/884waNSqg48eOHUuTJk145ZVXwlanaLFs2TJOO+00DjvsMC655BL36z/99BM33HAD3bt35/TTTycxMZGBAweyadOmgK/9wAMPMHv2bNLT00u9vnz5cgYMGECzZs0CvlYk8g6KiEhsUrBXQ/3zzz888sgjXoPG+Ph4rrjiCvbff/+ArpWQkMA555zDBx98QE5OTqirGjWWLVvGqaeeSrNmzVi8eDFNmzYFipc/O//882nSpAlz5swhPj4eoNJrxR566KFceOGFpV4rLCxk48aNHHrooZW6lmtt4kBbAkVERHwJ6TeJ0Urq1SI7O5uBAwdy2223MXz48IBX5PDn/PPPJzs7O2a7cssGei1btgSgqKiIIUOGsH37dt588013ABgqGzduxOl0csghh4T0uiIiIoEKOtgzxsQZY641xpxvjDkWikcoK+ALjK9BlE6n0+e+oqIiCgoKGDp0KD///DMXXXQR77zzDrfeeisOh4OioqKgrut0OunevTtNmjThzTffLLe/su8hkDKrct3K3ttvv/2WU045hWbNmvHZZ5/RokULHA4HDoeDm2++mSVLlvDMM89wzDHHUFRU5N6cTidA0PfVWsvatWsBvAZ74bh30aaqn6WIiFRdUMFeSUC3BDgOuBaYYIy5GYoDPj/njTbGLP/tt99IT09n2rRpwRQfE4wxXre4uDi/+2699Vbef/99nnjiCV566SWuueYannjiCaZNm1bhub72GWNKrd0brV2506ZNo2vXrgDtjDHLjTGjXfs8n62uXbu6ny3PFr3PPvuMVq1aud/za6+9xtNPP811113HiBEjSEhIKLW5ulATEhL83rf4+Hifm79gzx9/n5X+fyr0pk2b5hprWe7ZEhGp8YKZwgscBLxb8vOBwPXAIuCKQM6vzalX5s2bZwG7cuXKSqdXefrppy1gr7/+endqj7y8PNu/f38bHx9vP/zwwyrl7/vss88sYN98882A0nxUlAsu2PQhFV2XClKvuI775ptvbIMGDexhhx1mN27c6E6TUlRUZL///nubmJhou3fvbnNzc21eXp4tLCwstd122222Tp06flOvVJQO55ZbbrF169b1mnKlojQ6/u5hTVHRZxlt/D1bKPWKVIG/Z0ubtnBvlWrZK+m6HQxcCjQzxvSz1m4F3gS+BfoYYxpWOQKtBSo7zm7ZsmWMGTOGgQMH8sgjj7hfT0hI4LXXXuOoo45iyJAh/P3330HXqUePHjRp0oT//Oc/QV/DUyjThzidTrKzs91dq778/vvvnHjiiZx44onurlvPMXqua7kmZLz++uvUqVOnyvXzZd26dbRs2TLoiRY1PQVLoJ+biIiET8DfQCVdt18DQ4DBwLHATGPMUdbabcCTQCsgNQz1jBkdOnTAGMPcuXMrdd7PP/+M0+nkiSeecM8WdWnQoAHz588nPz+fxx9/POi6JSQkMHr0aObPn88nn3wS9HVcQpk+JNCgJy4ujvr161O/fn369+9fLtCD4hmyO3fu5LTTTvM7ISMxMZHCwkKKioqCrvdRRx3F2rVrg76fNT0FS00PVkVEYkFlmhsGAP9Ya88BegOfA78CPxhjrgSGAglAXshrGUOOOOIILrjgAqZMmcKuXbsCPq9Ro0aA7/x8rVu35oILLuCVV15h3759QdfvzjvvpH379owePbpK14HicWehSh8SaNBzxBFHsGDBAhYsWMDrr79eLtADqFevHgMGDODdd9/1G8i5ciLu3bs36HrfdttttG/fnssvvzyo+1nTU7DU9GBVRCQWVOYb5F+gkzGmC1AHaAacBVwGdASOBy611u4IcR1jzl133UV2dnalWuEaN24MwL///uvzmGuuuYbs7GxmzZoVdN0SExOZOXMmW7du5aabbvJ7bHV20YU66DnvvPPYsWMHX3zxhc9jXHkK9+zZE3Q5iYmJzJo1K6D76eJ0OsnJyYmJrs+aHqyKiMSCyvwF/hGYBGRS3FV7IFAAFAHbgOHW2v+GvIYxqF27dgwePJgpU6awc+fOUoMofaXccAUeu3fvxul0et06derECSecwLPPPovD4Qjout62rl27cvPNNzNz5ky/efc8u+iCGTAaLr7Kczgc7nt16qmnkpyczLx583A6ne7uWs8tNbV4RMKuXbt8XrOilDae93PGjBmccsopfPHFF37Prei+BlJmdd9zERGJXgEHe9baPGCmtfZPIBH4jOLxe+OB96y1wQ9sqmWMMYwfP57s7GyeeOKJgFKkuFr29uzZ4zfVx3XXXcfff//NJ598UqnUK2W3CRMm0K5dO6644gqfLVueXXSu86y15OTkYK0NOn1IVdOOBJImpUGDBgwYMID58+djraVOnTrl7qXrnmdkZASd0sbl/vvv55FHHmHlypX06NGDU089lW+++abc8a6ArX79+qXuq7fr1oTJD0ohIyISeZXqW7HW5pf8GA+cC9wHDLbW/hTqisW69u3bV2rsnmew588555zDgQceyDPPPFOl+nl2P44ZM8brMd666GrSgPzzzjuPnTt3+uzKDcWYPZc6depw00038ffff7uDvu7du3Pqqafy9ddfu4/Lzc0lKyuLuLi4Crs+a9K9FhGRyAl2IM2vwPPA6dbaX0NYn1ol0LF7RUVFrFy5EmOM3zF7UBxUXHnllXz66af8/vvvVarfsccey80338wrr7zCZ599FtA53gbkR2MLlNPppEWLFgDMmzfP6zGhGLNXVkpKSrmg7+STT+bUU09l9+7d7vuXmJhY4bUqmvwQjfc9VrVp08Zn62VaWlqkqycitVxQwZ61NgcYU9KlK0Fq3749vXv39pmW47fffmPIkCE0bdqUk08+mbi4OA499NAKr3v++ecDxbn5quq4444DYPXq1QEdXxNa+1avXs2hhx5K9+7dAUhOTvZ6nCtnoWvsXiilpKQwduxYfvnlF5o1a8bChQtZt25dpSY0VHRstN33WLZ+/Xqf4yQ3bNgQ6eqJSC2XEOyJ1trCUFaktmrdujW//lq+cTQ/P5/zzz+fLVu2cPbZZ3P66afTp08fd2uTP/Xq1QtJ3fbs2cO1115Lhw4duPrqq4O+jqvlKRrSb2zYsIHTTz8dp9PJzJkz6devHwceeCCFheUf5+nTp5OamsqZZ54Zlrrk5uYydOhQduzYwcyZM13LdQHFrXJ5eXkkJSUFPZM1mu67iIhETtDBnoRG8+bN2bFjB06ns9SX+qRJk/j111/54IMP6N+/P0C1d8fdeOON7Nixgw8++IC6desGfR1XC1SkuRIpZ2dns3TpUjp06ODz2H///Ze33nqLUaNGhaXuubm5DBo0iMWLFzNz5kxGjhxZan9eXh4ZGRkAQZcfLfddREQiS8FehDVr1gyHw8Hu3btp0qQJRUVF/Pbbbzz44IMMHjyYfv36uVud8vPz/S7t5XQ6SUxMdKfY8Ey3UTaYLHte2X3vv/8+r7zyCnfeeSedOnXymrbDNeM22nira2ZmJqeddhobN27kjTfeoHnz5uzcudO93+FwuBNXA8yePZv8/HxGjRpFUVERCQne/6lU9r5CxYEeFLfOusbulX0//sqM1s9EREQiR8FeBHh+GTdv3hyAHTt20LRpU6y1XHPNNaSkpPDYY4+VOtaV5sMXzzQgrnJcP/s7t+y+PXv2cNVVV9GhQwfuuusu9+tOp5Pc3Fx312IgKVQioWy5+fn5nHPOOfzyyy/MmjXLPQ7RU1xcnDuQttYyc+ZMjj32WLp06eI3gKrMfYXiQO+ss85yB3oXX3yxz3Nd76XsNcpe19vnItHDNXnD3/7169dXX4VEpNZRsBdhrmBv+/btHHXUUcyYMYOvv/6a6dOn06xZs4jUydV9+/7775fqvnUN+Ifguxarm8PhYPjw4SxevJinn36aU045pcJzvv76a3777TemT58e0rq4WvQWLVrks0XP89hA73VN/Fxqk4oCOQXnIhJuCvaqgcPhICcnh4KCAveWn59PQUEBW7ZsAWDbtm189913jBs3jl69ejFixIiw1ee7777jt99+c//u2XK1efNmXnnlFe666y46d+5cqguxJgz4dzgc7rx4e/fu5f7772fevHk8+uijDB48OKBrTJ8+nbi4OFq2bMnff/9N06ZN2W+//apUr7y8PAYNGsTChQuZMGECw4YN83t8YmIi27dvZ926dWzatKnUjNqyLY3p6ekcfPDBUf25iG/+Wv78tfqlpaX5nOmr1kIR/4wxE4HuwHZgREmWkbLHXAg8ba1tYow5Hnic4pXDtpacU2MmqppILKGUnp5uly9fXu3lRsKff/7Jaaedxrp16/weV79+fbKysqhfvz7ff/89hx12WLljCgoKfI4dg+IgoF69euzevZtmzZoxYsQIZs6c6d7n+kJp1qxZqfFqZaWnp/PVV19Rt27dCseARaJVwhizwlqb7mNfuQf6tttu46GHHmLHDt/LNltradKkCQDdunWj7POZnJxM8+bNadasGQ0aNHC/77L3p3Hjxpx77rn079+funXruvd99913HH/88e7j4uPjadmyJWlpaaSlpdGyZUt27drF+vXrWb9+PRs2bCAvLy+g+5GUlMQHH3xAr169XPcgoPOkvIqerer+e+laVSWU+yQy/D1bEl7GmKXW2p4evx8F3GWtHWKMuRJIsNZOKXNOHPAmcLC1trMx5kBgj7U2tyRQ/Mla6z1JaxRSy14Y/fe//6Vv375kZ2dzyCGHsHfvXvbu3VtuVm2zZs3cqVV69uxZ5e7bxo0bc8sttzBp0iQGDx7Maaed5t7ncDjYuXMn119/vXtljLIBy0EHHeR3Ikg0a9myJWPHjgWKJzmceOKJHH300ZW6xsKFC1mzZg3bt293bzt27HD/vG/fPp/n/vjjj/znP/9hv/3249xzz2XYsGF0796d4447jrVr17JmzZpSAd369etZuHAhW7dupXHjxqSlpdGhQwcGDhzoDgTbtGlD/fr13WV4fl7Z2dkMGTKEgQMHlgr4RETEp5MB18LvHwOTgClljhkKzANuArDWbvXYVwTUqGz1CvbC5Pvvv+fUU08lLy+PvLw8mjZtSrdu3WjTpg2tW7d2/7d169Y0aNDAfZ63fG/BuOeee3j//fcZPXo0q1atcicGdgUqBx98sDuzfyzN4GzWrJnP5d0CVb9+fTp27Oj+3VpLfHy812PL3ruioiIWLVrEnDlzmDt3LjNnzuTAAw/kggsu4IILLqB79+707dvXfbznOreeM2z9tcqULXPRokX06dPHHfD17t270u9ZolNFXbwioWaM+Qt4leLlUFsC/YBpQB9r7echuH4jYAZwCrALGGet/U8F5xwOrALmWWsvqsq1SuwPbCv5eR/QyHOnMSYeGAycRUmw57HvYOB0YGIA5UQNBXtV4OsL+YsvvmDAgAHuxLgPPvggV155pXv/t99+S7169dwtRZ78Ddrv168fXbt29bl/v/3246yzznL//vzzz9OrVy/GjBnDc889R1JSknvpr0CSM9dEq1at4pBDDvG6r3Xr1uTklBuWAUCHDh1KBd2eDjjgAJ8tZtu2bXMvu+ZSv359Lr/8ci6++GI2b97M3LlzmTJlCk888QR16tShbdu2dOjQgfbt29OxY0c6dOjgnqjj4nQ6fbaulg0+mzRpwsKFC+nbt6/fFr5AgvpYCfpjhcbdSQSdCbwThus+S/G4t2ZAR+BDY8xKa62/ZZqeBX6ozLWMMa2BV0qO62iMWVry8whgD+AaiL0fUHYd0ouAudZap+ffRGNMKvAyMNxaW1DxW40eCvZCbMGCBZx99tkYY8jLy+Opp55i+PDhAZ+/efNmn/t+/fVX2rdv73N/2TQdXbt2ZezYsTz66KOcffbZDBo0yB3sNWrUqFSKFn/XrEmcTidZWVle933xxRc+z8vNzfWakgWKx9f5Smidl5fnc1+dOnU499xzOffcc/n3339ZuHAhq1at4pdffuHzzz9nzpw57mObNm3KwIEDGT58ON26dQMq97k0a9asXAufunRFpDKMMeuANGACsJTiACodeDFE108BzgWOstZmAV8ZY94DhgO3+zhnCLAX+AY4zON1v9ey1m4EepYcW3bM3lfAOIoDt1OBr8sU2x7oZIy5CDjcGPM0MBaYA0yw1v5RhdsQEcGtwyRevfPOO5x55pnExcVRUFDAtGnTKhXoVeSPP/7gv//9L0VFRQGfc8cdd9CuXTuuueYa9u3bF/Mtew6Hg6ysLLKyssjMzHS/5927d/s9LycnJ6wrlDRq1IjBgwdz//33M3/+fP766y/Wrl3LJ598wqOPPkrPnj15/fXX6dWrFx06dGDy5Mls2rSpUmU0bdqUhQsXcsghhzBw4ECWLFkSpncjIjGqP7AJeAG4zFq7ElhT0UnGmJHGGOtlm1Xm0CMAh7X2T4/XVgJH+rhuKsXdyTd52V2pa3my1q4CNhhjvqQ42JtpjGlujLm3ZP9t1tpTrLWnAX9Za68HLgSOA+42xiw1xlxQUTnRRC17IfLaa68xcuRIEhMTKSoq4uWXX3Yvcxaob7/91u/+7OxsZs+eTXJyMp06daJr164cdNBBfs9JTEzkhRdeoFevXtx0003069cPiN1gz1pLfn6++/e4uDji4+OpU6eO39mt69atY9u2bbRq1YrWrVvTsmVLn+P0QqVRo0b07NmTnj17AsWrfLz99tvMnj2bCRMmcO+999KnTx9GjhzJOeecE9Cax02bNlULnwSdzgWU0qU2s9b+ZozJB/6x1v5diVPfo7gVsKyy3aP1KR4j52kf4H0MDdwPzLDWbvLyPAd8Lc9WPY/XxpV5KRu4x8tx6SX/nQ3M9lHPqKeWvRDYuXMnF198MU2bNsXpdPLee+9VOtBbu3ZtqdUqvOnUqROXXXYZRxxxBN999x1PPfUU//5b9t9SeV27duWGG25gxowZ/P138b/fquaNi1YJCQk0btzYve2///6kpqaWms3qTcuWLTnwwAPZsGEDixYt4ueff66eCnto0KABI0eOZOHChfzyyy/ceeed/PXXXwwfPpwHHngg4Ou4Ar60tDQuvPBC9+tOp5Ps7OxqX2NZqt/69evdyyWW3eB/Qz68bYDPc30FgVIsLS3N732NVdbaPdbalV62st0TWUBqmddSgcyy1zTGdAT6Ak/4KDbga4mCvZDYuXMnDoeDFi1acMQRRwS0SkNZru5Vf4wxtG3blmHDhnHBBReUa8XyxzWxw5Wc11++vtpov/32o2fPngwdOpS6detSUBDZsbeHHHIIEyZM4K+//qJhw4buFTIC1bRp01JjNKF4fGFGRkapBM1S+/gLBK21flvuXC2Gld1cM/+rk7/AK1z12bBhg98guwapVHRaiW7cP4GEktm1LscA3iZn9KR4/OBGY8w/wM3AucaYH4O4lrc6TzTGfGmMmWeMSfZ4/XhjzLfGmM+NMXOMMXVKXk8zxuws6cJdaoxpEkg50aJWBnvTpk0L6fUSExMrdfzHH39c8UEV8EzTEYzZsyPbGh3qzyBUKru2bLjHxcXFxZX7rINdxi0xMZHU1NSgV9qI1GcWrc9KuEXifVdUZkWBYrCtieHYwH8Lpa/36i9IrGiLkXQ4e4FjjTHtKnGOqxu37Ha350HW2mzgbeA+Y0yKMeZEYBDeu0enAYd6XOsF4EOKx9hV9lqlmOKkyodaa08GFgKXeOzeAPS21vYA1pZc0+Vza23Pks33ygRRSMFeCFQ28Prkk09CWn4wXn311YiWHytf4JGYBPHii8FNjIuLiyMlJSXo/1FQsFe9ojHYC1ZFQWKXLl2CCiKDbaH09179tc5Vtcwa4jWKlxF7PNATKtGNC3A1kATsoHh261XWI+2KMeZjY8wd1toca+0/ro3ibtu8MkGW32v5UTap8kke72WrtdbV/VE2efKJJa2BD5oa1jcfkeXSjDE7KY6eI6Ud8FuFR0Wm/DiKH66y38gJJVsBxQ9g2WMrOxArmu9BRdpYa702oXt5tuKAeMBR8nPZqcyu/XGAK6N12Xvp7/5G4j5G6rOrDeVW5tkKNz1bsVWmz2dLqpcx5g7gV2vtO8aYhhTn1DulzDEHA28AJ1lrC4wx9Sj+Ds4BpgMfW2vfquaqBy0iA7ci/cAbY5bbCK5RGOnyo6EO4Sq/up+tSNzHSH12ta3csvRsxVa50fJcSXgYY5pTvNxZWedRQVJl4yV5srU2H8gv2f820A1QsBflIt0vFOnyIfJ1iHT5oRKJ9xGpe1fbyo00PVuxV6ZUk5Ku35O87TN+kiobYxLwkjzZGJNqrXXNlDuZyPaMVVpEunFFREREIsUY8xDFweAOipdQawBcRXES6SeAX0oOfd5a+4Yx5gyKVxbJAdYBl1hrA1/hIMIU7ImIiIjEsFo5G9cYM7o2lx8NdYh0+aESifcRqXtX28qNND1bsVdmNKkt97y6y4zW56pWBntApD+MSJcPka9DpMsPlUi8j0jdu9pWbqTp2Yq9MqNJbbnn1V1mVD5XEZmgccABB9hIZFR3SU5OJj09Paz919baUsl5t23bxtatW+ncuTP16tXj4IMPtuvXr6dFixY0aPC/pfwyMjJ8rm4RFxdHUlISf/zxBy1btiy1vu26desoLCwsldRzzZo1pKam0rRpUzIzM9m2bRvt27cnKSmpWu6BP1Upf8WKFbt8zYzcb7/9bPPmzb2eZ60lMTGRgoKCcvewqKjI5333ty8xMZGjjz661PtYt24dTqeTQw89FIBVq1aRlpZG48aN3cf8+eefOJ1O2rZtW+6aW7ZsYceOHXTu3NlrmcHeO1cuMM+ks5URqWemOsv192zVq1fPpqSklHs9IyODuLg4Wrdu7V6OsHPnzlVeIisS97s2fMaRKtPfsxWJ78TacM8jUWYk3qO/Z8slImP20tPT7fLly6u9XI/yCUX5/tYYdTqdpZLXTpw4kbvvvpv8/Hw6dOjAuHHjGDVqFHfffXepheoXLFhAkybeP7OkpCSOOeYYTjzxRB577DHOP/98975hw4axY8cOXnjhBfdrgwYNok+fPlx//fV8/vnnTJgwgf/+97906NAhZPcgWFUp3xizwlfKhLZt29qpU6d6PS8/P5/27duzadOmcvdw9+7dpYIxT/72nXrqqSxYsKDUa8OGDSMnJ4f58+fjcDg4+OCDmTVrFhdffLH7mFNOOYXMzEyWLl1a7prjx4/nySef9LlkW3p6Ot9//73XfeA7yXdmZiaZmZk0aNAAb0GLv3Nd5UbimanOcv09W40aNbLelkL89NNPSUpK4rnnnuOss84CoKCggDp16lSpLpG437XhM45Umf6erUh8J9aGex6JMiPxHv09Wy61sht39OjQtrJWdoH5gQMHhrT8YIT6HtS08kPl3HPPrfYyL7vsslK/B/r8JSUl0aBBg6CXS4vUZxYrz0plReJ916bPuLY+Vy615Z5Xd5nR+lwp2AuB3NxcMjMz/S4wX1hY6P7ZM9jLzs4OqswdO3aUe23Xrl2sXLkSay1FRUUUFf1vVnh+fn6pYyP9QEay/A0b/C+CcO+99wa8pJ2vYK9si7m3FnSHw+Hzuk6nk8zMTK/7yt67QJ4/qPpyabUpEKgM12cb6l6S2vDFGMlyo/25Crfacs8V7BWrlcFeqFXUYpKdnc2sWbPo3Lkz8fHxABx44IG0atWK5557jl9++cXred60aNGCY445hsmTJ/PUU0+5v2AGDBhAXl4eY8aMYeTIkdxxxx3k5OTQqVMndu3axfTp02ndurV7HFltVFBQwKOPPsrw4cNp1qwZxx9/fLlj1q1bx4wZM7jrrrvIy8sLqpykpCRycnIAaNq0KQkJCfz555+ljunYsSM//vije4yXpz59+gBw+umn+wz4ypZXlRY7qRrXmE7P/6ELNqAWEQkH/UUKgYpaTCZOnMjmzZt56qmn3IO269aty2OPPUbjxo2544472Lx5c0BlJSQk8Oabb3LWWWfx2GOP8dZbxau1DB06lFmzZnHbbbeRmprKDz/8wNChQzn22GMZP348WVlZvP/++yQnJ4fmTdcwf/zxB9dffz1PP/00Z599Np999hktW7Ysd9yHH34IFLeczpkzJ6iyGjVqxO7du4HiwbrHHntsubF5Y8eOpW7dujz00EPlzu/ZsyevvPIKy5YtCyjgq2qLnQTPWktubi6JiYmlWlb1WYhINNFfpDD75ptvePTRRxk5ciQnnHBCqX1NmjRh0qRJGGO44447yMrKCuiaiYmJPPHEE3Tq1IkHHniAPXv2AFCvXj1OO+00pkyZwltvvcWll17KpEmT+OOPP7jrrrs4+uijQ/7+ol1BQQEzZ87k6quvJjMzk1mzZvH4449TWFjotdvtww8/pHPnzhx33HE8++yzQbXuNW7cmD179rjH0PXs2ZMffvih1OfbvHlzRo8ezZw5c7y27p177rnMmTMn4IBPIqOoqAiHw0FSUpI72IuLi6vyTFwRkVBSsBcmW7duZcWKFQwePJiDDjqIW265hS1btrBlyxYyMzPdaTBatGjBvffey7Zt25gwYQJ79uxh06ZNXre//vqLl19+mZdffpnZs2dzwgknsHfvXkaNGsXLL7/MqlWr3Nfdf//9mT17NkuWLOGyyy6ja9euAU8gqcmsteTl5ZGXl8eqVau44oormD17Nr179+bpp5+mYcOGjBw5ki5dujBlyhT32MadO3fyzTffsHr1ak488UT37ObnnnuOnTt3kpOT43Xbs2cPu3fvLrUlJSVRVFTE+vXrcTqddO/eHYfDwZdffklRURGZmZkUFRVx/fXXU6dOHR566CGcTmepLS8vjzPPPNPdwte2bVueeOIJ9u3bR15eXrnjPTcJj8LCQrZv315qc7XIFxQUuIM911ANEZFoEZE8e7HCX1dNbm4ut99+O7t37+btt98mNTXVva9Tp04cdNBB7t+7d+9OSkoKo0ePpnHjxlxyySVer/nee++Vao2qU6cOnTp1YsWKFRxyyCEcccQR9OjRA4A333yTl156ieHDh/Poo4+SlZVFTk4O9evXr+rbjmpr1qzhzDPPBIq/nFu0aMHrr79OkyZNePjhh/nss89o1KgRnTt35sknn2TIkCEceeSR7N27lw8++ACAM888k2bNmtGlSxdee+01LrjgApo1a+azzLJd461atQL+N2D/hBNOICEhgc8//5yTTjrJ3UrXunVrrrzySp555hnGjx/PYYcdVu7a5557Ls2bN+eee+5h7NixPPLII9x8881ceeWVJCYmVureqGuxavbbbz9OPfXUUq+tXbuW77//nhNPPNEd7PnKySgiEin66x8mL7/8MosXL2b8+PE+u0+//vprjj32WPr27cuwYcO48cYb+fTTT/nss898XtfpdPL333/z7rvv8sILL3DYYYeRmprKokWL3LNv//zzT0aNGsUJJ5zA1KlTSU5OpkGDBrVivF6TJk249tprufbaaxk/fjzPP/88M2fOpE+fPixfvpx7772X33//nXfffZeGDRty+eWXu+/bp59+yjHHHEPz5s0xxnD11Vezc+dO5s+fX6k6uHLyucbtpaSkcOyxx/L555+Xm0xxyy23UKdOHSZOnOjzeieeeCILFy5kwYIFHHroodx0000cdthhTJkyJehJJBIaGRkZADRo0EAteyIStRTshcF3333H5MmTGTBgACNGjCi331rLFVdcQffu3dmyZQtLly7l6quvZtKkSXTs2JFZs2axevXqcuf99ddfzJw5k/fff58dO3ZgreXzzz+nZ8+e7Nmzh++++46ioiKuuuoq6taty9y5c6lXr16tGsDvdDrJysoiKyuLb775hrPOOosffviBu+66i+XLl7snsDRp0oSnnnqKH3/8kfvvv58NGzbw+++/069fP/e1unbtSocOHZg5c6Y7dc2SJUt49tln/aZvca3K4Qr2oLj19ocffiA7O7vUZ9G8eXOuvPJKXn31VX799Ve/761Hjx4sXLiQTz75hMMPP5wbbriBww47jPfffz/o+yVV42qldQV7CQkJVU6mLCISarH/7R8BP/74Iw6Hg6FDh3odqJ2fn88777xDQkICn3zyCUceeSRff/018fHxXHfddTRv3pxnn33Wnb7DZevWre5B/q4cbZmZme5cfXv27GHEiBEsXbqURx99lBYtWoT5nUafPXv2MG/ePObNm8fvv//OXXfdxc8//8xNN91Ualk6gHPOOYdhw4YxadIkJk+eTJ06dZg9ezY///wzv/zyC9deey2rVq1yj6EDmDt3LnfddRcdO3akd+/ezJgxg40bN7qvmZeXx0MPPYQxxn3/N2/ezLx583wG3LfccgsNGzakf//+rF27tsL32L17d5YsWcJVV13Ftm3b+PDDDyuV1FtCw1rLf//7X/bbbz8SExP5+++/SUxMrBUt6CJSsyjYC4MRI0bQuHFjfC3blZiYyCeffEJqaiq9evVi9erV3HTTTUDx+K+rr76aPXv2MHv27FLn9ejRg0suuYRTTz2VQw89lObNm3P00UezaNEiWrVqRUFBAW+88QYPPfSQz3F/sa5Dhw6sXbuWtWvX8ssvv3DTTTeVGi9Z1vTp07n//vv5/vvv3d2vI0eO5MILL2TlypXccMMNvPvuu+y3334AnHbaaUDxUnTGGB555BFOOukkBg4cyAsvvMCll17K119/zaOPPkrHjh3ZvHkzffr0Yfv27Xz88cdelylr3rw5n376KdnZ2fTu3TuggO+ll17ihRdeoE+fPjzwwAMBJVWW0Fq1ahWbN2+mX79+OJ1OFi1aRGpqqteUPiIikaRgLwxSUlK48sor+eKLL3yukdepUyc+/fRTjDG0adOm1Lqphx12GGeccQZLlizh559/LnVeamoq7dq1o1+/fhxxxBF8++23tGrVinr16vH777/z0EMPceutt4bx3cWWuLg4brnlFqZOnYrT6eTff/+lc+fOXH/99SxYsIDLLrusVIDWr18/kpOTadSoEYsWLWLhwoXccccdGGN48MEH+eqrr3j00Uc5//zz2bZtmzvQ++STT+jWrZvPerieB1fAt27dOp/Hzp49m8suu4zevXvz7rvv0qhRIyVVrmZF/9/eWUdHcbVx+JlsXAmQIAUS3Iq7e3EpUKBIcS9a3FpokeJWXAoUigV3Le4OxT1ACXHi2d37/bHsfNlkNwkhmwQyzzlzIDs7c+/M3J35zXtfUas5ePAgWbNmpVSpUnh7e8v+e4rYU0jPeHp6IkmS0cXT0zO1u5duUcLGzMT333/PkiVLmDNnDmvWrDFYFxgYCOiqKxw4cACtVsu7d+8ACAoKws7OjqZNm3L58mV+//13eeovdl44rVYrC71Hjx5Rs2bNdC/0hBAGlQxiEhUVZXKqs2TJkmzatIlx48Zx5swZMmXKhEajQavVEhwcbFCerkaNGuzcuZNhw4bh5ORE79696d27Ny9evCAoKIhixYrx+vVr2rVrh5+fX4JCT49e8H3zzTfUr1+fgwcPkjt3boPvrFu3jj59+shCTy/wjFkMFZKXmNP5Fy5cwN/fn/bt2xMVFcXdu3eRJInAwEBF7Cmka54/f26ydKCSfzL1UMSemciXLx/Dhg1j9OjRPH/+3GhpLtBN4cWkVq1auLu7A1C2bFnWr19vUEPVxsZG/r+zszNXrlzBy8uLKVOmMHLkyHQRhBEfKpXK5LStVqs1OH8xKVGiBBYWFvzzzz/MmDGD8ePH8/jxYzZu3Eju3LkNblLNmjVj//793Lx5kypVqshpdPQPeW9vbzp27Iifnx+HDh0yKfSMXasyZcpw5MgR6tatS4MGDTh27Bh58uQBdFO3eqG3e/duxZKXwgQFBcnpee7du0fVqlWZMWMGkiSxZ88eSpcuzZUrVxSxp6CgkOZQxJ4Z6dWrF7Nnz2by5MnyQ+Jj8PT0ZOzYsfLfQgjZd8zX15eBAwfi5eWlTN0mIxYWFowcOZLKlSvToUMHqlSpwoQJE+jYsaMs+GrXro2dnR179+6lSpUqBtvH9NFLrEUvNjEtfLVr1+bYsWOcPHnSYOpWEXopjz6qHaBq1aqMHTsWSZIICgri+vXr9OjRQxF7CgoKaRJF7JkRBwcHhg4dyujRozl37pxJ615iOX/+PPv27ePEiRPcunULQBF6SWTfvn0GU7NCCFnMSZJEnTp1uHLlCp07d2bs2LGcO3eO33//XfaNq1OnDvv372fSpEnyPl6/fi0LvX379iVJ6OmJKfgqV66Mr6+vIvRSmTx58rBx48Y4n589exaNRkP+/PmB/yfVVlBQUEgrKGLPzOite7/99ht79+79pH317duX//77j2rVqtG2bVvq1q1LuXLlkqmn6YewsDBatmxp0rcPIEOGDKxcuZI9e/Ywbtw4Zs6cye3bt1m8eDFWVlbcu3ePgIAA/P398fDwAGDTpk08evSIjRs3xqmDnBRKlSrFgQMHaNiwIXXr1mX79u2K0EuDXLt2DSsrKzJmzAgQb7UVBQUFhdQgfTt4pQB6697Ro0c5e/bsJ+0rR44clC5dmoMHDzJ69GhF6CURe3t71q1bh0qlomDBgpw7d4779+/z+PFjHj9+zKVLl8ibNy+tWrVi2LBh9OjRg40bNxIREUGLFi1o2rQpgYGBrFu3TvavBF1Qjq2tLfv27Uu2vhYqVIgLFy6wdetWReilUYKDg3FxcZF9az+2jJ2CgoKCuVHEXgrQq1cv3Nzc4i2JlRgKFizInTt3TEY6KSSe7777jvXr1/Po0SOGDRuGg4MDnp6eeHp6Urp0aU6dOsWPP/7IvHnzaN26NdmzZ2f//v3UrFmTihUrsn//fqpVq2awT301DP1+kwM7Ozvc3d2VRL1pmPfv3+Pk5ERUVBQA1tbWqdwjBQUFBUOUadwUIKbv3tmzZ3FxcTEZNavP1WWMHDlyEBwcjLe3txwBGpv0Ho0rhJAfurF59+6dQSmrypUrs2DBAgYMGECLFi04dOgQTk5OaLVaoqOjmTVrFtWrV6d79+40bNiQWbNmsXz5coN9qtVquZQawMCBA1myZAm//vory5Yt+2RrXMygAIXURaPRGP19BgQEYGdnp4g9BQWFNEv6VgZmxMbGxmAZMGAAbm5uTJ06FTc3NzJlymR0cXZ2xs7OzuhStGhRAO7evYuFhYXRJb0jSRLW1tZGF30ARkBAAAsWLODly5c0adKEBQsWcO3aNZo2bUpoaCiRkZGEhIQQGRlJq1atuHr1Kvnz56dnz57Mnj2bLFmykDNnTnLmzEn27NkNzn/27Nnp1asXf//9d7yJkRPC1PVVrnPqYWlpafQ3GxERgaurq+wDqtTGVVBQSGsoT40UwsHBgREjRnD48GEuXbqUpH3oo/3u3r2bnF1LVxw8eJC6desyY8YMvvvuO54/fy4LvvPnz9O4cWPUarVBRYo8efJw5swZBgwYwNy5cyldujRbt241maB56NChWFlZMXXqVEJCQpSatV8479+/x9nZWbHsKSgopFkUsZeC9O3bFzc3N+bOnZuk7TNnzkzmzJm5c+eOwedarZbQ0FBFVMSDn58fgwYNomfPnri7u7NgwQLCwsJo27atLPjWr1/P+fPnadq0KVqt1sCCZmNjw/z589m5cycajYbvvvuOEiVKsG3btjg+lFmzZpWtezdu3CAsLCylD1chBQkJCTHw2bO0VLxjFBQU0haK2EtB9Na9U6dOJdm6V7x4cY4dO2ZQ9D48PJz3798bfKZgyLhx49i9ezcdO3Zk165dNG/enLVr1/L69Ws5cEYftHH+/HkKFy7MggUL4pzTZs2acfv2bdavX8/bt29p3749p0+fjtPe0KFDsbCwwMvLSwmu+MIJCwvDysoKjUajTLErKCikSZQ7UwrTt29fMmXKlCjrXmBgINevXzf4bOTIkTx9+pSff/5Z/szOzs5g2lEhLn379iVDhgwcO3aM169fA3D8+HEAWrduLX/vu+++49ixY+TPn5/BgweTP3/+OKJPpVIRHR0tJzo2lgIna9asODk5ER0drQiAL5y8efPy4MEDXFxc0Gq1hISEpHaXFBQUFAxQnkIpjIODA3369EnQuqfVaunRowfNmzfnyJEj8ud169aVEzWfO3cO+H/EpiIqTFO8eHHWrVsnT90eOHCABQsW0KJFC7755huD71atWpXjx49z9OhRWfTlzZuX+fPnEx4ezpo1a+jatSt16tTBy8tLyauWzilevDi3b9+Wkyr7+vqmco8UFBQUDEmyOpAkyUKSpMGSJPWTJMnuw2dSQtspQKdOnRK07v35559cvHiRTJkyMWjQIJ48eSKv+/3338mZMyfdunVTpm5j8fLlS4YMGcKQIUOYOHEi79+/l9cVLVqUDRs2EBYWRq9evXB1dWXixIkm91WzZk0D0Tdo0CA8PT1lobdr1y7FmqpAiRIlCA8PJyIiAlDEnoKCQtojSWLvg6i7BlQHfgOWA4gPnuqmRJ8kSb0kSbp89+5dypYty7Jly5LW688ArVZrdFGr1Wi1Wnr27MmpU6e4cOECarVaXi5fvsyqVauYPHkyRYsWZdCgQQghaNeuHQcOHCAoKAghBHPnzuXBgweMGjWKgIAA3r59S0BAQGoftllZtmwZZcuWBSgsSdJlSZJ66dfpx9a7d+9YtmwZf/75J9OmTaNhw4Y8fPiQN2/e4O/vT6FChVi3bh1FixZl+vTpODk5oVaref/+PUFBQUaXYsWKcejQIQ4ePEjRokVp1qwZW7ZswcLCgtDQUKKioowun5L8OvaYef/+vTx2lECc5CcxY+v+/ftUq1aNmTNn8ujRI3lxdXUF4O3bt4Aun6OCgp74xpaCQkohJeWBJElSbWCCEKKmJEl5gHXANMAbuCGEiPdpVLZsWXH58uWk9PezwdQDOTQ0lMDAQCRJonTp0pQoUYI9e/bI6+fNm8fSpUt5/fo1I0aMwMXFhUePHrF06VKKFy/O0aNH5enawYMHs3btWnbu3EnhwoVxcHAgW7ZsKXJ8qYkkSVeEEGWNrStevLjQ1yDes2cPAwYMoHTp0qxZswY7OzuT5yc4ONhkfrTo6GicnZ2NrouKijKZaiN79uy0b9+eBQsWJHhMsYk5fkJDQ+UqDfoEy8qUvXmIb2wVKVJErF+/Ps7nGo2GatWq0aZNG9auXcuaNWv44YcfzN5Xhc+L+MbWl/RMlCTJ5ItufOsUkk58Y0tPUp8Y/kB1SZJGAyuAjMBIYDGQL4n7TBfY2dnh4OBApkyZGDp0KEeOHJF97wDOnj3LkydPaN68OS4uLgDky5ePpk2bcv36dWbPni1/d9KkSXz11Vf89NNPWFhYYGNjk+LHk5bR58+7evUqnTt3/mwd55UAnLSNvsby/fv3AWUaV0FBIe2RJLEnhLgOtAI8ABchRGEhRFVAAO7xbZvesbCwwM7ODgsLC7lm7vDhw3n//j2hoaHs27ePHDlyxInwrFatGgUKFGDKlCncu3cPAGdnZ+bPn8/Dhw/ZvHmzYu0xQpMmTZg5cyaXLl2K1z8vufH29iYsLCxZrokSgJP2yZMnj1zZRhF7CgoKaY1EPT0kHZUlSaql/0wIsR2YBXhIklRfkqTvgUzAM7P09AvEwcGBhQsXcu3aNZo3b45Go6FAgQJ4e3tz5swZ+XtarZaDBw/y4MEDKlSoQM6cOeV1+ulFvRVQwZCIiAi8vLyQJInq1aunSJve3t7Ur18fKysrunTpkiJtKqQekZGRnDp1imLFiqHVasmePXtqd0lBQUHBgARTvX8ItjgJ+AGlJUn6F+gFvEQn7NYBU4FooJ0Qwttsvf0Cad68OevWraNTp060aNFCzvm2fft2AMqXL8/ff//NzZs3qVy5Mtu2bTOYrt2xYwdWVlY0atQoVfqflomIiKBbt26cOXOGmTNn0rRpU7O3qRd6Pj4+7Nmzh1KlSpm9TYXUZffu3fj6+jJo0CDOnDmjXHMFBYU0R2Lq+jQC3gohWkuS5AYcBFYCrYUQQZIkzUHnr2cvhAg0X1e/XFq2bAnoUrK8efOGLl26sHnzZrZv387Jkyfx9/enSZMmNGnSxEDoCSHYuXMnNWvWVCx7HwgICGDLli0AbNu2jbNnzzJz5ky+++47NBoNoDtv//zzD5UqVfroHHnPnj3j5MmT8t8ajQaVSiXvd/r06bLQq1ChQjIdlUJaRa1Ws2bNGr7++mu0Wi2SJFG8ePHU7paCgoKCAYkRez5AOUmSqgshTkqSVAm4A8yWJGk10Bb4SRF6icdYpG6LFi34888/6dy5M3/++acczffo0SO6dOlCkSJF0Gq1REdHy9tcvXqVly9fMnz4cKKjoxNMy5EefL68vb356aefAJ3j/PTp0/n222/ltCVCCIYPH87ChQvp3LkzS5cuBXQRr6Zqmmo0GpydnQkICKBWrVq8efPGZPsuLi6y0EtMmpT0cE2+FIQQBr8/gAMHDvDq1SsGDRrEuXPnyJcvH05OTqnUQwUFBQXjJEbs3QEOAd9LkhQqhLgiSVJLYAI6H715Qogoc3bycyS+h7ipqMr27dtjYWHBDz/8wOHDhzl+/DiWlpZybdXYaT4OHjyIlZUVTZs2xdHRMXkP4DOlaNGieHl5AeDk5ESmTJnkdUIIRo0axcKFCylcuDBr1qyhXbt2NGjQgAwZMsS7XxsbG0aOHImPjw9Hjhwhb968Rr+XKVMm+WH/KfnwFBGY9rCzs6NYsWLy31qtlq5du1KkSBH69evHokWLKF++fCr2UEFBQcE4CT5RhBBhwHR06VVGSZJUDyiDTujtF0I8Mm8X0xfNmzdnxYoVXLhwgRYtWshTj7ERQuDl5UWdOnVQqVRy9n7QPYRCQ0PTZfJdGxsbPD098fT0jCP0hg8fzrx58xg8eDBXr16lSJEi9OzZk6CgoAT3u3v3btauXcuYMWOoU6eO3EbsRbHqpB/27t3Lv//+y7BhwwgODubZs2eKv56CgkKaJDGWPYQQDyVJGgH0BCYBQcAQxaKXvGi1WgIDA6lVqxYrVqygR48elC1bVk4ErNVqZYtPdHQ0L168YNy4cTg6Ohr4noWHh8tlwvRJeNMzMadu+/fvz+zZs5EkidWrV1OpUiX69u3LwoULTVrTXr16Re/evSlWrBjjxo1Llj5ptVrCw8OVlCqfKdHR0UybNg1PT09at24t169WxJ6CgkJaJFFiD0AI8RwYJ0nSNHSVN94ntI3CxxEeHo5arcbS0pK2bdvi6OjI4sWL0Vef0/ucaTQabG1tadWqFd9++608zau35OmniZUkvIZCr0+fPsybN08+n+XLl+fXX39l7NixWFlZMWfOnDjC69WrVzRr1oyQkBD2799vslrGx6IX5JIkKVPwnwmBgYGyi8C+ffu4du0aa9euxcfHhx9//JFcuXJRpUqVVO6lgoKCQlwSLfb0CCE+zzIEnwExKyVYWFjQtGlTg3QhUVFRWFpaEhERga2trUmLkD4Jb3onptAbMGAA06dPlyNn9YwePZrw8HB+++03AAPBpxd6796948CBA5QoUSLZ+qYX4nqhrpD2efr0KZ06dZL/HjZsGI0bN+abb74hKCiIs2fPKsJdQUEhTfLRYk/BfMSsrhHfdxSBYJrw8HBu374NwKpVq1i0aJEs9PQWvZhIksSkSZOIiIhg5syZgE7wvXnzRhZ6Xl5eVK5cOVn7qVTF+PwoXLgwf/31FwC2trZ4enrSuXNnrl69yqZNm5SUKwpmwdPTk+fPn5tc7+HhwbNnz1KuQ2YivuP8Uo4xNVHE3mdEQqk8lALTcPfuXcqW/X896JhCL77zN2rUKABmzpxJREQEly9floVe7NJ1CukTW1tbChUqJP89depUtm7dyqRJk2jcuHEq9kzhS+b58+fx3tuNvcR+jsR3nF/KMaYmithLBZKSlgXAysrqk/adHsiTJw/Tp08HwNXVlVq1ahn4PJrC1dWV6dOnY2try2+//YaTkxOHDh2iYsWKSe5Ler8WXxqSJMm/wW3btvHbb7/xww8/MHbsWOVhpKCgkKZRxJ7CF4WrqyutWrVK0rb6Kd1ChQrx9ddfJ6uPnsLnz9WrVw0CdCpXrszSpUsVoaegoJDmUcSegkIMJEmiQ4cOqd0NhTRI9uzZ6dOnD6Cb0u3atatB+UIFBQWFtIoi9hQUFBQSQbZs2ZItz6KCgoJCSqI4FSkofMaEhobKCbQVFBQUFBSMoYi9z5T0XBItKXyJ58vb25uSJUvi6enJ33//rURjpwBf2hhSUFBIHyjTuGkMYw8SfWktGxsbLC11lyylSqJFRkaaXBcVFRVvRYn41se3TqvVJrsvVFhYGEFBQYSGhpIxY0ajkbLJHT2bkCiIWf7OGKbWBQYGGiR8zps3L+3bt2fDhg3MnDmT/PnzJ6m9+NYnta9fEvpyhiEhIWTOnDnOMad0oIaputl6YicQTwwJvTAk9RjNtd+01KbysqWQlvny79BfAHphFxkZiYWFhZyQ19nZWU7Mq1/SMlqtlrCwsFSxjNjb22NpaYlarTY4j5/LuYtJTKHn5eXF4cOH+eWXXzh8+DCVKlVSrHxmwsLCQh5DERERSJJksKQW3t7etGvXjhMnTqRaH5KCVqslJCREsZQqKKQAn88TLh0Ts4yaHgsLCxwdHVNFpCRVtEVERBASEkJERISZemYaCwsLMmfOjIuLS5qpGZyUqWVvb28DoVeuXDksLS0ZNGgQJ06cIHfu3HTs2JHWrVvz9u1bM/Y+feLm5kaGDBnSzBjy9vambt26bN26lSZNmnD8+HGztGMOYRYWFsb79+8JCwtLtn0qfJl4eHjEebnSL56enqndvc8CRex9BqS10lp60RbfFK8xbG1tcXR0xNbWFvi/aLx06VKihcmHh02STkRaPI/6h92+fft4+vRpvN/39vamVq1aJit7FCxYkAMHDjBt2jT279/P119/zbZt25Ldz+xL9H9MLGlpDL1+/Zq6devy9u1btm/fTp48eWjWrFmyW/i0Wi3v3r0jKCjok4VZTNFob2+Pk5OTUv5RIUGePXuGEMLoEl8pOYX/k/p3LIXPDr1o+1i/On1dX/2DMiwsjFu3blGnTh26deuWqH2Eh4cDfLwzUhpEfx7HjRtH06ZNyZcvHxUqVGDmzJlxhJ9e6L19+zbeEm6WlpYMHz6cXbt24e/vz+jRo3n//r3+vMl8imDTuxXE3qdCyuLl5cWjR4+YPn06TZs2ZfPmzYSHh7NgwYJkbScsLAy1Wo2lpeUnC7OY1rzUmp1Izy8rCukXRewpfDSxRVtsPmaad9myZURFRXH06FHOnj2b4Pc/TJ/F75n+mSBJEuPHj2fBggX07duX33//HYCRI0eSL18+ypUrx4wZMzh//rws9A4dOpRgrV5vb2/69++Pk5MTq1evjuMCAJ8m2Iy5FSikPJ07dyZXrlzMmTOH8PBwpk6dikqlYsyYMcnajr29PS4uLri5uX2yMEsL1rzw8HCCg4OV6WOFdIUSjZvOUKvV/PXXX9y5c4eAgAD8/f0JCAgwWKpUqcLmzZtxcnKSt9NqtURERGBraxvnhh97nX6a18bGRp6yjf398PBwAgMD2bJlC+3atePo0aNMnjyZvXv3xtv/D22n+it5aGgo9+7d486dO9y5c4d///2XO3fuYGVlxfz586lfv77JbfVieOzYsSxcuJCBAwcye/ZsJEli2LBhPH36FC8vL7Zs2cKIESMADGr1BgYGmtz3q1ev+Pbbb3n79i0HDhwwqO0bU3zrhVpMwaa/LnZ2dglG3pozAjw9IoRgxIgRbNiwgaZNm/Ldd99Ro0YNOfreGM7OzixbtowGDRrQpEkTTpw4wZAhQyhVqlSy9k1vgUtr+0oq+jGf3IIzsb8fBYXUIMliT5IkC+B34C2wVwhx98PnklBCAVOF+Hzo/v33X27dusXkyZN58OABNjY2uLi44OzsjKWlJRkzZiRLlixYWVmxf/9+qlSpwqRJk8iYMSMVKlSQBRz8/yYZHR0N6N6Uw8LCiI6Olm90NjY2SJJk1LqnF3pTpkxBq9UyfPhwChYsyMSJEzl8+DDly5eXC86nFPoI3fDwcIYMGcK9e/fkdTHTjgghePv2Lc+fP5cjXq2trcmXLx8lS5bk33//pUGDBnz//ff89ttv5MqVK05b79+/Z+jQofz555/07t2bn3/+meDgYHl9xowZad++Pb169eL58+fs37+fypUrU6hQIQIDA3n//r1RsfXq1SuaN2+Or68v+/bto3z58gbnP+ZxGBNs4eHhhIaGIoSI8yBMKPWKKWKOyY0bN7Jr1y4GDBhApUqVEpVi50t/aIaHh6PRaBg2bBhLly6lfPnyrFu3jqVLl5I5c2ZatmxJ69atqV69ehzhp9FoqFu3Ll27dmX16tUUK1aMH3/8kfDw8HiFTHy3Z61WazKyWKPREBERYdKqL4QwuW18+4WUTVujH/um2ozv/MR3jHqLISS/kFRQ+GRMOT3GtwAScAyYCEwF6gDugEq/3sR2vYDL9vb2okyZMmLp0qVCIfmIiIgwujx//lw0adJEACJr1qxizpw54ubNm+LWrVvi1q1bYtOmTeL48ePy8vPPPwsLCwtRrFgxsX37diGEEBqNRrx//15oNJo47YWFhQl/f38RFhZm0G5YWJjQaDRxlujoaHHp0iVhbW0tOnfuLAICAoS3t7fInDmzqFWrlvD39ze63eLFi0WZMmUEEApcBnqJZBpbERERIiAgQNSuXVtIkiRq1qwp6tSpI+rUqSNq1aol/79OnTriu+++ExMmTBAbN24UZ86cEe/evRMBAQEiICBAvHnzRgwePFhYWFiI7Nmzi3379hkcg1qtFn369BGA6N27t/D395e3jbm8fPnS6OcBAQFGz8/z589Fvnz5hJOTkzhz5ozR8xfz2hkjOjpaBAcHi+jo6I/eNr7zGhERIZYtWyYkSRIqlUoAok6dOuLYsWMm+/kpbSaFpUuXmm1sxUdoaKjo3bu3AMSPP/4oQkNDha+vr9iwYYNo1aqVsLe3F4Bwc3MTffv2Fa9evRJqtVqo1WoRHR0ttFqt8Pf3Fz169BDXrl0T79+/F2q1Wmi1WpNtarVak4tGozG5Ljg4WLx69UoEBwd/9LbxrYuvr6lBUs9PdHS0wfnXL0uWLDE5tvRLmTJlTPZH95g2TULrU5r4+pPUdUltL70AXBYJ6baEvmB0I7AFFgIOwHngNHASGKYXfPEt8Q1shaQTW+SFhISIWbNmCWdnZ2FpaSl69OghLly4IIu8mGLv6NGjYv369WLKlClizZo1BoIvODg43vY+VuxpNBrRvXt3YWlpKW7cuCGLmEmTJgkgjkCKvcQ3sJM6tmIKveXLlxscR3BwsEkh7ePjY1SQHTlyRBQoUEAAolu3bsLf31+o1WoxcOBAAYiBAwcKX19fk4LuY8ReTKF36tSpJIsncwivmEKvdu3a4tWrV2LatGnC3d1dAKJu3bri5MmTqS729JhjbJlCq9WK/v37y0Lv6dOnYvfu3SIkJESEhYWJkJAQERwcLDZv3izatGkjrK2tRaZMmcT69etFdHS0LPY+VkB9ipgJDg6OI2bUarX8kpCexV5Cx5jUsaWIvaS3l14wi9j7YNXLDDwCdgOjAUdgHHAU8ExoH4rYMw8xRcixY8dEsWLF5Afqtm3bZHF39OhRsWDBAjF48GDRtGlTkTt3bmFjYyMAAQgbGxsxe/ZsWfBVrVrVqODTt+Xv7y9evHgh/P39DfoQGhpq1FL0/PlzA6ueftFb92rWrJmiYi8sLMyk0PsYsefv7y+eP38u//369WsxcuRIYWFhIXLkyCHatm0rCz21Wi38/Pw+WezFtuiZssp9itjTP+STIr5iCr2AgAD5vPn5+YmpU6fKoq9OnTpGhWpKk1JiT6vVikGDBslC78GDByJv3rwCEEWLFhXr16+XrUT65datW6JcuXICEN9++614+fJlHEGhVqvl7eJrOznFTEIWv/Qo9mJeB0Xsffq6pLaXXjCbZU+3bzoCamBhjM8uAlUT2lYRe+YhIiJC+Pr6ytNCOXLkEBs3bhTh4eHizJkzYurUqaJixYpCkiRZ2Lm7u4vixYuL1q1bi2HDholZs2aJnDlziixZsojjMaZ0R48ebbS9+Cx7fn5+8kMg5gN81qxZAhD9+vUzmAK9ffu2+PrrrwUgXr9+bVRwREdHJ/sDedSoUSaF3seIvR49eggnJydx+vRpA1F27tw5UbhwYQOhp9FokkXsde7cWQBi/fr18nlKbrGnf5i/f//+o85raGioUKlUImvWrMLPzy/OuQsLCxPBwcFixowZwt3dXUiSFEfwpTQpJfYmTpxoMHXbsWNHAYhOnTrJv80jR44YiD21Wi0iIiLE1KlThY2NjbC2thbt27cX//zzjyw03r9/n+C1Sm6xF9uyZ0zopDexF/M6fGliz8PDQx6jxhYPDw+T2ypiL+kkcN6TR+x9sOZVBmrF+MwSGIMuDcakDxa+f4EcCe1PEXvm4cCBA8LT01NIkiQGDBggfH19xbFjx0TXrl2Fg4ODAMRXX30l+vXrJ9atWyfOnDlj1GevcuXKwsPDQ/47f/78om3btnHaMyWAErLs+fr6ihYtWghAlCxZUpw5c0YsWLBAODk5CXt7ezF9+nSTgiM4ODhZH8harVZ4eHiIBg0ayP0+f/688Pb2/iixd/LkSVlEe3p6iidPnhiIstDQUHHy5ElZ6CWX2Lt69arImDGjyJUrl3j48GGas+z99NNPAhBdu3aNd5o/ICBAuLu7i7p1637xYm/z5s0CED/88IMIDQ0VYWFh4p9//pGt61ZWVmLYsGEiMDAwjtjTL3fv3hX9+/cXLi4uAhCFChUShw8fThXLXuz1xoROehN7X7Jl71PaU8Re0kng3CUo9hIMdZN0oUcngRHAGkmSDkiSlEMIoRZCTAHaA0U+LO2EEN4J7VMheQkJCaF///40aNAAlUrFgQMHyJIlCxUqVKB27dps3ryZ2rVrs2rVKvbt20ffvn0pWbIkzs7ORvf37NkzgxI0bm5uvHv37qP7JUmS0WoDLi4u/PHHH6xevRpvb2+qVq3KgAEDKFasGKdPn6ZHjx5x9mWu3G6XLl3i+fPntGrVCoAbN25QtWpVmjVrJkcbJ4QQgrFjx2JpaYkkSbx8+ZKePXsaFKq3tbWlSpUqyR51WKJECQ4fPkxISAh16tThyZMnybp/+LSqEZMmTeKnn35i9erV9O/f32TuRWdnZ4YPH86RI0c4c+bMp3Y5zXLlyhU6d+5M5cqVWbZsmTweypcvz5o1a2jbti2XL19m0qRJ8aZdyZ8/P3PnzuXVq1esWrUKIQT169dn7ty58ebANIU2Gcuh2dnZ4ezs/MXmYdQmIilzWqq0oqAAiUuq3Ah4K4RoAZRBF3X7pyRJGT6s9xJCtAY6CyFumqWXCiY5fvw4xYsXZ/HixQwYMICjR48yY8YMxo0bR/bs2Vm+fDnPnz9n4sSJlCtXLsGbT0REBG/evCF37tzyZ6bEnlqtxtfXF7Va/VF91qdqqV+/PufOnaNLly5Mnz6d3bt3G7QbE3PdPDdv3oyVlRVNmzYlKiqKnj17YmFhwdWrV5k9e3ac7x87doyDBw8afLZv3z5OnTpFdHQ0jo6OWFlZyXkDU4KSJUvKgq9evXrcvn07zVQHUKlU/Pbbb4waNSpBwde7d2/c3d2ZNGlSmul/crJnzx5q166Nm5sb27dvj5N2plmzZqxevZr8+fMnep/29vZ06dKFixcv0qJFC4YNG0anTp0+OmFwWFgYQUFB+Pr6otVqP6nKRHy/1U/Zb2oSs9/6FCvxJST/XI9T4cslMXn2fIBykiRVF0KclCSpEnAHmClJ0hrgO0mSRgohlNpJZiZmMt3w8HDGjx/PypUryZMnD3v37mX16tWULl2agIAAqlWrRoECBTh//jznz58nR44cJoWUn5+f/P+nT58ihCBDhgz4+PggSRJubm6cP38etVptcAP38/PD398fjUaDq6sroLvJRUZGYmNjgxDCaL48Gxsb7Ozs5H+NiaqUQAjB1q1bqVevHg8ePGD16tXcvHlT7uOkSZPw8PAgf/78lClTBiEEffr0Qa1W8/jxYyRJ4vTp0/z0009YWlri5ubGmDFjGDt2LCqVirlz51K2bFlatmxptP3g4GCT1ht9UmpjhIaGxvksd+7cbN++nRYtWtC0aVN27dpF0aJFDb6j1WqxtLQ0mfw19vU1tq0p4hPhkiTx888/AzBt2jQA/vjjjziWU0tLS4YMGcLo0aM5cuQINWrU+GysQ7qZFONEREQwbdo0Jk2aRKlSpdi8eTMZMmQgKiqKqKgokzklo6OjUalMVwaMjIzE2toaACsrK9atW0fx4sWZOHEit2/fZseOHUaLxAsRN1ecvb09oaGhqNVqWcTEzhlnbLvEngP9tjFz0SUmMXfsfcYcu5IkJdif5MrtF7Pf+kTxtra2Bv3TxshFqS8LJ4RIswnIPT09TdaV9fDw4NmzZynbIQWzkhgzyR3gEPC9JEllhBCRQEsgA5AJmK8IvZQlPDyc9u3bs2rVKvr27cupU6dwdnZm9+7dBAUFUbx4cZ48ecKpU6d49uwZPj4+PHnyBFtbW6NLpkyZKFKkCEWKFJHfRGvWrCl/5ubmhq+vbxyRkTFjRtzc3MiUKRPW1tZYW1vLYk/7IWGuhYVFnMXS0pJs2bKRMWNGMmTIEGdxdXU1up1+SS70U7ht2rTh/v37rFmzBmtra7JlyyY/aKdMmUJYWBg2NjbcvHmTFy9e8Pr1a+7fv4+NjQ1eXl68ffsWtVpNx44dcXNzY9CgQYSHh+Po6EjPnj35999/jR6Hg4MDjo6ORpdMmTLh7OxsdNEnhI29FC9enB07dhASEkKzZs149uyZ0XNnqlRafOc8qefdxsZGrqQyZcoUxo0bx+rVqxk4cCBWVlZx2ujVqxfu7u7MmjXri5gCCw4Opl27dkyaNIkOHTpw/Phxg0Tbtra22NnZYWdnh6+vLydOnDD4LL7rIUkSERERXLp0ic2bNxMYGMiIESPw8vLi+fPnlCtXjqNHj8bpk7Gxo1KpcHd3J0OGDHLb+qlY/Xf0bZpa4luvX2dvb4+zszP29vYG6xNLYqxqn4qx/uv7bWdnh0qlwsHBAZVKZfL4Y5aF+9hjTCn0ieGNLaZEoMLnS4J3UyFEGDAdyAiMkiSpHrrp3EzAPiHEI/N2USEmeqF34sQJ/vjjD6ZMmcKlS5do2LAhQgiKFy/O3bt3efPmDQ8ePJBrzj5+/JiIiIgE9//48WMsLS0NHkhubm5oNJo4FTpUKhWZM2c2sPjY2tri6OhoUCYtrU5p6KdwGzVqxLRp07C0tEStVvPLL7/Qtm1boqKiePDgARs3bgRgy5Yt8rHu378fHx8fvLy8UKlUFCtWjNKlSwNQtGhROnbsSEhICCqVijZt2hAQEJAix1SsWDEDHz5jU7qpVdtWkiQmTZrEuHHjWLFihdEpXQcHB4YOHcqRI0c4d+5civYvubl//z4VK1Zk7969zJw5k5UrV5o850eOHKFUqVI0atSInj17Gq2Goxd2S5cupU+fPlStWhV3d3dq1KhBly5dqFq1Krdv36ZBgwacPHmSLFmyUL9+faZNm2bgP2qKmNOv5nKb0JdLS+p+U8sf8GP7/anHqaCQ3CRqJAohHqIL0LiPLvK2LTBECBFlxr4pxOL69es0btxYFnrff/89u3btonXr1uTIkYNChQpx8+ZNLC0tqV69OnXr1qVQoUJERERw9uxZfvzxR7nkWWyCgoJYv349u3fvxsPDw2BqST9F6+/vn2AfLSws4jiIm7IkpSahoaFs3LiRevXqsWjRIh4/fkxUVBQdOnSgQIECdOnSBQ8PD2xtbfnzzz/x9vZmz5491KpVi2LFinH06FGmTZsmWzF/+OEHg7f3hg0bUqNGDUJDQ3nx4gWjR49OsWMrWbIkhw4d4s2bN3To0MGoBc/YgzwlRHlMwWfKh69Tp06oVCoWLFhgtn6YG29vbypVqoSvry979+5lwIABSJLEjz/+SPPmzXn//r383Vu3btGgQQNcXV3p27cvq1aton///gb7u337NlmzZqVq1aoMHDiQnTt3kjlzZgYPHsyGDRvYtm0b4eHh1K5dGz8/P/Lly8e5c+do0aIFY8aMYd68eSl9CszCp4rQ5AxEScl9pzQeHh4mrbQeHh6p3T2FJJDo2rhCiOfAOEmSpqErh/Y+oW0Ukgd/f3/Gjh3L0qVLcXNzY82aNbJfVrdu3ShTpgyDBg2iU6dOODg4UKlSJdnfK3/+/OTLl4+LFy/KvnR6hBA8ePCAQ4cOcfnyZSIjIylatChDhw41aP/u3btYWVnx1VdfJan/+rfwtOR/NXbsWF69esXEiRPp3bs31tbW5MiRg44dOwK6erfNmzdn/vz5ABw+fJicOXPy+PFjAgMDKV26NIcOHcLe3p7s2bOTM2dOg/1LkiQL87x586botIgQgrVr1xIdHU29evUSfd5j1j82p5+RXvCp1WoDHz59beJOnTqh1Wpp3ry52fpgbqZNm0ZISAi3b9+W/eZu3rzJ8uXLAWjevDk7d+7E0dGRXLlyUbhwYZ48ecK1a9eQJIm6desa7M/Dw4NmzZrJ1uhu3brRr18/smbNCuiuea5cuXj9+jVOTk4AODk5sWXLFjJmzMjTp08/+hhM+XamVRLTX70vHSCfp+RC/1IL4OjomKz7TmkUf70vj4/+BQshQhShlzJotVpWrFhBgQIFWLZsGb179+bixYs0btyYJUuWGAi9Hj16YG9vbyD09ISHh+Pj40Pt2rWxsrIiNDSUAwcOMGLECH7++WeuXLlCs2bN2LRpExs3bqR8+fIG2584cYLy5csnWQCkpTQEarWasWPHMm/ePPr06cPChQuxsrJCrVYzcuRI2eEdkANa7O3t+eeff/juu+948uQJ/v7+VK9enTt37hAdHR1H6OnRW/pS0l9HCMHQoUOZP38+gwYNYsaMGYk+77a2tik2vasP2ogZpRsaGkqrVq04fvw4y5Yto23btmbvhznw9vZmxYoVdOnShQIFCsifT548GRcXFxYtWsT58+dlC5+LiwtHjx4lT548XLhwgVWrVtGuXTuDfTo5ObF27VquXbtGixYtmDVrFsWKFWPChAn4+vpy5MgRLl68yIgRIwzGsN4fLymkhH9ccpKY/sb0pUtu9O4R5ti3gsKnkmjLnkLKEh0dTYcOHdiyZQtVq1bljz/+IFeuXLx9+5bu3btz9OhRGjRoQKdOnejRoweenp5kz57daATn8+fPkSSJOnXqEBoayvDhw/H39ydPnjz07NmTMmXKUKVKFaP9CA0N5fLly4wcOdLch2x2Xr16xffff8+pU6fo0aMHmTNn5vr164Bu6jDmgxmQpyuyZcvGP//8w8yZM+nbty9CCPnNPSoqihw5cpi135cvX8bDwwM3N7d4vyeEYMyYMSxdupRBgwYxa9asjxKaelGeEMll8Ykdpbt//37evn3LsmXL6NSpU5L3m9pMnToVrVbLmDFj5M9u3rzJjh07GDduHN27dydDhgx06tSJ5s2bs3//ftzd3Tl9+jTPnj2jRIkSJvdduHBh1q1bx5gxY/j111+ZNWsWixcvxtXVlRw5cvDDDz8Y3S46OtogWjQxJNUin1oWwfj6q9VqCQsLw97e3mxWN72fnjE+TO2m/tuuQrpFEXtmIiG/DVM3wdu3bxMdHc2oUaM4dOgQQ4YMoWvXrkiSRNu2bTl58iTR0dFUq1YNS0tLOnfujLOzM+XLl+fGjRv4+vrG6cfTp0/Jli0brq6urFq1ioCAAEaPHs3XX38N6ASdqbxc58+fR6PRUL169TjH9LEPj5TC2Lk/cOAAnTp1IiIignnz5lGwYEEaN26Mra0tnp6eVKhQwejUhaOjI5Ik8eLFC/z8/GjcuDFhYWFcu3YNCwsL+Rw8fPgwzrb6VA36CLeoKEMX1/gsEO/evSM0NJStW7cyYsQIvv76a7Zt24alpSURERG4u7sbfF+f2DmpQg/iH7NqtVoOTgkLCyMkJAQhhGzFSOo4iCn4ZsyYYSD09PneTJEaY89UehF9AMSaNWtYsmQJPXr0IGfOnGg0GkJCQpg4cSLOzs50796d0NBQGjRowPLly+nZsycNGzZk586dODk5UbhwYXmcREdHm0zLUqhQIZYtW8aoUaP4/fff8fLy4o8//kClUqHRaNBqtQZ9jY6OJjQ0FEdHR102/XhS7MS0SMdMuxLfdvrvJCW9SnzEl84l9v0ndn+NpUKJ2SdTv4/EpJD52O0+3F+TZmJVUEgGFLGXxogp9IYNG0bnzp2JiIhg1qxZHDp0CGdnZypWrEhwcDBnzpzBwcGBsmXL4u/vL/vtxMTb2xuNRkPx4sX56quvOHr0KAMHDpQfsKATJaYeKhs2bMDKyorKlSsDGLyxfw6+PPro2qlTp1KwYEEWL16Mu7s7bdu2xdLSkujoaGbNmoVarTZaUSRPnjyyED558iTr169HCEHTpk3Jli0br169omLFimTIkCHOti9fvgT+n8oh5vQa6AJfYn+mJyQkhG3btjFixAjy5s3LrVu3WL58OT/++CMODg4G7emnbvVCb86cOUYfSAldq/jy6FlaWsrb69NJfOr1j2mFnjp1KuPHjzeYAvucHN0lSWLNmjX07NmTOnXqMHv2bPka3L17lz179jB69GgyZ84sb/Pdd98B0LNnT1q0aCELPj1WVlby+Hj16hWrV6+mb9++ZMqUCdBdz5IlS/L3338TEhJiYFWKLUqsrKzklD0JHUd834lpIYt97WOmHYH/j5NPIb7t9alOErM+5pj9lD7Fd37i+y18OCcJh0QrKJiJtP2kTmcYE3r379/n+++/Z+PGjXh4eFChQgUePnzI1atXcXFxMeqjF5Nnz57h4OBA1qxZ6d+/Pzly5GDChAmJ7tPp06dlf720GFUbH69evaJu3bpMnTqV7t27s3PnTsLCwmjUqBF3794lMjKS/v37U6xYMZP78PT05L///iNTpkycPHlSzg136dIlnJ2dcXJywsXFJdn7vnXrVoYNG0bVqlXZt28fjRs3Zs6cOdy/f9/gezF99Pr165cki97HYM4pus/Z12nNmjX06NGDOnXqsH37doOpxOnTp+Pi4kK/fv3ibNeiRQvWrVtn4MMXG29vb2rVqsWECRMoVaoUZ8+ejfOdhKYm9eP2U9FbyOKr0GGutCPmquqREnxo9/N5e1FIFTw9Pc0WBa2IvTREv379DITeyZMnad++PQEBASxZsoQ8efJw5swZXr16RYECBahcuXK8Qs/Hx4eAgAA8PDy4evUqd+7cYerUqYwZM4Yff/yRY8eOxVvq7Pr161y/fp3q1asDqZefLSk8fvyY0qVLc/XqVdauXcuyZcu4fv06LVu2JDo6GhcXFwoVKsTAgQPj3Y+npychISGUL1+ekydPAnDnzh3CwsKIiooiS5YsJsWVfjou9vRtQhw5coSRI0dSpEgRVq5cia2tLb/99htWVlYGKVwiIyPp27evLPTmzp1r9mCQ1BL8aTVXI+jK5ZkSenfv3mX37t306NHDqPUXoFWrVixYsIAzZ87I0eB6IiMjqVOnDm/fvmXFihVYW1tTo0YNdu3alai+CSESlWMvJvGda3MGOCREfAEYpvqclseNgkJs4kt0/akR0orYSyNERkby119/0apVKzp37gzAn3/+ibu7O15eXlSpUgUfHx/CwsIoX748BQsWjPct9fXr11y6dAlHR0fev3/PzZs3adu2LbNmzWLlypVs2LCBRo0akTt3boYNG8aJEydk4RcZGcnkyZOpW7cu7u7utG7dGkj9t+OPwdfXF19fX2bPnk2HDh0AuHjxIlFRUSxZsoSAgAC6du1qchpVjz7dTObMmXn58iVarZZs2bLh4uJCYGAgjx8/5sqVK3G2i4qKkoXakydPZMGcGBwdHcmYMSN37tyhe/funDx5krFjxxIWFkb27NkBnZitVq0ay5cvZ+TIkcyfPz/JUZcfQ2oJ/rRgVTblQ6j3X+zWrVuc82JnZ4erqyubN282mv4kMDCQSZMmMWrUKIA4kd0WFhZoNBpcXFz45ptvyJ07N1qtNtHX+tChQwQFBcUb9BHz+MLCwggNDTUpqlIzWXB8CZVNCcHPLaJYQcFcpP2ndjrh6tWrREREUK1aNUAnVq5cuULjxo1lHx1/f3+sra3jOOfH5uXLl1y5cgUXFxfs7Ox4+fIlBQoU4Ny5czx48ICtW7fy4MEDNmzYQM2aNdm6dSstWrSgcOHCDB48mFq1ajFz5ky+++47jh49SvHixc1+/MlN6dKlcXBw4MaNG/Jn+vOod6SOz0ct9jZ660hoaCju7u6sWbMGPz8/7O3tWbhwIa9fv5a3EUKwbNkynj59SnR0NN988w0jRoxIdN8rVqzIiRMnGDduHHfv3qVjx44cPHiQsWPHMn/+fPbu3UvZsmV5/Pgx27ZtY8qUKSmW3iW1BH9asCprNBqjoqFVq1YULFhQjsKNiaenJzt37iQkJITGjRvLgi8gIIDJkydTokQJJk+eTJ06dbh8+TILFy402N7KyopNmzbh4+ND4cKFOXbsGKtWraJx48YJ9lcIwcSJE8mVK5fJKN2YxAysSI0qFQkR39gzJQRTq+KGgnGUZM2phyL2UpD4phROnz4NIL+BHz16FK1WS/369QHdjdvPzw83N7d4H+wvX77k+vXrZMyYESEE7969I0+ePDx//pyIiAgOHjxIw4YNyZw5My1btmTdunX8+++/LF++nAoVKrBlyxYCAgLYuHEjixcvJmvWrJ+FJS82VlZWVKlShRMnTsif6YVbYGBgovejd6jXl6/S+1Q1aNCAX375Rb6es2bNkv2Y9u/fz+nTp7GxscHDw4M1a9agUqkICgpKdLv29vb06tWL06dPM3nyZLZv306XLl345Zdf6Nu3L4UKFeLKlStmTTyclqbA0oJVWaVSGRUNKpWKcePGcevWLXbs2BFnffHixdmzZ48s+H7++We+/vprpk2bRs2aNbl8+TIbN2406TtapkwZ5syZQ2RkJKtWrZIt/wlx6NAhzp8/z5gxY2QLdnzXVC+MHBwc4j3XabFShKnx8Snj5lPHf1r6/aQVnj17ZrZpSoX4UaJx4yExP9L4boix0aes0Gq1cbK3nz59mvz58/Ps2TOeP3+Ol5cX2bJlIygoiCtXruDt7U1UVBQWFha8ePHCaJvv3r3j9evXZMyYkYiICCIiIsibNy9Pnz7F0dGRrVu3UrRo0Tjb2dvb07JlS9laYGVllSirV1qnZs2ajBkzBh8fH9zd3eWIY32UbEhICH5+fgA8ffrU6DELIVCpVISGhgI6i4xeAA4ZMoRdu3Zx9epVXr9+zcKFC6lbty5//fUXDg4OREdHs2XLFlxdXVmyZAk//fQT27dv55tvvpHbN3Wew8LC5ILxnTp14tmzZ7Rs2ZJbt27Ro0cPFixYkOAUdGwSGs+xU1nErAgQn28opE4alJRGH91pLMVG69at+e233/j1119p0aKFwfkQQlC0aFF27txJ8+bNmT17Ns2aNWPEiBEUKlRITocSG7VaLe+nZ8+edOzYETs7O9ndIjo62uR0rkajka16nTt3lvevD7CQJClOWhR9hL2pY9RH+MZMYxI7Ajg+ktv6bKo9rVZLREQE1tbW8d7HEuqPsRQyH5OWJTlT0CgofCqf/xM9jWLs4acP/4/94BRCcObMGZo2bYqtrS1BQUHcu3ePli1bypYEfRRmgQIFjDpHv3z5ktevX1O1alVev37Nu3fvGDRoEPPmzaNs2bLs2LFDdqyO3beEkvV+buiPr1atWoBOSLdu3Zo8efIAuock6B5Uemufo6OjyYjGjBkzylY7/UNEz9atW2nUqBEvXrzg6tWr3Lp1CycnJ4KCgli9ejWlS5fm0aNHjB49Go1GQ79+/bh9+7Y8xW6Kr776Snbo9/Lyolu3blhYWLBjxw6zWfMsLCwMxoY+VUdqRcmmRQFpSiBYW1szfvx4OnbsyM6dO2nVqpW8Tv9iV7lyZS5fvkxYWBiFCxcG/v8yYYzYqUViXwdLS0uT2x49epTz58+zZMkSg/uN/h5kKi1KfOdcP90Wc1yY230godQqxoiIiCA4OBhnZ2eTKaUS0+bHppCJnZYlOVPQKCh8KmnvbvoFY2pK4f79+/j5+cm57C5evIgQgooVK8rfuX37NnZ2diaF3vXr13Fzc5OFXosWLZgzZw4NGzbkyJEjZMmSJdUcq1OLMmXK4ODgwD///AMkbRoXdPnw9JaM2KkxHB0d+euvv1CpVGTIkAFLS0uCgoIYPHgwLVq0QKvV0r17d6ytrdm2bRuvX7/mp59+SlS7kZGRDBw4kNatW1OoUCGuXbuWovViU9MZPy2SUJLntm3bUrBgQSZNmmTyex4eHrLQMxdCCCZNmkSuXLno0qWLwbrkmA5P6+NCPx1ta2v7Sfv51ONMC+fJnKk80grx+QFKkiTXpk7vpM1fazpDn9KjatWqCCE4d+4c2bNnl8twqdVq/v33X1xdXeNs+/r1a9lHLywsjHfv3tGsWTPWrVtHjx492LZtW7qdQrCysqJq1aocP34c0Dmdq1QqAgICPmo/rq6u+Pj4ALqp8tjkzZuXFStWEBgYSHh4OHXq1GHcuHEALF68mJMnTzJ37ly+/fZbhg8fzsqVKzl8+LC8fVRUFEuXLjXYt7+/P1WqVGHBggUMHTqUU6dOKTetVMZUgIYelUrF8OHDTfrupRSHDx/mwoULBr56etKDH1la8O9MK5gzlUdaIT4/QCEEz58/T+0upgmUX0Mq8/z5c37++WcKFixIgQIFOHToEHfv3qVWrVqy6f/OnTtERkYaFXtv3rzB2tqaggULEhoaysiRI7l16xaenp4sWbLki/C9Syp+fn7cuXNHnsrZvn07Go2GO3fuACT6zbZYsWK8evUKS0tLevXqxfr16+N855tvvmHq1KlUqlSJFStWyNNrt2/fRpIkypYtC0CFChUA5D4AzJ49m1GjRhlUNXnw4AFXrlzhl19+YdasWR/tn6eQ/JgK0ACdL+fPP//M0KFDgf+XyksN9C8mly5diiPq9H5k8SVFVlBQ+PJIFrEnKQ4JH8WLFy9YvHgxjRo1onDhwkRERODl5cXhw4dZs2YNZcqUMUitcP78eezs7MiYMWOcfTk6OhIVFUWGDBnIkCEDGzZsoGfPnjx79oyjR4+m5GGlCcLDw7l16xa3bt3ihx9+wMfHh1WrVnH37l3GjBmDp6cnV69epV+/fpQvXz5R+2zcuDF//vkntra2REVF0b17d8aPHx/nQdqnTx/27dtnkDx30qRJZM6cma5du/Lu3Tu5Yoe+msKtW7eYNWsWzs7ObN68mSdPngBQtmxZ3NzcuHv3rsl+RUZGMmrUKEaPHv1FW2rSCrF9GuH/Ii9Pnjz8+uuv1K1bl2vXrsWZPk1JOnTowJgxY1i5ciW9e/c2GBv6Kc7PuVpJYkkPVkwFhcSSLGYfIYSQJEkSCYVjfWYktjTUq1ev+PnnnxFCkD17drJly0bWrFnl/7u7u3P9+nX27NnD3r175dxv+fLlo2/fvnTt2hVJkmjXrh05c+bkxx9/lNtTq9VcunSJMmXKGO2D3vk7JCSEQoUKcf78eXLkyEG+fPno3Lkzly5dkhPxpgf+/fdfg7yAf/zxB4UKFaJChQpYWlri7e1NzZo1GT58+Eftt127dlSoUIHOnTtz8eJFZsyYwZMnT5g9e3a8TuBubm4sWrSI7777jjJlyuDj48OePXuwtrYmMDCQfv36kTFjRnbu3CnnN1y0aBGWlpa0atWKtWvXyrVIY/L48WPatm0rJ3T29fVl6dKlHzV1lRylz/T7SG/TZgEBAcydO5d58+YRHBzMt99+y4QJEyhevDhbtmwhIiJCtuKmNJIk8csvvyBJEpMnTwaQx4Z+ijOx7+cfM0bMWUovKegDNcD80bD6YzcWAKegkBZIstj7YM1bDRwXQqxJbcGnf3szdcMx9QPU508zRkhICJGRkWg0GiwsLLC1tTXYz71797h69SpDhw4lNDQUZ2dnfH19Tb5JqlQqKlSoQO/evalWrRq5cuUCdAEWvXv3xsLCgvr16xvUP33y5AkhISFkyZKFJ0+exAku0Pf/2bNn5M2bFzc3N2bMmMGMGTNo3749TZo0YfXq1VhbWxtNu/Kl4enpycSJEwFdQEblypXp06cPt2/fxt7eHldXV5o0acL+/fsNtnv79q0crRubW7duyQL9m2++ITIykhs3buDl5cXVq1fZtm2bQYF7PWq1Gjs7O5o2bUqrVq3w8vJi1KhRFC1alMjISMaOHcvt27cZPXo0HTt2pHLlymzevJm2bdvi4eFBq1atWLJkCXv37pWjO7VaLYsXL2bs2LFIksSKFSu4efMm8+fPJyQkhGnTpiV6ejpmahUHBwfUajWRkZFGH9Yx04DERJ9OSJKkBOuzpgXis/J8WGfySX316lU5slWtViOE4Ntvv2Xs2LHkzJkTjUZD165dWbt2LQAtW7ZkwoQJZM2a1WT95KioKJNpbdRqdbzT9xqNxqRo02q18gvolClTePLkCb/88guVK1dGCGHSveNT0ofoU7IIIYx+11RfPyadSWLXgS5dkD5QI3YbQgiTzwS9r5ex54hWq0WSJDm9i/6ZYOo8xXweKSikJp9i2csPfAdU/CDy/vwg+CzQGfvi/IIlSeoF9LK3t6ds2bL06tWLXr16fUIX4hL7AfYp2NnZYW1tLScRjZmbSgjBxo0b+f3338mePTsrVqwgX758aDQaAgICeP36NSqViv/++w8fHx88PDyoV68erq6unD9/HltbWzQaDWq1mvHjx+Pj48Ps2bN59eqVQSqUY8eOYWNjQ7ly5Xjz5o0cUapHCMGDBw+wsrKiSJEiFC9enPnz5yOEYPLkyQwdOpTJkycb+IN9rixbtoxly5YBFJYk6TKwTAixDP4/tuzs7JgzZw6dO3emWbNmbNy4kXXr1pEtWzb8/Pzo3LkzkiTFcbTPnj07zZo1A3T+Vg4ODrLf3fjx4w0ejjVr1sTe3p5z587x9OlTmjZtytatWylYsKDBPvUpXkBnYaxbt65cuu3GjRv8+eefNGzYkIMHD/L48WM5DceqVauYMmUK1atXx83Nja1bt9KqVSsiIyMZPHgwy5Yto2TJkvzxxx9kzpxZjuKeP38+AOvXrzf6IIv9WcwUGhYWFkRGRpr87RibwgTDVB6fMzHGViFTY8vS0pJMmTJRokQJypYtS8uWLeUk6E+fPqVnz578888/DBgwACsrKxYtWsT+/fvp27cv48aNM3o/ip1eJSaWlpbyuLt58ya//PILw4cPp1KlSvK28WFhYcHEiRPJli0bv/76KzVq1KBu3bqMHz9ertQTm09JH2Jra0toaCi2trYflWpEX6btY61isfsam5jn72MxJd70bca2GurFXGxRFx4ezqJFi9i4cSMYuW8lB/poVFPrFBQApE8xxEmStAbwBUoDq4QQ6yRJshBCxOskUbZsWXH58uUkt2sMc1j2tFotNjY2cfYZGRnJjz/+yKpVq6hWrRrTpk3D2dnZYNvo6Ghy585tdL96sefv78/cuXM5deoUo0eP5ptvvuHgwYOy2NNoNPzyyy8UKlSIDh064OXlFUfsgS6a18rKijZt2vD9999Tr149GjVqxMSJE5k/fz7Lly9n/PjxTJo0Kf6T+JkgSdIVIURZY+tKlSol9NG39+/fp3bt2jg6OuLj40P79u0pU6aM0X06OjrSpk0bHj9+TN26dalZsyarVq1CkqQ4Yk/PvXv3OHToEJIkYWdnx/r16w1q4EZHR8cZF6Cz5lStWpVXr17RsWNH5s2bR61atTh+/DjVq1fnzJkz7N69m/r169OvXz/++usvzp49S7du3bhy5Qo9evRg1KhRXLt2jR9//BGVSsWmTZvYsmUL8+fPp0ePHh89pQs6S5KpabjYCZdj87lMXSVk2bOysromhChtbH3ZsmXFhQsX4nz+/PlzGjVqxOPHj5k5c6Ys6F++fMmkSZPYsWMH2bNn57fffuP77783OFdRUVEmrXdCCKytrQkODqZMmTI8fvwYCwsLhgwZwqRJk+JNdB37eoWGhrJ06VJmzpyJj48P9erV4+eff5ZfFGKSVBfs9+/f8/79e5ycnIxaeU3tN/Z2McVfQoIuvnWfYjE0ZdnTb2fsOWNsnzG/Z2lpafK+Fd8z0VSSa4XE8Tmdv6T2Nb5nop5PvUNHAzbAX0BfSZIOAPU+cZ+fxKeWxwkLC4vzQIi5z7dv31KrVi1WrVpFz549WbBggdEHekLs2rWLH374gXPnztG7d2+5qkJMHj58SFhYWIJFzJ2dneW3zAwZMtC8eXP27NmDr68v/fv3p2rVqkydOpUzZ858dD8/V8LDw2VfSB8fHwYPHpxgjd+QkBA6duzI+/fv2bFjB15eXvF+v1ChQnTr1g0nJyfCw8Np2bIlf//9d4J9+/3337l58ya9evVi6dKlVKtWjTlz5uDp6Ym3tzcqlYqlS5cCusoMoaGhVKxYUa6FO378eFatWkW7du2ws7MjLCyMtm3b0qZNGwYOHMiKFSvo3bv3R980TP12TP0uvjQ+HPdHHeSlS5eoXLkyb968YdOmTbLQA8iZMyfLly9n165dZMuWjW7dulGtWjVu3ryZ6P1rtVp69erFs2fP2Lt3Lz169GDWrFmUKlWKs2fPJno/Dg4ODB06lIcPHzJ9+nRu3LhB1apVqV+//kf1Jz7s7e1xcnLC1tb2o8qp6bfTWxH108EJRQybo2ybfqwD8T5HEvucUdLAKKQVkjQCJUnSe6TvBK4LIVYC9kAdwLhzymdAREQEISEhREREmPzOtm3buHDhAp06dZKtKh9LVFQUixYtIjQ0lNGjR9OuXTuj33vw4AGWlpZxpgdjo1KpiI6Olm96xYoVIyoqigcPHqBSqejVqxdqtVr2JUoPHD9+nLt37+Li4kLJkiXlvHfxcfr0ae7du8eSJUsoV64cAwcO5NSpU/Fu4+HhwfTp09FoNLi6ujJo0KA4iZdjs2vXLqysrLh//z4RERE4Ojry/v17MmTIwNOnT3F2dubixYsAci6s8PBwtmzZQvPmzYmIiGDmzJnY2NiwefNmxo4dy+vXr9m1axc//fQT5cuXZ+XKlfj6+ibuZCVAeHg4ISEh8eaYS49s376d2rVrY2dnx549ewysujEpX748p0+fZsCAAVy+fJkZM2Ykav/BwcG0bt2azZs389tvv9GwYUOWLFnCoUOHiIiIoEaNGgwfPvyjroutrS1lypShUaNGgC4n35QpU4BPF0/6JMIRERGJEmuxt9MLotjizxTmSCOj36cy1hW+NBIl9iQdlSVJqgUghNA7I/0H9JMk6RTwAOgFnDNLT1MAW1tbHB0d5czrQog4ofvdu3enadOmrFu3Tu+H8dFYW1szffp0MmfOzLRp0zh06JDR77179w43N7d4oz01Gg2vXr0iW7ZsWFhYyAl68+bNS/ny5QkKCmLs2LFkzpz5i/DbSyz6+qFZsmTBysoqUaJcP+0eFRXF+vXr8fDwoG3btnh7e5vc5sWLF/z000/kyZOHKVOmEB0dLQs1U2zatIlixYqxbds2vv76a/bv30+9evW4ceMGrq6uhIeHM3PmTNasWUOvXr0oX748GTNmpFu3bjx58gRbW1vmzp1LZGQkffv2Zfbs2eTKlYsffviBbdu2cfHiRcaMGZNsZfDs7OxwdHRM907mAQEB7Nixgx07djBx4kTatGlD8eLFOXv2bIIvZBcvXuTPP/8kb968TJ06NcG27t+/T7Vq1di9ezezZ89mxIgR8rq6dety/vx5OnTowJw5cyhTpky8Vj6NRsOJEycYOHAgHh4e1KlTh40bN9KqVSv+/vtvVq1aBSTeopYQiRVrpkhs5YmkppGJz1Kt32d6H+sKXx4Jir0PUbcngRHAGkmS9kuSlPNDIMZV4B/gkhCijRBitRDipVl7bEYsLCwMnIT1b6gx3/Ksra3ZvHkzTZs2ZerUqfFO2/3333/MmDGDsmXL0q1bNznZKUDx4sVZvnw5hQsXZvbs2Qbr9Pj6+hqN8ozJmzdviI6OliN7169fz8uXLxkxYgSSJDFq1CjevHnD1KlT01UKFj1fffVVojOo58uXD0dHR7n03K5du/Dw8GDPnj28fBl3WL9+/Zq1a9fK323UqBGWlpacPn063nby5MnD8ePHadeuHbdv38bDw4PMmTPj6upKZGQkS5cu5dmzZ3Tv3p3atWtz7NgxDh06RGhoKLVr1+bFixc0adKEBQsWcP36dfz8/Fi0aBFv375lzJgxVK9enV9++SUpp8sosX8X6ZUnT57QunVrWrduza+//kqrVq04cuQI7u7u8W53+fJlmjRpgru7O4cOHZIr45hi7969VK1aFT8/Pw4dOsTgwYPj+IK5u7uzaNEiDhw4QEREBNWrV8fOzk5e9EED+hKLderUYdWqVVSuXJn169fj4+PDpk2baNy4sfxy+6kiTU9KlQlLajvxWe+Usa7wpZKYUKVGwFshRGtJktyAg8AqoLUQIkiSpL+EEFcBEhOcAbo3K1OpHD4l31d860xFZcXneK5Pt6JSqeIEcqxbt47mzZszZcoUhBDyVKwQgmvXrrFhwwaOHz+OWq2mUqVK7Nu3jxMnTjB9+nSyZs2KRqPBycmJkSNH0qVLF5YuXcqYMWP477//eP36NVqtFl9fX9zc3OS0H+Hh4fj5+Rn04/Hjx3L0m5+fH3v27KFatWpUrFiRBQsWcPr0aQYPHpyg39+XgkajMZiKd3Nzw8fHBx8fH/z9/U1e6+joaPz8/ChUqBCXL1+WxfeqVato3LgxO3fupEmTJvKD+s2bN+zatQtHR0dWr16NlZUVoaGhFCtWjH/++Ye+ffsihJD9OR8+fMiYMWP4/vvvadGiBTY2NgwfPpxy5crxyy+/8P79exwcHFiyZAlPnz5lwoQJ1K5dGy8vL2xsbChRogQHDhygQYMGtGnThs2bN9O4cWMyZsyIRqMhX758NG/eHHt7e9avX290vCc0PRffbyGhbVP64aivU2sqoMQc/cmfPz+LFi0CdC99+fLlk4MLgoKCjKZXuXz5Mu3btydLlizs37+fbNmyodFoDI4j5v+nTp3KpEmTKFWqFBs3biRfvnwIIeIcqz4oqG7duty4cYOlS5calAGMjo6W07pYWlpSvHhxGjdujKOjoxz1HRISIrsc6EWTPkLbmL9nzACExAYoJIb4fEsTCgxKSnumImc/FY1GQ2RkZJwUXQoKaYHEiD0foJwkSdWFECclSaoE3AFmS5K0CmgjSdIdICoxQg/+X2MyORNdJvXHZWNjk6RtbW1t2blzJ506dWLq1KlkzpwZW1tblixZwq1bt8iQIQM//vgjffr0ka1L3bp1o0ePHrRo0YJ58+aRJUsWQJdW4ffff2fChAk4OztjYWFBUFAQWq2WrFmzypUzihUrZiDa3r59y9mzZ2ncuDG1a9dm27ZtsmXo9u3brFixgm7dujFx4sTPPjVGYlGpVAZT8frUA76+vtSqVctkcXR9frkSJUqwfv16rKyssLS0JEeOHOzdu5du3bpx8OBBNm3ahI2NDa1btyZnzpysWrXKIOq6SpUqLF68GK1Wi4ODgxwxOWHCBHbv3s3u3bspVqwYP//8M82bN6dkyZI0a9aMX3/9lT59+nDv3j1Z6MVMhAtQpkwZDh06xDfffEP79u05duwY3333HQA9e/bkwYMH7Nu3L0HLkSlMpVcB0mTZvaSkWfqUh7CdnZ3JXJWOjo5xIuX1U61ZsmTh6NGjRq+LtbU1KpWKO3fu8OOPP/LPP//QsWNHli1bJo9VtVrNy5cv5Wugj3RVqVRIkkSGDBkYOXKkwX4Tk84kZuqdjxVNxlKTJBQ5a4r4tokvNU1S96u/RySlP/Gti4yMTLEkzgoKH0ti7nx3gEPA95IklRFCRAIt0QViZAYWCCEiPyaZcnw1JtMa8fl3xJzS/emnn+jfvz8qlYolS5bw8uVL5syZQ8GCBXF0dKRo0aKcOXOG33//nf3791OqVCk2bdqEEILhw4eTLVs2RowYIb/l6pMnxyy9FZvz58+jUqkoV64cr1+/5uzZs/Tv3x9bW1u6du1KxYoVWbhwYYpMqaRV9FPXiS36XaxYMSIiInj06JH8WaZMmeQp3bZt29K6dWt56jZr1qwG21esWBGNRkPMNArXr19n+/btjBs3jvXr1xMVFUXr1q0pXbo0Xl5eeHp6snr1au7duydP3Xp5eeHu7h7nd1KqVCkOHDggT+k+efKEv/76i1WrVsnpe9ILdnZ2ODk5pcl7yfnz5+Wp2wMHDpgU4O/fv2fYsGGULFmSGzdusGzZMtauXWtwTH5+fgQGBqJWqxP90paYKc5PmW41t2/b51jqTPH3U0jLJPgrF0KEAdOBjMAoSZLqAWWATMB+IcSj+LY32mg8FoS0RkIRunrBN2fOHE6fPs3ly5fp2bOn0ZuypaUlI0aM4MKFC+TNm5fOnTvTrl07QkNDmTRpEhcvXpSrZyQk9qKiorh8+TJff/01jo6O7NixAzs7O8aPH8+oUaPQarVs2LAh3lxcXyLh4eHcuXNH9rHTi73E+u3p07PcunXL4HO9uPP09CRz5szs2rWLr776Ks72ZcuWxdLSknPn/h+nNGnSJFxcXPjpp59o3749d+7ckUVfmzZtKF26NKNHj5aF3s6dO3FwcDCZsqFUqVKyD1+tWrXo168f1atXT1cBOJB201rEFHqmfPSEEGzatImiRYsya9YsunTpwoMHD+jZs2cc61GmTJnInj07OXPmTDPHau5zr7ccxpcZIa2RVsejggIkMhpXCPEQXYDGfWAS0BYYIoSIMmPfkpWkvinGjtA1hrW1NQMHDqRSpUqJmnIoVKgQx48f55dffmHnzp0MGTKEDh06kC1bNu7cuQMgR8Tpo0g1Gg3v3r3j6NGjLFu2jJ9//pmwsDAqVarEq1evePz4MQ0bNiQ0NJTNmzczePDgdJk9/f79+1StWlVOtWJpaYkkSbx79y5R2+fJk0cO0ohN5syZcXZ25u3bt6xYsSKO/yTo3u6zZcvGv//+C0BQUBDbt2+nR48esnBXqVS0b9+eW7dusW7dOqKiopg+fbos9BJjGdALPv1U3bp165I81fo5WlHSMvp6x/EFY/zxxx/yb/78+fMsX77cZDCWpaUlWbJkSZNT6eZCbyWL775rjjx7CgpfKom+ewghngPjJEmahq7yRvzJxNIYSS2jpo/OAuLUQ/wULC0t5fQg33//PYcPH+bNmzdUrVoVgIIFC3Lx4kX27duHSqXi9evXcvmtrFmzUq5cOYoUKUL+/Pl58uQJoLNihYaGAqSLOrjGiFkb18XFhTlz5mBjY0P79u0TlXfOwsKCMmXKYKxSwrFjx7hw4QKlSpVi3rx5LF++nPbt29O/f3/Zr/LkyZO8fPmSfv36AbqE15kzZzYqNvWir23btpw+fZoKFSpgbW1NaGhoonxJS5UqxbVr19BoNEn20wPD34aTk1OS96Ogw9XVFUdHR5PX5NGjR4wePZoGDRqwc+fOeOvfpiX09z9jATHJjd5KFp93UEy/wc+hLrOCQmry0a+KQogQc3QkuTAVoZccEVj6KV3gkwMebty4wbRp02jbti0NGjSgbNmy5MuXTw7AyJgxI6VKleLq1atkypSJIkWKkCFDBrn8l4JxMmTIINe43bhxIwcPHuS3334jX758iU4yXLFiRX7//XeD1DdCCH7//Xdy5MjBgQMHePLkCTNmzGDFihWsX7+eLl260KtXL+bMmcNXX30lB05IkkTNmjX5559/TEYrqlQqatSoAejKWumLySfmOn+KyNOTmN9GfJGvCoaUKlWKFStWoFar41jjtFotPXv2xNramqVLlyYpKXtKETvIIznrjicH+vGaXoLPFBQ+hS9uXsDUDUn/pvgp6KcU4ptaSAxRUVH07NmTTJkyMXv2bJYvX879+/fx8vLixIkT8veqVatGhQoVZL87f39/ReglgLe3t5yAdvPmzVSoUIE+ffp81D70heYvXLhA48aNAZ1V79KlS8yZMwdra2sKFSrEypUr6datG8uWLWPx4sWsXLmSyMhIpk6damCtqVmzJlu3buXZs2cm6yXr0T/AUtLXMjG/jbT2oE/LlC5dmvDwcO7du8fXX39tsG7RokWcOnWKlStXGvX5TEvokywDclJtfcqXtIA+wORL4NatWyZdgNKjO05ax9PT06QfuIeHR6IDAlOSVBF7+rxRsYkvH56ehPKA2djYIITAxsZG/szYG3Zi9mnse8nxFjlo0CBu3rzJ5MmTuXbtGj///DNly5Ylc+bMBAQExBGT+unekJAQHjx4EGd/b968AXRJnPXHbOwcpweLTEBAAJs3bwZ002mjRo3ixYsXgC4JsqmHQ3h4uDyF6erqip2dHYcPH6Z48eJotVqmTZtGjhw5aNeuncF59fDwYO7cufTv358FCxbw7t07vv32W9RqtZwLTl9G69ixY3Tt2lXe1lSuSTs7uyT7IcW3nan2YmJqvbHfVcw2k+P3ldZRq9UmfT9j5lTUC7wLFy7IefKsrKyIjo7m999/p1atWvzwww/ydqamKpOat06/bXzrTO035jr9vU7/76e8MCe1P+mFqKioj65lrZB6PH/+3OT1SqtjOVXEniRJyf4A0O/P2NuepaUlFhYWHz0VZQ7ryvXr11m7di116tShcuXKzJs3j9DQUPr27YtWq6VFixYUKFDA6LZbt26VHygx0T9os2bNKh+XOc7x50DBggXZtGmT0XU5cuQwWUXk9evXBue2dOnS3Lx5E2dnZ86ePcvly5dZvHhxnGoJ+rGUPXt2eSpWj14UFSlShMyZM3PixAkDsRdfVHpC6xLC2Fj/lCh4S0vLNGVFSY2xrVKpTEbHq9Vquaxh4cKFcXR05ObNm3Tp0gXQ9Xffvn28efOGJUuWyNO38T3gk5q3LjGYysMXs02VSmXgw2muvsR3nGn1wWmMz6mvCumPNKMG4stnl1zop6JSq8h1dHQ0Xbp0wdnZmQEDBnDz5k127txJ8+bNyZMnT6r0ScE4ZcuW5dGjR/j6+rJo0SJy5colP7g/FkmSqFGjBidOnEixt/eUHOtKNK8hFhYWlChRgmvXrhl8vnz5cr766isaNmyYSj37P8lVBzexKGNEQSF1STNiLzw8nJCQEIOH06feIGJvn9pJWBcuXMiNGzcYOnQoLi4uzJs3j6xZs9KzZ8+P3tfly5eZPHmy7FOjR5kKSB7KlSsH6KypN27cYMSIEVhbWzNixAg6der00furUaMGL168MFpj1xyYGuvJ/dDVarX4+/sTFBSUai9RaZF8+fLJaZRAV1f34MGDdO/eHUtLS6PXQa1W8/btW9ltIzmJ3V7sOrjmTmMSXz1aBQUF85NmxJ6dnZ3sBKznU60T+u31iTlTO+nlmjVrqFixIlWrVkWr1fLs2TPq1Knz0eLz0qVLbNy4EX9/f968eWNQrzIoKAjA6HRveiAyMpIHDx7w4MEDXr16leT9fP3112TPnp2TJ08C/w+cOHHiBBs2bODs2bMftT99pQ399TE3psZ6clv8wsPDZZ/YtOK4by6EEGg0GjQaTbyi6PXr13h5eVGtWjX5s1mzZmFlZUWvXr0A4+LHz8+P//77z2j+xk8ldnuxq2eY29KnVJdQUEhd0ozY0wc/xE6X8imWOP32nxo9mxw8fPiQGzdu0KZNG0BXJkmr1Rotnh4fly5dYtOmTWTLlg3QiQf9OdNqtbIDuZubWzL2/vPh8ePHtGrVilatWtGwYUOjyZETg0ql4rvvvuPWrVs4OzvLUdL68ztp0qRP7mtqTG0lt3Vb/xDPnDnzF+8jevv2bfLkyUOePHkoVKgQBw4cMPq9cePGER0dzYwZMwDw8fFh9erVdOrUSfYZNSZ+MmXKRNasWePU2E0OEhJbsS19yU1qv2grKKR30vQv71NvEGnpBrNlyxYAWrduDfzfwhNf7dvYXL9+nU2bNpEvXz769u0r70d/fBqNRs4lZyob/5eOq6sr7dq1o23btjg4OLBo0aIk76tly5ZYWVmRIUMGA7Hn6urK4cOHP9q6BzoLi17cRUREpLgPaXL/Joy9pH2pZMmShaFDhzJ06FA8PDzkKjYxUavVbNu2jS5dush+uIsWLSIyMpLhw4fL3zN2HcxZKSOh6/4pdXIVFBTSPl9cnr20QmRkpMHfmzZtomLFiri7u3PkyBE5hUpkZKRBTp7Xr18b9eu6ePEiO3fuJFu2bJQrV46HDx9iY2PD48eP5ci+58+fky9fPiD9ir2AgAA2btwo/33u3DmuXLkiJ6g2JdBevHjBgwcPePfuHW/fvsXd3Z2sWbPi6uqKt7c3Wq2WO3fuEBYWxpAhQ1i2bBmTJk1iy5YtJh+QGo1GjtDU+1KGhobKUZDW1tap6kOq8HFYW1vj6ekJQKNGjZg7dy6DBw+mcePGuLi40LJlSx4/fkxUVBQlS5ZEo9Hw/v17li1bxrfffmsyyl5BQUHB3KSa2DP2gPyUt8r4tk3tt9VHjx5x8+ZNpk+fDkCuXLnk3G8FCxaUHyCgq+0ae9pZP3Xr7u5Oq1at5Dd/Z2dnoqOjcXV1BXRTj76+vlhbW+Pi4pIuUwF8/fXX7Ny5EyEEDRs25MmTJ/z111906tSJVatWGT0nISEhrF692uCzoKAgHj58aPCZftouT548jBgxguHDh3Pt2jU5CXNstFqtQSoc0CUk1lvCrK2tk93FwFy/g7T8+0op9NPuoLPylShRgqNHj1KiRAk0Gg12dnbyi1vx4sWxs7Nj8eLFBAYGMnLkSKNjz5zpTJKyzlykx3uRgkJaIn3cpVMZLy8vAL799lv5s8DAQCDhaVy90MuXLx+VK1c2mOJxcnLi/fv3BgEa+hJf6fXmamNjg6enJ7lz52bChAloNBqOHDnCmTNnDL4nhODNmzfs37+fVatWJbhflUrF8ePHAZ0/ZN++fXFzc2Py5Mkf1T+l3NiXQ8OGDZEkiX379smf3bt3D9C9xEVGRjJ37lxq1qxJ+fLlU6ubCgoKCorYSwl27txJhQoVyJkzp/yZXuzFF6Bx9OhRNm7ciEqlokyZMoSHhxukVrG3tyckJMRA7L179y7dTuECXLlyBZVKhUqlolOnTuTJkwdLS0vZWR50vnLLli1j06ZN3L9/nyJFiiS4X41Gw7lz5wBwd3fHwcGBESNGcOTIEfnz+EipKFyFlMPFxYUaNWpw+/Zt2aJ3//59smbNSoYMGdi8eTOvX7+Wy/cpKCgopBbJIvak9GpGSiS2trYEBAQYCDV9ahR9QEVsNBqNbI3SarVs3LiRgwcPsmTJEry8vDh16hTPnj3D1dVVzueVOXNmLl26ROHChc18RJ8P/v7+aLVag0oA9+/fJzw8XBbJ//77b6L2FRISQu7cueVyWIm17p0/f56hQ4eSL18+2adS4ctAfz31+S7fvn0rR9zeu3cPS0tL6tWrl2r9U1BI73h4eMhVWmIvMV2oUgpPT0+T/TFnHeRkEXvig4pRRJ9xunTpwoMHDzh9+rT8WcmSJQFMpga5cuUKQUFBqFQqChYsyJAhQyhdujQFChQgKiqK69evExoaSqlSpbh27RqFChVCkiT+++8/6tevnwJHlTYpU6aMnAutc+fOBAUFYWtry9SpU+Xv6MWxh4cHLVu2pFSpUgnuV6VSyVG4+vqgDg4ODB06NF7r3vnz52nQoAFZsmRh9+7dSjDGF4a3tzeAbLUPCwszuMbmLHmmoKCQMM+ePZNrUMdenj9/nuL90dfVNbbEDNZMbpIs9iRJspAk6XdJksZLklRPkiQLoZRvMErr1q1xcXFh5cqV8meenp64uLgYFXt6P7McOXJQq1Yt7t27h52dHblz56ZOnTp8//339OvXjx49evDkyRMAKleuLA/cb775JkWOKy1y8+ZNcubMSc6cOVmzZg1CCKZMmUKOHDkAneXFx8cHKysr3r17R65cuQyS35rCzs6OQ4cOkTdvXoPPe/XqZdK6d/HiRQOhp1j1vjy8vb1xcnKSfW/Dw8PNlqtOQUFBIakkSex9sOAdAzIDpYD+QPrM4psI7O3tad++Pdu3b8fPzw8/Pz/evXtHoUKFuHr1qixAfHx8ePjwIWvWrMHPz4/MmTMTFRUFwPr16wkODiYyMpLIyEjUajX+/v7cu3eP4sWLY2Njw9OnT/n666/56quvUvmIUw+VSoWLiwsuLi7Y2tqSP39+SpQowZkzZ7hy5QrHjh3DwsKCLFmyEBoayunTp7l48WKC+929ezfFixdHq9WiVqt5//49arUaGxsbhgwZwpEjRzh79ixarRatVsv58+dp2rQpWbJk4ejRoxQoUEAJzPjMiY6O5u3btwbL8+fPcXNzw9fXV67vbWdnh1arld02lHqwCgoKqU1Snz5ZgSAhRHchREugBGAwd2hsSleSpF6SJF2+e/cuZcuWZdmyZUlsPu1jY2NjsPTt25fIyEg2bdpEkSJFKFKkCHXq1MHX1xcXFxf5sxs3bvDgwQNsbW158+YNFy9exNHRkZcvX2Jvb8/o0aMZPXo0o0aN4tWrV2TKlIm//vqLQYMG8e7duy96CnfZsmWULVsWoLAkSZclSeqlX6cfWxEREdy9e5e7d+9iaWnJsGHDAJ219NWrV7x79w4nJyc5N+F///1HQEAAFStWRKPRoFar6d27N6BLKSJJElu2bKF69epyP2KWHLOxseHHH3/Ezc2NqVOnYmNjw7Vr12Shd/z4cXLlyoWFhYXRRSFtkJixFRQUxN69e9FoNNSvX5+qVasSEBBA1apVqVmzJhYWFrJlTz92IP2kplEwTnxjS+HLIz4fQXP75cVHUu9ClkAFSZJaffj7MeANIEmSp6kpXSHEMiFE2cKFC3P58mW5TmR6oFixYlSsWJFly5bJb/zlypUDdOlV9AQHBxMdHU3GjBnx9fUlIiICGxsbtFqtnJsPdJam8+fPM2TIEJycnDh37hxRUVFftNjr1asXly9fBrgrhCgrhJDfFvRjK1++fOzbt499+/axbds2g0S2+kLvGTJkkPPb6esmg676QefOnVm6dCmSJKHValm+fDktWrQw6EfskmMODg4MHz6cw4cPM2/ePHnq9ujRo/L0sULaJjFjK1euXMyfP59GjRoBuvyZAPnz55f3o0zjKsQmvrGl8OURn4+guf3y4iNJYk8I8RLoAPhLkuQCfA3clySpPfAb4BTf9umV3r17c+/ePS5cuABA3rx5cXV1lcWeRqMhICAAW1tboqOj0Wg0qFQqQkJCsLW15dGjRwghePv2LWPGjKFkyZJ8//33AJw4cQJbW9tE+Z99yVhYWGBnZ4ednZ1cvUJPcHAw1tbWcq47a2trWexptVqaNm3K+vXrkSQJIQTTpk2ja9euBvvYs2cP27dvj1N6qk+fPri5uTF06FBF6KUT9FVwYoo9fXUU0P2eFRQUFNICiRJ7ko7KkiTV0n8mhDgqhDgORAG3gIHAWGC6EEJJKmaENm3akDNnTkaMGMGDBw+QJInq1atz+PBh3r59S1RUFNHR0djZ2REVFSXXs4yOjsbBwUH2E7tx4wbBwcGMHDlSFjSRkZFYWFgQEhKSykeZdhFCEBUVxcuXLwkICJDPsRCCR48ecejQIdlq9+233xrUMgX4888/adGiBW3atIlTc9fBwYHp06dTtWpVReilE3x8fHBycpJzZUZFRREcHIyLiwvnzp1j6dKlicrhqKCgoGBuEhR7H3zvTgIjgDWSJB2QJClnjHWuQE2gM9BSCHHTfN39vLG3t+fo0aNYWVnRo0cPHjx4QJ8+fdBoNCxcuBA7OzscHR0JDg7GwcEBrVYr/z8yMhInJyesrKzIlSsXAH5+fvK+u3fvTnh4OLNnz06tw0vzfPXVV7i7uxMeHs5///2HjY0NWbNm5e3bt/j7+2NpacmBAwewtLSUc6Xp+fPPP+nRowd16tShadOmDBgwII7g++GHHzhx4gQ5cuSQS2spzvlfLrHdkp8/fy4HZjRq1IgsWbKwa9euVOqdgoKCwv9JjGWvEfBWCNECKAO4A6skScrwwS/vHdAUqC2EuG+2nn4h5M+fn1WrVsmCLywsjA4dOrBz507u37+Ps7MzGo0GIYRBPdXIyEjZgqB38Iw591+gQAGaNm3KggULTCZqTu9YWFiQKVMm8ubNS5YsWciZMyeBgYEEBAQAsHHjRqpWrRpnuzVr1shCb8eOHWzevNmk4NOjD+IICwsz6zEppB56q7AefRqkZcuWyVP56TkyXkFBIe2QGLHnA5STJKm6EOIdUAnIDcyUJKkaMAs4IYRIXBmCdII+RYqxJXv27AaCr1atWjg7OzNz5kwsLCxQqVT4+/vLQQRarVaeyn358iW+vr64ublx584dXr58KS9t2rQhNDSU6dOnEx4eTkBAAOHh4XK76QG1Wi2nsYm96K0u+gTJoaGh+Pj4ADoB3bx5c9kSJ4RAq9WyevVqevbsSdWqVVm0aBH+/v68e/eOOXPmUK9ePQYNGsTChQvllCv6xcbGBgcHB2xtbeOs0y96C+PYsWP56quvWLBggcE4MYWp/enTwsS3XiHpRERE8ODBA3kJDAxErVbz4MEDHj9+LIs9X19f+vfvrwg9BQWFNINlIr5zBzgEfC9JUqgQ4ookSS2BCUAmYL4QItycnfxc0Wq1REREYGtra+DMX6RIEUqVKsWZM2eoWbMmEydOZMKECfz00080b96c//77jwsXLtC6dWvCwsJwdHRk8+bNlCpVSi6z5unpyZs3b+S/AUqUKEHr1q1ZvHgxvXr1wsbGBiBdRQfa2tqa9JPatWuX7Ex/5MgRmjVrBsDIkSP5+eefDa6RJEmsXbtWFnorV67ExsaGefPmERkZybBhw1i8eDF9+/Zl0KBBWFhY0K9fP3l7faBIeHi4HBASk4CAAGbNmsUff/xBcHAwefLkYeDAgWi1Wvr06ZPcp0UhGbC2tjaob63/7ek/O3PmDNbW1kRFRVGsWDE5vY+CgoJCapOgZU8IEQZMBzICoyRJqoduOjcTsE8I8ci8Xfx8iYiIICQkxCC9R0zy5cvHqlWrePLkCRqNhnz58nHq1Cm5tu3Lly8pUKCAPC2bJUsWedtcuXIZpGLRM2bMGEJDQ1m6dCmOjo6ydVBBZ23dtWsXnTt3pkWLFmi1Wjp06MAvv/wS57uHDh2Sp25XrFiBjY0NEydOZNasWSxcuJBRo0ZhaWnJ4sWL5SndOXPmGFjPIiIi5Hx8egICAvjll1/IkycPU6dOpXbt2ly8eJHr16/TpEkTBg8ezJIlS1LidCh8IiqVymAa9/Hjx7i6ugJQtGjR1OqWgoKCQhwSY9lDCPFQkqQRQE9gEhAEDBFCRJmzc587eqF169YtAMqWLRvnbf+bb76hfv36TJ06lTlz5tClSxdevnxJrly5+Pfff6lQoQK+vr5YW1vLPnugE3v+/v68f/8eJ6f/Z7opXLgwrVu3ZsmSJQwePDhdWfVMce3aNTZu3Mg///xDWFgYFhYWaLVa6tatK+fUi50W8uHDh9StW5cdO3bg5+fHpEmTWLVqFd27d8fe3p4FCxYA8Pvvv7N582batGnDsGHD+O+//2Sxrp9WtbKyQpIkHj16JFvyvv32W0aOHCnXSAbYsGED7du3Z/DgwVhYWDBgwIAkHa9+itiYRVEh+Yg9bp48eYKNjQ2urq4GL2YKCgqfH56eniZr56ZWYuRPIVFiD0AI8RwYJ0nSNEASQrw3X7e+DCwsLPD396devXpERUWRKVMm6tatS/369WnSpAlubroKc1WqVOHgwYNIkoSFhQVPnz7F0dGRFy9eEBkZSXR0NEIIgoL+n9FGX2f16tWr1KhRw6DdPn36sGXLFk6cOEGrVq1I70ydOpW7d+/Kf2u1WrJly8bixYuxtIz7E3B3d6dMmTJs374dOzs7goKCWLlyJQ0aNGDChAkAvHr1io0bNzJkyBBy5MjB5s2badeuHTNnzoy3L99++y3jx4+nRIkScfzyrK2t2bBhAyVKlGDatGlJFnv64BDQBfcomAc7Ozuio6MJDg7G2dmZd+/eYWFhQe7cueNE6iooKHxePH/+PI4R4HMm0WJPjxBCSeSWCPT+ejNmzECrRQqByQAAF8xJREFU1bJgwQLOnTvH4cOH2bRpE5IkUbZsWapWrcqCBQto1KgRixYtQqVSkSdPHo4cOUKpUqWws7OjYsWKPH78mK1bt9K5c2esrKyoVKkS7u7u/P3333HEnl4UKg7iOpYtW8bevXvZuXMnd+/eRZIk/vvvP2rWrMn+/fsNqmwAXL58GVtbW/mB7eLiQvHixXnz5o382Zs3b8iXLx9Zs2YFdELNy8uLly9fylO5Wq3WwLJmb2+Pu7t7vH29e/cuT58+ZezYsQkelykLnr6yh/5fBfNQpkwZNmzYwPnz5/nmm2/Inj073t7e6SYYSkFB4fNBmeMxExERETx8+JDVq1fTuXNnevbsyapVq3j+/Dlnzpxh4sSJqFQq5s6di6enJ9myZePChQvUqlWL8+fP4+rqipOTExs2bMDa2pp69erx6tUr5syZA4CVlRVt2rThzJkzchSgHn0ZJ731L72TIUMGOnTowObNm7l06RIDBw7EwcGB169fU6VKFc6cOWPwfTs7uziWmcaNG3Pjxg1evHjBu3fvuHDhAo0bNzb4niRJ5MqVC09PT6NLQkIPYMqUKbi4uCTKqhezRm9M9Mm4lSlc8+Lp6Un27Nnl8ePh4SH7aSooKCikJT7asqeQOIKCgpg/fz5arZYuXbrw5s0bed2zZ88oUKAABQoUICQkhAsXLrB8+XKaNGnC6dOnCQkJwdPTk1OnTiGEYMOGDeTOnZvChQuzbt06rK2tKVasGFmyZEGlUjFjxgxat26Ns7MzefPm5dGjR2TIkAFXV9d0l24jICCArVu3Gl0XGBgoVxhp0KABBw4c4P3799SrV0/2czS1zzp16jB16lR27tyJvb09Wq2W2rVrExgYiKOjo9Ht1Gq1yXWgCxjRR0wD3Lhxg507dzJixAgcHR1NXju9xVCx4KUsb9++Ze7cuQafWVpacuvWLaZPn06lSpUIDQ1VRLaCgkKaQxF7ZsLHx4ctW7bQpk2bOM6c4eHhsgjw8/NjzZo1FC5cmDx58rBnzx7AMGHy+/fvuXnzJuXLlydHjhxs2LCBoUOHkjFjRkqVKsXFixdp0KABlpaW2NjY8PTpU/Lly5cuI3E1Go2Bb2NMzp49K0+7AlSuXJkLFy4QFBTE5s2bKVq0KAMGDIhj1cuUKRMeHh6UKlWKo0ePYm9vT4ECBahWrRqhoaHx+mfF9+C3sbExEHu///47Li4uDB8+3OBzY/vULzGDcxJqT+HTCAkJ4eTJk0bXnT9/nnbt2qFWqxXLnoKCQppDeTKYiUWLFqHVavnxxx/jrBNC8PDhQ+bPn8+YMWNwcHCgS5curFq1Kt59hoaG8sMPPyCEYN26dajVaqpWrUpUVBSXLl2Sv/fo0SNlCjcehBBoNBpsbGyoXLmyHDn5888/M2zYMJMF7Js3b87Vq1c5c+YMzZs3T1Yn/OvXr7Njxw4GDRpExowZk22/CimH/qVOrVYTFaUkKlBQUEg7KGLPDHh7e/P333/Tpk0bcuTIIX+uVqvZtm0bkydPZvz48Vy7do1vvvmGSZMmsXHjxgQdux8+fIitrS1t27blxYsX7N27l5w5c+Lp6cnp06fRarVERUXx/Plz8uXLl+7rs/r5+fHo0SOuXr3KP//8w8OHDzlx4gQHDhxg37593LlzB5VKRbly5ShWrBgAq1at4vvvvyc0NDTO/lq0aAHoxGLz5s2Tta+//vorLi4uDBo0yOR30vv1TOvEnE5XrHsKCgppCWUa1wysWrUKtVodx6q3bds2hg0bho2NDV27dqVGjRrY2try7Nkzrl27hqenp8H0bWyioqI4f/48WbJkwc7OjpMnT1K7dm1cXV159uwZr169YtasWWi1WgoXLpyuU3C8ffuWv//+2+g6CwsLrK2tefLkCZkzZyZLlixUrVqVwoULs3nzZg4fPsyKFSviCC8PDw/KlClDaGioySodScHHx4cdO3YwevRoMmTIYPJ7Ma9n7OlbhdTn8ePH8v/DwsLIlClTKvZGQUFB4f8oYs8MPH36lKxZsxpY9UCXQLlmzZr8888/3Lt3j2rVqgG6BMlt27Zl27Zt8e43Q4YMXLt2jTdv3pApUyaaNWvG9u3buXHjBpUqVeL06dOcOXOGtm3b0rJlSzmHXHp04Hd3d6dJkyYEBAQQHBxMcHAwb9++JSoqSraAZsmShcyZMwNw+/ZtTp8+DegSU7dt29bofteuXYtGo0nWKdywsDAA8ubNS2hoqMnrpQRkpG2yZ88u/z8+0a6goKCQ0ihizwy8fv3aaJqNDBky8OeffzJ06FB27NjB06dPGTRoELlz5+bbb7+lUqVKDB482OR+AwMDUalUtG3blty5c7Np0yaePn1Kw4YNefr0Kffu3WPkyJH89ttvsqN+erPo6ZEkKY7f4pkzZ8iSJQtRUVFERUXJQTJ37tyR09fUrFmTtWvXmrScxXygJzfR0dGy5c6YoNOnVFFIm+hTHllZWcUbha2goKCQ0ig+e2bg9evXJsslWVhY0KhRI8aPH09UVBQTJkxg+/btBAYGGkSKGiNPnjz07NmT58+fM336dF6+fEnr1q25desW9+/fp1WrVkyZMkWJyIwHSZKwsbHByckJrVbLlStXZKHXsWNHNm/enGpTpFZWVjg5OSmWu8+Uy5cv4+joiLu7u1JBQ0FBIU2hWPbMwJs3b8ifPz8vX740uj44OJg8efIwceJEVq5cyaZNm9iyZQvFixePd78hISFMnz4dSZKoVKkSxYsXZ+PGjYSGhtKxY0fKli0bb5BHehARQgjUarXJdfryN1FRUVy8eJHAwEBAV7d4ypQpREZGxjmHYWFhJmsMa7VakwETxoI8YvfHysrKYHv9NYpdfUMh7XPlyhUcHR1l1wAFBQWFtIIi9pKZyMhI/Pz8+Oqrr3B2djb6nZo1a5I7d25AZ026f/8+f//9N5s3bwZ0070tWrSgdevWuLq6snDhQrZs2UJgYCD9+vVjyJAhHD58mFGjRmFjY8OhQ4dkoacv02Zra5suxULGjBlp164doMu55+fnx7t37/Dz86NKlSoEBwfj6+vLzZs3CQoKQqVSsWLFCho3boyVlZXRfTo4OODi4mJ0nbOzc7znOb51Qgg5Xx4g10bWb5cer19apnDhwqxfvx7QCfnmzZuTK1cuVq5cSUhICDVq1CBnzpxyzWsFBQWFtIIi9pIZfaWMxJTG0lOwYEF++eUXxo8fz/bt29m9ezd///03f/75J5IkYWVlRceOHenevTt37tyhVq1avHjxgsqVK7N+/Xo8PT3lfUVERMhVIkxZo75k7t+/T9WqVY2ukyRJFsGRkZHY29vj5eVFzZo1CQ4OTuGeKnzOrF69Gn9/f+bNm4ckSdy9exfQvWAolj0FBYW0hiL2khm92EtK2gWVSkW1atWoX78+gYGB7N69Gx8fH9q3b0+2bNlo0aIFV69epWLFiixfvpx69erF8Q3SV81Ij9Uz9KhUKoA4yZGFELi6upIrVy5y587NiBEjKFy48Efte/jw4SxYsMDk+pIlS3L+/PmP77RCmufu3buUL18e0OXMbNSoEUWLFgV0Lxmgc7VQxJ6CgkJaQxF7yYw+c/6niq0MGTLQqVMng8+ePHlCp06dWLNmjVEH8PQ+hQs6i2qHDh0AXd3Sr776ily5cpErVy7c3d0/yuJqjJs3b+Lm5sYPP/wA6ASk/lqcOHGCy5cvf9oBKKRZ9OmOQOdb+d1338nr9Olz1Gp1vKXuFBQU0g4eHh4mg6lilzn93EmXimDZsmWp2r6Xl1eSt3VxcTE5OPVTuBEREQnuJ7XPgbnaz549OxMmTGDChAmMGTOGzp07U6tWLfLmzZts1k4PDw8mT57M5MmTDf5fo0aNZNl/QqTWtUtv7cbG3d2dAQMGMGDAAHr06GHSjzO5SI3jTk/XOK2Mq9QivZzz+Np89uyZHLgXe4mvwEFS20tNFLGXCiSUPDmp2Nra4ujomChRk9rnILXbTy5WrFiR7PtMqCxaenogp2a7qU1aezB+ae2m13GlJ72c85RuM62OK0mfiiJFG5Wkd8DzFG/4/xQG7qZQWxaACtAA+qd3YeB+jL9Tg5Q8B8ndvocQwmjIYyqMrYSOw4KPv87GxszHtGku0kO7iRlbFoAVEI15f8Opcb7TwzVOrTbT0n0L0sc5T402U+MYTY4tPaki9lIbSZIuCyHKptf200IfUrv95CI1jiO1zl16aze1UcbWl9dmWiK9nPOUbjOtjqt0OY0LpLadNbXbh9TvQ2q3n1ykxnGk1rlLb+2mNsrY+vLaTEukl3Oe0m2myXGVLi17CgoKCgoKCgrphfRq2VNQUFBQUFBQSBcoYk9BQUFBQUFB4QtGEXsKCgoKCgrpEMlU0tYvrE2FdCb2JB25UqndypIk1UrptmP0wUKSpKGSJBVMxfZrSpJULTXaTy4+XMs6kiS1SOF2LSRJ6iRJUvsUbrO2JEn1U6pNhbikpwdyejrWtID44LSfkudACCHS8zlPLdJbubS9QIQkSeOFEHdSosEPg/ok4AeUliTpDtBVCPFfSrQfg5rATMBNkqS/Uur4QScagHOAGsgkSdIyIcTslGo/ufhwHCeBZ0B9SZIqCSFGpkC7+jF0EcgmSdIFIcRjM7epv2bhQBFJkq6hG7evzdlurPanAK+BJ0KIPSnRbmrz4bj7AW+B50KIi/qHozBTNF1qtJla7abWsaYlPpyDqUAIcB44KoQwa87XD/ew1cBxIcSaFBxfv6O71nuFEHf1fTHz+BoIRAGrhRDhaWVspTex9wJoDnwvSdJ2IcSVFGizEfBWCNFakiQ34CCwVpKkVkKI9ynQvv6H9gR4BVQCxAfB9Swl2gdGAv5CiIaSJI0AMkiSlNfcgsUMtAO0QoiOkiT1B6p+EHznzNxuWeCaEGKoJEnHgHkfXox7mPGloTvgK4RoLElSFuAmsEySpDZCiDAztQnI4/UQ8B/gAVSRJOmmEOKFOdtNbT4c93F096lcQKgkSceEEDPN+HBK8TZTq90PbR4DXqZUm2mNGOfgMZAfKIPut/3WzE3nB74DKn4QP39+EHwW6Ix9yXr+PxznEeAUkAnILkmSH+AnhNCYQ4B9aPMaunNbE6gMdIxpPU3NcZaupnGB6+gsBeWB9pIkFZUkqYCZ2/QBykmSVF0I8Q6d2MoDLDFzuzJCxzNgC7AHqAj0kSRpqiRJOVOgC08AT0mSqgB1gQ7AcUmS9qZA28mJP+AuSZIjOgFWBvhLkqRzkiRZmbHd9+gsiQeAf4CfgZzo3pTNxWt0x1paCPEWWI/u2s0wY5t6cgNRQoiOwCB057pECrSb2mQHAoUQnYDvgQNAQ0mSepuxzWxAUAq3qW83IIXbzQkEp8KxpiWyorve3YUQLdH9rgzcNMwxxSqEeABsRTe71lmSpE4x1plDANkA/wLTgVrAxA/tD5EkSWWmNmuhG9Mt0d2zckuS1FSSpFKSJFmk9gtFuhB7MQbvSWA/0Akojs7KZu4f+h10VorvJUkqI4SIBFoCzpIk2Zm5bUD2M7NE90M/ie4B2hnoQsqMgWPAdmA2ujfqAuhuMG6SJGVLgfaTi9PAAHRlsryEEAWArwFrdBYoc/EI3U0yP3D3g0W6NuAqSVJWM7V5+UO7syVJGoNO2PZEdxM1G5IkZUA3BZJDkqQ6QggfdNYf3w/rPc3Zfmog6Xwj26CzpmaRJKneh+nyLeim0ut8OC/J3eYwoDS6F7FG5m4zRrud0Y2lHJIkNU6hY62N7mUlsyRJ9VPiWNMolkAFSZJaffj7MeANut+WmUVJNLr7x19A3w8vr/WSu5EPz3tHoAGwEdj54f+HgIboRL858AeqS5I0GlgBZEQ3q7UYyGemNhNNuhB7MQavD1AdkND5j2UAQs3cdhi6t4uMwChJkuqhu8E6mbPdWH0QQgg1sAlojO4t5xZwAQhKgfbfCSHGoBv499EdeyV0IsmsU4LJiRAiBJ3PSSRwTZKkfEB7wB4IMGO7amA+OgtpN0mSmgPfYsbz98GaNxLdAzEjOiuiL7rpELNYMSVJWgn8JITwBsYD0ZIkuaOrNflAkqSOwBRJkpzN0X5q8OHBdAadi0AbdLMOqyRJ+loI8QaYi+7hlNzHXAvdfSk/8BSda4lZ2/xwrCfRvWhnRDeeV0iSVOBDu/OSu13p/76nk4Cu6CwuGyVJKmauNtMyQoiX6GZW/CVJckH3snpf0gV+/YYZnksx7hc7getCiJXo7pl1AJfkbu/D884X+AWduPtKCBEihNAfX47kbvNDu9eBVuhe/F2EEIWFEFUBAbibo82PIV347H14W9ECbuisTPvQWWkGonNANytCiIcffNV6orvpBAGDhBBmbzsWN9H9oE8JIfqlgg/BcyALOtGZC/heCGF2sZmcCCHUkiTZAD+i80GJAjoIIfzM3O5TSZJ6AP3R3cQCgG5CiGAztvkC+EOSpJborMA5gO5CiGgzNVkXXQDVe2CxEOK9pPNzvY/OGt0aaGPOY04FGgP/CSFafhC2m9CNqUuSJA1BJ+gtgYjkavCD6HqMzieyKTrfJlvgtCRJQ9E9gFXJ2eYH9L6nP0mSdASdRSkLcEiSpInoBFdytxvTX3g4sAGdP9V+SZLGoXvhN8expgk+XOtKgI0Q4jiAEOLoh3V26F76BwJNSKb7cew2Y9wv/gMmfrDsPkAntJPF39nYcaKz6uUCfpUkyR/ds94RXYCdWdoUQmyXJOk2cE7SZTHIiM5nMFna/BS++HJpeqEnSVJTdG8W09D5A/2SSv1xRHfeUyQ4w0j72T9MYfDBd0GTwu1nRSe6Az5YcD5LPkz7OAORH6xgKdWuhO7tVEopofzhoeAKqD9Mq5qjjTrAAuAwUA7YBfwJaNE51L8EmooPEXVfCpIkVUYnQFqhexgeRjdl3hKohk54TRVC3DRD27M/tFkf3Qtwf3RiMzcwJbnblCSpELrr+gTdrMJOdA/8KsBCdMJvcnK2K0lSW3QvRz3QvWjnAt6h81uejc6ql6xtphViWFL90M0m/Qv0FEK8/LAuG7oXcD+ghhDivhnavAP0QhccKPHB51cIMfRT24qnzX/RBa/pp6fbonsxDwdmJMe1NtFmL3T3KUt0VvMa6Kau+wohrn5qm5+MEOKLX9BF4EYC7WN9bpHafUvFcyKldh+URVn0C7q3X4cP/5+I7o1/OLobZ1egWGr30UzHbQv0RefHWhgIRGdpaosuEMfSDG1KH87rBqACUBJdQM4zdFNQKjMdqyUwB51V8bsPn2VE50vlbo77MboXyynoxOU9dD5jBdHN8GQ117GmhQWd1XhrjPNwFd3LRIYPn1mh82UrkgJtunz4rHSM7ybL9TbR5pEYx2mZnO0l8jhzobPKZ0jtcaBfvnifPUmSSqF7W+0phNjwIVjBAkCYObdQWkZ8GJEKCmkBIYSfECL0w/9/Rhc13hPoJ4RYLYS4laodNBNCiAhgldBFK9qie2C0Q+ezuEvo/DWTu00h/u/D2wiYgM4Ccx1dtKpZrP3CuO9payAzOgt5st+PRVx/YTt0FtOMQLi5jjWNYCwTRG5gpqRLbj8LOCGE+DcF2pwt6bIxdJYkyeaDC1FyXW9jbXry/+OcLUmSXTKPr/iOsyq6F1WEEIHJ2OYn8cWLPXT5g2oLIdbqfdTSs8hTUEjLfJgeQQgxGV1E2+7U7ZH5EbqAH9BZ9Fqhm25sI4S4Zuamb6KbLv5PCFEP+FYIYbZAI9D5nqKbUr2Fbnq1HTrfU3O7JMT0F/4J6JICbaY2pjJBZEBnSZ8vkt9v3FSbLuhE/QIhRGQyGxvS2nFmAuYJIaKSuc1P4ov32YtJKgQkKCgofCQxAqrSFZIk2aPzaZr3wdKXEm2mig9vKvmefhH+wh+DJEn50QXlWQDL0AVZ/QDUN5cYUdo0X5ufQroSewoKCgppGUmSrIT5op3ja1d5Ef5CkSTJA51LRB10mSBGCV2aEKXNz7DNpKKIPQUFBQUFhS+c1MgEobSZdlDEnoKCgoKCgoLCF0x6CNBQUFBQUFBQUEi3KGJPQUFBQUFBQeELRhF7CgoKCgoKCgpfMIrYU1BQUFBQUFD4glHEnoKCgoKCgoLCF4wi9hQUFBQUFBQUvmAUsaegoKCgoKCg8AXzP5dvhG6BhZqzAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 698.4x698.4 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "live_points_p = sampler.transform(sampler.region.u)\n",
     "corner.corner(live_points_p, titles=sampler.paramnames, show_titles=True, quiet=True);"
@@ -510,22 +391,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 698.4x698.4 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "corner.corner(sampler.region.u, show_titles=True, quiet=True);"
    ]
@@ -548,22 +416,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.plot(t, y, 'o ', ms=14, color='k')\n",
     "\n",
@@ -618,22 +473,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.scatter(live_points_p[:,0], live_points_p[:,1])\n",
     "plt.ylabel('Amplitude (A1)')\n",
@@ -649,22 +491,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.scatter(sampler.region.u[:,0], sampler.region.u[:,1])\n",
     "plt.ylabel('Amplitude (A1), untransformed')\n",
@@ -680,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -722,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index c8d1832d..aadb42d4 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -395,6 +395,9 @@ def get_auxiliary_contbox_parameterization(
         posterior_samples = aux_results['samples'][:,-1]
 
     """
+    mask = np.logical_and(upoints > 0, upoints < 1)
+    assert np.all(mask), (
+        'upoints must be between 0 and 1, have:', upoints[~mask,:])
     steps = 10**-(1.0 * np.arange(1, 8, 2))
     nsamples, ndim = upoints.shape
     assert nsamples > 10

From 00a8482eb44542882c1cb80c7e20aa4957e67cce Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 13 Sep 2022 20:44:26 +0200
Subject: [PATCH 103/313] =?UTF-8?q?Bump=20version:=203.5.4=20=E2=86=92=203?=
 =?UTF-8?q?.5.5?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 5ad486d6..7442b6dc 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.5.4',
+    version='3.5.5',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index f4b86270..a584a4d4 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.5.4'
+__version__ = '3.5.5'

From d864952fdb3fcc3e0420b3110b3d6de6a5f289ca Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 18 Sep 2022 15:01:25 +0200
Subject: [PATCH 104/313] fix sanity check

---
 docs/example-sine-highd.ipynb | 331 ++++------------------------------
 ultranest/hotstart.py         |   4 +-
 2 files changed, 38 insertions(+), 297 deletions(-)

diff --git a/docs/example-sine-highd.ipynb b/docs/example-sine-highd.ipynb
index a673d24d..58c0defc 100644
--- a/docs/example-sine-highd.ipynb
+++ b/docs/example-sine-highd.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,22 +89,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -130,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,57 +256,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "36c8a23fc9314d4382ddc2543cf55e32",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=-4e+01   [-35.7755..-35.7752]*| it/evals=8440/59678 eff=14.2380% N=400    0 0  \n",
-      "[ultranest] Likelihood function evaluations: 59697\n",
-      "[ultranest]   logZ = -52.21 +- 0.1439\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 2209.0, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.05 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.14, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.19 bs:0.14 tail:0.01 total:0.14 required:<0.50\n",
-      "[ultranest] done iterating.\n",
-      "\n",
-      "logZ = -52.252 +- 0.301\n",
-      "  single instance: logZ = -52.252 +- 0.190\n",
-      "  bootstrapped   : logZ = -52.214 +- 0.301\n",
-      "  tail           : logZ = +- 0.010\n",
-      "insert order U test : converged: True correlation: inf iterations\n",
-      "\n",
-      "    B                   : 0.36  │ ▁  ▁▁▁▁▁▁▁▁▂▂▃▄▅▆▆▇▇▇▇▆▆▅▄▄▂▁▁▁▁▁▁▁ ▁ │1.64      1.04 +- 0.15\n",
-      "    A1                  : 3.07  │ ▁▁▁▁▁▁▁▁▂▂▂▃▃▄▅▇▇▇▇▇▇▆▅▅▄▃▂▂▂▁▁▁▁▁▁▁▁ │4.80      3.94 +- 0.22\n",
-      "    P1                  : 2.878 │ ▁▁▁▁▁▁▁▁▂▃▃▄▄▅▇▇▇▇▇▆▅▅▄▄▃▃▂▂▁▁▁▁▁▁▁▁▁ │3.294     3.074 +- 0.055\n",
-      "    t1                  : 0.00  │▇▄▁                                  ▁▂│1.00      0.18 +- 0.36\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "result1 = sampler1.run(min_num_live_points=400)\n",
     "sampler1.print_results()"
@@ -334,70 +273,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1bddc773e7224f198f77288b819f15c0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Z=-184.4(0.00%) | Like=-174.71..-46.75 [-184.3100..-169.5248] | it/evals=2752/159174 eff=1.7333% N=400   \r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/user/.local/lib/python3.10/site-packages/ultranest-3.5.4-py3.10-linux-x86_64.egg/ultranest/integrator.py:1732: UserWarning: Sampling from region seems inefficient (0/40 accepted in iteration 2500). To improve efficiency, modify the transformation so that the current live points are ellipsoidal, or use a stepsampler, or set frac_remain to a lower number (e.g., 0.5) to terminate earlier.\n",
-      "  warnings.warn(warning_message)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=-4e+01  2 [-124.0658..-104.8495] | it/evals=3564/400459 eff=0.8909% N=400 \n",
-      "[ultranest] Likelihood function evaluations: 400459\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: overflow encountered in multiply\n",
-      "  x = um.multiply(x, x, out=x)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Reached maximum number of likelihood calls (400459 > 400000)...\n",
-      "[ultranest] done iterating.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "result2 = sampler2.run(min_num_live_points=400, max_ncalls=400000)"
    ]
@@ -419,74 +297,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Widening roots to 400 live points (have 400 already) ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f2ec77fc6ac474c92c5fad0ad5251f5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=-2e+01   [-19.8059..-19.8052]*| it/evals=12427/929849 eff=1.6740% N=400   \n",
-      "[ultranest] Likelihood function evaluations: 930279\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: overflow encountered in multiply\n",
-      "  x = um.multiply(x, x, out=x)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest]   logZ = -46.08 +- 0.2089\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 2942.8, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.07 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy wants 398 minimum live points (dlogz from 0.18 to 0.58, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.24 bs:0.21 tail:0.01 total:0.21 required:<0.50\n",
-      "[ultranest] done iterating.\n",
-      "\n",
-      "logZ = -46.147 +- 0.580\n",
-      "  single instance: logZ = -46.147 +- 0.241\n",
-      "  bootstrapped   : logZ = -46.079 +- 0.580\n",
-      "  tail           : logZ = +- 0.010\n",
-      "insert order U test : converged: True correlation: inf iterations\n",
-      "\n",
-      "    B                   : 0.41  │ ▁▁▁▁▁▁▁▁▁▂▃▃▄▄▆▆▆▇▇▇▇▇▅▄▄▃▂▂▁▁▁▁▁▁▁▁▁ │1.62      1.01 +- 0.15\n",
-      "    A1                  : 0.10  │▁▁▁                     ▁▁▁▂▄▆▇▇▄▂▁▁▁▁ │5.31      4.19 +- 0.24\n",
-      "    P1                  : 1.00  │▁▇▁ ▁             ▁      ▁           ▁ │77.45     3.07 +- 0.82\n",
-      "    t1                  : 0.00  │▇▃▁▁ ▁ ▁  ▁▁        ▁   ▁         ▁▁▁▁▃│1.00      0.22 +- 0.40\n",
-      "    A2                  : 0.10  │▁▁▁▁▁▁▃▄▇▇▇▆▄▂▁▁▁▁ ▁           ▁▁ ▁▁ ▁ │4.50      1.21 +- 0.25\n",
-      "    P2                  : 1.000 │ ▁▁▃▇▂▁▁              ▁           ▁▁▁▁ │3.264     1.254 +- 0.059\n",
-      "    t2                  : 0.000 │▁▁▁▁▁▁▁▁▁▂▃▄▅▆▇▇▅▄▂▂▁▁▁▁▁▁  ▁        ▁ │0.718     0.262 +- 0.053\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import ultranest.stepsampler\n",
     "\n",
@@ -525,22 +338,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 698.4x698.4 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from ultranest.plot import cornerplot\n",
     "cornerplot(result1)"
@@ -548,42 +348,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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918rlcmpvb9fVq1fV0tKiIAj08MMPK5lMqqur68AEISKRSDHThYwXAAAAAMB+QebLDoWBglgspiNHjqi9vV25XE4zMzMaGxvT0aNHlc/n9bM/+7NaWFi44/3ZbFb//J//8+IS1c45dXR0aHh4WLlcTjdu3NDS0pKSyaQiEW4TAAAAAADNisyXKjl27Jh839fIyIjS6bRisZhOnz6ts2fP6h3veIf+4A/+oJjNkU6n9U//6T/V008/rc7OTh06dEjLy8vyPE+HDx9WKpXSS17yEvX395MBAgAAAABAkyOlokry+bz6+/vV09Oj++67T845vfSlL9WrXvUqPfXUU/qxH/sxzc/P6+LFi/qBH/gBPfvss3rooYcUi8W0tLSkpaUlLS4uanFxUe3t7VpZWVEikWjqrJdw9aJKqxZttA/YCydOnJCZVXyMjIzUu3kAAAAA9hEyX6qkvb1dQRAoFovp+PHjmpqa0s2bN3X33XcrkUjo05/+tH74h39Y169fV1tbm+655x4dPnxYExMTOnTokAYGBvTa175W8Xhct27d0srKipLJpA4fPlzvj7ZjYVFi6c4aLhvtA/bClStX1t1XXhgbAAAAAHaD4EsVmJlaWlokSdevX9fQ0JAOHz4sz/N048YNDQ0N6b777tNzzz2ne++9VxcvXlQ+n9fNmzeVy+XU29urjo4OTU9Pa2FhQS0tLers7FQ8Hl8ToGi2LJiwUHClgsEb7QMAAAAAYD8h+FJl7e3tWlhY0KFDhxQEgfL5vMbHxzU8PKxsNqtr164VVzEyM83NzSmfzyudTqujo0NHjx7Vfffdp7vvvlu+72thYUGRSGTNSkDNYr02B0Eg3/eL06rCKUhScwaZAAAAAADYCMGXKkomkzpy5Iiy2ayeeeYZtbW1KZvNKpvNqqurS8eOHVNbW5t831dra6vi8biy2az6+/t1zz336NSpUzp9+rSGh4e1vLyshYUFdXZ2KplM7qsMkXDKUVjvxfM8zc/PN22QCQAAAACAjRB8qaJIJFKs2ZJMJnX79m11dHToW77lW9TT06MrV64oFovpwoULMjO1trYqlUopm81qcnJSAwMDWllZUWdnpyKRiNrb2zUwMKBodH/dpjCQFASBJiYmFATBvgwyAQAAAAAgEXypunQ6rdnZWbW3t6u3t1fz8/M6evSoHnroIXV0dGh8fFxmplgspgceeKBYcLevr08vfelL1d/fr6WlJXmep6WlJfX09Oy7TJAwuyWfz6ujo0OJREKpVIrpRgAAAACAfYngS5X19PRocnJSd999t5aXl+X7vmZmZvTkk08qm81qenpakUhEQ0ND6u/v18mTJ9XT06MHHnhAR48eVXt7uyYmJtTe3q6enh7F4/F6f6SayWazklTM7Gnm4sIAAAAAAKyH4EuV5XI59fT0KBqN6pWvfKWWl5d15coVPf/888rn8zp06JA6Ojp0+PBhdXV1KZ/Pq62tTX19fZKkmZkZzc7OqqOjQ0tLS8pms/su8yUUj8fleZ7i8bh839fExISk9Qv1AgAAAADQjAi+VFnpEsqe5+mBBx5QLpfTV7/6VbW3t+u+++5TT0+PlpaWdOPGDV29elXnz59XR0eHjhw5oiAI1Nvbq5GREcVisX1bAyUIAqXTaeXzefm+L0nq7+9XJBLZt58ZAAAAAHAwEXypsjB44Pu+4vG4YrGYent7dfLkSUWjUfX09Oiuu+5SNBrV888/ryAIZGbKZDLq6OiQJB06dEjxeHzfZH+ULy0tFVY8yufzkqSJiQm1tbXty/o2AAAAAAAQfKmBcCnlSCSitrY23Xfffert7VU2m1Umk9HRo0c1PT2tnp4eveY1r1E8HldXV5cOHTqkwcFBpVIpBUGgIAj2Re2T8PuQVAyuhNktmUxGc3NzOnToEBkvAAAAAIB9ieBLDYRBhHg8rmw2q0QioaNHj2p8fFzz8/Nqb2/X1NSUotGo+vr6lM1mtbS0pKWlpWK2TBi82Q+ZIKVTsULhZ4vH44pEIurr69sXgSYAAAAAAMoRfKmB0qBJafBkaGhIqVRKuVyuOD0pnU7r2LFj6uvrU1dX15ogxH7JBNkoiBSNRjU0NLTHLQIAAAAAYO+QarCHwoBLNptVe3u7rly5ohs3bsjzPHV1dam7u1vRaLQYrNjPmSBBEMjzPAVBUO+mAAAAAABQU/t3dN+gPM/T/Py8UqmUXvGKV+j06dN69atfrWg0qlgsdmACEmEdmHClIwAAAAAA9iumHdVBJBJRf3+/Ojs7lUwmiwGImZmZYuCl2Wu9VFrhqFSlOjAAAAAAAOxHBF/2SGkwYnh4uBiUWFhYUCKRUDQaXVOgt9mVr3BUHozZL8WEAQAAAADYDMGXPVJpueWwrktpdsh+CUiUZ7ZU+vwAAAAAABwE+yL4YmbmnHP1bsdGNlpueT8q/2xMMwIAAAAAHFRNV3DXzCJm9s/M7CfN7CVm1uKcc2ZmG7znUTM7e+HCBY2OjurMmTN72WRJqriC0UFa8aeZV3A6c+aMRkdHJekBMztrZo/Wu00AAAAAgOZh6yWMmNmx9d7knLtWsxZtYDXA8rik5ySdljS3+vq3nHPLm71/dHTUnT17tpZN3BbP85TJZJRKpfZtBsx+YmZPOudG19vfaP3roDMz7TQhbjfv3anN+hcAAACA5rXRtKOLFfabJCeppWYt2tiQpGnn3D8xs4Skfy7pWyXNmdmZRp96VI6pOAAAAAAA7H8bzQF5q6RZSXeVPEZW/60Xk/Q6M3uHc86X9B5JlyV9n6T2OrZrR5p5Kg4AAAAAANiajUb9n5B0zjl3NXyoEOD42b1p2p2cczcl/bKkf2dmb3bOZZxz/7ukQyoEhtBgDlJdGwAAAAAAKll32pFzbkWFLJO4pH8g6SckvVrSE3vUtrDGyxskdTrnPrq6+b9I6pL0YTP7l5J8Sa2SpvaqXSgIgkC+769ZKrscS0wDAAAAAA66dYMvZvYySf9E0j9Sof7LiKTDzrnJvWiYmUUkfUbSFUlvNLNXOef+D+fcoqTfMLOLkt4saUnSP3bOEXzZY1sJrFDXBgAAAABw0G1UcPcpSf9T0quccxfM7NJeBV5W/ZCkwDn3I2b2zyS9xsxeLenzq4V1/9w59yEzs2YrtLtfbCWwEta1AQAAAADgoNqo5sv/Kellkv7YzP6/2vsVjmYkDZhZUtKopG+S9H5Jnzazt6pQ94V0im2qZg2W0oLB1HYBAAAAAKCydYMvzrlfknRc0rslfYekITP7mJm9fY/a9lkVivsuq5Dlcq+kByW1SRqU9B+ccz5ZL9sTThXyfb8pzgsAAAAAQLPbcI1j59yKc+4vnHNvUmGJ6b+T9K69aJhzzpP01865JUlPmdndKtSf6Zb0Aefc5b1ox36TSCSUSqWqXoOlVucFAAAAAKDZbRh8KeWcu+mc+9fOuVO1bFDZNfNm1ibpZ1SoP/O/S3qbc25ur9rQzCpNBSqdKrTTc1Sy3fMCAAAAAHBQNPxIeTXz5TdVWHL69c65J2t9zb2qX1Lr62xlKtBmbWA6EQAAAAAAu7PRakcNwzk3K2l2r663lSWUm+E6W1mNaLM2sFQ0AAAAAAC70xTBl722VwGHWl9nK8s8b9YGlooGAAAAAGB3CL5UsFcBBwIbAAAAAADsfw1f8wW7Q00XAAAAAADqi8yXBhAEgXzfVyKRqPpqQdR0AQAAAACgvsh8aQC1yj4JgkBBECiZTG5a04UlogEAAAAAqA0yXxpArbJPfN+X53lKpVIEVwAAAAAAqBOCLw2gVoV3E4lEMfslCAICMAAAAAAA1AGj8X0sEokoEonI87wNpzQFQaBMJqNMJlMM1GxUpBcAAAAAAGwdmS/73FamNPm+r4mJCUkqZsdsVKQXAAAAAABsHcGXfW4rU5oSiYT6+/vl+77i8XgxAMMKSAAAAAAA7B7Tjg64cJnrMOCSzWZZAQkAAAAAgCpidL3PbVa/JVzmWpJSqRTZLgAAAAAAVBnTjva50uBKpelHpTVhyHQBAAAAAKD6CL7sc5sV3K3VMtcAAAAAAKCAVIcGt9tln6nfgv1sZGREZlbxceLEiXo3DwAAAAAkkfnS8DabNlQtYeFdph+hmYyNjck5V+9mAAAAAMCGGGU3uEQiUSyEu90smO0cHwZ5fN/fbZMBAAAAAEAJMl8aXGlNFs/ztpUFs52smc1qwwAAAAAAgJ05UMGXIAgUBEHTTqvZboCk9PjNphVReBcAAAAAgNpozijEDq2srDT1tJrtFs8tPZ5pRQAAAAAA1MeBynxpaWnZd9Nqtlool2lFAAAAAADUx4HKfIlEIk075Wg9W81oYclpAAAAAADqg5F4A1tvtaLS7aWrIW31/QAAAAAAYO80bfDFzPb9lKnSrJbSQErp9o0yWqjzAgAAAABA/TVd8MXMImb2eUnfU++21FppVovnebp165Y8z9sw22W99wMAAAAAgPpoquCLmUUk/Y2kjzvn/sLM7jazlJnZJu971MzOXrhwQaOjozpz5sya/Y06Pad0+edMJqP5+fniUtlbqd9CnZfqOHPmjEZHRyXpATM7a2aP1rtNAAAAAIDmYc65erdhy8zsn0j6QUk/IulPJB2WdEXSH0r6U7fJhxkdHXVnz56VtHaVoHB6TiqVKgY76qXS6kWe5+nGjRtaXFzUyZMnlUqldnV+z/MkicDMNpnZk8650fX2l/Yv7A0zUy1+h9XqvJtcc8P+BQAAAKB5NdvI+7OSOiW9X9IfSHpE0iUVgjHbmltTWg+lkabnVKrTkkgkNDw8rJMnT+46OOT7viYmJjQxMUEtGAAAAAAA9kBTBF9slXPugqRflHS3pKedczcl/YKkY5KOb+ecpQGXSCRSrKuSyWTqOv2oUiComtOHEomEBgcHNTg42BDBJgAAAAAA9ruGXzHIzLqcc3OrhXbNOffXZva9zrnzZvZKSadV+Bzp7Zy3tJ6K9GJGSKV91VZpatF67SptXyaTkaRtt638eruZtgQAAAAAALanoYMvZvb7ksbN7Lecc9MlGTDnzewHJf2OpOckvd05N7XT6wRBoCAI1N/fX8yCqaWdBFLCNu2kbbsJ3AAAAAAAgN1p6OCLpO+Q5EuaN7M/dM5Nlaxs9L8k9Utqd87N7+YiYXAiGo1qYGCg5kVoKwVSSldcqjTNaDfZOLsJ3AAAAAAAgN1p2JovZvZtkhYk/ZWkN0v6cTPrd845M3uzpHdLiuw28CIVghLRaFT5fF6+79d86elKwZVw2tOVK1c2LIa7k7ax5DQAAAAAAPXTyJkvX5H0Cufcgpn9K0lvkbRiZr+nQn2Xf++cy1XjQpFIRAMDA3csPS29OE1nozot5UqPlVTxfeXnSyQS6u/vV3t7u5LJpOLxuDzPK+4Pjy/PkCk973baCAAAAAAA9kbDBl+cc9Mlz99lZjlJPy4p55z7D9W+Xum0nkQiUawDE2aYTE5OKp/PS9q8bkpp8EZSxXor5QGeSCSiaDRa/Debza7Z7/u+Zmdni20NgzCl56W2CwAAAAAAjadhgy+h1QK7zjn362a2LOljtbxemD0iSZ7nFTNI8vm8otHoHXVaKmWaVKqxUp7JUumYzbZ5nqe5ublioKh8SWpquwAAAAAA0HgaPviyWuMl4pwLnHP/V62vF2aPhEGSWCymbDarVCpVsU5LpUyTMDhSmp3i+/4dmSqlKgVyys85MDBQDKxUquFS6yWyAQAAAADA9jV88EWSnHO1qXxbQRjcCKcczczMKAgCpVKpO4Idm2WahEV0JWlwcHBNpkp54CZ8XamWSygSiSiVSq3bdmq+AAAAAADQeJoi+LKXwuyRfD4vz/MUi8XumG5UfmyY4RKPx5XNZovBj3g8ro6ODiUSiTsyVcoDN+G0pNJsmbD471aDKdR8AQAAAACg8RB8WUc2m9XCwoIWFhY0PDy8YfAjDHqULgGdTCaVzWYlFYI0vu/fEZwpDZBks9li1ktpQGe9lY0qoeYLAAAAAACNh+DLOhKJhAYHB4vPS5WvNFSavRIGV0rfFwTBmuBMpWBK6QpL4ZLSqVRKqVSq+P7wepUw5QgAAAAAgMZE8GUdG9VXKa3lEmaqhNkq5ecIt4fTkMIMlzCYUj616MaNG5qamlJ/f3/xvOG5NspoYcoRAAAAAACNieDLFpRnlSQSCfX39xenEoU2Wv0oDLLEYjGl02nF43Hl83ndvHlT8/Pz6urq0sDAgKLRqHzf1/T0tKLRqKLR6JogzHqYcgQAAAAAQGMi+LIFnudpYmKiuGJRJBJRNBpVEARKp9MaGBhYk5kSTiEqDdj4vq+ZmRlduXJF6XRa9913n5aXl7W8vKy5ublidsyJEyfU0dGheDyuaDRaLMS72XQilpkGAAAAAKAxURxkG8JaL0EQFAMt169f1/Xr19dkvITBltnZWU1OTioIAsViMd28eVM3btzQ2NhY8TzJZFJHjhxRLBZTPp9XLpfTwMBAcXpSmE3j+349PzoAAAAAANghMl+2IAyoBEGg2dlZeZ5XzHaZmZmR53mKRqM6fvy4uru7i0V4M5mMbt++XawF09LSoiNHjujw4cNKJpOKRqMaHBwsZrj4vq98Pq8XXnhBly5dUkdHh0ZGRor7AQAAAABA8yHzZQtKi+pGo1Hl83n5vq+BgQGdPn1ayWRSmUxGV65c0cWLF3Xz5s3iVKGFhQVdvHhRuVxOw8PDevDBB3Xq1ClFIhF1dHRIKiwzffnyZeXzeU1NTWl+fl5mJt/3NTk5qYWFheKy1QBq78SJEzKzio+RkZF6Nw8AAABAkyHzZRsikYgGBgbW1HLp7u5WEATq6upSW1ub0um0xsfHlU6ndfz4cUWjUS0sLGhqakqtra1qbW1VJBLR4uKiOjs7dePGDX3xi1/UysqKXvWqV+nQoUPq7e3V0NCQ5ubmFI1G1d/fTyFdYA9duXJl3X1mtncNAQAAALAvEHzZptKVixKJRDEoEk4bSiaTmpiY0MLCgnzfVyqVKh47OTkpz/M0ODgoSZqcnCzWeQmCQIuLi+rq6lIsFlN3d3cxyyYajW5YbBcAAAAAADQugi87UL6kdDKZlOd5ymQyWlhY0K1bt7S0tKSenh7NzMzo9u3b6ujoUGtrq44fP66uri4999xzmp2d1QMPPKDR0VE555RKpTQzM1OsJ1OaZQMAAAAAAJoTwZcdKF1SWiqsghQuFT0/P69bt27J933Nzc0pk8motbVVuVxOPT09SiQSWlxc1NzcnFpbW9XV1aWlpSXl83ml02lls1mNjY0V68KwfDQA1I+Z/bqk10qakPR255y/2X4ze0TSL6tQV+0/Ouc+sqeNBgAAQMNhLssOlE49Kl0OOggC9fX16Z577tGRI0d048YNnTt3Tvl8XsvLy+rt7VVvb69u3LihiYkJDQ4OKplM6tChQ1paWlIymVQQBMrn8/I8r7gcNQCg9szs8bLXD0o65Zx7WNKnJL1js/1mFpf0c5K+yzn3egIvAAAAkMh82bHSqUeJREKe52lubk6Li4vq6Ogo1mu5ffu20um00um0zEzz8/O6efOmFhYWdOPGDcXjcXV2diqfz6uzs1P33ntvcVlrz/MkiewXAKiPhyU9tvr8MUm/Kek/b7L/vKRFSf/dzHxJP+WcG9+b5gIAAKBRkfmyQ4lEQqlUas0UpP7+fvX19RWnH504cULf+q3fqiNHjkiSvvSlL+l973ufrl69qu7ubsViMXmep0OHDmloaEiSNDc3pytXriiTySiZTFLvBUDdmdkJM3MljxUze97M3m1mLVZwzMzeYWZ5M3tdFa75ATO7ZWYZM3vOzH5ig2N7zOwjZrZgZmNm9g/L9j9uZlkz81YfX99iMw5Jmlt9PiepZwv7ByXdLelNkn5P0ru3eC0AAADsY2S+7FBpPZZMJqOJiQl1dHQoEomoo6NDy8vLymazyufz8v1CiYAbN25oZmZG7e3t6unp0Ve/+lW94hWvKK5mNDExoUgkIs/zlE6n1dPTwypHABrJr0j6mKRWSd8h6d9IuixpVtJHq3yt35D04865JTO7X9LjZvaUc+7JCse+R1JOhcDHyyV93Myeds49U3LMzzjnfr/8jWZ2XNL7V1++vGTq0dsl3ZbUtfq6S9JM2dsr7Z+V9DnnXM7M/pekX9zi5wUAAMA+RvBll/L5vCYmJpTP55VIJBSNRpXNZtXa2irP82Rmmpqa0tjYmLLZrG7fvq1Lly4pl8vphRdeUD6f1/333y+pENC59957lcvlJBWWrwaABnLVOff06vOzZvZzkl6pQoDh5ZJGJd0R4NiJssCJW32ckrQm+GJmHZJ+QNKDzjlP0mfN7GOS3qYtBD6cc1clPbJ6rsedc4+UnPuzkt4p6Y8kvVHS58reXmn/lyT9nJmZCt/JC1v5vAAAANjfCL7sUjqd1tzcnA4dOlRccvrKlSu6evWqJiYm9PWvf11Xr14trnq0sLCgiYkJSSoGYz71qU/p3nvv1dzcnCKRiFKplCYmJuT7vlKpVJ0/IQCsZWatkr5PUq+k551zGUlPm1n3Ju/7UUnvq7Drfc65f1zh+N+W9GOS2iU9JekTFd57r6QV59xzJduellQ+9ek3zOz/lPR1Sf9/59zjG7VVkpxzX1udxvSEpElJbzezIRXquLyr0n7n3IKZfUTS30gKVFakFwAAAAcTwZdd6uvrK/6bzWZ16dIlXbhwQc8884wuXbqkW7duKZfLKZFI6NSpU/I8T7dv39b09LSWl5d16dIlxeNxZTIZpVIpPfvss7r//vu1sLDASkcAGs37zOx9q8+dpL+QdGYb7/+YCtkg5cqn8xQu4NxPm9nPSvpWFbJTliocltSLdVdCc5I6S17/ggqFcHOSfkiFYrgvd86tyUopzXop2fbOsk0Lkt61wX45596jwlQoAAAAQBLBl12LRqMaGhpSEATKZrMaHx/Xl7/8Zd24cUOXL19WJpPR/Py82tvbZWa6ffu2FhYW5Hme2tra1NraqkuXLikWi2lxcVH9/f2ampqq98cCGsbIyIjGxsYq7jtx4sQet+bAC2u+BJKuOOfmt/Nm59xtFeqkbOc9KypMJfoRST8l6T+WHeJJKk8RTEkqts0598WSfX9kZj8s6bsl/afttAUAAADYKYIvVeL7vp599ll95jOf0e3bt9XX1yff97W0tKRbt25pZmZG09PTWlpaUi6XU3t7u7q7u5XL5bS8vKypqSk99NBDWlxcVFtbW7EIL3DQjY2NyTlX72agoLTmy7Ztd9pRmagKNV/KPScpamb3OOeeX932MknPVDg25CTZJteTJJnZr0t6raQJFaYV+RvtlzQg6e9Krv8PnHNE1AEAAA44RvcbOHNm69n0iURCx48f1yte8Qo9+OCDuvvuuyVJ8/Pzmp+f19LSkubm5pTNZhUEQTH7ZWlpSfPz87rnnnvU19en3t5efehDH9Lw8HBxNaW9sp3Py3UbXyN/Ptp2YIXTjsofv1J6kJkNmNkPmVlydSnrN0r6YUmfLj+hc25B0ocl/aqZdZjZqyW9RdJ/XT1Xt5m90cziZhY1s3+kQrDkLzdrrJk9KOmUc+5hSZ9SWf2WDfb/jXPukdUHgRcAAAAQfNnIdgZhkUhER48e1bd+67fqkUce0cjIiAYGBtTR0aGWlpY7jg8zW2ZnZzU1NaWrV69qeXlZnZ2d+uAHP6hkMrnnmS8HLQiy3wfZjfz5yts2MjIiM6v42OupRZt9bydOnFi3rSMjI3vTyCblnLvtnHu6wuNa+aEqTDG6rsI0pX8r6V845/4iPMDMHjOzX1p9+dMqFOWdlPTfVCiIG2aetEr6NUlTktKSflbS9znnvr6FJj8s6bHV549Jes0W97/azJ4ws3+zuuoRAAAADjg7SOn8ZjYlqXLxiMoekHRhB5eKqPA//G0qpMonJcX1YrArL2lRhQHGigrp75nV50u7uO5ucd2NnXDO9a+3cwf9q9bq9b1uBW2704b9C3tvNbhz3jn30dWVnD7onPvOjfZLepMKv/d9Sb8n6THn3J/veeMBAADQUA5UzZftDmzM7KxzbrRW7eG6++u6jTZwrtf3uhW0DY1idenoP6uw660qZN10rb7u0p2rMt2x3zm3pNVVmczsw5K+RRLBFwAAgAOOaUcbq9e8Da67v6+7Vxr589E2NATn3Lhz7jUVHuOSPivpjauHvlHS58refsd+MytdeelhSRdr2X4AAAA0hwM17QgAgO0ws99QoZbLpAqrGXWqUFPmXevsf4Okd6sw7eiypHc45/J733IAAAA0EoIvAAAAAAAANcS0ow2Y2aNcl+s2q0b+fLStMR2kn8WD9FkBAABQfwRfNlav/0nmuvv7unulkT8fbWtMB+ln8SB9VgAAANTZgVrtqK+vz42MjGz5+EQiodHR0V3PywqCQM45BUGglpYWRSIbx7yqdd3t4robe/LJJ9MbrWjU19fnjh8/rpWVleJ9DoJg0/tdK/X6XreCtt1po/613d9dO9UsP4vNes16XXez310AAACovQMVfBkZGdHZs2e3fPzo6Oi2jq/E8zxlMhklk0lFIhElEonioNz3/eLral93J7juxsxsbKP9IyMj+tu//Vul02n19fUpm80qk8kolUopmUzuur3bVa/vdSto25026l/b/d21U83ys9is16zXdTf73QUAAIDaO1DBl+169NHdZ4cnEgkFQVB8HgZafN9XJpORpDsG5tW47k5w3d3zfV/z8/NKJBLF+5pIJKp+na2o1/e6FbStMe2nn8VGvGY9rwsAAID6OlCrHY2Ojrq9/ItjmN0SBIE8z1uTAbFR5gsak5k96ZwbXW//6Oio+/SnP62JiQkNDg4qlUrdcQz3HevZqH/t9e8u7C+b/e4CAABA7ZH5UgPlQZdkMqlUKqV4PC7P84oD73pMRUFtlU4vC+9/6faNMp4AAAAAAPsTwZcaCAfYYdAlDLaE9V8kBt77VWlQzfM8TUxMrNkeTkGq11QkAAAAAMDeI/hSA6UD7NKpJQy8D5ZEIqHBwcHic0lkPAEAAADAAUTwpQYYYB9c5TVdKtV9AQAAAAAcLARf9kgQBJqcnFQ+n5d057SjWhVipcDr3gqnnIUrXEkv1ntpdPQVAAAAAKgNgi97IAy85HI5xWKxOwrvShsvPb0bFHjdW2FwLQgCTU1NSWqeTCj6CgAAAADUBsGXPeD7vvL5vGKxmAYGBtZkR4Qr49SqHgx1ZvbO8vKyJiYmisGW8novjY6+AgAAAAC1QfBlD5QX4A1fB0GwJtOgFtkGzZJ1sR8459TZ2Vm8l802dYe+AgAAAAC1QfBlD5QPasPXpZkvEjU3ml00GtXQ0JAkcR8BAAAAAEWMDOukUqAlnI7k+36dW4ediEQiikQia+5jEATyPG9NAV5gu772ta/JzCo+RkZG6t08AAAAAJsg82UHNspQ2Wr2iud5unXrljo7OzU0NLQmA4aaG83L8zzFYjFFIhHF4/FNi9iu11/IgkKpXC4n51zFfWa2x60BAAAAsF2M6nbA933Nzs5qcnLyjoyG7WSvLC4uan5+vnhsOB3pIAy2N8oIadZskbCGz8zMjIIgUDabVSKRUCqVWjegtl5/CYNznuftRdMb0m76QbP2IQAAAAD7E5kvO5BIJOR5nvL5vHzfX5PRsNXslUQioYGBgTUrHR0kG2WENOuSx5FIRKlUSrFYTDMzM4rH45sWsSXbaX276QfN2ocAAAAA7E8EX3YgEokUl4wuHzRvZcWYIAiUTqeLBXcP4vSSjYIOzRqQCIJA8Xhc6XRa+Xxe2Wx2076wXn8JM6Ca7Tuopt30g2btQwAAAAD2p4Mz2q+y3UwR8jxPc3NzxffWsshuo06/KP3+ytvYrNOv8vm8xsbGlMvlFI1GdzXwb5TvoFH7z2Ya5fsDAAAAAIngy45tdVC63nHh4DCZTG5YE2S3GnkFpfC78TyvYdu4HWam1tZWxWIxDQwMSNKae9+MgYx69p9K127G7xAAAAAAmHa0Q1utKVHpuNIpJVuZprQbjTz9Ivxuah2A2iutra3q6ekp3tcwqCQV7nkz1iGpZ/+pdO1m/A4BAAAAgODLDm2nsG7pv3u9hHCtgzu7Ufrd7MfpIeX3vpEDYeupZ/+pdO1m/A4BAAAAYP+NePfIVmtKlNc2mZyc1MzMTMVlqvdCI03b2G91OYIgWLMEeZjd5Pt+8XWtP28j3d9a2G99BgAAAMDBwAhmD/m+r3w+r+Xl5eIy1XuhdEDeyDVgml0kElE0Gl1zb/fi++b+AgAAAEBjY9rRHgmCQEEQFGubZLPZNVMnajkdqbROBtM2aiv8XuPxuDzPUzweX7O9Fri/AAAAANDYCL7skXCAHI1Gi6scVdovVb+QaHltlUatAdPsVlZWNDExocHBQWWz2T0rDMv9BQAAAIDGti+CL2ZmzjlX73ZsJJFIyPO84pSUvSwkyoB87+1lBgr3FwAAAAAaW9MFX8wsIumnJU1IGnPOfck55xo9ABOJRDQwMFCcWlRpPwPo5tbS0qLh4WEyUAAAAAAAazRVwV0zM0mflvRKST8j6d1m9vOStFHgxcweNbOzFy5c0OjoqM6cOVOzNpavNlP6er2VWvb7CjXN7syZMxodHZWkB8zsrJk9Wro/7F/nz5/Xww8/rN/93d/lfmLLNutfAAAAAJqfNXCyyB3M7Lik/+yce7OZHZb0VklvkfRB59zvbvb+0dFRd/bs2Zq20fM8ZTIZpVIpJZPJO16XC5efzufz6u7uJluigZnZk8650fX2P/jgg+4DH/iABgYGJGndew5UslH/2iixz8zUTL/Hsfc2+90FAACA2muKaUerU40ekTQiqc/M3uic+0sz+5CkAUnfZmZ/6pybrV8rC8prfZT/W76qUbj8dDQaZYWaJmdmamtrUyKR4H4CAAAAAIoaPviyGnj5gqRlSU7SqKQ/MbPXOue+Zmb/QdLHJKUkzdatoavKa32Uvy5f1ah0aeJaLTWNvWFmOnTokJLJpLLZbL2bAwAAAABoEM0wyv8FSTPOudeoEGT555L+StJjZvZjkv6RpBZJDTfarVTLJZFIKJVKFYMuYXAmXJrY9/16NRe7FI1Gi0WVb926Jc/z6t0kAAAAAEADaPjMF0mXJL3dzF4t6e9JOi5pStJhSQ9KOibpJ5xzk/VrYmXlWS7Si8GWMDATZrrs5dLEqI1IJFKxmDIZTQAAAABwsDVD8OXTkl4m6d9J6lIh4DIi6b2S/q2kKefcSt1at4GNAiqlgZlEIrFmgF46YA+P3a+D9/0WnAiCQEEQqKOjo3hfywNwG713P30Xu8F3AQAAAGA/afjgi3NuStIvmdkjkv43Se2SHpbUI2mxUQMv0p31XkqVBmbKB+ilryVtefDejLYTnGgGvu9rampKkpTNZreV0bTfvovd4LsAAAAAsJ80fPClxJikQUl/qsLUox92zs3Vt0nrC6cVSYXBY/lf78OpRr7vKx6PS1p/haTy59tpQ6NnD+y36VZBEKi/v1+SlM/n5XlexftfSTN/F9Xua838XQAAAABAuaYJvjjnLpvZ90nql3TbOXe9zk3akO/7mpiYUBAE6urqUl9fXzETIhycrvfX/fKMmZ3+5b8Zsgc2yg5qNsvLy8pkMkqlUvJ9X3Nzc8U6MFv5jM38XVS7rzXzdwEAAAAA5Zom+CJJzrlxSeO1vk41/oofj8fV0dGhIAiUz+eVTqeLqx6Fg8pa/3Wf7IG9tbKyUuwv+XxenZ2da5YT34/Cn5Xy7C0AAAAAwIuaKviyV6rxV/xstrDydSqVUiQSUTweX1MDRKr9X/fJHthbZqZkMlmcZtTI072qpRmyqwAAAACg3gi+VFCNjJHSc4QD8EpLTIdZMX19fYpGD97taIa6NFsVBtlKV6oqvdf7UT2yq/ZTnwEAAABwMDByqSDMGNnOwC6fz2t8fFz5fH7Dc4SZAr7vS5LS6bRu3rypdDot6cVCveEUpf2u/Ptodul0Wrdu3ZLneXd8tmrf20boKzv5Wdmt/dZnAAAAAOx/By/VokbCIIokDQ0N3bF/vdoYfX19a/49SNM4giBQEAT7pi5KJBIpfibpzqyQat/bg9ZXwmwXahkBAAAAaDYEX6qkPIhSzvM8TUxMaHBwUKlUqri9PHOgmgPLRp+e4fu+PM8r1sVpdvl8Xvl8Xl1dXcV7WhoUqXbQYC+DEPXuS+WBpv0ebAIAAACwvzT/iLdBRKNRDQ0NbbtuS/kUis2mcWxnqkmjT89IJBJKpVKKx+N1nz5TDSsrK1pZWdHAwEDF+1eNKTql938vp/zUuy+FfSUMNDXClCsAAAAA2CoyX/ZIIpFQZ2fnHVkK281e2M5Uk0afnhEGDzzP2xfTZ1pbW3XixIni9KNaZIrUa6pRvftSeRbRQZpyBQAAAKD5EXypodIBeDabVRAEymazxcHiTgbo2xkEN8NS0/up7ktra6ui0aiCIND4+Ljm5+c1PDy8ZprZbtUrCLLXfWmzn416B4MAAAAAYDsIvtRIEASanJwsrn4UDhLDKTaJRGJHf71vhoDKduy3ui/hVJj5+XktLi5Kqm69lP12/9ez2c/GQfkeAAAAAOwPBF9qxPd95fN5RaPR4qC7fIpN+V/v613UtB72UwZDEATKZDJKJpMaHh6WVAgclAcSDuJ93q791C8AAAAAgOBLjZRmupQOtEsHlQepjsV6AYf9lMHgnCuuxFP6GWu95HSz2igItZ/6BQAAAADwZ/caCQeP2WxWs7OzmpycXHeFmnDllng8vm9XdKn3ajl7Iby/5fc2DDBIqnifa92mRu1DB6FPAAAAAIBE5kvVlf81P5FIyPM85fN5+b5f8a/562VC7KcMiYMwjaSlpWVNTZ9IJLLmHkra8/vZyH3oIPQJAAAAAJAIvlRd6WA3LKrb19enbDarRCJRcarFeoPQ/TQ4PQjTSCKRiNLpdLHIcukKTqX3sPx+1rIGTCP3oYPQJwAAAABAIvhSdWGAJZzu4XmepBcLrZaugLTZwJPBaXNZXl5WLpdTLBZbkwFTeg+3k/lUDdXsQxQKBgAAAICdIfhSZWHNj3DVm9LaHp7naW5uTp2dnWuyYErrcoRTlRjcNh/nnJaXlzU0NHRHBoy0fvCikbNTSjXyFCYAAAAAaGQEX2qgfEWj8uBKOHANs2BSqZRSqVRxqWKJwW0zcs4pl8spnU4rCILiMuOh9YIXzZLh1CxBIgAAAABoNPsi+GJm5pxz9W5HaL0lpJPJpIaHh4u1YPL5vKLRaHH1o9LMl/Vsd+oHU0X2jnNOS0tLSiQSxcBLrTJc6nFfw34dBhPpUwAAAACwNU0XfDGziKSfljQhacw59yXnnGu0AEworP+STCbX1H3p6elRd3f3tgew2536wVSRvdPa2qqBgYFiMK10ienweTXuwXZrB1UbfQoAAAAAtqep/mxtZibpryW9UtLPSHq3mf28JG0UeDGzR83s7IULFzQ6OqozZ87UtJ2l04x835fnecVaMOl0Wjdv3tTMzExxkB4KB7W+76977kQisaaOzGa2ezzudObMGY2OjkrSA2Z21sweLd0f9q+vf/3r+q7v+i799m//tnzf1+zsrCYnJ+V53qb3dTtKs6bqcV/pU9W1Wf8CAAAA0PysAZNF1mVmhyX9tnPu+1afv1XSWyR90Dn3u5u9f3R01J09e7bWzSwOtlOplOLxuNLptPr6+hSNRpXP59e8LhUGbSTdEZhB/ZnZk8650fX2j46Ouk9/+tOSCgGKsOhuKpWqaiHljaYcMc2seW3UvzZK7DMzNdPvcey9zX53AQAAoPaaYtrR6lSjt0kakXTUzL7HOfdxM/uQpAFJ32Zmf+qcm61jM4tKa3uEqxlls1klk0lFo1ENDQ1VfF/pSknNUoQVa5Xev4GBgZoEQjbqG0wJAgAAAIDG0/B/Gl+davQZSQ9J6pF0SdLvm9m9zrlbkv6DpGOSUvVrZUF55kq4LZlM3jFFo3RqUum28uMrHYfGFQRBcRlxaWcZTFu955WO2+6UIPoXAAAAANRewwdfJI1Keso593OSXiLJlzQo6a/M7B9L+oeSWiRl69fEgvKaLeX1XsqPTafTOnfunHK5XMX3r7cNjSkMZGSzWXmeV3wEQaB8Pq/x8fFikdyNbPWeVzouzIrZasBnt/2L4A0AAAAAbK4Zph3NS3qjmf1PSZ+T9BeSTkl6taRvUCEQ8xPOucn6NbGgfCnh8N94PH7H0ryJREKZTEbXrl2TJD344INKJBLyPE/5fF6+76/JgNksk4FaH/UX1t3o6elRLpdTEATFKUCe5+nmzZuStO60s1CYORM+1ruf1Vi6erfnYJoTAAAAAGyuGYIvFyV9XNKbJf2Bc+7LZvYWSX8i6dckpZ1zDfFn9/JaHOHrsACv9GIdmEQiofvvv19SYdA6Pj6uZDKpvr4+ZbPZ4mB4q7VfGATX3/LyssbHxyW9GGAJC+3G43FJUl9f36bn2cvaP7s9fzUCQAAAAACw3zV88MU5lzez/yjpQUnvMLOcCtkufZKW9iLwst2sktLaL2EWQ5jFUh4kefDBBzU5Oam5uTktLCxoeHh4R4NhBsH1F4lE5Pu+bt++XSy4m0wmiwWXBwYGtpyVtN79LO2LjRBwozA0AAAAAGyu4YMvkuScu2xmPyHpn0l6t6Tbkt7hnJvbi+tvd5Dr+74mJiYkSZ2dnQqCYM1yw+VTSgYGBnYdPGEQXH8tLS3q7e3V0tKScrlccerYToIk693P0nMRcAMAAACA5tAUwRdJcs6NmdkvqDDVyPYq8CJtb5AbBlX6+/uLwZbyaUTlU0oikYhSqbov1lR1B60OTUtLiw4dOqRcLqdYLLal2j/bVXpOAm4Hr48BAAAAaE5NE3yRJFeoaJrZ6+tuZ5AbrnCUSqWK7wmnnoQD74OSsdAI02L2UhAEa2r2hMGASrV/dvp9EHBZ66D1MQAAAADNqamCL42o/C/v5YGVcH95HZhmsZvMgu1mDDV7BsPKyop839/26kThZ4/H43cEbprFXt6/0msdlEAmAAAAgOa2YfDFzB5eb59z7onqN6f5lP/lvTwzIdwfDkrj8bh839fMzIwmJiZ04sQJRaONGwPbTWbBdjOGmj2DoaWlRUEQaGJiojjtTHqxX2xWx8XzPAVBUHxPM9nL+1d+rWb7rgAAAAAcPJuN+j8mKSXJyrY7SS01aVGT2ewv76UZMOGqN/F4XLOzs1paWlJHR0dxWeLN5PN5pdNp9fX17VnAZqeZBdvNhNgPGQzhakfhZ19YWChu3yhAUFoTprQ+0HqqkWVS7UyVveonu7kWAAAAANTLZiP475b0Uefc4F40phmFU43CAaSkdZ+XHtvd3a3l5WX19fVt+VrpdFo3b96UpC0HbHZrpzVG9kMmy3atrKxofn5enZ2dGhgYKAZiSle2ku4MOJR+x1tdTWu3322170+t+0n5d3ZQ+hQAAACA/WGz4MvfSoqY2SPOucf3oD1NqXQAKani89LpEeUr1mwmrBcTi8U0NDS0rYBNvWw3O2G/BGvCwEA0GlUqlSoW2S0NGOz2s2723W6lhkyjZI9stR37pX8AAAAAOJg2C778B0mdkj5pZr/qnPvXe9CmplNpAFn6vHSJYUnbnmbh+74mJiYkScPDw7uecrQXxVG3m53QKMGA3WhpadHg4KCCIFAmk1Eymdywb+z0s2703QZBoMnJSeXzeUWj0XVryDRK9shW27Ef+gcAAACAg2uzUfz3SjohaUjSf5VE8KWC8gFk+fPSJYYlrfsX/PWCIolEQoODg8Xnu9WIWQTl07eabbWfUCQSKQbKwn6xl9+x7/vFwEvpstfNbrvBov2wehYAAACA/WOz4MuSc25C0oSZte5Fg/ajzTJjpLUZC9LaoEg48PR9v2btqaWtDoQbMSi0XVsJlPm+r9nZWXmep4GBgaoGByoV723G4MNugyf7oS8BAAAA2D82G9UE6zxHBWFtlnCqRyjM6vA8rzj9qHxAWZqxUGnQHg4mdxuAqUdGwFbbHo/Hi8txN6PSwrrh8tLh9tJ+kUgkFI1Glc/nqxZQC4XXzmazVekv27Xez8B2lfaZnZwzkUgolUrti6wfAAAAAM1vs8yXB8xsZfW5lTyXc+5ALTW9laDFRhkNpXVbKk2h2KwIb3m2yk6DKPXICNhqpk02my0ux92M2QorKytKp9NrgjDhVKrS7zwSiRRXQ9pJcGArS47Xq0ZKtfpXaft3cs5GqWkDAAAAANLmwZdH9qIRzWCjAWDp6jJhRoPnecXBd/hvf39/8bhymw0Wy/fvdJBbj0H5QSqq2tPTo1wuVyy6K1X+XFutcVMpyLaVJcfrFXyo1j0sbf9+6BcAAAAADrYNgy/Ouc/sVUMa3UYDwNJAyMDAQHF6UelKM5FIpJilUI3Mjp0OSBslI6BSUKFR2rYb2Wz2jqDbep9rKwG0SseES4034pLju7mH62Vz7Yd+AQAAAOBg292axQfIRgPA8ilDkUhEQRDcUb+lmn/Br9SenUxFqnUNmPXOvx8Lora0FGbibfVzbaU/JBKJ4jSmcCpTNBpdN+Nlr23Wf7bTv/ZjnwAAAAAAieBLVZQGQsJBciqVuqPoaq0L3e5k8FrrAe9659+vU0ni8fi6U8vKbSWjIwzmZTKZhswA2az/bKd/7dc+AQAAAAAEX6rM9315nqdUKrVhpke4gkv4uhoBmZ0MXqs14F0vuLTe+RsxkLBbKysrmpmZKd7bbDa7YVHcrWrkoMRmhaC30/b92CcAAAAAQCL4UnXrDUbDTIhw+2arH+3ETs4TvicMGOw0M2e9DIeDNKBuaWlRT0+PZmZm5HmeJicnJa1fFHeravkd7jYja7NC0Afp/gMAAADAegi+lNnqYHQrxUGDINDk5KTy+by6u7vvmHYzODhYfF6tdu3UbqcfNXJ2xl6JRCLKZrOan59Xb2+vDh8+vO2iuBvd59JAXjabrUpfqPa0M/oBAAAAANxpXwRfzMycc64a59rqYLT0uPIlg8NBchAEyuVyWlxclKTiUtRSYaCeSqXWHWyXb691bZbdDpqbOcOhmoGtfD6viYkJtbW16ejRo3ecL5/Pa3JyUolE4o6padLG/S/cl8lktLCwoMHBQaVSqV21t9rBkmbuB1u1F/WbAAAAAOwvTTdyMLOImf2Mmf0DM/tmSXLOOTOzapw/HBRXGoyGU3OCIFhzXDgo9n1f0toBdDQa1dTUlC5duqR0On3HOcvfu972eDyuSCSypUKuOxEOmg/SYDK8n57nVbwHO5HNZuX7vtLp9JrzhdeanJzUpUuXdOXKlTuuFxZrTiaTFfvfRn2z0ucKlzrfyEG877u13s9sJdu5FwAAAAD2r6Yaca0GWP5a0isl/Yykd5vZz0uFAMwG73vUzM5euHBBo6OjOnPmzLrX2GgwWjroKj2udFBcOoAOH729vRoeHq44BWW9AXX59mw2qyAIlM1mt/BNYStKg2QbBTXOnDmj0dFRSXrAzM6a2aOl+0v715ve9CZ95Stf0cmTJ9ecL7xWIpHQyZMndfz48WJfKT3G87ziCkflwj6XSqU0PDy8bobJdoID2L6tBsGkrd2LzfoXAAAAgOZnVZqtsyfM7LCk33HOvXn1+VslvUXSB51zv7vZ+0dHR93Zs2d3fP2tTDfwPE+zs7OKRqMaGBhQEARKp9MVV73ZzvQFpjpU33a/UzN70jk3ut7+sH+VnzcIguIAfGBgQNFotJhtEy5JvpP2VOtzoXa2cy826l8bzaw0MzXT73Hsvc1+dwEAAKD2mmJktjrV6OclfaOkETP7bufcTUkfkvQFSd9mZt21bsdWpmgkEglFo1Hl83n5vn9Hxko+n9f4+Ljy+bw8z9OtW7eKS07v9trYnmp/pysrK8pkMsXASjilyfM8TU1NaWFhodgPKmVPVKs99JXGwb0AAAAAIDVJ8EXS6yX9X5LukXRZ0vvN7EHn3C1J/17SMUm7qzxaJZFIRAMDA+ru7lYikVgzyM7n8zp37pwuXLigy5cvUwdin1laWtKNGzfkeV5x+e5bt24pCAINDg5qcHCwGGwJp6uFhZkBAAAAAPtXwwdfVuu8vCBpXNKbJH1R0pOSPmtm75D0NkktkhqmGErpwFp6cdWasbExTU9Pa2pqSrlcTpFIZMPaHXvlIBYFrcVnjkQiikajCoKgmOmwsLBQzIIqn3rS6LVZDmK/qCa+PwAAAAChhl9qerXQwRUz+xMVAjBvlPQJFbJg7pF0l6SfcM5N1q+VLypdZjqcTpRMJuV5nnK5nA4dOqTBwUHFYrHiFKV62+ky1s1cW6QWS3ebmY4dO6Z8Pq9EIiHP89TR0aHbt29rfn5ekjQ0NFT83sKVq6q1zHO17fY7aub+UQ21Xh4eAAAAQPOo/8h/E6uZLy2ShiT9qaS/UiH4kpP0O5KuO+dW6tfCtcIBV7gqTbgCUhiIGRgYKBZhzWazxQyZagxQdzrYDQf/2w0CNPPgcqefeSPLy8u6du2aTp06Vcx+6uzsVHd3t2ZnZ4urXTVLUGO331Ez949qqEUfAwAAANCcGj74spr5kjezP5X03ZJeKukZSQuSMo0UeJHWDrjCgXE49SAWixWnpISBmWoOUHd6rrAo6HY18+Byvc+828BGOMUomUwWiy3ncrk1RVebJaix034Raub+AQAAAADV1PDBlxJflfRrkp5wzn2/bbT2apVtZ0BeOmAtnV7S3d2teDyudDqtfD6vSCRSzI6QqjNA3evB7m4H541oN4GNtrY2DQ8P33EfwqWmw3Nu9L1tpa81S1BjP/aP7TjomT8AAAAAXtQ0wRfn3GUze+PqEtNSoVjwnmS9bHUQVTpwlqTJyUnl8/niyke+76unp6cYgAmzYKo1MDvog91q2E1gIwgCBUGg8fHx4ipX4apHpYG2jQIsW+lr3Ofm0CxBMgAAAAC11zTBF0kKAy+rWS97Nt2oUiZDaZAlfF46cJZUzHDJ5/MaHx8vDsInJyfV3t6uaDTKILrB7CawsbKyovPnzxcDbnfffXfFmj6+72t2dlae5xVrAIUYsO8fBMkAAAAAhJoq+BLaq+lGofJBVHmQJZPJFJeTTSaTawbOQRBoYmJCQRCos7NTQRCora1NnZ2dNR9gH/TVZvZaOL1oaGhIIyMj69b0SSQSymQympubK2bIhPZqwE7fAAAAAIC9w6hrHeEKRWFQpVQ4YE4kEmsGz57nFWu5hIPoZDKp/v5+dXZ2KhaLaXJyUp2dnRoaGloz6N3oejsVDvx936/aObG+aDSqjo4O9fX1KZVKKRKJKB6PF/8NhX1jK0GPWvQLib4BAAAAAHupKTNf9sJGtTfKsxMq1fUoPTYaLXzNt2/fLg52K01FCa8XFubt6+srvncnmMJSPavBj02jJa2trVpcXCwG4sI6MNls9o4+U9pf8vl8xXteq6Kt9I3GstX+BQAAAKA5EXxZx3YHp5Wmi5SudiRJfX196uzsVDwerzgVJfx3cnJSN28W6goPDQ3t+DNQc6J6VoNmLRsd09raqtOnTxeDLhMTE+rv7y9mSZUqvzfpdLriPa9VkIS+0Vi20r8AAAAANC+CL+vY6uB0s5Vr0um0MpmM7r//fsViMQ0NDRWnkGQyGcXjcUWj0TXX6+vrW/Mv6m81+LFhkefSuj+e5214XGmfCYJA8XhcQ0NDd9xzgiQHw1b6FwAAAIDmRZr7Lm1UOyMsrHrjxg1du3ZN0osDb9/3NT4+rnQ6fUddj2g0qqGhoV1NOUJ1rQbWNiy8sri4qEuXLsn3fSWTSQ0PDysSidzRP0r7TBAEmpyclOd5SqVS3PMDaiv9CwAAAEDzYqSn3a38stG0kEgkonvvvVdBEKirq6sYZJmYmFBvb68OHz6svr6+LdX1YHWa5tDT07OmL4T3q3xb+K/v+8rn84pGo7uaWrTe8uf0FQAAAACoP4Iv2l1R081qvczMzCifz+vq1auKxWLFY6LRqHp6eiRtra5HrQqvonrCzKUwoyWcelR6v8qDaKX3fjeBkkrLn5dfGwAAAABQHwRfVP2ipr7va3Z2VktLS8rlcvJ9v1jbI1yCuPRa5QGcSlkurE7T+CKRiJaWljQ7O1u8p+XFlcuDaNWq6VKpf9BXAAAAAKAxEHxR9YuaJhKJYg2X9vZ2dXZ2amFhYU1mw2aFesszF2pdeJVpTbvX1tamEydOaHZ2Vvl8XpFIRL7v69KlS2pvb9eRI0f2bPWiavUV+gUAAAAA7B7Bl12otGpN+HpgYKD4PAgCpdPpYo2PTCazpsBu+UC5HlkuTGvavUgkolwut+b+eZ5XDMDF4/FNAxmNFuygXwAAAADA7hF8WVVeo2MrA99wYBoEQTH4Ep6jNHDi+77m5+eVSCSUTCYVBEHxPWFwpnTAXY/lhUsDBo0WAGgWLS0tkiTP84pTjOLxuDo7O9cE3jKZjJLJZMV+ttNgRz6fVzqdVl9fX9VWTAr7aTKZZAoTAAAAAOwCI+tVvu9rYmJCExMTawqmhtkplSQSCaVSKUlaE4QJMxxmZ2c1Pj6u2dlZzc/PF88VrngkqTg1Zb3lqvdKGPDZy/Zs5TtuJs455fN5SYXPlslkNDMzo4WFBU1NTUkqfM83b97UxYsXi4G60GbBjo2+r3Q6rZs3byqdTlft8/i+L8/zigHBethvfQQAAADAwUTmy6pEIqHBwcHi861kIIQBi9LMlzBrJNw/Pz+v+fl55XI5SYUgzaVLlxSPxzU8PFy8Xum/9bZX7dlvU1qWlpZ0/vx59ff3K5lMKpVKKRaLKQiCNVlP2WxW8Xj8jveHwY6wKHOl/et9X319fWv+rYZG6Jf7rY8AAAAAOJgIvqyKRCLFLBZJxVVq8vl8Mbiy0XuTyaTy+Xwxa6a03ksQBGpra9PU1JTa2toUBIG6urruWOEonK5S76k+ezXtqREG99UUBIFisZg6OjokvRjEy+fzmp6eLt7b3t7eNcE6ScUpSuH7Ss8Z9qVwezwel+d5a6aFRaNRDQ0NVfXz1GP6W7n91kcAAAAAHEwEX9aRzWa1sLCghYUFRaPRLQ1Cs9lscUC9uLioeDyuxcVFSdL8/LxmZmZ04sQJHTlyREeOHFkz7aR0GlI4aN/vNVcaYXBfTZFIRJ2dnQqCQDdu3FBnZ6eSyaSWlpY0Nzen9vZ2JZNJdXV1aX5+XlNTU8X6LKXT1kqVZ34kk0l5nndgskH2Wx8BAAAAcDARfFlVmmEQvu7v7y8GQ7YiPC6TyWhubk5tbW1qb2/XwsKCOjs7NT8/r2g0qmg0qhs3biiXy2lpaUkjIyPq7++X7/uKxWIaHx/X/Py8BgcHFY1G910QZr8W9A2CQGNjYzp06JDm5+fV2dmplZUV3XvvvcXVjjKZjCKRiAYHB+/oW2GdGOnFoEqY5VI6TSnMqMrn88Xivfvpe9zMfu0/AAAAAPavfTFyMTPb7ntKC3kGQaDJyUnNzs7K9/1i7Y1oNHpH/Y3y95UWAw3/St/X16euri4lEoli9kxXV5dGRkY0MDCgaDSq1tZWRaNRtbW1yff94jVmZmY0Pz+vxcXFXRe+3Uqx0nw+r/Hx8WKh2L3QCAWGayEMiCwtLSmVSqmlpUX5fF65XE4DAwPFwrVBENzRt9YrthtmU2Wz2eK28DxTU1O6deuWJicn19zjjfroVj/HRu+pdxHc/dp/AAAAAOxfTZf5YmYRST8taULSmHPuS845Z2bmnHNbPU/pdA6pEIQIs0xClTJeyt9XafpHLpdTe3u7JGlhYUErKyvFQffMzIz6+vrW1IYJB7GpVErxeHxNnYtsNrvjehdbKVYarpIjqeo1Q9azX+t4RCIRDQ0NqbW1VVNTU3rooYcUBEHxfodZL6lU6o6lyMNpZ8PDw2uCfet9V2GBaM/zirWGwnu8lT66kc36Tb2L4O7X/gMAAABg/2qqzJfVDJe/lvRKST8j6d1m9vOStFHgxcweNbOzFy5c0OjoqM6cOVNcJjosZNrd3b0mO2G9qRzl7ysfSIfHJJNJTU9PK5PJaGlpqVgHJpvNFpcDDmu+lJ6j9NobtWMr1mtfqb6+Ph0+fLiqq+RsprSuTTMsIXzmzBmNjo5K0gNmdtbMHi3dH/avK1eu6Ed+5Ef04Q9/uJjxMjQ0VAy8lf5bHmAZHBzU4ODgmmlvYf+o1AfCIM7Q0JC6u7vvCBpu1kc3stl7dnLOair9uah3Fk41bNa/AAAAADQ/20aySN2Z2RFJv+2ce4uZHZb0VklvkfRB59zvbvb+b/zGb3Rnz56tWZ2I0loUvu9rcnJS4+PjOnz4sJaWlpTL5TQxMaHBwUEdO3ZM2WxW+Xxe3d3dklSs3xGukNPd3b1nmQV7XUcjLBqbSqWapqCqmT3pnBtdb/9dd93lfuVXfkUvfelLFYlENDAwoMOHD8v3fc3MzGhxcVHt7e1KpVLrLicdqtX3s9X73Cx1VZqxH61no/61UWKfmamZfo9j7232uwsAAAC117ijqhJmFjGz/4+kH5c0aGbf4Zy7KelDkr4g6dvMrHuz86ysrNS0TkRpLYpYLKbJyUm1tLQoGo2qs7NTy8vLmpub061bt+T7vnp6eiQV6ryENT3CuiHlU6Bqba/raGwne6JZshuCIFBLS4u6urokSXNzc8UloZeXlzU+Pq7nnntOExMTm37Ptcou2ep9bpa6KtX8npqlnwEAAABoPg1f82V1qtHnJN2SdK+k05L+0My+yzl3zsz+vaT/LiklaXajc7W0tNQ0oFFai2J8fFzZbFbJZFILCwtqb2/X8ePHFYlE1N3drXw+r2vXrmlubk6Tk5OKRCLq6+vTyZMni9NI9jLjYK/raGxnCeHyGiONmpWxsrKiWCymS5cuaWFhQX19fTp06JASiYR6e3uVz+eVz+fV2dm56fdcqyWWt3qfm6WuSjW/p0q1bBq1rwEAAABoLg0ffJH0PZLGnXPfb2YDkv5UUk7S35nZ/yYppsLnyG5wDkm6o9bGVm1lAFb6V/NwaeDu7m61t7cXl5iOxWIaGBgoTi2SVKy1sry8XGxjPaZP1Ou6W1EeCKh3wdf1RKNR9fX1KZPJqKurS4ODgwqCQOl0WkEQFIvplvej8mXOaznY3+p9buT+UCuVAk6N2tcAAAAANJdmCL7MSPoGM/smSeOSBiV9k6Tvl/SwpC5JP+6cm6xVA7YyAAtXrAmzXDo7O3Xy5EnF43Gl0+li8dNw8B2PxzU2NqbW1la1t7drYGBgX9StqIXyQECjZmXE43F1dHRoZmZG7e3tOnHihGZnZ9XT06NcLrdu0KW0uK60/dWJUB2VAk6N2tcAAAAANJdmCL58WdJvSppXYWrRYRUyX/IqTEX6GedcvpYN2MoALFyxJhxQ5/P5NavVeJ5XHHh7nifP83Tz5k3Nzs5qeHhYnZ2dkpjmsBWNmpWRz+e1srKi9vb2YhBGKiw9XjqNpTTQ4nmeksnkHXVL4vF4sV5MuKoP/WLvNWpfAwAAANBcGn4U55zLSvpD59xzkuKSPinphyT9sqSP1TrwIq1d2nazY6LRqAYGBhSLxTQzM6MvfelLyuVySqVSkqQbN27o3Llzikajam1tVTKZVCwW08LCgiYmJpROp5ui0CnuZGa6fv26rly5Usx2Kg+qhBlSExMTklTMdipfXjybza7pB81SABcAAAAAcKdmyHyRc25p9WmLpB+Q9I2S3uScO1/ra28n46B0elJPT4/OnTunmZkZ3b59W6dOndLs7Kymp6e1vLys27dvq6urS4lEQvfff79yuZykQgZNNput+jQHMif2xvXr13Xr1i319/crHo8rn18bGwwzpCRtGNArz7ZKJBIKgqD42Ok9pB8AAAAAwN5rttHXeUnvlfRdexF4kTbOOAinkIRTjOLxeHE1nnQ6rc7OTh0/flwnTpyQ7/u6evWqcrmc2tvb1dvbq+XlZbW1tRWnp4SBl3g8XqwFshefA9WxsrKiI0eO6CUveYkOHTqksbEx3bp1qzjNaKPARz6f1/j4eDFYU55tFWbFeJ63q3tIPwAAAACAvdcUmS8h55xvZv/CObe8V9fcqN5LOJANVzmSCoPk2dlZBUGgnp4eHTt2TL7vy/M89fX1aWxsTN3d3cpmsxoZGVEmk9GlS5fU29urQ4cOrVk1Sape0VUKh9ZeEAQ6fvy4ksmkrl+/rosXL6qnp0dtbW3FY8J6P+X3N51O6+bNm5KkoaGhiuevxj2kHwAAAADA3muq4Isk7WXgRdq44GZYFDVczSbMWIlEIpqfn9fi4qLGxsY0NTWllZUVTU9P68knn9SlS5f00EMP6ejRo2pvby9OI4nH48XVkMIMmNKiq7X6HKiObDarz372s3r1q1+t8fFxTUxM6PDhw8VaMPfff79SqZTi8XhxalmYDdPT0yPpxaXHa4V+AAAAAAB7r+mCL40km80qCILiajae5ymTySgSiaizs1OLi4uan59XLBZTLpfT4uKipqen5ZzTxYsXlclk9E3f9E269957FYvFNDExoY6ODkmFzIR0Ol2chrLegJkaHo0jl8vpK1/5ivL5vO666y4NDQ3pvvvuUzQa1fT09JogS3g/M5mMJiYmNDg4uG7GS6UlqbcbQKGfAAAAAED9EHzZhUpFUcMaMOEqNrOzs0qn01pZWdH8/Lzuvvtutbe3a3BwUG1tbUokEurr61Mmk9HCwoKmpqbU39+vzs5Ozc/Pq6OjY8Miq6VFfsloqK/W1latrKwok8komUzqpS99abGocrhcdLgvfF1aM2i9ArzhPa60JPVW0U8AAAAAoH4IvuxCOEWoNKNgYGBAvu8rFotpbGxM169f182bN7W4uKhMJlNcfjgcTGezWUmFAfHQ0JDm5ubU2dmpeDyuyclJdXR0yPO8daeLUMOjcZiZOjs7lclkND4+rmg0qvn5ed1///3q6urSwsKClpaWivcyzGYJjwuDK+VK7/FOs1boJwDQGMzs1yW9VtKEpLc75/ySfS2S3i/psKTLkh51zuXN7BFJv6zCQgn/0Tn3kb1uNwAA2B3mH+xSpdVj8vm8zp8/r2vXrml2dlbRaFT9/f26//77dc899+jo0aNqa2tTe3u7JK1ZQrijo6NYOyYs1Fq6zHC58lVxUD9BEOjo0aM6evSolpeXFQSBWlpaNDExoWw2q0gkov7+/jWBkO7ubh06dEjT09N3LEtdPs1oN/eYfgIAe8/MHi97/aCkU865hyV9StI7yt7y9yVdcs69XtKzkr7fzOKSfk6FlR5fT+AFAIDmRObLLpVnFHiep/Pnz2tpaUnd3d06efKkrl69qrGxMXV0dMg5p4GBAc3OzhZXNwpXQ7py5YqkwkD50KFDisVikl6sLRMOoKnf0ZiCIFAikSjWbkkkEpqbm9PCwoLy+byOHTumaDR6xz0L72+YBVWNGi+lbaKvAEDDeFjSY6vPH5P0m5L+c8n+U5K+svr8y5LeIiktaVHSfzczX9JPOefG96S1AACgahiN7VKljIKuri51dXWpp6dHi4uL6u3t1bFjx4q1XcbGxjQ+Pq7nn39emUxGs7Oz6u7u1sjIiLq6upTL5dTR0aETJ06ou7tbfX19a2p9VMq2Qf21trZqfn5eN27c0KFDhzQwMKDOzk7Nzc0V+0TptB/f94vLkg8ODhZXOgq3h3VgdjNViL4CoFmY2fNm9i4rOGZm7zCzvJm9rkrn7zGzj5jZgpmNmdk/3MmxZtZmZn+wun3ezJ4ys+/aYjMOSZpbfT4nqads/3lJb1h9/u2rxw9KulvSmyT9nqR3b/FaAACggZD5UgWlA9tIJFKs6XLz5k29+tWvXvd9w8PDeu9736u+vj4tLS3p0KFDunbtmpaXl+X7viYnJzUwMKBoNLom84H6HY1pZWVFn/3sZ4sZLl1dXero6FBbW5u6u7s1NDRULLTr+77i8bii0ahu376thYUFjY2N6a677lI8HtfS0pJaW1slaVeZK/uhrzjn1t13+fLldfedOnVq3X3f8A3foO/4ju+ouC+ZTOqd73znuu9taWlZd59UqP0DYFfeLOmjNTjveyTlVAhmvFzSx83saefcM9s8NirpmqTXSboq6bslfdDMXuqcu2Jmx1Wo2yJJLy+ZevR2Sbclda2+7pI0U3bd/yHpETP7tKRnVKgLMyvpc865nJn9L0m/uONvAAAA1A2ZL1VQOk0knDKUTCa3NFWkpaVFHR0dMjNNTk4qn8+rt7dXvu/r0qVLSqfTd7yH+h2NyTmnzs5OOefkeZ7Gx8fV0tKiaDSqQ4cOFQNymUxGmUxG2Wy2mB0zPT2t5557TpcvX5bv+2ptbdXy8rLy+bxu3bpVnH60XfQVAM3AzC6rkN3xbkn/QoWAx09U8fwdkn5A0i875zzn3GclfUzS27Z7rHNuwTn3bufcFedc4Jz7HyoUx/2m1f1XnXOPOOcekfSV8Llz7qqkz0p64+ql3ijpc6XXdgU/55x7g6RpSX8h6UuSHrBCZPflkl6o1vcCAAD2DpkvVbC4uKjp6WkdOnRILS0tisfj6u7u1o0bNzZ978rKiiKRiK5evSoz0/LyskZGRtTR0aFsNlucioLm0NnZqa6uLl26dKlYdDcejxezW6ampjQyMqLh4WHF43FlMhnNz8+rtbVVzrli0d1YLKZIJCLf94s1YzzPo3ZLlVy/fl3PP/+8jh8/XixsDaCuvlvSX0r6uKR/65x7wcy6N3uTmf2opPdV2PU+59w/Lnl9r6QV59xzJdueViF7pdx2jpWZDa6+p1IGzRrOua+tTld6QtKkpLeb2ZAKdVzetfr8TyStSPpfzrnPrF7jI5L+RlKgO4v0AgCAJkDwpYrCgqnOOd2+fVuZTGbT9ywsLOjs2bOKxWLq7u7Wvffeq4GBAbW0tKinp3wqOBrZ0tKS2traZGaan5/X9PS0pqamdOrUKcViMeXzeTnnlMvllMlkdOvWLd28eVM3btxQb2+vXvKSlxSXmu7p6dHMzIxisVgxEFe6LDl2J51O6yMf+YjMTIcPH9ZLXvISvfzlL693s4ADyzl3wcyWJI0757aT2fExFbJBypVP50nqxVoroTlJnRXeu+VjzaxV0v8j6Y+cc8+W71/NfinfVj6vcUHSu1b3jUuq9J73qDAVCgAANCmCL1XQ3t6uvr4+tbW1aWlpSc65O+q0rOeZZ56R7/saGRnRfffdVwy8oPksLy/r2Wef1dDQkMxMg4OD6u7u1s2bN7WysqLW1lb19vYqHo9rcnJSN2/e1JUrV9Ta2qqjR4+qu7tbQRDo4sWL6ujoUEdHhxKJRDFLJpvNNnXtlkby0EMP6YEHHtCVK1d04cIFPf744wRfgCbknLutQh2VzXiSUmXbUpLmd3qsmUUk/VcVasP8zFbaCwAADi7mL1RBJBJRIpFQS0uL2traND8/r6WlJR05cmTT9968eVNTU1Nqa2vT0NAQg+sm5pzTrVu3dP36dU1PT6ujo0Of//znde7cOb3wwguam5vT7OxssQhvmAXT29uru+++W/F4XBcvXtSFCxc0OztbnF6USCQUjUaVSCTk+75yuZzGx8eLU5SwfZFIRMePH9drX/ta3XPPPRsW9QWwp7ZVsdrMftTMXIXHfyk79DlJUTO7p2Tby1R5qtCmx67WX/kDFQry/oBzbnkbbf51M3vCzP7MzO74j375fjPrMrMvmZlnZg9u9ToAAKCxkPmygTNnzujRRx+tuC8IAl2/fl1//Md/rGvXrmlyclKXLl3SwsLCtq7xl3/5l5KkT3/60/rVX/1VnT59WrOzs3rHO96hU6dOKZfL6fTp0zpx4oSi0drero0+L9fdXEtLi2ZmZrS0tKTOzk59+MMf1srKirq6unTy5EktLS3p8ccf18WLF4tT0qLRqNra2hSNRvXUU0/pM5/5jIIg0IMPPqggCHTjxg11dXVpaGhImUxGV65cUVtbm6anpxUEgQ4fPlws+FxeD6Ze3+tGwrZ+4AMf0E/+5E8qn8/r5s2bmp+fVyQS0ZUrVzQ0NKSLFy8qEono8ccf1zPPPKOlpSUFQaDJyUldv369eL5v+ZZvqVoQ6pOf/KSkwvTB3/md35GZ6fjx47r33nvV3t6uRCKhU6dOaX5+Xq94xSt0//33a3p6WtlsVidOnCjW6YnH45qZmVFPT49yuVxT1enZLz+LjXrNel63ScxK+mYze8A5d2GL79nStCPn3IKZfVjSr5rZT6y+5y2SXlX+xi0e+15JD0j6dufc4hbbqtXgySnn3MNm9pMq1G/5z5vs/11J3yPp/97qdQAAQOOxg/QX39HRUXf27NntHK/1jvc8T7//+7+v//Sf/lNxSeBKvv71r6+7b3h4uOL2dDqtEydO6MSJE+rv79crXvEK/dAP/ZAOHz685bbvxEafl+tKZvakc250vf2dnZ2upaVFJ0+e1ODgoJaXl5XJZHT06FF9wzd8g5aXl3Xt2jUNDAzo9u3bamlp0dDQkIaHh9Xd3a1MJqNnnnlG/f39+nt/7++po6ND09PT6u3t1ZEjR5TJZHTp0iX19vZqaWlJIyMj6u7ulud5ymQySqVSa6a61et73UjY1u/93u/Vl7/8ZY2Pj+sLX/iCrly5opmZGd24cUPRaFTpdFqXL1/W008/veHP10bBl89//vPrvq90qelPf/rTevrpp/Xgg4U/KF+8eFGdnZXKQEgdHR3KZDJ66Utfqte//vWam5tTa2urvumbvkmDg4Pq7e1VNBrV3Nycurq61N7efsd9Wc9G/cvM3Hq/q82sapk7zfKz2KzXrNd1N/vd1SjM7F9I+jVJTzjnvsvMXifpcUmPOOf+pgrn75H0h5K+Q4WVhH7ROffHJfsfW732v9noWDM7IemKpCVJpb+A/qlz7v/ZpA0/Jcl3zv3R6nl+0zn3Q1vZb2bvU6EY8bndfA8AAKA+DlTwxcymJI1t4y0PSNror28xSX0qzAVv04vTuLaVNl2BScqqML98cfXhq7DKQS1t9nkP+nVPOOf619tpZjOSliVNqNAXYircs0CFlSucCv1kcfVfp8L/uLesPner7wtUqCFQ2o/Cex9dfU9Ea/tD+eudfL69EpF0n15sW5sKn8skxVUY0LRJ6lDhZ8tK3qeS17v9OVuPqXAvVPavU+H+OhV+Pv3VfcsqFMyUCvc5r/Xv00bW7V87+N21U83ys9is16zXdTf83YW9Y2a/JOm8c+6jq6s5fdA5951b2U/wBQCA5nagph1t938+zexsPf5ayHWb87rOuYZanqpe3+tW0Lbt2auB8375WWzUa9bzutg7q8tF/1mFXW9VoThw1+rrLt25KtNm+wEAQJNqjkIE9XOG63LdJtbIn4+2NaaD9LN4kD4r9pBzbtw595oKj3FJn5X0xtVD3yjpc2Vv32w/AABoUgdq2hEAAEA9mdlvSHqNpElJb5fUKemnnHPvqrR/tQDwJ1Qo/Dsm6Xedc++rQ9MBAMAuEHwBAAAAAACoIaYdbcDM6rIeKNfd39fdK438+WhbYzpIP4sH6bM2moNwvw/CZwQAANtD8GVj9fofGa67v6+7Vxr589G2xnSQfhYP0mdtNAfhfh+EzwgAALbhQK121NfX50ZGRrZ8fCKR0Ojo6KbzsrLZrBYWFuSc08LCgubn5yVJQRAoFotpvaldZibf99XS0qJUKlU8trW1VadOnXIdHR2KxWJbbu9ubfXzHtTrPvnkk+mNVp3p6+tzR44cUSQSUUtLS3H7ysqKJK3Ztt6+IAi0srKilpYWRSK7i43W63vdivK2BUGgIAiUy+UUjUaL23K5nMxMLS0tCoJA2WxWS0tLam9v1/Lysnzf18rKipxzxe8uEoloZWVFra2txX3lYrGYlpaW1mwL71skElEymXTd3d1yzsnMFI/HFY1Gi22RpNbWVpnZru9TaKP+FY1GXVtbm+65554124MgUGtrq4LgxdWsI5GInHM7alet+kwQBBu2px59tVl+71TDRn1ru/9drIaDcL8PwmeUNv/vIgAAeNGBCr6MjIzo7NmzWz5+dHR0S8dfuXJF73//+/XMM8/o2rVrunbtmubm5pTP5/Wyl73sjkFeKBaL6ctf/nLxeW9vrw4dOqTz58/rh3/4h/Xa175Wr3rVq5RMJrfc5t3Y6uc9qNc1s7GN9h85ckTvec97lEgkdPjwYQ0MDMj3fd26dUuSNDw8rGQyqSAI5Pu+giDQxMRExX2JRGLXg/p6fa9bUdo2z/OUyWS0sLCgiYkJjYyMKBqN6vz58zp79qwGBwd1zz33aGZmRteuXdP58+fl+77Onz+vhYUFtbS0KJ/Py/d9zc/PFwMup0+fLgZCy91111164YUXKu67fPmyent7ddddd+n48eMaGhrSyMiIYrGY+vr61N3drVQqpaGhIQVBoGg0qr6+PmWz2V3dt436V1tbm+6//3499thjxeDP4uKifN9XKpXS/Py8FhYW1NHRoc7OTgVBoFQqte3fHbXoM+H93ag99eirzfJ7pxo26lvb/e9iNRyE+30QPqO0+X8XAQDAiw5U8GW7Hn108wze8C/OQ0NDun79uvL5vLq6unTx4kWNj49v+VorKyvq7u7W6dOndfz4cb3hDW/QQw89pEQiseP2b9dWPi/XXV8kElH4F+QwGJBIJDQ4OChJxXvp+74ymYySyaT6+/vl+75isZg8z1MikahasK1e3+tWlLYt/F76+vrU2dmpvr4+RSIR3b59W4cOHdLy8rLm5uY0PT2tsbExRaNRLS4uKh6P66677pKZFTPEXnjhBU1PT2tubq5iptF6IpFIMZBy8uRJLS8va3JyUpFIRMPDw8rn8+ru7lZnZ6ey2axOnjypgYEBpdNp5fN5pdPp4u+CWgZLZ2dn1dvbK6kQkJEK31sikShmlyQSCWWzWcXj8WKf2mpAqBZ9Jry/G/0uq0df3S+/d5rRQbjfB+EzAgCA7TlQqx2Njo66WvxV9/nnn9fly5c1Njam6elp3b59W1/72td08+ZNDQ8Pa3FxseJ7w8yXwcFBPfDAA3rggQf0zd/8zert7dXp06d1+PDhqk1pwO6Z2ZPOudH19r/sZS9zn/vc5xSNRvXcc8/p3nvvVTwev+O40uyWMBATDv53kq2wX+VyOZ07d07Ly8saHBzUs88+q2eeeUbd3d26ePGibt68qePHjysejyuVSunuu+/Wl770JT3zzDO6ceOGXvrSl2piYqKYFRJOz4lGozpy5IguXbqktrY2dXd3q6OjQ3Nzc1peXtY999yjyf+XvT+PkiTL63vB77Uw38zX8DXWzMituqo6G+jqbBo40OIwIyHBe+JJrC0WaaShH4KnhUEjkA7DAx6MkEZHHKE5YqYeNDS0WDRIT4L3hJAQu1iaqlZ3LVlZWZmREZmx+RIRvpm5ubmF3fkj4t40tzDfIjwi3CN+n3PyRIYvZtfMfnbDft/7W0olWJaFO3fuoFAoyCgYVVXRaDRw+/ZtLCwsyGsZDofHEfnS076i0Sh/8cUX8W/+zb+BqqoIhULSZiKRiO9+h4k4Ia4G/WzrLP4uEleHQX8XCYIgCIJ4DkW+nBJN03Djxg3kcjl8yZd8CR4/fozt7W3oug7GGBKJBAKBADqdDlRVhW3b6HQ6SCQSWFpagmEYmJ2dxe3bt/HRj34Ui4uLaLfbaDabMAyDnKYpYmZmBuFwGG+99RYePXoEx3Fw9+7dY075UU0RAM+jAUSUQr1el/VFrjqWZSGfz0NVVeTzeczNzWFhYQHRaFRGeNy+fRumaSIYDGJvb0/Wgclms0in04hGo2i323AcR16D/f19FAoFLC0tYW5uTtZdsm0b9XpdCi2KoqBQKEDTNFy7dg3z8/PIZDKo1WrIZrMAuq/ledyrIs1K1KLhnKPT6fjuX9iUnwBIEARBEARBEMT5Qh7eKVEUBclkEslkEgCQy+WwubmJQCCAz33uc2i32zg4OIBlWYjFYnjvvffQarWwvLyMz//8z0ehUMD29rasHzEzMyNTT84z5Yg4PYqioFKpYGZmBqFQCIFAYGA6itt5N00TOzs7UBQFc3Nz5zr2ScSdrsIYQygUwt27d7GxsYEbN27g7t27AICdnR10Oh1EIhG88MILYIwhn8/jxo0bqFarUFUVnHOYpgnDMLC4uIjr168jkUjAsiwYhoFCoQDbtmUkSbvdRqvVQjqdRigUQigUgmmaODg4uNBrU6lUsLCwAOAwfS0SiSCRSPjOFaZpyiLFowhD46w7RBAEQRAEQRDEISS+jBlFUSA63oTDYezu7kLXdcTjcYTDYdy+fRvtdhsvvfQSwuEw7ty5g2fPnmF2dhYLCwsIhUIyMoYcn+mi0+nIKIzP//zPl07x3t7eUNEHIppC/PQ6waK2SDabvRKRMUKYcqdGGoaBVquFVCqFlZUVGIaBaDQqz0ckEkEul8OHP/xh2Rmp0+ng3XffRSQSwYsvvghFUZDJZKBpmqwdI6KWqtUqbNuGpmnyvEciEZk6GIlELuRcMMZw7949LCwsQHRhMk0ToVCoS1hx28wwtVa8OI6DUqkE27YBnE80D0EQBEEQBEFcBS6/B3cBKIqCRCKB69evg3OOarWK/f195PN5FAoFLC8vIxwOo9Pp4O7du7hz5w40TUM0GpVOXjQaveCjIEbFsiw8ffoUkUhEthkXdTn8og+84oqqql1RFaIeDHDoBFcqFWxtbQHAlY2M0TQN8/PzAA7PSSwWQzwel+c8FoshEokgHo+j0WggFouh2WwinU4jGAxicXFRpu3E43HE4/Gu7c/OzsI0TVn0V+D93HkTjUbx+Z//+TBNE4qioNVqgXOOdrstbUTTNJRKJTQaDczPz5+o1othGLBtG6qqDhRtHMdBs9kEcHgtSCwmCIIgCIIgiN6Q+DIGGGPHXovFYlhYWECn08GzZ88Qj8extLSEdrsNwzBgWRai0Shs25ZpBAAQCATOc+jEGOl0OqhUKsjn80ilUrAsS9YaEfVE3E6qV1zxIjrYiH/eyJirgvv+EnWUBIqidAkj7ntpdnYWAJBIJOR3hhEJRArhJBGLxfChD30It27dQr1eRz6fl/Yl2pULwalXge9hcEfLDDpPhmHIfbvT5wiCIAiCIAiCOA6JL2eEoijyXz6fx/z8PBYWFqDrOiKRiHRWqK7L5SEcDuOVV15BKpWSkSqi5XSpVEIkEsHi4uKxYru9bEDYj+iGFIvFrmzEy2kQkWjTTCAQwPLyMqrVKlZXV3Hz5k3ZbUkQDofhOA4KhcKJhZBRRBS/NuoEQRAEQRAEQfhD4ssY8KaPiHB8x3GQTCZx8+ZNHBwcIJvNIpFIoF6vo9FoXAqnkOim3W7L/wuHVBRxjcfjXa8JmwHQFQHjjjg4Sd0O4vIRCoVkJJSmabAsCxsbGzL1ShQT1nX93FKApm3+okLCBEEQBEEQxEVC4ssY8KaPiHB8x3Fkod1arYZqtYpYLAZd17G/vw8AMjKGnIHpx7ZtPHnyBK1WS3avEo5woVDousZumwHQM32D0jkI4LCNebPZlJ2wdnZ2ZIqbsDWiN1RImCAIgiAIgrhoSHwZA+FwGM1mU3a00TQNuVwO5XIZtm0jl8shHo8jlUqhUqnIriumacK2belU0YrsdKOqKl588UXZqUpcS3F9RQpSPp/vimhxHAfRaBSapklbIlsg3HDOZVRdIpHAysoKotEocrkcEomE7LIl5hHgeaRHOByGaZpX2qZGKSQMUJQMQRAEQRAEMX7oqXIMmKYpO9oAkJ1rRNtoTdNkDZC33noLa2trME0T+XweiUQCzWYT1WoVhmFc8JEQp+Hg4ABbW1toNpuyCwxwuMou2kMLAUZEtCiKIu1GVVWYpol6vX7MFtypbMTVQ6QbCfFAdDwSc00sFpMFeIU9ieiqSqXia1PubV9229I0DalUaugoQ3HuxjUnn8U5Htc233zzTTDGfP+trKyMZ7AEQRAEQRAERb6MA7+6HJqmyQfj+/fvIxQKyegGwzBQq9VkhITjOEOvyBKTC+cc9+/fh2VZePHFFwEA1WoVnU4H169fRz6f76rzIuhlP24GdUYiLjeMMaytrSGVSsn6Ls1mE4ZhIJVKAThuR+KnO/LFj162dZmiP0ZN3xt3raWzuH/HtU3LssA5933Pr5MfQRAEQRAEcTJIfBkDvR7sNU1DrVbD5uYmZmZm8PLLL2NlZQWlUqmr+Kr47LQ7OFedYDAoHeFgMAhN01Aul7G/v49oNIp8Pt/1ebdz67afXrbk/klcLSzLwqNHj3D79m1cu3YN4XAYiqIgnU53pan51QsaFBnRy7bOS/CbRJFn3LWWzuL+pTmBIAiCIAhiupiMJ91LgjsMXKxMR6NRxONxMMbQaDTQbrcRjUYBQKamnFd3EuJsCQQCeOGFF2SUQqlUwuzsLBYWFpDNZtFsNrG9vS2vuze1wS+NQLwGkJ1cdZrNJuLxOIDn4kCvNDU3g1Jo3ClwbjRNQyKROHPnftwpPpNIr3M8adskCIIgCIIgzg6KfBkj7pViUTg1m83Ctm2888470HUd0WgUiqJA13VZs4HSSC4P6XRaRr9sbW1BURTcvn0bqnr8VvMWavaLNKB0I0LQbrfxxhtvIJPJQFVVVKtVAIdOuLAhP04aIXFecxNFcBAEQRAEQRBXARJfxojbiTAMA47jwLIstNtt7OzsIBKJIBwOI5vNylVecjguD7ZtY21tDTdv3pQFlt2IVWpxzd2FmmOxWN/aL2QnVxtVVZHP5xEKhWAYBubm5tBsNlGr1WTR5l5CyaQLvJM+PoIgCIIgCIIYByS+jBG3E+EudhmJRHDz5k3k83nZ9SaRSFzkUIkzQFx/UcPl9u3bAJ5HrHidTLeNiLodQrgT9S/IMSWAw/SzD37wg+h0Oshms1AU5VjLcoIgCIIgCIIgJhdKFj9jRFvhhYUFKIpyqesaXHVmZmZw+/Zt2cXKHQHlh1/djqtQ/4IYHcYYgsEgotGobCltGAZisZi0N+LqcRXahBMEQRAEQVwW6In9jDAMA3t7eygWi4jFYshkMmi1WnAcp+uBmR6eLw/tdhvBYBDAYXHUUqmEra0tmX7U61q7C5ueV5FTYrpQFAXZbFbahmEYqFarKJVKQ80dvWyP5qLphsRagiAIgiCI6YHElzMiHA6jWq1if38fpmlCVVXZ5ahUKqFarUoHvVqtyggJcn6mF9M08eDBA9TrdWxvbyMcDstOR47jYGdnB5ubm7J7kcDdtYQ6mBB+HBwcoFKpyHQ0TdOgqips2x7K8XY76d6ubBR1Nb0RJCTWEgRBEARBTA9U8+WMME1T1nVJp9NQVRWKosC2bezv70NVVWiahlqthng8LlezqbPN9BIMBpFIJNBsNtFoNBAKhZBKpWS6WaPRQKvVGvt+hRMtHHPi8uE4DlZXV6FpmuymNUq9F28xcDHP9CvyPMmM2+ande6lmlAEQRAEQRDTA4kvZ4SmaQgGg7I7iaqqCIfDKJVKaLVaiEajME2zK9JhGGeKHO3JJRKJIJvNwrIsmKYpr3UymUQ2m8X8/DyA487dSa6p+zvT6jgSw6MoCprNJprNJhKJBEqlEmzbluLeMN/3FgMX9ua2mWmxn3HZvLiPRKvuixCeaE4nCIIgCIK4GpD4ckYIp6bRaKBYLAIAotEodF1HPp9HIpGQD/yi3sIwq5jkaE82sVgM9Xods7Oz8jXTNFGpVJDP56EoikxxEI6ecKTF94dhUPQCcbngnAM4TGc0DAO2bcvouVG5DNES47L5cc6nJxVRaE4nCIIgCIK4GpD4coYEg0Hs7+9LhykUCqHVaqFQKMj0FF3Xoes6VFUd6sGbHO3JhXOOnZ0dpNNpzM7OYn9/H7quQ1EUqKoqa714a0ucxJHuF71AXD5M08T29jYMw0A6nQYAXyf/qkRRjGLz/c7JOOfTk4ooNKcTBEEQBEFcDUh8OUM2Nzexu7uLUCiExcVF+eAvIh9E61jR5WYYyNGeXFqtFt59913cuXMHc3NzMpopl8tBVVU4joNisQjHcZBMJo/V2XA7hoOcaLKDq0en0xkYITctURTnKRL1OyfjvI9OKqLQvUwQBEEQBHE1mDrxhTGmAPjbACwAP8M5bzHGGBdx+RPE8vIydF2HpmlgjCEWi6HdbsM0TVnvBYAsxktMN51OB+12WzqUc3NzshOJSDcSuDsa+Tle0+JEE+eDqqqIx+MD54lpiaI4T/s+r3NCIgpBEARBEATRj6ny+BljDMB/A/BRAD8C4H8FACG8HL3v972PM8Zee+edd3Dv3j28+uqrZzZGd8vSYDCIF198EYZhYGZmBoqiIB6PI51OQ1EUpNNpxGIxWfNllG0T58err76Ke/fuAcBLjLHXGGMfd78v7OvZs2f4O3/n7+BnfuZnuiIUhMOsKAoSiQQSicRQTnSvFrJkB5eLfvYlbOvp06f4oR/6IfzKr/yKvPZ+djAtrcrH1SLZtm3s7OzImklEN4PmLoIgCIIgCOL8YBMYMNITxthXAPgBzvmXM8ZuAvh5AD8GYAPA5zjnfb3Re/fu8ddee+1Mx9hsNlGv15FIJBCLxbCzs4ONjQ0kk0lEo1E4jgNVVWFZFjqdDjKZDAzDkI6I6LxhmuaxkHzvtonzhTH2Ouf8Xq/3V1ZW+I/+6I/ixo0buHv3LhKJBGzbRqVSQTabhaoeBpr5pVyMkoZBdnA56Wdfd+/e5Z/61KeQz+cBQLaxv2g7cNstgHNJJXLvs1QqYWtrCwsLC5ibm/P9/EXcL5NUe8dxHMzMzPw3zvkrfu/3CxxljGGanhGI82fQ30WCIAiCIJ4zbWlHewA+yhj7BwD+LIA0gO/F4XF8G4CHFzg2AMdD3NPpNHRdx+zsLJrNJjqdDrLZLB4+fAjTNBGJRLqEl3q93rWa7XYWpiWl4KoSi8XwBV/wBV0djarVKtbW1uA4DhYWFgD4p1yMkoYRDofRbDZltyzi8hMIBJDP52UrczEHiAgYEWl13gi7FWLDqF27TrNPAMhms10//biIeXOS0gYNwwCAmQsdBEEQBEEQBDFd4gvn/LOMsa8F8JUAkpzzlwCAMfZHAPKYAPHFm/dvWRYikQhUVUUwGAQAPHv2DIwxBINBZLNZ+bqmaV2OlNdZoJoCk004HMZLL70EwzDgOA7q9Tps24ZhGDBNE/V6HbFYzNcZHMVBNE0TjuPANE2yhytEo9EAAMzNzXWlstXr9QubG9wi0GnaX59kn+7aSv0Qc+l5RqKcheBz0miaozEcjG0gBEEQBEEQxImYaPHlqIbLFwMIcc5/GwA45/8bY+wtAH/EGPtKHEa/ZACsXdhA++B+CI/FYiiVSmg0Gtje3kY6nYZhGHIlW1EUKIqCZrM5VF0QYvIQ7aTFdd/e3gbnHFtbWzg4OJBO8mm6rlAE1NWk1WoBQFf6zEXbgrBbt2B81vPWoHvFT6ToF4lymhShXt89CzHspNE0R+OiAlEEQRAEQRAXzMSKL0fCy+8B2AXwCmPsPoCPA3iGQ6Hl5wH8IwAdAN/EOd+4oKEORKSgiJbStm1D13UZDdFut5HL5WS6ijs6YtT9TEqdgVGY1nF7sSwLb7zxBjKZDObn57G7u4tAIICbN28ik8nIqABhDwCmojjqRXFZ7GIcHBwcIBQKIZfLdc0N/Zz88zx/JxFEzgo/kaKfSNVL1BhmzKdJL+q3fb/3LlpoIwiCIAiCIE7HxIovAL4KQJFz/nWMsRyA3wDw0wC+jnNeY4z9OA7rvWic8+oFjrMvzWYTq6uriEQiSCaTUlyZnZ2FbdtScCmXy3L1eH5+/kQOyjCOwCQ6tJNUH+E0WJaFer0OTdNQLBZRq9WQyWRw69atY4WTi8UigJOtkJ/VKv6kcVnsYhyYpomHDx8imUwOfV39zt9F2cdFt5bud5/1EjXEfVooFGSB42G/Owzuc+JNi/I7X5R2ShAEQRAEMd1MsvhSAvBhxthHOee/xxj7YgBvA/hnjLGfAfCNAL5nkoUXQSQSQTweRzabhWmasG0bjuMgmUwin8+jUqnAsiyoqnriqBdgOEdgEh3ay1JAVtSfiEQicBwHmUwG169fP+bkapqGQqEg/z8qJ1nFn0b6HedlEpmGIRgMYnFxUUZODXPsfufvouzjPKM2RhUpRvm899yfRhBxnxPvdaEoF4IgCIIgiMvHJIsvbwP4TwA+xhjTOeevM8b+MoAfwGGNl3/OObcudIRDEIvFpNMkHtSr1SparRYKhYKskyBqwpzGkRzGEZjEh3pvAdlpdqyz2ays19PreiqK0nMlfRhOsoo/jfQ7zkEiwjTbkB+hUAhLS0uIxWJDCyjueizutEfg/O1jGqM2xP17VuKV+5x4r4vftbsMdkwQBEEQBHGVmVjxhXNuMMb+CYAfAfB9jLFXASzhUHj59WkQXoDuB2zhEAJANBqV4eXnWWB3Ep0gr+MxzdEbrVYLqVTqwhylSby+Z8EgEWGabcgPx3HQaDSkSAsML6B4z8VlOB/ngd+9dFbiVa/79rLZMUEQBEEQxFVmYsUXAOCcv8cY+/sAvh3ADwOoAfjuaRFe3DiOg52dHdRqNcTjcRQKha48f3eL6cu2aj8Ir+MxrdEbqqoiFAphe3sboVAIwWBQOrtX4TqeJ4NEpmm1oV6I+QEYPU3Gr4j3qMVez4tx7vssjuO8xc3LZscEQRAEQRBXmYn3CDnn65zz7wfwZwF8Pef8sxc8pBNhGAYajQb29/eh6zoURZHdjsrlMra3t2UHHLHaaRiGDDsXjtdZcV778e7Ptu1j++2XsjPJ2LaNjY0NbG9vS6GtWCzKaKdBnPc1uKh9DoPjOKjX66jX6yca27TaUC8450gmkycqztxsNmV9EvfrYo7x+061WkWpVBqbXQxrZ81ms2suPA39jvGssW0bOzs7sG373PdNEARBEARBTCYTHfnihnN++qfxC0TTNMzPzyOXy8E0TYTDYSiKIgWIdrvd9Vnxc5Sw89Os9J53eLvYX71eh67rfTuKTAuKouDatWtot9uYmZnBwcHBsdbA/ThNy9uTMqlpDYZh9OwIddUiwwBgZmZmrMWZ+0VUaJom5yXDMMZiF6Pa2ThqnZxH1EgvW6xUKtja2gIAzM3NnXj7k3p/EgRBEARBEKMzNeLLtCMcyJ2dHVm7QVVVZLNZaJomI0DE670KMfbCcRyUSiW50jrqg/p5h7eLVKtqtYpGo4FcLncu+z1LDg4OkE6nu1b5VVUd2nl0XwO3U3eWDtikpjWEw2FEo9GuIrHA6e18WmGMyQiWYdpG27aNUqkETdN860n1S59RFAX5fF5uexwMa2ciWklEPonXTsI4i9b6net+tpjNZrt+nnRfk3p/EgRBEARBEKNzNZaNJwBR82Vrawu6rkuH2jRNJBIJWJaFnZ0dVCqVru8Nmz5hGAZs24aqqiM/qI8jkqBfWoHfeyINotVqyf9PagrMsLTbbXz6059GtVoFACQSiZGuhftau1MmhAN9Fg5YP/s6yfUY1zU0DEOm5wGQ2zyNnU8znPOu2lBA/7SaSqWC1dVVrK2tnSjtZtxpW26xp599uLsLuevUnCYNbRzpR37b6GeLqqpibm4Oqjr6+oZ7X5ctfY4gCIIgCOIqQ5Ev50Sz2cTOzg4cx0EymUQ2m5Utlh3HkSuk6XT6RKu07hXSi0g56rcNUUOi2Wwin8/L8YXDYVl8eJQWupPKzMwMkskk2u22FJRO6jSFw2E0m02ZnnYR5+Mk1+Ms0uTc9pPNZpFKpa5UyhEA2YpdFOUWwoSYP8Trgmw2C8dxfCOHBp3zi05z8+sA1y8NbRAniR7xngO/qLRwODyULY56PinahSAIgiAI4nJC4ss5IVZro9EoGo0Gms0mLMuCbdvSmZibm0Oz2TyRAHEaB30cD/u9tuF2DL01JEzTBACZmjPtTgfnHLqu49atWwCAer2OcDh8otVv4WibpnlhQtRJrof3O/0cz36OuFjtF9sRNUgu8nxcJIqiSCFOCHPinNbr9WP3v6qqWFhYOLadYcWPcYigftd+GJsSxxcMBuVxapqGQqEw8Lt+nGRu9J4Db+TOKOdn1PN5VdrFEwRBEARBXDVIfDknFEVBIBDAw4cPEQwGEYlE5OuapqFer3eF2Y9TgBi08jqOh/1e2xCr2F5n2tsC9yKKqLpXsE3TlGM76Tg6nQ7W19fxBV/wBSiXy9jf34eiKCcquDmKHZzVuTuJXXi/08/xHCTUeGtoVCoVhMPhEx3LOBn2fIsULOC5A39SGGNSuKxUKrLOSDAYRKvV6ltbxD0Oca7dAo7jOKhUKshms10pNKedg8S1F+Kr33X1ji8Wi0nhcW9vT9bCyufzxwpyn+WcMaggca/3/MY16ucJgiAIgiCIywmJL+dELBZDo9HA+vo65ubmEI1GUalUZG2LeDwuHZNxr3peRDqPW9gAjqdDeVML6vU6isXiuXU9chfLVFW1q47ESc9Vp9PBZz7zGRQKBbz44ouYnZ09UcFNYDThY5LTtfo5nsIxHaawsDsSyP2di3BWhz3fzWYTq6uriEQiWFxcPNW1YYzJukjuOiOlUgm1Wg3RaLSnyOdO2Zmfn0csFuuK3mg2m2PpzOPFW6/FK8L4jc8tVoTDYSk0+XVd8oo7bhH1tHYxqCBxv2vZL2pmmM8TBEEQBEEQlxMSX84JRVFw+/ZtlEol3Lp1C3Nzc1AUBTs7O7KOhTedYFyMkgoyLgY5FN56FWJc7iiZs8RdLFPU33ELBCdZ9Y/H43jppZeQzWbRbrexvLw8NmewH726JE3CKvoojqefUOMn4g3jrF5UVISXSCSCeDx+6igSx3HQaDQQDAal0AAM11XHL2XH/dO7Le81Oel5dHcb6tfByDs+t834dV1yF3UWn6vX612FfC9SxBg1cmjYz0/avU0QBEEQBEGMBokv50g4HMbKygqCwSAsy0IikUCr1ZLRHyIaBBiv8zBKKsgo9HMG+jkU4nsAutrnJpPJnqvc48ZboNi9P799Hzl1fT2eWCyGL/qiL4JpmigWi1AURaaXneXxnKYexVkyjLPY7zoA/rbqLkbci7OMJhg2KikWi2FxcXEoZ3kY+wIOI4B0XYeu67Il/aBoFUVRjkWTuY/BmxrnPr/Dnsd+11pEswhh1Tsn+I3Pb5wCbySP2Kc3fXDQuM6KUdP1hv08RcgQBEEQBEFMNyS+nBNiRXZpaQm7u7swTROqqsoVX1HzZZwthXs5HuOu6QAcdwb6ORTie+7jVRTFd5X7rBjVQToSi2b6fabT6cCyLGSzWRwcHGBxcRG2bZ/59XRzEVFOvRjGWRx0HfxsdZhixJNQvHnU1DH0sS/ROSuVSgGALELbD9u2u2q5DIOo/yKKGw97Hgdda78ORielX6TMMOLdtDIJNk0QBEEQBEGcHBJfzolmsylXa8vlMnZ3d5HJZGTbab9aCG5O4kT3cjzG1U3jpM6AN9ph3OM6C47GfNDvM5ZlYXd3V9bvEdFN46JXAVM3ZxXldBLG4Sz62cQw251kW/JjkH3Zto1arQbgMJVJdAjrNy9UKpWRa7m40/F6RSP1GX/PazLMNRt2jusXKXOS/Y6y74tk2myaIAiCIAiC6IbElzNGPNSLyJfZ2VkAh/VBdnZ2EAqFhioiOozj7eWsV0pP6gx4C61OqrPj5miMzqDPXLt2DaFQCHt7e8hkMmOt4SNqutTrdei6jvn5+YFO6EWulp+Vs+je7jQ4zcMwyL4YY4jH4zKaSqQGeTsFuRmmHoxARMmk02mkUqmRz6e7voufzQ9jC2chFI47peey2BtBEARBEARx/tDT4xnTbDaxvb0Nx3EQj8ehqioikQhKpRI6nQ46nY50WOr1uhRq3MUjgUPnWTja4nODEI7HaZwEv7GMA+HsDHMcZzWGs8BxHDx9+lQ6advb29I5Pi0i9UTXdbRaraG/cx4FjC/qGo1iR4OYZDuzbRu6rsOyLMRiMWlbjuP0TFVUVRVzc3O+KUfiWEUr51KphK2tLezt7Z3KXk5zPcQcdxKhUIiSQqA+q30Pe3yTbEsEQRAEQRDExUCRL+eEaZry/+12G7FYDNFo9Fh0RK8il97OIcN2RjrtSu1pVqNPWpB30BgmdfXZcRyUy2WYpilrUozb+RL1Ls6iNs5pzutFpTcNU3x3WPyOYVJsbWZmRhZvduMX2THMmEUaZDQaBXBoVwsLCydujS44TaRVvyiVQcckivA6joNkMol8Pn+iyJ1BjKsGzjjwRj1N0lxIEARBEARBHIfElzPA7ShomoZ4PI50Og3DMFAulxEIBBAMBmXnD1VVZcvlQUUuhZMwbFeb04bTn8aZOmlBXi/eMUxyEc1MJoPZ2Vl5jZLJZM/uSYMcZL/PKIoiIxn6FZw9CadpMXxR6U3DFN8dFr9jmBRbU1UV8Xgctm2jXq9D0zTZ6ceLEFYKhULPtDTHcaDrOjKZjLSnUQULP84q1WyY1vWFQkFG85xVx7RxizSnwd31ierBEARBEARBTD4kvpwBbkcBOEwZ2Nvbg+M4qNVq0jmv1+td7VdFLZRhGPbh/rQrtad5qB+XA+Idw6R2/bBtG61WC7lcrmsl2k9EGcap7/WZszp+93ZHFR0uyvkb57k4aXHf84BzDl3XUSqVEI1GMT8/3zVWt42Ngkhlq9frQ13Di4oEGnQdRBFekZLl/dx5j/s87gdv1yeCIAiCIAhispk68YUxpgD4xwCKAP4Pzvk7R68zzjm/0MEd4U6FUBRFrsbGYjEsLS3J7kb1eh0PHz4EANy8eVO2kR1Hi95RP+d2bsblqJxHwdVJQlVVhEIhPHr0CMFgEMFgENVqVaZDuAvkDuPUn7fj7z6vwg7Ev0lNaThrW5gUW2OMyTRF0YnIjTvaRQh/g7pBRaPRrs/1+ry7ZbVpmmOLBBplnjntfHdWEUxnJeoM2q54n9KNCIIgCIIgpoepempjjDEAvwnAAJABsMAYyzPGZjjn/Oh9v+99nDH22jvvvIN79+7h1VdfPdNxulMhFEVBPp9HKpVCIpGQBTDFinO1WsXW1haePn2KYrEoH7hF8cfzKtzoLsw6ziKml4FXX30V9+7dA4CXGGOvMcY+7n5f2Nf6+jo+9rGP4Rd+4Rewu7srU8pERIwb9/nudY17Fcs9j+sj7LPZbJIdnDH97Ms9d/2Fv/AX8KlPfQqJROLUDncsFpPRM4OKMouW1ZVK5VRFcb2c5zwzznG7OatjGLTdYfc7aO4iCIIgCIIgzg82IcEiQ8EYCwP4pwC+F8B/AWDjsD3rrwL4cc75Qb/v37t3j7/22mtnPk6xKhkOh7tquIiHZbGCLDqNAIfOgWVZsu6C+H6lUoFt20ilUiOv2Nq2jVKpJB0PP+fKvaotaj9MSpHRSYMx9jrn/F6v919++WX+r/7Vv0IwGJRRLqJ7VT8HV9TvEWkTbvyuxTiuT79tuO1XOHeD0qjIZk5PP/v64Ac/yH/3d3+3S7Bzn2/378Ixd0fA+H0HOJ6u5BYB3UW+vXNEP4a1hUmymZOOxXv+xnE8fu3DT3u/9bOtfkGjjDFM0zMCcf4M+rtIEARBEMRzpibt6CiqJQbgzwO4DuDfA/gXAP4ugL8A4FcArF3Q8LrwK4oLAKurqyiXyygUCrJziejMUa/XsbGxIaMlRD0Y27Z90wyGoVKpYHV1FbFYDDdv3uxyxASlUgmrq6tS4BHvT0KqxbRhGAZef/11XLt2Da1WC3Nzc0O1eu6X9tGv89VJEE6bn4Pnt08AsqinSJnyK8zbb3vE6RECnhBRSqUSbNuWr2ua1pUyJr7jvk7iO0C3U++91rquAwAikYicn+bm5uQ2Bzn9w6b4nKa70bjpN+Z+Y3EfQ68i6KMei2EYaDabUjD32y7N0QRBEARBENPH1IgvR0tzFcbYDwL4WQDrnPMmgB9hjP1FAEuYEPFF4K39ks/n0Wg0UK1WkU6nZUqKePBfW1tDKBTCnTt35PdF5IxwuIZdfQaAbDYLx3G6Im/czprXaZuEri7TTDQaxdLSEprNJra2tpDNZpHNZmHb9rHuM16HrNc59xNmho086VfoNxaL9UzDcO9TCCq93vdub9gW6MRozMzMdEVX2LYtHXMRqdKrLb24TpZlodPpdLXlFtsUUU65XA65XK5rP+7OQcN0xBpHrSI/McQbpXdagcbbla7XmIcVk3ptY5jv9xvLpBR9JgiCIAiCIE7HRIsvR9EuXwwgxDn/7aOXfwnANQD/C2NsD0ALhxExaxcyyD6I2i/1el22mI5Go9B1HaqqYm5uTj5wV6tVMMag63pX21zxs1QqYWtrCwDkKvQgVFXFwsICgOcr5+7VcOEw3759+1iKFDE6gUAAoVAIjUZDOoiiq5UXP4esV2vpfpEpfk5xv9fcjlwvh9W9T7/ird7CvO7tDdsCnRgNxhiePHmC5eVlhMNhqKoqhS73/3tdVyGkKYrSNb+4o/REtIV4T6TNua/9MB2xxhGV4Sc4iNozAOTceRpb835/FAHUj17HPcz3+42FolwIgiAIgiAuBxMrvhwJL78HYBfAK4yx+wD+r5zzDQD/T8bYYwBfj0Px5ZuOXp8oxMP29vY2PvOZz4BzjoWFBaRSKWSzWQDP8/vz+Tw+/OEPIxwOw7btrpoNAOTnxc9h8IuucK+GA8cdbeLk2LaNt99+G4FAAB/+8IeRz+elQyxawnrPv9shGyaqwP2dXk5xv9dGdeTcduPn3E9LG/Bpx7IsPHr0SKYhBgIBaJomUwWHEd/y+fwxMUXMEcFgEIqidEXF+NlKL+FtFIaJWPHbt3cOPK2tDRtV5h3LqBE3w9xzdN8QBEEQBEFcfiZWfAHwVQCKnPOvY4zlAPwGgJ9ljH0d57wK4N9wzn+ZMaZwzs+2FdAJEQ/d169fl92Ptre3EQgEYJomDMPA/v4+2u02bt68iYWFBTSbTWxvb3d9H4CMlBkFwzBQrValuCNqRpDIcjZwzpFKpaCqKqLRKEzTlGk7orW4uwaM9zoME1UgtnXS107KsMIQ2dfZIIo4h0IhVCoVzM7OdhXfdRwHsVisr/jmTkES105cV7Edd1TMIE56rU8aseKdA8/C1oYZ21m0rfY7lkkqSEwQBEEQBEGcnkkWX0oAPswY+yjn/PcYY18M4G0A/5Qx9kkAX88Y+17Oeav/Zi6eYDCIO3fu4PHjxzJlwF1M11svQURJeFMJRn0YF6kG3roNxNnQ6XSwuLiIUCiEUCgEAMfqqvRb2R5HVIGXXjZzElsSP8/C+ST6wzlHNpuFpmkIh8PIZrNddXzcBVoFw6SsuWu+nFfa4bijPE4qUvRLy+uVxtVL6DqL8dN9RhAEQRAEcbmYZPHlbQD/CcDHGGM65/x1xthfBvADADIAfmIahBdBs9mEZVlIJBIIBALY2trC3Nwcbty40eX0iAfxdDqNUqmERqMhxZhyuSyd+mEexkWRX2+qAXE2zMzMIBAIYG5uDuFwGJqmwTRNAL1X6d3twGOxmLSFYVb1exXZFZ2yRGSEXxeiUR27sxCGiOERnY1isRhSqVTXe0IoCAaDUjAA0HXdhX34RcOcd9rhadJ4/Fo7j9ppy91KHfBPy+tVu0gIXbFYbGxRKb3uRbrPCIIgCIIgLhcTK75wzg3G2D8B8CMAvo8x9ioOOxplAPwHzrl1oQMcEndr31arhb29PVSrVezt7cE0TeTz+a6uMs1mEzs7O9B1HbZto9VqwTAMlEol1Ot1LC4ujvQwTmkg5wfnHNVqFZlMBul0Gjs7O2g0Gkgmk7LttJdSqYS33noL2WwWCwsLxzrX9KNXnY+1tTWUSiXk83ncvHnTt6vRaRw7sqnzxzRNrK2tIRaLydbzIhquUqnAtm3s7e3JyAzDMFCr1Y6lGg4r6nnFjHFET/ViFCHQ2xp7UOeuk+6v1/3hFsnHFZXSa190nxEEQRAEQVwuJlZ8AQDO+XuMsb8P4NsB/DCAGoDvnhbhBehu7RuPx1EqlaCqKlqtFu7fv49cLofFxUUUi0U4joNoNIq5uTmk02ns7e0hl8tBURRcu3ZNijWU/z+ZHBwcoFaroVwuQ9d1cM5Rq9UAoKuLjBtN05DNZpHP55HNZqVQJwrz+tGvSKqmaVhZWUE2m5VdU/y2M6iQLjFZiE5o9XpdtpoX0RcifVHYj0g1jMfjvukxg0QTwzBQLBa7frdtW+7T/fo4BAgRueO24170Kiw9TMSMEJSGER57CR+9CpefBhJZCIIgCIIgrgYTLb4AAOd8HcD3M8Z+DADjnDcuekyj4H7Qz+VyePbsGaLRKDKZDGq1GiKRiKzzUq/XZaSEZR3qS5ZlwXEcJBIJpNPpY9unooyTQyQSQS6XQ7vdhqIoyOVyiMViSCaTPZ000eobgIxScBfm9aNfkVRFUZBKpY6lplBdiekmGAwiEolAUZSuKI9wOCwFOGE/tm2j3W5Lwc87Lwy65u66UwCkuDPO6Ck3pmkOXezXe18Ma7NuQWl+fv7Utk6CCUEQBEEQBDEqEy++CDjnzYsew0nwhvyn02mkUinpFIl0FFFnoNVqyd8TicTAQpjkPE8OnHN5za5duwbHcbC/v99VHNUrgngFl2Ec2pMUSaW6EtNNMBjE4uIi8vk8gsGgfN00Tei6Dl3XpRjXbrdh2zbK5TJUVe3bVcsPIfAA3a3RT9PZqp9IPA4bHCRCuwUlsnWCIAiCIAjiIpga8WVacRd3FGHv7XYbm5ubyGazME0TlmXBtm00Gg2k02npPKVSKd/2sG7IeZ4cOp0O4vE4FhcXoSgKVldXpeBRKBSkwFYsFlEoFKSDq2maTDUSvwubcRfgFZykSCrVlZhuOOeyzbQ7nUyICrZto1gswrZtKIqCZDLZsyPPoDnFK2T0sw/3/OZnq4JRW6ePyiAR2i0oTQPDRjRS5CNBEARBEMT00FN8YYytAuB+73HOb53ZiC4ZwtmORCIolUrSwU6lUohGo7JwY6t12Lip0+mgWCxid3cXd+/eRSKROHPHhRgPpmlid3cX8Xgcqqri2rVr2N3dhWVZWF1dxc2bN32/541+ASA7Fo1SgLcf47ATcvQuDsYY2u02LMuSUS5CgEkkEtjZ2cH+/j6CwSBWVlZk22m/mj6O46BUKvnWcQFOVgB3kK2etUh82u33s+2LsPthrwFFPhIEQRAEQUwP/SJffh7A3wXwt85nKJefSCQCXdfRbDYRiURQLpeRTCaxt7cnC/KKui6itay7ECVFt0w2nHPs7u4iFovJVKNoNIpAICA70Qin2Nta3O8aj5JWdB6Qo3dxiMiXSCTiG9GSzWah6zoCgQBUVZXCixBZ3OlDhmHAsiy02+2u4rGCfkKGV4gYJgXO+52zEDNOKy72s233e/0ihkZhmDQp989eUOQjQRAEQRDE9NBPfPkxAP8jgD/gnK+KFxljN858VJcITdOkqKIoiuyEMzMzIzvjhEIh3LhxA9lsFqqq4tatW7LrDTm70wNjDKVSCbFYDKZpYmFhAXt7e4jH44jH48fa/gK9nb5h0orOe0Xez9GjaJjzgTF2rLOVG1VVcePGDXktgOddikR3JLeAIOpLuevCuK/loGLPAGTkzSBb9X7H+/tZ2ZB7u2IcJxE73O+NS4AcJk3K/Xqvc0SRjwRBEARBENNDP/ElCOAvAvgNxtifBcAAhAD8JoClcxjbpUB08rAsC7FYDLOzs7AsC0tLS9ja2kK1WsX6+jqy2Syq1SoMw0A2m5U1QHrVbSAmD1Fro9FooFarIRwOY25uDtlsFpZlHYt20TQNmqbBtm3U63WEw2HpKA/DeUei+Dl6FA1zPnDOoes6dnZ2oCgKbt68eayGiYiUCwaDsCwL4XBYih2iG5Jw3vP5/DHBYZhreZJIC+933DWOxL1wFjbk3i6AkcSOXu+dJtLEe9+Psh26zwiCIAiCIKaffp7e3wHwg0f/F5EvDoD/cJYDumyIh+tgMIhKpQJVVREIBDAzM4Mv+ZIvwWuvvQbHcWRNh1arBcdxoOs6gMO2qBRRMB1wzsE5x/vf/348e/YMyWQSiUQCwWCwq0ONcKREuodhGNKpnpubG3p/k5ByMAljuAowxhCNRhEKhWR9KC+VSgUbGxsolUqyq5ppmr625S5Aa9u2/I67jbUfJ4m08H7nJB2+ToLfdk+7D3EsfrV0BuEVUEY5j3SfEQRBEARBTD89xRfO+Q8D+GHG2H/mnP/ZcxzT1OMNEQ+Hw3jrrbewv7+PSCSCdrsta73Mzs4il8uhUCjAcRysra0hEokgl8t1OSbE5BMIBBCJRPDkyROEQiGYponNzU1kMhkEg8GuDjUAZIeaTCaDhYUFZLPZkfY3CSkHkzCGqwDnHLZto9VqIRqNyu5pwpaazSZUVZUpRKqqQtM0mabUz7YqlQq2trYAYCTx7yT41TgatqNSP6HD7zPe7Yr/jyPN6SSRKKcRUOg+IwiCIAiCmH4G5jiQ8DI63gfzUqmE7e1tGdliWRb29/fhOA729/dld5JmswlFUdBqtZBOp3s+bFOdjcmEMYa9vT0kEgnZdnpjYwOxWAzXr1/v6lATi8WkjaiqKgstjxuylcsBYwzVahW7u7sIhUJdnYuazSZ2dnYAANFoFJqmIZ/PywgTIaj0sgUhzIwi/p3Urk4iWgzznZN0aBplDF5OIqSQgEIQBEEQBHG1Gb7ABDEUfrVaNE1DLpeT3UeSyaTsgHNwcIBGowHDMBCLxWQ74n4P9ZT/P5mIdJBYLIbFxUVZaDeTyUBVVVnbxS3AnHV0E9nK5YBzjkQigWq1CsuyEAwGEQ6H4TgOarUaDMPAtWvXZDctP3rZgqqqI0e8nNSuxlEzZtBnxtVJyIt3u2d1P5FgShAEQRAEcTmhJ7sxYxgGms2m/L9oL7ywsIBWqwXDMNBut/H06VMUi0W0221EIhFZAySRSMgimY7j+O5D07SBtRmI88dxHBnxUqvVoOs65ufnZRercrmMYrGIer2OnZ0dKdL1c7BEZEMvWxgE2crlgDGGg4MD2dJ5f38fwWBQtq0X11lVVTSbTRiGcWwbouiuSPkZZFv93u9nV/2+5xYdh2WY77g/I4Qhv3Pg/iyAke6tQdsdF+e1H4IgCIIgCOJ8ociXMePuaCMEFfF7OByGbdtoNBoIh8OYn59HJBJBp9NBpVJBq9WS7WJP2pmDuDhUVZVCGucciqJgeXkZwKFdFAoFAOhKE3FHHPiteHuL8466Gk62cjmwbRuBQAALCwuYnZ1FKBTCs2fPUKvVulqZ94vqME0Ttm2jUqkgn88PnGf6ve9nV2Lboh5Ns9mU6U/nybCRLaNG75xX0VsqrksQBEEQBHE5IfFlzAinxLZtGb1SLpfhOA5SqRTm5uZgmiZu3ryJubk57O3tIZVKQVEUzMzMoFQqdbWaFk43MfmoqopkMikLJs/Pz0unzt1dRggo7hobjuNgZ2cHjUYD8/PzXZ8V71P60NWFc45gMIh8Pg/gUDgIBoOIRCKyRbk7jc0vbUXTNDSbTTk3hcNh1Ot12LbtO88I23MX9+03F4nCvXNzczLNTqRTCs4jpWZYwXFUkeO8hEwSTAmCIAiCIC4n5NWfEYZhoFaroVqtwnEcZDIZRKNRJBIJzM3NIZVKwbIsOI4D27Zx48YNhMNhWJaFSqUCx3FQLBZlChMx+QSDQWQyGRwcHGB7exsAutLHRDqGKIKqqs+1T2Evu7u7XWkQ7mgGdx0h4mqhqmpXEd1YLIZgMIhEIgHTNOXrAFCv1/Ho0SMp1gkURUE+n0cqlYKmaTBNE7quo1wuH0txcYskpmkOlQaTzWaxsLCAfD6PfD6PRCIh6xwJm56klJqTpECdlmHSCE+bakgQBEEQBEFMJlMd+cIYY5xzftHj8CKiFETEi3CMLMuCoii4du3ascK8wjHa2dlBrVaDbdsXfRjEiCiKgkwmg2aziVKphLW1NWSzWZl+IRxP27ZhmqasBQMcrr4nk0lpK27nV9QR6ldMlbjciFomQnwT/xcFZt1RcsJeDMNAKpU6th0RVeFOhfOKeu6UnGEjRNyFe4X91mq1LsHovFJqThJhc5qonGG/O+7OTScdB0EQBEEQBHH+TLX4wjnnkyjAGIYBXdcRDAbBOQdjDDMzM9jf34dt23j69CkMw0Amk8Hy8vIxZ1vXdWiaBlVVKdJhihDRSrlcDoFAAJZlodFoyOKowhGuVqtYW1uD4zhYWFgAABkNk0gkEA6HUSqVZDoIQFEvVx3HcVCtVmX6T71eR71el+KcEF5isZiMkPGmtXmdcncqnBe3SCK2KyIyvI69qPXiFhMNw4Bt24jH4122e14pNadtae1N3Rokagy7v1E7N40KdTcjCIIgCIKYXKZuaYwd8rOMsb8KPBdgBnzn44yx19555x3cu3cPr7766pmOUdM0zM/PY3Z2Fvv7+9jc3MTGxgYymQzm5+dxcHCAZrPZJa4YhoFqtQrDMFAoFKCqKhzHgWmaZzpWYjCvvvoq7t27BwAvMcZeY4x93P2+sK/V1VV8x3d8B37t134N2WwWwWAQ7XYblUoFu7u70vEU/7zOlXjfNE1YloV2u92VqkQr2ZeTfvYlbOvdd9/FV3/1V+OTn/wkgMMok1qthvv376NWq3XNJSICxZvWNkq6j19KTq9tiFovlUpFvqZpmqxxdZ4RW+J+CYfDQ3X5cqf4uDs4eY910PkbtqvYqJ2bRsU7jkFzF0EQBEEQBHF+sAkLGhkIY+wFAP8NwDMAP8Y5/9mj1xUcajE9D+jevXv8tddeO9PxuVdIRRHVWq2GSqWCaDSKlZUVlMtlAMCNGzdg27Z8UBbRDqImA4WPTxaMsdc55/d6vX/nzh3+0z/903j55Zel42vbNnZ3d5HL5WR9DtEuuNe1dRxH2oJwXMkOLj/97OvevXv8N3/zN2EYhoxsEXNLPB6XLep72Yh7XgLQd27pFeXR63W/yJdBYzgrW242m6jX6/J8jPp5MUbvPTrt6Tz9bKtf8ChjDNP2jECcL4P+LhIEQRAE8ZypSzvinD9kjP0KgAqAv8oYO+Cc//zRexf+lOgO+xZdSDKZDB4+fAjbtrG2toZyuYxoNCpbEwOQ6QLuB3wKG58+RDTC/Pw8crkcLMtCJBLBgwcPsLS0BEVRZPFUAHKV3u3oifo/0+zsEePF3TVN2IeIKnFHR4mORu7W5MK2xHwiBAfgeWqKt8aQX+pKrznJXevFPd5xt00fhlFTdtyfd4ueqVRqqGOfNKZdJCIIgiAIgrjMTJ34ckQHQAjApwD8TcbYNwP4cQC/cZGD8hbR3dnZwaNHj2AYBsrlMlqtFiKRCEzTRKfTga7reP/733/u9RCIsyEYDCKZTKLZbGJra0sKa2trayiVSkgmk+h0OpiZmYFt23JFXaSYAc+dXbIFwg3nHLlcTtZSES2cRbQG8DyFplgsQtd1RKNRxONx2LYtiz67u2e5BQqR9thsNrta3Yt9AZCRNV4Hv5/QIr7n3t9J26YPIyyMet+4Py/acE9zrS2q+UIQBEEQBDG5TJX4whgLcM47AP49gALn/KcZY38LwIcBfOIix+ZdNVUURbaOTiQSyGQyiEQiiMfj6HQ6WF9fh2VZuHnzJq1QXiJmZ2cRDocRDAahaRqePn2Kg4MDLCwswHEcPHr0CKqqYmlpCfF4HMlkEtlsVkYnEIQfBwcHME0TqVQKjx496iqmK6Kl6vU6YrEYcrkcIpGIFGcqlUqXYOMWHNxpNqqqyk5cYnuGYaDRaMj9xGKxYw5+P6HFbdPuwr1uEWhYzlpY8BYZHsQkRpmcVzcpgiAIgiAIYnQmWnw5KqT7xQBCnPPfPhJeAGAHwA8dFd19COCfA/ijCxomgOfdPbyFL5eXlxEIBNBsNsEYQ7vdBmMMi4uLODg4QC6Xu8hhE2Ok0+lAURTMzs4iFApBVVWsrKwgn88jHA7jzTffRK1Ww40bN2TKiNcZJgg/GGOwbRuPHj3C9vY2otEobt26Jd93O92GYcj0NpHi5q73IhCCsWmaODg4wPLyMizLQjgclkKNaGft3QdwmFYp0ubcrwP9I1BOau9nLSyMOq5JjDKhuYQgCIIgCGJymVjx5Uh4+T0AuwBeYYy9DeDjADYBfAbA7wAA5/z/dlFjFIgQfW/RS7Ga3Gq1ZFFH0VUjn89LoWZjYwMAkM/nYVnWRK2kEqPRbrcRiURQrVZlxxkRUSC6YBUKBRmVAKCrva23EO8krq4T508gEEAqlUI2m0UkEkEymUS1WpX2EYvFuoozA4NFCiEY1+t1WJaFaDSKubk5NJtNNJtNuT1vfRbh4PvVjhGM027d25okYWHQeaZ7lyAIgiAIgnAzseILgK8CUOScfx1jLIfDei6fAPB1nPMaY+xTnPPPAIedjjjnzkUN1DAM6awAkEUly+UyarUaIpEIUqkUbt26BcuyEI/HoWkayuUyGo0GHjx4gHA4LB13YHJWUonhmZmZQTKZRL1eR61Ww/r6OgqFAprNJmKxGObn59Fut6HrOtbW1mTKWbFYBADE4/FjtV8mcXWduBhEyk48Hsfu7i5arZace/L5vLQnr0jhreciBD4hGuTzeezt7clUJm8UTS/76yc+jGK3g0QK9/hF3ZpJYFCUCd27BEEQBEEQhJtJFl9KAD7MGPso5/z3GGNfDOBtAP+MMfYJAN9wFA1jXaTwAhx3Vra3t7G7u4tkMonZ2VlZaDWZTMpaIPV6HbquI5VKYXFxEfF4XIb9U77+dMIYg+M4SKfTaDabsCwL9Xq9KxrBm2amaRoKhYL8v7f2C9VwINyIaJVoNIpoNCo7ZXmjqNzOvuiAJFpCuwU+8Tl3tyK3qNDP/vqJD6PY7SCRwj1+EeUzDVEl533vTsM5IQiCIAiCuMpMsvjyNoD/BOBjjDGdc/46Y+wvA/gBAFkA/4Jz3r6IgXkfcr3OiugwItpMA0A0GgVw2FEjGAzCMAwZEZPNZuW2gsHgRRwSMQZmZmYQCoUAACsrK2i1Wmg0GlBVVXaMEUKLsBlFUWTEFODf+pdWzQmgO70ReN6mPJ1Oy6K5foVs3a3L3W3Nh0Fsb5BT329OHMQgkcLbeh0Yf1TJWQgX46q/MuzYKNKGIAiCIAhispnY5THOuQHgnwBIA/g+xtifBfAhABkAv845f3RRYxMPucKhdiMchXA4DMdxUCwWYRgGCoUCTNPExsYG7t+/j/39fflwLpwbsSJNTCedTge1Wg3t9qEmGAqFkEwmpeDSbDZhmqYsiCpeE061+D/Q38aIq4lIMXILLKKNdL1elxEwfg66+I63ptCw+/Xa4kns1fsd99h6jbvXZzRNQyKR6Csi9drfsMc4KQw7tmHOCUEQBEEQBHFxTHLkCzjn7zHG/j6AbwfwwwBqAL6bc25d5Lg0TZMOs2ib6sYwDFiWBcdx0Gq1ZDSEiIrZ3d1FuVzG7du3pUNVq9UQj8ePFe0lpgfOOfb392HbNmZmZnDr1i3k83mYpimjEoLBoGz922w2Ua1WUSwWMTs7i93dXeRyOaiqimAwKNuVEwTwPDIkGAzi8ePHKBaLyOVyWFxcRCKRkN2HhLjijZg4SR0WISILkVjg3ZY3esUvWkN8x91q+qTz3DBRJe4xDore6Rd9c9HpPN6uUr2gTkcEQRAEQRCTzUSLLwDAOV8H8P2MsR8DwDjnjYsekwirF/U8/Dp9CCfccRyoqopUKgXDMBCPx1EulxEMBmWKUa1Ww+bmJlKpFFqtFj1ETynBYBCBQABvvvkmEokEbt26BdM0u5zUZrMJXdeh6zoKhQI6nQ729/e7IggASOfZXc+DIACgUqngyZMnaLVamJ+fl2Ktt/vQIIGkH+K7InJEdD4SeLflnbP8hB63MDOMCHRa0cOvcHAv4affnHvR6TymadJcQBAEQRAEcQmYePFFwDlvXvQY3AwqRJnJZBCJRKSjLRyYYDAoI2bc3Uvm5+ehqipyuRyFjU8pqqoiFoshGAwinU7LCCm34xsOhxGNRmUtF03TEIlEZFtykRoyam0O4vIjRIBwOIzr168DAK5fv96ViuP+OWzEhB/ubfjZ4aBaMH7zoxA43ALIMMcLnKxjkl/h4GGFn0HHcp5c9P4JgiAIgiCI8UC5LSfE7XyIqAVRYyAcDssUomg0ir29PTSbh9qRaZoy/aRcLmN1dRUAMDs7i2g0ClVVKeVoSrEsCxsbG0gmk7h58yZUVZU1OkQki+g2I66zqqpIJBIwDEPWgqG0M8IPUdMDAHZ3d6HrOkzTlO97bccdMQH0rx0i5i6RDifmtH72OKj2Va9aVsPa+Cg1TAbVRRH7jMVicpvD1oS56HvyovdPEARBEARBjIepiXyZRLwrs4ZhoFqtot1uw7Zt6LqOSCSCdDqNUqkEXdcxPz8vI2AajQYcx0Gj0UAymaRiiVNOp9PB+vo6UqmUbCntrg8k2gQLIcYt2imK0tVK96JTHYjJQzjhGxsbKJfLyGQyUkAYJvqkXwSFN83ILUr0inAZFJFxWhselH7pjnYZNjrEvU1vmtZ5c5a1ZC66Tg1BEARBEARxHBJfToGfUyO6jiiKgkgkAk3TkEwmsbm5CdM0kUwmkc/nUalUEAqFsLCwAOC5A04PytNLOBxGoVBAoVDA/v4+QqEQOp2OLLgMAIlEAo7jYHt7G/F4XNaBESvbwzjKxNVG0zTkcjlcu3ZN1p4CjgsI3tSgQdsEDusW7e3tIZ1Ow7Ksrnop3n0MEkeGseHTiATecY0qoIz7Hhv1WM5SYCXxliAIgiAIYvIg8eUUeJ0P4eysrq7KTkftdhuZTAbAYeth27axvr6OmZkZHBwcYG5uTqYFkKM93czMzGB5eRmKoiAQCKDT6SAQCMhiqLZtIxgMIhwOo9VqyeioXC53LK2Aii4TbtyRUiKFUQgO3vopbhHA7YQD6Fl0VtibiHixLOtYvZRR56dROxKdt3gy7nts1GM5S4GVxFuCIAiCIIjJg8SXMeBd8dQ0DbZtS7Gl2WwiFApBVVVZg6HdbiMSiaBSqQDAsW4ixPTR6XRQqVSwuLiIcDiMbDYL0zRh2zaKxSIAQNd1RKNRpNNpVCoV6LqOeDyOVCp1sYMnJhrbtlEqlaBpGkqlEnZ3d2EYBtLp9DFHv9lsyjbUQmDwijNukWBQ+s44RQq/uRLAsTbZw3x30hhV8DhLgZXEW4IgCIIgiMmDxJch6ffg725jCgA3b95Es9nE2toadF1HOp1Gp9NBMplEMplEp9OB4zhot9sIh8OwLOtEHUmIycI0TTSbTRwcHCCbzcraLiJSIRqNolKpyHbiQpBLpVIDHU83k+6EEuOHMSZb18fjcSwuLg4VVQI8r0clOmx5uw35pe/41ZIRdufugDSs/Ynvuu8HEbUjIm56RY30+u6kpdaQ4EEQBEEQBEH0g8SXIfE+6PutFluWhadPn2JlZQWJRAKzs7PQNA2hUAhPnz6FZVlQFEWuVqvq4ek3TROmadKD+5QTCASQz+cxMzODUqmEWCyGYrEo043y+TwSiQQsy0K5XAYAxONxVKvVgcVN3Uya00mcPe56UNFoVBbo9kPTNNltbXt7W4olQLfg4f68SGsSwozbxtxdi5rNZlcx3mHtT2zP3W3IO2bvGAZ9l1JrCIIgCIIgiGmCls3xvM1qv5aj3ran7tamwpkRkQ9idXZ2dhb5fB7xeByRSASmaWJrawsbGxuyxXAvZ4SYPoLBIHK5HBqNhuxkFY1GEYlEUKvVZCejWq2GcrkMXdfR6XSQTqelDQxqmQsMbsE7bAtdYnrodDoolUoAgFQqhXw+DwC+11m0mN7e3katVoNpmn3tRQg7zWZT2l04HIaiKAiHw11CTCKRQDabRSKRkKlCw9iZsFl3nRqBELLF8Xhtv9d3B7VgpvuAIAiCIAiCmCQo8gW9Iwm86R3e1WL3TwDI5/PSYanX61AURXa3CQaDODg4AOccyWSya/vCwaE0kulGURTMz8+jUCjISAPg0HE2DAOGYUBVVQQCAczPzyMSiQBAV3FTYTsixQTAMdsYlN5AkTGXj5mZGQQCAZTLZdy4cUOKJX7XWcxJ2WxWdlzzzi29aq+In6JWUaVSQTable+JbQxKFfLSz2YHRcWcNJ2H7gOCIAiCIAhikiDxBf5CiuM4KJVKsG0bwPFUIz+HQFVVzM3NwbZt7O7uyk43mqZhaWkJlUoFqVRK1l4wDAPVahXtdlu2IyYnYXo5ODjA7u4uZmdnoeu6XLHPZrOIx+OyDgzwPM2iUqnIej/i90ajAV3XZVqaN/1jkEhH6RiXj0AggGAwiO3tbUSjUSwsLHRd5171WBKJBOr1OlRV7ZpbvMKEn7gsOnSJ6D4v47Izd7FdYfPj4LLfB1T7iTgvVlZWsL6+ftHDIAiCIIiph8QX+K+sGoYB27ahqmrPwpRu3MUgAchiquFwGKVSCZVKRa5CiwgZ4eA4jtO1H2I64ZxjY2MDtVoN0WhUvi5EOYEQ8iqVCmzblvV+hM3F4/FjHWq8bYP7iXRU+PNyEolEuuzCfZ1FFIq3HksvAUIIHr0KfSuKgnw+L2u9+NldLzsbVRQQaVLjrnt12e+DYeYDd0oXQZyU9fV1cM5932OMnfNoCIIgCGJ6IfGlB26nxR2t4P7pdjIMw5DthHO5HFRVla2GG40GACCZTErhBeh2cGj1cvoRKUWBQOBY1Mow4p6IYBCfd6d4iPfdP4mrg7CLhYUF30K77pbNIvIF6C1A9BM83POauzvSMO2ggdHTfciuT8Yw5811LeiPC0EQBEEQxAUz1eILY4zxXssxp8TPafG+5u0IUigU5HvCsQmHw4hGo4hGo8hmsyPX7yCmB0VRcO3aNei6LlPOehU6dTvLbptQFEXWCxpkf8TVgTEmBRA/3LZxGsHDL91ymHbQw2y7F2TXJ2OY8+a6BlR1mCAIgiAI4oKZavFFCC9nKcL0w+tkuAtXAoBt2yiVSmg0Gmi32wCAVquFQqEg00wo4uXyYFkWdnZ2EI1GZUFUd6STG7dDW61W5WcpCoDwQ4gioVAIpmn2tKtetV/8PuP3nl9ElmBY27xqYsok1165ateCIAiCIAhikpk68YUxpgD4RwCaAP4YwH/hnJ/pqp7fw7X3Na8TrSgKisUibNuWBXUdx4Gu67I+TLFYRKFQ8E0jIKYPy7JQqVQQj8cBADs7O12pZd6UEPGaqqqysCmJLoQfjuPIOlLCVoRT7a03Jdrdu1PYxOeEuFIul33nHr90SwE58v6Ms6vSJAs5BEEQBEEQxOmYqqc7dljZ7bcAZAF8EMB3AcgN8b2PM8Zee+edd3Dv3j28+uqrI+232Wxie3u7y8ERD9yimKHo1CEcI5FuFI/HkU6nMTs7i0QigUgkIsP3hXNETDavvvoq7t27BwAvMcZeY4x93P2+sK+nT5/iu7/7u/HJT34SjuNgbm5OpprV63VUKhVpM+I1EcUgumD52RpxuelnX8K23nvvPXzTN30T/vN//s+ykLeg2WxidXUV29vbACA7bHlT3rxzlh9CYOnl+AuhZ9i5a9TPTyOiq9k4hNNhrtEoDJq7CIIgCIIgiPODXUC2zolhjM0D+P9wzr/m6PcnAP5nzvnPuT7TMwXp3r17/LXXXht5v/V6/ViUineF0r36LFKK6vW6dGREbY9ms4lGowHHcRAMBnH9+vWxtlclzg7G2Ouc83u93v+8z/s8/rM/+7NoNpvI5XJYXl7ualHujXxx24twdv1sjbga9LOvV155hX/605+WHbJSqZSMsqjX69je3kY8HpddtfyiJ4ZJSRqEEI4TicRQUR6jfn6aOIsolbOKfOlnW/2ydhljPbvcEFeHfnYw6O8iQRAEQRDPmarIFxymSX2EMfa1R78/BrABAIyxFcaYcha1X2KxGObn533brIoHZCGsOI6DnZ0d2U3EsizZAla0dlUUBdFoVNZvIC4Pe3t72NnZQaPRgKqqXQKLqKMhVrVFupp7ldvP1ghCURTs7OxgY2MDQHfHNQCYn5/H3NwcFEU5Fj0hhGHbtqU9CjscNSJl1CiPfp+f9qiYcUepAIMjjwiCIAiCIIjpZapCLjjnzxhj3wzAYYwlAdwF8C5j7K8A+CocpiHVxr1fb60Dv9VJ4VzU63Wsrq7K4rvCuX755ZeRSCRQLBZRq9UwNzd3LH2AmG4YY9jZ2cGjR4+gKApmZ2cBoCuKRThs7nocbseU6moQfghRd3NzU4oswHPRN5FIdM1FjuPIf8LmWq0WarXD6TEWi52oTomwTyGc9IrQ8Lar9mOctVIuAiqOTRAEQRAEQYzCRIsvRzVevhhAiHP+2wDAOf8vR+9FALwJ4G8D+O8AfIxzfmLhxR2SL1Yye61A+jkNwikRYoqIcBG1PoQQY1kWkskkgsEgHj16hGAwiHQ6fdJhExOEqqpYXl5Gp9PB8vIyQqEQWq1W12eEo+YueuqXGjKoSw3gn7ZEXE7chZuFqAIc2oSfgKcoCqrVKgzDQDabBQBks1nZ8t4rGruxbRuVSgXZbLZnSuQg4WQYYcUrXkxbsdlJF0q98wVBEARBEARxsUys+HIkvPwegF0ArzDG7gP49qPoFwZgFsCXA/gAgD/DOX/3NPsTzoKoyQL0frh2Ow3uWi+apsE0za6V6UQigVQqhWazid3dXWxvb+PFF1/E/v4+dnZ2oGkaUqnUVDgbRH86nQ4457KVuK7rSCaTvulqwjEF/LvVAMedVrdDCwDFYrFrm8TlhXOOVqslW06nUikAkPWBvPOHEHtFAXBFUaCqqqwJAxzvgiREj0qlgq2tLQDo+rx3++Jnv0jAfo6/126nPRJm0vDOFwRBEARBEMTFMrHiCw7TiIqc869jjOUA/AaATzDGvp5zXmWMlQH89wCenlZ4AbpD9UXUSi/Hwe00iJbRABCPx+WKtKip4C7Ie3BwAM45KpUKVlZWcPPmTQQCAZk2QEw3lmVhb28PsVgMrVarq8aLdzVfRKuI94UNiRQlP9vzOrSFQkH+Pm1RA8Ro2LaNYrEI0zShqiocx+l7nb2RMqNEqbgjZQRe+/LOgb0iAUfBT7Ahuz45lBZFEARBEAQxWUyy+FIC8GHG2Ec557/HGPtiAG8D+KeMsU8C+HoA38s5b/XdypAIh0IIIcM6DpqmIZfLwTAMpNNpWJYlHWp3bY9EIoF8Pi8dmlgshnw+L6NsiOlHVVW89NJLiEajUlxpNpvQdV0W0XU7ksLGNE2TQkq/FCKvQ+sW7PwcYOLyoKoqbt682TMqzy3GeOutiPd6OeFeJ90bIQP0j0oZl5PvJ9gMioYhcaY3FBFHEARBEAQxWUyy+PI2gP8E4GOMMZ1z/jpj7C8D+AEAGQA/MarwIiJbetXSEPVaRokkEOH8wGHkg9uhFtsWgk4wGEQqlUK9Xsfe3h4cxzmWlkJML8FgEIuLi2g2mwgGgwAAXddl3ZdejqSiKCNFPnlrOXhtd9jvkrM6PTDGZKqRqqpSpBN1pdyOttfO+jnhXnvo1Y66n8Bylk7+IGGHUpUIgiAIgiCIaWFixRfOucEY+ycAfgTA9zHGXgWwhEPh5T9wzq1Rt3lwcADDMORqsHA6DMNAtVqFqqrI5/MyYmHYh/p+DkI4HJa1FxzHkZ/xOjfE9ON2Qi3LgmEYyOVy0lkWnDZCwFvLQURY+UU+eG2LnNXpxT13iOvrN/e4P+cVYby47UHTNJRKJdi2LVObxHcvikHCDqXWEARBEARBENPCxIovAMA5f48x9vcBfDuAH8ZhG+nvPonwAgAzMzNd0QJup8NdnNLdPWSYh3q3gyAcX1Fnod1uw7Zt6LounfBedUCI6UY4q4qiYHV1FZubm/jQhz6EpaUl+ZlxOLK9amN4BRm//ZGzOv2I9tLA4fX1XmMxH7nrUQ1TPNwwDFiWhU6ng3w+L1MoxT4nUbSbpNQaiiojCIIgCIIg+jHR4gsAcM7XAXw/Y+zHADDO+YmLpIgwfaDb6XAXpxSvn/ShXjgpsVhMrh5HIhEpuohuJbZtA5gsR4Y4HZ1OB81mE7FYDPF4XKYCjRuvbfaq63HeKSLE2eKeW3oVZXYTDocRjUa7ImS8eGsPNZtNKIrSlUIp3nP/JI4zqQIVQRAEQRAEMRlMvPgi4Jw3B39qeLxO6Emc0kEtVt0FVoHDaIRyuYyZmRlwzs/MOScuhoODA+kc37p1C8lkUhZYdreT7ldUtx/9Vtb9BBnichEMBtFqtZDNZmVNITde+zAMA7quD21vfiK0+73ztKlpjCIhgYogCIIgCILox3Q81V4AwlkWRXrF/92IlU7DMHy/53ZYdnZ28MYbb+DBgwd49OgRTNOU6Ul+2yamj06ng729PZRKpWPXs9lsYnV1Fdvb22g2mz2vudce3L+L2kR+2ycuN5xz3L9/H0+fPsX6+jps20a1WsXW1pasJ1UqlVCtVrvmo1ERc9ZJBY9+89koc53f3DqucZwVpz13BEEQBEEQxOVmaiJfzhu/oqYAeobic84BAKVSCaurq3AcB/Pz8+CcwzAMNBoN+X/GmNwehapfHjjnqNfrCIVCaDQasi1wPp9Hs9lEKBRCPB4H0Lsmi9ceBtUmuuqI+67Xe4yxkd8D0Pe9i8C2bdRqNei6Dtu2UalUsLOzA13XpdMvCuWKeUkIAecZidFvPht2rhOCt7v21qiRMDSvEgRBEARBEJMGiS896NVFxOsExGKxLgdQpBuJ73HOYVkWHMdBNptFKpXCwcEBVlZWEI1GpZNHoerTj6qqmJubw8HBAWZnZ6EoCtLptIxwmp2dRT6fB4Aup9htU167G1SbiIAUNTVNmzjRZFyoqoqFhQXMzs4iGAwiHA7Dtm1ks1lks1nZJhrotqdxpFKOInz0S70ZNi1HFBROJBJyf6OKKZQCRBAEQRAEQUwaFB/dA3fKkLeOgjsc3nEc6LoOzjk451AUBTdv3oSmadje3sazZ8/wp3/6p3jjjTfw5ptvQlEURKNRqKratW0AMqqB0pCmE9u28d577yESicA0TViWhUqlgnA4jEQiIduOA911X9w25U1dcP8+jXUwvJxFOkiz2cTm5ia2trZkhJkQZEQUha7rQ+9zElMBG40G1tbW5LhKpRJ0XUcikYCqqtIeisUiSqVS33TIfq81m02ZGicYJd2tX+rNsGk5mqYdKyjs99pJx0EQBEEQBEEQFwFFvgzAu+LqXVEVjkmz2YSmaajX61BVFfV6HZ/97Gfx+PFj6LqOdruNdrsNVVXx/ve/H5Zl4eHDh6hWq8jlcjg4OEAqlQLnHKFQSO6PmB5s28bu7i5arRYcx8GzZ8+g6zo0TYNpmqjVarLj1kk6yVyGVArvMbgLEYvzNEzUhXhdCFqNRgPlchmzs7Oyy5ht2wiHw8hkMtje3kan08HLL7+MmZkZNBoNGIaBfD4Pxhh0XQcARKNR6LqOYrGIXC4n03gu2ol/+vQpfu7nfg7f/M3fjHw+j1arJdPYxHkTwoimaV3pR4C/7QxrT2Jeq9VqUgQ5S/yK+16GLl2XQTwlCGLyYYz9KICPAigC+DbOueF674sB/DMAFoCto/c7R+99DMBPcM5z5z9qgiCIqwGJLwPwOsZuJ0A4eMIREg/VtVoNkUgEjUYDq6urqNfryOVyqFar2NnZweLiIt555x2888478r18Po+lpSXcvHnzmONETAfhcBiLi4twHAfRaBTpdBrAYTSC4ziIx+O+q/ciBWmQY3YZUin8xMtisQjgufBRKBSkg+8WN/P5PBzHkdFEIj1FnG9d17G7u4t6vY5isYhQKIRwOIxcLidFsWg0ivn5eayurqJSqeDu3buIxWJYXV1FJBLB4uKiHKs7cuSiHf92u40/+ZM/wXd913chm81C13WoqgrbtrG+vo5QKIREIoFCoQDgeZqaW6QSrwv87MmvToyY84RANQwkNBznMoinBEFMFoyx3+Gcf7nr97sAbnHOv4wx9h0A/jqA/7frK+sAvoJz3joSab4GwK8wxhQAXwfg2fmNniAI4upB4ssA+q24GoaBcrksOxs1m02Ew2E0Gg04joPFxUVomoZSqYR2u43PfOYzqFQqqNVq2NraQrvdRj6fRyqVQjabxezsLDqdDpaWlgBARtOQ8zIdiJosuq4jHo9jbm5OFkjtdDpYXFzsGTUwjGN2GVb//aJ+hGAg0oPceIsMV6tVvPHGGzKiRVVVJJNJPHjwAK1WC5qmoVqtolqtIplM4tmzZ/i1X/s1rK2tYWtrC8vLy5ibm0Mul0MikUCj0cDS0hIcx0E6nYZlWTAMA7lcDrFYTEbi+DGO9uGjwDlHMplEKpVCKpWSAgdwWBPGXaBZCCilUgm2bSOVSvlGk3hFv142Nmrx3qsmNAwjNl0G8ZQgiInnywD8+tH/fx3AP4ZLfOGcb7k+awMQuaR/BcCvAPiecxgjQRDElYXEl1OgqioajQbS6TRs28bDhw9x+/Zt6LqON998E//xP/5H/MEf/AGazSauX7+O119/HZ/73Odw//59ZLNZqKqKnZ0dtFotMMbAOUcul0M0GkUsFuty7IjJxzRN6LqOpaUlaJqGcrkMAJidnZUpa70EtavqmCmKIgUpx3Fk1JcQNmzbhm3bUiR48803cf/+fSl6ZjIZpNNpPHz4EJVKBblcDowxPHnyBM+ePcP6+jp2dnZw69YtfOhDH8LGxgZ+//d/H1tbW131S3K5HH7kR34E165dQygUwsLCAgzDQDabRb1eR7PZRCwW6yoCK9qHi4iZs75P19bWsLi4iGg0ilarJUUWIay4xRFha7VaTaYm+eGNLOqV7jWq8HfV7PmqiKcEMY0wxiIAto9+neect1zvvQTg/wvgCwC8A+Bvcc4/fYp9/U8A/hqADwD4Rc75X+vxuRCAfwng/wwgDeARgH/IOf9112eG2paHWTw/1trRtv32fwPAXwDwo4yxGQDfAOB/AIkvBEEQZwqJL6dgc3MTT58+RavVQjgchmVZ+O3f/m0cHBzgwYMHWF9fx6NHj3BwcIDNzU0oioKDgwO8++67eO+997CwsIB4PA5d12GaJu7du4dAIABVVbGysjJSgUni4uGc4+DgQP6eyWRgmiby+bysA1QsFrvSagTedLarmLLhPgfNZhPFYhHFYhG6riOfz+Px48f4r//1vyKRSKBWq6FYLMK2bViWhXq9Ds453nvvPdy/fx9PnjyBbdtgjEFRFKytrWF9fV3u6yMf+Qi+9Eu/FLVaDfv7+/it3/otfNd3fRc++tGP4iMf+Qi+5mu+BhsbG8jlcnAcB7u7u4jFYrh9+zY6nQ7S6XRX+3BRe+asr1kmkzn22u7ubs99Dio826t9+WkjVyZRaDjL++qqiU0EMWV8DQ6FlSCAvwjglwGAMaYC+N8B/DGA/weAvwPgVxljK5xz84T72gLwIwC+EkCkz+dUHKb4/BkATwF8FYB/zRj7AOd8rd+2GGPXAPzc0a9fwBj7naP/fxuAfQDJo9+TAPa8O2aMJQB8EsC3cs4txthfBfCvOefOZe0YSBAEMSmQ+HIKFhcXsb+/j06nA13X8eDBA6ytraFarULXdTx8+LDLGfd2GhHfFa2qRavh2dlZGIYhOyIR00EkEkE6nUaj0UCtVkOn00EqlYJpml2OqIjqcNc9cf/fm85y2bFtG5VKBalUCtVqFalUCnt7ezBNE6qqot1u43Of+xweP36M3d1dGQXz1ltvIRAIYG1tDYZhoFKp4DOf+QwSiQS+7du+DZ/4xCdk5yMvosB1KpXC9evX8cILL+BTn/oUfuu3fguGYeCLvuiL0GodLo4WCgXMzs4iEAjg0aNHskBvKBSS7cPPI83Gtu2+74suSOJzw6QK9WpfPo1iwiBx5Syv0VmKTVdVjCWIMfKtAP4tgBAOBYpfPnr9IwBuAvgazvlbjLEWgD8B8H4Ar59kR5zzfwsAjLF7AJb6fE4H8IOul/53xtgTAB8CsNZvW5zzpwC+/Og9b82XPwDwD3AornwlgP/q3u+R4PSLAH6Qc/7u0csvA/ggY+xbANxhjP0E5/xvj3bkBEEQxDCQd+/DsA+7tm3j4OAAb775Jtrttiz2WS6X8ad/+qfY39/vux9d16U4Y1kWGGNIJpOYn5/H+vo6UqkUMplMVzoAMbmEw2Fcv34djx8/xrNnzxCPxxGJRGS7Y3fLaOEEAjj2f5HeMg2O72kdQ8dxsL6+jv39fSkc7Ozs4MmTJ9jZ2UG9XodpmtjY2ABjDK1WC9VqFZ/97GdRKpWwv7+PZrOJUqmEra0t/OAP/iD+0l/6S9A0DZ/4xCd67nd9fR1PnjzB9evXpUDx1//6X8e//bf/Fn/8x3+Mv/f3/h6+6qu+CoVCAV/4hV+IcDiMdruNmZkZBINB2VUpFouBMXahYoU7WsWyLHQ6HYTD4aEFgXF2FzqpPdi2jVKpJLspncSWBokrZ3WNzlocuWr1cwhinDDGCgD+HA6jWkIAfpAxVuCcF3HYDejvAnhw9PFbAPjR6+5t/FUAP+uz+Z/lnP9fxjjOFwC8fZrtcM7fZIytM8Z+H0AJh2ITGGNzAP4mDtObPgLgBxhjPwDgJznn3+sax2skvBAEQZwdJL74MOzDbjAYxOPHj/Hee+8hFArBMAwcHByg3W5jf38f3/md34l/+S//Zc/vi7ouX/EVX4HXX38d29vbePDgASKRCF566SUcHBzIgq0LCwskwEw4iqKgWq3i0aNHaDQaSCQSCIVC2NzclJ2OhD15IxK8/5+Wa31ax9AwDAQCAcTjcQQCAXQ6HczOzqJer6NSqeDZs2fY2tqS95bjONjc3MTu7i4ODg5gmiaePXuGWq2Gn/mZn8G9e/cAdEeZ+VEqlfBTP/VT0DQNL730El555RWsrKzg67/+6zE3N4ff+I3fwOc+9zncuXNHpol1Oh1kMhk0Gg3ZNQmAbCcejUa79nFeEQti2yKFSFGUY9FW58VJ7EEIcJubm0gkErh9+/aJxj5IXPEKSuO6PmctjkxjFBJBTBAfA/AuDlN8GA6jSv4KgB/nnD8C8M8BgDH2RQB+AsBPc843PNv4VRzWhPFyLKXnJDDGAgD+FYBPcs4fDPq8G3fUi+u1f+Dz2g6A//no15/vs717o+yfIAiCGA0SX3wY9mG3Uqng4cOHePbsGRhjaDQaePDgAR4/foyf/MmfxLd8y7cMFF9+6Id+CN/zPd+DVColRZs33ngDwWAQr7zyClZXV7uKahKTi+iY88EPfhC6rsv2x/v7+2i1Wmi1WtIBdF/LXv+fBk7rGIrv1et1rK6uypbIKysrmJubw7Nnz3D//n3U63VEo1EYhoH19XWYpomZmRmsra1hd3cXv/iLv4i7d+/K7f7Jn/xJ3/2+8soruHPnDu7fv4+33noL9+/fx/d///eDMYY//+f/PL7hG74Bf+Nv/A3UajXMz89D0zR0Oh187nOfQ6PRgK7r+IZv+AaUSiVYliUL1jLGpFN/3uljvVKIzpOT2IMQ4Obn55HL5U489lGjdcYlmpy1ODKJ9XMIYor4VhymEZme135c/MIY+7sA/l8AfgbAd3g3wDnfx2EtlbFz1OL55wFYAP6ns9gHQRAEMTlMx/L6OTOoSKUgHA7jzp07uH37tkwlEc7WN37jNw61r6/+6q8GcFistd1uwzAMbGxsYG9vD81mE/Pz88jn87TqOQUcHBzAsiwsLS1hdnYWABCPx3Ht2jVcu3YN0WhUiguXhWHvlUHfz+fzuHnzJuLxOHZ2drCxsYFgMIhUKoVQKIREIoFwOIzZ2Vn5M5PJwHEczM7OdgkvAGQL5l7MzMzgAx/4AL7xG78R9+7d66rNBABf+7VfCwCo1Wp47bXX8O6770JVVaTTaaiqCs45Hj9+jK2tLbRaLdi2jWazCV3XUa/Xsb29Dcdxzi197LzbXvdiWHsQ43UcB5qmIZ1O49atW0ilUuc2dpHidNrrc9p7YJK5fv06GGO+/1ZWVi56eATRF8bY+wG8AuAbAXzR0b+/hsP6Jh84+sz3APhHAP4G5/zjnPNjYZOMsb/KGOM+/37mlONjAH4aQAHA13LOO6fZnmu7P8oY+33G2K8wxjTPe1/MGPsjxtjvMsZ+8SjqRrz3McZYeRxjIAiCIPy5fE+LY+TVV1/t+Z5wHoRz2Gq1oOv6wHSHfoRCIViWhVqthpmZGTDGEIlE0G63T7XdYel3vLTfwczMzCAcDmNnZwf7+/tQVVXWrxBFUE3zeAMFtyM6Ti7qvA6Dd2yqqmJhYQG3bt1CPp+Hbdsol8uo1+t43/veh+XlZXDOEY/HMTc3h1deeUW2lR43f/iHfyj/b5omtra28PDhQ6ytrUHTNMzNzaHZbMrxAZDdq+r1uhR+ztMpF1Ecg0SnQZyXzbjHqygKfuEXfuHU52nU++infuqnLkQ0meT70sva2posWu395+4eNioXcQ7Oe59X4RingG8FsMU5/9ec8z/hnP8JgE/hMF3oWxljSwD+KYB/AeBzjLHPP/oX9WxHpB15//2Ad4eMMZUxFgYwA2CGMRY+KnLrx08CeAnAf+9uf33CbYnv3AVwi3P+ZQB+E8Bf93xkHcBXcM7/DIBVHHaCEhE4X4fD9CyCIAjijCDxpQ/9HmREd5V2u407d+7gxo0bmJ+fP1WHolAohGQyic3NTdnitl6vY29vD5VK5cTbHZbLIoJc1H4VRUGlUkGtVkO73UYsFpP1JNLpdM9V9nE5zl4m+UG819hUVUUymYSiKHj69CmazSay2axMOTJNE/F4XHZCOgvH+Y/+6I/k/znnsp5Lq9WCqqr48Ic/jBdffBGRSASJREI6+6JQcj6fx/z8/LmmiowriuO8bMY73nHsd9T76LLMO9PIVRAmrsIxTjJHYsI3A/h19+uc8wMA//Hovf/T0cv/dwCfdf275/nOPuf8cz7//ISK7wfQAvB9AL7l6P/f7xrXrzPG/iFj7DqA/xGHIs4OY6x59O+bh91WD77Mdcy/DuBLPcey5RJ6bABCrf4rAH7F9TtBEARxBjC/NqyXlaNwylGW614C8E6f9xUAgaOfQQCzACJHv6s4LO4m/vXCOfpn4/APa/toO4+O3p85ev94yMT4GXS8V32/1znnuV5vMsYqADZx2C1BXFcFh9fwAP0fapQB75+EizqvwzBobCoOz2Mch/eGCiCKw/Oo4nm9Kn70WhiH92IAz++3Xj/9EBOhuGYWDu9H8XB6gMPraBy9fnC0PfH5Dk5//XraF2OMH43lzaOXhrGpkzAt92IvRrmPpv1YR6GfbY36d3EcXMQ5OO99XoVjBAb8XSTOF8bYPwRwn3P+7xhjKQD/mnP+53w+dwOHLbe/FId/S/43AP8DgE9T0V2CIIiz40oV3B31AeGo5d65/xGi/U7nfjnn2XFtaxxc1HkdBhrbaHDOx59f5cNluRcndZ8Xud9eXITjfBWu91U4RuJiOGob/Ss+b30dDgsDJ49+T8KnIxNjLAHgkwC+lXNuHbXS/tecc+csUnkJgiCI51wp8eUEXFQML+33cu/3vJjk46OxTSZX6V68Ssc6aVyF630VjpG4AI7aRn+p33uMsT8A8A9wKK58JYD/6nlfBfCLAH6Qc/7u0csv47AI8bcAuMMY+wnO+d8+q/ETBEFcZa5U2hFBEARBEARBXFYYY/8Ih+JMCcC3cc71o2iZv4nDlPYfB/DW0cd/knP+y67vUvQUQRDEGULiC0EQBEEQBEEQBEEQxBlC3Y76wBj7OO2X9jutTPLx0dgmk6t0L16lY500rsL1vgrHOO1M0/misRIEQVwOSHzpz0X9AaH9Xu79nheTfHw0tsnkKt2LV+lYJ42rcL2vwjFOO9N0vmisBEEQl4ArVXA3m83ylZWVoT+vaRru3bs3cl6W4zg4ODjAzMwMFGV0feuk+z0ttN/+vP7665V+nUFGta+TMqx9XdR5HQYa23H62Vc2m+WLi4snnlOGZVruxWnd50Xtd5Btnce85eayX2/HcRCJRPDKK6/ws7xfvZBtjcYk/x3ychXHOuiZiyAIYhq5UuLLysoKXnvttaE/f+/evZE+L3AcB4ZhQNO0EzlKJ93vaaH99ocxtt7v/VHt66QMa18XdV6HgcZ2nH72tbKygt/5nd858ZwyLNNyL07rPi9qv4Ns6yqc+/Pcp+M4cn/nKb6QbY3GJP8d8nIVxzromYsgCGIauVLiy6h8/OMni5xUFAWxWOzc93taaL/TwbD2NcnHR2MbndPMKcNyle7Fq3Ssk8Zlv96KouA7vuM7zlV4Aci2RmWazheNlSAI4nJwpbod3bt3j0/LygExeTDGXu/XgpHsizgN/eyLbIs4DWRbxFlBtkWcFYOeuQiCIKYRKrh7TjiOg3q9jnq9DsdxLno4xATiOA6azaa0D+/vxNWG5g+CmDz6zdM0hxMEQRAE4YbSjs4JwzBQLBYBnD4tibicGIaBer0O4DDFxPs7cXVxHIfmD4KYQPrN0zSHEwRBEAThhsSXc0LTNBQKBfl/gvAi7KLXT+LqoigKzR8EMYH0m6dpDicIgiAIwg2JL+eEoihIJBLy99N2RCIuH96IBu/vZDNXG0VR6NoTxITRLxLN7z2axwmCIAji6kJ/+S+IZrOJ7e1tNJvNix4KMSWIEPZms0l1BK4YBwcHNF8QxBQj6r80m03U63UYhnHRQyIIgiAI4pwh8YUgpgRN02T0FD28EwRBTA/u+i+JRIJSkQiCIAjiCkJpRxdELBaTaQQEMQwihN1xHLKdK8bMzAzm5+fpmhPElOKu/0LpRgRBEARxNaEngDNgmNaTwHMBhrh6+NnIsG1JhQhDtnN1ODg4oDQzgpgARmkf7f4szdsEQRAEQdBTwBkgwov90kIMw0C1WkWpVCJn6grjV7+ln90QV5tOp4PV1VWq+UIQF8wo9dpoTicIgiAIwg2JL2eAqM3Rq/WkqqqwbZseyK4wfvVb+tkNcbWZmZlBJBK56GEQBDECNKefPysrK2CM+f5bWVm56OERBEEQVxyq+XIGDGovmc/n5f+Jy0u/lqJ+9Vv6tSwlrjaKomB+fp7sgyAuGE3TEI/Hh/r7TXP6+bO+vg7Oue97jLFzHg1BEARBdEORL2eIO9/bHX5Mud+Xm1FaipItEMMgRDq3nYxSe4IgiPFgmiYcx4Fpmhc9lKGhuYIgCIIgJgPy+HD6B5NexVNLpRKq1eqxlBJ6ELrc9Gsp6jgO6vU66vW6vP6nsQeypauBoiio1+uwbRvA8fllXJA9Eadhmuxn2LF6PzeNqUTD1J6ZpmtHEARBENPK1KYdMcZUzrk9jm25neWThAj7fd8wDNi2DVVVZUqJpmldBVZPuj9icnEcB47jIBaLddmCsAHDMFAsFgE8j3o5jf2d1naJ6cBxHDx69Ai6ruPWrVvH5pdxQfZEnIZpsp9hx+r9nDeVqF966Vkyyn7dba57MU3XjiAIgiCmlakTXxhjCoA/APCPAfx71+uM90r0HcAwDyajft/7mhBcisUiHMdBMpmcqpUzYjhKpRJs20YqlYKiKDL1CDh8oNU0DYVCAcBxGzmJPZzWdonpQKQc1Wo1NJtN6RyN2+EjeyJOwzTZz7BjHfS5ixItRtnvMILRNF07giAIgphWpirt6Eh4+V0A/wfn/N8zxm4zxhKDhBfG2McZY6+98847uHfvHl599dWu98+i7ob7YadUKmFvbw/NZhO5XA7z8/PI5/NU52NKePXVV3Hv3j0AeIkx9hpj7OPu94V93b9/H3/uz/05/MIv/IKMgAmHw1AUBeFwGMChXSQSCSQSibFcf6oZM/30sy9hWw8fPsR3fud34t/9u38nbessUgTIni4Xw9hWr7+LJ2Ga7GeYsQ4TXTJsGtK479nTpD/5pSG5n1mGGeegv4sEQRAEQRyHnTBY5EJgjH07gG8E8C0AfgnAAoA1AJ8A8MuDIl/u3bvHX3vttbGPS0Q3JBKJYytQzWYTe3t72NvbQyAQQDKZlJ+bhgdU4jmMsdc55/d6vf+hD32I/9Iv/RICgQDa7Tbm5+dlrQ4/2xD0sx/i6tDPvu7du8d/9Vd/FY1GA4VCAbu7u9jf38fS0lJX97STzCkXlTZBnB+DbOss/i5OE0IYAdD1t3mYuXnY++ek8/xZ3J/9tjnqOCfNthhjfbsdTdMz71Vn0DMXQRDENDJtT9p/ACAO4OcA/DSALwewikMx5sJiZfutQAWDQVSrVczMzKBWq2FnZwerq6uoVqvY2dmBZVmXsside5XvqhTys20bT548wdOnT9FqteA4DizLgq7rCAaDPb83ajHmy3Q+ex3LZTrGcWBZFt544w2EQiEoioJAIIDZ2Vlks9mhimn247TfPy+myVYmcUxEb0QtrmKxKO8Dd/0uMTd7i6WL7w5TzNa9Lffrbjvxs5t+2+81pkH0i/qZxoLCBEEQBDEtTFXNF875O4yx78Oh8PI5zvkWY+x7AfwegGsA3rmIcYkHGfHgJFaTHMfBkydPsLGxgRs3buDFF1/Es2fP8N5772Fvbw/xeBy6riMSichWspdl9dmdjw6gb276ZVl5b7fb4JwjGAwilUqhWq3i4cOHCIVCiMfjPSMUvOHeg/L4L1NhxF7HMugYL4vNDIthGPjN3/xNJBIJXLt2rWuucNdq8J6XYdMm3D/HdW7HfY2GtZWzHL/7NbFvv/1cpnt0UjjLe17TNORyORiGgWAwKAUQUV/JMAw4joPt7W20Wi3cvHkTiURCftf90w/DMNBsNrvSTUXHMtHBTOynWq2iXq93iT6xWAzhcFjalKZpsuW1t4D7ac+Ttz4MQRAEQRDjY6LFl6MaL38XhzVe3gUAzvlvM8b+O875fcbYRwC8jMPjqFzcSA+p1+tYW1vDysoKUqkUDMNAo9FAu90GcOhYv/XWW3jw4AFu376N973vfVhcXIRt23IFC7gcD+v9ihB7uSyOimEYePjwoXwwVlUVtm0jn893RSgIoS0cDsM0zS5H2W911MtlKozY61gmtcjlRcE5R6vVQqvVOuYc9RPv3OdJ07SewoH4vp9DeFLGdY2EMynqJg2ylXHtt1cXu2FE5XHeo5MkNI5jLCcRCIHx3vN++xRixt7enpyHE4mE/NssPhsKhbq25b1//I7Fzx78OpaJjoi1Wg26riMej8NxHCQSCZimKYUW8XosFjtWwF0IOM1mk2rLEQRBEMSEMdHiCw7Tiv4pgBxj7FOc87cB4Eh4+SYAPwngIYBv45yXx7VT98O+2zl2v+d9uHIcB+VyWYYHx2IxVKtVpNNpcM5RLBZxcHCAWq2GbDaLO3fuYG5uDnt7e9LZdq9iTzteB7Hfw/JlERM455iZmUG5XMa1a9eQTqcRDofxwgsvwDRN6TyKh3l3WpZwQvxWR732Nkkrk2e1yjroGC+LzQxLMBjESy+9hHg8LsXaZ8+eSfFWzFVegcL9cxjhQDh+8Xgc4XAYzWbTdx4cBr+InGAwiL29PWSzWajqcH9+BjndXlsZl20M6mIn7lcRKdHrHj3tPXLeQmO/8RqGgb29PRSLRVy/fn3oa+jerre+ileU7iXKjOu6+gmMbrvPZrPHRHHxMxKJQFXVntF4vYRLv/nMfTzu+T8cDsv7WHxXfNYttPS6J4WAY9s2ms1m1/eHidrqd94mRQQkCIIgiGllYsUXxhjDYT2XTQBfDIAzxv5XzvmTo4/8ZwA5ABHOeWOc+xYrR+12G4FAoGsFqdeDonjYKRQKCAaDeO211/DZz34W0WgUS0tLaLVaqNfr2Nvbw61bt7C0tIR2u41KpYJ4PI7FxcWulemr9HAzSWLCabBtG2+++Sbu3LmDzc1NvPvuuygUClBVFel0GgDQarWQyWSk4CZWW0WYuTfXvtlsolgsolAoSEdlkuzjoiJQLovNDEsoFMIrr7yCdruN1157DclkEk+fPkWj0UAkEoGiKIhEIkilUl1OvxD4vO3OBV5H1nEc6LqOaDQqUyXcNSgGpTV43/NG5LRaLdRqNQDA3NzcUMcuhA63UOm3L8FJbMOv4KrfdrzH1Gg0umpt+O231z0ybFpTr4iffufgNPS7p8PhMKrVKkzTRCQSkfPVMBEsYrvife9xee1UzH25XE5ur1edEu958yue6xZ/RMSJEBiFXcVisWPiijudp5845BfJ0u+ciO3ato1SqYRwOIxy+XANaX5+HgBk4VvxXZHqJI7Nm+ostitSXEUkT7FYxOzsLHZ3d1EoFGQhePc1HrToNK6IOIIgCIK4ykys+HLUuWiNMfb/A7AF4KsAKIyxFoAygPcB+PvjFl6A5ytHjuOg0+nIByu34yIeFN2rYqqqIhqN4unTp/jTP/1TPHjwAJlMBuFwGJlMBqurq6jVanK10LZtKIqCeDx+bGWaHm6mj4ODA2xtbclrLh6KZ2Zm0G638fjxY2xubuLOnTt44YUXEIvF5MO3eEDvtZI8qQ+/Vy0C5aJgjGF2dhabm5tSvBB1hNbW1pBMJuU8IhBFRHVdBwBEo1HZgQvoHUWiKAoajYZ0DsPhMPb29qSz7J2nbNtGpVKREQN+c5gYVzabRTQaRTqdPuY09kKMqV6vd4kf/eZLPzGlH+Jcif2Ncn+J+7bXPdDrHjlNWhMw3jnBLRKIcbrnMHdqTiqVQqfTgaZpPVPc/EQ6999Ox3Fgmqa8Nl5xw3uedF2H4zhIJpO+qTTe8+ZXB2VnZ0d2C0ulUl1/c2OxGObn5/vOY2Js4pjcwrhbNHefr16LNW4qlQq2trYwNzd3LIXIT3Tsddy9BKNyuYz9/f0ukdBrk7ZtY319HYFAAMFgsEtsFfsRzyv9xkMQBEEQRH8mVnw5inyZATCHw7bS/wnAbwBoA/hRAD/OOW+Pe7/igVE4Eu5VIOD4Kpg7amF+fh6qqmJrawsLCwsy2uWzn/0sMpmMrNuws7OD/f19ZDKZrodJd5j/sOMc14onhRSfHkVRwBiT9pDP53Hr1i2oqopQKIREIgFd1xEKhbC/vw9N02Quvzj/ArdTIuzMsix0Oh3fNIeTQsUZpwPOOd59913Mzc1B13UcHBzAtm2sra0hHo8jGAwim812rVTbto1IJIJMJnPs9WazCcMwkM/nARw6gCI6a2VlRUZ1aJqGvb092LYtnWWv41apVLCxsQFd13H9+vWu9wRuO5mbm/MtLN1PMHHPjYNqwACjiynhcBjRaLRLfBD0ukfEGAfdO+6/Ge77dlBak/c1v2P0Rlp48c4rvRDCxPz8vGwx3Gw2u2qHiO25o6u89UrcP921R9ypPO6xCSfffT0F4vyGw2EZheUWqt1RGsIuhBAlomXckS2NRuNYzSQ/wcR77vzEFD96pRaJZwRd12XUiXjPNE2k02k4jgNN03wjdYTo4WfDYvuWZWFrawv5fL5LwFcUBdevX0ckEkE4HD42D+zs7CCfz6NUKmFzcxPz8/NYWFg4dq3cYpA7lWkY2yIIgiAI4jkTK74cRb7YjLFfBvDVAD4A4C0ATQC/dBYRL2Il0bIsBINBKYr0WiEWq18iggEAHj9+LNtVcs5RrVZRKpXw4MEDAIdpJzMzM3jf+96HTCYjVwA1TUOpVJIOjzu82DtGv7z503JWUTdXSdQRdQOEuLa9vY2ZmRkYhiHrvwjnVBRhBrofbP26aYjPGIYBRVFQqVRkBNWNGzegquqJz/NZR1tdpet/lrTbbfzu7/4uQqGQdJADgQAODg7k6nupVMLc3JyM1CsWi2i1WlhZWelKjSyXy6jVatKeAGBjYwOlUgnpdLorzUHMNYqiwLZtVKvVY6k36XQauq4jEAhIgWYQ3topYj9uwcQdZSAEStM0AfSPCBHb9UYReHHPoQJVVftGVQyq49GPYdOPTlIrq9e95dpnz5vPcRzUajXs7u4iEonI/bprh7ivj1tA6FXXxi2s1Go1+X/3okW5XJbXVIhI4nPeFFwhCLmFF3fdLODwegj7EVEsIjIokUjIVJ5hr6Hf9fLarRA+t7a25N9tbwqcoijQdR2tVgvNZhOVSgWRSATJZBKO48iomdXVVeTzeSwsLPRMY3KfayE8AcDTp0+7BB4h9omItFgshmKxiGg0Ko9ta2sLlUoFd+/elWPP5XJdaVfeiCjxvOOJ1KWJnSAIgiCGZGLFFxdvAPgRAL/POf/LjDGFc+4M+tKouIUXb6qR32fdAoh44BIPqoFAAPF4HKZpym4J29vbqNVqUBQF8/Pz4Jzj8ePHuHHjhtyOWJnrhztM2lsf5DScJH1kGMf6KqVSicKni4uLKBaL6HQ6KJVK4JzDNE288MILSCaTALoFF/fKeDwe79qm+/yJPH4R8WBZFhRFwY0bN058ns8ybWhSU6WmFc45bNtGu93Gzs6OjLRTFAVra2vS0ctms3AcB4FAAOvr64jH4ygWi4jH4+CcI5FISGE5m80CAHRdx8zMjEzvaTQaMipPRB0IMcftwAmh+vr1612r5d65wZ2aJMbsrQfj7dzitulRIkIASIe9H+7oGHekhJdB98iwAqNfZIhXQBjlHhlG/HGNueffTEVRkEwm0Wq1UKlU0G635bbF9RcpRiL9B+g+bu/8I2wGgKyL465jImoLCbFHVdWu6Bi/+cwvYkUUcBZRHV77EeKFX+rZoOvWK/1KbFsITJubm9jc3ISiKLh27RpUVe2KQNE0TR67u6aSGLOmadjZ2UGlctiwUUQWee1FjEHci+I+1DQN2WwWkUgEtm2jWCzK55Gtra2ubYgUuXA4LAt3i6ibubk5eX7F/SqOXUSdlcvlrpbcR+dt7M9jBEEQBHFZmXjxhXP+hDH2lZzzraOX2FnsRzyoBYNBLCwsdD1ciffFSpMQS7wCiHjIikajePLkCeLxOOr1OjjnyGazsCwLBwcHaDQaePPNN8EYQzKZRKlUQjQa7VpR7JVTPcxq56icZdSEn+PiV1TyskRGbG1tIZVKoVqt4tq1a/IhO5VKdR2fZVnSQXFHTnkf+P1SK2KxGF5++WWUy2UEAoGuczmqiNLPgTtt1MowaREnwevIXwU450in01heXpYtzPf29vC+970PrVYLkUgErVYLu7u7siZMo9HA7u6utKFOp4NQKIROp4OFhQVYlgXg8Dq5U5M2Nzexu7uLeDwuX2s0GsfmKMuyUK1WkUqluiJevPU1VFVFvV7Hzs4OHMeRn3OnlQjH0n09vXPdsBEhgwrZitfcqUYiGsMvcmfQPTKswOiN5nHf6940j3HgnWf7IRxvkV4mbMZ97IpyWNTZMAwkEoljxcCB4/O9sBFd1zE7O4tsNivFpkgkIgvcqqqK9fV1LC8vy/M0qN4JgK6IKDGPilQYv7+V7qiRUqmEWq0mRUZvhyMhHrmFpHA4LGujCJFECBmNRgNra2swTRN3797FwsKCPG9CCBTHUq/XUSwWZQ2mfD6Pl19+WaYDuaONxPkql8soFApSwMlkMl31Wdw1nxzHkWmEQmCNx+PynhMph61WS9Z3EuczFot11aER840YT7lcll2fjvY9/X+4CYIgCOKcmArPRQgvjDHGOT84i314H9TEA5J4KHGvULpFF78Q4729PaRSKdy6dQu2bePJkycIBoNQVRWNRgNbW1uo1+tQVRWf/vSnZfjxl33ZlyGRSGB7exvxeFymEAjOKoXjLKMm/ByXUYpKThOMMTQaDayuroIxhk6nA9M0kclk0G635Sq7cEJE+pCu6/J6im4XwgEAnp8XESHgOI5MOXI7DL26X5yU00YtnYVQCDwvUAkM3zFn2qnX69jd3cX8/DwKhQIWFxfR6XSk7XDOwTnHxsYGbNtGLBZDKpVCJpPB7du30Wq1kEwmsbGxAUVR8OzZM4RCISiKglKphEgkIjuuiSgIYKheFQABAABJREFUXdelI+tN/RB1OBKJBDqdTlfEiGEYMorP7QgvLCwgHA7L+0BsVzjYXuFjmMiOQXU5/ArZuiMXgOd1S9x1WIbdn1tgHNSa20+ocQtR46ZffRIv4vqK6BZxbcW4xTEnk8kuR1x023Ifi9ieiJaZn5+XNYlM0+wqniuiRt977z28/fbbcBwHd+7ckX9LRc0YsX3vNfYugHivfa+/PSLSdH9/X+7LLfSIFCsRjShElvX1dVQqFVnDS1EUmKaJ+fl5zM/PY29vDxsbGwgGg13jENsU561UKskxCdtPJBIoFosoFovY3d2Fbdsy0kxEuYhzG41GZUSPO3JW7Es8u8zNzXXZnTttT4yp3W4fSxcTgo34KWpCKYoCy7K6IpVAkS8EQRAEMTRTIb4IjurAjAXvQ7T3Qd9PWPA+VItCesDzByzbtrG/v492u43bt2/Lh72nT58iGAxidnYWzWZTOjibm5syXWVzc1Ouarm36Y24Ee+Ni7OImhhmf8KBc4dYA9NbJ4QxJkPKk8kk0uk02u02LMtCq9WCbdvQNE0+SKfTaTQaDczOzsJxnK6V+Fgsdmw13F08spcdGIYhW4tev35dOognOZenTUk6qX248bMFr2Mw7PemnWw2i0KhAF3X0el0EI1Gpb1lMhkUi0Xs7e1hZ2cHy8vLCIfD0jkLhUKyuGetVkOpVML169dlMehoNCoL8YrzGg6HZYRWLpeTwos7Dc7t1LmjHwqFAgzDkBF/mqYhlUp1ObduMdud8jPstXM7lUJIEfsXP92RDuKY3FE5bhG9Xx0Wgde5d++rXxqRO601GAweu6f8aqa4oy17RQn2iyI86f0r/q65I07EfJNOp7G+vt5VBFfULXHXZBHzjuM4yGQyMs1N2IY4N2L74jOZTKbrOoqIqWg0isXFRWnPQsBy70tEoACH6UjValVGeLg/I+xA0zSZQqMohx2+gsEg9vf3kcvlkMvlUCwWsb+/L8+LZVkIBAKwLAurq6uyNpdIf0okEigUCjBNE0+fPgUA3Lx5U6YBiXMq0q329vawvb0t28TncjmUy2XMzMwgEonISDORoiTOu/g7ATyfZ4WoJWxPFGYX58vbAluI+Lu7u7AsC7VaTQqysVhMCttiG5ZldaWhjWN+JwiCIIirxlSJL+Nk0Kp+rzB3b2i9eDAUOd3FYhGrq6uwLAs7OzvY3t7GH/7hH2JzcxPJZFL+EyvWotZCu93Gw4cP0W63u4qt9oq48eOkDud5P0SJ/Ylccm+RwmmtE3NwcIBarYZWq4WXX35ZHl8sFpMrlW+//TZ2d3ehqiparZasAxQKhVAulxEKhY4V/XSvmIqH90Qi4dsKVtM06TBEo1HEYrETn8tJeLj2swVVVQdGvEyrDfXi4OAA29vbCAaDiMViSCaTMtql2Wxid3cX5XJZdiYSjlqxWJSpCcI2S6USKpUKZmZmkMlkZHqRiEhRVRWBQAD7+/uyMO/t27dle17gudObz+fl/OR21oVYbFmWbxoPAOn8eVN+DMPo6rLTay5zR50A6Cq2Kpx0d8FV4YC7u94I0cQrrPtFyoiOOm7RxX2P9Eoj8gov3mPyi4hxCzlCFBP1PsRnxDnoFUV4kvtX2FatVkOlUkE0GkUulwMAWXD5yZMnKBaLXQsXAHxbL/vVh/HW4hF2cPfuXQSDQbz33nvIZDJd9ibSN4WtbW9vY39/XwqEolabsH0AWFtbk+cUQFc3JwAy0qdSqaBer0ubFccktiOKmzuOI1P8RLcwcc52dnawsbGBeDyOeDzeJSSKwsIAkEwmZVeyra0tPHz4EMFgEIuLi7LLUCQSkddOXEdx3JVKRUaqCcFL2J6oF+aNnhRRcO6/E2KbrVYL5XIZz549QzAYxPz8/LF7uV/6H0EQBEEQo3FlxRfvqmCvlcdeQoZ4iAcgH3qKxaLsNrC+vi5DdAuFAjjn8vdWq4V2u41YLIY7d+7g+vXriEajqNfrCIVCXWN0r3a5X/NjWIdzUqICeuX1nzbi4qIQD6bCUXj06JFcybUsS4Zz53I5OI6DmZkZzM7OYnl5GXt7e9IJESu+a2trWFlZkTUzhPgmnLtmsykLILrPXSaTQSgUQjgc7tuOd5LxRiyMOv5ptaFeBAIBJBIJmQ7SbrdlpFw0GsXS0hKWlpZw9+5dPHr0CMlkEru7u3j27BmWl5fxvve9T95vS0tLWFtbQ7VaRblcRjwel05rJBKR0XflchlLS0uYm5tDOp2WAoBfeptIYxDCQa9aP+50Ebdz7Y3UcIsO4j33vCzSOsQ9Ic6Du5aKGIfbefV2vfFGq4h5yG13Qgxqt9sIhUJIpVIAcEz4dDum7vnXXU/MT0zyni8hcgCH0U5CUAPQJWr43R9+8+kwdTncx5LP5+U4hCCxs7Mjx5NOp2X9HzF+sU+RhqRpWleKixCWxblx/w0SETSJRAKbm5t4++23kc/n8Xmf93lQFAXxeByJRAKmacpuW8ChmCLqj1SrVSQSCblvEV0qoozE32bv38d6vY52uw3OOWZnZ2W3H+BQRCsUClLEBoBSqYRqtSqFHHFuUqkUdF2HZVnQdR2apiGfz6PRaMCyLJliJcSUfD6PYDAoI2Ci0agU7ERHMneLaXF9Rft4b30X0SkxGAx2RU+60+vcCCFrcXERkUgEwWBQRqh5W0mLbXkR15AgCIIgiOG5suKL+0G538ojcLztpXhNPMSHw2FUKhUkk0n5wBkMBlEul2XoczqdRjKZRL1eh23b2NnZQaFQwP7+Pvb39/GBD3xAFqcUK7NiBVeM1+3g+OF1OHsVJp2EqAD3g5t42HMf6zSurjnOYYcZTdOwu7uLg4MDXLt2TRY+FE7N7OwsdnZ2MDc3h4WFBezt7SGdTsuHX+Bw9VI4heKh3e0AuFd4DcOQqWrCkRatrN1tQ71jHacAN+7tCYdXdA4ZdZvTakO9mJmZkTV/Wq0Wrl+/jng8jmQyib29PTx79gxf+IVfCMMwYBiGrCEhnKx6vd4VYWZZFnZ3d6WTtb+/L+tYAIc1HiqVCnK5nCxOCnS3Chb2KdKU3JEfoiYE0B2N4V1FF7jnWPe2AMgoGFEnSdj3/v4+ZmdnZfHXUqkk22qLVAtxfGKOEePwCr3u1E6/9sJCnBECid8c2use8Dtm92e975dKJaytrXWJreKzYh4X19S9f8D/78TRPDvTy7b8jll8z93dSLzf6XQQCASgqqr8jKhNIqI5K5WKjLoQBVvb7TZUVcXOzo48Jnd6lqZpWFxcRK1Wk/VFtre3kclk5DkX1zYejyOXy8nCr6lUSs51IiUok8lI8VukwYmIHpEKpOu6LDgbCoUQi8Wwt7cH0zRRLpfl94XQFY1GUS6X0Wg0kM/nuwpc37hxA9VqFbu7uzJKRkRYJRKJrogfRVEQDAaRTCa77Ac4jBoThYDdQpqwQ/Fc4J7/xTODsGFxroDuSBixL9Heu1qtdtWW86YF1ut1KXK69wlALhCACu4SBEEQxNBcWfHFjd9KrV8uP9Dd9lKsuD558gT7+/sIBoN48uQJ1tfXoes6QqGQXNULh8NYWlpCKpXCzs4OVFVFOp2GaZo4ODhAIpFAMpnEzMwMDg4OpNMjViSHWcn3OpyVSgUbGxvQdR03btw408iSUZ3vXgUTpxnOORhj0nEQq5mmaSKXy8kUtWq1iu3tbQCHAtmDBw9w48YNWWiy2WwiFArJGgd+7czdBQ+9kVHC8enXZWjcUVJie25H4TQijDv6QdQFusqINtDA4X3+7rvvIhqN4sUXX0Sn00GlUsHa2hoikYi0wevXr8sUibW1NelwilX597///XJ7It1NCM/ChgHg0aNHiMVisnuLuG+FU+0tlGuaJizLwvr6OjKZzDHnz0/Mdtuj4zhdXXTE/CyEFyHIAZDdvsT3y+WyjBAS+xX3QKPR6Iom8Nq2eM0dhSPGLWpdiHvNL+JE1JJxp7a4j9tNv8Kw6XQahUJBOv7iOLx1ddw/BX6vH/2/Z6F6UUpNpFWJLkCNRgOmaSIUCslUGUVREAgEZIpPrVZDNBqVc7h7fCKSQ6TlNBoNPHv2DI1GQ3ZXEqKW6AZn2zbi8Tg6nQ4ePXok50mRWqSqqtyXsD93QVshwm1ubsI0Tdk2++bNm8jn81KkFmOcn59HJpPBxsYGEomEjDIqlUp4/PgxPvKRj0DTNFknKRKJ4KWXXkK73UYmk5GF80Vq3+7urqzhUigUZFSi3/zV6xkjHA5jYWGhSzQU94FIY3WnyoXDYbkQpGkaSqUStra25HG722+79+2dX92Ci67ryOVysmaZSJN2C0VigQAkvhAEQRDE0JD4Av+VSb9cfm+KklgRNk0TyWQSi4uLCAQCcoVWPKBEo1HMzMxA0zSUy2XUajXMzc0hmUzK1UBRFFO0gxURNMViEfF4HPl8Xj4ceVegepFOp1EqlTAzM9PlwJ5FVMCo0TTuFWf3Q+VFpkGdFvdq5r1792T0iVjFtm0bjDGEQiFUq1U8evRIOo7b29vy+i8vL8vUBuFkuG1QrJC6r6Pb2fMWRfTD28q6F8OKKu57Y1xRVV5H9yrDOcfu7i6uXbsm29ULp29paUnOIevr67hz5w5mZ2ehaRp0XUe73ca1a9cAdEehiO4q4v8AuopzZrNZ2LYtHch0Oi1rtLiviZ8AIOoOCSfU/TnvXCFW991piAK38CGKmgohQKTviYLl8/PzUvDWtOeFXQHIuh6ioxNwvEaJ2J9fWpV4vdls+kZkiaiK/f19FAoFWJYlU76CweCx69lPALcsC5FIBJZlIZFIIJfLodlswrIsKXz1msN7jR99OtK02200Gg15DoQAEo1GUSqVsLe3J7etaZqMfDFNUy5CCEHAG1XkTlsR7ZHj8Tiy2ayM2HCn0ITDYYRCIbRaLdy8eRPRaFQWqd/b25PRKcI+d3Z2UK/XZavoWCyGdDqNSCQii8e2Wi1ZLyUQCMg5KhgMyvlFRIeKv5nuGkrZbLar9bY4LiFwi7/zlmV1LeKIa+EWhtxCuVt0E7bvFqOCwSB2d3ePRZZZloWnT5/Kgu5ukdK9T7dNeOdt933lvi+LxaKMBDJNU9ajKxQKXbZqGIZcIADwvLAOQRAEQRB9IfEFg8UI7/vuGgCi0N/y8rIMh261WuCcyzDqRqOBRqOBZrMpV/7a7TZSqRQMw8ALL7wgV5dyuRx2d3eh6zri8Th0XYeu67BtGxsbG5idncXi4uJQ0QXi4b1er8tV67Ni1GgatzMjWnomk8kTpZhMCkKU293dxTvvvIOPfvSjMl0jn89jeXkZm5ub0mGZmZmRYp3jOHjw4AEMw0Cr1cLKysqxdCzgeTvzQSloYjzuAp3u8+oNT3c/gHvT68S2+qXh9YscOAnuyAVvZMWk1Cw6T0StFrEa3Wg0cP36ddy8eVN2K3n69CkM47DDkOiutb29LVv9ivMl6koUi0VYliXtTKRrdDodtFotaNphe2hVVbG2toZ4PI6FhQV533qLZbuvi4i6EY631wEVoolIw3Q7k7lcDvPz89KG3HOFKMwLPK/XIaJZcrmcjAQQ95gY18rKSld7XfFTzK1CqACe1zDx6yjWKyJL0w7bMIv789mzZ3j06BEA4NatW8euZ797170P0zRlcW5RANcbgeB3L4wihiuKIovEippCblFXnAvRglrUHRGChxivN1rJfQ7FNYnFYl1ilFcsMAwDa2tr2N3dRTQaxa1bt+A4h4WbZ2ZmsLm5iUKhICN01tbW4DiOrMcjhA8hHC0tLXVF6YhUo2KxiI2NDVm82x1JWCqVcHBwgJdfflnOw+5zKCLJxP4ikYis81UoFKQduOdLkSoq0ordiBRAUf9FCDqVSkV2WhKRq7FYDFtbW1IQF53z3CI5cNhhSbzuTV1yz9nuuVwIlplMBqZpIpvN4tmzZ7BtG5ZlyXtGCFZigYAgCIIgiOEh8WVIvDn6whEQxeqKxSKWl5dl61bRYrVUKmF/fx+maSIej2N+fh6MMbTbbZRKJRQKBYRCIWxtbaFcLuP27dvSESoUCpibm5P53O7OHsM8WGuahoODA1iWhb29vYEdYvyOdVjn9qTRNOKBT6zslkqlcxdgxuXMu4sqVyoVvP3227h16xZCoRCWl5fBOcfW1hYODg6QzWYRiURgmiYYY9JRzWaz0ml0RwW4xzUoasVdRFQ86Huvj5+oAuBYRIJ7ZdT9sN7L/sYVVeWOXPAKOcM4lmcp0FyE+CNSQzKZjFyh3tjYwMOHD9HpdLC/v49EIiHb9Yq2sYFAQAoUwPPzJVJLgsEgarWarP/S6XTQ6XS6nGXxXdHS2S2uiRV74ZSLSL25uTkp/LrTmcR1U5Tn3ZUASBEGACKRCBYWFo6JOsFgUEaXiaiLmZkZOI4jo3NEepJ7DhFCzO7uLvb396XD7U0NdI+lVzqkiBjwFuhVFEUes6Zp8t5cXl4e+Vr7RSUIwcErbPa6F0YRw4PBIObm5roiEIVoIu53kS4mOveI6BO/+08IMbOzs9B1HeFwWKZaimsrRAIRUeEWv8T1bjQaXQWERaSXKDa+tbUF27YxPz8vi5aL6y86egmbEHVZVlZWuto5c867UnpEyh0A2XVQCBhikcCdphcKhRCPxxEMBrG2tiajsoSw5xYaRcSYuLccx8Ha2hqKxSK2trZw69YtvPDCC13nI5lMSoFU/F3I5/NotVqYmZmRLbPdQqD7s+K8Af7PDGIhSXQWE/WTROqgEFHFcXjvYUo7IgiCIIjRIPFlCPwK8rofjp88eYLV1VUoioIbN27IXPLHjx8jHA4jEAiAc45QKCRXLkWHhUQiAV3XEQgEABw+0Ig6DaJIJgBZUFAIPMNGF2QyGUQika5uJYMcxnEV5B3GSRWOkaZpWF9fl/s/zxof4zrecDgsU4YYY9B1He+++y4WFxfxx3/8x/joRz+KxcVFxONxmTridvoYY7hx44aMUhDdPYRDJFaIRdSKOyLEr4CneNB3p+64P9crUqVXesigehPjxB250Ov43I7/aVb+R+Ust92Lg4MDWWR2YWEB8/PzUFUVm5ubCAQCUrQtFAqyoPedO3ewsLAgI1pEaoVIo1hYWEA2m8X6+rp0OnO5HGZmZlCtVnHt2jXpiIu0Ebc4KpxEIZxWq9WuOVI42N76KG5BATh0/lutFhhjaDQaMsLCXTDWHbUiOoNpmoZWq9UV8dBsNlGr1aBp3S2NDcNAIBDA7OysTOMQ95O3NbQYs/vedOOOPhO/u9NMxDH5RbwMi1fE9LZnFvS6F0cRQcXxuMUPIYzF4/EuAbfRaGB2dvbYtsW5FNcAAPb392WXIVFDKBwOw7ZtrK+vIxAIyLpn7vvp9u3bXYWFy+Uy9vf3ZaquuO/FHCEiu7LZLEqlkkwfCoVCiEQi6HQ6sCwLtVpNphtdv34dlmXJ+VHY+dbWlhQPvWJWoVBAvV6XoqXYhqZp2NraQrFYRCAQQDKZRLlclscuOta59yVsORKJYH5+HoZhYGlpSdYycl8PEd0jUFUV169fR6VSQTqdlrWQ3O2kg8EgKpVKV0qh3zODO8pKRMAJkVOIRIpyWGvMrybeET1T2giCIAiC6IbElyHwK8jrfrgVq5vLy8vygXBrawvNZhOf93mfh0gkggcPHsgOCiKMfHl5GcvLy+h0OggGg7L2wOrqqnRostks4vG4FGXEiu+wIop42BOrgsMURB3GuR32vA3rpJqmKQs5nneNj3GJCZFIBJlMBrlcDtevX8fs7Cz29vZQLBZlOpFoSZpKpTAzM/P/Z+/Pw+vKyjtf/LPPPA86OhqtwZbLdrlcVVDlKiimAAkhJARoQugEAiQdIJCkIZfbhExA0oEmI/eXhNx06lINhAQu5DYkdNIJQ4BQhDDUXJTLdtmyJGs4RzrSmedh//6Q16qt46PJlqzp/TyPH0s6w15777X3Xuu73vf7EovFWFxc1NELw8PDV1WpUuaX4XBYpyiplWhYKQ62n181iVFCTfs5aZ+krbV63i7cWNnuSJN28dO6ynu9K/+b5UaIT+2oKA+32008HtdRbA899JCOHlhaWmJiYoJ6vc7FixcxTZNbbrlFn5uzZ8/idrv1pE0JuqpqjOoP3//+90kkErhcLrq6uggEAhw5cuSqdBsVgaPSPur1OpFIREdpKANga4lda0SV+luhUNCTc3UPbRcD21MnlABpFV7WwnrO2lPl1N+tfXu1fmVNu1C/X68Qt1Z64Hqf28prznp+1PcDK/ZVpRu1b896LNU+qFLK6ntisRgOh0Mb4qp+Yj2/AAMDAyvEJiWaqegcFVXj9/txu906smR+fl6XxG61WszMzOByubj11lu1ie/U1BSVSoWTJ08SiUS0uNPb26sNeVWUjOoH7SbKqVQKh8OhhRd1DlTKsfJt6+/vXxGRpcQNq3fb4OCgjtCxCk7W46oWbKznXB0v9Vxv95lJJBIkEgkGBgZ0etBqHkFWjzoV0ZVKpbT4ks1mtS9Pe8qbIAiCIAib40CJL51SODZCp4G7FZfLxeHDhymVSnoAZd2Ow+Gg2WzqQZP6W6lU4siRIzidTj0JeeKJJ3QYeDQaJRwO68iQ1UqhrtduNThVueRWk8lOA3jrAGu1Schmj9t6EwW1AqciO9by+NiuScdWfI/b7dYGhbfeeitTU1PMzMyQTqdZWFigWq3i9Xq5dOmSTkc6cuSIXmlWK7zK70GVp1YTXzVItnptWMVBWL20L2zcaLfTcVnLfHejxrxWNnoeO4mfiq1Y+V+rPetdHzcKVQWt0WjoykcXLlzQq9vlcpmZmRmazSYjIyMcPnwYWE4vCgaDeuJot9v1+wOBAJlMhqmpKYaHh/W1pVIfVMqFWj1Xgov1mKvoFY/Ho81JHQ4HuVwOr9e7wkxV0W50q+4PsFJ8aD/21hSm9qgtdf9stVoryvcq1jtn7aKkx+PRqVBKcLK2X71vLY+jzfRvdf1vpm91MgzeKlR7rSksKhJT3Wc6YRWSlKCgxAx1XpR4rL7TGkmk9j8UCukILpvNxsjIyIpn69TUlDazr9frVCoV0uk0qVSKY8eOUa1WcTqdGIYBoEVrZeartm3F4XBoQcjv9+soqmq1qvfL5/NpPxVrCqbP59PCuVqAUfdoFXmm2qTSmVS/yGQy2u9FpQOqY2KNKIKn04TUa2pc0N7HVHTXRiJe1bhCtVtFW65Wnl1huWYk7UgQBEEQNsiBEl+UALLZidNGBsSlUomlpSWd164mSaVSiUgkwsmTJ6lUKoyPj+uBULVaZXJykrGxMcLhMI1Gg2g0qsP/1UTDOgBqH+yvNcC3rmS2h4Qr1hvAX88q/3oiTnvb230OrMfW+tmdSPu4Mjlcc5CpPF+WlpZ49NFHqVarHDlyhJtvvpmhoSHS6bSuIjIxMYHdbtfeE9FoVIfLWwfcakCs9rtTikT7ObL2D2sfsPpUtAtcG8Ha56xlUVUkhRLzNnpuNnoerZPzdrZSCOnUnhvV19brX06nk4GBAY4cOYLb7eb8+fPEYjFCoZCOArj99tupVqscPXqU8fFxTNOkXq/TarWIxWKUy2UOHTpENpul0WiwsLCgU0BUBAlAf38/p06d0qvfqn3t9w51/SqfC+u17vP5dGWhdsG0HXXfUgKE6kfWaCerAGmNmlkt2qn9XrgR8df6f6VS0WbnlUpFp94oQ1lrpMZqbKZ/r+ZvtBPkcjmdpqbORbt4u9oxtooHwWBQV06ypk2GQiGdDmcVVNUxVUbMZ86cIZfLaS8dlQrjcDhwOp34/X6Gh4d1ZaDx8XHsdjv1ep3Dhw8TDAb1OVpaWqJcLuNwOHQZZ2sUlXW/+vv7deSpw+HQZZzn5uauek5a214sFllYWFghXKqItFwup01s1XbV68rw1+VyaUHJKs6093F1HShxq1PfUr5GG1k8ab9u1DFWxyQYDOp9tJ5zS1+VEBhBEARB2CAHSnxR5Z63A5/Pp43z7HY7vb29ukSmqiij0o7UxESlBRUKBfx+Pz09PXpyrkSbTpEenaIRVBs6TTA6hYRv9Dhs1eS2UwpLIpFYEdK92rbaP7sTaR9XVr7ta72n0WjgcrkIBoO61GmpVKKnpwe/368jWmw2G7feeivwtI+Cqq5hDR+3rrauliJhXSFVdDqGqp+oVeXrEdOsYt78/Dyzs7MA9PX1rRkJ0M5aaU3t/VetjKvftyO9qVO/ulF9bb3+5fV6ue222xgcHGRhYQGbzcaRI0e0X4SaRDYaDZ16VK/XSSaT+Hw+XZUlnU5rLwwV+aKqtaj0BCWoqegra4oRPN0PUqmUPveBQECbPFujWBTt1XDUObSmUbQfD3WtqEn5eudirXvVekJIp0muVRBR27SKv6r/rfa9G+07SpBQbDRiZrP38o1SKpVIpVLAclSnuqat6VjW86Xaoc6ZElusURnqezsJ7daIslwuR6lUYnx8nEuXLhEKheju7tb9ymaz6UWKkZERve1EIoHD4dCpSLVaTRv7KtHaepxU2o8SXlKpFLVaTftttVotms0mTqeTxcVFotHomsfM5/Ot8MexRkqpqBslarQvIPh8y6luqr+vlXpmTRPaSPTiRvpge99Xwo0y622PZlrtuSMIgiAIwvocKPFFDRi267uHhob0qrLKbw8Gg8zNzQHLA75yuUy1WuXkyZPMz8/rCiWqpOXExAQej4dDhw6tGLRuZIC/kZXWThP17RjAr7fdQqGg8/M3+9mtGvRtJn3pyvFprvWeQCDAT/7kTzI3N8fY2BiZTAafz6dXkm+++WZcLhe1Wo1cLqfTzfx+vxZdVBnWTj4nimuJxlgrKmCzWI+/Cm9X/2/m3KwlJK7Wz9sja7YyBa1T26+3r220fRvtXyq18eabb8br9TI1NYXb7daT0kwmQyAQwOv14nQ6cTgchMNhRkZGWFpa0hNMq2muWuW2bsOa5qXED+Vfoc6F9dyrfesUmaVQ1ZDUZBOW7wMqjUIJM5bjoSd+SqCxpttths2KaO2CSKdoh04iXft3XEvf2ej1vV2T356eHk6ePAksp60obxNYeV5UOqXyT2m/x1gFmvW8pqzf7fF4cLlc2j/L4XCQyWSo1+u6gpJK61G0Cxyq3yrRpt3oVv2fyWSoVqvY7XZyuZzej0gkgsfjYXJyknQ6rY1x14rKtJr0WoUnm822Qgiy7quKZEkmk7rE+nrRg50M11ej/Thv5n7ZLrZvhb+RIAiCIBx0DpT4st3UajX8fr8Ob7Ya9Kn86e7ubhqNhjbOjEajnDp1CpvNxuLiIsViUYcwt0cRrDfA34mIkOvB7/frVdKdYDMixpWB6prh1V6vl2c84xmcOnVKn+NIJEK9XufBBx/E5/PxzGc+c4X3i6qgkUwmdXSMmmi2r/yrwfK1nOfNTNQ2M0BXq6RbwUb6efs1sRMpaJthMxNp1uhf1mgSp9PJ6Ogoi4uLzM3N6RL2qqKPx+PRkSjDw8NaaFEr5ko0UVFVoVBIC8QKFR2l+qLy/ui0Qq5YLfLO6nOVzWZJpVL6c+1pFO39zZoKsZqosxG2UqjYSKTZ9bDT93GHw0EkEiGXy1Gr1ToK3yoqolarsbCw0DFysVAo6P6pqvhsNAKjq6tLl3xW9yOVZqNEEOt9Sj1vlS+NzWbTokaxWNSmtdbFhkwmQyqVIhQK0Ww2tQE1PC0iDg4OAsvijhLGrbQLTkq87NTf2u8Fqwm960UPXk//uJb75UbbJQiCIAjC+oj4soX4fD6dH67CiFUlDyW+uN1ums0mdrtdl9pU4dZLS0v4/X5isdiKScpGJg7XGgGwU5PXQCCwwhNiJ9jqSU6j0WBmZgabzUYkEmFkZITBwUG+//3v09PTo81zVfnvnp4eHA6HnniqUqTKwFH5AFi9MOBqH5etPoY71Sc20s87pYdY/99tbFX71KRHGYwqcc7nWzb6VBPOYrGot2WtMgQrV8yBFWkPKsUGnjZZVSWAN5qqtlq/UamV8XicYDCoxR+1X528njp912b7ojXlpJMp6Wrv38l7ElyfULRV+7CRFK/Dhw9js9lwOp1Xeampe1OxWAS4SrjbDKulKVlToNT909pmJZYcOnRIp88oIVClSypPk6GhIZaWlrTR8sLCgvZB8nq9OmKsU9s2G73XfkxvZCpwpzZstM9ImpEgCIIgXD8ivmwh7aHqqvRlMBjUIfs333wz2WyWaDTK9PQ0xWJRe5/Mzc3hdrvp7e2lr68Pj8ez4epM1zph3qnJ604P5LZjouVyubRPRm9vL4cPH2Z+fh7TNDl27BhjY2MrvAWsXgeqHeo8NBoN8vm8roLRie0SSXa7oGFlp/vRWmxlH7Pb7QSDQT0ZnJ6eJpPJ4HK5tLmnx+PRJqOqfK6KnLKWR25Pl7Hetzqtbm+0/av1G2s6icPhuOq71kpBuZ4+qK6P1XxoVnv/eu/bzWzVPmzkunI4HLrKX6d0nFarpVNx2iNVNns9rNVHVDqMuveqEslLS0vk83mCwSAej4dMJqO332g06Onpoa+vj56eHi1MAjoNyeFw0N3dvamIq41GqSo6HZPt7IcbicgRBEEQBGH7EPFlG1F56C6Xi8XFRZ2T3tPTQ6PRoFar0Wq1yGazFAoFDh06hN/vx+v16lLUq1UzaOdaJyvbHUWxW9mOAafb7aa/vx+bzUY8HgeWw+eLxSJDQ0PUajUajQb1el2HxcPyCq7X6+143AOBgPYhaB+ob5dIor57N0QB7GW2uo+pPgVQr9cJhUKMjo7qyDkltKjIlvXaoX5ey9C7U7vXMkZey8x2M2Xrt0JUs4o+G5lAX8/1tFuiZm60cLraeWpPxwF0euVGSmOvdzwbjYZOoVPparlcTvuI9fX1XeVJlMvlyGazeDwefU9tT+VU5agdDodO19tMP9ysx0qna/NGn8P1trdb+rYgCIIg7AdEfNlGVB763NwcxWJxxcDFutIGUC6XicfjDA4OYrPZcLlcOgx6I1zvZOWgrX5txwDXbrczNDSky/iqSYEKW2+fDLZaLZLJJK1WS5fztPq/WCvDwNXlurcz6uOg9YftYCv7WLPZ1P4VyjdKGTUDK6rLtIsN67Vjs+d6r0TZWa+PjbTzeq6n3XK93MhIsLUm5dfSDuv3rXU8W62WNsKFp41uVcSLEl3aPYkCgYBOg2qnk6/JVggN6/WLTtfEjY7mW297u6VvC4IgCMJ+QMSXLaTTYFT5KcRiMT0hUn4N5XJZ/x8IBAiHw3pSrULlN1LRYCvweDwUCoUNiz17ndUGnNe7yqeMThVrHVefz0c8HtfbU5Ew5XIZQJcat77f+v92YhWKDlJE1G7FNE1mZmbI5XJ0d3dz5MgRgBUpRtYouU4mqauxmX6lfDOU2LMRrNfUfp287aVUva2iVCqRyWQoFAo6SmQt1vM2aS+/bP2/XZhxOp1Eo9EV/kHrmX9bqx2tJiZstd/ORrxzdnuUYadzoZ5V+/V6FgRBEITtQsSXLaTTCpHyU8jlchSLRQqFApVKhXQ6TbFYJBgMMjo6qidOavDlcrkol8srBpfbyWbKV+5ntmKVzzrJUN4H1kgXv9+v36uMIJVhpfL5AVYVwm5EGPi1pIoIK9nKFeNms0m5XMbj8axIO1P3FWsU3WbZzIRTmedupuTzesdhP6Q17Gbvoe1CCcaNRmOF4e5m09Ks36f+X83IVvXzSCSy4nm5HtY2qbZsVii7lhSijfSLvRZZUiqVtAn8FfbmRSsIgiAIO4CIL1vIWitEatCoJuPKeHI1wWNpaYlsNqvLD9/oth9UtuI4WAfc1u9TfcHj8VCr1XA4HCwtLQHoCQw8HfnS3i82ayC6FUi/uHa28tip861Si5RfVKlUWtUvaDu4ln3a6rQnYXewmUpV67GesNH+TN2M+NepTdfic3YtKUQbYTffY1utFvPz8zQaDQC9QOD3+63ttd/odo2OjjI5OdnxtZGRkRvcGkEQBEHYOCK+bCGdVuvUClFvby/9/f3aj6Gvr08P5hKJBH6/n8HBQf15q1ngTrT9oLLVx6Hdd8IquM3MzJDNZunr69Mlp2G5r6j0Dmu1q80aiG51+4XNsZXHzmazEQwGdbqPEl9arZZOV7wRXMs+bWXak7C72KpKVesJGypSpVAobCrlba02bVYk2kgK0bVch7v5HlsqlWg0GjgcDr3fytDb8rfmjW7X5OQkpmne6M0KgiAIwnUj4ss2ovxegBUh0mqgFQqFaDQautqNNc1kvfx1Ye/RbuoYiUQoFot0dXXhcrl0OWA1uC0UCisG5ps1EBX2D263m1OnTlEoFFhYWMDr9eLz+a6q2rIX2c2TT2HzXMv5VF5o6h+w4n6oUjivJepltTZtViQ6iP20U9WqDn+79pxHQRAEQThgiPiyTVh9PNYaKFYqFSqVCuVy+cD7rRwU1CA+kUjQaDTIZDL09fXh8/l0iHcoFCIUCkk0gAAsV9Kq1WqkUikKhYKOktvLoosgKKzloq2lqa0pllsdIXUQxZTNYj1GB8E4WxAEQRC2GxFftolOIc2d8tp7eno4deoUPp9PJtoHjPbUskKhQDab1eklMrEWFM1mk2w2S3d3N319fRuqLiMIe4lO4oo18kXEkp2lUCiQTCbp7e0lFArtdHMEQRAEYU8i4ss2sVqOeXtpTofDwcDAwE41U9hBJLVM2CjWKi8y8RH2I+3iiqRY3lj2Q9UxQRAEQdjtyBN2m1ADSesgxufzaWNVVdlGEBSBQIBwOKwHwYKgME3zhhrrCoJwsFDRuqs9ewKBAP39/XIPEgRBEITrYF9EvhiGYZi70Pq+fSVptdKcggCdS7fKaqQAYBgG3d3d0gcEQdgW2qN1O41fRHgRBEEQhOtjz4kvhmHYgLezXN7wfuCsaZrN3SjAdPJ9kQGMsBadypVvphyqsH8RQ25BELYLefYIgiAIwvazp5ZRDcMwgK8BzwDeAPwh8H8ahuFcS3gxDOOthmE88OSTT3L69GnuvffeG9Jen88nFWv2Affeey+nT58GuNkwjAcMw3ir9fXt7F/Sh/Y/a/Uv1beeeuopXvjCF96we5ewP9hI37rRz0Vhb7Des2e956IgCIIgCFdj7LJgkTUxDKMf+HPTNF9tGIYPeCdwN/DPwL3rRb6cPn3afOCBB25AS4X9iGEYD5qmeXq116V/CdfDWv1L+pZwPUjfEraLnehbhmFwLWPXa/2csDOsN+YSBEHYi+ypyBfAAH7AMIz/ZJpmCfhz4BLwKsC7kw0TBEEQBEEQBEEQBEHoxJ4SX0zTnAXeC3zYMIxXmKaZM03zXUAUGN3RxgmCIAiCIAiCIAiCIHRgVxvuXvF4eTEQNE3z7678+WNAGPicYRi/BZQAJ7CwI40UBEEQBEEQBEEQBEFYg10rvlypavQNYAJ4qWEYzzFN81dN0ywDHzIM4wLwCqAK/JxpmiK+CIIgCIIgCIIgCIKw61g17cgwjCHDMP6HYRh/ZRjG8yx//9iNaRo/BbRM0/wZ4LeBIcMwnnslGgbgf5qm+QbgLaZpPnaD2iQIgiAIgiAIgiAIgrAp1vJ8+SRQAMaB/2UYxg9d+fvd296qZZaAHsMwAsBp4E7gr4CvGobxGpZ9X6T+riAIgiAIgiAIgiAIu5q10o76TdN8IYBhGI8Df2MYxqkb0qplvgn8Z6DOcpTLzxmG4QX+BegF/uRKxSNBEARBEARBWJWRkRGeDp6++rWJiYkb2yBBEAThwLGW+DJhGMaHgM+Zpvk/DcN4IfCvQOQGtAvTNAuGYXzNNM2GYRgPG4ZxFHjhle3/tWma2RvRDkEQBEEQBGFvs5a4spooIwiCIAhbyVppRz8LDACvATBN8z8DjwHF7W/WMleEFzfwy8A/A+8C3iDCiyAIgiAIgiAIgiAIe4VVI19M05wD3mQYhscwjDcAbwaeC9x/oxp3pR1VwzB+H/gLoGqaZvJGbl8QBEEQBEEQBEEQBOF6WFV8MQzjduAtwOuAi8AoMGCa5vyNadrTmKaZATI3eruCIAiCIAiCIAiCIAjXy1ppRw8DR4DnmqZ5F5DfCeFFEARBEARBEARBEARhL7OW+PJ7wO3ApwzDeAdgvzFNEgRBEARBEARBEARB2D+sKr6YpvkbwDDw28BLgD7DML5gGMYbb1DbBEEQBEEQBEEQBEEQ9jxrRb5gmmbTNM2/N03zx4HDwPeA99+QlgmCIAiCIAiCIAiCIOwD1hRfrJimOWua5u+apjm2nQ0SBEEQBEEQBEEQBEHYT2xYfBEEQRAEQRAEQRAEQRA2j4gvN5BWq0WhUKDVau10UwRBaGM3X5+tVmtXtkvYH+zmvi8IgiAIgrBfEPHlBlIqlcjlcpRKpZ1uiiAIbezm67PZbO7Kdgn7g93c9wVBEARBEPYLjp1uwEHC5/Ot+F8QhN3Dbr4+7Xb7rmyXsD/YzX1fEARBEARhvyDiyw3EZrMRCAR2uhmCIHRgN1+fNpsNm00CFYXtYTf3fUEQBEEQhP2CjOYFQRAEQRAEQRAEQRC2ERFftgmrgaFpmqv+EwThxmO9Bjtdq7sNZbgr9xBhO1jreSUIgiAIgiBsDSK+bBPtBoamaVIsFmk2mxSLRRnUCsIW0alSy2aqt+wFs9FKpUIul9vpZgj7lFwux4ULF6SPCYIgCIIgbCMivmwDapU6EAhoA0M1wUulUrt+oicIe4lO4sl6gooSQ03TxOfzEQqFdrXZaKPRoFAo7HQzhH2Kul4WFhZkYUAQBEEQBGGbEPFlGyiVShQKBWw2G4ZhAODxeLDZbHR1de36iZ4g7BU6CZ3AuoKKVZwxDAO/36+v1d2IzWaTe4awbfh8Pvr7+3G73bIwIAiCIAiCsE1ItaMtpNVqUSqV8Hg8KzwaDMOgUqnQarWo1Wr4/f6On/P5fFLRRBA2gRI6Q6GQvnZUylEnrNco7J3Suup+Ighbjbpe/H4/Pp+PVqtFs9mkUqng8/l2tSgpCIIgCIKwlxDxZYtotVrMz8/TaDSIRCLYbDZyuZxese60Oq9Qq/CAlPsUhE2grifrdVUqlUgmk8DVJXSt11q7CLoWVtFGTUpvpFBaq9VYWFggFovdsG0KBwObzYbD4aDRaFAqlSgWiyuuDXkmCe1YF4wEQRAEQdg4EmaxRRQKBbLZrBZbVJqRy+Vifn5eT/hKpdJVOfXqvSpiZqNGoYJw0FHXjRI+YVmI6e3tJR6PXxUx0p6OtFEj7E6eTTfyWq3X69u+DeFgUq/X6erqIhKJ4HK5WFhYwOVyEQqF8Hg88jwSrqJUKpHJZJifn9/ppgiCIAjCnkIiX7YAVa62XC4Tj8f1xKzRaDA9PY3NZtMTQ/V+r9dLuVzG7/frlCSVQpHP5+nv7ycUCu3gXgnC7kZdM7lcjmQySavVIhQK4fV6CQaDFAoF0uk0qVSKoaEh7HY7gI5Es9lsFItFstmsvgZ7e3sJBAIroloajQa5XA6fz4ff76dareLz+W5oxFqlUuHcuXMcO3bsqtfUfgnCtVCv10mlUgSDQXK5HLOzs4RCIQYHB8nlclp4kQiYg0knQdrr9VIoFKjVaiDjSEEQBEHYMBL5skXYbDYtpKgJmcPhwDAMkskkmUxGm/C2Wi096UskEjQaDT2wzefzlMvlndwVQdgTKPHD4/HoVfpMJsPCwgKtVguv10uj0dB/UxEu+Xxer+Z7vV6dclGpVDpuZ3FxkWQySaVSweFwaHHG5/MRCARuiB+L2g9B2GqcTider5dsNsvc3BzpdJpLly5x6dIlHdkgnkOCFZvNRiwWUxF5YgokCIIgCBtEViy2CL/fr6tFlMtlGo0GtVqNTCZDOp0mGAzS19cHQDabxeFYPvSJRAK/38/g4KCuOAGyyigI66EiWAqFAm63G4fDoYWUYrGIYRj6unK73aTTaTKZDI1Gg2AwiN1ux+v14vV6cTqdVKtVvF7vim20Wi08Hg+9vb1X+a3YbLYV3k7bfc26XK5t/X7hYKKug3Q6TSAQIBqNEgwGta+RzWbTCwfyXBJgWQyemJhQgrWIL4IgCIKwQUR82SJU5IviySefZH5+HpfLxdLSEr29vdorAsAwDLxeL61WS1eZMAyDYDC4U7sgCHsKNTFsNBrU63W8Xi9+v59yuUyr1dIeTH6/H4/Hw+TkJJOTk8RiMbq7u1lcXCSVSmEYBvF4nFqtRrlcxufzUS6XcbvdLC4uUqvVcLlcHQ12Oxn+bgf1el2bCAvCVtJoNHjwwQep1+sEAgFisRj5fJ7h4WGi0SimaVKpVHSFMEFYWFjgwoULKspXxBdBEARB2CAivmwRyvNFrZy7XC7t+VIoFJibm+MrX/kKPT09vPa1r6VSqRAOh7XwoioHSFlPQXia9cqwe71eLYxYI1c8Hg+pVIparUapVMIwDL7//e+zsLCgfWC+853vkE6niUajvPjFL2ZkZIR8Pk+xWNSCjt1uJ5fLEYlEtEfTRtu2lTQaDR577LFt3YZwMKlWqzz00EPMz88zMDBAMBgkm80SiUTw+Xzk83lg2XdIIl8ONiplWnnYXbnvSfq6IAiCIGwQEV+2iHK5rM0JVVlp9feJiQm+/vWvc/78eWDZwO7Vr3619p+Ym5vDZrMRDofp6+sTAUYQrrCWqW2j0SCVStHV1UWtVsPtdjM3N0cymSQQCOB2u7HZbFy8eJFf+IVfYHJyctXt/PEf/zHvfe97+eEf/mFcLhcej4dSqUQgENBVYNpTkm6k4a5pmqRSqW3dhnAwmZqa4sMf/jD5fB6v18urX/1qjh8/TrPZxG63E4vFJPJFoNVqsbCwQDabpdVqEYvFVCSviC+CIAiCsEFEfNki1MSs0Whw7tw5Ll68CEAqleKLX/wi6XSar371q3zkIx/hU5/6FI1Gg9e+9rU6vUGZGfp8PiqVCt3d3doXRhAOKmul9aRSKWZmZigWi4yOjpLNZnnwwQfJ5XIcOnSIWCzGN77xDX7913+dUCjEfffdRyAQwOFwYLfbcTqdOBwObDYbv/u7v8v73/9+zpw5w2te8xrcbjd2u514PE5XVxder1ebZavUpBuVcqRQ4q0gbCUOh4Ph4WEAzp49y2c/+1le//rX02g08Pl8RKNRvaAgFfgOLsrLTqVGJ5NJvvOd7wA4d7RhgiAIgrCHkNn9daJSD5SAksvleOqppzh37hymafI3f/M3VCoVvvKVr3DnnXdyzz338KpXvYq//du/xefz8epXv5p4PE61WiUYDFIqlUgkEgDaoFcQDiqqqlB7eo8ywg0EApRKJcbHx7XRrhJU7rvvPv7sz/6M06dP87nPfY6BgQH9varUtOKee+7hp3/6p/l//9//l8uXL/MTP/ET3HTTTTqiRYkujUaDhYUFent7CYVCNzQNQ6qgCdvB8ePH+epXvwrAE088wfOf/3y+9KUv8ZKXvIQjR47g9/up1+va3PpGpNkJuw+1wKT+/+pXv8rs7CxI5IsgCIIgbBgRX66TUqlEJpOhWq1SrVaZm5vD7XaTz+f5m7/5GxqNBr/5m7+pS3cCvP71r+fy5cv81V/9FW63m3e84x0kEglisRgDAwO6jKMgCCvTe5TnSqlUolQq4ff7yWQyZDIZvF4vx48fx+Fw8O53v5svf/nLvPCFL+Td7343586d49y5c/o7Z2dnGRsbW7Gd//Jf/gutVot//Md/xOv18kd/9EfauBegWCwCyylANxqPx8Phw4f5u7/7u6te+4mf+Ikb3h5h/5BOp/nc5z6nf3/HO97Bhz70Iex2O319ffT19XHkyBFd2Quevg4lRXb/c6WcNLAcJZXNZrXBeXd3N0BlxxonCIIgCHuMPSu+GIbhME2zsdPt8Pl8ejBSKpVYWlri/PnzfPzjHycUCvF//B//B4ODgys+43a7+dVf/VU++MEP8rGPfYxwOMzRo0f1YKa3t3eH9kYQdh/WFVcVgeLxeGi1WlSrVfr6+lhaWiKXyzE7O8uf/dmf8e///u+88Y1v5A1veMOGJ4gOh4Nf//VfJxqN8td//dd88IMf5Ld/+7dxOp04nU5KpRIej4dIJCLGo8K+5bbbbuPNb34zf/mXf4ndbuf48eNEo1GGhob0NSAcTEqlEhMTE1y6dIkzZ84oM2b/ep8TBEEQBGGZPSe+GIZhA74J/D7w9zvcHGw2G16vl4WFBUzT5Pz583zoQx+iv7+ff/7nf+Z73/tex895vV5+8zd/kw9/+MN85CMf4QMf+ADDw8M0m82rUiIE4SDRXkXIWsa9WCzq1fdWq8X4+DiBQICuri6+//3v81u/9VssLCzw6U9/mq6uLgzDoNls8ulPf5pz584RCoWIRCIAjI2NEY1GCYfDHDp0iHA4jGEY/PIv/zK33nor73nPe1haWuJNb3oTJ06cYGBgAL/fTygU6nh93sjqR4KwnfzgD/4giUSCv//7v+cf/uEfOHXqFJcvX8bpdJJKpYjH49LHDxCtVotKpUKr1WJpaYnHH3+cbDbL0tISwI0xvRIEQRCEfcCeGj1dEV7+FfhH0zT/3jCMo4ZhhIx1lrYNw3irYRgPPPnkk5w+fZp77713S9qjPF4ymQyXL19menqa3//932doaIivfvWrjIyMrPn5YDDIF7/4RQ4dOsQf/dEfsbCwwPz8PMlkUldOEnaee++9l9OnTwPcbBjGA4ZhvNX6+nb1r4OKSjO6UkljBS6Xi3K5jMvlolQqUavVKBQKTExM8M53vpNms8lXv/pVXv3qV+vPfPSjH+XjH/8409PTPPDAA3zuc5/jM5/5DP/tv/033v3ud/PWt76V173udVQqT0fPv+td7+K+++7ja1/7Gp/85Ce1yNqO8sFQwstq7V6LtfqX6lulUomHHnqIL3/5y5v6buFgs5G+NT09za/92q/xla98ZcVnf/qnf5pnP/vZfPSjH+XJJ5+kUqlQrVZpNBriP3TAqFQq+t6WTCaZmJjgS1/6krofdXV6LgqCIAiCcDV7LfLl54EqcJ9hGF8HBoAJ4H8YhvEZcxUzBtM07wXuPX36tPnAAw9sWWNKpRJzc3NcvHiRRCLBww8/TD6f15Ev7dRqNW0Gqujt7eU//af/xK/92q/piipqkGO32yW9YRfw1re+lbe+9a0YhvGkaZqn21/frv51UNlIhaNGo0EsFiMUCuHxeHj/+99PsVjkX//1Xzl+/Lh+/0MPPcTf/u3f8vKXv5xf+ZVfAZY9W8bHx4lGo2QyGc6ePcuHP/xh/uVf/oUf+7Ef05994xvfyAc+8AFKpRKpVIr5+Xni8TjHjh3TVV8KhQLJZJLe3l59rW62+tFa/Uv1Lb/fbx4/fpzjx4+TTqeJRqOb2oZwMNlI3+ru7jZ/4Ad+gHq9zpe//GWe//zn4/F4sNlsvPzlL+fb3/423/rWtwgGg5w8eZJGoyGpRwcMj8ej74MAPT09HD16FNM0efLJJ6udnouCIAiCIFzNnop8YTndKAj8FXAf8EJgHPgZdiD01efz4ff7icfj3HzzzTz++OOMjIzwIz/yI1e9t1Qq8Y53vINPf/rTV702NDQEwOOPP04mkwGgu7v7hpWwFYTdhM1mIxAIrBApW60WxWJRT/ouXbrExMQEHo+HL37xi3zmM5/hPe95zwrhJZfL8Qd/8AcMDQ3xtre9Tf/dMAy8Xi+Dg4PccsstvPrVr2Z0dLSjma3T6cRms3Hu3Dm+//3v6+tzo+3eKkqlEg8++CDvete7eMtb3sIjjzyy5dsQDiaLi4t87GMf42Mf+xj/z//z//C//tf/0q+pCmFPPPEErVaLdDpNo9FYESUm7H9sNhvRaJSlpSUKhQJ9fX0Ui0UV5Wff6fYJgiAIwl5hT4gvxhVM03wS+DXgKPCoaZqzwHuAIWD4RrfLZrNpD4lKpcI3v/lNfvmXfxmH4+qAon/8x38knU7zz//8z9qzQnHo0CEAJiYmmJmZYWlpCcMwJKdeOFBYU3hUSp9KvyuXy+RyOS1wNBoNTNPEMAz+5E/+hJtuuol3v/vd+rtM0+RP//RPSafTvOc97+EP/uAPeMc73sFjjz121XYNw+BVr3oVZ86c4fz58yteczqdeDwejhw5wp133snRo0dpNBrMzs7SaDQIBAL09/d3jFCz7s/14vf7ec5znsOzn/1sAD75yU9e93cKAsDIyAj33Xcf9913H894xjP40pe+pCvcBAIB4vE4pVIJm81GtVpdNQVP2N+Uy2UuXbrE+973Pj7wgQ/w0EMPqbGMlLwSBEEQhA2y69OODMMIm6aZNQzDdkWA+ZphGC83TfOMYRjPAk6yvB+pnWhfIBCgXC7zf//f/zfBYPAq74hPfOITZDIZvve97xEMBsnn8/zX//pfGR0dpbu7mx//8R/XKUpnzpzh9OnTuN1ums3mTuyOIOwY1pLSAMlkklarRTgcpru7W4sZTqdTR8L8xV/8BePj43zqU59icXFRf/Zzn/sc999/P4cPH+aP//iPuXTpEg6Hg3e9613EYjFe9rKXceTIEf1+p9OJw+HgIx/5CK973et45jOfCSxXQMpkMoyMjBCJRDAMg4mJCT0Z7e3t1WbAKutRWVBZ9+d60wcNw9CirsfjYXJykj/4gz/A4/FIqWnhulhcXOQTn/gEAI1Gg2w2y3/9r/+V3t5eHA4Hx48fp1KpkM/nKZfL2rBaiZ/C/kZF+5XLZT7+8Y/TarVWeGrdd999EvkiCIIgCBtkV4svhmF8FEgYhvF/maa5aImAOWMYxn8E/jtwHnijaZoLO9FGm83GzMwMX/jCF/jFX/xFPTBV/PM//7P++UpZRi5fvszly5cZGBjAMAwGBwcxDIOnnnqKc+fO4XK56OnpIRKJSPSLcGBo93qJx+Mkk0nS6bSuINRoNKjX64TDYWZnZ/nrv/5rXv3qV/Oc5zxHf8/ly5d5//vfDyynJykajeXK9IuLi/z1X/81d955J8997nO119LNN9/MY489xktf+lLs9uX5hMvlYmlpicuXL9PV1YVpmnR3d1OtVunq6trU/lwPhUKBb3zjGyv+9t3vfve6v1cQOvWtc+fOce7cOZxOJ294wxv4h3/4BxwOBz6fj2KxSLlc1qKjsL9R98JLly7xjW98g/e+9728853v1K/fd999Hb32BEEQBEG4mt0+s38J8BPAmw3DiLcZ6v4LEAd+yDTNB3ekdVf47//9v2OaJr/4i794TZ93uVz09vbSaDTw+/0Ui0Wy2Szz8/NS8Ug4MKiUIkBHlsDyimuhUMDj8eBwOGi1Wng8Hv7iL/4Cv9/Pb/7mb+rvaDabvOtd71p3W93d3Tz00EP85V/+Jd/5zndoNBo885nPpF6vr0hNcjqdWgwNBoO63KoqvbqR/REBVdjLHD9+nPn5eYLBICMjI/T29kra0QHDNE0+8pGP4Pf7edOb3rTTzREEQRCEPcuunRUYhvGDQBH4EvAK4OeVAGMYxiuA3wZspmnmd6qNrVaLfD7PZz/7WV75ylcyPHzttjPK2LDRaNBsNmm1WtRqNRFghH1NJ18Ua7pOf38/fX19upQzgNfr5Zvf/Cb3338/v/7rv04sFtOf/fznP8/3vvc9fud3fmfN7Q4PD/NzP/dzDAwM8PWvf53Pfvaz9PX1EY/HefTRR/X77HY7brebfD7PN7/5TcbHx5mampJSu8KBQZlYnz17lscee4xGoyGC4gHCNE0uX77MF77wBX7qp37qquheQCJfBEEQBGGD7OYR1CPAXaZpvhP4CvBK4GcNw4iw7O/y/zNNs7ZzzVueJC4tLREIBDA7V7neMKZp6hX9UChEX18fDoeDRqOhJ52CsN9QQou1j/t8PkKhEIFAgFAoRG9vL6FQiEKhQKVSIZvN6pKn1nQjQE8Kb7755nW33d3drUs2Hzp0iHq9TjabXTG5mJiYwO/3k0gkWFpaIpvN4vV6GR4e1td9sVi87utfEHYrKl2v1WqRSCR46qmnZEHgAFGtVnE4HNjt9o7FBBDDXUEQBEHYMLvW88U0zUXLz+83DKMG/DxQM03zT3auZU/j8/lIJpPcfvvtfP3rX6fZbOr86M2SyWS0qWg+n2dhYYFoNEooFJKS08K+pZMvikrXUVExKsUhm82yuLhINpvVYk0qldKl2gGe9axnAfDtb397ze2apsmXvvQlHnnkEe6++26e//zn88gjj1Cr1fR3KK+XF7/4xbrKUbPZpFwuU6st676JRIJ8Pq/NSX0+H6ZpUiqVtE+NIOxl5ubmALjllluoVCrE43HxfDlAuN1uurq6uOuuu/jWt761080RBEEQhD3Nrp8ZGFfKKZim+UHgfwBf2NkWrSQWi/GiF72ITCbDQw89dM3fk06nsdvtjI+Pk0wmWVhY0OHdMoETDiIqKkal+BSLRQqFArBcHhdYUeEIYHBwkKGhoXXFl6mpKR555BGe/exn88IXvhCABx98kL6+Pi3mqPSjoaEhFhcXsdlsjI6O6vLyhUKBRCJBsVhcEcHTKZpHEPYqc3NzOuphbGyMQCAgni8HCMMwcDqd3HbbbXz/+9/X1Y8EQRAEQdg8uzbyRXHF48VmmmbLNM0/2On2KFqtFvPz81QqFe655x4Mw+ArX/mKLlG7me9ptVpks1nq9To2m42uri56enpwOBx4PB4KhYKsogv7krXKMatoGI/HA0A4HKZQKGAYhvZImpmZ0YKM4s477+RrX/vamttdXFzkjjvu4PbbbyeXyzE9Pc3i4iIvetGLKBQKmKbJI488AkAoFOKRRx7B5XJx+vRpAoEAmUwGn8+H3+8nGAzS09NDpVJZEcEjEWvCfmBubo7e3l48Hg8ul4vu7m4Mw5BS0weESqVCLpdjeHgY0zT5xje+wQ//8A9b3yI5aIIgCIKwQXa9+AJgmuaue7iXSiUajQa5XI5Wq8UznvEM/vVf/5X3ve99K973O7/zO7RaLf7wD/+Qrq4upqen+ZEf+RGe+9zn4na7cTgcZDIZPZC95ZZbeN7znkcqlcLn8+mBD1w9ORWEvU6ntCNlruvz+fD5fCuiXVqtFk899RTpdBqAWq1GX1/fiu98yUtewt/93d/xxje+kYGBAT760Y/icDh405vexP/+3/+bxx9/nB/4gR/g937v9/Tk8Vd/9VeJRCL89m//Nh6PB7vdzve//336+vo4dOgQrVaLaDTKsWPHtDeTz+djcHBQC6Pq+twOP4yBgQFdTe3DH/4wS0tLvO9978Plcm35toSDxeDgIO94xzuA5eivz372s/zSL/0Sw8PDGIbB1772Nfr7+ykWiwwODmIYhoguB4hIJILD4eCee+7B7Xbz8MMP89rXvtb6FukMgiAIgrBBJJTiGlETQ6fTqSdl//7v/04+f3XxJYfDwW233cb09DQAp06dWvG6mkiOjo5y6tQpCoUCDoeDUqmEy+XCZrPp1X9B2Mu0VzfqVI65PYUnmUwyNzdHKpXSUWLqc8p414oy4b18+TKGYXDnnXeSSCT49Kc/zeOPP87zn/98XvSiF+kJ5OzsLN/85jd55Stfidvt1t/zyCOPcPvtt3PkyBFe+cpXcscddxAOhykWi8zMzDA/P6+FF+t+bWfaUaPRYGlpiUgkIsKLsOU8/vjjhMPhFT5Kc3NzZDIZnnjiCcbHx1lYWBDD3QOG3+9nZGSEu+++m/vvv7/9ZekMgiAIgrBBRHy5Rmw2G+l0momJCYrFIj/4gz9Io9Hgi1/8Ysf3q3SkkZERwuHwiteUb4XP5+P8+fM6laLRaJBKpcjn8+IfIewLOgkT7YKMz+cjEAjQaDRoNBrEYjFsNhuLi4tMTk4SDoeJx+P09PQwOzt71TaGhoYYGRnhqaeeotlscurUKdxuN1NTU7zgBS/gec97nn7vhQsX+OAHP4hhGLz61a/Wf0+n05w5cwaXy0U8HufSpUvUajVmZ2eZm5tjbm5uxXVp3S9VrWkr047m5ub43d/9XT7wgQ8AG6vmJAgbYWFhgb/8y7/kL//yLzl//jy33nqrFiabzSbT09P6OrDZbNRqNSm1fgBxOp2cOHGCRx99lB/5kR/R/4DGTrdNEARBEPYKeyLtaLcyODhIMpnE6/Vy8uRJjh07xjvf+U6e9axnrVg5BOjv7+c5z3kON91001Xfc9999+F0OjFNk4sXL1Iqlbj77rtxOBzaE0YQ9gOd0ozafV+UyfTMzAyLi4vEYjEajQazs7PkcjkcDgczMzPY7Xb+9//+3zz88MNXeS29+c1v5r3vfS+f+cxn+A//4T/woz/6o9RqNW677TYApqenefe7383999+Pz+fjXe96F729vcCyx8ErXvEKAF72spcxMTGB0+lkcXGR06dP4/V6cblcVCoVGo0GrVZrxX5ZU5C2CtM0aTQauqLSC17wgi39fkEAOHLkCM9+9rP171/+8pdJpVK84AUv4PDhwzgcDhwOhxjuHjBKpRITExM861nP4pvf/Cbnz5/X3liAudPtEwRBEIS9gogv10GtVsPj8ehUgx/4gR/g05/+ND/7sz/Ll770pRVlpw3D4GUve9lV33Hu3Dnuu+8+brrpJoaGhmg2mywtLZFOp+nv7yeXyxEOh8XvRdgXdBImrMa6ylza5/PhcDjI5/MUCgUqlQq1Wo1ms0k8HsftdvMf/sN/4JOf/CS/9Eu/xL/8y7+smBC+/e1v59/+7d/48pe/zCc+8Ql+8id/kq6uLiYnJ/nWt77FxMQEoVCIt7zlLbzmNa/R0WiNRoPf+I3f4Fvf+have93rCIVCmKZJMpkEliNQbrrpJgqFAul0mnK5jMPhIBAIbOs16vP5uOWWW3jFK14hfhvClhKPx/mFX/iFq/5+9uxZvvrVrzIyMsIrXvEKqtUq9Xqd/v5+MX8/gIRCIW666Sbe85738K//+q889thjNBoNHn30URFfBEEQBGGDyAjqOvH7/cRiMaLRKOFwmHvuuYf777+fP/qjP9rQ59/73vfi9Xp5/vOfz1133cXdd9/N6Ogovb29+Hw+IpEIPT09MtgV9i1KkCmVSszNzVEoFLDZbIyMjBCPx2k0GjidToLBIDfddBNjY2P4/X78fj8/9mM/xvnz5/nQhz501ffecsstvO51r6NarfKJT3yCv/qrv+JTn/oUCwsLvOQlL+Hzn/88P//zP6+Fl1arxQc/+EH+7d/+jR/90R9laGiIUChEd3c3x44do6uri1gsBiyLIfF4HL/ff0P9mER4EW4E6XSaz3zmM/T39/P85z+fdDqtU20lEvPg4fP56O3tZWBggGAwyNjYGL29vfj9ftgmz5fR0VFt7tz+b2RkZDs2KQiCIAjbjkS+XAeBQIDBwUHq9Trnz58nHA5z6tQpIpEIv/M7v8OLX/xims2mCs29isnJSf7n//yf3H777YTDYXK5HGNjY/h8Pu0fEQqFbvBeCcLuoFQqkUqlyOVyDA4OMjQ0pNOOZmdnqdfr3HXXXTzxxBP8xV/8BT/yIz+yImWi1WrR39/PG97wBj7/+c9TKBR4yUtewq233kogEMDn8+lr0zRN/uzP/ox/+qd/4sd+7Mf4oR/6IdLpNLVajbGxMV1qNRgMAsuCkcPh0O202WzbWg7ebrcTCARk4itsC9Z+1Wg0+Ju/+RuazSb33HMPY2NjHDt2jHQ6jcPhoFKp7GBLhZ3AMAwcDgfZbJZCocDi4iJOp1NFG25L5Mvk5OSqYydBEARB2KuI+HIdqBX7ZrPJkSNHSCQSdHV1kUwmGRgY4E1vehP/9m//pidsVkzT5GUvexnxeJwjR44wPj6uUwuGhoYoFos7sEeCsHMovxeVhpTL5VhcXMTn82EYBvPz81y8eJFz585x/vx5HZXyspe9jGQyya/8yq/w0EMP6fSf3/3d39ViyB//8R+vKJFrmuaKiJU//MM/5NOf/jS33347r3nNa4hEIpRKJRYXF/nud7/L0aNHcTgcK8QV1c5Wq7Xt5eBrtZr2qRGEraS3t5df+ZVf0b+/+93v5vLly4yOjhIIBHjBC15APB6nUCjgdrvp7u6WCKwDhM/no9Vqcf78eR599FESiQSTk5NMTU0p42Ux3BUEQRCEDSK5LFuE0+mkr68P0zTp7+9nZGSE8fFx/st/+S8d3//lL3+Zb3zjG7qKy9LSEplMhlarRXd3N8FgUMx2hQONzWajq6sLm81GMpnk/PnzTE1NMTc3RzabpVgsks1mCYVCvOhFL+LSpUvcc889fPzjH6darV71Xe0TxqWlJf72b/+Wn/u5n+PXfu3X+Kmf+ile/vKX02g0qNVqDA0NaX+ZTCaDy+WiWCxeVSY7EAhseXUjQdgJPv/5z/ORj3yErq4uQqEQpVKJBx98kGw2qwXGWq22w60UbjTlcplGo0G9Xuf48eM8+9nPJhaLqXL3Ep4iCIIgCBtEIl+2gGKxyMLCAvl8nkAgwOjoKKVSidnZWT75yU/ygz/4g7z2ta/V76/Varz3ve/l8OHDDA4O0tfXR6vVIhQKkU6nefTRRwmFQhSLRW3mKQj7nUKhQDKZpLe3l1AoRF9fH+VymXw+z+XLl3G5XGQyGQKBACMjI9hsNnK5HH6/n1e+8pXY7Xa+/e1v8+Y3v5n3ve99vP3tb+ctb3nLitS9SqXCn//5n/OFL3yBBx98kFarpb2afvRHfxTDMLSY0mg0eMYznkE+n8fpdDI/P08qlWJ0dJRIJKK/czuqGwnCjeKxxx7j8OHDALqil6pk43K5CIVC2O12Dh06hMPhUBNu4QDh9Xrp7e1leHiYSqWiheYrkYDNHW2cIAiCIOwhRHzZAlqtFo1Gg56eHjweD6dOndKCSk9PD29+85txuVy86lWvolqt8vrXv57HH3+cZz7zmXR1ddFoNIhGowDMzs7i9/txOBzE43Ed/SKGu8JBodVq6apHsViMM2fO0Gw26erq4s477+TixYt4PB6q1Sp2ux2n00k6nebEiRMMDw8Tj8cxTZP3vve9/PEf/zFve9vb+MVf/EUuXbrE2972Np588knuvvtubr/9dp75zGcyMjLCwMAA+XxeVx3z+Xy6kpHT6aRcLuN2uykUCpRKpRXiy40+NnIvELYSh8NBs9mkXq8TDAbJ5/P6uaMiyGw2G/V6nXK5zNLSEn19fTvcauFGYrPZiEQi+P1+EomEFmCuGJCLCZAgCIIgbBARX7YAm81GqVTizJkzeL1eisUiuVwOwzA4deoUrVaLN7zhDfz5n/85n/vc5/jSl77E0aNHueOOO0in01SrVV22ulqt0mw2dVUVVeJWjHeF/Yoq1e7z+ejv76fRaDA3N0csFmNiYkKXuFViCCxfc+FwGL/fTygU0pNCt9vNbbfdxuLiIj/1Uz9FqVTi937v9/jTP/1TKpUK/f39/Mf/+B8ZHBzEbrczPDysS0lPTk5iGAb9/f3AciWzubk5HnnkEbxeL6dPn+bo0aN0d3fv2LGyrjoLwlYQiUQ4evQomUyGRqOBYRi0Wi0Mw6BcLjM3N8cdd9xBJBKh2WzuaP8Xdg6/309fX5+OAOzr61PjE8mNFgRBEIQNIuLLFuD1evXq4czMDE6nk2Qyyblz5+jp6QHgOc95Dm9729swDINjx45xyy23YBiGdvMPBoPEYjFsNhvFYpGpqSkZ5AoHglKppP0kfD4fs7Oz5PN5Go0Gs7OzOJ1ObrrpJhYWFhgYGGBychJY9mwpFArUajUKhQLpdJqlpSUMw2BgYIBGo0GhUOClL30pk5OTeDwefuZnfoaxsTFmZ2e5cOEC2WyWZrOpKwnlcjnm5uZYWlqi0WgwMzNDoVAgGo0SCoUIh8M0m00SiQTd3d1aDLpR3Miy1sLBwDRN6vW6jnZRqUWmaZLJZLh8+TKmaRKJRLDZbBJ5dUCx2WwMDg7y8MMPYxgGfr9fFROQDiEIgiAIG0TEl+uk0WgwPj4OQDQaZX5+Xht2wvLE0ul0Uq1W6e/vJxKJMDg4SDqdplwuMzExwZEjRzBNk76+PuLxOIFAgEajgc/nIxKJyEq3sK9R/dvn81EqlSgWi9hsNi1qer1e8vk8pmny5S9/mbm5OWq1Gjabjb6+PhKJBPC0MFGr1Uin00SjUSqVCjabjdOnTzM8PEwoFKJQKNBoNDBNk2azyfDwsE5dUgJLpVLR12hfXx+nTp0CIJvNsrCwQDabBbhh6ReGYWC321cItoKwFdRqNcrlsk5ps9vthEIhms2mjixzu91UKhUajQalUomenh4RYQ4I6n5jmiYXLlwgl8tRKBRotVqq7LiMIwVBEARhg8hD8zpoNBo8+OCD3H///fj9frxeL81mk1KpRLVaJR6PEwwGmZ+fJ51O43a7AZicnKTValGv16lUKgwMDDA0NEQgEMDhcJBOp3VZWzHyFA4CrVaLTCZDoVDQ5o6tVotAIEChUMBms+mKK8FgkKGhIZ566ikWFhYwTZNoNEpPTw/ZbFZHxDgcDmZnZzl//jzRaJRwOMzS0hLBYJBSqUS9XsflcjE9PU25XKZYLFIoFGg2m/r98XicEydO4HK5mJiY0OlP8Xgcj8dzwzxYKpUKFy5cwOVyyaRX2FLq9Trj4+NqIo3L5dJVxo4ePUo8HiebzXLTTTcxNzen0wTl2XQwUFXiSqUSNpsNt9tNvV7nqaeeUn1GbkiCIAiCsEFEfLkOUqkUjz76KBcuXODQoUPcfffd+Hw+Ll++TKlUor+/H4fDgd/vp1AoYBjGCvPOYDBIMpmkVCqRTqeZmZmht7cXn8+Hz+eTSZZwICiVSiSTSRYWFigUCvT09BCJRCiVSkSjUWKxGH6/n8HBQer1Oj09PZRKJaanp6lUKgSDQQYGBqhUKjrKrFAoMDw8THd3N7VaDYfDQT6fp1QqaRHU7/frVf9ms0kwGMRutzM7O8tNN91Es9nEZrPpSDblhwHLJqWlUumGVyMrFosq1F8QtgTDMHTf9ng8OvXV7Xbj9/spl8ssLi5y4cIFIpEIDodDjOAPIB6PB5fLhc/no1gsUqvVlD9WcafbJgiCIAh7BRFfroPu7m7uuecenSIRjUa1KWG1WmVmZoZ0Oq0nbqqKi5roDQ4OEggEiMVi9PT0EI/HOXnyJKFQCJvNhs/n01VfZJAr7Fd8Ph+9vb3EYrEVK+qtVguPx4Pb7dZlpaPRqI4+sdvt+vNPPPEEiUSCSCRCpVLBNE0CgQA9PT2Ew2FmZ2eJRqPkcjm9clsulzl+/DjlcplgMMjo6CgAhw4dIhKJaM8Zl8vF4uIiDoeDQ4cOkc1mGRwc1KmBgrDXsdvtWmxxuVz6d1XBz+PxEAgEqNfrOnVPIjMPFioyqquri/7+flKplEr13BeGuyMjI1pcX+31iYmJG9cgQdiFGIbxQeAFQBJ4o2mapfVeNwyjF/g8UGe5NP3rTdOcu7EtF4Tdg4gv14HD4eCWW24hGo1y4cIFFhcXtQDT29vLwsICxWKRaDRKs9lkYWGBUqmE3W6nWCwyOTlJNBrFZrPRarUIh8N65TEQCKwwIpVBrrDfsdlshEKhFeILLPf9Wq3G9PQ0drsdj8dDvV7n5ptvZn5+HoDp6WlM09ReLko8eeKJJ6jValpw8Xq9ZDIZHA4HmUyGyclJnvWsZ3Hy5Enq9TputxuHw8Hi4qIWS1WVJPW7ioTZiQpkXq/3hm9T2N80Gg0CgQDBYBC3202xWMTn8xGPx4nH47jdbgYGBnC5XOTzeQqFAqFQSITHA4aKHlTinErvBPbF4GQ9YWUtYUYQ9iOGYXzdNM0XWn4/BYyZpvl8wzDeBvwn4CMbeD0FPM80zZZhGD8L/DzwgRu3J4KwuxDx5Tqx2WwMDAxQLpf15DAUCvHwww9TrVbx+/3EYjFcLhe5XI6lpSVdIjeTydBqtajVanz7298mk8lw6tQpbr/9dmKxmM67lwonwn5GpR0Vi0X8fr+O+lpcXCSdTtPX18f4+DhPPfUUNpsN0zRxuVxa1JyfnyccDtPV1UUgEODMmTNEo1EWFxe1SKJMeMPhMF6vl1gshs/nw+Px4HQ6mZ+fx+1202q1cLvdZLNZnZ7k8/n0an+xWKRcLu/YsapWqze8wpKwv3E4HEQiEQzDIJvNUqlUsNvtdHV1US6XKZVKTExM4HK5KJfL2Gw2XflIOBiYpsn58+d58sknSaVSZLNZvF6vmH8LwsHi+cA/Xfn5n4DfxyK+rPa6aZpNy3uCwBPb3E5B2NXI6Ok6Uavh4XAYp9NJsVjk3//935mZmSGRSJBIJJiYmOCpp54il8vp6itqEgjo0p7VahXDMLQR79mzZ6nVajrcVxD2IyrtaHR0VHseFQoFSqUS+XyeRx55hCeeeIJLly7x8MMP89hjj/HUU0+RSCSYn5/HZrPhdDqJRqP6+5aWlshkMthsNg4fPkyj0WBpaYnDhw/jcrkoFosMDg7SarV46KGH+NznPsejjz5KPp9nYWGBubk5SqUSPp+PSqVCq9XS7ezp6dmxVX+JfBG2GsMwWFxcJJlMsrS0pKMtK5UKExMTTE5OMjExQaFQoK+vj/7+fol6OWBciXDB5/Nx/Phxuru78Xg8SnyRkBDhwGEYxohhGKblX9MwjKcMw/htwzDsV95zs2EY3zAMI2cYxncMw7j7Orf5y4ZhPGAYRtUwjI+v8T63YRj3GYYxaRhG3jCMhw3DeFnbe75uGEbFMIzClX/nNtCEKJC98nMW6Nro64ZhPMMwjO8Avww8tIFtCcK+RZZQr5N2s9CZmRlSqRQulwun04lhGExPT5NMJnG5XLp8bqVSwefzUa/XdSTM4cOHOX78OCdOnOD8+fMUi8s+dgMDAzu8l4Kwfah0o3ay2Sxnz55lfn6eSqWio1Vg+ZpQ15KKIEsmk0SjUQzD0Ca7pmlSrVap1Wrk83kMw8Dr9ZJOp8lkMjidThYWFnRZab/fT6vV0ka6DoeDbDZLIBAgFArpqJNKpbIjqYASbSBsNaZprvgHyxFWHo+HcDhMLBYjHA7T3d1NIBAgEAhIPzxAmKZJLpdjdnaWarXKwsIC9XqdTCajFoYk/EU4yLwP+ALgBF4C/DfgkmEYfwP8A/Bt4L3AO4EvGIYxaprmta6ozrKcrvNSYK2VGAdwGfgBYAr4UeCzhmHcaprmhOV9v2ya5ketHzQMYxj4qyu/PsMwjK9f+fmNQBoIX/k9DCy1bXfV103TfAR4lmEYrwV+HXjbGu0XhH2NiC/XSbtZaG9vL4VCgWQyqVeLTNOkXq8TiUS04JLJZBgeHsblcjEwMEA8Huf48eOMjo5Sq9WIRCIA2kBUPF+Eg4Lyejl06BD5fF6LMyoVaW5uDsMwiEajpNNpgsEg586do1qtUq/X9bXicrmAZTNRVaI5nU7T09OjfVwAbrvtNiYnJxkZGQGWBQ6V/tRoNMhms/q9SvzZiZV/a7UlQdgqQqEQ8XicxcVFSqUSpVKJVqtFvV6nq6uLWCyG2+2mVquJ0e4BpFQqUSwW8Xg8FAoFHVE4MjLCI488AsuTTkE4qEyZpvnolZ8fMAzj/wSeBVwAjgCvNE3z+4ZhlIHvALcAD17LhkzT/ByAYRingUNrvK8I/LblT/9gGMYl4E5gYp1tTAEvvLKdds+Xb7IsnHyCZQHo39o+3vF1wzDcpmlWr7wnC5QQhAOMiC/XiXXVPhKJ0Gq16Ovr4/Lly5imqSsd2Ww2KpUKlUpF+1DMzMxgmibZbBa/30+1WuXs2bOcOHGCrq4uXT5XQryFg0SpVKJQKOBwOLTgUC6XiUaj9Pb2atPP+++/n2azST6fZ2pqSvuxxONxarWa9nwZGBggHA6Tz+dJJBJMTU2t8FOy2+288IUvxOFwYLPZyOfzOsLF5XIRi8X0Sr9MPIX9hvIpm5tbLj7hdDppNpuMj4/TarWYn5/XIozH45Hn0QHD5/PR39+P3++n2WwyMTHBzMwM09PTJBIJAPdOt1EQdhrDMJzAq4AY8BTL1X5+BTh75S1jLEeJJds+9ybg4x2+8uOmaf7cFrWtFzjG1V4rHzIM4/eAc8Bvmqb59bW+xzTNx6+kMt0PzLMcDYNhGH3A203TfH+n14E7DMP4fZYrHVVYNuIVhAOLiC9bRKPRYHJyknK5TLVapdVqEQqFyOVyuFwu/H4/hUKBcrlMrVbTBqBerxefz4fT6aRer7O0tMTCwgI33XSTTPSEA4ma3NVqNWKxGHa7Hb/fTzabJRgMEgqFmJiYIJFI0Gg0sNvtGIZBtVplYmKCdDqN3++nVqtRLBa5fPkyLpeLY8eOkcvlOH/+vP49kUhgmibNZhO3283w8DD9/f309/dr418RQIX9TDab5fHHH6dWq+k0vUqlQnd3N61WS19/KhVPOFhYq9BVq8uL14lEgrm5OfV7c80vEIT9zcct/ism8PfAvVeiT/4EwDCMZwN/CtxnmuZ02+e/ADyjw/e2p/RcE1dEob8BPmGa5lnLS+8BzgA14KeA/2UYxjNM07yo3mCNerH87dc7/C0BvH+N1/+d5fLTgiAg4suWkUqlSKfTOtrFMAxarZb2gqlUKpRKJV0Kt16vU6/XgeXBbzKZJJfL0dXVRavVolQqyUBXONCoEuyVSoWFhQXOnj1LMBgkmUwyOzvL3NwcpmkSDAYJBAK6vHuz2dTVxdLpNN/73vdwu908+9nP1sbYtVqNixcvapGmUqlQr9dJpVI6gq1QKODz+eQ6FPY19XqdUqmE3W6n2VyeRytBs7u7m76+PqLRqPi8HHBUVGCxWGR2dlYL10jhBuFgozxfWsCEaZp564uGYfwK8IfAx+jgc2KaZpplr5QtxzAMG/BJlgWWX27b7ncsv37CMIyfZtkb5s+2oy2CIDyNiC9bhFolvHz5MhcvXmRpaYlDhw4xNTVFtVrV5rlqQunxeHC73QSDQUZGRrjppps4fvw4x44dw+FwyEq7cGAplUrkcjkCgQCjo6M0m00WFhZwu9309/dro12n00mlUiGdTpNKpejq6uLQoUO43W5dNlqJKh6PR3stWf1bBgYGsNlsDA8PE41GtdCiqrvYbDb6+vp28nAIwrZiGAYul4tarab/FgqFGB4eZnBwkLvuuou77rpLREiBY8eOcfPNN/Pkk0/qSnNA41q+a3R0lMnJyVVfVx5cgrDLsXq+rOCK/8sHgJ83TfOvVnnPtqQdGcsGcfcBvcCPmqZZX+cjJhuoXGYYxgdZjmJJAm80TbO00devCDx/appmfDP7Igj7DVmxWIN77713w+91OBza+8XlcmEYBqZpMjY2htvtxuPx4Pf7tfGnzWbjtttu46677uKHfuiHeMYznsGJEyeIRCJ8/OMf36Y9WpvN7K9sd/ezm/dvrbb5fD4d5j4wMMDY2BinTp3iOc95Dq985Su56667eNaznsWxY8d47nOfy4kTJzBNk0KhoKPOZmdn8Xq9BINB6vU6pmliGAZut5uenh5isRiwXDXp+PHjOrQ+EonwqU99itHRUY4cOUJ3d/eNOiS7goN0LR6kfV2LYDDI6OgoHo9nxd9U5Mvtt9+Ox+PZ0siXg3C+9+M+tlotnvvc5/LDP/zD3H777bowwLUwOTl5VaUt67+JiYkta/dq7LZrcS2krXsLwzAOAX/EciTJo4Zh3H7ln7/trSrtqP3f+zp8p8MwDA9gB+yGYXgMw1htEf0vgJuBHzdNs9z2PRHDMF6qPm8YxutZFky+uM4+nQLGTNN8PvAV2rxb1nr9ShTOa1iuwiQIBxoRX9Zgsw8Qn8/H4OAgt956K0NDQwQCAcLhMEeOHNETynA4TDgcpq+vj7GxMU6ePElPTw/Hjh0DoFAocN9991Eq3Xgz8IM2GdnvA4TdvH9rtU15HSmRMhQK4ff7GRoaYmJigmw2i81m05EqPT09nD59mrGxMfr6+pienmZpaYlIJMKzn/1s7rjjDl70ohdx1113cfr0aaLRKKFQCLvdrktOKy8DgI9+9KNEIhEGBga08e5B4SBdiwdpX9fCZrNx9OhRLb6o6keVSoVGYzmoYasjMQ/C+d6P++jz+RgZGeE1r3kNP/7jP87NN98M1xj5shvYbdfiWkhb9xwvvvL/u4FHLP9OW99kmmbaNM1HO/zrJFL8FlAGfg34mSs//5Z60TCMfzIM4zcMwxgBfoFlESdhGEbhyr/XX3mrk+WInAUgBfxn4FWmaZ5bZ5+eD/zTlZ//CXjeJl5/HfD/sZyeJQgHGuNKzu6BwDCMBWD1ONeruRl48ho2ZQc8gI/lm5wdcLF802mxbFBXBXKA1/K3BlAHjl/jdq+Xa93fg7LdkbXCJa+hf203O3VcN8Jm22a78s/JcnWNBsshsjWW0ydtV/538fQ1V2A5lLZ15X32Kz8rRaXBymtSTSJ26rit2r8MwzBZ3p/1BkfXy165FvfqNndqu2v1rSLLzyN1XVR5+nrJAvlOn7tODsL53u/7qMY5A6Zphju9YRc+E9vZzc/Idg5iW9cccwk3FsMwfgM4Y5rm3xmGEQE+a5rmD6/3umEYduDzLFeD+q5pmqev/nZBODgcqGXdzd7EDcN4YCduErLdvbnd3TZI2KnjuhGkbZvDNM11c7G3gv1yLe7Wbe7kdlfDNM32MPht5yCc74Owj+ux256J7ey247UW0lbhRnClbPT/1+Gl17BsDKyE1jBXV2Ra7fWfYVmIaS1b0QjCwUbSjtZmp2InZbv7e7s3it28f9K23clBuhYP0r7uNg7C+T4I+7jX2UvHS9oqbDumaSZM03xeh38J4JvAS6+89aXAv7V9fLXXTwJvNAzjn4GbDMP4023eDUHY1RyotCNBEARBEARBEARhcxiG8SGWvVzmWa5mVLwSLfN20zTf3+n1ts9LVJRw4BHxRRAEQRAEQRAEQRAEYRuRtKM1MAzjrbJd2e5eZTfvn7Rtd3KQrsWDtK+7jYNwvg/CPu519tLxkrbuLnZyH2XbgrC3EfFlbXbqQpft7u/t3ih28/5J23YnB+laPEj7uts4COf7IOzjXmcvHS9p6+5iJ/dRti0Ie5gDVe2ou7vbHB0d3fD7fT4fp0+f3lBeVqvVwmazXfW3ZrOJ3W6/6rWt2u5WIttdmwcffDC1VvWGzfYvK536T/vrm+1LO3VcN4K07WrW6l/tfavZbNJoNHA4HNjt9i1rw165FvfqNndqu5vpWzeCg3C+99I+tj9f1nseWdltfWsz7ObnUDu7pa3rjUVardaWtXU3962dPB+y7etnvfG8IGwrpmkemH933nmnuRk2+v58Pm/OzMyY2WzWzOfzZrPZNE3TNJvN5orft3q7W41sd22AB8wt7F8K1X/y+fxVr6k+VK/XN92Xduq4bgRp29Ws1b/a25ROp82HH37YTKfTW9qGvXIt7tVt7tR2N9O3bgQHYZu7fR+t4xPrz2s9jzqx2/rWZtjt7bOy023dyFhE9Z1nPvOZW7LN3dy3dnL7su3rZ73xvPyTf9v570BFvmyWt751YxFuPp8PWFb8c7kcAIFAAJvNRiAQ2LbtbjWy3Z1B9R/1v5VSqbSiT22G3bJ/nZC2XR82mw2/37+piLqNcJCuxYO0r7uNg3C+d/s+tj9b1PNlrefRfmMvXYs73daNjEVUn9nptt4IdnIfZduCsLc5UNWOTp8+bT7wwAPb9v2tVotSqYTP59vySZGw8xiG8aC5Rom87ehf0qcODmv1r/a+Jf1C2Ayb6VvCwWCr7iHStw4GO/HMkb4lbBfrjecFYTuRUfsWoiJdOnm/FAoFGo0GhUKBVqvV8fX2vwt7E+v5vN5zu1qf2gmkn+4ebDYbPp+PQqFALpeTc3KdSN8W2tnvfeJani2dnm3C3uN6+vZ+vy4EQRC2m52f0e1hrKJKLpfTk6D2Acr8/DxLS0tcunSJmZkZPWBR71MTqFKptMN7dDXyoN08pVKJTCbD/Pz8que2vY/kcjkymcyGJtKNRoNEIkGj0djO3bgKFXZs3Zf1hEVhazBNk4sXL1Kr1QD0fWVubo5kMtnx3nEt1+5WXO87dc9Ya7vrtalT3xaun430hY32lxvVrzb7XO7Urva/bVfbt/J7N/Jd1uvEkoYi48g9gPVZPT8/TyaT6di3G40Gs7OzZDKZFX1Bne9UKrXqOOBa+/tq1xDStwRB2IeI58t1UCgUSCaT+P1+8vk85XKZ0dFRSqUS6XQal8tFPB6n0WhQLpdJJBIYhkEsFiORSODxeCiVSgQCAUKhED6fb9elE1yP58hBo9lssrS0RKlUotVqkc1m8Xg8hEIhPB4PmUyGQqGgj+PCwgLxeJxKpUI6nSadThOLxRgcHFzzWKdSKWZnZwHo6+u7IfsGnb0AVP+wDpykn2w9tVqNxx9/nFarxeHDh5mcnASgXC4zPDy8ql+Q6nM9PT0r7ifWVWvr6vdan9koW/Ed14L1XuXz+VbcR9e7j12Lz8Vuu1dvB2ofPR4PlUpF/6+O03r7r467qp7T6b0bfcasdX47tbn9tY2eL7Ud5YOiBHL1mfbv6dT+9r+tt4/X2pe28vm8kXO1ynUiivsup9FoMDk5idPpxOVy6Up56jzWajUuX77M0NAQ8/PzPPjgg/T39zM8PKzv4R6Ph1wuh8PhoFar4fF49Pev19/X69+rXUPA1pXyEwRB2CWI+LIFWAcihUKB+fl5UqkULpeLVquF3+/X0Q2NRoOLFy+STqc5efIkXV1dKx5IuVyOZDJJb28voVDoutq12gTrWvbtIJjvXS/1ep3vfve7lMtljh07RrlcBmBsbIxSqcSZM2fI5XL09PQQDAb1gKTVauFwOAiHw3qQsxbd3d0AdHV1USgUrmnydy2DfRWmrvqVz+fT/aJ9UiZsPYZhEA6HSaVSpNNpPYCu1WodJ5kejwebzUY2m8Xn8624n5RKJZLJpP5dTbZ8Ph+5XI50Ok2r1aKvr2/Tfcvj8VCtVnU7bpQYZ71XtQ/mO93H2q+BzbRTRR6p6LOt3MfdJOq0i6vtq9MbNeBsN6Pv9J5O9w7rM8z6PrXw0f6ctE4y27e1UVGwUz+y9o9cLsfExATDw8O4XC59v7Yunlj/tt4+qrZdizhzPc/n9u/cyLlqv05EaN8bqGdGNBplYGBAR600Gg2WlpbIZrNcvHiRhYUFurq6WFpaIh6Ps7i4SD6f5/Dhw1QqFYrFIvPz8/j9fiqVyqomze3/r3ftderHV35ubttBEQRB2CFEfGFzg13re30+H8FgEI/HQ6FQwO12k8/nMQwDt9uNw+HgwoULeL1eDMPAbrdTLpeZmpqiUqkwNzcHLD8AQ6HQlg+0rROs1SYX6+37tVZs2ii7aaKxFWQyGdLpNM1mk9nZWQYHBykWi3R1deF0OhkZGcHv91MsFgmHw3R3dzM/P4/T6aTVauF2u/Wgpv3YNBoNUqkU3d3d9PX16bB4WDkI3sgxvZ4V0/YVaNj+fmJlv/WZjaAGyQsLC9x0003A8nkbHx8nEAiQSCTo7u7GZrNpUSAQCFAsFnVKmBJg1Up+PB7Xv6uJrM/no1gsUiwWsdlsOmpL9TuH4+lHxmrnoVKp6Ptf+6Rwo+eu0/s28rfNVGxZK5Ki/Xs7RTs0Go0Vx3OjERjrcaOjDVdrPzwtKDQajRWCXqvV0gJDo9HoGBnSLsi2T/A7nbd2sUVNGsvlMn19fWuKga1Wi8nJSZaWlohEIiv2S6UGq/aqba92jtfqR6VSiUKhwOLiIn6/H3h6dV9de5FIRItE6nX1nvaKiJ22s5HoGnVMrzWSpv07reK69VythtqGsLtR1+rAwAChUIgzZ84wMDBAoVDgwoUL2Gw2Dh06RC6XI5vN8sQTT+hrZmFhgUwmg9vtJhQK4fV6iUajVCoVarWajhBrp71fqmtBXXudxjft1+OVfitRVYIg7DsOlPiy2iBzM4Nd63theSB3/vx5MpkMxWKRpaUlPB4Pi4uLGIZBLpdjaWmJarXKzTffDMDi4iI2m42FhQW6u7s5cuQIt912Gx6Ph2QySSwWu6odKk9XrWBvZDDv8/no7e3VP6+3P9cz0N/oZGm7tr8bWFxc5Ctf+QrpdJru7m6cTifpdJpqtYrX66W/vx+Hw4HL5eLMmTMMDQ3pCcb8/DxdXV36detKMywfG5Vu1Gq19KQYrj63a4WPq3Picrk2FGXTibUiDG6EMLLR9IP9RL1eJ5lMMjc3RywWo7u7m4sXLzI9Pa1z8/v7+wkGgzQaDVwuF+l0mu9+97scOnSIYDCoB71qFbJarRKLxSgUCuTzeeLxOKlUimw2Szgc1mLM/Pz8VWluKsLAbrfj8XhWrGZao6Haz81a17sSF9XKa61WW7FS2t6vlSik3tfd3a0ng9bSuWpSbH2fSqFR7e0UNm9dqe207VAopAXQTpPgzdzb2kV9dfzWimxb61rbzHXYbDZXRCipZ43L5aJSqQBQLBYpl8v4/X4dtaeiPJVg3NPTA6AFCJvNRrFYJB6PayFuvX5QKpWYmZkhnU4zPDwMgMPhoNVq6T7Y09OjI0rbRRG73Y7b7db9Wp2XVCpFIpGgr69Pp/iqc5zL5fTvSqhUfa7TeVXbV6KTx+O56npQk83x8XHcbjfhcFjfHycmJvB6vTq91HquWq0WiURiRXRRe+SW9TpR6R9K5FKT5tWif6z9o9Vq6e+2tsGKVfC3Cq9t53D/3nj3AUosdDgcPPHEE3zlK1/h1ltvxel0cvHiRZxOJ3Nzczz22GNks1mi0ShdXV2Ew2FmZmY4dOgQ+Xye6elpgsEgzWaTer1OtVolGo3i8XiIxWIr7r3t2Gw2fR+1RsNsJjVPEARhv3CgxJf2QabCOrBZTUToFPoMy2lGakUglUrx+OOP4/P5ME0TwzD49re/TTabZWlpiQsXLtBsNjEMg0gkQqVS4eabbyYUCnH58mXK5TITExMcPXqUSCSyoo2pVEqvch89enRDD6dGo6HFHMVqocbXmy6ykbz3dtoHgHudTCbDJz/5SeLxOLfccgu33norDoeD73//+xw/fhy3283i4iLFYpFz586RSCRwOBwMDAzQarXo6uoC4OzZs0QiESKRiJ4owHKakYpIeOyxxzhx4oT+jMJ6TFWbOk1g1UqvNXS4E51WsTtFGKiJolot24q0udXaY+0zB2XAptLWhoaGsNls9Pb2asPdeDyOaZq4XC4mJyc5duwYPp+PqakppqamCIfDegLbarVwuVxMT0+TSqVotVp6QD06OkokEiGRSOD3+0kmk3g8no5pbioiweFw4PV68Xg8KybYHo9nxQRQCSVdXV0r+rSV+fl5xsfH6e3txev1Uq/Xsdls5HI5KpUKkUhE91sleuTzeT1JTqVS5PN5YGWK3Pz8PJVKRa/gKg8uJQqo46Lu/+pnq5E0sOKaUs8GJXR1ena0Xxvt11D7+VX92HotqbTETh4ca/X9zVwXdrt9xflIpVJcuHCBbDarfcv6+vq0P1UkEiGTyeByuXC73VSrVWq12opUBofDgcfjIZ/Ps7CwgNfr1W1pNBpaMGw0GjQaDX1c1D6qYxyNRnW04OzsLIlEAkB7rPl8Pt3vPB4PzWaTUCikj9vS0pKOLE0kEvpc2Ww2XC4XS0tL2O12bWDeaDR0VID1HFrvgSqtbmZmhkAgoFP/zpw5g81mY3R0VN+X1fHJZrO6r6prQok31hSqUqmkoxFGR0d1xI5VTEwkEly4cIFQKKSj3YrFor7nzs/PMzMzQ39//6oCnpqQq3t0IpEgm81q8XZqaorR0VEcDocWg9T2raLmlZ8lOmEXsFqkbFdXFzabjcXFRS5evMj4+DiFQgG73U4+n6der2vRMxaLUS6XaTQa3H///fj9fu644w58Ph+Li4s4nU7K5TLpdJqhoSFyuZy+p/b396+4j7S3pz3CrX3st1VjUUEQhN3OgRJfrIPM1cKeO6VyWNN3+vv79QBSPdhKpZIeeNpsNiqVCvF4nAcffJBLly5RLBap1WosLCxoHwW3260Hg2rA4/V69YphIpFYMeBSg+B4PL7hh9Ply5d5/PHHCYVC3H333XpQ2inU+HpZI2d3zagbNQDcD1EL9XqdZrOp86THx8cxTVOnd3R1dfHEE09gt9up1WpUq1V9LkqlEl1dXczOzlIqlXC73UQikRXRA7VaDa/XSyKRYH5+nmg0Sq1W0wNwNeHIZDLU63WGhob0aq4SHa0TwnaPlk7CY7vnA3QOe7deN9tJe59ZL1x/v1CpVDh79ixOp5NYLKZTMNRqdDgcplqtcunSJZrNpjbhfcYznqF/VhUqWq0W1WqVpaUlfD4f2WwWm81GoVAgnU6Ty+WYnJzU97SxsTEtuCgxoqurS0+ey+XyivQDFWmwuLiIx+Ohr6+PyclJ0uk0sBw9o9IvrMKC8k9Rq6mqj2YyGSYmJujt7cXv9+t0qFqtptOtlKBjFVDUPbpWq1Gv1/VkNxgMAsv3ehXNAWhhR/Xp7u5uLVBZRXubzaZFnnA4rIXN9mdH+7XRfg11inaxRhqpKA0lorcLmuqY5XI5LX5ZX1PHwRpF0Qk1MVLt6erq0lFUxWKR4eHhFc+5mZkZ3G43Pp9P34OU90mpVCIUCun9W1hYoFKp6IlWq9UilUoxMTGBzWbTgo5qo8fjodVqUavVmJqaIplM4vV6sdlsuN1ubWSvUjeVSKJEjFqthsPhoFqtkkwmmZ2d5ejRo0SjUQqFAlNTU9RqNXw+HwsLC2SzWTKZDCdOnODSpUs4nU7q9foK4U0JcblcjmKxiNfrpVar6ft6pVIhlUoxNTVFJpPRouXIyIgWqJrNJl6vV+9nPp8nmUxq8S+fz+P1eunp6aG/v1+PAxYWFvQ5UsdPiVXRaFSLXNlsVm9XHetoNLpCALVGCSpRxuVyMT8/Tzab1eldqVSKmZkZAEZHR3Vq9dzcHOVymSNHjmhRtD0aZjX26315N9E+tlNi9ujoqO5LKno6k8lQqVQoFArMzs6u8C4yTZMnn3ySarWqhbhyuUwgECAWizE+Pq59j8bGxvRzyO/3XxXFtpY43D72u5Gpy4IgCDvJgRJfLHmkq6YuqEGwy+UikUjoSYYaaKnXU6kU09PTejCmVuTi8TgzMzMsLCxgs9mIxWJUKhXK5TLVapV6vY7b7aa3t5d4PE4oFCIUChGJRDAMQ0+yZ2dnCQaDDA4OAsuVccrlMg6HY8ODl6GhIfL5vPYTUauvap9hZYk/9fC7lsFR+4NzPc8E1QY1mFQrnde6/d2AYRjA8uBlenpah9oahoHf7+exxx7DMAyazab2BEilUjrNrFqtUq1WKZfLeL1eXe5UrWqqScDRo0ex2Wx4vV7dB+PxuPb1qNfrLC0tATAyMtLRCNc6mFcTjE4pRNbVKfU9q51L2JzxbntalfW8rzZYt/aZ9hVh2F9pbFbsdjvNZhOXy8XMzAwzMzOcPHkSt9uNx+MhEokQDodpNpvMzc3ptI1wOIzb7db3JK/Xy9jYGIcOHaK7uxuXy4XT6dTpbOrcBYNBkskk0WiUXC7HmTNn8Pv92nujVqsRCoVYWlrC4XDQ3d2tUyDUpMzlcun3OJ1OHc6eyWRYWFig0WjolDwVPaD6mUqLU0K3GviryZ46DspryxrRAMvCihIe1QS3VCoxPDxMILBcXa7VaunoCavwovqYEpsUSixyOBz09vau8CmwTnrbr5HVro32vqr6q4o0UqkeartW3x7F4uIi6XT6qrB+9ayzCKLr3lRVe0KhEGNjY1y8eJF8Pk+lUlnh91Kr1XC73TrlxufzaZFpZmZGR6IoUSWfz+NyuXQ1QJfLRSwWw+v1EggEdPSdEp6SyaSuyuL1eikWiywsLJBOp3WKk0Id05mZGfL5PA6Hg+HhYdLpNOFwmIWFBfx+P0NDQ1r8yOfzOtJlbm6OYrHIpUuXCIVCuFwuotEoCwsLVKtVIpGIFkIcDoeO6kun0xw6dAibzUa1WsXtdtPT06OFq/7+flKpFHa7nUwmg9PpxOv16u9TYkutViOZTJLL5Wg2l71Fo9Eo6XRaR79aRcX5+XntIeZyubSpf7lc1oKg1+ulr6+PdDqtrz1YjtiCpw11VUTY0tIS3d3d9Pf302q1iEQiRKNRfL5l/yeHw4HD4dCipWqTEqg207dUXxe2nk4RJNZIE5fLRbVaJRQKUS6XiUajzM7O6sgXlY45NTWlo7sOHz5MNpulWq3y4z/+45RKJU6dOsXZs2c5e/YsgUCAm266ifHxcf0cska3AStSm62RtNa2CoIgHCQOlPhixfqAsuZ+q0GwWn1U4opazQN0eHIwGMRut9Nqtejp6dFhz5lMhu9+97uMj4/Tai2XHC4WixiGQavV0itePT09HDp0iGKxSDab1YNRtfLX09OjB/9qELraKqYaUJVKJXp6evTk57bbbtOTeOUVAuiBvIrqKRaLelV5o4OjtVaz1svnVZ+FZWHpWra/21DnRoWkO51OKpUKwWCQdDqtJ6JqEqtWMIeHhzl06JA2YFZ9KZ/PEw6HyefzK1bOc7mc7iu5XA7TNJmbm8Pv9+vJtN1ux263UyqV1kxVsIa8t6cSWVenHA7HmtFh1vNmHfCttdppjShrN9xbbbCuJpWrGUnv19Dler3O5OQk/f392nRUrVwnk0mCwSDz8/P09vaSy+VYWFjA6XTS3d3NxMQEyWRSG4Q6nU5OnjxJJpMhEokwOTmpJ9b1el1P8tSxLpVK1Ot1FhYWCAaD+j0ej4d6vY7dbmdpaYmuri4mJyep1WqUy2UdYaMmtaVSifn5eebn5ykUCnqSqiJu6vU63d3d2vBXGVLbbDaOHDmy4j6STCa1d4b67gceeIC+vj6d2qTSSlwulw61z2QyOBwOPUmwppaq0u+lUkkL4CqqRd17lfitBAblJaOqO/X3918VBaP64mp9VQlWgE5jsZaQV6k11gg2WL4OK5UKDodDR6ZY0wytAjtrlGxtNpssLS2tiLDL5XJEo1Ft2HzhwgUtnMzMzHD06FEt8p08eVIfx3Q6Ta1WA9CRml6vl0wmo++JKgXC5/NRr9d1uovqz+oZtbi4SCAQ0M8FZVoOaKFhYWGBy5cva4Gut7dXRx56vV78fv+KCBYVLeVwOFhaWuLo0aNkMhm6urpIpVIMDg5SqVRYXFzUKUOpVEqL3Sr9NxQKEQ6HCYfDRKNR3G63rggTj8fJ5XL6ODqdTrLZrO53KkqgWCxy9uxZLW7F43Ed2VOtVrXIUqlUdNSqqmymfHempqb0M6FWqxEOh3WEl9PpxOl06mtlaGhICzqJREIL9KVSSd/fJycnqVQq+hpXCwGNRkNfiyrCKRKJqAiZddOO9ut9eSfplBKsxlaqsqbb7aZer2MYBvPz80xMTOhowrGxMT1WKJfLLCwsaK+tRqOhvYxyuRyJRIJUKkU4HOaee+7R/cw0TR195nQ6VyxOTkxMEAgE9L0Wdo8INzo6yuTkZMfXRkZGmJiYuLENEgThQHFgxZd234pCoaBFEkCbSFpXRdXKqDLvU14KasUNlgec4XAYu91OsVgknU5rc8t6va6/e2FhgUKhwMjICD09PczMzDAwMKAHsmrAp8KOrUaHKiJHrRSrCdLExIR+EKrBuzWkXLWxfcLb29u7YtV3o6z1IO3kd2D9u/qsGjAr4cIaVr3XQpStwpiKIlhcXOS73/0uz33uc/H7/XpC4HQ6V6Q/ZLNZnnzySVwuF/39/aTTaYrFoq4ao/wCVIpaOp0mn8/T1dVFLpcjk8kQCoWoVqvMzc3p0HJYWQ52rUHwRoWMjQ6k2yeD7edS9T31c3s02mrbaP9cp31QpqHt10k7eyUcXpnFPvHEEzrFplKp0Gw2KRQKfOtb36LVanHkyBGcTie1Wk1HQUWjUS2ClEolzp49y/z8PIODg3oSpiZX3d3dZLNZnnrqKW0Abu2zPp+PWCym/66E6HK5TCKR0FEAKmVHeXP4/X4tLsZiMfx+v07LaLVaPPzww/j9fvr6+ujt7dXpKpOTk7rShhIfVbqNNf3qzJkzOo3q0KFDVCoVHXlRqVR0dJma/FrPt9oXJcB4vV6dStV+TXg8Hm2qriJrVEpTuVzWorb1vreWkGgVQK1Y78epVAqn07nCn0a1vV6v6wgKlVpmLS2unm2sUbK10Whw9uxZXaWnVquxtLTEiRMn6O7uJpFIaG+HxcVFvRIOy5P4aDSq9+vEiRM6nUele9XrdWZmZrSg4XK5dPSM2+3WUVbFYhGn08ng4KBOYVCChxJ+VApWMBgkHA4zOzur+64SqGBZUHriiSe0EJPNZmk2m1y6dEkfv+7ubvx+P0eOHNGTVWv1QhXJtbi4SFdXF263m9nZWRYXFxkcHNTRJXa7Xad5+v1+stksoVCIubk5HdEYjUaJxWK6fcFgUEdu+Xw+HekYDAbx+/3UajXsdruOhFTiqBIC1XNBReaqVMBGo6EFM+XHpK4ZFe1VLBb1dWOduM/Pz2sfJ3XPSKVSlMtlstksw8PDWlSFZRPmK/123XFkp0jL1SJihY2xWkqwWtRYWFjQEYbqGlRV8+bn56lWqywuLmpvLdM09f1bpaVGo1EMw6BarXLmzBkAQqGQFrh7e3ux2+0MDg5y4sQJLQZ5PB5GR0ev8vPbLSLc5OQkpml2fE1FMAuCIGwXB1Z8saJCtq0PhEKhoAeY7SufyvhUrcKpEGe/30+1WsVmszEyMoLdbuexxx7TAzlYvrGrQVAul+PRRx+lXq9rH4SxsTFM09TfbU13Ug9VJRJZTQx9Ph+jo6M6fNTKWpNo5Z9gZaMDoo1M5Fcrh2z9rBqEqSoZypei/TN7CbXKWalUaDQaTE9PMzw8TKu1XE7a6/XqAX21WiUYDOqIht7eXmKxmD4udrsdh8OhKwrMzc1x6dIlLfSdOHGCVCqlw9O9Xq8ebMPKEq/tk0m1+r+ZwdBGo5OsoemdjK7b+157n1grjW09M19VGar9Omlnt6zEbQS3241hGFQqFX1/GRkZ4cknn9TVtY4fP65TIlUqhqqq4vF4yGQy2lwzl8tx4sQJPbGbmprC6/VimiZ9fX3aiHRxcRG73c7Q0JCODFHXp/LimJqa0qlRKnpQTYbVZLG3t5d8Pk+1WtUTcFXZq16vEwqF6Onp0aWqlaDYLhoHg0FsNhsDAwP6vnjs2DH8fj+Dg4Pa50VNVpUnzFqGtz6fj2q1qq+1kZERgsGgvpda78HW9Cx4uqqTmgSpiD7r+6z+K+2TTo/Hs6JyjzUS0Vq9Sl1P6toIBAIcOXJEt1/9XS0gwNPRa2vhcDg4ceKEPs9KXILl66hYLOJyuZiYmKBcLuNyuRgaGiKVSlGtVpmfn9f+MMFgUAuj5XKZTCZDrVajv7+fQ4cOAeg0JGWgHAwGqdVqOmpE+eioe93CwgKLi4uMjY2Ry+W0WbI6t8FgEK/XSzKZ1NFTTz31FA8++CDxeJybb76ZSCTC+fPnteF9OBzWRvmZTEZHgfh8T3vBqJTPWq2mzWe7uroIBoPcc889Ogro4YcfplQqMTs7y+HDh5mbm9PeM4Ce4Fr9alQ1MdUWlcIBy+nChmHgcDhwOp0sLS3p826z2XQkkIpMUenNyv9F9RNllHv06FEd2aj6phJbFhYW9HVarVbp6+ujp6dHX5tdXV3Mzc3paJhisUg0GqXRaOi0LqDzLLYD60XEChtH3S+sEX4qRVF5Mfn9fpaWlpidnaWrq4uTJ0/SbDa1t1c2myUSiWij3Xw+TyQSYX5+Xi/qxONx7ZmUyWTI5/MEg0GCwSBHjx4F0JFkKtXRagBtZS9HNguCIGwVIr5YsE5C1SBbDWiKxaIeuIRCIQ4fPqxXPmOxGMFgEJ/Pp3PBPR4Pbrebc+fO6dBPm23Zgd4wDC2u1Ot1ba64tLTE448/jmEY9Pf368gaa2qH2+0mGo0yNDSkVzvVa6pCTqf9sj7w1pu4bnRAtJEHaaeJh8K6CqYmTC6XS+ce7/TqyPWgRA0luMCyAbLb7ebYsWP09fUxNjbGpUuXdFiwCoFXVbny+byeeKnVbZWipjwwRkZGdJqCCh8fGBi4qi2rsdY5vN6BcbsPxUbeb01ZsvrBWNOjrP13Nd8Ya4Ue63XSzm5ZiVsP5RtkmqZOmxgbG6NSqTA4OMjg4KBepT9//jyA9nxQAkcwGKRarXLs2DEikYiOPFAVUlKplPYuKZVKDA0NkclkdIrc0NAQsHzMw+GwLgmtJpn1el2nkpRKJT1JVhELyjBXpT+qCANYPnfKuFFNHiORCC6XS5cwthrC1mo1ncIJaA8tFcVTr9d1OpDyeFHbsaL6jBJF1TPAmvrTnkKk7mXW71Npqe19vd1/RfVx67WlUPsOy5FOyvej0WisuM9bf26/l7eLqZa2rJoaonxchoaGmJmZ0Qa36v6lBOQnnnhCp7ZcuHCBvr4+otEopmnqiIpisciRI0cYHBzUZq4q8mJxcVFPCNU2lIikFjOUr9H8/LxOe1CplCoSye/3U6lUyOfzLC4uEovFtI9FPp/H4/Fw+PBh8vm8rhxYLpcZHBwkFAoRi8W0GbTyPVF+MmoRxDRNfQ78fj9ut5tAIMDIyIiOIotEIhSLRV0dKRAIMDMzQzabpVQq4XQ66erq0tubn5+nVCpx++23a28VVQ2vXC7r8zc9Pb2ixLc6/qp/uFwuFhcXiUajZLNZ7emmohyi0eiKPtXf36/7iTXtWB0/tQCgSs/39fXpfh2JRLThqjJ8T6fT+vzF43HYoPjS7iFm7Z+7/f67W2hfhFDPRuVRNT8/r8/NzMwMPp8Pv9+PYRjYbMsVtNLptBboHQ4H4XCYY8eOkc1mmZqaIhQKYRgG+XyeWq2mow/V/TgajXLkyBGy2ayumFQoFIjFYng8HsLhsF6slMgmQRCEqxHx5QrtKQ/qnxqEKBGmvdyuEmjUhKFarWIYBqVSiXQ6TXd3ty7L2Ww2dR64inAIhUIcP36cY8eO4fF4tFeB8gVR728fsNhsy6Uyr5dOecNbOSDqNPGA5cHfhQsX9EROhVF3inrYLjYzMLgy2drQ6MHv92O323X6RW9vL263m5tuukmbkapJXFdXF6Zp6kGzWn1PpVLA09VUVIlTn8+nK4Qobx/Y2ggOa59YTThrf+9qx/Baz6W1f6hJcCe/o3bfGOv12m5A2qm9293XNtrHNtK/lLluo9HAbrfT1dWlRdtWq0U6nebcuXNUKhX8fj+HDx8mEAhoo1vlNXH48GEdqRCLxZidnSWfz5NOpzl16hT1el2Lx93d3SuqGSkfl+7ubi10tFotHallLd+sVuXVeVB/U/dM9bdAIMDY2Bg9PT36eCkBVkWVuFwuPbm0GuZaSSaT+n4yMDDA/Py89gZZ79zA09WOrNElcHVEVvv9TH2PErmUB43VpHatNEy1TfVeVRY5n8+ztLTUUSxfrV+19+eN9G/l6RKPx1ccUxW9oQQJ5SM2NTXF4uLiCh8QlV6jzGl9vuWKKqlUikgkQjqd1h49/f392mNGiSPxeFxXAzx//rw+z6VSiXA4TL1e16lksVgMQE/+rOemVCrpSJORkRH6+/u1QXKlUtGpUCMjI9qDSFUkUtdIOp3mec97nq4qp6oZKRHo3LlzLC4uctdddxGPxymXyzrVrVwuk0wmGRwcZH5+nkAgQFdXF729vdx///00m00cDoeuYpXP5/UxPHnypPYmUp+bnp7WVbvU9Vsul5mbm6Nareo2OxwOkskk1WqVRCKB1+tlYGBAjxsymcwKPzifz0d/fz9+v5/5+XldSUo9e6yeQ7FYjN7eXkZGRlhaWtL7qVL52IDni0qXzufz2hdpo/1TeJpOhSLUvaRSqej0QGWYrEyhla+WivyyGuP6fD6mp6fx+/06ddkwDH1eVFWtnp4estksfr9fR+fW63VM08Tlcmnjb0BHZ0lkkyAIwtXsC/HFMAzDXC2Bc4NYB9hWo1H1MGk3E1VYBw+BwHJ50snJSVKpFKVSiVtuuYVwOKyNzFTOucfj4dChQ9pI0jRNms0muVyO48ePU6/XV/glWFcJtxL1cGwPFd3K7awl5qiJooocap/4bBfWNCdYf2BwZfK5qmmlwm63rxDlRkZG9OT3yJEjNBoN+vv78fl82tBSTaTS6bQ2pOzt7dWmyyr6SQkgKlzfylYKZsqrxSoqrnZOtntwValUSCQS2Gy2FSuyCuX/ogQiq19Qp7atFkGzHWymj63Xv5SwoSZNx48f56abbsLtduuyvN3d3QwMDFAqlRgbG9O+GarfxONxTNOkt7dXT2pVuofK24/H4zp1UqUYqUgQ9blsNgugBVM1eVRRCu2itOo7jUZDR75ks1ldOUmJHpVKRQsb6nhZS/xevHiRQCCgK8BZIz+sooGK/lClmle7Jtr7gvVYWT/T3vet15r1HFerVZ0SqlaDVTs6pWFa05jU/qvtqMgtlSpmLXms2KrrzvrsAfR9qFar0dfXRyKR0JFJrVaLxcVFXWXK6veiqlYp0eTs2bMkEgmOHj3KoUOHyOfzOopErc6r/QyFQpw6dUqb46poK3UMrKJMJBLB6XSyuLio90FVpurv7ycWi1EsFrHZbFq0UP4ThUJBi0gq4sbtduuIgPPnzzM1NUUgENCpbDbbstmu8thSYtD8/DxHjhyhr69Pp5gq0bxd9AuFQjz3uc9lenqakZERGo2GNsK22+3a+2h0dJREIkFfX58u+6yOvTrOKurr6NGjOqrIKo6pCk9qvFAqlRgfHyeVSnHq1CkGBgb0taPeUygUOHz4MLVabUXaciAQoL+/f0W6khJgFxcXVYrZqqJxq7VcPODy5cs6dcn6mkRFbA5r5J0qpADL18iFCxcoFot4PB7tExQKhfR9pVarMTExwdzcHPF4nGc961mkUim++93vMjExgdvtxu/3U6/XcTqdDA0NMTAwoMdmqpLYyMgIIyMjWmiMx+M6/TGdTjM0NLQiWk+1WxAEQVhmz4kvhmHYgLezbCB4P3DWNM3mWgKMYRhvBd7q8/k4ffo0b33rW3nrW9+64j2rrXRuBpvNpvPCVZiwMjhTq4qlUgnDMIjFYtx5552cPn1ar/CqUHnDMPB4PNroUk1sOkUfXO8ARu2j8mbYDjpN3AOBgB48Wl/frP/ItaJSxqyh/tD5eN57773ce++9AEcNw3gAuNc0zXvVZ1T/Uuetv78fr9fL4OCgNpsNhUI6V7rZbGpRTa1q9vT0EIvFmJ6e1kamKpRYmdWuJXSsJ1htpp9YvVqANSew2xU2bu0fKkTeinV/lBmrqvalJuI7HdKujJTV4HU11upfqm/ZbDbOnTvHHXfcwXOf+1xe8IIXMD8/j2EYuN1uHRGVzWbp7++n0Whor5uhoSHC4bA2oFV+RGogr/qiMgVXqR3WMr1K0FD/qyos0Dm9rFMKmbXimjXqSKWsqCgYuPrcqepfPT09Hft5pxScaxFxN3sdqetErQz7/X5deajdf6s97cJqOt5+jVkjTlTaCVx9La7Xty196+bV+pbH4+Etb3kLL3/5y3n1q1+9Qiir1+u68o7aXxW9oHxElHhmxefzceLECeLxuE6NVNGd7eW21bFU17FV6FPRp6oa0OLiIk6nk3q9rtN9rUayygtGTQaVb4sSD6xpWYlEgsnJSbq7u2k2m9r/pK+vj+7ubnK5nC4prgTAeDzO85//fF0Zyyoc+nw+hoeHWVhYwO12A+hoHXjaxF/5v3V3d+vxgPLZUSW5lf8RLEeeKMG9UCjo69J6vSiRr9VqaSFHiX8+n09vq1PZX3Utejyeq/zibDabFsDL5bJOzevt7eUTn/gEH//4xwFOrNa3vF4vd955Jy996Ut5y1vewtDQkH6+tKeMCutjjbyz3jdU1TXlt5TNZnG73Tp9Wd2fFhYWGB0dpb+/X5dBV9GQoVCI4eFhqtUqPT09DA8Paw+wVqtFMBjUEbfqfq4qcql+pcY01vN5Led2rfuWIAjCXse4zoCRG4qxbEP+deA8cBLIXvn9/zJNs776J5c5ffq0+cADD2xnE/VkOhQKceHCBdLpNN/73veIRqOEw2G+973vMTExQTgc5u6772ZsbIyZmRmCwaAuDdvT06MjZ9TgKRgM0t/ff9WDTE06rzda5UauQu2GFa/2lAP1s5oQdTqehmE8aJrm6dW+MxKJmG9/+9t58YtfzOLiIqlUimw2y6lTp+jr69NGpqosbW9vL2fPnmV8fJzR0VFuu+22FT44atKlfC+UGKMqe2yGzfaT3XSOPB7PVZ5AaqVbTeTVpG4tU1Xrd17Pfm30O1RJ5I1G2azVv2KxmPn6179e+wXdcccdNJtNnSagVr2V6aEqDe1wOLj99tsZGxtbtc8DejCtJvmpVIpms0koFNKGve1stE8VCgXm5uZ0SpCaVKrtq9etptPtbEd/vJbvbN/nzXzHtX52K/Z9rb71zGc+0/yXf/kXbTSrSpCrCZtVeLPeg5QgbBWi1P6pe+labe7Uf9S+qvugVSxRixCpVOqqKmbtn1Pfqd4fiUTIZDIr7p2ZTIbx8XF6enro7u7WnlzlcpnFxUV9j7ZWDzpy5Iie/Hbaz1wux/j4uI4KavcvU21UaWs+n4+5uTmmpqa45ZZbdOTaWvevTh5Xnd7X3q72e5E6/u3HuB0VuTI4OEgmk9HRR6o/2O32h03TvKNTO+644w7zS1/6EufPn9fi22rntlO7havplCpeq9U4f/48fX19LC4uaoGs0Wjw0EMPEQ6HyefzTE9Pa3FxYWGBM2fOaFGzu7ubw4cP67Q+h8OhzZbV9ZZOpxkcHGR0dFS3w+qptdXnbq371vWM5w3DWLPa0V6aFwnXxnrjeUHYTvZa5EsfsGia5lsMw/AB7wTuAbKGYdx7valHW4HVqPHUqVNkMhmCwSD1el1XDHnsscd0mUubzaYrSKjIGfU9Xq8XwzB05ESnVc6tWt2/kbnXuyEP2Lq/7akA1v83Qzgc5md/9mcZGxujVCrpdJB8Pk+lUuHQoUN0dXWRyWR0hIHKo1YrjCoCqVwu09vbq6MnrJMMa3rCRtnsfu2GXHxrP+np6dHpHUp4UddKe19aq91bsV8b7b/WycX14vP5OHXqFMeOHaNYLDI1NaWNMn2+pyud1Wo1hoeHqdVq3HLLLWSzWW2Uu1qfV4NnFWEwNzdHMpmkv79fp8it1ibr/2u1Xa3IWieM1vMVDodXrYrV3vat4lq+s32fN/Md1/rZ7b4WlX+Qw+HQK9nWdLB20/dORsQqmgeuLhu/Wts79R+ryKO2ryq4qN9VKovV80KlrF24cIFDhw7p71TRXktLSxSLRR0pB8tRRMeOHdPfoypOqcpyqtR0q9XSfhjt6Wedzo3X6yUYDOqIxfYoADVpVfew/v5+bQy93rnuFOG12vvat6tEaiXuW4//WpNlZbTbaDQ6Rrixjpmzy+UiHo9Tq9Wo1WorIjbat7sbxga7nU4pjEo4z2azOBwOHZWVTCZJJBI0Gg2OHTum0+ZmZmZwu92cPHkSr9fL8573PJrNpo5SUx5WKpJFefyo54nyG+zq6lrRNjlngiAI67PXxBcD+AHDMP6TaZr/wzCMPwfiwKuATwKlnWxcJ2q1Gl1dXdTrdeLxOB6PR6/CpVIpnE7nCoNKVfEhEonoz1i9Z9rZDZPkzbLT6SDttA9Cr/V4xmIxXYFGDXZVOVKHw8HQ0BCVSmWFSW6z2SSTyTA9Pc309DQnT57E7XYTj8f1QF1VDGlPydgMe72fWFNbrCt+1lXAnWjXWmzlMXe73dx1112Ew2Gmp6dJJBK6msXAwABHjx7V6UIqOgjQUSYb2QfV3pGREWw224pB9vXs33oTxk5pS7uV6zmnu/0abE8FVdF3GxXfrMKJul+5XC4SiUTHaL21jsdqIoyauLtcLi5duoTT6dRpNlNTU4yPj2OzPe0l0ymt1hpd0X5PVelTKgVHpfG0CwWrtVt5Eimj6E4CQ6f+fvTo0W3t+50m7MrvZTXa763X+ny0Cj/W6MRO7LaxwW6hU0SQ9VgpkVCZdqt0I1U+XhlNDwwMMD09zdLSkq4IFolE6O7uXnGPVud4YGBgxbbb09IEQRCEzbOnxBfTNGcNw3gv8GHDMFKmaX4BeJdhGN8GRoEzO9rANtTqVr1e14NIFdafTqepVCp0d3czMjKiVx/D4TAA5XJZGxjut/Db3TYJ2ar2qOiV9sGtynNXRs5qEKyMS0dGRnQqmgrpVgMoq5fCbjtu2037/lp/V/+rY2SNBtrO0PWdDItvNpvMzs7SaDSo1WqMjIxoj6GpqSkikQjhcPi6oylcLhdjY2Nb3fxrbo+w/VirLanfNxKF0el1q2H90tISs7OzACsq6GyE9jQ56wQ+lUrpctEnTpygVqtx22234fP5OHbs2FVttPqlWf2HrGkv1vd1EpRUxMha1/1qIsd6x+5G9f3NiBtbFYVi3VflT2L1sLMeU7kPdKbTubAeK/VzMpnU0So223IFrXg8TqFQIBgM0tXVRTab5dChQzSbTdLpNLVabcU1046cE0EQhK1lV4svVzxeXgwETdP8uyt//hgQBj5nGMZvsRzt4gQWdqSRa6AGOIFAgIWFBf23YDDI0NCQNris1WpEo1FttpdMJnUovqwA7S3WSi1ofy2RSOhqFWNjY3i9Xo4dO6bz6hcXF1eYLh9kVhM91LG0htNvZ+j6ToXFq0gCh8OB3W7XFYlisRiGYVCr1Wg2mzrVQRA2SqvVIpPJUK1WcbvdKyIVroX2FXlg3RXzTl4m7deaVdTw+XzEYjFdlUldi7fddlvH71aGu0r8Via9a01orWz2ut+tERzXkyZ3PVg9eVZLGRW/l9XpdC7avV+sRvMqDc/j8TA4OMjc3JxeHPL7/YTDYSKRiC7wsLS0tGlxVBAEQbg2dq34cqWq0TeACeClhmE8xzTNXzVNswx8yDCMC8ArgCrwc6Zp7jrxRQ10arXaisGsGki2Wi3Gx8fx+XzU63WKxSJer5f+/v41040O2iClfX938/5vJJQelvtAX18f+Xxe58Mrc0YVyaHepyrTXAu7+VhthtUmP+qYWg0l10rPWs10drV+1f63nZpUmaZJJpPRKUWxWKxjLv5Gz3En08a93D+Ea8dms63wnFJci7eU+r5OHjFrUSqVSCaTKz7f6Vqz/i0UCm0oVa1TVSl1vbf7oKx2DVyPZ9ZGTXJ3G1sV8aD8SBYWFojFYivK01vvqeL3sjqdzoU6XiqKKxAIaI+u9mtDRdVa+7zL5eKOO+7g8uXLV3m3CIIgCNvHrhVfgJ8CWqZp/oxhGL8EPM8wjOcC37pirPs/TdP827VKTO8GWq0Wly9fZnFxEUCHgIZCITweD8FgkOHhYb0SpB6yaw3QDtogpX1/98P+q0lJu/GgNZJjKybF++FYwdWTH+uExuPxkEwm9cRxrUmD9XgA6/ar9r/tVAh2vV7H5XIxMDCg/TOsq8ler1dXg9oI7QN32Nv9Q7h2TNPUpWptNhvFYpG+vr4bKjBaTZnXMiS+lnSd1YxlN5oitFpbNkonYemgYDVMLhaLK0zCrelcq4ltwuqsN1aw9jPlMaTOQ7FY1GPOzT47BEEQhOtjN4svS0CPYRgB4DRwJ/BXwNQVo93nGYbxG0B5B9u4Jsos1W634/F4cDqdAHrCnUgkSCaTeL1eHd2wkYn2QRuktO/vftl/tepnjazo9LfrYT8dK+tgsn1Ck81mtV+S1d+hnfVW09d7bSep1+taAOrkh7GZNnYauAsHk1qtxtzcHP39/XR3d5PP5294G9T1rdKD1mMzEX3rCR7bfY13EpYOCspsW0XpWc9VJ1FqM/46B51OHmirRTRavYsUnZ5zgiAIwvazm8WXbwL/GaizHOXyc4ZheIF/AXqBPzFNc9dVN7KiTFWDwSAnT57UA0v1QFQGh51yz9fioK2erWW8utfZSLTF9bCfjhWsHFyqCY3H48Hv9+v0AXhajOhUacR6PNqFrvVW2ncKp9NJV1fXqlE7m21jp4G7cDBxuVwcOXIEr9dLd3c31Wp1zbLf28Vm7nt76R6pBIiDjPKrahd74/H4VdXqSqUSmUyGQqEgHlabZL2Ixk59Ue7/giAIN5ZdK76YplkwDONrpmk2DMN42DCMo8ALgQjw16ZpZne0gRtAlY9WAwyV+5zL5bT4YvV2USU6ZbBxcOiUTqPyt2U1arlE7Pz8/Io8djXpUoNIdbw8Hg8Oh4NGo7HC/2WtFfK9kpZVrVbJ5XL09/frMrbSR4StwDAMbDYb4+PjpFIp3G63Llt7Iz2jNhOBooTWG1liXtg8amGp0WjoMtMej4f5+fkV5ceVIKOEGHUfV/e5/eJdtt34fL4V40h5PgiCIOw+dq34AnBFeHEDvwz8JFAD3rAXhBdYXmXw+/1UKhWKxaJ+ICaTSVqtFkeOHMHv9+v3b6S8p7C/6JROo3LgZbAJqVSK8fFxAoEAR44cAbhKdGgXUDKZjL7e1hNXdlNa0VrU63WSySR33nnnCnPhg9w3hK1BVaCp1Wpks1nq9TpDQ0Pa9PtGiZPrPfusE3CrMbk8L3cvStgrFAokEgldVCCRSADLXiTwdASi6mvKo0TMeNenXZhSx3u1gg2CIAjCzrKrxRcA0zSrhmH8PvAXQNU0zeROt2mjKH8Gh8NBsVgkFotRqVR0WWlVgrpYLNLb26u9YISDizUCRgabyyVqVTluQA8q1c8ej0cb7apVcCV6bqQ60V4RO10uF6OjozvdDGEfYhgGDoeDo0ePMj09TSAQIBwO7zpPCOsEfK+Ipgcd0zT1/TkcDuN0OvH5fAwMDBCJRHQETPu9ulN6qPV/4WnahSk5VoIgCLubXS++AJimmQEyO9yMTaMGEIlEgmw2q93+o9EofX19K1Ys9lIJSmH7sBrjHdSwYet14XA4GBgY0H9X6XqqOku9XqfRaOhSucr/xOr5shfElfWw2Ww0m02AFfsnCNdLo9Egk8lQr9eJxWJEo9EVkQe75fpZa3Iu7E7U4lOj0SAej+t7dCQSIZFIMD09TbFY5PDhw1eV526P5pDz3Zl2sUWOlSAIwu5mT4gve53u7m6KxSKVSoVqtaonT8qA1+FwiPAirOAgD6BWCzG3loZtNBrU63WcTider3fFit9+PHZ2u/2qcvSCsBU4HI4VPi99fX27Ms1D+v3exFrm2JoqpsZFTqfzKnPn3dj/dityXQiCIOwtRHy5ATgcDg4fPqwnjaqMooSHCsIy1pXO1a4La6WjSCRyVTnN/YzX62VkZGSnmyHsQ0zT5MSJEywtLdHV1bWi+ow8m4TrwVpxpz2aU42LrN4u6jNiOi8IgiDsV/b3jGWHabVaKwSXVqvF4uIis7OzpFIpvWKx3yeOgrAeaqWzVCqtel2o9yjBRVXPUKuq8PQ1p37vxEbes9uoVqvMz8/vdDOEfUitVtMVxSqVir4OBeF6qVQqPPXUUzo1tP2+3ulvynRepRsJgiAIwn5CIl+2ETVZLBQK5PN58vk8rVaLYDCoy3jK4EIQNmaoaH1PqVQik8lQrVZxu90AukJUe7h6u3/AXgxpVxNkMd0VtppWq8X8/DzlcplYLEa5XNZRZbB3rhFh91Gr1RgfH9dRLmuNd6yRjSBRV4IgCML+RMSXbUQNHjweDz6fD6/XS7FYJBgMUigUqFQq9PT0YLPZrpogCoKwEmtuu8/n09ErDofjKvHG4/Ho9L79UA3CbrcTi8V2uhnCPsTtdusy7slkkmQyidvtJhKJ7KlrRNh9+Hw+ent7sdvtV/m6KNTYR0Ukggh+giAIwv5FxJdtxDpZDIVCemXe4/GQSqVoNBp6QLIXV+MFYavYbP+32WwrKrIowdJqyrtaWdq9aFDodrt1hI8gbCV2u51IJLJCzFRVwwThenA4HPT09KwQyNtR9/5AIEAoFBLBTxAEQdjXiPhyA2gvKQ2smDjCxtIuBGG/ci39f63SpPutLK3b7aanp2enmyHsYzweD+FwmP7/P3t/Hh/ZetX3wt9n1655lFQqlbpbQ/fp0+4z2H2O3T6ehzcYbDBmMNgBgnlfSHJecm/yIYEbjHlJgBCS68AlF0hywwnGTkJiY5wEg20c4BoP+BqbPh6PTzd9+qjVg4YqlaSaJ+3a+/1D/exTqi6VpG5JVSWt7+fTn5ZUw3521TOsZz1r/dbkJLFYrN/NEY4AhmG4EVTbRfh2ztWCIAiCcJSRle4QKJfLLC0tuSG13RDxXeE4s5v+3ymU2/57u2Dvbt9vmGi1WiKCKhwoWuPFNE13o1wsFikWi0MlTi0MFu3zcOc8DXfP1d0KFQiCIAjCUUEiXw4ZbdCurKxsEQoVhONKu9Bir9LRnalJ7b/3ipw5CnpK9XqdfD5PIpHod1OEI4rWSQoEAliWxY0bN2g2m64zRtYp4V7ZrZiubdssLy9TKpWIRqPu36XvCYIgCEcFcb4cMPrUZmJiwt0wzs3NkclkmJyc5MSJE31uoSD0l/aqYHq8dDO2t0vR2y61qF3IUb9/Mpns6eAZVDY2NnpGzgnC/VKtVimVSq6Y9cLCAhMTE0xMTEg6rNCVO/N1z4lUz72WZeHz+dwiA93QfbBWqzExMdFTK0YQBEEQhhFxvhwwemOpjQjLsvD7/Sil8Hq91Ot1OdURjjXtFYq0Y6Qb252+d0a26N8ty2JlZYXx8XFM08SyLHK5XE8Hz6DSarXY2NjodzOEI4p2UBYKBYLBoKtPFg6Hj1T6nrC/3Ekf8mz3eKvVYm5uDo/HQ6VSIZ1O96x6ZNs2ExMTRy5tVBAEQRA0srIdMKFQaMvGb2VlhUajQTKZxOfzuWG4gnBc0Ya2rrLSbnB36ry0ox2buVxui45AN/2XVCpFIpEgmUwOZUUNwzCwLKvfzRCOKNVqlUqlQrPZpFKpYJomJ06cwLZtstms6G4IXbkzj7Z6PScYDNJqtfB4PLRarS1zb6duV7lcxjRNYrGYOF4EQRCEI4lEvhwg2rAIBAJuBZZQKMT4+LibDiGRL4KwPb1KUGsj3ufzsba2dpeeQLdImmHVrvD7/UxNTfW7GcIRJRQKMTk5uWVtSiaT5HI5LMvaNlpBON7ccZBs65nzeDycPHnSnaOTyeQWp0q5XCaTyTA+Pu7OzcPmGBcEQRCEvSDOlwNCC8ctLy8TDAaJx+PAC8r/sVhsS6lpQRDuppeQrjbW209OtZNTbxT1/+Vy2a3aYhjGls2AaQ7+NKjvTxAOAj1utMhuqVTC5/MxNTVFs9mUdUq4JxzHoVgsEgqFumq92LZNpVIhGAy6dpFEvAiCIAhHGVnlDggtHAebpTsLhQJzc3OuaKbkNAvCzuxmnIRCIWKxGMBdZUzhBS0B7YgpFovcunWLxcVFcrncwTV+H7Esi7W1tX43QzjCZLNZFhYWMAwD0zRZX19nbW1N1inhnrEsi7m5Oebn5ymXy3elkBqG4eoKDWM6qCAIgiDslcE/8h1COoXjQqEQy8vL1Gq1rrnzR6EUriAcBu1jBXB/1hVauoWtay0BbdwbhkEymSQcDpNMJvtxG3tGa3AIwkGh02ODwSDpdJpcLkcgEHCjxQRhr5imyZkzZwiFQti2TSaT2VL5MRAIEI1GXc0vQRAEQTjqDO1qp5QyHccZSAXK9s2ePm2PxWLUarWuRmwvXQtBEF6gfawAW37WY65zjG1XkjqdTh9Ci/cHXS1EEA4KLXhdq9VoNpvEYjG3Up+sS8K9oJRyncbtc7Wex3V1OtG+EwRBEI4LQ+d8UUoZwF8A7wU+2vZ35TiO07eGtdFNp0KHbusTIH3qU6/X7xIKFQShO3r82LZ91zjTf+88qR9Wkd12vF4vJ0+e7HczhCOM1uXQkWSaQCBAuVyWyEzhvmifg7XN0ymWLgiCIAhHnaGypO44Xj4DfNxxnI8qpc4qpWI7OV6UUk8qpS5dvnyZixcv8tRTTx1oO7vpVLT/rbNErj71EcN2MHnqqae4ePEiwENKqUtKqSfbHz/s/nWcMQwDwzAol8tbxk3734dNmLZX/9J9a35+nle96lXSt4Q9sZu+peetf/fv/h2wGQGjx1MkEqFer3fVUhKON3vpW0899VTXubvZbLqRL4Kg2cnmEgRBGGbUgASL7Aql1N8F/ibww8CHgBPAPPA7wO/tFPly8eJF59KlSwfdTJf2CBctNheJRFyD1jRNFhYWmJqawufzHVq7hHtDKfW04zgXt3v8sPvXMNKpb2RZFrlc7q6qQ710kHQJd2CL03K37z2o9Opfp0+fdp5++mlGR0cPu1nCEaBX37p48aLzpS99yR1ToVDILTet0VGb2gEjhwWCplffevzxx53PfOYzW6rOwQv9Zz/m6O10wHRKk+jpDS87zVv3am8ppdhuu9DrMeHosJM9LwgHybCtRn8BRIH/BLwPeCMwx6YzZuBydtojXObm5nj66aeZm5tzT33y+TyFQkGqmAjHBj0m9CYul8t1rTrU+bx2OqNctnPGbPfew0i1WuXzn/98v5shHFG0TplhGNTrdTKZDPPz88zPz5PJZKhWq1SrVTKZjPu7IOyEZVlb+o/uY8CW+ft+Il/a14rOdaPXOiIIgiAI/WDwj4PbcBznslLqPcBvA19zHGdRKfVu4LPANHC5rw28Q3vEi9agSCaT2Lbt5tQDbqWVYam4Igj3S6dOy3ZjoJtuUjs6miwQCNwlWK3Hn44SOQrjy+/3c+HChX43Qzii6HGmx9P4+DhjY2PuptWyNrXtJyYmtjxfEHphmibhcNitpAUvRFYVi8WeJaZ3G7XSba3o/Jv0V0EQBGFQGGjnyx2Nl+8DvI7j/FcAx3E+pZT6TsdxnlVKvQJ4mM37GJjj7Wq1Sj6fxzRNAoEAKysrjI+Pk0gktjzPNE1SqZSExQrHhk7xW9M0u1Ydak9zaB8b2iDXTs16vb7FwLZtm+XlZUqlEpOTk0NV0agXHo/H3QALwn6jI8faq9BodAUxWaOEe6FUKgGb1eX03N9ZgU5HL+oiBO0OGuhdBbJzTWn/+SiIrQuCIAhHi4F1viilFPB5YBl4tVLq7Y7jfD/AHcfL3wT+PXAV+BHHcVb619qthEIhyuUylmW5J4c6VUIbttogkDLTgtCdbmOj24lp+3gql8uUSiVqtVrf2n0Q2LZNPp/vdzOEI4ht22QyGWAzssU0TQqFAgDxeFz0XYT7Qs/FsVjMnac7nSJ6Xi+Xy67WkEStCIIgCEeRgXW+AG8Flh3H+V6l1ATwWaXU48BX7wjrfgpIAQHHcUr9bGgnhmG4ES3tJzkan8/H8vIyo6Oj7mm2lFoUjjud2i07hZN32xCGQiEmJyddA17/P+zRZaZpcubMmX43QziCGIbBxMQElmVRLpdJJpP4fD5WV1cZHR0d2jEj9B+Px+POWzoysdtcrOd1XYQgmUze5aAR8VxBEAThKDDIK9ga8LhS6mV3fneANcdxHKXUdwM/BxiD5njRbFduOhQKcevWLW7fvs2tW7dYWVmhUqlIqUXh2NMp6NlrDFWrVXez2FmVJRaLYZqmK+h4FEQXLctibm6u380QjiixWIxms8nt27e5cuUK1WqVUqkkYvDCfROLxdzUte3mYj3X5/N51tfXuXHjBsViccvcfhTmcUEQBEEY5MiXLwPvBUrAKDAOLCql3gX8r8C7HMdp9LF9PdGn+O2bw0gkQrlcpl6vYxgG8XgcgPX1dSk1LRx7QqHQXYKe3cqIdo4t/a9arZJKpTBN866oGf0cy7LcSLRhOj1tNBp4vd5+N0M4oti27YqiLiwsEIlECIfDNJtNLMvCNE2JPBDuiXK57PYZn89HrVYjkUiwvLx8V4npZDJJpVKh2WySyWS2RL/oyBnLstzUU+mHgiAIwrAxsM4Xx3HqSqnfcRyncSfd6FPAO4CfBn7QcZzn+tvC3lSrVZaWliiVSsTjcdLptLtpbDQ2fUbr6+sAZDIZwuEwJ06c6GeTBaGv6KiVdtp1XwDX6I5EIq4jxbZt5ufn3TKmnSK72kDX40wzTBpLzWaTK1eu8OIXv7jfTRGOIOVymZWVFcLhMGtrazQaDVZXVymVSjQaDR544AHRJxP2TKvVYmlpiYmJCWKxGGtraxQKBSqVipty3Vl0YHx83J3b29OxtW6e1icSMV1BEARhGBlY5wtAW2SLh82qRy8FvstxnG/2r1W7IxQKEQ6HXYNWbxILhQLhcJixsTGazSY+n49KpSKicoLQhfaS0vDCKWq1WmV+fh6AM2fOMDs7S7VadctKb7dRDIVCWyJjhgWllFs6WxAOAtu2CQaDzMzM4Pf78Xq9NJtNms3mluizYRs7Qv/RB0+jo6PYtu2KOo+OjrpztY6s0pFWAPV6/a75W8qdC4IgCMPMQDtf2ngW+L+AX3cc52q/G7Mb9Cn++Pg40WjUrYDUaDTw+/0UCgX8fj8+n4+zZ8+KISEIXdBOS62J1P6zRo+19lLunRvFUChENBolEolsCXMfFvx+/5aoHUHYTyKRCPF4nPX1ddbX1xkZGWF0dJRYLEY8Hr+rqpgg7AaPx+MKoOfzeUzTJBKJuNErzWZzS2qodrwkk8m7ChVA9+hIQRAEQRgmhmIX4jhOVSn1Dx3H2eh3W/ZCKBQiHo+7p/GAe0KvQ27L5TKpVEpyl4VjSy8tiV4Vj86ePQuwbUUM/XfbtsnlcjSbTXK53FCON7/fz9jYGLZtD13bhcFGa5KlUilXG8k0Taampsjn827lGdF8Ee4FffBkGIZr97RHr2gR9XK57Oq47MXRJ/1SEARBGCaGZqUaNscLbJ7aW5ZFLpdzBUK1UZFIJDAMg+XlZXK5XL+bKgh9o1cVi/aKR50/x2IxV8S6PWy98720s3NjYwPLsoayWoZt2ywsLAxl24XBptVqudXFUqkUPp8Py7JYW1vbEmkm1WaEeyGbzbpaXTpqRf+snSXVatUtRqCr1O0W6ZeCIAjCMDEUkS/Dij7NsSzLPbG2LIvV1VUmJibcE/j2yBhBOG7cj5aELk8NL5Shbn+v9soY6XS6ayj7MKCUYmxsbCjbLgw2Ho/H1VbSqR/r6+tMTEzg8/m6pvAJwm6wbZtms0mj0XAd5+2HUBrdpwKBwJ7naOmXgiAIwjAxNJEvw4g+SdRRLp16FaZpkk6nh1KDQhD2i/aIlr2iBRgnJia26FLo98rlciwvL1Ov1129gWEMTff5fIyMjAxl24XBxjAM6vW6K1Adj8cZGxu7a7zczzgVjie6vLRt26ysrAAQi8W6arloPa699jHpl4IgCMIwIbv+A6YznzkUCrklFNursYjhIAjd0ZUy4O6x0kuA0bZtAoEA6XR66KPLPB6PVDsSDgzbtt31CXB/1tXFZH0S7hWdyra6ukogEMDn87mPiV6LIAiCcNyQ1e4A0JtFLWSo85m1VoVt22SzWZaWlshkMpKrLAg90KlFvcZK55jTY6xYLLrjbpip1WrcuHGj380QjiCtVstN3dNjTYtU5/N5WZ+E+0JHVi0tLXHjxg0sy3LnatFrEQRBEI4bPSNflFIvAX4d8AD/znGcD935+584jvNth9C+oUQbFPDCCaKuIlEul13jVpe+lVxlQdgenVqkf9a0n5p2jrl2kV3DMKhWq0NdJtdxHNexJAgHTbVadaseyfokdEPPvztRLpcJBAKYpukWINBzmei1CIIgCMeNndKO3gd8EFgHfl0pZTiO81+BUwfesiGm3dmihXYNw3CFP/VGUtKNBGFntkstane4tAs2amM/kUjck4DjIOL3+915QxD2E4/Hw+TkJIFAgGq16uon6XEja5TQjbb5d9sOYts2+XwewzB4+OGH3TTs9r41zE5xQRAEQdgrOzlfoo7j/BqAUup54A+VUl8CnANv2RDT7mzRxoZt2+7/+jmCIOyd9hSjSCTibho7I2D070dhAymRL8JBodcmHZWpnXzaiTnsY0c4GNoc2ttOTIZhuBEvWky3cx7T0TDJZNItPiBaMIIgCMJRZSfny9eVUh8E/rPjOJ9QSv1L4C/YTEMSetAeTtuu+VIqlahUKgSDQeLxuFtuWhCE3dFeXnpycnJLpZbOcdeeijTMBn2z2eT27dukUql+N0U4YrRaLcrl8hbNpFwux+LiIgDpdLqfzRMGlN1GraRSKXfehc35O5/PUy6XSaVSXfta59wtCIIgCEeFnZwvfxv4M+Bh4BOO47xXKZUFfujAWzbkGIZBIBAgm80yOjpKLBYjEAi40S/lcplCoUAoFNq2WosgCHeznQZMZ2pRp56APtkfHx93tSyGxQljGIZsgoUDw7IsSqWSq0MWCASoVCpSYUu4b7STxrZtisWim4rdbDZd+wjYUpFOtGAEQRCEo8pOzpd/DlwAXqqUCjqO80uO47wfeP/BN2346XaiE4lE3LK5giDsnW4aMDrSpbMstTb6O6uPaYblVNVxHAqFAidOnOh3U4QjhuM4rK6uAi/okNXrdWzbpl6vbykNLAj3gq4+VygUAIhGo246UrPZvMuxLFowgiAIwlFlp2PftwEzwEXgHQffnKNFMpkklUphWRaWZQGbG7+lpSWy2SzhcBhAtBwEYY9YlsXi4iL5fH5LyVKAWCy25cRUP2YYBpOTkySTSTcybVjQlWcEYb9RSuH1ejFN0xWstiyLSqWypSywINwL2vHSbDaJRqOEw2FKpRKhUIhEInFXBbtisehGyAiCIAjCUWMna77hOE4GyCilvIfRoKOEaZqYpsni4iKmaZJOpwmFQkSjUWDzdEdrwcgpjyDsnlwux9zcHJFIhLNnz24JU+9MJep8TG8m6/X60Iw7pRSlUqnfzRCOIF6vl0AggGVZrK2tuZvecDhMvV6nXq8DwxMlJgwW1WoVy7Lw+XykUinK5TK1Wq2r3dOu5yV2kSAIgnAU2cn5Ym/zs7BLdB6z/l9rN8RiMXw+H2tra0N1Ai8Ih8l2IrnJZNKt0rJTydLOx4ZRTyAUCnH+/Pl+N0M4ouhNsdbj0OWAj0qpdqF/dDq/dWqbjrJqn9u30/MSBEEQhKPCTmlHDymlWkqpVvvPd34XdoFpmqRSKTeHHl7YDDabTbfMooTYCsLd6JShdp0WwI0k65WK06n1omnfAAwLSimazWa/myEcUQzDwDAMd5xph8swjRFhMGl3fheLRdfhoqvUtc/tWs8rFotJ3xMEQRCOJDtFvrzxMBpx1KlWq6ytrZHJZJiamqLZbLon9jq/vlqtSoitIHTQK0pFO2banSvtTpX2x/Vp/rAa9PV6nXw+TyKR6HdThCOKz+ejVqvh8/lYX19nYmICwzCk5K9w39i2zfLyMsvLy4TDYSYnJ4HNPiURLoIgCMJxoqfzxXGczx5WQ+4HpZRyHMfpdzu2IxQKkclkWF9fdzeK4+Pjbuit3hgKgrCVXulE7WXbS6WS61jR40mPKdu2yefzlMtlUqnUUDpgNjY2pEqacKBks1mef/55pqamXG2kVCoFSAqIcO/o6MNCoUC1WiWVSmHbNisrK66DTxAEQRCOC0NXPkMpZQB/D2gBnwOuOI7TGmQHjGEYzMzMEAwG3SoSKysr7on85OSkGCCCsEd0qoRlWRiGwfj4OMCWk3pdarpardJsNslms0PrgBnQ6U0Ycmzb3hI9Fg6H8fl8NJtNcrnc0I4Xof/o6kWRSIR4PA6wq760ndaXIAiCIAw7Q7WqKaUU8OfAY8C7gF8Bfkop5e3leFFKPamUunT58mUuXrzIU089dTgNvkNnZZWNjQ08Ho+7YZRTxcHmqaee4uLFi7Cpe3RJKfVk++P97l9Hne20W2DzRN7n8xEMBjFNk0gksqXUtH5tIBDANE03xW+Q6NW/dN/KZrP80A/9kPQtYU/spm9duXKFixcv8tGPfpR0Ok0ymSQUCg3seBEGg930rb/+67/mrW99K7/zO79DKBQiHo+7h05aWLfbvL6d1pdwPNjJ5hIEQRhm1DCdpiqlJoF/6zjO25VSIeAngCeATwJP7RT5cvHiRefSpUuH0NKtlMtlisUioVCIlZUVms2mu2FMJBKSSz8kKKWedhzn4naP96t/HXX0+InFYu5YaT8ZBbY9JS2XyywsLFCr1ZidncU0zYE9Te3Vv2ZmZpxPfOITPPLII4fdLOEI0KtvvfSlL3UuXbpENptlcXGReDyO3+/fopM0bALVwuHRq29dvHjR+fSnP83S0hK2bRONRt2IxHK5TCaTYWJiglgstuV1EvkiwM59617tLaXUtpGkvR4Tjg472fOCcJAM26qmgDcopX7McZwq8G+B68D3AMF+NqwXoVDIVe+3bZuNjQ3Gx8dJJBJuuUWpdiQI3dHjpz1CrP1ktH1z2BklEwqFiEajBIPBod5EejwewuFwv5shHEF0+t7o6CjxeJyTJ09imqYbsVkulyUCQbhndPnoaDTqRr10zsGd8/Ywz9WCIAiC0Iuh0nxxHGdRKfVPgF9TSuUcx/lD4CeVUn8JzALP9rWB26ANCdu2t6j8a1FDqSYhCNvTTXR3uypI2ikDL4yxdDp9l/Nm2AgEAlLpSDhQms2mq0uWSqWoVqsEAgG37LQg3Au6fHQkEtkSrajn51AodNe8LQiCIAhHlYF2vtwR1/0+wOs4zn+98+f3AzHgvyulfg6oAl5gpT+t3D3aCNFooUMptygIe6PdoVkul93w9G5OmV4Vk4YFr9c79PcgDDa6epiOPtD9TfqdsB90m7N139rOmS4IgiAIR42Bdb7cEdf9PLAMvFop9XbHcb7fcZwa8L8rpZ4HvgtoAD/qOM5AOl+0oQFsSY2oVqtYliXlFgVhj+jxEwgEyGazlEolJicn3dS+nTaLw6gnUKvVyOfzJJPJfjdFOIK0r0k3b95kenoan883VGNEGEx03/L5fKytrREIBKhWq24KUruukCAIgiAcdQbW+QK8FVh2HOd7lVITwGeVUo8DX70jrPsRx3F+f5BLTMNmGkQmkwFeOPmpVqvk83lqtZpovQjCHtEh6uVymVKpRK1W2/L4Ts6VYQxxbzQa3Lx5U5wvwoGQzWaxLItarUa5XGZ1ddXVGBqWMSIMJnq+rVQqZDIZZmdnSSQSbhlqkD4mCIIgHB8G2fmyBjyulHoZcBtwgDXHcRyl1PcAb1RK/SxQ6/EefUeLzemf9f/lchm/34/P5xPDQxD2gB5HgUBgi36AZifnyjCGuPv9fqanp/vdDOEIYts2lmVhmiYzMzNEo1FGR0dpNptDNUaEwaR9vq5UKm61o/bIF0EQBEE4Lgyy8+XLwHuBEjAKjAOLSql3Af8r8K47FY8GGm1c5HI59yTeMAxSqZSbjiQIwu5pD1HXGkqWZZHNZkkmk0PpXNkJ0XwRDgqtRdYeMWaaJj6fr99NE44AhmEQCAQoFotMT0+7qdeSaiQIgiAcRwY2mdtxnDrwO47jXAUCwKeAdwA/Dfwdx3Ge62f7tqOzZCJALpfj9u3b3LhxY0spRV3tSMp4CsK9ocdbNptlcXGRXC63Y5nScrnM0tLSUDk/a7UaN27c6HczhCNKtVplaWmJZ599lqWlJVmThH0ll8uxsLDA1atXyefz0r8EQRCEY8sgR77gOE7jzo8eNqsevRT4Lsdxvtm/VvWmW8pDMpmkUqng9Xrdk0UtGApH64ReEA4TPd5CoRAnTpzYookyjMK6vfD7/f1ugnAEaU87isfjbqSCIOwH5XKZ0dFRstks1WqVjY0NsXkEQRCEY8tAO1/aeBb4v4BfvxMJM7B0S3kwTZPTp09vqdBiWRaJRELCbgXhPmgfb4lEAnjB6dJZaUyjo2KGaQPg8/m2lKkXhP3CMAwSiQTpdJpcLodlWdTrdVmbhB25E8m7rWe71WqxtLTExMQEDz/8MLlcjmQyeSSc4YIgCIJwLwzFCnhH2+UfDrrjBdgx5UGX8zRNk1Ao1DVNSRCE3dGuG6DHUXv0WSwW2+JkOWrRMIKwH7RHY3aOGUHYjjvpQ55ez6lUKti2jWmapNNpN91abB5BEAThODIskS84jrPR7zbcD3pDGIlESCQS7uavXC5LuUVBuE/aHS7t0TCdDpZhLDOtqdfr/W6CcESpVqtkMhkAJicnxTEp7Io7c22r13PC4fCW/jTMc7AgCIIg3C9D43wZdrbbEB7FyiyCcNh0jq/tjPphHW9+v59UKtXvZghHlFAoxMTEhPuzIOyGO7bMtiEsHo+HycnJLX1qWOdgQRAEQdgPxPlySGy3IZRyi4Jw/+x2HA3rePN6vZimTNfCwaDLTQvCftM53w7rHCwIgiAI+4HEFvcB0XkRhPvjuI0h27aPzb0KgjD4tAua9+I4zdPC4DA7O4tSquu/mZmZfjdPEIRjjDhfDon2zaLOeb4jVicIwh65nzE0jI6bjY2NXW10BOFeGcZxIfSPNu2WHasdydwlHDY3btzAcZyu/+bn5/vdPEEQjjESx35IVKtV8vk85XKZZDIJSM6zINwru9UN6FbdaBgFH1utlmyKhQPDtm2y2SyWZQHDMy6E/tE29/acmHS1I0EQBEEQJPLl0AgEAjQaDer1Orlczt0MymmjIOydnUq663Glq4npCBmdvqPLvA/LuLMsy90YC8J+k81maTabmKYphwLCrrkzf+5oR1qWxfLyssxhgiAIwrFHnC+HRL1ex+/3s7GxQaFQcMNwJQVJEPafcrnM0tIStm0Ti8XcDWW1WqVcLlOv1ymXy0Mz7izLYnV1td/NEI4gtm1jWRY+n49UKuU6NOVgQOhFW3lyb6/nhcNh1tfXWVxcJJfLSb8SBEEQjjWSdnRItJ+0awNE/739f0EQ9gdt5LdXcdHjLBAIUK/Xh2bceTweRkZG+t0M4QjSrdKRpCEJO9FWnnxju+foUtOmaXLr1i18Pp8bkQjSrwRBEITjh0S+HALtKRClUol6ve7+vVOPQhCE+0M7OQ3DoFQqkclk7jL4TdN0Df9hOIW1LEtEK4UDo1wus7CwwOLiIsVikXK5jGVZkoYkbEub067n5GnbNmtra9i27UbvRSIRLMuiWCwO/NwrCIIgCPuJ7Pj3mW4htTo8t1Qq4fP5GBsbwzAMNzVCNlWCsH9Uq1VWVlawLAvDMAgGg9i23TW9b1jS/prNpqQdCQeCbduUSiXW19dZWlrimWeewbIsEonEljQkQdgrtm2TyWQoFAoYhsHY2Jj72MrKCplMZuDnXkEQBEHYT8Sq2mfaN3PaERMIBBgfHycajTI1NUU8HpfTREE4IHQ4fDweBzarbegIs87T1lAotEUTZlAxDMO9H0HYT5RSRKNRzp07h1KKQqFAtVrtKWgtCLvBMAzGx8cB3H6lD5smJiaYmJgY+LlXEARBEPYT0XzZZ9o1XNpL2hqGQaVScZ9Xr9dd41aMD0G4dzrT93Q4fCQSIRKJuJFo9XqdUqkEbK2WNAy6Ax6Ph1ar1e9mCEcQ27bdtSkcDtNsNodiTAjDgWma1Go11tbWSKVSbiqbaYr5KQiCIBw/ZPXbZ9o3c+2OmGKxSKVSYWxsDJ/PJw4XQdgnqtUq+XyecrlMKpVy/6ajWiKRCNVqlUAgMLQC116vd0vIviDsN4FAANiMSDAMw9VNEoT7wbZtZmdn3bnZsiz38EkQBEEQjhvifDlAdFSLFvsMBoN3CX3qyBgxRATh3tBjzLIsVz+gU8hRR7l0VnUZFhzHcYW6BWE/0RVp9HjREWLRaJR0Oi0OGOGe0VpbpmkSi8UoFouuY0+ce4IgCMJxRFa+A6ZarbK0tEQ2myUajW7ZKNq2vaUEtSAIu0drKgGkUikikYhr6GsDP5PJHAlRx42NDZrNZr+bIRxRIpGIm7bn8/lYWVlhcXHRTdkbhopgwuCh00ALhQK2bROJRKhUKm6kovQrQRAE4bgxtM4XpdRQRO2EQiGi0SjhcJhQKEQ2m2VhYYFsNku5XKZer1Mul4d+cygIh41ON8pms8Dmif3y8jILCwvuqepuRB2HYXNpWRbr6+v9boZwhKnX69i2TbPZJBAIuL9LVT7hfgiFQtRqNWCzjxUKBTY2NrBtW/qVIAiCcOwYCgdGO0opA/gL4L3AR/vcnLvoFP8E3OiWcrlMoVCgUCgwPj6Oz+cjEAi4lZEkDFcQuo+hTtqrFhUKBUKhEMlkEtu2CQQC7ut2U7GlXRh7UNP/bNsmGAz2uxnCEUWPJR0BY9s2i4uLZDIZt1qNILSj5+mdqFar7tzl8/kIh8N4vd4dnd27WQcEQRAEYdgYqhXtjuPlM8DHHcf5qFLqrFIqppRSO7zuSaXUpcuXL3Px4kWeeuqpA2tje6lp/fvKygrZbJbl5WWq1Sq1Wo25uTl8Ph+GYdz1GmGweOqpp7h48SLAQ0qpS0qpJ9sfP8z+dRzoNh46o1NyuRzLy8vUajVqtZrruNTjqVqtsrCwwHPPPUc+n8eyrG2jW/pdbrpX/9J9a2Vlhe/+7u+WviXsid30rWeffZZXv/rV/MZv/Ab5fJ7nn3+eZrNJPp9neXmZer3O5OTkwDomhf7wm7/5m7zqVa+CHfrWW97yFv7gD/6ApaUlnnnmGVZWVrh58ybVatVNxe6c323bJpvNks/n3XVgGCIUhf1hJ5tLEARhmFGO4/S7DbtGKfV3gb8J/DDwIeAEMA/8DvB7zg43c/HiRefSpUsH2sbO0xrLstwTRG3M5vN5/H4/jz/+OB6PB4/HQygU4vTp01J+cYBRSj3tOM7F7R4/jP51lNFjR6c8aGeIjgzTYo2RSATTNLl27RrpdJr19XXXiF9aWsI0TeLxOF/4whfY2NjgzJkzpNNpV0Q0lUq57z9IJ6q9+pff73d+6Zd+iZ/+6Z8+7GYJR4BefeslL3mJ84lPfIJSqcQ3v/lNbt26RSgUIpVKMTIywsMPP0wgEHCdLxKNIMBmtFQmk+HcuXNfcRznpd2e87KXvcz5oz/6IwqFAs8++yzVapUzZ87QarU4deoUpmm6EcArKytMTEwQiURYXl6mUCgQjUa3RC+Wy2W3gp1w9Ok1b+1kbymluJf9zb2+ThgudrLnBeEgGbad/l8Afwf4T8D7gP8b+KdsOmP+CKj0r2mbtJeahk0D5Wtf+xq2bbO6uko+nyccDjM2NsatW7eoVCr4/X5e/OIXS/lF4VjTLf1HG/jj4+OYpkmhUKBSqbiOTcuyiEajlEolbNum0WhgGAbr6+u0Wi1CoRCjo6OUSiVKpZJ7rVKpRDgcdg35Qd9Itlotrl271tUo3CHwTxB64vF4qNVqlEol8vk88/PzjI6OUqlUcByHa9euMT4+7o6RQU/REw6H3Xz/juNQKpVYWVlxU4+WlpZYX1+nWq3ywAMPALjzN2yuA6VSiVqthmma7nw/MTFBLBYjEAhQLpcJhUL3NPfJfCkIgiD0k6Fwvui0IsdxLiulfoZNx8vXHMdZVEq9G/gsMA1c7mMz78KyLJ599llyuRxer5eJiQmazSYTExOk02k2NjZQSjE7O0s8Hu9b2oMgDALaqA4EAl0fD4VCrp5LIBAgHA6TTCbJ5/PcuHGDBx54gNHRUWzbplKpcOrUKU6cOAHAzZs3mZiYYHJyknq9ztWrVxkfH2dsbOwuh+kgopQilUr1uxnCEUQfDNTrdbxeL5FIhGg0yuzsLCdPnuSBBx7Yok+mdWGE44tOAbojluvZ7nlKKZrNJplMhlOnTpFMJikWizSbTUZGRhgZGWFxcdHtc6FQiGq1ysTEBPl8nps3bzI1NYXf7ycUCuHxeKhUKq4DMBwOH9IdC4IgCML+MPDOF6VU3HGcglLKUEopx3H+XCn1nY7jPKuUegXwMJv3ketzU13aK0Tk83ls26bVarG6uorjOMzNzRGNRmm1Wvj9fndjNein74JwkOjqKu0RYDoqxbIslpaWiEajpNNpANdQ11oC+XzedbbYtu3qVBSLRSKRiBvSPjc3R6PRwOfz7VgJaVBwHIebN2/SarXweLbd6wjCnlFKuRX39NiLx+MsLCzgOA4PPPDAFm2yvYhZC0eTarVKJpPR0Srb5mg4jsPS0hK3bt0iGo3ykpe8hGazic/nY3JykmazSbPZdKsfFYtFVldXGRsbY3FxkWw2SywWI5VKuX1Up8CFQiEcx7krVVUiWwRBEIRBZqCdL0qp3waWlVL/2nGcVXWHO46Xvwn8e+Aq8COO46z0q52dOi/lcpm5uTlqtRqtVouHHnqI69evA3D16lUCgQBer5dYLEY2m6XRaJDNZkmn02LMCscW7QRpd4boqJR8Ps/Kygr5fN5NGSqVSjQaDRqNBq1Wi7W1NcbGxkin00SjUQDm5uYIBoOcOHGCWCxGtVolkUhw9uxZTp06RbPZ5LnnnmN8fJxYLDaQWjCA65TK5XJMTEz0uznCEaJer5PNZrEsi4mJCR5//HEuX75MNpsll8uRTCZdPTLDMCiVSm4qyKBHjAkHQygUap+HrO2ep5Ti5MmTxONxN3X02rVrrK+vE4lEOH/+PLVaDa/Xy+LiIqZp4vf7uXnzJkopTpw44WrDrKyssLS0RCwW48yZM27kjRZbr1QqTExMEI1GxSkjCIIgDCwD7XwBvhWoAiWl1O84jrPSVtno/wbGgaDjOKVt3+EQ0FEuWkPCsiw32iWXyxGLxQgGg3zyk5/km9/8JtPT08zPzwObVVuuXr2qld3FASMcWzo3c/oktFgsUqlUXAN9YWGBkydP0mg03AgXrVGxtLREMBikUqmwsrKCz+dzo1+04yUWi7GxsUEmk2Fubo5iscjMzAyTk5P4/X5g8PQsHMfh61//OqOjo/1uinDEaLVa3Lhxg2w2SzAYZGZmhkuXLpHJZJienubWrVs0Gg1mZ2dRSrmb6GGIGBMODh3hS4+qmc1mk9XVVSYmJlhbW+Pzn/88i4uLJJNJIpEIX//61zlx4gShUMjV71pfXyeRSFAul4lGozz//PNuYQLLsmg0GqytrblzfCQSIZlMbqmCVC6XyWQy7lqgIybFASMIgiD0m4F1viilvoVNAd0/Bb5r80/qfXccMN8FfBvwk/12vGhqtRqVSoVarYbf76der1Or1bh+/Tq3bt3ic5/7HJ/61KeYnJyk0Wi40S8bGxs4jsP6+joAlUpFqh4JApuh7XNzc1y9epVwOEwwGCSVSlEoFKjVauRyOTweD5FIBI/HQyaTYWNjg+XlZVf3RYemX79+nY2NDV772tfyqle9inw+TzabJRqNYlkWk5OTeL3egd5UXr16lWazidfr7XdThCPEwsIC/+E//AdKpRKtVosLFy4AmyKoXq+Xp59+msnJSUqlEqFQiGAwyKlTp/rcaqGfVKtVrl27xtWrVwGC2z1PR+xZlkW5XHa1hbTOnRbWbTQaKKW4deuW62RZWVnh+vXrlMtl14nSaDQwTZNz584RjUb5whe+wOzsLADRaJR8Pk+z2WRxcZGvfOUrFItFvF4vDzzwAKFQiFgs5q4L+pCrW5U9OQATBEEQDopB3uF/FXi54zgVpdQvAt8NtJRS/4FNfZf/03GcZj8bqIlEIkxPT5PJZNyT80qlwrVr11hbW2NpaYlPfepT/PAP/zCf/vSnuXr1KpVKhXPnzjExMUEgEKBSqfDVr36V06dPA3D69Gmq1ar7/mIMCEcdfZKqw8hDoRDJZNJ1tmxsbFAsFvH5fNi2zcjICH6/nxMnTnD58mU8Hg/FYpFcLodpmm5kTKFQ4Pd///dxHIf3v//9jI+P88gjj2DbNmNjY8zMzLgGuo546UwlHBS2EyMWhHvF4/EwNTUFwHPPPcfTTz/N1NQUXq+XtbU1Wq0WlUqFWCyG1+vloYce6nOLhX5i2zbNZpNqtaojSba1I03TdKMSI5EIDz/8MLdv32ZsbIxgMMjGxgY3btxgenqaVqtFMBikVCoRjUbdvqeUYmFhgWKxSDweZ2pqilgsxjPPPMPt27eZmJhgeXmZ//E//gdf/epXKRaLfPnLX3btJ9js4w8//DDnz5/n3LlzvOENb+DVr3414XDYjZJpF+8dtMhHQRAE4egwsM4Xx3FW237+eaVUE/jbQNNxnF/vX8u2YlkWy8vLruZEoVBgdnaWXC7Hn//5n/P7v//77nN/93d/1/35ypUrZDIZXv/613P27Fl8Ph+hUMjdaN64cQPLsjAMQ3LrhSONdnTYtk0mk6FSqRAMBonH40QiEQKBAMFgkGvXrlEqldwxFo1GWVhY4LnnnqPZbBKNRt1T01wux1NPPdX1eisrK1y6dImNjQ0ajQYATz31FG9+85t55JFHiEQi/NiP/RiFQoGxsTE8Hg/BYNB1wuzkjDnI0Hafz3fX39rD7QVhr5TLZT73uc8BuKXMn332WWCzv33rt34rzWaTy5cvu4cD+nmSxnH0qdfrW36vVCpcvnyZmzdvUqlUALadgEzTJB6Ps7q66kaXTE1Nsbi4iGVZ5PN5crkcKysrpNNpSqUS2WyWmzdvsri4SKFQIBQKsb6+TigUIhKJsLq6ysrKCrdu3eIb3/gGH/jAB1xHi8/no9m8+0yu1WrxjW98gytXrrCxscEv//Ivo5Tida97HW9729vweDyMjo7yvd/7vXi9XorFIsFgcEv/7iV0LuNAEARB2C0D63zR3BHYdRzH+WWl1Abwh/1uk8a2bW7cuOGKx8ViMRYXF/nmN7/Jpz71Kdeg3Y719XW++MUv0mg0iMVihEIhotEozz33HBcuXCAajUpZT+HIo6uo6IpE2hlTrVa5evUqrVaLUCjE9PQ0ly5dYnFxEdu28fl8FAoFyuUyrVYLn8/H+vo6X/jCF/jrv/7rntd89NFHeec730k+n2dxcZE/+qM/4qMf/SjlcplXvvKVbqqTLm0N/S9rqje8grDfbNe3Wq0WKysrnDx5EsdxqFQqVCoVNzpNOJ6USiW+9KUvaefLthOjbdvYto1pmuTzedLpNJVKhUuXLrni4broQLlcZm1tjeeee46RkRFg05ni9/uxLIv19XXGx8fZ2Nig1WrxJ3/yJzz77LM88sgjvPrVr+b06dNMTk7yoz/6o9u2+4knnuDtb387N27c4Otf/zqf/vSnWV5e5h3veAeRSIRms+nqg6VSKYLBIGtra4yOjkqVOUEQBGFfGHjni+M4jlLKcBzHdhznX/W7Pe3osojVapWVlRUMw+DZZ5/lk5/8JJ/97GfdkrjbEQwGWV5e5nOf+xwPPvgg6XTajXY5ffo0qVRKUo6EI097lSPd12OxGF/96le5dOkSxWKRM2fOYBgGmUyGfD4PbI4fj8dDLpfDsiy++MUvcuPGDXw+Hz/6oz/K+973vm2vefXqVb7xjW9w/vx5HnnkEc6cOcMHPvAB/vRP/5RischrX/taLMui1Wq5AqMrKyuMjo7KeBSODa1Wi6997WukUikee+wxxsbGyOVyroipcLxotVqUy2UajQYrKyusrq5CD80Xjd/vx+fz4TgOmUyGtbU1vF4voVCIRCJBpVJhZGTEFdFdWFggHA6TTCY5e/YspVLJjZyZn5/nT/7kT5ibm+O7v/u7+d7v/V6UUly5coU//MPeZ3Nf/epXmZ6e5lWvehWPPfYY586d4/3vfz///t//e37gB36AkZER1+4Kh8NUKhUymQzVatWN+tLpqBLtIgiCINwLA+98AXAcZyDj6svlMrdu3aJQKJDJZLBtm49//ON84Qtf4NWvfjUf+tCHmJ6e3vb1IyMj/P2///f5lV/5Faamppifn8eyLAKBAIuLi8RiMYrFIoZhkEqlRIRXOBbotKFoNEqhUGB9fZ1yucz09DQejwfDMFhYWCCfzzM5OUk2m6VSqXD9+nXe9a538eM//uMUCoWezpdSqcT73/9+/H4/jz76KK95zWt48skn+chHPsJf/uVf8r73vY9HHnkEr9fLY489RiKRwLIsTp8+TSwWA17QBbhT8YNIJCIGuXAkuXbtGm94wxuoVCo0m01JdTuGOI7D/Pw8X//617l16xa5XI61tTXoUWoaNh0vX/7yl7l9+zYnT56kVqsRj8fZ2NggmUy6Wl6GYRAIBGi1WlSrVVqtFqdOnXLTuUulEl/4whf4xCc+wcbGBj/xEz/BhQsX+J//83/y53/+5ywvL+8qSviDH/wgH/7wh7lw4QLf8z3fw8/8zM/wm7/5m/z2b/82KysrvOlNb2JqagrLskgmkxiGQblc5utf/zqnTp1ifX3dLWktCIIgCHtFdvP3gT4dqdfr2LbN/Pw8X/jCF/i2b/s2PvzhD7viu9uhlOJf/It/gWEYvPe97+XRRx91NS4WFxfdU6FqtYphGDtG0gjCMKLTjnQqQz6fZ25uDsuymJ6eJhgMuuVFM5kM8/Pz5PN5VldXCYVCbqrfd33Xd/FzP/dzAPzar/1az2u+9KUvZXZ2lo985CM8/fTT3Lp1i/e85z28853vxHEcPvzhD/Pyl7+cF7/4xdi2zYMPPkgoFKJcLlOpVFwtJtu2mZubw+/3u6em4oARjgqmadJsNpmYmOD27dtEIhFKpZJEfx0zHMdhaWmJz33uc8zNzbGyssL8/LxOV4tv97pms8nTTz/NV77yFW7fvk02m3UPkSqVCvPz8zz//PMUCgUMw8BxHEKhELZt4/V63cOtQqGAaZqsra1RLpf5pV/6Jaanp7ly5Qof/OAHAXj961/Pj/zIj/B3/s7f2fY+Hn/8cV7zmtfw3ve+ly9/+csEg0H+9t/+2/zET/wEv/iLv8jly5epVCo88sgjJBIJJiYmmJycpFAokMvlePnLX+6mQemqSZ3z/aCKtQuCIAiDgThf7gNdjUWX6CwUCpw7d45Wq7Wj46Wd27dvE4vFKBQK3LhxA6WUm3ZUq9U4deoUyWTyAO9EEPqHPq20LItMJoPX66XZbLKxscHKygrhcJhSqeRu+iqVCvl8no2NDTY2NkgkErzpTW/iYx/7GG984xt561vfyk/+5E/y3/7bf9v2mtevX+ev/uqvUErxmte8hm/5lm8BNh1B3/zmN3nFK17hjut6vc7S0hJjY2PcunULr9fL7OwsgUCAarXqivFalkW1Wh34ikmCsFtarRYej8cdh4lEglOnThEM7phpIhwh9Lx45coV5ubmKBQK7TpB29qRuppRvV6n2WzyjW98g5GREWZnZymXy9y8eZObN28SCARIJBIopchkMm5lrUajwY0bN4DNvpjL5XAchy9/+ctMT0/zohe9iH/wD/4BH/zgB/nsZz9LrVbreR8LCwv8m3/zb9x5//u+7/sol8v89m//Nn6/n9XVVdex8uCDD7K0tMTq6qpbsWllZYXTp0+zurpKMBjcUplPz/H6MAGkapIgCIJwN+J8uQ/W1tYoFossLCxw7do1stks6+vrzM3Nkc/nSSQSu3qPj3zkI5imiW3blEolV0D0c5/7HLFYjNHRUUk5Eo4supqXNlgNwyCRSLC0tARsVpkoFAqsra0xNTXF+Pg4tVoNj8dDKBSiXq9TqVR47LHH+Mmf/Em+/vWv87/9b/9bz2uurKy4Tpf2cfqxj33MFfoNh8OuEGQgEKBUKnHjxg1s26ZSqWCaJpFIhMnJSbcd7WHvYoQLw47jOJimydmzZ5mcnCSRSLiRaLImHQ8cx6FcLrtOidu3b7vz2h229Sx7vV5OnTrF6OgoGxsblMtlHMdxU0vn5+ddh4ku9+z3+zFNk2g0imEYrhO8VCrRbDZ54okn+KM/+iNe+cpXkk6nefnLX86FCxf4xCc+wcc+9rGe93L9+nUeeeQR3vGOdzA9PU2tVuNXf/VXyWQyvPzlL+f27dtuSl21WmVkZATHcYjFYgSDQYLBoHu4pm22TCbDxMSEm47armEmCIIgCJ2I9XQfaAV8r9fL2NgYS0tLRCIRVlZW+OQnP8kP/MAP7Pgev/u7v0uj0SAejxMMBolGowQCARqNBjMzM4yOjnLy5MlDuBtB6C9aXNqyLNbW1giFQoyMjOD1ehkfH8eyLMLhMKOjo24aks/nIxaLUS6XUUoxOTnJBz7wAb72ta/1vNbjjz/O933f923529zcHH/5l3/J7OwslUqFRqPB888/7wpMptNpHMdxhbaXlpbweDyk02k3BbEdMcKFYUcpxdTUFA8++CB+v59QKOSWfheOB7VajUql4lZ+63C89EQpRTQadSNWTNOkUChQLBbxeDycOnWKSqVCOBym1Wpx5swZNjY2WFxcZG1tjVqtRrPZxOPxsLGxgWVZhEIhTNPkAx/4AO9+97tRSuHz+fie7/keXve61/GP/tE/2rY9jzzyCD/1Uz8FbKZE/cZv/AY3b97k3LlzjI+Pb6lcGYvFXHssEAjw8MMPE4/HSaVSNJtNNw21E32YIAiCIAjdEOfLfVCv16lWq9RqNTY2NtwTnHQ6zcc//nHe9a53MT8/v23KgeM4fPu3fzuveMUruHz5MtFolLNnzzIxMcH4+DjxeJxQKMTa2hrpdFpSF4RjQSQS4cyZM8RiMebn56lWqzz66KOcPn2aXC7nhqxvbGyQy+VotVqsr6+zvr7OxsaGW659dHSUf/tv/y2vfe1r+c3f/E0KhQJPPvkk6XSa9fV1RkdH3Ws2m03e/OY3c/LkSaamplznza1bt1ydF51yYRgGsVgMy7K4ffu26wAKhUJuRQ4dBbNfRng4HObChQv87u/+7r68nyBoHnvsMT71qU+5v6+srPDGN76RYDBIIpFwU0Je9rKXEQ6HmZmZkbK7xwh9KGQYxp6jnTweD36/n1u3bnHr1i3W19ddp/XLXvYyTp8+TTabdSsgLS0tkU6n2djYYG5ujnq9TjAYJBKJYFkW9Xodr9dLKpXi2WefpVQq3eVEf+c738mXvvQl/sk/+Sc888wzTExM8I//8T/mne98J0opkskkf/Znf8a/+lf/iqtXr7opRdlslrGxMRKJBGfPniUWi/HEE08wOjrqlstOJBL4fD58Ph+w6ViPRqPiYBcEQRB2jThf7gPbtgkEAvj9fiqVCvV6nXg8ztLSEp/85Cep1+s9X3/p0iWeffZZPB4P4+PjjI2N0Wg08Hq9wAuRAKVSiVgsJqcpwpGmPU1HV/rSRu7jjz8OwPz8PMvLy64hPj4+zpUrV1xHaL1edwURx8fH+aEf+iF+6qd+ip/4iZ/o6bz8rd/6Lf76r/+aBx98cMvGUp9++v1+bNsmEokQDAbx+XwUCgWq1ap7GqvbXy6X3dB1GbPCMNFqtXjyySdZX193T/+TyaSraxSNRmk2m+64FI4H+vtutVp7ep1pmjQaDWq1Gq1WC9u28Xg8br8KhUJuFEmj0aBQKLjzr9frdXW+QqEQsVjMvX4qlWJ0dJRf+IVf4G/8jb/ByMjIlus+8cQTfOITn+ArX/kKDz30EOFwmOeee44Pf/jD/MEf/AGLi4uMjo4yMjLCyMgIJ06c4Du/8zuxbZtms8mDDz7I2bNn3UOAarUK3B3FqIst1Ot1mesFQRCEXSHOl/vAMAzi8TgPP/ww1WoV0zSpVCpYlsUzzzzDpz71KV7ykpds+/r/8l/+C7FYjFqthm3bWJaFz+fDcRx3Y6lP5+VkRTjqdKbpRCIRHnroIVdjYnl5mYWFBarVqpvuY5qmezKrS+BqkdupqSlyuRy/+qu/yl/91V/xsz/7syiltqQ1NZtNSqUS//pf/2u+4zu+g1wuB+BuEMLhsJvrv7y8zMTEBDMzM8zMzDA2Nsby8jLnz58HcJ0z3fRfBGFQqdfrXLt2Ddhckz7zmc8wNTVFMBhkdnaWxx57jIsXL3Lq1Cl8Pp/062NGrVZjaWmJ9fX1rmk2wLZ1xy3LotVqEQqF8Pv9GIbB6dOnSafTKKW4cuUK169fZ3R0lNHRUQzDYGFhgUAgwIkTJ0gmkzQaDcLhMBsbGzSbTbxeLz6fj3q9zvr6Or/4i7/IT/3UT7kRKaZpuv+fPXuWj3zkI/z+7/8+X/nKV/B4PLzlLW/h5MmTzM7OYlkWwWCQhx56iEcffZRWq4Vpmpw/f96tNOnz+YhGo12r2ElqqSAIwv2jlPpl4PVABvgRx3GqOz2ulJoF/gr45p2nvcNxnJXDa/W9I86X+yASiTA7O0utVmNycpJMJsPy8jKtVot4PM673/1ufv3Xf93dnLXzJ3/yJ6443Pj4OIlEggcffJBHH32Uqakp/H6/m3ssCMeB9lx5XUXi3LlzFItFvv71r7O2tkYul+PGjRuu6OGJEydcA3p+fp6xsTEWFxfx+/0kEgkuXLjAwsICX/jCF3jzm9+87bVjsZj7ung8zoMPPkggEGB8fBzDMFydC6/Xi9/vd43y0dFR8vm8m/8fi8VcIV5BGAauXLnCE0884f4+OjrK2NgYU1NTzM7Oks1maTQa1Ot1V/dDOD4Eg0EmJyf55je/6TqlO3RfnO1ea5omJ06c4NFHH2VxcZH19XWi0SjJZBLHccjlchSLReLxOIlEwtWXGR0dJZVKYRgGS0tLFAoFN/UokUi4B1WTk5P83u/9Hr/3e79317WVUm5Fpoceeohv+ZZv4dy5c5RKJd7whjeQSCQwDIPx8XECgQCnTp3C6/ViWRYbGxtuemkvx4rouwiCIOwNpdSnHcd5Y9vvjwIPOI7zOqXUjwM/BvybXT7+Gcdxvv/wWr8/iPPlHtEn7PV6nVar5aY5XL9+nVAoRDweZ3V1le/6ru/i53/+5/mhH/oh9+Tkwx/+MO9+97t59NFHuXHjBqdPn+alL30p6XSa2dlZ0um0G/4qCMcRncJjmqYbch6NRl1j2rIsNyx9enraNeTL5TJerxev14vH40EpxezsLBMTExQKBWBzQ3Dy5EkWFhYwTROPx0MkEnHLw7/kJS/h8ccfp1QqEQwG8Xg8xONxTNNkamqKSCRCo9HAsixg82Q4EAi4hrqUmBaGCb2Jhc0Nq04JmZ2dRSnF7du3uX79OufOnZMT/mOIUmqLIyIajW5JraSH88VxHJLJJCdPnuT06dNUKhUMwyAcDuPxeDh9+jTj4+M89thjzM3Nsb6+7s7ty8vLmKaJaZrE43EajQajo6MkEgny+TytVosLFy4wOTlJs9mkXq/jOA6GYeDxeNwImLNnzzI1NcW5c+eIx+NcvXqVdDrNhQsXUEoxMzNDuVxmZGQEv9/vRvdEIpGu0S6CIAjCvvI64I/v/PzHwHtpc77s8PhrlFKfAz4H/P8cvUkYcMT5co9Uq1Xy+Ty2bROLxdzN2szMDH/2Z3/G+vo63/jGN3jRi17Ee97zHj772c/y3ve+lw9+8IP8y3/5L3nd615HOBwmk8mglOLcuXMkk0kSiQR+v5+RkRFSqVS/b1MQ+kIoFCKfz7O4uMjs7CyPPvoozzzzjFv5QinF4uIilmUxOTlJOBwmEom40SqxWIyZmRnXeIdNUd3x8XFSqRThcJibN29SKBQwTdN1oAYCAaanp3nd615HJpMhHo+zsbHBrVu3KBQKBAIBUqkUExMTbqnRlZWVLSeg5XJZSkwLQ4OuKtZOIpEgkUgQjUYZGxujVCoBiDPxmKIPmiYmJlhZWaFer7sOEGDb8kftzvJwOEw4HHa1XRzHwePx8Mgjj7C+vk4ul3OjGLVWDMCpU6dQSrG2tsbq6irBYJCzZ8/SbDY5efIkY2Nj1Go1FhcXqdVqJBIJkskk5XLZTdtuNBqcOHGC2dlZzp49SzQaJZFIuLp6unqXrs4kCIIwKCilngN+13GcX1RKPQT8FvAYcBn4B47jfOk+338UeB/wbUAOeI/jOP+1y/P8wL8D3gSMAteAn3Uc54/bnvMDwM8D08Ay8P9xHOdzOzRhBFi683Phznvv5vEl4CxQBf4D8Hbgv+1wrYFAnC/3iE4zWF1dpVKpEAwG3RLRk5OTTExMcPbsWQqFAqVSiT/90z/l85//PMVikbe97W0YhsGpU6dYXl4mFovRbDbdUFyv1+tu7gThOGIYBoZhUK1WXT2leDyO3+9nbW3NFV7Up7HBYJDHH3+cWCzGxsYGGxsbpNNp/H4/GxsbLC8vMzo6yokTJxgbG8Pv9+Pz+SiVSrRaLZRS7utGRkaoVCrEYjHGx8cZGRlxS6RqMchms0kikQBwo3M0ogMgDBPhcJixsTGKxSJ+v9/VOCqXy0xNTXHx4kVOnz4tjsRjTCAQYGRkhPPnz5PP5920HIDnn39+29fZtk2tVuP27dusrq4Sj8ddrTzbtolGo9RqNW7evInjOIyMjOA4DtFo1BU61ymfJ06cYGFhwRXJDYfDKKUYHR2l1WoxNTVFtVolHo+Tz+dRSnHmzBnC4TChUAjHcQiFQkxOTlIul/F4POJMFARhaFBKmcDHgL8E/gnwE8AfKqVmHcfpXeGlN/8WaAITbDp1Pq6U+prjON/seJ4J3ALeANwEvgP4sFLqxY7jzCulvpXNqJS/CXwJmGxr+zTwn+78+phS6tN3fv4RYB2I3/k9Dqx1XLfr447jNIDGnff/78ArEefL0cYwDFKpFKVSyU09unnzJplMhkajQTKZZGZmhpWVFcLhMOfPn+frX/+6G9atReNe8pKXuPnNwWCQBx980D39EYTjjM759/l8LCwsUCgUePTRR/H7/e5JphZmXFtbIxAI0Gw22djYYG1tDa/XSzqdxnEcJicn8fl8BINBqtUqyWRySzUNHaaeTqeZmpoimUxy8+ZNWq0W1WrVHaOBQIDV1VWazSbVapVIJHLXplR0AIRhwrZtN1UvHo8zNTVFPB5nfHyc8fFxHnzwQcbGxkR/7BhjmiaTk5M0Gg3Onj1LIBAgnU7TbDb5y7/8y23zo3Ulx1gsxtjYGMFg0BW11fNuPp+nWq0yNjbGI4884j7eaDRIpVLU63V8Ph+JRIJYLIZhGCSTSWKxGH6/n2g06gr76tRSn89HPB5ncnIS0zTd+bpSqZBOpwG22FjaSRSLxSTVSBCEgUEpdR2YBX4BeCNwBvhux3GeUUrVgC8CjwBP3+P7h4HvAx51HKcM/IVS6g+BdwE/0/5cx3Eqd9qh+did9r0MmAd+EfhnjuP85Z3HF9pee/NO+7tpvvwF8B7gPwJvBj7f0cyujyulYo7j6MjL17EZCTQUiPPlPtDK/eFwmEKhQLFYxHEcpqamqNVqnDhxgng8TjKZ5LnnnuPxxx/nypUreL1estkst2/fZnp6mqmpKRzHIRaLuXnU7SWnBeE4op0hxWKRpaUlcrkc586d48EHH+TEiRNcu3aN9fV11tfXWV5eJpFIuNEuCwsLxGIxt1JFIBBgcXGRpaUlEokEL3rRixgZGeHy5cvU63WWl5cBmJyc5OzZs664aK1WIxQKEYlESKVSVKtVNyz+MCNbKpUKX/nKVzh9+vShXVM4HmxsbPDss8/SaDQ4efIkk5OThEIhLl68yEMPPUShUHAr0QjHj0AggM/nI5/PMzo6isfj4fz58/j9fpaWlvjABz5Q3e61hmGQSCR43eteRzAYZGFhgUaj4eoJPfPMM67jRClFKBRidHSUYDDI2toajuNw6tQpYrEYlmUxNjbGzMwMjUYD27aJxzcPQ7XtNDc3h2VZRCIR0un0FufN2toayWQS0zRdfS9NuVx2IyzFcS4IwgDxHcD/BD4O/J/AW4Ardx57gE3NrUzni5RS/2/gA13e7wOO4/xo2+/ngJbjOFfb/vY1NqNbeqKUmrjz+m8qpTzARTYjca4BAeAPgH/sOE6t1/s4jvMNpdSNO9otWTajYVBKpYG/5zjOz3d7HHiDUuoX2Ew7us5mNNBQIM6X+0Sr+etFfWRkhNXVVRYXF4lGo7z4xS8mn89jGAbPPvss8Xic2dlZLl68SL1eJxgMUq/XKRQK7omOlKkVhBeIRCKcP3+e+fl5RkdH3ZKi58+fZ25ujmw2S71ep16v8/DDDxMOh5mdnWV9fZ3V1VU2NjYYHx/nla98JdlsFo/Hw8zMDMVikRMnTlCpVBgfHycajTIxMYFhGFiWRTwe58yZM65BbhjGlpSiw96MvuY1rznU6wnHg1AoxIMPPuim2T344IOcPn2aCxcucPPmTRqNBtFo1I0YEI4fhmEwMzNDOBzGNE1u377tOkmAjZ1ea5omgUCASCRCPB7n/PnzrK2tucUJXvnKVzIyMsITTzyBbduMjo5y8+ZNms0mo6OjTE9Pu9pasVjMjWIJhUJbHOFnzpxxK+XpOVs7U3r1X0kVFQRhEHEc57JSqgEsO47z18BfAyilXgn8BvA+x3Fud3npH7KZQtRJZ0pPhE0dlXYKQE/xK6WUF/gvwH90HOeKUuoE4AW+n80olA3go8DPAf+/jnt6Y+f7OY7zni5/W2ZTP2a7x/8I+KNe7RxUxPmyTzSbTcLhMIFAgNOnT1Or1fB6vVuEeX0+H6lUipmZGVerYnV1lZWVFTf9QU5eBGErWh/J5/O5EWa1Wo2JiQlOnTrF6Oiom8oXDAYplUpupYulpSVXkDcYDOI4DrVajatXr+L1ejFNk9nZ2S0OFl2tSKc8tY/HfqUUKaXuEkUVhP3AMAxOnjzJi170IpRSblTAlStX3DLryWSyz60U+k17JGI4HMa2ber1OmxqBXTFcRyWl5ep1+usrKyQz+cBuHnzJrOzs67Gy+TkJNPT00xPT+Pz+dxom+XlZcLhMD6fj5GREQqFAjdv3iQYDBKPx900JM29psZJqqggCMOCUuofAr8CvB/48W7PcRxnnU2tlJ0oA50TZwwo9bi+AfxnNuf+v3/nzzq65Tcdx1m687xfo4vzRRDny77RfnKiUxNqtc2+GA6HeeUrX8mpU6doNpuYpkm9XmdpaYlgMMgjjzxCo9FgYmKin7cgCAOL1ljSpZy1sRyJRKhWq0xPT5PL5bh9+zYLCwuMjY0BbNGw0KKROqWvUCgwNjbW1cGiU4wG5STUNE3OnDnT72YIRxDDMFyB0/HxcU6ePInH4+HRRx+lXC67UZ2CALhOau1IoUepacuyWFxcJBwOE4/HKZfLrK+v4zgOiUSC1772tVy5coV4PO46w9sjVfTPeq7vnP8lFU4QhGOCAlBK/RTwz4G/7TjOf9r2ybtPO7oKmEqpBx3Hee7O3y4AnWK7+n0Vm5WRJoDvcBxnAzadPUqp2/RYD3qhlPpl4PVsplD9iOM41Z0eV0rNAn/V1tZ3OI6zci/XP2xk5erBU089tevnthsDgUCAaDTK7Ows6XSakydPMjs7y2OPPcbU1BRnzpxhZmaGBx54gIceeogHHniAl7zkJW71lL1cdz+R6x4tBvn+7qVtOuS8XXhRpwLV63WSySRnz57l4YcfZmxsjOnpaV75yldy9uxZYNOBMTU1xaOPPsrJkyeZnp5mYmLiLgfLU089NXDGfTwe5x3veMeBX+c4jcXjdK+9CIVCvOENb+DixYu86U1v4oEHHmB6ehrY3AAfhOPlOHzfR/Ue9dyYTqe1Q9je7rm6St3MzAxPPPEEjz76KFNTU0SjUSKRCKdOneKVr3wlL3rRizhz5sxdTvBYLLZlru+c/++XQRuLvZC2Dhb9vEe59rEjDzyhlHod8KvAbwJfU0pduPMv3OU1Ou2o898/bX/SHRHd/w78M6VUWCn1GuC72Yxs6cb/BTwEvK2Llsv7gX+glEoppUaAf8hmdaaeKKUeBR5wHOd1wJ8BP7aHxz/jOM4b7/wbCscLiPOlJ/c60O+E4mKa5l16EfF43DUwxsbGmJycpF6vu8bF/Vz3fpHrHi0G+f72s23VapVisUi9XieRSLjVWQzDIBwOb4mS0Y4aPd501Y2Datt+ocPwD5rjNBaP0732wjRNTp48SSKRwOfzuXpGgUDgwK55HL7vo36PpmnqNB/Pds9xHIdgMIhlWSQSCWZmZnAcB6/X687D++1Q2QuDNhZ7IW09HGZmZlBKdf03OzvrPu+4OiGO67X7zH9hM+rjs3d+/8fAV9v+Xex8geM4647jfK3Lv1td3v9/AYJsitl+kE2RWzfyRSn1x0qpn1VKzQD/XzadOMtKqfKdf3/rzlN/ic1IlKtsVh76CvDLu7i/1wF/fOfnPwZeu4fHX6OU+pxS6l+oISpVpxznniKEhhKl1ApwYw8veYh7L11lcPeJkMGmoeKwKUxk84JYXftz7+e694NctzczjuOMb/fgPfSvg6Zfn+tu2O+2dRtvJi9sDDbuPK7HYOvO37ud2vbrc9u2fymlSmyW8msccBuGZSwO6zX7dd1efSsHLLE5Jmw21yZ4YcwcBMfh+z4O9wgw6zhOV1GgO2viLV7oRz42jfwGUD+c5vVkkNfITo5jW3vNW/22t/r5fci175+e9rxweCilfhZ41nGcP1BKJYAPO47zbTs9rpTys2nnV4H/APyx4zj/7fDvYO8cq0TuvQ40pdQlx3Hu8igeNHLd4bzuoE3k/fpcd4O0bW84jtNTeX6/OCpjcVCv2c/rbsd2G+eD5Dh838fhHndi0NbETgbt8+qFtHUr/e5b/fw+5NrCsHGnbPRHujz0/WwKA8fv/B7n7opMXR93HKfBnQNJpdR/B14JDIXzRdKOetOvGDe57tG+7mExyPcnbRtMjtNYPE73Omgch+/7ONzjsDNMn5e0dbDo5z3KtYWhwnGcZcdxXtvl3zLwF8Cb7zz1zcDnO17e9XGlVHuVptcB1w7uDvaXY5V2JAiCIAiCIAiCIAhC/1FK/Us2tVyybFYzqtyJlvl7juP8/DaPvw34BTbTjq4DP+Y4jtWfO9gb4nwRBEEQBEEQBEEQBEE4QCTtSBAEQRAEQRAEQRAE4QAR50sPlFJPynXlusPKIN+ftG0wOU5j8Tjd66BxHL7v43CPw84wfV7S1sGin/co1z56HJf14rhccyfE+dKbfn1hct2jfd3DYpDvT9o2mBynsXic7nXQOA7f93G4x2FnmD4vaetg0c97lGsfPY7LenFcrtmTY1VqOplMOrOzs7t+figU4uLFi/csimPbNs1mk2aziVIK27ap1WpsbGzg9XpRSuHxeLBtG7/fj2EYNBoNfD4f58+fdwAsy8Ln8xEIBDCMu31ltm3TarXweDxdH98L93u/w3bdYDDIxYsXnfbPENj283z66adXe5VlDQaDTjAYRP/z+XwYhoFlWTiOg2VZGIbhfuehUAjLsiiXy9i2TSQSIRQKYds2GxsbAJimiW3bmKaJUmpP99f+ue5nP9kPDvo7t237nu9zp7bt9rNstVpYloVpmm7f6kWv/hWPx51EIoHf7ycSiVAul933tm0bj8ez5XpKKXw+n3vt9nZ29nfLsmi1WrRaLQKBAI8//rijlHL77G7bv91nrq9nGAZKKQzD2NJWPf89/PDDzmHMc5pgMMiFCxecwxwTety/9KUvdQ5zPD799NO57UqzJpNJZ2pqaksb27+rbtzv97HdGGuf+7xe7139Vv/earXctdJxHLR2nV4v9XprGIb7fzAY5PHHH3fa5/mNjQ33HjY2NrAsi2az6c7d+jF9v16vF4BGo4Hf78fj8dzV3o2NDRzHcdeX7T4z27ZRSlGr1Wg2mwSDQfx+/54/y50+V9u2qdfr7jjT99D+uGVZKKXuemw37NS3Om2u+5mb95t+2R73wm7aqvuYnrv1/x6PB9M03Xm32WwCYBgGjuPQbDYxTROv14vjOO73Y5qm2yfb+2+vNcG2bYLBIC996Uud9vF3L+y1bx0m/ew7x+XalmVRq9Uol8s0m008Hg/BYNBxHIdwOEw4HHbt5r3Sq2/B4fevfnyncs2DY6f+daycL7Ozs1y6dGnXz7948eKent+JbdssLi5y69Ytstksfr+fq1evcvv2bVqtFkopxsfHqVarxONxwuEwSil+5Vd+hd/6rd8iGAyilGJycpITJ05suympVquEQqH7Nmju936H6brlcplXv/rVfOxjHyOZTFKv190JvNvnWS6XiUajC73ec2RkhB/7sR/jta99LefPn8cwDObn58lkMuRyOZrNJqOjo8RiMTY2Njhz5gzlcpnLly9TLL+gG+EAANVxSURBVBY5f/48jz/+OOl0mmq1CkCxWGR+fp4zZ85w4sSJPd1j++e6n/1kP9iPsbXd/ZTLZYrFIrFYjEgksu9t2+1nWSwWWVpaIhqNkk6nez53p/4ViUR4+9vfzkMPPcSZM2e4ceMGS0tLpNNpvF4vDz30ECsrKywvL3P+/Hmi0Sjnzp3Dsqy72mnbNuVyGdhclHK5HPV6nY2NDd75znfymc98xn28UqkwMTFBLBbr2q729m/3mevPS183Foth2zaZTIaJiQkCgQAve9nL+MxnPkMikTjweU5z8eJFPv3pT+/pPe+nHfoz+s7v/E4uXbp0qONRKXVju8empqZ4//vfTzAYZHJy0nUM6O8qFAptaWt7/4lEIvfU/u3GWOd7A1v6TigUol6v02w2mZubw+fzceLECQKBAM1mkytXrjAzM+Ma7GfOnCEWi1EsFvkbf+Nv8P73v58zZ85gGIb7/JGREeLxONlslrW1NbLZLBcvXnTX3mKxSCKRIJ/Pk0wmKZfLzM/PMzs7SywW29LearVKPp/HMAze+ta38qUvfcn9fNr7TrVapVgsYhgGKysrzM/P86pXvYpkclvf/j1/rrZtUywWqVarpFIpTNO86/FsNotlWSQSiT3Pmb36VqfNVSwW3XG/05xyGPTL5rkXdrMu6YOcTCbD/Pw8rVaLK1euYJomZ8+eJZFIcPnyZT7/+c+TTqdJp9NMT09TKpUIhUK84hWvoFKp4PV6uX37NqFQiLNnz2KaJj6fj1u3brmOmsnJya59xbZtt626n9/rWryXvnXY9LPvHNVr6zkyEAiwuLjI5z73OZ555hlWVlZoNBp8/OMfZ3Z2FsMw+PZv/3YuXLjA9PQ058+f3/N80qtvweH3r358p3LNg2On/nWsnC975ckn7y9SyTAMYrGYe2KWTCZ50YteRLlcplQqcf36dRzHYXl52T11OHv2LD/8wz9MKBRCKcWJEyd6btwMw7inRa0b93u/w3TdUCjEu971LizLol6vb/kMu32edxwzrV7vGQ6Hectb3sL09DSjo6PYts34+DiGYRCPxwkEAoyPj+P1eqlWq6TTaW7evMnjjz/OrVu3aDQaLC8vu99pJBLZEhGzV9o/1/3sJ/vBXr7zbhtebdTB3d+X/qx285l1e++d2rbbzzISiRCPx7Esi2q12vM1O/UvpRSvec1rSKVSnDlzhlwuh2marK+vc/LkSfcU6OzZs+7GptecYRiGu/lLpVLuZ/D3/t7fczfUgUBgi1OyF70+c/156RNQ/Rz9c7Va5V3vepcbbbBdm/e7/z755JN7fs9e/W4n9H0/+eSTAzcebdt2TxFh01Gk553Oe65Wq65j5l4dR51jbDuHjnZYRSIR14myvLxMKpVidHSUWq1Gq9UilUpRLBZJpVLunNvubPD5fPytv/W3mJ6eBnDvZ2xsjGg0SiqVIhKJMD097fZ5fd86glW3KxaLcfbsWXfOaDf69Xds2zY/+IM/uGXct3/n+nmBQADbtvH5fPh8vnv6LHt9rvq6iUSCRCLR9TWdc8BBo7/r9u+5X4cD/bJ57oVuY0ZvVPWY1FGywWCQM2fO4PV6CQQC5PN5xsfHeeCBB9jY2HCdLT6fD7/f744T0zR50YtehG3briOw/Xs6ffr0Fsd9NwzD4Md//Me3zPWH0a+6cZD9qp995yhdu5tTem1tjf/n//l/WF5eJhaLuYcyXq+Xhx9+GKUUb3rTmxgbG6NSqexre/pFP75TuWb/OFalpi9evOgctvdLR79ks1lSqdSWjUixWKRUKrGysoLf7ycYDDI9PU21WqVWq2HbNpOTk3d5dActimFY2evnqJR62nGci9s9fuHCBeev/uqvaDabW6JofD4fa2trJJNJd1MQCATIZrOUSiXC4TClUgnDMAiHw1QqFQzDYHJy8q5T54O+x0GkW1TFvd5X5+vuN0pmr9frRa/+debMGedDH/oQpmmSSqWwbZtCoeDOE6Zp7uiovdd2HTSD1JadGKa2ttOrbz3++OPOhz/8YcbHx900MO1c0WtVZ+TLfn8G5XKZpaUlAPdEvZtDxrIscrmcuzHUP+t27yYaTs+p2rnY7mTsdAboxzo/k53Yy2c0rH1K06tvddpc20XZ7DQPD/tndBDoz8wwDEqlErZtE4/H74rk1U4Z7QwpFousrq4yOTnJ1atXabVanD59Gsuy3LE0KOylb8Hhr+/C/dM5N5fLZa5du8aVK1d0ajBKKRYWFjh//jymabqOdW1P38u8sJM934/9onB02Kl/Dc4se0QxDIMTJ064ofZ6glhZWXEXy3g8vsWwNQyDmZmZLZv4du7n9HUYOSjjf79Pn71e710nmPr90+n0lr9pg0gbS4lEwtV7WV5eZmNjg4WFBU6fPn1fbTwKfaXb6dm9fnftp9k6ukO/d7d+db99bb/6mM/nY3p6mkqlgs/nY3FxkenpaUzTJJ/Pk8vl7qmNg7CpGbQokF4MU1t3i8fjYXJyEtu23ZSZ9mi7zns+iM8gFAq5Kbh6THZG2GjtklQq5T6uDfBe7bJt29XasW0beGEu1HNx5xzZ/l7dorZ2ov31O6VpHcU+tR3tUTaBQMBNJdspQuIorGP7jf6sfD6f2791ZFp7dJG2PfV4WllZIRqNuilFmUwGn8933ylvg0BnP9F9TM8pwmBhWRbFYpFQKORGcFmWxcbGBqOjo5w8eZJkMkkkEmF8fNy1U9qj+GQ+EIYRcb4cIO1GF7AlBFkbmqVSCdgU8NPiUuFwuKenvt+hnIdNt7D3YTfE2sPO209dddTTjRs3KJfLmKbJAw88cN/XGda+0u4cAFxj/V4dBXoDpp1f3U5fYavexCD0Nb3xDAaDFAoFisUiN2/edE8qo9Honj4TfV/6c4DhHUvC/aMj8WCrIOr9jrfdYhiGe+Ku00A75672sdiuGbRTrr/edLYfbrT39d3MkffjIKlWq2Qymft+n6OCnn91OiawJc22W58b9nXsINB9SduYpmlSr9fJZDJuJK3WPGofO7VaDcC1MffiVBx0OvuJjlrrTC0/agzCIcq9kMvlWFxcZGRkBHjhULpQKBAKhWi1emb6C8LQIs6XA6Td6NJGYnv4cjqd3lK5JBgMMjExseNieNwMuM4F9SgYYu2GU/vmPhQKMTExwcjICOvr67RXIbmf6wwr7UYjcN+OEK13olN12vtQt341KH3Ntm0ajQbJZNJNX9NVJzrDzHdDN+efcHzJ5XIsLy+TTqe3pOYcpuOxc6z1cpK0H2rs9n236+sHPUfqOb29LccZveFq72ua7frcsK9jB0nnuJmYmCAYDG7RwmgfA+26Q0ftc70Xx+pRYFAOifZKMpl0BZ61I3Z8fJxoNEogEMA0zSNz4CoI7Yjz5YDQoc5a/E+fMLSHL2uxPj25DJvX+rA4jLD3ftFtw6FPco9CGPD90sshshu6nQi1v2f7eOvWrwalr/n9fmZmZtzw6dOnT2+5r722sTOtQjje6LmmU5cKDm/jslM/7uyzuz2x73df7xTlPe6097VOfZHO+X5YT/QPk85x02lTdj7nqPbFbn1lUNbvg2ZYnUymabq2TDftLc2w3p8gbIesZgeEDnVu99zqMNvtNn1iXBw/5LvvTfvn0+2zak8h6oY+MdGnKu3vCfR87SDhOA71et39XfqNsJ+09yctfqvTZAexj0n/H146D6Pa5+DO77Xb/C3szHEcH7rMezabHYo1fT8Z5u9bt13vldr19trFoof1/gShGxL5ckC0e2pFX0EQDoadwlF7nZgMUyirPtXbrlysINwPnel9gnBQ7CWVVE68hd2i0xH1Ieegr+lCb4bJPhOEvSLOlwOi/XS9XZV+bW1NlNcFYQ/0Cj3fyTjvFXY8TIa91+uVNDThwGivCqKjzIZhXAiDT+f8vZdU0uOSNiLcP+2VtGTuGn4kBVE4ykgPPgS0AdFsNrEsi1wud+zCIgWhF73Sh3qFnt9POOqwhbK2px0Jwn6iq4JUq1UxcIV9pXP+vt9UUkHYjmFb048bexnbkoIoHGVkhjoEbNumWCxiWRaGYWwpsSgIQu+FNRQKuWVId1q0j6rhvrGxgWVZ/W6GcEQJhUKuEKfWTbAs60iOJeFw6Zy/70WnSxCE4ed+xraOyuyWOXBU7T7h6CLOlwOifTIol8tcu3aNubk5QqEQiURiS1ikTBzCcUdv/jrDhfVpPGyK4+p/nWNFj6FyuXwkRfcajQb5fL7fzRCOKO3VjfQBQTabZWFhgeXl5SM1loTDRUe4lMtlisUi169fZ21tjWq1usX20T8HAoGua4EgbIfY0IfHbj/rbs/rZuft9v10dGa3CODj4LCdnZ1FKdX13+zsbL+bJ+wRcb4cALZtk81myefzWyYDPclYlkWxWKRYLLqby6M+cQjCdugxEAgEXINc0y66pk/mtXh1+4KtK7TYto1pmruKLhsmg61Wq7G0tNTvZghHENu2yefzXL9+nYWFBWzbJhQKUSqV3H+yNgn3inashEIhVlZWWF1dpdFouPOvtn2Oc7Ua4f7Q63+5XO53U448O33WlmWxvLzs7nG6VZpsTwvb7f6nV+TLdod3R4kbN27gOE7Xfzdu3Oh384Q9IoK7B4AuK22apjsZnDlzhpWVFQqFAjdv3iQQCBCNRrcVoBOE40KvamDtY6M9ZU8b7u3PhZ1F99pF24ZJTd+2bWq1Wr+bIRxBlFI0Gg3q9TqZTIaTJ0/SaDQolUrE43EmJiZkbRLuGT3PNhoNvF4vY2NjjI2NUS6X3Xldb6zK5TKFQoFKpSJiu8KesCyLTCZDKBTCNGVr0y9yuRyLi4uk0+ldOUR2u/9pj3zpnBdkrhCGDZmhDoDODSOAaZoEg0EARkZGiEaj7sQkE4dwnNHjJRAIUK/XtyzCnWNDL8CGYWxZ2PVpyk7jqd3hMkxOz3A4zAMPPNDvZghHEMdx8Pv92LbNyZMnicfjJJNJwuEwyWRSNjLCfaFLANu2jc/nI5VKAZs2kY6K0RuqVCo1VPOyMBhEIhF8Ph/r6+vkcjnS6XS/m3Rkabe1uqGrMu527djt/kfmBeEoIVbVAdA5mWgDIxaLkU6n3Q2mKLILwtbxstMi3LkAt1dm2esCPkxOz0gkwsmTJ/vdDOEIYhgGiUTirrWpPYJM1irhXtmuL2kR3vaNnHaqwwtpodL/hJ0wDIOZmRnXYSwcHDvZTQdVcWqY7DVB2AlxvhwwWv/FsiwSiQSmacoEIgj3iDbUe6Ue7fT6YR1/9Xodn8/X72YIR5D2NL9hTMsTBpvt5t3t/t5uN4H0P2FnTNOUiJf7pD0t+16dJ7JuCMLOiPPlgOmm/6LZj4lOEI4benFv1ws46rRaLRGhFA4MvRa1OzQlzFvYb3Zr8/SymwShF2JX3zv74TiRdUMQdkacLwdMN/2XboaueIgFYXfoMdWuF9B+Yi8GlyDsDR1lEIvFRItM2FfuReS8m90kCLtBIi/unf1wnBzkuiGONeGoIM6XA2S7iUIvDpFI5MiXRxOE/UYv7u16AbpMablcJpVKycIsCLvEtm03ykAbzWLgCvvFXkXOZYMl3A8SeXHv9NPhvptxL4414aggK9sBUi6XWVpacqNbNLqsYigUOhBhKkE4yti27Zam1gu1Li+pS1EfNXSUjyDsN47jEIvFXKeldmRms1npc8J9EwqFiMViBAKBXaccSf8T9kpnJLnY1btHf3b9HG/asdLLftNziTjWhGFHZqcDxLZtKpXKXRNatVqlVCpRLpf7PuEJQj/ZbtHvZQxUq1UymQxLS0uuga4raiQSiSO5MDebTffERxD2Ez1+YPPAwOfz0Wg0aDabskYJ940+Ta/X63dtrtrnef1zIBC4Z0f6IGwihf6wm8270J2dPjvLslheXnYFsLtxv2NvN46Vg6qkJAiHjaQdHSCGYRAOh3uG0GkkhE44jmwXRtorvDQUCjExMUG5XHYNdL0gH+Vx1Gg0+t0E4Qji8Xi26HEYhoHf78c0N80DCfMW9oNu6SDt8zy80NfaS1PvBUlLOL5IutG9s9Nnl8vlWFxcBNi2otT9jr2jbr8JQjvifDlA9IawfULTXuHx8XGAux4XhOPEdot+t7+35wTHYjEikUhPA/0oaQf4/X6mpqb63QzhCKJTjQKBgBuB0G4Iyxol7AftkS16Pu42z9+P2LNswI8nR2mt7wc7jbdkMrnl/27cy9iT7004rkhvP0A6Q+Rs2yabzbrhfSsrK+7zBOE4sl0YabsxoENZO0NjdwpB3U5zaRhptVqsra31uxnCEcS2bfL5PLlcDtg8wTQMQ9YlYV/JZrNcu3aNbDbb9fH9SCmQtITjySCmHB2lFDjTNEmn0240ZCf36kQZxO9NEA4DiXw5RKrVKpZlYRiGW2GiXC6LsSAI29CtUkYgEHDFdvVzjvrJiWVZPfOtBeFeMQwD0zRpNpvYtk0kEnHHWKeIpSDsFb0xCwQCRCKRbdOO2vuXnIgLe2EQI56OUwrcXu+1fU6Anb83mQ+Eo8bQ9WKllKGU+odKqf9FKRW88ze1w2ueVEpdunz5MhcvXuSpp546lLZqw7XZbLK4uEiz2XTTJWDTm6yFd4XB5amnnuLixYsADymlLimlnmx/vF/96zjQLsLWKdxYLpfJZrPk83n35ERXQioWiwQCAcLh8MBXCurVv3Tfun37Nm9729ukbwl7Yjd96/Lly7z1rW/l/e9/P9euXaPZbJLL5cjn8wBSXULoym771uOPP87/8X/8H5TLZVKp1JbNWXslpPYoATkRP97stm9pe2sQI572uzLPfkfS3Ov7dXudrty6W1trr+N7P+eDnex5QTgMlOM4/W7DrrnjZPkq8DzwRuATjuP8cPvjTo8bunjxonPp0qWDbqZLuVymWCxSq9VYWFjANE0effRRV6vCsixWVlaYmJggFosdWruEe0Mp9bTjOBe3e/yw+9dxRZ+CaEeLNrz0uFpaWgIgGo1SKpUAmJycHPjTp17968SJE86HPvQhXv/61x92s4QjQK++dfHiRefTn/40X/7yl3n++ee5cOECqVQK0zTd8tOCsB29+tZLX/pS54Mf/KCbYj0+Ps7Jkyfvmou1raQPp+SkW4Cd563jZG+1jxEtkH4/46NzzN3v6/byfu02XLlc3vE1BzEfDJs9r5Riu+1tr8eE/rBT/xq2tKP/F7DuOM7blVJngP+slHobcBv4muM4A3W8HQqFsG2bQCCAZVmuR1hvFm3bxjRNOVUUhD3QPn50Cl+5XHaFQScmJgBcAdFQKDT0YywYDHLu3Ll+N0M4ooRCIWZnZwEYHR11jWHZ+Ar3g2EYTE5OEgwGyWazhMPhrnNxZ9qIVD4R9sJxcNa1j5H9SGm611StvRRJ2I5OG26n18h8IBw1hs35sga8Xin1HuBbgVHg3Wzex48AV/vYtrvQooV6Y6grHGknjKQbCcIL7GRAdT7evoDrxwE3AqZer2PbNvV6XSLLBKEHhmFw6tQpTNOkUCiQz+cpl8skk0mazeaR3tQIB0skEnEjFLc7sW+fy9s3ldLnhJ3QhSy0Jtphb9L3y/Gz0/u0OyD2Q+NmO4fGXtqx3d93+5mIU0U4rgyV88VxnK8qpb4PeDMQdxznIQCl1BeAFAPmfIHNyTGTyVAoFDAMA7/f7wpNzc/P4/f7GRkZkfBu4djT7TSnfRHf7rRHO2L0YzqlLxaLYZomlmVRrVaHOpy9Wq1y5coV0ul0v5siHEHax8Xy8jK3bt0iEAhQq9UIBoMA226cBaEX1WqVSqXiHkStra0xOztLIpHo+txMJgPc++ZQOB5ofZFqtUqz2WRjY8MVcD1M9ktYdy/vcxBOi85UoN20YzuOk9iwINwLA+18uaPx8irA7zjOnwM4jvM/lFLPAF9QSr2ZzeiXMWC+bw3tgWEYzMzMEAwGCQQC1Ot1ms0m6+vr5PN5IpEI4XB4S9qEGBTCcaJdwK1XNYxepz3tgo3tFcW0MdZuqA2jUdBqtXAcxw3TFYT9wnEcrl+/jtfrJRaLEQ6HaTQa+P1+Tp48iWVZhEIhyuUymUxGNMqEXaPn4MnJSQDW1ta4ceMGo6OjrvOlvfKJbduMj4/3TEXonMPFGXM8abVa7pqv56t6vX7o6/p+VVo67IpNneNGj6tIJHKXUPBex1j7vez1tTKehePAwDpf7jhePgusAi9VSj0LPAncYtPR8p+BfwlsAD/gOM7tPjV1W9onkUgkQiaTYXx8nHq9TjabpVAoEIlE8Pl8AEO5KRSE+0GHDOvIsMnJyS0Lbvsi3uu0R6cYGYZBIpHAtm33BDUajW7JLdaOmGFzZKysrLgRPIKwX1iWxcLCwpax5zgO4XAYy7K2RKFVKpWBrhwmDBaWZZHL5dzI3mKxSDAY3DLv6k2f3kjvJL7ZuUnt5VCXjdzRxePxAJvrom3b+Hy+vmi77VcUymGn4HSOm05bq529Ot7b76VcLrtprLuJ8B/WAzJB2AsD63wBvgPIOI7z/UqpceB/Au8Dvt9xnIJS6l+zqfcSchwn38d2bku1WnUnHX0CbxgGyWQSy7JIJBLk83lXdHc3wlOCMMx0O22xLItoNHpX1AvcnUfcHg6rF3HLsigWi66TU0e96GuNjo5u0a3Qm4Bhyjc2DIPp6WmZH4R9RymFaZqMjY3h8/mo1WqcPXsWn8+3xUlpGAbBYJBqtUosFpPNrLAjSikKhYJbdjeVStFoNEilUsALEYl67q/X6z1Py7v9XUc9dks5kY3c0aZdYF80gnZP57jbzfPv1fGuoybb07+7vX979Ntu2yUIw8ogz1RZ4OVKqdc7jrPCZvrRaeDXlFKvBf4xwKA6XuCFk5nFxUUsyyIcDruTjN543r59m6tXr7rhkrJ4CEcZbQxXq1Vgc4wkEgnS6fRdG7r2dCT92kwmQyaTcV8PkM1mmZubc/+mHTSmuelb1o4Xnc+sNwLDtLjrsvQyPwj7jVKKQCBAtVrl1q1bLC0tsbi4eNdYjUQixONxdw0ThJ0wDINKpUKxWHTFz/X/sDmn65RrfQil/9be9zSdfRK46z3bGca5XtgdrVbLFdMfBGdwp70yyLSPO31Ylc1myefzrp1ULBbdcWsYBuFw+J4+Y8MwSKVSJBKJHVMJc7nclnYJwlFlkCNfvgn8CfCDSqmK4zhPK6XeDvxTNjVeft1xnGZfW9iDdgO1Wq2yurpKpVIBYHZ2FsMwOHv2LH6/39WDEYSjzk4lRXsJ7LafcrUv4j6fj3q9js/nu0sjRp/wlMvlLVEze1XlHwTC4XC/myAcQfRmoVQqEQwGicfjeDweSqUS0WjUjSyLRCKkUil3vAjCbtAOGL2pq1QqWJZFuVx2U64ty3LnaZ2e3c1p0k0Xo5dWxjBFNwp7Z5Ds5kGLsupl23RL3WvXytPjsJ2JiYl7vq+dxqFuh9bF3O36Mkz2myC0M7DOF8dxqkqpfwX8c+BnlFJPAafYdLz88aA7XnTpO71hNAyDbDbL2NiYe0Jj2zYnT56kWCz2RShMEA6bnRbhXgK7hmF0zTduNpsEAgGazSajo6PAC6HonToCWpC3Xq8TCATI5XJ9K1G5FxzHodFo9LsZwhHEcRyi0Sgej4erV6+STqcJBAKu8Gln9ZlBHifCYKE3c36/342uCgaDrK6uEgwGMU2TSqVCpVJxIxX163YqZ7vTc4Wjj96o92sD3r757yUy2w8nQS9nUOeY0W3XjpdIJOIedMFmNPFBRhe1l5rfC4Pm8BKE3TKwzhcAx3GeU0r9NPB3gX8GFIB/NMiOF3jBi2ya5pawW8dx8Hq9jI6Oks/nCQQCd+UtiwdXOM7sVmBXCzkmk0lXxC2ZTLqvKRaLzM3N4fV6CQQCrp6ADlnXRoYep8Nwkr+pQS4I+4tSCr/fz82bN8lms4RCIUZHR911aKfqM8LxZDfpZ41Ggy9+8Ys8/PDDJJNJN3Wt2WximibJZLKrk/1eqqQIxwuPx9M1QvYw6bx2u8hs+9/bI7raNR4Pkr1UT2p3frTP9VqH5X7m/72M471+l4ddIUoQ9ouBdr4AOI5zA/g5pdT/DijHcUr9btNO6FDIer3O+vo6Xq+X2dlZGo2Ge7qYSCTcaBc9OYsHVzju7PYUM5fLsbi4CEA6nSadTmPbtpuzHAgECAaD2LZNoVCgWCximqabvqQdMPp6g27cO47jVncQhP1E59drQd14PE4gECCTyeD3+xkdHZU1SbiLto3StpOndpLXajX3wKk9da1bNON2G1hBaEev2f0UaN1u899ZVVHT7qw86D6tHSa7cXx0VmaF3Y2/3ThW9lKNbCdnSufzJepNGFYG3vmicRyn3O827JZ6vU4ul+PGjRv4/X4efPBBTpw44QqGNhoNGo3GlkoS4sEVhN2TTCaxbdtVx9cC1vPz85TLZc6cOcPk5CRLS0uUy2VWV1cJh8PEYjE3Iu2gQ2n3m/X19X43QTiC6PHj8/kwTZO1tTUcx6HZbDIyMiJrktAVHa0LbJsroKN9q9Uqc3NzNBoNTp8+Lafawr6ghWP7tY73So/rrKpoGMaeNU3ul/aKq73KPHdzkOxm/HWmiXdzxPR6n87r7iUtXZwuwjAzNM6XYSIUCjE7O0sikaDRaDA1NeU6XsbGxtyqR/qUXk+KMpkIwu4wTZNYLOYaN9qwmZ2ddcdUvV4nGAwyOTnJ2NgYpmm6Gwafz+e+Zhjw+Xw89NBD/W6GcATxeDxu6d9gMIhhGPj9fhqNBiMjI31unTCo6CpD9Ih88fv9PPbYY5w9e5ZWq0Wz2XQ3y+20l5rVm1OxiYSdGGQH3XYp1IfZp3uVed5Or0azm/EXCAQoFouugHZ7UYPdvM9ev7/OiKJhOTgThE7E+XIAGIZBIpHANE1XTFdPgDrVqFgssrGxAWyWyu3llRYE4W7aReL0aUgikSAWi23Rc4nFYlSrVWKxmKv7okXwhkXoWkf2CMJ+o8v86nLvxWKRa9euuVX6Hn30URKJRL+bKQwYbRumbSNfPB4P6XTa1ePqrKACm6lJN27cwOv14vP53DSNYZiXhf4yyA66frat3bGyXYW6bsUN9kq9XqdSqbiV8faa/rXXz6hbRJEgDCPifDlA2jeHtm1jmiaFQsGtvhIKhdxqK51eaUEQtkcL5sILJ0vtInGZTIZKpUI4HCYSidxVtvSww3/vl2GK0hGGF53KNzEx4absVatVcb4Id7HbzU84HN6SetE55+ZyOdbX14nH425EjJxsC8K9s50QcDvt5Z11ddZ2GYTdjD1dzVVXldTOkYNkkKOdBGG3iPPlAOksn2bbNplMBsuyaDQazMzMbOuVFgRhe6rVKktLS9RqNc6cOXOX0T42Nobf73cNifYypv0I/71flFIyRwgHSrVaZWFhgfX1dc6fP8/4+Dirq6skk8l+N00YQHZT7cgwDKLR6Ja5q1M0U/cvn8/HysoK4XDYfe0wzdFC/zjIyljDWHWr00HR7R70+GqPEgb2pKmiBbMjkci2+5j9/vxkXhCOAuJ8OQS0N7hWq+Hz+chkMkSjUYLBoHsiPyyTuiD0G+1g0UZ6+9/1CY5pmjQaDYAtqUXDaEgBtFot1tbW+t0M4YhSLpcJBAKYpulGYpqmSTAYpF6v02w2h27MCAfLbqod6WpzuiKN1oXQB1KhUIh6vU4qldoSyajL8QrCbjhIIda9VOvppF/2RqeDotc9bFfqvRvb3U8vh4iI5ArC3Yjz5QBon6AAV0hubGyMGzdu4DgOfr+fUCgkk5Ig7JH2xTwajboiurZt02w2aTQajI2NMT4+7hoSeky2pysN05hrtVoopfrdDOEI0q6ZNDU1Ra1WwzAMrl27xqlTp7ZslodpzAgHy240XxzH4erVq0xPTwO4mhA65XO7jVkgEBhKJ7nQHw4yFWUv1Xr2+vhBspOgbjc6nSidzpbt7mc7wez2a2o7Tca0IIjz5UBon6B0qtHExATr6+tks1lCoRDBYNCdqHw+n0xKgrAL2pXub968ydjYGAClUsnVFiiVSti2zcmTJ10DoVwuUywWu+q/DAPNZpPbt2/zxBNP9LspwhFDG9yWZbG0tESz2WRubo7r169TrVY5d+4cgUBg6MaM0H8syyKbzRKLxbaI6WrHi9ax0raPFtPUjnIQh5+wMweVirJdpEe7swG2d2ochFOoVzRN+2O70X2BF/Yr3fReOh/b7n7187o56tvTm+SwWRA2EefLPqM3hvqER5+yA4yNjXHq1Cnq9Trr6+uYponf7+fWrVv4/X5AJiVB6EW5XCaTyWwRsQ6FQmSzWWAzEiYcDrs6A/pUv1wuuwbIMDo4HcdxjRpBOAjm5+dRSmFZFrOzsywuLqKUotVqSTU+4S52k3ZkGAajo6MEAgFWV1epVCpUKhXX9kkkEq4unk59g+ETRBeOJp0VgbZzbGg6I2sPwinUK5qmWwWjncZQt6qRekzqvQz01oJpj27ZbtyKUK4gvIA4X/aZarVKuVx2BUABJiYmXEXxWCyG1+vFtm3i8TjNZhOv1ys5zoLQhe1OecbHx4nH424J0zNnzmw5VW0/uZmfn6dcLnPmzBm3xHR79Y1hwDAM4vF4v5shHFF0yt7a2hper5dsNstDDz1Eq9ViampqaMaJcHjsJu3I6/XywAMPuNov6XSaYrGI1+slGAxuSc3u3NzJQZTQb9odBtVqlXw+T7lcdkWi21Oa9WHr3NwcwWBwS+TtQbWp22PaaQJ7G0PdqkbqvYx+bLvUoXYn03bXFKHc3szOznLjxo1tH5+ZmTnE1ggHjThf9pn2iVGf0k9MTLC2tsb8/DyBQIBwOIxlWayurjI5Obmn0m6CcJzodpIzMTFxl+MkFou5Ya3tDpZQKMT09DSrq6sEAoEtJaiHyRgwDIN6vd7vZghHFMMwsCyLVqtFo9Fwx9fo6CjNZhOfz9fvJgoDxm7mT8dxGB0dJZfLEQgEKBaLrKysEAwG3ZRRkFNxYTBp7+Paprcsa4uIf3s6DUAwGLyrwtdBtanbYzp1b7f2Tfs+JRaL3RWFpvcmkjp0sGg9UOF4IM6XfWa7CS+ZTGLbtiskVyqV3AlaNlWC0J3Okyd9EtMt17k93a9d9M3n8xEOhzFNk/HxcYLBoPu8YSEQCHDu3Ll+N0M4omgn5alTpwC2VA0bpnEiDBZKKUzT5Pz58+TzeUzTdB3juqrWQaVnCMJ+YhgGqVTqrpLKnY7DkydP7uow9aAqIXVzZO7lWnupjCQIwr0hzpcDRBsVOlRPU6lUiMfjpNNpKcMmCD3oPHlq/x9eMCr0aU0sFnM3jNrY0OMvEAi45ZpN0xyqSDOPx4NpynQt7D96DBmGsSVdNpvNis6QcF84jkOz2WRhYYGZmRnX6aKjqXpt4rrpZwhCP+hWJbE9/abddt+tHX9Qtn83R2a3a7U7ZCYnJ++yr7qNTXGSCsL+INb8PtNZZho2J+lnn32WZrNJJBKhXq9vKYML4kkWhJ3oZVRoMV3Lslx1/kwm46YX2bZNLpfbEnE2TNTrdZaWlnjggQf63RThiGFZFnNzc2454GKxSKPRAGB5eRnDMEin07L5FfaMYRg0Gg0WFxexbZuZmRksywJw0xq2o1qtkslk3PeRTZ9w2LQf7rT3xfYqptph3RldslO0yX7Y/ruNaGm/VueBFWx1/shYE4SDR6ypfaZcLrO0tESxWCSbzboCXR6Ph2azSbPZZHV1lWq16r5GT4JyyigIW8fDdmOjPS85FosRCARYWVlhaWmJTCZDtVqlVquRzWYplUpb0if0ay3LGppxV6/XWVxc7HczhCOIZVk8//zzzM3Nsba25lYOazab2LZNqVTasl4Jwl4YHx/H6/VSr9f50pe+xOc//3k++9nPcvXq1S0RwZ2EQiEmJiaYmJgYOme5cDRojxgZGRlxK3XZtk2lUtliO+jn6rmy8/dO9OGrdoTcyz5gp2u0X0tHj7XfkxbThc11YHl52XWOCoJwcAxd5ItSygDeC2SAjzuOc/nO35UzQGpF1WoVy7LY2NhgZGSERqNBqVRiZGSEqakpIpEIzWaTK1eu4PF48Pl84nEWBLYaPNC9xGGxWGR+fp7p6Wl8Ph+5XI7V1VV8Ph+Tk5NbSr1HIhFXSG51dZVSqUStViMajd5TVYB+YFkWuVyu380QjiA+nw/TNMlkMty4cQPDMBgfH2djY8PVS9opSkEQtiMWi/Hwww9z9epVnnnmGXK5HJFIhFQq1fX57af57WlwgnDYtEeMPP/888zPzxMOhxkfH+/53G7/d0PbOrrPa8fHbu2RbhEt7eWedTRM+5hqf017tEwul3MPeNLp9K6uLwjCvTFUkS9KKQX8GVAFxoATSqmUUsrjOI5z5/Fur3tSKXXp8uXLXLx4kaeeeurA2hiJRJicnCSZTLKxseGWkdZpRslkknQ6jWVZXLp0iRs3brCxsSGnOwPMU089xcWLFwEeUkpdUko92f74Yfav44A2urWhEIlEtpRPhBeMlvn5eRYWFvD5fAQCARKJBKZpupoutVrNrQBQr9e3jMdkMrnl5Kdf9Opfum/V63V+9Vd/VfqWsCd207euXr3Ke97zHj772c8SDodJp9OEw2EymQwbGxvUajURhRfuYjd96/LlyzzxxBP8x//4H1ldXWV1dZVoNMorXvEKLl686DrIe0UQCMeP3fatg7S3OlN6xsbGSKVSjI2NYRgG4XAYwO2/7dElwF2/d6PdwbgXgfNuekh63ORyubvGT/uY2q5do6OjxONxRkdH9/QZHWb08H5cbyd7XhAOAzVAwSI7opQKAL8KvBv4vwELsIE/BP614zitXq+/ePGic+nSpQNvp23bLC8vUygUiMfjpFIplpeXyWazJJNJ1tbWKJfL7oT4lre8ZeBP3gVQSj3tOM7F7R4/rP513NDlDWOxmDtOLMvixo0b1Ot1NjY2SKVSbqTZ1NQU9XqdYrFIqVQiHo+TTCbJ5XJYlkWtVmNycnLgTlV79a9YLOb81m/9Fj/4gz942M0SjgC9+tbLXvYy56Mf/aibspdIJGi1Wty8eZPTp0/z8MMPY5qmiJ4KXenVt/SauLa2xhe/+EVu376Nz+fjwoULnD9/nqtXrxKLxUgmk3cJgXY7wReOF7vpWweBbdtuxbdEIrHFSajTher1OpZlsbKyskX7Rb9+L5WM9vr8bjZRr3GjI2eTyeS2wv3d3vNe2nFQdH4n93u9QbPnlVL3XGr6fl4rHAw79a+hSTu6E9USAd4CzAAfBX4T+IfAtwMfAeb71Lwt6FLSOrVBV404ceIEPp+PbDbLyMgIABsbG2QyGXG+CMI2dAvf1SdROq0oEomQy+UwDIO1tTVKpRK2bePz+bBtmxs3bpDJZNwINM1BlXs8CCQXWzgIHMehUqmwurpKoVAgEokwOztLNBrF7/e7+knAwDkshcFHHzSFw2Fe/epX02g08Hg8fPnLX2Z1dZXx8XG3xDm8EDGgN3Yw+GmhwtFCpwC1V07UkSaVSsV1trSnR3e+fi99d6+SA9vZRPo9Ot+rXq9j2zb1ev2ux9qdNp3vuZt26Khk7Zi6F3Zjh3V+J4IwzAyN8+WOnktOKfULwAeAG47jlIF/rpT6LuAUA+J80UJxKysrlEolDMMgGo26JW9t22Z9fZ2zZ8+SzWY5efJkv5ssCAPLdlWOMpkMtVqNSCTiGg+6tLtt227KUrlcplQqEQgEGB8f31LidFhKvXs8nm01EgThfnAch3A4zOTkJAB+vx/TNJmdneXmzZtDIUgtDCa2bbO2tkatVmN2dtbV3rpy5YpbiS6dTnfdFHbqWUjZaeGw6Ox7xWLR/VkfpsILfbHTGXDQVUz3w1mjuVcbSDtMYNPBul2bdnKstEe09GrDdlo1gjCMDLTz5U60y6sAv+M4f37nzx8CpoFfUkqtATU2I2Lm+9LILhiGgWmaBINB9/dQKMTS0hIrKyuuQTI2Nsbo6Cj5fJ5UKiUTiiD0oH0R144Wv99PuVwml8vh9/sZGRlxKxFEIhFM06Rer1OpVBgZGSEWi23Rr9ivk5uDxnEcSqVSv5shHEFs2yYWi1Gr1XAch0KhgMfjccdCKBTa4rAUBM2dTei2k6YuNZ3JZJicnKRcLlOr1RgZGSEajd61eW3XdGjXptBVJGu1GmfOnJEILOFA0X1PR82ur69z6tQpQqEQwWCQarXqHvr0ev1BsJ0zo5eTo1d77tVRpJ02uqBBu+hvext2cu7sNqJFCpIIR4mBdb7ccbx8FlgFXqqUehb4O47j3Ab+hVLqeeAdbDpffuDO3weGdmOiWCy6p+/z8/M4jkMkEiEcDmOaJpZluZO5IAgv0L6Yd1ZBCgaDmKZJIBCgVCrRaDTc8rj6teVy2RWQSyaTVKtVlpaWCIfDbp6yYRgUi8WBXtxbrda2Ic6CcD94PB53o1EoFKjX6zzzzDOMjo6SSqVoNpsAXaMThOOLPrEGPL2e5/f7AfB6vcBm+mQ0GnXnXZ/P5/YrHdFYqVTcyBhduS4ajR7o/QhCJ9VqFa/XSzweJxAI4PP5aDQa+P1+crmceyDSy3bY79TmarVKPp+nXC5vObS91wiWe7V7ukWidEsV3Mm50/k+w5QKLgj3ysA6X4DvADKO43y/Umoc+J/AB5RS3+84Th74b47j/J5SynAcZ+Diots957oqS6PRoFwuMzY2xgMPPMCJEydclXI5VRSEu2kvxQibC3ogEKBarW4ReavX6zSbTXw+H8lk0s1x1mJw7aUTa7UalUqFSqXiivHCwYUI7wemaRKPx/vdDOEI0r4GxeNxbt68SalU4tSpUzz88MNEIhFXwFEQNG2lcbdVemy1WuRyOWCzlK12uuhN1sbGhtv/tJNlYmJii7Ap4KYnDUJ1OuHo066DMjo66h7krK6u0mw2abVaJJPJPZWThntL6+l0Qmjx385D253ast9OjW5Om520aHbzPsOSCi4I98MgO1+ywMuVUq93HOezSqlXAd8EflUp9R+Bdyil3u04Tq2/zexNu6ERDoeZnZ1ldHSU6elpAPHwCkIPtE6SZVmsrq4yMTFBvV6nXC67oefVatV1uLQr/QOuBky5XHY1YM6cOeMaIpZlDcWJvmmanDhxot/NEI4grVaLpaUlgsEghUKBaDTKi170Ih577DFisdhAR4QJ/aNtg7WjEvj4+Di1Wg3LsggEAiSTScrlspsO2j5fb5dSJP1QOCzaD320E9AwDDeKdmpqCtM0d5X+dr9pPfq17XuFVCrl/t7uVOk1Pnq9336xH2P0oPVyBGEQGGTnyzeBPwF+UClVcRznaaXU24F/CowBvzHojhftLQ8EApw9e5Zms8ny8jJra2vMzc0Rj8ddXRgxKgThbnQ5R129CLbqtOgwV9M0XYOk3cCAzVDY9nKQ2mCKxWKuvsAg673A5lySz+f73QzhiFKpVPB6vdTrdUzTxHEcVlZWsCzLrSY2yONDOHx2u9GKRqOMjY25VSCbzSbVapXbt29z69YtGo0GiURi19eVtAThoAmFQliWRSaTwe/3Mzo6SigUIpvN4vf73Sjb3bAfaT3dUo30e+62Kpg+hNKRwwcRXbIfY1OcrMJxYGBXLsdxqsC/AkaBn1FKfSvwMjYdL59wHOdaP9u3E7Zts7y8zLVr18hkMu6JTjweZ21tjUKhQKFQ6KqULgjCJnoR1zoBehOoRRiBLbpJ+vTHsiyWl5fJ5/OuKJ7+W7PZdHWY4IVSqIOMZVkUCoV+N0M4ogSDQWq1mqvPUSwWqdVqlEolMpnMwI8P4fDRaZ3sILgbDAbJZDIsLy9TqVTc9GstmK43tbutqqU3jtInhYNAH+qsrKyQz+dpNBru35rNJo1Gwy3LvJ/X6+z/7YLToVAI0zRpNpt3jZVQKHRXOl6392wvN93tNfuBjE1B2B2DHPmC4zjPKaV+Gvi7wD8DCsA/chyn2d+W7Yw+5YHNSbRQKDAxMcHIyAgrKyu0Wi3C4fCBhf8JwlFAGyC6epEeI52pRJ1jaG5uDtu2OXHiBKlUinq9TrFYdDcA2ijR0TDD4ABttVr9boJwBNHV+arVKuvr62xsbLilzXVEwjCMD+Fw0eK4gHe75ziOQy6XI5PJuGnXsVgMy7K4efMmk5OTKKXu0q/odoLersMB0ieFg0H3a8uySCQSBINBVzvO5/NhGMaOqcp7iQDZTRSKTjXSJZnbx0q3SJFu79kpbHsQAsGSMiQIu2OgnS8AjuPcAH5OKfW/A8pxnKGotxoKhZicnMSyLLe8NMCpU6eoVqukUilCoRDFYpFsNusauhLeLQhbaT/9sSyLXC6Hz+ejUChg2zbpdHqL0a5PqHQJd00ymXTztnW56WEZb0opJicn+90M4Qhi27Zb6lOXZh8bGyOZTGJZljumhmWsCIeDFscFNrZ7jmEYzM7OkkwmXUHdtbU1t0pdPB4nnU5TrVa3pH/q0tLRaNR1nmtbSjvMeyGpScK9EgqFGB8fJ5/Ps7a2hsfjIRQKbdFX8fl87uFPZ//SVcDuiFHvmEKzG4eFvq7WttvJuXEvwrea+0lJkpQhQdgdA+980TiOU+53G/aCTjNaXl6mWCy64dzr6+uMjY25hsi1a9dYWFigXq/TaDRk8hKEDtrHRDabZXFxkWg0Sq22KfnU7pzRRnwgEEAp5TpqbNveUvVot/nag8T169f73QThiGIYBuFwmNX/P3t/HmVZVd7/469z53moO9S9NU890HRD092AgCBOiQNOUdAoH/3EAX+uqKgxmqgYzUcTTYxR8StKFHFKRCNRHAiTAiIitAw9FtU1V92qO8/zdH5/FHtbVfQE9FzntVatVVV3OvfeffbZ+/08z/tJpaT/y/j4OGazmUqlsqLtr4YGrDDHPWy9kDAL1+l0RKNROX87nU5cLhcGg0G2nV4+xsT8LhoWPBO0jikazxaRCbiwsEA0GmVkZIS+vj6SyaS0Clg+Jg+WdSLE7KPJADmaefWZjufnMldr2SsaGsefQ4oviqIYgb986j63PtXeGUVR3qOq6g0n5vBOb8Qm0OPxYDQaKZfL+Hw+qtUqHo+HdDpNX1+fbHV7upQ/aGicLMS5sjx7pdlsMj4+jsFgYNOmTXg8HoLBIMFgEIvFIhdLp/O5ZTabOffcc095Y2CN0w+9Xk8gECCfz9Pd3Y3X65W32e12Ojs75fmjZRRoPBOEL4zYqJpMJkKhEH6/n3q9Ludkk8lEqVSSQrnNZmNoaAhY2gQerJPd4dA2kBpH4nBzmcVioaurS3Y0Eh0Xhe+cGJMHG1+ry3uOBcdyPB9pDtdEdg2N48/hZob/AD4AvBXYqyhK31P/f+/xPqgzhXK5TLFYxGq10mg0ZFvbUqnE3Nwc0WiUarVKo7GUtSsiQBoaGkuIkodmsylNGkOhECaTSXYuqlarjI+P8/jjjzMzMwP8KSorvCyEAHO60m63mZiY0IzsNI4L1WqViYkJHnnkEQC8Xq88xzwej2w5LcpBhFm1xtpFzM2HQ1VVYEkgn5ycJJFISO+M5WVs6XSaWCzG2NiYNHgWgp/Y5BoMhqMufVtuVqqhcTAOZw5bLBZZWFigUqkwNjZGsViUQR2xljjU+HouY+9ozHefK5oprobGyedwZUcXARtVVVUVRfkn4KeKolwAKCfm0E5/LBYLtVqNer2OTqfDZDJhMplYWFjAarUSDAZpt9tEIhHC4bAWpdHQWIVYKCxfkCyPyogo6bnnnksqlZLmePl8nkKhgNVqlf4CB+N0ieS3Wq3TPntH49Sk1WphMplQFIVsNkulUmHdunXaeNM4LMtKIQ45cTYaDeLxOH6/H7PZjE6nWzEXi/m3o6ODoaEhLBaL7F43OzsrfbpAKx/SOLYcKpuk3W4Ti8XI5XL4fD6Z7eJwOCgWi1Io7O/vx2A4+Bbq2a4rTkS5nJYVpqFx8jmc+FIEXqooyu+ATwCXAt8DTj+zhJNAu90mmUxiNBqxWq04HA4sFgsTExPMzc3h8Xhwu91UKhVp8HUqb/40NE4Gy9PS0+k0FotlxcKmWCwSiUSo1Wps3rxZZsI0m03i8TilUgmn07kikrr8PDtdvAFUVZVRZA2NY4kwMrXZbBiNRrxeL/F4nHg8LstmBauFT+2atXZZtnk7pCGLXq+Xfndut1tG9qvVKh0dHaTTadlVpqurS87tImu4o6NjhXh+uojlGqc+q8trxNhqNpsUCgWcTidDQ0NUq1VpOG6z2YjFYmQyGex2u/SQW82zXVeIc2p5N8dnO84Pda5oZUUaGiefw4kv1wLfBx5QVfVqRVGuAG4DBk/IkZ3mFItFMpmMNJszGAxEo1GmpqZIJBJ4PB6i0SiVSoVGo3HIyLyGxlpGLBREJotYSIiFDcDi4iL5fB63201HRwcWi4V4PE4oFGJ+fh6DwUA2m6VUKsnuGmIxcrpEgRqNBgsLCytaTGpoHAsURaFQKFAoFHC73WQyGUqlEplMhmAwiMPhIJlM4vf7D2mOqrG2WL6xOxwGgwGTyUS9XqfVahEIBCgWi0xPTxMIBKSA53A45HPm83kcDgcjIyNyExqPx+VYE6VOyx+jiTEaz5TlnYuSyaTMrBXm48KAvFAoUC6XqdVqDA4O0t/fj91ul/5zB+OZriuWj2ORYfNMxZvV58LpEljS0FiLHE582Q28CLhDUZQBlsqN/n/AnhNwXKc1wmQulUrhdDrZt28fGzduxO/3EwwGicfjzM/P43Q6yefzeDweIpEIw8PDJ/vQNTROGodbSLfbbUqlkiwzKhaLUrAcGRkhnU7T3d0NQDQaZc+ePdKjIpvNrjDohT+Z4Yl04lOdRqNBLBY75UUijdOPZrPJ6OgoTqcTs9lMNBrFarXK9sDJZJKFhQXq9TqNRoPu7m7NHH6Ns3xjdzhUVcVsNpNMJsnn81itVtlSutlsUiqVyGaztNttmeEiIv7i73K5LMek2+2WGQirj+Ng87gmzmgcjOXtoCuVClNTU3R0dNDV1YXFYqFQKDA/P080GuWcc87BbrfLTqUej+eQGS+CZypMrx7HzyYD5lDPoc3TGhqnHkfKfPkUoAKTT/2vBfziOB/TaU+5XKZUKmGxWGTtaLvdZvPmzQSDQdxuN7VajXQ6jdfrxe/309vbe7IPW0PjpHKohbRYQJvNZorFomwnLcSUWq0GLJk2hkIh2u022WyW7u5uGUEVraXL5TKFQoF4PH5atc8VJUfaBkLjWCN8OMS5VSqV2LhxI+edd548N0KhEIVCgenpaQAtULDGEZ2vjtQCWqfT4fF4sNlsTE9PUyqVaDab1Ot1ebvIZBGieqFQWPG8DocDu90OQKFQkMaj4vWXbzRXo0X/NQ6GKC/S6XQYjUYsFovM8hNr9qmpKUqlEpVKBbfbLcvhlpdhHitWCyViXfJMMmAO9RwaGhqnHocUX1RV/UfgHxVFuUtV1ZeewGM67TGZTOh0Ovx+v6wfXVhYwO12E4lECAaDMsW7Vquxfv162WJRi9JorFUOFqlZHqHS6XRMT0/j9Xpxu91ks9kVXgCiLKlSqTA8PCwX6SaTCYPBIFN6AQKBgPzfMznvTtY5qigKPp/vhL2extrBbDZz9tln8+STT5JOpymXy8TjcXbu3Mm2bduo1+u4XC5ZdqQFCjREx5cjGe4CUkhxOp3o9XoKhQIGgwGr1UqlUsFisWC32+VGs9ls0m63V2RXdXd3S2+i5R1hisUiLpcL4KAZAkJU1Mq6NZYjxlWz2SQajWKxWKhUKjgcDlkOFwwGSaVS5HI5KpUKoVCIjo4OotEoHR0dslX6sVgHHEooORWyV07GmkfbC2mc6Rwu8wUATXg5epbXLAvDLrfbTT6fJ5vNMjo6SqFQQK/XYzQaZTcWEcHXojQaa5mDLUCKxSK5XA6n04nVaqVYLEpz6snJSXQ6HT6fT/q4iMhpR0cHPp+PWCxGLBYDIBwOo9PpaDabpFIpfD6fXMQnEgk6OzvlQv5QnKxztN1uMzk5eVDTXUXRGtBpPHtEBsH8/DyxWAxVVZmYmJBdakZGRmRZiJbxogF/GjNH2hS2220WFxdJpVK43W4ajQbFYpFKpUI0GkWv19PV1QXA+Pi47E5XKBTk9UBRFOx2O6qqYjAYZGajuN1ms62Yl8V9RYakyJIU2TPafLm2UVVVjqlCoYDdbiefz7Nz505CoRB9fX309vZKs/5KpUKr1cJutxOJRKQnltVqBY7tOmC16PBMsleO19rkZKx5tL2QxpnOEcUXjaOnWCwSi8Xw+Xx0dXXR0dFBMpmk1WrJCWRiYgKDwSCd/0VK7vLojFajqaHxJ5YbMoZCISm+DAwMSG8A0R7SbDbjdrsJh8Nks1msVivxeBxYWpQ7HA5qtRqLi4skEgkCgYBclB8NJzMSdSRhSEPj2SAyCoxGI36/n87OTjweD4lEQp5boluYthDWAGQ3oqei0vpD3U+n0+F0OqV4J3A6nQSDQZnRt7i4SLlclqUf09PT5HI5mUFcLpeBP23EhMguxJnlGS6qqspsSZfLpfkTaRwSh8NBd3e3FOgikQi5XI5UKkU2m8Xj8UjBuVQqyW5wvb29MvPlWPJcRIfjtTY5GWueo31NLUNmif7+/kOKyv39/bJcWOPUQRNfjgMGg4GOjg5gafKIRqP09PTQbreZnZ0lkUhgt9vJ5XIYDAa5kDGZTASDwTU9iWhowMqLajgclhfhoaEhma3icDioVqu4XC6KxSKJREIuuCORiIxq6XQ6KcKUSiU6Ozux2+1YLBZZelStVp9W7nQqtWkUmxgNjWONwWBgaGiIWq1GJpOh3W6Ty+UoFovMzc3h8XhoNBqH7e6hsbawWCzk83nhpdU63H1DoRAOh4Nms0ksFkOn0+H1enE4HFJYcTqd6HQ6XC4XDoeDoaEh8vm89MsrFApUKhWGhoae5gcmjNiX+4A1m005t4v1lRBqNDREZpTNZsNms9HZ2cnFF19MPp9nbm6O0dFRkskk27Ztw+VySa84r9fL4OCgLGc+1jwToeO5ZMk8E07GmudoX1PLkFnicOKKNuedmmjiyzFEeEyIiVN0PYrFYhQKBamWZzIZhoeHMRqN6HQ6otGoFGy0VrIaaxWxmLBYLCSTSSmkCPFDLDBEFLVcLrOwsEChUMBsNtNqtahUKthsNoxGI3a7HbPZDPC07BbRrUCYjIpF+qnaplFsiDU0jjV6vZ5QKESpVMJkMjE7O0sqlaLVauF0OhkdHZXnj9h4aKxtRAeio0HM26lUilKphE6nw+12EwwGKRaLLC4uYrVacTqdmEwmWe5Wr9fZt28f69atWzF/C7NUkZW1uLhIIBBYkeEifGbK5TKLi4tSuDmYgK1Fz9ceq7t1lctl2V3LYDAwNTWFwWAgEongcrkYGBjA7Xbj9XqP63GdCmVGpxOngieOhsazQRNfngPC8A3+JLwsnwSLxSLT09PMz89jsVhQFIV2u02r1WJmZoZAICBLjZZHasRzawsCjbWEWEwUi0V5PgArFhgmk4lKpYLP58PhcFCpVKjX6xQKBRRFkZ4BpVKJTCaDxWKRkVdYakNdKpUYGRlBp9NJPxiRGt9ut2X3F3hmrR6PJ+12m2QyedJeX+PMJp1OE4vFSCaT6HQ6zGYzlUqFhYUFZmdn6enpobu7WwsOaKzgKZHjsBOjyOq12+0YjUbS6bQUu9vtNpVKZYW/hhCZC4UCqVQKi8XCyMgI4XB4RdmRTqeTmS4i6CWuHyIjRog2QoQ/GNomdu2xetOezWaZn59nfn6evXv3MjY2xrnnnovL5aJarcruXKlUCpPJdEqME0140Do6aZy+nNbii6IoinowB8oTRLlcJhaL0W63cTqdOBwOKcKIFFuj0YjP58NkMlGr1fB4PLjdbjmZiw4ShULhac+tLQg01hJiEWEymUin0/j9fhk1FWnui4uL0g+gp6eH/v5+GQmNxWLSu6LRaMhsmNnZWXw+H3Nzc1itVlRVlWVLgUCAcrksuxeIbDXgGbd6PJ6cxGlO4wxHVVWq1SqlUolEIiHNSZ1O5wpzykajoXWN0QCW5upgMEipVAI4ZF67mE9FxmKj0cBqtZJKpbBarbhcLoaGhqToLbo+6nQ69Ho9w8PD2Gw2mTUjRHCRNSNMf9vtNtFolEgkwsTEBKFQSAr1drtdljkdDK0j0tpDzGmVSoXHH38cm83G/Pw8yWSScrlMvV6np6dH+jdaLBY59k4VseNYCA9akFdD4+RwWosvqqqqJ1OAEbWiwvxNpNTabDYOHDjA3r176ejoIBwOk8vlyGQy0vnfbDazbt06arUaHR0d6HQ6aSC3PH32VJnoNTSON2IxIRbVwuRTREljsRi5XE4unJrNpoyeplIpKaiYTCaazSZms5lUKkUymWTv3r1y0S66t0SjUZmeXq/Xpanv8gXWqXQeZjKZk30IGmcg5XKZvXv3kslkKBaLK7p59Pb2yuBBs9mkWq1qwQANqtUqlUpF/HnI9ZfwcRGlpCIDRYh9wuMuHo9js9mkAL5r1y5Zdlqv10mn0zITsrOzUwa5RAZMJpORbahFEGxoaEiK98vLSld7IFSrVekXo43ttYHwfNm1axf33nsvOp2ODRs20Gg0ZFatz+cjmUzKbPVwOCw7aZ0K2bDHAi3Iq6FxcjjtxBdl6cr5beA3qqp+52QKMMsN4sTEJToXzczMEIvFZDu7vXv3yi4S2WwWp9NJs9mU3hadnZ20220ikQj5fH5FqYSGxpnM6uiLEDqazSaLi4uyM0ZnZydmsxmdTofdbpfdfyYnJ+X9+vr6AGRGmqqqTE9P09PTQ7PZpLu7G4PBQLFYZP/+/VitVhl5FT8iCno8TeyeKaqqatkvGseFRqNBtVrF6/UyPj7O9PQ0Op2OUCjEOeecI73KarWaPHfOhI2HxrNHBJ6eEq/bh7uvELUtFosUR0ZHR2k0GszNzWE2m9m3bx9+v5+hoSFgqbObyL5yOBz09fUxPT1NJBKR87iYrw0GA6lUinQ6LTNrqtUq09PTbNq0SYr0y0vEBeI5RMtqjbVBuVwmm80SDAZxuVwkEgnGxsaYnp6mXC7T398vM2cDgQCdnZ1yfIiupp2dnad9B8JTKbikobGWOB1XT+uAK4G/VxTl/4LMgNEph7B1VhTlGkVRdu7fv58dO3Zw4403HtMDUhQFp9OJzWYjHo9TrVbl/5xOJ9lslv379zMxMUEul6OrqwudTseuXbv42c9+xs9+9jOmpqYAePzxx9mzZ4/m73AKceONN7Jjxw6AsxRF2akoyjXLbz/e4+tMR0RfRDvR5YJHKpUik8mQTCblAiGZTLK4uEg2myWTydBqtWg2mxQKBfbt20cikaDRaKCqKo888gjxeJydO3fidDqJRCJEo1FarZbsgqTT6cjn88TjceLxOIuLi8TjcZmBc7w53PgSYwvgzjvv5Bvf+MYKoehEHJ/G6cvRjK1IJMLf/u3f8h//8R/MzMywsLAgNyePPvoo+/fvJ5fLyU4g4lzVxMC1zU033cTzn/98gI2HGlvimvjVr36VSCTCvn37aLfbrF+/HqPRKH3wbDab7KYlOhsJQ/VYLEaxWCSZTMqykIWFBSYnJ0kmkxw4cIBkMkmj0aBYLJJOpxkbG2NsbIw//vGPPPHEE6TTacxmM/l8nkajQavVolgsyh9AZlEKoXv1j8aJ42jmrWe73lJVFZPJRLVaJZfL0dfXR6FQ4MEHH+Thhx/miSeeYGZmhunpaVm+vDxjanmHreOFCAId7+u7WGutJSH9SOt5DY0TgXI6XlQURfkOkAS2ATepqvo9RVF0qqoedqbasWOHunPnzmN+POIzjMVizM/PS5f+vXv3EggEsNlsPPnkk/h8Pux2O8FgkGw2SzQaJZ/P09HRwaZNm9i0aRNPPPEEZ599Ntu3b5ddV7Qo46mBoih/VFV1x6FuP17j60xHZL4s93oxGAxks1kmJyex2+1YrVYajQaZTIaZmRnpA1MoFDCZTFgsFiqVCs1mk2AwKFtLP/LIIywuLmIwGNi4cSNerxe3282GDRvI5/MYDAbOOuss0uk0hUKBQCBAvV7HYrFQLpdlZtuJ4HDjy2g0qsFgkJmZmaelzev1+hNyfBqnL4cbW5s2bVLf//73Mzc3x9jYGPF4HK/XKzcdXq+XP//zP8dgMOB0OhkZGcHtdqPT6bQ2lmuUYrHI+Pg48/PzvOpVrzqgqur6g91PXBPb7TaPP/44e/fu5ayzzsLpdLK4uEh3dzeBQIBYLIbT6cRisbBv3z5KpZIUXPx+P3q9XraLDgQCFItFarUa4XCYZrPJgQMHGBgYoNFo0NXVRbFYRFEUCoUCY2NjdHV1sW7dOlKpFA6HQ3qFiY2n8KYxGAwEg8GDjmttrJ94DjdvPdv1lhBPIpEI8/PzzMzMcN9997F3715KpRKBQICNGzcSCoVwu910dXVx+eWXEw6HgSVx8HhnvgivucOtPzS/lufGqbaeVxTluIi8x+t5NQ7PkcbXaVd29BQNwAx8H3iPoihvAf4duONkHpTf76dQKFAul6XPS61WQ1EU+vr6pLN/sVjEYDBgNpvlxrJUKjE7O4vJZJIRIa0eU2MtIKIv0WiUhYUFYKkVtE6nw+l0SlNc4RHgdDpl9F0sPAqFAolEgnQ6Ta1WkwvoarVKX18f5XIZr9dLR0cHnZ2ddHR0kEwmpcHounXrVpQZicjYqWTCaLVaZSttDY1jRbPZxO12Uy6X8Xg8VCoVurq6ZPeZcrnM+Pg4Pp9PdhBzu90n+7A1TgLLhXLRDvpo0Ol09PX1MTc3R7FYlNmKPp9PdpRpNpsy2t/Z2YnJZMLtdsuudi6Xi0AggF6vx2KxMDs7y969e2XLaVVVMZvN5HI5dDodRqORQCBAJpMhn88zPT2N0Wik2WzK1tQi81G8r3q9TjweP6QAo3H6Ir5ji8WC1WqVwZt6vY7P56OjowOj0YjVamV6eppKpcL5559PV1fXimuuEOyO53X4aMqBtP2Bhsbpy2klviiKYlRVtQH8DOhUVfVbiqK8DzgfuOlkHNPyCb1arRIIBJienqa/vx+9Xs/s7KxcbPz6179Gr9dz9tln02q10Ov1VKtVhoeHUVWVrq4uWQ6xe/duNm7cuMJ8V0PjTMbv99NutzGZTOTzeWw2G+FweIXAYjAYGBwcJJlMYjAYiEajMiq6uLhILpcjm83S29tLKBQiFArh9/uZm5tjcXGRVqsFQH9/P729vUQiEdlFo7+/n2KxKNueivbOwWAQ4KRHmdrtNlar9aS8tsaZi6qq0s9FmL7rdDoWFxdRVZVWq4XNZmPTpk3k83m5GRZtfDXWDss3fLlcTpjuto7msSaTaUW7cjFvGwwGOjo6ZNlnsVgkkUiQzWYZGhrC7XZjMBiw2+2ypHR4eJhqtUoqlSKbzRIOhzGZTDz55JPyGCORCMViUQo54XCY5z3veTIbWQg1IrvG5/ORSqWkR4w2vs8sxLhQVVUa9sdiMekZl0wmZUmS1+ulr6+PLVu2cM4556zIcDkRPnBH8xqaX4uGxunLKS2+POXhchFgVlX1N08JLwBR4NOKorwNGAO+DPz+ZByjMN8S2StWqxWPx0M6nSYQCFCtVuns7OTf/u3f+O1vfyvLI7xer1xQiIt/Nptlw4YNHDhwgGKxiNPpZHh4+GS8LQ2NE47BYMDlcrG4uAgsdbUApAGuwWCgWq2yb98+jEYjtVqNXC6H0WjEaDTK+uhyuczs7CztdhtFUfD7/dxxxx04HA7ZrUB4DzgcDrLZLK1WS3oL6HQ6Ojs7ZTRWeNGc7ChToVDQorEax5zZ2Vk+/elPA0vZVd3d3ej1erLZLHa7XQqYIkJss9k0EXCNIto6Z7NZxsbGxFxtPNT92+22nEObzSZ2u112lAOYnp7G4XAQDAaZnZ2lVqvh8XhQVZVCoUAmk5GmumazmWw2S7Vapb+/n1qtxr59+9i5cyfT09MsLi7SbDYPe/w9PT2cffbZ9Pf3s2HDBoaGhiiXywSDQTZu3HhCsho0Tg5i7DabTTKZDIqiYLFYiMViPPnkk6TTaWw2G41Gg3q9LjP/hIfjieZIZUWnQjOAZ1L6pJVJaWj8iVNWfHlKeLkfSAHbFEXZC1wDRIBHgXsBVFX90Mk6xuU0m01mZmYwmUyoqkokEkGn05FKpfjP//xPHnroIT7wgQ9w55138vjjjzM8PMzQ0BAmkwmr1UosFqNarWIwGFi3bh2NRoPe3t6T/bY0NE4oy7toFItFGV11Op0YjUYWFhbkwqnVapFKpTAajdhsNtl1zOPxoCgK4+PjPPLII/zTP/0TiUQCgOHhYdavX89ll11GIpGQJRY6nY6BgQHy+Tx9fX1y8S2y2sRi4WQvylVV1QQYjWOKw+Hg0ksvBeAPf/gDDz30ED09PRiNRgYHB/F4PLhcLilMDg8Pa4vnNYoozSyVShgMBplJeCharZbMaBHiuBBInE6nNLltNps0Gg0cDgeDg4MyW7hUKvHEE0/Q1dVFJBIhl8vRbrcZHx/ngQce4Nvf/jYOh4MXvvCF9PT00N3dTVdXF93d3fT09OD3+5mZmWHv3r3s37+fvXv3sm/fPu677z6q1SojIyN89rOfxe12E4vFSCaT9PX1EY/H8fv9sp01sOaMSc80xNhdWFhgampKllNOTk7K8el0OikUCjSbTXK5nBQLT8Z193QoK3omx3g6vB8NjRPFKSu+AK8AYqqqvkFRlABLfi43AW9QVTWnKMr3VVV9FOBozHaPFzabDbvdLv1dCoUCZrOZ+fl5kskk//u//8vevXv5m7/5Gz7/+c8Ti8V44QtfyNTUFOFwmHXr1lGtVllcXCSRSBAKhdi+fTtut1t2AtDUYo21gog6zszMoNfrMRiWpqh6vS67YOh0OrmorlarmEwmgsEgjUZDtow8cOAAP//5zwF45Stfybvf/W5KpRJf+MIXuP3223nsscfo7u5meHhYpsBXq1XZknRubo5ms4lOp6NarZ500UWgeb5oHGsGBwf59re/DcDCwgKXXHKJzCiLx+Pkcjl5XszPz+P3+7XAwBpGrHmazSYdHR1HvL/w2FieLSXKOERWCyDbRVssFkwmE+eccw5TU1MkEgny+bz08qpWq0xOTvLNb34Tu93Offfdx8DAwCFff3h4mOHhYV796lejqio6nY5Wq8V9993HG97wBj74wQ9yww03sHnzZrLZLIlEglKpBCxtEmOxGHBqZBpoPHuEyFetVtHr9UxNTcl1uvAuSqfTBINBAoEA5557LuFwWJo/O53OE3q8p0NZ0TM5xtPh/WhonChOZfElDpyvKMplqqreryjKRcBe4IuKotwEXPVUNkz9ZAkvgNywKYpCIBAgl8vJaNAdd9zB3r17ef3rX89rX/taHnroIQA+97nP8d73vpcHH3yQQCDAli1bMJvNDA4O4vP5yOfz2O12arWazAAATS3WWBskk0kymQxer5f+/n4WFhaIRCK0223Gxsbo7e3FZDLhdDoJhUIoikKz2eRnP/sZO3fuZGxsDJ/Pxxvf+EZe97rXMTU1xbve9S4APvCBD/DmN7+ZH/zgB+zcuZPx8XEuuugi1q9fj8vloqOjA5PJRL1elx2WTCaTbL1bLBYJBoMnXAi12+1cfPHFPPHEE0+LwF1yySUn9Fg0ziwKhQL333+//PtTn/oU73//+wkEAoyMjMhuCel0mkQiwdatW2X3BC0L68xndacMVVUplUqoqioMyQ+5/mo0GrTbbdxuNyaTidnZWcxmMwaDQZqZZ7NZbDYbNpuNZrPJ4uIiiqLg8/kIh8MYjUZUVeXJJ59kZGSEAwcO8IlPfIJms8mtt96Ky+UinU7L14xEIod8L+VyWQqHGzZs4Ec/+hFXX301b3vb2/i3f/s3LrvsMjweD0ajEa/Xi06nk6KRxWI5ZNcQ7Tw4tWm321JIHhsbI5FIyIBnsVjkvPPOWzEeN2zYgNvtlibjJ4PTQex7psco9jNaFpnGWudUHv17gTuBv1QUZbuqqjXgLwA34AeuV1W1pp7kHloWi0WWSpx33nn09vaSz+f51a9+xZ49e3jlK1/Je9/7XtrtNq1Wi1arhc/n49///d+x2Wzcfvvt3HfffeRyOZxOJ/V6nWQySSqVkil6mumuxlrC7/fT09NDf38/1WqViYkJYrEY6XSaVqvF4uIiv/nNbxgbG2NmZoZ4PM53vvMd/vM//xObzcYNN9zAnj17ePnLX87111/PRz/6UUwmEyaTiY9+9KP8z//8Dx/5yEe45557eMELXsAdd9zBRz/6UeLxOB0dHbjdbqrVKplMhmw2y9zcnNwwLPeAOZG0Wi2Zci/mEfGjofFcUFWVZrMpf3p7e/nkJz/J/Pw8e/bsYXp6mkgkQl9fH+FwGL1ej6Io2oZzjSI6YAlh+nA0m00SiQS5XI5EIiHn0Vwux+joKIlEgoWFBSqVCps2bZKlQqVSiXQ6zczMjGwnXSqVWFhY4LrrriOXy3HLLbewfv3TO1y3223Z2vpf/uVf+NGPfiTF+9WeMGeddZYUcN73vvexZ88e6fVVq9WoVqsrfpYLTxqnD8JzaLmR/uzsLA8++CCRSISZmRnZFaurqwu/30+j0SCTydDZ2YnD4dDmvOdIuVwmFosRi8VOyhpKQ+NU4pTNfFFVtawoyr8AnwH+TlGUG4EewAfcrqrq0fU4PI6ISVwYyNXrdXQ6Hbfeeiu//e1vufrqq/mrv/qrgyq8oVCIr3zlK3zwgx9k586dnH/++ZhMJnw+H8FgEJ/PR61W08qNNNYcBoOBUCgkuxH4fD4sFgt+v59Wq0UikZCdj+r1Or/73e+47bbbePGLX8wPf/hDVFXl61//Ov/0T/+Eqqq8613v4sorrwTgRz/6Ed/97nd55JFH+MQnPsG3vvUtxsbGuPLKK/nQhz7E1772NXp6eojFYgwMDGA0GqlWqyQSCQKBgDwnBVpZoMaZyHnnnceHPvQh/uVf/oVEIsFrX/taNmzYwMjICH19fSf78DROIlarlcHBQdrtNjMzMwCH3JHqdDppcGo2m+no6KC/v192NXI6nQQCATo7O6WXTCqVotVq8dhjj2E0GjEYDCQSCcrlMv/wD/9AJBLhlltu4dxzz33a6zUaDe644w5+8IMfsGfPHimYw1L50QUXXMDrXvc6duzYgdG45BPc39/PrbfeytVXX81VV13F5z//eS688EJsNhs+nw8Aj8cjo/YiE1nrhnT6IK7ZJpOJTCZDKpXi0UcfpV6v8453vINvfetbhMNh2a68t7eX7u5uzGaz9Io5la71p9KxHC0iSC1+19BYy5zSZ62qqgeAjwBPAv8IvBH44KkgvMCSkluv12WU/MCBA9x11138+Mc/5sorr+Rb3/oWer0egFqtxrXXXsv//M//yMd3d3dz9913oygKu3fvlnXRInKjobGWSSaT5HI5FEXBZrPJdNVWq4XBYGDbtm0oisLdd9/Nxo0buemmmzAYDFx99dV88pOf5JxzzuHmm2/mzW9+s+yI9Ja3vIWbb76Zc845h49//OO84AUvIBaLcdttt6HX63n/+9/Prl27SKVSqKpKR0cH6XSaxcVFKpUKVqtVmkQCssW1WJAfL0qlEg8//DAve9nLuPLKK6UPgYbGcyWfz3PXXXdx11138Zvf/Eb6H7zkJS/hbW97G5FIhFtvvZVyuSxb+mqsPdrtNoVCQfqhLCwssGvXLjiC+BKJRKhWqxw4cEB2cnS5XOj1elKplBRWYGk+zWQytFotnE4nIyMj+Hw+AoEAO3fuZM+ePXz729/moosuetqx/eQnP+HCCy/k7//+78nlcnz0ox/l3nvv5Sc/+Qkf+tCH8Pl8/PjHP+aqq65i69atfPzjHycajQJLnfV+/OMfc/755/PhD3+Yffv2EY/HGR0dJRqNkk6nicVitNttLRP5NER4yQkvt1//+tdMT0/zxje+kTvuuIOrrrqK++67j2QySX9/P319fbIhhsVikWVL2Wz2lMjaEOa1p8KxHC06nQ6Xy4XL5TptBCMNjePFKZv5IlBVdQb4hKIonwMUVVULJ/uYBBaLRbalm5mZoVgs8tOf/hSv18v3vvc9aSQH8N3vfpfHH3+c/fv3c8kllxAMBoGluuPrrruO9773vbJzy8TEBOeee67ccJ4MjwkNjZON3+8HljJh5ubm8Pl8sjNYpVJhYmKC3bt3UygU+PSnP43L5SIajXLnnXfy/ve/n5e97GUHbYkbDof553/+ZyKRCJ/4xCf4i7/4C1796lfzgx/8gD/7sz/jZz/7GR/96Efp7u5e4R8gBJaT4cHk8Xh46Utfiqqq/PSnP+V73/seH/7wh0/Y62ucucRiMf71X/9V/v3Wt76Vq6++GoC3vOUt3HHHHUxNTVGr1bQW02uYSqVCLBajXq+zsLDA448/zpNPPgmHaTUtMl+y2SxTU1M8+uijdHd3c9ZZZ6HT6VBVlVQqJTvVNRoNFhYWZKcZWBLhx8bG+OUvf8l73vMeXvSiF614jT/84Q988pOf5NFHH2XLli186EMf4tJLL5VrpsHBQQYHB7n66quJx+PMzc3xq1/9iltuuYXJyUn+67/+C1iaY3/xi1/g8/n4wx/+wPbt26UhsMlkIpvNEggEtIyX04xms0kymcRgMJBOp4nH4xw4cICXvexl3H///czPz1Ov1xkaGpIBl40bN6LT6SgUCjK7pNlsYjAYTgnhTTOv1dA4vTltdvSqqhZPJeEFlsx2RQ2zy+XC4/EwPT3NpZdeKlNaAaanp/nhD3/IhRdeiKqqfOMb31jxPN3d3QDkcjnq9TpGoxGLxXJSPSY0NE42Op0Oi8UiuxGIiKjD4WBgYIBQKCSj9Bs2bADg7rvvBuANb3jDivrsVqu1IptMURSuuOIK/vCHP/Dxj3+c2267jZ///Oe89a1v5c4772Tfvn089thj6HQ6+vv7cblcMn3d5XJhsVgoFosrUmmPZ7ZaMBjkr//6r3nve9/LFVdcwa9+9SsWFxeP2+tprB0GBgb4zne+w3e+8x0uuOAC/ud//kdecxRFob+/n87OTrxeL7CUhaVlZq49rFYrPp+PaDRKJpPB6XSKbkeHNEAxGo2YTCZ0Oh2NRgNFUUin0+zbt49EIoFOp2NoaAir1SrncpvNRnd3N263m0qlQrFYZHp6mlqtxgtf+MIVz/+Tn/yEK664goWFBa6//nruvvtuXvCCF6DT6bjnnnt4+ctfzte//nU5d9tsNl72spfxla98hb//+7/ngQceWGE27fF4WLduHYuLi3g8HiqVCkNDQ3g8Him6aJ4vpw+iNG52dpaxsTGSySRPPPEE9XqdUCjExMQEwWCQ22+/ne3bt7N//34mJiYYHx9f8Rwi4+lUCYQKo9uDHYsoj9PmaA2NU5eTP4ucxthsNlwuFyaTiXQ6zSOPPMLk5CSXX365vI+qqnzxi1/EarXysY99jDe96U3cfffd7NmzR94nHA4DsLi4iNPplN4Woga6Xq9rE6nGmqNYLLJv3z4mJibYv38/Bw4cIBKJSG8kVVVJJBLYbDa6uroAuOuuu+jq6mLz5s3yeRqNBtdeey1vfvObeeCBB1YsnM1mMx/+8If5q7/6K7785S9z0UUXYbfbue2227j//vuJRqO4XC7MZjONRgOr1YrD4aBarZLP56lWq+h0OorF4nEVSWdmZnjPe97De97zHqamptDpdHz3u989bq+nsXYwGo2Ew2HC4TBXX301hUKBX/ziF/L2/v5+ZmdnqVQqjI+PMzc3J0tPNNYOwtfObrfLUoynxsEh69CEeB0Oh7nooou47LLL6OrqYnJyklgsRiKRYHJyUm6Qp6amKJfLOBwOrFYri4uLdHZ2ksvlALj00kvlc6dSKT72sY9x/vnn89BDD/GmN71JZih86Utf4m//9m9pt9vceOONXHPNNU8Tq6+++mp6e3v53Oc+t2J9dd5557Fnzx4effRRxsbGWFxcRKfTSV+a063cYy1TLpdlMDMUChEIBJiYmKCnp4d77rmH7u5uPvWpT1Gr1eScNjY2JrPWRXtpke26vOT4VOVYlSRpIo6GxvHjlC87OpURRlyZTIZYLMYdd9yBy+Xida97HbVaDUA66K9fv54vfvGLtFotTCYTf/d3f8fLX/5yLrjgAhk5T6VSZDIZ2R2g3W5jNBqpVCqYTKZTvu2chsZzYbWJnEj5NhqN6PV6MpkMfr9fprEXCgUWFxfp7+8nnU5Tr9e55557uOKKK0ilUtx8881Eo1HGx8eJRCJYLBauu+46Ojo6eN3rXsfu3bvla2/evJmenh4++MEPcvnll/OrX/0Kl8vF85//fIrFIjqdjlarRalUwu12HzTt12azoarqcemI0Gg05OZBRJ1/9atfkUwm+cMf/nDMX09j7bC4uMhnP/tZ+bfX6+U73/kOo6OjWCwWtm7dys9+9jPi8Tj1eh2Xy0V3d/dxG+sapxb1ep1ms0k6nZalQtlslscee4z5+XkAy6Eeq6oqdrudrq4uEokEXq+XQmEpgXlwcJB4PC6FFrfbTa1WQ6/Xy640c3Nz6HQ6pqam2Lx5My6Xi//93/8F4Mtf/jK5XI7/83/+j/CeIZfL8b73vY9sNks4HGZ4eJiOjg52797Na17zGl71qldx1VVXyeN71atexde+9jW+8IUv8MIXvpBQKMS5557LLbfcgtFopLe3l1AoRD6fx2Aw4Pf7ZUZmqVTCZrNp58ApiBAMLBYLbrcbp9OJTqdjfn6effv2EQgESCQSnHXWWdxyyy34fD7uvPNO2W5alB2JZhoul4t2uy07kJ7Ka/FjVZIkRBw4td+vhsbpiCa+PEdsNhsdHR1MTExw991384UvfIFQKAQs1SqLDJexsTHGxsbk4+r1OnfccQeKohAKhVAURbr85/N5/H4/fX19WCwWOjs7tdpOjTMWIbqISAv86WLfbDZxOByUy2W6urro7OyUbU6tViv5fJ7zzjsPq9XKo48+SqlU4qUvfSlWq5VbbrllxetUq1UA0uk0N910Ey94wQu47LLLZInglVdeyQ033MCuXbsYGRnh8ccfx2Qykc/nZfaZz+eTmwfRfhKOf+eLVqtFJpORf4tjePjhh4/r62qc+RSLRe67776n/f+BBx4A4DWveY3cFJ911lm4XK4VY1/jzCebzTI3NyeFGCHAPDVfH7LfvaIo6HQ6WbIkMgqCwaA0QRdlnLt27UJRFPn/XC6HqqocOHCABx98kLe+9a0oikKz2WTv3r3cc889vP71r6evr492u83Y2Bif//znyWazwJKouDrb5X/+53+kYGM2m7n44ov5+c9/zo9+9CMuvvhiFEVh69atAMTjcV7ykpfQaDSoVquYTCaq1Sp2u51SqUQ+n5eb89Op68xaQnwnqVQKr9fL97//fQASiQQA+/fvZ//+/fL+woxXlBkt/26Xf9enMqIk6bmi+cpoaBw/tKvFc0Sn02G327n55ps5++yzefe73y1v+/jHP37YxxYKBdkyNxgMUq/X2bBhA/39/fj9fmw2G8PDw3g8Hu3CrnHGsjzCsryThE6nw+120263GRoawuv1Ui6XyWQyRKNRVFVlbm6OoaEhAO655x5MJtOK1PRD4fV6uffee7n++uuFaaTMiJmfn2fjxo2MjY1x3333Ua/XZceLRCLB+Pi47HB0KtT/j46OntTX1zizEa2lU6kUJpMJl8ulCS9riHa7jclkoqurS3qxmEwmKpXKEcuOVFWlVCpRKpUwmUx0dnYSj8fJZDJUKhVarRblcpmHHnqIqakpAM466yxgqRw7GAzyxBNPUCqV5LzeaDT4+te/TmdnJ1dddRWqqnLHHXfwsY99THaXPBQ9PT38+te/5hOf+ASzs7PodDre9KY3EYvFuOeeewCk+BKJRDCbzTIwYDAYaLfbqKoqS84BrQzpFEcEdcbGxrjzzjuPeP9ms8mtt976NF+Vw/msnImstferoXEi0c6qY8D/9//9f8zOzvKlL31JRnZ27tx5RE+GdrvNv//7vwMQCoWYmZmRqd7CIFRE6zU0zlTEQlZ0vBAXe4vFQqFQQFEU8vk8uVxOdiqYm5uT/i3Dw8MA/OY3v+F5z3veUWWhDA8P8/a3vx2DwcD3v/99fvrTnwKwadMmLrvsMn7xi1+wbds2vvKVrxCJRGT0ViyyRS348oW32GiIlOcTVTP9z//8z8f1+TXWNv39/cBSlHh+fl4KpRprg2q1SrVaxWg0smvXLt75znfy0Y9+lIceeoiZmRk4gudLZ2cndrudTCbDgw8+yK9//WseeeQR6eeiqiqBQEBm+O7Zs4d9+/YxNzdHb28vk5OTwJ/8Xn75y18SiUS45pprMJvN/OAHP+CGG25gy5YtfOELXzjsexkcHOTv//7vKZVKXHfddTzxxBNs3bqVjRs38t///d+0Wi2CwSBdXV089NBDeDwegsEgwWAQh8NBPp8nHo8DS9mODofjaa2nNa+MUwuRtfLf//3fYrweFrfbzS9/+csTcGTPHG1saWicGWjiy3Ok3W7z6KOPMjAwwPOf/3z5/+Utag+FoigyLdZgMFAul3niiSfI5XL4fD6t3EhjTbA6wiIWGPF4nFqthqIomEwm2Q3M7/fT3d2Nx+NBURQOHDggn+uZROT7+vpk1kyj0ZD//7M/+zMARkZGyOVyPPzww/h8PtavX08wGKSnp0dGcpcvvFcb3R0r47sjoXU90jietFpLVSWlUol0Oq1F+dcYFosFh8OBx+Ph+9//PmNjYwwMDLBjxw7OP/98OEL5usvlkm1+jUYjW7Zs4cILL+Tyyy/H6/WSzWal11cwGOSss86iq6uLTCbD2NgYCwsLdHV1yXLuubk5vF4v27dvB5ZapRuNRj7ykY/IbJTDsWXLFllOJK4vRqNRZrWoqkqz2aRarTI1NUWpVMJsNgN/ap19uHPgRM37GkeH1WrFYDDw+9//Xnq6HI5yuSyDK6ca2tjS0Dgz0DxfngPtdptoNIrBYJAeDILnPe95R3y8qqpcdtllwFJKdygUYmBgQAoxwtBXQ2MtsbwMye1209fXh06no6uri2azSTKZlNkwl19+OT/84Q+lUe53v/tduZA/HM1mk+9///uMj49z0UUX8ed//ufytkqlAiwt2gCZbp7JZCgUCqRSKRqNBjqdjsHBQfl6q2ukT1TN9LZt247r82usbSYmJgC4+OKL2bx5M8Fg8CQfkcaJRPhcpNNp7rjjDt785jfzta99Td5usViOuEgJBoNs3rwZk8lEKpWi3W5js9kwGAxYLBb0ej25XI6enh78fj979+5lfHycRqOBxWKR2VewVDIqGhLodDpe9rKX8dvf/pbf/va3Ujg/HHNzc/z0pz/l/PPP5/zzz+fAgQPs3r2bq6++GoPBwPT0NPF4XLawzuVyVKtVzGazDBQIoeZgpqSaV8apgejGVSqVsFgsLC4u8uIXv/iIWS3NZpMPfOADcnydSmhjS0PjzODUmllOM4rFItFoFL1eTzabXeH90NnZKcshDocQXxKJBF1dXQQCAbxe7yk36WtonChEGZLf78fpdOJwOHC73XR3d9PT04Pb7WZxcZFms0k4HCYWi/Gb3/yGF77whdRqNR588MEjvsb+/fuZnJzkNa95Da94xStWeAUIIdVkWsqmF2VMU1NT7N27l0wmg9frpbu7m3g8LiO3iqJgt9tPeI24iABraBwPhPhy9tlnEwqFjuiroXFmcvPNN1OpVHjnO9/5jB4nusV1dXVhMpkoFAokEgmy2SyRSITZ2VmKxSKpVIp4PM5DDz3EwsIC1WoVj8fD7OzsCvGlo6NjReeZTZs2MTg4yC9+8Ysj+m+1221uuOEGbDYb73jHO1AUhVtvvRWn0ykFeGFifskll3D22WcTCAQIBAKyo161WqVYLErRfXXZkeaVcWpQqVSYn5/nj3/8I3fffTfT09NcccUVR3zcG9/4RkKh0CmZXaKNLQ2NMwPtDH6O2O12+vr6aLVapFIp6vW6/LnwwgsP+1i9Xk84HJbqfDKZJB6P4/F4CAQCmrqtsaZY3e0om82SyWSYmZmhUCjQarWk/0AkEqFUKvHiF78Yv9/PD37wA7Zu3YrVauXuu++Wrd4PRbPZ5JWvfCWhUIj5+fmn/QByk7lt2zbZ8cLr9WI0Gunq6pKdPwwGw0k9V7XMF43jycTEhNx8noobEo3jSyaTIZ1Oc/PNN7N161YGBwfJZDLyBzik4tFut8lmsywsLDA5Ockf//hHOXcvLCzw8MMPMzs7S6PR4JxzzsFms5HP5/F4PAwODtLZ2Sm9X5rNJs1mU5aWjo6OEolEWFhY4JJLLmF2dpZ77733sO9ldnaW6elp3vKWt6AoCrt27eKJJ57gJS95Ca1WC1VV+cMf/oDFYmFwcJBisYjFYiGZTFKv12m1Wvj9fim4rBbcNU4NZmdnSSaTsuPVXXfdBcB55513xMf+7d/+7dMENQ0NDY1jiVZ29Byw2Wy43W4GBweBJaU9EAjI25///Ofzn//5n1x77bX4/X75/3a7zec+9zk2b96MwWAgl8sBUKvVZLRHURSq1eoxaRmnoXE6sDqF2+/3UyqVqNfrxGIxwuEwJpOJSCTC4uIirVYLnU7HWWedxW9+8xtMJhOXX345DzzwAJ2dnVx//fUYDAa+9a1v8cQTT/Dyl7+cX/ziF3R2dvLa176WSy655KDHIVrs6nQ6FEUhkUiwdetWLBYL7Xabubk5kskkHR0deDweuUgrlUpyQX6sCYfDvOMd7wDg5z//OU8++SSbNm1i79690rdGQ+PZMDw8zL/9278BS1kK73jHO7j44ou59tprAfjKV77C8PAwdrsdi8WidTpaYwi/jLGxMf793/9dNhVYxiEHhDA7feSRR5iamqJSqeDxeOjt7UVRFAwGg/xRVRWLxUKz2cRkMklhu9FoMDAwIMVwsYH2+Xyce+65AGzcuJFf/OIXPPbYY/y///f/Dnosc3NzfPOb3+T1r389H/nIRwB4xzvegcfj4R//8R9xOBzo9Xoefvhhtm/fzoEDB3A4HDidThkM8/v9GAwGbV12itNut6nX6wSDQbLZLLOzs2zZsoXOzk7uvfdefD4fd911Fx/60Ifwer3UajVarRaXXnopwWAQi8WyQlBrt9sy00kT2jQ0NJ4r2izyHKhWq9TrdZnqms1mV9x+8cUXAzzNYT0Wi1GpVOSmKZlMAkstFjs7O0X7Rkwmk+ZsrrFmWJ3CbTAY6O/vx+12EwgEsFgszM3NUa/XMZvNdHZ2smnTJl796lfTarX43ve+x5//+Z8zPT29woRX3H7bbbexadMm/vZv/xav13vI41jezcVutzM9Pc3c3ByDg4P09/cTCARk9wuRAnyijPBUVWViYoLBwUFisRihUEjbDGscM+68805qtRqvec1r5P8mJibo6OjAarWSTCa169Ea5KabbsLpdPK6173uGT1OVVVmZ2eZm5vDbrezfft2LrjgApxOJ7VaTbaTnp+fl5ktwni3s7OTdDoN/KndOSyJLrDkkycwm8286lWv4oEHHhDZOCuo1+v85Cc/weVy8clPfhKAXbt28etf/5p3vOMdUkyp1+s8+uijdHV14fP5cDqd+Hw+Go0GwWDwYMKTxilIvV6nUqmg1+uxWCw89thjvPjFL15xn5e85CVcfvnlZDIZNm3aRKPR4BWveAXj4+NyTQ5Lwsvy8mINDQ2N54omvjwHLBYL2WxWdkpZfdFfv349Vqv1aeLL9PQ0gMyYERO91+ulXC4zMTFBPp8nmUwSiUSIRqPaglfjjOdg9czlclmKkfF4nGq1SldXFyMjI+h0Omq1Gps3b+ayyy7j5ptv5kUvehEAt99+u3yOQCDAVVddxatf/Wre8573SCPdQ5HNZnE4HNTrdRwOByaTiWq1SiqVwmAw0NnZicfjWXGcB6v9P5bk83nuuusufvWrX1EsFhkaGpLii4bGcyGZTHLTTTdx0003cdttt3HuuecyMDAALHUBm5ubY+vWrTIbQduArC3S6TS33347V111lfS/OlqazSaNRoO+vj62bt1Kf38//f39mM1mKpUK6XSaTCZDrVbDYDAwMDBAIBDAaDQyMDBALBYDWOH5IsSX5RtkgNe+9rUoisJvfvObFWWn7Xab22+/nVQqxV/+5V/icrmo1+t84QtfwO1287a3vU3e9/HHH6dWq3HRRRfR2dmJTqejVCppHWZOM0wmE1arFZ1Ox3333Sc7WD3xxBNyPaEoCp/+9Kd53/vex549e7j44osJhUKYTCY6Ojrkc5XL5VOivFhDQ+PMQZPxnwPlchmj0Uh/fz96vZ5bbrmFF7zgBfJ2nU7Hhg0beOKJJ9i8eTPr16+nUChw//33EwqFZPT997//PbC0wHA6nZhMJjnRx+NxYKldo5bqqrFWKZfLFAoFGo0GIyMjWK1WMpkMqVSKYDDIpk2buP/++7nxxht53vOex2c+8xne9a53sXHjRoAV5+XhWFhY4N5772XTpk386Ec/Yvv27TidTkqlEpVKhUqlIlvAF4tFmYYshKPj+f4fe+wxYGkuyOfzNJtNuUnW0Hi25PN57rzzTgCMRiNveMMb5G233nor7XabrVu3EgwGj6qTmMaZRT6fp1arHe57P2RkyGAwsGHDBorFInfffTeKojA8PIzb7SYWi+FwONi4cSNOp5PnPe95NJtNLBYL0WiU7u5uwuEwsDQvi7ncZDIxODjIbbfdxute9zrcbjew1OTgVa96FT/96U/Zt28f27ZtY8OGDdxzzz3Mzc3x/Oc/n/Xr17Nv3z4+/OEPs3//fq677jrZVjifz3PNNddgt9u56KKL6OjokCXhDofjqNoUa5w6tFotarUaJpMJvV7PV7/6Vb761a8CSwGZgYEBBgcHefzxx4GlDFmXy0VPTw/1el0a7i/vMKSVHGloaBwLTuuZRDkF8u0NBgPbtm3jda97Hd/+9relU77g5S9/OaFQiB/+8IfMzc3x3//939RqNbnAbbVa3HTTTbzkJS+ht7eX4eFhwuEwvb29uFwuhoaGCIfD2oJXY00hzHdtNhvhcJiOjg7ZMaNYLGK1WnE4HBSLRarVKpdeeikXX3wxX/3qV7n00kvp6+vjhhtukJ1ajoZSqcQXv/hF9Ho9yWQSi8XCW9/6ViwWC2azmVarJQ2Bq9XqIaOh4tiPZbZaKBTiIx/5CB/5yEd485vfzIMPPsiGDRsYGRk5Zq+hsTYZGhrihz/8IT/84Q/53ve+x5YtW4CljmC33HILL37xi7nkkku0ThtrlIGBAa688kpuvPFGmbV7tCiKgsvlYmFhgYmJCaLRKPV6HaPRiMViwev1snnzZoaHh3n44YdJJpNSdDEYDFx22WX4fD6+/vWvr3jej3/846TTaT772c+u6HD0N3/zN1xzzTVs2LCBhx9+mJtvvplkMsmVV17Ji1/8Yu68805e85rXkEgkuPHGG3n7298OLM3n73znO9m/fz833ngjHo9HHp/w+6hWq8/5s9Q4MdTrdeLxuCwXfutb38qOHTvYsGED27ZtQ6fTUa/X+d///V8ikQgf+MAHZAmv8HFb3QDgcPPeoa75x2MtoKGhcfpzWq+i1KeuuidLhHE4HITDYfx+P9dddx3hcJhrr72WVqsl72M2m7n66qux2+1861vfYnJykle+8pV0dnYCcPfddzM7O4vf76dSqcg2t9VqFZ1Oh8vlwuVyaQtejTWF8FCpVqtYrVbm5uaYm5tj165djI2N8eSTT6LX67FarVQqFcbHx9m2bRtXXXUV//qv/8rVV1+Nx+Phq1/96lFtGFqtFl/96ldJJBIEAgEikQjXXHONzEbr6uqiu7tbnoeHKzM6Hv4vjUaDaDRKNBrltttuw2Qy8fKXv1zze9E4LgghMhAI8PnPf36FYbzG2uO6665Dr9fzqU996mA3H7bbUTqdptVq4Xa7UVWV3bt3Mzo6isFgQFEUSqUSjz/+OL/+9a8ZHR0FlrL7FhcXeeyxxzjvvPP4+c9/LrOAYclg96//+q958MEH+dGPfiT/rygKvb29XHnllfzN3/wNf/EXf8H73vc+Ojs7+Y//+A9uv/12XvGKV3DnnXfy0pe+FFgqjXr/+9/Pzp07ufnmm7niiitk84NgMEhXVxfBYFALgJ0mtNttms0mqqpiMplkZpTJZMLn87FlyxbWr1+Py+XiBS94AV/84he54oorCAaDsnPVM/VxO9R9T5QXnMbpwcDAAIqiHPRneWmlxpnPabejVxRFpyjK5xVFuU5RlJcqiqJTl4c+TiAiElir1SiVSrzhDW/gscce4xvf+AaqqqKqKq1WC6vVylve8hbMZjNbt27lnHPOodls0mq1+OY3v4nf72fjxo0kEgnMZjPtdptCoaCp5RprFpvNhsPhoN1uUyqVaDabsla73W5jMplotVoEAgHZjaKjo4P169fzspe9jOuuu47LLrsMh8PBV77yFaampqShozj3lv/9n//5n+zevZv169czOTnJpZdeitfrla1Na7UalUpFdjyCpcXc1NQU9Xr9acd+rP1fkskk3/zmN/nmN7/JwsICL3nJSzCbzdJvSkPj2SL8EMRPo9Hga1/7GqlUiiuvvJKFhQUZAdZYe7RaLYLBIO985zv55S9/yX333Uer1ZI/HEZ80el0co4aGRnhnHPOwWw2Y7PZ0Ov1OJ1O4vE4sViMVCpFIpEglUphtVrl8+/YsYNWq8WPf/xjVFWlVqtRr9d59atfzSWXXML111/PG9/4Rj72sY/xrW99i3379pFOp7FarZx99tk8+uij3HDDDWSzWd72trfxpS99CY/Hg6qqtNttPvaxj3HXXXfxwQ9+kDe96U3Y7XYGBwcJhULY7XYcDgd2u51yuaxlNpwGlMtlEokEtVoNo9GIyWTC7/fT29tLb28vHR0d6PV6Of70ej3NZhOv14vVaqXdbtNut5/RdfxQ9z3eXnAapxczMzNyb7j655lmFWqc3ignSbd4VjyV4fIbYALwsiQevVtV1dgRHncNcI3NZtt+1llncc0113DNNdccs+NKp9Pcd999xONxfvzjH7Nz5072799PZ2cnhUJBRstF/amIVsfjcTZu3MjWrVu5+OKLcbvdvPKVr8RsNmO1Wunu7tZ8Xk4BbrzxRm688Ub++Mc/loH9wI2qqt4obj/e42utUiwWyefz8hxoNpukUikURSESiZDP5+nq6sJgMDAxMUEqlWJxcZGZmRnGxsb4wx/+wNe+9jU+85nPUCgU+Od//mcajQbZbJZkMkmxWJS/33vvvVx++eXce++9nH/++Vx44YV0dHRwySWXUK1WsVgs+P1+9Hq9LIWKxWKMj48zMjLC8PCwPO5n2pbycONLjC1FUbYbjUb+z//5P/zlX/4lO3bskI8XkT0NjdUczdiy2WzbN27cyNvf/nbe+c538r3vfY93vetd/Nmf/RnvfOc7UVWV8847j3Xr1p3U96JxcqjX65TLZaLRKC9/+csxm8288Y1v5I9//COPPvooi4uLbeAxDjO2XvOa1/DSl76UZDJJMpnEaDRiNpsxGo0Ui0V0Oh3RaJSBgQFsNhtWq5WJiQlarRZdXV187GMfw26388ADD8j7w5JPy4033sjjjz/O7t27mZyclMft8/lwu91MTk7y2te+li996Uuya57g4x//OJ/73Od405vexPXXXy/NfFcjTHdXe++Ja5TmyXdsOdp562DrrXa7zfz8PNPT0yQSCR555BEeeugh2TnLYDCwe/duYMnn5RWveAXd3d0MDg7idrtpt9va93kGc6T1vGDHjh3qzp07j+lrK4rCid5zn4zX1ABFUf6oquqOQ95+On0piqKEga+rqvqap/6eAv5BVdXvLruPcqhMmGN9MolNVjabZefOnUSjUeLxOJ/5zGd44xvfyHe+850V4stqvvzlL3Pdddfxvve9D5PJxMaNG3nRi16Ex+ORKa9aa8NThyOdTMdjsl7L1Ot15ubm6O7uli1IAaamphgdHSUWizE0NITJZCIWi6GqKvV6nbGxMSYmJnjssceYmJjgG9/4Bn/3d39HJBKRz20wGPB6vXg8HjweD11dXfzyl7/k8ssv55JLLpEiy7Zt22g0GhgMBjweD319fZhMJhwOB81mk5mZGXw+34ruR892QX648WWz2dSRkRF++9vfPu02TXzROBKHG1vbt29XH3zwQWCprfQFF1wgfcye97znYbfb2bhxozSg1Fhb5HI5YrEY0WiU//3f/+XLX/7y6jKKsqqqB22DtGPHDvXhhx8mGo0yOTnJxMQEExMTbNiwAZ/Px/DwMJFIhHK5jMViweFwUKlUSCaTTE5O0tvbS3d3N9/85jf57ne/y4EDBwgGg4dcU+XzeUZHR3niiSfYtWsXMzMz/N//+395wxveIDchFouFer3Ov/3bv/GJT3yCl7/85Xz2s5+lv7+fer0uRfblqKp6UEH9mQrtGs+Mw81bh1tvTU1NMT4+ztjYGLt27WLXrl0yc1aIL+vWreMNb3gDdrudHTt2cNZZZxEIBKS5tPZ9ntmcjPW8Jr6sHY40vk63nb0BuFBRlNerqvoTljJg5gEURRkAZlVVPWH5n6KeUzj2K4qCTqdj69atfP/73+ftb38727ZtO+hjVVXlO9/5Ds9//vNZt24dJpOJYDBIKpWiUqmQyWSwWCwrWt5paKwlRBvSQqGA1WolHA7jcrno7++n2WxiNpvJ5XLY7XbMZjPlcpn169fjdDql0WO5XOZ973sfP/nJT7DZbFJsEZHXubk5nnzySa655hoGBwc5++yz2bJly4oFdavVIp/Po9fryefz9PX1ydvD4bCs6RZCy/LuCBoapzqRSIRPfOITwFKLdpPJRCgUotVqySzMdrtNPp8Hjmw+qXFmYbVasdvt1Go1BgcH+ehHP8revXtJJpPEYjH27t1bP9zjRblOX18f9XqdbDZLKpXC4/HQbDZxuVxYLBaazSY+n48nnnhCmq2rqkosFpN+CP/1X//Ftddee8jXcrlcXHTRRVx00UUHvX1xcZFvfOMb3HzzzSQSCV7/+tfz4Q9/mM7OTpndUyqVGBwcXOGnpSjKQYX0493lTuOZIcQwl8vFwMAA5XKZ8fFxms3m0+67YcMGgsEgXq8Xr9dLIBDAYDBgNBpPwpFraGisJU4r8UVV1TlFUd4CtBVFcQObgScVRXkz8Argr4HciToesbkym804HA66urro6uriM5/5DO9+97t54xvfyE9+8hO2bt36tMd+9atfZXx8nEAgQCqVoq+vj2KxSKlUkpvOzs5OTXzRWLP4/X4KhQLZbJZms4nT6cRms1Eul+ns7KTZbPLwww+zuLiI2WyW54/wYWq1WlxyySXcfffdXHnllVx77bUsLCwwOTnJ5OQks7Oz0o/A6/Xyghe8QC74YSmKajQasdvthEIhMpkMqqoyMzMjvZlgaTO6XGjRFuQapxPxeJzrr78eWLqmBQIBYMnkeWFhgWq1SiKRwOfznZC26hqnFjqdjkAgQF9fH08++SQej4dAIEC1WhXZBIcNeIm5sd1uS28ug8GA3W6Xc7ZOp6PdbjM7OyszFBOJBKOjo1QqFQwGAy9+8Yv5/Oc/z5YtW3jJS17yjN/Hrbfeyvve9z7y+TyvetWreOlLX8rw8LDsmuf3+/F6vRiNRsrlMnb7QZN5NE5hREBUmJhms1kWFhZWNMGAJb+2SqVCvV5nYGBANrzQ6XRa1ouGhsZx55QWX57yeLkIMKuq+hsAVVXveeo2K7AbeD9wBfCXqqqeMOEF/rTJyuVyJJNJma7o9Xq54oor+NnPfsarXvUqfvKTn3DBBRfIx919991cd911vOY1r8HhcOBwOFAUBbfbTTgcxufzkclkNPdrjTWNwWCgs7OTdrstF+fJZFKaUddqNdxuN4qiMDQ0RCaToVKpSENdg8FAd3c3r3zlK7nnnnv4h3/4B1wul6ztftGLXkRvby9Go5He3l6KxSK1Wo1sNiszXtLptDwfhemi2+2W5YDFYhGXyyV/1xZuGqcbHR0dDAwMoKoqoVAIt9tNb28vLpcLVVWlMaXT6Xya0KixNtDpdKTTaRKJBHq9Xq57crkcwGHr0cQ6qd1uYzAYqNfrck4PBAIYjUZqtRrRaBSHw0FPTw9er5fJyUn0ej1erxen00lfXx/JZJKrrrqKm266ide+9rVHdez5fJ4Pf/jDfP/732fHjh1ce+21eL1e2X0pGo3i9XpxOBz09/dTrVa1MX6aIr63fD5PJBJhZmaGZrP5NEPk/v5+duzYQTgcxuPxyGv48uw+DQ0NjePFKSu+PCW83A+kgG2KouwD3vVU9ovCkuHu5cAW4AWqqj550g6WpUnfYDAwOjrKxMQEOp2Obdu2sWvXLl772tdyyy23cOmll3LgwAH+6q/+ik2bNrFjxw7ZocVms+Hz+RgcHKRerzM0NPS0umMNjbWGw+Ggu7sbk8lEOp2mo6MDm81GNpsll8tJg0aXy4Ver+fJJ5emgbPPPptCoUBvby+Li4uMjIxIUaWjo4ONGzfKdqa5XI75+XmazSb1eh2DwUAoFKJUKlEqlYhEIvj9fhRFYWRkhFartWJxLjpeiI4w2sJN43RCnBPiGtTR0UF3d7fsCNPf3y/FGE1YXLsMDQ1x7rnnUigUmJycxOVyUavVAFpHeqwoBxFZhDqdDovFgsFgoKOjg1KphM1mo1qtEgqFCIVC9Pb2smvXLtkl8ve//z0XX3wxNpuNt771rXzpS1/i7W9/+2Ff98EHH+Sd73wns7OzvPe97+W9730vmUwGRVGo1+vodDoMBgONRoNms0m5XNbG+GmMEPry+TyFQgFYynIRGbOqquJ0Otm0aRNbt24lEAjIAEsgEJCZLxoaGhrHk1NWfGGpjCimquobFEUJAHcANymKcqWqqllFURLAq1jyeTlpwotQ1Ds7O4nFYkxPTzMxMSE9KpxOJzt37uT1r389N9xwA//8z/+MwWDgkksuIZVK0W632bJlC5VKRabyxmIxOjs7pRqvobFWEYsp0c6zXq/jcrkoFovU63VCoRAmkwmn08n+/fsZGxujVCqxfft2zj33XBYXF6nX62QyGRltbTQaVCoV0um0zKB5ahNBKBRCVVUMBoN8bY/HI/+u1Wp4PJ4Vxyd8n7SWkhqnK61Wi3K5TLPZlBkJ1WpVtmYV419j7eJ2uwkEAuzatYu5uTkcDgdmsxmOIL6IubtYLGKxWLBarbKpgMlkIhqNyg52xWKRoaEhHA4HkUiEWCxGq9Wis7MTRVEIh8Occ845uFwurr32WtLpNH/zN38jTSUnJib4/e9/z+9+9zseeOABZmZmGBgY4MYbb+TFL34xOp0Oq9VKvV4nn8/TbrdxOp24XC5MJhPtdluuv5xO54n5YDWOOYqiUKlUiMViLCwsUCwW0ev1mM1mvF4vNpsNh8OB3++X13SdTofdbl/h9aOhoaFxPDiVxZc4cL6iKJepqnq/oigXAXuBLyiK8h3gSuCjqqpWTuZBlstl6TPh9/u58MILMRgMGAwGstksBw4coKurC6vVyl/91V9hMBh42ctexuTkJGazmU2bNkkzT2EUpqGhsbKThMVikYt3gGAwKO/TbDalN4Xb7cZqtcrNYzQaZWFhgUajgV6vx2Kx0Gq1SCaTMstlYGCATCbD4OAgAJVKhWg0iqqq+P1+TCYTrVaLSCRCKBRaIb4sN9c9npvTWq3G9PS0Nj9oHHMURaHValGv13G73dIANZPJUCwWZeRYY20iMnDL5TLxeJxIJEI2m8VsNgtflEO20hBGzWL8zMzMMD8/T61Wk3536XRalnmWSiXi8TjBYFCWuxkMBgqFAgaDAZ/Ph9Pp5F3vehc+n49Pf/rTjI+PU6/X+e1vf0s0GgWW/MK6urq4+uqredvb3obf75dZDQsLC4yMjGCz2SgUCnR2dsq5e3l5irYJP/0QIp/VauWss87id7/7HaVSiVgsJgVkq9XK8PAwNpuNWq0mO5aGQqGTffgaT6F1EdM40zmVxZe9wJ3AXyqKUlJV9Y+KovwF8EnAB3zlZAsvsNJMrlqtyou+uC2VSpHP5/F6vdRqNbZs2YLX65UlEb29vfh8Ptm+VnSSsNls2gSksaYR5nkCcT6I88PlcpHNZuWivLOzk2AwKAXReDxOu93mwgsvlH4uZrOZQqGAx+Nhfn4eq9VKo9HAZDIRCAQwmUwsLCxgNBrx+XyEw2ECgQCJRAK73f60kqITaT6qRWI1jgdirLvd7hUdwWBpA6pdfzSEiOLxeDj77LOJx+NUKhUhVhyy25FOp8PlctFutykUCiQSCdlFy2AwyPlYiCpOpxNVVWm325hMJkZGRojFYjSbTex2O5s3byaRSHDuuefS3d1NMpnkBz/4AT09PWzatIlXvvKVnHXWWaxbt46uri4GBgao1+s0m01MJhOZTIa5uTnK5TLbtm2T2YrLO1eGw2FNbDxNKZfLLC4uUiqVGBsbk2VsBoOBarWKTqcjFApx8cUXs2PHDur1OuVyGb/ff7IPXWMZy9d+Whm3xpnIKSu+qKpaVhTlX4DPAH+nKMqNQA9LwsuvVFU9bHvDE40QSyKRCIuLi1QqFWnMKcoi3v3ud6PT6ZienmZoaIht27bhdrvxer0rIi5isikWi9oEpLFmOVjL5nq9zuzsLAMDAzILxGQykUwmCYVCtNttSqUSi4uLso0kIFtTG41GLBYLOp1Ophpns1kymQzxeFwKoMKIb3k7a7fbjU6no9lsSlPGE7kp9fl8J+y1NNYOrVYLv98vN7ypVIqenh7Wr19PR0eHJrxoUKlUKJfLWCwWzj33XCYmJmQGDFA93GMdDocsZ+vu7iYYDOL3+6XnVq1Wo16v09nZSSAQkCWi9Xodk8nE0NAQjUYDo9HI3NycnN8B3vKWt/Da174Wr9fLunXrKJfLzM7OMjc3h9frxWKx4Ha7SaVS0s8okUhQqVRIJpOEw2HgxGUwahwfRMZLvV6nUCiQTCbleO3u7qZQKNBqteju7mZwcBCr1Uq73V4hNGucHA4WZD7Y2k9D40zilBVfAFRVPaAoykeAdwH/yFIb6Q+eSsLLcoVWtF40mUzs3buXgYEBhoeHMRgMZDIZFhcX6e3txev14vF4ZLr34uKiTK0dGhrCYDBgs9m0CUhjTbM6q8ThcDA/P088Hpc+FCaTiT179sjF9saNG4lGo0QiEXQ6HalUCr/fj9/vl501MpkMrVaLbDZLMpnEZrPR19fHyMgIJpNJbgza7TaJRIJms4nVapXeM+VyeUWb6RNFs9k8Ya+lsXZotVrk83kymQzNZpPzzjuPUCjEWWedhclkkiV/2sZ07WK1WgkGg9hsNtLpNOPj46iqKlr4Ng732Hq9zp49e6jX63i9Xkwmk8xYjMfjxGIx3G43sNReulgsUqlUKJVKWCwWQqEQLpeLVCqFyWSiXq8Tj8cJBAJs2rRJdpq02+1yXhYm0e12W3bASyQSOBwONm7cyOzs7Ip1laIomt/HaUy5XCYSichxmc1micVibNq0iQMHDlCpVGi1WvT09OB0OrHb7ZhMS026tAzzk8vBslxOZEaxhsbJ4JQWXwBUVZ0BPqEoyucARVXVwsk+puWYTCZKpRIGg0FO4C6XS27u3G43oVCIWq1GPB7HbrfLxawQZIxGo0y5zefz1Go1abirTUAaGn/C4XAQDAZlxko8Hmffvn3EYjF8Pp/scFCv1zGbzTJbxWg0EggEZD34/Pw80WiURCKB0+lk+/btGI1G/H4/4XCYWq0m05fFQuApfwNp3HuiRdFSqXRCX09j7ZDNZkkkEtTrdUqlEt3d3dJvo1QqaRmYGlSrVZrNJtFoVJroWq1WOIL4Mjc3x+joKADbt2/HbrdTqVSo1+uk02n0er30FzIYDLJEKJfLYTAYyOVysh26z+ej2WwyNzeHwWBg06ZNDAwMsLCwQHd3N3v37sVgMOD1ejEYDKTTaRqNBlarlVwuRzweZ2BgQHq+aJwZWCwWarWa9HFZXFyU13fhTxQMBlFVlYWFBXbv3i1L07QSl5PL8iCzJoRprBVOefFFoKpq8WQfw3LEJJHP54lEIjz22GPodDqq1SqFQoFQKERPTw+NRoNqtcrIyAgLCwv4/X5CoRBzc3MsLi7i9XoxGo309fVpHSU0NA6DiGqKtPV0Oi2zUs4991z5e6vVoqurC5/PJ8+zubk54vE4gBQ1N23axMTEhKz7npmZke2oLRYL7Xabvr4+TCYTdrsdVVWl/0B3d/cJP1cNhtNmutY4jTAajZjNZjZs2EClUiGTyfDYY4/R09MjM19Ay8BcywgBrlQq0Ww2ZcbuU5ki5sM9tre3l3q9TrFYlOVHTqeTWq1Gs9lEr9ejKIrsRuPxeJicnJTlIOl0moWFBbq6urDZbPT09BCNRimXy1SrVQwGA+12m7m5ORnwcrlclEolWdIES55ZOp1OdrXROHOoVqt0dHTgdDpRFIVMJkOlUmH37t1MTU3Jsrbe3l7sdjvr169/Wma5Nr+dHJZnueTzea3bq8aaQFvNP0uE8GKxWDCZTLjdblqtFqlUilwuR1dXlzTWrVarKIoio/BigWswGOjp6aHZbMoNncPhkH4SGhprldURkHa7zfz8PHv27JHdj2w2GyMjI7jdbvL5PM1mk1KphF6vp1QqUalUmJ+fJxAIEA6H6ejoYHJyEp1Ox/r16+UmYHx8nJ6eHvR6PQaDgUQiQaFQIJPJsH37djwejyw/WlxcRKfT4Xa7pTn2iUKkSWtoHEssFguVSoXOzk68Xi9zc3PMz8+zf/9+uru7pRm8xtpFlO84HA5arRabNm0ikUhgNBoP+7hWq0W1WqW7u5uZmRnGxsYYGBjAZrPJwJOqqng8HtmCOpFIkE6nabVanHPOObJEaWJiAqvVitPpZHBwkGq1Srvdxu12Uy6XSafTzMzMyHEsunbNzMzQbDZxOBx4PB5tbXUGIdYJFouFjo4ObDYbyWQSp9PJunXrGB8fl2M3mUySSqXYvn07AwMDWomLhobGSUMTX54ly7sc9fT0kM1mqVarFItFHA4HVquV8fFxms0moVAIi8WC1+uVKbuASNmVNakOh0MrNdLQANn+sVgsyg5Gs7OzTE9P4/V66e7ult2HnE4nhUKBXC5Hu92mv78fr9fLnj17OHDgALVajQ0bNuD1eunr60NVVVwuF1arVXbOEKVMoqTIZDJRLpcBiMfjTE5OSl+YdrstF30n8lxVVRVVfXpXV82nQOO5oKoqdrudarVKMBikp6cHm80mu79YLJYV5XcaawtVVdHpdJjNZmZmZmg0GrhcLrZs2SKMbw+pQDebTRnJbjQaRCIR2ep5aGgIr9dLKpWS/l31eh273U6z2aRWqxGNRtm6dSvlchm73U4sFsNutxMMBkmn05TLZelV5PP52LJli1xD6XQ69Ho9nZ2dLC4uUi6X6ezs1ObLM4iDlQylUinm5+dlGZvL5ZI+iiJYYrVaV3ROPF0yzs/0spzl3V41NM5kNPHlWSIWoqIUQhh0ut1u2Vml3W4Tj8fR6/Wy60q1WmVqakq2t6vVamzcuBGv14terz/hGzoNjVMRm80m09TFYmPTpk34fD6sVisulwudTkexWKRaXWq2ITyTRMbZueeeS7VaZXh4GIfDIbNhzGYzsViMdrtNJpOR5r2Tk5PU63V6e3vp7u7G6/Vis9lkqrzD4aCjo2PFAuhE0W63GRgY0DYOGsccRVHo6enB4/HQ399PsVhkYGCAjo4Oent7ZbmbtiBemyiKgsPhQK/XS+H6vPPOo1QqceDAATjMOtJgMBAIBGg2mxiNRoLBIGazmZ6eHrxeL9lsFovFQqFQoFKp4Pf76evr4xWveAWpVAqj0YjNZqO/v592u43RaKRQKKCqqsxusdlsRKNRWq0WPp+Pzs5OdDqd9Hvp7e2V6zIR5NI4MxBzksViIZ/P0263qVarLCwsUCwWCYfDssuhz+cjGAwyNDQkS1pON6+XM82fZrWYJISXwwlMZ7oApbE20MSXY0C5XKbZbOLxeFi3bh3xeJxMJoPL5WL37t2USiXcbjfnnnsu3d3dTE1NyY4rrVaLgYEBAoEAJpNJW+BqHJG1cPHR6XT4/X6SyeSK1tAiOjo7O0tHR4fsiiFakIoyovHxcUwmE93d3fT09NDb28vo6Chut5tarcbevXsxmUy0Wi06OztxOBwEAgHi8bgUdwwGg1zQWSwWyuUyLpfrpLSZBohGo6iqqgkwGseUZrNJoVCgXq/T19dHLpfjwIED+Hw+eR5onJqcqGuBoij4fD6mpqZwuVxEIhFyuRy5XA7g6el4TyFKOcfGxpienkZRFMxmM3a7nfn5eVqtFnq9nkqlIjvJ1et1BgYGCIfDsrNcLBbD6XTSbrdZXFxEVVVZpm2xWHA6nSwuLlKpVHC73QSDQRKJBKlUCljqfiSeS2Q3ahw7TtaaRARFisUisViMXC7H2NiYzGI1mUy0222cTifVapVyuUyxWKSzs1Me6+m05j7T/GkOJiat/t/qsfVMBai1sF7WOP3QVlXPESG8ANIDpqOjg4WFBRYXF8nn81SrVVkGsWnTJux2O7Vajd7eXpxOJ16vl1qtdsI9JDROT8606MehEDX91WoVh8Mh33cul2NmZga9Xk+r1aKjo4NGo0GlUpGRe5PJhMFgQFEUnE4njz76KKVSSWbOiIyaoaEhaSpaKpVIpVIMDg6uWJQ1m02SySSlUolkMnlS2kwDNBoNmX6voXGscDgcuN1uGo0GU1NTsrtMPB5nfn4ej8cjNzna9enU4kReCwwGAx0dHSSTSZLJJIlEQmT0HrbbkcViwW634/F4yGQylMtlCoUC2WyWcrmMXq+X4jcgxT8hosTjccrlMlarFYPBgNFopFarUalU5GdgNpvp7OzEaDTK8dvf3w8sGUpXq1V0Oh35fF4rnzsOnOw1iRhjIghqNptxOBzs3r2bYrGIz+fDYDDILqNirX66jYMzbeweTExa/b/VY+uZClAne2yebPr7+w8ZsOvv72d6evrEHpAGoIkvzxkRlc/lcsRiMUqlEna7nWKxiM1mo6urC4vFQqPRIJfLyY1kMBiUZQ2FQmGFAZjG2uKZKvNnWvTjUBysG0Gz2ZQRU+HNIjYFwvPF6XQyNDSEXq/HaDQSjUZZXFykVqvJtqPBYJB8Pk+xWCQUCjE2NkYsFqNer8uo2fLSoqGhIXkM1WpVtos/kdEUu90uj0dVVXl8WiaMxnOh0VjaOwthUXgbpdNpRkdHCYVC2O32M27hfyZwPK4Fh7oeORwOurq65Bw8MDDABRdcwKc+9any4Z6vWq1it9vp7e3F4XDIOTYSiVAsFunp6WFoaAi73c7U1BTpdJpKpYKiKNIXT6fTyczGTZs2kUwm5fOKDElRaiRM0cPh8IqMl7Vy3TwZLC//OdHXRUCWHofDYSKRCDabjVQqRb1ep9VqYTab0ev19PX1EQgEiEaj6HQ6QqHQCTtGjZUcap5ZfZ1Zfd4+0+vQwc77pwJoayKScDhxRVs7njw08eU5Ui6Xicfj1Go1CoUCtVoNq9VKX18fdruddrtNsVgkm83KjJdQKEStVmNgYIDR0VFyuRzbt2/XoopnCOI7F9kVDodjRRcrnU4nsyn8fj/lcvmI7fVWX6iWX3zO1LTKg11kU6kUpVJJGu2KNqM+nw9YEigWFhZwOp2ynalIVzcajYyOjhKLxdi2bRuFQkFGw1wuFyMjI9jtdkKh0NOiJcu/F5HmfKKjKRaLhXq9jtlsplgsEolE0Ol0ctycKvXRZ+p4PFMRZRihUAiPx0NPTw/Dw8MUCgV6e3tlt6PVm9Zj9T0f6Xm08bSSw10Llt8uSiWBg2YtrRaYxe+HixSLOajZbKIoylGVpNlsNlkCks1m5XwsykjdbjdWq5VarYbNZsPn86GqKrVajXa7LVtMF4tFafws2kz7fD50Op00Zff7/djtdrxer3wPyzNe7Hb7mtxwHOocWj5WisXiCuFkdbabuK/JZCKdTuP3++X3v7z852RkGYjATLFYpL+/nyeffBKHwyHNw1VVpdVqYTKZ8Pl8tNtt/H7/CTs+jadztBkpRxJbVs934npmsVhk2TosVSYEg0G55gMO36pNQ+M4ookvzwGxyRaGXqJsQUz0+/fvZ8+ePXIhEQgE2LdvH36/X5rrigXF2NgYHR0dx9RPQlu0nniy2SyJRIJqtcr09LTsUDMyMiJbG9tsNqanp8lms/T29hIIBORYWr1IFt+huB2efqF6NmmVz2ZsnMzx1Gw2mZqaolqt0tXVJUtvRE2waAU9OTlJoVDAbrfTarXI5/PEYjHZ1j2Xy8nWpeFwGLvdTmdnJ4Ds9iIyX8SmQZQZLX/vJyOKurCwwO7du9m+fTv5fJ65uTlpIjkyMvK0777ZbDIzMyPbwR5KsANW/F4sFuUCZnkd9tF+76L+/nBi4qnGwVqbH+k9H8vz4WSeWyIiLMxLk8kkjz/+OBdddBEWi4V2u43BYHjacR3NvLN8YXyoa9vR1viLMSnOSyFeLxcAms0m8Xgcm822QpA83vPd0bzPY8XBPo/lr1UsFmX2h7jPwTYw4n5Op1NunOFP2Qti07L8/qOjo6TTadLpNHq9HpPJBKA/0jEXi0X27NnD/Py8FEs6Ojpkmcjs7Kz0v3O5XORyOXQ6HV6vV2YnCCHJ5/PJwJb43EVWYLlcZnBwkHa7Tb1el5nJsLYzXpZ3D/T7/dJfR3xm2WyWPXv2EI/H5TrUbrdz/vnnS98UsVYFSCQSUhhbzsnMLkokEoyPj6Oqqmw/rigKbrebzs5OYrEYrVaLyclJzjnnHM3L6iQizt2DifrPFDEf5vN5afdQqVQwGAzMzc3R19cHgNls5pxzzsHj8cg1nYbGyUKbfVbxTBZcxWJRbvSSySTpdJpqtUo6nZau/na7ndHRUVKpFA8//LD8n8fjQVVVWYakKArRaFSWLS1vl/hsOdji+FQTZE6143kulEolvvnNb8rUfZ1Ox9zcHDqdjng8LkWDUqnE4uIibrcbl8slo3e5XA6bzYbBYJCL+OWijNiYNJvNFQv8Z7PgeTaCzTPZbB3rDWkymWRxcRGDwcCmTZvkxkIIL5lMBq/Xy/r160mlUlitVtrttqwDj0ajjI2N4Xa7CQQCGI1GEomEbN2s0+lk2WBnZyfFYhGdTke9XpfeBqlUakWWyYkuwYjFYsTjcRYWFlhYWECv12OxWOjq6lohEolNVDab5cknnyQUCj1tkbx8MS6iyoLFxUVSqRQ+n4/u7m7gT10hVnciOFxE9WBi4uF4NhkQR4roHo2Y0m63iUajFAoFwuEwLpfraa3OD3Y8R7rPMzkXlp9bz+QzPhbnWq1WIxKJMDMzw/Of/3wsFosU7Or1Ovl8no6ODqLRKB0dHdTrdWw22yE36cuPSwhxy32KVr+/1fPXcvFOzHniXBOfUT6fZ3JycsUGsN1uMzMzQyQSkZlsy8XDbDYr22cfzbg8mjEgEMdstVqpVCrSv6RcLsto6+rP5lAZCKKs8VAijujsIj4LWHl9F+sSQL72wb6j5Ztp0XGo2WzicrlWiFjL5zqv10u1WmVubo58Ps/ZZ58N0DrS51goFHA6nXR1dTE0NISqqkxOTjIzM0OlUqFarZJMJrHb7TidTtxuNz6fj2q1Kr1hCoUCbrcbj8cjP3NAZjYsP4cBCoUCNpsNp9N5xvhkHWkeO9RtFouFWq1GqVTikUcekeea2Wymr6+PZrPJwsIClUqFdDrN/Py89MoBCIVCVCoVotEoGzZsAJY8gKLR6EEzYMT4ei5z0zMpDSkWi+RyORRFoV6vy/Evumg1m006OjpwuVysX7/+tBDizqS18WqWz/HP5r0tzxw3mUzkcjny+TxTU1P8/Oc/l0bbRqORcDjM9u3bpQeUuAZwBK+qZ8vAwAAzMzMHvU0cg4bGmhJfjsbp/kgbzNURY51OR61Wo1arAUhfiQMHDmA0Gjlw4ADj4+PMzs5itVpxOBxUKhXa7TaPPvooRqORwcFBEokE3d3dFAoFZmZm6O3tlTXSz+T9rV7UNptNaS5mMBiOm1P4s71QnElmWMlkkm9961vYbDZe+tKXypKjer1Od3c3CwsLeL1euUnu7OzE6XTKCKTIYujo6KDVamE0GjEYDCsWCiJitXws22y2Z5zxIhbjq+tgD/cdHqyuG1ixaWg2myQSieeU9bA620ekrHd2dhIIBGQaeaVSoVAoSMPc3t5eWd4nDHrFMYiFIyz5t0SjUaLRKCaTiVAoRE9PjxTAAoEALpcLi8XCzMwMmUzmlIiUFItF9u3bRzAYJJvNUqvVyGaztNttAoEAbrcbnU5HoVBAp9NRrVaZnZ3F4/FIkReQAl6lUsFoNMpW2iLLoVKpSI8PMU7EJnt11H35d7R8LLrdbur1OvF4HL/ff9hsgIN93+L/y1PfD5ZRcyjRYvVxNptNmQ2w+vHtdlsu0gVCOFje6ny1ILL8ucW5vpwjZXQsZ7kAsfpxh5ojn4k4cDiazSbNZpN6vU4ymaS/v59qtYrb7WZ2dpZ2u82uXbtQFIXp6WlCoRAWi0Vu2MW1RYyP5d/h8vcn5rKDHffysZnP5ykUCgQCAfnedbolf6dSqYTH45Gvu3wuKhaL1Ot1OU8s/47E5rNer8vSFXEsq4WO5XOkwWB42vcrFv7LhShx7ELMEOXIB8tWXH2+CNFIzPelUkmKOGKsLh871WqVUqlEu93G7XavmMPFeHa73TJjSWTA9ff3y02yuF8wGKTdbjM9PU0sFkNRFHp7e8nlcrhcLtn9ze1209HRgdfrledSpVJhdnb2iOPLZrMRCASIxWI0m01arRbhcFh2ruvo6GB2dpbOzk7sdvuK83BmZgabzbZCBBLzv5gLAOLxOLlcTpYqifcHZ5avweFE2kPNN6L8QlEUfvvb3/L444/T39+Px+OR15T+/n50Oh1ut5vR0VFmZ2dRFEX67lgsFtLpNBMTE7KBhBBnCoUCw8PDK+afYxH4eyrT6YhZVQKn00kwGGRsbIz9+/fL65rVasVut2O32xkZGVmRDXUqcyatjVcj5spnu65KJpPMz89TKBSoVqv8/Oc/JxqNMjExwUMPPUSj0cDpdBIKhTCbzZRKJTZt2kQoFJJzPXBcFnUzMzMyqKehcSjWlPjSarUOukhezqE2mMsjw6sXVCI6Xq/XGRsbo1KpkM/nKZVKLCwsMDs7Kxdcer2efD6P3W5nbm4Oq9UqPSbEwk10T1q+eFxddnKoSOzqyTqVSpFOp2VEf3WU8UgXxKO9ADzb0pdjlXp4KpDL5UilUrjdbu655x66u7sxm80MDAzITcnY2Bivf/3r8Xq9MooYj8dlFx+R9jsyMkKj0ZDp9R6PZ0XGxeTkpFy8Ll/Iw+FLRMQGNhAIPC3t9kjf4cHqutvt9oqIrzBIfC6I4xBeK+12m1QqhU6nkx4vhUKBWCwmI5tms5lIJIKiKGSzWRn5EpsHYbjX3d2NzWajr68Pq9WK1WrF4/HgcDgwGAxSuBCLV9Edye/3r9honSzm5uaYmprCYrHITJhSqUQ0GqW/v59arYbdbicQCJDNZmUrVpPJJKOU5XJZtn1NpVI4nU6i0SgXXXQR9XqdbDaLyWSS4xNWlnMsz0JYvmFdvqns6OhgZmZGLrDE7UKIWR7hTyaTNJtNOa+JMSWybwKBAJlMBrfbLT8H8XzNZlOKSfl8nkQiIQU6cZzpdJp4PM7c3BxGo5FCoSDPv1QqRSAQkNFy8RhRGiE2L/F4XL6WEFzEawlR4WDiN/zpWrJ6Dl/+XpY/bnVGyfLyt+XBA5vNJucNUWaz+vNZ/VoHw2Qycc455zA/Py8FR4A9e/aQy+Ww2+10dXWRTCYxm81MTk4yNDREvV6XG2rhv1QoFOTm32QyyfNPnF/iuyoUCrK9tTj/xAZSXAeFwGg2m2VmqcVikZkSYhOVz+dpNpvEYjGKxSLhcFi2iReZJ+XyUldCu90uhaJiscjc3By5XI5gMEir1ZLlK4lEQm7mxGJdiC71ep3JyUkMBgM+n0+OnXq9ztzcHP39/fj9fikItdttKfyJsbn8+1meoTYwMEChUJDnjcgeWz43P1XqQyAQkFkg6XSaVCpFOBymVCphNBqxWq10dHQwNjYmveiEqCxEKKvVSjKZJJ/P09nZidVqZXZ2Vopbfr+fQqHA/Pz8is3sxo0bpZcdYDrsAGMpa2/fvn0rsiGsVivr16+nXq/T1dVFKBRieHiYZDJJoVCQ867f73/a2F7+ezwep16v43a7V2z0l/99pnAwkXa5WL18rlhejpFKpRgdHWVqaopMJkOtVsPlcjE/P0+7veSH5nQ6ZZZuKpVCr9ej1+sZGBjAbDbT0dFBOp1GVVXGx8c5cOAA3d3dtNttzGbzivP4YNm4h1tfHGwd+tRjD5tVJXA4HHR3d8ty73q9Tq1WY2hoiM7OTkwmExdccAGBQOCkX8OPlpNZwnWqsXp8+P1+UqkUjz76KKOjo/z0pz+VpfwCm83G0NAQO3bsYPPmzbhcLgwGwxk3J2icnqwp8UWv1x90Ilt9Yq/eYMLS4gGQqdBikwJLm+56vY7BYJCRplqtRjweZ2xsjGw2K83A4vH4CgMov99PIBCgp6eHWCyGTqeT0Z/lE0k6nWZsbIzu7m76+/sPOoGsXrSLi269XpcL5dVpfqs346s38Ed7ATiYqHM0YpEwOz0dIhFHQnQNyWaz7N69m0Qigd/vJ5fL0dvbS6VSoVwu89BDD3HOOecwMzMjNw7CUyESiZDL5QiHw3i9XkqlksymEuURjUaD8fFx9Hr9CmNBWPrchRGrWOgvjxgKRP28KGESmwVYOY7E/UTEdfWYWC5KwtJ37fF4jnrBcJhF19OyDMQGanZ2VrZwD4fDMkIuPp9UKkWj0eCss86ir69PmvQqiiKj2CLKJ8r7hAgofsRnJLJnTCaT3PScTH7/+9+TyWRkVLy7u1sKI5VKhUajgc1m449//CPr1q0jGAzi9/u5//77MZlMLCwsYDQaSaVS5PN59Ho98XiceDyO2+2mu7ubxcVFuru7ZflkuVwmmUxKX6twOLzC3C6TyaDT6fD5fMRiMcxms9yIw1IHCrHRXVhYkJld4n5ioSw2t8FgUHo6OJ1OUqmU3PCLSHc+n+fAgQMsLCys+F9nZyeJREJuNGEpGzGRSNBqtQgEAtjtdjKZjMz+EZlOhxIsREq76GIn5nchug8ODgJIgQYOnoUjxMTV58bqTBBRYiEyL0wmE4lEAqPRuEJUEr8LsQL+lJVhsVikL4PT6YTDpO8bDAYajQbBYJB6vS7HvRDG+vr6iMfj8rNtt9tks1k6OjqkuOB0OqXIJz6jaDTK6OgoLpeLZDJJT08P9XqdXC7H3NwcpVKJer1OpVKRbYVFlkShUODhhx8mm82yceNGLBYLev1SEDwYDMp2sXNzcxgMBnK5HIuLi9KM22KxMDExQSaTkddMIc719PTIa8/c3ByJRIJ8Pi8/NyGWi42tELnq9Tqjo6PYbDZGR0fx+XykUimazSapVIrx8XEpZAgBCJbWB6LEWJRzFQoFfD4fzWaTXC4nM+4MBgNOp1N6nuRyOfmexXe1c+dOKbLqdDqy2Sy/+93vqNfr8ryuVCr09vbKTba4fqTTadl5UYipgUBAzhNzc3PSk66rq4uFhQVisZgU1r1eL7VaDb1eTygUYmJi4ohzVrlcJpFIUCqVpP+G2OSn02na7bb08jIYDPK9WiwWeS1ZXoK7/HomxDyTybTCr0sIAGfCumI5y8//5WstkYkkMpnEdUt09dmzZw+jo6MsLCxgsVhIpVIkEgnm5uaIRqNSgLHb7aiqis1mo9VqkU6niUQizM7O0tfXx+LiIuvXr5fni6qqNBoNFhYWWLduHRs3bpTz7nLxUPwtxKHVpdMHE2ae+u6OKjtBfC6RSASDwcDg4KAUIbPZLFarVZbUnS6cjNLmE4XY5xzt+SnGhwiylEolZmdn+a//+i9uv/126vX60x4TDAa55ppr6Onpka+poXGqsKbEF7ERXY0o+1jutbJ8AygW1D6fT0ahpqenmZ6eltE3ESUPhUKk02kSiQSLi4skk8mnvV61WmVhYUEueM1mM+Vymd27d2Oz2QiHwzJtDpaEn8nJSSKRyIpN8nJEhoS4qInjFItkr9f7tLr51Snsy1l+n6O5AKy+UJTLSx18xMJ8ddqwuAC32+0VQtbBhILTkVqtxsLCAoVCgVarJbNZFhcXefLJJ9m/fz/hcBhFUTCZTDKjoFQq0Wg0SCaTZDIZfD4fHo+H3t5e4vE40WgUs9ksS0Smp6dxu9243W5ZXpZKpUgmkyQSCRwOx4o2seKzFZkH+XxeluOEQqEVwuNy7wDRoQJWjonVz3coL4ND1agfasMqsg38fr8sORF/m81mOZ59Pp98jFh8CoNAschvt9uytKijo0OeEyKTZflxeDwemdkgMjtOpcjT9PQ0uVwOi8Ui69jNZrMsW7NarRgMBmZnZ5mfn8fn87F//34ajQaKosjvQafTYTab6enpwefzYTKZMBqN7NmzR7ZrHRkZAZZExbm5Obq7u3G5XHLcVKtVJicnZdmlqK0XGSrtdltm2rTbbRYWFjCZTPK1xTgWG1DRPhagq6tLZhSYTCYGBgZwu93EYjGMRiONRoN0Oi03salUikgkQj6fZ9u2bXLMiHlZbEy7urqkoaQQNcT4mZmZIZfL4XQ6CYfDUuwT41aUaWWzWXkMQviwWq3kcjm5CVzuPyFESlF6IzhYeYvoOCJuE68rPI2Wj0UxRq1W64rylUwmQ1dXl/QueuocPmRnB2GcKkoeRSmez+eTHUHcbjetVgur1crc3ByhUEiet0JQEKJmOp2Wx9VsNslkMuRyOfbv3y+DF6qqylJCkbUSjUbZuHEjmUxGznVCmLPZbFK4cTgcMlurUCig1+tl5oroMjg6OsoDDzzAxMQEmzdvlsEQcZ6XSiXpIaKqKg6HQ6aLi3IaMceK0kabzUY8HpclGSKLZNeuXbLkyWQykclkmJubo9Vqkc1myeVyUmAQc6Q4jkqlIsXiSqXC/v37AThw4IA87rm5ObZt24bJZGJ0dJT5+XnMZrP8/oUnitvtZvPmzWSzWXlNFZtjs9lMLpej0Wjg8Xjw+/2yxKtYLOJ2u6Wvlk6nw2g0yrT+Uqkkx3Aul5OlUMVikUwmA0fIfLHZbGzevFl6m3V2dlIul7FarQSDQXK5HPPz8+RyOSl+iowpkc2wXLwUwQHx3OL6szqAdiax/DoKfwqOORwOOUeZzWYpVJXLZf7whz8wMzODyWSi0WhIMUZ4hvn9fqLRKJlMBkVRZBaZwWDAaDTKa5/I3n7wwQexWCwsLi7S29tLKpViaGhIrjcMBgPr16+X11MhIIr5UwhnywMpQnA+VHbfM/2M9Ho97Xabs846i2g0KsVuk8kkfZg0Tj6rM6QOxfJrZLvdZmpqiltvvZXZ2Vl2797NH//4x4M+zmQy8bznPY9wOCyzMs+0OUHj9GZNiS/LWX0xq1QqlEqlFfXG4n4iNdlut1MuL7Xas9vtWK1W0um0rIcWpmRTU1Po9XoZtToYy7Mk9u3bx+LiIjabjVAoRDabZXh4WG5O7HY769evlyUS0Wh0hYkfIC9my7NXgsEgmzdvlinp4sIn3peIRNjtdrkBWD4ZPpdNp81mW7H4j8fjK0QAEQ0TNZsmk0kuTEdGRk6rCMWhaDQaUgybmZmRwoaiKNJxXaRHu1wumZkhvmeRsWEwGGS6e6VSwWZbasdZqVSo1WpyQyvGhYio9vT0SDFBfJfLF6YiQizSOAXivkLEEIs88f0cLFNMPN9qDpdqvHrMLr/vckNNUWYl2lt6vV4WFhYol8vU63WZtSAyKXp6ekgmkysyGUTZlijTEIKMWHCKTbd478lkkmg0SldXl0zth5NvgidMBYPBII1Gg3g8TiaTwe/3y+hyoVAgnU5jMBikl5TBYKBWqzE7O8vQ0BBdXV2ybC2dTjMwMIDVapWZaNlslvn5efr6+mQXN/FdpFIp+X2JMdfR0YFer8dqtcoMGGE+3mq1cLlcnHfeeXIzK7JTREaHmEcHBwelSbLIKhBZSgsLC+TzeVwuF0ajka6uLnw+n+xmsGvXLqxWK63WUqa6iIiLzA4AVVVxuVx4PB75GrlcjkKhIMu53G63FIScTid+v59KpYJer5fndLvdZmBggEwmI71txOZCCDGLi4sEAgEZtRfnjxg/y7MOl8+1qwVr0T63o6NjxTVrubmsGKNGoxGv1yuvD8uE0kOaC+p0S8bg9XqdRqMhBQlYMuTs7OyUpSozMzOydFaIDOJ6JVoQ9/T0kMlkSCaTLCwsyIwOkYXpdrvluBAi6fj4uDyfxeuKzL16vS4z8cScGQ6H5WcaiUSo1Wqys5zIgBPH3tPTI/1L2u02jz32mHx/gUCArq4umSHQ09ODoiiy/MlqtWIymWTpVDKZpFKp0N3dzeWXX87Y2BiRSESWNdZqNSYmJhgaGpLzucjyMpvNOJ1OFEWhWq1Kjx2RXbCwsEAul6PVatFoNOTrim6IPp9PZqS43W4WFxeZmppCVVV0Oh2qqpLNZunq6iIej5NIJKSXirjOChFNZCPpdDoymcwKUVZ8Hu12W3qoiHGUSCTI5XKMjIwsF1rLhxhacnx5PB45PkT5qBBmhdGuKEsTmVOiBEycE2LtIgI7sJRVtzpT+UzcZC2/NgJP890S5b7iPBEZoAAdHR00Gg3y+bzMms3lcmQyGRqNxgo/JCG8iNcQYzWdTstNrN1uZ2FhgWQySVdXl5zjRAYLIEQ5ubYU18qDlZsKKwBxLX424pkQ93fv3i2zvBVFobOzk+HhYQwGwxmxpjxTONrvWASQRUDs/vvv51e/+hU7d+485GMcDgevf/3rOf/889Hr9TJ77nQP6GqcWazZ0SguZmLiHxgYIBgMSqO+8fFxPve5z0khRUQd8/k86XQau93Oli1buOSSS+jq6pKbGavVSk9Pj6wXD4VCMl36YDSbTSKRiFyY9/b2Mjo6iqqqxONxuYk0m82MjIyQzWaZnJyUi32xILHZbHg8nhXGiwaDQW4exf/EAksYtwrzvlgsxvXXXy8v6EeTfbLc52E1Op0Ol8tFKBQiHA4zMDAgPWdEtkuz2cRsNnPHHXfIyM2RnvdYcuONNx7X54elTYTYlDmdThlV93g8eL1eaQoWDofx+/3ydq/XS3d3t8x+yuVyLCwsAEvCwPj4OLFYDJ/Px+DgIH6/X5ovisXuz372sxVeMQfDYDAQCoVWCHlCrEmn01I8Wj4mlp87h/uuxIL5YJ4+N95449PGrBAJRcTNYrHI1tDCf8DlcuH3+wkGgwwMDOD3+2UE2OFwyNKJQqHA6OiojG4vv9ALo97l2V7L39+NN96I3++nq6trhSgFK+eNk0Gj0aBer8v2sMsz7LZs2UJvby9er5fe3l656BZCqBAtRNZBo9GQ0ehMJsP+/fv50Y9+hE6nI5VKSXFAdGEzGo10d3fLbjhOpxOj0ShNDEUmSiqVkpFovV6P2WzGaDTK7JgDBw6QSqWkSXmlUuEXv/gFZrOZ4eFhQqEQjUYDvV4vjRJdLhdWq5WBgQH6+vpoNBo0Gg1ZcmKz2bj88ssZHByUGReAzFoYGBhgZGREGksCfO1rX5MbTBGFD4fDWK3WFWO+Xq9LITwQCMhz1uPx0N/fLwUace0AZJmamNdFaVuxWORrX/vaivEjPmdARvdF23QhjgmvnMXFRTl3i04OAwMD8pzp6OhgcHBwRfeRpzYdh5xMRbaHyARTFEVuwlRVJZfL4Xa75Ya5UChIAVkIbplMhsnJSXbu3Cm7g4lyydnZWfR6Pc1mU3p7HDhwgK1bt8qMpnA4jNlsZmZmhunpaVkWJq6dqqrKrjWJRIKxsTEpbPT398vvNZVKMTU1RbPZJBgMcvbZZ+P3+1EUhUceeYRwOCz9okqlkjx/6vU6VquV3t5ewuEw3d3deDwe4vG4NCCdmprCZDLhcDh43vOeR7vdlp/VwMAAF154ISMjI3R2dqIo///27jzMjqrM4/j37TWJSbqzdugQ0p2EQDMRIWERjCgiMjOyuIG4BJAQRhRRBMdh8FHReZjBcUDHUWdwQYIw8QGDURRFVo1GHFxREAxJIIQQAlnoDk2nSc78cepUVy6300tu3arb9/d5nvvk5nbS7zlVp+qte+qcU8att95Ka2trfH7bsWMHq1atYtOmTfHIvrBOVVtbW9y258yZw/z58+MnjY0ePTq+a9/Q0MC4ceNYs2YNmzdv5qmnnoqn83R3d/OVr3wlXkg8jIQI7f+AAw6gqamJxsbG+FHC++23HzNmzIgX+g1taN68ecydO5eDDz44XicpPKUxTJGdPn06CxcuBOjZ2zkruS5L+L+h4wz8F/S2trb4kdLhhkPIW+G1efPmPaZ/F65hV2xK30DKcQ1QCmPGjOGWW26Jj/Nww2Hjxo3s3r07fjz3fffdx/3334+ZMWfOHI4//nimT5/O1q1befrpp+PjIozIC6NIwzk2/Bk6a7Zs2UJPTw+jRo3CzOI239LSEo/Ybm5u5rDDDmPWrFmsWbOGbdu2cdNNN9Hd3R2vzxTOpaFDOEwvS05RHu4+hL4FrkNbnzZtWjwiOHS6hPZWKlm2nWqJXVdXx5NPPsn69eu54YYbuOaaa/ba8bJgwQIuv/xyLrzwQo4++mhmz56dykj6tra2eEHqwtdwn2iUxT6dNGlSv/Voa2tLJWYW9czjeb5qR74UrisRLgrC3ReA5cuXs2jRorgjBdjjMcDhLmZY62XUqFH09vbGi0POmTOHadOm8cADD8R39JIX3WGtgXDXbNSoUXR0dHD99ddz1llnxXfxwsXK+PHjmTVrVnz3M3mnZyh3CwrXZ2lpaWHTpk3ceOONLF68OL4IGshgFtlNXPzHurq6eOKJJ+JpJMuXL2fx4sV7DA0sx12sa6+9lvPPPz+13z9u3Lh4pf2DDjqIU045hcceeywendDQ0BCP8mlvb2fMmDG0trbGi9eFzgcgniv/wgsvsH37dtauXUtrayuHHHIIdXV1dHV1xVNOwnSN8847jw996EPDGqkRRqX09vbGHS6Fc80LR6sUG9nS35o+Ydsn/0+yDY8fP54JEyawYcMGuru72X///eO7r2F6w/jx49m5cyfr1q2jq6uLOXPmxBdzYZpUWEsixB87diyzZs3aox6FQ2BD2YodB4XHTrmEL1HhPLXffvsxb968+EkhYYHTpqYmzIyOjg42bNgQTyGqr69nypQptLS0xHetw53PsWPH8sgjjwBwzz33cNRRR7F161Y6OztZv359vFbFhAkT4rukYTpDuDPf3NwcP957+/bt8flwzJgxtLe38/jjj7Nx40YAJk+eTEtLC+3t7fF6VHfccQcXXHBBvN5IfX19PNUjTAvt7OyMv5TNmjUrPk8n13cIo3YaGhro7OxkzZo11NTUMHv27D1GMAEsXbqUc845J96+EydOjM/tYTRZsbWIwkitcFyE0ZKh0zR02IVOmfDlMXQQLlu2jEsvvTQue5h+Ev5eU1PDs88+G49MCNNkJk2atEd7CH8mj63hnC8bGho48sgjGTduXNzhEBbc7O3tpbOzk9WrV8cjL8LaP5MnT2b27Nnx49vDaKfw6Pfu7u74y3pYv2PChAnU1dXxgx/8gMsuuyyeMhK+zK1fvx7nHFOmTIk7xcKUiPAY4rB+T2jX4bHEYSROWEco5NXQkbNixQouvvhiZsyYEd/dD+2ju7ubCRMmxNMbQwdnWNA8TGmqra1l48aNPPPMM4B/dGhTUxO1tbV0dnbGUzdbWlq46667WLJkSdwWamtrefHFF2lsbKSmpoa1a9eyadMmZs+ezcyZM5k6dSrPP/98fKyFRZ0L2+2OHTt46aWXmD59Ooceemi8DZ944gk++clPcvbZZ7Njxw7a2tri9bzClKfkNNEwhSc8pS6UM3m+rqmpYcaMGbz44ovU1dUxevToPUakhVyzNyGXhCl54feGNhwWbQ03KQoXvw7tPKztVOzL1HCnGqV9DVAqNTU1LF26lIsuugjw2yDk3XDOeO655+IbFbt27YqfphhG8G3fvp3m5mZ6enpoa2tj9erVrFmzht7eXtra2qivr4+npoWpo+Hat6enh/r6erq6upg0aRILFy6MRyt1dXXR0dGBmfHss88yZcoUbrvtNi655JK9Ti0p3Gf7Ml1szBi/uGp4EtumTZvo6elh4sSJcV4odc7Osu1US+wNGzawdetWNm/ezMqVK/t9ulpjYyOnnXYaF110ER0dHfF3iX3pdHnwwQf7fVLazJkzS/5Eoyz2aVtbW9GlMSC9p8RlUc88nuetmh6JZWabgeIPYPdq6LtDWAMcBDzCwIt+Jf9fPdAIuOiz3UBT9Ocu/KPzGqO/90af1QOhW74bmAOsiX6Ho2/F98Jy1BT5bF8cAjw0xP8z3DLU4LfFLopv51LXrZgO4OEh/PuZzrl+Jw6bWS+wBXgJv49fwO+/3uh9L77Ojr42EA7AXvZse8n38PJtURfFSSrcZh347Rq281C3Z/h9A+2Lvf28v58NdtvX0Xcc9fd7a6LXQNtjsIbaLkql3/ZlZluB5/Dbow7Yjq9v2D+h/qFDfWf02Suiz130mdG3nRrw7a4m8f9mAasLfk/YB6ENhbaXjJvc1nX0neuS+6SOvnNa4X4JbRX6jpHwf8PvbqDvfDjY9tgQ1aGYve3n5PlpMOd/BvnvdvcTt7Cthm0b8sPe8sBg7K1tbQbWJ353bRQzeX4K7130s13RK3keCtus8DxmiXr0Rn8/iOLbviH6t/1dmIT2TvR7ku05xE/WozdR3rlFYibLHMqZvClVR18bDyM8Qjusoe+8XZf4PaHNHBLFC5+HbRe2QW1iG/VS/HzbEP3b5LSx8HsK23XymiVsg8KfD+c8nmzfhd9mdjNw23p8ELEHU740ZHWuH46Bzhu19B2PyeM2eVzUR6+e6Gfj6TtPvwCMpe94Cm01tOdw3dIVfRbyxm76clFol6XaroNpW8UM9tplX2TZdqopdrhOeQX++mQ7MJq+dvkivs110vc9ajAGup4f6PtiqWWxTxUzPXtvX9XU+TJUZvaAc+4IxVXcSpTn+qls+VRNx2I11TVvqmF/V0MdK10lbS+VNV+yrKNijzzVki+qJeZAqnbNl0HKaqKY4o7suOWS5/qpbPlUTcdiNdU1b6phf1dDHStdJW0vlTVfsqyjYo881ZIvqiXmXmnki4iIiIiIiIhIijTyRUREREREREQkRep8ERERERERERFJkTpfRERkD5bWcwYVNxdx8yCLulfz9hYRkXwrd45STsyG1nwZJDMzV8aNZWZ1zrnCR+eWTTnrm3VdE+Uo6z5OW16260Dytt0rZbuVipnVAB8ANgGPO+d+HX2e6n5R3PLEzbsMcmtVb2/pU6nn+ry34UrdrkORlzpW23eTpLwfB/uizN/BRux2zCt1vhQRXSRfgH+O/M+BvzjndpWjgUaxVwJXOedWpBmrIGbZ69tfXcu4nTPZx2nLog0NVp63e5btMSvRXY97gSeAA4AdwN3Ouc8rbsnj3gOsL2fcvImOsX8FuoBfAXc553anHNOA64B7nHPXh8/KkNuuwne0/dA593CacaN4FwE7geucc90j+by1r/KcIwvlOWcWqoYcmmXbyeN3k3Lt20o6Doaq3HmxGnJiImYu82Jd1gXIm8RF8qPAIcCpwL1mdo1zrjfl2DXAffiGucLM5gDPAJ0pNs5M6ptFXROxM9vHactyuw6ibLnd7nnebilrBbY55xaZWSvwDuA0M+t0zv1PinH3A7ZnFHdrBnFnAM9nEDc3ouP/buAx4EBgAfBH/MVYmg4ETgdeHV14fcs556Jj3pX6+I7qeSf+C8IkoNXMngOeS+PLQhTvd/jt+nrgWOC9IUZeLjbzopLO9XnOmYUqabsOV7Vet2a9byvpOBiqjPLiiM6JiZi5zYta8+XlpuEbxBLgRHxjOQY4N9qZaVoM9ADfMLN7gR8BtwDvTDF2VvXNoq5Blvs4bVlu14HkebvnebuVnJnVmNkZ+Hq3mNmJzrmngJuBVcAJZtacUtxLgflAm5n9fRnjng0sAfY3szeXMe4bgDcCk83spHLEzalp+A63xc65twGvAk5K/oM0jjXn3KP4Y/mHwNlmtijxszQuvhqBh4DPAccDV0TxLzaz2hRiHo/vUHwbcATQbmanmNnhZlYzUr74llAlnevznDMLVdJ2Ha5qvW7Net9W0nEwVGXPi1WQEyHneVGdLy9nwOvM7Fzn3AvAl4G1wFuA0SnHXgmMA5YC38D31q0B3guMSSlmVvXNoq5Blvs4bVlu14HkebvnebuVVJTIfwGcCZwBHAV808zmOec2Al/Aj9QYn0L44/EJ+ED8vl+adtyovj8DDgUm4vfr181sbhT3iynFrcF3sHwGeB/+AmCZmb0yzbg5VgccbWZvj/7+GPAkgJm1pXxB1Iu/APw2cIGZ/Rh/EV9SUVsbC/wtsAxYEb2/A/g7/P4utS3AcWZ2GfB1fBv/OPBVYE4K8SpdJZ3r85wzC1XSdh2uar1uzXrfVtJxMFRZ5cWRnBMh73nROadXwQu/KOI24NTEZ78CDkkpntG3/s7x+JPaodHfm4A/AB0jpb6JGGWva9Z1TrE+mbahkbDd87zdSlzPk4Fbo/dT8cN5fwJ0A+/Hz5H9P2BqieMa0AY8hR9m+89R3G3AucDFwK9TiHsk8KXo/Z3At4DdwDp8p8iHU4p7GXB79P5j+Pnq38FfWJ0DfCSNuHl+ASdEx1kT8DQwHXg3/gKwKYV49dGfpwDnRe9/j7/wPCPFer4XeAn4r8RnvwYWphTvrcB/A79JfLYqrXiV+KqUHFmk3LnNmUXKWjHbtRLbTpZtAXhDlvu2ko6DYdStbHmxWnJi9Ptzmxerfs2XqFfuGKDROXdP9PHXgGZguZl9AngBqAc2pxC/yTm3PRqebs65e8zsZOfcQ2Z2NH5+Yx3wbIniGf4kOs45973o4+vwB31q9Y3uAn8EP2f0EYC061oQ+z3ALufcTdHH3yTlOpdLudvQEMqVSVsbZNkya485sAU43MwW4BN9C36e8duA1+L3z2Ln3DOlDOp85ltnZsuiuCfhhy8fGL3a8RcDJY0LdAInRXd2foG/8zIbeA1wOL7+acRdA5xlZq/B3+k5AN/OW4F5+Ds+acTNhWK51Tl3V/Sz0cCD+I6+k4F3Oee2lzqm61sP4Gngimjq2aP4UUer9jVesZjRx8vw+/uzZrYF37E5Ft/hV/J4zrlbzexPwCozOwl/l29SKeKNBHnNkUXKmducWahacmhWbSfavm/Hf1kO163X4UdKptoWisV2zt1drn1bScfBUJU7L1ZDTuwvZq7zYta9P1m+8L3ZPwe+h3/6xY+B/RM/Px24AT9k6dAU4n8d+BdgUqI8oXf9ncBW4H5gQYnihdXKv40/YX2u4Oep1Rd/It2NX9H7bwp+dmap61qwj1cCVwP/C8wpV53L8Sp3G6qEtjbI8mXSHvPwAkbhR2HMBTrwd5Nqo/byKaAupbiGv1i7CTgaOAw/CmYdMBOoTSluHXANfjjv6dFnE/HDXqcCNSnFnQJcGbWjv+CH+B6EH/UzLa365uFF8dw6I/GzVvydtqeBg1KKeTu+g6smat9XA1eXoZ7Ja4h34ue231CK81w/8Q6IPq+P2vlvozY3P+t2kIcXOc2RRcqZ65xZpLwjPodm1XaiOKuAW/ELr95SrrYwiNipHjOVdhwMY9uWLS8WiTficuJeYuY6L2ZegEwrD28OJxb8hfJv8cPSm6PP6sKOTSn+48DD+HloU5KxgMn4Lw3jShjv3cDPovcfxHdGvCYRsyaN+tI33WA9/jGvVwLtiZ9PKnVdE787OeXgbuA2/N32/aPPatPcx2m/yt2G8t7WBlm2zNpjXl74uwPgR37cjB8Z9ifg8DLEPg2/4Npy4KdRwpyQcsz2KNbtUfzzo/N9Uxnq+3r8aJtm4Dz8EN/U42b5onhu/Sl9ubUePxqoZEPG9xKzKfpsfuLflqTDrZ+Yxa4h0oyXrOMBQEOIr1d+c2SRcuY2ZxYpa1Xk0KzaDntODW4BHsHnaisoQ8nbwiBiT4nO36ns20o6DoZRt7LmxUHki4rPiYOsZ+7yYrUvuPsMcKSZHeec24wfstQGfN7MXgtcbWapLCZlZicAO/B3X08FFpvZFOecM7NTgU/jG2dnCcNuAaaa2Vj84o8L8Ato3W1m7yCl+jpvHf5L3m3Aq4F/MLNPmdkHgE/iO0FKWdcgOeXgXvyd/VbgS2b2RlLcx2nLqA0NViZtbTAybo+54Jzrid7W4ocXfwY/3/d3ZQj/R/wUp6edcycCb3XObU0zoHNuLb7j40H8cXEmcK4rwVSXQXgcfxH7HeAS4Jwyxc1SsdzaTl9u/Q/gPufcQ2WIeXU09etsM2uMpg7sTjFmG3teQ4xOOV6o40L82kI457aVKF5Fy3mOLJTbnFmoGnJoxm0nOTUYwAFbothvAa5JsS3sLfZpwCdI95ipmONgGMqdF6shJ/YXM995Mevenyxf+FW6v4Zf/XhB9Nmh+CFRb6FgikqJY08CXhG9vwI/zO9j+Lujx6YRGz+/7kT88PeTo89GA7/E9zC3lzpmFCM53eCoaBtvxE83WAK0pbidi005mATcB5wFzM66HVZSG8p7Wxtk2TJrj3l7RefALwNzyxy3NfG+bNNvon0/njKPPMFPM3olieG3I/mVRW4dIOZpGcQseT2zqGMlv/KcI4uUNbc5s0hZR3wOzbLt8PKpwc/hR0Uswi8ye+BIjB3Fr5jjYBh1G/H5otx1zKqe+/oKw7iqlpkdiJ/TWQNcC+yP/1J+knNuZxnLcTn+5PZV59wXU4xT55x7ycym409orwc+ChzjUr4bG/Waz8d/CRkHdAFnuZTvjphZO37fvoRf+Xoq/qkuxznndqQZu5zK1YaGUJ7M2tpgZNUe88bM6l3fImzljm2u2pPQCJVFbq2GmHm5ZqlEecuRhfKeMwtVUw4td9sxs0bnXI+ZHY5/MuCt+Cfovcs596eRGjuKX1HHwVBUQ76olpj7ouo7XwDMbCa+t/4EYDvwT86535cpdvzlw8z+EbjZ+SHyacZsxA+ZPB3YCSxyzv0mzZhR3Hbg+8DPnXMfMP/8+lIOPdtb7Jn4XvMT8YuFXeLKM8UidVm0ocHKqq0NRpbtUaQaZJFbqyFmltcslSjPObJQnnNmoWrIoVm3HTM7Av9I3rX4xyz/uUpiV8xxMFTVkC+qJeZwqfMlIZpjaOXutc8iYZlZM374fY9zblMZ47Y6556K3tc653aVMbbh785YpfeeF8rzRU9WbW0wsmyPItUii9xaDTGzumapRHnOkYXynDMLVUMOzbLtROub/DvwRefco9USO4rfTIUcB8NRDfmiWmIOlTpfJBOabiB5ovYoIiIyPMqh6cl4anBmsUVGKnW+iIiIiIiIiIikqNofNS0iIiIiIiIikip1voiIiIiIiIiIpEidLyIiIiIiIiIiKVLni4iIiIiIiIhIitT5IiIiIiIiIiKSInW+iIiIiIiIiIikSJ0vIiIiIiIiIiIpUueLiIiIiIiIiEiK1PkiIiIiIiIiIpIidb6IiIiIiIiIiKRInS8iIiIiIiIiIilS54uIiIiIiIiISIrU+SIiIiIiIiIikiJ1voiIiIiIiIiIpEidLyIiIiIiIiIiKVLni4iIiIiIiIhIitT5IiIiIiIiIiKSInW+iIiIiIiIiIikSJ0vIiIiIiIiIiIpUueLiIiIiIiIiEiK1PkiIiIiIiIiIpIidb4IZvY6M3Nm9nD0WmNm3zCzxqzLJpXLzK6M2tXh0d/rzOwUM9tpZjOzLp9UnoJz1V/MbJ2ZfcnMaszsGDPbZmavy7qcUtnM7FNmNsHMxpjZFWa2NusyychQJC+2mtkdZvaomf1MuVGGai95cbyZ3Wxmj5jZb8zssKzLKpUpkRMXmtlvzeyvZrZU3xOHR50vEnPOdTjnOoD5wHuAkzIuklQoM6sDzgF+AiyJPr4buB6oz6hYMkJE56qDgWOA84HNwPeBpkwLJiPFp4G5wFrgo9kWRUaKfvLivwG/c87NBf4AXJFN6aTSFcmLdwK7nXMHATcBX8iweFLZPg1MBG4ELgcOBl4JvCvDMlUsdb5IMZOA3cCjWRdEKtab8V+ILwHebWZjnHPHAYdnWywZYaYCdcAZzrkpWRdGKp+Z3Rm9vQlYCFyYYXFkZHlZXgQ2Acujn2/Bn89E9kXIi98Fbog+U9uSYUnkxDuAXwIrnXO7gOdRmxoWbTSJmdnD0duZwO3AuuxKIxVuCXCdc+7PZrYaOB0/6kVkn0XnKgNeAj7rnLsr4yLJCOGce6OZOeBNzrnHzOzYrMskI8bL8qJz7mMAZvZ24CPAqRmWTypYkbx4VfT5ccBVwMUZFk8qVJGcWGtmV+JHhy4f4L9LEep8kVg05QgzGwesAt6PhinKEJnZdOBNwKvM7MPAeOA81PkiJRLOVSIilaC/vGhmS4H/BM7Ej+C7L8NiSgUrlhfN7OP4aSIfdM7dWP5SyUhiZs3AbcA44Fjn3JZsS1SZ1PkixbwI7AT2y7ogUpHOBe5wzp0MYGYtwFNm1gG8kGnJREQGthvQQoJSSkXzIrAMeBUwzzm3KcPyyQhjZu/Dr1l1hHNOywjIvgg5cQWwAVjknNuZbZEql9Z8kVh42hHwV/zB9fmMiyQVxswMf5H53fBZdEG5Ej/6RUQk734E3Glms7MuiFS+AfLiEUAzcG90DaYRolIq78HfZF8Rta07B/oPIv34EfBn4DjgSOAPUZv6YLbFqkzmnMu6DCIiIiIiIiIiI5ZGvoiIiIiIiIiIpEidLyIiIiIiIiIiKVLni4iIiIiIiIhIitT5IiIiIiIiIiKSInW+iIiIiIiIiIikSJ0vIiIiIiIiIiIpUueLiIiIiIiIiEiK/h8t/ST9ax8bugAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1152x1152 with 49 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cornerplot(result2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "59697"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sampler1.ncall"
    ]
@@ -605,22 +381,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.title(\"1-sine fit\")\n",
@@ -650,22 +413,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.title(\"2-sine fit\")\n",
@@ -722,20 +472,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "448.08936178184297"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "K = np.exp(result2['logz'] - result1['logz'])\n",
     "K"
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index aadb42d4..44d03f9e 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -395,7 +395,9 @@ def get_auxiliary_contbox_parameterization(
         posterior_samples = aux_results['samples'][:,-1]
 
     """
-    mask = np.logical_and(upoints > 0, upoints < 1)
+    upoints = np.asarray(upoints)
+    assert upoints.ndim == 2, ('expected 2d array for upoints, got shape: %s' % upoints.shape)
+    mask = np.logical_and(upoints > 0, upoints < 1).all(axis=1)
     assert np.all(mask), (
         'upoints must be between 0 and 1, have:', upoints[~mask,:])
     steps = 10**-(1.0 * np.arange(1, 8, 2))

From f12f974ce7a9ded3119668c5ae2776116bc3a131 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 18 Sep 2022 15:48:41 +0200
Subject: [PATCH 105/313] rst formatting; demote harmless warning

---
 ultranest/integrator.py |  2 +-
 ultranest/netiter.py    | 79 ++++++++++++++++++++---------------------
 2 files changed, 39 insertions(+), 42 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 20367e76..fd906377 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2016,7 +2016,7 @@ def _update_region(
                 good_region = nextregion.inside(active_u).all()
                 # assert good_region
                 if not good_region and self.log:
-                    self.logger.warning("Proposed region is inconsistent (maxr=%f,enlarge=%f) and will be skipped.", r, f)
+                    self.logger.debug("Proposed region is inconsistent (maxr=%g,enlarge=%g) and will be skipped.", r, f)
 
                 # avoid cases where every point is its own cluster,
                 # and even the largest cluster has fewer than x_dim points
diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index 46be9f82..cfc4afe9 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -29,26 +29,22 @@
 
 
 class TreeNode(object):
-    """Simple tree node."""
+    """Simple tree node.
 
-    def __init__(self, value=None, id=None, children=None):
-        """Define TreeNode.
+    Parameters
+    ----------
+    value: float
+        value used to order nodes (typically log-likelihood)
+    id: int
+        identifier, refers to the order of discovery and storage (PointPile)
+    children: list
+        children nodes, should be :class:`TreeNode` objects. if None, a empty list is used.
+    """
 
-        Parameters
-        ----------
-        value:
-            used to order nodes
-        id: int
-            refers to the order of discovery and storage (PointPile)
-        children: list of :class:TreeNode objects
-            children nodes. if None, a empty list is used.
-        """
+    def __init__(self, value=None, id=None, children=None):
         self.value = value
         self.id = id
-        if children is None:
-            self.children = []
-        else:
-            self.children = children
+        self.children = children or []
 
     def __str__(self, indent=0):
         """Visual representation of the node and its children (recursive)."""
@@ -171,8 +167,8 @@ def print_tree(roots, title='Tree:'):
 
     Parameters
     ----------
-    roots: list of :pyclass:TreeNode
-        tree
+    roots: list
+        list of `:py:class:TreeNode` specifying the roots of the tree.
     """
     print()
     print(title)
@@ -222,8 +218,8 @@ def dump_tree(filename, roots, pointpile):
     ----------
     filename: str
         output filename
-    roots: list of :pyclass:TreeNode
-        tree to store
+    roots: list
+        list of `:py:class:TreeNode` specifying the roots of the tree.
     pointpile: :class:PointPile
         information on the node points
     """
@@ -259,8 +255,8 @@ def count_tree(roots):
 
     Parameters
     ----------
-    roots: list of :pyclass:TreeNode
-        tree
+    roots: list
+        list of `:py:class:TreeNode` specifying the roots of the tree.
 
     Returns
     --------
@@ -291,8 +287,8 @@ def count_tree_between(roots, lo, hi):
 
     Parameters
     ----------
-    roots: list of :pyclass:TreeNode
-        tree
+    roots: list
+        list of `:py:class:TreeNode` specifying the roots of the tree.
     lo: float
         lower value threshold
     hi: float
@@ -335,7 +331,7 @@ def find_nodes_before(root, value):
 
     Parameters
     ----------
-    root: :pyclass:TreeNode
+    root: :py:class:`TreeNode`
         tree
     value: float
         selection threshold
@@ -384,9 +380,9 @@ def find_nodes_before(root, value):
 class PointPile(object):
     """A in-memory linearized storage of point coordinates.
 
-    :pyclass:TreeNodes only store the logL value and id,
+    :py:class:`TreeNode`s only store the logL value and id,
     which is the index in the point pile. The point pile stores
-    the point coordinates.
+    the point coordinates in u and p-space (transformed and untransformed).
     """
 
     def __init__(self, udim, pdim, chunksize=1000):
@@ -464,10 +460,7 @@ def make_node(self, value, u, p):
 
 
 class SingleCounter(object):
-    """Evidence log(Z) and posterior weight summation for a Nested Sampling tree."""
-
-    def __init__(self, random=False):
-        """Initialise counter.
+    """Evidence log(Z) and posterior weight summation for a Nested Sampling tree.
 
         Parameters
         ----------
@@ -476,6 +469,8 @@ def __init__(self, random=False):
             if True, draw a random sample
 
         """
+
+    def __init__(self, random=False):
         self.reset()
         self.random = random
 
@@ -568,7 +563,7 @@ def passing_node(self, node, parallel_nodes):
 
 
 class MultiCounter(object):
-    """Like SingleCounter, but bootstrap capable.
+    """Like :py:class:`SingleCounter`, but bootstrap capable.
 
     **Attributes**:
 
@@ -719,9 +714,9 @@ def passing_node(self, rootid, node, rootids, parallel_values):
 
         Parameters
         ----------
-        rootid: :pyclass:TreeNode
+        rootid: :py:class:`TreeNode`
             root node this `node` is from.
-        node: :pyclass:TreeNode
+        node: :py:class:`TreeNode`
             node being processed.
         rootids: array of ints
             for each parallel node, which root it belongs to.
@@ -856,9 +851,9 @@ def combine_results(saved_logl, saved_nodeids, pointpile, main_iterator, mpi_com
         loglikelihoods of dead points
     saved_nodeids: list of ints
         indices of dead points
-    pointpile: :pyclass:PointPile
+    pointpile: :py:class:`PointPile`
         Point pile.
-    main_iterator: :pyclass:BreadthFirstIterator
+    main_iterator: :py:class:`BreadthFirstIterator`
         iterator used
     mpi_comm:
         MPI communicator object, or None if MPI is not used.
@@ -971,9 +966,9 @@ def logz_sequence(root, pointpile, nbootstraps=12, random=True, onNode=None, ver
 
     Parameters
     ----------
-    root: :pyclass:TreeNode
+    root: :py:class:`TreeNode`
         Tree
-    pointpile: :pyclass:PointPile
+    pointpile: :py:class:`PointPile`
         Point pile
     nbootstraps: int
         Number of independent iterators
@@ -1039,14 +1034,16 @@ def logz_sequence(root, pointpile, nbootstraps=12, random=True, onNode=None, ver
         with np.errstate(invalid='ignore'):
             # first time they are all the same
             logzerr.append(main_iterator.logZerr_bs)
+        
+        nactive = len(active_values)
 
-        if len(np.unique(active_values)) == len(active_values) and len(node.children) > 0:
+        if len(np.unique(active_values)) == nactive and len(node.children) > 0:
             child_insertion_order = (active_values > node.children[0].value).sum()
-            insert_order.append(2 * (child_insertion_order + 1.) / len(active_values))
+            insert_order.append(2 * (child_insertion_order + 1.) / nactive)
         else:
             insert_order.append(np.nan)
 
-        nlive.append(len(active_values))
+        nlive.append(nactive)
         logvol.append(main_iterator.logVolremaining)
 
         niter += 1

From 655f3b430d30d0c464f99f81940fa880affd938d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 19 Sep 2022 03:47:14 +0200
Subject: [PATCH 106/313] rst formatting

---
 docs/issues.rst         |  4 +--
 ultranest/dychmc.py     | 68 ++++++++++++++++++++++-------------------
 ultranest/dyhmc.py      | 21 +++++++------
 ultranest/flatnuts.py   | 17 +++++++++--
 ultranest/hotstart.py   |  2 +-
 ultranest/netiter.py    | 16 +++++-----
 ultranest/stepfuncs.pyx |  2 ++
 7 files changed, 76 insertions(+), 54 deletions(-)

diff --git a/docs/issues.rst b/docs/issues.rst
index 7183dc3b..61f01798 100644
--- a/docs/issues.rst
+++ b/docs/issues.rst
@@ -20,7 +20,7 @@ To suppress the live point visualisations, set ``viz_callback=False`` in ``sampl
 
 To suppress the status line, set ``show_status=False`` in `sampler.run()``.
 
-See the documentation of `:py:meth:ultranest.ReactiveNestedSampler.run()`.
+See the documentation of :py:meth:`ultranest.ReactiveNestedSampler.run()`.
 
 To suppress the logging to stderr, set up a logging handler::
 
@@ -33,7 +33,7 @@ To suppress the logging to stderr, set up a logging handler::
     logger.addHandler(handler)
     logger.setLevel(logging.WARNING)
 
-You may want to alter the above to log to a file instead. See the `logging python module<https://docs.python.org/3/library/logging.html>`_ docs.
+You may want to alter the above to log to a file instead. See the `logging python module <https://docs.python.org/3/library/logging.html>`_ docs.
 
 To completely turn off logging, you can use::
 
diff --git a/ultranest/dychmc.py b/ultranest/dychmc.py
index 6bfb6ac8..13fb8396 100644
--- a/ultranest/dychmc.py
+++ b/ultranest/dychmc.py
@@ -8,20 +8,26 @@
 import matplotlib.pyplot as plt
 
 def stop_criterion(thetaminus, thetaplus, rminus, rplus):
-    """ Compute the stop condition in the main loop
-    dot(dtheta, rminus) >= 0 & dot(dtheta, rplus >= 0)
+    """Compute the stop condition in the main loop
 
-    INPUTS
-    ------
-    thetaminus, thetaplus: ndarray[float, ndim=1]
-        under and above position
-    rminus, rplus: ndarray[float, ndim=1]
-        under and above momentum
+    computes:
+    `dot(dtheta, rminus) >= 0 & dot(dtheta, rplus >= 0)`
 
-    OUTPUTS
+    Parameters
+    ------
+    thetaminus: ndarray[float, ndim=1]
+        under position
+    thetaplus: ndarray[float, ndim=1]
+        above position
+    rminus: ndarray[float, ndim=1]
+        under momentum
+    rplus: ndarray[float, ndim=1]
+        above momentum
+
+    Returns
     -------
     criterion: bool
-        return if the condition is valid
+        whether the condition is valid
     """
     dtheta = thetaplus - thetaminus
     #print("stop?", dtheta, rminus, rplus, np.dot(dtheta, rminus.T), np.dot(dtheta, rplus.T))
@@ -29,7 +35,7 @@ def stop_criterion(thetaminus, thetaplus, rminus, rplus):
 
 
 def step_or_reflect(theta, v, epsilon, transform, loglike, gradient, Lmin):
-    """Make a step from theta towards v with stepsize epsilon. """
+    """Make a step from `theta` towards `v` with stepsize `epsilon`. """
     # step in position:
     thetaprime = theta + epsilon * v
     # check if still inside
@@ -154,7 +160,7 @@ def build_tree(theta, v, direction, j, epsilon, transform, loglike, gradient, Lm
     return thetaminus, vminus, pminus, thetaplus, vplus, pplus, thetaprime, vprime, pprime, logpprime, sprime, can_continue, alphaprime, nalphaprime, nreflectprime
 
 def tree_sample(theta, p, logL, v, epsilon, transform, loglike, gradient, Lmin, maxheight=np.inf):
-    """Build NUTS-like tree of sampling path from theta towards p with stepsize epsilon."""
+    """Build NUTS-like tree of sampling path from `theta` towards `p` with stepsize `epsilon`."""
     # initialize the tree
     thetaminus = theta
     thetaplus = theta
@@ -225,7 +231,7 @@ def tree_sample(theta, p, logL, v, epsilon, transform, loglike, gradient, Lmin,
     return alpha, nreflect, nalpha, theta, p, logp, j
 
 def generate_uniform_direction(d, massmatrix):
-    """ draw unit direction vector according to mass matrix """
+    """Draw unit direction vector according to mass matrix."""
     momentum = np.random.multivariate_normal(np.zeros(d), np.dot(massmatrix, np.eye(d)))
     momentum /= (momentum**2).sum()**0.5
     return momentum
@@ -241,27 +247,27 @@ class DynamicCHMCSampler(object):
     A No-U-turn criterion and randomized doubling of forward or backward
     steps is used to avoid repeating circular trajectories.
     Because of this, the number of steps is dynamic.
+
+    Parameters
+    -----------
+    nsteps: int
+        number of accepted steps until the sample is considered independent.
+    adaptive_nsteps: False, 'proposal-distance', 'move-distance'
+        if not false, allow earlier termination than nsteps.
+        The 'proposal-distance' strategy stops when the sum of
+        all proposed vectors exceeds the mean distance
+        between pairs of live points.
+        As distance, the Mahalanobis distance is used.
+        The 'move-distance' strategy stops when the distance between
+        start point and current position exceeds the mean distance
+        between pairs of live points.
+    delta: float
+        step size
+    nudge: float
+        change in step size, must be >1.
     """
 
     def __init__(self, scale, nsteps, adaptive_nsteps=False, delta=0.9, nudge=1.04):
-        """Initialise sampler.
-
-        Parameters
-        -----------
-        nsteps: int
-            number of accepted steps until the sample is considered independent.
-
-        adaptive_nsteps: False, 'proposal-distance', 'move-distance'
-            if not false, allow earlier termination than nsteps.
-            The 'proposal-distance' strategy stops when the sum of
-            all proposed vectors exceeds the mean distance
-            between pairs of live points.
-            As distance, the Mahalanobis distance is used.
-            The 'move-distance' strategy stops when the distance between
-            start point and current position exceeds the mean distance
-            between pairs of live points.
-
-        """
         self.history = []
         self.nsteps = nsteps
         self.scale = scale
diff --git a/ultranest/dyhmc.py b/ultranest/dyhmc.py
index 2d3ff035..edf1f356 100644
--- a/ultranest/dyhmc.py
+++ b/ultranest/dyhmc.py
@@ -14,17 +14,21 @@ def stop_criterion(thetaminus, thetaplus, rminus, rplus):
     """ Compute the stop condition in the main loop
     dot(dtheta, rminus) >= 0 & dot(dtheta, rplus >= 0)
 
-    INPUTS
+    Parameters
     ------
-    thetaminus, thetaplus: ndarray[float, ndim=1]
-        under and above position
-    rminus, rplus: ndarray[float, ndim=1]
-        under and above momentum
-
-    OUTPUTS
+    thetaminus: ndarray[float, ndim=1]
+        under position
+    thetaplus: ndarray[float, ndim=1]
+        above position
+    rminus: ndarray[float, ndim=1]
+        under momentum
+    rplus: ndarray[float, ndim=1]
+        above momentum
+
+    Returns
     -------
     criterion: bool
-        return if the condition is valid
+        whether the condition is valid
     """
     dtheta = thetaplus - thetaminus
     return (np.dot(dtheta, rminus.T) >= 0) & (np.dot(dtheta, rplus.T) >= 0)
@@ -197,7 +201,6 @@ def find_beta_params_dynamic(d, u10):
     """ Define auxiliary distribution taking into account
     kinetic energy of a d-dimensional HMC.
     Make exp(-d/2) quantile to be at u=0.1, and 95% quantile at u=0.5. """
-    del d
 
     u50 = (u10 + 1) / 2.
 
diff --git a/ultranest/flatnuts.py b/ultranest/flatnuts.py
index 2f02373a..1817e98b 100644
--- a/ultranest/flatnuts.py
+++ b/ultranest/flatnuts.py
@@ -716,10 +716,21 @@ def next(self, Llast=None):
     
     def sample_chain_point(self, a, b):
         """
-        Gets a point on the track between a and b (inclusive)
-        returns tuple ((point coordinates, likelihood), is_independent)
-            where is_independent is always True
+        Gets a point on the track between a and b (inclusive).
         
+        Parameters
+        ----------
+        a: array
+            starting point
+        b: array
+            end point
+        
+        Returns
+        --------
+        newpoint: tuple
+            tuple of point_coordinates and loglikelihood
+        is_independent: bool
+            always True
         """
         if self.plot: 
             for i in range(a, b+1):
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index 44d03f9e..72775373 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -381,7 +381,7 @@ def get_auxiliary_contbox_parameterization(
         Takes d u-space coordinates, and returns d + 1 p-space parameters.
         The first d return coordinates are identical to what ``transform`` would return.
         The final coordinate is the log of the correction weight.
-      vectorized: bool
+    vectorized: bool
         whether the returned functions are vectorized
 
     Usage
diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index cfc4afe9..9743889f 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -121,9 +121,9 @@ def expand_children_of(self, rootid, node):
         Parameters
         ----------
         rootid: int
-            index of the root returned by the most recent call to :pyfunc:BreadthFirstIterator.next_node
+            index of the root returned by the most recent call to :py:method:`BreadthFirstIterator.next_node`
         node: :pyclass:TreeNode
-            node returned by the most recent call to :pyfunc:BreadthFirstIterator.next_node
+            node returned by the most recent call to :py:method:`BreadthFirstIterator.next_node`
         """
         # print("replacing %.1f" % node.value, len(node.children))
         i = self.next_index
@@ -168,7 +168,7 @@ def print_tree(roots, title='Tree:'):
     Parameters
     ----------
     roots: list
-        list of `:py:class:TreeNode` specifying the roots of the tree.
+        list of :py:class:`TreeNode` specifying the roots of the tree.
     """
     print()
     print(title)
@@ -219,7 +219,7 @@ def dump_tree(filename, roots, pointpile):
     filename: str
         output filename
     roots: list
-        list of `:py:class:TreeNode` specifying the roots of the tree.
+        list of :py:class:`TreeNode` specifying the roots of the tree.
     pointpile: :class:PointPile
         information on the node points
     """
@@ -256,7 +256,7 @@ def count_tree(roots):
     Parameters
     ----------
     roots: list
-        list of `:py:class:TreeNode` specifying the roots of the tree.
+        list of :py:class:`TreeNode` specifying the roots of the tree.
 
     Returns
     --------
@@ -288,7 +288,7 @@ def count_tree_between(roots, lo, hi):
     Parameters
     ----------
     roots: list
-        list of `:py:class:TreeNode` specifying the roots of the tree.
+        list of :py:class:`TreeNode` specifying the roots of the tree.
     lo: float
         lower value threshold
     hi: float
@@ -452,7 +452,7 @@ def make_node(self, value, u, p):
 
         Returns
         ---------
-        node: :pyclass:TreeNode
+        node: :py:class:`TreeNode`
             node
         """
         index = self.add(u, p)
@@ -985,7 +985,7 @@ def logz_sequence(root, pointpile, nbootstraps=12, random=True, onNode=None, ver
     Returns
     --------
     results: dict
-        Run information, see :pyfunc:combine_results
+        Run information, see :py:func:`combine_results`
     sequence: dict
         Each entry of the dictionary is results['niter'] long,
         and contains the state of information at that iteration.
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 07727fcf..23c0453f 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -127,6 +127,8 @@ cpdef evolve_update(
     success: np.array(nwalkers, dtype=bool)
         whether the walker accepts the point.
 
+    Notes
+    -----
     Writes to `currentt`, `current_left`, `current_right`, 
     `searching_left`, `searching_right`, `success`.
     """

From 6ef7ae19c06e4d4630cedfaa582fe001ea12275b Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 19 Sep 2022 09:26:57 +0200
Subject: [PATCH 107/313] rst formatting typos py:method instead of py:meth

---
 ultranest/netiter.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index 9743889f..4b88caa6 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -121,9 +121,9 @@ def expand_children_of(self, rootid, node):
         Parameters
         ----------
         rootid: int
-            index of the root returned by the most recent call to :py:method:`BreadthFirstIterator.next_node`
-        node: :pyclass:TreeNode
-            node returned by the most recent call to :py:method:`BreadthFirstIterator.next_node`
+            index of the root returned by the most recent call to :py:meth:`BreadthFirstIterator.next_node`
+        node: :py:class:`TreeNode`
+            node returned by the most recent call to :py:meth:`BreadthFirstIterator.next_node`
         """
         # print("replacing %.1f" % node.value, len(node.children))
         i = self.next_index

From 2abaa287212163b0f9fbd30d4bc981f7a5c45c71 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 19 Sep 2022 15:49:25 +0200
Subject: [PATCH 108/313] more rst formatting issues of directive

---
 ultranest/netiter.py        | 2 +-
 ultranest/popstepsampler.py | 2 +-
 ultranest/stepsampler.py    | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index 4b88caa6..9a07dc51 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -380,7 +380,7 @@ def find_nodes_before(root, value):
 class PointPile(object):
     """A in-memory linearized storage of point coordinates.
 
-    :py:class:`TreeNode`s only store the logL value and id,
+    :py:class:`TreeNode` objects only store the logL value and id,
     which is the index in the point pile. The point pile stores
     the point coordinates in u and p-space (transformed and untransformed).
     """
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 78221e8b..cbd8b945 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -81,7 +81,7 @@ def _setup(self, ndim):
         self.searching_right = np.zeros(self.popsize, dtype=bool)
 
     def step_back(self, Lmin):
-        """see `:func:ultranest.stepfuncs.step_back` :func:ultranest.stepfuncs.step_back."""
+        """see :py:func:`ultranest.stepfuncs.step_back`"""
         step_back(Lmin, self.allL, self.generation, self.currentt)
 
     def setup_start(self, us, Ls, starting):
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index d4890102..e5378986 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -662,7 +662,7 @@ def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=10, plot=Fals
             number of draws to attempt simultaneously.
         plot: bool
             whether to produce debug plots.
-        tregion: WrappingEllipsoid
+        tregion: :py:class:`WrappingEllipsoid`
             optional ellipsoid in transformed space for rejecting proposals
 
         """
@@ -1114,7 +1114,7 @@ def ellipsoid_bracket(ui, v, ellipsoid_center, ellipsoid_inv_axes, ellipsoid_rad
     ellipsoid_center: array
         center of the ellipsoid
     ellipsoid_inv_axes: array
-        ellipsoid axes matrix, as computed by :class:WrappingEllipsoid
+        ellipsoid axes matrix, as computed by :py:class:`WrappingEllipsoid`
     ellipsoid_radius_square: float
         square of the ellipsoid radius
 

From aad770bc9b8a88ebd55ba2911d07e23f71606f38 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 19 Sep 2022 23:14:03 +0200
Subject: [PATCH 109/313] rst reference formatting, found more issues with
 pystrict3

---
 ultranest/integrator.py |  7 ++++---
 ultranest/netiter.py    | 16 +++++++++++++---
 2 files changed, 17 insertions(+), 6 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index fd906377..b5b946e1 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2231,7 +2231,7 @@ def run(
         viz_callback: function
             callback function when region was rebuilt. Allows to
             show current state of the live points.
-            See :func:`nicelogger` or :class:`LivePointsWidget`.
+            See :py:func:`nicelogger` or :py:class:`LivePointsWidget`.
             If no output desired, set to False.
 
         dlogz: float
@@ -2278,9 +2278,10 @@ def run(
         insertion_test_window: int
             Number of iterations after which the insertion order test is reset.
 
-        region_class: MLFriends or RobustEllipsoidRegion
+        region_class: :py:class:`MLFriends` or :py:class:`RobustEllipsoidRegion` or :py:class:`SimpleRegion`
             Whether to use MLFriends+ellipsoidal+tellipsoidal region (better for multi-modal problems)
-            or just ellipsoidal sampling (faster for high-dimensional, gaussian-like problems).
+            or just ellipsoidal sampling (faster for high-dimensional, gaussian-like problems)
+            or a axis-aligned ellipsoid (fastest, to be combined with slice sampling).
         """
         for result in self.run_iter(
             update_interval_volume_fraction=update_interval_volume_fraction,
diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index 9a07dc51..d9478060 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -38,7 +38,7 @@ class TreeNode(object):
     id: int
         identifier, refers to the order of discovery and storage (PointPile)
     children: list
-        children nodes, should be :class:`TreeNode` objects. if None, a empty list is used.
+        children nodes, should be :py:class:`TreeNode` objects. if None, a empty list is used.
     """
 
     def __init__(self, value=None, id=None, children=None):
@@ -169,6 +169,8 @@ def print_tree(roots, title='Tree:'):
     ----------
     roots: list
         list of :py:class:`TreeNode` specifying the roots of the tree.
+    title: str
+        Print this string first.
     """
     print()
     print(title)
@@ -220,7 +222,7 @@ def dump_tree(filename, roots, pointpile):
         output filename
     roots: list
         list of :py:class:`TreeNode` specifying the roots of the tree.
-    pointpile: :class:PointPile
+    pointpile: :py:class:`PointPile`
         information on the node points
     """
     import h5py
@@ -596,6 +598,8 @@ def __init__(self, nroots, nbootstraps=10, random=False, check_insertion_order=F
         random: bool
             if False, use mean estimator for volume shrinkage
             if True, draw a random sample
+        check_insertion_order: bool
+            whether to run insertion order rank U test
 
         """
         allyes = np.ones(nroots, dtype=bool)
@@ -621,7 +625,13 @@ def __init__(self, nroots, nbootstraps=10, random=False, check_insertion_order=F
         self.reset(len(self.rootids))
 
     def reset(self, nentries):
-        """Reset counters/integrator."""
+        """Reset counters/integrator.
+
+        Parameters
+        ----------
+        nentries: int
+            number of iterators
+        """
         self.logweights = []
         self.istail = []
         self.logZ = -np.inf

From 2ebc622b11dd33a8089fbb53902b212473a9e679 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 20 Sep 2022 22:07:22 +0200
Subject: [PATCH 110/313] set seed to make docs reproducible

---
 docs/example-intrinsic-distribution.ipynb | 13 +++++++++++--
 docs/example-outliers.ipynb               |  5 +++--
 docs/example-sine-bayesian-workflow.ipynb |  4 ++--
 3 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/docs/example-intrinsic-distribution.ipynb b/docs/example-intrinsic-distribution.ipynb
index e6a1ab53..65e8dfbb 100644
--- a/docs/example-intrinsic-distribution.ipynb
+++ b/docs/example-intrinsic-distribution.ipynb
@@ -75,6 +75,15 @@
     "Each point represents a possible true solution of that galaxy.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(42)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -303,7 +312,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -317,7 +326,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/example-outliers.ipynb b/docs/example-outliers.ipynb
index 072be5e5..c631ef96 100644
--- a/docs/example-outliers.ipynb
+++ b/docs/example-outliers.ipynb
@@ -58,6 +58,7 @@
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
+    "np.random.seed(42)\n",
     "\n",
     "samples = []\n",
     "for i in range(n_data):\n",
@@ -316,7 +317,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -330,7 +331,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/example-sine-bayesian-workflow.ipynb b/docs/example-sine-bayesian-workflow.ipynb
index 3efb101a..5379449f 100644
--- a/docs/example-sine-bayesian-workflow.ipynb
+++ b/docs/example-sine-bayesian-workflow.ipynb
@@ -302,7 +302,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -316,7 +316,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

From fe270ee3bdf58e6af1922c072073a094ad44180e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 20 Sep 2022 22:39:28 +0200
Subject: [PATCH 111/313] add missing json libraries

---
 .../static/mcmc-demo/lib/canvas-5-polyfill.js |  12 +
 docs/static/mcmc-demo/lib/conrec.min.js       |  66 ++
 docs/static/mcmc-demo/lib/dat.gui.min.js      |  95 ++
 docs/static/mcmc-demo/lib/linalg.core.js      | 877 ++++++++++++++++++
 docs/static/mcmc-demo/lib/linalg.opt.js       |  99 ++
 5 files changed, 1149 insertions(+)
 create mode 100644 docs/static/mcmc-demo/lib/canvas-5-polyfill.js
 create mode 100644 docs/static/mcmc-demo/lib/conrec.min.js
 create mode 100644 docs/static/mcmc-demo/lib/dat.gui.min.js
 create mode 100644 docs/static/mcmc-demo/lib/linalg.core.js
 create mode 100644 docs/static/mcmc-demo/lib/linalg.opt.js

diff --git a/docs/static/mcmc-demo/lib/canvas-5-polyfill.js b/docs/static/mcmc-demo/lib/canvas-5-polyfill.js
new file mode 100644
index 00000000..f24b5821
--- /dev/null
+++ b/docs/static/mcmc-demo/lib/canvas-5-polyfill.js
@@ -0,0 +1,12 @@
+/**
+ * Copyright 2014 Google Inc. All rights reserved.
+ *
+ * Use of this source code is governed by a BSD-style
+ * license that can be found in the LICENSE file.
+ *
+ * @fileoverview Description of this file.
+ *
+ * A polyfill for HTML Canvas features, including
+ * Path2D support.
+ */
+void 0==CanvasRenderingContext2D.prototype.ellipse&&(CanvasRenderingContext2D.prototype.ellipse=function(t,r,n,e,o,a,i,s){this.save(),this.translate(t,r),this.rotate(o),this.scale(n,e),this.arc(0,0,1,a,i,s),this.restore()}),"function"!=typeof Path2D&&!function(){function t(t){if(this.ops_=[],void 0!=t)if("string"==typeof t)try{this.ops_=parser.parse(t)}catch(r){}else{if(!t.hasOwnProperty("ops_"))throw"Error: "+typeof t+"is not a valid argument to Path";this.ops_=t.ops_.slice(0)}}function r(t){return function(){this.ops_.push({type:t,args:Array.prototype.slice.call(arguments,0)})}}parser=function(){function t(t,r){function n(){this.constructor=t}n.prototype=r.prototype,t.prototype=new n}function r(t,r,n,e,o,a){this.message=t,this.expected=r,this.found=n,this.offset=e,this.line=o,this.column=a,this.name="SyntaxError"}function n(t){function n(r){function n(r,n,e){var o,a;for(o=n;e>o;o++)a=t.charAt(o),"\n"===a?(r.seenCR||r.line++,r.column=1,r.seenCR=!1):"\r"===a||"\u2028"===a||"\u2029"===a?(r.line++,r.column=1,r.seenCR=!0):(r.column++,r.seenCR=!1)}return Ar!==r&&(Ar>r&&(Ar=0,xr={line:1,column:1,seenCR:!1}),n(xr,Ar,r),Ar=r),xr}function e(t){mr>Cr||(Cr>mr&&(mr=Cr,Pr=[]),Pr.push(t))}function o(e,o,a){function i(t){var r=1;for(t.sort(function(t,r){return t.description<r.description?-1:t.description>r.description?1:0});r<t.length;)t[r-1]===t[r]?t.splice(r,1):r++}function s(t,r){function n(t){function r(t){return t.charCodeAt(0).toString(16).toUpperCase()}return t.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\x08/g,"\\b").replace(/\t/g,"\\t").replace(/\n/g,"\\n").replace(/\f/g,"\\f").replace(/\r/g,"\\r").replace(/[\x00-\x07\x0B\x0E\x0F]/g,function(t){return"\\x0"+r(t)}).replace(/[\x10-\x1F\x80-\xFF]/g,function(t){return"\\x"+r(t)}).replace(/[\u0180-\u0FFF]/g,function(t){return"\\u0"+r(t)}).replace(/[\u1080-\uFFFF]/g,function(t){return"\\u"+r(t)})}var e,o,a,i=new Array(t.length);for(a=0;a<t.length;a++)i[a]=t[a].description;return e=t.length>1?i.slice(0,-1).join(", ")+" or "+i[t.length-1]:i[0],o=r?'"'+n(r)+'"':"end of input","Expected "+e+" but "+o+" found."}var u=n(a),c=a<t.length?t.charAt(a):null;return null!==o&&i(o),new r(null!==e?e:s(o,c),o,c,a,u.line,u.column)}function a(){var t,r,n,e,o;for(t=Cr,r=[],n=B();n!==lt;)r.push(n),n=B();if(r!==lt)if(n=i(),n===lt&&(n=gt),n!==lt){for(e=[],o=B();o!==lt;)e.push(o),o=B();e!==lt?(_r=t,r=yt(n),t=r):(Cr=t,t=vt)}else Cr=t,t=vt;else Cr=t,t=vt;return t}function i(){var 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diff --git a/docs/static/mcmc-demo/lib/conrec.min.js b/docs/static/mcmc-demo/lib/conrec.min.js
new file mode 100644
index 00000000..ff5796a2
--- /dev/null
+++ b/docs/static/mcmc-demo/lib/conrec.min.js
@@ -0,0 +1,66 @@
+/**
+ * Copyright (c) 2010, Jason Davies.
+ *
+ * All rights reserved.  This code is based on Bradley White's Java version,
+ * which is in turn based on Nicholas Yue's C++ version, which in turn is based
+ * on Paul D. Bourke's original Fortran version.  See below for the respective
+ * copyright notices.
+ *
+ * See http://local.wasp.uwa.edu.au/~pbourke/papers/conrec/ for the original
+ * paper by Paul D. Bourke.
+ *
+ * The vector conversion code is based on http://apptree.net/conrec.htm by
+ * Graham Cox.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above copyright
+ *       notice, this list of conditions and the following disclaimer in the
+ *       documentation and/or other materials provided with the distribution.
+ *     * Neither the name of the <organization> nor the
+ *       names of its contributors may be used to endorse or promote products
+ *       derived from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ * ARE DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
+ * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+/*
+ * Copyright (c) 1996-1997 Nicholas Yue
+ *
+ * This software is copyrighted by Nicholas Yue. This code is based on Paul D.
+ * Bourke's CONREC.F routine.
+ *
+ * The authors hereby grant permission to use, copy, and distribute this
+ * software and its documentation for any purpose, provided that existing
+ * copyright notices are retained in all copies and that this notice is
+ * included verbatim in any distributions. Additionally, the authors grant
+ * permission to modify this software and its documentation for any purpose,
+ * provided that such modifications are not distributed without the explicit
+ * consent of the authors and that existing copyright notices are retained in
+ * all copies. Some of the algorithms implemented by this software are
+ * patented, observe all applicable patent law.
+ *
+ * IN NO EVENT SHALL THE AUTHORS OR DISTRIBUTORS BE LIABLE TO ANY PARTY FOR
+ * DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT
+ * OF THE USE OF THIS SOFTWARE, ITS DOCUMENTATION, OR ANY DERIVATIVES THEREOF,
+ * EVEN IF THE AUTHORS HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * THE AUTHORS AND DISTRIBUTORS SPECIFICALLY DISCLAIM ANY WARRANTIES,
+ * INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT.  THIS SOFTWARE IS
+ * PROVIDED ON AN "AS IS" BASIS, AND THE AUTHORS AND DISTRIBUTORS HAVE NO
+ * OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR
+ * MODIFICATIONS.
+ */
+!function(e){function t(e,t){var a=e.x-t.x,r=e.y-t.y;return i>a*a+r*r}function a(e){for(var t=e.head;t;){var a=t.next;t.next=t.prev,t.prev=a,t=a}var a=e.head;e.head=e.tail,e.tail=a}function r(e){this.level=e,this.s=null,this.count=0}function n(e){if(e)this.drawContour=e;else{var t=this;t.contours={},this.drawContour=function(e,a,n,i,s,h){var l=t.contours[h];l||(l=t.contours[h]=new r(s)),l.addSegment({x:e,y:a},{x:n,y:i})},this.contourList=function(){var e=[],a=t.contours;for(var r in a)for(var n=a[r].s,i=a[r].level;n;){var s=n.head,h=[];for(h.level=i,h.k=r;s&&s.p;)h.push(s.p),s=s.next;e.push(h),n=n.next}return e.sort(function(e,t){return e.k-t.k}),e}}this.h=new Array(5),this.sh=new Array(5),this.xh=new Array(5),this.yh=new Array(5)}e.Conrec=n;var i=1e-10;r.prototype.remove_seq=function(e){e.prev?e.prev.next=e.next:this.s=e.next,e.next&&(e.next.prev=e.prev),--this.count},r.prototype.addSegment=function(e,r){for(var n=this.s,i=null,s=null,h=!1,l=!1;n&&(null==i&&(t(e,n.head.p)?(i=n,h=!0):t(e,n.tail.p)&&(i=n)),null==s&&(t(r,n.head.p)?(s=n,l=!0):t(r,n.tail.p)&&(s=n)),null==s||null==i);)n=n.next;var o=(null!=i?1:0)|(null!=s?2:0);switch(o){case 0:var u={p:e,prev:null},v={p:r,next:null};u.next=v,v.prev=u,i={head:u,tail:v,next:this.s,prev:null,closed:!1},this.s&&(this.s.prev=i),this.s=i,++this.count;break;case 1:var c={p:r};h?(c.next=i.head,c.prev=null,i.head.prev=c,i.head=c):(c.next=null,c.prev=i.tail,i.tail.next=c,i.tail=c);break;case 2:var c={p:e};l?(c.next=s.head,c.prev=null,s.head.prev=c,s.head=c):(c.next=null,c.prev=s.tail,s.tail.next=c,s.tail=c);break;case 3:if(i===s){var c={p:i.tail.p,next:i.head,prev:null};i.head.prev=c,i.head=c,i.closed=!0;break}switch((h?1:0)|(l?2:0)){case 0:a(i);case 1:s.tail.next=i.head,i.head.prev=s.tail,s.tail=i.tail,this.remove_seq(i);break;case 3:a(i);case 2:i.tail.next=s.head,s.head.prev=i.tail,i.tail=s.tail,this.remove_seq(s)}}},n.prototype.contour=function(e,t,a,r,n,s,h,l,o){var u=this.h,v=this.sh,c=this.xh,p=this.yh,x=this.drawContour;this.contours={};for(var d,f,k,y,b,w,m=function(e,t){return(u[t]*c[e]-u[e]*c[t])/(u[t]-u[e])},M=function(e,t){return(u[t]*p[e]-u[e]*p[t])/(u[t]-u[e])},A=0,C=0,q=0,_=0,g=[0,1,1,0],S=[0,0,1,1],L=[[[0,0,8],[0,2,5],[7,6,9]],[[0,3,4],[1,3,1],[4,3,0]],[[9,6,7],[5,2,0],[8,0,0]]],j=n-1;j>=r;j--)for(var z=t;a-1>=z;z++){var B,D;if(B=Math.min(e[z][j],e[z][j+1]),D=Math.min(e[z+1][j],e[z+1][j+1]),b=Math.min(B,D),B=Math.max(e[z][j],e[z][j+1]),D=Math.max(e[z+1][j],e[z+1][j+1]),w=Math.max(B,D),w>=o[0]&&b<=o[l-1])for(var E=0;l>E;E++)if(o[E]>=b&&o[E]<=w){for(var F=4;F>=0;F--)F>0?(u[F]=e[z+g[F-1]][j+S[F-1]]-o[E],c[F]=s[z+g[F-1]],p[F]=h[j+S[F-1]]):(u[0]=.25*(u[1]+u[2]+u[3]+u[4]),c[0]=.5*(s[z]+s[z+1]),p[0]=.5*(h[j]+h[j+1])),u[F]>i?v[F]=1:u[F]<-i?v[F]=-1:v[F]=0;for(F=1;4>=F;F++)if(d=F,f=0,k=4!=F?F+1:1,y=L[v[d]+1][v[f]+1][v[k]+1],0!=y){switch(y){case 1:A=c[d],q=p[d],C=c[f],_=p[f];break;case 2:A=c[f],q=p[f],C=c[k],_=p[k];break;case 3:A=c[k],q=p[k],C=c[d],_=p[d];break;case 4:A=c[d],q=p[d],C=m(f,k),_=M(f,k);break;case 5:A=c[f],q=p[f],C=m(k,d),_=M(k,d);break;case 6:A=c[k],q=p[k],C=m(d,f),_=M(d,f);break;case 7:A=m(d,f),q=M(d,f),C=m(f,k),_=M(f,k);break;case 8:A=m(f,k),q=M(f,k),C=m(k,d),_=M(k,d);break;case 9:A=m(k,d),q=M(k,d),C=m(d,f),_=M(d,f)}x(A,q,C,_,o[E],E)}}}}}("undefined"!=typeof exports?exports:window);
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diff --git a/docs/static/mcmc-demo/lib/dat.gui.min.js b/docs/static/mcmc-demo/lib/dat.gui.min.js
new file mode 100644
index 00000000..35733d96
--- /dev/null
+++ b/docs/static/mcmc-demo/lib/dat.gui.min.js
@@ -0,0 +1,95 @@
+/**
+ * dat-gui JavaScript Controller Library
+ * http://code.google.com/p/dat-gui
+ *
+ * Copyright 2011 Data Arts Team, Google Creative Lab
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var dat=dat||{};dat.gui=dat.gui||{};dat.utils=dat.utils||{};dat.controllers=dat.controllers||{};dat.dom=dat.dom||{};dat.color=dat.color||{};dat.utils.css=function(){return{load:function(f,a){a=a||document;var d=a.createElement("link");d.type="text/css";d.rel="stylesheet";d.href=f;a.getElementsByTagName("head")[0].appendChild(d)},inject:function(f,a){a=a||document;var d=document.createElement("style");d.type="text/css";d.innerHTML=f;a.getElementsByTagName("head")[0].appendChild(d)}}}();
+dat.utils.common=function(){var f=Array.prototype.forEach,a=Array.prototype.slice;return{BREAK:{},extend:function(d){this.each(a.call(arguments,1),function(a){for(var c in a)this.isUndefined(a[c])||(d[c]=a[c])},this);return d},defaults:function(d){this.each(a.call(arguments,1),function(a){for(var c in a)this.isUndefined(d[c])&&(d[c]=a[c])},this);return d},compose:function(){var d=a.call(arguments);return function(){for(var e=a.call(arguments),c=d.length-1;0<=c;c--)e=[d[c].apply(this,e)];return e[0]}},
+each:function(a,e,c){if(a)if(f&&a.forEach&&a.forEach===f)a.forEach(e,c);else if(a.length===a.length+0)for(var b=0,p=a.length;b<p&&!(b in a&&e.call(c,a[b],b)===this.BREAK);b++);else for(b in a)if(e.call(c,a[b],b)===this.BREAK)break},defer:function(a){setTimeout(a,0)},toArray:function(d){return d.toArray?d.toArray():a.call(d)},isUndefined:function(a){return void 0===a},isNull:function(a){return null===a},isNaN:function(a){return a!==a},isArray:Array.isArray||function(a){return a.constructor===Array},
+isObject:function(a){return a===Object(a)},isNumber:function(a){return a===a+0},isString:function(a){return a===a+""},isBoolean:function(a){return!1===a||!0===a},isFunction:function(a){return"[object Function]"===Object.prototype.toString.call(a)}}}();
+dat.controllers.Controller=function(f){var a=function(a,e){this.initialValue=a[e];this.domElement=document.createElement("div");this.object=a;this.property=e;this.__onFinishChange=this.__onChange=void 0};f.extend(a.prototype,{onChange:function(a){this.__onChange=a;return this},onFinishChange:function(a){this.__onFinishChange=a;return this},setValue:function(a){this.object[this.property]=a;this.__onChange&&this.__onChange.call(this,a);this.updateDisplay();return this},getValue:function(){return this.object[this.property]},
+updateDisplay:function(){return this},isModified:function(){return this.initialValue!==this.getValue()}});return a}(dat.utils.common);
+dat.dom.dom=function(f){function a(b){if("0"===b||f.isUndefined(b))return 0;b=b.match(e);return f.isNull(b)?0:parseFloat(b[1])}var d={};f.each({HTMLEvents:["change"],MouseEvents:["click","mousemove","mousedown","mouseup","mouseover"],KeyboardEvents:["keydown"]},function(b,a){f.each(b,function(b){d[b]=a})});var e=/(\d+(\.\d+)?)px/,c={makeSelectable:function(b,a){void 0!==b&&void 0!==b.style&&(b.onselectstart=a?function(){return!1}:function(){},b.style.MozUserSelect=a?"auto":"none",b.style.KhtmlUserSelect=
+a?"auto":"none",b.unselectable=a?"on":"off")},makeFullscreen:function(b,a,c){f.isUndefined(a)&&(a=!0);f.isUndefined(c)&&(c=!0);b.style.position="absolute";a&&(b.style.left=0,b.style.right=0);c&&(b.style.top=0,b.style.bottom=0)},fakeEvent:function(b,a,c,e){c=c||{};var r=d[a];if(!r)throw Error("Event type "+a+" not supported.");var n=document.createEvent(r);switch(r){case "MouseEvents":n.initMouseEvent(a,c.bubbles||!1,c.cancelable||!0,window,c.clickCount||1,0,0,c.x||c.clientX||0,c.y||c.clientY||0,!1,
+!1,!1,!1,0,null);break;case "KeyboardEvents":r=n.initKeyboardEvent||n.initKeyEvent;f.defaults(c,{cancelable:!0,ctrlKey:!1,altKey:!1,shiftKey:!1,metaKey:!1,keyCode:void 0,charCode:void 0});r(a,c.bubbles||!1,c.cancelable,window,c.ctrlKey,c.altKey,c.shiftKey,c.metaKey,c.keyCode,c.charCode);break;default:n.initEvent(a,c.bubbles||!1,c.cancelable||!0)}f.defaults(n,e);b.dispatchEvent(n)},bind:function(a,e,d,f){a.addEventListener?a.addEventListener(e,d,f||!1):a.attachEvent&&a.attachEvent("on"+e,d);return c},
+unbind:function(a,e,d,f){a.removeEventListener?a.removeEventListener(e,d,f||!1):a.detachEvent&&a.detachEvent("on"+e,d);return c},addClass:function(a,e){if(void 0===a.className)a.className=e;else if(a.className!==e){var d=a.className.split(/ +/);-1==d.indexOf(e)&&(d.push(e),a.className=d.join(" ").replace(/^\s+/,"").replace(/\s+$/,""))}return c},removeClass:function(a,e){if(e){if(void 0!==a.className)if(a.className===e)a.removeAttribute("class");else{var d=a.className.split(/ +/),f=d.indexOf(e);-1!=
+f&&(d.splice(f,1),a.className=d.join(" "))}}else a.className=void 0;return c},hasClass:function(a,c){return(new RegExp("(?:^|\\s+)"+c+"(?:\\s+|$)")).test(a.className)||!1},getWidth:function(b){b=getComputedStyle(b);return a(b["border-left-width"])+a(b["border-right-width"])+a(b["padding-left"])+a(b["padding-right"])+a(b.width)},getHeight:function(b){b=getComputedStyle(b);return a(b["border-top-width"])+a(b["border-bottom-width"])+a(b["padding-top"])+a(b["padding-bottom"])+a(b.height)},getOffset:function(a){var c=
+{left:0,top:0};if(a.offsetParent){do c.left+=a.offsetLeft,c.top+=a.offsetTop;while(a=a.offsetParent)}return c},isActive:function(a){return a===document.activeElement&&(a.type||a.href)}};return c}(dat.utils.common);
+dat.controllers.OptionController=function(f,a,d){var e=function(c,b,f){e.superclass.call(this,c,b);var q=this;this.__select=document.createElement("select");if(d.isArray(f)){var l={};d.each(f,function(a){l[a]=a});f=l}d.each(f,function(a,b){var c=document.createElement("option");c.innerHTML=b;c.setAttribute("value",a);q.__select.appendChild(c)});this.updateDisplay();a.bind(this.__select,"change",function(){q.setValue(this.options[this.selectedIndex].value)});this.domElement.appendChild(this.__select)};
+e.superclass=f;d.extend(e.prototype,f.prototype,{setValue:function(a){a=e.superclass.prototype.setValue.call(this,a);this.__onFinishChange&&this.__onFinishChange.call(this,this.getValue());return a},updateDisplay:function(){this.__select.value=this.getValue();return e.superclass.prototype.updateDisplay.call(this)}});return e}(dat.controllers.Controller,dat.dom.dom,dat.utils.common);
+dat.controllers.NumberController=function(f,a){function d(a){a=a.toString();return-1<a.indexOf(".")?a.length-a.indexOf(".")-1:0}var e=function(c,b,f){e.superclass.call(this,c,b);f=f||{};this.__min=f.min;this.__max=f.max;this.__step=f.step;a.isUndefined(this.__step)?this.__impliedStep=0==this.initialValue?1:Math.pow(10,Math.floor(Math.log(this.initialValue)/Math.LN10))/10:this.__impliedStep=this.__step;this.__precision=d(this.__impliedStep)};e.superclass=f;a.extend(e.prototype,f.prototype,{setValue:function(a){void 0!==
+this.__min&&a<this.__min?a=this.__min:void 0!==this.__max&&a>this.__max&&(a=this.__max);void 0!==this.__step&&0!=a%this.__step&&(a=Math.round(a/this.__step)*this.__step);return e.superclass.prototype.setValue.call(this,a)},min:function(a){this.__min=a;return this},max:function(a){this.__max=a;return this},step:function(a){this.__impliedStep=this.__step=a;this.__precision=d(a);return this}});return e}(dat.controllers.Controller,dat.utils.common);
+dat.controllers.NumberControllerBox=function(f,a,d){var e=function(c,b,f){function q(){var a=parseFloat(n.__input.value);d.isNaN(a)||n.setValue(a)}function l(a){var b=u-a.clientY;n.setValue(n.getValue()+b*n.__impliedStep);u=a.clientY}function r(){a.unbind(window,"mousemove",l);a.unbind(window,"mouseup",r)}this.__truncationSuspended=!1;e.superclass.call(this,c,b,f);var n=this,u;this.__input=document.createElement("input");this.__input.setAttribute("type","text");a.bind(this.__input,"change",q);a.bind(this.__input,
+"blur",function(){q();n.__onFinishChange&&n.__onFinishChange.call(n,n.getValue())});a.bind(this.__input,"mousedown",function(b){a.bind(window,"mousemove",l);a.bind(window,"mouseup",r);u=b.clientY});a.bind(this.__input,"keydown",function(a){13===a.keyCode&&(n.__truncationSuspended=!0,this.blur(),n.__truncationSuspended=!1)});this.updateDisplay();this.domElement.appendChild(this.__input)};e.superclass=f;d.extend(e.prototype,f.prototype,{updateDisplay:function(){var a=this.__input,b;if(this.__truncationSuspended)b=
+this.getValue();else{b=this.getValue();var d=Math.pow(10,this.__precision);b=Math.round(b*d)/d}a.value=b;return e.superclass.prototype.updateDisplay.call(this)}});return e}(dat.controllers.NumberController,dat.dom.dom,dat.utils.common);
+dat.controllers.NumberControllerSlider=function(f,a,d,e,c){function b(a,b,c,e,d){return e+(a-b)/(c-b)*(d-e)}var p=function(c,e,d,f,u){function A(c){c.preventDefault();var e=a.getOffset(k.__background),d=a.getWidth(k.__background);k.setValue(b(c.clientX,e.left,e.left+d,k.__min,k.__max));return!1}function g(){a.unbind(window,"mousemove",A);a.unbind(window,"mouseup",g);k.__onFinishChange&&k.__onFinishChange.call(k,k.getValue())}p.superclass.call(this,c,e,{min:d,max:f,step:u});var k=this;this.__background=
+document.createElement("div");this.__foreground=document.createElement("div");a.bind(this.__background,"mousedown",function(b){a.bind(window,"mousemove",A);a.bind(window,"mouseup",g);A(b)});a.addClass(this.__background,"slider");a.addClass(this.__foreground,"slider-fg");this.updateDisplay();this.__background.appendChild(this.__foreground);this.domElement.appendChild(this.__background)};p.superclass=f;p.useDefaultStyles=function(){d.inject(c)};e.extend(p.prototype,f.prototype,{updateDisplay:function(){var a=
+(this.getValue()-this.__min)/(this.__max-this.__min);this.__foreground.style.width=100*a+"%";return p.superclass.prototype.updateDisplay.call(this)}});return p}(dat.controllers.NumberController,dat.dom.dom,dat.utils.css,dat.utils.common,"/**\n * dat-gui JavaScript Controller Library\n * http://code.google.com/p/dat-gui\n *\n * Copyright 2011 Data Arts Team, Google Creative Lab\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n */\n\n.slider {\n  box-shadow: inset 0 2px 4px rgba(0,0,0,0.15);\n  height: 1em;\n  border-radius: 1em;\n  background-color: #eee;\n  padding: 0 0.5em;\n  overflow: hidden;\n}\n\n.slider-fg {\n  padding: 1px 0 2px 0;\n  background-color: #aaa;\n  height: 1em;\n  margin-left: -0.5em;\n  padding-right: 0.5em;\n  border-radius: 1em 0 0 1em;\n}\n\n.slider-fg:after {\n  display: inline-block;\n  border-radius: 1em;\n  background-color: #fff;\n  border:  1px solid #aaa;\n  content: '';\n  float: right;\n  margin-right: -1em;\n  margin-top: -1px;\n  height: 0.9em;\n  width: 0.9em;\n}");
+dat.controllers.FunctionController=function(f,a,d){var e=function(c,b,d){e.superclass.call(this,c,b);var f=this;this.__button=document.createElement("div");this.__button.innerHTML=void 0===d?"Fire":d;a.bind(this.__button,"click",function(a){a.preventDefault();f.fire();return!1});a.addClass(this.__button,"button");this.domElement.appendChild(this.__button)};e.superclass=f;d.extend(e.prototype,f.prototype,{fire:function(){this.__onChange&&this.__onChange.call(this);this.getValue().call(this.object);
+this.__onFinishChange&&this.__onFinishChange.call(this,this.getValue())}});return e}(dat.controllers.Controller,dat.dom.dom,dat.utils.common);
+dat.controllers.BooleanController=function(f,a,d){var e=function(c,b){e.superclass.call(this,c,b);var d=this;this.__prev=this.getValue();this.__checkbox=document.createElement("input");this.__checkbox.setAttribute("type","checkbox");a.bind(this.__checkbox,"change",function(){d.setValue(!d.__prev)},!1);this.domElement.appendChild(this.__checkbox);this.updateDisplay()};e.superclass=f;d.extend(e.prototype,f.prototype,{setValue:function(a){a=e.superclass.prototype.setValue.call(this,a);this.__onFinishChange&&
+this.__onFinishChange.call(this,this.getValue());this.__prev=this.getValue();return a},updateDisplay:function(){!0===this.getValue()?(this.__checkbox.setAttribute("checked","checked"),this.__checkbox.checked=!0):this.__checkbox.checked=!1;return e.superclass.prototype.updateDisplay.call(this)}});return e}(dat.controllers.Controller,dat.dom.dom,dat.utils.common);
+dat.color.toString=function(f){return function(a){if(1==a.a||f.isUndefined(a.a)){for(a=a.hex.toString(16);6>a.length;)a="0"+a;return"#"+a}return"rgba("+Math.round(a.r)+","+Math.round(a.g)+","+Math.round(a.b)+","+a.a+")"}}(dat.utils.common);
+dat.color.interpret=function(f,a){var d,e,c=[{litmus:a.isString,conversions:{THREE_CHAR_HEX:{read:function(a){a=a.match(/^#([A-F0-9])([A-F0-9])([A-F0-9])$/i);return null===a?!1:{space:"HEX",hex:parseInt("0x"+a[1].toString()+a[1].toString()+a[2].toString()+a[2].toString()+a[3].toString()+a[3].toString())}},write:f},SIX_CHAR_HEX:{read:function(a){a=a.match(/^#([A-F0-9]{6})$/i);return null===a?!1:{space:"HEX",hex:parseInt("0x"+a[1].toString())}},write:f},CSS_RGB:{read:function(a){a=a.match(/^rgb\(\s*(.+)\s*,\s*(.+)\s*,\s*(.+)\s*\)/);
+return null===a?!1:{space:"RGB",r:parseFloat(a[1]),g:parseFloat(a[2]),b:parseFloat(a[3])}},write:f},CSS_RGBA:{read:function(a){a=a.match(/^rgba\(\s*(.+)\s*,\s*(.+)\s*,\s*(.+)\s*\,\s*(.+)\s*\)/);return null===a?!1:{space:"RGB",r:parseFloat(a[1]),g:parseFloat(a[2]),b:parseFloat(a[3]),a:parseFloat(a[4])}},write:f}}},{litmus:a.isNumber,conversions:{HEX:{read:function(a){return{space:"HEX",hex:a,conversionName:"HEX"}},write:function(a){return a.hex}}}},{litmus:a.isArray,conversions:{RGB_ARRAY:{read:function(a){return 3!=
+a.length?!1:{space:"RGB",r:a[0],g:a[1],b:a[2]}},write:function(a){return[a.r,a.g,a.b]}},RGBA_ARRAY:{read:function(a){return 4!=a.length?!1:{space:"RGB",r:a[0],g:a[1],b:a[2],a:a[3]}},write:function(a){return[a.r,a.g,a.b,a.a]}}}},{litmus:a.isObject,conversions:{RGBA_OBJ:{read:function(b){return a.isNumber(b.r)&&a.isNumber(b.g)&&a.isNumber(b.b)&&a.isNumber(b.a)?{space:"RGB",r:b.r,g:b.g,b:b.b,a:b.a}:!1},write:function(a){return{r:a.r,g:a.g,b:a.b,a:a.a}}},RGB_OBJ:{read:function(b){return a.isNumber(b.r)&&
+a.isNumber(b.g)&&a.isNumber(b.b)?{space:"RGB",r:b.r,g:b.g,b:b.b}:!1},write:function(a){return{r:a.r,g:a.g,b:a.b}}},HSVA_OBJ:{read:function(b){return a.isNumber(b.h)&&a.isNumber(b.s)&&a.isNumber(b.v)&&a.isNumber(b.a)?{space:"HSV",h:b.h,s:b.s,v:b.v,a:b.a}:!1},write:function(a){return{h:a.h,s:a.s,v:a.v,a:a.a}}},HSV_OBJ:{read:function(b){return a.isNumber(b.h)&&a.isNumber(b.s)&&a.isNumber(b.v)?{space:"HSV",h:b.h,s:b.s,v:b.v}:!1},write:function(a){return{h:a.h,s:a.s,v:a.v}}}}}];return function(){e=!1;
+var b=1<arguments.length?a.toArray(arguments):arguments[0];a.each(c,function(c){if(c.litmus(b))return a.each(c.conversions,function(c,f){d=c.read(b);if(!1===e&&!1!==d)return e=d,d.conversionName=f,d.conversion=c,a.BREAK}),a.BREAK});return e}}(dat.color.toString,dat.utils.common);
+dat.GUI=dat.gui.GUI=function(f,a,d,e,c,b,p,q,l,r,n,u,A,g,k){function v(a,b,h,d){if(void 0===b[h])throw Error("Object "+b+' has no property "'+h+'"');d.color?b=new n(b,h):(b=[b,h].concat(d.factoryArgs),b=e.apply(a,b));d.before instanceof c&&(d.before=d.before.__li);y(a,b);g.addClass(b.domElement,"c");h=document.createElement("span");g.addClass(h,"property-name");h.innerHTML=b.property;var f=document.createElement("div");f.appendChild(h);f.appendChild(b.domElement);d=w(a,f,d.before);g.addClass(d,m.CLASS_CONTROLLER_ROW);
+g.addClass(d,typeof b.getValue());t(a,d,b);a.__controllers.push(b);return b}function w(a,b,h){var c=document.createElement("li");b&&c.appendChild(b);h?a.__ul.insertBefore(c,params.before):a.__ul.appendChild(c);a.onResize();return c}function t(a,c,h){h.__li=c;h.__gui=a;k.extend(h,{options:function(b){if(1<arguments.length)return h.remove(),v(a,h.object,h.property,{before:h.__li.nextElementSibling,factoryArgs:[k.toArray(arguments)]});if(k.isArray(b)||k.isObject(b))return h.remove(),v(a,h.object,h.property,
+{before:h.__li.nextElementSibling,factoryArgs:[b]})},name:function(a){h.__li.firstElementChild.firstElementChild.innerHTML=a;return h},listen:function(){h.__gui.listen(h);return h},remove:function(){h.__gui.remove(h);return h}});if(h instanceof l){var d=new q(h.object,h.property,{min:h.__min,max:h.__max,step:h.__step});k.each(["updateDisplay","onChange","onFinishChange"],function(a){var K=h[a],b=d[a];h[a]=d[a]=function(){var a=Array.prototype.slice.call(arguments);K.apply(h,a);return b.apply(d,a)}});
+g.addClass(c,"has-slider");h.domElement.insertBefore(d.domElement,h.domElement.firstElementChild)}else if(h instanceof q){var e=function(b){return k.isNumber(h.__min)&&k.isNumber(h.__max)?(h.remove(),v(a,h.object,h.property,{before:h.__li.nextElementSibling,factoryArgs:[h.__min,h.__max,h.__step]})):b};h.min=k.compose(e,h.min);h.max=k.compose(e,h.max)}else h instanceof b?(g.bind(c,"click",function(){g.fakeEvent(h.__checkbox,"click")}),g.bind(h.__checkbox,"click",function(a){a.stopPropagation()})):
+h instanceof p?(g.bind(c,"click",function(){g.fakeEvent(h.__button,"click")}),g.bind(c,"mouseover",function(){g.addClass(h.__button,"hover")}),g.bind(c,"mouseout",function(){g.removeClass(h.__button,"hover")})):h instanceof n&&(g.addClass(c,"color"),h.updateDisplay=k.compose(function(a){c.style.borderLeftColor=h.__color.toString();return a},h.updateDisplay),h.updateDisplay());h.setValue=k.compose(function(b){a.getRoot().__preset_select&&h.isModified()&&E(a.getRoot(),!0);return b},h.setValue)}function y(a,
+b){var c=a.getRoot(),d=c.__rememberedObjects.indexOf(b.object);if(-1!=d){var e=c.__rememberedObjectIndecesToControllers[d];void 0===e&&(e={},c.__rememberedObjectIndecesToControllers[d]=e);e[b.property]=b;if(c.load&&c.load.remembered){c=c.load.remembered;if(c[a.preset])c=c[a.preset];else if(c.Default)c=c.Default;else return;c[d]&&void 0!==c[d][b.property]&&(d=c[d][b.property],b.initialValue=d,b.setValue(d))}}}function L(a){var b=a.__save_row=document.createElement("li");g.addClass(a.domElement,"has-save");
+a.__ul.insertBefore(b,a.__ul.firstChild);g.addClass(b,"save-row");var c=document.createElement("span");c.innerHTML="&nbsp;";g.addClass(c,"button gears");var d=document.createElement("span");d.innerHTML="Save";g.addClass(d,"button");g.addClass(d,"save");var e=document.createElement("span");e.innerHTML="New";g.addClass(e,"button");g.addClass(e,"save-as");var f=document.createElement("span");f.innerHTML="Revert";g.addClass(f,"button");g.addClass(f,"revert");var r=a.__preset_select=document.createElement("select");
+a.load&&a.load.remembered?k.each(a.load.remembered,function(b,c){F(a,c,c==a.preset)}):F(a,"Default",!1);g.bind(r,"change",function(){for(var b=0;b<a.__preset_select.length;b++)a.__preset_select[b].innerHTML=a.__preset_select[b].value;a.preset=this.value});b.appendChild(r);b.appendChild(c);b.appendChild(d);b.appendChild(e);b.appendChild(f);if(x){var n=function(){u.style.display=a.useLocalStorage?"block":"none"},b=document.getElementById("dg-save-locally"),u=document.getElementById("dg-local-explain");
+b.style.display="block";b=document.getElementById("dg-local-storage");"true"===localStorage.getItem(document.location.href+".isLocal")&&b.setAttribute("checked","checked");n();g.bind(b,"change",function(){a.useLocalStorage=!a.useLocalStorage;n()})}var m=document.getElementById("dg-new-constructor");g.bind(m,"keydown",function(a){!a.metaKey||67!==a.which&&67!=a.keyCode||B.hide()});g.bind(c,"click",function(){m.innerHTML=JSON.stringify(a.getSaveObject(),void 0,2);B.show();m.focus();m.select()});g.bind(d,
+"click",function(){a.save()});g.bind(e,"click",function(){var b=prompt("Enter a new preset name.");b&&a.saveAs(b)});g.bind(f,"click",function(){a.revert()})}function M(a){function b(f){f.preventDefault();e=f.clientX;g.addClass(a.__closeButton,m.CLASS_DRAG);g.bind(window,"mousemove",c);g.bind(window,"mouseup",d);return!1}function c(b){b.preventDefault();a.width+=e-b.clientX;a.onResize();e=b.clientX;return!1}function d(){g.removeClass(a.__closeButton,m.CLASS_DRAG);g.unbind(window,"mousemove",c);g.unbind(window,
+"mouseup",d)}a.__resize_handle=document.createElement("div");k.extend(a.__resize_handle.style,{width:"6px",marginLeft:"-3px",height:"200px",cursor:"ew-resize",position:"absolute"});var e;g.bind(a.__resize_handle,"mousedown",b);g.bind(a.__closeButton,"mousedown",b);a.domElement.insertBefore(a.__resize_handle,a.domElement.firstElementChild)}function G(a,b){a.domElement.style.width=b+"px";a.__save_row&&a.autoPlace&&(a.__save_row.style.width=b+"px");a.__closeButton&&(a.__closeButton.style.width=b+"px")}
+function C(a,b){var c={};k.each(a.__rememberedObjects,function(d,e){var f={};k.each(a.__rememberedObjectIndecesToControllers[e],function(a,c){f[c]=b?a.initialValue:a.getValue()});c[e]=f});return c}function F(a,b,c){var d=document.createElement("option");d.innerHTML=b;d.value=b;a.__preset_select.appendChild(d);c&&(a.__preset_select.selectedIndex=a.__preset_select.length-1)}function E(a,b){var c=a.__preset_select[a.__preset_select.selectedIndex];c.innerHTML=b?c.value+"*":c.value}function H(a){0!=a.length&&
+u(function(){H(a)});k.each(a,function(a){a.updateDisplay()})}f.inject(d);var x;try{x="localStorage"in window&&null!==window.localStorage}catch(N){x=!1}var B,I=!0,z,D=!1,J=[],m=function(a){function b(){var a=c.getRoot();a.width+=1;k.defer(function(){--a.width})}var c=this;this.domElement=document.createElement("div");this.__ul=document.createElement("ul");this.domElement.appendChild(this.__ul);g.addClass(this.domElement,"dg");this.__folders={};this.__controllers=[];this.__rememberedObjects=[];this.__rememberedObjectIndecesToControllers=
+[];this.__listening=[];a=a||{};a=k.defaults(a,{autoPlace:!0,width:m.DEFAULT_WIDTH});a=k.defaults(a,{resizable:a.autoPlace,hideable:a.autoPlace});k.isUndefined(a.load)?a.load={preset:"Default"}:a.preset&&(a.load.preset=a.preset);k.isUndefined(a.parent)&&a.hideable&&J.push(this);a.resizable=k.isUndefined(a.parent)&&a.resizable;a.autoPlace&&k.isUndefined(a.scrollable)&&(a.scrollable=!0);var d=x&&"true"===localStorage.getItem(document.location.href+".isLocal"),e;Object.defineProperties(this,{parent:{get:function(){return a.parent}},
+scrollable:{get:function(){return a.scrollable}},autoPlace:{get:function(){return a.autoPlace}},preset:{get:function(){return c.parent?c.getRoot().preset:a.load.preset},set:function(b){c.parent?c.getRoot().preset=b:a.load.preset=b;for(b=0;b<this.__preset_select.length;b++)this.__preset_select[b].value==this.preset&&(this.__preset_select.selectedIndex=b);c.revert()}},width:{get:function(){return a.width},set:function(b){a.width=b;G(c,b)}},name:{get:function(){return a.name},set:function(b){a.name=
+b;r&&(r.innerHTML=a.name)}},closed:{get:function(){return a.closed},set:function(b){a.closed=b;a.closed?g.addClass(c.__ul,m.CLASS_CLOSED):g.removeClass(c.__ul,m.CLASS_CLOSED);this.onResize();c.__closeButton&&(c.__closeButton.innerHTML=b?m.TEXT_OPEN:m.TEXT_CLOSED)}},load:{get:function(){return a.load}},useLocalStorage:{get:function(){return d},set:function(a){x&&((d=a)?g.bind(window,"unload",e):g.unbind(window,"unload",e),localStorage.setItem(document.location.href+".isLocal",a))}}});if(k.isUndefined(a.parent)){a.closed=
+!1;g.addClass(this.domElement,m.CLASS_MAIN);g.makeSelectable(this.domElement,!1);if(x&&d){c.useLocalStorage=!0;var f=localStorage.getItem(document.location.href+".gui");f&&(a.load=JSON.parse(f))}this.__closeButton=document.createElement("div");this.__closeButton.innerHTML=m.TEXT_CLOSED;g.addClass(this.__closeButton,m.CLASS_CLOSE_BUTTON);this.domElement.appendChild(this.__closeButton);g.bind(this.__closeButton,"click",function(){c.closed=!c.closed})}else{void 0===a.closed&&(a.closed=!0);var r=document.createTextNode(a.name);
+g.addClass(r,"controller-name");f=w(c,r);g.addClass(this.__ul,m.CLASS_CLOSED);g.addClass(f,"title");g.bind(f,"click",function(a){a.preventDefault();c.closed=!c.closed;return!1});a.closed||(this.closed=!1)}a.autoPlace&&(k.isUndefined(a.parent)&&(I&&(z=document.createElement("div"),g.addClass(z,"dg"),g.addClass(z,m.CLASS_AUTO_PLACE_CONTAINER),document.body.appendChild(z),I=!1),z.appendChild(this.domElement),g.addClass(this.domElement,m.CLASS_AUTO_PLACE)),this.parent||G(c,a.width));g.bind(window,"resize",
+function(){c.onResize()});g.bind(this.__ul,"webkitTransitionEnd",function(){c.onResize()});g.bind(this.__ul,"transitionend",function(){c.onResize()});g.bind(this.__ul,"oTransitionEnd",function(){c.onResize()});this.onResize();a.resizable&&M(this);this.saveToLocalStorageIfPossible=e=function(){x&&"true"===localStorage.getItem(document.location.href+".isLocal")&&localStorage.setItem(document.location.href+".gui",JSON.stringify(c.getSaveObject()))};c.getRoot();a.parent||b()};m.toggleHide=function(){D=
+!D;k.each(J,function(a){a.domElement.style.zIndex=D?-999:999;a.domElement.style.opacity=D?0:1})};m.CLASS_AUTO_PLACE="a";m.CLASS_AUTO_PLACE_CONTAINER="ac";m.CLASS_MAIN="main";m.CLASS_CONTROLLER_ROW="cr";m.CLASS_TOO_TALL="taller-than-window";m.CLASS_CLOSED="closed";m.CLASS_CLOSE_BUTTON="close-button";m.CLASS_DRAG="drag";m.DEFAULT_WIDTH=245;m.TEXT_CLOSED="Close Controls";m.TEXT_OPEN="Open Controls";g.bind(window,"keydown",function(a){"text"===document.activeElement.type||72!==a.which&&72!=a.keyCode||
+m.toggleHide()},!1);k.extend(m.prototype,{add:function(a,b){return v(this,a,b,{factoryArgs:Array.prototype.slice.call(arguments,2)})},addColor:function(a,b){return v(this,a,b,{color:!0})},remove:function(a){this.__ul.removeChild(a.__li);this.__controllers.splice(this.__controllers.indexOf(a),1);var b=this;k.defer(function(){b.onResize()})},destroy:function(){this.autoPlace&&z.removeChild(this.domElement)},addFolder:function(a){if(void 0!==this.__folders[a])throw Error('You already have a folder in this GUI by the name "'+
+a+'"');var b={name:a,parent:this};b.autoPlace=this.autoPlace;this.load&&this.load.folders&&this.load.folders[a]&&(b.closed=this.load.folders[a].closed,b.load=this.load.folders[a]);b=new m(b);this.__folders[a]=b;a=w(this,b.domElement);g.addClass(a,"folder");return b},open:function(){this.closed=!1},close:function(){this.closed=!0},onResize:function(){var a=this.getRoot();if(a.scrollable){var b=g.getOffset(a.__ul).top,c=0;k.each(a.__ul.childNodes,function(b){a.autoPlace&&b===a.__save_row||(c+=g.getHeight(b))});
+window.innerHeight-b-20<c?(g.addClass(a.domElement,m.CLASS_TOO_TALL),a.__ul.style.height=window.innerHeight-b-20+"px"):(g.removeClass(a.domElement,m.CLASS_TOO_TALL),a.__ul.style.height="auto")}a.__resize_handle&&k.defer(function(){a.__resize_handle.style.height=a.__ul.offsetHeight+"px"});a.__closeButton&&(a.__closeButton.style.width=a.width+"px")},remember:function(){k.isUndefined(B)&&(B=new A,B.domElement.innerHTML=a);if(this.parent)throw Error("You can only call remember on a top level GUI.");var b=
+this;k.each(Array.prototype.slice.call(arguments),function(a){0==b.__rememberedObjects.length&&L(b);-1==b.__rememberedObjects.indexOf(a)&&b.__rememberedObjects.push(a)});this.autoPlace&&G(this,this.width)},getRoot:function(){for(var a=this;a.parent;)a=a.parent;return a},getSaveObject:function(){var a=this.load;a.closed=this.closed;0<this.__rememberedObjects.length&&(a.preset=this.preset,a.remembered||(a.remembered={}),a.remembered[this.preset]=C(this));a.folders={};k.each(this.__folders,function(b,
+c){a.folders[c]=b.getSaveObject()});return a},save:function(){this.load.remembered||(this.load.remembered={});this.load.remembered[this.preset]=C(this);E(this,!1);this.saveToLocalStorageIfPossible()},saveAs:function(a){this.load.remembered||(this.load.remembered={},this.load.remembered.Default=C(this,!0));this.load.remembered[a]=C(this);this.preset=a;F(this,a,!0);this.saveToLocalStorageIfPossible()},revert:function(a){k.each(this.__controllers,function(b){this.getRoot().load.remembered?y(a||this.getRoot(),
+b):b.setValue(b.initialValue)},this);k.each(this.__folders,function(a){a.revert(a)});a||E(this.getRoot(),!1)},listen:function(a){var b=0==this.__listening.length;this.__listening.push(a);b&&H(this.__listening)}});return m}(dat.utils.css,'<div id="dg-save" class="dg dialogue">\n\n  Here\'s the new load parameter for your <code>GUI</code>\'s constructor:\n\n  <textarea id="dg-new-constructor"></textarea>\n\n  <div id="dg-save-locally">\n\n    <input id="dg-local-storage" type="checkbox"/> Automatically save\n    values to <code>localStorage</code> on exit.\n\n    <div id="dg-local-explain">The values saved to <code>localStorage</code> will\n      override those passed to <code>dat.GUI</code>\'s constructor. This makes it\n      easier to work incrementally, but <code>localStorage</code> is fragile,\n      and your friends may not see the same values you do.\n      \n    </div>\n    \n  </div>\n\n</div>',
+".dg {\n  /** Clear list styles */\n  /* Auto-place container */\n  /* Auto-placed GUI's */\n  /* Line items that don't contain folders. */\n  /** Folder names */\n  /** Hides closed items */\n  /** Controller row */\n  /** Name-half (left) */\n  /** Controller-half (right) */\n  /** Controller placement */\n  /** Shorter number boxes when slider is present. */\n  /** Ensure the entire boolean and function row shows a hand */ }\n  .dg ul {\n    list-style: none;\n    margin: 0;\n    padding: 0;\n    width: 100%;\n    clear: both; }\n  .dg.ac {\n    position: fixed;\n    top: 0;\n    left: 0;\n    right: 0;\n    height: 0;\n    z-index: 0; }\n  .dg:not(.ac) .main {\n    /** Exclude mains in ac so that we don't hide close button */\n    overflow: hidden; }\n  .dg.main {\n    -webkit-transition: opacity 0.1s linear;\n    -o-transition: opacity 0.1s linear;\n    -moz-transition: opacity 0.1s linear;\n    transition: opacity 0.1s linear; }\n    .dg.main.taller-than-window {\n      overflow-y: auto; }\n      .dg.main.taller-than-window .close-button {\n        opacity: 1;\n        /* TODO, these are style notes */\n        margin-top: -1px;\n        border-top: 1px solid #2c2c2c; }\n    .dg.main ul.closed .close-button {\n      opacity: 1 !important; }\n    .dg.main:hover .close-button,\n    .dg.main .close-button.drag {\n      opacity: 1; }\n    .dg.main .close-button {\n      /*opacity: 0;*/\n      -webkit-transition: opacity 0.1s linear;\n      -o-transition: opacity 0.1s linear;\n      -moz-transition: opacity 0.1s linear;\n      transition: opacity 0.1s linear;\n      border: 0;\n      position: absolute;\n      line-height: 19px;\n      height: 20px;\n      /* TODO, these are style notes */\n      cursor: pointer;\n      text-align: center;\n      background-color: #000; }\n      .dg.main .close-button:hover {\n        background-color: #111; }\n  .dg.a {\n    float: right;\n    margin-right: 15px;\n    overflow-x: hidden; }\n    .dg.a.has-save > ul {\n      margin-top: 27px; }\n      .dg.a.has-save > ul.closed {\n        margin-top: 0; }\n    .dg.a .save-row {\n      position: fixed;\n      top: 0;\n      z-index: 1002; }\n  .dg li {\n    -webkit-transition: height 0.1s ease-out;\n    -o-transition: height 0.1s ease-out;\n    -moz-transition: height 0.1s ease-out;\n    transition: height 0.1s ease-out; }\n  .dg li:not(.folder) {\n    cursor: auto;\n    height: 27px;\n    line-height: 27px;\n    overflow: hidden;\n    padding: 0 4px 0 5px; }\n  .dg li.folder {\n    padding: 0;\n    border-left: 4px solid rgba(0, 0, 0, 0); }\n  .dg li.title {\n    cursor: pointer;\n    margin-left: -4px; }\n  .dg .closed li:not(.title),\n  .dg .closed ul li,\n  .dg .closed ul li > * {\n    height: 0;\n    overflow: hidden;\n    border: 0; }\n  .dg .cr {\n    clear: both;\n    padding-left: 3px;\n    height: 27px; }\n  .dg .property-name {\n    cursor: default;\n    float: left;\n    clear: left;\n    width: 40%;\n    overflow: hidden;\n    text-overflow: ellipsis; }\n  .dg .c {\n    float: left;\n    width: 60%; }\n  .dg .c input[type=text] {\n    border: 0;\n    margin-top: 4px;\n    padding: 3px;\n    width: 100%;\n    float: right; }\n  .dg .has-slider input[type=text] {\n    width: 30%;\n    /*display: none;*/\n    margin-left: 0; }\n  .dg .slider {\n    float: left;\n    width: 66%;\n    margin-left: -5px;\n    margin-right: 0;\n    height: 19px;\n    margin-top: 4px; }\n  .dg .slider-fg {\n    height: 100%; }\n  .dg .c input[type=checkbox] {\n    margin-top: 9px; }\n  .dg .c select {\n    margin-top: 5px; }\n  .dg .cr.function,\n  .dg .cr.function .property-name,\n  .dg .cr.function *,\n  .dg .cr.boolean,\n  .dg .cr.boolean * {\n    cursor: pointer; }\n  .dg .selector {\n    display: none;\n    position: absolute;\n    margin-left: -9px;\n    margin-top: 23px;\n    z-index: 10; }\n  .dg .c:hover .selector,\n  .dg .selector.drag {\n    display: block; }\n  .dg li.save-row {\n    padding: 0; }\n    .dg li.save-row .button {\n      display: inline-block;\n      padding: 0px 6px; }\n  .dg.dialogue {\n    background-color: #222;\n    width: 460px;\n    padding: 15px;\n    font-size: 13px;\n    line-height: 15px; }\n\n/* TODO Separate style and structure */\n#dg-new-constructor {\n  padding: 10px;\n  color: #222;\n  font-family: Monaco, monospace;\n  font-size: 10px;\n  border: 0;\n  resize: none;\n  box-shadow: inset 1px 1px 1px #888;\n  word-wrap: break-word;\n  margin: 12px 0;\n  display: block;\n  width: 440px;\n  overflow-y: scroll;\n  height: 100px;\n  position: relative; }\n\n#dg-local-explain {\n  display: none;\n  font-size: 11px;\n  line-height: 17px;\n  border-radius: 3px;\n  background-color: #333;\n  padding: 8px;\n  margin-top: 10px; }\n  #dg-local-explain code {\n    font-size: 10px; }\n\n#dat-gui-save-locally {\n  display: none; }\n\n/** Main type */\n.dg {\n  color: #eee;\n  font: 11px 'Lucida Grande', sans-serif;\n  text-shadow: 0 -1px 0 #111;\n  /** Auto place */\n  /* Controller row, <li> */\n  /** Controllers */ }\n  .dg.main {\n    /** Scrollbar */ }\n    .dg.main::-webkit-scrollbar {\n      width: 5px;\n      background: #1a1a1a; }\n    .dg.main::-webkit-scrollbar-corner {\n      height: 0;\n      display: none; }\n    .dg.main::-webkit-scrollbar-thumb {\n      border-radius: 5px;\n      background: #676767; }\n  .dg li:not(.folder) {\n    background: #1a1a1a;\n    border-bottom: 1px solid #2c2c2c; }\n  .dg li.save-row {\n    line-height: 25px;\n    background: #dad5cb;\n    border: 0; }\n    .dg li.save-row select {\n      margin-left: 5px;\n      width: 108px; }\n    .dg li.save-row .button {\n      margin-left: 5px;\n      margin-top: 1px;\n      border-radius: 2px;\n      font-size: 9px;\n      line-height: 7px;\n      padding: 4px 4px 5px 4px;\n      background: #c5bdad;\n      color: #fff;\n      text-shadow: 0 1px 0 #b0a58f;\n      box-shadow: 0 -1px 0 #b0a58f;\n      cursor: pointer; }\n      .dg li.save-row .button.gears {\n        background: #c5bdad url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAsAAAANCAYAAAB/9ZQ7AAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAQJJREFUeNpiYKAU/P//PwGIC/ApCABiBSAW+I8AClAcgKxQ4T9hoMAEUrxx2QSGN6+egDX+/vWT4e7N82AMYoPAx/evwWoYoSYbACX2s7KxCxzcsezDh3evFoDEBYTEEqycggWAzA9AuUSQQgeYPa9fPv6/YWm/Acx5IPb7ty/fw+QZblw67vDs8R0YHyQhgObx+yAJkBqmG5dPPDh1aPOGR/eugW0G4vlIoTIfyFcA+QekhhHJhPdQxbiAIguMBTQZrPD7108M6roWYDFQiIAAv6Aow/1bFwXgis+f2LUAynwoIaNcz8XNx3Dl7MEJUDGQpx9gtQ8YCueB+D26OECAAQDadt7e46D42QAAAABJRU5ErkJggg==) 2px 1px no-repeat;\n        height: 7px;\n        width: 8px; }\n      .dg li.save-row .button:hover {\n        background-color: #bab19e;\n        box-shadow: 0 -1px 0 #b0a58f; }\n  .dg li.folder {\n    border-bottom: 0; }\n  .dg li.title {\n    padding-left: 16px;\n    background: black url(data:image/gif;base64,R0lGODlhBQAFAJEAAP////Pz8////////yH5BAEAAAIALAAAAAAFAAUAAAIIlI+hKgFxoCgAOw==) 6px 10px no-repeat;\n    cursor: pointer;\n    border-bottom: 1px solid rgba(255, 255, 255, 0.2); }\n  .dg .closed li.title {\n    background-image: url(data:image/gif;base64,R0lGODlhBQAFAJEAAP////Pz8////////yH5BAEAAAIALAAAAAAFAAUAAAIIlGIWqMCbWAEAOw==); }\n  .dg .cr.boolean {\n    border-left: 3px solid #806787; }\n  .dg .cr.function {\n    border-left: 3px solid #e61d5f; }\n  .dg .cr.number {\n    border-left: 3px solid #2fa1d6; }\n    .dg .cr.number input[type=text] {\n      color: #2fa1d6; }\n  .dg .cr.string {\n    border-left: 3px solid #1ed36f; }\n    .dg .cr.string input[type=text] {\n      color: #1ed36f; }\n  .dg .cr.function:hover, .dg .cr.boolean:hover {\n    background: #111; }\n  .dg .c input[type=text] {\n    background: #303030;\n    outline: none; }\n    .dg .c input[type=text]:hover {\n      background: #3c3c3c; }\n    .dg .c input[type=text]:focus {\n      background: #494949;\n      color: #fff; }\n  .dg .c .slider {\n    background: #303030;\n    cursor: ew-resize; }\n  .dg .c .slider-fg {\n    background: #2fa1d6; }\n  .dg .c .slider:hover {\n    background: #3c3c3c; }\n    .dg .c .slider:hover .slider-fg {\n      background: #44abda; }\n",
+dat.controllers.factory=function(f,a,d,e,c,b,p){return function(q,l,r,n){var u=q[l];if(p.isArray(r)||p.isObject(r))return new f(q,l,r);if(p.isNumber(u))return p.isNumber(r)&&p.isNumber(n)?new d(q,l,r,n):new a(q,l,{min:r,max:n});if(p.isString(u))return new e(q,l);if(p.isFunction(u))return new c(q,l,"");if(p.isBoolean(u))return new b(q,l)}}(dat.controllers.OptionController,dat.controllers.NumberControllerBox,dat.controllers.NumberControllerSlider,dat.controllers.StringController=function(f,a,d){var e=
+function(c,b){function d(){f.setValue(f.__input.value)}e.superclass.call(this,c,b);var f=this;this.__input=document.createElement("input");this.__input.setAttribute("type","text");a.bind(this.__input,"keyup",d);a.bind(this.__input,"change",d);a.bind(this.__input,"blur",function(){f.__onFinishChange&&f.__onFinishChange.call(f,f.getValue())});a.bind(this.__input,"keydown",function(a){13===a.keyCode&&this.blur()});this.updateDisplay();this.domElement.appendChild(this.__input)};e.superclass=f;d.extend(e.prototype,
+f.prototype,{updateDisplay:function(){a.isActive(this.__input)||(this.__input.value=this.getValue());return e.superclass.prototype.updateDisplay.call(this)}});return e}(dat.controllers.Controller,dat.dom.dom,dat.utils.common),dat.controllers.FunctionController,dat.controllers.BooleanController,dat.utils.common),dat.controllers.Controller,dat.controllers.BooleanController,dat.controllers.FunctionController,dat.controllers.NumberControllerBox,dat.controllers.NumberControllerSlider,dat.controllers.OptionController,
+dat.controllers.ColorController=function(f,a,d,e,c){function b(a,b,d,e){a.style.background="";c.each(l,function(c){a.style.cssText+="background: "+c+"linear-gradient("+b+", "+d+" 0%, "+e+" 100%); "})}function p(a){a.style.background="";a.style.cssText+="background: -moz-linear-gradient(top,  #ff0000 0%, #ff00ff 17%, #0000ff 34%, #00ffff 50%, #00ff00 67%, #ffff00 84%, #ff0000 100%);";a.style.cssText+="background: -webkit-linear-gradient(top,  #ff0000 0%,#ff00ff 17%,#0000ff 34%,#00ffff 50%,#00ff00 67%,#ffff00 84%,#ff0000 100%);";
+a.style.cssText+="background: -o-linear-gradient(top,  #ff0000 0%,#ff00ff 17%,#0000ff 34%,#00ffff 50%,#00ff00 67%,#ffff00 84%,#ff0000 100%);";a.style.cssText+="background: -ms-linear-gradient(top,  #ff0000 0%,#ff00ff 17%,#0000ff 34%,#00ffff 50%,#00ff00 67%,#ffff00 84%,#ff0000 100%);";a.style.cssText+="background: linear-gradient(top,  #ff0000 0%,#ff00ff 17%,#0000ff 34%,#00ffff 50%,#00ff00 67%,#ffff00 84%,#ff0000 100%);"}var q=function(f,n){function u(b){v(b);a.bind(window,"mousemove",v);a.bind(window,
+"mouseup",l)}function l(){a.unbind(window,"mousemove",v);a.unbind(window,"mouseup",l)}function g(){var a=e(this.value);!1!==a?(t.__color.__state=a,t.setValue(t.__color.toOriginal())):this.value=t.__color.toString()}function k(){a.unbind(window,"mousemove",w);a.unbind(window,"mouseup",k)}function v(b){b.preventDefault();var c=a.getWidth(t.__saturation_field),d=a.getOffset(t.__saturation_field),e=(b.clientX-d.left+document.body.scrollLeft)/c;b=1-(b.clientY-d.top+document.body.scrollTop)/c;1<b?b=1:0>
+b&&(b=0);1<e?e=1:0>e&&(e=0);t.__color.v=b;t.__color.s=e;t.setValue(t.__color.toOriginal());return!1}function w(b){b.preventDefault();var c=a.getHeight(t.__hue_field),d=a.getOffset(t.__hue_field);b=1-(b.clientY-d.top+document.body.scrollTop)/c;1<b?b=1:0>b&&(b=0);t.__color.h=360*b;t.setValue(t.__color.toOriginal());return!1}q.superclass.call(this,f,n);this.__color=new d(this.getValue());this.__temp=new d(0);var t=this;this.domElement=document.createElement("div");a.makeSelectable(this.domElement,!1);
+this.__selector=document.createElement("div");this.__selector.className="selector";this.__saturation_field=document.createElement("div");this.__saturation_field.className="saturation-field";this.__field_knob=document.createElement("div");this.__field_knob.className="field-knob";this.__field_knob_border="2px solid ";this.__hue_knob=document.createElement("div");this.__hue_knob.className="hue-knob";this.__hue_field=document.createElement("div");this.__hue_field.className="hue-field";this.__input=document.createElement("input");
+this.__input.type="text";this.__input_textShadow="0 1px 1px ";a.bind(this.__input,"keydown",function(a){13===a.keyCode&&g.call(this)});a.bind(this.__input,"blur",g);a.bind(this.__selector,"mousedown",function(b){a.addClass(this,"drag").bind(window,"mouseup",function(b){a.removeClass(t.__selector,"drag")})});var y=document.createElement("div");c.extend(this.__selector.style,{width:"122px",height:"102px",padding:"3px",backgroundColor:"#222",boxShadow:"0px 1px 3px rgba(0,0,0,0.3)"});c.extend(this.__field_knob.style,
+{position:"absolute",width:"12px",height:"12px",border:this.__field_knob_border+(.5>this.__color.v?"#fff":"#000"),boxShadow:"0px 1px 3px rgba(0,0,0,0.5)",borderRadius:"12px",zIndex:1});c.extend(this.__hue_knob.style,{position:"absolute",width:"15px",height:"2px",borderRight:"4px solid #fff",zIndex:1});c.extend(this.__saturation_field.style,{width:"100px",height:"100px",border:"1px solid #555",marginRight:"3px",display:"inline-block",cursor:"pointer"});c.extend(y.style,{width:"100%",height:"100%",
+background:"none"});b(y,"top","rgba(0,0,0,0)","#000");c.extend(this.__hue_field.style,{width:"15px",height:"100px",display:"inline-block",border:"1px solid #555",cursor:"ns-resize"});p(this.__hue_field);c.extend(this.__input.style,{outline:"none",textAlign:"center",color:"#fff",border:0,fontWeight:"bold",textShadow:this.__input_textShadow+"rgba(0,0,0,0.7)"});a.bind(this.__saturation_field,"mousedown",u);a.bind(this.__field_knob,"mousedown",u);a.bind(this.__hue_field,"mousedown",function(b){w(b);a.bind(window,
+"mousemove",w);a.bind(window,"mouseup",k)});this.__saturation_field.appendChild(y);this.__selector.appendChild(this.__field_knob);this.__selector.appendChild(this.__saturation_field);this.__selector.appendChild(this.__hue_field);this.__hue_field.appendChild(this.__hue_knob);this.domElement.appendChild(this.__input);this.domElement.appendChild(this.__selector);this.updateDisplay()};q.superclass=f;c.extend(q.prototype,f.prototype,{updateDisplay:function(){var a=e(this.getValue());if(!1!==a){var f=!1;
+c.each(d.COMPONENTS,function(b){if(!c.isUndefined(a[b])&&!c.isUndefined(this.__color.__state[b])&&a[b]!==this.__color.__state[b])return f=!0,{}},this);f&&c.extend(this.__color.__state,a)}c.extend(this.__temp.__state,this.__color.__state);this.__temp.a=1;var l=.5>this.__color.v||.5<this.__color.s?255:0,p=255-l;c.extend(this.__field_knob.style,{marginLeft:100*this.__color.s-7+"px",marginTop:100*(1-this.__color.v)-7+"px",backgroundColor:this.__temp.toString(),border:this.__field_knob_border+"rgb("+l+
+","+l+","+l+")"});this.__hue_knob.style.marginTop=100*(1-this.__color.h/360)+"px";this.__temp.s=1;this.__temp.v=1;b(this.__saturation_field,"left","#fff",this.__temp.toString());c.extend(this.__input.style,{backgroundColor:this.__input.value=this.__color.toString(),color:"rgb("+l+","+l+","+l+")",textShadow:this.__input_textShadow+"rgba("+p+","+p+","+p+",.7)"})}});var l=["-moz-","-o-","-webkit-","-ms-",""];return q}(dat.controllers.Controller,dat.dom.dom,dat.color.Color=function(f,a,d,e){function c(a,
+b,c){Object.defineProperty(a,b,{get:function(){if("RGB"===this.__state.space)return this.__state[b];p(this,b,c);return this.__state[b]},set:function(a){"RGB"!==this.__state.space&&(p(this,b,c),this.__state.space="RGB");this.__state[b]=a}})}function b(a,b){Object.defineProperty(a,b,{get:function(){if("HSV"===this.__state.space)return this.__state[b];q(this);return this.__state[b]},set:function(a){"HSV"!==this.__state.space&&(q(this),this.__state.space="HSV");this.__state[b]=a}})}function p(b,c,d){if("HEX"===
+b.__state.space)b.__state[c]=a.component_from_hex(b.__state.hex,d);else if("HSV"===b.__state.space)e.extend(b.__state,a.hsv_to_rgb(b.__state.h,b.__state.s,b.__state.v));else throw"Corrupted color state";}function q(b){var c=a.rgb_to_hsv(b.r,b.g,b.b);e.extend(b.__state,{s:c.s,v:c.v});e.isNaN(c.h)?e.isUndefined(b.__state.h)&&(b.__state.h=0):b.__state.h=c.h}var l=function(){this.__state=f.apply(this,arguments);if(!1===this.__state)throw"Failed to interpret color arguments";this.__state.a=this.__state.a||
+1};l.COMPONENTS="r g b h s v hex a".split(" ");e.extend(l.prototype,{toString:function(){return d(this)},toOriginal:function(){return this.__state.conversion.write(this)}});c(l.prototype,"r",2);c(l.prototype,"g",1);c(l.prototype,"b",0);b(l.prototype,"h");b(l.prototype,"s");b(l.prototype,"v");Object.defineProperty(l.prototype,"a",{get:function(){return this.__state.a},set:function(a){this.__state.a=a}});Object.defineProperty(l.prototype,"hex",{get:function(){"HEX"!==!this.__state.space&&(this.__state.hex=
+a.rgb_to_hex(this.r,this.g,this.b));return this.__state.hex},set:function(a){this.__state.space="HEX";this.__state.hex=a}});return l}(dat.color.interpret,dat.color.math=function(){var f;return{hsv_to_rgb:function(a,d,e){var c=a/60-Math.floor(a/60),b=e*(1-d),f=e*(1-c*d);d=e*(1-(1-c)*d);a=[[e,d,b],[f,e,b],[b,e,d],[b,f,e],[d,b,e],[e,b,f]][Math.floor(a/60)%6];return{r:255*a[0],g:255*a[1],b:255*a[2]}},rgb_to_hsv:function(a,d,e){var c=Math.min(a,d,e),b=Math.max(a,d,e),c=b-c;if(0==b)return{h:NaN,s:0,v:0};
+a=(a==b?(d-e)/c:d==b?2+(e-a)/c:4+(a-d)/c)/6;0>a&&(a+=1);return{h:360*a,s:c/b,v:b/255}},rgb_to_hex:function(a,d,e){a=this.hex_with_component(0,2,a);a=this.hex_with_component(a,1,d);return a=this.hex_with_component(a,0,e)},component_from_hex:function(a,d){return a>>8*d&255},hex_with_component:function(a,d,e){return e<<(f=8*d)|a&~(255<<f)}}}(),dat.color.toString,dat.utils.common),dat.color.interpret,dat.utils.common),dat.utils.requestAnimationFrame=function(){return window.requestAnimationFrame||
+window.mozRequestAnimationFrame||window.oRequestAnimationFrame||window.msRequestAnimationFrame||function(f,a){window.setTimeout(f,1E3/60)}}(),dat.dom.CenteredDiv=function(f,a){var d=function(){this.backgroundElement=document.createElement("div");a.extend(this.backgroundElement.style,{backgroundColor:"rgba(0,0,0,0.8)",top:0,left:0,display:"none",zIndex:"1000",opacity:0,WebkitTransition:"opacity 0.2s linear",transition:"opacity 0.2s linear"});f.makeFullscreen(this.backgroundElement);this.backgroundElement.style.position=
+"fixed";this.domElement=document.createElement("div");a.extend(this.domElement.style,{position:"fixed",display:"none",zIndex:"1001",opacity:0,WebkitTransition:"-webkit-transform 0.2s ease-out, opacity 0.2s linear",transition:"transform 0.2s ease-out, opacity 0.2s linear"});document.body.appendChild(this.backgroundElement);document.body.appendChild(this.domElement);var d=this;f.bind(this.backgroundElement,"click",function(){d.hide()})};d.prototype.show=function(){var d=this;this.backgroundElement.style.display=
+"block";this.domElement.style.display="block";this.domElement.style.opacity=0;this.domElement.style.webkitTransform="scale(1.1)";this.layout();a.defer(function(){d.backgroundElement.style.opacity=1;d.domElement.style.opacity=1;d.domElement.style.webkitTransform="scale(1)"})};d.prototype.hide=function(){var a=this,c=function(){a.domElement.style.display="none";a.backgroundElement.style.display="none";f.unbind(a.domElement,"webkitTransitionEnd",c);f.unbind(a.domElement,"transitionend",c);f.unbind(a.domElement,
+"oTransitionEnd",c)};f.bind(this.domElement,"webkitTransitionEnd",c);f.bind(this.domElement,"transitionend",c);f.bind(this.domElement,"oTransitionEnd",c);this.backgroundElement.style.opacity=0;this.domElement.style.opacity=0;this.domElement.style.webkitTransform="scale(1.1)"};d.prototype.layout=function(){this.domElement.style.left=window.innerWidth/2-f.getWidth(this.domElement)/2+"px";this.domElement.style.top=window.innerHeight/2-f.getHeight(this.domElement)/2+"px"};return d}(dat.dom.dom,dat.utils.common),
+dat.dom.dom,dat.utils.common);
\ No newline at end of file
diff --git a/docs/static/mcmc-demo/lib/linalg.core.js b/docs/static/mcmc-demo/lib/linalg.core.js
new file mode 100644
index 00000000..e54b1c9f
--- /dev/null
+++ b/docs/static/mcmc-demo/lib/linalg.core.js
@@ -0,0 +1,877 @@
+/*
+Copyright (c) 2016 Chi Feng
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+*/
+
+"use strict";
+
+/**
+ * Creates a "matrix" from an existing array-like object with optional dimensions
+ * @param  {ArrayLike} array existing array, can be nested or flat (row-major)
+ * @param  {int}       rows  number of rows
+ * @param  {int}       cols  number of columns
+ * @return {Float64Array}
+ */
+Float64Array.matrix = function(array, rows, cols) {
+
+  if (Array.isArray(array[0])) { // flatten nested arrays
+    rows = array.length;
+    cols = array[0].length;
+    var matrix = new Float64Array(rows * cols);
+    matrix.rows = rows;
+    matrix.cols = cols;
+    for (var i = 0; i < rows; ++i)
+      for (var j = 0; j < cols; ++j)
+        matrix[i*cols+j] = array[i][j];
+    return matrix;
+  } else {
+    var matrix = new Float64Array(array);
+    matrix.rows = rows || array.length
+    matrix.cols = cols || 1;
+    return matrix;
+  }
+};
+
+/**
+ * String representation
+ * @param  {int} precision (optional)
+ * @return {string}
+ */
+Float64Array.prototype.toString = function(precision) {
+  precision = precision || 4;
+  var str = '';
+  for (var i = 0; i < this.rows; ++i) {
+    str += (i == 0) ? '[[ ' : ' [ ';
+    str += this[i * this.cols + 0].toPrecision(precision);
+    for (var j = 1; j < this.cols; ++j)
+      str += ', ' + this[i * this.cols + j].toPrecision(precision);
+    str += (i == this.rows - 1) ? ' ]]' : ' ],\n';
+  }
+  return str;
+};
+
+/**
+ * Returns a copy of a "matrix"
+ * @return {Float64Array}
+ */
+Float64Array.prototype.copy = function() {
+  var copy = new Float64Array(this);
+  copy.rows = this.rows || this.length;
+  copy.cols = this.cols || 1;
+  return copy;
+};
+
+/**
+ * Creates a "matrix" with all entries set to zero
+ * @param  {int} rows number of rows
+ * @param  {int} cols number of columns
+ * @return {Float64Array}
+ */
+Float64Array.zeros = function(rows, cols) {
+  cols = cols || 1;
+  var matrix = new Float64Array(rows * cols);
+  matrix.rows = rows;
+  matrix.cols = cols;
+  return matrix;
+};
+
+/**
+ * Creates a column vector with linearly-spaced elements
+ * @param  {float} min minimum value (inclusive)
+ * @param  {float} max maximum value (inclusive)
+ * @param  {int}   n   number of elements
+ * @return {Float64Array}
+ */
+Float64Array.linspace = function(min, max, n) {
+  var matrix = new Float64Array(n);
+  var dx = (max - min) / (n - 1);
+  for (var i = 0; i < n; ++i)
+    matrix[i] = i * dx + min;
+  return matrix;
+};
+
+/**
+ * Creates an n x n identity "matrix"
+ * @param  {int} n number of rows and columns
+ * @return {Float64Array}
+ */
+Float64Array.eye = function(n) {
+  var matrix = new Float64Array(n * n);
+  matrix.rows = n;
+  matrix.cols = n;
+  for (var i = 0; i < n; ++i)
+    matrix[i * n + i] = 1;
+  return matrix;
+};
+
+/**
+ * Creates a "matrix" filled with ones
+ * @param  {int} rows number of rows
+ * @param  {int} cols number of columns
+ * @return {Float64Array}
+ */
+Float64Array.ones = function(rows, cols) {
+  cols = cols || 1;
+  var matrix = new Float64Array(rows * cols);
+  matrix.rows = rows;
+  matrix.cols = cols;
+  for (var i = 0; i < matrix.length; ++i)
+    matrix[i] = 1;
+  return matrix;
+};
+
+/**
+ * Creates a "matrix" filled with constant
+ * @param  {float} const constant
+ * @param  {int}   rows number of rows
+ * @param  {int}   cols number of columns
+ * @return {Float64Array}
+ */
+Float64Array.constant = function(constant, rows, cols) {
+  cols = cols || 1;
+  var matrix = new Float64Array(rows * cols);
+  matrix.rows = rows;
+  matrix.cols = cols;
+  for (var i = 0; i < matrix.length; ++i)
+    matrix[i] = constant;
+  return matrix;
+};
+
+/**
+ * Build a "matrix" where each element is a function applied to element index
+ * @param  {function} f    takes ([i, [j]]) as arguments
+ * @param  {int}      rows number of rows
+ * @param  {int}      cols number of cols
+ * @return {Float64Array}
+ */
+Float64Array.build = function(f, rows, cols) {
+  cols = cols || 1;
+  var matrix = Float64Array.zeros(rows, cols);
+  for (var i = 0; i < rows; ++i)
+    for (var j = 0; j < cols; ++j)
+      matrix[i*cols+j] = f(i, j);
+  return matrix;
+};
+
+/**
+ * (in place) Each element is replaced by a function applied to the element index
+ * @param  {function} f takes ([i, [j]]) as arguments
+ * @return {Float64Array}
+ */
+Float64Array.prototype.rebuild = function(f) {
+  if (this.cols == 1)
+    for (var i = 0; i < this.rows; ++i)
+      this[i] = f(i, i);
+  else
+    for (var i = 0; i < this.rows; ++i)
+      for (var j = 0; j < this.cols; ++j)
+        this[i*this.cols+j] = f(i, j);
+  return this;
+};
+
+/**
+ * Matrix transpose (copy)
+ * @return {Float64Array}
+ */
+Float64Array.prototype.transpose = function() {
+  var m = this.rows, n = this.cols;
+  var transposed = Float64Array.zeros(n, m);
+  for (var i = 0; i < m; ++i)
+    for (var j = 0; j < n; ++j)
+      transposed[j * m + i] = this[i * n + j];
+  return transposed;
+};
+
+/**
+ * Outer product (form matrix from vector tensor product)
+ * @param  {Float64Array} u vector (column or row)
+ * @param  {Float64Array} v vector (column or row)
+ * @return {Float64Array}
+ */
+Float64Array.outer = function(u, v) {
+  var A = Float64Array.zeros(u.length, v.length);
+  for (var i = 0; i < u.length; ++i)
+    for (var j = 0; j < v.length; ++j)
+      A[i * v.length + j] = u[i] * v[j];
+  return A;
+};
+
+/**
+ * cwise map function onto matrix copy
+ * @param  {function} f arguments (A[i], i)
+ * @return {Float64Array}
+ */
+Float64Array.prototype.map = function(f) {
+  var A = Float64Array.zeros(this.rows, this.cols);
+  for (var i = 0; i < this.length; ++i)
+    A[i] = f(this[i], i);
+  return A;
+}
+
+/**
+ * Add two matrices and return sum
+ * @param  {Float64Array} other
+ * @return {Float64Array}
+ */
+Float64Array.prototype.add = function(other) {
+  if (this.cols != other.cols || this.rows != other.rows) throw 'matrix dimension mismatch';
+  var sum = Float64Array.zeros(this.rows, this.cols);
+  for (var i = 0; i < this.rows; ++i)
+    for (var j = 0; j < this.cols; ++j)
+      sum[i*this.cols+j] = this[i*this.cols+j] + other[i*this.cols+j];
+  return sum;
+};
+
+/**
+ * Increment matrix (in place)
+ * @param  {Float64Array} other
+ * @return {Float64Array}
+ */
+Float64Array.prototype.increment = function(other) {
+  if (this.cols != other.cols || this.rows != other.rows) throw 'matrix dimension mismatch';
+  for (var i = 0; i < this.rows; ++i)
+    for (var j = 0; j < this.cols; ++j)
+      this[i*this.cols+j] += other[i*this.cols+j];
+  return this;
+};
+
+/**
+ * Decrement matrix (in place)
+ * @param  {Float64Array} other
+ * @return {Float64Array}
+ */
+Float64Array.prototype.decrement = function(other) {
+  if (this.cols != other.cols || this.rows != other.rows) throw 'matrix dimension mismatch';
+  for (var i = 0; i < this.rows; ++i)
+    for (var j = 0; j < this.cols; ++j)
+      this[i*this.cols+j] -= other[i*this.cols+j];
+  return this;
+};
+
+/**
+ * Subtract two matrices and return difference
+ * @param  {Float64Array} other
+ * @return {Float64Array}
+ */
+Float64Array.prototype.subtract = function(other) {
+  if (this.cols != other.cols || this.rows != other.rows) throw 'matrix dimension mismatch';
+  var difference = Float64Array.zeros(this.rows, this.cols);
+  for (var i = 0; i < this.rows; ++i)
+    for (var j = 0; j < this.cols; ++j)
+      difference[i*this.cols+j] = this[i*this.cols+j] - other[i*this.cols+j];
+  return difference;
+};
+
+/**
+ * Compute squared euclidian distance
+ * @param  {Float64Array} other
+ * @return {float}       square euclidian distance
+ */
+Float64Array.prototype.dist2 = function(other) {
+  var d2 = 0;
+  for (var i = 0; i < this.length; ++i)
+    d2 += Math.pow(this[i] - other[i], 2);
+  return d2;
+};
+
+/**
+ * Compute euclidian distance
+ * @param  {Float64Array} other
+ * @return {float}       euclidian distance
+ */
+Float64Array.prototype.dist = function(other) {
+  return Math.sqrt(this.dist2(other));
+};
+
+/**
+ * Multiply by scalar and return copy
+ * @param  {Float64Array} scalar
+ * @return {Float64Array}
+ */
+Float64Array.prototype.scale = function(scalar) {
+  var scaled = Float64Array.zeros(this.rows, this.cols);
+  for (var i = 0; i < this.rows; ++i)
+    for (var j = 0; j < this.cols; ++j)
+      scaled[i*this.cols+j] = scalar * this[i*this.cols+j];
+  return scaled;
+};
+
+/**
+ * cwise negate matrix copy
+ * @return {Float64Array}
+ */
+Float64Array.prototype.negate = function() {
+  var A = Float64Array.zeros(this.rows, this.cols);
+  for (var i = 0; i < this.length; ++i)
+    A[i] = -this[i];
+  return A;
+};
+
+/**
+ * Trace of a "matrix," i.e. sum along diagonal
+ * @return {float}
+ */
+Float64Array.prototype.trace = function() {
+  var trace = 0;
+  for (var i = 0; i < Math.min(this.rows, this.cols); ++i)
+    trace += A[i*this.cols+j];
+  return trace;
+};
+
+/**
+ * Element-wise 2-norm (Frobenius-norm for matrices)
+ * @return {float}
+ */
+Float64Array.prototype.norm = function() {
+  var norm = 0;
+  for (var i = 0; i < this.length; ++i)
+    norm += this[i] * this[i];
+  return Math.sqrt(norm);
+};
+
+/**
+ * Element-wise squared norm
+ * @return {float}
+ */
+Float64Array.prototype.norm2 = function() {
+  var norm = 0;
+  for (var i = 0; i < this.length; ++i)
+    norm += this[i] * this[i];
+  return norm;
+};
+
+/**
+ * Sum of all elements
+ * @return {float}
+ */
+Float64Array.prototype.sum = function() {
+  var sum = 0;
+  for (var i = 0; i < this.length; ++i)
+    sum += this[i];
+  return sum;
+};
+
+/**
+ * Get diagonal as a column vector
+ * @return {Float64Array}
+ */
+Float64Array.prototype.diagonal = function() {
+  var diagonal = Float64Array.zeros(Math.min(this.rows, this.cols));
+  for (var i = 0; i < diagonal.length; ++i)
+    diagonal[i] = this[i*this.cols+i];
+  return diagonal;
+};
+
+/**
+ * Create diagonal matrix from a vector
+ * @return {Float64Array} a diagonal matrix
+ */
+Float64Array.prototype.asDiagonal = function() {
+  var D = Float64Array.zeros(this.length, this.length);
+  for (var i = 0; i < this.length; ++i) {
+    D[i * this.length + i] = this[i];
+  }
+  return D;
+};
+
+/**
+ * Get row i as a row vector
+ * @param  {int} i row index
+ * @return {Float64Array}
+ */
+Float64Array.prototype.row = function(i) {
+  var row = Float64Array.zeros(1, this.cols);
+  for (var j = 0; j < this.cols; ++j)
+    row[j] = this[i*this.cols+j];
+  return row;
+};
+
+/**
+ * Get column j as as column vector
+ * @param  {int} j column index
+ * @return {Float64Array}
+ */
+Float64Array.prototype.col = function(j) {
+  var col = Float64Array.zeros(this.cols, 1);
+  for (var i = 0; i < this.rows; ++i)
+    col[i] = this[i*this.cols+j];
+  return col;
+};
+
+Float64Array.prototype.setRow = function(i, row) {
+  for (var j = 0; j < this.cols; ++i)
+    this[i*this.cols + j] = row[i];
+  return this;
+};
+
+Float64Array.prototype.setCol = function(j, col) {
+  for (var i = 0; i < this.rows; ++i)
+    this[i*this.cols + j] = col[i];
+  return this;
+};
+
+/**
+ * Swap rows i and k
+ * @param  {int} i row index
+ * @param  {int} k row index
+ * @return {Float64Array} (for chaining)
+ */
+Float64Array.prototype.swap_rows = function(i, k) {
+  for (var j = 0; j < this.cols; ++j) {
+    var tmp = this[i*this.cols+j];
+    this[i*this.cols+j] = this[k*this.cols+j];
+    this[k*this.cols+j] = tmp;
+  }
+  return this;
+};
+
+/**
+ * Computes determinant using upper triangulation
+ * @return {float} NaN if uninvertible
+ */
+Float64Array.prototype.det = function() {
+  if (this.rows != this.cols) throw 'det() requires square matrix';
+  if (this.rows == 2 && this.cols == 2) {
+    return this[0] * this[3] - this[1] * this[2];
+  }
+  // upper triangularize, then return product of diagonal
+  var U = this.copy();
+  for (var i = 0; i < n; ++i) {
+    var max = 0;
+    for (var row = i; row < n; ++row)
+      if (Math.abs(U[row*n+i]) > Math.abs(U[max*n+i]))
+        max = row;
+    if (max > 0)
+      U.swap_rows(i, max);
+    if (U[i*n+i] == 0) return NaN;
+    for (var row = i + 1; row < n; ++row) {
+      var r = U[row*n+i] / U[i*n+i];
+      if (r == 0) continue;
+      for (var col = i; col < n; ++col);
+        U[row*n+col] -= U[i*n+col] * r;
+    }
+  }
+  var det = 1;
+  for (var i = 0; i < n; ++i)
+    det *= U[i*n+i];
+  return det;
+};
+
+/**
+ * Generalized dot product (sum of element-wise multiplication)
+ * @param  {Float64Array} other another "matrix" of same size
+ * @return {float}
+ */
+Float64Array.prototype.dot = function(other) {
+  var prod = 0;
+  for (var i = 0; i < this.length; ++i)
+    prod += this[i] * other[i];
+  return prod;
+};
+
+/**
+ * Matrix multiplication (naive implementation)
+ * @param  {Float64Array} other
+ * @return {Float64Array}
+ */
+Float64Array.prototype.multiply = function(other) {
+  var A = this, B = other;
+  if (A.cols != B.rows) throw 'multiply() dimension mismatch';
+  var n = A.rows, l = A.cols, m = B.cols;
+  var C = Float64Array.zeros(n, m)
+  // vector-vector product
+  if (m == 1 && n == 1) {
+    C[0] = A.dot(B);
+    return C;
+  }
+  // matrix-vector product
+  if (m == 1) {
+    for (var i = 0; i < n; ++i)
+      for (var j = 0; j < l; ++j)
+        C[i] += A[i*l+j] * B[j];
+    return C;
+  }
+  // vector-matrix product
+  if (n == 1) {
+    for (var j = 0; j < m; ++j) {
+      for (var k = 0; k < l; ++k)
+        C[j] += A[k] * B[k * m + j];
+    }
+    return C;
+  }
+  // matrix-matrix product
+  for (var i = 0; i < n; ++i) {
+    for (var j = 0; j < m; ++j) {
+      var cij = 0;
+      for (var k = 0; k < l; ++k)
+        cij += A[i * l + k] * B[k * m + j];
+      C[i * m + j] = cij;
+    }
+  }
+  return C;
+};
+
+/**
+ * Computes PA = LU decomposition
+ * @return {object} {L, U, P}
+ */
+Float64Array.prototype.lu = function() {
+  if (this.rows != this.cols) throw 'lu() requires square matrix';
+  var n = this.rows;
+  var L = Float64Array.zeros(n, n);
+  var U = Float64Array.zeros(n, n);
+  var P = Float64Array.eye(n, n);
+  for (var j = 0; j < n; ++j) {
+    var max = j;
+    for (var i = j; i < n; ++i)
+      if (Math.abs(this[i*n+j]) > Math.abs(this[max*n+j]))
+        max = i;
+    if (j != max)
+      P.swap_rows(j, max);
+  }
+  var PA = P.multiply(this);
+  for (var j = 0; j < n; ++j) {
+    L[j*n+j] = 1;
+    for (var i = 0; i < j+1; ++i) {
+      var s = 0;
+      for (var k = 0; k < i; ++k)
+        s += U[k*n+j] * L[i*n+k]
+      U[i*n+j] = PA[i*n+j] - s
+    }
+    for (var i = j; i < n; ++i) {
+      var s = 0;
+      for (var k = 0; k < i; ++k)
+        s += U[k*n+j] * L[i*n+k]
+      L[i*n+j] = (PA[i*n+j] - s) / U[j*n+j];
+    }
+  }
+  return {L:L, U:U, P:P};
+};
+
+/**
+ * Cholesky A = LL^T decomposition (in-place)
+ * @return {[type]} [description]
+ */
+Float64Array.prototype.chol_inplace = function() {
+  if (this.rows != this.cols) throw 'chol_inplace() requires square matrix';
+  var A = this;
+  var m = A.rows, n = A.cols;
+  var i, j, k, s = 0.0;
+  for (i = 0; i < n; ++i) {
+    for (j = 0; j < (i + 1); ++j) {
+      s = 0.0;
+      for (k = 0; k < j; ++k)
+        s += A[i * n + k] * A[j * n + k];
+      if (i != j) A[j * n + i] = 0;
+      if (i == j && A[i * n + i] - s < 0) throw "chol_inplace() matrix not positive definite";
+      A[i * n + j] = (i == j) ? Math.sqrt(A[i * n + i] - s) : ((A[i * n + j] - s) / A[j * n + j]);
+    }
+  }
+  return A;
+};
+
+/**
+ * Cholesky A = LL^T decomposition (returns copy)
+ * @return {Float64Array}
+ */
+Float64Array.prototype.chol = function() {
+  return this.copy().chol_inplace();
+};
+
+/**
+ * Solves Lx = b using foward substitution, updates b
+ * @param  {Float64Array} b rhs
+ * @return {Float64Array}
+ */
+Float64Array.prototype.fsolve_inplace = function(b) {
+  var L = this;
+  var m = L.rows, n = L.cols;
+  for (var i = 0; i < n; ++i) {
+    var s = 0.0
+    for (var j = 0; j < i; ++j)
+      s += L[i * n + j] * b[j];
+    b[i] = (b[i] - s) / L[i * n + i];
+  }
+  return b;
+};
+
+/**
+ * Solves Lx = b using foward substitution
+ * @param  {Float64Array} b rhs
+ * @return {Float64Array}
+ */
+Float64Array.prototype.fsolve = function(b) {
+  return this.fsolve_inplace(b.copy());
+};
+
+/**
+ * Solves Ux = b using backward substitution, updates b
+ * @param  {Float64Array} b rhs
+ * @param  {object} options {transpose: false}
+ * @return {Float64Array}
+ */
+Float64Array.prototype.bsolve_inplace = function(b, options) {
+  var U = this;
+  var m = U.rows, n = U.cols;
+  options = options || {};
+  var transpose = options.hasOwnProperty('transpose') ? options.transpose : false;
+  for (var i = n - 1; i >= 0; --i) {
+    var s = 0.0;
+    for (var j = i + 1; j < n; ++j)
+      s += (transpose ? U[j * n + i] : U[i * n + j]) * b[j];
+    b[i] = (b[i] - s) / U[i * n + i];
+  }
+  return b;
+};
+
+/**
+ * Solves Ux = b using backward substitution
+ * @param  {Float64Array} b rhs
+ * @param  {object} options {transpose: false}
+ * @return {Float64Array}
+ */
+Float64Array.prototype.bsolve = function(b, options) {
+  return this.bsolve_inplace(b.copy(), options);
+};
+
+/**
+ * Solve Ax = b using PA = LU decomposition
+ * @param  {Float64Array} b rhs
+ * @return {Float64Array} x
+ */
+Float64Array.prototype.lu_solve = function(b) {
+  var res = this.lu(), P = res.P, L = res.L, U = res.U;
+  return U.bsolve(L.fsolve(P.multiply(b)));
+};
+
+/**
+ * Computes the matrix inverse using PA = LU decomposition
+ * @return {Float64Array} A^-1
+ */
+Float64Array.prototype.lu_inverse = function() {
+  var res = this.lu(), P = res.P, L = res.L, U = res.U;
+  var inverse = Float64Array.zeros(this.rows, this.cols);
+  var eye = Float64Array.eye(this.rows, this.cols);
+  for (var j = 0; j < this.cols; ++j) {
+    inverse.setCol(j, U.bsolve(L.fsolve(P.multiply(eye.col(j)))));
+  }
+  return inverse;
+};
+
+/**
+ * Solve Ax = b using A = LL^T decomposition
+ * @param  {Float64Array} b rhs
+ * @return {Float64Array} x
+ */
+Float64Array.prototype.llt_solve = function(b) {
+  var L = this.chol();
+  return L.bsolve(L.fsolve(b), {transpose: true});
+};
+
+/**
+ * Computes the matrix inverse using LL^T decomposition
+ * @return {Float64Array} A^-1
+ */
+Float64Array.prototype.llt_inverse = function() {
+  var L = this.chol();
+  var inverse = Float64Array.zeros(this.rows, this.cols);
+  var eye = Float64Array.eye(this.rows, this.cols);
+  for (var j = 0; j < this.cols; ++j) {
+    inverse.setCol(j, L.bsolve(L.fsolve(eye.col(j)), {transpose: true}));
+  }
+  return inverse;
+};
+
+/**
+ * Solve Ax = b using A = LL^T decomposition (in-place)
+ * @param  {Float64Array} b rhs
+ * @return {Float64Array} x
+ */
+Float64Array.prototype.llt_solve_inplace = function(b) {
+  var L = this.chol_inplace();
+  return L.bsolve_inplace(L.fsolve_inplace(b), {transpose: true});
+};
+
+
+/**
+ * Computes diagonal matrix D of eigenvalues and matrix V whose columns are the corresponding
+ * right eigenvectors so that AV = VD
+ * @param  {object} options tolerance and maxIter
+ * @return {object}         V:V D:D
+ */
+Float64Array.prototype.jacobiRotation = function(options) {
+
+  if (this.cols != this.rows) throw 'matrix must be square';
+
+  if (arguments.length < 1)
+    options = {};
+
+  var maxIter = options.maxIter || 100;
+  var tolerance = options.tolerance || 1e-5;
+
+  var n = this.rows;
+  var D = this.copy();
+  var V = Float64Array.eye(n, n);
+
+  var iter, maxOffDiag, p, q;
+  for (iter = 0; iter < maxIter; ++iter) {
+
+    // find max off diagonal term at (p, q)
+    maxOffDiag = 0;
+    for (var i = 0; i < n - 1; ++i) {
+      for (var j = i + 1; j < n; ++j) {
+        if (Math.abs(D[i * n + j]) > maxOffDiag) {
+          maxOffDiag = Math.abs(D[i * n + j]);
+          p = i; q = j;
+        }
+      }
+    }
+
+    if (maxOffDiag < tolerance)
+      break;
+
+    // Rotates matrix D through theta in pq-plane to set D[p][q] = 0
+    // Rotation stored in matrix V whose columns are eigenvectors of D
+    // d = cot 2 * theta, t = tan theta, c = cos theta, s = sin theta
+    var d = (D[p * n + p] - D[q * n + q]) / (2.0 * D[p * n + q]);
+    var t = Math.sign(d) / (Math.abs(d) + Math.sqrt(d * d + 1));
+    var c = 1.0 / Math.sqrt(t * t + 1);
+    var s = t * c;
+    D[p * n + p] += t * D[p * n + q];
+    D[q * n + q] -= t * D[p * n + q];
+    D[p * n + q] =      D[q * n + p] = 0.0;
+    for (var k = 0; k < n; k++) {  // Transform D
+      if (k != p && k != q) {
+        var akp =  c * D[k * n + p] + s * D[k * n + q];
+        var akq = -s * D[k * n + p] + c * D[k * n + q];
+        D[k * n + p] = akp;
+        D[p * n + k] = akp;
+        D[k * n + q] = akq;
+        D[q * n + k] = akq;
+      }
+    }
+    for (var k = 0; k < n; k++) {  // Store V
+      var rkp =  c * V[k * n + p] + s * V[k * n + q];
+      var rkq = -s * V[k * n + p] + c * V[k * n + q];
+      V[k * n + p] = rkp;
+      V[k * n + q] = rkq;
+    }
+  }
+
+  if (iter == maxIter) {
+    console.log('Hit maxIter: ', maxOffDiag, ' > ', tolerance);
+  }
+
+  return {V:V, D:D, eigenvalues: D.diagonal(), eigenvectors: V};
+
+};
+
+Float64Array.prototype.maxCoeff = function() {
+  var max = this[0];
+  for (var i = 0; i < this.length; ++i) {
+    if (this[i] > max)
+      max = this[i];
+  }
+  return max;
+};
+
+Float64Array.prototype.cwiseProduct = function(other) {
+  var A = this.copy();
+  for (var i = 0; i < this.length; ++i) {
+    A[i] = this[i] * other[i];
+  }
+  return A;
+};
+
+Float64Array.prototype.cwiseQuotient = function(other) {
+  var A = this.copy();
+  for (var i = 0; i < this.length; ++i) {
+    A[i] = this[i] / other[i];
+  }
+  return A;
+};
+
+Float64Array.prototype.cwiseInverse = function() {
+  var A = this.copy();
+  for (var i = 0; i < this.length; ++i) {
+    A[i] = 1.0 / this[i];
+  }
+  return A;
+};
+
+Float64Array.prototype.cwiseSqrt = function() {
+  var A = this.copy();
+  for (var i = 0; i < this.length; ++i) {
+    A[i] = Math.sqrt(this[i]);
+  }
+  return A;
+};
+
+// get sub-block of matrix
+Float64Array.prototype.getBlock = function(top, left, rows, cols) {
+	var B = new Float64Array(rows*cols);
+	B.rows = rows;
+	B.cols = cols;
+	for (var i = 0; i < rows; i++) {
+		for (var j = 0; j < cols; j++) {
+			B[i*B.cols+j] = this[(i+top)*this.cols + (j+left)];
+		}
+	}
+	return B;
+}
+
+// set sub-block of matrix to another matrix
+Float64Array.prototype.setBlock = function(top, left, A) {
+	for (var i = 0; i < A.rows; i++) {
+		for (var j = 0; j < A.cols; j++) {
+			this[(i+top)*this.cols + (j+left)] = A[i*A.cols+j];
+		}
+	}
+};
+
+// QR decomposition using Householder reflections.
+Float64Array.prototype.qr = function() {
+	var make_householder = function(a) {
+		var v = a.scale(1 / (a[0] + Math.sign(a[0]) * a.norm()));
+		v[0] = 1;
+		var H = Float64Array.eye(a.length);
+		H.decrement(Float64Array.outer(v, v).scale(2 / v.dot(v)));
+		return H;
+	};
+	var A = this.copy();
+	var m = A.rows;
+	var n = A.cols;
+	var Q = Float64Array.eye(m);
+	var upper = n - ((m == n) ? 1 : 0);
+	for (var i = 0; i < upper; i++) {
+		var a = A.getBlock(i, i, m - i, 1);
+		var H = Float64Array.eye(m);
+		H.setBlock(i, i, make_householder(a));
+		Q = Q.multiply(H);
+		A = H.multiply(A);
+	}
+	return {Q:Q, R:A};
+};
+
+var zeros = Float64Array.zeros;
+var eye = Float64Array.eye;
+var linspace = Float64Array.linspace;
+var matrix = Float64Array.matrix;
+
diff --git a/docs/static/mcmc-demo/lib/linalg.opt.js b/docs/static/mcmc-demo/lib/linalg.opt.js
new file mode 100644
index 00000000..c1a00089
--- /dev/null
+++ b/docs/static/mcmc-demo/lib/linalg.opt.js
@@ -0,0 +1,99 @@
+/*
+Copyright (c) 2016 Chi Feng
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+*/
+
+"use strict";
+
+/**
+ * Unconstrained gradient-based optimization
+ * @param  {function} f       objective function
+ * @param  {function} g       gradient of objective function
+ * @param  {object} user_opts
+ */
+Float64Array.opt = function(f, g, user_opts) {
+
+  // defaults
+  var options = {
+    method: 'bfgs',
+    tolerance: 1e-6,
+    step_size: 1.0,
+    max_iter: 100,
+    warn_max_iter: false,
+    line_search_iter: 20,
+    line_search_tolerance: 1e-4
+  };
+
+  // assign dim
+  if (!user_opts.hasOwnProperty('x0'))
+    throw 'x0 not set';
+  else
+    options.dim = user_opts.x0.length;
+
+  // assign user options
+  for (var key in user_opts) {
+    options[key] = user_opts[key];
+  }
+
+  // run optimization
+  if (options.method == 'bfgs') {
+    return Float64Array.bfgs(f, g, options);
+  } else {
+    throw 'unrecognized method';
+  }
+
+};
+
+Float64Array.bfgs = function(f, grad, options) {
+  var n = options.dim;
+  var geval = 0;
+  // bisection line search in the direction p
+  var line_search = function(x, p) {
+    var a, a_lo = 0, a_hi = options.step_size;
+    for (var k = 0; k < options.line_search_iter; k++) {
+      a = (a_hi + a_lo) / 2;
+      var h = grad(x.add(p.scale(a))).dot(p); geval++;
+      if (h > options.line_search_tolerance) a_hi = a;
+      else if (h < -options.line_search_tolerance) a_lo = a;
+      else break;
+    }
+    return a;
+  };
+  // BFGS
+  var B = Float64Array.eye(options.dim, options.dim);
+  var x = [options.x0.copy()];
+  var p = [ ], a = [ ], g = [grad(x[0])]; geval++;
+  var k = 0;
+  for (k = 0; k < options.max_iter; k++) {
+    if (g[k].dot(g[k]) < options.tolerance) break;
+    p.push(B.lu_solve(g[k].negate()));
+    a.push(line_search(x[k], p[k]));
+    x.push(x[k].add(p[k].scale(a[k])));
+    g.push(grad(x[k+1])); geval++;
+    var s = p[k].scale(a[k]);
+    var y = g[k+1].subtract(g[k]);
+    var first = Float64Array.outer(y, y).scale(1.0 / y.dot(s));
+    var second = B.multiply(Float64Array.outer(s, s.transpose().multiply(B))).scale(1.0 / s.dot(B.multiply(s)));
+    B.increment(first.subtract(second));
+  }
+  if (k == options.max_iter && options.warn_max_iter)
+    console.log('max_iter exceeded, gradient is', g[k]);
+  return {x: x[x.length-1], trajectory: x, p: p, a:a, geval: geval};
+};

From 66cf7ba3a44628ffaf35d337bb797816d50cfdc2 Mon Sep 17 00:00:00 2001
From: QZ Gao <gaoqz.ph@gmail.com>
Date: Tue, 25 Oct 2022 13:53:00 +0800
Subject: [PATCH 112/313] add encoding="utf-8" parameter

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 7442b6dc..3be51c28 100644
--- a/setup.py
+++ b/setup.py
@@ -24,10 +24,10 @@
 )
 
 
-with open('README.rst') as readme_file:
+with open('README.rst', encoding="utf-8") as readme_file:
     readme = readme_file.read()
 
-with open('HISTORY.rst') as history_file:
+with open('HISTORY.rst', encoding="utf-8") as history_file:
     history = history_file.read()
 
 requirements = ['numpy', 'cython', 'matplotlib', 'corner']

From dd54fe55ebc8e30e74d695fd9f2679a4ac1a4b91 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 14 Nov 2022 12:54:44 +0100
Subject: [PATCH 113/313] skip live point test if using step sampler

When using a step sampler with few live points in a high-d scenario,
it can occur that the ellipsoid moves and does not cover the live points.
This is because the ellipsoid is continuously updated with the
live point center, but may be too small (especially in d>10).
However, when a step sampler is used which ignores the region
(region filtering off), this is inconsequential.

This commit turns the check off.

For MLFriends runs, the check remains, but I have not yet seen any
problem there.

It can be turned off by running python with optimization (-O etc),
which disables all asserts.
---
 ultranest/integrator.py | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index b5b946e1..6652668c 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1760,16 +1760,17 @@ def _create_point(self, Lmin, ndraw, active_u, active_values):
             loglikelihoods of current live points
 
         """
-        assert self.region.inside(active_u).any(), \
-            ("None of the live points satisfies the current region!",
-             self.region.maxradiussq, self.region.u, self.region.unormed, active_u,
-             getattr(self.region, 'bbox_lo'),
-             getattr(self.region, 'bbox_hi'),
-             getattr(self.region, 'ellipsoid_cov'),
-             getattr(self.region, 'ellipsoid_center'),
-             getattr(self.region, 'ellipsoid_invcov'),
-             getattr(self.region, 'ellipsoid_cov'),
-             )
+        if self.stepsampler is None:
+            assert self.region.inside(active_u).any(), \
+                ("None of the live points satisfies the current region!",
+                 self.region.maxradiussq, self.region.u, self.region.unormed, active_u,
+                 getattr(self.region, 'bbox_lo'),
+                 getattr(self.region, 'bbox_hi'),
+                 getattr(self.region, 'ellipsoid_cov'),
+                 getattr(self.region, 'ellipsoid_center'),
+                 getattr(self.region, 'ellipsoid_invcov'),
+                 getattr(self.region, 'ellipsoid_cov'),
+                 )
 
         nit = 0
         while True:

From 8af6881c885e7ad1b7e0d1fe8d3cc96c4e9aac03 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 14 Nov 2022 12:59:50 +0100
Subject: [PATCH 114/313] =?UTF-8?q?Bump=20version:=203.5.5=20=E2=86=92=203?=
 =?UTF-8?q?.5.6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 3be51c28..8f497e7f 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.5.5',
+    version='3.5.6',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index a584a4d4..9081b11e 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.5.5'
+__version__ = '3.5.6'

From 27bb175f62b9f4f2f5b5219be52b15bbecf8981d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 25 Nov 2022 10:22:52 +0100
Subject: [PATCH 115/313] fix issue 77: wider initial sampling to overcome
 initial plateaus

---
 ultranest/integrator.py | 30 +++++++++++++++++++++++++++---
 1 file changed, 27 insertions(+), 3 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 6652668c..70118fbf 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -440,7 +440,6 @@ def __init__(self,
             of points.
         run_num: int
             unique run number. If None, will be automatically incremented.
-        
 
         """
         self.paramnames = param_names
@@ -1384,6 +1383,31 @@ def _widen_nodes(self, weighted_parents, weights, nnodes_needed, update_interval
 
         return target_min_num_children
 
+    def _widen_roots_beyond_initial_plateau(self, nroots):
+        """Widen roots, but populate ahead of initial plateau.
+
+        calls _widen_roots, and if there are several points with the same
+        value equal to the lowest loglikelihood, widens some more until
+        there are `nroots`-1 that are different to the lowest
+        loglikelihood value.
+        """
+        nroots_needed = nroots
+        while True:
+            self._widen_roots(nroots_needed)
+            Ls = np.unique([node.value for node in self.root.children])
+            Lmin = np.min(Ls)
+            # number of plateau points
+            P = (Ls == Lmin).sum()
+            if P > 1 and P < nroots_needed:
+                # guess the number of points needed: P-1 are useless
+                nroots_needed = nroots_needed + (P - 1)
+                self.logger.debug(
+                    'Found plateau with %d points at %e. Widening to %d live points.'
+                    'Avoid this by a loglikelihood that continuously increases towards good regions.',
+                    P, Lmin, nroots_needed)
+            else:
+                break
+
     def _widen_roots(self, nroots):
         """Ensure root has `nroots` children.
 
@@ -2379,7 +2403,7 @@ def run_iter(
         if viz_callback == 'auto':
             viz_callback = get_default_viz_callback()
 
-        self._widen_roots(min_num_live_points)
+        self._widen_roots_beyond_initial_plateau(min_num_live_points)
 
         Llo, Lhi = -np.inf, np.inf
         Lmax = -np.inf
@@ -2720,7 +2744,7 @@ def run_iter(
             if dlogz_min_num_live_points > self.min_num_live_points:
                 # more live points needed throughout to reach target
                 self.min_num_live_points = dlogz_min_num_live_points
-                self._widen_roots(self.min_num_live_points)
+                self._widen_roots_beyond_initial_plateau(self.min_num_live_points)
 
             elif Llo <= Lhi:
                 # if self.log:

From 42d68fad6c27f39328bf9e5bd19461459a4a6181 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 25 Nov 2022 10:28:13 +0100
Subject: [PATCH 116/313] =?UTF-8?q?Bump=20version:=203.5.6=20=E2=86=92=203?=
 =?UTF-8?q?.5.7?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 8f497e7f..9bec9f91 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.5.6',
+    version='3.5.7',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 9081b11e..836b2951 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.5.6'
+__version__ = '3.5.7'

From ec15d59f13f828bacd5271919545069042c9ce0d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 25 Nov 2022 11:59:06 +0100
Subject: [PATCH 117/313] fix detection of initial plateau

bug was that np.unique got rid of all duplicates
---
 ultranest/integrator.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 70118fbf..133430af 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1394,17 +1394,17 @@ def _widen_roots_beyond_initial_plateau(self, nroots):
         nroots_needed = nroots
         while True:
             self._widen_roots(nroots_needed)
-            Ls = np.unique([node.value for node in self.root.children])
+            Ls = np.array([node.value for node in self.root.children])
             Lmin = np.min(Ls)
             # number of plateau points
             P = (Ls == Lmin).sum()
-            if P > 1 and P < nroots_needed:
+            if P > 1 and len(Ls) - P + 1 < nroots:
                 # guess the number of points needed: P-1 are useless
-                nroots_needed = nroots_needed + (P - 1)
                 self.logger.debug(
-                    'Found plateau with %d points at %e. Widening to %d live points.'
-                    'Avoid this by a loglikelihood that continuously increases towards good regions.',
-                    P, Lmin, nroots_needed)
+                    'Found plateau of %d/%d initial points at L=%g. '
+                    'Avoid this by a continuously increasing loglikelihood towards good regions.',
+                    P, nroots_needed, Lmin)
+                nroots_needed = nroots_needed + (P - 1)
             else:
                 break
 

From adab4e9826aa590b0358ffdd426d2160e84f0869 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 6 Dec 2022 14:02:14 +0100
Subject: [PATCH 118/313] [docs] add reference for warm-starting; formatting
 typo

---
 docs/example-warmstart.ipynb | 7 +++++--
 docs/performance.rst         | 1 +
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index f9ac7fc5..dd4d870b 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -586,7 +586,10 @@
     "\n",
     "without needing to start the computation from scratch (potentially costly).\n",
     "\n",
-    "These features are experimental and feedback is appreciated. It is recommended to do a full, clean run to obtain final, reliable results before publication.\n"
+    "These features are experimental and feedback is appreciated. It is recommended to do a full, clean run to obtain final, reliable results before publication.\n",
+    "\n",
+    "References:\n",
+    " * \"SuperNest\" by Aleksandr Petrosyan and Will Handley https://arxiv.org/abs/2212.01760 \n"
    ]
   }
  ],
@@ -606,7 +609,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/performance.rst b/docs/performance.rst
index 0a144130..fa24767a 100644
--- a/docs/performance.rst
+++ b/docs/performance.rst
@@ -176,6 +176,7 @@ All of the above can be written, but are never read, by ultranest.ReactiveNested
 read the state of a previous run is:
 
 * results/points.hdf5: file storing all sampled points. Used for resuming.
+
   * this is an internal file.
   * ncalls: number of likelihood calls
   * points: the columns are: likelihood threshold under which the point was sampled, likelihood of the point, a quality indicator (0 for MLFriends, otherwise the number of steps in the step sampler), u-space (unit cube) coordinates, p-space (transformed parameters) coordinates.

From 1d9b72003b091f923fd049f802a5535a97e23e53 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 11 May 2023 18:38:58 +0200
Subject: [PATCH 119/313] update call, fixes issue
 https://github.com/JohannesBuchner/UltraNest/issues/95

---
 docs/performance.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/performance.rst b/docs/performance.rst
index fa24767a..e3717b54 100644
--- a/docs/performance.rst
+++ b/docs/performance.rst
@@ -49,7 +49,7 @@ To understand it, have a look first the `Basic usage <using-ultranest.html>`_ pa
         min_num_live_points=400,
         dlogz=0.5, # desired accuracy on logz
         min_ess=400, # number of effective samples
-        update_interval_iter_fraction=0.4, # how often to update region
+        update_interval_volume_fraction=0.4, # how often to update region
         max_num_improvement_loops=3, # how many times to go back and improve
     )
 

From 6ada1aa470af00757d97441638b5a1db5c1ce9c5 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 11 May 2023 19:00:38 +0200
Subject: [PATCH 120/313] move demo code that was inline to scripts that can be
 included and run with CI

---
 .circleci/config.yml |   2 +
 docs/gauss.py        |  61 +++++++++++++++++++++++++
 docs/performance.rst | 103 ++-----------------------------------------
 docs/simple.py       |  30 +++++++++++++
 4 files changed, 97 insertions(+), 99 deletions(-)
 create mode 100644 docs/gauss.py
 create mode 100644 docs/simple.py

diff --git a/.circleci/config.yml b/.circleci/config.yml
index bcb165d0..3f78a630 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -29,6 +29,8 @@ jobs:
 
       - run: python3 examples/testfeatures.py
       - run: python3 examples/rundirichlet.py
+      - run: coverage3 run --parallel-mode docs/simple.py
+      - run: coverage3 run --parallel-mode docs/gauss.py --x_dim=1 --log_dir=tmp
       - run: coverage3 combine
       - run: coverage3 report --include="$PWD/*" --omit="$PWD/.eggs/*"
       - run: coverage3 html --include="$PWD/*" --omit="$PWD/.eggs/*" && mv htmlcov test-reports
diff --git a/docs/gauss.py b/docs/gauss.py
new file mode 100644
index 00000000..d757c929
--- /dev/null
+++ b/docs/gauss.py
@@ -0,0 +1,61 @@
+import argparse
+import numpy as np
+from numpy import log
+
+# define command line arguments:
+parser = argparse.ArgumentParser()
+
+parser.add_argument('--x_dim', type=int, default=2,
+                    help="Dimensionality")
+parser.add_argument("--num_live_points", type=int, default=400)
+parser.add_argument('--sigma', type=float, default=0.1)
+parser.add_argument('--slice', action='store_true')
+parser.add_argument('--slice_steps', type=int, default=100)
+parser.add_argument('--log_dir', type=str, default='logs/loggauss')
+
+args = parser.parse_args()
+
+ndim = args.x_dim
+sigma = args.sigma
+width = max(0, 1 - 5 * sigma)
+centers = (np.sin(np.arange(ndim)/2.) * width + 1.) / 2.
+
+# Here, we implement a vectorized loglikelihood, which can
+# process many points at the same time. This reduces function calls.
+def loglike(theta):
+    like = -0.5 * (((theta - centers)/sigma)**2).sum(axis=1) - 0.5 * np.log(2 * np.pi * sigma**2) * ndim
+    return like
+
+def transform(x):
+    return x
+
+paramnames = ['param%d' % (i+1) for i in range(ndim)]
+
+# set up nested sampler:
+
+from ultranest import ReactiveNestedSampler
+
+sampler = ReactiveNestedSampler(paramnames, loglike, transform=transform, 
+    log_dir=args.log_dir + 'RNS-%dd' % ndim, resume=True,
+    vectorized=True)
+
+if args.slice:
+    # set up step sampler. Here, we use a differential evolution slice sampler:
+    import ultranest.stepsampler
+    sampler.stepsampler = ultranest.stepsampler.SliceSampler(
+        nsteps=args.slice_steps,
+        generate_direction=ultranest.stepsampler.generate_mixture_random_direction,
+    )
+
+# run sampler, with a few custom arguments:
+sampler.run(dlogz=0.5 + 0.1 * ndim,
+    update_interval_volume_fraction=0.4 if ndim > 20 else 0.2,
+    max_num_improvement_loops=3,
+    min_num_live_points=args.num_live_points)
+
+sampler.print_results()
+
+if args.slice:
+    sampler.stepsampler.plot(filename = args.log_dir + 'RNS-%dd/stepsampler_stats_regionslice.pdf' % ndim)
+
+sampler.plot()
diff --git a/docs/performance.rst b/docs/performance.rst
index e3717b54..0d620c1f 100644
--- a/docs/performance.rst
+++ b/docs/performance.rst
@@ -23,40 +23,8 @@ and analyses it.
 
 To understand it, have a look first the `Basic usage <using-ultranest.html>`_ page.
 
-.. code-block:: python3
-   :caption: simple.py
-   :name: simple.py
-    
-    import scipy.stats
-    
-    paramnames = ['param1', 'param2', 'param3']
-    centers = [0.4, 0.5, 0.6]
-    sigma = 0.1
-    
-    def transform(cube):
-        return cube
-
-    def loglike(theta):
-        return scipy.stats.norm(centers, sigma).logpdf(theta).sum()
-
-    from ultranest import ReactiveNestedSampler
-    sampler = ReactiveNestedSampler(paramnames, loglike, transform=transform, 
-        log_dir='my_gauss', # folder where to store files
-        resume=True, # whether to resume from there (otherwise start from scratch) 
-        )
-
-    sampler.run(
-        min_num_live_points=400,
-        dlogz=0.5, # desired accuracy on logz
-        min_ess=400, # number of effective samples
-        update_interval_volume_fraction=0.4, # how often to update region
-        max_num_improvement_loops=3, # how many times to go back and improve
-    )
-
-    sampler.print_results()
-
-    sampler.plot()
-    sampler.plot_trace()
+.. literalinclude:: simple.py
+   :language: python3
 
 Running this, you should see outputs like:
 
@@ -234,71 +202,8 @@ Vectorized full program
 Below is a Python program that implements a gaussian likelihood,
 and allows the user to specify the problem dimension and a few sampler parameters.
 
-.. code-block:: python3
-   :caption: gauss.py
-   :name: gauss.py
-
-    import argparse
-    import numpy as np
-    from numpy import log
-
-    # define command line arguments:
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--x_dim', type=int, default=2,
-                        help="Dimensionality")
-    parser.add_argument("--num_live_points", type=int, default=400)
-    parser.add_argument('--sigma', type=float, default=0.1)
-    parser.add_argument('--slice', action='store_true')
-    parser.add_argument('--slice_steps', type=int, default=100)
-    parser.add_argument('--log_dir', type=str, default='logs/loggauss')
-
-    args = parser.parse_args()
-
-    ndim = args.x_dim
-    sigma = args.sigma
-    width = max(0, 1 - 5 * sigma)
-    centers = (np.sin(np.arange(ndim)/2.) * width + 1.) / 2.
-
-    # Here, we implement a vectorized loglikelihood, which can
-    # process many points at the same time. This reduces function calls.
-    def loglike(theta):
-        like = -0.5 * (((theta - centers)/sigma)**2).sum(axis=1) - 0.5 * np.log(2 * np.pi * sigma**2) * ndim
-        return like
-
-    def transform(x):
-        return x
-
-    paramnames = ['param%d' % (i+1) for i in range(ndim)]
-
-    # set up nested sampler:
-    
-    from ultranest import ReactiveNestedSampler
-    
-    sampler = ReactiveNestedSampler(paramnames, loglike, transform=transform, 
-        log_dir=args.log_dir + 'RNS-%dd' % ndim, resume=True,
-        vectorized=True)
-
-    if args.slice:
-        # set up step sampler. Here, we use a differential evolution slice sampler:
-        import ultranest.stepsampler
-        sampler.stepsampler = ultranest.stepsampler.SliceSampler(
-            nsteps=args.slice_steps,
-            generate_direction=ultranest.stepsampler.generate_mixture_random_direction,
-        )
-
-    # run sampler, with a few custom arguments:
-    sampler.run(dlogz=0.5 + 0.1 * ndim,
-        update_interval_iter_fraction=0.4 if ndim > 20 else 0.2,
-        max_num_improvement_loops=3,
-        min_num_live_points=args.num_live_points)
-
-    sampler.print_results()
-
-    if args.slice:
-        sampler.stepsampler.plot(filename = args.log_dir + 'RNS-%dd/stepsampler_stats_regionslice.pdf' % ndim)
-
-    sampler.plot()
+.. literalinclude:: gauss.py
+   :language: python3
 
 Note that our likelihood is vectorized, and we pass ``vectorized=True``.
 
diff --git a/docs/simple.py b/docs/simple.py
new file mode 100644
index 00000000..f6116ff0
--- /dev/null
+++ b/docs/simple.py
@@ -0,0 +1,30 @@
+import scipy.stats
+
+paramnames = ['param1', 'param2', 'param3']
+centers = [0.4, 0.5, 0.6]
+sigma = 0.1
+
+def transform(cube):
+    return cube
+
+def loglike(theta):
+    return scipy.stats.norm(centers, sigma).logpdf(theta).sum()
+
+from ultranest import ReactiveNestedSampler
+sampler = ReactiveNestedSampler(paramnames, loglike, transform=transform, 
+    log_dir='my_gauss', # folder where to store files
+    resume=True, # whether to resume from there (otherwise start from scratch) 
+)
+
+sampler.run(
+    min_num_live_points=400,
+    dlogz=0.5, # desired accuracy on logz
+    min_ess=400, # number of effective samples
+    update_interval_volume_fraction=0.4, # how often to update region
+    max_num_improvement_loops=3, # how many times to go back and improve
+)
+
+sampler.print_results()
+
+sampler.plot()
+sampler.plot_trace()

From 4b7d8b350cb6a6f019c5802aa7fd64b02801e533 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 1 Jun 2023 09:52:33 +0200
Subject: [PATCH 121/313] avoid run-away root widening. closes
 https://github.com/JohannesBuchner/UltraNest/issues/81

A stopping is added to avoid a infinite loop.
A warning is added with user instructions.
---
 ultranest/integrator.py | 45 ++++++++++++++++++++++++++++++++++++++---
 1 file changed, 42 insertions(+), 3 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 133430af..ca6aac9f 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1383,20 +1383,55 @@ def _widen_nodes(self, weighted_parents, weights, nnodes_needed, update_interval
 
         return target_min_num_children
 
-    def _widen_roots_beyond_initial_plateau(self, nroots):
+    def _widen_roots_beyond_initial_plateau(self, nroots, num_warn=100000, num_stop=500000):
         """Widen roots, but populate ahead of initial plateau.
 
         calls _widen_roots, and if there are several points with the same
         value equal to the lowest loglikelihood, widens some more until
         there are `nroots`-1 that are different to the lowest
         loglikelihood value.
+
+        Parameters
+        -----------
+        nroots: int
+            Number of root live points, after the plateau is traversed.
+
+        num_warn: int
+            Warn if the number of root live points reached this.
+
+        num_stop: int
+            Do not increasing the number of root live points beyond this limit.
+
         """
         nroots_needed = nroots
         while True:
             self._widen_roots(nroots_needed)
             Ls = np.array([node.value for node in self.root.children])
             Lmin = np.min(Ls)
-            # number of plateau points
+            if nroots_needed > num_warn:
+                self.logger.warn("""The log-likelihood has a large plateau with L=%g.
+
+Probably you are returning a low value when the parameters are problematic/unphysical.
+ultranest can handle this correctly, by discarding live points with the same loglikelihood.
+(arxiv:2005.08602 arxiv:2010.13884). To mitigate running out of live points,
+the initial number of live points is increased. But now this has reached over %d points.
+
+You can avoid this making the loglikelihood increase towards where the good region is.
+For example, let's say you have two parameters where the sum must be below 1. Replace this:
+
+    if params[0] + params[1] > 1:
+         return -1e300
+
+with:
+
+    if params[0] + params[1] > 1:
+         return -1e300 * (params[0] + params[1])
+
+The current strategy will continue until %d live points are reached.
+It is safe to ignore this warning.""", Lmin, num_warn, num_stop)
+
+            if nroots_needed > num_stop:
+                break
             P = (Ls == Lmin).sum()
             if P > 1 and len(Ls) - P + 1 < nroots:
                 # guess the number of points needed: P-1 are useless
@@ -1412,6 +1447,11 @@ def _widen_roots(self, nroots):
         """Ensure root has `nroots` children.
 
         Sample from prior to fill up (if needed).
+
+        Parameters
+        -----------
+        nroots: int
+            Number of root live points, after the plateau is traversed.
         """
         if self.log and len(self.root.children) > 0:
             self.logger.info('Widening roots to %d live points (have %d already) ...', nroots, len(self.root.children))
@@ -1532,7 +1572,6 @@ def _adaptive_strategy_advice(self, Lmin, parallel_values, main_iterator, minima
             maximum fraction of integral in remainder for termination
         Lepsilon: float
             loglikelihood accuracy threshold
-
         """
         Ls = parallel_values.copy()
         Ls.sort()

From deca8a31aa411780870174d6378bbabb217c5658 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 1 Jun 2023 10:28:31 +0200
Subject: [PATCH 122/313] only log in the main MPI process

---
 ultranest/integrator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index ca6aac9f..a6b62235 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1408,7 +1408,7 @@ def _widen_roots_beyond_initial_plateau(self, nroots, num_warn=100000, num_stop=
             self._widen_roots(nroots_needed)
             Ls = np.array([node.value for node in self.root.children])
             Lmin = np.min(Ls)
-            if nroots_needed > num_warn:
+            if self.log and nroots_needed > num_warn:
                 self.logger.warn("""The log-likelihood has a large plateau with L=%g.
 
 Probably you are returning a low value when the parameters are problematic/unphysical.

From 2ed7e6c64fa992e4f17f7f382cc848368efa7edf Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 1 Jun 2023 22:44:23 +0200
Subject: [PATCH 123/313] expose new limits to root widening to users

---
 ultranest/integrator.py | 21 ++++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index a6b62235..9ba76e95 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2274,6 +2274,8 @@ def run(
             insertion_test_window=10,
             insertion_test_zscore_threshold=4,
             region_class=MLFriends,
+            widen_before_initial_plateau_num_warn=100000,
+            widen_before_initial_plateau_num_max=500000,
     ):
         """Run until target convergence criteria are fulfilled.
 
@@ -2346,6 +2348,19 @@ def run(
             Whether to use MLFriends+ellipsoidal+tellipsoidal region (better for multi-modal problems)
             or just ellipsoidal sampling (faster for high-dimensional, gaussian-like problems)
             or a axis-aligned ellipsoid (fastest, to be combined with slice sampling).
+
+        widen_before_initial_plateau_num_warn: int
+            If a likelihood plateau is encountered, increase the number
+            of initial live points so that once the plateau is traversed,
+            *min_num_live_points* live points remain.
+            If the number exceeds *widen_before_initial_plateau_num_warn*,
+            a warning is raised.
+
+        widen_before_initial_plateau_num_max: int
+            If a likelihood plateau is encountered, increase the number
+            of initial live points so that once the plateau is traversed,
+            *min_num_live_points* live points remain, but not more than
+            *widen_before_initial_plateau_num_warn*.
         """
         for result in self.run_iter(
             update_interval_volume_fraction=update_interval_volume_fraction,
@@ -2362,6 +2377,8 @@ def run(
             insertion_test_window=insertion_test_window,
             insertion_test_zscore_threshold=insertion_test_zscore_threshold,
             region_class=region_class,
+            widen_before_initial_plateau_num_warn=widen_before_initial_plateau_num_warn,
+            widen_before_initial_plateau_num_max=widen_before_initial_plateau_num_max,
         ):
             if self.log:
                 self.logger.debug("did a run_iter pass!")
@@ -2390,7 +2407,9 @@ def run_iter(
             viz_callback='auto',
             insertion_test_window=10000,
             insertion_test_zscore_threshold=2,
-            region_class=MLFriends
+            region_class=MLFriends,
+            widen_before_initial_plateau_num_warn=100000,
+            widen_before_initial_plateau_num_max=500000,
     ):
         """Iterate towards convergence.
 

From 60fad8c435091d284460ba4aa2e5cca693d48e3f Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 20 Jun 2023 21:53:13 +0300
Subject: [PATCH 124/313] add tests, pass arguments through

passing the plateau (passing_node is slow)
---
 tests/test_run.py       | 30 +++++++++++++++++++++-
 ultranest/integrator.py | 56 +++++++++++++++++++++--------------------
 2 files changed, 58 insertions(+), 28 deletions(-)

diff --git a/tests/test_run.py b/tests/test_run.py
index c6c4f1c4..850c33a7 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -116,6 +116,33 @@ def transform(x):
     sampler.plot()
 
 
+def test_plateau_SLOW():
+    def loglike(y):
+        a = -0.5 * ((y/0.1)**2).sum()
+        if a < -1:
+            return -1e100
+        return a
+
+    def transform(x):
+        return x * 2 - 1
+
+    paramnames = ['Hinz', 'Kunz']
+
+    sampler = ReactiveNestedSampler(paramnames, loglike, transform=transform)
+    print(sampler.run(min_num_live_points=400)['logz'])
+    print(sampler.run_sequence['nlive'][:-400])
+    assert sampler.run_sequence['nlive'][-400] == 400, sampler.run_sequence['nlive'][:-400]
+
+def test_flat():
+    def loglike(y):
+        return 0
+    paramnames = ['Hinz', 'Kunz']
+
+    sampler = ReactiveNestedSampler(paramnames, loglike)
+    print(sampler.run(min_num_live_points=400)['logz'])
+    print(sampler.run_sequence['nlive'][:-400])
+
+
 def test_reactive_run_extraparams():
     np.random.seed(1)
 
@@ -556,5 +583,6 @@ def transform(x):
     #test_run()
     #test_reactive_run_warmstart_gauss()
     #test_reactive_run_extraparams()
-    test_reactive_run_resume_eggbox('hdf5')
+    #test_reactive_run_resume_eggbox('hdf5')
     #test_dlogz_reactive_run()
+    test_plateau()
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 9ba76e95..66158062 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -474,7 +474,7 @@ def __init__(self,
         assert p.shape == (2, self.num_params), ("Error in transform function: returned shape is %s, expected %s" % (p.shape, (2, self.num_params)))
         logl = loglike(p)
         assert np.logical_and(u > 0, u < 1).all(), ("Error in transform function: u was modified!")
-        assert logl.shape == (2,), ("Error in loglikelihood function: returned shape is %s, expected %s" % (p.shape, (2, self.num_params)))
+        assert np.shape(logl) == (2,), ("Error in loglikelihood function: returned shape is %s, expected %s" % (p.shape, (2, self.num_params)))
         assert np.isfinite(logl).all(), ("Error in loglikelihood function: returned non-finite number: %s for input u=%s p=%s" % (logl, u, p))
 
         def safe_loglike(x):
@@ -1383,7 +1383,7 @@ def _widen_nodes(self, weighted_parents, weights, nnodes_needed, update_interval
 
         return target_min_num_children
 
-    def _widen_roots_beyond_initial_plateau(self, nroots, num_warn=100000, num_stop=500000):
+    def _widen_roots_beyond_initial_plateau(self, nroots, num_warn, num_stop):
         """Widen roots, but populate ahead of initial plateau.
 
         calls _widen_roots, and if there are several points with the same
@@ -1404,42 +1404,44 @@ def _widen_roots_beyond_initial_plateau(self, nroots, num_warn=100000, num_stop=
 
         """
         nroots_needed = nroots
+        user_has_been_warned = False
         while True:
             self._widen_roots(nroots_needed)
             Ls = np.array([node.value for node in self.root.children])
             Lmin = np.min(Ls)
-            if self.log and nroots_needed > num_warn:
-                self.logger.warn("""The log-likelihood has a large plateau with L=%g.
+            if self.log and nroots_needed > num_warn and not user_has_been_warned:
+                self.logger.warn("""Warning: The log-likelihood has a large plateau with L=%g.
 
-Probably you are returning a low value when the parameters are problematic/unphysical.
-ultranest can handle this correctly, by discarding live points with the same loglikelihood.
-(arxiv:2005.08602 arxiv:2010.13884). To mitigate running out of live points,
-the initial number of live points is increased. But now this has reached over %d points.
+  Probably you are returning a low value when the parameters are problematic/unphysical.
+  ultranest can handle this correctly, by discarding live points with the same loglikelihood.
+  (arxiv:2005.08602 arxiv:2010.13884). To mitigate running out of live points,
+  the initial number of live points is increased. But now this has reached over %d points.
 
-You can avoid this making the loglikelihood increase towards where the good region is.
-For example, let's say you have two parameters where the sum must be below 1. Replace this:
+  You can avoid this making the loglikelihood increase towards where the good region is.
+  For example, let's say you have two parameters where the sum must be below 1. Replace this:
 
     if params[0] + params[1] > 1:
          return -1e300
 
-with:
+  with:
 
     if params[0] + params[1] > 1:
          return -1e300 * (params[0] + params[1])
 
-The current strategy will continue until %d live points are reached.
-It is safe to ignore this warning.""", Lmin, num_warn, num_stop)
+  The current strategy will continue until %d live points are reached.
+  It is safe to ignore this warning.""", Lmin, num_warn, num_stop)
+                user_has_been_warned = True
 
-            if nroots_needed > num_stop:
+            if nroots_needed >= num_stop:
                 break
             P = (Ls == Lmin).sum()
-            if P > 1 and len(Ls) - P + 1 < nroots:
+            if 1 < P < len(Ls) and len(Ls) - P + 1 < nroots:
                 # guess the number of points needed: P-1 are useless
                 self.logger.debug(
                     'Found plateau of %d/%d initial points at L=%g. '
                     'Avoid this by a continuously increasing loglikelihood towards good regions.',
                     P, nroots_needed, Lmin)
-                nroots_needed = nroots_needed + (P - 1)
+                nroots_needed = min(num_stop, nroots_needed + (P - 1))
             else:
                 break
 
@@ -1841,7 +1843,6 @@ def _create_point(self, Lmin, ndraw, active_u, active_values):
             if ib >= len(self.samples) and self.use_point_stack:
                 # root checks the point store
                 next_point = np.zeros((1, 3 + self.x_dim + self.num_params)) * np.nan
-                # print("1", self.mpi_rank, next_point)
 
                 if self.log_to_pointstore:
                     _, stored_point = self.pointstore.pop(Lmin)
@@ -1849,17 +1850,13 @@ def _create_point(self, Lmin, ndraw, active_u, active_values):
                         next_point[0,:] = stored_point
                     else:
                         next_point[0,:] = -np.inf
-                    # print("2", self.mpi_rank, next_point)
                     self.use_point_stack = not self.pointstore.stack_empty
 
                 if self.use_mpi:  # and informs everyone
                     self.use_point_stack = self.comm.bcast(self.use_point_stack, root=0)
-                    # print("3", self.mpi_rank, next_point)
                     next_point = self.comm.bcast(next_point, root=0)
 
                 # unpack
-                if np.ndim(next_point) != 2:
-                    print("XXXX ", self.mpi_rank, next_point, self.use_point_stack)
                 self.likes = next_point[:,1]
                 self.samples = next_point[:,3:3 + self.x_dim]
                 self.samplesv = next_point[:,3 + self.x_dim:3 + self.x_dim + self.num_params]
@@ -2274,8 +2271,8 @@ def run(
             insertion_test_window=10,
             insertion_test_zscore_threshold=4,
             region_class=MLFriends,
-            widen_before_initial_plateau_num_warn=100000,
-            widen_before_initial_plateau_num_max=500000,
+            widen_before_initial_plateau_num_warn=10000,
+            widen_before_initial_plateau_num_max=50000,
     ):
         """Run until target convergence criteria are fulfilled.
 
@@ -2408,8 +2405,8 @@ def run_iter(
             insertion_test_window=10000,
             insertion_test_zscore_threshold=2,
             region_class=MLFriends,
-            widen_before_initial_plateau_num_warn=100000,
-            widen_before_initial_plateau_num_max=500000,
+            widen_before_initial_plateau_num_warn=10000,
+            widen_before_initial_plateau_num_max=50000,
     ):
         """Iterate towards convergence.
 
@@ -2461,7 +2458,9 @@ def run_iter(
         if viz_callback == 'auto':
             viz_callback = get_default_viz_callback()
 
-        self._widen_roots_beyond_initial_plateau(min_num_live_points)
+        self._widen_roots_beyond_initial_plateau(
+            min_num_live_points, 
+            widen_before_initial_plateau_num_warn, widen_before_initial_plateau_num_max)
 
         Llo, Lhi = -np.inf, np.inf
         Lmax = -np.inf
@@ -2802,7 +2801,10 @@ def run_iter(
             if dlogz_min_num_live_points > self.min_num_live_points:
                 # more live points needed throughout to reach target
                 self.min_num_live_points = dlogz_min_num_live_points
-                self._widen_roots_beyond_initial_plateau(self.min_num_live_points)
+                self._widen_roots_beyond_initial_plateau(
+                    self.min_num_live_points,
+                    widen_before_initial_plateau_num_warn,
+                    widen_before_initial_plateau_num_max)
 
             elif Llo <= Lhi:
                 # if self.log:

From 6554c4b560c7e86b18bc25f79b88bcbda3201e36 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 17 Jan 2022 09:11:56 +0100
Subject: [PATCH 125/313] first try at population step sampler; has issues

in particular, the posterior and lnZ seem to come out biased for
some reason, which I do not understand yet. Needs more debugging
---
 ultranest/integrator.py  |   7 +-
 ultranest/stepsampler.py | 514 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 518 insertions(+), 3 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 66158062..2b06b483 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -18,7 +18,7 @@
 
 from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify as _listify
 from .utils import is_affine_transform, normalised_kendall_tau_distance, distributed_work_chunk_size
-from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid
+from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
 from .store import HDF5PointStore, TextPointStore, NullPointStore
 from .viz import get_default_viz_callback
 from .ordertest import UniformOrderAccumulator
@@ -2935,8 +2935,8 @@ def print_results(self, use_unicode=True):
                     # add a bit of padding, but not outside parameter limits
                     lo, hi = edges[0], edges[-1]
                     step = edges[1] - lo
-                    lo = max(min(lo, self.transform_limits[i,0]), lo - 2 * step)
-                    hi = min(max(hi, self.transform_limits[i,1]), hi + 2 * step)
+                    lo = max(self.transform_limits[i,0], lo - 2 * step)
+                    hi = min(self.transform_limits[i,1], hi + 2 * step)
                     H, edges = np.histogram(v, bins=np.linspace(lo, hi, 40))
                     lo, hi = edges[0], edges[-1]
 
@@ -2947,6 +2947,7 @@ def print_results(self, use_unicode=True):
                     print(fmts % (p, med, sigma))
             print()
 
+
     def plot(self):
         """Make corner, run and trace plots.
 
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index e5378986..8d0cf826 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -1219,3 +1219,517 @@ def crop_bracket_at_unit_cube(ui, v, left, right, epsilon=1e-6):
 
     assert left <= 0 <= right, (left, right)
     return left, right, cropped_left, cropped_right
+
+def _prepare_steps(
+    nsteps_done, nsteps, directions, ndraw,
+    current_interval, loglike, transform, region, ndim, region_filter, 
+    Lmin, verbose,
+):
+    point_sequence = []
+    point_expectation = []
+    intervals = []
+    nsteps_prepared = 0
+    while nsteps_prepared + nsteps_done < nsteps and len(point_sequence) < ndraw:
+        if verbose:
+            print("loop:", nsteps_prepared, nsteps_done, 'of', nsteps)
+        v = directions[nsteps_done + nsteps_prepared]
+        if verbose:
+            print("direction:", v)
+        if len(point_sequence) == 0:
+            ucurrent, left, right = current_interval
+            assert (ucurrent >= 0).all(), ucurrent
+            assert (ucurrent <= 1).all(), ucurrent
+            assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), (
+                'cannot start from outside ellipsoid!', region.inside_ellipsoid(ucurrent.reshape((1, ndim))))
+            if region_filter:
+                assert region.inside(ucurrent.reshape((1, ndim))), (
+                    'cannot start from outside region!', region.inside(ucurrent.reshape((1, ndim))))
+            assert loglike(transform(ucurrent.reshape((1, ndim)))) >= Lmin, (
+                'cannot start from outside!', loglike(transform(ucurrent.reshape((1, ndim)))), Lmin)
+        else:
+            left, right = None, None
+        assert (ucurrent >= 0).all(), ucurrent
+        assert (ucurrent <= 1).all(), ucurrent
+        if verbose:
+            print("preparing step: %d from %s" % (nsteps_prepared + nsteps_done, ucurrent))
+
+        if left is None or right is None:
+            # in each, find the end points using the expanded ellipsoid
+            assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), ('current point outside ellipsoid!')
+            left, right = ellipsoid_bracket(ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.enlarge)
+            left, right, _, _ = crop_bracket_at_unit_cube(ucurrent, v, left, right)
+            assert (ucurrent + v * left <= 1).all(), (
+                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
+            assert (ucurrent + v * right <= 1).all(), (
+                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
+            assert (ucurrent + v * left >= 0).all(), (
+                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
+            assert (ucurrent + v * right >= 0).all(), (
+                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
+
+            assert left <= 0 <= right, (left, right)
+            if verbose:
+                print("   ellipsoid bracket found:", left, right)
+
+        while True:
+            # sample in each a point until presumed success:
+            assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), ('current point outside ellipsoid!')
+            t = np.random.uniform(left, right)
+            unext = ucurrent + v * t
+            assert (unext >= 0).all(), unext
+            assert (unext <= 1).all(), unext
+            assert region.inside_ellipsoid(unext.reshape((1, ndim))), ('proposal landed outside ellipsoid!', t, left, right)
+
+            # compute distance vector to center
+            d = unext - region.ellipsoid_center
+            # distance in normalised coordates: vector . matrix . vector
+            # where the matrix is the ellipsoid inverse covariance
+            r = np.einsum('j,jk,k->', d, region.ellipsoid_invcov, d)
+            if verbose:
+                print("   proposed slice point", t, r)
+
+            likely_inside = r <= 1
+            if not likely_inside and r <= region.enlarge:
+                # The exception is, when a point is between projected ellipsoid center and current point
+                # then it is also likely inside (if still inside the ellipsoid)
+
+                # project ellipsoid center onto line
+                # region.ellipsoid_center = ucurrent + tc * v
+                tc = np.dot(region.ellipsoid_center - ucurrent, v)
+                # current point is at 0 by definition
+                if 0 < t < tc or tc < t < 0:
+                    if verbose:
+                        print("   proposed point is further inside than current point")
+                    likely_inside = True
+                #    print("   proposed point %.3f is going towards center %.3f" % (t, tc))
+                # else:
+                #    print("   proposed point %.3f is going away from center %.3f" % (t, tc))
+                else:
+                    # another exception is that points very close to the current point
+                    # are very likely also inside
+                    # to find that out, project all live points on the line
+                    tall = np.einsum('ij,j->i', region.u - ucurrent, v)
+                    # find the range and identify a small part of it
+                    epsilon_nearby = 1e-6
+                    if tc < (tall.max() - tall.min()) * epsilon_nearby:
+                        likely_inside = True
+                        if verbose:
+                            print("   proposed point is very nearby")
+
+            if verbose:
+                print("   proposed point %s (%f) is likely %s (r=%f)" % (unext, t, 'inside' if likely_inside else 'outside', r))
+            intervals.append((nsteps_prepared, ucurrent, v, left, right, t))
+            point_sequence.append(unext)
+            point_expectation.append(likely_inside)
+            #   If point radius in ellipsoid is <1, presume that it will be successful
+            if likely_inside:
+                nsteps_prepared += 1
+                ucurrent = unext
+                assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), ('current point outside ellipsoid!')
+                break
+
+            #   Else, presume it will be unsuccessful, and sample another point
+            #   shrink interval
+            if t > 0:
+                right = t
+            else:
+                left = t
+
+    assert len(point_sequence) == len(point_expectation)
+    assert len(point_sequence) == len(intervals)
+    assert nsteps_prepared <= len(point_sequence)
+
+    assert len(point_sequence) > 0, (len(point_sequence), ndraw, nsteps_prepared, nsteps_done, nsteps)
+
+    if verbose:
+        print("proposed sequence:", point_sequence)
+        print("expectations:", point_expectation)
+
+    return np.array(point_sequence, dtype=float), np.array(point_expectation, dtype=bool), intervals, nsteps_prepared
+
+
+def _evaluate_with_filter(
+    region_filter, loglike, transform, Lmin, region, tregion,
+    point_sequence, point_expectation, 
+    verbose
+):
+    truncated = False
+    # region-filter, transform, tregion-filter, and evaluate the likelihood
+    if region_filter:
+        mask_inside = region.inside(point_sequence)
+        # identify first point that was expected to be inside, but was marked outside-of-region
+        i = np.where(np.logical_and(point_expectation, ~mask_inside))[0]
+        if verbose:
+            print("region filter says:", mask_inside, i)
+        if len(i) > 0:
+            imax = i[0] + 1
+            # truncate there
+            point_sequence = point_sequence[:imax]
+            point_expectation = point_expectation[:imax]
+            mask_inside = mask_inside[:imax]
+            truncated |= True
+            del imax
+        if not mask_inside.any():
+            return None
+    else:
+        mask_inside = None
+
+    t_point_sequence = transform(point_sequence)
+    if region_filter and tregion is not None:
+        tmask = tregion.inside(t_point_sequence)
+        # identify first point that was expected to be inside, but was marked outside-of-region
+        i = np.where(np.logical_and(point_expectation, ~tmask))[0]
+        if verbose:
+            print("tregion filter says:", tmask, i)
+        mask_inside[~tmask] = False
+        del tmask
+        if len(i) > 0:
+            imax = i[0] + 1
+            # truncate there
+            point_sequence = point_sequence[:imax]
+            point_expectation = point_expectation[:imax]
+            t_point_sequence = t_point_sequence[:imax]
+            mask_inside = mask_inside[:imax]
+            truncated |= True
+            del imax
+        if not mask_inside.any():
+            return None
+
+    # we expect the last point to be an accept, otherwise we would not terminate the sequence
+    assert point_expectation[-1]
+    if region_filter:
+        # set filtered ones to -np.inf
+        L = np.ones(len(t_point_sequence)) * -np.inf
+        nc = mask_inside.sum()
+        L[mask_inside] = loglike(t_point_sequence[mask_inside,:])
+    else:
+        nc = len(point_sequence)
+        L = loglike(t_point_sequence)
+    Lmask = L > Lmin
+
+    i = np.where(point_expectation != Lmask)[0]
+    if verbose:
+        print("reality:", Lmask)
+        print("difference:", point_expectation == Lmask)
+    return point_sequence, t_point_sequence, L, Lmask, i, nc, truncated
+
+class AHARMSampler(StepSampler):
+    """Accelerated hit-and-run/slice sampler, vectorised.
+
+    Uses region ellipsoid to propose a sequence of points
+    on a randomly drawn line.
+
+    (in development)
+    """
+
+    def __init__(
+        self, nsteps, adaptive_nsteps=False, max_nsteps=1000,
+        region_filter=False, log=False, direction=generate_region_random_direction,
+        orthogonalise=True,
+    ):
+        """Initialise vectorised hit-and-run/slice sampler.
+
+        Parameters
+        -----------
+        nsteps: int
+            number of accepted steps until the sample is considered independent.
+
+        adaptive_nsteps: False, 'proposal-distance', 'move-distance'
+            Select a strategy to adapt the number of steps. The strategies
+            make sure that:
+
+            * 'move-distance' (recommended): distance between
+              start point and final position exceeds the mean distance
+              between pairs of live points.
+            * 'move-distance-midway': distance between
+              start point and position in the middle of the chain
+              exceeds the mean distance between pairs of live points.
+
+        max_nsteps: int
+            Maximum number of steps the adaptive_nsteps can reach.
+
+        region_filter: bool
+            if True, use region to check if a proposed point can be inside
+            before calling likelihood.
+
+        direction: function
+            function that draws slice direction given a point and
+            the current region.
+
+        orthogonalise: bool
+            If true, make subsequent proposed directions orthogonal
+            to each other.
+
+        log: file
+            log file for sampler statistics, such as acceptance rate,
+            proposal scale, number of steps, jump distance and distance
+            between live points
+
+        """
+        self.history = []
+        self.nsteps = nsteps
+        self.nrejects = 0
+        self.max_nsteps = max_nsteps
+        self.last = None, None
+        self.generate_direction = direction
+        adaptive_nsteps_options = [
+            False,
+            'move-distance', 'move-distance-midway',
+        ]
+
+        if adaptive_nsteps not in adaptive_nsteps_options:
+            raise ValueError("adaptive_nsteps must be one of: %s, not '%s'" % (adaptive_nsteps_options, adaptive_nsteps))
+        self.adaptive_nsteps = adaptive_nsteps
+        self.region_filter = region_filter
+        self.log = log
+        self.adaptive_nsteps_needs_mean_pair_distance = False
+        self.nsteps_nudge = 1.01
+        self.orthogonalise = orthogonalise
+
+        self.logstat = []
+        self.logstat_labels = ['rejection_rate', 'steps']
+        if adaptive_nsteps:
+            self.logstat_labels += ['jump-distance', 'reference-distance']
+
+    def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=1024, plot=False, tregion=None, verbose=False):
+        """Get next point.
+
+        Parameters
+        ----------
+        region: MLFriends
+            region.
+        Lmin: float
+            loglikelihood threshold
+        us: array of vectors
+            current live points
+        Ls: array of floats
+            current live point likelihoods
+        transform: function
+            transform function
+        loglike: function
+            loglikelihood function
+        ndraw: int
+            number of draws to attempt simultaneously.
+        plot: bool
+            whether to produce debug plots.
+        tregion: WrappingEllipsoid
+            optional ellipsoid in transformed space for rejecting proposals
+
+        """
+        # find most recent point in history conforming to current Lmin
+        ui, Li = self.last
+        if Li is not None and not Li >= Lmin:
+            print("wandered out of L constraint; resetting", ui[0])
+            ui, Li = None, None
+
+        if ui is not None and not region.inside_ellipsoid(ui.reshape((1, -1))):
+            print("wandered out of ellipsoid; resetting", ui[0])
+            ui, Li = None, None
+
+        if Li is None and self.history:
+            # try to resume from a previous point above the current contour
+            for j, (uj, Lj) in enumerate(self.history[::-1]):
+                if Lj > Lmin and region.inside(uj.reshape((1,-1))) and (tregion is None or tregion.inside(transform(uj.reshape((1, -1))))):
+                    ui, Li = uj, Lj
+                    # print("recovering at point %d/%d " % (j+1, len(self.history)))
+                    self.last = ui, Li
+
+                    # pj = transform(uj.reshape((1, -1)))
+                    # Lj2 = loglike(pj)[0]
+                    # assert Lj2 > Lmin, (Lj2, Lj, uj, pj)
+                    assert region.inside_ellipsoid(ui.reshape((1, -1)))
+
+                    break
+            pass
+
+        # select starting point
+        ndim = us.shape[1]
+        if Li is None:
+            self.directions = None
+
+            self.history = []
+            self.last = None, None
+            self.nrejects = 0
+
+            # choose a new random starting point
+            i = np.random.randint(len(us))
+            self.starti = i
+            ui = us[i,:]
+            assert region.inside_ellipsoid(ui.reshape((1, -1)))
+            assert np.logical_and(ui > 0, ui < 1).all(), ui
+            Li = Ls[i]
+            self.history.append((ui.copy(), Li.copy()))
+            del i
+            print("starting at", ui)
+            # set initially nleft = nsteps
+            self.nsteps_done = 0
+
+            # generate nsteps directions
+            self.directions = []
+            for i in range(self.nsteps):
+                v = self.generate_direction(ui, region)
+                self.directions.append(v)
+            self.directions = np.array(self.directions)
+
+            if verbose:
+                print("directions:", self.directions)
+            if self.orthogonalise:
+                # orthogonalise relative to this previous direction
+                for i in range(self.nsteps // ndim):
+                    # go back only ndim steps, then start fresh
+                    self.directions[i * ndim:(i + 1) * ndim], _ = np.linalg.qr(self.directions[i * ndim:(i + 1) * ndim])
+
+            assert (ui >= 0).all(), ui
+            assert (ui <= 1).all(), ui
+            self.current_interval = ui, None, None
+            if self.region_filter:
+                assert region.inside(ui.reshape((1, ndim))), ('cannot start from outside region!', region.inside(ui.reshape((1, ndim))))
+
+        del ui
+        nc = 0
+        while True:
+            # prepare a sequence of points until nsteps are reached
+            point_sequence, point_expectation, intervals, nsteps_prepared = _prepare_steps(
+                self.nsteps_done, self.nsteps, self.directions, ndraw,
+                self.current_interval, loglike, transform, region, ndim, self.region_filter, 
+                Lmin, verbose
+            )
+            point_sequence, t_point_sequence, L, Lmask, indices_deviating, nc_here, truncated = _evaluate_with_filter(
+                self.region_filter, loglike, transform, Lmin, region, tregion,
+                point_sequence, point_expectation, 
+                verbose
+            )
+            del point_expectation
+            nc += nc_here
+
+            self.nrejects += (~Lmask).sum()
+            #print("calling likelihood with %5d prepared points, accepted:" % (
+            #    len(point_sequence)), '=' * (i[0] + Lmask[i[0]] * 1 if len(i) > 0 else len(Lmask)))
+            # identify first point that was unexpected
+            any_deviating = len(indices_deviating) > 0
+            if any_deviating and nsteps_prepared + self.nsteps_done == self.nsteps:
+                # everything according to prediction.
+                if verbose:
+                    print("everything according to prediction and done")
+                # done, return last point
+                for ui, Li in zip(point_sequence[Lmask], L[Lmask]):
+                    self.history.append((ui, Li))
+                self.finalize_chain(region=region, Lmin=Lmin, Ls=Ls)
+                return point_sequence[-1], t_point_sequence[-1], L[-1], nc
+            elif any_deviating:
+                # everything according to prediction.
+                if verbose:
+                    print("everything according to prediction")
+                # continue from last point
+                for ui, Li in zip(point_sequence[Lmask], L[Lmask]):
+                    self.history.append((ui, Li))
+                self.nsteps_done += nsteps_prepared
+                assert self.nsteps_done == len(self.history), (self.nsteps_done, len(self.history))
+                nsteps_prepared, ucurrent, v, left, right, t = intervals[-1]
+                assert (ucurrent >= 0).all(), ucurrent
+                assert (ucurrent <= 1).all(), ucurrent
+                self.current_interval = ucurrent, None, None
+                if self.region_filter:
+                    assert region.inside(ucurrent.reshape((1, ndim))), ('suggested point outside region!', region.inside(ucurrent.reshape((1, ndim))))
+            else:
+                # point i unexpectedly inside or outside
+                imax = indices_deviating[0]
+                for ui, Li in zip(point_sequence[:imax][Lmask[:imax]], L[:imax][Lmask[:imax]]):
+                    self.history.append((ui, Li))
+                nsteps_prepared, ucurrent, v, left, right, t = intervals[imax]
+                if self.region_filter:
+                    assert region.inside(ucurrent.reshape((1, ndim))), ('suggested point outside region!', region.inside(ucurrent.reshape((1, ndim))))
+                assert (ucurrent >= 0).all(), ucurrent
+                assert (ucurrent <= 1).all(), ucurrent
+                if point_expectation[imax]:
+                    if verbose:
+                        print("following prediction until %d, which was unexpectedly rejected" % imax)
+                    # expected point to lie inside, but rejected
+                    # need to repair interval
+                    self.nsteps_done += nsteps_prepared
+                    assert self.nsteps_done + 1 == len(self.history), (self.nsteps_done, len(self.history))
+                    if t > 0:
+                        right = t
+                    else:
+                        left = t
+                    if verbose:
+                        print("%d steps done, continuing from unexpected outside point" % self.nsteps_done, imax, point_sequence[imax], "interval:", t)
+                    self.current_interval = ucurrent, left, right
+                else:
+                    if verbose:
+                        print("following prediction until %d, which was unexpectedly accepted" % imax)
+                    if imax == len(point_sequence) - 1 and truncated:
+                        assert False
+                    ucurrent = point_sequence[imax]
+                    if self.region_filter:
+                        assert region.inside(ucurrent.reshape((1, ndim))), ('accepted point outside region!', region.inside(ucurrent.reshape((1, ndim))))
+                    # expected point to lie outside, but actually inside
+                    # adopt as point and continue
+                    # print(len(self.history), self.nsteps_done, nsteps_prepared, Lmask[:imax].sum())
+                    self.nsteps_done += nsteps_prepared + 1
+                    self.history.append((ucurrent.copy(), L[imax]))
+                    assert self.nsteps_done + 1 == len(self.history), (self.nsteps_done, len(self.history))
+                    self.current_interval = ucurrent, None, None
+                    if self.nsteps_done == self.nsteps:
+                        # last point was inside, so we are actually done there
+                        self.finalize_chain(region=region, Lmin=Lmin, Ls=Ls)
+                        return point_sequence[-1], t_point_sequence[-1], L[-1], nc
+                    else:
+                        if verbose:
+                            print("%d steps done, continuing from unexpected inside point" % self.nsteps_done, imax, point_sequence[imax])
+
+            # need to exit here to only do one likelihood evaluation
+            # per function call
+            if verbose:
+                print("breaking")
+            break
+
+        # do not have a independent sample yet
+        return None, None, None, nc
+
+    def region_changed(self, Ls, region):
+        assert region.inside_ellipsoid(region.u).all()
+        ui, Li = self.last
+        if ui is not None and not region.inside(ui.reshape((1, -1))):
+            print("wandered out of ellipsoid; resetting", ui[0])
+            self.last = None, None
+
+    def finalize_chain(self, region=None, Lmin=None, Ls=None):
+        """Store chain statistics and adapt proposal."""
+        self.logstat.append([self.nrejects / self.nsteps, self.nsteps])
+        if self.log:
+            ustart, Lstart = self.history[0]
+            ufinal, Lfinal = self.history[-1]
+            # mean_pair_distance = region.compute_mean_pair_distance()
+            mean_pair_distance = np.nan
+            tstart, tfinal = region.transformLayer.transform(np.vstack((ustart, ufinal)))
+            # L index of start and end
+            # Ls_sorted = np.sort(Ls)
+            iLstart = np.sum(Ls > Lstart)
+            iLfinal = np.sum(Ls > Lfinal)
+            # nearest neighbor index of start and end
+            itstart = np.argmin((region.unormed - tstart.reshape((1, -1)))**2)
+            itfinal = np.argmin((region.unormed - tfinal.reshape((1, -1)))**2)
+            np.savetxt(self.log, [_listify(
+                [Lmin], ustart, ufinal, tstart, tfinal,
+                [self.nsteps, region.maxradiussq**0.5, mean_pair_distance,
+                 iLstart, iLfinal, itstart, itfinal])])
+
+        if self.adaptive_nsteps:
+            self.adapt_nsteps(region=region)
+
+        self.last = None, None
+        self.history = []
+        self.nrejects = 0
+
+    def generate_new_interval(self, ui, region):
+        v = self.generate_direction(ui, region)
+        assert region.inside_ellipsoid(ui.reshape((1, -1)))
+        assert (ui > 0).all(), ui
+        assert (ui < 1).all(), ui
+
+        # use region ellipsoid to identify limits
+        # rotate line so that ellipsoid is a sphere
+        left, right = ellipsoid_bracket(ui, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.enlarge)
+        left, right, _, _ = crop_bracket_at_unit_cube(ui, v, left, right)
+        self.interval = (v, left, right, 0)

From 4deb415eeab2da0fb80b3c6948165888fd14c1fe Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 19 Jun 2023 10:38:02 +0300
Subject: [PATCH 126/313] add gaussian population sampler

---
 examples/test_popsampler.py |  58 ++++++++++++
 ultranest/popstepsampler.py | 184 ++++++++++++++++++++++++++++++++++++
 2 files changed, 242 insertions(+)
 create mode 100644 examples/test_popsampler.py

diff --git a/examples/test_popsampler.py b/examples/test_popsampler.py
new file mode 100644
index 00000000..c54e4075
--- /dev/null
+++ b/examples/test_popsampler.py
@@ -0,0 +1,58 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+import numpy as np
+from ultranest import ReactiveNestedSampler
+from ultranest.mlfriends import RobustEllipsoidRegion, SimpleRegion, ScalingLayer
+import ultranest.popstepsampler
+import matplotlib.pyplot as plt
+
+def main(generate_direction_method, ndim, nsteps, popsize, verbose=False):
+
+    np.random.seed(1)
+
+    logsigma = -5
+
+    sigma = np.logspace(-1, logsigma, ndim)
+    width = 1 - 5 * sigma
+    width[width < 1e-20] = 1e-20
+    centers = (np.sin(np.arange(ndim)/2.) * width + 1.) / 2.
+    #sigma[:] = 0.01
+    #centers[:] = 0.5
+
+    norm = -0.5 * np.log(2 * np.pi * sigma**2).sum()
+    def loglike(theta):
+        return -0.5 * (((theta - centers) / sigma)**2).sum(axis=1) + norm
+
+    def transform(x):
+        return x
+
+    paramnames = ['param%d' % (i+1) for i in range(ndim)]
+
+    sampler = ReactiveNestedSampler(paramnames, loglike, transform=transform, vectorized=True)
+    
+    # ellipsoidal:
+    region_class = RobustEllipsoidRegion
+    # ellipsoidal axis-aligned:
+    #sampler.transform_layer_class = ScalingLayer
+    #region_class = SimpleRegion
+    
+    sampler.stepsampler = ultranest.popstepsampler.PopulationRandomWalkSampler(
+        popsize=popsize, nsteps=nsteps, scale=1. / len(paramnames),
+        generate_direction=getattr(ultranest.popstepsampler, generate_direction_method), log=verbose,
+    )
+    results = sampler.run(
+        frac_remain=0.01, update_interval_volume_fraction=0.01, 
+        max_num_improvement_loops=0, min_num_live_points=400, 
+        viz_callback=None, region_class=region_class
+    )
+    sampler.print_results()
+    stats = results['posterior']
+    plt.errorbar(x=np.arange(ndim), y=stats['mean'] - centers, yerr=stats['stdev'] / sigma, color='k')
+    plt.savefig('populationstepsampler_%d.pdf' % ndim)
+    plt.close()
+    #sampler.plot_trace()
+
+if __name__ == '__main__':
+    import sys
+    main(sys.argv[1], *map(int, sys.argv[2:5]), verbose='--verbose' in sys.argv[1:])
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index cbd8b945..0374be8a 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -6,6 +6,190 @@
 from ultranest.stepfuncs import evolve, step_back
 from ultranest.stepfuncs import generate_cube_oriented_direction, \
    generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
+import scipy.stats
+
+def unitcube_line_intersection(ray_origin, ray_direction):
+    r"""Compute intersection of a line (ray) and a unit box (0:1 in all axes).
+
+    Based on
+    http://www.iquilezles.org/www/articles/intersectors/intersectors.htm
+
+    Parameters
+    -----------
+    ray_origin: array of vectors
+        starting point of line
+    ray_direction: vector
+        line direction vector
+
+    Returns
+    --------
+    tleft: array
+        negative intersection point distance from ray\_origin in units in ray\_direction
+    tright: array
+        positive intersection point distance from ray\_origin in units in ray\_direction
+
+    """
+    # make sure ray starts inside the box
+    assert (ray_origin >= 0).all(), ray_origin
+    assert (ray_origin <= 1).all(), ray_origin
+    assert ((ray_direction**2).sum()**0.5 > 1e-200).all(), ray_direction
+
+    # step size
+    with np.errstate(divide='ignore', invalid='ignore'):
+        m = 1. / ray_direction
+        n = m * (ray_origin - 0.5)
+        k = np.abs(m) * 0.5
+        # line coordinates of intersection
+        # find first intersecting coordinate
+        t1 = -n - k
+        t2 = -n + k
+        return np.nanmax(t1, axis=1), np.nanmin(t2, axis=1)
+
+
+
+class PopulationRandomWalkSampler():
+    def __init__(
+        self, popsize, nsteps, generate_direction, scale=1.0, 
+        scale_adapt_factor=0.9, log=False, logfile=None
+    ):
+        """
+        Vectorized Gaussian Random Walk sampler.
+
+        Revert until all previous steps have likelihoods allL above Lmin.
+        Updates currentt, generation and allL, in-place.
+
+        Parameters
+        ----------
+        popsize: int
+            number of walkers to maintain
+        nsteps: int
+            number of steps to take until the found point is accepted as independent.
+        generate_direction: function `(u, region, scale) -> v`
+            function such as `generate_unit_directions`, which
+            generates a random slice direction.
+        scale: float
+            initial guess scale for the length of the slice
+        scale_adapt_factor: float
+            smoothing factor for updating scale.
+            if near 1, scale is barely updating, if near 0,
+            the last slice length is used as a initial guess for the next.
+
+        """
+        self.nsteps = nsteps
+        self.nrejects = 0
+        self.scale = scale
+        self.ncalls = 0
+        assert scale_adapt_factor <= 1
+        self.scale_adapt_factor = scale_adapt_factor
+
+        self.log = log
+        self.logfile = logfile
+        self.prepared_samples = []
+
+        self.popsize = popsize
+        self.generate_direction = generate_direction
+
+    def __str__(self):
+        return 'PopulationRandomWalkSampler(popsize=%d, nsteps=%d, generate_direction=%s, scale=%.g)' % (
+                self.popsize, self.nsteps, self.generate_direction, self.scale)
+
+    def region_changed(self, Ls, region):
+        """notification that the region changed. Currently not used."""
+        pass
+
+    def __next__(
+        self, region, Lmin, us, Ls, transform, loglike, ndraw=10,
+        plot=False, tregion=None, log=False
+    ):
+        """Sample a new live point.
+
+        Parameters
+        ----------
+        region: MLFriends object
+            Region
+        Lmin: float
+            current log-likelihood threshold
+        us: np.array((nlive, ndim))
+            live points
+        Ls: np.array(nlive)
+            loglikelihoods live points
+        transform: function
+            prior transform function
+        loglike: function
+            loglikelihood function
+        ndraw: int
+            not used
+        plot: bool
+            not used
+        tregion: bool
+            not used
+        log: bool
+            not used
+
+        Returns
+        -------
+        u: np.array(ndim) or None
+            new point coordinates (None if not yet available)
+        p: np.array(nparams) or None
+            new point transformed coordinates (None if not yet available)
+        L: float or None
+            new point likelihood (None if not yet available)
+        nc: int
+
+        """
+        nlive, ndim = us.shape
+
+        # fill if empty:
+        if len(self.prepared_samples) == 0:
+            # choose live points
+            ilive = np.random.randint(0, nlive, size=self.popsize)
+            allu = us[ilive,:]
+            allp = None
+            allL = Ls[ilive]
+            nc = self.nsteps * self.popsize
+            nrejects_expected = self.nrejects + self.nsteps * self.popsize * (1 - 0.234)
+
+            for i in range(self.nsteps):
+                # perturb walker population
+                v = self.generate_direction(allu, region, self.scale)
+                # compute intersection of u + t * v with unit cube
+                tleft, tright = unitcube_line_intersection(allu, v)
+                #print(tleft.shape, tright.shape, self.popsize, tleft, tright)
+                proposed_t = scipy.stats.truncnorm.rvs(tleft, tright, loc=0, scale=1).reshape((-1, 1))
+                # proposed_t = np.random.normal(size=(self.popsize, 1))
+                
+                proposed_u = allu + v * proposed_t
+                mask_outside = ~np.logical_and(proposed_u > 0, proposed_u < 1).all(axis=1)
+                assert not mask_outside.any(), proposed_u[mask_outside,:]
+                #while mask_outside.any():
+                #    proposed_u[mask_outside,:] = allu[mask_outside,:] + v[mask_outside,:] * np.random.normal(size=(mask_outside.sum(), 1))
+                #    mask_outside = ~np.logical_and(proposed_u > 0, proposed_u < 1).all(axis=1)
+                
+                proposed_p = transform(proposed_u)
+                # accept if likelihood threshold exceeded
+                proposed_L = loglike(proposed_p)
+                mask_accept = proposed_L > Lmin
+                self.nrejects += (~mask_accept).sum()
+                allu[mask_accept,:] = proposed_u[mask_accept,:]
+                if allp is None:
+                    allp = proposed_p * np.nan
+                allp[mask_accept,:] = proposed_p[mask_accept,:]
+                allL[mask_accept] = proposed_L[mask_accept]
+            assert np.isfinite(allp).all(), 'some walkers never moved! Double nsteps of PopulationRandomWalkSampler.'
+            self.prepared_samples = list(zip(allu, allp, allL))
+
+            # adapt slightly
+            print('%.1f%%  %.1f%%  %f ' % (mask_accept.mean() * 100, 100 - (self.nrejects - (nrejects_expected - self.nsteps * self.popsize * (1 - 0.234))) * 100. / (self.nsteps * self.popsize), self.scale))
+            if self.nrejects > nrejects_expected and self.scale > 1e-20:
+                # lots of rejects, decrease scale
+                self.scale *= self.scale_adapt_factor
+            elif self.nrejects < nrejects_expected and self.scale < 10:
+                self.scale /= self.scale_adapt_factor
+        else:
+            nc = 0
+
+        u, p, L = self.prepared_samples.pop(0)
+        return u, p, L, nc
 
 
 class PopulationSliceSampler():

From f57c3a5e3ae57e32da9b2225153f80b1a0acb06f Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 20 Jun 2023 23:05:52 +0300
Subject: [PATCH 127/313] remove AHARM, not needed anymore with popstepsampler
 implementation

---
 ultranest/stepsampler.py | 514 ---------------------------------------
 1 file changed, 514 deletions(-)

diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 8d0cf826..e5378986 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -1219,517 +1219,3 @@ def crop_bracket_at_unit_cube(ui, v, left, right, epsilon=1e-6):
 
     assert left <= 0 <= right, (left, right)
     return left, right, cropped_left, cropped_right
-
-def _prepare_steps(
-    nsteps_done, nsteps, directions, ndraw,
-    current_interval, loglike, transform, region, ndim, region_filter, 
-    Lmin, verbose,
-):
-    point_sequence = []
-    point_expectation = []
-    intervals = []
-    nsteps_prepared = 0
-    while nsteps_prepared + nsteps_done < nsteps and len(point_sequence) < ndraw:
-        if verbose:
-            print("loop:", nsteps_prepared, nsteps_done, 'of', nsteps)
-        v = directions[nsteps_done + nsteps_prepared]
-        if verbose:
-            print("direction:", v)
-        if len(point_sequence) == 0:
-            ucurrent, left, right = current_interval
-            assert (ucurrent >= 0).all(), ucurrent
-            assert (ucurrent <= 1).all(), ucurrent
-            assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), (
-                'cannot start from outside ellipsoid!', region.inside_ellipsoid(ucurrent.reshape((1, ndim))))
-            if region_filter:
-                assert region.inside(ucurrent.reshape((1, ndim))), (
-                    'cannot start from outside region!', region.inside(ucurrent.reshape((1, ndim))))
-            assert loglike(transform(ucurrent.reshape((1, ndim)))) >= Lmin, (
-                'cannot start from outside!', loglike(transform(ucurrent.reshape((1, ndim)))), Lmin)
-        else:
-            left, right = None, None
-        assert (ucurrent >= 0).all(), ucurrent
-        assert (ucurrent <= 1).all(), ucurrent
-        if verbose:
-            print("preparing step: %d from %s" % (nsteps_prepared + nsteps_done, ucurrent))
-
-        if left is None or right is None:
-            # in each, find the end points using the expanded ellipsoid
-            assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), ('current point outside ellipsoid!')
-            left, right = ellipsoid_bracket(ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.enlarge)
-            left, right, _, _ = crop_bracket_at_unit_cube(ucurrent, v, left, right)
-            assert (ucurrent + v * left <= 1).all(), (
-                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
-            assert (ucurrent + v * right <= 1).all(), (
-                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
-            assert (ucurrent + v * left >= 0).all(), (
-                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
-            assert (ucurrent + v * right >= 0).all(), (
-                ucurrent, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.ellipsoid_invcov, region.enlarge)
-
-            assert left <= 0 <= right, (left, right)
-            if verbose:
-                print("   ellipsoid bracket found:", left, right)
-
-        while True:
-            # sample in each a point until presumed success:
-            assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), ('current point outside ellipsoid!')
-            t = np.random.uniform(left, right)
-            unext = ucurrent + v * t
-            assert (unext >= 0).all(), unext
-            assert (unext <= 1).all(), unext
-            assert region.inside_ellipsoid(unext.reshape((1, ndim))), ('proposal landed outside ellipsoid!', t, left, right)
-
-            # compute distance vector to center
-            d = unext - region.ellipsoid_center
-            # distance in normalised coordates: vector . matrix . vector
-            # where the matrix is the ellipsoid inverse covariance
-            r = np.einsum('j,jk,k->', d, region.ellipsoid_invcov, d)
-            if verbose:
-                print("   proposed slice point", t, r)
-
-            likely_inside = r <= 1
-            if not likely_inside and r <= region.enlarge:
-                # The exception is, when a point is between projected ellipsoid center and current point
-                # then it is also likely inside (if still inside the ellipsoid)
-
-                # project ellipsoid center onto line
-                # region.ellipsoid_center = ucurrent + tc * v
-                tc = np.dot(region.ellipsoid_center - ucurrent, v)
-                # current point is at 0 by definition
-                if 0 < t < tc or tc < t < 0:
-                    if verbose:
-                        print("   proposed point is further inside than current point")
-                    likely_inside = True
-                #    print("   proposed point %.3f is going towards center %.3f" % (t, tc))
-                # else:
-                #    print("   proposed point %.3f is going away from center %.3f" % (t, tc))
-                else:
-                    # another exception is that points very close to the current point
-                    # are very likely also inside
-                    # to find that out, project all live points on the line
-                    tall = np.einsum('ij,j->i', region.u - ucurrent, v)
-                    # find the range and identify a small part of it
-                    epsilon_nearby = 1e-6
-                    if tc < (tall.max() - tall.min()) * epsilon_nearby:
-                        likely_inside = True
-                        if verbose:
-                            print("   proposed point is very nearby")
-
-            if verbose:
-                print("   proposed point %s (%f) is likely %s (r=%f)" % (unext, t, 'inside' if likely_inside else 'outside', r))
-            intervals.append((nsteps_prepared, ucurrent, v, left, right, t))
-            point_sequence.append(unext)
-            point_expectation.append(likely_inside)
-            #   If point radius in ellipsoid is <1, presume that it will be successful
-            if likely_inside:
-                nsteps_prepared += 1
-                ucurrent = unext
-                assert region.inside_ellipsoid(ucurrent.reshape((1, ndim))), ('current point outside ellipsoid!')
-                break
-
-            #   Else, presume it will be unsuccessful, and sample another point
-            #   shrink interval
-            if t > 0:
-                right = t
-            else:
-                left = t
-
-    assert len(point_sequence) == len(point_expectation)
-    assert len(point_sequence) == len(intervals)
-    assert nsteps_prepared <= len(point_sequence)
-
-    assert len(point_sequence) > 0, (len(point_sequence), ndraw, nsteps_prepared, nsteps_done, nsteps)
-
-    if verbose:
-        print("proposed sequence:", point_sequence)
-        print("expectations:", point_expectation)
-
-    return np.array(point_sequence, dtype=float), np.array(point_expectation, dtype=bool), intervals, nsteps_prepared
-
-
-def _evaluate_with_filter(
-    region_filter, loglike, transform, Lmin, region, tregion,
-    point_sequence, point_expectation, 
-    verbose
-):
-    truncated = False
-    # region-filter, transform, tregion-filter, and evaluate the likelihood
-    if region_filter:
-        mask_inside = region.inside(point_sequence)
-        # identify first point that was expected to be inside, but was marked outside-of-region
-        i = np.where(np.logical_and(point_expectation, ~mask_inside))[0]
-        if verbose:
-            print("region filter says:", mask_inside, i)
-        if len(i) > 0:
-            imax = i[0] + 1
-            # truncate there
-            point_sequence = point_sequence[:imax]
-            point_expectation = point_expectation[:imax]
-            mask_inside = mask_inside[:imax]
-            truncated |= True
-            del imax
-        if not mask_inside.any():
-            return None
-    else:
-        mask_inside = None
-
-    t_point_sequence = transform(point_sequence)
-    if region_filter and tregion is not None:
-        tmask = tregion.inside(t_point_sequence)
-        # identify first point that was expected to be inside, but was marked outside-of-region
-        i = np.where(np.logical_and(point_expectation, ~tmask))[0]
-        if verbose:
-            print("tregion filter says:", tmask, i)
-        mask_inside[~tmask] = False
-        del tmask
-        if len(i) > 0:
-            imax = i[0] + 1
-            # truncate there
-            point_sequence = point_sequence[:imax]
-            point_expectation = point_expectation[:imax]
-            t_point_sequence = t_point_sequence[:imax]
-            mask_inside = mask_inside[:imax]
-            truncated |= True
-            del imax
-        if not mask_inside.any():
-            return None
-
-    # we expect the last point to be an accept, otherwise we would not terminate the sequence
-    assert point_expectation[-1]
-    if region_filter:
-        # set filtered ones to -np.inf
-        L = np.ones(len(t_point_sequence)) * -np.inf
-        nc = mask_inside.sum()
-        L[mask_inside] = loglike(t_point_sequence[mask_inside,:])
-    else:
-        nc = len(point_sequence)
-        L = loglike(t_point_sequence)
-    Lmask = L > Lmin
-
-    i = np.where(point_expectation != Lmask)[0]
-    if verbose:
-        print("reality:", Lmask)
-        print("difference:", point_expectation == Lmask)
-    return point_sequence, t_point_sequence, L, Lmask, i, nc, truncated
-
-class AHARMSampler(StepSampler):
-    """Accelerated hit-and-run/slice sampler, vectorised.
-
-    Uses region ellipsoid to propose a sequence of points
-    on a randomly drawn line.
-
-    (in development)
-    """
-
-    def __init__(
-        self, nsteps, adaptive_nsteps=False, max_nsteps=1000,
-        region_filter=False, log=False, direction=generate_region_random_direction,
-        orthogonalise=True,
-    ):
-        """Initialise vectorised hit-and-run/slice sampler.
-
-        Parameters
-        -----------
-        nsteps: int
-            number of accepted steps until the sample is considered independent.
-
-        adaptive_nsteps: False, 'proposal-distance', 'move-distance'
-            Select a strategy to adapt the number of steps. The strategies
-            make sure that:
-
-            * 'move-distance' (recommended): distance between
-              start point and final position exceeds the mean distance
-              between pairs of live points.
-            * 'move-distance-midway': distance between
-              start point and position in the middle of the chain
-              exceeds the mean distance between pairs of live points.
-
-        max_nsteps: int
-            Maximum number of steps the adaptive_nsteps can reach.
-
-        region_filter: bool
-            if True, use region to check if a proposed point can be inside
-            before calling likelihood.
-
-        direction: function
-            function that draws slice direction given a point and
-            the current region.
-
-        orthogonalise: bool
-            If true, make subsequent proposed directions orthogonal
-            to each other.
-
-        log: file
-            log file for sampler statistics, such as acceptance rate,
-            proposal scale, number of steps, jump distance and distance
-            between live points
-
-        """
-        self.history = []
-        self.nsteps = nsteps
-        self.nrejects = 0
-        self.max_nsteps = max_nsteps
-        self.last = None, None
-        self.generate_direction = direction
-        adaptive_nsteps_options = [
-            False,
-            'move-distance', 'move-distance-midway',
-        ]
-
-        if adaptive_nsteps not in adaptive_nsteps_options:
-            raise ValueError("adaptive_nsteps must be one of: %s, not '%s'" % (adaptive_nsteps_options, adaptive_nsteps))
-        self.adaptive_nsteps = adaptive_nsteps
-        self.region_filter = region_filter
-        self.log = log
-        self.adaptive_nsteps_needs_mean_pair_distance = False
-        self.nsteps_nudge = 1.01
-        self.orthogonalise = orthogonalise
-
-        self.logstat = []
-        self.logstat_labels = ['rejection_rate', 'steps']
-        if adaptive_nsteps:
-            self.logstat_labels += ['jump-distance', 'reference-distance']
-
-    def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=1024, plot=False, tregion=None, verbose=False):
-        """Get next point.
-
-        Parameters
-        ----------
-        region: MLFriends
-            region.
-        Lmin: float
-            loglikelihood threshold
-        us: array of vectors
-            current live points
-        Ls: array of floats
-            current live point likelihoods
-        transform: function
-            transform function
-        loglike: function
-            loglikelihood function
-        ndraw: int
-            number of draws to attempt simultaneously.
-        plot: bool
-            whether to produce debug plots.
-        tregion: WrappingEllipsoid
-            optional ellipsoid in transformed space for rejecting proposals
-
-        """
-        # find most recent point in history conforming to current Lmin
-        ui, Li = self.last
-        if Li is not None and not Li >= Lmin:
-            print("wandered out of L constraint; resetting", ui[0])
-            ui, Li = None, None
-
-        if ui is not None and not region.inside_ellipsoid(ui.reshape((1, -1))):
-            print("wandered out of ellipsoid; resetting", ui[0])
-            ui, Li = None, None
-
-        if Li is None and self.history:
-            # try to resume from a previous point above the current contour
-            for j, (uj, Lj) in enumerate(self.history[::-1]):
-                if Lj > Lmin and region.inside(uj.reshape((1,-1))) and (tregion is None or tregion.inside(transform(uj.reshape((1, -1))))):
-                    ui, Li = uj, Lj
-                    # print("recovering at point %d/%d " % (j+1, len(self.history)))
-                    self.last = ui, Li
-
-                    # pj = transform(uj.reshape((1, -1)))
-                    # Lj2 = loglike(pj)[0]
-                    # assert Lj2 > Lmin, (Lj2, Lj, uj, pj)
-                    assert region.inside_ellipsoid(ui.reshape((1, -1)))
-
-                    break
-            pass
-
-        # select starting point
-        ndim = us.shape[1]
-        if Li is None:
-            self.directions = None
-
-            self.history = []
-            self.last = None, None
-            self.nrejects = 0
-
-            # choose a new random starting point
-            i = np.random.randint(len(us))
-            self.starti = i
-            ui = us[i,:]
-            assert region.inside_ellipsoid(ui.reshape((1, -1)))
-            assert np.logical_and(ui > 0, ui < 1).all(), ui
-            Li = Ls[i]
-            self.history.append((ui.copy(), Li.copy()))
-            del i
-            print("starting at", ui)
-            # set initially nleft = nsteps
-            self.nsteps_done = 0
-
-            # generate nsteps directions
-            self.directions = []
-            for i in range(self.nsteps):
-                v = self.generate_direction(ui, region)
-                self.directions.append(v)
-            self.directions = np.array(self.directions)
-
-            if verbose:
-                print("directions:", self.directions)
-            if self.orthogonalise:
-                # orthogonalise relative to this previous direction
-                for i in range(self.nsteps // ndim):
-                    # go back only ndim steps, then start fresh
-                    self.directions[i * ndim:(i + 1) * ndim], _ = np.linalg.qr(self.directions[i * ndim:(i + 1) * ndim])
-
-            assert (ui >= 0).all(), ui
-            assert (ui <= 1).all(), ui
-            self.current_interval = ui, None, None
-            if self.region_filter:
-                assert region.inside(ui.reshape((1, ndim))), ('cannot start from outside region!', region.inside(ui.reshape((1, ndim))))
-
-        del ui
-        nc = 0
-        while True:
-            # prepare a sequence of points until nsteps are reached
-            point_sequence, point_expectation, intervals, nsteps_prepared = _prepare_steps(
-                self.nsteps_done, self.nsteps, self.directions, ndraw,
-                self.current_interval, loglike, transform, region, ndim, self.region_filter, 
-                Lmin, verbose
-            )
-            point_sequence, t_point_sequence, L, Lmask, indices_deviating, nc_here, truncated = _evaluate_with_filter(
-                self.region_filter, loglike, transform, Lmin, region, tregion,
-                point_sequence, point_expectation, 
-                verbose
-            )
-            del point_expectation
-            nc += nc_here
-
-            self.nrejects += (~Lmask).sum()
-            #print("calling likelihood with %5d prepared points, accepted:" % (
-            #    len(point_sequence)), '=' * (i[0] + Lmask[i[0]] * 1 if len(i) > 0 else len(Lmask)))
-            # identify first point that was unexpected
-            any_deviating = len(indices_deviating) > 0
-            if any_deviating and nsteps_prepared + self.nsteps_done == self.nsteps:
-                # everything according to prediction.
-                if verbose:
-                    print("everything according to prediction and done")
-                # done, return last point
-                for ui, Li in zip(point_sequence[Lmask], L[Lmask]):
-                    self.history.append((ui, Li))
-                self.finalize_chain(region=region, Lmin=Lmin, Ls=Ls)
-                return point_sequence[-1], t_point_sequence[-1], L[-1], nc
-            elif any_deviating:
-                # everything according to prediction.
-                if verbose:
-                    print("everything according to prediction")
-                # continue from last point
-                for ui, Li in zip(point_sequence[Lmask], L[Lmask]):
-                    self.history.append((ui, Li))
-                self.nsteps_done += nsteps_prepared
-                assert self.nsteps_done == len(self.history), (self.nsteps_done, len(self.history))
-                nsteps_prepared, ucurrent, v, left, right, t = intervals[-1]
-                assert (ucurrent >= 0).all(), ucurrent
-                assert (ucurrent <= 1).all(), ucurrent
-                self.current_interval = ucurrent, None, None
-                if self.region_filter:
-                    assert region.inside(ucurrent.reshape((1, ndim))), ('suggested point outside region!', region.inside(ucurrent.reshape((1, ndim))))
-            else:
-                # point i unexpectedly inside or outside
-                imax = indices_deviating[0]
-                for ui, Li in zip(point_sequence[:imax][Lmask[:imax]], L[:imax][Lmask[:imax]]):
-                    self.history.append((ui, Li))
-                nsteps_prepared, ucurrent, v, left, right, t = intervals[imax]
-                if self.region_filter:
-                    assert region.inside(ucurrent.reshape((1, ndim))), ('suggested point outside region!', region.inside(ucurrent.reshape((1, ndim))))
-                assert (ucurrent >= 0).all(), ucurrent
-                assert (ucurrent <= 1).all(), ucurrent
-                if point_expectation[imax]:
-                    if verbose:
-                        print("following prediction until %d, which was unexpectedly rejected" % imax)
-                    # expected point to lie inside, but rejected
-                    # need to repair interval
-                    self.nsteps_done += nsteps_prepared
-                    assert self.nsteps_done + 1 == len(self.history), (self.nsteps_done, len(self.history))
-                    if t > 0:
-                        right = t
-                    else:
-                        left = t
-                    if verbose:
-                        print("%d steps done, continuing from unexpected outside point" % self.nsteps_done, imax, point_sequence[imax], "interval:", t)
-                    self.current_interval = ucurrent, left, right
-                else:
-                    if verbose:
-                        print("following prediction until %d, which was unexpectedly accepted" % imax)
-                    if imax == len(point_sequence) - 1 and truncated:
-                        assert False
-                    ucurrent = point_sequence[imax]
-                    if self.region_filter:
-                        assert region.inside(ucurrent.reshape((1, ndim))), ('accepted point outside region!', region.inside(ucurrent.reshape((1, ndim))))
-                    # expected point to lie outside, but actually inside
-                    # adopt as point and continue
-                    # print(len(self.history), self.nsteps_done, nsteps_prepared, Lmask[:imax].sum())
-                    self.nsteps_done += nsteps_prepared + 1
-                    self.history.append((ucurrent.copy(), L[imax]))
-                    assert self.nsteps_done + 1 == len(self.history), (self.nsteps_done, len(self.history))
-                    self.current_interval = ucurrent, None, None
-                    if self.nsteps_done == self.nsteps:
-                        # last point was inside, so we are actually done there
-                        self.finalize_chain(region=region, Lmin=Lmin, Ls=Ls)
-                        return point_sequence[-1], t_point_sequence[-1], L[-1], nc
-                    else:
-                        if verbose:
-                            print("%d steps done, continuing from unexpected inside point" % self.nsteps_done, imax, point_sequence[imax])
-
-            # need to exit here to only do one likelihood evaluation
-            # per function call
-            if verbose:
-                print("breaking")
-            break
-
-        # do not have a independent sample yet
-        return None, None, None, nc
-
-    def region_changed(self, Ls, region):
-        assert region.inside_ellipsoid(region.u).all()
-        ui, Li = self.last
-        if ui is not None and not region.inside(ui.reshape((1, -1))):
-            print("wandered out of ellipsoid; resetting", ui[0])
-            self.last = None, None
-
-    def finalize_chain(self, region=None, Lmin=None, Ls=None):
-        """Store chain statistics and adapt proposal."""
-        self.logstat.append([self.nrejects / self.nsteps, self.nsteps])
-        if self.log:
-            ustart, Lstart = self.history[0]
-            ufinal, Lfinal = self.history[-1]
-            # mean_pair_distance = region.compute_mean_pair_distance()
-            mean_pair_distance = np.nan
-            tstart, tfinal = region.transformLayer.transform(np.vstack((ustart, ufinal)))
-            # L index of start and end
-            # Ls_sorted = np.sort(Ls)
-            iLstart = np.sum(Ls > Lstart)
-            iLfinal = np.sum(Ls > Lfinal)
-            # nearest neighbor index of start and end
-            itstart = np.argmin((region.unormed - tstart.reshape((1, -1)))**2)
-            itfinal = np.argmin((region.unormed - tfinal.reshape((1, -1)))**2)
-            np.savetxt(self.log, [_listify(
-                [Lmin], ustart, ufinal, tstart, tfinal,
-                [self.nsteps, region.maxradiussq**0.5, mean_pair_distance,
-                 iLstart, iLfinal, itstart, itfinal])])
-
-        if self.adaptive_nsteps:
-            self.adapt_nsteps(region=region)
-
-        self.last = None, None
-        self.history = []
-        self.nrejects = 0
-
-    def generate_new_interval(self, ui, region):
-        v = self.generate_direction(ui, region)
-        assert region.inside_ellipsoid(ui.reshape((1, -1)))
-        assert (ui > 0).all(), ui
-        assert (ui < 1).all(), ui
-
-        # use region ellipsoid to identify limits
-        # rotate line so that ellipsoid is a sphere
-        left, right = ellipsoid_bracket(ui, v, region.ellipsoid_center, region.ellipsoid_inv_axes, region.enlarge)
-        left, right, _, _ = crop_bracket_at_unit_cube(ui, v, left, right)
-        self.interval = (v, left, right, 0)

From f593814013f714dae4db956d3885f1a62c1bc97d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 21 Jun 2023 21:28:58 +0300
Subject: [PATCH 128/313] make PopulationRandomWalkSampler interface more
 transparent and configurable

---
 examples/test_popsampler.py   | 25 ++++++++++++++++++++-----
 tests/test_popstepsampling.py | 28 +++++++++++++++++++++++-----
 ultranest/popstepsampler.py   | 18 ++++++++++++------
 ultranest/stepfuncs.pyx       | 29 +++++++++++++++++++++++++++++
 4 files changed, 84 insertions(+), 16 deletions(-)

diff --git a/examples/test_popsampler.py b/examples/test_popsampler.py
index c54e4075..495fda6e 100644
--- a/examples/test_popsampler.py
+++ b/examples/test_popsampler.py
@@ -6,9 +6,10 @@
 from ultranest.mlfriends import RobustEllipsoidRegion, SimpleRegion, ScalingLayer
 import ultranest.popstepsampler
 import matplotlib.pyplot as plt
+import sys
+import argparse
 
-def main(generate_direction_method, ndim, nsteps, popsize, verbose=False):
-
+def main(generate_direction_method, ndim, nsteps, popsize, log_dir=None, verbose=False):
     np.random.seed(1)
 
     logsigma = -5
@@ -29,7 +30,9 @@ def transform(x):
 
     paramnames = ['param%d' % (i+1) for i in range(ndim)]
 
-    sampler = ReactiveNestedSampler(paramnames, loglike, transform=transform, vectorized=True)
+    sampler = ReactiveNestedSampler(
+        paramnames, loglike, transform=transform, 
+        vectorized=True, log_dir=log_dir, resume=True)
     
     # ellipsoidal:
     region_class = RobustEllipsoidRegion
@@ -40,6 +43,7 @@ def transform(x):
     sampler.stepsampler = ultranest.popstepsampler.PopulationRandomWalkSampler(
         popsize=popsize, nsteps=nsteps, scale=1. / len(paramnames),
         generate_direction=getattr(ultranest.popstepsampler, generate_direction_method), log=verbose,
+        #logfile=sys.stderr
     )
     results = sampler.run(
         frac_remain=0.01, update_interval_volume_fraction=0.01, 
@@ -54,5 +58,16 @@ def transform(x):
     #sampler.plot_trace()
 
 if __name__ == '__main__':
-    import sys
-    main(sys.argv[1], *map(int, sys.argv[2:5]), verbose='--verbose' in sys.argv[1:])
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--x_dim', type=int, default=2,
+                        help="Dimensionality")
+    parser.add_argument("--num_live_points", type=int, default=400)
+    parser.add_argument("--generate_direction_method", type=str, required=True)
+    parser.add_argument("--num_steps", type=int, required=True)
+    parser.add_argument("--popsize", type=int, required=True)
+    parser.add_argument('--log_dir', type=str)
+    parser.add_argument('--verbose', action='store_true')
+
+    args = parser.parse_args()
+    main(args.generate_direction_method, args.x_dim, args.num_steps, args.popsize, args.log_dir, verbose=args.verbose)
diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 9d656636..54743387 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -1,7 +1,8 @@
 import numpy as np
 from ultranest import ReactiveNestedSampler
-from ultranest.popstepsampler import PopulationSliceSampler, generate_cube_oriented_direction, \
-    generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
+from ultranest.popstepsampler import PopulationSliceSampler, PopulationRandomWalkSampler
+from ultranest.popstepsampler import generate_cube_oriented_direction, generate_random_direction, generate_cube_oriented_direction_scaled
+from ultranest.popstepsampler import generate_region_oriented_direction, generate_region_random_direction
 
 def loglike_vectorized(z):
     a = np.array([-0.5 * sum([((xi - 0.7 + i*0.001)/0.1)**2 for i, xi in enumerate(x)]) for x in z])
@@ -35,7 +36,25 @@ def test_stepsampler_cubeslice(plot=False):
     assert a.sum() > 1
     assert b.sum() > 1
 
-from ultranest.mlfriends import update_clusters, AffineLayer, ScalingLayer, MLFriends, RobustEllipsoidRegion, SimpleRegion
+def test_stepsampler_cubegausswalk(plot=False):
+    np.random.seed(2)
+    nsteps = np.random.randint(10, 50)
+    popsize = np.random.randint(1, 20)
+    sampler = ReactiveNestedSampler(paramnames, loglike_vectorized, transform=transform, vectorized=True)
+
+    sampler.stepsampler = PopulationRandomWalkSampler(
+        popsize=popsize, nsteps=nsteps, 
+        generate_direction=generate_cube_oriented_direction,
+        scale=0.1,
+    )
+    r = sampler.run(viz_callback=None, log_interval=50, max_iters=200, max_num_improvement_loops=0)
+    sampler.print_results()
+    a = (np.abs(r['samples'] - 0.7) < 0.1).all(axis=1)
+    b = (np.abs(r['samples'] - 0.3) < 0.1).all(axis=1)
+    assert a.sum() > 1
+    assert b.sum() > 1
+
+from ultranest.mlfriends import AffineLayer, ScalingLayer, MLFriends, RobustEllipsoidRegion, SimpleRegion
 
 def test_direction_proposals():
     proposals = [generate_cube_oriented_direction, generate_random_direction, 
@@ -58,10 +77,9 @@ def test_direction_proposals():
             for prop in proposals:
                 print("test of proposal:", prop, "with region:", region_class, "layer:", layer)
                 directions = prop(points, region, scale=scale)
-                norms = np.linalg.norm(directions, axis=1)
-                #print(norms[0], directions[0])
                 assert directions.shape == points.shape, (directions.shape, points.shape)
                 #assert np.allclose(norms, scale), (norms, scale)
 
 if __name__ == '__main__':
+    test_stepsampler_cubegausswalk()
     test_direction_proposals()
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 0374be8a..3330ec67 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -4,7 +4,7 @@
 import numpy as np
 from ultranest.utils import submasks
 from ultranest.stepfuncs import evolve, step_back
-from ultranest.stepfuncs import generate_cube_oriented_direction, \
+from ultranest.stepfuncs import generate_cube_oriented_direction, generate_cube_oriented_direction_scaled, \
    generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
 import scipy.stats
 
@@ -49,8 +49,8 @@ def unitcube_line_intersection(ray_origin, ray_direction):
 
 class PopulationRandomWalkSampler():
     def __init__(
-        self, popsize, nsteps, generate_direction, scale=1.0, 
-        scale_adapt_factor=0.9, log=False, logfile=None
+        self, popsize, nsteps, generate_direction, scale, 
+        scale_adapt_factor=0.9, scale_min=1e-20, scale_max=20, log=False, logfile=None
     ):
         """
         Vectorized Gaussian Random Walk sampler.
@@ -81,6 +81,8 @@ def __init__(
         self.ncalls = 0
         assert scale_adapt_factor <= 1
         self.scale_adapt_factor = scale_adapt_factor
+        self.scale_min = scale_min
+        self.scale_max = scale_max
 
         self.log = log
         self.logfile = logfile
@@ -179,11 +181,15 @@ def __next__(
             self.prepared_samples = list(zip(allu, allp, allL))
 
             # adapt slightly
-            print('%.1f%%  %.1f%%  %f ' % (mask_accept.mean() * 100, 100 - (self.nrejects - (nrejects_expected - self.nsteps * self.popsize * (1 - 0.234))) * 100. / (self.nsteps * self.popsize), self.scale))
-            if self.nrejects > nrejects_expected and self.scale > 1e-20:
+            if self.logfile:
+                self.logfile.write("rescale\t%.4f\t%.4f\t%g\n" % (
+                    mask_accept.mean() * 100, 
+                    100 - (self.nrejects - (nrejects_expected - self.nsteps * self.popsize * (1 - 0.234))) * 100. / (self.nsteps * self.popsize), 
+                    self.scale))
+            if self.nrejects > nrejects_expected and self.scale > self.scale_min:
                 # lots of rejects, decrease scale
                 self.scale *= self.scale_adapt_factor
-            elif self.nrejects < nrejects_expected and self.scale < 10:
+            elif self.nrejects < nrejects_expected and self.scale < self.scale_max:
                 self.scale /= self.scale_adapt_factor
         else:
             nc = 0
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 23c0453f..b2bcc31c 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -332,6 +332,7 @@ cdef _fill_directions(
     for i in range(nsamples):
         v[i, indices[i]] = scale
 
+
 def generate_cube_oriented_direction(ui, region, scale=1):
     """Draw a unit direction vector in direction of a random unit cube axes.
 
@@ -356,6 +357,34 @@ def generate_cube_oriented_direction(ui, region, scale=1):
     _fill_directions(v, j, scale)
     return v
 
+
+def generate_cube_oriented_direction_scaled(ui, region, scale=1):
+    """Draw a unit direction vector in direction of a random unit cube axes.
+    Scale by the live point min-max range.
+
+    Parameters
+    ----------
+    ui: np.array((npoints, ndim), dtype=float)
+        starting points (not used)
+    region:
+        not used
+    scale: float
+        length of returned vector
+
+    Returns
+    ---------
+    v: np.array((npoints, ndim), dtype=float)
+        Random axis vectors of length `scale`, one for each starting point.
+    """
+    nsamples, ndim = ui.shape
+    v = np.zeros((nsamples, ndim))
+    scales = region.u.std(axis=0)
+    # choose axis
+    j = np.random.randint(ndim, size=nsamples)
+    _fill_directions(v, j, scale)
+    v *= scales[j].reshape((-1, 1))
+    return v
+
 def generate_random_direction(ui, region, scale=1):
     """Draw uniform direction vector in unit cube space of length `scale`.
 

From a69893e720c7103210fca32c6919966a501b5fac Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jun 2023 03:38:08 +0300
Subject: [PATCH 129/313] =?UTF-8?q?Bump=20version:=203.5.7=20=E2=86=92=203?=
 =?UTF-8?q?.6.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 9bec9f91..eabf73e5 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.5.7',
+    version='3.6.0',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 836b2951..578c66e3 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.5.7'
+__version__ = '3.6.0'

From 9f34057cd44a0f435e0c71d83e338d27bbc1b9f3 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jun 2023 04:02:02 +0300
Subject: [PATCH 130/313] add changelog notes

---
 HISTORY.rst | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 45eda263..7e2f4d80 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,6 +2,14 @@
 Release Notes
 ==============
 
+
+3.6.0 (2023-06-22)
+------------------
+
+* add PopulationRandomWalkSampler: vectorized Gaussian random walks for GPU/JAX-powered likelihoods
+* limit initial widening to escape plateau (issue #81)
+
+
 3.5.0 (2022-09-05)
 ------------------
 

From 10af0b5840ae0aa62cf43964f9a39e905db96b35 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jun 2023 04:49:28 +0300
Subject: [PATCH 131/313] avoid scipy.optimize.minimize_scalar error

---
 docs/example-warmstart.ipynb | 310 +++++++++++++++++++++++++++++------
 1 file changed, 259 insertions(+), 51 deletions(-)

diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index dd4d870b..cbedb2c9 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,9 +98,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -128,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,9 +154,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.title(\"Prior predictive checks\")\n",
@@ -169,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,9 +207,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abaa11159f0f40478c3ff39ea1e99388",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=2e+02  7 [173.8606..173.8613]*| it/evals=6000/22771 eff=26.8204% N=400    400  0 0 400 \n",
+      "[ultranest] Likelihood function evaluations: 22781\n",
+      "[ultranest] Writing samples and results to disk ...\n",
+      "[ultranest] Writing samples and results to disk ... done\n",
+      "[ultranest]   logZ = 159.9 +- 0.124\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 981.6, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.07 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.42, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.18 bs:0.12 tail:0.41 total:0.42 required:<0.50\n",
+      "[ultranest] done iterating.\n",
+      "\n",
+      "logZ = 159.914 +- 0.447\n",
+      "  single instance: logZ = 159.914 +- 0.185\n",
+      "  bootstrapped   : logZ = 159.917 +- 0.189\n",
+      "  tail           : logZ = +- 0.405\n",
+      "insert order U test : converged: True correlation: inf iterations\n",
+      "\n",
+      "    Temperature         : 0.00945│ ▁▁▁▁▁▁▁▂▂▂▄▄▅▆▆▆▇▇▆▇▆▅▅▃▃▂▁▁▁▁▁▁▁▁▁ ▁ │0.01038    0.00989 +- 0.00012\n",
+      "    Amplitude           : 37.3  │ ▁▁▁▁▁▁▁▂▂▂▃▄▄▅▇▇▇▇▆▆▇▅▆▅▃▃▂▁▁▁▁▁▁▁▁▁▁ │58.1      47.3 +- 2.7\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from ultranest import ReactiveNestedSampler\n",
     "\n",
@@ -202,9 +276,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -229,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,9 +377,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a673c1fd51be473994ca0c5577ae6d5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=2e+02  6 [169.4184..169.4186]*| it/evals=1629/5477 eff=32.0859% N=400 00 \n",
+      "[ultranest] Likelihood function evaluations: 5484\n",
+      "[ultranest]   logZ = 166.7 +- 0.05516\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 485.6, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.11 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.41, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.08 bs:0.06 tail:0.41 total:0.41 required:<0.50\n",
+      "[ultranest] done iterating.\n"
+     ]
+    }
+   ],
    "source": [
     "sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized=vectorized)\n",
     "res = sampler.run(frac_remain=0.5)"
@@ -300,9 +423,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -332,9 +468,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Speed-up of warm-start: 315%\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Speed-up of warm-start: %d%%\" % ((results_ref['ncall'] / res['ncall'] - 1)*100))"
    ]
@@ -394,9 +538,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "posterior_samples = results_ref['samples']\n",
     "\n",
@@ -416,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -426,9 +583,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.subplot(2, 1, 1)\n",
     "plt.plot(uguess, pguess[:,0])\n",
@@ -449,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -458,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -467,15 +637,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 6410/6410 [00:01<00:00, 3357.17it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "nparams = len(parameters)\n",
     "u = np.ones(nparams) * 0.5\n",
     "stdevs = posterior_samples.std(axis=0)\n",
     "\n",
     "def minfunc(ui, i, u, pi):\n",
+    "    if not 0 < ui < 1: return 1e100\n",
     "    u[i] = ui\n",
     "    p = prior_transform(u)\n",
     "    return (p[i] - pi)**2\n",
@@ -488,7 +667,6 @@
     "            minfunc, \n",
     "            args=(i, u, sample[i]), \n",
     "            method='brent',\n",
-    "            bounds=(0,1),\n",
     "            bracket=(ui0 - 1e-4, ui0, ui0 + 1e-4),\n",
     "            tol=0.001 * stdevs[i],\n",
     "        )\n",
@@ -504,9 +682,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "weights = results_ref['weighted_samples']['weights']\n",
     "i = np.random.choice(len(weights), p=weights, size=1000)\n",
@@ -542,7 +733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,9 +750,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# weight logl Temperature Amplitude\r\n",
+      "0.000156 0.000000 0.009827 0.584641\r\n",
+      "0.000156 0.000000 0.009769 0.585080\r\n",
+      "0.000156 0.000000 0.009959 0.581489\r\n",
+      "0.000156 0.000000 0.009796 0.584856\r\n",
+      "0.000156 0.000000 0.009792 0.584156\r\n",
+      "0.000156 0.000000 0.009872 0.583356\r\n",
+      "0.000156 0.000000 0.009916 0.582792\r\n",
+      "0.000156 0.000000 0.009762 0.584928\r\n",
+      "0.000156 0.000000 0.010057 0.582260\r\n"
+     ]
+    }
+   ],
    "source": [
     "!head custom-weighted_post_untransformed.txt"
    ]

From 314a26762ade4503bff99f55c85bb072c51d89c5 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jun 2023 06:10:37 +0300
Subject: [PATCH 132/313] update docstrings

---
 docs/example-warmstart.ipynb | 308 ++++++-----------------------------
 ultranest/popstepsampler.py  | 117 +++++++------
 ultranest/stepfuncs.pyx      |  28 +++-
 3 files changed, 139 insertions(+), 314 deletions(-)

diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index cbedb2c9..d06a3641 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,22 +98,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -141,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,22 +141,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.title(\"Prior predictive checks\")\n",
@@ -195,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,57 +181,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "abaa11159f0f40478c3ff39ea1e99388",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=2e+02  7 [173.8606..173.8613]*| it/evals=6000/22771 eff=26.8204% N=400    400  0 0 400 \n",
-      "[ultranest] Likelihood function evaluations: 22781\n",
-      "[ultranest] Writing samples and results to disk ...\n",
-      "[ultranest] Writing samples and results to disk ... done\n",
-      "[ultranest]   logZ = 159.9 +- 0.124\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 981.6, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.07 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.42, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.18 bs:0.12 tail:0.41 total:0.42 required:<0.50\n",
-      "[ultranest] done iterating.\n",
-      "\n",
-      "logZ = 159.914 +- 0.447\n",
-      "  single instance: logZ = 159.914 +- 0.185\n",
-      "  bootstrapped   : logZ = 159.917 +- 0.189\n",
-      "  tail           : logZ = +- 0.405\n",
-      "insert order U test : converged: True correlation: inf iterations\n",
-      "\n",
-      "    Temperature         : 0.00945│ ▁▁▁▁▁▁▁▂▂▂▄▄▅▆▆▆▇▇▆▇▆▅▅▃▃▂▁▁▁▁▁▁▁▁▁ ▁ │0.01038    0.00989 +- 0.00012\n",
-      "    Amplitude           : 37.3  │ ▁▁▁▁▁▁▁▂▂▂▃▄▄▅▇▇▇▇▆▆▇▅▆▅▃▃▂▁▁▁▁▁▁▁▁▁▁ │58.1      47.3 +- 2.7\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from ultranest import ReactiveNestedSampler\n",
     "\n",
@@ -276,22 +202,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -316,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -342,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,45 +290,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a673c1fd51be473994ca0c5577ae6d5e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=2e+02  6 [169.4184..169.4186]*| it/evals=1629/5477 eff=32.0859% N=400 00 \n",
-      "[ultranest] Likelihood function evaluations: 5484\n",
-      "[ultranest]   logZ = 166.7 +- 0.05516\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 485.6, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.11 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.41, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.08 bs:0.06 tail:0.41 total:0.41 required:<0.50\n",
-      "[ultranest] done iterating.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized=vectorized)\n",
     "res = sampler.run(frac_remain=0.5)"
@@ -423,22 +300,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -468,17 +332,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Speed-up of warm-start: 315%\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"Speed-up of warm-start: %d%%\" % ((results_ref['ncall'] / res['ncall'] - 1)*100))"
    ]
@@ -538,22 +394,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "posterior_samples = results_ref['samples']\n",
     "\n",
@@ -573,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -583,22 +426,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.subplot(2, 1, 1)\n",
     "plt.plot(uguess, pguess[:,0])\n",
@@ -619,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -628,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -637,17 +467,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████| 6410/6410 [00:01<00:00, 3357.17it/s]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "nparams = len(parameters)\n",
     "u = np.ones(nparams) * 0.5\n",
@@ -682,22 +504,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "weights = results_ref['weighted_samples']['weights']\n",
     "i = np.random.choice(len(weights), p=weights, size=1000)\n",
@@ -733,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -750,26 +559,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# weight logl Temperature Amplitude\r\n",
-      "0.000156 0.000000 0.009827 0.584641\r\n",
-      "0.000156 0.000000 0.009769 0.585080\r\n",
-      "0.000156 0.000000 0.009959 0.581489\r\n",
-      "0.000156 0.000000 0.009796 0.584856\r\n",
-      "0.000156 0.000000 0.009792 0.584156\r\n",
-      "0.000156 0.000000 0.009872 0.583356\r\n",
-      "0.000156 0.000000 0.009916 0.582792\r\n",
-      "0.000156 0.000000 0.009762 0.584928\r\n",
-      "0.000156 0.000000 0.010057 0.582260\r\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "!head custom-weighted_post_untransformed.txt"
    ]
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 3330ec67..657584e3 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -1,13 +1,23 @@
 #!/usr/bin/env python
 # coding: utf-8
+"""
+Vectorized step samplers
+------------------------
+
+Likelihood based on GPUs (model emulators based on neural networks,
+or JAX implementations) can evaluate hundreds of points as efficiently
+as one point. The implementations in this module leverage this power,
+by providing random walks of populations of walkers.
+"""
 
 import numpy as np
 from ultranest.utils import submasks
 from ultranest.stepfuncs import evolve, step_back
-from ultranest.stepfuncs import generate_cube_oriented_direction, generate_cube_oriented_direction_scaled, \
-   generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
+from ultranest.stepfuncs import generate_cube_oriented_direction, generate_cube_oriented_direction_scaled
+from ultranest.stepfuncs import generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
 import scipy.stats
 
+
 def unitcube_line_intersection(ray_origin, ray_direction):
     r"""Compute intersection of a line (ray) and a unit box (0:1 in all axes).
 
@@ -46,33 +56,44 @@ def unitcube_line_intersection(ray_origin, ray_direction):
         return np.nanmax(t1, axis=1), np.nanmin(t2, axis=1)
 
 
-
 class PopulationRandomWalkSampler():
     def __init__(
-        self, popsize, nsteps, generate_direction, scale, 
+        self, popsize, nsteps, generate_direction, scale,
         scale_adapt_factor=0.9, scale_min=1e-20, scale_max=20, log=False, logfile=None
     ):
         """
         Vectorized Gaussian Random Walk sampler.
 
-        Revert until all previous steps have likelihoods allL above Lmin.
-        Updates currentt, generation and allL, in-place.
-
         Parameters
         ----------
         popsize: int
-            number of walkers to maintain
+            number of walkers to maintain.
+            this should be fairly large (~100), if too large you probably get memory issues
+            Also, some results have to be discarded as the likelihood threshold increases.
+            Observe the nested sampling efficiency.
         nsteps: int
             number of steps to take until the found point is accepted as independent.
-        generate_direction: function `(u, region, scale) -> v`
-            function such as `generate_unit_directions`, which
-            generates a random slice direction.
+            To calibrate, try several runs with increasing nsteps (doubling).
+            The ln(Z) should become stable at some value.
+        generate_direction: function
+            Function that gives proposal kernel shape, one of:
+            :py:func:`ultranest.popstepsampler.generate_cube_oriented_direction`
+            :py:func:`ultranest.popstepsampler.generate_cube_oriented_direction_scaled`
+            :py:func:`ultranest.popstepsampler.generate_random_direction`
+            :py:func:`ultranest.popstepsampler.generate_region_oriented_direction`
+            :py:func:`ultranest.popstepsampler.generate_region_random_direction`
         scale: float
-            initial guess scale for the length of the slice
+            initial guess for the proposal scaling factor
         scale_adapt_factor: float
-            smoothing factor for updating scale.
-            if near 1, scale is barely updating, if near 0,
-            the last slice length is used as a initial guess for the next.
+            if 1, no adapting is done.
+            if <1, the scale is increased if the acceptance rate is below 23.4%,
+            or decreased if it is above, by *scale_adapt_factor*.
+        scale_min: float
+            lowest value allowed for scale, do not adapt down further
+        scale_max: float
+            highest value allowed for scale, do not adapt up further
+        logfile: file
+            where to print the current scaling factor and acceptance rate
 
         """
         self.nsteps = nsteps
@@ -93,7 +114,7 @@ def __init__(
 
     def __str__(self):
         return 'PopulationRandomWalkSampler(popsize=%d, nsteps=%d, generate_direction=%s, scale=%.g)' % (
-                self.popsize, self.nsteps, self.generate_direction, self.scale)
+            self.popsize, self.nsteps, self.generate_direction, self.scale)
 
     def region_changed(self, Ls, region):
         """notification that the region changed. Currently not used."""
@@ -156,17 +177,12 @@ def __next__(
                 v = self.generate_direction(allu, region, self.scale)
                 # compute intersection of u + t * v with unit cube
                 tleft, tright = unitcube_line_intersection(allu, v)
-                #print(tleft.shape, tright.shape, self.popsize, tleft, tright)
                 proposed_t = scipy.stats.truncnorm.rvs(tleft, tright, loc=0, scale=1).reshape((-1, 1))
-                # proposed_t = np.random.normal(size=(self.popsize, 1))
-                
+
                 proposed_u = allu + v * proposed_t
                 mask_outside = ~np.logical_and(proposed_u > 0, proposed_u < 1).all(axis=1)
-                assert not mask_outside.any(), proposed_u[mask_outside,:]
-                #while mask_outside.any():
-                #    proposed_u[mask_outside,:] = allu[mask_outside,:] + v[mask_outside,:] * np.random.normal(size=(mask_outside.sum(), 1))
-                #    mask_outside = ~np.logical_and(proposed_u > 0, proposed_u < 1).all(axis=1)
-                
+                assert not mask_outside.any(), proposed_u[mask_outside, :]
+
                 proposed_p = transform(proposed_u)
                 # accept if likelihood threshold exceeded
                 proposed_L = loglike(proposed_p)
@@ -174,6 +190,7 @@ def __next__(
                 self.nrejects += (~mask_accept).sum()
                 allu[mask_accept,:] = proposed_u[mask_accept,:]
                 if allp is None:
+                    del allp
                     allp = proposed_p * np.nan
                 allp[mask_accept,:] = proposed_p[mask_accept,:]
                 allL[mask_accept] = proposed_L[mask_accept]
@@ -183,8 +200,8 @@ def __next__(
             # adapt slightly
             if self.logfile:
                 self.logfile.write("rescale\t%.4f\t%.4f\t%g\n" % (
-                    mask_accept.mean() * 100, 
-                    100 - (self.nrejects - (nrejects_expected - self.nsteps * self.popsize * (1 - 0.234))) * 100. / (self.nsteps * self.popsize), 
+                    mask_accept.mean() * 100,
+                    100 - (self.nrejects - (nrejects_expected - self.nsteps * self.popsize * (1 - 0.234))) * 100. / (self.nsteps * self.popsize),
                     self.scale))
             if self.nrejects > nrejects_expected and self.scale > self.scale_min:
                 # lots of rejects, decrease scale
@@ -200,7 +217,7 @@ def __next__(
 
 class PopulationSliceSampler():
     def __init__(
-        self, popsize, nsteps, generate_direction, scale=1.0, 
+        self, popsize, nsteps, generate_direction, scale=1.0,
         scale_adapt_factor=0.9, log=False, logfile=None
     ):
         """
@@ -250,7 +267,7 @@ def __init__(
 
     def __str__(self):
         return 'PopulationSliceSampler(popsize=%d, nsteps=%d, generate_direction=%s, scale=%.g)' % (
-                self.popsize, self.nsteps, self.generate_direction, self.scale)
+            self.popsize, self.nsteps, self.generate_direction, self.scale)
 
     def region_changed(self, Ls, region):
         """notification that the region changed. Currently not used."""
@@ -270,10 +287,6 @@ def _setup(self, ndim):
         self.searching_left = np.zeros(self.popsize, dtype=bool)
         self.searching_right = np.zeros(self.popsize, dtype=bool)
 
-    def step_back(self, Lmin):
-        """see :py:func:`ultranest.stepfuncs.step_back`"""
-        step_back(Lmin, self.allL, self.generation, self.currentt)
-
     def setup_start(self, us, Ls, starting):
         """Initialize walker starting points.
 
@@ -289,7 +302,8 @@ def setup_start(self, us, Ls, starting):
             which walkers to initialize.
 
         """
-        if self.log: print("setting up:", starting)
+        if self.log:
+            print("setting up:", starting)
         nlive = len(us)
         i = np.random.randint(nlive, size=starting.sum())
 
@@ -308,12 +322,13 @@ def setup_start(self, us, Ls, starting):
     @property
     def status(self):
         s1 = ('G:' + ''.join(['%d' % g if g >= 0 else '_' for g in self.generation]))
-        s2 = ('S:' + ''.join(['S' if not np.isfinite(self.currentt[i]) else 'L' if self.searching_left[i] else 'R' if self.searching_right[i] else 'B'
+        s2 = ('S:' + ''.join([
+            'S' if not np.isfinite(self.currentt[i]) else 'L' if self.searching_left[i] else 'R' if self.searching_right[i] else 'B'
             for i in range(self.popsize)]))
         return s1 + '  ' + s2
 
     def setup_brackets(self, mask_starting, region):
-        """Pick starting direction and range for slice
+        """Pick starting direction and range for slice.
 
         Parameters
         ----------
@@ -323,7 +338,8 @@ def setup_brackets(self, mask_starting, region):
             which walkers to set up.
 
         """
-        if self.log: print("starting brackets:", mask_starting)
+        if self.log:
+            print("starting brackets:", mask_starting)
         i_starting, = np.where(mask_starting)
         self.current_left[i_starting] = -self.scale
         self.current_right[i_starting] = self.scale
@@ -336,11 +352,12 @@ def setup_brackets(self, mask_starting, region):
             region)
 
     def _setup_currentp(self, nparams):
-        if self.log: print("setting currentp")
+        if self.log:
+            print("setting currentp")
         self.currentp = np.zeros((self.popsize, nparams)) + np.nan
 
     def advance(self, transform, loglike, Lmin):
-        """Advance the walker population
+        """Advance the walker population.
 
         Parameters
         ----------
@@ -381,7 +398,8 @@ def advance(self, transform, loglike, Lmin):
                 self.searching_left[movable],
                 self.searching_right[movable]
             ]
-        if self.log: print("evolve will advance:", movable)
+        if self.log:
+            print("evolve will advance:", movable)
 
         (
             (
@@ -389,17 +407,20 @@ def advance(self, transform, loglike, Lmin):
             current_left, current_right, searching_left, searching_right),
             (success, unew, pnew, Lnew),
             nc
-        ) = evolve(transform, loglike, Lmin, *args, log=self.log)
+        ) = evolve(transform, loglike, Lmin, *args)
 
-        if self.log: print("movable", movable.shape, movable.sum(), success.shape)
+        if self.log:
+            print("movable", movable.shape, movable.sum(), success.shape)
         moved = submasks(movable, success)
-        if self.log: print("evolve moved:", moved)
+        if self.log:
+            print("evolve moved:", moved)
         self.generation[moved] += 1
         if len(pnew) > 0:
             if len(self.currentp) == 0:
                 self._setup_currentp(nparams=pnew.shape[1])
 
-            if self.log: print("currentp", self.currentp[moved,:].shape, pnew.shape)
+            if self.log:
+                print("currentp", self.currentp[moved,:].shape, pnew.shape)
             self.currentp[moved,:] = pnew
 
         # update with what we learned
@@ -470,16 +491,13 @@ def __next__(
         if len(self.allu) == 0:
             self._setup(ndim)
 
-        #print(str(self), "(start)")
-        self.step_back(Lmin)
+        step_back(Lmin, self.allL, self.generation, self.currentt)
 
         starting = self.generation < 0
         if starting.any():
             self.setup_start(us[Ls > Lmin], Ls[Ls > Lmin], starting)
         assert (self.generation >= 0).all(), self.generation
 
-        #if self.log: print("generation:", self.generation)
-
         # find those where bracket is undefined:
         mask_starting = ~np.isfinite(self.currentt)
         if mask_starting.any():
@@ -508,3 +526,8 @@ def __next__(
             return u, p, L, nc
         else:
             return None, None, None, nc
+
+
+__all__ = [generate_cube_oriented_direction, generate_cube_oriented_direction_scaled,
+   generate_random_direction, generate_region_oriented_direction, generate_region_random_direction,
+   PopulationRandomWalkSampler, PopulationSliceSampler]
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index b2bcc31c..ab745f83 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -8,6 +8,7 @@ from numpy import nan as np_nan
 cimport cython
 from cython.parallel import prange
 
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 cdef _within_unit_cube(
@@ -42,6 +43,7 @@ def within_unit_cube(u):
     _within_unit_cube(u, acceptable)
     return acceptable
 
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 cdef _evolve_prepare(
@@ -58,6 +60,7 @@ cdef _evolve_prepare(
         search_right[i] = not searching_left[i] and searching_right[i]
         bisecting[i] = not (searching_left[i] or searching_right[i])
 
+
 def evolve_prepare(searching_left, searching_right):
     """Get auxiliary slice sampler state selectors.
 
@@ -82,6 +85,7 @@ def evolve_prepare(searching_left, searching_right):
     _evolve_prepare(searching_left, searching_right, search_right, bisecting)
     return search_right, bisecting
 
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 cpdef evolve_update(
@@ -137,10 +141,10 @@ cpdef evolve_update(
     cdef size_t i
     cdef float my_nan = np_nan
     
-    for i in range(popsize):
-        if acceptable[i]:
+    for k in range(popsize):
+        if acceptable[k]:
             if Lnew[j] > Lmin:
-                success[i] = 1
+                success[k] = 1
             j += 1
 
     for i in prange(popsize, nogil=True):
@@ -177,8 +181,7 @@ Lnew_empty = np.empty(0)
 def evolve(
     transform, loglike, Lmin, 
     currentu, currentL, currentt, currentv,
-    current_left, current_right, searching_left, searching_right,
-    log=False
+    current_left, current_right, searching_left, searching_right
 ):
     """Evolve each slice sampling walker.
 
@@ -190,10 +193,6 @@ def evolve(
         loglikelihood function
     Lmin: float
         current log-likelihood threshold
-    search_right: np.array(nwalkers, dtype=bool)
-        whether stepping out in the positive direction
-    bisecting: np.array(nwalkers, dtype=bool)
-        whether bisecting. If neither search_right nor bisecting, then 
     currentu: np.array((nwalkers, ndim))
         slice starting point (where currentt=0)
     currentL: np.array(nwalkers)
@@ -274,6 +273,7 @@ def evolve(
         nc
     )
 
+
 def step_back(Lmin, allL, generation, currentt, log=False):
     """Revert walkers which have wandered astray.
 
@@ -290,6 +290,9 @@ def step_back(Lmin, allL, generation, currentt, log=False):
         how many iterations each walker has completed.
     currentt: np.array(nwalkers)
         current slice coordinate
+    log: bool
+        whether to print when steps are reverted
+
 
     """
     # step back where step was excluded by Lmin increase
@@ -317,6 +320,7 @@ def step_back(Lmin, allL, generation, currentt, log=False):
         if log:
             print("resetting %d%%" % (problematic.meancount_good_generations() * 100), 'by', step, 'steps', 'to', g)
 
+        del problematic
         problematic = np.any(below_threshold_parent, axis=1)
         if not problematic.any():
             break
@@ -390,6 +394,8 @@ def generate_random_direction(ui, region, scale=1):
 
     Parameters
     -----------
+    ui: np.array((npoints, ndim), dtype=float)
+        starting points (not used)
     region: MLFriends object
         current region (not used)
     scale: float
@@ -415,6 +421,8 @@ def generate_region_oriented_direction(ui, region, scale=1):
 
     Parameters
     -----------
+    ui: np.array((npoints, ndim), dtype=float)
+        starting points (not used)
     region: MLFriends object
         current region
     scale: float
@@ -439,6 +447,8 @@ def generate_region_random_direction(ui, region, scale=1):
 
     Parameters
     -----------
+    ui: np.array((npoints, ndim), dtype=float)
+        starting points (not used)
     region: MLFriends object
         current region
     scale: float:

From cb15fad19edf018776bf7a512a641e64e017af30 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jun 2023 10:58:21 +0300
Subject: [PATCH 133/313] avoid unused warning with __all__

---
 ultranest/popstepsampler.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 657584e3..36a7016b 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -528,6 +528,6 @@ def __next__(
             return None, None, None, nc
 
 
-__all__ = [generate_cube_oriented_direction, generate_cube_oriented_direction_scaled,
-   generate_random_direction, generate_region_oriented_direction, generate_region_random_direction,
-   PopulationRandomWalkSampler, PopulationSliceSampler]
+__all__ = ["generate_cube_oriented_direction", "generate_cube_oriented_direction_scaled",
+   "generate_random_direction", "generate_region_oriented_direction", "generate_region_random_direction",
+   "PopulationRandomWalkSampler", "PopulationSliceSampler"]

From 529e690b7ba3b2435f1f16632a28a6fdf815d8f9 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jun 2023 11:03:40 +0300
Subject: [PATCH 134/313] signing is not supported by pypi anymore

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 8101839e..0432b9b2 100644
--- a/Makefile
+++ b/Makefile
@@ -97,7 +97,7 @@ release-test: install
 	#grep -- --random examples/runfeatures.sh | sed s,python3,,g | xargs -rt --max-lines=1 mpiexec -np 5 coverage run --parallel-mode 
 
 release: release-test dist ## package and upload a release
-	twine upload -s dist/*.tar.gz
+	twine upload dist/*.tar.gz
 
 dist: clean ## builds source and wheel package
 	$(PYTHON) setup.py sdist

From 55ad630aa393c01fc414a7d528c1ddb0f95540e7 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jun 2023 11:17:18 +0300
Subject: [PATCH 135/313] =?UTF-8?q?Bump=20version:=203.6.0=20=E2=86=92=203?=
 =?UTF-8?q?.6.1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index eabf73e5..5dd9fb65 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.6.0',
+    version='3.6.1',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 578c66e3..66ac50ff 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.6.0'
+__version__ = '3.6.1'

From 18a4cb4b207db9605fa5b2cdaabc02424f016aff Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jun 2023 23:19:26 +0300
Subject: [PATCH 136/313] improve docs

---
 ultranest/hotstart.py       | 11 ++++++++-
 ultranest/integrator.py     | 12 +++++++---
 ultranest/mlfriends.pyx     | 14 ++++++++++--
 ultranest/netiter.py        | 41 ++++++++++++++++++---------------
 ultranest/ordertest.py      |  6 ++++-
 ultranest/pathsampler.py    |  3 +--
 ultranest/plot.py           | 24 +++++++++++---------
 ultranest/popstepsampler.py | 45 +++++++++++++++++++++++--------------
 ultranest/samplingpath.py   | 35 +++++++++++++++++++++++------
 ultranest/solvecompat.py    |  2 +-
 ultranest/stepfuncs.pyx     |  5 ++++-
 ultranest/stepsampler.py    | 41 ++++++++++++++++++++-------------
 ultranest/store.py          |  4 +++-
 ultranest/utils.py          | 13 ++++++++---
 ultranest/viz.py            | 14 +++++++++---
 15 files changed, 185 insertions(+), 85 deletions(-)

diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index 72775373..627e0778 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -1,4 +1,13 @@
-"""Warm start and hot start helper functions."""
+"""
+Warm start
+----------
+
+Helper functions for deforming the parameter space to enable
+a more efficient sampling.
+
+Based on ideas from Petrosyan & Handley (2022, https://arxiv.org/abs/2212.01760).
+
+"""
 
 import numpy as np
 import scipy.stats
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 2b06b483..d91ad228 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1,4 +1,11 @@
-"""Ultranest calculates the Bayesian evidence and posterior samples of arbitrary models."""
+"""
+Nested sampling integrators
+---------------------------
+
+This module provides the high-level class :py:class:`ReactiveNestedSampler`,
+for calculating the Bayesian evidence and posterior samples of arbitrary models.
+
+"""
 
 # Some parts are from the Nestle library by Kyle Barbary (https://github.com/kbarbary/nestle)
 # Some parts are from the nnest library by Adam Moss (https://github.com/adammoss/nnest)
@@ -2459,7 +2466,7 @@ def run_iter(
             viz_callback = get_default_viz_callback()
 
         self._widen_roots_beyond_initial_plateau(
-            min_num_live_points, 
+            min_num_live_points,
             widen_before_initial_plateau_num_warn, widen_before_initial_plateau_num_max)
 
         Llo, Lhi = -np.inf, np.inf
@@ -2947,7 +2954,6 @@ def print_results(self, use_unicode=True):
                     print(fmts % (p, med, sigma))
             print()
 
-
     def plot(self):
         """Make corner, run and trace plots.
 
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index a5133d14..22251870 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -1,7 +1,17 @@
 # cython: language_level=3,annotate=True,profile=True,fast_fail=True,warning_errors=True
-"""Construct and sample from region.
+"""
+Region construction methods
+---------------------------
+
+Construct and sample from regions of neighbourhoods around the live points.
+Includes
+
+* an efficient implementation of MLFriends, with transformation layers and clustering.
+  * RadFriends: Buchner (2014) https://arxiv.org/abs/1407.5459
+  * MLFriends: Buchner (2019) https://arxiv.org/abs/1707.04476
+* a single-ellipsoid region (Mukherjee et al., 2006, https://arxiv.org/abs/astro-ph/0508461)
+* a very fast single-ellipsoid, axis-aligned region, for use with step-samplers in high dimensions
 
-Implements MLFriends efficiently, with transformation layers and clustering.
 """
 
 import numpy as np
diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index d9478060..160e8f85 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -1,6 +1,11 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-"""Functions and classes for treating nested sampling exploration as a tree.
+"""
+Graph-based nested sampling
+---------------------------
+
+A formulation of nested sampling exploration as a tree, presented in
+section 3.4 of Buchner (2023, https://arxiv.org/abs/2101.09675).
 
 The root represents the prior volume, branches and sub-branches split the volume.
 The leaves of the tree are the integration tail.
@@ -29,19 +34,20 @@
 
 
 class TreeNode(object):
-    """Simple tree node.
-
-    Parameters
-    ----------
-    value: float
-        value used to order nodes (typically log-likelihood)
-    id: int
-        identifier, refers to the order of discovery and storage (PointPile)
-    children: list
-        children nodes, should be :py:class:`TreeNode` objects. if None, a empty list is used.
-    """
+    """Simple tree node."""
 
     def __init__(self, value=None, id=None, children=None):
+        """Initialise.
+
+        Parameters
+        ----------
+        value: float
+            value used to order nodes (typically log-likelihood)
+        id: int
+            identifier, refers to the order of discovery and storage (PointPile)
+        children: list
+            children nodes, should be :py:class:`TreeNode` objects. if None, a empty list is used.
+        """
         self.value = value
         self.id = id
         self.children = children or []
@@ -462,17 +468,17 @@ def make_node(self, value, u, p):
 
 
 class SingleCounter(object):
-    """Evidence log(Z) and posterior weight summation for a Nested Sampling tree.
+    """Evidence log(Z) and posterior weight summation for a Nested Sampling tree."""
+
+    def __init__(self, random=False):
+        """Initialise.
 
         Parameters
         ----------
         random: bool
             if False, use mean estimator for volume shrinkage
             if True, draw a random sample
-
         """
-
-    def __init__(self, random=False):
         self.reset()
         self.random = random
 
@@ -600,7 +606,6 @@ def __init__(self, nroots, nbootstraps=10, random=False, check_insertion_order=F
             if True, draw a random sample
         check_insertion_order: bool
             whether to run insertion order rank U test
-
         """
         allyes = np.ones(nroots, dtype=bool)
         # the following is a masked array of size (nbootstraps+1, nroots)
@@ -1044,7 +1049,7 @@ def logz_sequence(root, pointpile, nbootstraps=12, random=True, onNode=None, ver
         with np.errstate(invalid='ignore'):
             # first time they are all the same
             logzerr.append(main_iterator.logZerr_bs)
-        
+
         nactive = len(active_values)
 
         if len(np.unique(active_values)) == nactive and len(node.children) > 0:
diff --git a/ultranest/ordertest.py b/ultranest/ordertest.py
index 06df14de..c60acf75 100644
--- a/ultranest/ordertest.py
+++ b/ultranest/ordertest.py
@@ -1,7 +1,11 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
-Mann-Whitney-Wilcoxon U test for a uniform distribution of integers.
+Mann-Whitney-Wilcoxon U test for a uniform distribution of integers
+-------------------------------------------------------------------
+
+A test for biased nested sampling, presented in
+section 4.5.2 of Buchner (2023, https://arxiv.org/abs/2101.09675).
 
 This implements the same idea as https://arxiv.org/abs/2006.03371
 except their KS test is problematic because the variable (insertion order)
diff --git a/ultranest/pathsampler.py b/ultranest/pathsampler.py
index 5ec13049..47df8f7c 100644
--- a/ultranest/pathsampler.py
+++ b/ultranest/pathsampler.py
@@ -172,13 +172,12 @@ def __init__(self, nresets, nsteps, scale=1.0, balance=0.01, nudge=1.1, log=Fals
 
     def __str__(self):
         """Get string representation."""
-        return '(nsteps=%d, nresets=%d, AR=%d%%)' % (
+        return '%s(nsteps=%d, nresets=%d, AR=%d%%)' % (
             type(self).__name__, self.nsteps, self.nresets, (1 - self.balance) * 100)
 
     def start(self):
         """Start sampler, reset all counters."""
         if hasattr(self, 'naccepts') and self.nrejects + self.naccepts > 0:
-            nr, na = self.nrejects, self.naccepts
             self.logstat.append([
                 self.naccepts / (self.nrejects + self.naccepts),
                 self.nreflects / (self.nreflects + self.nrejects + self.naccepts),
diff --git a/ultranest/plot.py b/ultranest/plot.py
index ab0bf6b8..dab981b9 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -1,4 +1,8 @@
-"""Plotting utilities."""
+"""
+Plotting utilities
+------------------
+
+"""
 
 from __future__ import (print_function, division)
 from six.moves import range
@@ -117,7 +121,7 @@ def get_line(self, q=0.5):
     def shade(self, q=0.341, **kwargs):
         """Plot a shaded region between 0.5-q and 0.5+q. Default is 1 sigma."""
         if not 0 <= q <= 0.5:
-            raise ValueError("quantile distance from the median, q, must be between 0 and 0.5, not %s. For a 99% quantile range, use q=0.48." % q)
+            raise ValueError("quantile distance from the median, q, must be between 0 and 0.5, not %s. For a 99%% quantile range, use q=0.48." % q)
         shadeargs = dict(self.shadeargs)
         shadeargs.update(kwargs)
         lo = self.get_line(0.5 - q)
@@ -283,7 +287,7 @@ def runplot(results, span=None, logplot=False, kde=True, nkde=1000,
     for i, _ in enumerate(span):
         try:
             ymin, ymax = span[i]
-        except:
+        except Exception:
             span[i] = (max(data[i]) * span[i], max(data[i]))
     if lnz_error and no_span:
         if logplot:
@@ -302,7 +306,7 @@ def runplot(results, span=None, logplot=False, kde=True, nkde=1000,
         fig, axes = fig
         try:
             axes.reshape(4, 1)
-        except:
+        except Exception:
             raise ValueError("Provided axes do not match the required shape "
                              "for plotting samples.")
         # If figure is provided, keep previous bounds if they were larger.
@@ -594,12 +598,12 @@ def traceplot(results, span=None, quantiles=[0.025, 0.5, 0.975], smooth=0.02,
         try:
             samples_id = results['samples_id']
             uid = np.unique(samples_id)
-        except:
+        except Exception:
             raise ValueError("Sample IDs are not defined!")
         try:
             ids = connect_highlight[0]
             ids = connect_highlight
-        except:
+        except Exception:
             ids = np.random.choice(uid, size=connect_highlight, replace=False)
 
     # Determine plotting bounds for marginalized 1-D posteriors.
@@ -611,7 +615,7 @@ def traceplot(results, span=None, quantiles=[0.025, 0.5, 0.975], smooth=0.02,
     for i, _ in enumerate(span):
         try:
             xmin, xmax = span[i]
-        except:
+        except Exception:
             q = [0.5 - 0.5 * span[i], 0.5 + 0.5 * span[i]]
             span[i] = _quantile(samples[i], q, weights=weights)
 
@@ -630,7 +634,7 @@ def traceplot(results, span=None, quantiles=[0.025, 0.5, 0.975], smooth=0.02,
         fig, axes = fig
         try:
             axes.reshape(ndim, 2)
-        except:
+        except Exception:
             raise ValueError("Provided axes do not match the required shape "
                              "for plotting samples.")
 
@@ -683,7 +687,7 @@ def traceplot(results, span=None, quantiles=[0.025, 0.5, 0.975], smooth=0.02,
             try:
                 [ax.axhline(t, color=truth_color, **truth_kwargs)
                  for t in truths[i]]
-            except:
+            except Exception:
                 ax.axhline(truths[i], color=truth_color, **truth_kwargs)
 
         # Plot marginalized 1-D posterior.
@@ -752,7 +756,7 @@ def traceplot(results, span=None, quantiles=[0.025, 0.5, 0.975], smooth=0.02,
             try:
                 [ax.axvline(t, color=truth_color, **truth_kwargs)
                  for t in truths[i]]
-            except:
+            except Exception:
                 ax.axvline(truths[i], color=truth_color, **truth_kwargs)
         # Set titles.
         if show_titles:
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 36a7016b..7bfbd011 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -57,12 +57,13 @@ def unitcube_line_intersection(ray_origin, ray_direction):
 
 
 class PopulationRandomWalkSampler():
+    """Vectorized Gaussian Random Walk sampler."""
+
     def __init__(
         self, popsize, nsteps, generate_direction, scale,
         scale_adapt_factor=0.9, scale_min=1e-20, scale_max=20, log=False, logfile=None
     ):
-        """
-        Vectorized Gaussian Random Walk sampler.
+        """Initialise.
 
         Parameters
         ----------
@@ -113,11 +114,12 @@ def __init__(
         self.generate_direction = generate_direction
 
     def __str__(self):
+        """Return string representation."""
         return 'PopulationRandomWalkSampler(popsize=%d, nsteps=%d, generate_direction=%s, scale=%.g)' % (
             self.popsize, self.nsteps, self.generate_direction, self.scale)
 
     def region_changed(self, Ls, region):
-        """notification that the region changed. Currently not used."""
+        """Act upon region changed. Currently unused."""
         pass
 
     def __next__(
@@ -216,15 +218,17 @@ def __next__(
 
 
 class PopulationSliceSampler():
+    """Vectorized slice/HARM sampler.
+
+    Can revert until all previous steps have likelihoods allL above Lmin.
+    Updates currentt, generation and allL, in-place.
+    """
+
     def __init__(
         self, popsize, nsteps, generate_direction, scale=1.0,
         scale_adapt_factor=0.9, log=False, logfile=None
     ):
-        """
-        Vectorized slice/HARM sampler.
-
-        Revert until all previous steps have likelihoods allL above Lmin.
-        Updates currentt, generation and allL, in-place.
+        """Initialise.
 
         Parameters
         ----------
@@ -266,11 +270,12 @@ def __init__(
         self.generate_direction = generate_direction
 
     def __str__(self):
+        """Return string representation."""
         return 'PopulationSliceSampler(popsize=%d, nsteps=%d, generate_direction=%s, scale=%.g)' % (
             self.popsize, self.nsteps, self.generate_direction, self.scale)
 
     def region_changed(self, Ls, region):
-        """notification that the region changed. Currently not used."""
+        """Act upon region changed. Currently unused."""
         # self.scale = region.us.std(axis=1).mean()
         if self.logfile:
             self.logfile.write("region-update\t%g\t%g\n" % (self.scale, region.us.std(axis=1).mean()))
@@ -321,6 +326,7 @@ def setup_start(self, us, Ls, starting):
 
     @property
     def status(self):
+        """Return compact string representation of the current status."""
         s1 = ('G:' + ''.join(['%d' % g if g >= 0 else '_' for g in self.generation]))
         s2 = ('S:' + ''.join([
             'S' if not np.isfinite(self.currentt[i]) else 'L' if self.searching_left[i] else 'R' if self.searching_right[i] else 'B'
@@ -403,8 +409,9 @@ def advance(self, transform, loglike, Lmin):
 
         (
             (
-            currentt, currentv,
-            current_left, current_right, searching_left, searching_right),
+                currentt, currentv,
+                current_left, current_right, searching_left, searching_right
+            ),
             (success, unew, pnew, Lnew),
             nc
         ) = evolve(transform, loglike, Lmin, *args)
@@ -503,13 +510,16 @@ def __next__(
         if mask_starting.any():
             self.setup_brackets(mask_starting, region)
 
-        if self.log: print(str(self), "(before)")
+        if self.log:
+            print(str(self), "(before)")
         nc = self.advance(transform, loglike, Lmin)
-        if self.log: print(str(self), "(after)")
+        if self.log:
+            print(str(self), "(after)")
 
         # harvest top individual if possible
         if self.generation[self.ringindex] == self.nsteps:
-            if self.log: print("have a candidate")
+            if self.log:
+                print("have a candidate")
             u, p, L = self.allu[self.ringindex, self.nsteps, :].copy(), self.currentp[self.ringindex, :].copy(), self.allL[self.ringindex, self.nsteps].copy()
             assert np.isfinite(u).all(), u
             assert np.isfinite(p).all(), p
@@ -528,6 +538,7 @@ def __next__(
             return None, None, None, nc
 
 
-__all__ = ["generate_cube_oriented_direction", "generate_cube_oriented_direction_scaled",
-   "generate_random_direction", "generate_region_oriented_direction", "generate_region_random_direction",
-   "PopulationRandomWalkSampler", "PopulationSliceSampler"]
+__all__ = [
+    "generate_cube_oriented_direction", "generate_cube_oriented_direction_scaled",
+    "generate_random_direction", "generate_region_oriented_direction", "generate_region_random_direction",
+    "PopulationRandomWalkSampler", "PopulationSliceSampler"]
diff --git a/ultranest/samplingpath.py b/ultranest/samplingpath.py
index 0a800a44..82ddd705 100644
--- a/ultranest/samplingpath.py
+++ b/ultranest/samplingpath.py
@@ -73,6 +73,13 @@ def nearest_box_intersection_line(ray_origin, ray_direction, fwd=True):
 def box_line_intersection(ray_origin, ray_direction):
     """Find intersections of a line with the unit cube, in both sides.
 
+    Parameters
+    -----------
+    ray_origin: vector
+        starting point of line
+    ray_direction: vector
+        line direction vector
+
     Returns
     --------
     left: nearest_box_intersection_line return value
@@ -156,6 +163,13 @@ def get_sphere_tangent(sphere_center, edge_point):
     so that edge_point is on the surface. At edge_point, in
     which direction does the normal vector point?
 
+    Parameters
+    -----------
+    sphere_center: vector
+        center of sphere
+    edge_point: vector
+        point at the surface
+
     Returns
     --------
     tangent: vector
@@ -175,10 +189,17 @@ def get_sphere_tangents(sphere_center, edge_point):
 
     This function is vectorized and handles arrays of arguments.
 
+    Parameters
+    -----------
+    sphere_center: array
+        centers of spheres
+    edge_point: array
+        points at the surface
+
     Returns
     --------
-    tangent: vector
-        vector pointing to the sphere center.
+    tangent: array
+        vectors pointing to the sphere center.
 
     """
     arrow = sphere_center - edge_point
@@ -190,14 +211,14 @@ def reflect(v, normal):
     return v - 2 * (normal * v).sum() * normal
 
 
-def distances(l, o, r=1):
+def distances(direction, center, r=1):
     """Compute sphere-line intersection.
 
     Parameters
     -----------
-    l: vector
+    direction: vector
         direction vector (line starts at 0)
-    o: vector
+    center: vector
         center of sphere (coordinate vector)
     r: float
         radius of sphere
@@ -209,8 +230,8 @@ def distances(l, o, r=1):
         If no intersection, throws AssertError.
 
     """
-    loc = (l * o).sum()
-    osqrnorm = (o**2).sum()
+    loc = (direction * center).sum()
+    osqrnorm = (center**2).sum()
     # print(loc.shape, loc.shape, osqrnorm.shape)
     rootterm = loc**2 - osqrnorm + r**2
     # make sure we are crossing the sphere
diff --git a/ultranest/solvecompat.py b/ultranest/solvecompat.py
index 61ee4612..4fabf7ca 100644
--- a/ultranest/solvecompat.py
+++ b/ultranest/solvecompat.py
@@ -73,7 +73,7 @@ def pymultinest_solve_compat(
             generate_direction=generate_mixture_random_direction,
             adaptive_nsteps='move-distance',
             region_filter=kwargs.get('region_filter', True)
-            )
+        )
     else:
         sampler.stepsampler = SliceSampler(
             generate_direction=generate_mixture_random_direction,
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index ab745f83..8b84f61e 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -1,5 +1,8 @@
 # cython: language_level=3,annotate=True,profile=True,fast_fail=True,warning_errors=True
-"""Efficient Helper functions for stepsamplers
+"""
+Efficient helper functions for vectorized step-samplers
+-------------------------------------------------------
+
 """
 
 import numpy as np
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index e5378986..50c502db 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -1,9 +1,11 @@
-"""MCMC-like step sampling within a region.
+"""
+MCMC-like step sampling
+-----------------------
 
 The classes implemented here are generators that, in each iteration,
-only make one likelihood call. This allows keeping a population of
-samplers that have the same execution time per call, even if they
-do not terminate at the same number of iterations.
+only make one likelihood call. This allows running in parallel a
+population of samplers that have the same execution time per call,
+even if they do not terminate at the same number of iterations.
 """
 
 from __future__ import print_function, division
@@ -162,8 +164,8 @@ def generate_partial_differential_direction(ui, region, scale=1):
             # repeat if live points are identical
             break
     # use doubling procedure to identify left and right maxima borders
-    #v = np.zeros(ndim)
-    #v[mask] = (region.u[i,mask] - region.u[i2,mask]) * scale
+    # v = np.zeros(ndim)
+    # v[mask] = (region.u[i,mask] - region.u[i2,mask]) * scale
     return v
 
 
@@ -380,7 +382,7 @@ def __init__(
             * :py:func:`generate_partial_differential_direction` (differential evolution slice proposal on only 10% of the parameters)
             * :py:func:`generate_mixture_random_direction` (generate_differential_direction and generate_cube_oriented_differential_direction)
 
-            Additionally, :py:class:`OrthogonalDirectionGenerator` 
+            Additionally, :py:class:`OrthogonalDirectionGenerator`
             can be applied to a generate_direction.
 
             When in doubt, try :py:func:`generate_mixture_random_direction`.
@@ -466,6 +468,7 @@ def __init__(
             self.logstat_labels += ['jump-distance', 'reference-distance']
 
     def __str__(self):
+        """Return string representation."""
         if not self.adaptive_nsteps:
             return type(self).__name__ + '(nsteps=%d, generate_direction=%s)' % (self.nsteps, self.generate_direction)
         else:
@@ -792,7 +795,7 @@ def new_chain(self, region=None):
         self.nrejects = 0
 
     def adjust_accept(self, accepted, unew, pnew, Lnew, nc):
-        """see :py:meth:`StepSampler.adjust_accept`"""
+        """See :py:meth:`StepSampler.adjust_accept`."""
         v, left, right, u = self.interval
         if not self.found_left:
             if accepted:
@@ -833,7 +836,7 @@ def adjust_outside_region(self):
         self.adjust_accept(False, unew=None, pnew=None, Lnew=None, nc=0)
 
     def move(self, ui, region, ndraw=1, plot=False):
-        """Advance the slice sampling move. see :py:meth:`StepSampler.move`"""
+        """Advance the slice sampling move. see :py:meth:`StepSampler.move`."""
         if self.interval is None:
             v = self.generate_direction(ui, region)
 
@@ -914,12 +917,13 @@ def RegionBallSliceSampler(*args, **kwargs):
 
 
 class SequentialRegionDirectionGenerator(object):
+    """Sequentially proposes one region axes after the next."""
     def __init__(self):
-        """Sequentially proposes one region axes after the next."""
+        """Initialise."""
         self.axis_index = 0
 
     def __call__(self, ui, region, scale=1):
-        """Iteratively choose the next axis in t-space.
+        """Choose the next axis in t-space.
 
         Parameters
         -----------
@@ -952,14 +956,17 @@ def __call__(self, ui, region, scale=1):
     def __str__(self):
         return type(self).__name__ + '()'
 
+
 def RegionSequentialSliceSampler(*args, **kwargs):
     """Slice sampler, sequentially iterating region axes."""
     return SliceSampler(*args, **kwargs, generate_direction=SequentialRegionDirectionGenerator())
 
 
 class OrthogonalDirectionGenerator(object):
+    """Orthogonalizes proposal vectors."""
+
     def __init__(self, generate_direction):
-        """Orthogonalizes proposal vectors.
+        """Initialise.
 
         Parameters
         -----------
@@ -969,12 +976,13 @@ def __init__(self, generate_direction):
         self.axis_index = 0
         self.generate_direction = generate_direction
         self.directions = None
-    
+
     def __str__(self):
+        """Return string representation."""
         return type(self).__name__ + '(generate_direction=%s)' % self.generate_direction
 
     def __call__(self, ui, region, scale=1):
-        """Iteratively return a orthogonalized vector.
+        """Return next orthogonalized vector.
 
         Parameters
         -----------
@@ -1005,7 +1013,10 @@ def __call__(self, ui, region, scale=1):
 
 
 class SpeedVariableGenerator(object):
-    """Propose directions in region, but only some dimensions at a time, completely user-definable.
+    """Propose directions with only some parameters variable.
+
+    Propose in region direction, but only include some dimensions at a time.
+    Completely configurable.
     """
 
     def __init__(self, step_matrix, generate_direction=generate_region_random_direction):
diff --git a/ultranest/store.py b/ultranest/store.py
index 7bf424f8..6501a0d1 100644
--- a/ultranest/store.py
+++ b/ultranest/store.py
@@ -1,4 +1,6 @@
-"""Storage for nested sampling points.
+"""
+Storage for nested sampling points
+-----------------------------------
 
 The information stored is a table with
 
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 8ea524d5..6fee2178 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -1,4 +1,7 @@
-"""Utility functions for logging and statistics."""
+"""
+Utility functions for logging and statistics
+--------------------------------------------
+"""
 
 from __future__ import print_function, division
 import logging
@@ -446,8 +449,11 @@ def verify_gradient(ndim, transform, loglike, gradient, verbose=False, combinati
         assert np.allclose(Lprime, Lexpected, atol=0.1 / ndim), \
             (u, uprime, theta, thetaprime, grad, eps * grad / L, L, Lprime, Lexpected)
 
+
 def distributed_work_chunk_size(num_total_tasks, mpi_rank, mpi_size):
     """
+    Divide tasks uniformly.
+
     Computes the number of tasks for process number `mpi_rank`, so that
     `num_total_tasks` tasks are spread uniformly among `mpi_size` processes.
 
@@ -465,8 +471,9 @@ def distributed_work_chunk_size(num_total_tasks, mpi_rank, mpi_size):
 
 def submasks(mask, *masks):
     """
-    Get indices for an array, so that
-    array[indices] is equivalent to a[mask][mask1][mask2].
+    Get indices for a submasked array.
+
+    Returns indices, so that a[indices] is equivalent to a[mask][mask1][mask2].
 
     Parameters
     ----------
diff --git a/ultranest/viz.py b/ultranest/viz.py
index 78e13873..252b999d 100644
--- a/ultranest/viz.py
+++ b/ultranest/viz.py
@@ -1,4 +1,12 @@
-"""Visual impression of current exploration."""
+"""
+Live point visualisations
+-------------------------
+
+Gives a live impression of current exploration.
+This is powerful because the user can abort partial runs if the fit
+converges to unreasonable values.
+
+"""
 
 from __future__ import print_function, division
 
@@ -107,9 +115,9 @@ def nicelogger(points, info, region, transformLayer, region_fresh=False):
     plo_rounded, phi_rounded, paramformats = round_parameterlimits(plo, phi, paramlimitguess=info.get('paramlims'))
 
     if sys.stderr.isatty() and hasattr(shutil, 'get_terminal_size'):
-        columns, _rows = shutil.get_terminal_size(fallback=(80, 25))
+        columns, _ = shutil.get_terminal_size(fallback=(80, 25))
     else:
-        columns, _rows = 80, 25
+        columns, _ = 80, 25
 
     paramwidth = max([len(pname) for pname in paramnames])
     width = columns - 23 - paramwidth

From c1f20f2f3d6b746f23bb17a28f6a95550ee47ea8 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jun 2023 23:34:28 +0300
Subject: [PATCH 137/313] more user-friendly API list

---
 docs/API.rst   | 153 +++++++++++++++++++++++++++++++++++++++++++++++++
 docs/index.rst |   2 +-
 2 files changed, 154 insertions(+), 1 deletion(-)
 create mode 100644 docs/API.rst

diff --git a/docs/API.rst b/docs/API.rst
new file mode 100644
index 00000000..e0ddc6fb
--- /dev/null
+++ b/docs/API.rst
@@ -0,0 +1,153 @@
+ultranest package
+=================
+
+Modules common to be used directly:
+
+ultranest.integrator module
+---------------------------
+
+.. automodule:: ultranest.integrator
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.hotstart module
+-------------------------
+
+.. automodule:: ultranest.hotstart
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.plot module
+---------------------
+
+.. automodule:: ultranest.plot
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.stepsampler module
+----------------------------
+
+.. automodule:: ultranest.stepsampler
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.popstepsampler module
+-------------------------------
+
+.. automodule:: ultranest.popstepsampler
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.solvecompat module
+----------------------------
+
+.. automodule:: ultranest.solvecompat
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Internally used modules:
+
+ultranest.mlfriends module
+--------------------------
+
+.. automodule:: ultranest.mlfriends
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.netiter module
+------------------------
+
+.. automodule:: ultranest.netiter
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.ordertest module
+--------------------------
+
+.. automodule:: ultranest.ordertest
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+ultranest.stepfuncs module
+--------------------------
+
+.. automodule:: ultranest.stepfuncs
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.store module
+----------------------
+
+.. automodule:: ultranest.store
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.utils module
+----------------------
+
+.. automodule:: ultranest.utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.viz module
+--------------------
+
+.. automodule:: ultranest.viz
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Experimental modules, no guarantees:
+
+ultranest.dychmc module
+-----------------------
+
+.. automodule:: ultranest.dychmc
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.dyhmc module
+----------------------
+
+.. automodule:: ultranest.dyhmc
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.flatnuts module
+-------------------------
+
+.. automodule:: ultranest.flatnuts
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.pathsampler module
+----------------------------
+
+.. automodule:: ultranest.pathsampler
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ultranest.samplingpath module
+-----------------------------
+
+.. automodule:: ultranest.samplingpath
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/index.rst b/docs/index.rst
index 53a7cc4f..bd7930ed 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -11,7 +11,7 @@ Welcome to UltraNest's documentation!
    using-ultranest.ipynb
    priors.ipynb
    performance
-   modules
+   API
    issues
    contributing
    history

From 09fcc3dc7fda70f0647691c169655342076f3c31 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jun 2023 23:39:31 +0300
Subject: [PATCH 138/313] add pointer to popstepsamplers

---
 docs/performance.rst | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/docs/performance.rst b/docs/performance.rst
index 0d620c1f..e3ee853a 100644
--- a/docs/performance.rst
+++ b/docs/performance.rst
@@ -381,6 +381,17 @@ coordinates them.
 
 Use as many scripts as processors. If memory is a concern, look into shared memory solutions.
 
+GPU-acceleration
+====================
+
+Some models today use probabilistic programming languages, such as JAX,
+which allows fast model evaluations on GPUs and CPUs.
+
+UltraNest supports such models with vectorization (see above).
+
+For high-dimensional, cheap, vectorized models, the 
+:py:mod:`popstepsampler` implements vectorized versions.
+
 More features
 ===================
 

From 2f78e09c5f36af9f3e6c8bf28ffcd4816b53e1ea Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 23 Jun 2023 00:31:06 +0300
Subject: [PATCH 139/313] rename API page title

---
 docs/API.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/API.rst b/docs/API.rst
index e0ddc6fb..f0eb1ae4 100644
--- a/docs/API.rst
+++ b/docs/API.rst
@@ -1,5 +1,5 @@
-ultranest package
-=================
+API
+===
 
 Modules common to be used directly:
 

From 64cdafc3e3f255a37b5e6dfaf38326d2018d51c6 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 23 Jun 2023 00:39:36 +0300
Subject: [PATCH 140/313] add module list to API page

---
 docs/API.rst | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/docs/API.rst b/docs/API.rst
index f0eb1ae4..08eae254 100644
--- a/docs/API.rst
+++ b/docs/API.rst
@@ -1,6 +1,9 @@
 API
 ===
 
+.. toctree::
+   :maxdepth: 4
+
 Modules common to be used directly:
 
 ultranest.integrator module

From aada49d38abc6443b0a1292094a53b97b844c5f8 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 23 Jun 2023 11:43:15 +0300
Subject: [PATCH 141/313] friendlier API docs

---
 docs/API.rst                | 166 +++++++-----------------------------
 ultranest/netiter.py        |   3 +-
 ultranest/ordertest.py      |   2 -
 ultranest/pathsampler.py    |   2 +-
 ultranest/popstepsampler.py |   2 -
 5 files changed, 31 insertions(+), 144 deletions(-)

diff --git a/docs/API.rst b/docs/API.rst
index 08eae254..04f4356b 100644
--- a/docs/API.rst
+++ b/docs/API.rst
@@ -1,156 +1,48 @@
 API
 ===
 
-.. toctree::
-   :maxdepth: 4
-
-Modules common to be used directly:
-
-ultranest.integrator module
----------------------------
-
-.. automodule:: ultranest.integrator
-   :members:
-   :undoc-members:
-   :show-inheritance:
+`Full API documentation on one page <ultranest.html>`_
 
-ultranest.hotstart module
--------------------------
+The main interface is :py:class:`ultranest.integrator.ReactiveNestedSampler`, 
+also available as `ultranest.ReactiveNestedSampler`.
 
-.. automodule:: ultranest.hotstart
-   :members:
-   :undoc-members:
-   :show-inheritance:
 
-ultranest.plot module
----------------------
+Modules commonly used directly:
+--------------------------------------------------------------------------------
 
-.. automodule:: ultranest.plot
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-ultranest.stepsampler module
-----------------------------
-
-.. automodule:: ultranest.stepsampler
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-ultranest.popstepsampler module
--------------------------------
-
-.. automodule:: ultranest.popstepsampler
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-ultranest.solvecompat module
-----------------------------
-
-.. automodule:: ultranest.solvecompat
-   :members:
-   :undoc-members:
-   :show-inheritance:
+ * :py:mod:`ultranest.integrator`: Nested sampling integrators
+ * :py:mod:`ultranest.hotstart`: Warm start
+ * :py:mod:`ultranest.plot`: Plotting utilities
+ * :py:mod:`ultranest.stepsampler`: MCMC-like step sampling
+ * :py:mod:`ultranest.popstepsampler`: Vectorized step samplers
+ * :py:mod:`ultranest.solvecompat`: Drop-in replacement for pymultinest.solve.
 
 Internally used modules:
+--------------------------------------------------------------------------------
 
-ultranest.mlfriends module
---------------------------
-
-.. automodule:: ultranest.mlfriends
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-ultranest.netiter module
-------------------------
-
-.. automodule:: ultranest.netiter
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-ultranest.ordertest module
---------------------------
-
-.. automodule:: ultranest.ordertest
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-
-ultranest.stepfuncs module
---------------------------
-
-.. automodule:: ultranest.stepfuncs
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-ultranest.store module
-----------------------
-
-.. automodule:: ultranest.store
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-ultranest.utils module
-----------------------
-
-.. automodule:: ultranest.utils
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-ultranest.viz module
---------------------
-
-.. automodule:: ultranest.viz
-   :members:
-   :undoc-members:
-   :show-inheritance:
+ * :py:mod:`ultranest.mlfriends`: Region construction methods
+ * :py:mod:`ultranest.netiter`: Graph-based nested sampling
+ * :py:mod:`ultranest.ordertest`: Mann-Whitney-Wilcoxon U test for a uniform distribution of integers
+ * :py:mod:`ultranest.stepfuncs`: Efficient helper functions for vectorized step-samplers
+ * :py:mod:`ultranest.store`: Storage for nested sampling points
+ * :py:mod:`ultranest.viz`: Live point visualisations
 
 Experimental modules, no guarantees:
+--------------------------------------------------------------------------------
 
-ultranest.dychmc module
------------------------
-
-.. automodule:: ultranest.dychmc
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-ultranest.dyhmc module
-----------------------
+ * :py:mod:`ultranest.dychmc`: Constrained Hamiltanean Monte Carlo step sampling.
+ * :py:mod:`ultranest.dyhmc`: Experimental constrained Hamiltanean Monte Carlo step sampling
+ * :py:mod:`ultranest.flatnuts`: FLATNUTS is a implementation of No-U-turn sampler 
+ * :py:mod:`ultranest.pathsampler`: MCMC-like step sampling on a trajectory
+ * :py:mod:`ultranest.samplingpath`: Sparsely sampled, virtual sampling path.
 
-.. automodule:: ultranest.dyhmc
-   :members:
-   :undoc-members:
-   :show-inheritance:
 
-ultranest.flatnuts module
--------------------------
-
-.. automodule:: ultranest.flatnuts
-   :members:
-   :undoc-members:
-   :show-inheritance:
+Alphabetical list of submodules
+-------------------------------
 
-ultranest.pathsampler module
-----------------------------
+.. toctree::
+   :maxdepth: 2
 
-.. automodule:: ultranest.pathsampler
-   :members:
-   :undoc-members:
-   :show-inheritance:
+   ultranest
 
-ultranest.samplingpath module
------------------------------
 
-.. automodule:: ultranest.samplingpath
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index 160e8f85..548f66e7 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-"""
+__doc__ = """
 Graph-based nested sampling
 ---------------------------
 
@@ -24,7 +24,6 @@
 
 """
 
-from __future__ import print_function, division
 import numpy as np
 from numpy import log, log1p, exp, logaddexp
 import math
diff --git a/ultranest/ordertest.py b/ultranest/ordertest.py
index c60acf75..f473fbc1 100644
--- a/ultranest/ordertest.py
+++ b/ultranest/ordertest.py
@@ -1,5 +1,3 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
 """
 Mann-Whitney-Wilcoxon U test for a uniform distribution of integers
 -------------------------------------------------------------------
diff --git a/ultranest/pathsampler.py b/ultranest/pathsampler.py
index 47df8f7c..1370cc9e 100644
--- a/ultranest/pathsampler.py
+++ b/ultranest/pathsampler.py
@@ -1,4 +1,4 @@
-"""MCMC-like step sampling on a trajectory.
+"""MCMC-like step sampling on a trajectory
 
 These features are experimental.
 """
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 7bfbd011..47813288 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -1,5 +1,3 @@
-#!/usr/bin/env python
-# coding: utf-8
 """
 Vectorized step samplers
 ------------------------

From 36395beb93aca63bf18b4c6525a55624e1c26a20 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 23 Jun 2023 11:50:20 +0300
Subject: [PATCH 142/313] add module overview script

---
 Makefile            |  2 ++
 docs/modoverview.py | 40 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 42 insertions(+)
 create mode 100644 docs/modoverview.py

diff --git a/Makefile b/Makefile
index 0432b9b2..19e1e660 100644
--- a/Makefile
+++ b/Makefile
@@ -77,9 +77,11 @@ coverage: ## check code coverage quickly with the default Python
 docs: ## generate Sphinx HTML documentation, including API docs
 	rm -f docs/ultranest.rst
 	rm -f docs/modules.rst
+	rm -f docs/API.rst
 	python3 setup.py build_ext --inplace
 	#nbstripout docs/*.ipynb
 	sphinx-apidoc -H API -o docs/ ultranest
+	pushd docs; python3 modoverview.py; popd
 	$(MAKE) -C docs clean
 	$(MAKE) -C docs html O=-jauto
 	sed --in-place '/href="ultranest\/mlfriends.html"/d' docs/build/html/_modules/index.html
diff --git a/docs/modoverview.py b/docs/modoverview.py
new file mode 100644
index 00000000..2e336c9b
--- /dev/null
+++ b/docs/modoverview.py
@@ -0,0 +1,40 @@
+import importlib
+
+sections = [
+	('Modules commonly used directly', ['integrator', 'hotstart', 'plot', 'stepsampler', 'popstepsampler', 'solvecompat']),
+	('Internally used modules', ['mlfriends', 'netiter', 'ordertest', 'stepfuncs', 'store', 'viz']),
+	('Experimental modules, no guarantees', ['dychmc', 'dyhmc', 'flatnuts', 'pathsampler', 'samplingpath']),
+]
+
+fout = open('API.rst', 'w')
+fout.write("""API
+===
+
+`Full API documentation on one page <ultranest.html>`_
+
+The main interface is :py:class:`ultranest.integrator.ReactiveNestedSampler`, 
+also available as `ultranest.ReactiveNestedSampler`.
+
+""")
+
+for section, modules in sections:
+	fout.write("\n%s:\n%s\n\n" % (section, '-'*80))
+	for mod in modules:
+		moddoc = importlib.import_module('ultranest.%s' % mod).__doc__
+		modtitle = moddoc.strip().split('\n')[0]
+		
+		print('%-15s: %s' % (mod, modtitle))
+		fout.write(" * :py:mod:`ultranest.%s`: %s\n" % (mod, modtitle))
+
+fout.write("""
+
+Alphabetical list of submodules
+-------------------------------
+
+.. toctree::
+   :maxdepth: 2
+
+   ultranest
+
+
+""")

From 540290d2a10fd2838ab5273c2f2af45ebfbaeaa4 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 23 Jun 2023 11:53:03 +0300
Subject: [PATCH 143/313] syntax fix, make calls sh, not bash

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 19e1e660..70e18f13 100644
--- a/Makefile
+++ b/Makefile
@@ -81,7 +81,7 @@ docs: ## generate Sphinx HTML documentation, including API docs
 	python3 setup.py build_ext --inplace
 	#nbstripout docs/*.ipynb
 	sphinx-apidoc -H API -o docs/ ultranest
-	pushd docs; python3 modoverview.py; popd
+	cd docs; python3 modoverview.py
 	$(MAKE) -C docs clean
 	$(MAKE) -C docs html O=-jauto
 	sed --in-place '/href="ultranest\/mlfriends.html"/d' docs/build/html/_modules/index.html

From 82f987ff66d65b609f77b60c9ed40097c49973ea Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 23 Jun 2023 12:27:25 +0300
Subject: [PATCH 144/313] fix link to API doc

---
 docs/using-ultranest.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/using-ultranest.ipynb b/docs/using-ultranest.ipynb
index 08908472..a09159e0 100644
--- a/docs/using-ultranest.ipynb
+++ b/docs/using-ultranest.ipynb
@@ -212,7 +212,7 @@
     "\n",
     "\n",
     "\n",
-    "Both [ReactiveNestedSampler](modules.html#ultranest.integrator.ReactiveNestedSampler) and [its .run() function](modules.html#ultranest.integrator.ReactiveNestedSampler.run)  have several options to specify what logging and file output they should produce, and how they should explore the parameter space.\n",
+    "Both [ReactiveNestedSampler](ultranest.html#ultranest.integrator.ReactiveNestedSampler) and [its .run() function](ultranest.html#ultranest.integrator.ReactiveNestedSampler.run)  have several options to specify what logging and file output they should produce, and how they should explore the parameter space.\n",
     "\n"
    ]
   },
@@ -263,7 +263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

From 8574c763f916b15cdd2ff7d453c486819d3a0cd4 Mon Sep 17 00:00:00 2001
From: Alexander Harvey Nitz <alex.nitz@gmail.com>
Date: Tue, 18 Jul 2023 16:09:29 -0400
Subject: [PATCH 145/313] use explicit sqrt

---
 ultranest/mlfriends.pyx | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 22251870..6cbbf929 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -18,7 +18,7 @@ import numpy as np
 cimport numpy as np
 from numpy import pi
 cimport cython
-
+from cython.cimports.libc.math import sqrt
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
@@ -187,7 +187,7 @@ def compute_mean_pair_distance(
                 pair_dist = 0.0
                 for k in range(ndim):
                     pair_dist += (pts[i,k] - pts[j,k])**2
-                total_dist += pair_dist**0.5
+                total_dist += sqrt(pair_dist)
                 Npairs += 1
 
     assert np.isfinite(total_dist), total_dist

From 557539d327bb325f5d002207de4e6cd3a1228d50 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 18 Jul 2023 20:46:17 -0400
Subject: [PATCH 146/313] loosen stochastic test requirement

---
 tests/test_clustering.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_clustering.py b/tests/test_clustering.py
index aa1d93df..b607854a 100644
--- a/tests/test_clustering.py
+++ b/tests/test_clustering.py
@@ -62,7 +62,7 @@ def test_clusteringcase_eggbox():
     transformLayer.optimize(points, points)
     region = MLFriends(points, transformLayer)
     maxr = region.compute_maxradiussq(nbootstraps=30)
-    assert 1e-10 < maxr < 5e-10
+    assert 1e-10 < maxr < 6e-10
     print('maxradius:', maxr)
     nclusters, clusteridxs, overlapped_points = update_clusters(points, points, maxr)
     # plt.title('nclusters: %d' % nclusters)

From d344775ef10793383d08df79fc8d1f05dfab1f63 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 18 Jul 2023 20:50:09 -0400
Subject: [PATCH 147/313] =?UTF-8?q?Bump=20version:=203.6.1=20=E2=86=92=203?=
 =?UTF-8?q?.6.2?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 5dd9fb65..9dcf9d09 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.6.1',
+    version='3.6.2',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 66ac50ff..8d459f57 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.6.1'
+__version__ = '3.6.2'

From e9851a38e6b9ada531c7329d6ac8e1929b91e1d9 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sat, 30 Sep 2023 13:24:53 +0200
Subject: [PATCH 148/313] better doc for step sampler functions.

---
 ultranest/stepsampler.py | 71 +++++++++++++++++++++++++++++++++-------
 1 file changed, 60 insertions(+), 11 deletions(-)

diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 50c502db..2adf043f 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -15,7 +15,9 @@
 
 
 def generate_random_direction(ui, region, scale=1):
-    """Draw uniform direction vector in unit cube space of length `scale`.
+    """Sample uniform direction vector in unit cube space of length `scale`.
+
+    Samples a direction from a unit multi-variate Gaussian.
 
     Parameters
     -----------
@@ -38,7 +40,9 @@ def generate_random_direction(ui, region, scale=1):
 
 
 def generate_cube_oriented_direction(ui, region, scale=1):
-    """Draw a unit direction vector in direction of a random unit cube axes.
+    """Sample a unit direction vector in direction of a random unit cube axes.
+
+    Chooses one parameter, randomly uniformly, upon which the slice will be defined.
 
     Parameters
     -----------
@@ -65,8 +69,9 @@ def generate_cube_oriented_direction(ui, region, scale=1):
 
 
 def generate_cube_oriented_differential_direction(ui, region, scale=1):
-    """Draw a unit direction vector in direction of a random unit cube axes.
+    """Sample a direction vector on a randomly chose parameter based on two randomly selected live points.
 
+    Chooses one parameter, randomly uniformly, upon which the slice will be defined.
     Guess the length from the difference of two points in that axis.
 
     Parameters
@@ -101,7 +106,7 @@ def generate_cube_oriented_differential_direction(ui, region, scale=1):
 
 
 def generate_differential_direction(ui, region, scale=1):
-    """Draw a vector using the difference between two points.
+    """Sample a vector using the difference between two randomly selected live points.
 
     Parameters
     -----------
@@ -130,7 +135,9 @@ def generate_differential_direction(ui, region, scale=1):
 
 
 def generate_partial_differential_direction(ui, region, scale=1):
-    """Draw a unit direction vector in direction of a random unit cube axes.
+    """Sample a vector using the difference between two randomly selected live points.
+
+    Only 10% of parameters are allowed to vary at a time.
 
     Parameters
     -----------
@@ -170,7 +177,10 @@ def generate_partial_differential_direction(ui, region, scale=1):
 
 
 def generate_region_oriented_direction(ui, region, scale=1):
-    """Draw a random direction vector in direction of one of the `region` axes.
+    """Sample a vector along one `region` principle axes, chosen at random.
+
+    The region transformLayer axes are considered (:py:class:`AffineLayer` or :py:class:`ScalingLayer`).
+    One axis is chosen at random.
 
     Parameters
     -----------
@@ -193,9 +203,11 @@ def generate_region_oriented_direction(ui, region, scale=1):
 
 
 def generate_region_random_direction(ui, region, scale=1):
-    """Draw a direction vector in a random direction of the region.
+    """Sample a direction vector based on the region covariance.
 
-    The vector length is `scale` (in unit cube space).
+    The region transformLayer axes are considered (:py:class:`AffineLayer` or :py:class:`ScalingLayer`).
+    With this covariance matrix, a random direction is generated.
+    Generating proceeds by transforming a unit multi-variate Gaussian.
 
     Parameters
     -----------
@@ -219,7 +231,13 @@ def generate_region_random_direction(ui, region, scale=1):
 
 
 def generate_mixture_random_direction(ui, region, scale=1):
-    """Draw either from a region-direction or a unit axis.
+    """Sample randomly uniformly from two proposals.
+
+    Randomly applies either :py:func:`generate_differential_direction`,
+    which transports far, or :py:func:`generate_region_oriented_direction`,
+    which is stiffer.
+
+    Best method according to https://arxiv.org/abs/2211.09426
 
     Parameters
     -----------
@@ -243,6 +261,32 @@ def generate_mixture_random_direction(ui, region, scale=1):
         return generate_region_oriented_direction(ui, region, scale=scale)
 
 
+def generate_region_sample_direction(ui, region, scale=1):
+    """Sample a point directly from the region, and return the difference vector to the current point.
+
+    Parameters
+    -----------
+    region: MLFriends
+        region
+    ui: array
+        vector of starting point
+    scale: float
+        length of the vector.
+
+    Returns
+    --------
+    v: array
+        new direction vector
+    """
+    while True:
+        upoints = region.sample(nsamples=200)
+        if len(upoints) != 0:
+            break
+    # we only need the first one
+    u = upoints[0,:]
+    return (u - ui) * scale
+
+
 def _inside_region(region, unew, uold):
     """Check if `unew` is inside region.
 
@@ -874,8 +918,9 @@ def move(self, ui, region, ndraw=1, plot=False):
             else:
                 self.found_right = True
 
-                # adjust scale
+                # adjust scale to final slice length
                 if -left > self.next_scale or right > self.next_scale:
+                #if right - left > self.next_scale:
                     self.next_scale *= 1.1
                 else:
                     self.next_scale /= 1.1
@@ -963,7 +1008,11 @@ def RegionSequentialSliceSampler(*args, **kwargs):
 
 
 class OrthogonalDirectionGenerator(object):
-    """Orthogonalizes proposal vectors."""
+    """Orthogonalizes proposal vectors.
+
+    Samples N proposed vectors by a provided method, then orthogonalizes
+    them with Gram-Schmidt (QR decomposition).
+    """
 
     def __init__(self, generate_direction):
         """Initialise.

From fa5b3d1709f73b0908708997ea1f37d9a007d37b Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 1 Oct 2023 17:47:30 +0200
Subject: [PATCH 149/313] stepsampler: customizable random live point selection
 strategies

---
 tests/test_stepsampling.py |  56 +++++++++++++++++++
 ultranest/integrator.py    |   2 +-
 ultranest/stepsampler.py   | 107 ++++++++++++++++++++++++++++++++++++-
 3 files changed, 163 insertions(+), 2 deletions(-)

diff --git a/tests/test_stepsampling.py b/tests/test_stepsampling.py
index 2443de22..251c4ec7 100644
--- a/tests/test_stepsampling.py
+++ b/tests/test_stepsampling.py
@@ -4,6 +4,7 @@
 from ultranest.stepsampler import RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler, SpeedVariableRegionSliceSampler, RegionBallSliceSampler
 from ultranest.stepsampler import generate_region_random_direction, ellipsoid_bracket, crop_bracket_at_unit_cube
 from ultranest.pathsampler import SamplingPathStepSampler
+from ultranest.stepsampler import select_random_livepoint, IslandPopulationRandomLivepointSelector
 from numpy.testing import assert_allclose
 
 #here = os.path.dirname(__file__)
@@ -297,6 +298,61 @@ def test_crop_bracket(plot=False):
     assert (ucurrent + v * left >= 0).all(), (ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
     assert (ucurrent + v * right >= 0).all(), (ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
 
+def test_random_point_selector():
+    np.random.seed(41)
+    K = 10
+    ndim = 2
+    i1 = np.random.randint(0, K)
+    i2 = np.random.randint(0, K)
+    i3 = np.random.randint(0, K)
+    us = np.random.normal(size=(K, ndim))
+    Ls = np.random.normal(size=K)
+    Lmin = Ls.min()
+    np.random.seed(41)
+    j1 = select_random_livepoint(us, Ls, Lmin)
+    j2 = select_random_livepoint(us, Ls, Lmin)
+    j3 = select_random_livepoint(us, Ls, Lmin)
+    assert i1 == j1, (i1, j1)
+    assert i2 == j2, (i2, j2)
+    assert i3 == j3, (i3, j3)
+
+
+def test_island_point_selector():
+    K = 10
+    ndim = 2
+    self_selector = IslandPopulationRandomLivepointSelector(1)
+    selector = IslandPopulationRandomLivepointSelector(5)
+    imbalanced_selector = IslandPopulationRandomLivepointSelector(9)
+    for i in range(100):
+        us = np.random.normal(size=(K, ndim))
+        Ls = np.random.normal(size=K)
+        Lmin = Ls.min()
+        j1 = np.argmin(Ls)
+        j2 = selector(us, Ls, Lmin)
+        assert j1 == self_selector(us, Ls, Lmin)
+        if j1 >= 5:
+            assert j2 >= 5, (j1, j2)
+        if j1 < 5:
+            assert j2 < 5, (j1, j2)
+        if j1 == 9:
+            assert j1 == imbalanced_selector(us, Ls, Lmin)
+        else:
+            assert imbalanced_selector(us, Ls, Lmin) < 9
+
+    np.random.seed(421)
+    leaked = False
+    selector = IslandPopulationRandomLivepointSelector(5, 0.1)
+    for i in range(100):
+        j1 = np.argmin(Ls)
+        j2 = selector(us, Ls, Lmin)
+        if j1 >= 5 and j2 >= 5 or j1 < 5 and j2 < 5:
+            pass
+        else:
+            # leak, as expected
+            leaked = True
+            break
+    assert leaked
+
 
 if __name__ == '__main__':
     #test_stepsampler_cubemh(plot=True)
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index d91ad228..1cfd7dd8 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1417,7 +1417,7 @@ def _widen_roots_beyond_initial_plateau(self, nroots, num_warn, num_stop):
             Ls = np.array([node.value for node in self.root.children])
             Lmin = np.min(Ls)
             if self.log and nroots_needed > num_warn and not user_has_been_warned:
-                self.logger.warn("""Warning: The log-likelihood has a large plateau with L=%g.
+                self.logger.warning("""Warning: The log-likelihood has a large plateau with L=%g.
 
   Probably you are returning a low value when the parameters are problematic/unphysical.
   ultranest can handle this correctly, by discarding live points with the same loglikelihood.
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 2adf043f..abddc122 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -387,6 +387,100 @@ def adapt_proposal_move_distances_midway(region, history, mean_pair_distance, nd
     return far_enough, [d2, region.maxradiussq**0.5]
 
 
+def select_random_livepoint(us, Ls, Lmin):
+    """Select random live point as chain starting point.
+
+    Parameters
+    -----------
+    us: array
+        positions of live points
+    Ls: array
+        likelihood of live points
+    Lmin: float
+        current log-likelihood threshold
+
+    Returns
+    -------
+    i: int
+        index of live point selected
+    """
+    return np.random.randint(len(Ls))
+
+
+class IslandPopulationRandomLivepointSelector(object):
+    def __init__(self, island_size, exchange_probability=0):
+        """Set up multiple isolated islands.
+
+        To replace dead points, chains are only started from the same
+        island as the dead point. Island refers to chunks of 
+        live point indices (0,1,2,3 as stored, not sorted).
+        Each chunk has size ´island_size´.
+
+        If ´island_size´ is large, for example, the total number of live points,
+        then clumping can occur more easily. This is the observed behaviour
+        that a limited random walk is run from one live point, giving 
+        two similar points, then the next dead point replacement is 
+        likely run again from these, giving more and more similar live points.
+        This gives a run-away process leading to clumps of similar,
+        highly correlated points.
+        
+        If ´island_size´ is small, for example, 1, then each dead point
+        is replaced by a chain started from it. This is a problem because
+        modes can never die out. Nested sampling can then not complete.
+
+        In a multi-modal run, within a given number of live points, 
+        the number of live points per mode is proportional to the mode's
+        prior volume, but can fluctuate. If the number of live points
+        is small, a fluctuation can lead to mode die-out, which cannot
+        be reversed. Therefore, the number of island members should be 
+        large enough to represent each mode.
+
+        Parameters
+        -----------
+        island_size: int
+            maximum number of members on each isolated live point
+            population.
+
+        exchange_probability: float
+            Probability that a member from a random island will be picked.
+
+        """
+        assert island_size > 0
+        self.island_size = island_size
+        assert 0 <= exchange_probability <= 1
+        self.exchange_probability = exchange_probability
+
+    def __call__(self, us, Ls, Lmin):
+        """Select live point as chain starting point.
+
+        Parameters
+        -----------
+        us: array
+            positions of live points
+        Ls: array
+            likelihood of live points
+        Lmin: float
+            current log-likelihood threshold
+
+        Returns
+        -------
+        i: int
+            index of live point selected
+        """
+        mask_deadpoints = Lmin == Ls
+        if not mask_deadpoints.any() or (self.exchange_probability > 0 and np.random.uniform() < self.exchange_probability):
+            return np.random.randint(len(Ls))
+        
+        # find the dead point we should replace
+        j = np.where(mask_deadpoints)[0][0]
+        # start in the same island
+        island = j // self.island_size
+        # pick a random member from the island
+        return np.random.randint(
+            island * self.island_size,
+            min(len(Ls), (island + 1) * self.island_size))
+
+
 class StepSampler(object):
     """Base class for a simple step sampler, staggering around.
 
@@ -397,6 +491,7 @@ def __init__(
         self, nsteps, generate_direction,
         scale=1.0, adaptive_nsteps=False, max_nsteps=1000,
         region_filter=False, log=False,
+        starting_point_selector=select_random_livepoint,
     ):
         """Initialise sampler.
 
@@ -475,6 +570,15 @@ def __init__(
             proposal scale, number of steps, jump distance and distance
             between live points
 
+        starting_point_selector: func
+            function which given the live point positions us,
+            their log-likelihoods Ls and the current log-likelihood 
+            threshold Lmin, returns the index i of the selected live 
+            point to start a new chain from.
+            Examples: :py:func:`select_random_livepoint`, which has
+            always been the default behaviour,
+            or an instance of :py:class:`IslandPopulationRandomLivepointSelector`.
+
         """
         self.history = []
         self.nsteps = nsteps
@@ -502,6 +606,7 @@ def __init__(
         self.adaptive_nsteps_needs_mean_pair_distance = self.adaptive_nsteps in (
             'proposal-total-distances', 'proposal-summed-distances',
         )
+        self.starting_point_selector = starting_point_selector
         self.mean_pair_distance = np.nan
         self.region_filter = region_filter
         self.log = log
@@ -728,7 +833,7 @@ def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=10, plot=Fals
             # mask = region.inside(us)
             # assert mask.any(), ("One of the live points does not satisfies the current region!",
             #    region.maxradiussq, region.u, region.unormed, us)
-            i = np.random.randint(len(us))
+            i = self.starting_point_selector(us, Ls, Lmin)
             self.starti = i
             ui = us[i,:]
             # print("starting at", ui[0])

From 602f21d946aaa0c7ed647cad5beefe09236efd9a Mon Sep 17 00:00:00 2001
From: Gregory David Martinez <gregory.david.martinez@gmail.com>
Date: Wed, 11 Oct 2023 13:00:06 -0700
Subject: [PATCH 150/313] Added mpi check and 'break' on logger statement.

---
 ultranest/integrator.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 1cfd7dd8..e5407f70 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1442,13 +1442,14 @@ def _widen_roots_beyond_initial_plateau(self, nroots, num_warn, num_stop):
             if nroots_needed >= num_stop:
                 break
             P = (Ls == Lmin).sum()
-            if 1 < P < len(Ls) and len(Ls) - P + 1 < nroots:
+            if 1 < P < len(Ls) and len(Ls) - P + 1 < nroots and self.log:
                 # guess the number of points needed: P-1 are useless
                 self.logger.debug(
                     'Found plateau of %d/%d initial points at L=%g. '
                     'Avoid this by a continuously increasing loglikelihood towards good regions.',
                     P, nroots_needed, Lmin)
                 nroots_needed = min(num_stop, nroots_needed + (P - 1))
+                break
             else:
                 break
 

From 996e4fe39f28c46d8b843fa78e4fe09928803a41 Mon Sep 17 00:00:00 2001
From: Gregory David Martinez <gregory.david.martinez@gmail.com>
Date: Wed, 11 Oct 2023 13:31:59 -0700
Subject: [PATCH 151/313] Revert "Added mpi check and 'break' on logger
 statement."

This reverts commit 602f21d946aaa0c7ed647cad5beefe09236efd9a.
---
 ultranest/integrator.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index e5407f70..1cfd7dd8 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1442,14 +1442,13 @@ def _widen_roots_beyond_initial_plateau(self, nroots, num_warn, num_stop):
             if nroots_needed >= num_stop:
                 break
             P = (Ls == Lmin).sum()
-            if 1 < P < len(Ls) and len(Ls) - P + 1 < nroots and self.log:
+            if 1 < P < len(Ls) and len(Ls) - P + 1 < nroots:
                 # guess the number of points needed: P-1 are useless
                 self.logger.debug(
                     'Found plateau of %d/%d initial points at L=%g. '
                     'Avoid this by a continuously increasing loglikelihood towards good regions.',
                     P, nroots_needed, Lmin)
                 nroots_needed = min(num_stop, nroots_needed + (P - 1))
-                break
             else:
                 break
 

From ab11d6616b52ee7f0a48d6faa1397e7b4774088b Mon Sep 17 00:00:00 2001
From: Gregory David Martinez <gregory.david.martinez@gmail.com>
Date: Wed, 11 Oct 2023 13:42:42 -0700
Subject: [PATCH 152/313] Added mpi check on logger statement.

---
 ultranest/integrator.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 1cfd7dd8..a463ffa3 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1444,10 +1444,11 @@ def _widen_roots_beyond_initial_plateau(self, nroots, num_warn, num_stop):
             P = (Ls == Lmin).sum()
             if 1 < P < len(Ls) and len(Ls) - P + 1 < nroots:
                 # guess the number of points needed: P-1 are useless
-                self.logger.debug(
-                    'Found plateau of %d/%d initial points at L=%g. '
-                    'Avoid this by a continuously increasing loglikelihood towards good regions.',
-                    P, nroots_needed, Lmin)
+                if self.log:
+                    self.logger.debug(
+                        'Found plateau of %d/%d initial points at L=%g. '
+                        'Avoid this by a continuously increasing loglikelihood towards good regions.',
+                        P, nroots_needed, Lmin)
                 nroots_needed = min(num_stop, nroots_needed + (P - 1))
             else:
                 break

From d87653cb8a71b7270fa94457f46c6d6af77bdc7e Mon Sep 17 00:00:00 2001
From: facero <fabio.f.acero@gmail.com>
Date: Mon, 16 Oct 2023 13:43:03 +0200
Subject: [PATCH 153/313] Add the possibility to provide an ax for plots

In a spectral plot with a residual panel the current `band.line` plots the line on the 2nd residual panel.
This allows to select the ax where the line or shade will be overlaid.
---
 ultranest/plot.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/ultranest/plot.py b/ultranest/plot.py
index dab981b9..6384b006 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -118,22 +118,28 @@ def get_line(self, q=0.5):
         assert len(self.ys) > 0, self.ys
         return scipy.stats.mstats.mquantiles(self.ys, q, axis=0)[0]
 
-    def shade(self, q=0.341, **kwargs):
+   def shade(self, q=0.341, ax=None, **kwargs):
         """Plot a shaded region between 0.5-q and 0.5+q. Default is 1 sigma."""
         if not 0 <= q <= 0.5:
-            raise ValueError("quantile distance from the median, q, must be between 0 and 0.5, not %s. For a 99%% quantile range, use q=0.48." % q)
+            raise ValueError("quantile distance from the median, q, must be between 0 and 0.5, not %s. For a 99% quantile range, use q=0.48." % q)
         shadeargs = dict(self.shadeargs)
         shadeargs.update(kwargs)
         lo = self.get_line(0.5 - q)
         hi = self.get_line(0.5 + q)
-        return plt.fill_between(self.x, lo, hi, **shadeargs)
+        if ax is None:
+            return plt.fill_between(self.x, lo, hi, **shadeargs)
+        else:
+            return ax.fill_between(self.x, lo, hi, **shadeargs)
 
-    def line(self, **kwargs):
+    def line(self, ax= None, **kwargs):
         """Plot the median curve."""
         lineargs = dict(self.lineargs)
         lineargs.update(kwargs)
         mid = self.get_line(0.5)
-        return plt.plot(self.x, mid, **lineargs)
+        if ax is None:
+            return plt.plot(self.x, mid, **lineargs)
+        else:
+            return ax.plot(self.x, mid, **lineargs)
 
 
 # the following function is taken from https://github.com/joshspeagle/dynesty/blob/master/dynesty/plotting.py

From 3afda7341ba9ead4b026bad690d157f084488896 Mon Sep 17 00:00:00 2001
From: facero <fabio.f.acero@gmail.com>
Date: Mon, 16 Oct 2023 14:52:57 +0200
Subject: [PATCH 154/313] Update plot.py

---
 ultranest/plot.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/plot.py b/ultranest/plot.py
index 6384b006..649f6231 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -131,7 +131,7 @@ def shade(self, q=0.341, ax=None, **kwargs):
         else:
             return ax.fill_between(self.x, lo, hi, **shadeargs)
 
-    def line(self, ax= None, **kwargs):
+    def line(self, ax=None, **kwargs):
         """Plot the median curve."""
         lineargs = dict(self.lineargs)
         lineargs.update(kwargs)

From 87f2464a14424b2ab93ca6e0ea7c3bf7bb20c799 Mon Sep 17 00:00:00 2001
From: facero <fabio.f.acero@gmail.com>
Date: Tue, 17 Oct 2023 14:04:05 +0200
Subject: [PATCH 155/313] Updated code and example following requested changes

---
 ultranest/plot.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/ultranest/plot.py b/ultranest/plot.py
index 649f6231..afaddad6 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -77,8 +77,8 @@ class PredictionBand(object):
         band = PredictionBand(x)
         for c in chain:
             band.add(c[0] * x + c[1])
-        # add median line
-        band.line(color='k')
+        # add median line. As an option a matplotlib ax can be given.
+        band.line(color='k') # or band.line(color='k', ax = ax)
         # add 1 sigma quantile
         band.shade(color='k', alpha=0.3)
         # add wider quantile
@@ -127,9 +127,8 @@ def shade(self, q=0.341, ax=None, **kwargs):
         lo = self.get_line(0.5 - q)
         hi = self.get_line(0.5 + q)
         if ax is None:
-            return plt.fill_between(self.x, lo, hi, **shadeargs)
-        else:
-            return ax.fill_between(self.x, lo, hi, **shadeargs)
+            ax = plt
+        return ax.fill_between(self.x, lo, hi, **shadeargs)
 
     def line(self, ax=None, **kwargs):
         """Plot the median curve."""
@@ -137,9 +136,8 @@ def line(self, ax=None, **kwargs):
         lineargs.update(kwargs)
         mid = self.get_line(0.5)
         if ax is None:
-            return plt.plot(self.x, mid, **lineargs)
-        else:
-            return ax.plot(self.x, mid, **lineargs)
+            ax = plt
+        return ax.plot(self.x, mid, **lineargs)
 
 
 # the following function is taken from https://github.com/joshspeagle/dynesty/blob/master/dynesty/plotting.py

From d3e37e34265c0a75c363045cf039a722fce14e1f Mon Sep 17 00:00:00 2001
From: facero <fabio.f.acero@gmail.com>
Date: Tue, 17 Oct 2023 15:32:37 +0200
Subject: [PATCH 156/313] Update plot.py

---
 ultranest/plot.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ultranest/plot.py b/ultranest/plot.py
index afaddad6..6b0d6f65 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -78,7 +78,8 @@ class PredictionBand(object):
         for c in chain:
             band.add(c[0] * x + c[1])
         # add median line. As an option a matplotlib ax can be given.
-        band.line(color='k') # or band.line(color='k', ax = ax)
+        band.line(color='k')
+        # to plot onto a specific axis, use `band.line(..., ax=myaxis)`
         # add 1 sigma quantile
         band.shade(color='k', alpha=0.3)
         # add wider quantile

From 0f4c54f5480fc9d38a7ad2450675841982f90ea7 Mon Sep 17 00:00:00 2001
From: Adipol Phosrisom <85479134+adipol-ph@users.noreply.github.com>
Date: Tue, 17 Oct 2023 14:54:52 +0100
Subject: [PATCH 157/313] Update integrator.py

fix logger bug in _widen_roots_beyond_initial_plateau
---
 ultranest/integrator.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 1cfd7dd8..a463ffa3 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1444,10 +1444,11 @@ def _widen_roots_beyond_initial_plateau(self, nroots, num_warn, num_stop):
             P = (Ls == Lmin).sum()
             if 1 < P < len(Ls) and len(Ls) - P + 1 < nroots:
                 # guess the number of points needed: P-1 are useless
-                self.logger.debug(
-                    'Found plateau of %d/%d initial points at L=%g. '
-                    'Avoid this by a continuously increasing loglikelihood towards good regions.',
-                    P, nroots_needed, Lmin)
+                if self.log:
+                    self.logger.debug(
+                        'Found plateau of %d/%d initial points at L=%g. '
+                        'Avoid this by a continuously increasing loglikelihood towards good regions.',
+                        P, nroots_needed, Lmin)
                 nroots_needed = min(num_stop, nroots_needed + (P - 1))
             else:
                 break

From 48859063b32c6925f84ad29a11a30f6ddab31583 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 17 Oct 2023 15:56:53 +0200
Subject: [PATCH 158/313] fix some typos and move new axis feature doc line

---
 ultranest/plot.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/ultranest/plot.py b/ultranest/plot.py
index 6b0d6f65..e58ff236 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -79,13 +79,14 @@ class PredictionBand(object):
             band.add(c[0] * x + c[1])
         # add median line. As an option a matplotlib ax can be given.
         band.line(color='k')
-        # to plot onto a specific axis, use `band.line(..., ax=myaxis)`
         # add 1 sigma quantile
         band.shade(color='k', alpha=0.3)
         # add wider quantile
         band.shade(q=0.01, color='gray', alpha=0.1)
         plt.show()
 
+    To plot onto a specific axis, use `band.line(..., ax=myaxis)`.
+
     Parameters
     ----------
     x: array
@@ -119,10 +120,10 @@ def get_line(self, q=0.5):
         assert len(self.ys) > 0, self.ys
         return scipy.stats.mstats.mquantiles(self.ys, q, axis=0)[0]
 
-   def shade(self, q=0.341, ax=None, **kwargs):
-        """Plot a shaded region between 0.5-q and 0.5+q. Default is 1 sigma."""
+    def shade(self, q=0.341, ax=None, **kwargs):
+        """Plot a shaded region between 0.5-q and 0.5+q, by default 1 sigma."""
         if not 0 <= q <= 0.5:
-            raise ValueError("quantile distance from the median, q, must be between 0 and 0.5, not %s. For a 99% quantile range, use q=0.48." % q)
+            raise ValueError("quantile distance from the median, q, must be between 0 and 0.5, not %s. For a 99%% quantile range, use q=0.48." % q)
         shadeargs = dict(self.shadeargs)
         shadeargs.update(kwargs)
         lo = self.get_line(0.5 - q)

From 4817c4eae921ea779c63b88682324a223d962706 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 17 Oct 2023 17:44:21 +0200
Subject: [PATCH 159/313] fix for
 https://github.com/JohannesBuchner/UltraNest/issues/1#issuecomment-1765536315

---
 ultranest/integrator.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 1cfd7dd8..a463ffa3 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1444,10 +1444,11 @@ def _widen_roots_beyond_initial_plateau(self, nroots, num_warn, num_stop):
             P = (Ls == Lmin).sum()
             if 1 < P < len(Ls) and len(Ls) - P + 1 < nroots:
                 # guess the number of points needed: P-1 are useless
-                self.logger.debug(
-                    'Found plateau of %d/%d initial points at L=%g. '
-                    'Avoid this by a continuously increasing loglikelihood towards good regions.',
-                    P, nroots_needed, Lmin)
+                if self.log:
+                    self.logger.debug(
+                        'Found plateau of %d/%d initial points at L=%g. '
+                        'Avoid this by a continuously increasing loglikelihood towards good regions.',
+                        P, nroots_needed, Lmin)
                 nroots_needed = min(num_stop, nroots_needed + (P - 1))
             else:
                 break

From 66246e507b087c27fc888022e338759ba1d3ce86 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 17 Oct 2023 17:49:59 +0200
Subject: [PATCH 160/313] =?UTF-8?q?Bump=20version:=203.6.2=20=E2=86=92=203?=
 =?UTF-8?q?.6.3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 9dcf9d09..6e0a0ccb 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.6.2',
+    version='3.6.3',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 8d459f57..7d7bddba 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.6.2'
+__version__ = '3.6.3'

From 273c8bac14617d946ec3e236fd2ff06ebbf11377 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 17 Oct 2023 18:15:54 +0200
Subject: [PATCH 161/313] add requirements for doc building

---
 pip-requirements.txt | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/pip-requirements.txt b/pip-requirements.txt
index 7078dd05..34c1ed52 100644
--- a/pip-requirements.txt
+++ b/pip-requirements.txt
@@ -8,3 +8,9 @@ pandas
 flake8
 coveralls
 pytest-html
+pytest-xdist
+sphinx_rtd_theme
+sphinx
+nbsphinx
+fastkde
+getdist

From f3b6e82f523a49a79449697dfd0e3f2f8a661811 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 17 Oct 2023 20:17:00 +0200
Subject: [PATCH 162/313] more libraries for the tests towards a release

---
 pip-requirements.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pip-requirements.txt b/pip-requirements.txt
index 34c1ed52..4e1c8ad5 100644
--- a/pip-requirements.txt
+++ b/pip-requirements.txt
@@ -14,3 +14,5 @@ sphinx
 nbsphinx
 fastkde
 getdist
+nbstripout
+mpi4py

From 9f3cacc7d2726c76fdda53c1d8b308b25f7bcd10 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 19 Oct 2023 20:21:11 +0200
Subject: [PATCH 163/313] remove extraneous doc and test outputs from sdist

---
 MANIFEST.in | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/MANIFEST.in b/MANIFEST.in
index d6c81dd0..400e3a90 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -12,5 +12,14 @@ recursive-include tests *
 recursive-exclude * __pycache__
 recursive-exclude * *.py[co]
 recursive-exclude * *.c
+recursive-exclude * *.orig
+recursive-exclude * *.pdf
 
 recursive-include docs *.rst conf.py Makefile make.bat *.jpg *.png *.gif
+
+# remove extraneous doc and test outputs
+prune docs/static/mcmc-demo
+prune tests/reports
+prune tests/.pytype
+recursive-exclude tests conetestdata.npz
+recursive-exclude tests region-stuck*.npz

From 1d6edf734da21bc84f21b1279e71f442bece0b38 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 19 Oct 2023 20:21:31 +0200
Subject: [PATCH 164/313] =?UTF-8?q?Bump=20version:=203.6.3=20=E2=86=92=203?=
 =?UTF-8?q?.6.4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 6e0a0ccb..6688bf44 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.6.3',
+    version='3.6.4',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 7d7bddba..ce2fc020 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.6.3'
+__version__ = '3.6.4'

From f3795df988ff63b8abf407f8e0aea5e1b80a025d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 30 Nov 2023 09:18:05 +0100
Subject: [PATCH 165/313] add 2 missing direction proposals to population step
 sampler

---
 ultranest/popstepsampler.py |  1 +
 ultranest/stepfuncs.pyx     | 58 +++++++++++++++++++++++++++++++++++++
 2 files changed, 59 insertions(+)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 47813288..89cebfad 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -13,6 +13,7 @@
 from ultranest.stepfuncs import evolve, step_back
 from ultranest.stepfuncs import generate_cube_oriented_direction, generate_cube_oriented_direction_scaled
 from ultranest.stepfuncs import generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
+from ultranest.stepfuncs import generate_differential_direction, generate_mixture_random_direction
 import scipy.stats
 
 
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 8b84f61e..68a5fd7b 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -468,3 +468,61 @@ def generate_region_random_direction(ui, region, scale=1):
     v1 *= scale / np.linalg.norm(v1, axis=1).reshape((nsamples, 1))
     v = np.einsum('ij,kj->ki', region.transformLayer.axes, v1)
     return v
+
+def generate_differential_direction(ui, region, scale=1):
+    """Sample a vector using the difference between two randomly selected live points.
+
+    Parameters
+    -----------
+    ui: np.array((npoints, ndim), dtype=float)
+        starting point
+    region: MLFriends object
+        current region
+    scale: float:
+        length of direction vector (in t-space)
+
+    Returns
+    --------
+    v: array
+        new direction vector
+    """
+    nsamples, ndim = ui.shape
+    nlive, ndim = region.u.shape
+    # choose pair
+    i = np.random.randint(nlive, size=nsamples)
+    i2 = np.random.randint(nlive - 1, size=nsamples)
+    i2[i2 >= i] += 1
+
+    # compute difference vector
+    v = (region.u[i,:] - region.u[i2,:]) * scale
+    return v
+
+
+
+def generate_mixture_random_direction(ui, region, scale=1):
+    """Sample randomly uniformly from two proposals.
+
+    Randomly applies either :py:func:`generate_differential_direction`,
+    which transports far, or :py:func:`generate_region_oriented_direction`,
+    which is stiffer.
+
+    Best method according to https://arxiv.org/abs/2211.09426
+
+    Parameters
+    -----------
+    ui: np.array((npoints, ndim), dtype=float)
+        starting point
+    region: MLFriends object
+        current region
+    scale: float:
+        length of direction vector (in t-space)
+
+    Returns
+    --------
+    v: array
+        new direction vector
+    """
+    nsamples, ndim = ui.shape
+    v_DE = generate_differential_direction(ui, region, scale=scale)
+    v_axis = generate_region_oriented_direction(ui, region, scale=scale)
+    return np.where(np.random.uniform(size=nsamples).reshape((-1, 1)) < 0.5, v_DE, v_axis)

From 8c5e5b7cb239641617b8714717263427936935c2 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 30 Nov 2023 09:24:11 +0100
Subject: [PATCH 166/313] whitespace stripping

---
 ultranest/stepsampler.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index abddc122..924b4799 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -412,27 +412,27 @@ def __init__(self, island_size, exchange_probability=0):
         """Set up multiple isolated islands.
 
         To replace dead points, chains are only started from the same
-        island as the dead point. Island refers to chunks of 
+        island as the dead point. Island refers to chunks of
         live point indices (0,1,2,3 as stored, not sorted).
         Each chunk has size ´island_size´.
 
         If ´island_size´ is large, for example, the total number of live points,
         then clumping can occur more easily. This is the observed behaviour
-        that a limited random walk is run from one live point, giving 
-        two similar points, then the next dead point replacement is 
+        that a limited random walk is run from one live point, giving
+        two similar points, then the next dead point replacement is
         likely run again from these, giving more and more similar live points.
         This gives a run-away process leading to clumps of similar,
         highly correlated points.
-        
+
         If ´island_size´ is small, for example, 1, then each dead point
         is replaced by a chain started from it. This is a problem because
         modes can never die out. Nested sampling can then not complete.
 
-        In a multi-modal run, within a given number of live points, 
+        In a multi-modal run, within a given number of live points,
         the number of live points per mode is proportional to the mode's
         prior volume, but can fluctuate. If the number of live points
         is small, a fluctuation can lead to mode die-out, which cannot
-        be reversed. Therefore, the number of island members should be 
+        be reversed. Therefore, the number of island members should be
         large enough to represent each mode.
 
         Parameters
@@ -470,7 +470,7 @@ def __call__(self, us, Ls, Lmin):
         mask_deadpoints = Lmin == Ls
         if not mask_deadpoints.any() or (self.exchange_probability > 0 and np.random.uniform() < self.exchange_probability):
             return np.random.randint(len(Ls))
-        
+
         # find the dead point we should replace
         j = np.where(mask_deadpoints)[0][0]
         # start in the same island
@@ -572,8 +572,8 @@ def __init__(
 
         starting_point_selector: func
             function which given the live point positions us,
-            their log-likelihoods Ls and the current log-likelihood 
-            threshold Lmin, returns the index i of the selected live 
+            their log-likelihoods Ls and the current log-likelihood
+            threshold Lmin, returns the index i of the selected live
             point to start a new chain from.
             Examples: :py:func:`select_random_livepoint`, which has
             always been the default behaviour,

From 35a7565f199e955b7208dc989616ecbdc7470c57 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 30 Nov 2023 09:31:53 +0100
Subject: [PATCH 167/313] bug fix: pass on user-provided scale

---
 ultranest/stepsampler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 924b4799..c206733f 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -583,7 +583,7 @@ def __init__(
         self.history = []
         self.nsteps = nsteps
         self.nrejects = 0
-        self.scale = 1.0
+        self.scale = scale
         self.max_nsteps = max_nsteps
         self.next_scale = self.scale
         self.nudge = 1.1**(1. / self.nsteps)

From 3bf6ee3344052d2b775077d66eb540d320d43e92 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 29 Dec 2023 16:16:29 +0100
Subject: [PATCH 168/313] add MaxPrincipleGapAffineLayer and test

this builds a more local covariance even for cases where
clusters could not (yet) been detected, by splitting the live points
along the distribution principle axis. The split point is at the
largest gap.
---
 tests/test_run.py       | 129 +++++++++++++++++++++++++++++++++++++++-
 ultranest/mlfriends.pyx |  87 ++++++++++++++++++++++++++-
 2 files changed, 212 insertions(+), 4 deletions(-)

diff --git a/tests/test_run.py b/tests/test_run.py
index 850c33a7..ceeb1ad4 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -5,11 +5,135 @@
 import pytest
 import json
 import pandas
+from ultranest.mlfriends import MLFriends, AffineLayer, MaxPrincipleGapAffineLayer
 from ultranest import NestedSampler, ReactiveNestedSampler, read_file
-from ultranest.integrator import warmstart_from_similar_file
+from ultranest.integrator import warmstart_from_similar_file, _update_region_bootstrap
 import ultranest.mlfriends
 from numpy.testing import assert_allclose
 
+def sample_ellipsoid(rng, nsamples, ndim, sigma=0.01, center=0.5):
+    """ Sample from a unit sphere with constant density """
+    z = rng.normal(size=(nsamples, ndim))
+    z /= ((z**2).sum(axis=1)**0.5).reshape((nsamples, 1))
+    u = z * rng.uniform(size=(nsamples, 1))**(1./ndim)
+    return u * sigma + center
+
+def generate_two_blob_points(rng, d, Nlive1, Nlive2, offset2, sigma):
+    """ generate live points from two spheres """
+    return np.vstack((sample_ellipsoid(rng, Nlive1, d, sigma=sigma), sample_ellipsoid(rng, Nlive2, d, sigma=sigma) + offset2))
+
+def test_clustering_recursion(plot=False):
+    # generate two blobs separated by 2 sigma
+    # check that they are *not* separated with AffineLayer+MLFriends
+    # check that they are separated with MaxPrincipleGapAffineLayer+MLFriends
+    nbootstraps = 30
+
+    sigma = 0.001
+    d = 20
+    
+    Nlive = 100
+    Nlive1 = Nlive // 2
+    Nlive2 = Nlive // 2
+    
+    offset2 = 1.0 * sigma
+    
+    nwithclusters = 0
+    nwithclusters2 = 0
+    noverclustered = 0
+    gapped_nwithclusters = 0
+    gapped_nwithclusters2 = 0
+    gapped_noverclustered = 0
+
+    for seed in range(25):
+        rng = np.random.RandomState(54 + seed)
+        u = generate_two_blob_points(rng, d, Nlive1, Nlive2, offset2, sigma)
+
+        # boot-strap an affine layer after clustering
+        transformLayer = AffineLayer()
+        transformLayer.optimize(u, u)
+        region = MLFriends(u, transformLayer)
+        _update_region_bootstrap(region, nbootstraps)
+        region.create_ellipsoid()
+        layer = transformLayer.create_new(u, region.maxradiussq)
+        nextregion = MLFriends(u, layer)
+        _update_region_bootstrap(nextregion, nbootstraps=30)
+        nextLayer = layer.create_new(u, nextregion.maxradiussq)
+        nextNextLayer = nextLayer.create_new(u, region.maxradiussq)
+
+        nwithclusters += layer.nclusters
+        nwithclusters2 += nextLayer.nclusters
+        noverclustered += nextLayer.nclusters > 2
+        if plot:
+            import matplotlib.pyplot as plt
+            plt.figure("AffineLayer", figsize=(20, 20))
+            plt.subplot(5, 5, seed + 1)
+            plt.title('%d -> %d -> %d' % (transformLayer.nclusters, nextLayer.nclusters, nextNextLayer.nclusters))
+            plt.scatter(u[:,0], u[:,1], label='points')
+            # Plot the principal vectors
+            plt.quiver(0.5, 0.5, transformLayer.invT[0, 0], transformLayer.invT[0, 1], angles='xy', scale_units='xy', scale=1, color='r', label='First Principal Vector')
+            plt.quiver(0.5, 0.5, transformLayer.invT[1, 0], transformLayer.invT[1, 1], angles='xy', scale_units='xy', scale=1, color='b', label='Second Principal Vector')
+            ylo, yhi = plt.ylim()
+            ymax = max(0.5 - ylo, yhi - 0.5)
+            xlo, xhi = plt.xlim()
+            xmax = max(0.5 - xlo, xhi - 0.5)
+            xymax = 1.5 * max(ymax, xmax)
+            plt.xlim(0.5 - xymax, 0.5 + xymax)
+            plt.ylim(0.5 - xymax, 0.5 + xymax)
+
+        # boot-strap an MaxPrincipleGapAffineLayer layer after clustering
+        transformLayer = MaxPrincipleGapAffineLayer()
+        transformLayer.optimize(u, u)
+        region = MLFriends(u, transformLayer)
+        _update_region_bootstrap(region, nbootstraps)
+        region.create_ellipsoid()
+        layer = transformLayer.create_new(u, region.maxradiussq)
+        nextregion = MLFriends(u, layer)
+        _update_region_bootstrap(nextregion, nbootstraps=30)
+        nextLayer = layer.create_new(u, nextregion.maxradiussq)
+        nextNextLayer = nextLayer.create_new(u, region.maxradiussq)
+
+        gapped_nwithclusters += layer.nclusters
+        gapped_nwithclusters2 += nextLayer.nclusters
+        gapped_noverclustered += nextLayer.nclusters > 2
+        if plot:
+            plt.figure("MaxPrincipleGapAffineLayer", figsize=(20, 20))
+            plt.subplot(5, 5, seed + 1)
+            plt.title('%d -> %d -> %d' % (transformLayer.nclusters, nextLayer.nclusters, nextNextLayer.nclusters))
+            plt.scatter(u[:,0], u[:,1], label='points')
+            # Plot the principal vectors
+            plt.quiver(0.5, 0.5, transformLayer.invT[0, 0], transformLayer.invT[0, 1], angles='xy', scale_units='xy', scale=1, color='r', label='First Principal Vector')
+            plt.quiver(0.5, 0.5, transformLayer.invT[1, 0], transformLayer.invT[1, 1], angles='xy', scale_units='xy', scale=1, color='b', label='Second Principal Vector')
+            ylo, yhi = plt.ylim()
+            ymax = max(0.5 - ylo, yhi - 0.5)
+            xlo, xhi = plt.xlim()
+            xmax = max(0.5 - xlo, xhi - 0.5)
+            xymax = 1.5 * max(ymax, xmax)
+            plt.xlim(0.5 - xymax, 0.5 + xymax)
+            plt.ylim(0.5 - xymax, 0.5 + xymax)
+
+    if plot:
+        plt.savefig('layercov_MaxPrincipleGapAffineLayer.pdf')
+        plt.close()
+        plt.savefig('layercov_AffineLayer.pdf')
+        plt.close()
+
+    print("clustering statistics: (%d runs)" % (seed+1))
+    print("  number of clusters iteration 1, iteration 2, number of overclusterings")
+    print("AffineLayer:")
+    print("  ", nwithclusters, nwithclusters2, noverclustered)
+    print("MaxPrincipleGapAffineLayer:")
+    print("  ", gapped_nwithclusters, gapped_nwithclusters2, gapped_noverclustered)
+    # with the affine layer we only see one cluster, because they are
+    # so close together and the covariance spans them
+    assert nwithclusters in (25, 26, 27)
+    assert nwithclusters2 in (25, 26, 27)
+    # MaxPrincipleGapAffineLayer builds a more local covariance
+    # so the subsequent iteration splits the cluster
+    assert gapped_nwithclusters in (25, 26, 27)
+    assert gapped_nwithclusters2 in (49, 50, 51, 52)
+    assert noverclustered in (0, 1, 2)
+    assert gapped_noverclustered in (0, 1, 2)
+
 def test_run():
     def loglike(y):
         z = np.log10(y)
@@ -585,4 +709,5 @@ def transform(x):
     #test_reactive_run_extraparams()
     #test_reactive_run_resume_eggbox('hdf5')
     #test_dlogz_reactive_run()
-    test_plateau()
+    #test_plateau()
+    test_clustering_recursion(plot=True)
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 6cbbf929..bf3494b8 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -548,6 +548,12 @@ class AffineLayer(ScalingLayer):
     """Affine whitening transformation.
 
     Learns the covariance of points.
+
+    For learning the next layer's covariance, the clustering
+    is considered: the sample covariance is computed after subtracting
+    the cluster mean. This co-centers all clusters and gets
+    the average cluster shape, avoiding learning a covariance dominated
+    by the distance between clusters.
     """
 
     def __init__(self, ctr=0, T=1, invT=1, nclusters=1, wrapped_dims=[], clusterids=None):
@@ -560,9 +566,11 @@ class AffineLayer(ScalingLayer):
         ctr: vector
             Center of points
         T: matrix
-            transformation matrix
+            Transformation matrix. This matrix whitens the points 
+            to a unit Gaussian.
         invT: matrix
-            inverse transformation matrix
+            Inverse transformation matrix. For transforming a unit
+            Gaussian into something with the sample cov.
         nclusters: int
             number of clusters
         wrapped_dims: array of bools
@@ -599,18 +607,28 @@ class AffineLayer(ScalingLayer):
         """
         self.optimize_wrap(points)
         wrapped_points = self.wrap(points)
+        # point center
         self.ctr = np.mean(wrapped_points, axis=0)
+        # compute sample covariance
         cov = np.cov(centered_points, rowvar=0)
         cov *= (len(self.ctr) + 2)
         self.cov = cov
+        # Eigen decomposition of the covariance, with numerical stability
         eigval, eigvec = np.linalg.eigh(cov)
         eigvalmin = eigval.max() * 1e-40
         eigval[eigval < eigvalmin] = eigvalmin
+        # Try explicit inversion; if this fails, the error is escalated.
         a = np.linalg.inv(cov)
+        # log-volume of the space
         self.logvolscale = np.linalg.slogdet(a)[1] * -0.5
 
+        # Transformation matrix with the correct scale
+        # this matrix whitens the points to a unit Gaussian.
         self.T = eigvec * eigval**-0.5
+        # Inverse transformation matrix, for transforming a unit 
+        # Gaussian into something with the sample cov.
         self.invT = np.linalg.inv(self.T)
+        # These also are the principle axes of the space
         self.axes = self.invT
         # print('transform used:', self.T, self.invT, 'cov:', cov, 'eigen:', eigval, eigvec)
         self.set_clusterids(clusterids=clusterids, npoints=len(points))
@@ -657,6 +675,71 @@ class AffineLayer(ScalingLayer):
             u = w.reshape(ww.shape)
         return u
 
+class MaxPrincipleGapAffineLayer(AffineLayer):
+    """Affine whitening transformation.
+
+    For learning the next layer's covariance, the clustering
+    and principal axis is considered: 
+    the sample covariance is computed after subtracting
+    the cluster mean. All points are projected onto the line
+    defined by the principle axis vector starting from the origin.
+    Then, on the sorted line positions, the largest gap is identified.
+    All points before the gap are mean-subtracted, and all points
+    after the gap are mean-subtracted. Then, the final
+    sample covariance is computed. This should give a more "local"
+    covariance, even in the case where clusters could not yet be 
+    clearly identified.
+    """
+
+    def create_new(self, upoints, maxradiussq, minvol=0.):
+        """Learn next layer from this optimized layer's clustering.
+
+        Parameters
+        ----------
+        upoints: array
+            points to use for optimize (in u-space)
+        maxradiussq: float
+            square of the MLFriends radius
+        minvol: float
+            Minimum volume to regularize sample covariance
+
+        Returns
+        ---------
+        A new, optimized MaxPrincipleGapAffineLayer.
+        """
+        # perform clustering in transformed space
+        uwpoints = self.wrap(upoints)
+        tpoints = self.transform(upoints)
+        nclusters, clusteridxs, overlapped_uwpoints = update_clusters(uwpoints, tpoints, maxradiussq, self.clusterids)
+
+        cov = np.cov(overlapped_uwpoints, rowvar=0)
+        cov *= (len(self.ctr) + 2)
+        eigval, eigvec = np.linalg.eigh(cov)
+        # identify principle axis
+        principal_vector = eigvec[:, -1]
+        # project all overlapped_uwpoints onto principle axis,
+        # obtaining position on line
+        t = np.dot(overlapped_uwpoints - overlapped_uwpoints.mean(axis=0).reshape((1,-1)), principal_vector)
+        # sort positions, identify largest gap
+        tsorted = np.sort(t)
+        tgapindex = np.argmax(np.diff(tsorted))
+        # compute center of largest gap
+        tsep = (tsorted[tgapindex] + tsorted[tgapindex + 1]) / 2
+        # assign point to left and right cluster
+        left_cluster = t < tsep
+        # subtract the respective cluster mean from overlapped_uwpoints
+        left_mean = overlapped_uwpoints[left_cluster, :].mean(axis=0)
+        right_mean = overlapped_uwpoints[~left_cluster, :].mean(axis=0)
+        halved_overlapped_uwpoints = overlapped_uwpoints.copy()
+        halved_overlapped_uwpoints[left_cluster, :] -= left_mean
+        halved_overlapped_uwpoints[~left_cluster, :] -= right_mean
+
+        # re-optimize with the new subtracted points
+        s = MaxPrincipleGapAffineLayer(nclusters=nclusters, wrapped_dims=self.wrapped_dims, clusterids=clusteridxs)
+        s.optimize(upoints, halved_overlapped_uwpoints, minvol=minvol)
+        return s
+
+
 
 def vol_prefactor(np.int_t n):
     """Volume constant for an ``n``-dimensional sphere.

From a9dcfae6b81d457ed18fd44a028402e6d05cbaf8 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 29 Dec 2023 16:22:23 +0100
Subject: [PATCH 169/313] use MaxPrincipleGapAffineLayer by default now

---
 ultranest/integrator.py | 6 +++---
 ultranest/viz.py        | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index a463ffa3..d33b2a76 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -25,7 +25,7 @@
 
 from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify as _listify
 from .utils import is_affine_transform, normalised_kendall_tau_distance, distributed_work_chunk_size
-from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
+from ultranest.mlfriends import MLFriends, MaxPrincipleGapAffineLayer, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
 from .store import HDF5PointStore, TextPointStore, NullPointStore
 from .viz import get_default_viz_callback
 from .ordertest import UniformOrderAccumulator
@@ -661,7 +661,7 @@ def run(
         ncall = num_live_points_missing  # number of calls we already made
         first_time = True
         if self.x_dim > 1:
-            transformLayer = AffineLayer(wrapped_dims=self.wrapped_axes)
+            transformLayer = MaxPrincipleGapAffineLayer(wrapped_dims=self.wrapped_axes)
         else:
             transformLayer = ScalingLayer(wrapped_dims=self.wrapped_axes)
         transformLayer.optimize(active_u, active_u)
@@ -1115,7 +1115,7 @@ def __init__(self,
 
         self.sampler = 'reactive-nested'
         self.x_dim = x_dim
-        self.transform_layer_class = AffineLayer if x_dim > 1 else ScalingLayer
+        self.transform_layer_class = MaxPrincipleGapAffineLayer if x_dim > 1 else ScalingLayer
         self.derivedparamnames = derived_param_names
         self.num_bootstraps = int(num_bootstraps)
         num_derived = len(self.derivedparamnames)
diff --git a/ultranest/viz.py b/ultranest/viz.py
index 252b999d..02d03b3b 100644
--- a/ultranest/viz.py
+++ b/ultranest/viz.py
@@ -98,7 +98,7 @@ def nicelogger(points, info, region, transformLayer, region_fresh=False):
 
     region: MLFriends
         Current region.
-    transformLayer: ScaleLayer or AffineLayer
+    transformLayer: ScaleLayer or AffineLayer or MaxPrincipleGapAffineLayer
         Current transformLayer (for clustering information).
     region_fresh: bool
         Whether the region was just updated.
@@ -274,7 +274,7 @@ def __call__(self, points, info, region, transformLayer, region_fresh=False):
 
         region: MLFriends
             Current region.
-        transformLayer: ScaleLayer or AffineLayer
+        transformLayer: ScaleLayer or AffineLayer or MaxPrincipleGapAffineLayer
             Current transformLayer (for clustering information).
         region_fresh: bool
             Whether the region was just updated.

From 2a4743f1053c58304d664a00e175bdbed0bafdf0 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Mon, 29 Jan 2024 15:31:23 +0100
Subject: [PATCH 170/313] Implementation of the vectorised slice sampler with
 fixed number of likelihood calls

---
 examples/test_PopEllSliceSampler.py | 139 +++++++++++++++++++
 tests/test_popstepsampling.py       |  24 +++-
 ultranest/popstepsampler.py         | 206 +++++++++++++++++++++++++++-
 ultranest/stepfuncs.pyx             |  36 +++++
 4 files changed, 401 insertions(+), 4 deletions(-)
 create mode 100644 examples/test_PopEllSliceSampler.py

diff --git a/examples/test_PopEllSliceSampler.py b/examples/test_PopEllSliceSampler.py
new file mode 100644
index 00000000..45382d22
--- /dev/null
+++ b/examples/test_PopEllSliceSampler.py
@@ -0,0 +1,139 @@
+import argparse
+import numpy as np
+
+def main(args):
+
+    ndim = args.x_dim
+    paramnames = ['param%d' % (i+1) for i in range(ndim)]
+    if args.seed is not None:
+        np.random.seed(args.seed)
+    if args.rosenbrock:
+        def loglike(theta):
+            a = theta[:,:-1]
+            b = theta[:,1:]
+            return -2 * (100 * (b - a**2)**2 + (1 - a)**2).sum(axis=1)
+
+        def transform(u):
+            return u * 20 - 10
+    if args.multishell:
+        from numpy import exp, log, pi
+        import scipy
+        def shell_vol(ndim, r, w):
+            # integral along the radius
+            mom = scipy.stats.norm.moment(ndim - 1, loc=r, scale=w)
+            # integral along the angles is surface of hyper-ball
+            # which is volume of one higher dimension x (ndim + 1)
+            vol = pi**((ndim)/2.) / scipy.special.gamma((ndim)/2. + 1)
+            surf = vol * ndim
+            return mom * surf
+
+        r = 0.2
+        # the shell thickness is 
+        #w = (r**(ndim+1) + C * scipy.special.gamma((ndim+3)/2)*ndim*pi**(-(ndim+1)/2) / (
+        #        scipy.special.gamma((ndim+2)/2) * pi**(-ndim/2)))**(1 / (ndim+1)) - r
+        w = 0.001 / ndim
+    
+        r1, r2 = r, r
+        w1, w2 = w, w
+        c1, c2 = np.zeros(ndim) + 0.5, np.zeros(ndim) + 0.5
+        c1[0] -= r1 / 2
+        c2[0] += r2 / 2
+        N1 = -0.5 * log(2 * pi * w1**2)
+        N2 = -0.5 * log(2 * pi * w2**2)
+        Z_analytic = log(shell_vol(ndim, r1, w1) + shell_vol(ndim, r2, w2))
+    
+        def loglike(theta):
+            d1 = ((theta - c1)**2).sum(axis=1)**0.5
+            d2 = ((theta - c2)**2).sum(axis=1)**0.5
+            L1 = -0.5 * ((d1 - r1)**2) / w1**2 + N1
+            L2 = -0.5 * ((d2 - r2)**2) / w2**2 + N2
+            return np.logaddexp(L1, L2)
+
+        def transform(x):
+            return x
+
+    if args.gaussian:
+        sigma = args.sigma
+        width = max(0, 1 - 5 * sigma)    
+        centers = (np.sin(np.arange(ndim)/2.) * width + 1.) / 2.
+        sigma = np.random.uniform(0.01, 1., ndim)*sigma
+        centers=centers.reshape((1,ndim))
+        sigma=np.array(sigma.reshape((1,ndim)))
+
+        norm = -0.5 * np.log(2 * np.pi * sigma**2).sum()
+        def loglike(theta):
+            return -0.5 * (((theta - centers) / sigma)**2).sum(axis=1) + norm
+
+        def transform(x):
+            return x
+    if args.eggbox:
+        def loglike(theta):
+            return np.cos(theta).prod(axis=1)**2
+
+        def transform(x):
+            return x * 10 * np.pi
+
+    if args.funnel:
+        sigma = args.sigma
+        centers = np.sin(np.arange(ndim) / 2.)
+        data = np.random.normal(centers, sigma).reshape((1, -1))
+
+        def loglike(theta):
+            sigma = 10**theta[:,0]
+            
+            like = -0.5 * (((theta[:,1:] - data)/sigma.reshape((-1, 1)))**2).sum(axis=1) - 0.5 * np.log(2 * np.pi * sigma**2) * ndim
+            return like
+
+        def transform(x):
+            z = x * 20 - 10
+            z[:,0] = x[:,0] * 6 - 3
+            return z
+        import string
+        paramnames = ['sigma'] + list(string.ascii_lowercase)[:ndim]
+
+    
+    from ultranest import ReactiveNestedSampler
+    sampler = ReactiveNestedSampler(paramnames, loglike,\
+    transform=transform, log_dir=args.log_dir, resume='overwrite',\
+    draw_multiple=False, vectorized=True,)
+    if args.ElliSlice:
+        import ultranest.popstepsampler as ultrapop
+        direction=[ultrapop.generate_random_direction, ultrapop.generate_region_oriented_direction, ultrapop.generate_region_random_direction]
+        sampler.stepsampler = ultrapop.PopulationEllipticalSliceSampler(popsize=args.popsize,nsteps=args.nstep,generate_direction=direction[1],scale=1.0)
+    if args.PopSlice:
+        import ultranest.popstepsampler as ultrapop
+        direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_random_direction, ultrapop.generate_region_oriented_direction, ultrapop.generate_region_random_direction]
+        sampler.stepsampler = ultrapop.PopulationSliceSampler(popsize=args.popsize,nsteps=args.nstep,generate_direction=direction[1],scale=1.0)
+    if args.Slice:
+        import ultranest.stepsampler as stepsampler
+        sampler.stepsampler = stepsampler.SliceSampler(nsteps=args.nstep,generate_direction=stepsampler.generate_mixture_random_direction,)
+    if args.PopGaussWalk:
+        import ultranest.popstepsampler as ultrapop
+        direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_random_direction, ultrapop.generate_region_oriented_direction, ultrapop.generate_region_random_direction]
+        sampler.stepsampler = ultrapop.PopulationRandomWalkSampler(popsize=args.popsize, nsteps=args.nstep, generate_direction=direction[1],scale=1.0,)
+
+    sampler.run(frac_remain=0.5, min_num_live_points=args.num_live_points, max_num_improvement_loops=1)
+    sampler.print_results()
+    if ndim <= 20:
+        sampler.plot()
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--x_dim', type=int, default=2,
+                        help="Dimensionality")
+    parser.add_argument("--num_live_points", type=int, default=400)
+    parser.add_argument('--log_dir', type=str)
+    parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--rosenbrock', action='store_true')
+    parser.add_argument('--multishell', action='store_true')
+    parser.add_argument('--gaussian', action='store_true')
+    parser.add_argument('--sigma', type=float, default=1)
+    parser.add_argument('--eggbox', action='store_true')
+    parser.add_argument('--funnel', action='store_true')
+    parser.add_argument('--ElliSlice', action='store_true')
+    parser.add_argument('--PopSlice', action='store_true')
+    parser.add_argument('--Slice', action='store_true')
+    parser.add_argument('--PopGaussWalk', action='store_true')
+    parser.add_argument('--popsize', type=int)
+    parser.add_argument('--nstep', type=int)
+    main(parser.parse_args())
diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 54743387..a3354d7d 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -1,6 +1,6 @@
 import numpy as np
 from ultranest import ReactiveNestedSampler
-from ultranest.popstepsampler import PopulationSliceSampler, PopulationRandomWalkSampler
+from ultranest.popstepsampler import PopulationSliceSampler, PopulationRandomWalkSampler, PopulationEllipticalSliceSampler
 from ultranest.popstepsampler import generate_cube_oriented_direction, generate_random_direction, generate_cube_oriented_direction_scaled
 from ultranest.popstepsampler import generate_region_oriented_direction, generate_region_random_direction
 
@@ -54,6 +54,25 @@ def test_stepsampler_cubegausswalk(plot=False):
     assert a.sum() > 1
     assert b.sum() > 1
 
+def test_stepsampler_randomEllSlice(plot=False):
+    np.random.seed(4)
+    nsteps = np.random.randint(10, 50)
+    popsize = np.random.randint(1, 20)
+    sampler = ReactiveNestedSampler(paramnames, loglike_vectorized, transform=transform, vectorized=True)
+
+    sampler.stepsampler = PopulationEllipticalSliceSampler(
+        popsize=popsize, nsteps=nsteps, 
+        generate_direction=generate_random_direction,
+        scale=1.0,
+    )
+    r = sampler.run(viz_callback=None, log_interval=50, max_iters=200, max_num_improvement_loops=0)
+    sampler.print_results()
+    a = (np.abs(r['samples'] - 0.7) < 0.1).all(axis=1)
+    b = (np.abs(r['samples'] - 0.3) < 0.1).all(axis=1)
+    assert a.sum() > 1
+    assert b.sum() > 1
+
+
 from ultranest.mlfriends import AffineLayer, ScalingLayer, MLFriends, RobustEllipsoidRegion, SimpleRegion
 
 def test_direction_proposals():
@@ -81,5 +100,6 @@ def test_direction_proposals():
                 #assert np.allclose(norms, scale), (norms, scale)
 
 if __name__ == '__main__':
-    test_stepsampler_cubegausswalk()
+    #test_stepsampler_cubegausswalk()
+    test_stepsampler_randomEllSlice()
     test_direction_proposals()
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 89cebfad..79a679ed 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -10,7 +10,7 @@
 
 import numpy as np
 from ultranest.utils import submasks
-from ultranest.stepfuncs import evolve, step_back
+from ultranest.stepfuncs import evolve, step_back, update_vectorised_slice_sampler
 from ultranest.stepfuncs import generate_cube_oriented_direction, generate_cube_oriented_direction_scaled
 from ultranest.stepfuncs import generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
 from ultranest.stepfuncs import generate_differential_direction, generate_mixture_random_direction
@@ -536,8 +536,210 @@ def __next__(
         else:
             return None, None, None, nc
 
+class PopulationEllipticalSliceSampler():
+    """
+       Vectorized Slice sampler taking inspiration from the elliptical slice sampler.
+       In Comparison the PopulationSliceSampler, the sampler calls the likelihood on
+       batch of points of the same size.
+
+       Sliced are defined by the generate_direction function on a interval defined
+       around the current point. The centred interval has the width of the scale parameter.
+       Slices are then shrink towards the current point until a point is found with a
+       likelihood above the threshold.
+
+       A slice can be searched with more than one point at a time. In that case, we 
+       read points as if they were the next selected each after the other. For a points
+       to update the slice, it needs to be still in the part of the slices searched after
+       the first point have been read. In that case, we update as normal, otherwise we
+       discard the point.
+
+    """
+
+    def __init__(
+        self, popsize, nsteps,scale, generate_direction
+        ,scale_adapt_factor=0.9):
+        """Initialise.
+
+        Parameters
+        ----------
+        popsize: int
+            number of walkers to maintain.
+        nsteps: int
+            number of steps to take until the found point is accepted as independent.
+            To calibrate, try several runs with increasing nsteps (doubling).
+            The ln(Z) should become stable at some value.
+        generate_direction: function
+            Function that gives proposal kernel shape, one of:
+            :py:func:`ultranest.popstepsampler.generate_random_direction`
+            :py:func:`ultranest.popstepsampler.generate_region_oriented_direction`
+            :py:func:`ultranest.popstepsampler.generate_region_random_direction`
+        scale: float
+            initial guess for the proposal scaling factor
+        scale_adapt_factor: float
+            if 1, no adapting is done.
+            if <1, the scale is increased if the slice final size is under 1/2 the scale
+            or decreased if it is above, by *scale_adapt_factor*.
+
+        """
+        self.nsteps = nsteps
+        
+        
+        self.nrejects = 0
+        self.generate_direction =  generate_direction
+        self.scale_adapt_factor = scale_adapt_factor
+        self.ncalls = 0
+        self.throwed=0
+        self.scale = scale
+       
+        
+       
+
+        
+        self.prepared_samples = []
+
+        self.popsize = popsize
+        
+
+        
+        
+
+    def __str__(self):
+        """Return string representation."""
+        return 'PopulationEllipticalSliceSampler(popsize=%d, nsteps=%d, generate_direction=%s, scale=%.g)' % (
+            self.popsize, self.nsteps, self.generate_direction, self.scale)
+
+    def region_changed(self, Ls, region):
+        """Act upon region changed. Currently unused."""
+        pass
+
+    
+    
+    def __next__(
+        self, region, Lmin, us, Ls, transform, loglike, ndraw=10,
+        plot=False, tregion=None, log=False
+    ):
+        """Sample a new live point.
+
+        Parameters
+        ----------
+        region: MLFriends object
+            Region
+        Lmin: float
+            current log-likelihood threshold
+        us: np.array((nlive, ndim))
+            live points
+        Ls: np.array(nlive)
+            loglikelihoods live points
+        transform: function
+            prior transform function
+        loglike: function
+            loglikelihood function
+        ndraw: int
+            not used
+        plot: bool
+            not used
+        tregion: bool
+            not used
+        log: bool
+            not used
+
+        Returns
+        -------
+        u: np.array(ndim) or None
+            new point coordinates (None if not yet available)
+        p: np.array(nparams) or None
+            new point transformed coordinates (None if not yet available)
+        L: float or None
+            new point likelihood (None if not yet available)
+        nc: int
+
+        """
+        nlive, ndim = us.shape
+        
+        
+        # fill if empty:
+        if len(self.prepared_samples) == 0:
+            # choose live points
+            ilive = np.random.randint(0, nlive, size=self.popsize)
+            allu = np.array(us[ilive,:])
+            allp = np.zeros((self.popsize, ndim))
+            allL = np.array(Ls[ilive])
+            nc = 0#self.nsteps * self.popsize
+            n_throws=0
+                                       
+                
+            
+            interval_final=0. 
+            Likelihood_threshold=np.ones(self.popsize)*Lmin
+            for k in range(self.nsteps):
+                # Defining scale jitter
+                factor_scale=scipy.stats.truncnorm.rvs(-0.5, 5., loc=0, scale=1)+1.
+                # Defining slice direction
+                v = self.generate_direction(allu, region,scale= 1.0)*self.scale*factor_scale
+                # limite of the slice based on the unit cube boundaries
+                tleft,tright= unitcube_line_intersection(allu, v)
+                # Defining bound of the slice
+                Theta_min_worker,Theta_max_worker = np.fmax(tleft,-1.+np.zeros(self.popsize)),np.fmin(tright,1.+np.zeros(self.popsize))
+                Theta_min,Theta_max=Theta_min_worker.copy(),Theta_max_worker.copy()
+                # Index of the workers working concurrently
+                worker=np.arange(0,self.popsize,1,dtype=int)
+                # Status indicating if a points has already find its next position
+                status=np.zeros(self.popsize,dtype=int) # one for success, zero for running
+               
+                # Loop until each points has found its next position or we reached 100 iterations
+                loop_n=0
+                while (status==0).any() and loop_n<100:
+                
+                    # Sampling points on the slices
+                    Theta=Theta_min_worker+(Theta_max_worker-Theta_min_worker)*np.random.uniform(size=(self.popsize,))
+                
+                    points=allu[worker,:]
+                    v_worker=v[worker,:]
+                    proposed_u=points+Theta.reshape((-1,1))*v_worker
+                
+                    proposed_p = transform(proposed_u)
+                    proposed_L = loglike(proposed_p)
+                    nc+=self.popsize
+                    # Updating the pool of points based on the newly sampled points
+                    Theta_min,Theta_max,proposed_L,proposed_u,proposed_p,worker,status,Likelihood_threshold,allu,allL,allp,nth=\
+                    update_vectorised_slice_sampler(Theta,Theta_min,Theta_max,proposed_L,proposed_u,proposed_p,worker,status,Likelihood_threshold,allu,allL,allp,self.popsize)
+                    n_throws+=nth
+                    # Update of the limits of the slices
+                    Theta_min_worker=Theta_min[worker]
+                    Theta_max_worker=Theta_max[worker]
+                    loop_n+=1
+                # Record of the final interval on theta for scale adaptation
+                interval_final+=np.median(Theta_max-Theta_min)
+
+
+            
+            interval_final=interval_final/self.nsteps
+            
+            
+            self.throwed+=n_throws
+            self.ncalls+=nc
+            
+            assert np.array([p!=np.zeros(ndim) for p in allp]).all(), 'some walkers never moved! Double nsteps of PopulationEllipticalSliceSampler.'
+            self.prepared_samples = list(zip(allu, allp, allL))
+
+            
+            # Scale adaptation such that the final interval is
+            # half the scale. There may be better things to do 
+            # here, but it seems to work.
+            if interval_final>=1./2.:
+                self.scale *= 1./self.scale_adapt_factor
+            else:
+                self.scale *= self.scale_adapt_factor
+            #print("percentage of throws %.3f\n\n"%((self.throwed/self.ncalls)*100.))
+            
+        else:
+            nc = 0
+
+        u, p, L = self.prepared_samples.pop(0)
+        return u, p, L, nc
+
 
 __all__ = [
     "generate_cube_oriented_direction", "generate_cube_oriented_direction_scaled",
     "generate_random_direction", "generate_region_oriented_direction", "generate_region_random_direction",
-    "PopulationRandomWalkSampler", "PopulationSliceSampler"]
+    "PopulationRandomWalkSampler", "PopulationSliceSampler","PopulationEllipticalSliceSampler"]
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 68a5fd7b..c52f383c 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -526,3 +526,39 @@ def generate_mixture_random_direction(ui, region, scale=1):
     v_DE = generate_differential_direction(ui, region, scale=scale)
     v_axis = generate_region_oriented_direction(ui, region, scale=scale)
     return np.where(np.random.uniform(size=nsamples).reshape((-1, 1)) < 0.5, v_DE, v_axis)
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cpdef tuple update_vectorised_slice_sampler(\
+                          np.ndarray[np.float_t, ndim=1] Theta, np.ndarray[np.float_t, ndim=1] Theta_min,\
+                          np.ndarray[np.float_t, ndim=1] Theta_max, np.ndarray[np.float_t, ndim=1] proposed_L,\
+                          np.ndarray[np.float_t, ndim=2] proposed_u, np.ndarray[np.float_t, ndim=2] proposed_p,\
+                          np.ndarray[np.int_t, ndim=1] worker, np.ndarray[np.int_t, ndim=1] status,\
+                          np.ndarray[np.float_t, ndim=1] Likelihood_threshold, np.ndarray[np.float_t, ndim=2] allu,\
+                          np.ndarray[np.float_t, ndim=1] allL, np.ndarray[np.float_t, ndim=2] allp, int popsize):
+                            
+    cdef int j, k
+    cdef throwed = 0
+    for l in range(popsize):
+        if Theta[l] > Theta_max[worker[l]] or Theta[l] < Theta_min[worker[l]]:
+            if proposed_L[l]>Likelihood_threshold[worker[l]]:
+                throwed+=1
+            continue
+        if 0 < Theta[l] < Theta_max[worker[l]]:
+            Theta_max[worker[l]] = Theta[l]
+        if 0 > Theta[l] > Theta_min[worker[l]]:
+            Theta_min[worker[l]] = Theta[l]
+        if proposed_L[l] > Likelihood_threshold[worker[l]] and status[worker[l]] == 0:
+            status[worker[l]] = 1
+            allu[worker[l], :] = proposed_u[l, :]
+            allL[worker[l]] = proposed_L[l]
+            allp[worker[l], :] = proposed_p[l, :]
+
+    j = 0
+    while j < popsize and (status == 0).any():
+        for k in range(popsize):
+            if status[k] == 0 and j < popsize:
+                worker[j] = k
+                j += 1
+
+    return (Theta_min, Theta_max, proposed_L, proposed_u, proposed_p, worker, status, Likelihood_threshold, allu, allL, allp,throwed)

From a3c4ea937b229c88aa9f725ef3fd5d901dbbc45c Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Thu, 8 Feb 2024 12:31:53 +0100
Subject: [PATCH 171/313] Add docs and remove useless output of the update
 functions for the Slice sampler

---
 ultranest/popstepsampler.py | 15 ++++++-----
 ultranest/stepfuncs.pyx     | 53 ++++++++++++++++++++++++++++++++++++-
 2 files changed, 61 insertions(+), 7 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 79a679ed..c090183f 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -656,11 +656,12 @@ def __next__(
         """
         nlive, ndim = us.shape
         
-        
+        assert nlive>=self.popsize, "The number of live points should be greater than the population size" 
         # fill if empty:
         if len(self.prepared_samples) == 0:
             # choose live points
-            ilive = np.random.randint(0, nlive, size=self.popsize)
+            #ilive = np.random.randint(0, nlive, size=self.popsize)
+            ilive = np.random.choice(nlive, size=self.popsize, replace=False)
             allu = np.array(us[ilive,:])
             allp = np.zeros((self.popsize, ndim))
             allL = np.array(Ls[ilive])
@@ -670,7 +671,7 @@ def __next__(
                 
             
             interval_final=0. 
-            Likelihood_threshold=np.ones(self.popsize)*Lmin
+            #Likelihood_threshold=np.ones(self.popsize)*Lmin
             for k in range(self.nsteps):
                 # Defining scale jitter
                 factor_scale=scipy.stats.truncnorm.rvs(-0.5, 5., loc=0, scale=1)+1.
@@ -685,7 +686,8 @@ def __next__(
                 worker=np.arange(0,self.popsize,1,dtype=int)
                 # Status indicating if a points has already find its next position
                 status=np.zeros(self.popsize,dtype=int) # one for success, zero for running
-               
+                mask_threshold=(np.random.uniform(size=(self.popsize,))>0.5).astype(int)
+                Likelihood_threshold=Lmin*mask_threshold+np.fmax(allL+np.log(np.random.uniform(size=(self.popsize,))),Lmin)*(1-mask_threshold)
                 # Loop until each points has found its next position or we reached 100 iterations
                 loop_n=0
                 while (status==0).any() and loop_n<100:
@@ -701,8 +703,9 @@ def __next__(
                     proposed_L = loglike(proposed_p)
                     nc+=self.popsize
                     # Updating the pool of points based on the newly sampled points
-                    Theta_min,Theta_max,proposed_L,proposed_u,proposed_p,worker,status,Likelihood_threshold,allu,allL,allp,nth=\
-                    update_vectorised_slice_sampler(Theta,Theta_min,Theta_max,proposed_L,proposed_u,proposed_p,worker,status,Likelihood_threshold,allu,allL,allp,self.popsize)
+                    Theta_min,Theta_max,worker,status,allu,allL,allp,nth=update_vectorised_slice_sampler(\
+                    Theta,Theta_min,Theta_max,proposed_L,proposed_u,proposed_p,worker,status,Likelihood_threshold\
+                    ,allu,allL,allp,self.popsize)
                     n_throws+=nth
                     # Update of the limits of the slices
                     Theta_min_worker=Theta_min[worker]
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index c52f383c..881d837a 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -536,6 +536,57 @@ cpdef tuple update_vectorised_slice_sampler(\
                           np.ndarray[np.int_t, ndim=1] worker, np.ndarray[np.int_t, ndim=1] status,\
                           np.ndarray[np.float_t, ndim=1] Likelihood_threshold, np.ndarray[np.float_t, ndim=2] allu,\
                           np.ndarray[np.float_t, ndim=1] allL, np.ndarray[np.float_t, ndim=2] allp, int popsize):
+
+    """Update the slice sampler state of each walker in the populations.
+
+    Parameters
+    -----------
+    Theta: array
+        proposed slice coordinate
+    Theta_min: array
+        current slice negative end
+    Theta_max: array
+        current slice positive end
+    proposed_L: array
+        log-likelihood of proposed point
+    proposed_u: array
+        proposed point in unit cube space
+    proposed_p: array
+        proposed point in transformed space
+    worker: array
+        index of the point associated with each worker
+    status: array
+        integer status of the point
+    Likelihood_threshold: array
+        current log-likelihood threshold
+    allu: array
+        Accepted points in unit cube space
+    allL: array
+        log-likelihoods of accepted points
+    allp: array
+        Accepted points in transformed space
+    popsize: int
+        number of points
+
+    Returns
+    --------
+    Theta_min: array
+        updated current slice negative end
+    Theta_max: array
+        updated current slice positive end
+    worker: array
+        updated index of the point associated with each worker
+    status: array
+        updated integer status of the point
+    allu: array
+        updated accepted points in unit cube space
+    allL: array
+        updated log-likelihoods of accepted points
+    allp: array
+        updated accepted points in transformed space
+    throwed: int
+        number of points that were rejected because they were outside the slice
+    """
                             
     cdef int j, k
     cdef throwed = 0
@@ -561,4 +612,4 @@ cpdef tuple update_vectorised_slice_sampler(\
                 worker[j] = k
                 j += 1
 
-    return (Theta_min, Theta_max, proposed_L, proposed_u, proposed_p, worker, status, Likelihood_threshold, allu, allL, allp,throwed)
+    return (Theta_min, Theta_max, worker, status, allu, allL, allp,throwed)

From dcf9093109fc490d3cb534a1d2fa67e1366be7ae Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Thu, 8 Feb 2024 13:54:39 +0100
Subject: [PATCH 172/313] Add slice size argument for the scale adaptation

---
 ultranest/popstepsampler.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index c090183f..bd8aa92f 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -557,7 +557,7 @@ class PopulationEllipticalSliceSampler():
 
     def __init__(
         self, popsize, nsteps,scale, generate_direction
-        ,scale_adapt_factor=0.9):
+        ,scale_adapt_factor=0.9, slice_size=2.0):
         """Initialise.
 
         Parameters
@@ -579,7 +579,8 @@ def __init__(
             if 1, no adapting is done.
             if <1, the scale is increased if the slice final size is under 1/2 the scale
             or decreased if it is above, by *scale_adapt_factor*.
-
+        slice_size: float
+            size of the slice in units of distance between the previous and next point.
         """
         self.nsteps = nsteps
         
@@ -590,6 +591,7 @@ def __init__(
         self.ncalls = 0
         self.throwed=0
         self.scale = scale
+        self.slice_size=slice_size
        
         
        
@@ -729,7 +731,7 @@ def __next__(
             # Scale adaptation such that the final interval is
             # half the scale. There may be better things to do 
             # here, but it seems to work.
-            if interval_final>=1./2.:
+            if interval_final>=1./self.slice_size
                 self.scale *= 1./self.scale_adapt_factor
             else:
                 self.scale *= self.scale_adapt_factor

From 32eaeba5bb587b282dfee8e2100e9e2edfea7f15 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Thu, 8 Feb 2024 14:12:20 +0100
Subject: [PATCH 173/313] Add the test code update

---
 examples/test_PopEllSliceSampler.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/examples/test_PopEllSliceSampler.py b/examples/test_PopEllSliceSampler.py
index 45382d22..814913e1 100644
--- a/examples/test_PopEllSliceSampler.py
+++ b/examples/test_PopEllSliceSampler.py
@@ -89,7 +89,8 @@ def transform(x):
             z[:,0] = x[:,0] * 6 - 3
             return z
         import string
-        paramnames = ['sigma'] + list(string.ascii_lowercase)[:ndim]
+        #print(ndim, len(list(string.ascii_lowercase)))
+        paramnames = ['sigma'] + ['param%d' % (i+1) for i in range(ndim)][:ndim]
 
     
     from ultranest import ReactiveNestedSampler
@@ -98,8 +99,8 @@ def transform(x):
     draw_multiple=False, vectorized=True,)
     if args.ElliSlice:
         import ultranest.popstepsampler as ultrapop
-        direction=[ultrapop.generate_random_direction, ultrapop.generate_region_oriented_direction, ultrapop.generate_region_random_direction]
-        sampler.stepsampler = ultrapop.PopulationEllipticalSliceSampler(popsize=args.popsize,nsteps=args.nstep,generate_direction=direction[1],scale=1.0)
+        direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_random_direction, ultrapop.generate_region_oriented_direction, ultrapop.generate_region_random_direction]
+        sampler.stepsampler = ultrapop.PopulationEllipticalSliceSampler(popsize=args.popsize,nsteps=args.nstep,generate_direction=direction[1],scale=1.0,scale_adapt_factor=0.9)
     if args.PopSlice:
         import ultranest.popstepsampler as ultrapop
         direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_random_direction, ultrapop.generate_region_oriented_direction, ultrapop.generate_region_random_direction]

From 248b5d71aae4c39e3924984a7e0fed449afa20cb Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Thu, 8 Feb 2024 14:24:31 +0100
Subject: [PATCH 174/313] Update the regions documentation of the slice sampler

---
 ultranest/popstepsampler.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index bd8aa92f..2949a90a 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -573,6 +573,10 @@ def __init__(
             :py:func:`ultranest.popstepsampler.generate_random_direction`
             :py:func:`ultranest.popstepsampler.generate_region_oriented_direction`
             :py:func:`ultranest.popstepsampler.generate_region_random_direction`
+            :py:func:`ultranest.popstepsampler.generate_differential_direction`
+            :py:func:`ultranest.popstepsampler.generate_mixture_random_direction`
+            :py:func:`ultranest.popstepsampler.generate_cube_oriented_direction` -> no adaptation in that case
+            :py:func:`ultranest.popstepsampler.generate_cube_oriented_direction_scaled` -> no adaptation in that case
         scale: float
             initial guess for the proposal scaling factor
         scale_adapt_factor: float

From 8b19d92088e4512959195200dbd9e83cdd31f0f7 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Fri, 9 Feb 2024 13:10:22 +0100
Subject: [PATCH 175/313] Change name of the sampler + setup the default as a
 static slice width

---
 examples/test_PopSliceSampler.py | 141 +++++++++++++++++++++++++++++++
 ultranest/popstepsampler.py      |  31 +++----
 ultranest/stepfuncs.pyx          |   8 +-
 3 files changed, 159 insertions(+), 21 deletions(-)
 create mode 100644 examples/test_PopSliceSampler.py

diff --git a/examples/test_PopSliceSampler.py b/examples/test_PopSliceSampler.py
new file mode 100644
index 00000000..15933c11
--- /dev/null
+++ b/examples/test_PopSliceSampler.py
@@ -0,0 +1,141 @@
+import argparse
+import numpy as np
+
+def main(args):
+
+    ndim = args.x_dim
+    paramnames = ['param%d' % (i+1) for i in range(ndim)]
+    if args.seed is not None:
+        np.random.seed(args.seed)
+    if args.rosenbrock:
+        def loglike(theta):
+            a = theta[:,:-1]
+            b = theta[:,1:]
+            return -2 * (100 * (b - a**2)**2 + (1 - a)**2).sum(axis=1)
+
+        def transform(u):
+            return u * 20 - 10
+    if args.multishell:
+        from numpy import exp, log, pi
+        import scipy
+        def shell_vol(ndim, r, w):
+            # integral along the radius
+            mom = scipy.stats.norm.moment(ndim - 1, loc=r, scale=w)
+            # integral along the angles is surface of hyper-ball
+            # which is volume of one higher dimension x (ndim + 1)
+            vol = pi**((ndim)/2.) / scipy.special.gamma((ndim)/2. + 1)
+            surf = vol * ndim
+            return mom * surf
+
+        r = 0.2
+        # the shell thickness is 
+        #w = (r**(ndim+1) + C * scipy.special.gamma((ndim+3)/2)*ndim*pi**(-(ndim+1)/2) / (
+        #        scipy.special.gamma((ndim+2)/2) * pi**(-ndim/2)))**(1 / (ndim+1)) - r
+        w = 0.001 / ndim
+    
+        r1, r2 = r, r
+        w1, w2 = w, w
+        c1, c2 = np.zeros(ndim) + 0.5, np.zeros(ndim) + 0.5
+        c1[0] -= r1 / 2
+        c2[0] += r2 / 2
+        N1 = -0.5 * log(2 * pi * w1**2)
+        N2 = -0.5 * log(2 * pi * w2**2)
+        Z_analytic = log(shell_vol(ndim, r1, w1) + shell_vol(ndim, r2, w2))
+    
+        def loglike(theta):
+            d1 = ((theta - c1)**2).sum(axis=1)**0.5
+            d2 = ((theta - c2)**2).sum(axis=1)**0.5
+            L1 = -0.5 * ((d1 - r1)**2) / w1**2 + N1
+            L2 = -0.5 * ((d2 - r2)**2) / w2**2 + N2
+            return np.logaddexp(L1, L2)
+
+        def transform(x):
+            return x
+
+    if args.gaussian:
+        sigma = args.sigma
+        width = max(0, 1 - 5 * sigma)    
+        centers = (np.sin(np.arange(ndim)/2.) * width + 1.) / 2.
+        sigma = np.random.uniform(0.01, 1., ndim)*sigma
+        centers=centers.reshape((1,ndim))
+        sigma=np.array(sigma.reshape((1,ndim)))
+
+        norm = -0.5 * np.log(2 * np.pi * sigma**2).sum()
+        def loglike(theta):
+            return -0.5 * (((theta - centers) / sigma)**2).sum(axis=1) + norm
+
+        def transform(x):
+            return x
+    if args.eggbox:
+        def loglike(theta):
+            return np.cos(theta).prod(axis=1)**2
+
+        def transform(x):
+            return x * 10 * np.pi
+
+    if args.funnel:
+        sigma = args.sigma
+        centers = np.sin(np.arange(ndim) / 2.)
+        data = np.random.normal(centers, sigma).reshape((1, -1))
+
+        def loglike(theta):
+            sigma = 10**theta[:,0]
+            
+            like = -0.5 * (((theta[:,1:] - data)/sigma.reshape((-1, 1)))**2).sum(axis=1) - 0.5 * np.log(2 * np.pi * sigma**2) * ndim
+            return like
+
+        def transform(x):
+            z = x * 20 - 10
+            z[:,0] = x[:,0] * 6 - 3
+            return z
+        import string
+        #print(ndim, len(list(string.ascii_lowercase)))
+        paramnames = ['sigma'] + ['param%d' % (i+1) for i in range(ndim)][:ndim]
+
+    
+    from ultranest import ReactiveNestedSampler
+    sampler = ReactiveNestedSampler(paramnames, loglike,\
+    transform=transform, log_dir=args.log_dir, resume='overwrite',\
+    draw_multiple=False, vectorized=True,)
+    if args.SimSlice:
+        import ultranest.popstepsampler as ultrapop
+        direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_mixture_random_direction,ultrapop.generate_differential_direction,ultrapop.generate_region_random_direction,ultrapop.generate_region_oriented_direction,ultrapop.generate_random_direction]
+        sampler.stepsampler = ultrapop.PopulationSimpleSliceSampler(popsize=args.popsize,nsteps=args.nstep,generate_direction=direction[args.direction],scale=1.0,scale_adapt_factor=1.0)
+    if args.PopSlice:
+        import ultranest.popstepsampler as ultrapop
+        direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_mixture_random_direction,ultrapop.generate_differential_direction,ultrapop.generate_region_random_direction,ultrapop.generate_region_oriented_direction,ultrapop.generate_random_direction]
+        sampler.stepsampler = ultrapop.PopulationSliceSampler(popsize=args.popsize,nsteps=args.nstep,generate_direction=direction[args.direction],scale=1.0)
+    if args.Slice:
+        import ultranest.stepsampler as stepsampler
+        sampler.stepsampler = stepsampler.SliceSampler(nsteps=args.nstep,generate_direction=stepsampler.generate_mixture_random_direction,)
+    if args.PopGaussWalk:
+        import ultranest.popstepsampler as ultrapop
+        direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_random_direction, ultrapop.generate_region_oriented_direction, ultrapop.generate_region_random_direction]
+        sampler.stepsampler = ultrapop.PopulationRandomWalkSampler(popsize=args.popsize, nsteps=args.nstep, generate_direction=direction[args.direction],scale=1.0,)
+
+    sampler.run(frac_remain=0.5, min_num_live_points=args.num_live_points, max_num_improvement_loops=3)
+    sampler.print_results()
+    if ndim <= 20:
+        sampler.plot()
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--x_dim', type=int, default=2,
+                        help="Dimensionality")
+    parser.add_argument("--num_live_points", type=int, default=400)
+    parser.add_argument('--log_dir', type=str)
+    parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--rosenbrock', action='store_true')
+    parser.add_argument('--multishell', action='store_true')
+    parser.add_argument('--gaussian', action='store_true')
+    parser.add_argument('--sigma', type=float, default=1)
+    parser.add_argument('--eggbox', action='store_true')
+    parser.add_argument('--funnel', action='store_true')
+    parser.add_argument('--SimSlice', action='store_true')
+    parser.add_argument('--PopSlice', action='store_true')
+    parser.add_argument('--Slice', action='store_true')
+    parser.add_argument('--PopGaussWalk', action='store_true')
+    parser.add_argument('--popsize', type=int)
+    parser.add_argument('--nstep', type=int)
+    parser.add_argument('--direction', type=int)
+    main(parser.parse_args())
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 2949a90a..4297b53a 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -536,9 +536,9 @@ def __next__(
         else:
             return None, None, None, nc
 
-class PopulationEllipticalSliceSampler():
+class PopulationSimpleSliceSampler():
     """
-       Vectorized Slice sampler taking inspiration from the elliptical slice sampler.
+       Vectorized Slice sampler without stepping out procedure.
        In Comparison the PopulationSliceSampler, the sampler calls the likelihood on
        batch of points of the same size.
 
@@ -557,7 +557,7 @@ class PopulationEllipticalSliceSampler():
 
     def __init__(
         self, popsize, nsteps,scale, generate_direction
-        ,scale_adapt_factor=0.9, slice_size=2.0):
+        ,scale_adapt_factor=1.0, slice_size=2.0, scale_jitter=False):
         """Initialise.
 
         Parameters
@@ -597,12 +597,11 @@ def __init__(
         self.scale = scale
         self.slice_size=slice_size
        
-        
-       
-
-        
+        if scale_jitter:
+            self.scale_jitter_func= lambda : scipy.stats.truncnorm.rvs(-0.5, 5., loc=0, scale=1)+1.
+        else:
+            self.scale_jitter_func= lambda : 1.              
         self.prepared_samples = []
-
         self.popsize = popsize
         
 
@@ -666,21 +665,20 @@ def __next__(
         # fill if empty:
         if len(self.prepared_samples) == 0:
             # choose live points
-            #ilive = np.random.randint(0, nlive, size=self.popsize)
             ilive = np.random.choice(nlive, size=self.popsize, replace=False)
             allu = np.array(us[ilive,:])
             allp = np.zeros((self.popsize, ndim))
             allL = np.array(Ls[ilive])
-            nc = 0#self.nsteps * self.popsize
+            nc = 0
             n_throws=0
                                        
                 
             
             interval_final=0. 
-            #Likelihood_threshold=np.ones(self.popsize)*Lmin
+            
             for k in range(self.nsteps):
                 # Defining scale jitter
-                factor_scale=scipy.stats.truncnorm.rvs(-0.5, 5., loc=0, scale=1)+1.
+                factor_scale=self.scale_jitter_func()
                 # Defining slice direction
                 v = self.generate_direction(allu, region,scale= 1.0)*self.scale*factor_scale
                 # limite of the slice based on the unit cube boundaries
@@ -692,8 +690,7 @@ def __next__(
                 worker=np.arange(0,self.popsize,1,dtype=int)
                 # Status indicating if a points has already find its next position
                 status=np.zeros(self.popsize,dtype=int) # one for success, zero for running
-                mask_threshold=(np.random.uniform(size=(self.popsize,))>0.5).astype(int)
-                Likelihood_threshold=Lmin*mask_threshold+np.fmax(allL+np.log(np.random.uniform(size=(self.popsize,))),Lmin)*(1-mask_threshold)
+                
                 # Loop until each points has found its next position or we reached 100 iterations
                 loop_n=0
                 while (status==0).any() and loop_n<100:
@@ -710,7 +707,7 @@ def __next__(
                     nc+=self.popsize
                     # Updating the pool of points based on the newly sampled points
                     Theta_min,Theta_max,worker,status,allu,allL,allp,nth=update_vectorised_slice_sampler(\
-                    Theta,Theta_min,Theta_max,proposed_L,proposed_u,proposed_p,worker,status,Likelihood_threshold\
+                    Theta,Theta_min,Theta_max,proposed_L,proposed_u,proposed_p,worker,status,Lmin\
                     ,allu,allL,allp,self.popsize)
                     n_throws+=nth
                     # Update of the limits of the slices
@@ -735,7 +732,7 @@ def __next__(
             # Scale adaptation such that the final interval is
             # half the scale. There may be better things to do 
             # here, but it seems to work.
-            if interval_final>=1./self.slice_size
+            if interval_final>=1./self.slice_size:
                 self.scale *= 1./self.scale_adapt_factor
             else:
                 self.scale *= self.scale_adapt_factor
@@ -751,4 +748,4 @@ def __next__(
 __all__ = [
     "generate_cube_oriented_direction", "generate_cube_oriented_direction_scaled",
     "generate_random_direction", "generate_region_oriented_direction", "generate_region_random_direction",
-    "PopulationRandomWalkSampler", "PopulationSliceSampler","PopulationEllipticalSliceSampler"]
+    "PopulationRandomWalkSampler", "PopulationSliceSampler","PopulationSimpleSliceSampler"]
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 881d837a..dbce3fc3 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -534,7 +534,7 @@ cpdef tuple update_vectorised_slice_sampler(\
                           np.ndarray[np.float_t, ndim=1] Theta_max, np.ndarray[np.float_t, ndim=1] proposed_L,\
                           np.ndarray[np.float_t, ndim=2] proposed_u, np.ndarray[np.float_t, ndim=2] proposed_p,\
                           np.ndarray[np.int_t, ndim=1] worker, np.ndarray[np.int_t, ndim=1] status,\
-                          np.ndarray[np.float_t, ndim=1] Likelihood_threshold, np.ndarray[np.float_t, ndim=2] allu,\
+                          np.float_t Likelihood_threshold, np.ndarray[np.float_t, ndim=2] allu,\
                           np.ndarray[np.float_t, ndim=1] allL, np.ndarray[np.float_t, ndim=2] allp, int popsize):
 
     """Update the slice sampler state of each walker in the populations.
@@ -557,7 +557,7 @@ cpdef tuple update_vectorised_slice_sampler(\
         index of the point associated with each worker
     status: array
         integer status of the point
-    Likelihood_threshold: array
+    Likelihood_threshold: float
         current log-likelihood threshold
     allu: array
         Accepted points in unit cube space
@@ -592,14 +592,14 @@ cpdef tuple update_vectorised_slice_sampler(\
     cdef throwed = 0
     for l in range(popsize):
         if Theta[l] > Theta_max[worker[l]] or Theta[l] < Theta_min[worker[l]]:
-            if proposed_L[l]>Likelihood_threshold[worker[l]]:
+            if proposed_L[l]>Likelihood_threshold:
                 throwed+=1
             continue
         if 0 < Theta[l] < Theta_max[worker[l]]:
             Theta_max[worker[l]] = Theta[l]
         if 0 > Theta[l] > Theta_min[worker[l]]:
             Theta_min[worker[l]] = Theta[l]
-        if proposed_L[l] > Likelihood_threshold[worker[l]] and status[worker[l]] == 0:
+        if proposed_L[l] > Likelihood_threshold and status[worker[l]] == 0:
             status[worker[l]] = 1
             allu[worker[l], :] = proposed_u[l, :]
             allL[worker[l]] = proposed_L[l]

From 1c7f79d2fca72da4ef09a05858a8ed7a7095576f Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Fri, 9 Feb 2024 13:31:55 +0100
Subject: [PATCH 176/313] some small modificaton

---
 examples/test_PopSliceSampler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/test_PopSliceSampler.py b/examples/test_PopSliceSampler.py
index 15933c11..97f1db14 100644
--- a/examples/test_PopSliceSampler.py
+++ b/examples/test_PopSliceSampler.py
@@ -100,7 +100,7 @@ def transform(x):
     if args.SimSlice:
         import ultranest.popstepsampler as ultrapop
         direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_mixture_random_direction,ultrapop.generate_differential_direction,ultrapop.generate_region_random_direction,ultrapop.generate_region_oriented_direction,ultrapop.generate_random_direction]
-        sampler.stepsampler = ultrapop.PopulationSimpleSliceSampler(popsize=args.popsize,nsteps=args.nstep,generate_direction=direction[args.direction],scale=1.0,scale_adapt_factor=1.0)
+        sampler.stepsampler = ultrapop.PopulationSimpleSliceSampler(popsize=args.popsize,nsteps=args.nstep,generate_direction=direction[args.direction],scale=1.0,scale_adapt_factor=1.0,scale_jitter=False)
     if args.PopSlice:
         import ultranest.popstepsampler as ultrapop
         direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_mixture_random_direction,ultrapop.generate_differential_direction,ultrapop.generate_region_random_direction,ultrapop.generate_region_oriented_direction,ultrapop.generate_random_direction]

From dd0d867593de94943ee080d82fc61725665d7fcb Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Tue, 13 Feb 2024 09:24:49 +0100
Subject: [PATCH 177/313] Correct the default setup to use the bound of the
 unit cube instead of a fixed scale

---
 ultranest/popstepsampler.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 4297b53a..3e6e03b4 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -600,10 +600,13 @@ def __init__(
         if scale_jitter:
             self.scale_jitter_func= lambda : scipy.stats.truncnorm.rvs(-0.5, 5., loc=0, scale=1)+1.
         else:
-            self.scale_jitter_func= lambda : 1.              
+            self.scale_jitter_func= lambda : 1.         
         self.prepared_samples = []
         self.popsize = popsize
-        
+        if self.scale_adapt_factor!=1.:
+            self.slice_limit=lambda tleft,tright:np.fmax(tleft,-1.+np.zeros(self.popsize)),np.fmin(tright,1.+np.zeros(self.popsize))
+        else:
+            self.slice_limit=lambda tleft,tright:tleft,tright
 
         
         
@@ -684,7 +687,7 @@ def __next__(
                 # limite of the slice based on the unit cube boundaries
                 tleft,tright= unitcube_line_intersection(allu, v)
                 # Defining bound of the slice
-                Theta_min_worker,Theta_max_worker = np.fmax(tleft,-1.+np.zeros(self.popsize)),np.fmin(tright,1.+np.zeros(self.popsize))
+                Theta_min_worker,Theta_max_worker = self.slice_limit(tleft,tright)
                 Theta_min,Theta_max=Theta_min_worker.copy(),Theta_max_worker.copy()
                 # Index of the workers working concurrently
                 worker=np.arange(0,self.popsize,1,dtype=int)

From 51e94cb4aab2ca203b01d1ff308afde5f9a10a7e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 14 Feb 2024 17:31:45 +0100
Subject: [PATCH 178/313] add test for speed variable generator

---
 tests/test_stepsampling.py | 28 ++++++++++++++++++++++++++--
 1 file changed, 26 insertions(+), 2 deletions(-)

diff --git a/tests/test_stepsampling.py b/tests/test_stepsampling.py
index 251c4ec7..c6b8a5ca 100644
--- a/tests/test_stepsampling.py
+++ b/tests/test_stepsampling.py
@@ -1,8 +1,8 @@
 import numpy as np
 from ultranest.mlfriends import ScalingLayer, AffineLayer, MLFriends
 from ultranest import ReactiveNestedSampler
-from ultranest.stepsampler import RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler, SpeedVariableRegionSliceSampler, RegionBallSliceSampler
-from ultranest.stepsampler import generate_region_random_direction, ellipsoid_bracket, crop_bracket_at_unit_cube
+from ultranest.stepsampler import RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler, SpeedVariableRegionSliceSampler, RegionBallSliceSampler, SpeedVariableGenerator
+from ultranest.stepsampler import generate_region_random_direction, generate_random_direction, ellipsoid_bracket, crop_bracket_at_unit_cube
 from ultranest.pathsampler import SamplingPathStepSampler
 from ultranest.stepsampler import select_random_livepoint, IslandPopulationRandomLivepointSelector
 from numpy.testing import assert_allclose
@@ -73,6 +73,30 @@ def test_stepsampler_regionslice(plot=False):
     assert b.sum() > 1
 
 
+def test_SpeedVariableGenerator():
+    np.random.seed(4)
+    ndims = [3, 10]
+    matrices = [
+        np.array([[True, True, True], [False, True, True], [False, False, True]]),
+        [Ellipsis, slice(1,None), slice(2,4)]
+    ]
+    for matrix, ndim in zip(matrices, ndims):
+        direction_generator = SpeedVariableGenerator(matrix, generate_direction=generate_random_direction)
+        for i in range(10):
+            u0 = np.random.uniform(size=ndim)
+            for mask_varying in matrix:
+                mask = np.zeros(ndim, dtype=bool)
+                mask[mask_varying] = True
+                print("starting at u0", u0)
+                print("varying:", mask_varying, mask)
+                v = direction_generator(u0, None)
+                print("direction:", v)
+                assert_allclose(v[~mask], 0)
+                u1 = u0 + np.random.uniform() * v
+                print("new point:", u1)
+                assert_allclose(u1[~mask], u0[~mask])
+
+
 def test_stepsampler_variable_speed_SLOW(plot=False):
     matrices = [
         np.array([[True, True, True], [False, True, True], [False, False, True]]),

From 83a1b5768cad171a4a7422a43ee6f19356ca3b7c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 08:21:23 +0100
Subject: [PATCH 179/313] higher tolerance on flaky test

---
 tests/test_run.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_run.py b/tests/test_run.py
index 850c33a7..2aef799e 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -56,7 +56,7 @@ def loglike(y):
         print("logzerr in iteration %d" % niter, results['logzerr'])
     print()
     print({k:v for k, v in results.items() if 'logzerr' in k})
-    assert results['logzerr'] < 0.1 * 2
+    assert results['logzerr'] < 0.1 * 3
 
 def test_reactive_run():
     np.random.seed(1)

From a4ee31fed80aa2d304ad48cb3d3a9145741ae8c5 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 08:24:37 +0100
Subject: [PATCH 180/313] =?UTF-8?q?Bump=20version:=203.6.4=20=E2=86=92=203?=
 =?UTF-8?q?.6.5?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 6688bf44..e2c5381f 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.6.4',
+    version='3.6.5',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index ce2fc020..e130a2b1 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.6.4'
+__version__ = '3.6.5'

From 27479f5b79c94e76890e2167ac979ec3b78dbb67 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 09:12:51 +0100
Subject: [PATCH 181/313] =?UTF-8?q?Bump=20version:=203.6.5=20=E2=86=92=204?=
 =?UTF-8?q?.0.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index e2c5381f..8bffbba6 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='3.6.5',
+    version='4.0.0',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index e130a2b1..5b49fb72 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '3.6.5'
+__version__ = '4.0.0'

From a07783d0746e1ef034c1e6ead539feab56282f1b Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 09:22:08 +0100
Subject: [PATCH 182/313] add to changelog

---
 HISTORY.rst | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 7e2f4d80..81024d45 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,6 +2,11 @@
 Release Notes
 ==============
 
+3.6.5 (2023-07-18)
+------------------
+* documentation improvements
+* logging with MPI fixes `by adipol-ph <https://github.com/JohannesBuchner/UltraNest/issues/109>`_ and `by gregorydavidmartinez <https://github.com/JohannesBuchner/UltraNest/issues/110>`_
+* more flexible plotting `by facero <https://github.com/JohannesBuchner/UltraNest/issues/108>`_
 
 3.6.0 (2023-06-22)
 ------------------

From df379fef2a5a1310f04216b5471dc8e679a65bde Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 09:31:26 +0100
Subject: [PATCH 183/313] add notes for 4.0.0

---
 HISTORY.rst | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 81024d45..358b3dda 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,6 +2,11 @@
 Release Notes
 ==============
 
+4.0.0 (2023-02-15)
+------------------
+* replace :py:class:`AffineLayer` with new :py:class:`MaxPrincipleGapAffineLayer`
+  * This changes the learned covariance to be hopefully boost local features, make MLFriends identify smaller neighbourhoods, and thereby make sampling faster.
+
 3.6.5 (2023-07-18)
 ------------------
 * documentation improvements

From 0cda0684ad714a988a201330fe8467c6485a158d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 29 Dec 2023 17:49:38 +0100
Subject: [PATCH 184/313] add first version of auto-calibration of nsteps

---
 ultranest/calibrator.py | 105 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 ultranest/calibrator.py

diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
new file mode 100644
index 00000000..8df92ba2
--- /dev/null
+++ b/ultranest/calibrator.py
@@ -0,0 +1,105 @@
+from ultranest.integrator import ReactiveNestedSampler
+
+def substitute_log_dir(init_args, nsteps):
+    if 'log_dir' in init_args:
+        args = dict(init_args)
+        args['log_dir'] = init_args['log_dir'] + '-nsteps%d' % nsteps
+        return args
+    return init_args
+
+
+class ReactiveNestedCalibrator():
+    """Calibrator for the number of steps in step samplers.
+
+    Usage
+    -----
+
+    Usage is designed to be a drop-in replacement for ReactiveNestedSampler.
+    
+    If your code was::
+        sampler = ReactiveNestedSampler(my_param_names, my_loglike, my_transform)
+        sampler.stepsampler = SliceSampler(nsteps=10, generate_direction=region_oriented_direction)
+        sampler.run(min_num_livepoints=400)
+    
+    You would change it to::
+        sampler = ReactiveNestedCalibrator(my_param_names, my_loglike, my_transform)
+        sampler.stepsampler = SliceSampler(nsteps=10, generate_direction=region_oriented_direction)
+        sampler.run(min_num_livepoints=400)
+    
+    The run() command will print the number of slice sampler steps
+    that appear safe for the inference task.
+
+    The initial value for nsteps is ignored, and set to len(param_names)
+    instead.
+    """
+    def __init__(self,
+                 param_names,
+                 loglike,
+                 transform=None,
+                 **kwargs
+                 ):
+        """Initialise nested sampler calibrator.
+
+        Parameters
+        -----------
+        param_names: list of str
+            Names of the parameters.
+            Length gives dimensionality of the sampling problem.
+        loglike: function
+            log-likelihood function.
+        transform: function
+            parameter transform from unit cube to physical parameters.
+        kwargs: dict
+            further arguments passed to ReactiveNestedSampler
+
+        if `log_dir` is set, then the suffix `-nsteps%d` is added for each
+        run where %d is replaced with the number of steps (2, 4, 8 etc).
+        """
+        
+        self.init_args = dict(param_names=param_names, loglike=loglike, transform=transform, **kwargs)
+        self.stepsampler = None
+        
+    def run(self, **kwargs):
+        """Run a sequence of ReactiveNestedSampler with nsteps doubling.
+
+        All arguments are passed to :py:meth:`ReactiveNestedSampler.run`.
+
+        """
+        assert self.stepsampler is not None
+        self.run_args = kwargs
+        
+        # start with nsteps=d
+        nsteps = len(self.init_args['param_names'])
+        self.results = []
+        self.nsteps = []
+
+        while True:
+            print("running with %d steps ..." % nsteps)
+            sampler = ReactiveNestedSampler(**substitute_log_dir(self.init_args, nsteps))
+            sampler.stepsampler = self.stepsampler.__class__(nsteps, generate_direction=self.stepsampler.generate_direction)
+            result = sampler.run(**self.run_args)
+            self.results.append(result)
+            self.nsteps.append(nsteps)
+            print("lnZ=%(logz).2f +- %(logzerr).2f" % result)
+            if len(self.results) > 2:
+                last_result = self.results[-2]
+                last_result2 = self.results[-3]
+                # check if they agree within the error bars
+                last_significant = abs(result['logz'] - last_result['logz']) > (result['logzerr']**2 + last_result['logzerr']**2)**0.5
+                last2_significant = abs(last_result2['logz'] - last_result['logz']) > (last_result2['logzerr']**2 + last_result['logzerr']**2)**0.5
+                # check if there is order
+                monotonic_increase = result['logz'] > last_result['logz'] > last_result2['logz']
+                monotonic_decrease = result['logz'] < last_result['logz'] < last_result2['logz']
+                if last_significant:
+                    print("not converged: last two Z were significantly different")
+                elif last2_significant:
+                    print("not yet converged: previous two Z were significantly different")
+                elif monotonic_increase:
+                    print("not converged: monotonic increase in the last three Z results")
+                elif monotonic_decrease:
+                    print("not converged: monotonic decrease in the last three Z results")
+                else:
+                    print("converged! nsteps=%d appears safe" % nsteps)
+                    break
+            
+            nsteps *= 2

From 3f9c0b23ca3af1194bbccdef7b74d98f226f521f Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 31 Dec 2023 17:26:02 +0100
Subject: [PATCH 185/313] make Calibrator work and provide more useful plots
 and prints Make stepsamplers provide diagnostics add doc to move-distance
 functions

---
 ultranest/calibrator.py  | 107 ++++++++++++++++++++---
 ultranest/integrator.py  |   2 +
 ultranest/mlfriends.pyx  |  11 +--
 ultranest/stepsampler.py | 184 +++++++++++++++++++++++++++++++++++----
 4 files changed, 269 insertions(+), 35 deletions(-)

diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index 8df92ba2..6fffc3f8 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -1,6 +1,14 @@
+"""
+Calibration of step sampler
+"""
+
+import numpy as np
 from ultranest.integrator import ReactiveNestedSampler
+import os
+
 
 def substitute_log_dir(init_args, nsteps):
+    """Append nsteps to log_dir argument, if set."""
     if 'log_dir' in init_args:
         args = dict(init_args)
         args['log_dir'] = init_args['log_dir'] + '-nsteps%d' % nsteps
@@ -15,23 +23,24 @@ class ReactiveNestedCalibrator():
     -----
 
     Usage is designed to be a drop-in replacement for ReactiveNestedSampler.
-    
+
     If your code was::
         sampler = ReactiveNestedSampler(my_param_names, my_loglike, my_transform)
         sampler.stepsampler = SliceSampler(nsteps=10, generate_direction=region_oriented_direction)
         sampler.run(min_num_livepoints=400)
-    
+
     You would change it to::
         sampler = ReactiveNestedCalibrator(my_param_names, my_loglike, my_transform)
         sampler.stepsampler = SliceSampler(nsteps=10, generate_direction=region_oriented_direction)
         sampler.run(min_num_livepoints=400)
-    
+
     The run() command will print the number of slice sampler steps
     that appear safe for the inference task.
 
     The initial value for nsteps is ignored, and set to len(param_names)
     instead.
     """
+
     def __init__(self,
                  param_names,
                  loglike,
@@ -55,32 +64,53 @@ def __init__(self,
         if `log_dir` is set, then the suffix `-nsteps%d` is added for each
         run where %d is replaced with the number of steps (2, 4, 8 etc).
         """
-        
         self.init_args = dict(param_names=param_names, loglike=loglike, transform=transform, **kwargs)
         self.stepsampler = None
-        
+
     def run(self, **kwargs):
         """Run a sequence of ReactiveNestedSampler with nsteps doubling.
 
-        All arguments are passed to :py:meth:`ReactiveNestedSampler.run`.
-
+        Parameters
+        -----------
+        **kwargs: dict
+            All arguments are passed to :py:meth:`ReactiveNestedSampler.run`.
         """
         assert self.stepsampler is not None
         self.run_args = kwargs
-        
+
         # start with nsteps=d
         nsteps = len(self.init_args['param_names'])
         self.results = []
         self.nsteps = []
+        self.relsteps = []
 
         while True:
             print("running with %d steps ..." % nsteps)
-            sampler = ReactiveNestedSampler(**substitute_log_dir(self.init_args, nsteps))
-            sampler.stepsampler = self.stepsampler.__class__(nsteps, generate_direction=self.stepsampler.generate_direction)
+            init_args = substitute_log_dir(self.init_args, nsteps)
+            sampler = ReactiveNestedSampler(**init_args)
+            sampler.stepsampler = self.stepsampler.__class__(
+                nsteps=nsteps, generate_direction=self.stepsampler.generate_direction,
+                check_nsteps=self.stepsampler.check_nsteps,
+                adaptive_nsteps=self.stepsampler.adaptive_nsteps,
+                log=open(init_args['log_dir'] + '/stepsampler.log', 'w') if 'log_dir' in self.init_args else None)
+            self.sampler = sampler
             result = sampler.run(**self.run_args)
+            print("lnZ=%(logz).2f +- %(logzerr).2f" % result)
+            if self.sampler.log_to_disk:
+                sampler.stepsampler.plot(os.path.join(self.sampler.logs['plots'], 'stepsampler.pdf'))
+                sampler.stepsampler.plot_jump_diagnostic_histogram(
+                    os.path.join(self.sampler.logs['plots'], 'stepsampler-jumphist.pdf'),
+                    histtype='step', bins='auto')
+            sampler.stepsampler.print_diagnostic()
+            if 'jump-distance' in sampler.stepsampler.logstat_labels and 'reference-distance' in sampler.stepsampler.logstat_labels:
+                i = sampler.stepsampler.logstat_labels.index('jump-distance')
+                j = sampler.stepsampler.logstat_labels.index('reference-distance')
+                jump_distances = np.array([entry[i] for entry in sampler.stepsampler.logstat])
+                reference_distances = np.array([entry[j] for entry in sampler.stepsampler.logstat])
+                self.relsteps.append(jump_distances / reference_distances)
+
             self.results.append(result)
             self.nsteps.append(nsteps)
-            print("lnZ=%(logz).2f +- %(logzerr).2f" % result)
             if len(self.results) > 2:
                 last_result = self.results[-2]
                 last_result2 = self.results[-3]
@@ -101,5 +131,58 @@ def run(self, **kwargs):
                 else:
                     print("converged! nsteps=%d appears safe" % nsteps)
                     break
-            
+
             nsteps *= 2
+
+    def plot(self):
+        """Visualise the convergence diagnostics.
+
+        Stores into `<log_dir>/plots/` folder:
+        * stepsampler.pdf: diagnostic of stepsampler, see :py:meth:`StepSampler.plot`
+        * nsteps-calibration-jumps.pdf: distribution of relative jump distance
+        * nsteps-calibration.pdf: evolution of ln(Z) with nsteps
+        """
+        self.sampler.stepsampler.plot(os.path.join(self.sampler.logs['plots'], 'stepsampler.pdf'))
+
+        # plot U-test convergence run length (at 4 sigma) (or niter) vs nsteps
+        # plot step > reference fraction vs nsteps
+        calibration_results = []
+
+        import matplotlib.pyplot as plt
+        plt.figure("jump-distance")
+        print("jump distance diagnostic:")
+        for nsteps, relsteps, result in zip(self.nsteps, self.relsteps, self.results):
+            calibration_results.append([
+                nsteps, result['logz'], result['logzerr'],
+                min(result['niter'], result['insertion_order_MWW_test']['independent_iterations']),
+                result['insertion_order_MWW_test']['converged'] * 1,
+                np.nanmean(relsteps > 1)])
+            plt.hist(np.log10(relsteps), histtype='step', bins='auto', label=nsteps)
+            print('%-4d: %.2f%%' % (nsteps, np.nanmean(relsteps > 1) * 100.0))
+        if 'log_dir' in self.init_args:
+            print('calibration results:', np.shape(calibration_results))
+            np.savetxt(
+                self.init_args['log_dir'] + 'calibration.csv',
+                calibration_results, delimiter=',', comments='',
+                header='nsteps,logz,logzerr,maxUrun,Uconverged,stepfrac',
+                fmt='%d,%.3f,%.3f,%d,%d,%.5f')
+        plt.xlabel('$log_{10}$(relative step distance)')
+        plt.ylabel('Frequency')
+        plt.legend(title='nsteps', loc='best')
+        if self.sampler.log_to_disk:
+            plt.savefig(os.path.join(self.sampler.logs['plots'], 'nsteps-calibration-jumps.pdf'), bbox_inches='tight')
+            plt.close()
+
+        plt.figure("logz")
+        plt.errorbar(
+            x=self.nsteps,
+            y=[result['logz'] for result in self.results],
+            yerr=[result['logzerr'] for result in self.results],
+        )
+        plt.title('Step sampler calibration')
+        plt.xlabel('Number of steps')
+        plt.ylabel('ln(Z)')
+        if self.sampler.log_to_disk:
+            plt.savefig(os.path.join(self.sampler.logs['plots'], 'nsteps-calibration.pdf'), bbox_inches='tight')
+            plt.close()
+            self.sampler.logger.debug('Making nsteps calibration plot ... done')
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index d33b2a76..7d182c8b 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2923,6 +2923,8 @@ def print_results(self, use_unicode=True):
             print('  tail           : logZ = +- %(logzerr_tail).3f' % self.results)
             print('insert order U test : converged: %(converged)s correlation: %(independent_iterations)s iterations' % (
                 self.results['insertion_order_MWW_test']))
+            if self.stepsampler and hasattr(self.stepsampler, 'print_diagnostic'):
+                self.stepsampler.print_diagnostic()
 
             print()
             for i, p in enumerate(self.paramnames + self.derivedparamnames):
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index bf3494b8..86803a65 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -20,6 +20,7 @@ from numpy import pi
 cimport cython
 from cython.cimports.libc.math import sqrt
 
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 cdef count_nearby(
@@ -275,7 +276,7 @@ def update_clusters(
     np.float_t maxradiussq,
     clusterids=None,
 ):
-    """Clusters `upoints`, so that clusters are distinct if no 
+    """Clusters `upoints`, so that clusters are distinct if no
     member pair is within a radius of sqrt(`maxradiussq`).
 
     Parameters
@@ -433,7 +434,7 @@ class ScalingLayer(object):
             |      ********      |
         """
         if not self.has_wraps:
-            return 
+            return
 
         N, ndims = points.shape
         self.wrap_cuts = []
@@ -1059,7 +1060,7 @@ class MLFriends(object):
         ----------
         nsamples: int
             number of samples to draw
-        
+
         Returns
         -------
         samples: array of shape (nsamples, dimension)
@@ -1091,7 +1092,7 @@ class MLFriends(object):
         """
         # require points to be inside bounding ellipsoid
         mask = self.inside_ellipsoid(pts)
-        
+
         if mask.any():
             # additionally require points to be near neighbours
             bpts = self.transformLayer.transform(pts[mask,:])
@@ -1247,7 +1248,7 @@ class RobustEllipsoidRegion(MLFriends):
         ----------
         nsamples: int
             number of samples to draw
-        
+
         Returns
         -------
         samples: array of shape (nsamples, dimension)
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index c206733f..d2afb005 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -365,6 +365,29 @@ def adapt_proposal_summed_distances_NN(region, history, mean_pair_distance, ndim
 
 
 def adapt_proposal_move_distances(region, history, mean_pair_distance, ndim):
+    """Compares random walk travel distance to MLFriends radius.
+
+    Compares in whitened space (t-space), the L2 norm between final
+    point and starting point to the MLFriends bootstrapped radius.
+
+    Parameters
+    ----------
+    region: MLFriends
+        built region
+    history: list
+        list of tuples, containing visited point and likelihood.
+    mean_pair_distance: float
+        not used
+    ndim: int
+        dimensionality
+
+    Returns
+    -------
+    far_enough: bool
+        whether the distance is larger than the radius
+    info: tuple
+        distance and radius (both float)
+    """
     # compute distance from start to end
     ustart, _ = history[0]
     ufinal, _ = history[-1]
@@ -376,6 +399,30 @@ def adapt_proposal_move_distances(region, history, mean_pair_distance, ndim):
 
 
 def adapt_proposal_move_distances_midway(region, history, mean_pair_distance, ndim):
+    """Compares first half of the travel distance to MLFriends radius.
+
+    Compares in whitened space (t-space), the L2 norm between the
+    middle point of the walk and the starting point,
+    to the MLFriends bootstrapped radius.
+
+    Parameters
+    ----------
+    region: MLFriends
+        built region
+    history: list
+        list of tuples, containing visited point and likelihood.
+    mean_pair_distance: float
+        not used
+    ndim: int
+        dimensionality
+
+    Returns
+    -------
+    far_enough: bool
+        whether the distance is larger than the radius
+    info: tuple
+        distance and radius (both float)
+    """
     # compute distance from start to end
     ustart, _ = history[0]
     middle = max(1, len(history) // 2)
@@ -489,7 +536,7 @@ class StepSampler(object):
 
     def __init__(
         self, nsteps, generate_direction,
-        scale=1.0, adaptive_nsteps=False, max_nsteps=1000,
+        scale=1.0, check_nsteps=False, adaptive_nsteps=False, max_nsteps=1000,
         region_filter=False, log=False,
         starting_point_selector=select_random_livepoint,
     ):
@@ -529,10 +576,21 @@ def __init__(
             with robustness against collapse to a subspace.
             :py:func:`generate_cube_oriented_direction` works well too.
 
-        adaptive_nsteps: False, 'proposal-distance', 'move-distance'
-            Strategy to adapt the number of steps. The strategies
-            make sure that:
+        adaptive_nsteps: False or str
+            Strategy to adapt the number of steps. 
+            The possible values are the same as for `check_nsteps`.
 
+            Adapting can give usable results. However, strictly speaking,
+            detailed balance is not maintained, so the results can be biased.
+            You can use the stepsampler.logstat property to find out the `nsteps` learned
+            from one run (third column), and use the largest value for `nsteps`
+            for a fresh run.
+            The forth column is the jump distance, the fifth column is the reference distance.
+
+        check_nsteps: False or str
+            Method to diagnose the step sampler walks. The options are:
+
+            * False: no checking
             * 'move-distance' (recommended): distance between
               start point and final position exceeds the mean distance
               between pairs of live points.
@@ -552,11 +610,9 @@ def __init__(
               between chain points exceeds mean distance
               between pairs of live points.
 
-            Adapting can give usable results. However, strictly speaking,
-            detailed balance is not maintained, so the results can be biased.
-            You can use the logstat property to find out the `nsteps` learned
-            from one run (third column), and use the largest value for `nsteps`
-            of a fresh run.
+            Each step sampler walk adds one row to stepsampler.logstat.
+            The jump distance (forth column) should be compared to 
+            the reference distance (fifth column).
 
         max_nsteps: int
             Maximum number of steps the adaptive_nsteps can reach.
@@ -589,7 +645,7 @@ def __init__(
         self.nudge = 1.1**(1. / self.nsteps)
         self.nsteps_nudge = 1.01
         self.generate_direction = generate_direction
-        adaptive_nsteps_options = {
+        check_nsteps_options = {
             False: None,
             'move-distance': adapt_proposal_move_distances,
             'move-distance-midway': adapt_proposal_move_distances_midway,
@@ -598,13 +654,22 @@ def __init__(
             'proposal-summed-distances': adapt_proposal_summed_distances,
             'proposal-summed-distances-NN': adapt_proposal_summed_distances_NN,
         }
+        adaptive_nsteps_options = dict(check_nsteps_options)
 
         if adaptive_nsteps not in adaptive_nsteps_options.keys():
             raise ValueError("adaptive_nsteps must be one of: %s, not '%s'" % (adaptive_nsteps_options, adaptive_nsteps))
+        if check_nsteps not in check_nsteps_options.keys():
+            raise ValueError("check_nsteps must be one of: %s, not '%s'" % (adaptive_nsteps_options, adaptive_nsteps))
         self.adaptive_nsteps = adaptive_nsteps
+        if self.adaptive_nsteps:
+            assert nsteps <= max_nsteps, 'Invalid adapting configuration: provided nsteps=%d exceeds provided max_nsteps=%d' % (nsteps, max_nsteps)
         self.adaptive_nsteps_function = adaptive_nsteps_options[adaptive_nsteps]
+        self.check_nsteps = check_nsteps
+        self.check_nsteps_function = check_nsteps_options[check_nsteps]
         self.adaptive_nsteps_needs_mean_pair_distance = self.adaptive_nsteps in (
             'proposal-total-distances', 'proposal-summed-distances',
+        ) or self.check_nsteps in (
+            'proposal-total-distances', 'proposal-summed-distances',
         )
         self.starting_point_selector = starting_point_selector
         self.mean_pair_distance = np.nan
@@ -613,7 +678,7 @@ def __init__(
 
         self.logstat = []
         self.logstat_labels = ['rejection_rate', 'scale', 'steps']
-        if adaptive_nsteps:
+        if adaptive_nsteps or check_nsteps:
             self.logstat_labels += ['jump-distance', 'reference-distance']
 
     def __str__(self):
@@ -654,6 +719,85 @@ def plot(self, filename):
                    header=','.join(self.logstat_labels), delimiter=',')
         plt.close()
 
+    @property
+    def mean_jump_distance(self):
+        """Geometric mean jump distance."""
+        if len(self.logstat) == 0:
+            return np.nan
+        if 'jump-distance' not in self.logstat_labels or 'reference-distance' not in self.logstat_labels:
+            return np.nan
+        i = self.logstat_labels.index('jump-distance')
+        j = self.logstat_labels.index('reference-distance')
+        jump_distances = np.array([entry[i] for entry in self.logstat])
+        reference_distances = np.array([entry[j] for entry in self.logstat])
+        return np.exp(np.nanmean(np.log(jump_distances / reference_distances)))
+
+    @property
+    def far_enough_fraction(self):
+        """Fraction of jumps exceeding reference distance."""
+        if len(self.logstat) == 0:
+            return np.nan
+        if 'jump-distance' not in self.logstat_labels or 'reference-distance' not in self.logstat_labels:
+            return np.nan
+        i = self.logstat_labels.index('jump-distance')
+        j = self.logstat_labels.index('reference-distance')
+        jump_distances = np.array([entry[i] for entry in self.logstat])
+        reference_distances = np.array([entry[j] for entry in self.logstat])
+        return np.nanmean(jump_distances > reference_distances)
+
+    def get_info_dict(self):
+        return dict(
+            num_logs=len(self.logstat),
+            rejection_rate=np.nanmean([entry[0] for entry in self.logstat]),
+            mean_scale=np.nanmean([entry[1] for entry in self.logstat]),
+            mean_nsteps=np.nanmean([entry[2] for entry in self.logstat]),
+            mean_distance=self.mean_jump_distance,
+            frac_far_enough=self.far_enough_fraction,
+            last_logstat=dict(zip(self.logstat_labels, self.logstat[-1]))
+        )
+
+
+    def print_diagnostic(self):
+        """Print diagnostic of step sampler performance."""
+        if len(self.logstat) == 0:
+            print("diagnostic unavailable, no recorded steps found")
+            return
+        if 'jump-distance' not in self.logstat_labels or 'reference-distance' not in self.logstat_labels:
+            print("turn on check_nsteps in the step sampler for diagnostics")
+            return 
+        frac_farenough = self.far_enough_fraction
+        average_distance = self.mean_jump_distance
+        if frac_farenough < 0.5:
+            advice = ': very fishy. Double nsteps and see if fraction and lnZ change)'
+        elif frac_farenough < 0.66:
+            advice = ': fishy. Double nsteps and see if fraction and lnZ change)'
+        else:
+            advice = ' (should be >50%)'
+        print('step sampler diagnostic: jump distance %.2f (should be >1), far enough fraction: %.2f%% %s' % (
+            average_distance, frac_farenough * 100, advice))
+
+    def plot_jump_diagnostic_histogram(self, filename, **kwargs):
+        """Plot jump diagnostic histogram."""
+        if len(self.logstat) == 0:
+            return
+        if 'jump-distance' not in self.logstat_labels:
+            return 
+        if 'reference-distance' not in self.logstat_labels:
+            return 
+        i = self.logstat_labels.index('jump-distance')
+        j = self.logstat_labels.index('reference-distance')
+        jump_distances = np.array([entry[i] for entry in self.logstat])
+        reference_distances = np.array([entry[j] for entry in self.logstat])
+        plt.hist(np.log10(jump_distances / reference_distances), **kwargs)
+        ylo, yhi = plt.ylim()
+        plt.vlines(self.mean_jump_distance, ylo, yhi)
+        plt.ylim(ylo, yhi)
+        plt.title(self.check_nsteps or self.adaptive_nsteps)
+        plt.xlabel('log(relative step distance)')
+        plt.ylabel('Frequency')
+        plt.savefig(filename, bbox_inches='tight')
+        plt.close()
+
     def move(self, ui, region, ndraw=1, plot=False):
         """Move around point ``ui``. Stub to be implemented by child classes."""
         raise NotImplementedError()
@@ -670,7 +814,7 @@ def adjust_outside_region(self):
         assert self.scale > 0
         assert self.next_scale > 0
         # reset chain
-        if self.adaptive_nsteps:
+        if self.adaptive_nsteps or self.check_nsteps:
             self.logstat.append([-1.0, self.scale, self.nsteps, np.nan, np.nan])
         else:
             self.logstat.append([-1.0, self.scale, self.nsteps])
@@ -709,18 +853,22 @@ def adapt_nsteps(self, region):
         region: MLFriends object
             current region
         """
-        if not self.adaptive_nsteps:
+        if not (self.adaptive_nsteps or self.check_nsteps):
             return
-        elif len(self.history) < self.nsteps:
+        if len(self.history) < self.nsteps:
             # incomplete or aborted for some reason
-            print("not adapting, incomplete history", len(self.history), self.nsteps)
+            print("not adapting/checking nsteps, incomplete history", len(self.history), self.nsteps)
             return
 
-        # assert self.nrejects < len(self.history), (self.nsteps, self.nrejects, len(self.history))
-        # assert self.nrejects <= self.nsteps, (self.nsteps, self.nrejects, len(self.history))
         if self.adaptive_nsteps_needs_mean_pair_distance:
             assert np.isfinite(self.mean_pair_distance)
         ndim = region.u.shape[1]
+        if self.check_nsteps:
+            far_enough, extra_info = self.check_nsteps_function(region, self.history, self.mean_pair_distance, ndim)
+            self.logstat[-1] += extra_info
+        if not self.adaptive_nsteps:
+            return
+
         far_enough, extra_info = self.adaptive_nsteps_function(region, self.history, self.mean_pair_distance, ndim)
         self.logstat[-1] += extra_info
 
@@ -762,7 +910,7 @@ def finalize_chain(self, region=None, Lmin=None, Ls=None):
                 [self.nsteps, region.maxradiussq**0.5, mean_pair_distance,
                  iLstart, iLfinal, itstart, itfinal])])
 
-        if self.adaptive_nsteps:
+        if self.adaptive_nsteps or self.check_nsteps:
             self.adapt_nsteps(region=region)
 
         if self.next_scale > self.scale * self.nudge**10:

From 3c242d280baf9cc9fed6248a5ee10ab66b7255f3 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 31 Dec 2023 17:39:14 +0100
Subject: [PATCH 186/313] turn on step sampler diagnostics by default

---
 ultranest/stepsampler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index d2afb005..64d006a9 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -536,7 +536,7 @@ class StepSampler(object):
 
     def __init__(
         self, nsteps, generate_direction,
-        scale=1.0, check_nsteps=False, adaptive_nsteps=False, max_nsteps=1000,
+        scale=1.0, check_nsteps='move-distance', adaptive_nsteps=False, max_nsteps=1000,
         region_filter=False, log=False,
         starting_point_selector=select_random_livepoint,
     ):

From 9e683aab655444de4c5d0180a9b0ab57298b5193 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 31 Dec 2023 17:51:44 +0100
Subject: [PATCH 187/313] test that diagnostics work

---
 tests/test_stepsampling.py | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/tests/test_stepsampling.py b/tests/test_stepsampling.py
index c6b8a5ca..4689ca91 100644
--- a/tests/test_stepsampling.py
+++ b/tests/test_stepsampling.py
@@ -39,6 +39,15 @@ def test_stepsampler_cubemh(plot=False):
     assert a.sum() > 1, a.sum()
     assert b.sum() > 1, b.sum()
 
+    # check that diagnostics fail
+    print("mean jump distance:", sampler.stepsampler.mean_jump_distance)
+    print("far enough fraction:", sampler.stepsampler.far_enough_fraction)
+    assert sampler.stepsampler.mean_jump_distance < 1.0, sampler.stepsampler.mean_jump_distance
+    assert sampler.stepsampler.far_enough_fraction < 0.5, sampler.stepsampler.far_enough_fraction
+
+    print("Diagnostic print:")
+    sampler.stepsampler.print_diagnostic()
+
 def test_stepsampler_regionmh(plot=False):
     np.random.seed(2)
     sampler = ReactiveNestedSampler(paramnames, loglike_vectorized, transform=transform, vectorized=True)
@@ -64,7 +73,7 @@ def test_stepsampler_cubeslice(plot=False):
 def test_stepsampler_regionslice(plot=False):
     np.random.seed(4)
     sampler = ReactiveNestedSampler(paramnames, loglike, transform=transform)
-    sampler.stepsampler = RegionSliceSampler(nsteps=len(paramnames))
+    sampler.stepsampler = RegionSliceSampler(nsteps=2 + len(paramnames))
     r = sampler.run(log_interval=50, min_num_live_points=400)
     sampler.print_results()
     a = (np.abs(r['samples'] - 0.7) < 0.1).all(axis=1)
@@ -72,6 +81,14 @@ def test_stepsampler_regionslice(plot=False):
     assert a.sum() > 1
     assert b.sum() > 1
 
+    # check that diagnostics pass
+    print("mean jump distance:", sampler.stepsampler.mean_jump_distance)
+    print("far enough fraction:", sampler.stepsampler.far_enough_fraction)
+    assert sampler.stepsampler.mean_jump_distance > 1.0, sampler.stepsampler.mean_jump_distance
+    assert sampler.stepsampler.far_enough_fraction > 0.6, sampler.stepsampler.far_enough_fraction
+
+    print("Diagnostic print:")
+    sampler.stepsampler.print_diagnostic()
 
 def test_SpeedVariableGenerator():
     np.random.seed(4)

From 7bc5f40e1385d7681cdf91ab098eb1c4805a1436 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 1 Jan 2024 14:45:18 +0100
Subject: [PATCH 188/313] distance was erroneuously in squares, now without
 squares print step sampler diagnostics avoid nans/inf errors in plots

---
 ultranest/calibrator.py  |  7 +++----
 ultranest/integrator.py  |  1 +
 ultranest/stepsampler.py | 10 +++++-----
 ultranest/viz.py         | 15 +++++++++++++++
 4 files changed, 24 insertions(+), 9 deletions(-)

diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index 6fffc3f8..b9db7179 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -95,7 +95,7 @@ def run(self, **kwargs):
                 log=open(init_args['log_dir'] + '/stepsampler.log', 'w') if 'log_dir' in self.init_args else None)
             self.sampler = sampler
             result = sampler.run(**self.run_args)
-            print("lnZ=%(logz).2f +- %(logzerr).2f" % result)
+            print("Z=%(logz).2f +- %(logzerr).2f" % result)
             if self.sampler.log_to_disk:
                 sampler.stepsampler.plot(os.path.join(self.sampler.logs['plots'], 'stepsampler.pdf'))
                 sampler.stepsampler.plot_jump_diagnostic_histogram(
@@ -157,10 +157,9 @@ def plot(self):
                 min(result['niter'], result['insertion_order_MWW_test']['independent_iterations']),
                 result['insertion_order_MWW_test']['converged'] * 1,
                 np.nanmean(relsteps > 1)])
-            plt.hist(np.log10(relsteps), histtype='step', bins='auto', label=nsteps)
-            print('%-4d: %.2f%%' % (nsteps, np.nanmean(relsteps > 1) * 100.0))
+            plt.hist(np.log10(relsteps + 1e-10), histtype='step', bins='auto', label=nsteps)
+            print('  %-4d: %.2f%%  avg:%.2f' % (nsteps, np.nanmean(relsteps > 1) * 100.0, np.exp(np.nanmean(np.log(relsteps)))))
         if 'log_dir' in self.init_args:
-            print('calibration results:', np.shape(calibration_results))
             np.savetxt(
                 self.init_args['log_dir'] + 'calibration.csv',
                 calibration_results, delimiter=',', comments='',
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 7d182c8b..f0e393d6 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2621,6 +2621,7 @@ def run_iter(
                                     paramlims=self.transform_limits,
                                     order_test_correlation=insertion_test_quality,
                                     order_test_direction=insertion_test_direction,
+                                    stepsampler_info=self.stepsampler.get_info_dict() if hasattr(self.stepsampler, 'get_info_dict') else {}
                                 ),
                                 region=self.region, transformLayer=self.transformLayer,
                                 region_fresh=region_fresh,
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 64d006a9..7ab48129 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -395,7 +395,7 @@ def adapt_proposal_move_distances(region, history, mean_pair_distance, ndim):
     d2 = ((tstart - tfinal)**2).sum()
     far_enough = d2 > region.maxradiussq
 
-    return far_enough, [d2, region.maxradiussq**0.5]
+    return far_enough, [d2**0.5, region.maxradiussq**0.5]
 
 
 def adapt_proposal_move_distances_midway(region, history, mean_pair_distance, ndim):
@@ -431,7 +431,7 @@ def adapt_proposal_move_distances_midway(region, history, mean_pair_distance, nd
     d2 = ((tstart - tfinal)**2).sum()
     far_enough = d2 > region.maxradiussq
 
-    return far_enough, [d2, region.maxradiussq**0.5]
+    return far_enough, [d2**0.5, region.maxradiussq**0.5]
 
 
 def select_random_livepoint(us, Ls, Lmin):
@@ -730,7 +730,7 @@ def mean_jump_distance(self):
         j = self.logstat_labels.index('reference-distance')
         jump_distances = np.array([entry[i] for entry in self.logstat])
         reference_distances = np.array([entry[j] for entry in self.logstat])
-        return np.exp(np.nanmean(np.log(jump_distances / reference_distances)))
+        return np.exp(np.nanmean(np.log(jump_distances / reference_distances + 1e-10)))
 
     @property
     def far_enough_fraction(self):
@@ -753,7 +753,7 @@ def get_info_dict(self):
             mean_nsteps=np.nanmean([entry[2] for entry in self.logstat]),
             mean_distance=self.mean_jump_distance,
             frac_far_enough=self.far_enough_fraction,
-            last_logstat=dict(zip(self.logstat_labels, self.logstat[-1]))
+            last_logstat=dict(zip(self.logstat_labels, self.logstat[-1] if len(self.logstat) > 1 else [np.nan] * len(self.logstat_labels)))
         )
 
 
@@ -788,7 +788,7 @@ def plot_jump_diagnostic_histogram(self, filename, **kwargs):
         j = self.logstat_labels.index('reference-distance')
         jump_distances = np.array([entry[i] for entry in self.logstat])
         reference_distances = np.array([entry[j] for entry in self.logstat])
-        plt.hist(np.log10(jump_distances / reference_distances), **kwargs)
+        plt.hist(np.log10(jump_distances / reference_distances + 1e-10), **kwargs)
         ylo, yhi = plt.ylim()
         plt.vlines(self.mean_jump_distance, ylo, yhi)
         plt.ylim(ylo, yhi)
diff --git a/ultranest/viz.py b/ultranest/viz.py
index 02d03b3b..277179ef 100644
--- a/ultranest/viz.py
+++ b/ultranest/viz.py
@@ -139,6 +139,13 @@ def nicelogger(points, info, region, transformLayer, region_fresh=False):
         ("Quality: correlation length: %d (%s)" % (info['order_test_correlation'], '+' if info['order_test_direction'] >= 0 else '-'))
         if np.isfinite(info['order_test_correlation']) else "Quality: ok",
     )
+    if info.get('stepsampler_info', {}).get('num_logs', 0) > 0:
+        print(
+            'Step sampler performance: %(rejection_rate).1f%% rej/step, %(mean_nsteps)d steps/it' % (info['stepsampler_info']),
+            ('mean rel jump distance: %.2f (should be >1), %.2f%% (should be >50%%)' % (
+                info['stepsampler_info']['mean_distance'], 100 * info['stepsampler_info']['frac_far_enough']
+            )) if 'mean_distance' in info['stepsampler_info'] else ''
+        )
 
     print()
     if ndim == 1:
@@ -315,6 +322,14 @@ def __call__(self, points, info, region, transformLayer, region_fresh=False):
              (" | Quality: correlation length: %d (%s)" % (info['order_test_correlation'], '+' if info['order_test_direction'] >= 0 else '-'))
              if np.isfinite(info['order_test_correlation']) else " | Quality: ok")
 
+        if info.get('stepsampler_info', {}).get('num_logs', 0) > 0:
+            labeltext += ("<br/>" +
+                'Step sampler performance: %(rejection_rate).1f%% rej/step, %(mean_nsteps)d steps/it' % (info['stepsampler_info']) +
+                ('mean rel jump distance: %.2f (should be >1), %.2f%% (should be >50%%)' % (
+                    info['stepsampler_info']['mean_distance'], 100 * info['stepsampler_info']['frac_far_enough']
+                )) if 'mean_distance' in info['stepsampler_info'] else ''
+            )
+
         if ndim == 1:
             pass
         elif ndim == 2 and spearman is not None:

From a32fc9a9f8f64cf9034afcc1b9919dc1d09478a2 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 14 Feb 2024 17:04:50 +0100
Subject: [PATCH 189/313] add logging of jump distance to population sampler

---
 ultranest/calibrator.py     |   1 +
 ultranest/ordertest.py      |   6 +-
 ultranest/popstepsampler.py | 164 ++++++++++++++++++++++++++++++++++--
 ultranest/stepsampler.py    |   2 +-
 4 files changed, 162 insertions(+), 11 deletions(-)

diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index b9db7179..4bd037b3 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -111,6 +111,7 @@ def run(self, **kwargs):
 
             self.results.append(result)
             self.nsteps.append(nsteps)
+            yield nsteps, result
             if len(self.results) > 2:
                 last_result = self.results[-2]
                 last_result2 = self.results[-3]
diff --git a/ultranest/ordertest.py b/ultranest/ordertest.py
index f473fbc1..bad945fe 100644
--- a/ultranest/ordertest.py
+++ b/ultranest/ordertest.py
@@ -46,9 +46,13 @@ def infinite_U_zscore(sample, B):
 
 
 class UniformOrderAccumulator():
-    """Mann-Whitney-Wilcoxon U test accumulator.
+    """U test accumulator.
 
     Stores rank orders (1 to N), for comparison with a uniform order.
+
+    See section 4.5.2 of Buchner (2023, https://arxiv.org/abs/2101.09675),
+    based on the Mann-Whitney-Wilcoxon U test against a uniform integer
+    distribution.
     """
 
     def __init__(self):
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 89cebfad..39e2a788 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -54,8 +54,132 @@ def unitcube_line_intersection(ray_origin, ray_direction):
         t2 = -n + k
         return np.nanmax(t1, axis=1), np.nanmin(t2, axis=1)
 
+def diagnose_move_distances(region, ustart, ufinal):
+    """Compares random walk travel distance to MLFriends radius.
 
-class PopulationRandomWalkSampler():
+    Compares in whitened space (t-space), the L2 norm between final
+    point and starting point to the MLFriends bootstrapped radius.
+
+    Parameters
+    ----------
+    region: MLFriends
+        built region
+    ustart: array
+        starting positions
+    ufinal: array
+        final positions
+
+    Returns
+    -------
+    far_enough: bool
+        whether the distance is larger than the radius
+    move_distance: float
+        distance between start and final point in whitened space
+    reference_distance: float
+        MLFriends radius
+    """
+    assert ustart.shape == ufinal.shape, (ustart.shape, ufinal.shape)
+    tstart = region.transformLayer.transform(ustart)
+    tfinal = region.transformLayer.transform(ufinal)
+    d2 = ((tstart - tfinal)**2).sum(axis=1)
+    far_enough = d2 > region.maxradiussq
+
+    return far_enough, [d2**0.5, region.maxradiussq**0.5]
+
+class GenericPopulationSampler():
+    def plot(self, filename):
+        """Plot sampler statistics.
+
+        Parameters
+        -----------
+        filename: str
+            Stores plot into ``filename`` and data into
+            ``filename + ".txt.gz"``.
+        """
+        if len(self.logstat) == 0:
+            return
+
+        import matplotlib.pyplot as plt
+        plt.figure(figsize=(10, 1 + 3 * len(self.logstat_labels)))
+        for i, label in enumerate(self.logstat_labels):
+            part = [entry[i] for entry in self.logstat]
+            plt.subplot(len(self.logstat_labels), 1, 1 + i)
+            plt.ylabel(label)
+            plt.plot(part)
+            x = []
+            y = []
+            for j in range(0, len(part), 20):
+                x.append(j)
+                y.append(np.mean(part[j:j + 20]))
+            plt.plot(x, y)
+            if np.min(part) > 0:
+                plt.yscale('log')
+        plt.savefig(filename, bbox_inches='tight')
+        np.savetxt(filename + '.txt.gz', self.logstat,
+                   header=','.join(self.logstat_labels), delimiter=',')
+        plt.close()
+
+    @property
+    def mean_jump_distance(self):
+        """Geometric mean jump distance."""
+        if len(self.logstat) == 0:
+            return np.nan
+        return np.exp(np.nanmean(np.log([entry[-1] for entry in self.logstat])))
+
+    @property
+    def far_enough_fraction(self):
+        """Fraction of jumps exceeding reference distance."""
+        if len(self.logstat) == 0:
+            return np.nan
+        return np.nanmean([entry[-2] for entry in self.logstat])
+
+    def get_info_dict(self):
+        return dict(
+            num_logs=len(self.logstat),
+            rejection_rate=np.nanmean([entry[0] for entry in self.logstat]),
+            mean_scale=np.nanmean([entry[1] for entry in self.logstat]),
+            mean_nsteps=np.nanmean([entry[2] for entry in self.logstat]),
+            mean_distance=self.mean_jump_distance,
+            frac_far_enough=self.far_enough_fraction,
+            last_logstat=dict(zip(self.logstat_labels, self.logstat[-1] if len(self.logstat) > 1 else [np.nan] * len(self.logstat_labels)))
+        )
+
+
+    def print_diagnostic(self):
+        """Print diagnostic of step sampler performance."""
+        if len(self.logstat) == 0:
+            print("diagnostic unavailable, no recorded steps found")
+            return
+        if 'jump-distance' not in self.logstat_labels or 'reference-distance' not in self.logstat_labels:
+            print("turn on check_nsteps in the step sampler for diagnostics")
+            return 
+        frac_farenough = self.far_enough_fraction
+        average_distance = self.mean_jump_distance
+        if frac_farenough < 0.5:
+            advice = ': very fishy. Double nsteps and see if fraction and lnZ change)'
+        elif frac_farenough < 0.66:
+            advice = ': fishy. Double nsteps and see if fraction and lnZ change)'
+        else:
+            advice = ' (should be >50%)'
+        print('step sampler diagnostic: jump distance %.2f (should be >1), far enough fraction: %.2f%% %s' % (
+            average_distance, frac_farenough * 100, advice))
+
+    def plot_jump_diagnostic_histogram(self, filename, **kwargs):
+        """Plot jump diagnostic histogram."""
+        if len(self.logstat) == 0:
+            return
+        import matplotlib.pyplot as plt
+        plt.hist(np.log10([entry[-1] for entry in self.logstat]), **kwargs)
+        ylo, yhi = plt.ylim()
+        plt.vlines(self.mean_jump_distance, ylo, yhi)
+        plt.ylim(ylo, yhi)
+        plt.xlabel('log(relative step distance)')
+        plt.ylabel('Frequency')
+        plt.savefig(filename, bbox_inches='tight')
+        plt.close()
+    
+
+class PopulationRandomWalkSampler(GenericPopulationSampler):
     """Vectorized Gaussian Random Walk sampler."""
 
     def __init__(
@@ -107,6 +231,8 @@ def __init__(
 
         self.log = log
         self.logfile = logfile
+        self.logstat = []
+        self.logstat_labels = ['accept_rate', 'efficiency', 'scale', 'far_enough', 'mean_rel_jump']
         self.prepared_samples = []
 
         self.popsize = popsize
@@ -196,14 +322,21 @@ def __next__(
                 allp[mask_accept,:] = proposed_p[mask_accept,:]
                 allL[mask_accept] = proposed_L[mask_accept]
             assert np.isfinite(allp).all(), 'some walkers never moved! Double nsteps of PopulationRandomWalkSampler.'
+            far_enough, (move_distance, reference_distance) = diagnose_move_distances(region, us[ilive[mask_accept],:], allu[mask_accept,:])
             self.prepared_samples = list(zip(allu, allp, allL))
 
-            # adapt slightly
+            self.logstat.append([
+                mask_accept.mean(),
+                1 - (self.nrejects - (nrejects_expected - self.nsteps * self.popsize * (1 - 0.234))) / (self.nsteps * self.popsize),
+                self.scale,
+                self.nsteps,
+                np.mean(far_enough),
+                np.exp(np.mean(np.log(move_distance / reference_distance + 1e-10)))
+            ])
             if self.logfile:
-                self.logfile.write("rescale\t%.4f\t%.4f\t%g\n" % (
-                    mask_accept.mean() * 100,
-                    100 - (self.nrejects - (nrejects_expected - self.nsteps * self.popsize * (1 - 0.234))) * 100. / (self.nsteps * self.popsize),
-                    self.scale))
+                self.logfile.write("rescale\t%.4f\t%.4f\t%g\t%.4f%g\n" % self.logstat[-1])
+
+            # adapt slightly
             if self.nrejects > nrejects_expected and self.scale > self.scale_min:
                 # lots of rejects, decrease scale
                 self.scale *= self.scale_adapt_factor
@@ -216,7 +349,7 @@ def __next__(
         return u, p, L, nc
 
 
-class PopulationSliceSampler():
+class PopulationSliceSampler(GenericPopulationSampler):
     """Vectorized slice/HARM sampler.
 
     Can revert until all previous steps have likelihoods allL above Lmin.
@@ -264,6 +397,8 @@ def __init__(
 
         self.log = log
         self.logfile = logfile
+        self.logstat = []
+        self.logstat_labels = ['accept_rate', 'efficiency', 'scale', 'far_enough', 'mean_rel_jump']
 
         self.popsize = popsize
         self.generate_direction = generate_direction
@@ -361,7 +496,7 @@ def _setup_currentp(self, nparams):
             print("setting currentp")
         self.currentp = np.zeros((self.popsize, nparams)) + np.nan
 
-    def advance(self, transform, loglike, Lmin):
+    def advance(self, transform, loglike, Lmin, region):
         """Advance the walker population.
 
         Parameters
@@ -414,6 +549,17 @@ def advance(self, transform, loglike, Lmin):
             (success, unew, pnew, Lnew),
             nc
         ) = evolve(transform, loglike, Lmin, *args)
+        
+        far_enough, (move_distance, reference_distance) = diagnose_move_distances(region, args[0][success,:], unew)
+        self.logstat.append([
+            success.mean(),
+            self.scale,
+            self.nsteps,
+            np.mean(far_enough),
+            np.exp(np.mean(np.log(move_distance / reference_distance + 1e-10)))
+        ])
+        if self.logfile:
+            self.logfile.write("rescale\t%.4f\t%.4f\t%g\t%.4f%g\n" % self.logstat[-1])
 
         if self.log:
             print("movable", movable.shape, movable.sum(), success.shape)
@@ -511,7 +657,7 @@ def __next__(
 
         if self.log:
             print(str(self), "(before)")
-        nc = self.advance(transform, loglike, Lmin)
+        nc = self.advance(transform, loglike, Lmin, region)
         if self.log:
             print(str(self), "(after)")
 
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 7ab48129..89d15612 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -790,7 +790,7 @@ def plot_jump_diagnostic_histogram(self, filename, **kwargs):
         reference_distances = np.array([entry[j] for entry in self.logstat])
         plt.hist(np.log10(jump_distances / reference_distances + 1e-10), **kwargs)
         ylo, yhi = plt.ylim()
-        plt.vlines(self.mean_jump_distance, ylo, yhi)
+        plt.vlines(np.log10(self.mean_jump_distance), ylo, yhi)
         plt.ylim(ylo, yhi)
         plt.title(self.check_nsteps or self.adaptive_nsteps)
         plt.xlabel('log(relative step distance)')

From b4a4a128636dd15ca7c6b22c339353e980e80bf7 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 09:39:07 +0100
Subject: [PATCH 190/313] =?UTF-8?q?Bump=20version:=204.0.0=20=E2=86=92=204?=
 =?UTF-8?q?.1.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 8bffbba6..9e096438 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.0.0',
+    version='4.1.0',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 5b49fb72..48795886 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.0.0'
+__version__ = '4.1.0'

From 889becbe42f1b25bb06f418e1e044478a85396d0 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 09:42:13 +0100
Subject: [PATCH 191/313] update changelog

---
 HISTORY.rst | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 358b3dda..8959afbc 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,13 +2,22 @@
 Release Notes
 ==============
 
+4.1.0 (2023-02-15)
+------------------
+
+* add number of steps calibrator :py:class:`ultranest.calibrator.ReactiveNestedCalibrator`
+* add relative jump distance diagnostic for step samplers
+* make population step samplers more consistent with other step samplers
+
 4.0.0 (2023-02-15)
 ------------------
-* replace :py:class:`AffineLayer` with new :py:class:`MaxPrincipleGapAffineLayer`
+
+* replace :py:class:`ultranest.mlfriends.AffineLayer` with new :py:class:`ultranest.mlfriends.MaxPrincipleGapAffineLayer`
   * This changes the learned covariance to be hopefully boost local features, make MLFriends identify smaller neighbourhoods, and thereby make sampling faster.
 
 3.6.5 (2023-07-18)
 ------------------
+
 * documentation improvements
 * logging with MPI fixes `by adipol-ph <https://github.com/JohannesBuchner/UltraNest/issues/109>`_ and `by gregorydavidmartinez <https://github.com/JohannesBuchner/UltraNest/issues/110>`_
 * more flexible plotting `by facero <https://github.com/JohannesBuchner/UltraNest/issues/108>`_

From 5560443e94e24676561616a23c187be825a4df91 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 09:59:59 +0100
Subject: [PATCH 192/313] fix rst issue on pypi, by removing reference to
 classes

---
 Makefile | 2 +-
 setup.py | 5 ++++-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 70e18f13..628e7771 100644
--- a/Makefile
+++ b/Makefile
@@ -99,7 +99,7 @@ release-test: install
 	#grep -- --random examples/runfeatures.sh | sed s,python3,,g | xargs -rt --max-lines=1 mpiexec -np 5 coverage run --parallel-mode 
 
 release: release-test dist ## package and upload a release
-	twine upload dist/*.tar.gz
+	twine upload --verbose dist/*.tar.gz
 
 dist: clean ## builds source and wheel package
 	$(PYTHON) setup.py sdist
diff --git a/setup.py b/setup.py
index 9e096438..6483b99f 100644
--- a/setup.py
+++ b/setup.py
@@ -6,6 +6,7 @@
 except:
     from distutils.core import setup
 
+import re
 from Cython.Build import cythonize
 from distutils.extension import Extension
 from Cython.Distutils import build_ext
@@ -28,7 +29,9 @@
     readme = readme_file.read()
 
 with open('HISTORY.rst', encoding="utf-8") as history_file:
-    history = history_file.read()
+    history = re.sub(r':py:class:`([^`]+)`', r'\1', 
+        history_file.read())
+    
 
 requirements = ['numpy', 'cython', 'matplotlib', 'corner']
 

From 118c9419f47b9df736bb6a71e34e05f4cd573109 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 10:24:55 +0100
Subject: [PATCH 193/313] rst formatting

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 8959afbc..a2ecd1ec 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -13,6 +13,7 @@ Release Notes
 ------------------
 
 * replace :py:class:`ultranest.mlfriends.AffineLayer` with new :py:class:`ultranest.mlfriends.MaxPrincipleGapAffineLayer`
+
   * This changes the learned covariance to be hopefully boost local features, make MLFriends identify smaller neighbourhoods, and thereby make sampling faster.
 
 3.6.5 (2023-07-18)

From 141d15b5ecac3fee5f0e2f908590913a95b309f1 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 11:53:36 +0100
Subject: [PATCH 194/313] add more tests and fix newly identified bugs

* add SequentialDirectionGenerator which was mentioned in docs but did not exist
* add tests for the directions proposal
* fix logic bug in generate_partial_differential_direction, which did not ensure at least one parameter is varying
---
 tests/test_run.py          | 39 ++++++++++++++++++++-
 tests/test_stepsampling.py | 70 +++++++++++++++++++++++++++++++++++++-
 ultranest/integrator.py    |  7 ++--
 ultranest/stepsampler.py   | 67 ++++++++++++++++++++++++++++++------
 4 files changed, 168 insertions(+), 15 deletions(-)

diff --git a/tests/test_run.py b/tests/test_run.py
index 36dcac0a..31379a91 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -7,7 +7,7 @@
 import pandas
 from ultranest.mlfriends import MLFriends, AffineLayer, MaxPrincipleGapAffineLayer
 from ultranest import NestedSampler, ReactiveNestedSampler, read_file
-from ultranest.integrator import warmstart_from_similar_file, _update_region_bootstrap
+from ultranest.integrator import warmstart_from_similar_file, _update_region_bootstrap, _get_cumsum_range
 import ultranest.mlfriends
 from numpy.testing import assert_allclose
 
@@ -22,6 +22,43 @@ def generate_two_blob_points(rng, d, Nlive1, Nlive2, offset2, sigma):
     """ generate live points from two spheres """
     return np.vstack((sample_ellipsoid(rng, Nlive1, d, sigma=sigma), sample_ellipsoid(rng, Nlive2, d, sigma=sigma) + offset2))
 
+def test_get_cumsum_range1():
+    # cumulative probabilities are: array([0.1, 0.3, 0.6, 1. ])
+    pi = np.array([0.1, 0.2, 0.3, 0.4])
+    dp = 0.2
+    ilo, ihi = _get_cumsum_range(pi, dp)
+    assert ilo == 1, ilo
+    assert ihi == 2, ihi
+
+def test_get_cumsum_range_equal_prob():
+    p = np.ones(100) * 1.0
+    p = p * 1. / p.sum()
+    print(p, p.sum())
+    for percentile in 1, 5, 10, 20, 45:
+        ilo, ihi = _get_cumsum_range(p, percentile / 100.)
+        print(percentile, ilo, ihi, np.cumsum(p))
+        print(np.cumsum(p)[ilo], np.cumsum(p)[ihi])
+        # due to rounding issues, can slip to a lower index
+        assert ilo in (percentile, percentile - 1)
+        assert ihi in (100 - percentile - 1, 100 - percentile - 2)
+        assert np.cumsum(p)[ilo] >= percentile / 100.
+        assert np.cumsum(p)[ihi] <= 1 - percentile / 100.
+        assert p[ilo:ihi].sum() <= (1 - percentile / 100.) * 2, (p[ilo:ihi], p[ilo:ihi].sum(), percentile)
+
+
+def test_get_cumsum_range_random_prob():
+    np.random.seed(100)
+    for i in range(100):
+        size = int(10**np.random.uniform(0, 4))
+        p = np.random.uniform(size=size)
+        p = p * 1. / p.sum()
+        dp = np.random.uniform()
+        ilo, ihi = _get_cumsum_range(p, dp)
+        print(dp, p, np.cumsum(p), '-->', ilo, ihi, np.cumsum(p)[ilo], np.cumsum(p)[ihi])
+        # check that the selected interval contains the desired probability
+        assert p[ilo:ihi].sum() <= (1 - dp) * 2, (p[ilo:ihi], p[ilo:ihi].sum(), (1 - dp) * 2)
+
+
 def test_clustering_recursion(plot=False):
     # generate two blobs separated by 2 sigma
     # check that they are *not* separated with AffineLayer+MLFriends
diff --git a/tests/test_stepsampling.py b/tests/test_stepsampling.py
index 4689ca91..12b9b419 100644
--- a/tests/test_stepsampling.py
+++ b/tests/test_stepsampling.py
@@ -2,7 +2,12 @@
 from ultranest.mlfriends import ScalingLayer, AffineLayer, MLFriends
 from ultranest import ReactiveNestedSampler
 from ultranest.stepsampler import RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler, SpeedVariableRegionSliceSampler, RegionBallSliceSampler, SpeedVariableGenerator
-from ultranest.stepsampler import generate_region_random_direction, generate_random_direction, ellipsoid_bracket, crop_bracket_at_unit_cube
+from ultranest.stepsampler import ellipsoid_bracket, crop_bracket_at_unit_cube, _inside_region
+from ultranest.stepsampler import generate_random_direction, generate_cube_oriented_direction
+from ultranest.stepsampler import SequentialDirectionGenerator, OrthogonalDirectionGenerator, SequentialRegionDirectionGenerator
+from ultranest.stepsampler import generate_region_random_direction, generate_region_oriented_direction, generate_cube_oriented_differential_direction
+from ultranest.stepsampler import generate_differential_direction, generate_partial_differential_direction, generate_mixture_random_direction
+
 from ultranest.pathsampler import SamplingPathStepSampler
 from ultranest.stepsampler import select_random_livepoint, IslandPopulationRandomLivepointSelector
 from numpy.testing import assert_allclose
@@ -59,6 +64,69 @@ def test_stepsampler_regionmh(plot=False):
     assert a.sum() > 1, a
     assert b.sum() > 1, b
 
+def test_direction_proposals():
+    ndim = 10
+    np.random.seed(12)
+    region = make_region(ndim)
+    ui = region.u[0]
+    
+    scale = np.random.uniform()
+    vcube = generate_cube_oriented_direction(ui, region, scale)
+    assert (vcube != 0).sum() == 1, vcube
+    assert np.linalg.norm(vcube) == scale, vcube
+    
+    vcubede = generate_cube_oriented_differential_direction(ui, region, scale)
+    assert (vcubede != 0).sum() == 1, vcubede
+    assert np.linalg.norm(vcubede) > 0, vcubede
+    
+    vharm = generate_random_direction(ui, region, scale)
+    assert (vharm != 0).all(), vharm
+    
+    vde = generate_differential_direction(ui, region, scale)
+    assert (vde != 0).all(), vde
+    
+    vregionslice = generate_region_oriented_direction(ui, region, scale)
+    assert (vregionslice != 0).all(), vregionslice
+    
+    vmix = generate_mixture_random_direction(ui, region, scale)
+    assert (vmix != 0).all(), vmix
+    
+    vregionharm = generate_region_random_direction(ui, region, scale)
+    assert (vregionharm != 0).all(), vregionharm
+
+    direction_generator = SequentialDirectionGenerator()
+    for i in range(ndim * 2):
+        vdir = direction_generator(ui, region, scale)
+        assert (vdir != 0).sum() == 1, vdir
+        assert np.abs(vdir[i % ndim]) > 0, vdir
+
+    region_direction_generator = SequentialRegionDirectionGenerator()
+    for i in range(ndim * 2):
+        vdirharm = region_direction_generator(ui, region, scale)
+        assert (vdirharm != 0).all(), vdirharm
+    
+    vpartialde = generate_partial_differential_direction(ui, region, scale)
+    assert (vpartialde != 0).sum() > 1, vpartialde
+    assert (vpartialde != 0).sum() < ndim, vpartialde
+    
+    # test that applying OrthogonalDirectionGenerator to SequentialDirectionGenerator has no effect
+    ortho_direction_generator = OrthogonalDirectionGenerator(SequentialDirectionGenerator())
+    for i in range(ndim * 2):
+        vdir = ortho_direction_generator(ui, region, scale)
+        assert (vdir != 0).sum() == 1, vdir
+        assert np.abs(vdir[i % ndim]) > 0, vdir
+
+def test_inside_region():
+    ndim = 10
+    np.random.seed(12)
+    region = make_region(ndim, us = np.random.uniform(0.5, 0.51, size=(400, ndim)))
+    i = np.random.randint(400)
+    ui = region.u[i]
+    assert _inside_region(region, ui, ui)
+    # corner case where a new point is close to a old case, but both are somehow outside the region
+    unew, uold = np.random.uniform(0.4, 0.401, size=(2, ndim))
+    assert _inside_region(region, unew, uold)
+
 def test_stepsampler_cubeslice(plot=False):
     np.random.seed(3)
     sampler = ReactiveNestedSampler(paramnames, loglike, transform=transform1)
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index f0e393d6..072c76e3 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -55,9 +55,12 @@ def _get_cumsum_range(pi, dp):
         Index of the item corresponding to quantile ``1-dp``.
     """
     ci = pi.cumsum()
-    ilo = np.where(ci > dp)[0]
+    # this builds a conservatively narrow interval
+    # find first index where the cumulative is surely above
+    ilo, = np.where(ci >= dp)
     ilo = ilo[0] if len(ilo) > 0 else 0
-    ihi = np.where(ci < 1. - dp)[0]
+    # find last index where the cumulative is surely below
+    ihi, = np.where(ci <= 1. - dp)
     ihi = ihi[-1] if len(ihi) > 0 else -1
     return ilo, ihi
 
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 89d15612..2d5ee764 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -163,9 +163,10 @@ def generate_partial_differential_direction(ui, region, scale=1):
 
         v = region.u[i] - region.u[i2]
 
+        # choose which parameters to be off
         mask = np.random.uniform(size=ndim) > 0.1
-        # at least one must be on
-        mask[np.random.randint(ndim)] = True
+        # at least one must be free to vary
+        mask[np.random.randint(ndim)] = False
         v[mask] = 0
         if (v != 0).any():
             # repeat if live points are identical
@@ -558,18 +559,19 @@ def __init__(
 
             Available are:
 
-            * :py:func:`generate_cube_oriented_direction` (slice sampling)
-            * :py:func:`generate_region_oriented_direction` (slice sampling on the whitened parameter space)
-            * :py:class:`SequentialDirectionGenerator` (sequential slice sampling on the whitened parameter space)
-            * :py:func:`generate_random_direction` (hit-and-run sampling)
-            * :py:func:`generate_region_random_direction` (hit-and-run sampling on the whitened parameter space)
-            * :py:func:`generate_cube_oriented_differential_direction` (slice sampling with better proposal scale)
-            * :py:func:`generate_differential_direction` (differential evolution slice proposal)
+            * :py:func:`generate_cube_oriented_direction` (slice sampling, picking one random parameter to vary)
+            * :py:func:`generate_random_direction` (hit-and-run sampling, picking a random direction varying all parameters)
+            * :py:func:`generate_differential_direction` (differential evolution direction proposal)
+            * :py:func:`generate_region_oriented_direction` (slice sampling, but in the whitened parameter space)
+            * :py:func:`generate_region_random_direction` (hit-and-run sampling, but in the whitened parameter space)
+            * :py:class:`SequentialDirectionGenerator` (sequential slice sampling, i.e., iterate deterministically through the parameters)
+            * :py:class:`SequentialRegionDirectionGenerator` (sequential slice sampling in the whitened parameter space, i.e., iterate deterministically through the principle axes)
+            * :py:func:`generate_cube_oriented_differential_direction` (like generate_differential_direction, but along only one randomly chosen parameter)
             * :py:func:`generate_partial_differential_direction` (differential evolution slice proposal on only 10% of the parameters)
-            * :py:func:`generate_mixture_random_direction` (generate_differential_direction and generate_cube_oriented_differential_direction)
+            * :py:func:`generate_mixture_random_direction` (combined proposal)
 
             Additionally, :py:class:`OrthogonalDirectionGenerator`
-            can be applied to a generate_direction.
+            can be applied to any generate_direction function.
 
             When in doubt, try :py:func:`generate_mixture_random_direction`.
             It combines efficient moves along the live point distribution,
@@ -1214,6 +1216,49 @@ def RegionBallSliceSampler(*args, **kwargs):
     return SliceSampler(*args, **kwargs, generate_direction=generate_region_random_direction)
 
 
+class SequentialDirectionGenerator(object):
+    """Sequentially proposes one parameter after the next."""
+    def __init__(self):
+        """Initialise."""
+        self.axis_index = 0
+
+    def __call__(self, ui, region, scale=1):
+        """Choose the next axis in u-space.
+
+        Parameters
+        -----------
+        ui: array
+            current point (in u-space)
+        region: MLFriends object
+            pick random two live points for length along axis
+        scale: float
+            length of direction vector
+
+        Returns
+        --------
+        v: array
+            new direction vector (in u-space)
+        """
+        nlive, ndim = region.u.shape
+        j = self.axis_index % ndim
+        self.axis_index = j + 1
+
+        v = np.zeros(ndim)
+        # choose pair of live points
+        while v[j] == 0:
+            i = np.random.randint(nlive)
+            i2 = np.random.randint(nlive - 1)
+            if i2 >= i:
+                i2 += 1
+
+            v[j] = (region.u[i,j] - region.u[i2,j]) * scale
+
+        return v
+
+    def __str__(self):
+        return type(self).__name__ + '()'
+
+
 class SequentialRegionDirectionGenerator(object):
     """Sequentially proposes one region axes after the next."""
     def __init__(self):

From 1e8e907a5f048bdf5461061bf84892f2463742ee Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 12:35:23 +0100
Subject: [PATCH 195/313] test plotting and adapting the proposal; change
 behaviour of CubeSliceSampler

CubeSliceSampler now uses a differential scale, which make it more efficient
---
 tests/test_stepsampling.py | 36 +++++++++++++++++++++++++++++++-----
 ultranest/stepsampler.py   |  5 ++++-
 2 files changed, 35 insertions(+), 6 deletions(-)

diff --git a/tests/test_stepsampling.py b/tests/test_stepsampling.py
index 12b9b419..eb4e9a44 100644
--- a/tests/test_stepsampling.py
+++ b/tests/test_stepsampling.py
@@ -1,7 +1,9 @@
 import numpy as np
+import os
+
 from ultranest.mlfriends import ScalingLayer, AffineLayer, MLFriends
 from ultranest import ReactiveNestedSampler
-from ultranest.stepsampler import RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler, SpeedVariableRegionSliceSampler, RegionBallSliceSampler, SpeedVariableGenerator
+from ultranest.stepsampler import RegionMHSampler, CubeMHSampler, SliceSampler, CubeSliceSampler, RegionSliceSampler, SpeedVariableRegionSliceSampler, RegionBallSliceSampler, SpeedVariableGenerator
 from ultranest.stepsampler import ellipsoid_bracket, crop_bracket_at_unit_cube, _inside_region
 from ultranest.stepsampler import generate_random_direction, generate_cube_oriented_direction
 from ultranest.stepsampler import SequentialDirectionGenerator, OrthogonalDirectionGenerator, SequentialRegionDirectionGenerator
@@ -258,7 +260,7 @@ def test_stepsampler_adapt_when_stuck(plot=False):
     assert new_scale < 0.01, (new_scale, unew)
     
     print('CubeSliceSampler')
-    stepsampler = CubeSliceSampler(nsteps=1, region_filter=True)
+    stepsampler = SliceSampler(nsteps=1, region_filter=True, generate_direction=generate_cube_oriented_direction)
     np.random.seed(23)
     old_scale = stepsampler.scale
     for j in range(100):
@@ -273,7 +275,7 @@ def test_stepsampler_adapt_when_stuck(plot=False):
     assert new_scale != old_scale
     assert new_scale < 0.01, (new_scale, unew)
 
-def test_stepsampler_regionmh_adapt(plot=False):
+def test_stepsampler_adapt(plot=True):
     np.random.seed(8)
     region = make_region(len(paramnames))
     Ls = loglike_vectorized(region.u)
@@ -284,9 +286,15 @@ def test_stepsampler_regionmh_adapt(plot=False):
         pass
     
     for sampler_class in RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler: 
-        for adaptation in False, 'move-distance', 'proposal-total-distances', 'proposal-summed-distances':
+        for adaptation in False, 'move-distance', 'move-distance-midway', 'proposal-total-distances', 'proposal-summed-distances':
             print()
-            stepsampler = sampler_class(nsteps=len(paramnames), adaptive_nsteps=adaptation)
+            if sampler_class in (CubeMHSampler, CubeSliceSampler):
+                logfilename = 'test-stepsampler-%s.log' % adaptation
+                log = open(logfilename, 'w')
+            else:
+                logfilename = None
+                log = False
+            stepsampler = sampler_class(nsteps=len(paramnames), adaptive_nsteps=adaptation, log=log)
             print(stepsampler)
             stepsampler.region_changed(Ls, region)
             np.random.seed(23)
@@ -304,6 +312,24 @@ def test_stepsampler_regionmh_adapt(plot=False):
             else:
                 assert stepsampler.nsteps == len(paramnames)
 
+            if logfilename:
+                print(np.loadtxt(logfilename).shape)
+                log_nentries, log_ncolumns = np.loadtxt(logfilename).shape
+                assert log_nentries == 5
+                assert log_ncolumns == (1 + 4 * len(unew) + 7)
+                os.unlink(logfilename)
+            
+            if adaptation == 'move-distance' and sampler_class == RegionSliceSampler and plot:
+                # test plotting
+                if os.path.exists('test-stepsampler-plot.pdf'):
+                    os.unlink('test-stepsampler-plot.pdf')
+                stepsampler.plot('test-stepsampler-plot.pdf')
+                assert os.path.exists('test-stepsampler-plot.pdf')
+                if os.path.exists('test-stepsampler-plot-jumps.pdf'):
+                    os.unlink('test-stepsampler-plot-jumps.pdf')
+                stepsampler.plot_jump_diagnostic_histogram('test-stepsampler-plot-jumps.pdf')
+                assert os.path.exists('test-stepsampler-plot-jumps.pdf')
+
 def assert_point_touches_ellipsoid(ucurrent, v, t, ellipsoid_center, ellipsoid_invcov, enlarge):
     unext = ucurrent + v * t
     d = unext - ellipsoid_center
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 2d5ee764..8f4fc61c 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -676,6 +676,8 @@ def __init__(
         self.starting_point_selector = starting_point_selector
         self.mean_pair_distance = np.nan
         self.region_filter = region_filter
+        if log:
+            assert hasattr(log, 'write'), 'log argument should be a file, use log=open(filename, "w") or similar' 
         self.log = log
 
         self.logstat = []
@@ -911,6 +913,7 @@ def finalize_chain(self, region=None, Lmin=None, Ls=None):
                 [Lmin], ustart, ufinal, tstart, tfinal,
                 [self.nsteps, region.maxradiussq**0.5, mean_pair_distance,
                  iLstart, iLfinal, itstart, itfinal])])
+            self.log.flush()
 
         if self.adaptive_nsteps or self.check_nsteps:
             self.adapt_nsteps(region=region)
@@ -1198,7 +1201,7 @@ def move(self, ui, region, ndraw=1, plot=False):
 
 def CubeSliceSampler(*args, **kwargs):
     """Slice sampler, randomly picking region axes."""
-    return SliceSampler(*args, **kwargs, generate_direction=generate_cube_oriented_direction)
+    return SliceSampler(*args, **kwargs, generate_direction=SequentialDirectionGenerator())
 
 
 def RegionSliceSampler(*args, **kwargs):

From 5cf75ef6c390a7829812dc70aed71f0f68523e20 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 12:39:39 +0100
Subject: [PATCH 196/313] test reverse case in crop_bracket_at_unit_cube,
 hopefully covering the second if

---
 tests/test_stepsampling.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/tests/test_stepsampling.py b/tests/test_stepsampling.py
index eb4e9a44..71830cde 100644
--- a/tests/test_stepsampling.py
+++ b/tests/test_stepsampling.py
@@ -433,6 +433,20 @@ def test_crop_bracket(plot=False):
     assert (ucurrent + v * left >= 0).all(), (ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
     assert (ucurrent + v * right >= 0).all(), (ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
 
+    left, right, cropleft, cropright = crop_bracket_at_unit_cube(ucurrent, -v, eleft, eright)
+    if plot:
+        plt.plot([ucurrent[0] - left * v[0], ucurrent[0] - right * v[0]],
+            [ucurrent[1] - left * v[1], ucurrent[1] - right * v[1]],
+            's--', ms=8)
+        plt.savefig('test_crop_bracket_negative.pdf', bbox_inches='tight')
+        plt.close()
+    assert cropleft
+    assert cropright
+    assert (ucurrent - v * left <= 1).all(), (ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
+    assert (ucurrent - v * right <= 1).all(), (ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
+    assert (ucurrent - v * left >= 0).all(), (ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
+    assert (ucurrent - v * right >= 0).all(), (ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
+
 def test_random_point_selector():
     np.random.seed(41)
     K = 10

From 3269c7485c9283573c55d6a35840cd6aa662f8bc Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 13:30:51 +0100
Subject: [PATCH 197/313] test verify_gradient function

---
 tests/test_utils.py | 156 +++++++++++++++++++++++++++-----------------
 1 file changed, 96 insertions(+), 60 deletions(-)

diff --git a/tests/test_utils.py b/tests/test_utils.py
index ae7793d6..32649788 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,79 +1,115 @@
 import numpy as np
 import tempfile
 import os
-from ultranest.utils import vectorize, is_affine_transform, normalised_kendall_tau_distance, make_run_dir
+from ultranest.utils import vectorize, is_affine_transform, normalised_kendall_tau_distance, make_run_dir, verify_gradient
 from ultranest.utils import distributed_work_chunk_size
 from numpy.testing import assert_allclose
 import pytest
 
 def test_vectorize():
-	
-	def myfunc(x):
-		return (x**2).sum()
-
-	myvfunc = vectorize(myfunc)
-	
-	a = np.array([1.2, 2.3, 3.4])
-	
-	assert_allclose(np.array([myfunc(a)]), myvfunc([a]))
-	b = np.array([[1.2, 2.3, 3.4], [1.2, 2.3, 3.4]])
-	assert_allclose(np.array([myfunc(b[0]), myfunc(b[1])]), myvfunc(b))
-	
-	class FuncClass(object):
-		def __call__(self, x):
-			return (x**2).sum()
-		def foo(self, x):
-			return x
-	
-	mycaller = FuncClass()
-	vectorize(mycaller)
-	vectorize(mycaller.foo)
+    
+    def myfunc(x):
+        return (x**2).sum()
+
+    myvfunc = vectorize(myfunc)
+    
+    a = np.array([1.2, 2.3, 3.4])
+    
+    assert_allclose(np.array([myfunc(a)]), myvfunc([a]))
+    b = np.array([[1.2, 2.3, 3.4], [1.2, 2.3, 3.4]])
+    assert_allclose(np.array([myfunc(b[0]), myfunc(b[1])]), myvfunc(b))
+    
+    class FuncClass(object):
+        def __call__(self, x):
+            return (x**2).sum()
+        def foo(self, x):
+            return x
+    
+    mycaller = FuncClass()
+    vectorize(mycaller)
+    vectorize(mycaller.foo)
 
 
 def test_is_affine_transform():
-	na = 2**np.random.randint(1, 10)
-	d = 2**np.random.randint(1, 3)
-	a = np.random.uniform(-1, 1, size=(na, d))
-	
-	assert is_affine_transform(a, a)
-	assert is_affine_transform(a, a * 2.0)
-	assert is_affine_transform(a, a - 1)
-	assert is_affine_transform(a, a * 10000 - 5000.)
-	assert not is_affine_transform(a, a**2)
+    na = 2**np.random.randint(1, 10)
+    d = 2**np.random.randint(1, 3)
+    a = np.random.uniform(-1, 1, size=(na, d))
+    
+    assert is_affine_transform(a, a)
+    assert is_affine_transform(a, a * 2.0)
+    assert is_affine_transform(a, a - 1)
+    assert is_affine_transform(a, a * 10000 - 5000.)
+    assert not is_affine_transform(a, a**2)
 
 def test_tau():
-	
-	assert normalised_kendall_tau_distance(np.arange(400), np.arange(400)) == 0
-	assert normalised_kendall_tau_distance(np.arange(2000), np.arange(2000)) == 0
-	a = np.array([1, 2, 3, 4, 5])
-	b = np.array([3, 4, 1, 2, 5])
-	assert normalised_kendall_tau_distance(a, b) == 0.4
-	i, j = np.meshgrid(np.arange(len(a)), np.arange(len(b)))
-	assert normalised_kendall_tau_distance(a, b, i, j) == 0.4
-	assert normalised_kendall_tau_distance(a, a, i, j) == 0
-	
-	try:
-		normalised_kendall_tau_distance(np.arange(5), np.arange(10))
-		raise Exception("expect error")
-	except AssertionError:
-		pass
+    
+    assert normalised_kendall_tau_distance(np.arange(400), np.arange(400)) == 0
+    assert normalised_kendall_tau_distance(np.arange(2000), np.arange(2000)) == 0
+    a = np.array([1, 2, 3, 4, 5])
+    b = np.array([3, 4, 1, 2, 5])
+    assert normalised_kendall_tau_distance(a, b) == 0.4
+    i, j = np.meshgrid(np.arange(len(a)), np.arange(len(b)))
+    assert normalised_kendall_tau_distance(a, b, i, j) == 0.4
+    assert normalised_kendall_tau_distance(a, a, i, j) == 0
+    
+    try:
+        normalised_kendall_tau_distance(np.arange(5), np.arange(10))
+        raise Exception("expect error")
+    except AssertionError:
+        pass
+
+
+def test_verify_gradient():
+    ndim = 4
+    sigma = 0.01
+    sigma = np.logspace(-1, np.log10(sigma), ndim)
+    width = 1 - 5 * sigma
+    width[width < 1e-20] = 1e-20
+    centers = (np.sin(np.arange(ndim)/2.) * width + 1.) / 2.
+
+    def loglike(theta):
+        return -0.5 * (((theta - centers)/sigma)**2).sum(axis=1) - 0.5 * np.log(2 * np.pi * sigma**2).sum()
+
+    def transform(x):
+        return x
+
+    def transform_loglike_gradient(u):
+        theta = u
+        like = -0.5 * (((theta - centers)/sigma)**2).sum() - 0.5 * np.log(2 * np.pi * sigma**2).sum()
+        grad = (theta - centers) / sigma
+        return u, like, grad
+
+    def gradient(theta):
+        return (theta - centers) / sigma
+
+    def wrong_gradient(theta):
+        return -1000 * (theta - centers) / sigma
+
+    verify_gradient(ndim, transform, loglike, transform_loglike_gradient, combination=True, verbose=True)
+    verify_gradient(ndim, transform, loglike, gradient, verbose=True)
+    failed = False
+    try:
+        verify_gradient(ndim, transform, loglike, wrong_gradient, verbose=True)
+    except AssertionError:
+        failed = True
+    assert failed
 
 
 def test_make_log_dirs():
-	import shutil
-	try:
-		filepath = tempfile.mkdtemp()
-		make_run_dir(filepath, max_run_num=3)
-		assert os.path.exists(os.path.join(filepath, 'run1'))
-		make_run_dir(filepath, max_run_num=3)
-		assert os.path.exists(os.path.join(filepath, 'run2'))
-		try:
-			make_run_dir(filepath, max_run_num=3)
-			assert False
-		except ValueError:
-			pass
-	finally:
-		shutil.rmtree(filepath)
+    import shutil
+    try:
+        filepath = tempfile.mkdtemp()
+        make_run_dir(filepath, max_run_num=3)
+        assert os.path.exists(os.path.join(filepath, 'run1'))
+        make_run_dir(filepath, max_run_num=3)
+        assert os.path.exists(os.path.join(filepath, 'run2'))
+        try:
+            make_run_dir(filepath, max_run_num=3)
+            assert False
+        except ValueError:
+            pass
+    finally:
+        shutil.rmtree(filepath)
 
 @pytest.mark.parametrize("mpi_size", [1, 4, 10, 37, 53, 100, 1000, 513])
 @pytest.mark.parametrize("num_live_points_missing", [0, 1, 4, 10, 17, 31, 100, 1000, 513])

From 3c6445c569d3911c03e8d8fc5f30c1219a23e107 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 14:31:54 +0100
Subject: [PATCH 198/313] add more tests for population stepsamplers

---
 tests/test_popstepsampling.py | 59 +++++++++++++++++++++++++++++++++++
 1 file changed, 59 insertions(+)

diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 54743387..70d93b7d 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -1,9 +1,26 @@
+import os
 import numpy as np
+
 from ultranest import ReactiveNestedSampler
+from ultranest.mlfriends import AffineLayer, ScalingLayer, MLFriends
 from ultranest.popstepsampler import PopulationSliceSampler, PopulationRandomWalkSampler
 from ultranest.popstepsampler import generate_cube_oriented_direction, generate_random_direction, generate_cube_oriented_direction_scaled
 from ultranest.popstepsampler import generate_region_oriented_direction, generate_region_random_direction
 
+def make_region(ndim, us=None, nlive=400):
+    if us is None:
+        us = np.random.uniform(size=(nlive, ndim))
+    
+    if ndim > 1:
+        transformLayer = AffineLayer()
+    else:
+        transformLayer = ScalingLayer()
+    transformLayer.optimize(us, us)
+    region = MLFriends(us, transformLayer)
+    region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
+    region.create_ellipsoid(minvol=1.0)
+    return region
+
 def loglike_vectorized(z):
     a = np.array([-0.5 * sum([((xi - 0.7 + i*0.001)/0.1)**2 for i, xi in enumerate(x)]) for x in z])
     b = np.array([-0.5 * sum([((xi - 0.3 - i*0.001)/0.1)**2 for i, xi in enumerate(x)]) for x in z])
@@ -36,6 +53,16 @@ def test_stepsampler_cubeslice(plot=False):
     assert a.sum() > 1
     assert b.sum() > 1
 
+    if os.path.exists('test-popstepsampler-plot.pdf'):
+        os.unlink('test-popstepsampler-plot.pdf')
+    sampler.stepsampler.plot('test-popstepsampler-plot.pdf')
+    assert os.path.exists('test-popstepsampler-plot.pdf')
+    if os.path.exists('test-popstepsampler-plot-jumps.pdf'):
+        os.unlink('test-popstepsampler-plot-jumps.pdf')
+    sampler.stepsampler.plot_jump_diagnostic_histogram('test-popstepsampler-plot-jumps.pdf')
+    assert os.path.exists('test-popstepsampler-plot-jumps.pdf')
+    sampler.stepsampler.print_diagnostic()
+
 def test_stepsampler_cubegausswalk(plot=False):
     np.random.seed(2)
     nsteps = np.random.randint(10, 50)
@@ -54,6 +81,16 @@ def test_stepsampler_cubegausswalk(plot=False):
     assert a.sum() > 1
     assert b.sum() > 1
 
+    if os.path.exists('test-popstepsampler-plot.pdf'):
+        os.unlink('test-popstepsampler-plot.pdf')
+    sampler.stepsampler.plot('test-popstepsampler-plot.pdf')
+    assert os.path.exists('test-popstepsampler-plot.pdf')
+    if os.path.exists('test-popstepsampler-plot-jumps.pdf'):
+        os.unlink('test-popstepsampler-plot-jumps.pdf')
+    sampler.stepsampler.plot_jump_diagnostic_histogram('test-popstepsampler-plot-jumps.pdf')
+    assert os.path.exists('test-popstepsampler-plot-jumps.pdf')
+    sampler.stepsampler.print_diagnostic()
+
 from ultranest.mlfriends import AffineLayer, ScalingLayer, MLFriends, RobustEllipsoidRegion, SimpleRegion
 
 def test_direction_proposals():
@@ -80,6 +117,28 @@ def test_direction_proposals():
                 assert directions.shape == points.shape, (directions.shape, points.shape)
                 #assert np.allclose(norms, scale), (norms, scale)
 
+
+def test_direction_proposal_values():
+    ndim = 10
+    np.random.seed(12)
+    region = make_region(ndim, nlive=400)
+    ui = region.u[::2]
+    
+    scale = np.random.uniform()
+    vcube = generate_cube_oriented_direction(ui, region, scale)
+    assert vcube.shape == ui.shape
+    assert vcube.sum(axis=1).shape == (len(ui),)
+    assert ((vcube != 0).sum(axis=1) == 1).all(), vcube
+    assert np.allclose(np.linalg.norm(vcube, axis=1), scale), (vcube, np.linalg.norm(vcube, axis=1), scale)
+    
+    vharm = generate_random_direction(ui, region, scale)
+    assert (vharm != 0).all(), vharm
+    vregionslice = generate_region_oriented_direction(ui, region, scale)
+    assert (vregionslice != 0).all(), vregionslice
+    vregionharm = generate_region_random_direction(ui, region, scale)
+    assert (vregionharm != 0).all(), vregionharm
+
+
 if __name__ == '__main__':
     test_stepsampler_cubegausswalk()
     test_direction_proposals()

From 22c7f2db56db8b95d2dd919a8ba01080a898a82b Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 14:46:57 +0100
Subject: [PATCH 199/313] add test for generate_cube_oriented_direction_scaled

---
 tests/test_popstepsampling.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 70d93b7d..3a4cc7ff 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -2,7 +2,7 @@
 import numpy as np
 
 from ultranest import ReactiveNestedSampler
-from ultranest.mlfriends import AffineLayer, ScalingLayer, MLFriends
+from ultranest.mlfriends import AffineLayer, ScalingLayer, MLFriends, RobustEllipsoidRegion, SimpleRegion
 from ultranest.popstepsampler import PopulationSliceSampler, PopulationRandomWalkSampler
 from ultranest.popstepsampler import generate_cube_oriented_direction, generate_random_direction, generate_cube_oriented_direction_scaled
 from ultranest.popstepsampler import generate_region_oriented_direction, generate_region_random_direction
@@ -91,8 +91,6 @@ def test_stepsampler_cubegausswalk(plot=False):
     assert os.path.exists('test-popstepsampler-plot-jumps.pdf')
     sampler.stepsampler.print_diagnostic()
 
-from ultranest.mlfriends import AffineLayer, ScalingLayer, MLFriends, RobustEllipsoidRegion, SimpleRegion
-
 def test_direction_proposals():
     proposals = [generate_cube_oriented_direction, generate_random_direction, 
         generate_region_oriented_direction, generate_region_random_direction]
@@ -130,13 +128,17 @@ def test_direction_proposal_values():
     assert vcube.sum(axis=1).shape == (len(ui),)
     assert ((vcube != 0).sum(axis=1) == 1).all(), vcube
     assert np.allclose(np.linalg.norm(vcube, axis=1), scale), (vcube, np.linalg.norm(vcube, axis=1), scale)
-    
+
     vharm = generate_random_direction(ui, region, scale)
     assert (vharm != 0).all(), vharm
     vregionslice = generate_region_oriented_direction(ui, region, scale)
     assert (vregionslice != 0).all(), vregionslice
     vregionharm = generate_region_random_direction(ui, region, scale)
     assert (vregionharm != 0).all(), vregionharm
+    vcubestd = generate_cube_oriented_direction_scaled(ui, region, scale)
+    assert vcubestd.shape == ui.shape
+    assert vcubestd.sum(axis=1).shape == (len(ui),)
+    assert ((vcubestd != 0).sum(axis=1) == 1).all(), vcubestd
 
 
 if __name__ == '__main__':

From 6af3a8008aa51c78e0cfda2d82204ee6b87da9b2 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 14:54:45 +0100
Subject: [PATCH 200/313] fix
 https://github.com/JohannesBuchner/UltraNest/issues/116

---
 docs/conf.py          | 3 ++-
 ultranest/dychmc.py   | 2 +-
 ultranest/dyhmc.py    | 2 +-
 ultranest/hotstart.py | 1 -
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index ccdb0764..23cdc4bc 100755
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -39,6 +39,7 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.viewcode',
     'sphinx.ext.mathjax',
+    'sphinx.ext.doctests',
     'sphinx.ext.autosectionlabel',
     'nbsphinx',
     'sphinx_rtd_theme',
@@ -60,7 +61,7 @@
 
 # General information about the project.
 project = u'UltraNest'
-copyright = u"2014-2022, Johannes Buchner"
+copyright = u"2014-2024, Johannes Buchner"
 author = u"Johannes Buchner"
 
 # The version info for the project you're documenting, acts as replacement
diff --git a/ultranest/dychmc.py b/ultranest/dychmc.py
index 13fb8396..01343f60 100644
--- a/ultranest/dychmc.py
+++ b/ultranest/dychmc.py
@@ -326,7 +326,7 @@ def plot(self, filename):
             header=','.join(self.logstat_labels), delimiter=',')
         plt.close()
 
-    def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=40, plot=False):
+    def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=40, plot=False, tregion=None):
         """Get a new point.
 
         Parameters
diff --git a/ultranest/dyhmc.py b/ultranest/dyhmc.py
index edf1f356..2bbcc18f 100644
--- a/ultranest/dyhmc.py
+++ b/ultranest/dyhmc.py
@@ -467,7 +467,7 @@ def plot(self, filename):
         plt.savefig(filename, bbox_inches='tight')
         plt.close()
 
-    def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=40, plot=False):
+    def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=40, plot=False, tregion=None):
         """Get a new point.
 
         Parameters
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index 627e0778..6f1a5add 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -48,7 +48,6 @@ def get_auxiliary_problem(loglike, transform, ctr, invcov, enlargement_factor, d
     enlargement_factor: float
         Factor by which the scale of the auxiliary distribution is enlarged
         in all dimensions.
-
         For Gaussian-like posteriors, sqrt(ndim) seems to work,
         Heavier tailed or non-elliptical distributions may need larger factors.
     df: float

From 131f3dfe9277f91df6c863da990356a5c2d3705c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 15:05:33 +0100
Subject: [PATCH 201/313] improve front-facing docs

---
 README.rst | 32 ++++++++++++++++++--------------
 1 file changed, 18 insertions(+), 14 deletions(-)

diff --git a/README.rst b/README.rst
index dbab5f9d..4203749e 100644
--- a/README.rst
+++ b/README.rst
@@ -71,7 +71,7 @@ Features
   * Can control the run programmatically and check status
   * Reasonable defaults, but customizable
   * Thoroughly tested with many unit and integration tests
-  * NEW: allows likelihood functions written in `Python <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/python>`_, `C <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/c>`_, `C++ <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/c%2B%2B>`_, `Fortran <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/fortran>`_, `Julia <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/julia>`_ and `R <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/r>`_
+  * NEW: supports likelihood functions written in `Python <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/python>`_, `C <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/c>`_, `C++ <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/c%2B%2B>`_, `Fortran <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/fortran>`_, `Julia <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/julia>`_ and `R <https://github.com/JohannesBuchner/UltraNest/tree/master/languages/r>`_
 
 * Robust exploration easily handles:
 
@@ -79,25 +79,17 @@ Features
   * Multiple modes/solutions in the parameter space
   * Robust, parameter-free MLFriends algorithm 
     (metric learning RadFriends, Buchner+14,+19), with new improvements
-    (region follows new live points, clustering improves metric iteratively).
+    (region follows new live points, clustering improves metric iteratively, 
+    NEW in v4.0: refined local metric).
   * High-dimensional problems with hit-and-run sampling
   * Wrapped/circular parameters, derived parameters
   * Fast-slow parameters
 
-* strategic nested sampling
-
-  * can vary (increase) number of live points (akin to dynamic nested sampling, but with different targets)
-  * can sample clusters optimally (e.g., at least 50 points per cluster/mode/solution)
-  * can target minimizing parameter estimation uncertainties
-  * can target a desired evidence uncertainty threshold
-  * can target a desired number of effective samples
-  * or any combination of the above
-  * Robust ln(Z) uncertainties by bootstrapping live points.
-
 * Lightweight and fast
 
   * some functions implemented in Cython
-  * `vectorized likelihood function calls <https://johannesbuchner.github.io/UltraNest/performance.html>`__
+  * `vectorized likelihood function calls <https://johannesbuchner.github.io/UltraNest/performance.html>`__, 
+    optimally supporting models with deep learning emulators
   * Use multiple cores, fully parallelizable from laptops to computing clusters
   * `MPI support <https://johannesbuchner.github.io/UltraNest/performance.html>`__
 
@@ -107,7 +99,17 @@ Features
   * Publication-ready visualisations
   * Corner plots, run and parameter exploration diagnostic plots
   * Checkpointing and resuming, even with different number of live points
-  * NEW: `Warm-start: resume from modified data / model <https://johannesbuchner.github.io/UltraNest/example-warmstart.html>`__
+  * `Warm-start: resume from modified data / model <https://johannesbuchner.github.io/UltraNest/example-warmstart.html>`__
+
+* strategic nested sampling
+
+  * can vary (increase) number of live points (akin to dynamic nested sampling, but with different targets)
+  * can sample clusters optimally (e.g., at least 50 points per cluster/mode/solution)
+  * can target minimizing parameter estimation uncertainties
+  * can target a desired evidence uncertainty threshold
+  * can target a desired number of effective samples
+  * or any combination of the above
+  * Robust ln(Z) uncertainties by bootstrapping live points.
 
 Usage
 ^^^^^
@@ -130,3 +132,5 @@ How to `cite UltraNest <https://johannesbuchner.github.io/UltraNest/issues.html#
 GPLv3 (see LICENCE file). If you require another license, please contact me.
 
 The cute hedgehog icon was made by `Freepik <https://www.flaticon.com/authors/freepik>`_.
+It symbolises UltraNest's approach of carefully walking up a likelihood,
+ready to defend against any encountered danger.

From 9ef0ec17f1ec5b6c046a53718cbcd5286eb50690 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 15:26:00 +0100
Subject: [PATCH 202/313] improve docs for calibrator

---
 docs/API.rst            |  7 +++---
 ultranest/calibrator.py | 49 ++++++++++++++++++++++++++++++++++++-----
 2 files changed, 47 insertions(+), 9 deletions(-)

diff --git a/docs/API.rst b/docs/API.rst
index 04f4356b..0bcdfb2a 100644
--- a/docs/API.rst
+++ b/docs/API.rst
@@ -11,18 +11,19 @@ Modules commonly used directly:
 --------------------------------------------------------------------------------
 
  * :py:mod:`ultranest.integrator`: Nested sampling integrators
- * :py:mod:`ultranest.hotstart`: Warm start
  * :py:mod:`ultranest.plot`: Plotting utilities
  * :py:mod:`ultranest.stepsampler`: MCMC-like step sampling
  * :py:mod:`ultranest.popstepsampler`: Vectorized step samplers
+ * :py:mod:`ultranest.calibrator`: Automatic calibration of the number of steps
  * :py:mod:`ultranest.solvecompat`: Drop-in replacement for pymultinest.solve.
+ * :py:mod:`ultranest.hotstart`: Warm start
 
 Internally used modules:
 --------------------------------------------------------------------------------
 
  * :py:mod:`ultranest.mlfriends`: Region construction methods
  * :py:mod:`ultranest.netiter`: Graph-based nested sampling
- * :py:mod:`ultranest.ordertest`: Mann-Whitney-Wilcoxon U test for a uniform distribution of integers
+ * :py:mod:`ultranest.ordertest`: U test for a uniform distribution of integers
  * :py:mod:`ultranest.stepfuncs`: Efficient helper functions for vectorized step-samplers
  * :py:mod:`ultranest.store`: Storage for nested sampling points
  * :py:mod:`ultranest.viz`: Live point visualisations
@@ -44,5 +45,3 @@ Alphabetical list of submodules
    :maxdepth: 2
 
    ultranest
-
-
diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index 4bd037b3..d4e97070 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -7,8 +7,23 @@
 import os
 
 
-def substitute_log_dir(init_args, nsteps):
-    """Append nsteps to log_dir argument, if set."""
+def _substitute_log_dir(init_args, nsteps):
+    """Append `nsteps` to `log_dir` argument, if set.
+
+    Parameters
+    -----------
+    init_args: dict
+        arguments passed :py:class:`ReactiveNestedSampler`,
+        may contain the key `'log_dir'`.
+    nsteps: int
+        number of steps
+
+    Returns
+    -------
+    new_init_args: dict
+        same as init_args, but if `'log_dir'` was set,
+        it now has `'-nsteps'+str(nsteps)` appended.
+    """
     if 'log_dir' in init_args:
         args = dict(init_args)
         args['log_dir'] = init_args['log_dir'] + '-nsteps%d' % nsteps
@@ -19,6 +34,14 @@ def substitute_log_dir(init_args, nsteps):
 class ReactiveNestedCalibrator():
     """Calibrator for the number of steps in step samplers.
 
+    The number of steps in a step sampler needs to be chosen.
+    A calibration recommended (e.g. https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.2044H)
+    is to run a sequence of nested sampling runs with increasing number of steps,
+    and stop when log(Z) converges.
+
+    This class automates this. See the :py:meth:`ReactiveNestedCalibrator.run`
+    for details.
+
     Usage
     -----
 
@@ -37,8 +60,8 @@ class ReactiveNestedCalibrator():
     The run() command will print the number of slice sampler steps
     that appear safe for the inference task.
 
-    The initial value for nsteps is ignored, and set to len(param_names)
-    instead.
+    The initial value for nsteps (e.g. in `SliceSampler(nsteps=...)`) 
+    is overwritten by this class.
     """
 
     def __init__(self,
@@ -68,12 +91,28 @@ def __init__(self,
         self.stepsampler = None
 
     def run(self, **kwargs):
-        """Run a sequence of ReactiveNestedSampler with nsteps doubling.
+        """Run a sequence of ReactiveNestedSampler runs until convergence.
+
+        The first run is made with the number of steps set to the number of parameters.
+        Each subsequent run doubles the number of steps.
+        Runs are made until convergence is reached.
+        Then this generator stops yielding results.
+
+        Convergence is defined as three consecutive runs which
+        1) are not ordered in their log(Z) results,
+        and 2) the consecutive log(Z) error bars must overlap.
 
         Parameters
         -----------
         **kwargs: dict
             All arguments are passed to :py:meth:`ReactiveNestedSampler.run`.
+
+        Yields
+        -------
+        nsteps: int
+            number of steps for the current run
+        result: dict
+            return value of :py:meth:`ReactiveNestedSampler.run` for the current run
         """
         assert self.stepsampler is not None
         self.run_args = kwargs

From 785997671002068c591e163de90ca790323eafbc Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 15:30:03 +0100
Subject: [PATCH 203/313] cross-links in docs to ReactiveNestedCalibrator

---
 ultranest/popstepsampler.py | 4 ++--
 ultranest/stepsampler.py    | 3 +--
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 39e2a788..bff21286 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -197,8 +197,7 @@ def __init__(
             Observe the nested sampling efficiency.
         nsteps: int
             number of steps to take until the found point is accepted as independent.
-            To calibrate, try several runs with increasing nsteps (doubling).
-            The ln(Z) should become stable at some value.
+            To find the right value, see :py:class:`ultranest.calibrator.ReactiveNestedCalibrator`
         generate_direction: function
             Function that gives proposal kernel shape, one of:
             :py:func:`ultranest.popstepsampler.generate_cube_oriented_direction`
@@ -368,6 +367,7 @@ def __init__(
             number of walkers to maintain
         nsteps: int
             number of steps to take until the found point is accepted as independent.
+            To find the right value, see :py:class:`ultranest.calibrator.ReactiveNestedCalibrator`
         generate_direction: function `(u, region, scale) -> v`
             function such as `generate_unit_directions`, which
             generates a random slice direction.
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 8f4fc61c..640951b9 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -551,8 +551,7 @@ def __init__(
         nsteps: int
             number of accepted steps until the sample is considered independent.
 
-            To find the right value, run nested sampling several time,
-            always doubling nsteps, until Z is stable.
+            To find the right value, see :py:class:`ultranest.calibrator.ReactiveNestedCalibrator`
 
         generate_direction: function
             direction proposal function.

From 8cd160db0b94219d01dc68aadf9bf593fdeec177 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 15:38:32 +0100
Subject: [PATCH 204/313] typo in sphinx conf

---
 docs/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/conf.py b/docs/conf.py
index 23cdc4bc..e6250347 100755
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -39,7 +39,7 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.viewcode',
     'sphinx.ext.mathjax',
-    'sphinx.ext.doctests',
+    'sphinx.ext.doctest',
     'sphinx.ext.autosectionlabel',
     'nbsphinx',
     'sphinx_rtd_theme',

From 89a15eeee2c82401f91e82073dc64aca53f09ac2 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 16:04:56 +0100
Subject: [PATCH 205/313] add test for _update_region_bootstrap

---
 tests/test_run.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/tests/test_run.py b/tests/test_run.py
index 31379a91..7fce9d0f 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -59,6 +59,22 @@ def test_get_cumsum_range_random_prob():
         assert p[ilo:ihi].sum() <= (1 - dp) * 2, (p[ilo:ihi], p[ilo:ihi].sum(), (1 - dp) * 2)
 
 
+def test_failing_update_region_bootstrap():
+    rng = np.random.RandomState(10)
+    u = rng.uniform(size=(200, 4))
+    # make linearly dependent, so building a region should fail
+    u[:,-1] = u[:,0]
+    # boot-strap an affine layer after clustering
+    transformLayer = AffineLayer()
+    transformLayer.optimize(u, u)
+    region = MLFriends(u, transformLayer)
+    try:
+        _update_region_bootstrap(region, nbootstraps=30)
+        assert False, 'expected a linalg error'
+    except np.linalg.LinAlgError:
+        pass
+
+
 def test_clustering_recursion(plot=False):
     # generate two blobs separated by 2 sigma
     # check that they are *not* separated with AffineLayer+MLFriends

From 134280c9d048a1330ce18829524cf17304c827dc Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 19:10:55 +0100
Subject: [PATCH 206/313] update modoverview instead of API.docs

---
 docs/modoverview.py    | 2 +-
 ultranest/ordertest.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/modoverview.py b/docs/modoverview.py
index 2e336c9b..268a94c3 100644
--- a/docs/modoverview.py
+++ b/docs/modoverview.py
@@ -1,7 +1,7 @@
 import importlib
 
 sections = [
-	('Modules commonly used directly', ['integrator', 'hotstart', 'plot', 'stepsampler', 'popstepsampler', 'solvecompat']),
+	('Modules commonly used directly', ['integrator', 'plot', 'stepsampler', 'popstepsampler', 'calibrator', 'solvecompat', 'hotstart']),
 	('Internally used modules', ['mlfriends', 'netiter', 'ordertest', 'stepfuncs', 'store', 'viz']),
 	('Experimental modules, no guarantees', ['dychmc', 'dyhmc', 'flatnuts', 'pathsampler', 'samplingpath']),
 ]
diff --git a/ultranest/ordertest.py b/ultranest/ordertest.py
index bad945fe..e29ced91 100644
--- a/ultranest/ordertest.py
+++ b/ultranest/ordertest.py
@@ -1,6 +1,6 @@
 """
-Mann-Whitney-Wilcoxon U test for a uniform distribution of integers
--------------------------------------------------------------------
+U test for a uniform distribution of integers
+---------------------------------------------
 
 A test for biased nested sampling, presented in
 section 4.5.2 of Buchner (2023, https://arxiv.org/abs/2101.09675).

From 7d2b1e5dfd49be5661f8ebfad66bca489636f4e0 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 19:16:28 +0100
Subject: [PATCH 207/313] update modoverview.py (API.rst is generated)

---
 docs/API.rst            | 31 -------------------------------
 ultranest/calibrator.py |  4 ++--
 2 files changed, 2 insertions(+), 33 deletions(-)

diff --git a/docs/API.rst b/docs/API.rst
index 0bcdfb2a..76777e86 100644
--- a/docs/API.rst
+++ b/docs/API.rst
@@ -14,34 +14,3 @@ Modules commonly used directly:
  * :py:mod:`ultranest.plot`: Plotting utilities
  * :py:mod:`ultranest.stepsampler`: MCMC-like step sampling
  * :py:mod:`ultranest.popstepsampler`: Vectorized step samplers
- * :py:mod:`ultranest.calibrator`: Automatic calibration of the number of steps
- * :py:mod:`ultranest.solvecompat`: Drop-in replacement for pymultinest.solve.
- * :py:mod:`ultranest.hotstart`: Warm start
-
-Internally used modules:
---------------------------------------------------------------------------------
-
- * :py:mod:`ultranest.mlfriends`: Region construction methods
- * :py:mod:`ultranest.netiter`: Graph-based nested sampling
- * :py:mod:`ultranest.ordertest`: U test for a uniform distribution of integers
- * :py:mod:`ultranest.stepfuncs`: Efficient helper functions for vectorized step-samplers
- * :py:mod:`ultranest.store`: Storage for nested sampling points
- * :py:mod:`ultranest.viz`: Live point visualisations
-
-Experimental modules, no guarantees:
---------------------------------------------------------------------------------
-
- * :py:mod:`ultranest.dychmc`: Constrained Hamiltanean Monte Carlo step sampling.
- * :py:mod:`ultranest.dyhmc`: Experimental constrained Hamiltanean Monte Carlo step sampling
- * :py:mod:`ultranest.flatnuts`: FLATNUTS is a implementation of No-U-turn sampler 
- * :py:mod:`ultranest.pathsampler`: MCMC-like step sampling on a trajectory
- * :py:mod:`ultranest.samplingpath`: Sparsely sampled, virtual sampling path.
-
-
-Alphabetical list of submodules
--------------------------------
-
-.. toctree::
-   :maxdepth: 2
-
-   ultranest
diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index d4e97070..09a626c3 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -60,7 +60,7 @@ class ReactiveNestedCalibrator():
     The run() command will print the number of slice sampler steps
     that appear safe for the inference task.
 
-    The initial value for nsteps (e.g. in `SliceSampler(nsteps=...)`) 
+    The initial value for nsteps (e.g. in `SliceSampler(nsteps=...)`)
     is overwritten by this class.
     """
 
@@ -125,7 +125,7 @@ def run(self, **kwargs):
 
         while True:
             print("running with %d steps ..." % nsteps)
-            init_args = substitute_log_dir(self.init_args, nsteps)
+            init_args = _substitute_log_dir(self.init_args, nsteps)
             sampler = ReactiveNestedSampler(**init_args)
             sampler.stepsampler = self.stepsampler.__class__(
                 nsteps=nsteps, generate_direction=self.stepsampler.generate_direction,

From 03d7e6a552570f74af9acfcf5a4ea6eb14f6a861 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 21:45:20 +0100
Subject: [PATCH 208/313] fix broken file

---
 docs/API.rst | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/docs/API.rst b/docs/API.rst
index 76777e86..fb55a5a8 100644
--- a/docs/API.rst
+++ b/docs/API.rst
@@ -14,3 +14,36 @@ Modules commonly used directly:
  * :py:mod:`ultranest.plot`: Plotting utilities
  * :py:mod:`ultranest.stepsampler`: MCMC-like step sampling
  * :py:mod:`ultranest.popstepsampler`: Vectorized step samplers
+ * :py:mod:`ultranest.calibrator`: Calibration of step sampler
+ * :py:mod:`ultranest.solvecompat`: Drop-in replacement for pymultinest.solve.
+ * :py:mod:`ultranest.hotstart`: Warm start
+
+Internally used modules:
+--------------------------------------------------------------------------------
+
+ * :py:mod:`ultranest.mlfriends`: Region construction methods
+ * :py:mod:`ultranest.netiter`: Graph-based nested sampling
+ * :py:mod:`ultranest.ordertest`: U test for a uniform distribution of integers
+ * :py:mod:`ultranest.stepfuncs`: Efficient helper functions for vectorized step-samplers
+ * :py:mod:`ultranest.store`: Storage for nested sampling points
+ * :py:mod:`ultranest.viz`: Live point visualisations
+
+Experimental modules, no guarantees:
+--------------------------------------------------------------------------------
+
+ * :py:mod:`ultranest.dychmc`: Constrained Hamiltanean Monte Carlo step sampling.
+ * :py:mod:`ultranest.dyhmc`: Experimental constrained Hamiltanean Monte Carlo step sampling
+ * :py:mod:`ultranest.flatnuts`: FLATNUTS is a implementation of No-U-turn sampler 
+ * :py:mod:`ultranest.pathsampler`: MCMC-like step sampling on a trajectory
+ * :py:mod:`ultranest.samplingpath`: Sparsely sampled, virtual sampling path.
+
+
+Alphabetical list of submodules
+-------------------------------
+
+.. toctree::
+   :maxdepth: 2
+
+   ultranest
+
+

From 8cba314570cd2fb1ce5606ef5156154aebae21ee Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 22:51:16 +0100
Subject: [PATCH 209/313] remove always-false if

---
 ultranest/popstepsampler.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index bff21286..ff8eb916 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -150,9 +150,6 @@ def print_diagnostic(self):
         if len(self.logstat) == 0:
             print("diagnostic unavailable, no recorded steps found")
             return
-        if 'jump-distance' not in self.logstat_labels or 'reference-distance' not in self.logstat_labels:
-            print("turn on check_nsteps in the step sampler for diagnostics")
-            return 
         frac_farenough = self.far_enough_fraction
         average_distance = self.mean_jump_distance
         if frac_farenough < 0.5:

From 6ff29d1c23117871ffb482b6dd21ebb54910629f Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 23:10:28 +0100
Subject: [PATCH 210/313] better test coverage by enabling log=True for step
 sampler, and (doc)test for PredictionBand

---
 tests/test_plot.py            | 39 +++++++++++++++++++++++++++++++++++
 tests/test_popstepsampling.py |  5 ++++-
 ultranest/plot.py             |  7 ++++++-
 3 files changed, 49 insertions(+), 2 deletions(-)
 create mode 100644 tests/test_plot.py

diff --git a/tests/test_plot.py b/tests/test_plot.py
new file mode 100644
index 00000000..7dc5628b
--- /dev/null
+++ b/tests/test_plot.py
@@ -0,0 +1,39 @@
+import numpy as np
+import tempfile
+import os
+from ultranest.plot import PredictionBand
+from numpy.testing import assert_allclose
+import matplotlib.pyplot as plt
+import pytest
+
+def test_PredictionBand():
+    
+    import numpy
+    chain = numpy.random.uniform(size=(20, 2))
+
+
+    x = numpy.linspace(0, 1, 100)
+    band = PredictionBand(x)
+    for c in chain:
+        band.add(c[0] * x + c[1])
+    # add median line. As an option a matplotlib ax can be given.
+    band.line(color='k')
+    # add 1 sigma quantile
+    band.shade(color='k', alpha=0.3)
+    # add wider quantile
+    band.shade(q=0.01, color='gray', alpha=0.1)
+    plt.savefig('test-predictionband.pdf')
+    plt.close()
+    
+    # add median line. As an option a matplotlib ax can be given.
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    band.line(color='k', ax=ax1)
+    band.line(color='k', ax=ax2)
+    # add 1 sigma quantile
+    with pytest.raises(ValueError):
+        band.shade(q=0.6, ax=ax1)
+    with pytest.raises(ValueError):
+        band.shade(q=np.nan, ax=ax2)
+    band.shade(q=0.01, color='gray', alpha=0.3, ax=ax1)
+    plt.savefig('test-predictionband2.pdf')
+    plt.close()
diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 3a4cc7ff..da3f5873 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -45,6 +45,7 @@ def test_stepsampler_cubeslice(plot=False):
     sampler.stepsampler = PopulationSliceSampler(
         popsize=popsize, nsteps=nsteps, 
         generate_direction=generate_cube_oriented_direction,
+        log=True,
     )
     r = sampler.run(viz_callback=None, log_interval=50)
     sampler.print_results()
@@ -62,6 +63,8 @@ def test_stepsampler_cubeslice(plot=False):
     sampler.stepsampler.plot_jump_diagnostic_histogram('test-popstepsampler-plot-jumps.pdf')
     assert os.path.exists('test-popstepsampler-plot-jumps.pdf')
     sampler.stepsampler.print_diagnostic()
+    print(sampler.stepsampler)
+    print(sampler.stepsampler.status)
 
 def test_stepsampler_cubegausswalk(plot=False):
     np.random.seed(2)
@@ -72,7 +75,7 @@ def test_stepsampler_cubegausswalk(plot=False):
     sampler.stepsampler = PopulationRandomWalkSampler(
         popsize=popsize, nsteps=nsteps, 
         generate_direction=generate_cube_oriented_direction,
-        scale=0.1,
+        scale=0.1, log=True,
     )
     r = sampler.run(viz_callback=None, log_interval=50, max_iters=200, max_num_improvement_loops=0)
     sampler.print_results()
diff --git a/ultranest/plot.py b/ultranest/plot.py
index e58ff236..14225fd2 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -71,7 +71,12 @@ class PredictionBand(object):
 
     call add(y) to add predictions from each chain point
 
-    Example::
+    .. testsetup::
+
+        import numpy
+        chain = numpy.random.uniform(size=(20, 2))
+
+    .. testcode::
 
         x = numpy.linspace(0, 1, 100)
         band = PredictionBand(x)

From 7c464204f2db83edf83ad87f80f3bbb88adde7e6 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 23:21:30 +0100
Subject: [PATCH 211/313] =?UTF-8?q?Bump=20version:=204.1.0=20=E2=86=92=204?=
 =?UTF-8?q?.1.1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 6483b99f..1b12361c 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.1.0',
+    version='4.1.1',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 48795886..8b0b2b56 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.1.0'
+__version__ = '4.1.1'

From af77614066e2c81806ca8834d81c91180445e1d7 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 15 Feb 2024 23:56:08 +0100
Subject: [PATCH 212/313] cleaner test expectations for Exceptions

---
 tests/test_ordertest.py    |  6 ++--
 tests/test_run.py          |  5 +---
 tests/test_stepsampling.py | 56 ++++++++++++++++++--------------------
 3 files changed, 30 insertions(+), 37 deletions(-)

diff --git a/tests/test_ordertest.py b/tests/test_ordertest.py
index 9301d345..e03c47fd 100644
--- a/tests/test_ordertest.py
+++ b/tests/test_ordertest.py
@@ -1,15 +1,13 @@
 from __future__ import print_function, division
 import numpy as np
+import pytest
 from ultranest.ordertest import UniformOrderAccumulator, infinite_U_zscore
 
 def test_invalid_order():
 	sample_acc = UniformOrderAccumulator()
 	sample_acc.add(2, 3)
-	try:
+	with pytest.raises(ValueError):
 		sample_acc.add(4, 3)
-		assert False
-	except ValueError:
-		pass
 
 def test_diff_expand():
 	sample_acc = UniformOrderAccumulator()
diff --git a/tests/test_run.py b/tests/test_run.py
index 7fce9d0f..4afc65a6 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -68,11 +68,8 @@ def test_failing_update_region_bootstrap():
     transformLayer = AffineLayer()
     transformLayer.optimize(u, u)
     region = MLFriends(u, transformLayer)
-    try:
+    with pytest.raises(np.linalg.LinAlgError):
         _update_region_bootstrap(region, nbootstraps=30)
-        assert False, 'expected a linalg error'
-    except np.linalg.LinAlgError:
-        pass
 
 
 def test_clustering_recursion(plot=False):
diff --git a/tests/test_stepsampling.py b/tests/test_stepsampling.py
index 71830cde..5c83157c 100644
--- a/tests/test_stepsampling.py
+++ b/tests/test_stepsampling.py
@@ -1,5 +1,6 @@
 import numpy as np
 import os
+import pytest
 
 from ultranest.mlfriends import ScalingLayer, AffineLayer, MLFriends
 from ultranest import ReactiveNestedSampler
@@ -71,28 +72,28 @@ def test_direction_proposals():
     np.random.seed(12)
     region = make_region(ndim)
     ui = region.u[0]
-    
+
     scale = np.random.uniform()
     vcube = generate_cube_oriented_direction(ui, region, scale)
     assert (vcube != 0).sum() == 1, vcube
     assert np.linalg.norm(vcube) == scale, vcube
-    
+
     vcubede = generate_cube_oriented_differential_direction(ui, region, scale)
     assert (vcubede != 0).sum() == 1, vcubede
     assert np.linalg.norm(vcubede) > 0, vcubede
-    
+
     vharm = generate_random_direction(ui, region, scale)
     assert (vharm != 0).all(), vharm
-    
+
     vde = generate_differential_direction(ui, region, scale)
     assert (vde != 0).all(), vde
-    
+
     vregionslice = generate_region_oriented_direction(ui, region, scale)
     assert (vregionslice != 0).all(), vregionslice
-    
+
     vmix = generate_mixture_random_direction(ui, region, scale)
     assert (vmix != 0).all(), vmix
-    
+
     vregionharm = generate_region_random_direction(ui, region, scale)
     assert (vregionharm != 0).all(), vregionharm
 
@@ -106,11 +107,11 @@ def test_direction_proposals():
     for i in range(ndim * 2):
         vdirharm = region_direction_generator(ui, region, scale)
         assert (vdirharm != 0).all(), vdirharm
-    
+
     vpartialde = generate_partial_differential_direction(ui, region, scale)
     assert (vpartialde != 0).sum() > 1, vpartialde
     assert (vpartialde != 0).sum() < ndim, vpartialde
-    
+
     # test that applying OrthogonalDirectionGenerator to SequentialDirectionGenerator has no effect
     ortho_direction_generator = OrthogonalDirectionGenerator(SequentialDirectionGenerator())
     for i in range(ndim * 2):
@@ -203,7 +204,7 @@ def test_stepsampler_variable_speed_SLOW(plot=False):
 def make_region(ndim, us=None):
     if us is None:
         us = np.random.uniform(size=(1000, ndim))
-    
+
     if ndim > 1:
         transformLayer = AffineLayer()
     else:
@@ -219,7 +220,7 @@ def test_stepsampler(plot=False):
     np.random.seed(6)
     region = make_region(len(paramnames))
     Ls = loglike_vectorized(region.u)
-    
+
     stepsampler = CubeMHSampler(nsteps=len(paramnames))
     while True:
         u1, p1, L1, nc = stepsampler.__next__(region, -1e100, region.u, Ls, transform, loglike)
@@ -254,11 +255,11 @@ def test_stepsampler_adapt_when_stuck(plot=False):
         unew, pnew, Lnew, nc = stepsampler.__next__(region, Lmin, us, Ls, transform, loglike, ndraw=10)
         if unew is not None:
             break
-    
+
     new_scale = stepsampler.scale
     assert new_scale != old_scale
     assert new_scale < 0.01, (new_scale, unew)
-    
+
     print('CubeSliceSampler')
     stepsampler = SliceSampler(nsteps=1, region_filter=True, generate_direction=generate_cube_oriented_direction)
     np.random.seed(23)
@@ -270,7 +271,7 @@ def test_stepsampler_adapt_when_stuck(plot=False):
             unew, pnew, Lnew, nc = stepsampler.__next__(region, Lmin, us, Ls, transform, loglike, ndraw=10)
             if unew is not None:
                 break
-    
+
     new_scale = stepsampler.scale
     assert new_scale != old_scale
     assert new_scale < 0.01, (new_scale, unew)
@@ -279,13 +280,10 @@ def test_stepsampler_adapt(plot=True):
     np.random.seed(8)
     region = make_region(len(paramnames))
     Ls = loglike_vectorized(region.u)
-    try:
+    with pytest.raises(ValueError):
         RegionMHSampler(nsteps=len(paramnames), adaptive_nsteps='Hello')
-        assert False, 'expected error'
-    except ValueError:
-        pass
-    
-    for sampler_class in RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler: 
+
+    for sampler_class in RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler:
         for adaptation in False, 'move-distance', 'move-distance-midway', 'proposal-total-distances', 'proposal-summed-distances':
             print()
             if sampler_class in (CubeMHSampler, CubeSliceSampler):
@@ -306,7 +304,7 @@ def test_stepsampler_adapt(plot=True):
                         break
             new_scale = stepsampler.scale
             assert new_scale != old_scale
-            
+
             if adaptation:
                 assert stepsampler.nsteps != len(paramnames)
             else:
@@ -318,7 +316,7 @@ def test_stepsampler_adapt(plot=True):
                 assert log_nentries == 5
                 assert log_ncolumns == (1 + 4 * len(unew) + 7)
                 os.unlink(logfilename)
-            
+
             if adaptation == 'move-distance' and sampler_class == RegionSliceSampler and plot:
                 # test plotting
                 if os.path.exists('test-stepsampler-plot.pdf'):
@@ -346,11 +344,11 @@ def test_ellipsoid_bracket(plot=False):
             us = us * 0.1 + 0.5
         else:
             us = np.random.uniform(size=(2**np.random.randint(3, 10), 2))
-        
+
         if plot:
             import matplotlib.pyplot as plt
             plt.plot(us[:,0], us[:,1], 'o ', ms=2)
-        
+
         transformLayer = ScalingLayer()
         region = MLFriends(us, transformLayer)
         try:
@@ -375,15 +373,15 @@ def test_ellipsoid_bracket(plot=False):
         uleft = ucurrent + v * left
         uright = ucurrent + v * right
 
-        if plot: 
+        if plot:
             plt.plot([uleft[0], uright[0]], [uleft[1], uright[1]], 'x-')
-            
+
             plt.savefig('test_ellipsoid_bracket.pdf', bbox_inches='tight')
             plt.close()
         print("ellipsoid bracket:", left, right)
         assert left <= 0, left
         assert right >= 0, right
-        
+
         assert_point_touches_ellipsoid(ucurrent, v, left, region.ellipsoid_center, region.ellipsoid_invcov, region.enlarge)
         assert_point_touches_ellipsoid(ucurrent, v, right, region.ellipsoid_center, region.ellipsoid_invcov, region.enlarge)
 
@@ -396,7 +394,7 @@ def test_crop_bracket(plot=False):
     ellipsoid_invcov = np.array([[11.29995701, -3.17051875], [-3.17051875,  4.76837493]])
     #enlarge = 1.0
     #ellipsoid_inv_axes = np.array([[1.0, 0.], [0., 1]])
-    
+
 
     eleft, eright = ellipsoid_bracket(ucurrent, v, ellipsoid_center, ellipsoid_inv_axes, enlarge)
     if plot:
@@ -404,7 +402,7 @@ def test_crop_bracket(plot=False):
         d = us - ellipsoid_center
         r = np.einsum('ij,jk,ik->i', d, ellipsoid_invcov, d)
         mask_inside = r <= enlarge
-        
+
         import matplotlib.pyplot as plt
         plt.plot(us[mask_inside,0], us[mask_inside,1], '+', ms=2)
         plt.plot(ucurrent[0], ucurrent[1], 'o ', ms=2)

From bf78633c890cee314342feaad7266743c009870e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 16 Feb 2024 00:13:04 +0100
Subject: [PATCH 213/313] avoid LinAlgError in AffineLayer.optimize

---
 tests/test_run.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_run.py b/tests/test_run.py
index 4afc65a6..b7095f32 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -5,7 +5,7 @@
 import pytest
 import json
 import pandas
-from ultranest.mlfriends import MLFriends, AffineLayer, MaxPrincipleGapAffineLayer
+from ultranest.mlfriends import MLFriends, ScalingLayer, AffineLayer, MaxPrincipleGapAffineLayer
 from ultranest import NestedSampler, ReactiveNestedSampler, read_file
 from ultranest.integrator import warmstart_from_similar_file, _update_region_bootstrap, _get_cumsum_range
 import ultranest.mlfriends
@@ -65,7 +65,7 @@ def test_failing_update_region_bootstrap():
     # make linearly dependent, so building a region should fail
     u[:,-1] = u[:,0]
     # boot-strap an affine layer after clustering
-    transformLayer = AffineLayer()
+    transformLayer = ScalingLayer()
     transformLayer.optimize(u, u)
     region = MLFriends(u, transformLayer)
     with pytest.raises(np.linalg.LinAlgError):

From 036614e65768cd13df2d1d5c2f84d52c77dde569 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 16 Feb 2024 08:58:27 +0100
Subject: [PATCH 214/313] =?UTF-8?q?Bump=20version:=204.1.1=20=E2=86=92=204?=
 =?UTF-8?q?.1.2?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 1b12361c..3c74a0f2 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.1.1',
+    version='4.1.2',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 8b0b2b56..02b3abd7 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.1.1'
+__version__ = '4.1.2'

From 7eec8c767faa9c43fc114de2254267ebddd2c255 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 16 Feb 2024 16:24:38 +0100
Subject: [PATCH 215/313] use temporary files for testing, because windows
 testing breaks

---
 tests/test_popstepsampling.py | 38 +++++++--------
 tests/test_stepsampling.py    | 89 ++++++++++++++++++-----------------
 ultranest/calibrator.py       |  1 +
 3 files changed, 64 insertions(+), 64 deletions(-)

diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index da3f5873..0a109a5f 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -1,4 +1,5 @@
 import os
+import tempfile
 import numpy as np
 
 from ultranest import ReactiveNestedSampler
@@ -54,17 +55,15 @@ def test_stepsampler_cubeslice(plot=False):
     assert a.sum() > 1
     assert b.sum() > 1
 
-    if os.path.exists('test-popstepsampler-plot.pdf'):
-        os.unlink('test-popstepsampler-plot.pdf')
-    sampler.stepsampler.plot('test-popstepsampler-plot.pdf')
-    assert os.path.exists('test-popstepsampler-plot.pdf')
-    if os.path.exists('test-popstepsampler-plot-jumps.pdf'):
-        os.unlink('test-popstepsampler-plot-jumps.pdf')
-    sampler.stepsampler.plot_jump_diagnostic_histogram('test-popstepsampler-plot-jumps.pdf')
-    assert os.path.exists('test-popstepsampler-plot-jumps.pdf')
-    sampler.stepsampler.print_diagnostic()
-    print(sampler.stepsampler)
-    print(sampler.stepsampler.status)
+    with tempfile.TemporaryDirectory() as tempdir:
+        prefix = os.path.join(tempdir, 'test-stepsampler')
+        sampler.stepsampler.plot(prefix + '-plot.pdf')
+        assert os.path.exists('test-popstepsampler-plot.pdf')
+        sampler.stepsampler.plot_jump_diagnostic_histogram(prefix + '-plot-jumps.pdf')
+        assert os.path.exists(prefix + '-plot-jumps.pdf')
+        sampler.stepsampler.print_diagnostic()
+        print(sampler.stepsampler)
+        print(sampler.stepsampler.status)
 
 def test_stepsampler_cubegausswalk(plot=False):
     np.random.seed(2)
@@ -84,15 +83,14 @@ def test_stepsampler_cubegausswalk(plot=False):
     assert a.sum() > 1
     assert b.sum() > 1
 
-    if os.path.exists('test-popstepsampler-plot.pdf'):
-        os.unlink('test-popstepsampler-plot.pdf')
-    sampler.stepsampler.plot('test-popstepsampler-plot.pdf')
-    assert os.path.exists('test-popstepsampler-plot.pdf')
-    if os.path.exists('test-popstepsampler-plot-jumps.pdf'):
-        os.unlink('test-popstepsampler-plot-jumps.pdf')
-    sampler.stepsampler.plot_jump_diagnostic_histogram('test-popstepsampler-plot-jumps.pdf')
-    assert os.path.exists('test-popstepsampler-plot-jumps.pdf')
-    sampler.stepsampler.print_diagnostic()
+    with tempfile.TemporaryDirectory() as tempdir:
+        prefix = os.path.join(tempdir, 'test-stepsampler')
+        sampler.stepsampler.plot(prefix + '-plot.pdf')
+        assert os.path.exists('test-popstepsampler-plot.pdf')
+        sampler.stepsampler.plot_jump_diagnostic_histogram(prefix + '-plot-jumps.pdf')
+        assert os.path.exists(prefix + '-plot-jumps.pdf')
+        sampler.stepsampler.print_diagnostic()
+        print(sampler.stepsampler)
 
 def test_direction_proposals():
     proposals = [generate_cube_oriented_direction, generate_random_direction, 
diff --git a/tests/test_stepsampling.py b/tests/test_stepsampling.py
index 5c83157c..eed40bdd 100644
--- a/tests/test_stepsampling.py
+++ b/tests/test_stepsampling.py
@@ -1,6 +1,7 @@
 import numpy as np
 import os
 import pytest
+import tempfile
 
 from ultranest.mlfriends import ScalingLayer, AffineLayer, MLFriends
 from ultranest import ReactiveNestedSampler
@@ -283,50 +284,50 @@ def test_stepsampler_adapt(plot=True):
     with pytest.raises(ValueError):
         RegionMHSampler(nsteps=len(paramnames), adaptive_nsteps='Hello')
 
-    for sampler_class in RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler:
-        for adaptation in False, 'move-distance', 'move-distance-midway', 'proposal-total-distances', 'proposal-summed-distances':
-            print()
-            if sampler_class in (CubeMHSampler, CubeSliceSampler):
-                logfilename = 'test-stepsampler-%s.log' % adaptation
-                log = open(logfilename, 'w')
-            else:
-                logfilename = None
-                log = False
-            stepsampler = sampler_class(nsteps=len(paramnames), adaptive_nsteps=adaptation, log=log)
-            print(stepsampler)
-            stepsampler.region_changed(Ls, region)
-            np.random.seed(23)
-            old_scale = stepsampler.scale
-            for i in range(5):
-                while True:
-                    unew, pnew, Lnew, nc = stepsampler.__next__(region, -1e100, region.u, Ls, transform, loglike)
-                    if unew is not None:
-                        break
-            new_scale = stepsampler.scale
-            assert new_scale != old_scale
-
-            if adaptation:
-                assert stepsampler.nsteps != len(paramnames)
-            else:
-                assert stepsampler.nsteps == len(paramnames)
-
-            if logfilename:
-                print(np.loadtxt(logfilename).shape)
-                log_nentries, log_ncolumns = np.loadtxt(logfilename).shape
-                assert log_nentries == 5
-                assert log_ncolumns == (1 + 4 * len(unew) + 7)
-                os.unlink(logfilename)
-
-            if adaptation == 'move-distance' and sampler_class == RegionSliceSampler and plot:
-                # test plotting
-                if os.path.exists('test-stepsampler-plot.pdf'):
-                    os.unlink('test-stepsampler-plot.pdf')
-                stepsampler.plot('test-stepsampler-plot.pdf')
-                assert os.path.exists('test-stepsampler-plot.pdf')
-                if os.path.exists('test-stepsampler-plot-jumps.pdf'):
-                    os.unlink('test-stepsampler-plot-jumps.pdf')
-                stepsampler.plot_jump_diagnostic_histogram('test-stepsampler-plot-jumps.pdf')
-                assert os.path.exists('test-stepsampler-plot-jumps.pdf')
+    with tempfile.TemporaryDirectory() as tempdir:
+        for sampler_class in RegionMHSampler, CubeMHSampler, CubeSliceSampler, RegionSliceSampler:
+            for adaptation in False, 'move-distance', 'move-distance-midway', 'proposal-total-distances', 'proposal-summed-distances':
+                print()
+                if sampler_class in (CubeMHSampler, CubeSliceSampler):
+                    logfilename = os.path.join(tempdir, 'test-stepsampler-%s.log' % adaptation)
+                    log = open(logfilename, 'w')
+                else:
+                    logfilename = None
+                    log = False
+                stepsampler = sampler_class(nsteps=len(paramnames), adaptive_nsteps=adaptation, log=log)
+                print(stepsampler)
+                stepsampler.region_changed(Ls, region)
+                np.random.seed(23)
+                old_scale = stepsampler.scale
+                for i in range(5):
+                    while True:
+                        unew, pnew, Lnew, nc = stepsampler.__next__(region, -1e100, region.u, Ls, transform, loglike)
+                        if unew is not None:
+                            break
+                new_scale = stepsampler.scale
+                assert new_scale != old_scale
+
+                if adaptation:
+                    assert stepsampler.nsteps != len(paramnames)
+                else:
+                    assert stepsampler.nsteps == len(paramnames)
+
+                if logfilename:
+                    print(np.loadtxt(logfilename).shape)
+                    log_nentries, log_ncolumns = np.loadtxt(logfilename).shape
+                    assert log_nentries == 5
+                    assert log_ncolumns == (1 + 4 * len(unew) + 7)
+
+                if adaptation == 'move-distance' and sampler_class == RegionSliceSampler and plot:
+                    # test plotting
+                    prefix = os.path.join(tempdir, 'test-stepsampler')
+                    assert not os.path.exists(prefix + '-plot.pdf')
+                    stepsampler.plot(prefix + '-plot.pdf')
+                    assert os.path.exists(prefix + '-plot.pdf')
+
+                    assert not os.path.exists(prefix + '-plot-jumps.pdf')
+                    stepsampler.plot_jump_diagnostic_histogram(prefix + '-plot-jumps.pdf')
+                    assert os.path.exists(prefix + '-plot-jumps.pdf')
 
 def assert_point_touches_ellipsoid(ucurrent, v, t, ellipsoid_center, ellipsoid_invcov, enlarge):
     unext = ucurrent + v * t
diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index 09a626c3..07601772 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -147,6 +147,7 @@ def run(self, **kwargs):
                 jump_distances = np.array([entry[i] for entry in sampler.stepsampler.logstat])
                 reference_distances = np.array([entry[j] for entry in sampler.stepsampler.logstat])
                 self.relsteps.append(jump_distances / reference_distances)
+            # TODO: handle population step samplers
 
             self.results.append(result)
             self.nsteps.append(nsteps)

From b6528686d52eb43eadf17870fc019de1b261e5a3 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 16 Feb 2024 16:29:13 +0100
Subject: [PATCH 216/313] =?UTF-8?q?Bump=20version:=204.1.2=20=E2=86=92=204?=
 =?UTF-8?q?.1.3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 3c74a0f2..0d6a31d1 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.1.2',
+    version='4.1.3',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 02b3abd7..860c78d9 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.1.2'
+__version__ = '4.1.3'

From 2966747418a52cde38677e896e0fb0ab814677b7 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 16 Feb 2024 19:56:04 +0100
Subject: [PATCH 217/313] test had mistake in path filename

---
 tests/test_popstepsampling.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 0a109a5f..b65ca6ba 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -58,7 +58,7 @@ def test_stepsampler_cubeslice(plot=False):
     with tempfile.TemporaryDirectory() as tempdir:
         prefix = os.path.join(tempdir, 'test-stepsampler')
         sampler.stepsampler.plot(prefix + '-plot.pdf')
-        assert os.path.exists('test-popstepsampler-plot.pdf')
+        assert os.path.exists(prefix + '-plot.pdf')
         sampler.stepsampler.plot_jump_diagnostic_histogram(prefix + '-plot-jumps.pdf')
         assert os.path.exists(prefix + '-plot-jumps.pdf')
         sampler.stepsampler.print_diagnostic()
@@ -86,7 +86,7 @@ def test_stepsampler_cubegausswalk(plot=False):
     with tempfile.TemporaryDirectory() as tempdir:
         prefix = os.path.join(tempdir, 'test-stepsampler')
         sampler.stepsampler.plot(prefix + '-plot.pdf')
-        assert os.path.exists('test-popstepsampler-plot.pdf')
+        assert os.path.exists(prefix + '-plot.pdf')
         sampler.stepsampler.plot_jump_diagnostic_histogram(prefix + '-plot-jumps.pdf')
         assert os.path.exists(prefix + '-plot-jumps.pdf')
         sampler.stepsampler.print_diagnostic()

From 23106953059856b25dd28a86d8552ebf75f591c4 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sat, 17 Feb 2024 06:35:55 +0100
Subject: [PATCH 218/313] =?UTF-8?q?Bump=20version:=204.1.3=20=E2=86=92=204?=
 =?UTF-8?q?.1.4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 0d6a31d1..5c42d9e1 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.1.3',
+    version='4.1.4',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 860c78d9..8fa48620 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.1.3'
+__version__ = '4.1.4'

From 152f31d462d23461ebf1f99d21e34f418cda2a41 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sat, 17 Feb 2024 22:12:08 +0100
Subject: [PATCH 219/313] =?UTF-8?q?Bump=20version:=204.1.4=20=E2=86=92=204?=
 =?UTF-8?q?.1.5?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 5c42d9e1..047e9460 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.1.4',
+    version='4.1.5',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 8fa48620..cce36728 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.1.4'
+__version__ = '4.1.5'

From 1b19db1c937b8f3603c4ca46a12659e650ec00d5 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 18 Feb 2024 19:24:09 +0100
Subject: [PATCH 220/313] correct dates

---
 HISTORY.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index a2ecd1ec..3bd51193 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,14 +2,14 @@
 Release Notes
 ==============
 
-4.1.0 (2023-02-15)
+4.1.0 (2024-02-15)
 ------------------
 
 * add number of steps calibrator :py:class:`ultranest.calibrator.ReactiveNestedCalibrator`
 * add relative jump distance diagnostic for step samplers
 * make population step samplers more consistent with other step samplers
 
-4.0.0 (2023-02-15)
+4.0.0 (2024-02-15)
 ------------------
 
 * replace :py:class:`ultranest.mlfriends.AffineLayer` with new :py:class:`ultranest.mlfriends.MaxPrincipleGapAffineLayer`

From 2111ea483ffb95158934cc0fd5aa7150a8ba7e33 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Fri, 23 Feb 2024 13:26:19 +0100
Subject: [PATCH 221/313] Change some notation and how the code was split
 between different functions in PopulationSimpleSliceSampler.

---
 examples/parser_res_sampler.py   |  97 +++++++++++++++++
 examples/test_PopSliceSampler.py |   2 +-
 tests/test_popstepsampling.py    |   5 +-
 ultranest/popstepsampler.py      | 172 +++++++++++++++++++++++--------
 4 files changed, 227 insertions(+), 49 deletions(-)
 create mode 100644 examples/parser_res_sampler.py

diff --git a/examples/parser_res_sampler.py b/examples/parser_res_sampler.py
new file mode 100644
index 00000000..aabe2cd4
--- /dev/null
+++ b/examples/parser_res_sampler.py
@@ -0,0 +1,97 @@
+
+import glob
+import numpy as np
+from astropy.table import Table
+files=glob.glob('slurm-*.out')
+type_likelihoods=[]
+type_sampler=[]
+dimensions=[]
+populations=[]
+logZs=[]
+logZ_errs=[]
+nb_call_likelihood=[]
+ESS=[]
+time=[]
+U_test_convergence=[]
+U_test_nb_it_corr=[]
+for file in files:
+    type_opti=False
+    logZ_founded=False
+    time_founded=False
+    ESS_founded=False
+    U_test_founded=False
+    nb_call_likelihood_founded=False
+    try:
+        lines=open(file).readlines()
+        for i in range(min(len(lines),300)):
+            if len(lines[-i-1])<2: continue
+            line=lines[-i-1][:-1]
+            #print(len(line))
+            if len(line)>10:
+                if line[:9]=="Namespace":
+                    name_dir=line.split("log_dir='")[1].split("',")[0]
+                    name_dir=name_dir.split("_")
+                    dimensions.append(int(name_dir[2]))
+                    type_likelihoods.append(name_dir[0])
+                    type_sampler.append(name_dir[1])
+                    populations.append(int(name_dir[4]))
+                    type_opti=True
+            if len(line)>4:
+                 if line[:4]=="real":
+                    time.append(float(line.split("real")[1]))
+                    time_founded=True
+            if len(line)>4:
+                 if line[:4]=="logZ":
+                    logz_all=line.split("logZ =")[1].split("+-")
+                    logZs.append(float(logz_all[0]))
+                    logZ_errs.append(float(logz_all[1]))
+                    logZ_founded=True
+            line_ESS="[ultranest] Effective samples strategy satisfied (ESS ="
+            if len(line)>len(line_ESS):
+                if line[:len(line_ESS)]==line_ESS:
+                    ESS.append(float(line.split(line_ESS)[1].split(",")[0]))
+                    ESS_founded=True
+            line_nb_call="[ultranest] Likelihood function evaluations:"
+            if len(line)>len(line_nb_call):
+                if line[:len(line_nb_call)]==line_nb_call and not nb_call_likelihood_founded:
+                    nb_call_likelihood.append(int(line.split(line_nb_call)[1]))
+                    nb_call_likelihood_founded=True
+            line_U_test="insert order U test :"
+            if len(line)>len(line_U_test):
+                if line[:len(line_U_test)]==line_U_test:
+                    U_test_conv=bool(line.split(line_U_test+" converged: ")[1].split("correlation")[0])
+                    nb_it_corr=line.split(line_U_test+" converged: ")[1].split("correlation:")[1].split("iterations")[0]
+                    nb_it_corr= np.inf if nb_it_corr==' inf ' else int(nb_it_corr)
+                    U_test_convergence.append(U_test_conv)
+                    U_test_nb_it_corr.append(nb_it_corr)
+                    U_test_founded=True
+            if type_opti and logZ_founded and time_founded and ESS_founded and nb_call_likelihood_founded and U_test_founded:
+                break
+        if not ESS_founded and type_opti and logZ_founded and time_founded and nb_call_likelihood_founded and U_test_founded:
+            ESS_founded=True
+            ESS.append(0)
+        if not type_opti or not logZ_founded or not time_founded or not ESS_founded or not nb_call_likelihood_founded or not U_test_founded:
+            if type_opti:
+                type_likelihoods.pop()
+                type_sampler.pop()
+                dimensions.pop()
+                populations.pop()
+            if logZ_founded:
+                logZs.pop()
+                logZ_errs.pop()
+            if time_founded:
+                time.pop()
+            if ESS_founded:
+                ESS.pop()
+            if nb_call_likelihood_founded:
+                nb_call_likelihood.pop()
+            if U_test_founded:
+                U_test_convergence.pop()
+                U_test_nb_it_corr.pop()
+        
+    except:
+        continue
+
+dict_Table={"type_likelihood":type_likelihoods,"type_sampler":type_sampler,"dimensions":dimensions,"populations":populations,"logZ":logZs,"logZ_err":logZ_errs,"nb_call_likelihood":nb_call_likelihood,"ESS":ESS,"time":time,"U_test_convergence":U_test_convergence,"U_test_nb_it_corr":U_test_nb_it_corr}
+print(len(type_likelihoods),len(type_sampler),len(dimensions),len(populations),len(logZs),len(logZ_errs),len(nb_call_likelihood),len(ESS),len(time),len(U_test_convergence),len(U_test_nb_it_corr))
+Table(dict_Table).write("results_sampler.fits",format="fits",overwrite=True)
diff --git a/examples/test_PopSliceSampler.py b/examples/test_PopSliceSampler.py
index 97f1db14..158d645e 100644
--- a/examples/test_PopSliceSampler.py
+++ b/examples/test_PopSliceSampler.py
@@ -100,7 +100,7 @@ def transform(x):
     if args.SimSlice:
         import ultranest.popstepsampler as ultrapop
         direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_mixture_random_direction,ultrapop.generate_differential_direction,ultrapop.generate_region_random_direction,ultrapop.generate_region_oriented_direction,ultrapop.generate_random_direction]
-        sampler.stepsampler = ultrapop.PopulationSimpleSliceSampler(popsize=args.popsize,nsteps=args.nstep,generate_direction=direction[args.direction],scale=1.0,scale_adapt_factor=1.0,scale_jitter=False)
+        sampler.stepsampler = ultrapop.PopulationSimpleSliceSampler(popsize=args.popsize,nsteps=args.nstep,generate_direction=direction[args.direction])
     if args.PopSlice:
         import ultranest.popstepsampler as ultrapop
         direction=[ultrapop.generate_cube_oriented_direction,ultrapop.generate_mixture_random_direction,ultrapop.generate_differential_direction,ultrapop.generate_region_random_direction,ultrapop.generate_region_oriented_direction,ultrapop.generate_random_direction]
diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index a3354d7d..85cebe62 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -54,16 +54,15 @@ def test_stepsampler_cubegausswalk(plot=False):
     assert a.sum() > 1
     assert b.sum() > 1
 
-def test_stepsampler_randomEllSlice(plot=False):
+def test_stepsampler_randomSimSlice(plot=False):
     np.random.seed(4)
     nsteps = np.random.randint(10, 50)
     popsize = np.random.randint(1, 20)
     sampler = ReactiveNestedSampler(paramnames, loglike_vectorized, transform=transform, vectorized=True)
 
-    sampler.stepsampler = PopulationEllipticalSliceSampler(
+    sampler.stepsampler = PopulationSimpleSliceSampler(
         popsize=popsize, nsteps=nsteps, 
         generate_direction=generate_random_direction,
-        scale=1.0,
     )
     r = sampler.run(viz_callback=None, log_interval=50, max_iters=200, max_num_improvement_loops=0)
     sampler.print_results()
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 3e6e03b4..7322ca1f 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -536,28 +536,93 @@ def __next__(
         else:
             return None, None, None, nc
 
+
+
+
+def slice_limit_to_unitcube(tleft, tright):
+    
+    """
+    return the slice limits as a copy of intersection between the slice and the unit cube boundaries
+
+
+    parameters
+    ----------
+        tleft: float
+                Intersection of the slice and the unit cube boundaries in the inverse direction of the slice
+        tright: float
+                Intersection of the slice and the unit cube boundaries in the direction of the slice
+    Returns
+    -------
+        (tleft_new,tright_new): tuple
+                Positive and negative slice limits
+    """
+    tleft_new,tright_new = tleft.copy(),tright.copy()
+    return (tleft_new,tright_new)
+
+
+def slice_limit_to_scale(tleft, tright):
+
+    """
+    return the slice limits as an interval of size `2*scale` or the intersection between the slice and the unit cube boundaries
+    if the interval is larger than the unit cube boundaries.
+
+    parameters
+    ----------
+        tleft: float
+                Intersection of the slice and the unit cube boundaries in the inverse direction of the slice
+        tright: float
+                Intersection of the slice and the unit cube boundaries in the direction of the slice
+    Returns
+    -------
+        (tleft_new,tright_new): tuple
+                Positive and negative slice limits
+    """
+
+    
+    tleft_new = np.fmax(tleft,-1.+np.zeros_like(tleft)).copy()
+    tright_new = np.fmin(tright,1.+np.zeros_like(tright)).copy()
+    return (tleft_new,tright_new)
+
+
+
 class PopulationSimpleSliceSampler():
     """
-       Vectorized Slice sampler without stepping out procedure.
-       In Comparison the PopulationSliceSampler, the sampler calls the likelihood on
-       batch of points of the same size.
+       Vectorized Slice sampler without stepping out procedure for quick look fits.
+       Unlike `:py:class:PopulationSliceSampler`, in `:py:class:PopulationSimpleSliceSampler`,
+       the likelihood is always called with the same number of points.
 
-       Sliced are defined by the generate_direction function on a interval defined
-       around the current point. The centred interval has the width of the scale parameter.
+       Sliced are defined by the `:py:func:generate_direction` function on a interval defined
+       around the current point. The centred interval has the width of the scale parameter,
+       i.e, there is no stepping out procedure as in `:py:class:PopulationSliceSampler`.
        Slices are then shrink towards the current point until a point is found with a
        likelihood above the threshold.
 
-       A slice can be searched with more than one point at a time. In that case, we 
-       read points as if they were the next selected each after the other. For a points
-       to update the slice, it needs to be still in the part of the slices searched after
-       the first point have been read. In that case, we update as normal, otherwise we
-       discard the point.
+       In the default case, i.e. `scale=None`, the slice width is defined as the
+       intersection between itself and the unit cube. To improve the efficiency of the sampler,
+       the slice can be reduced to an interval of size `2*scale` centred on the point. `scale`
+       can be adapted with the `scale_adapt_factor` parameter based on the median distance 
+       between the current and the next point in a chains among all the chains. If the median
+       distance is above `scale/adapt_slice_scale_target`, the scale is increased by `scale_adapt_factor`,
+       and decreased otherwise. The `scale` parameter can also be jittered by a user supplied
+       function `:py:func:scale_jitter_func` to counter balance the effect of a strong adaptation.
+
+       In the case `scale!=None`, the detailed balance is not guaranteed, so this sampler should
+       be use with caution.
+
+       Multiple (`popsize`) slice sampling chains are run independently and in parallel. 
+       In that case, we read points as if they were the next selected each after the other.
+       For a points to update the slice, it needs to be still in the part of the slices
+       searched after the first point have been read. In that case, we update as normal, 
+       otherwise we discard the point.
+
 
     """
 
     def __init__(
-        self, popsize, nsteps,scale, generate_direction
-        ,scale_adapt_factor=1.0, slice_size=2.0, scale_jitter=False):
+        self, popsize, nsteps, generate_direction,
+        scale_adapt_factor=1.0, adapt_slice_scale_target=2.0,
+        scale=None, scale_jitter_func=None,slice_limit=slice_limit_to_unitcube,
+        max_it=100):
         """Initialise.
 
         Parameters
@@ -577,43 +642,59 @@ def __init__(
             :py:func:`ultranest.popstepsampler.generate_mixture_random_direction`
             :py:func:`ultranest.popstepsampler.generate_cube_oriented_direction` -> no adaptation in that case
             :py:func:`ultranest.popstepsampler.generate_cube_oriented_direction_scaled` -> no adaptation in that case
+        scale_jitter_func: function
+            User supplied function to multiply the `scale` by a random factor. For example, 
+            :py:func:`lambda : scipy.stats.truncnorm.rvs(-0.5, 5., loc=0, scale=1)+1.`
         scale: float
-            initial guess for the proposal scaling factor
+            initial guess for the slice width. If None, the slice width is defined as the
+            intersection between itself and the unit cube.
         scale_adapt_factor: float
             if 1, no adapting is done.
             if <1, the scale is increased if the slice final size is under 1/2 the scale
             or decreased if it is above, by *scale_adapt_factor*.
-        slice_size: float
-            size of the slice in units of distance between the previous and next point.
+        adapt_slice_scale_target: float
+            Target size of the median distance between a current point and the next one among all chains in unit of
+            `scale`. If the median distance is above `scale/adapt_slice_scale_target`, the scale is increased by 
+            `scale_adapt_factor`, and decreased otherwise.
+        slice_limit: function
+            function to define the slice limit based on the intersection of the slice and the unit cube. The slice
+            limit are defined in unit of `scale`. The default is `:py:func:slice_limit_to_unitcube` which defines
+            the slice limit as the intersection between the slice and the unit cube. An alternative when the `scale`
+            is used is `:py:func:slice_limit_to_scale` which defines the slice limit as an interval of size `2*scale`.
+            This function should return a copy of the `tleft` and `tright` or another arrays of the same shape. 
+        max_it: int
+            maximum number of iterations to find a point on the slice. If the maximum number of iterations is reached,
+            the current point is returned as the next one.
         """
         self.nsteps = nsteps
         
-        
+        self.max_it=max_it
         self.nrejects = 0
         self.generate_direction =  generate_direction
         self.scale_adapt_factor = scale_adapt_factor
         self.ncalls = 0
         self.throwed=0
-        self.scale = scale
-        self.slice_size=slice_size
-       
-        if scale_jitter:
-            self.scale_jitter_func= lambda : scipy.stats.truncnorm.rvs(-0.5, 5., loc=0, scale=1)+1.
+        if scale is None:
+            self.scale = 1.0
+        else:
+            self.scale = scale
+        self.adapt_slice_scale_target=adapt_slice_scale_target
+        
+        if scale_jitter_func is None:
+            self.scale_jitter_func= lambda : 1.
         else:
-            self.scale_jitter_func= lambda : 1.         
+            self.scale_jitter_func= scale_jitter_func      
         self.prepared_samples = []
         self.popsize = popsize
-        if self.scale_adapt_factor!=1.:
-            self.slice_limit=lambda tleft,tright:np.fmax(tleft,-1.+np.zeros(self.popsize)),np.fmin(tright,1.+np.zeros(self.popsize))
-        else:
-            self.slice_limit=lambda tleft,tright:tleft,tright
+        
+        self.slice_limit=slice_limit
 
         
         
 
     def __str__(self):
         """Return string representation."""
-        return 'PopulationEllipticalSliceSampler(popsize=%d, nsteps=%d, generate_direction=%s, scale=%.g)' % (
+        return 'PopulationSimpleSliceSampler(popsize=%d, nsteps=%d, generate_direction=%s, scale=%.g)' % (
             self.popsize, self.nsteps, self.generate_direction, self.scale)
 
     def region_changed(self, Ls, region):
@@ -685,40 +766,41 @@ def __next__(
                 # Defining slice direction
                 v = self.generate_direction(allu, region,scale= 1.0)*self.scale*factor_scale
                 # limite of the slice based on the unit cube boundaries
-                tleft,tright= unitcube_line_intersection(allu, v)
+                tleft_unitcube,tright_unitcube= unitcube_line_intersection(allu, v)
                 # Defining bound of the slice
-                Theta_min_worker,Theta_max_worker = self.slice_limit(tleft,tright)
-                Theta_min,Theta_max=Theta_min_worker.copy(),Theta_max_worker.copy()
+                tleft_worker,tright_worker = self.slice_limit(tleft_unitcube,tright_unitcube)
+                tleft,tright = self.slice_limit(tleft_unitcube,tright_unitcube)
                 # Index of the workers working concurrently
-                worker=np.arange(0,self.popsize,1,dtype=int)
+                worker_running=np.arange(0,self.popsize,1,dtype=int)
                 # Status indicating if a points has already find its next position
                 status=np.zeros(self.popsize,dtype=int) # one for success, zero for running
                 
                 # Loop until each points has found its next position or we reached 100 iterations
-                loop_n=0
-                while (status==0).any() and loop_n<100:
+                
+                for it in range(self.max_it):
                 
                     # Sampling points on the slices
-                    Theta=Theta_min_worker+(Theta_max_worker-Theta_min_worker)*np.random.uniform(size=(self.popsize,))
+                    t=tleft_worker+(tright_worker-tleft_worker)*np.random.uniform(size=(self.popsize,))
                 
-                    points=allu[worker,:]
-                    v_worker=v[worker,:]
-                    proposed_u=points+Theta.reshape((-1,1))*v_worker
+                    points=allu[worker_running,:]
+                    v_worker=v[worker_running,:]
+                    proposed_u=points+t.reshape((-1,1))*v_worker
                 
                     proposed_p = transform(proposed_u)
                     proposed_L = loglike(proposed_p)
                     nc+=self.popsize
                     # Updating the pool of points based on the newly sampled points
-                    Theta_min,Theta_max,worker,status,allu,allL,allp,nth=update_vectorised_slice_sampler(\
-                    Theta,Theta_min,Theta_max,proposed_L,proposed_u,proposed_p,worker,status,Lmin\
+                    tleft,tright,worker_running,status,allu,allL,allp,nth=update_vectorised_slice_sampler(\
+                    t,tleft,tright,proposed_L,proposed_u,proposed_p,worker_running,status,Lmin\
                     ,allu,allL,allp,self.popsize)
                     n_throws+=nth
                     # Update of the limits of the slices
-                    Theta_min_worker=Theta_min[worker]
-                    Theta_max_worker=Theta_max[worker]
-                    loop_n+=1
+                    tleft_worker=tleft[worker_running]
+                    tright_worker=tright[worker_running]
+                    if not np.any(status==0):
+                        break
                 # Record of the final interval on theta for scale adaptation
-                interval_final+=np.median(Theta_max-Theta_min)
+                interval_final+=np.median(tright-tleft)
 
 
             
@@ -728,14 +810,14 @@ def __next__(
             self.throwed+=n_throws
             self.ncalls+=nc
             
-            assert np.array([p!=np.zeros(ndim) for p in allp]).all(), 'some walkers never moved! Double nsteps of PopulationEllipticalSliceSampler.'
+            assert np.array([p!=np.zeros(ndim) for p in allp]).all(), 'some walkers never moved! Double nsteps of PopulationSimpleSliceSampler.'
             self.prepared_samples = list(zip(allu, allp, allL))
 
             
             # Scale adaptation such that the final interval is
             # half the scale. There may be better things to do 
             # here, but it seems to work.
-            if interval_final>=1./self.slice_size:
+            if interval_final>=1./self.adapt_slice_scale_target:
                 self.scale *= 1./self.scale_adapt_factor
             else:
                 self.scale *= self.scale_adapt_factor

From eb75841207c0b54b91477ce02be5a3336f7f3347 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Fri, 23 Feb 2024 13:31:02 +0100
Subject: [PATCH 222/313] Clarify the count of the likelihood call that are not
 used

---
 ultranest/popstepsampler.py |  6 ++---
 ultranest/stepfuncs.pyx     | 52 ++++++++++++++++++-------------------
 2 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 7322ca1f..9487eeb9 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -754,7 +754,7 @@ def __next__(
             allp = np.zeros((self.popsize, ndim))
             allL = np.array(Ls[ilive])
             nc = 0
-            n_throws=0
+            n_discarded=0
                                        
                 
             
@@ -790,10 +790,10 @@ def __next__(
                     proposed_L = loglike(proposed_p)
                     nc+=self.popsize
                     # Updating the pool of points based on the newly sampled points
-                    tleft,tright,worker_running,status,allu,allL,allp,nth=update_vectorised_slice_sampler(\
+                    tleft,tright,worker_running,status,allu,allL,allp,n_discarded_it=update_vectorised_slice_sampler(\
                     t,tleft,tright,proposed_L,proposed_u,proposed_p,worker_running,status,Lmin\
                     ,allu,allL,allp,self.popsize)
-                    n_throws+=nth
+                    n_discarded+=n_discarded_it
                     # Update of the limits of the slices
                     tleft_worker=tleft[worker_running]
                     tright_worker=tright[worker_running]
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index dbce3fc3..b5a142f9 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -530,10 +530,10 @@ def generate_mixture_random_direction(ui, region, scale=1):
 @cython.boundscheck(False)
 @cython.wraparound(False)
 cpdef tuple update_vectorised_slice_sampler(\
-                          np.ndarray[np.float_t, ndim=1] Theta, np.ndarray[np.float_t, ndim=1] Theta_min,\
-                          np.ndarray[np.float_t, ndim=1] Theta_max, np.ndarray[np.float_t, ndim=1] proposed_L,\
+                          np.ndarray[np.float_t, ndim=1] t, np.ndarray[np.float_t, ndim=1] tleft,\
+                          np.ndarray[np.float_t, ndim=1] tright, np.ndarray[np.float_t, ndim=1] proposed_L,\
                           np.ndarray[np.float_t, ndim=2] proposed_u, np.ndarray[np.float_t, ndim=2] proposed_p,\
-                          np.ndarray[np.int_t, ndim=1] worker, np.ndarray[np.int_t, ndim=1] status,\
+                          np.ndarray[np.int_t, ndim=1] worker_running, np.ndarray[np.int_t, ndim=1] status,\
                           np.float_t Likelihood_threshold, np.ndarray[np.float_t, ndim=2] allu,\
                           np.ndarray[np.float_t, ndim=1] allL, np.ndarray[np.float_t, ndim=2] allp, int popsize):
 
@@ -541,11 +541,11 @@ cpdef tuple update_vectorised_slice_sampler(\
 
     Parameters
     -----------
-    Theta: array
+    t: array
         proposed slice coordinate
-    Theta_min: array
+    tleft: array
         current slice negative end
-    Theta_max: array
+    tright: array
         current slice positive end
     proposed_L: array
         log-likelihood of proposed point
@@ -553,7 +553,7 @@ cpdef tuple update_vectorised_slice_sampler(\
         proposed point in unit cube space
     proposed_p: array
         proposed point in transformed space
-    worker: array
+    worker_running: array
         index of the point associated with each worker
     status: array
         integer status of the point
@@ -570,11 +570,11 @@ cpdef tuple update_vectorised_slice_sampler(\
 
     Returns
     --------
-    Theta_min: array
+    tleft: array
         updated current slice negative end
-    Theta_max: array
+    tright: array
         updated current slice positive end
-    worker: array
+    worker_running: array
         updated index of the point associated with each worker
     status: array
         updated integer status of the point
@@ -584,32 +584,32 @@ cpdef tuple update_vectorised_slice_sampler(\
         updated log-likelihoods of accepted points
     allp: array
         updated accepted points in transformed space
-    throwed: int
-        number of points that were rejected because they were outside the slice
+    discarded: int
+        Point where the likelihood was evaluated but was not taken into account.
     """
                             
     cdef int j, k
-    cdef throwed = 0
+    cdef discarded = 0
     for l in range(popsize):
-        if Theta[l] > Theta_max[worker[l]] or Theta[l] < Theta_min[worker[l]]:
+        if t[l] > tright[worker_running[l]] or t[l] < tleft[worker_running[l]]:
             if proposed_L[l]>Likelihood_threshold:
-                throwed+=1
+                discarded+=1
             continue
-        if 0 < Theta[l] < Theta_max[worker[l]]:
-            Theta_max[worker[l]] = Theta[l]
-        if 0 > Theta[l] > Theta_min[worker[l]]:
-            Theta_min[worker[l]] = Theta[l]
-        if proposed_L[l] > Likelihood_threshold and status[worker[l]] == 0:
-            status[worker[l]] = 1
-            allu[worker[l], :] = proposed_u[l, :]
-            allL[worker[l]] = proposed_L[l]
-            allp[worker[l], :] = proposed_p[l, :]
+        if 0 < t[l] < tright[worker_running[l]]:
+            tright[worker_running[l]] = t[l]
+        if 0 > t[l] > tright[worker_running[l]]:
+            tright[worker_running[l]] = t[l]
+        if proposed_L[l] > Likelihood_threshold and status[worker_running[l]] == 0:
+            status[worker_running[l]] = 1
+            allu[worker_running[l], :] = proposed_u[l, :]
+            allL[worker_running[l]] = proposed_L[l]
+            allp[worker_running[l], :] = proposed_p[l, :]
 
     j = 0
     while j < popsize and (status == 0).any():
         for k in range(popsize):
             if status[k] == 0 and j < popsize:
-                worker[j] = k
+                worker_running[j] = k
                 j += 1
 
-    return (Theta_min, Theta_max, worker, status, allu, allL, allp,throwed)
+    return (tleft, tright, worker_running, status, allu, allL, allp,discarded)

From 51268a2906de71699db419f234ed23ab30b08b42 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Fri, 23 Feb 2024 13:40:29 +0100
Subject: [PATCH 223/313] Remove a file use for testing

---
 examples/parser_res_sampler.py | 97 ----------------------------------
 1 file changed, 97 deletions(-)
 delete mode 100644 examples/parser_res_sampler.py

diff --git a/examples/parser_res_sampler.py b/examples/parser_res_sampler.py
deleted file mode 100644
index aabe2cd4..00000000
--- a/examples/parser_res_sampler.py
+++ /dev/null
@@ -1,97 +0,0 @@
-
-import glob
-import numpy as np
-from astropy.table import Table
-files=glob.glob('slurm-*.out')
-type_likelihoods=[]
-type_sampler=[]
-dimensions=[]
-populations=[]
-logZs=[]
-logZ_errs=[]
-nb_call_likelihood=[]
-ESS=[]
-time=[]
-U_test_convergence=[]
-U_test_nb_it_corr=[]
-for file in files:
-    type_opti=False
-    logZ_founded=False
-    time_founded=False
-    ESS_founded=False
-    U_test_founded=False
-    nb_call_likelihood_founded=False
-    try:
-        lines=open(file).readlines()
-        for i in range(min(len(lines),300)):
-            if len(lines[-i-1])<2: continue
-            line=lines[-i-1][:-1]
-            #print(len(line))
-            if len(line)>10:
-                if line[:9]=="Namespace":
-                    name_dir=line.split("log_dir='")[1].split("',")[0]
-                    name_dir=name_dir.split("_")
-                    dimensions.append(int(name_dir[2]))
-                    type_likelihoods.append(name_dir[0])
-                    type_sampler.append(name_dir[1])
-                    populations.append(int(name_dir[4]))
-                    type_opti=True
-            if len(line)>4:
-                 if line[:4]=="real":
-                    time.append(float(line.split("real")[1]))
-                    time_founded=True
-            if len(line)>4:
-                 if line[:4]=="logZ":
-                    logz_all=line.split("logZ =")[1].split("+-")
-                    logZs.append(float(logz_all[0]))
-                    logZ_errs.append(float(logz_all[1]))
-                    logZ_founded=True
-            line_ESS="[ultranest] Effective samples strategy satisfied (ESS ="
-            if len(line)>len(line_ESS):
-                if line[:len(line_ESS)]==line_ESS:
-                    ESS.append(float(line.split(line_ESS)[1].split(",")[0]))
-                    ESS_founded=True
-            line_nb_call="[ultranest] Likelihood function evaluations:"
-            if len(line)>len(line_nb_call):
-                if line[:len(line_nb_call)]==line_nb_call and not nb_call_likelihood_founded:
-                    nb_call_likelihood.append(int(line.split(line_nb_call)[1]))
-                    nb_call_likelihood_founded=True
-            line_U_test="insert order U test :"
-            if len(line)>len(line_U_test):
-                if line[:len(line_U_test)]==line_U_test:
-                    U_test_conv=bool(line.split(line_U_test+" converged: ")[1].split("correlation")[0])
-                    nb_it_corr=line.split(line_U_test+" converged: ")[1].split("correlation:")[1].split("iterations")[0]
-                    nb_it_corr= np.inf if nb_it_corr==' inf ' else int(nb_it_corr)
-                    U_test_convergence.append(U_test_conv)
-                    U_test_nb_it_corr.append(nb_it_corr)
-                    U_test_founded=True
-            if type_opti and logZ_founded and time_founded and ESS_founded and nb_call_likelihood_founded and U_test_founded:
-                break
-        if not ESS_founded and type_opti and logZ_founded and time_founded and nb_call_likelihood_founded and U_test_founded:
-            ESS_founded=True
-            ESS.append(0)
-        if not type_opti or not logZ_founded or not time_founded or not ESS_founded or not nb_call_likelihood_founded or not U_test_founded:
-            if type_opti:
-                type_likelihoods.pop()
-                type_sampler.pop()
-                dimensions.pop()
-                populations.pop()
-            if logZ_founded:
-                logZs.pop()
-                logZ_errs.pop()
-            if time_founded:
-                time.pop()
-            if ESS_founded:
-                ESS.pop()
-            if nb_call_likelihood_founded:
-                nb_call_likelihood.pop()
-            if U_test_founded:
-                U_test_convergence.pop()
-                U_test_nb_it_corr.pop()
-        
-    except:
-        continue
-
-dict_Table={"type_likelihood":type_likelihoods,"type_sampler":type_sampler,"dimensions":dimensions,"populations":populations,"logZ":logZs,"logZ_err":logZ_errs,"nb_call_likelihood":nb_call_likelihood,"ESS":ESS,"time":time,"U_test_convergence":U_test_convergence,"U_test_nb_it_corr":U_test_nb_it_corr}
-print(len(type_likelihoods),len(type_sampler),len(dimensions),len(populations),len(logZs),len(logZ_errs),len(nb_call_likelihood),len(ESS),len(time),len(U_test_convergence),len(U_test_nb_it_corr))
-Table(dict_Table).write("results_sampler.fits",format="fits",overwrite=True)

From 1555cb5de6e57937b3c236a99248f0faf73c2993 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Mon, 26 Feb 2024 10:44:02 +0100
Subject: [PATCH 224/313] correct typo

---
 ultranest/popstepsampler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 9487eeb9..5ced1b47 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -807,7 +807,7 @@ def __next__(
             interval_final=interval_final/self.nsteps
             
             
-            self.throwed+=n_throws
+            self.discarded+=n_discarded
             self.ncalls+=nc
             
             assert np.array([p!=np.zeros(ndim) for p in allp]).all(), 'some walkers never moved! Double nsteps of PopulationSimpleSliceSampler.'

From f432b3d45960a8a41306be0521a643f0361acee8 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Mon, 26 Feb 2024 13:54:20 +0100
Subject: [PATCH 225/313] Finish to correct the previous typo

---
 ultranest/popstepsampler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 5ced1b47..48412e17 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -673,7 +673,7 @@ def __init__(
         self.generate_direction =  generate_direction
         self.scale_adapt_factor = scale_adapt_factor
         self.ncalls = 0
-        self.throwed=0
+        self.discarded=0
         if scale is None:
             self.scale = 1.0
         else:

From cfa3cdde887d57958a599c590dbb92f4333e389d Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Wed, 28 Feb 2024 14:18:05 +0100
Subject: [PATCH 226/313] Correct a typo in the update of the slice bounds, the
 code was only shrinking on one axes and not correctly. Added a shrinking
 factor to increase the shrinking speed

---
 ultranest/popstepsampler.py | 16 ++++++++++++----
 ultranest/stepfuncs.pyx     | 10 ++++++----
 2 files changed, 18 insertions(+), 8 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 48412e17..3e7e0f89 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -622,7 +622,7 @@ def __init__(
         self, popsize, nsteps, generate_direction,
         scale_adapt_factor=1.0, adapt_slice_scale_target=2.0,
         scale=None, scale_jitter_func=None,slice_limit=slice_limit_to_unitcube,
-        max_it=100):
+        max_it=100,shrink_factor=1.0):
         """Initialise.
 
         Parameters
@@ -665,6 +665,10 @@ def __init__(
         max_it: int
             maximum number of iterations to find a point on the slice. If the maximum number of iterations is reached,
             the current point is returned as the next one.
+        shrink_factor: float
+            During the shrinking procedure, a new slice bound is set to the last tested point that don't respect the Lmin
+            condition if `shrink_factor` is set to 1.0. If its value is larger than 1.0, the new slice bound is set to the
+            `1/shrink_factor` of the distance between the current point and the last tested point that don't respect the Lmin condition.
         """
         self.nsteps = nsteps
         
@@ -674,6 +678,8 @@ def __init__(
         self.scale_adapt_factor = scale_adapt_factor
         self.ncalls = 0
         self.discarded=0
+        self.shrink_factor=shrink_factor
+        assert shrink_factor>=1.0, "The shrink factor should be greater than 1.0 to be efficient"
         if scale is None:
             self.scale = 1.0
         else:
@@ -765,6 +771,8 @@ def __next__(
                 factor_scale=self.scale_jitter_func()
                 # Defining slice direction
                 v = self.generate_direction(allu, region,scale= 1.0)*self.scale*factor_scale
+                
+                
                 # limite of the slice based on the unit cube boundaries
                 tleft_unitcube,tright_unitcube= unitcube_line_intersection(allu, v)
                 # Defining bound of the slice
@@ -781,7 +789,7 @@ def __next__(
                 
                     # Sampling points on the slices
                     t=tleft_worker+(tright_worker-tleft_worker)*np.random.uniform(size=(self.popsize,))
-                
+                    
                     points=allu[worker_running,:]
                     v_worker=v[worker_running,:]
                     proposed_u=points+t.reshape((-1,1))*v_worker
@@ -791,8 +799,8 @@ def __next__(
                     nc+=self.popsize
                     # Updating the pool of points based on the newly sampled points
                     tleft,tright,worker_running,status,allu,allL,allp,n_discarded_it=update_vectorised_slice_sampler(\
-                    t,tleft,tright,proposed_L,proposed_u,proposed_p,worker_running,status,Lmin\
-                    ,allu,allL,allp,self.popsize)
+                    t,tleft,tright,proposed_L,proposed_u,proposed_p,worker_running,status,Lmin,self.shrink_factor,\
+                    allu,allL,allp,self.popsize)
                     n_discarded+=n_discarded_it
                     # Update of the limits of the slices
                     tleft_worker=tleft[worker_running]
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index b5a142f9..44e7f2d5 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -534,7 +534,7 @@ cpdef tuple update_vectorised_slice_sampler(\
                           np.ndarray[np.float_t, ndim=1] tright, np.ndarray[np.float_t, ndim=1] proposed_L,\
                           np.ndarray[np.float_t, ndim=2] proposed_u, np.ndarray[np.float_t, ndim=2] proposed_p,\
                           np.ndarray[np.int_t, ndim=1] worker_running, np.ndarray[np.int_t, ndim=1] status,\
-                          np.float_t Likelihood_threshold, np.ndarray[np.float_t, ndim=2] allu,\
+                          np.float_t Likelihood_threshold,np.float_t shrink_factor, np.ndarray[np.float_t, ndim=2] allu,\
                           np.ndarray[np.float_t, ndim=1] allL, np.ndarray[np.float_t, ndim=2] allp, int popsize):
 
     """Update the slice sampler state of each walker in the populations.
@@ -559,6 +559,8 @@ cpdef tuple update_vectorised_slice_sampler(\
         integer status of the point
     Likelihood_threshold: float
         current log-likelihood threshold
+    shrink_factor: float
+        factor by which to shrink the slice
     allu: array
         Accepted points in unit cube space
     allL: array
@@ -596,9 +598,9 @@ cpdef tuple update_vectorised_slice_sampler(\
                 discarded+=1
             continue
         if 0 < t[l] < tright[worker_running[l]]:
-            tright[worker_running[l]] = t[l]
-        if 0 > t[l] > tright[worker_running[l]]:
-            tright[worker_running[l]] = t[l]
+            tright[worker_running[l]] = t[l]/shrink_factor
+        if 0 > t[l] > tleft[worker_running[l]]:
+            tleft[worker_running[l]] = t[l]/shrink_factor
         if proposed_L[l] > Likelihood_threshold and status[worker_running[l]] == 0:
             status[worker_running[l]] = 1
             allu[worker_running[l], :] = proposed_u[l, :]

From 6a0286ab7c9bd9b452aa3ea00b931b07eddfc21e Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Thu, 29 Feb 2024 14:30:05 +0100
Subject: [PATCH 227/313] Remove the assertion that the live point number
 should be larger than the population of the sampler. It breaks in case of
 likelihood plateau

---
 ultranest/popstepsampler.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 3e7e0f89..827f5794 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -751,11 +751,11 @@ def __next__(
         """
         nlive, ndim = us.shape
         
-        assert nlive>=self.popsize, "The number of live points should be greater than the population size" 
+         
         # fill if empty:
         if len(self.prepared_samples) == 0:
             # choose live points
-            ilive = np.random.choice(nlive, size=self.popsize, replace=False)
+            ilive = np.random.randint(0, nlive, size=self.popsize)
             allu = np.array(us[ilive,:])
             allp = np.zeros((self.popsize, ndim))
             allL = np.array(Ls[ilive])

From 503bc3ed60cad68e553a3953c51dac10ad87f9bc Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 3 Mar 2024 13:11:35 +0100
Subject: [PATCH 228/313] avoid empty warnings; strip whitespace

---
 ultranest/stepsampler.py | 18 +++++++++---------
 ultranest/viz.py         |  4 ++--
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 640951b9..7e5efc5e 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -578,7 +578,7 @@ def __init__(
             :py:func:`generate_cube_oriented_direction` works well too.
 
         adaptive_nsteps: False or str
-            Strategy to adapt the number of steps. 
+            Strategy to adapt the number of steps.
             The possible values are the same as for `check_nsteps`.
 
             Adapting can give usable results. However, strictly speaking,
@@ -612,7 +612,7 @@ def __init__(
               between pairs of live points.
 
             Each step sampler walk adds one row to stepsampler.logstat.
-            The jump distance (forth column) should be compared to 
+            The jump distance (forth column) should be compared to
             the reference distance (fifth column).
 
         max_nsteps: int
@@ -676,7 +676,7 @@ def __init__(
         self.mean_pair_distance = np.nan
         self.region_filter = region_filter
         if log:
-            assert hasattr(log, 'write'), 'log argument should be a file, use log=open(filename, "w") or similar' 
+            assert hasattr(log, 'write'), 'log argument should be a file, use log=open(filename, "w") or similar'
         self.log = log
 
         self.logstat = []
@@ -751,9 +751,9 @@ def far_enough_fraction(self):
     def get_info_dict(self):
         return dict(
             num_logs=len(self.logstat),
-            rejection_rate=np.nanmean([entry[0] for entry in self.logstat]),
-            mean_scale=np.nanmean([entry[1] for entry in self.logstat]),
-            mean_nsteps=np.nanmean([entry[2] for entry in self.logstat]),
+            rejection_rate=np.nanmean([entry[0] for entry in self.logstat]) if len(self.logstat) > 0 else np.nan,
+            mean_scale=np.nanmean([entry[1] for entry in self.logstat]) if len(self.logstat) > 0 else np.nan,
+            mean_nsteps=np.nanmean([entry[2] for entry in self.logstat]) if len(self.logstat) > 0 else np.nan,
             mean_distance=self.mean_jump_distance,
             frac_far_enough=self.far_enough_fraction,
             last_logstat=dict(zip(self.logstat_labels, self.logstat[-1] if len(self.logstat) > 1 else [np.nan] * len(self.logstat_labels)))
@@ -767,7 +767,7 @@ def print_diagnostic(self):
             return
         if 'jump-distance' not in self.logstat_labels or 'reference-distance' not in self.logstat_labels:
             print("turn on check_nsteps in the step sampler for diagnostics")
-            return 
+            return
         frac_farenough = self.far_enough_fraction
         average_distance = self.mean_jump_distance
         if frac_farenough < 0.5:
@@ -784,9 +784,9 @@ def plot_jump_diagnostic_histogram(self, filename, **kwargs):
         if len(self.logstat) == 0:
             return
         if 'jump-distance' not in self.logstat_labels:
-            return 
+            return
         if 'reference-distance' not in self.logstat_labels:
-            return 
+            return
         i = self.logstat_labels.index('jump-distance')
         j = self.logstat_labels.index('reference-distance')
         jump_distances = np.array([entry[i] for entry in self.logstat])
diff --git a/ultranest/viz.py b/ultranest/viz.py
index 277179ef..ebf77962 100644
--- a/ultranest/viz.py
+++ b/ultranest/viz.py
@@ -141,8 +141,8 @@ def nicelogger(points, info, region, transformLayer, region_fresh=False):
     )
     if info.get('stepsampler_info', {}).get('num_logs', 0) > 0:
         print(
-            'Step sampler performance: %(rejection_rate).1f%% rej/step, %(mean_nsteps)d steps/it' % (info['stepsampler_info']),
-            ('mean rel jump distance: %.2f (should be >1), %.2f%% (should be >50%%)' % (
+            ('Step sampler performance: %(rejection_rate).1f rej/step, %(mean_nsteps)d steps/it' % (info['stepsampler_info'])) +
+            (', rel jump distance: %.2f (should be >1), %.2f%% (should be >50%%)' % (
                 info['stepsampler_info']['mean_distance'], 100 * info['stepsampler_info']['frac_far_enough']
             )) if 'mean_distance' in info['stepsampler_info'] else ''
         )

From bb067d2d9a6af761adabfb4f784b632a7b323975 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 4 Mar 2024 00:20:04 +0100
Subject: [PATCH 229/313] =?UTF-8?q?Bump=20version:=204.1.5=20=E2=86=92=204?=
 =?UTF-8?q?.1.6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 047e9460..52fec46a 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.1.5',
+    version='4.1.6',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index cce36728..eb5843fd 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.1.5'
+__version__ = '4.1.6'

From 00338bb3f360979f4706441532f4d67addb47cf7 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Mar 2024 23:41:45 +0100
Subject: [PATCH 230/313] closes
 https://github.com/JohannesBuchner/UltraNest/issues/123

inf can occur if all iterations have equal weight, which can
occur if the likelihood is weirdly uninformative.
This patch avoids this case.

The bug only occurred when max_num_improvement_loops>0 and on ARM,
and probably for somewhat special likelihoods.
---
 ultranest/integrator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 072c76e3..b0e125aa 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1720,7 +1720,7 @@ def _find_strategy(self, saved_logl, main_iterator, dlogz, dKL, min_ess):
             with np.errstate(divide='ignore', invalid='ignore'):
                 widthratio = 1 - np.exp(logweights[1:,0] - logweights[:-1,0])
                 nlive = 1. / np.log((1 - np.sqrt(1 - 4 * widthratio)) / (2 * widthratio))
-                nlive[~(nlive > 1)] = 1
+                nlive[~np.logical_and(np.isfinite(nlive), nlive > 1)] = 1
 
             # build iteration groups
             nlive_sets, niter = np.unique(nlive.astype(int), return_counts=True)

From b568f24319f072073ed50005de9584e78da88c6c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 5 Mar 2024 23:45:31 +0100
Subject: [PATCH 231/313] =?UTF-8?q?Bump=20version:=204.1.6=20=E2=86=92=204?=
 =?UTF-8?q?.1.7?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 52fec46a..c508166f 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.1.6',
+    version='4.1.7',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index eb5843fd..6bb5aa26 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.1.6'
+__version__ = '4.1.7'

From 0199c874ced963001e715bcad615e3678f365300 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 20 Mar 2024 00:32:59 +0100
Subject: [PATCH 232/313] implement HDI

---
 tests/test_plot.py | 49 ++++++++++++++++++++++++++-
 ultranest/plot.py  | 82 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 130 insertions(+), 1 deletion(-)

diff --git a/tests/test_plot.py b/tests/test_plot.py
index 7dc5628b..bccd9d1d 100644
--- a/tests/test_plot.py
+++ b/tests/test_plot.py
@@ -1,7 +1,7 @@
 import numpy as np
 import tempfile
 import os
-from ultranest.plot import PredictionBand
+from ultranest.plot import PredictionBand, highest_density_interval_from_samples
 from numpy.testing import assert_allclose
 import matplotlib.pyplot as plt
 import pytest
@@ -37,3 +37,50 @@ def test_PredictionBand():
     band.shade(q=0.01, color='gray', alpha=0.3, ax=ax1)
     plt.savefig('test-predictionband2.pdf')
     plt.close()
+
+
+def test_hdi():
+    rng = np.random.RandomState(2)
+    x = rng.normal(size=100000)
+    xmid, xerrlo, xerrhi = highest_density_interval_from_samples(x, xlo=None, xhi=None, probability_level=0.68)
+    assert -0.02 < xmid < 0.02
+    assert 0.98 < xerrlo < 1.02
+    assert 0.98 < xerrhi < 1.02
+
+    xpmid, xperrlo, xperrhi = highest_density_interval_from_samples(np.abs(x), xlo=0, xhi=None, probability_level=0.68)
+    assert 0 <= xpmid < 0.02
+    assert 0.98 < xperrhi < 1.02
+    assert 0 <= xperrlo < 0.02
+
+    xpmid, xperrlo, xperrhi = highest_density_interval_from_samples(-np.abs(x), xlo=None, xhi=0, probability_level=0.68)
+    assert -0.02 < xpmid <= 0
+    assert 0.98 < xperrlo < 1.02
+    assert 0 <= xperrhi < 0.02
+
+    xmid, xerrlo, xerrhi = highest_density_interval_from_samples(x, xlo=None, xhi=None, probability_level=0.955)
+    assert -0.02 < xmid < 0.02
+    assert 1.98 < xerrlo < 2.02
+    assert 1.98 < xerrhi < 2.02
+
+    xpmid, xperrlo, xperrhi = highest_density_interval_from_samples(np.abs(x), xlo=0, xhi=None, probability_level=0.955)
+    assert 0 <= xpmid < 0.02
+    assert 1.98 < xperrhi < 2.02
+    assert 0 <= xperrlo < 0.02
+
+    xpmid, xperrlo, xperrhi = highest_density_interval_from_samples(-np.abs(x), xlo=None, xhi=0, probability_level=0.955)
+    assert -0.02 < xpmid <= 0
+    assert 1.98 < xperrlo < 2.02
+    assert 0 <= xperrhi < 0.02
+
+    u = rng.beta(2, 2, size=100000)
+    umid, uerrlo, uerrhi = highest_density_interval_from_samples(u, xlo=0, xhi=1, probability_level=0.68)
+    print(umid, uerrlo, uerrhi)
+    assert abs(umid - 0.5) < 0.02, umid
+    assert abs(uerrlo - 0.25) < 0.02, umid
+    assert abs(uerrhi - 0.25) < 0.02, umid
+
+    umid, uerrlo, uerrhi = highest_density_interval_from_samples(u, xlo=None, xhi=None, probability_level=0.68)
+    print(umid, uerrlo, uerrhi)
+    assert abs(umid - 0.5) < 0.02, umid
+    assert abs(uerrlo - 0.25) < 0.02, umid
+    assert abs(uerrhi - 0.25) < 0.02, umid
diff --git a/ultranest/plot.py b/ultranest/plot.py
index 14225fd2..0659027e 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -66,6 +66,88 @@ def cornerplot(results, logger=None):
     logging.warning = oldfunc
 
 
+def highest_density_interval_from_samples(xsamples, xlo=None, xhi=None, probability_level=0.68):
+    """
+    Compute the highest density interval (HDI) from posterior samples.
+
+    Parameters
+    ----------
+    xsamples : array_like
+        The posterior samples from which to compute the HDI.
+    xlo : float or None, optional
+        Lower boundary limiting the space. Default is None.
+    xhi : float or None, optional
+        Upper boundary limiting the space. Default is None.
+    probability_level : float, optional
+        The desired probability level for the HDI. Default is 0.68.
+
+    Returns
+    -------
+    x_MAP: float
+        maximum a posteriori (MAP) estimate.
+    xerrlo: float
+        lower uncertainty (lower HDI bound minus x_MAP).
+    xerrhi: float
+        upper uncertainty (x_MAP minus upper HDI bound).
+
+    Notes
+    -----
+    The function starts at the highest density point and accumulates neighboring points
+    until the specified probability level is reached. If `xlo` or `xhi` is provided,
+    the HDI is constrained within these bounds.
+
+    Requires getdist to be installed for a kernel density estimation.
+
+    For uniform distributions, this function will give unpredictable results for the MAP.
+
+    Examples
+    --------
+    >>> xsamples = np.random.normal(loc=0, scale=1, size=100000)
+    >>> hdi = highest_density_interval_from_samples(xsamples)
+    >>> print('x = %.1f + %.2f - %.2f' % hdi)
+    x = 0.0 + 1.02 - 0.96
+    """
+    from getdist.mcsamples import MCSamples
+    import getdist.chains
+    getdist.chains.print_load_details = False
+    samples = MCSamples(
+        samples=xsamples, names=['x'], ranges={'x':[xlo,xhi]},
+        settings = dict(mult_bias_correction_order=1))
+    samples.raise_on_bandwidth_errors = True
+    density_bounded = samples.get1DDensityGridData('x')
+
+    x = density_bounded.x
+    y = density_bounded.P / np.sum(density_bounded.P)
+
+    # Sort the y values in descending order
+    sorted_indices = np.argsort(y)[::-1]
+
+    # define MAP as the peak. This works well if the peak is declining to both sides
+    MAP = x[sorted_indices[0]]
+    # if the peak is very flat, this is unstable, so lets use the top 10 per cent
+    # if KDE is multi-peaked or very flat, then the part identified with map_tolerance may be very large
+    #MAP_mask = y > map_tolerance * y.max()
+    #peak_weight = y[MAP_mask]
+    #if peak_weight.sum() > probability_level:
+    #    MAP = np.average(x[MAP_mask], weights=peak_weight)
+
+    total_probability = y[sorted_indices[0]]
+    i_lo = sorted_indices[0]
+    i_hi = sorted_indices[0]
+    for i in sorted_indices[1:]:
+        # Add the current probability to the total
+        i_lo = min(i_lo, i)
+        i_hi = max(i_hi, i)
+        total_probability = y[i_lo : i_hi + 1].sum()
+        # Check if the total probability exceeds or equals the desired level
+        if total_probability >= probability_level:
+            break
+
+    x_lo = x[i_lo]
+    x_hi = x[i_hi]
+    return MAP, MAP - x_lo, x_hi - MAP
+
+
 class PredictionBand(object):
     """Plot bands of model predictions as calculated from a chain.
 

From db45aee782acb5643bc342d923599a19bffe7dee Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 20 Mar 2024 00:35:49 +0100
Subject: [PATCH 233/313] simplify code

---
 ultranest/plot.py | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/ultranest/plot.py b/ultranest/plot.py
index 0659027e..574b560a 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -124,13 +124,6 @@ def highest_density_interval_from_samples(xsamples, xlo=None, xhi=None, probabil
 
     # define MAP as the peak. This works well if the peak is declining to both sides
     MAP = x[sorted_indices[0]]
-    # if the peak is very flat, this is unstable, so lets use the top 10 per cent
-    # if KDE is multi-peaked or very flat, then the part identified with map_tolerance may be very large
-    #MAP_mask = y > map_tolerance * y.max()
-    #peak_weight = y[MAP_mask]
-    #if peak_weight.sum() > probability_level:
-    #    MAP = np.average(x[MAP_mask], weights=peak_weight)
-
     total_probability = y[sorted_indices[0]]
     i_lo = sorted_indices[0]
     i_hi = sorted_indices[0]

From 10015fc12cb2d87c48f240bc8479db98bba34731 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 20 Mar 2024 01:38:50 +0100
Subject: [PATCH 234/313] add simpler alternative to MaxPrincipleGapAffineLayer

---
 tests/test_clustering.py |  25 +++++++++-
 tests/test_run.py        |  26 +++++++++-
 ultranest/mlfriends.pyx  | 103 +++++++++++++++++++++++++++++++++++++++
 3 files changed, 152 insertions(+), 2 deletions(-)

diff --git a/tests/test_clustering.py b/tests/test_clustering.py
index b607854a..790916c8 100644
--- a/tests/test_clustering.py
+++ b/tests/test_clustering.py
@@ -4,7 +4,7 @@
 import matplotlib.pyplot as plt
 from ultranest.utils import create_logger
 from ultranest import ReactiveNestedSampler
-from ultranest.mlfriends import MLFriends, AffineLayer
+from ultranest.mlfriends import MLFriends, AffineLayer, LocalAffineLayer
 
 here = os.path.dirname(__file__)
 
@@ -55,6 +55,29 @@ def test_clusteringcase():
     plt.close()
 
 
+def test_subtract_nearby():
+    from ultranest.mlfriends import subtract_nearby
+    
+    rng = np.random.RandomState(2)
+    u = rng.uniform(size=(20, 2))
+    u[:10,:] += 10
+    assert not np.all(np.abs(u) < 0.5)
+    print(u)
+    overlapped_points = subtract_nearby(u, 1.0)
+    print(overlapped_points)
+    assert np.all(np.abs(overlapped_points) < 0.5)
+
+    u = rng.uniform(size=(200, 2))
+    u[:100,0] = rng.uniform(0, 10, size=100)
+    u[100:,1] = rng.uniform(0, 10, size=100)
+    print(u)
+    assert not np.all(np.abs(u) < 0.5)
+    overlapped_points = subtract_nearby(u, 1.0)
+    print(overlapped_points)
+    print(overlapped_points.min(axis=0), overlapped_points.max(axis=0))
+    assert np.all(np.abs(overlapped_points) < 0.6)
+
+
 def test_clusteringcase_eggbox():
     from ultranest.mlfriends import update_clusters, ScalingLayer, MLFriends
     points = np.loadtxt(os.path.join(here, "eggboxregion.txt"))
diff --git a/tests/test_run.py b/tests/test_run.py
index b7095f32..6283e995 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -5,7 +5,7 @@
 import pytest
 import json
 import pandas
-from ultranest.mlfriends import MLFriends, ScalingLayer, AffineLayer, MaxPrincipleGapAffineLayer
+from ultranest.mlfriends import MLFriends, ScalingLayer, AffineLayer, MaxPrincipleGapAffineLayer, LocalAffineLayer
 from ultranest import NestedSampler, ReactiveNestedSampler, read_file
 from ultranest.integrator import warmstart_from_similar_file, _update_region_bootstrap, _get_cumsum_range
 import ultranest.mlfriends
@@ -90,6 +90,9 @@ def test_clustering_recursion(plot=False):
     nwithclusters = 0
     nwithclusters2 = 0
     noverclustered = 0
+    n2withclusters = 0
+    n2withclusters2 = 0
+    n2overclustered = 0
     gapped_nwithclusters = 0
     gapped_nwithclusters2 = 0
     gapped_noverclustered = 0
@@ -130,6 +133,22 @@ def test_clustering_recursion(plot=False):
             plt.xlim(0.5 - xymax, 0.5 + xymax)
             plt.ylim(0.5 - xymax, 0.5 + xymax)
 
+        # boot-strap an affine layer after clustering
+        transformLayer = LocalAffineLayer()
+        transformLayer.optimize(u, u)
+        region = MLFriends(u, transformLayer)
+        _update_region_bootstrap(region, nbootstraps)
+        region.create_ellipsoid()
+        layer = transformLayer.create_new(u, region.maxradiussq)
+        nextregion = MLFriends(u, layer)
+        _update_region_bootstrap(nextregion, nbootstraps=30)
+        nextLayer = layer.create_new(u, nextregion.maxradiussq)
+        nextNextLayer = nextLayer.create_new(u, region.maxradiussq)
+
+        n2withclusters += layer.nclusters
+        n2withclusters2 += nextLayer.nclusters
+        n2overclustered += nextLayer.nclusters > 2
+
         # boot-strap an MaxPrincipleGapAffineLayer layer after clustering
         transformLayer = MaxPrincipleGapAffineLayer()
         transformLayer.optimize(u, u)
@@ -171,17 +190,22 @@ def test_clustering_recursion(plot=False):
     print("  number of clusters iteration 1, iteration 2, number of overclusterings")
     print("AffineLayer:")
     print("  ", nwithclusters, nwithclusters2, noverclustered)
+    print("LocalAffineLayer:")
+    print("  ", nwithclusters, nwithclusters2, noverclustered)
     print("MaxPrincipleGapAffineLayer:")
     print("  ", gapped_nwithclusters, gapped_nwithclusters2, gapped_noverclustered)
     # with the affine layer we only see one cluster, because they are
     # so close together and the covariance spans them
     assert nwithclusters in (25, 26, 27)
     assert nwithclusters2 in (25, 26, 27)
+    assert n2withclusters in (25, 26, 27)
+    assert n2withclusters2 in (25, 26, 27)
     # MaxPrincipleGapAffineLayer builds a more local covariance
     # so the subsequent iteration splits the cluster
     assert gapped_nwithclusters in (25, 26, 27)
     assert gapped_nwithclusters2 in (49, 50, 51, 52)
     assert noverclustered in (0, 1, 2)
+    assert n2overclustered in (0, 1, 2)
     assert gapped_noverclustered in (0, 1, 2)
 
 def test_run():
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 86803a65..eb1247b3 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -63,6 +63,76 @@ cdef count_nearby(
                 nnearby[j] += 1
 
 
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def _subtract_nearby(np.ndarray[np.float_t, ndim=2] apts, np.ndarray[np.float_t, ndim=2] bpts, np.float_t radiussq):
+    """Subtract from each point apts the mean of points within square radius `radiussq`, store in bpts.
+
+    Parameters
+    ----------
+    apts: array
+        points
+    bpts: array
+        resulting points
+    radiussq: float
+        square of the MLFriends radius
+
+    """
+    cdef size_t n = apts.shape[0]
+    cdef size_t ndim = apts.shape[1]
+    assert n == bpts.shape[0]
+    assert ndim == bpts.shape[1]
+
+    cdef unsigned long i, j
+    cdef size_t nnearby
+    cdef np.float_t d
+
+    # go through each point
+    for j in range(n):
+        # find all nearest points
+        bpts[j,:] = 0
+        nnearby = 0
+        for i in range(n):
+            # check if it is within the radius
+            d = 0.0
+            for k in range(ndim):
+                d += (apts[i,k] - apts[j,k])**2
+            if d <= radiussq:
+                # accumulate to point average
+                nnearby += 1
+                for k in range(ndim):
+                    bpts[j,k] += apts[i,k]
+
+        # compute and subtract mean
+        for k in range(ndim):
+            bpts[j,k] = apts[j,k] - bpts[j,k] / float(nnearby)
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def subtract_nearby(
+    np.ndarray[np.float_t, ndim=2] upoints,
+    np.float_t maxradiussq):
+    """Subtract from each point apts the mean of points within square radius `radiussq`, store in bpts.
+
+    Parameters
+    ----------
+    apts: array
+        points
+    radiussq: float
+        square of the MLFriends radius
+
+    Returns
+    ---------
+    overlapped_points:
+        upoints with the nearby centers subtracted.
+
+    """
+    upoints_out = np.zeros_like(upoints)
+    _subtract_nearby(upoints, upoints_out, maxradiussq)
+    return upoints_out
+
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def find_nearby(
@@ -741,6 +811,39 @@ class MaxPrincipleGapAffineLayer(AffineLayer):
         return s
 
 
+class LocalAffineLayer(AffineLayer):
+    """Affine whitening transformation.
+
+    For learning the next layer's covariance, the points within 
+    the MLradius are co-centered. This should give a more "local"
+    covariance.
+    """
+
+    def create_new(self, upoints, maxradiussq, minvol=0.):
+        """Learn next layer from this optimized layer's clustering.
+
+        Parameters
+        ----------
+        upoints: array
+            points to use for optimize (in u-space)
+        maxradiussq: float
+            square of the MLFriends radius
+        minvol: float
+            Minimum volume to regularize sample covariance
+
+        Returns
+        ---------
+        A new, optimized LocalAffineLayer.
+        """
+        # perform clustering in transformed space
+        uwpoints = self.wrap(upoints)
+        tpoints = self.transform(upoints)
+        nclusters, clusteridxs, overlapped_uwpoints = update_clusters(uwpoints, tpoints, maxradiussq, self.clusterids)
+        s = LocalAffineLayer(nclusters=nclusters, wrapped_dims=self.wrapped_dims, clusterids=clusteridxs)
+        local_overlapped_uwpoints = subtract_nearby(uwpoints, maxradiussq)
+        s.optimize(upoints, local_overlapped_uwpoints, minvol=minvol)
+        return s
+
 
 def vol_prefactor(np.int_t n):
     """Volume constant for an ``n``-dimensional sphere.

From a7bdb9197c6ecdc0719c1db61750137d7858e905 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Wed, 20 Mar 2024 16:53:52 +0100
Subject: [PATCH 235/313] remove the last mention of Ellislice in
 test_popstepsampling.py and adds two unit tests. One for the two functions to
 define the slice limits and ones for the update function

---
 tests/test_popstepsampling.py | 87 +++++++++++++++++++++++++++++++++--
 1 file changed, 84 insertions(+), 3 deletions(-)

diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 85cebe62..239c9e58 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -1,8 +1,9 @@
 import numpy as np
 from ultranest import ReactiveNestedSampler
-from ultranest.popstepsampler import PopulationSliceSampler, PopulationRandomWalkSampler, PopulationEllipticalSliceSampler
+from ultranest.popstepsampler import PopulationSliceSampler, PopulationRandomWalkSampler, PopulationSimpleSliceSampler
 from ultranest.popstepsampler import generate_cube_oriented_direction, generate_random_direction, generate_cube_oriented_direction_scaled
 from ultranest.popstepsampler import generate_region_oriented_direction, generate_region_random_direction
+from ultranest.popstepsampler import slice_limit_to_unitcube,slice_limit_to_scale
 
 def loglike_vectorized(z):
     a = np.array([-0.5 * sum([((xi - 0.7 + i*0.001)/0.1)**2 for i, xi in enumerate(x)]) for x in z])
@@ -98,7 +99,87 @@ def test_direction_proposals():
                 assert directions.shape == points.shape, (directions.shape, points.shape)
                 #assert np.allclose(norms, scale), (norms, scale)
 
+
+def test_slice_limit():
+
+    slice_limit_func = [slice_limit_to_unitcube, slice_limit_to_scale]
+    fake_tleft = [-0.5, -0.2, -1.5]
+    fake_tright = [0.2, 2.4, 0.2]
+
+    fake_tleft_scale = [-0.5, -0.2, -1.]
+    fake_tright_scale = [0.2, 1.0, 0.2]
+
+    true_tleft = [fake_tleft, fake_tleft_scale]
+    true_tright = [fake_tright, fake_tright_scale]
+
+    for i,func in enumerate(slice_limit_func):
+        tleft, tright = func(fake_tleft, fake_tright)
+        assert np.allclose(tleft, true_tleft[i]), (tleft, true_tleft[i])
+        assert np.allclose(tright, true_tright[i]), (tright, true_tright[i])
+
+
+from ultranest.stepfuncs import update_vectorised_slice_sampler
+
+def test_update_slice_sampler():
+    """
+    Test goal: Testing the update in each different typical cases.
+    
+    There are 3 points searched with 4 points sampled on their slices:
+        - In the first case, no point is satisfying the Lmin condition. 
+    The functions should just update the slice limits and keep the status
+    unchanged.
+        - In the second case, one point is satisfying the Lmin condition.
+    But it will be discarded as it will be outside the slice limits. The
+    function should update the slice limits and keep the same status.
+        - In the third case, one point is satisfying the Lmin condition and
+    the slice limits. The function should update the slice limits and change
+    the status.
+
+    The workers should be split among the 2 unfinished points at the end.
+    """
+    
+    worker_running = np.array([0,0,0,0,1,1,1,1,2,2,2,2])
+    popsize = 12
+    status = np.zeros(12, dtype=int)
+    status[3:] = 1
+    Lmin = 1.
+    shrink = 1.0 
+    proposed_L = np.array([-12.,0.5,0.09,-2.,0.4,-5,2.4,0.3,-3.4,1.2,0.1,0.5])
+    tleft = -np.ones(12)
+    tright = np.ones(12)
+    t = np.array([-0.8,-0.2,0.4,-0.5,-0.3,0.9,-0.7,0.2,-0.8,0.5,-0.4,0.6])
+    proposed_u = np.array([[0.,0.,0.,0.,1.,1.,1.,1.,2.,2.5,2.,2.]]).T
+    proposed_p = np.array([[0.,0.,0.,0.,1.,1.,1.,1.,2.,2.5,2.,2.]]).T
+    allL = np.zeros(12)
+    allu = np.zeros((12,1))
+    allp = np.zeros((12,1))
+   
+    
+    tleft, tright, worker_running, status, allu, allL, allp,discarded= update_vectorised_slice_sampler(
+        t, tleft,tright,proposed_L,proposed_u,proposed_p,worker_running,status,Lmin,shrink,allu,allL,allp,popsize)
+
+    true_worker= np.array([0,1,0,1,0,1,0,1,0,1,0,1])
+    true_status = np.array([0,0,1,1,1,1,1,1,1,1,1,1])
+    true_allL = np.array([0.,0.,1.2,0,0,0,0,0,0,0,0,0])
+    true_allu = np.array([[0.,0.,2.5,0,0,0,0,0,0,0,0,0]]).T
+    true_allp = np.array([[0.,0.,2.5,0,0,0,0,0,0,0,0,0]]).T
+    true_discarded = 1
+    true_tleft = np.array([-0.2,-.3,-0.4,-1,-1,-1,-1,-1,-1,-1,-1,-1])
+    true_tright = np.array([0.4,0.2,0.5,1,1,1,1,1,1,1,1,1])
+
+    assert np.allclose(worker_running, true_worker), (worker_running, true_worker)
+    assert np.allclose(status, true_status), (status, true_status)
+    assert np.allclose(allL, true_allL), (allL, true_allL)
+    assert np.allclose(allu, true_allu), (allu, true_allu)
+    assert np.allclose(allp, true_allp), (allp, true_allp)
+    assert np.allclose(discarded, true_discarded), (discarded, true_discarded)
+    assert np.allclose(tleft, true_tleft), (tleft, true_tleft)
+    assert np.allclose(tright, true_tright), (tright, true_tright)
+
+
 if __name__ == '__main__':
     #test_stepsampler_cubegausswalk()
-    test_stepsampler_randomEllSlice()
-    test_direction_proposals()
+    #test_stepsampler_randomSimSlice()
+    #test_direction_proposals()
+    test_slice_limit()
+    test_update_slice_sampler()

From 8272923c2ab8a93c98bb91c4ee8241e0769d7453 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 20 Mar 2024 18:39:38 +0100
Subject: [PATCH 236/313] addressing algorithm performance regression,
 reverting default to AffineLayer

Joshuaalbert reports in https://github.com/JohannesBuchner/UltraNest/issues/124
that MaxPrincipleGapAffineLayer performs poorer than AffineLayer.

I was able to reproduce with the rosenbrock toy likelihood.
---
 ultranest/integrator.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index b0e125aa..83e3bc6c 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -25,7 +25,7 @@
 
 from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify as _listify
 from .utils import is_affine_transform, normalised_kendall_tau_distance, distributed_work_chunk_size
-from ultranest.mlfriends import MLFriends, MaxPrincipleGapAffineLayer, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
+from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
 from .store import HDF5PointStore, TextPointStore, NullPointStore
 from .viz import get_default_viz_callback
 from .ordertest import UniformOrderAccumulator
@@ -664,7 +664,7 @@ def run(
         ncall = num_live_points_missing  # number of calls we already made
         first_time = True
         if self.x_dim > 1:
-            transformLayer = MaxPrincipleGapAffineLayer(wrapped_dims=self.wrapped_axes)
+            transformLayer = AffineLayer(wrapped_dims=self.wrapped_axes)
         else:
             transformLayer = ScalingLayer(wrapped_dims=self.wrapped_axes)
         transformLayer.optimize(active_u, active_u)
@@ -1118,7 +1118,7 @@ def __init__(self,
 
         self.sampler = 'reactive-nested'
         self.x_dim = x_dim
-        self.transform_layer_class = MaxPrincipleGapAffineLayer if x_dim > 1 else ScalingLayer
+        self.transform_layer_class = AffineLayer if x_dim > 1 else ScalingLayer
         self.derivedparamnames = derived_param_names
         self.num_bootstraps = int(num_bootstraps)
         num_derived = len(self.derivedparamnames)

From cd5b02ee5cb14af38fd10c3633809338e9272dbc Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 21 Mar 2024 09:45:33 +0100
Subject: [PATCH 237/313] switch default to new local covariance,
 LocalAffineLayer

LocalAffineLayer uses the MLFriends distance to identify neighbours,
and subtracts the neighbour mean from each point before computing
a covariance. In a L-shape for example, the neighbour covariance
computed in the I part and the _ part can come out different,
and would be averaged. This is different to AffineLayer,
which takes the "diagonal", which is not optimal.
L-shape likelihood contours occur with additive multi-component fitting,
where the normalisations have log-uniform priors.

Based on this, LocalAffineLayer should lead to a covariance that
better captures local covariance in a way that is useful to MLFriends.

Indeed, in the rosenbrock example, a slightly better sampling
efficiency can be observed with the LocalAffineLayer than AffineLayer.

However, for a Gaussian likelihood, LocalAffineLayer is
sampling slightly less efficiently than AffineLayer.
This can be understood because AffineLayer uses all points to build
the covariance, and thereby probably gets a estimate of the covariance
that converges better to the truth.
The sampling efficiency however is already high for ellipsoidal contours
such as from Gaussians, partially supported by the encircling ellipsoid
in the MLFriends class.

This commit therefore probably helps over a wider class of problems,
while compromising with slightly poorer performance for ellipsoidal
contours.
---
 ultranest/integrator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 83e3bc6c..5db71918 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -25,7 +25,7 @@
 
 from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify as _listify
 from .utils import is_affine_transform, normalised_kendall_tau_distance, distributed_work_chunk_size
-from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
+from ultranest.mlfriends import MLFriends, AffineLayer, LocalAffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
 from .store import HDF5PointStore, TextPointStore, NullPointStore
 from .viz import get_default_viz_callback
 from .ordertest import UniformOrderAccumulator
@@ -1118,7 +1118,7 @@ def __init__(self,
 
         self.sampler = 'reactive-nested'
         self.x_dim = x_dim
-        self.transform_layer_class = AffineLayer if x_dim > 1 else ScalingLayer
+        self.transform_layer_class = LocalAffineLayer if x_dim > 1 else ScalingLayer
         self.derivedparamnames = derived_param_names
         self.num_bootstraps = int(num_bootstraps)
         num_derived = len(self.derivedparamnames)

From 2805fa6615d8dbc2ee6e2653febb5bcd7364bffd Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 21 Mar 2024 10:10:40 +0100
Subject: [PATCH 238/313] update Changelog

---
 HISTORY.rst | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 3bd51193..422e72d3 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,6 +2,12 @@
 Release Notes
 ==============
 
+4.2.0 (2024-02-15)
+------------------
+
+* new :py:class:`ultranest.mlfriends.LocalAffineLayer` for metric learning, set as default (see `issue 124 <https://github.com/JohannesBuchner/UltraNest/issues/124>`_)
+* add Highest Density Interval function (ultranest.plot.hdi)
+
 4.1.0 (2024-02-15)
 ------------------
 
@@ -12,9 +18,7 @@ Release Notes
 4.0.0 (2024-02-15)
 ------------------
 
-* replace :py:class:`ultranest.mlfriends.AffineLayer` with new :py:class:`ultranest.mlfriends.MaxPrincipleGapAffineLayer`
-
-  * This changes the learned covariance to be hopefully boost local features, make MLFriends identify smaller neighbourhoods, and thereby make sampling faster.
+* new :py:class:`ultranest.mlfriends.MaxPrincipleGapAffineLayer` for metric learning, set as default
 
 3.6.5 (2023-07-18)
 ------------------

From 19a98c50bcb97da42e15253d5c715d7cd4f80f00 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 22 Mar 2024 20:15:49 +0100
Subject: [PATCH 239/313] fix to avoid NaNs, wrong counting (rejection rate vs
 acceptance rate) and averaging

---
 ultranest/popstepsampler.py | 42 ++++++++++++++++++++++---------------
 1 file changed, 25 insertions(+), 17 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index ff8eb916..a644f4c7 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -124,21 +124,27 @@ def mean_jump_distance(self):
         """Geometric mean jump distance."""
         if len(self.logstat) == 0:
             return np.nan
-        return np.exp(np.nanmean(np.log([entry[-1] for entry in self.logstat])))
+        return np.exp(np.average(
+            np.log([entry[-1] + 1e-10 for entry in self.logstat]),
+            weights=([entry[0] for entry in self.logstat])
+        ))
 
     @property
     def far_enough_fraction(self):
         """Fraction of jumps exceeding reference distance."""
         if len(self.logstat) == 0:
             return np.nan
-        return np.nanmean([entry[-2] for entry in self.logstat])
+        return np.average(
+            [entry[-2] for entry in self.logstat],
+            weights=([entry[0] for entry in self.logstat])
+        )
 
     def get_info_dict(self):
         return dict(
             num_logs=len(self.logstat),
-            rejection_rate=np.nanmean([entry[0] for entry in self.logstat]),
-            mean_scale=np.nanmean([entry[1] for entry in self.logstat]),
-            mean_nsteps=np.nanmean([entry[2] for entry in self.logstat]),
+            rejection_rate=1 - np.nanmean([entry[0] for entry in self.logstat]) if len(self.logstat) > 0 else np.nan,
+            mean_scale=np.nanmean([entry[1] for entry in self.logstat]) if len(self.logstat) > 0 else np.nan,
+            mean_nsteps=np.nanmean([entry[2] for entry in self.logstat]) if len(self.logstat) > 0 else np.nan,
             mean_distance=self.mean_jump_distance,
             frac_far_enough=self.far_enough_fraction,
             last_logstat=dict(zip(self.logstat_labels, self.logstat[-1] if len(self.logstat) > 1 else [np.nan] * len(self.logstat_labels)))
@@ -174,7 +180,7 @@ def plot_jump_diagnostic_histogram(self, filename, **kwargs):
         plt.ylabel('Frequency')
         plt.savefig(filename, bbox_inches='tight')
         plt.close()
-    
+
 
 class PopulationRandomWalkSampler(GenericPopulationSampler):
     """Vectorized Gaussian Random Walk sampler."""
@@ -538,6 +544,7 @@ def advance(self, transform, loglike, Lmin, region):
         if self.log:
             print("evolve will advance:", movable)
 
+        uorig = args[0].copy()
         (
             (
                 currentt, currentv,
@@ -546,17 +553,18 @@ def advance(self, transform, loglike, Lmin, region):
             (success, unew, pnew, Lnew),
             nc
         ) = evolve(transform, loglike, Lmin, *args)
-        
-        far_enough, (move_distance, reference_distance) = diagnose_move_distances(region, args[0][success,:], unew)
-        self.logstat.append([
-            success.mean(),
-            self.scale,
-            self.nsteps,
-            np.mean(far_enough),
-            np.exp(np.mean(np.log(move_distance / reference_distance + 1e-10)))
-        ])
-        if self.logfile:
-            self.logfile.write("rescale\t%.4f\t%.4f\t%g\t%.4f%g\n" % self.logstat[-1])
+
+        if success.any():
+            far_enough, (move_distance, reference_distance) = diagnose_move_distances(region, uorig[success,:], unew)
+            self.logstat.append([
+                success.mean(),
+                self.scale,
+                self.nsteps,
+                np.mean(far_enough) if len(far_enough) > 0 else 0,
+                np.exp(np.mean(np.log(move_distance / reference_distance + 1e-10))) if len(far_enough) > 0 else 0
+            ])
+            if self.logfile:
+                self.logfile.write("rescale\t%.4f\t%.4f\t%g\t%.4f%g\n" % self.logstat[-1])
 
         if self.log:
             print("movable", movable.shape, movable.sum(), success.shape)

From ef167dd5fb928f707c41cb0b26849c32f2ec9175 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 22 Mar 2024 20:59:56 +0100
Subject: [PATCH 240/313] corner plot style with higher signal-to-ink ratio

in particular
better contour levels that encompass 99% of the probability
add legend that indicate 1, 2, 3 sigma levels
skip ragged outer points in plot
skip density inside that does not mean much (low posterior mass)
---
 HISTORY.rst       |  4 ++-
 ultranest/plot.py | 89 +++++++++++++++++++++++++++++++++++++++++++----
 2 files changed, 85 insertions(+), 8 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 422e72d3..2437597b 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -6,7 +6,9 @@ Release Notes
 ------------------
 
 * new :py:class:`ultranest.mlfriends.LocalAffineLayer` for metric learning, set as default (see `issue 124 <https://github.com/JohannesBuchner/UltraNest/issues/124>`_)
-* add Highest Density Interval function (ultranest.plot.hdi)
+* add Highest Density Interval function (ultranest.plot.highest_density_interval_from_samples)
+* corner plot style with higher signal-to-ink ratio.
+* bug fixes in popstepsampler
 
 4.1.0 (2024-02-15)
 ------------------
diff --git a/ultranest/plot.py b/ultranest/plot.py
index 574b560a..a59bc6de 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -38,14 +38,63 @@
 __all__ = ["runplot", "cornerplot", "traceplot", "PredictionBand"]
 
 
-def cornerplot(results, logger=None):
-    """Make a corner plot with corner."""
+def cornerplot(
+    results, min_weight=1e-4, with_legend=True, logger=None,
+    levels=[0.9973, 0.9545, 0.6827, 0.3934],
+    plot_datapoints=False, plot_density=False, show_titles=True, quiet=True,
+    contour_kwargs=dict(linestyles=['-','-.',':','--'], colors=['navy','navy','navy','purple']),
+    color='purple', quantiles=[0.15866, 0.5, 0.8413], **corner_kwargs
+):
+    """Make a healthy corner plot with corner.
+
+    Essentially does::
+
+        paramnames = results['paramnames']
+        data = results['weighted_samples']['points']
+        weights = results['weighted_samples']['weights']
+
+        return corner.corner(
+            results['weighted_samples']['points'],
+            weights=results['weighted_samples']['weights'],
+            labels=results['paramnames'])
+
+    Parameters
+    ----------
+    min_weight: float
+        cut off low-weight posterior points. Avoids meaningless
+        stragglers when plot_datapoints is True.
+    with_legend: bool
+        whether to add a legend to show meaning of the lines.
+    color : str
+        ``matplotlib`` style color for all histograms.
+    plot_density : bool
+        Draw the density colormap.
+    plot_contours : bool
+        Draw the contours.
+    show_titles : bool
+        Displays a title above each 1-D histogram showing the 0.5 quantile
+        with the upper and lower errors supplied by the quantiles argument.
+    quiet : bool
+        If true, suppress warnings for small datasets.
+    contour_kwargs : dict
+        Any additional keyword arguments to pass to the `contour` method.
+    quantiles: list
+        fractional quantiles to show on the 1-D histograms as vertical dashed lines.
+    **corner_kwargs: dict
+        Any remaining keyword arguments are sent to :func:`corner.corner`.
+
+    Returns
+    -------
+    fig : `~matplotlib.figure.Figure`
+        The ``matplotlib`` figure instance for the corner plot.
+
+    """
     paramnames = results['paramnames']
     data = np.array(results['weighted_samples']['points'])
     weights = np.array(results['weighted_samples']['weights'])
     cumsumweights = np.cumsum(weights)
 
-    mask = cumsumweights > 1e-4
+    mask = cumsumweights > min_weight
 
     if mask.sum() == 1:
         if logger is not None:
@@ -61,9 +110,35 @@ def cornerplot(results, logger=None):
     # monkey patch to disable a useless warning
     oldfunc = logging.warning
     logging.warning = lambda *args, **kwargs: None
-    corner.corner(data[mask,:], weights=weights[mask],
-                  labels=paramnames, show_titles=True, quiet=True)
+    fig = corner.corner(
+        data[mask,:], weights=weights[mask],
+        labels=paramnames, show_titles=show_titles, quiet=quiet,
+        plot_datapoints=plot_datapoints, plot_density=plot_density,
+        levels=levels, quantiles=quantiles,
+        contour_kwargs=contour_kwargs, color=color, **corner_kwargs
+    )
+    # Create legend handles
+    if with_legend and data.shape[1] > 1:
+        legend_handles = [
+            plt.Line2D(
+                [0], [0], linestyle='--', color=color,
+                label='%.1f%% marginal' % (100 * (quantiles[-1] - quantiles[0]))),
+        ] + [plt.Line2D(
+            [0], [0], linestyle=ls, color=linecolor,
+            label='%.1f%%' % (100 * level))
+            for ls, linecolor, level in zip(
+                contour_kwargs.get('linestyles', [])[::-1],
+                contour_kwargs.get('colors', [color] * 100)[::-1],
+                levels[::-1])
+        ]
+        if len(legend_handles) == len(levels) + 1 and len(legend_handles) > 0:
+            plt.legend(
+                title='credible prob level',
+                handles=legend_handles,
+                loc='lower right', bbox_to_anchor=(1.01,1.2), frameon=False
+            )
     logging.warning = oldfunc
+    return fig
 
 
 def highest_density_interval_from_samples(xsamples, xlo=None, xhi=None, probability_level=0.68):
@@ -112,7 +187,7 @@ def highest_density_interval_from_samples(xsamples, xlo=None, xhi=None, probabil
     getdist.chains.print_load_details = False
     samples = MCSamples(
         samples=xsamples, names=['x'], ranges={'x':[xlo,xhi]},
-        settings = dict(mult_bias_correction_order=1))
+        settings=dict(mult_bias_correction_order=1))
     samples.raise_on_bandwidth_errors = True
     density_bounded = samples.get1DDensityGridData('x')
 
@@ -131,7 +206,7 @@ def highest_density_interval_from_samples(xsamples, xlo=None, xhi=None, probabil
         # Add the current probability to the total
         i_lo = min(i_lo, i)
         i_hi = max(i_hi, i)
-        total_probability = y[i_lo : i_hi + 1].sum()
+        total_probability = y[i_lo:i_hi + 1].sum()
         # Check if the total probability exceeds or equals the desired level
         if total_probability >= probability_level:
             break

From a6265712b9db28f37442436417fc221c931979d1 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 22 Mar 2024 21:39:02 +0100
Subject: [PATCH 241/313] =?UTF-8?q?Bump=20version:=204.1.7=20=E2=86=92=204?=
 =?UTF-8?q?.2.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index c508166f..f7e5ca2d 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.1.7',
+    version='4.2.0',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 6bb5aa26..1970f7a1 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.1.7'
+__version__ = '4.2.0'

From b0ca4d7c486650cf238ad10982a444141a95ef87 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Wed, 17 Apr 2024 12:07:28 +0200
Subject: [PATCH 242/313] Test implementation of a reversibility test for the
 vectorized slice sampler

---
 tests/test_popstepsampling.py | 52 +++++++++++++++++++++++++++++++++++
 ultranest/popstepsampler.py   | 21 ++++++++++----
 2 files changed, 68 insertions(+), 5 deletions(-)

diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 239c9e58..c1f5602f 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -177,9 +177,61 @@ def test_update_slice_sampler():
     assert np.allclose(tright, true_tright), (tright, true_tright)
 
 
+
+
+
+
+
+
+
+
+def Test_SimpleSliceSampler_reversibility(seed):
+
+    
+    Loglikelihood = lambda x: np.zeros(x.shape[0]) # flat likelihood so we don't need to care about Lmin contours
+    Lmin = -1.
+    transform = lambda x: x
+    popsize = 5
+    np.random.seed(seed)
+    us = np.zeros((popsize,3)) +0.5
+    Ls = np.zeros(popsize)
+    
+    stepsampler = PopulationSimpleSliceSampler(
+        popsize=popsize, nsteps=20,
+        generate_direction=generate_random_direction,
+        slice_limit=slice_limit_to_scale,scale=1e-3
+    )
+    np.random.seed(seed)
+    u,L=np.zeros((popsize,3)),np.zeros(popsize)
+    for i in range(popsize):
+        u[i],_,L[i],_= stepsampler.__next__(None,Lmin,us.copy(),Ls.copy(),transform,Loglikelihood,test=True)
+     
+    def opposite_direction(ui, region, scale=1):
+        return -generate_random_direction(ui, region, scale)
+
+    
+    stepsampler.generate_direction = opposite_direction
+
+    us_2 = u.reshape((popsize,-1))
+    Ls_2 = np.zeros(popsize)+L
+    np.random.seed(seed)
+    u2,L2=np.zeros((popsize,3)),np.zeros(popsize)
+    
+    for i in range(popsize):
+        u2[i],_,L2[i],_= stepsampler.__next__(None,Lmin,us_2,Ls_2,transform,Loglikelihood,test=True)
+    
+    assert np.allclose(us, u2), (us, u2)
+    assert np.allclose(Ls, L2), (Ls, L2)
+
+    
+
+
 if __name__ == '__main__':
     #test_stepsampler_cubegausswalk()
     #test_stepsampler_randomSimSlice()
     #test_direction_proposals()
     test_slice_limit()
     test_update_slice_sampler()
+    Test_SimpleSliceSampler_reversibility(4)
+    
+    
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 827f5794..50b96d41 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -711,7 +711,7 @@ def region_changed(self, Ls, region):
     
     def __next__(
         self, region, Lmin, us, Ls, transform, loglike, ndraw=10,
-        plot=False, tregion=None, log=False
+        plot=False, tregion=None, log=False, test=False
     ):
         """Sample a new live point.
 
@@ -737,6 +737,10 @@ def __next__(
             not used
         log: bool
             not used
+        test: bool
+            In case of test of the reversibility of the sampler, the points drawn
+            from the live points needs to be deterministic. This parameters is
+            ensuring that.
 
         Returns
         -------
@@ -756,12 +760,13 @@ def __next__(
         if len(self.prepared_samples) == 0:
             # choose live points
             ilive = np.random.randint(0, nlive, size=self.popsize)
-            allu = np.array(us[ilive,:])
+            allu = np.array(us[ilive,:]) if not test else np.array(us)
             allp = np.zeros((self.popsize, ndim))
             allL = np.array(Ls[ilive])
             nc = 0
             n_discarded=0
-                                       
+            
+                                         
                 
             
             interval_final=0. 
@@ -771,7 +776,7 @@ def __next__(
                 factor_scale=self.scale_jitter_func()
                 # Defining slice direction
                 v = self.generate_direction(allu, region,scale= 1.0)*self.scale*factor_scale
-                
+                 
                 
                 # limite of the slice based on the unit cube boundaries
                 tleft_unitcube,tright_unitcube= unitcube_line_intersection(allu, v)
@@ -783,12 +788,17 @@ def __next__(
                 # Status indicating if a points has already find its next position
                 status=np.zeros(self.popsize,dtype=int) # one for success, zero for running
                 
+
                 # Loop until each points has found its next position or we reached 100 iterations
                 
                 for it in range(self.max_it):
                 
                     # Sampling points on the slices
-                    t=tleft_worker+(tright_worker-tleft_worker)*np.random.uniform(size=(self.popsize,))
+                    slice_position = np.random.uniform(size=(self.popsize,))
+                    
+                    t=tleft_worker+(tright_worker-tleft_worker)*slice_position
+                    
+                    
                     
                     points=allu[worker_running,:]
                     v_worker=v[worker_running,:]
@@ -819,6 +829,7 @@ def __next__(
             self.ncalls+=nc
             
             assert np.array([p!=np.zeros(ndim) for p in allp]).all(), 'some walkers never moved! Double nsteps of PopulationSimpleSliceSampler.'
+            
             self.prepared_samples = list(zip(allu, allp, allL))
 
             

From b5f4e535b83e1a9594e2e4d2b4a15d0b9e05018f Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Thu, 18 Apr 2024 15:33:12 +0200
Subject: [PATCH 243/313] Add a sanity check for the vectorised slice sampler,
 just checking that everything went well

---
 tests/test_popstepsampling.py | 73 +++++++++++++++++++++--------------
 1 file changed, 43 insertions(+), 30 deletions(-)

diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 18b65cca..d0d7baec 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -214,43 +214,56 @@ def test_update_slice_sampler():
     assert np.allclose(tright, true_tright), (tright, true_tright)
 
 
-def Test_SimpleSliceSampler_reversibility(seed):
+# aim at checking the sanity of the results of 
+# one iteration of the slice sampler. 
+def Test_SimpleSliceSampler(seed):
 
-    
-    Loglikelihood = lambda x: np.zeros(x.shape[0]) # flat likelihood so we don't need to care about Lmin contours
-    Lmin = -1.
-    transform = lambda x: x
-    popsize = 5
     np.random.seed(seed)
-    us = np.zeros((popsize,3)) +0.5
-    Ls = np.zeros(popsize)
-    
-    stepsampler = PopulationSimpleSliceSampler(
-        popsize=popsize, nsteps=20,
+    nsteps = 1 
+    popsize = 100
+    ndim = 10
+    sampler = ReactiveNestedSampler(paramnames, loglike_vectorized, transform=transform, vectorized=True)
+
+    sampler.stepsampler = PopulationSimpleSliceSampler(
+        popsize=popsize, nsteps=nsteps, 
         generate_direction=generate_random_direction,
-        slice_limit=slice_limit_to_scale,scale=1e-3
     )
+    stepsampler = sampler.stepsampler
+    # start with a random point in the unit cube
+    us = (np.random.uniform(size=(popsize, ndim))-0.5)*0.9+0.5
+    Ls = loglike_vectorized(us)
+    Lmin = np.min(Ls)
+
+    u,L=np.zeros((popsize,ndim)),np.zeros(popsize)
+
+    # initialising a region
+    #print(us) 
+    region= RobustEllipsoidRegion(us, AffineLayer())
+    region.maxradiussq, region.enlarge = region.compute_enlargement(nbootstraps=30)
+    region.create_ellipsoid(minvol=1.0)
+
+    # resetting the seed to check the slice axes
     np.random.seed(seed)
-    u,L=np.zeros((popsize,3)),np.zeros(popsize)
     for i in range(popsize):
-        u[i],_,L[i],_= stepsampler.__next__(None,Lmin,us.copy(),Ls.copy(),transform,Loglikelihood,test=True)
-     
-    def opposite_direction(ui, region, scale=1):
-        return -generate_random_direction(ui, region, scale)
+        u[i],_,L[i],_= stepsampler.__next__(region,Lmin,us.copy(),Ls.copy(),transform,loglike_vectorized,test=True)
 
+    # Basic check
+    assert (L>Lmin).all(), (L,Lmin) # Lmin check
+    assert (u>0).all() and (u<1).all(), u # u in the unit cube check
     
-    stepsampler.generate_direction = opposite_direction
-
-    us_2 = u.reshape((popsize,-1))
-    Ls_2 = np.zeros(popsize)+L
     np.random.seed(seed)
-    u2,L2=np.zeros((popsize,3)),np.zeros(popsize)
-    
+    # resetting the random generation inside the sampler
+    _=np.random.randint(0, us.shape[0], size=stepsampler.popsize)
+    _=stepsampler.scale_jitter_func()
+
+    # Getting the slice axes
+    slice_axes =  stepsampler.generate_direction(us.copy(), region,scale= 1.0)
     for i in range(popsize):
-        u2[i],_,L2[i],_= stepsampler.__next__(None,Lmin,us_2,Ls_2,transform,Loglikelihood,test=True)
-    
-    assert np.allclose(us, u2), (us, u2)
-    assert np.allclose(Ls, L2), (Ls, L2)
+        v=(u[i,:]-us[i,:])/slice_axes[i,:]
+        mean_v = np.mean(v)
+        assert np.allclose(mean_v, v, atol=1e-10), (mean_v, v)
+
+
 
     
 def test_direction_proposal_values():
@@ -282,8 +295,8 @@ def test_direction_proposal_values():
     #test_stepsampler_cubegausswalk()
     #test_stepsampler_randomSimSlice()
     #test_direction_proposals()
-    test_slice_limit()
-    test_update_slice_sampler()
-    Test_SimpleSliceSampler_reversibility(4)
+    #test_slice_limit()
+    #test_update_slice_sampler()
+    Test_SimpleSliceSampler(4)
     
     

From d106d7219a69119d20609142d0ef2f3cff850715 Mon Sep 17 00:00:00 2001
From: Benjamin Beauchesne <benjamin.beauchesne@epfl.ch>
Date: Mon, 22 Apr 2024 12:32:37 +0200
Subject: [PATCH 244/313] Few changes, mainly docstring and comment in the code

---
 ultranest/popstepsampler.py | 124 ++++++++++++++++++------------------
 1 file changed, 63 insertions(+), 61 deletions(-)

diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 82be49d3..dfaa8812 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -693,22 +693,22 @@ def __next__(
 def slice_limit_to_unitcube(tleft, tright):
     
     """
-    return the slice limits as a copy of intersection between the slice and the unit cube boundaries
+    return the slice limits as of the intersection between the slice and the unit cube boundaries
 
 
     parameters
     ----------
         tleft: float
-                Intersection of the slice and the unit cube boundaries in the inverse direction of the slice
+                Intersection of the unit cube with the slice in the negative direction
         tright: float
-                Intersection of the slice and the unit cube boundaries in the direction of the slice
+                Intersection of the unit cube with the slice in the positive direction
     Returns
     -------
         (tleft_new,tright_new): tuple
                 Positive and negative slice limits
     """
-    tleft_new,tright_new = tleft.copy(),tright.copy()
-    return (tleft_new,tright_new)
+    tleft_new, tright_new = tleft.copy(), tright.copy()
+    return (tleft_new, tright_new)
 
 
 def slice_limit_to_scale(tleft, tright):
@@ -720,9 +720,9 @@ def slice_limit_to_scale(tleft, tright):
     parameters
     ----------
         tleft: float
-                Intersection of the slice and the unit cube boundaries in the inverse direction of the slice
+                Intersection of the unit cube with the slice in the negative direction
         tright: float
-                Intersection of the slice and the unit cube boundaries in the direction of the slice
+                Intersection of the unit cube with the slice in the positive direction
     Returns
     -------
         (tleft_new,tright_new): tuple
@@ -730,9 +730,9 @@ def slice_limit_to_scale(tleft, tright):
     """
 
     
-    tleft_new = np.fmax(tleft,-1.+np.zeros_like(tleft)).copy()
-    tright_new = np.fmin(tright,1.+np.zeros_like(tright)).copy()
-    return (tleft_new,tright_new)
+    tleft_new = np.fmax(tleft , -1. + np.zeros_like(tleft))
+    tright_new = np.fmin(tright , 1. + np.zeros_like(tright))
+    return (tleft_new, tright_new)
 
 
 
@@ -772,7 +772,7 @@ class PopulationSimpleSliceSampler(GenericPopulationSampler):
     def __init__(
         self, popsize, nsteps, generate_direction,
         scale_adapt_factor=1.0, adapt_slice_scale_target=2.0,
-        scale=None, scale_jitter_func=None,slice_limit=slice_limit_to_unitcube,
+        scale=1.0, scale_jitter_func=None,slice_limit=slice_limit_to_unitcube,
         max_it=100,shrink_factor=1.0):
         """Initialise.
 
@@ -793,49 +793,46 @@ def __init__(
             :py:func:`ultranest.popstepsampler.generate_mixture_random_direction`
             :py:func:`ultranest.popstepsampler.generate_cube_oriented_direction` -> no adaptation in that case
             :py:func:`ultranest.popstepsampler.generate_cube_oriented_direction_scaled` -> no adaptation in that case
+        scale: float
+            initial guess for the slice width.
         scale_jitter_func: function
             User supplied function to multiply the `scale` by a random factor. For example, 
             :py:func:`lambda : scipy.stats.truncnorm.rvs(-0.5, 5., loc=0, scale=1)+1.`
-        scale: float
-            initial guess for the slice width. If None, the slice width is defined as the
-            intersection between itself and the unit cube.
         scale_adapt_factor: float
-            if 1, no adapting is done.
-            if <1, the scale is increased if the slice final size is under 1/2 the scale
-            or decreased if it is above, by *scale_adapt_factor*.
+            adaptation of `scale`. If 1: no adaptation. if <1, the scale is increased/decreased by this factor if the
+            final slice length is shorter/longer than the `adapt_slice_scale_target*scale`.        
         adapt_slice_scale_target: float
-            Target size of the median distance between a current point and the next one among all chains in unit of
-            `scale`. If the median distance is above `scale/adapt_slice_scale_target`, the scale is increased by 
-            `scale_adapt_factor`, and decreased otherwise.
+            Targeted ratio of the median distance between slice mid and final point among all chains of `scale`.
+            Default: 2.0. Higher values are more conservative, lower values are faster. 
         slice_limit: function
-            function to define the slice limit based on the intersection of the slice and the unit cube. The slice
-            limit are defined in unit of `scale`. The default is `:py:func:slice_limit_to_unitcube` which defines
-            the slice limit as the intersection between the slice and the unit cube. An alternative when the `scale`
-            is used is `:py:func:slice_limit_to_scale` which defines the slice limit as an interval of size `2*scale`.
-            This function should return a copy of the `tleft` and `tright` or another arrays of the same shape. 
+            Function setting the initial slice upper and lower bound. The default is `:py:func:slice_limit_to_unitcube`
+            which defines  the slice limit as the intersection between the slice and the unit cube. An alternative 
+            when the `scale` is used is `:py:func:slice_limit_to_scale` which defines the slice limit as an interval
+            of size `2*scale`. This function should either return a copy of the `tleft` and `tright` arguments or 
+            new arrays of the same shape. 
         max_it: int
             maximum number of iterations to find a point on the slice. If the maximum number of iterations is reached,
             the current point is returned as the next one.
         shrink_factor: float
-            During the shrinking procedure, a new slice bound is set to the last tested point that don't respect the Lmin
-            condition if `shrink_factor` is set to 1.0. If its value is larger than 1.0, the new slice bound is set to the
-            `1/shrink_factor` of the distance between the current point and the last tested point that don't respect the Lmin condition.
+            For standard slice sampling shrinking, `shrink_factor=1`, the slice bound is updated to the last 
+            rejected point. Setting `shrink_factor>1` aggressively accelerates the shrinkage, by updating the 
+            new slice bound to `1/shrink_factor` of the distance between the current point and rejected point.
         """
+
         self.nsteps = nsteps
         
-        self.max_it=max_it
+        self.max_it = max_it
         self.nrejects = 0
         self.generate_direction =  generate_direction
         self.scale_adapt_factor = scale_adapt_factor
         self.ncalls = 0
-        self.discarded=0
-        self.shrink_factor=shrink_factor
+        self.discarded = 0
+        self.shrink_factor = shrink_factor
         assert shrink_factor>=1.0, "The shrink factor should be greater than 1.0 to be efficient"
-        if scale is None:
-            self.scale = 1.0
-        else:
-            self.scale = scale
-        self.adapt_slice_scale_target=adapt_slice_scale_target
+
+        self.scale = float(scale)
+
+        self.adapt_slice_scale_target = adapt_slice_scale_target
         
         if scale_jitter_func is None:
             self.scale_jitter_func= lambda : 1.
@@ -844,7 +841,7 @@ def __init__(
         self.prepared_samples = []
         self.popsize = popsize
         
-        self.slice_limit=slice_limit
+        self.slice_limit = slice_limit
 
         self.logstat = []
         self.logstat_labels = ['accept_rate', 'efficiency', 'scale', 'far_enough', 'mean_rel_jump']
@@ -918,29 +915,35 @@ def __next__(
             allp = np.zeros((self.popsize, ndim))
             allL = np.array(Ls[ilive])
             nc = 0
-            n_discarded=0
+            n_discarded = 0
             
                                          
                 
             
-            interval_final=0. 
+            interval_final = 0. 
             
             for k in range(self.nsteps):
                 # Defining scale jitter
-                factor_scale=self.scale_jitter_func()
+                factor_scale = self.scale_jitter_func()
                 # Defining slice direction
-                v = self.generate_direction(allu, region,scale= 1.0)*self.scale*factor_scale
+                v = self.generate_direction(allu, region, scale = 1.0)*self.scale*factor_scale
                  
                 
                 # limite of the slice based on the unit cube boundaries
-                tleft_unitcube,tright_unitcube= unitcube_line_intersection(allu, v)
+                tleft_unitcube, tright_unitcube = unitcube_line_intersection(allu, v)
+
                 # Defining bound of the slice
-                tleft_worker,tright_worker = self.slice_limit(tleft_unitcube,tright_unitcube)
-                tleft,tright = self.slice_limit(tleft_unitcube,tright_unitcube)
+                # Bounds for each points and likelihood calls are identical initially
+
+                # Slice bounds for each likelihood call
+                tleft_worker, tright_worker = self.slice_limit(tleft_unitcube,tright_unitcube)
+
+                # Slice bounds for each points
+                tleft, tright = self.slice_limit(tleft_unitcube,tright_unitcube)
                 # Index of the workers working concurrently
-                worker_running=np.arange(0,self.popsize,1,dtype=int)
+                worker_running = np.arange(0,self.popsize,1,dtype=int)
                 # Status indicating if a points has already find its next position
-                status=np.zeros(self.popsize,dtype=int) # one for success, zero for running
+                status = np.zeros(self.popsize,dtype=int) # one for success, zero for running
                 
 
                 # Loop until each points has found its next position or we reached 100 iterations
@@ -950,45 +953,44 @@ def __next__(
                     # Sampling points on the slices
                     slice_position = np.random.uniform(size=(self.popsize,))
                     
-                    t=tleft_worker+(tright_worker-tleft_worker)*slice_position
+                    t = tleft_worker+(tright_worker-tleft_worker)*slice_position
                     
                     
                     
-                    points=allu[worker_running,:]
-                    v_worker=v[worker_running,:]
-                    proposed_u=points+t.reshape((-1,1))*v_worker
+                    points = allu[worker_running,:]
+                    v_worker = v[worker_running,:]
+                    proposed_u = points+t.reshape((-1,1))*v_worker
                 
                     proposed_p = transform(proposed_u)
                     proposed_L = loglike(proposed_p)
-                    nc+=self.popsize
+                    nc += self.popsize
                     # Updating the pool of points based on the newly sampled points
-                    tleft,tright,worker_running,status,allu,allL,allp,n_discarded_it=update_vectorised_slice_sampler(\
+                    tleft,tright,worker_running,status,allu,allL,allp,n_discarded_it = update_vectorised_slice_sampler(\
                     t,tleft,tright,proposed_L,proposed_u,proposed_p,worker_running,status,Lmin,self.shrink_factor,\
                     allu,allL,allp,self.popsize)
-                    n_discarded+=n_discarded_it
+                    n_discarded += n_discarded_it
                     # Update of the limits of the slices
-                    tleft_worker=tleft[worker_running]
-                    tright_worker=tright[worker_running]
+                    tleft_worker = tleft[worker_running]
+                    tright_worker = tright[worker_running]
                     if not np.any(status==0):
                         break
                 # Record of the final interval on theta for scale adaptation
-                interval_final+=np.median(tright-tleft)
+                interval_final += np.median(tright-tleft)
 
 
             
-            interval_final=interval_final/self.nsteps
+            interval_final = interval_final/self.nsteps
             
             
-            self.discarded+=n_discarded
-            self.ncalls+=nc
+            self.discarded += n_discarded
+            self.ncalls += nc
             
             assert np.array([p!=np.zeros(ndim) for p in allp]).all(), 'some walkers never moved! Double nsteps of PopulationSimpleSliceSampler.'
             far_enough, (move_distance, reference_distance) = diagnose_move_distances(region, us[ilive,:], allu)
             self.prepared_samples = list(zip(allu, allp, allL))
 
             self.logstat.append([
-                1., # we always find a point or we will break at the assert before
-                1., # same here?
+                self.popsize/nc,
                 self.scale, # will always be 1. in the default case
                 self.nsteps,
                 np.mean(far_enough) if len(far_enough) > 0 else 0,

From 8137ff74dab1c027c924fbebae57f0d81460cef5 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 22 Apr 2024 15:25:12 +0200
Subject: [PATCH 245/313] =?UTF-8?q?Bump=20version:=204.2.0=20=E2=86=92=204?=
 =?UTF-8?q?.3.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index f7e5ca2d..a781aa5d 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.2.0',
+    version='4.3.0',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 1970f7a1..99756a9e 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,4 +10,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.2.0'
+__version__ = '4.3.0'

From 969a66a7b3c3e87560e5bfde958debfe739e4b2b Mon Sep 17 00:00:00 2001
From: svaverbe <sigfried.vanaverbeke@telenet.be>
Date: Tue, 21 May 2024 16:46:51 +0200
Subject: [PATCH 246/313] An attempt to raise an error if the parameter names
 forwarded to ultranest are not a list.

line 455
---
 ultranest/integrator.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 5db71918..db506b28 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -452,6 +452,7 @@ def __init__(self,
             unique run number. If None, will be automatically incremented.
 
         """
+        assert isinstance(param_names, list), "param_names must be of type list !"
         self.paramnames = param_names
         x_dim = len(self.paramnames)
         self.num_live_points = num_live_points

From ca3a0998fa68b50fc4c3e374641def257b94cc0d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 26 May 2024 16:07:45 +0200
Subject: [PATCH 247/313] use np.int_ instead of np.int_t; improve docs

---
 ultranest/__init__.py       |   7 +-
 ultranest/calibrator.py     |   2 +
 ultranest/hotstart.py       |   1 +
 ultranest/integrator.py     |   8 +-
 ultranest/mlfriends.pyx     |  10 +-
 ultranest/netiter.py        |   7 +-
 ultranest/ordertest.py      |   1 +
 ultranest/plot.py           |   1 +
 ultranest/popstepsampler.py | 211 ++++++++++++++++--------------------
 ultranest/solvecompat.py    |   1 +
 ultranest/stepfuncs.pyx     |   4 +-
 ultranest/stepsampler.py    |  64 ++++++++---
 ultranest/store.py          |   1 +
 ultranest/utils.py          |   5 +-
 ultranest/viz.py            |  28 +++--
 15 files changed, 186 insertions(+), 165 deletions(-)

diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 99756a9e..c64c4e4f 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -1,7 +1,10 @@
+# noqa: D400 D205
 """
 Performs nested sampling to calculate the Bayesian evidence and posterior samples
-Some parts are from the Nestle library by Kyle Barbary (https://github.com/kbarbary/nestle)
-Some parts are from the nnest library by Adam Moss (https://github.com/adammoss/nnest)
+
+Some ellipsoid code is adopted from the Nestle library by Kyle Barbary (https://github.com/kbarbary/nestle)
+Some of the architecture and parallelisation is adopted from the nnest library by Adam Moss (https://github.com/adammoss/nnest)
+Some visualisations are adopted from the dynesty library by Josh Speagle (https://github.com/joshspeagle/dynesty/)
 """
 
 from .integrator import NestedSampler, ReactiveNestedSampler, read_file
diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index 07601772..ad404542 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -1,5 +1,7 @@
+# noqa: D400 D205
 """
 Calibration of step sampler
+---------------------------
 """
 
 import numpy as np
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index 6f1a5add..5946a243 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -1,3 +1,4 @@
+# noqa: D400 D205
 """
 Warm start
 ----------
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index db506b28..b3930987 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -1,3 +1,4 @@
+# noqa: D400 D205
 """
 Nested sampling integrators
 ---------------------------
@@ -452,8 +453,7 @@ def __init__(self,
             unique run number. If None, will be automatically incremented.
 
         """
-        assert isinstance(param_names, list), "param_names must be of type list !"
-        self.paramnames = param_names
+        self.paramnames = list(param_names)
         x_dim = len(self.paramnames)
         self.num_live_points = num_live_points
         self.sampler = 'nested'
@@ -2015,8 +2015,8 @@ def _update_region(
 
             # instead, track the clusters from before by matching manually
             oldt = self.transformLayer.transform(oldu)
-            clusterids = np.zeros(len(active_u), dtype=int)
-            nnearby = np.empty(len(self.region.unormed), dtype=int)
+            clusterids = np.zeros(len(active_u), dtype=np.int_)
+            nnearby = np.empty(len(self.region.unormed), dtype=np.int_)
             for ci in np.unique(self.transformLayer.clusterids):
                 if ci == 0:
                     continue
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index eb1247b3..df2ad7b6 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -27,7 +27,7 @@ cdef count_nearby(
     np.ndarray[np.float_t, ndim=2] apts,
     np.ndarray[np.float_t, ndim=2] bpts,
     np.float_t radiussq,
-    np.ndarray[np.int_t, ndim=1] nnearby
+    np.ndarray[np.int_, ndim=1] nnearby
 ):
     """Count the number of points in ``apts`` within square radius ``radiussq`` for each point ``b`` in `bpts``.
 
@@ -139,7 +139,7 @@ def find_nearby(
     np.ndarray[np.float_t, ndim=2] apts,
     np.ndarray[np.float_t, ndim=2] bpts,
     np.float_t radiussq,
-    np.ndarray[np.int_t, ndim=1] nnearby
+    np.ndarray[np.int_, ndim=1] nnearby
 ):
     """Gets the index of a point in `a` within square radius `radiussq`, for each point `b` in `bpts`.
 
@@ -223,7 +223,7 @@ cdef float compute_maxradiussq(np.ndarray[np.float_t, ndim=2] apts, np.ndarray[n
 @cython.wraparound(False)
 def compute_mean_pair_distance(
     np.ndarray[np.float_t, ndim=2] pts,
-    np.ndarray[np.int_t, ndim=1] clusterids
+    np.ndarray[np.int_, ndim=1] clusterids
 ):
     """Compute the average distance between pairs of points.
     Pairs from different clusters are excluded in the computation.
@@ -271,7 +271,7 @@ cdef _update_clusters(
     np.ndarray[np.float_t, ndim=2] upoints,
     np.ndarray[np.float_t, ndim=2] tpoints,
     np.float_t maxradiussq,
-    np.ndarray[np.int_t, ndim=1] clusterids,
+    np.ndarray[np.int_, ndim=1] clusterids,
 ):
     """same signature as ``update_clusters()``, see there."""
     assert upoints.shape[0] == tpoints.shape[0], ('different number of points', upoints.shape[0], tpoints.shape[0])
@@ -845,7 +845,7 @@ class LocalAffineLayer(AffineLayer):
         return s
 
 
-def vol_prefactor(np.int_t n):
+def vol_prefactor(np.int_ n):
     """Volume constant for an ``n``-dimensional sphere.
 
     for ``n`` even:  $$    (2pi)^(n    /2) / (2 * 4 * ... * n)$$
diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index 548f66e7..5a7b8f40 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -1,6 +1,5 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-__doc__ = """
+# noqa: D400 D205
+"""
 Graph-based nested sampling
 ---------------------------
 
@@ -20,8 +19,6 @@
 The exploration is bootstrap-capable without requiring additional
 computational effort: The roots are indexed, and the bootstrap explorer
 can ignore the rootids it does not know about.
-
-
 """
 
 import numpy as np
diff --git a/ultranest/ordertest.py b/ultranest/ordertest.py
index e29ced91..f50fd456 100644
--- a/ultranest/ordertest.py
+++ b/ultranest/ordertest.py
@@ -1,3 +1,4 @@
+# noqa: D400 D205
 """
 U test for a uniform distribution of integers
 ---------------------------------------------
diff --git a/ultranest/plot.py b/ultranest/plot.py
index a59bc6de..45e66f79 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -1,3 +1,4 @@
+# noqa: D400 D205
 """
 Plotting utilities
 ------------------
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index dfaa8812..e32c00c8 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -1,3 +1,4 @@
+# noqa: D400 D205
 """
 Vectorized step samplers
 ------------------------
@@ -54,8 +55,9 @@ def unitcube_line_intersection(ray_origin, ray_direction):
         t2 = -n + k
         return np.nanmax(t1, axis=1), np.nanmin(t2, axis=1)
 
+
 def diagnose_move_distances(region, ustart, ufinal):
-    """Compares random walk travel distance to MLFriends radius.
+    """Compare random walk travel distance to MLFriends radius.
 
     Compares in whitened space (t-space), the L2 norm between final
     point and starting point to the MLFriends bootstrapped radius.
@@ -86,6 +88,7 @@ def diagnose_move_distances(region, ustart, ufinal):
 
     return far_enough, [d2**0.5, region.maxradiussq**0.5]
 
+
 class GenericPopulationSampler():
     def plot(self, filename):
         """Plot sampler statistics.
@@ -150,7 +153,6 @@ def get_info_dict(self):
             last_logstat=dict(zip(self.logstat_labels, self.logstat[-1] if len(self.logstat) > 1 else [np.nan] * len(self.logstat_labels)))
         )
 
-
     def print_diagnostic(self):
         """Print diagnostic of step sampler performance."""
         if len(self.logstat) == 0:
@@ -423,7 +425,7 @@ def _setup(self, ndim):
         self.allL = np.zeros((self.popsize, self.nsteps + 1)) + np.nan
         self.currentt = np.zeros(self.popsize) + np.nan
         self.currentv = np.zeros((self.popsize, ndim)) + np.nan
-        self.generation = np.zeros(self.popsize, dtype=int) - 1
+        self.generation = np.zeros(self.popsize, dtype=np.int_) - 1
         self.current_left = np.zeros(self.popsize)
         self.current_right = np.zeros(self.popsize)
         self.searching_left = np.zeros(self.popsize, dtype=bool)
@@ -688,13 +690,9 @@ def __next__(
             return None, None, None, nc
 
 
-
-
 def slice_limit_to_unitcube(tleft, tright):
-    
     """
-    return the slice limits as of the intersection between the slice and the unit cube boundaries
-
+    Return the slice limits as of the intersection between the slice and the unit cube boundaries.
 
     parameters
     ----------
@@ -708,14 +706,12 @@ def slice_limit_to_unitcube(tleft, tright):
                 Positive and negative slice limits
     """
     tleft_new, tright_new = tleft.copy(), tright.copy()
-    return (tleft_new, tright_new)
 
+    return tleft_new, tright_new
 
-def slice_limit_to_scale(tleft, tright):
 
-    """
-    return the slice limits as an interval of size `2*scale` or the intersection between the slice and the unit cube boundaries
-    if the interval is larger than the unit cube boundaries.
+def slice_limit_to_scale(tleft, tright):
+    """Return -1..+1 or the intersection between slice and unit cube if that is shorter.
 
     parameters
     ----------
@@ -728,52 +724,49 @@ def slice_limit_to_scale(tleft, tright):
         (tleft_new,tright_new): tuple
                 Positive and negative slice limits
     """
+    tleft_new = np.fmax(tleft, -1. + np.zeros_like(tleft))
+    tright_new = np.fmin(tright, 1. + np.zeros_like(tright))
 
-    
-    tleft_new = np.fmax(tleft , -1. + np.zeros_like(tleft))
-    tright_new = np.fmin(tright , 1. + np.zeros_like(tright))
-    return (tleft_new, tright_new)
-
+    return tleft_new, tright_new
 
 
 class PopulationSimpleSliceSampler(GenericPopulationSampler):
-    """
-       Vectorized Slice sampler without stepping out procedure for quick look fits.
-       Unlike `:py:class:PopulationSliceSampler`, in `:py:class:PopulationSimpleSliceSampler`,
-       the likelihood is always called with the same number of points.
-
-       Sliced are defined by the `:py:func:generate_direction` function on a interval defined
-       around the current point. The centred interval has the width of the scale parameter,
-       i.e, there is no stepping out procedure as in `:py:class:PopulationSliceSampler`.
-       Slices are then shrink towards the current point until a point is found with a
-       likelihood above the threshold.
-
-       In the default case, i.e. `scale=None`, the slice width is defined as the
-       intersection between itself and the unit cube. To improve the efficiency of the sampler,
-       the slice can be reduced to an interval of size `2*scale` centred on the point. `scale`
-       can be adapted with the `scale_adapt_factor` parameter based on the median distance 
-       between the current and the next point in a chains among all the chains. If the median
-       distance is above `scale/adapt_slice_scale_target`, the scale is increased by `scale_adapt_factor`,
-       and decreased otherwise. The `scale` parameter can also be jittered by a user supplied
-       function `:py:func:scale_jitter_func` to counter balance the effect of a strong adaptation.
-
-       In the case `scale!=None`, the detailed balance is not guaranteed, so this sampler should
-       be use with caution.
-
-       Multiple (`popsize`) slice sampling chains are run independently and in parallel. 
-       In that case, we read points as if they were the next selected each after the other.
-       For a points to update the slice, it needs to be still in the part of the slices
-       searched after the first point have been read. In that case, we update as normal, 
-       otherwise we discard the point.
-
-
+    """Vectorized Slice sampler without stepping out procedure for quick look fits.
+
+    Unlike `:py:class:PopulationSliceSampler`, in `:py:class:PopulationSimpleSliceSampler`,
+    the likelihood is always called with the same number of points.
+
+    Sliced are defined by the `:py:func:generate_direction` function on a interval defined
+    around the current point. The centred interval has the width of the scale parameter,
+    i.e, there is no stepping out procedure as in `:py:class:PopulationSliceSampler`.
+    Slices are then shrink towards the current point until a point is found with a
+    likelihood above the threshold.
+
+    In the default case, i.e. `scale=None`, the slice width is defined as the
+    intersection between itself and the unit cube. To improve the efficiency of the sampler,
+    the slice can be reduced to an interval of size `2*scale` centred on the point. `scale`
+    can be adapted with the `scale_adapt_factor` parameter based on the median distance
+    between the current and the next point in a chains among all the chains. If the median
+    distance is above `scale/adapt_slice_scale_target`, the scale is increased by `scale_adapt_factor`,
+    and decreased otherwise. The `scale` parameter can also be jittered by a user supplied
+    function `:py:func:scale_jitter_func` to counter balance the effect of a strong adaptation.
+
+    In the case `scale!=None`, the detailed balance is not guaranteed, so this sampler should
+    be use with caution.
+
+    Multiple (`popsize`) slice sampling chains are run independently and in parallel.
+    In that case, we read points as if they were the next selected each after the other.
+    For a points to update the slice, it needs to be still in the part of the slices
+    searched after the first point have been read. In that case, we update as normal,
+    otherwise we discard the point.
     """
 
     def __init__(
         self, popsize, nsteps, generate_direction,
         scale_adapt_factor=1.0, adapt_slice_scale_target=2.0,
-        scale=1.0, scale_jitter_func=None,slice_limit=slice_limit_to_unitcube,
-        max_it=100,shrink_factor=1.0):
+        scale=1.0, scale_jitter_func=None, slice_limit=slice_limit_to_unitcube,
+        max_it=100, shrink_factor=1.0
+    ):
         """Initialise.
 
         Parameters
@@ -796,59 +789,55 @@ def __init__(
         scale: float
             initial guess for the slice width.
         scale_jitter_func: function
-            User supplied function to multiply the `scale` by a random factor. For example, 
+            User supplied function to multiply the `scale` by a random factor. For example,
             :py:func:`lambda : scipy.stats.truncnorm.rvs(-0.5, 5., loc=0, scale=1)+1.`
         scale_adapt_factor: float
             adaptation of `scale`. If 1: no adaptation. if <1, the scale is increased/decreased by this factor if the
-            final slice length is shorter/longer than the `adapt_slice_scale_target*scale`.        
+            final slice length is shorter/longer than the `adapt_slice_scale_target*scale`.
         adapt_slice_scale_target: float
             Targeted ratio of the median distance between slice mid and final point among all chains of `scale`.
-            Default: 2.0. Higher values are more conservative, lower values are faster. 
+            Default: 2.0. Higher values are more conservative, lower values are faster.
         slice_limit: function
             Function setting the initial slice upper and lower bound. The default is `:py:func:slice_limit_to_unitcube`
-            which defines  the slice limit as the intersection between the slice and the unit cube. An alternative 
+            which defines  the slice limit as the intersection between the slice and the unit cube. An alternative
             when the `scale` is used is `:py:func:slice_limit_to_scale` which defines the slice limit as an interval
-            of size `2*scale`. This function should either return a copy of the `tleft` and `tright` arguments or 
-            new arrays of the same shape. 
+            of size `2*scale`. This function should either return a copy of the `tleft` and `tright` arguments or
+            new arrays of the same shape.
         max_it: int
             maximum number of iterations to find a point on the slice. If the maximum number of iterations is reached,
             the current point is returned as the next one.
         shrink_factor: float
-            For standard slice sampling shrinking, `shrink_factor=1`, the slice bound is updated to the last 
-            rejected point. Setting `shrink_factor>1` aggressively accelerates the shrinkage, by updating the 
+            For standard slice sampling shrinking, `shrink_factor=1`, the slice bound is updated to the last
+            rejected point. Setting `shrink_factor>1` aggressively accelerates the shrinkage, by updating the
             new slice bound to `1/shrink_factor` of the distance between the current point and rejected point.
         """
-
         self.nsteps = nsteps
-        
+
         self.max_it = max_it
         self.nrejects = 0
-        self.generate_direction =  generate_direction
+        self.generate_direction = generate_direction
         self.scale_adapt_factor = scale_adapt_factor
         self.ncalls = 0
         self.discarded = 0
         self.shrink_factor = shrink_factor
-        assert shrink_factor>=1.0, "The shrink factor should be greater than 1.0 to be efficient"
+        assert shrink_factor >= 1.0, "The shrink factor should be greater than 1.0 to be efficient"
 
         self.scale = float(scale)
 
         self.adapt_slice_scale_target = adapt_slice_scale_target
-        
+
         if scale_jitter_func is None:
-            self.scale_jitter_func= lambda : 1.
+            self.scale_jitter_func = lambda: 1.
         else:
-            self.scale_jitter_func= scale_jitter_func      
+            self.scale_jitter_func = scale_jitter_func
         self.prepared_samples = []
         self.popsize = popsize
-        
+
         self.slice_limit = slice_limit
 
         self.logstat = []
         self.logstat_labels = ['accept_rate', 'efficiency', 'scale', 'far_enough', 'mean_rel_jump']
 
-        
-        
-
     def __str__(self):
         """Return string representation."""
         return 'PopulationSimpleSliceSampler(popsize=%d, nsteps=%d, generate_direction=%s, scale=%.g)' % (
@@ -858,8 +847,6 @@ def region_changed(self, Ls, region):
         """Act upon region changed. Currently unused."""
         pass
 
-    
-    
     def __next__(
         self, region, Lmin, us, Ls, transform, loglike, ndraw=10,
         plot=False, tregion=None, log=False, test=False
@@ -905,30 +892,25 @@ def __next__(
 
         """
         nlive, ndim = us.shape
-        
-         
+
         # fill if empty:
         if len(self.prepared_samples) == 0:
             # choose live points
             ilive = np.random.randint(0, nlive, size=self.popsize)
             allu = np.array(us[ilive,:]) if not test else np.array(us)
-            allp = np.zeros((self.popsize, ndim))
+            allp = np.zeros((self.popsize, ndim)) * np.nan
             allL = np.array(Ls[ilive])
             nc = 0
             n_discarded = 0
-            
-                                         
-                
-            
-            interval_final = 0. 
-            
+
+            interval_final = 0.
+
             for k in range(self.nsteps):
                 # Defining scale jitter
                 factor_scale = self.scale_jitter_func()
                 # Defining slice direction
-                v = self.generate_direction(allu, region, scale = 1.0)*self.scale*factor_scale
-                 
-                
+                v = self.generate_direction(allu, region, scale=1.0) * self.scale * factor_scale
+
                 # limite of the slice based on the unit cube boundaries
                 tleft_unitcube, tright_unitcube = unitcube_line_intersection(allu, v)
 
@@ -941,74 +923,65 @@ def __next__(
                 # Slice bounds for each points
                 tleft, tright = self.slice_limit(tleft_unitcube,tright_unitcube)
                 # Index of the workers working concurrently
-                worker_running = np.arange(0,self.popsize,1,dtype=int)
+                worker_running = np.arange(0, self.popsize, 1, dtype=np.int_)
                 # Status indicating if a points has already find its next position
-                status = np.zeros(self.popsize,dtype=int) # one for success, zero for running
-                
+                status = np.zeros(self.popsize, dtype=np.int_)  # one for success, zero for running
 
                 # Loop until each points has found its next position or we reached 100 iterations
-                
                 for it in range(self.max_it):
-                
                     # Sampling points on the slices
                     slice_position = np.random.uniform(size=(self.popsize,))
-                    
-                    t = tleft_worker+(tright_worker-tleft_worker)*slice_position
-                    
-                    
-                    
-                    points = allu[worker_running,:]
-                    v_worker = v[worker_running,:]
-                    proposed_u = points+t.reshape((-1,1))*v_worker
-                
+                    t = tleft_worker + (tright_worker - tleft_worker) * slice_position
+
+                    points = allu[worker_running, :]
+                    v_worker = v[worker_running, :]
+                    proposed_u = points + t.reshape((-1,1)) * v_worker
+
                     proposed_p = transform(proposed_u)
                     proposed_L = loglike(proposed_p)
                     nc += self.popsize
+
                     # Updating the pool of points based on the newly sampled points
-                    tleft,tright,worker_running,status,allu,allL,allp,n_discarded_it = update_vectorised_slice_sampler(\
-                    t,tleft,tright,proposed_L,proposed_u,proposed_p,worker_running,status,Lmin,self.shrink_factor,\
-                    allu,allL,allp,self.popsize)
+                    tleft, tright, worker_running, status, allu, allL, allp, n_discarded_it = update_vectorised_slice_sampler(
+                        t, tleft, tright, proposed_L, proposed_u, proposed_p, worker_running, status, Lmin, self.shrink_factor,
+                        allu, allL, allp, self.popsize)
                     n_discarded += n_discarded_it
+
                     # Update of the limits of the slices
                     tleft_worker = tleft[worker_running]
                     tright_worker = tright[worker_running]
-                    if not np.any(status==0):
+
+                    if not np.any(status == 0):
                         break
-                # Record of the final interval on theta for scale adaptation
-                interval_final += np.median(tright-tleft)
 
+                # Record of the final interval on theta for scale adaptation
+                interval_final += np.median(tright - tleft)
 
-            
-            interval_final = interval_final/self.nsteps
-            
-            
+            interval_final = interval_final / self.nsteps
             self.discarded += n_discarded
             self.ncalls += nc
-            
-            assert np.array([p!=np.zeros(ndim) for p in allp]).all(), 'some walkers never moved! Double nsteps of PopulationSimpleSliceSampler.'
+
+            assert np.isfinite(allp).all(), 'some walkers never moved! Double nsteps of PopulationSimpleSliceSampler.'
             far_enough, (move_distance, reference_distance) = diagnose_move_distances(region, us[ilive,:], allu)
             self.prepared_samples = list(zip(allu, allp, allL))
 
             self.logstat.append([
-                self.popsize/nc,
-                self.scale, # will always be 1. in the default case
+                self.popsize / nc,
+                self.scale,  # will always be 1. in the default case
                 self.nsteps,
                 np.mean(far_enough) if len(far_enough) > 0 else 0,
                 np.exp(np.mean(np.log(move_distance / reference_distance + 1e-10))) if len(far_enough) > 0 else 0
             ])
 
-            
-
-            
             # Scale adaptation such that the final interval is
-            # half the scale. There may be better things to do 
+            # half the scale. There may be better things to do
             # here, but it seems to work.
-            if interval_final>=1./self.adapt_slice_scale_target:
-                self.scale *= 1./self.scale_adapt_factor
+            if interval_final >= 1. / self.adapt_slice_scale_target:
+                self.scale *= 1. / self.scale_adapt_factor
             else:
                 self.scale *= self.scale_adapt_factor
-            #print("percentage of throws %.3f\n\n"%((self.throwed/self.ncalls)*100.))
-            
+            # print("percentage of throws %.3f\n\n"%((self.throwed/self.ncalls)*100.))
+
         else:
             nc = 0
 
diff --git a/ultranest/solvecompat.py b/ultranest/solvecompat.py
index 4fabf7ca..2afbb107 100644
--- a/ultranest/solvecompat.py
+++ b/ultranest/solvecompat.py
@@ -1,3 +1,4 @@
+# noqa: D400 D205
 """Drop-in replacement for pymultinest.solve.
 
 Example::
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 44e7f2d5..fd4c0111 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -331,7 +331,7 @@ def step_back(Lmin, allL, generation, currentt, log=False):
 
 cdef _fill_directions(
     np.ndarray[np.float_t, ndim=2] v,
-    np.ndarray[np.int_t, ndim=1] indices,
+    np.ndarray[np.int_, ndim=1] indices,
     float scale
 ):
     cdef size_t nsamples = v.shape[0]
@@ -533,7 +533,7 @@ cpdef tuple update_vectorised_slice_sampler(\
                           np.ndarray[np.float_t, ndim=1] t, np.ndarray[np.float_t, ndim=1] tleft,\
                           np.ndarray[np.float_t, ndim=1] tright, np.ndarray[np.float_t, ndim=1] proposed_L,\
                           np.ndarray[np.float_t, ndim=2] proposed_u, np.ndarray[np.float_t, ndim=2] proposed_p,\
-                          np.ndarray[np.int_t, ndim=1] worker_running, np.ndarray[np.int_t, ndim=1] status,\
+                          np.ndarray[np.int_, ndim=1] worker_running, np.ndarray[np.int_, ndim=1] status,\
                           np.float_t Likelihood_threshold,np.float_t shrink_factor, np.ndarray[np.float_t, ndim=2] allu,\
                           np.ndarray[np.float_t, ndim=1] allL, np.ndarray[np.float_t, ndim=2] allp, int popsize):
 
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 7e5efc5e..e161c03e 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -1,3 +1,4 @@
+# noqa: D400 D205
 """
 MCMC-like step sampling
 -----------------------
@@ -12,6 +13,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from .utils import listify as _listify
+from warnings import warn
 
 
 def generate_random_direction(ui, region, scale=1):
@@ -326,8 +328,10 @@ def inside_region(region, unew, uold):
 
 
 def adapt_proposal_total_distances(region, history, mean_pair_distance, ndim):
+    """Check jump distance (deprecated function)."""
     # compute mean vector of each proposed jump
     # compute total distance of all jumps
+    warn('adapt_proposal_total_distances is deprecated and will be removed in future versions of UltraNest.', DeprecationWarning, stacklevel=2)
     tproposed = region.transformLayer.transform(np.asarray([u for u, _ in history]))
     assert len(tproposed.sum(axis=1)) == len(tproposed)
     d2 = ((((tproposed[0] - tproposed)**2).sum(axis=1))**0.5).sum()
@@ -337,8 +341,10 @@ def adapt_proposal_total_distances(region, history, mean_pair_distance, ndim):
 
 
 def adapt_proposal_total_distances_NN(region, history, mean_pair_distance, ndim):
+    """Check jump distance (deprecated function)."""
     # compute mean vector of each proposed jump
     # compute total distance of all jumps
+    warn('adapt_proposal_total_distances_NN is deprecated and will be removed in future versions of UltraNest.', DeprecationWarning, stacklevel=2)
     tproposed = region.transformLayer.transform(np.asarray([u for u, _ in history]))
     assert len(tproposed.sum(axis=1)) == len(tproposed)
     d2 = ((((tproposed[0] - tproposed)**2).sum(axis=1))**0.5).sum()
@@ -348,7 +354,9 @@ def adapt_proposal_total_distances_NN(region, history, mean_pair_distance, ndim)
 
 
 def adapt_proposal_summed_distances(region, history, mean_pair_distance, ndim):
+    """Check jump distance (deprecated function)."""
     # compute sum of distances from each jump
+    warn('adapt_proposal_summed_distances is deprecated and will be removed in future versions of UltraNest.', DeprecationWarning, stacklevel=2)
     tproposed = region.transformLayer.transform(np.asarray([u for u, _ in history]))
     d2 = (((tproposed[1:,:] - tproposed[:-1,:])**2).sum(axis=1)**0.5).sum()
     far_enough = d2 > mean_pair_distance / ndim
@@ -357,7 +365,9 @@ def adapt_proposal_summed_distances(region, history, mean_pair_distance, ndim):
 
 
 def adapt_proposal_summed_distances_NN(region, history, mean_pair_distance, ndim):
+    """Check jump distance (deprecated function)."""
     # compute sum of distances from each jump
+    warn('adapt_proposal_summed_distances_NN is deprecated and will be removed in future versions of UltraNest.', DeprecationWarning, stacklevel=2)
     tproposed = region.transformLayer.transform(np.asarray([u for u, _ in history]))
     d2 = (((tproposed[1:,:] - tproposed[:-1,:])**2).sum(axis=1)**0.5).sum()
     far_enough = d2 > region.maxradiussq**0.5
@@ -366,7 +376,7 @@ def adapt_proposal_summed_distances_NN(region, history, mean_pair_distance, ndim
 
 
 def adapt_proposal_move_distances(region, history, mean_pair_distance, ndim):
-    """Compares random walk travel distance to MLFriends radius.
+    """Compare random walk travel distance to MLFriends radius.
 
     Compares in whitened space (t-space), the L2 norm between final
     point and starting point to the MLFriends bootstrapped radius.
@@ -400,7 +410,7 @@ def adapt_proposal_move_distances(region, history, mean_pair_distance, ndim):
 
 
 def adapt_proposal_move_distances_midway(region, history, mean_pair_distance, ndim):
-    """Compares first half of the travel distance to MLFriends radius.
+    """Compare first half of the travel distance to MLFriends radius.
 
     Compares in whitened space (t-space), the L2 norm between the
     middle point of the walk and the starting point,
@@ -558,16 +568,26 @@ def __init__(
 
             Available are:
 
-            * :py:func:`generate_cube_oriented_direction` (slice sampling, picking one random parameter to vary)
-            * :py:func:`generate_random_direction` (hit-and-run sampling, picking a random direction varying all parameters)
-            * :py:func:`generate_differential_direction` (differential evolution direction proposal)
-            * :py:func:`generate_region_oriented_direction` (slice sampling, but in the whitened parameter space)
-            * :py:func:`generate_region_random_direction` (hit-and-run sampling, but in the whitened parameter space)
-            * :py:class:`SequentialDirectionGenerator` (sequential slice sampling, i.e., iterate deterministically through the parameters)
-            * :py:class:`SequentialRegionDirectionGenerator` (sequential slice sampling in the whitened parameter space, i.e., iterate deterministically through the principle axes)
-            * :py:func:`generate_cube_oriented_differential_direction` (like generate_differential_direction, but along only one randomly chosen parameter)
-            * :py:func:`generate_partial_differential_direction` (differential evolution slice proposal on only 10% of the parameters)
-            * :py:func:`generate_mixture_random_direction` (combined proposal)
+            * :py:func:`generate_cube_oriented_direction`
+                (slice sampling, picking one random parameter to vary)
+            * :py:func:`generate_random_direction`
+                (hit-and-run sampling, picking a random direction varying all parameters)
+            * :py:func:`generate_differential_direction`
+                (differential evolution direction proposal)
+            * :py:func:`generate_region_oriented_direction`
+                (slice sampling, but in the whitened parameter space)
+            * :py:func:`generate_region_random_direction`
+                (hit-and-run sampling, but in the whitened parameter space)
+            * :py:class:`SequentialDirectionGenerator`
+                (sequential slice sampling, i.e., iterate deterministically through the parameters)
+            * :py:class:`SequentialRegionDirectionGenerator`
+                (sequential slice sampling in the whitened parameter space, i.e., iterate deterministically through the principle axes)
+            * :py:func:`generate_cube_oriented_differential_direction`
+                (like generate_differential_direction, but along only one randomly chosen parameter)
+            * :py:func:`generate_partial_differential_direction`
+                (differential evolution slice proposal on only 10% of the parameters)
+            * :py:func:`generate_mixture_random_direction`
+                (combined proposal)
 
             Additionally, :py:class:`OrthogonalDirectionGenerator`
             can be applied to any generate_direction function.
@@ -749,6 +769,20 @@ def far_enough_fraction(self):
         return np.nanmean(jump_distances > reference_distances)
 
     def get_info_dict(self):
+        """Return diagnostics of the step sampler performance.
+
+        Returns
+        --------
+        v: dict
+            the keys are:
+                * num_logs: number of log entries being summarized
+                * rejection_rate: fraction of steps rejected
+                * mean_scale: average value of `scale`
+                * mean_nsteps: average `nsteps`
+                * mean_distance: mean jump distance (see `Buchner+24 <https://arxiv.org/abs/2402.11936>`_)
+                * frac_far_enough: fraction of jumps with sufficient distance (see `Buchner+24 <https://arxiv.org/abs/2402.11936>`_)
+                * last_logstat: content of the last log entry
+        """
         return dict(
             num_logs=len(self.logstat),
             rejection_rate=np.nanmean([entry[0] for entry in self.logstat]) if len(self.logstat) > 0 else np.nan,
@@ -759,7 +793,6 @@ def get_info_dict(self):
             last_logstat=dict(zip(self.logstat_labels, self.logstat[-1] if len(self.logstat) > 1 else [np.nan] * len(self.logstat_labels)))
         )
 
-
     def print_diagnostic(self):
         """Print diagnostic of step sampler performance."""
         if len(self.logstat) == 0:
@@ -1177,7 +1210,6 @@ def move(self, ui, region, ndraw=1, plot=False):
 
                 # adjust scale to final slice length
                 if -left > self.next_scale or right > self.next_scale:
-                #if right - left > self.next_scale:
                     self.next_scale *= 1.1
                 else:
                     self.next_scale /= 1.1
@@ -1220,6 +1252,7 @@ def RegionBallSliceSampler(*args, **kwargs):
 
 class SequentialDirectionGenerator(object):
     """Sequentially proposes one parameter after the next."""
+
     def __init__(self):
         """Initialise."""
         self.axis_index = 0
@@ -1258,11 +1291,13 @@ def __call__(self, ui, region, scale=1):
         return v
 
     def __str__(self):
+        """Create string representation."""
         return type(self).__name__ + '()'
 
 
 class SequentialRegionDirectionGenerator(object):
     """Sequentially proposes one region axes after the next."""
+
     def __init__(self):
         """Initialise."""
         self.axis_index = 0
@@ -1299,6 +1334,7 @@ def __call__(self, ui, region, scale=1):
         return v
 
     def __str__(self):
+        """Create string representation."""
         return type(self).__name__ + '()'
 
 
diff --git a/ultranest/store.py b/ultranest/store.py
index 6501a0d1..b3b22fc1 100644
--- a/ultranest/store.py
+++ b/ultranest/store.py
@@ -1,3 +1,4 @@
+# noqa: D400 D205
 """
 Storage for nested sampling points
 -----------------------------------
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 6fee2178..65fd4707 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -1,3 +1,4 @@
+# noqa: D400 D205
 """
 Utility functions for logging and statistics
 --------------------------------------------
@@ -198,7 +199,7 @@ def resample_equal(samples, weights, rstate=None):
     # make N subdivisions, and choose positions with a consistent random offset
     positions = (rstate.random() + np.arange(N)) / N
 
-    idx = np.zeros(N, dtype=int)
+    idx = np.zeros(N, dtype=np.int_)
     cumulative_sum = np.cumsum(weights)
     i, j = 0, 0
     while i < N:
@@ -484,7 +485,7 @@ def submasks(mask, *masks):
 
     Returns
     -------
-    indices : np.array(dtype=int)
+    indices : np.array(dtype=np.int_)
         indices which select the subselection in the original array
 
     """
diff --git a/ultranest/viz.py b/ultranest/viz.py
index ebf77962..150a1a2d 100644
--- a/ultranest/viz.py
+++ b/ultranest/viz.py
@@ -1,3 +1,4 @@
+# noqa: D400 D205
 """
 Live point visualisations
 -------------------------
@@ -140,12 +141,13 @@ def nicelogger(points, info, region, transformLayer, region_fresh=False):
         if np.isfinite(info['order_test_correlation']) else "Quality: ok",
     )
     if info.get('stepsampler_info', {}).get('num_logs', 0) > 0:
-        print(
-            ('Step sampler performance: %(rejection_rate).1f rej/step, %(mean_nsteps)d steps/it' % (info['stepsampler_info'])) +
-            (', rel jump distance: %.2f (should be >1), %.2f%% (should be >50%%)' % (
-                info['stepsampler_info']['mean_distance'], 100 * info['stepsampler_info']['frac_far_enough']
-            )) if 'mean_distance' in info['stepsampler_info'] else ''
-        )
+        stepsampler_info = dict(info['stepsampler_info'])
+        stepsampler_info['frac_far_enough'] *= 100
+        if 'mean_distance' in stepsampler_info:
+            print((
+                'Step sampler performance: %(rejection_rate).1f rej/step, %(mean_nsteps)d steps/it, '
+                'rel jump distance: %(mean_distance).2f (should be >1), %(frac_far_enough).2f%% (should be >50%%)') % stepsampler_info
+            )
 
     print()
     if ndim == 1:
@@ -323,12 +325,14 @@ def __call__(self, points, info, region, transformLayer, region_fresh=False):
              if np.isfinite(info['order_test_correlation']) else " | Quality: ok")
 
         if info.get('stepsampler_info', {}).get('num_logs', 0) > 0:
-            labeltext += ("<br/>" +
-                'Step sampler performance: %(rejection_rate).1f%% rej/step, %(mean_nsteps)d steps/it' % (info['stepsampler_info']) +
-                ('mean rel jump distance: %.2f (should be >1), %.2f%% (should be >50%%)' % (
-                    info['stepsampler_info']['mean_distance'], 100 * info['stepsampler_info']['frac_far_enough']
-                )) if 'mean_distance' in info['stepsampler_info'] else ''
-            )
+            stepsampler_info = dict(info['stepsampler_info'])
+            stepsampler_info['frac_far_enough'] *= 100
+            if 'mean_distance' in stepsampler_info:
+                labeltext += (
+                    "<br/>"
+                    'Step sampler performance: %(rejection_rate).1f%% rej/step, %(mean_nsteps)d steps/it'
+                    'mean rel jump distance: %(mean_distance).2f (should be >1), %(frac_far_enough).2f%% (should be >50%%)'
+                ) % stepsampler_info
 
         if ndim == 1:
             pass

From 4e9f375d2d039828b85402807baff104210e3dbf Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 26 May 2024 16:41:54 +0200
Subject: [PATCH 248/313] try to fix cython int type error; add github CI for
 quick checks

closes issue https://github.com/JohannesBuchner/UltraNest/issues/134
(hopefully)
---
 .circleci/config.yml        |  7 ++++-
 .github/workflows/tests.yml | 44 ++++++++++++++++++++++++++++++++
 ultranest/integrator.py     |  6 +++--
 ultranest/mlfriends.pyx     | 10 ++++----
 ultranest/popstepsampler.py |  8 +++---
 ultranest/stepfuncs.pyx     |  4 +--
 ultranest/stepsampler.py    | 51 ++++++++++++++++++++-----------------
 7 files changed, 93 insertions(+), 37 deletions(-)
 create mode 100644 .github/workflows/tests.yml

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 3f78a630..1cf5aebf 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -17,7 +17,8 @@ jobs:
       
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 
-      - run: sudo python3 -m pip install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py
+      - run: sudo python3 -m pip install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py pydocstyle pycodestyle flake8
+
       - run: mkdir -p test-reports
 
       - run: python3 -m pip install -e .
@@ -35,6 +36,10 @@ jobs:
       - run: coverage3 report --include="$PWD/*" --omit="$PWD/.eggs/*"
       - run: coverage3 html --include="$PWD/*" --omit="$PWD/.eggs/*" && mv htmlcov test-reports
 
+      - run: flake8 $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc).py')
+      - run: pycodestyle $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc).py')
+      - run: pydocstyle $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc).py')
+
       - run: coveralls
       
       - store_test_results:
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
new file mode 100644
index 00000000..2c00f278
--- /dev/null
+++ b/.github/workflows/tests.yml
@@ -0,0 +1,44 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: build
+
+on:
+  push:
+  pull_request:
+  schedule:
+    - cron: '42 4 5,20 * *'
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [3.7, 3.8, 3.9, "3.10", 3.11, 3.12]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install dependencies
+      run: python -m pip install -r pip-requirements.txt
+
+    - name: Lint with flake8
+      run: flake8 $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc).py')
+
+    - name: Check code style
+      run: pycodestyle $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc).py')
+
+    - name: Check doc style
+      run: pydocstyle $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc).py')
+
+    - name: Install package
+      run: python -m pip install -e .
+
+    - name: Test with pytest
+      run: pytest -n -x
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index b3930987..3c4fb252 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -37,6 +37,8 @@
 
 __all__ = ['ReactiveNestedSampler', 'NestedSampler', 'read_file', 'warmstart_from_similar_file']
 
+int_t = int
+
 
 def _get_cumsum_range(pi, dp):
     """Compute quantile indices from probabilities.
@@ -2015,8 +2017,8 @@ def _update_region(
 
             # instead, track the clusters from before by matching manually
             oldt = self.transformLayer.transform(oldu)
-            clusterids = np.zeros(len(active_u), dtype=np.int_)
-            nnearby = np.empty(len(self.region.unormed), dtype=np.int_)
+            clusterids = np.zeros(len(active_u), dtype=int_t)
+            nnearby = np.empty(len(self.region.unormed), dtype=int_t)
             for ci in np.unique(self.transformLayer.clusterids):
                 if ci == 0:
                     continue
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index df2ad7b6..eb1247b3 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -27,7 +27,7 @@ cdef count_nearby(
     np.ndarray[np.float_t, ndim=2] apts,
     np.ndarray[np.float_t, ndim=2] bpts,
     np.float_t radiussq,
-    np.ndarray[np.int_, ndim=1] nnearby
+    np.ndarray[np.int_t, ndim=1] nnearby
 ):
     """Count the number of points in ``apts`` within square radius ``radiussq`` for each point ``b`` in `bpts``.
 
@@ -139,7 +139,7 @@ def find_nearby(
     np.ndarray[np.float_t, ndim=2] apts,
     np.ndarray[np.float_t, ndim=2] bpts,
     np.float_t radiussq,
-    np.ndarray[np.int_, ndim=1] nnearby
+    np.ndarray[np.int_t, ndim=1] nnearby
 ):
     """Gets the index of a point in `a` within square radius `radiussq`, for each point `b` in `bpts`.
 
@@ -223,7 +223,7 @@ cdef float compute_maxradiussq(np.ndarray[np.float_t, ndim=2] apts, np.ndarray[n
 @cython.wraparound(False)
 def compute_mean_pair_distance(
     np.ndarray[np.float_t, ndim=2] pts,
-    np.ndarray[np.int_, ndim=1] clusterids
+    np.ndarray[np.int_t, ndim=1] clusterids
 ):
     """Compute the average distance between pairs of points.
     Pairs from different clusters are excluded in the computation.
@@ -271,7 +271,7 @@ cdef _update_clusters(
     np.ndarray[np.float_t, ndim=2] upoints,
     np.ndarray[np.float_t, ndim=2] tpoints,
     np.float_t maxradiussq,
-    np.ndarray[np.int_, ndim=1] clusterids,
+    np.ndarray[np.int_t, ndim=1] clusterids,
 ):
     """same signature as ``update_clusters()``, see there."""
     assert upoints.shape[0] == tpoints.shape[0], ('different number of points', upoints.shape[0], tpoints.shape[0])
@@ -845,7 +845,7 @@ class LocalAffineLayer(AffineLayer):
         return s
 
 
-def vol_prefactor(np.int_ n):
+def vol_prefactor(np.int_t n):
     """Volume constant for an ``n``-dimensional sphere.
 
     for ``n`` even:  $$    (2pi)^(n    /2) / (2 * 4 * ... * n)$$
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index e32c00c8..48c9d4d3 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -17,6 +17,8 @@
 from ultranest.stepfuncs import generate_differential_direction, generate_mixture_random_direction
 import scipy.stats
 
+int_t = int
+
 
 def unitcube_line_intersection(ray_origin, ray_direction):
     r"""Compute intersection of a line (ray) and a unit box (0:1 in all axes).
@@ -425,7 +427,7 @@ def _setup(self, ndim):
         self.allL = np.zeros((self.popsize, self.nsteps + 1)) + np.nan
         self.currentt = np.zeros(self.popsize) + np.nan
         self.currentv = np.zeros((self.popsize, ndim)) + np.nan
-        self.generation = np.zeros(self.popsize, dtype=np.int_) - 1
+        self.generation = np.zeros(self.popsize, dtype=int_t) - 1
         self.current_left = np.zeros(self.popsize)
         self.current_right = np.zeros(self.popsize)
         self.searching_left = np.zeros(self.popsize, dtype=bool)
@@ -923,9 +925,9 @@ def __next__(
                 # Slice bounds for each points
                 tleft, tright = self.slice_limit(tleft_unitcube,tright_unitcube)
                 # Index of the workers working concurrently
-                worker_running = np.arange(0, self.popsize, 1, dtype=np.int_)
+                worker_running = np.arange(0, self.popsize, 1, dtype=int_t)
                 # Status indicating if a points has already find its next position
-                status = np.zeros(self.popsize, dtype=np.int_)  # one for success, zero for running
+                status = np.zeros(self.popsize, dtype=int_t)  # one for success, zero for running
 
                 # Loop until each points has found its next position or we reached 100 iterations
                 for it in range(self.max_it):
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index fd4c0111..44e7f2d5 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -331,7 +331,7 @@ def step_back(Lmin, allL, generation, currentt, log=False):
 
 cdef _fill_directions(
     np.ndarray[np.float_t, ndim=2] v,
-    np.ndarray[np.int_, ndim=1] indices,
+    np.ndarray[np.int_t, ndim=1] indices,
     float scale
 ):
     cdef size_t nsamples = v.shape[0]
@@ -533,7 +533,7 @@ cpdef tuple update_vectorised_slice_sampler(\
                           np.ndarray[np.float_t, ndim=1] t, np.ndarray[np.float_t, ndim=1] tleft,\
                           np.ndarray[np.float_t, ndim=1] tright, np.ndarray[np.float_t, ndim=1] proposed_L,\
                           np.ndarray[np.float_t, ndim=2] proposed_u, np.ndarray[np.float_t, ndim=2] proposed_p,\
-                          np.ndarray[np.int_, ndim=1] worker_running, np.ndarray[np.int_, ndim=1] status,\
+                          np.ndarray[np.int_t, ndim=1] worker_running, np.ndarray[np.int_t, ndim=1] status,\
                           np.float_t Likelihood_threshold,np.float_t shrink_factor, np.ndarray[np.float_t, ndim=2] allu,\
                           np.ndarray[np.float_t, ndim=1] allL, np.ndarray[np.float_t, ndim=2] allp, int popsize):
 
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index e161c03e..09dec8e1 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -466,33 +466,36 @@ def select_random_livepoint(us, Ls, Lmin):
 
 
 class IslandPopulationRandomLivepointSelector(object):
+    """Mutually isolated live point subsets.
+
+    To replace dead points, chains are only started from the same
+    island as the dead point. Island refers to chunks of
+    live point indices (0,1,2,3 as stored, not sorted).
+    Each chunk has size ´island_size´.
+
+    If ´island_size´ is large, for example, the total number of live points,
+    then clumping can occur more easily. This is the observed behaviour
+    that a limited random walk is run from one live point, giving
+    two similar points, then the next dead point replacement is
+    likely run again from these, giving more and more similar live points.
+    This gives a run-away process leading to clumps of similar,
+    highly correlated points.
+
+    If ´island_size´ is small, for example, 1, then each dead point
+    is replaced by a chain started from it. This is a problem because
+    modes can never die out. Nested sampling can then not complete.
+
+    In a multi-modal run, within a given number of live points,
+    the number of live points per mode is proportional to the mode's
+    prior volume, but can fluctuate. If the number of live points
+    is small, a fluctuation can lead to mode die-out, which cannot
+    be reversed. Therefore, the number of island members should be
+    large enough to represent each mode.
+    """
+
     def __init__(self, island_size, exchange_probability=0):
         """Set up multiple isolated islands.
 
-        To replace dead points, chains are only started from the same
-        island as the dead point. Island refers to chunks of
-        live point indices (0,1,2,3 as stored, not sorted).
-        Each chunk has size ´island_size´.
-
-        If ´island_size´ is large, for example, the total number of live points,
-        then clumping can occur more easily. This is the observed behaviour
-        that a limited random walk is run from one live point, giving
-        two similar points, then the next dead point replacement is
-        likely run again from these, giving more and more similar live points.
-        This gives a run-away process leading to clumps of similar,
-        highly correlated points.
-
-        If ´island_size´ is small, for example, 1, then each dead point
-        is replaced by a chain started from it. This is a problem because
-        modes can never die out. Nested sampling can then not complete.
-
-        In a multi-modal run, within a given number of live points,
-        the number of live points per mode is proportional to the mode's
-        prior volume, but can fluctuate. If the number of live points
-        is small, a fluctuation can lead to mode die-out, which cannot
-        be reversed. Therefore, the number of island members should be
-        large enough to represent each mode.
-
         Parameters
         -----------
         island_size: int

From 18db9fec6841b2bef8cdb4434ffe05a607d39937 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 26 May 2024 16:52:10 +0200
Subject: [PATCH 249/313] skip testing pathsampler in CI

---
 .github/workflows/tests.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 2c00f278..aa7601e7 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -29,13 +29,13 @@ jobs:
       run: python -m pip install -r pip-requirements.txt
 
     - name: Lint with flake8
-      run: flake8 $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc).py')
+      run: flake8 $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc|pathsampler).py')
 
     - name: Check code style
-      run: pycodestyle $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc).py')
+      run: pycodestyle $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc|pathsampler).py')
 
     - name: Check doc style
-      run: pydocstyle $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc).py')
+      run: pydocstyle $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc|pathsampler).py')
 
     - name: Install package
       run: python -m pip install -e .

From 86f26b16356b1f68fe44524ce79d1483217100a4 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 26 May 2024 16:59:07 +0200
Subject: [PATCH 250/313] avoid installing lots of deps

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index aa7601e7..2f96af54 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -26,7 +26,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
 
     - name: Install dependencies
-      run: python -m pip install -r pip-requirements.txt
+      run: python -m pip install cython numpy scipy matplotlib corner getdist h5py pandas flake8 pycodestyle pydocstyle pytest-html pytest-xdist
 
     - name: Lint with flake8
       run: flake8 $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc|pathsampler).py')

From 066def8ea2f8508e9e1c9d9d46b5dcf1cb1deb02 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 26 May 2024 17:01:31 +0200
Subject: [PATCH 251/313] [ci] pycodestyle settings

---
 setup.cfg | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/setup.cfg b/setup.cfg
index c47123ad..8b32c62f 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -28,5 +28,8 @@ addopts = --junitxml=test-reports/junit.xml  --html=tests/reports/index.html
 
 
 [pycodestyle]
-ignore = E231
+count = False
+ignore = W191,W291,W293,E231
+max-line-length = 160
+statistics = False
 

From 1b6a887325dd3861f4495c347b4946f678aba3b8 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 26 May 2024 17:03:36 +0200
Subject: [PATCH 252/313] [ci] pytest args

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 2f96af54..7b6fe97a 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -41,4 +41,4 @@ jobs:
       run: python -m pip install -e .
 
     - name: Test with pytest
-      run: pytest -n -x
+      run: pytest

From 6da81d81b54358c527c024d422ef5bfaaf86241c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 26 May 2024 17:22:20 +0200
Subject: [PATCH 253/313] skip slow tests

---
 .github/workflows/tests.yml   |  2 +-
 tests/test_popstepsampling.py | 11 +++++------
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 7b6fe97a..8cd5e1b9 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -41,4 +41,4 @@ jobs:
       run: python -m pip install -e .
 
     - name: Test with pytest
-      run: pytest
+      run: pytest -v -k 'not SLOW'
diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index d0d7baec..6995c9e0 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -216,8 +216,7 @@ def test_update_slice_sampler():
 
 # aim at checking the sanity of the results of 
 # one iteration of the slice sampler. 
-def Test_SimpleSliceSampler(seed):
-
+def test_SimpleSliceSampler_SLOW(seed=4):
     np.random.seed(seed)
     nsteps = 1 
     popsize = 100
@@ -245,7 +244,7 @@ def Test_SimpleSliceSampler(seed):
     # resetting the seed to check the slice axes
     np.random.seed(seed)
     for i in range(popsize):
-        u[i],_,L[i],_= stepsampler.__next__(region,Lmin,us.copy(),Ls.copy(),transform,loglike_vectorized,test=True)
+        u[i],_,L[i],_= stepsampler.__next__(region, Lmin, us.copy(), Ls.copy(), transform, loglike_vectorized, test=True)
 
     # Basic check
     assert (L>Lmin).all(), (L,Lmin) # Lmin check
@@ -253,13 +252,13 @@ def Test_SimpleSliceSampler(seed):
     
     np.random.seed(seed)
     # resetting the random generation inside the sampler
-    _=np.random.randint(0, us.shape[0], size=stepsampler.popsize)
-    _=stepsampler.scale_jitter_func()
+    np.random.randint(0, us.shape[0], size=stepsampler.popsize)
+    stepsampler.scale_jitter_func()
 
     # Getting the slice axes
     slice_axes =  stepsampler.generate_direction(us.copy(), region,scale= 1.0)
     for i in range(popsize):
-        v=(u[i,:]-us[i,:])/slice_axes[i,:]
+        v = (u[i,:] - us[i,:]) / slice_axes[i, :]
         mean_v = np.mean(v)
         assert np.allclose(mean_v, v, atol=1e-10), (mean_v, v)
 

From 074accba2ce22c6725de51a0ca3d7210b113bd47 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 26 May 2024 17:26:45 +0200
Subject: [PATCH 254/313] remove unnecessay setup.cfg sections

---
 setup.cfg | 13 -------------
 1 file changed, 13 deletions(-)

diff --git a/setup.cfg b/setup.cfg
index 8b32c62f..1625a0fd 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,16 +1,3 @@
-[bumpversion]
-current_version = 2.0.1
-commit = True
-tag = True
-
-[bumpversion:file:setup.py]
-search = version='{current_version}'
-replace = version='{new_version}'
-
-[bumpversion:file:ultranest/__init__.py]
-search = __version__ = '{current_version}'
-replace = __version__ = '{new_version}'
-
 [flake8]
 exclude = docs
 ignore = E501,F401,E128,E231,E124

From 4ea4a0e7911c82f41d50dfa2f48e156866a415af Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 26 May 2024 17:28:25 +0200
Subject: [PATCH 255/313] =?UTF-8?q?Bump=20version:=204.3.0=20=E2=86=92=204?=
 =?UTF-8?q?.3.1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index a781aa5d..198ad016 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.3.0',
+    version='4.3.1',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index c64c4e4f..aa36dc9e 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -13,4 +13,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.3.0'
+__version__ = '4.3.1'

From 7b01abecb51f9df981a7004935824d1b1460165a Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 26 May 2024 19:35:12 +0200
Subject: [PATCH 256/313] keep number of printed lines consistent

---
 ultranest/viz.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ultranest/viz.py b/ultranest/viz.py
index 150a1a2d..92beebc6 100644
--- a/ultranest/viz.py
+++ b/ultranest/viz.py
@@ -148,6 +148,8 @@ def nicelogger(points, info, region, transformLayer, region_fresh=False):
                 'Step sampler performance: %(rejection_rate).1f rej/step, %(mean_nsteps)d steps/it, '
                 'rel jump distance: %(mean_distance).2f (should be >1), %(frac_far_enough).2f%% (should be >50%%)') % stepsampler_info
             )
+        else:
+            print()
 
     print()
     if ndim == 1:

From dacc8dc2fec533d66b18bbb80b023ffba9ddbf32 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Mon, 27 May 2024 11:16:56 +0200
Subject: [PATCH 257/313] fix some string syntax issues in docs, and apply
 isort

---
 docs/example-line.ipynb      | 348 +++++++++++++++++++++++++++++------
 docs/example-warmstart.ipynb | 306 ++++++++++++++++++++++++------
 ultranest/__init__.py        |   1 -
 ultranest/calibrator.py      |   4 +-
 ultranest/dychmc.py          |   6 +-
 ultranest/dyhmc.py           |   2 +-
 ultranest/flatnuts.py        |   3 +-
 ultranest/hotstart.py        |   3 +-
 ultranest/integrator.py      |  44 +++--
 ultranest/netiter.py         |   8 +-
 ultranest/ordertest.py       |   2 +-
 ultranest/pathsampler.py     |  17 +-
 ultranest/plot.py            |  20 +-
 ultranest/popstepsampler.py  |  15 +-
 ultranest/samplingpath.py    |   2 +-
 ultranest/solvecompat.py     |   3 +-
 ultranest/stepsampler.py     |   9 +-
 ultranest/store.py           |   8 +-
 ultranest/utils.py           |   8 +-
 ultranest/viz.py             |  10 +-
 20 files changed, 641 insertions(+), 178 deletions(-)

diff --git a/docs/example-line.ipynb b/docs/example-line.ipynb
index 2ae804ef..8bb5423b 100644
--- a/docs/example-line.ipynb
+++ b/docs/example-line.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,15 +69,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "plt.figure()\n",
-    "xlabel = 'Bulge mass [log, $M_\\odot$]'\n",
-    "ylabel = 'Velocity dispersion [km/s]'\n",
+    "xlabel = r'Bulge mass [log, $M_\\odot$]'\n",
+    "ylabel = r'Velocity dispersion [km/s]'\n",
     "plt.xlabel(xlabel)\n",
     "plt.ylabel(ylabel)\n",
     "plt.errorbar(x=mB, xerr=mBerr, y=sigma, yerr=sigmaerr, \n",
@@ -104,9 +115,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Velocity dispersion [log, km/s]')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "samples = []\n",
     "\n",
@@ -129,17 +161,28 @@
     "    plt.scatter(samples_mBi, samples_logsigmai, s=2, marker='x')\n",
     "\n",
     "samples = np.array(samples)\n",
-    "xlabel = 'Bulge mass [log, $M_\\odot$]'\n",
-    "ylabel = 'Velocity dispersion [log, km/s]'\n",
+    "xlabel = r'Bulge mass [log, $M_\\odot$]'\n",
+    "ylabel = r'Velocity dispersion [log, km/s]'\n",
     "plt.xlabel(xlabel)\n",
     "plt.ylabel(ylabel)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(42, 2, 400)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "samples.shape"
    ]
@@ -165,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,18 +304,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] To achieve the desired logz accuracy, min_num_live_points was increased to 64\n",
+      "[ultranest] Sampling 64 live points from prior ...\n",
+      "[ultranest] Widening roots to 65 live points (have 64 already) ...\n",
+      "[ultranest] Sampling 1 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f9f279059404b20acd5fbacf674a7ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=4e+01  41.4092..41.4608] | it/evals=1116/2721 eff=41.9804% N=64          \n",
+      "[ultranest] Likelihood function evaluations: 2734\n",
+      "[ultranest]   logZ = 28.69 +- 0.3423\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 313.8, need >100)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.43+-0.16 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy wants 62 minimum live points (dlogz from 0.27 to 0.81, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.42 bs:0.34 tail:0.01 total:0.34 required:<0.50\n",
+      "[ultranest] done iterating.\n"
+     ]
+    }
+   ],
    "source": [
     "result = sampler.run(min_num_live_points=50, min_ess=100) # you can increase these numbers later"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 760x760 with 9 Axes>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 760x760 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from ultranest.plot import cornerplot\n",
     "cornerplot(sampler.results)"
@@ -280,9 +384,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure()\n",
     "plt.xlabel(xlabel)\n",
@@ -328,9 +443,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Posterior probability')"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x250 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "scatter_samples = result['weighted_samples']['points'][:,2]\n",
     "weights = result['weighted_samples']['weights']\n",
@@ -355,16 +491,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1186, 30)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "result['weighted_samples']['bootstrapped_weights'].shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -373,15 +520,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure()\n",
     "bins=np.linspace(0.01, 0.2, 64+1)\n",
     "scatter_samples = result['samples'][:,2]\n",
     "\n",
-    "pdf, _ = fastKDE.pdf(scatter_samples, axes=(bins,))\n",
+    "pdf = fastKDE.pdf_at_points(scatter_samples, list_of_points=bins)\n",
     "plt.plot(bins, pdf, color='k')\n",
     "\n",
     "from ultranest.plot import PredictionBand\n",
@@ -390,15 +548,15 @@
     "\n",
     "for weights in result['weighted_samples']['bootstrapped_weights'].transpose():\n",
     "    scatter_samples = resample_equal(result['weighted_samples']['points'][:,2], weights)\n",
-    "    pdf, _ = fastKDE.pdf(scatter_samples, axes=(bins,))\n",
+    "    pdf = fastKDE.pdf_at_points(scatter_samples, list_of_points=bins)\n",
     "    band.add(pdf)\n",
     "\n",
     "band.line(ls='--', color='r', alpha=0.5)\n",
     "band.shade(0.49, color='r', alpha=0.1)\n",
     "\n",
     "\n",
-    "plt.xlabel('$\\sigma$')\n",
-    "plt.ylabel(\"Posterior probability\")\n",
+    "plt.xlabel(r'$\\sigma$')\n",
+    "plt.ylabel(r\"Posterior probability\")\n",
     "#plt.yscale('log')\n",
     "plt.ylim(1e-3, 50);\n"
    ]
@@ -443,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -481,9 +639,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n",
+      "[ultranest] Widening roots to 457 live points (have 400 already) ...\n",
+      "[ultranest] Sampling 57 live points from prior ...\n",
+      "[ultranest] Widening roots to 520 live points (have 457 already) ...\n",
+      "[ultranest] Sampling 63 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edbc65e2aed04b56b395d92f59895bde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=2e+01  21.9772..21.9772]*| it/evals=4576/6329 eff=76.7086% N=400     0 0 \n",
+      "[ultranest] Likelihood function evaluations: 6374\n",
+      "[ultranest]   logZ = 15.1 +- 0.0855\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 1620.4, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.08 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.09, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.12 bs:0.09 tail:0.01 total:0.09 required:<0.50\n",
+      "[ultranest] done iterating.\n"
+     ]
+    }
+   ],
    "source": [
     "sampler0 = ultranest.ReactiveNestedSampler(parameters0, log_likelihood0, prior_transform0)\n",
     "result0 = sampler0.run()"
@@ -503,9 +701,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AIC of constant model: -41\n",
+      "AIC of line model    : -80\n"
+     ]
+    }
+   ],
    "source": [
     "Lmax0 = result0['weighted_samples']['logl'].max()\n",
     "AIC0 = -2 * Lmax0 + len(parameters0)\n",
@@ -539,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -548,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,7 +766,38 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n",
+      "[ultranest] Widening roots to 413 live points (have 400 already) ...\n",
+      "[ultranest] Sampling 13 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58fcef30127e463ab57f0bb4bb632a2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Z=12.5(0.00%) | Like=22.41..40.30 [14.7169..22.4322] | it/evals=2993/6028 eff=53.0721% N=400 0   0 0   \r"
+     ]
+    }
+   ],
    "source": [
     "Kpredicts = []\n",
     "\n",
@@ -650,8 +888,8 @@
    "outputs": [],
    "source": [
     "plt.figure()\n",
-    "plt.xlabel('Black Hole mass [log, $M_\\odot$]')\n",
-    "plt.ylabel('Bulge mass [log, $M_\\odot$]')\n",
+    "plt.xlabel(r'Black Hole mass [log, $M_\\odot$]')\n",
+    "plt.ylabel(r'Bulge mass [log, $M_\\odot$]')\n",
     "plt.errorbar(y=mB, yerr=mBerr, x=mBH, xerr=[mBHhi-mBH, mBH-mBHlo], \n",
     "             marker='o', ls=' ', color='orange');\n"
    ]
@@ -659,7 +897,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -673,9 +911,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index d06a3641..054ecff6 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,15 +98,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
     "plt.plot(wavelength, y_true, ':', color='gray')\n",
     "plt.ylabel('Spectral flux density [Jy]');\n",
-    "plt.xlabel('Wavelength [$\\mu$m]');\n"
+    "plt.xlabel(r'Wavelength [$\\mu$m]');\n"
    ]
   },
   {
@@ -128,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,9 +152,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.title(\"Prior predictive checks\")\n",
@@ -155,7 +177,7 @@
     "    y_predicted = black_body_model(wavelength, ampl, T)\n",
     "    plt.plot(wavelength, y_predicted, '-', color='gray')\n",
     "plt.ylabel('Spectral flux density [Jy]');\n",
-    "plt.xlabel('Wavelength [$\\mu$m]');\n"
+    "plt.xlabel('Wavelength [$\\\\mu$m]');\n"
    ]
   },
   {
@@ -169,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,9 +203,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74a0712aa182456fa12822d7251d85ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=2e+02  7 [173.9047..173.9128]*| it/evals=6080/46921 eff=13.0694% N=400    400  400 400 \n",
+      "[ultranest] Likelihood function evaluations: 46953\n",
+      "[ultranest] Writing samples and results to disk ...\n",
+      "[ultranest] Writing samples and results to disk ... done\n",
+      "[ultranest]   logZ = 159.8 +- 0.1527\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 981.1, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.06 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.43, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.19 bs:0.15 tail:0.41 total:0.43 required:<0.50\n",
+      "[ultranest] done iterating.\n",
+      "\n",
+      "logZ = 159.705 +- 0.538\n",
+      "  single instance: logZ = 159.705 +- 0.186\n",
+      "  bootstrapped   : logZ = 159.775 +- 0.353\n",
+      "  tail           : logZ = +- 0.406\n",
+      "insert order U test : converged: True correlation: inf iterations\n",
+      "\n",
+      "    Temperature         : 0.00945│ ▁▁▁▁▁▁▁▁▂▂▂▂▄▅▄▅▅▇▇▅▆▅▅▃▃▂▂▂▁▁▁▁▁▁▁ ▁ │0.01035    0.00989 +- 0.00012\n",
+      "    Amplitude           : 37.2  │ ▁▁▁▁▁▁▁▂▂▃▃▅▄▅▆▆▇▇▆▆▄▄▃▃▂▂▁▁▁▁▁▁▁▁▁▁▁ │59.7      47.4 +- 2.7\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from ultranest import ReactiveNestedSampler\n",
     "\n",
@@ -202,9 +272,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -215,7 +296,7 @@
     "band.line(color='k')\n",
     "band.shade(color='k', alpha=0.5)\n",
     "plt.ylabel('Spectral flux density [Jy]');\n",
-    "plt.xlabel('Wavelength [$\\mu$m]');\n"
+    "plt.xlabel('Wavelength [$\\\\mu$m]');\n"
    ]
   },
   {
@@ -229,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,9 +371,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Sampling 400 live points from prior ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0eb4138742fd484683ad88f6385be5fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ultranest] Explored until L=2e+02  5 [169.4613..169.4636]*| it/evals=1600/5358 eff=32.2711% N=400 \n",
+      "[ultranest] Likelihood function evaluations: 5393\n",
+      "[ultranest]   logZ = 166.7 +- 0.0473\n",
+      "[ultranest] Effective samples strategy satisfied (ESS = 763.2, need >400)\n",
+      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.08 nat, need <0.50 nat)\n",
+      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.41, need <0.5)\n",
+      "[ultranest]   logZ error budget: single: 0.08 bs:0.05 tail:0.41 total:0.41 required:<0.50\n",
+      "[ultranest] done iterating.\n"
+     ]
+    }
+   ],
    "source": [
     "sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized=vectorized)\n",
     "res = sampler.run(frac_remain=0.5)"
@@ -300,9 +417,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -320,7 +448,7 @@
     "band.shade(color='orange', alpha=0.5)\n",
     "plt.plot(wavelength, y_true, ':', color='gray')\n",
     "plt.ylabel('Spectral flux density [Jy]');\n",
-    "plt.xlabel('Wavelength [$\\mu$m]');\n"
+    "plt.xlabel('Wavelength [$\\\\mu$m]');\n"
    ]
   },
   {
@@ -332,9 +460,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Speed-up of warm-start: 770%\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Speed-up of warm-start: %d%%\" % ((results_ref['ncall'] / res['ncall'] - 1)*100))"
    ]
@@ -394,9 +530,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "posterior_samples = results_ref['samples']\n",
     "\n",
@@ -416,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -426,9 +573,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.subplot(2, 1, 1)\n",
     "plt.plot(uguess, pguess[:,0])\n",
@@ -449,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -458,7 +616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -467,9 +625,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████| 6505/6505 [00:00<00:00, 12544.08it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "nparams = len(parameters)\n",
     "u = np.ones(nparams) * 0.5\n",
@@ -504,9 +670,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "weights = results_ref['weighted_samples']['weights']\n",
     "i = np.random.choice(len(weights), p=weights, size=1000)\n",
@@ -542,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,9 +736,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# weight logl Temperature Amplitude\n",
+      "0.000154 0.000000 0.009828 0.584218\n",
+      "0.000154 0.000000 0.009933 0.582950\n",
+      "0.000154 0.000000 0.009911 0.583998\n",
+      "0.000154 0.000000 0.010134 0.580971\n",
+      "0.000154 0.000000 0.009717 0.585696\n",
+      "0.000154 0.000000 0.009823 0.584407\n",
+      "0.000154 0.000000 0.009729 0.585907\n",
+      "0.000154 0.000000 0.009938 0.582713\n",
+      "0.000154 0.000000 0.009849 0.582950\n"
+     ]
+    }
+   ],
    "source": [
     "!head custom-weighted_post_untransformed.txt"
    ]
@@ -609,9 +803,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index aa36dc9e..64bc2c06 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -10,7 +10,6 @@
 from .integrator import NestedSampler, ReactiveNestedSampler, read_file
 from .utils import vectorize
 
-
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
 __version__ = '4.3.1'
diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index ad404542..d4f03788 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -4,9 +4,11 @@
 ---------------------------
 """
 
+import os
+
 import numpy as np
+
 from ultranest.integrator import ReactiveNestedSampler
-import os
 
 
 def _substitute_log_dir(init_args, nsteps):
diff --git a/ultranest/dychmc.py b/ultranest/dychmc.py
index 01343f60..f9e25f8c 100644
--- a/ultranest/dychmc.py
+++ b/ultranest/dychmc.py
@@ -3,9 +3,11 @@
 Uses gradient to reflect at nested sampling boundaries.
 """
 
-from __future__ import print_function, division
-import numpy as np
+from __future__ import division, print_function
+
 import matplotlib.pyplot as plt
+import numpy as np
+
 
 def stop_criterion(thetaminus, thetaplus, rminus, rplus):
     """Compute the stop condition in the main loop
diff --git a/ultranest/dyhmc.py b/ultranest/dyhmc.py
index 2bbcc18f..f1e19679 100644
--- a/ultranest/dyhmc.py
+++ b/ultranest/dyhmc.py
@@ -4,8 +4,8 @@
 A helper surface is created using the live points.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 import scipy.special
 import scipy.stats
 
diff --git a/ultranest/flatnuts.py b/ultranest/flatnuts.py
index 1817e98b..2d5684c7 100644
--- a/ultranest/flatnuts.py
+++ b/ultranest/flatnuts.py
@@ -51,9 +51,10 @@
 """
 
 
+import matplotlib.pyplot as plt
 import numpy as np
 from numpy.linalg import norm
-import matplotlib.pyplot as plt
+
 from .samplingpath import angle, extrapolate_ahead
 
 
diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index 5946a243..e4aa4c50 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -12,7 +12,8 @@
 
 import numpy as np
 import scipy.stats
-from .utils import vectorize, resample_equal
+
+from .utils import resample_equal, vectorize
 
 
 def get_auxiliary_problem(loglike, transform, ctr, invcov, enlargement_factor, df=1):
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 3c4fb252..5b52fc8a 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -11,29 +11,36 @@
 # Some parts are from the Nestle library by Kyle Barbary (https://github.com/kbarbary/nestle)
 # Some parts are from the nnest library by Adam Moss (https://github.com/adammoss/nnest)
 
-from __future__ import print_function, division
+from __future__ import division, print_function
 
-import os
-import sys
 import csv
 import json
 import operator
+import os
+import sys
 import time
 import warnings
 
-from numpy import log, exp, logaddexp
 import numpy as np
+from numpy import exp, log, logaddexp
 
-from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify as _listify
-from .utils import is_affine_transform, normalised_kendall_tau_distance, distributed_work_chunk_size
-from ultranest.mlfriends import MLFriends, AffineLayer, LocalAffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
-from .store import HDF5PointStore, TextPointStore, NullPointStore
-from .viz import get_default_viz_callback
-from .ordertest import UniformOrderAccumulator
-from .netiter import PointPile, SingleCounter, MultiCounter, BreadthFirstIterator, TreeNode, count_tree_between, find_nodes_before, logz_sequence
-from .netiter import dump_tree, combine_results
-from .hotstart import get_auxiliary_contbox_parameterization
+from ultranest.mlfriends import (AffineLayer, LocalAffineLayer, MLFriends,
+                                 RobustEllipsoidRegion, ScalingLayer,
+                                 WrappingEllipsoid, find_nearby)
 
+from .hotstart import get_auxiliary_contbox_parameterization
+from .netiter import (BreadthFirstIterator, MultiCounter, PointPile,
+                      SingleCounter, TreeNode, combine_results,
+                      count_tree_between, dump_tree, find_nodes_before,
+                      logz_sequence)
+from .ordertest import UniformOrderAccumulator
+from .store import HDF5PointStore, NullPointStore, TextPointStore
+from .utils import (create_logger, distributed_work_chunk_size,
+                    is_affine_transform)
+from .utils import listify as _listify
+from .utils import (make_run_dir, normalised_kendall_tau_distance,
+                    resample_equal, vectorize, vol_prefactor)
+from .viz import get_default_viz_callback
 
 __all__ = ['ReactiveNestedSampler', 'NestedSampler', 'read_file', 'warmstart_from_similar_file']
 
@@ -914,8 +921,8 @@ def print_results(self):
     def plot(self):
         """Make corner plot."""
         if self.log_to_disk:
-            import matplotlib.pyplot as plt
             import corner
+            import matplotlib.pyplot as plt
             data = np.array(self.results['weighted_samples']['points'])
             weights = np.array(self.results['weighted_samples']['weights'])
             cumsumweights = np.cumsum(weights)
@@ -2989,8 +2996,9 @@ def plot_corner(self):
             cornerplot(results)
 
         """
-        from .plot import cornerplot
         import matplotlib.pyplot as plt
+
+        from .plot import cornerplot
         if self.log:
             self.logger.debug('Making corner plot ...')
         cornerplot(self.results, logger=self.logger if self.log else None)
@@ -3011,8 +3019,9 @@ def plot_trace(self):
             traceplot(results=results, labels=paramnames + derivedparamnames)
 
         """
-        from .plot import traceplot
         import matplotlib.pyplot as plt
+
+        from .plot import traceplot
         if self.log:
             self.logger.debug('Making trace plot ... ')
         paramnames = self.paramnames + self.derivedparamnames
@@ -3035,8 +3044,9 @@ def plot_run(self):
             runplot(results=results)
 
         """
-        from .plot import runplot
         import matplotlib.pyplot as plt
+
+        from .plot import runplot
         if self.log:
             self.logger.debug('Making run plot ... ')
         # get dynesty-compatible sequences
diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index 5a7b8f40..930cc6b1 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -21,12 +21,14 @@
 can ignore the rootids it does not know about.
 """
 
-import numpy as np
-from numpy import log, log1p, exp, logaddexp
 import math
 import sys
-from .utils import resample_equal
+
+import numpy as np
+from numpy import exp, log, log1p, logaddexp
+
 from .ordertest import UniformOrderAccumulator
+from .utils import resample_equal
 
 
 class TreeNode(object):
diff --git a/ultranest/ordertest.py b/ultranest/ordertest.py
index f50fd456..33d7f07d 100644
--- a/ultranest/ordertest.py
+++ b/ultranest/ordertest.py
@@ -22,7 +22,7 @@
 
 """
 
-from __future__ import print_function, division
+from __future__ import division, print_function
 
 __all__ = ['infinite_U_zscore', 'UniformOrderAccumulator']
 
diff --git a/ultranest/pathsampler.py b/ultranest/pathsampler.py
index 1370cc9e..85b3c182 100644
--- a/ultranest/pathsampler.py
+++ b/ultranest/pathsampler.py
@@ -3,16 +3,17 @@
 These features are experimental.
 """
 
-import numpy as np
-
 import matplotlib.pyplot as plt
+import numpy as np
 
-from ultranest.samplingpath import SamplingPath, ContourSamplingPath, extrapolate_ahead
-from ultranest.stepsampler import StepSampler
-from ultranest.stepsampler import generate_region_oriented_direction, generate_region_random_direction, generate_random_direction
-
-from ultranest.flatnuts import ClockedStepSampler, ClockedBisectSampler, ClockedNUTSSampler
-from ultranest.flatnuts import SingleJumper, DirectJumper, IntervalJumper
+from ultranest.flatnuts import (ClockedBisectSampler, ClockedNUTSSampler,
+                                ClockedStepSampler, DirectJumper,
+                                IntervalJumper, SingleJumper)
+from ultranest.samplingpath import (ContourSamplingPath, SamplingPath,
+                                    extrapolate_ahead)
+from ultranest.stepsampler import (StepSampler, generate_random_direction,
+                                   generate_region_oriented_direction,
+                                   generate_region_random_direction)
 
 
 class SamplingPathSliceSampler(StepSampler):
diff --git a/ultranest/plot.py b/ultranest/plot.py
index 45e66f79..9ed17743 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -5,25 +5,23 @@
 
 """
 
-from __future__ import (print_function, division)
-from six.moves import range
+from __future__ import division, print_function
 
 import logging
 import types
 import warnings
 
-import numpy as np
 import matplotlib.pyplot as pl
-from matplotlib.ticker import MaxNLocator, NullLocator
-# from matplotlib.colors import LinearSegmentedColormap, colorConverter
-from matplotlib.ticker import ScalarFormatter
-
-import scipy.stats
 import matplotlib.pyplot as plt
 import numpy
+import numpy as np
+import scipy.stats
+# from matplotlib.colors import LinearSegmentedColormap, colorConverter
+from matplotlib.ticker import MaxNLocator, NullLocator, ScalarFormatter
+from six.moves import range
 
-from .utils import resample_equal
 from .utils import quantile as _quantile
+from .utils import resample_equal
 
 try:
     str_type = types.StringTypes
@@ -183,8 +181,8 @@ def highest_density_interval_from_samples(xsamples, xlo=None, xhi=None, probabil
     >>> print('x = %.1f + %.2f - %.2f' % hdi)
     x = 0.0 + 1.02 - 0.96
     """
-    from getdist.mcsamples import MCSamples
     import getdist.chains
+    from getdist.mcsamples import MCSamples
     getdist.chains.print_load_details = False
     samples = MCSamples(
         samples=xsamples, names=['x'], ranges={'x':[xlo,xhi]},
@@ -431,6 +429,7 @@ def runplot(results, span=None, logplot=False, kde=True, nkde=1000,
         try:
             # from scipy.ndimage import gaussian_filter as norm_kde
             from scipy.stats import gaussian_kde
+
             # Derive kernel density estimate.
             wt_kde = gaussian_kde(resample_equal(-logvol, weights))  # KDE
             logvol_new = np.linspace(logvol[0], logvol[-1], nkde)  # resample
@@ -723,6 +722,7 @@ def traceplot(results, span=None, quantiles=[0.025, 0.5, 0.975], smooth=0.02,
         try:
             from scipy.ndimage import gaussian_filter as norm_kde
             from scipy.stats import gaussian_kde
+
             # Derive kernel density estimate.
             wt_kde = gaussian_kde(resample_equal(-logvol, weights))  # KDE
             logvol_grid = np.linspace(logvol[0], logvol[-1], nkde)  # resample
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 48c9d4d3..df3c2706 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -10,13 +10,18 @@
 """
 
 import numpy as np
-from ultranest.utils import submasks
-from ultranest.stepfuncs import evolve, step_back, update_vectorised_slice_sampler
-from ultranest.stepfuncs import generate_cube_oriented_direction, generate_cube_oriented_direction_scaled
-from ultranest.stepfuncs import generate_random_direction, generate_region_oriented_direction, generate_region_random_direction
-from ultranest.stepfuncs import generate_differential_direction, generate_mixture_random_direction
 import scipy.stats
 
+from ultranest.stepfuncs import (evolve, generate_cube_oriented_direction,
+                                 generate_cube_oriented_direction_scaled,
+                                 generate_differential_direction,
+                                 generate_mixture_random_direction,
+                                 generate_random_direction,
+                                 generate_region_oriented_direction,
+                                 generate_region_random_direction, step_back,
+                                 update_vectorised_slice_sampler)
+from ultranest.utils import submasks
+
 int_t = int
 
 
diff --git a/ultranest/samplingpath.py b/ultranest/samplingpath.py
index 82ddd705..4fe85935 100644
--- a/ultranest/samplingpath.py
+++ b/ultranest/samplingpath.py
@@ -4,9 +4,9 @@
 """
 
 
+import matplotlib.pyplot as plt
 import numpy as np
 from numpy.linalg import norm
-import matplotlib.pyplot as plt
 
 
 def nearest_box_intersection_line(ray_origin, ray_direction, fwd=True):
diff --git a/ultranest/solvecompat.py b/ultranest/solvecompat.py
index 2afbb107..274defcf 100644
--- a/ultranest/solvecompat.py
+++ b/ultranest/solvecompat.py
@@ -12,9 +12,10 @@
 """
 
 
-import numpy as np
 import string
 
+import numpy as np
+
 from .integrator import ReactiveNestedSampler
 from .stepsampler import SliceSampler, generate_mixture_random_direction
 
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 09dec8e1..f9ca468e 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -9,11 +9,14 @@
 even if they do not terminate at the same number of iterations.
 """
 
-from __future__ import print_function, division
-import numpy as np
+from __future__ import division, print_function
+
+from warnings import warn
+
 import matplotlib.pyplot as plt
+import numpy as np
+
 from .utils import listify as _listify
-from warnings import warn
 
 
 def generate_random_direction(ui, region, scale=1):
diff --git a/ultranest/store.py b/ultranest/store.py
index b3b22fc1..0764c069 100644
--- a/ultranest/store.py
+++ b/ultranest/store.py
@@ -10,10 +10,12 @@
 
 """
 
-from __future__ import print_function, division
-import numpy as np
-import warnings
+from __future__ import division, print_function
+
 import os
+import warnings
+
+import numpy as np
 
 
 class NullPointStore(object):
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 65fd4707..4e774269 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -4,13 +4,15 @@
 --------------------------------------------
 """
 
-from __future__ import print_function, division
+from __future__ import division, print_function
+
+import errno
 import logging
-import sys
 import os
+import sys
+
 import numpy as np
 from numpy import pi
-import errno
 
 
 def create_logger(module_name, log_dir=None, level=logging.INFO):
diff --git a/ultranest/viz.py b/ultranest/viz.py
index 92beebc6..3c44e700 100644
--- a/ultranest/viz.py
+++ b/ultranest/viz.py
@@ -9,15 +9,15 @@
 
 """
 
-from __future__ import print_function, division
+from __future__ import division, print_function
 
-import sys
 import shutil
-from numpy import log10
-import numpy as np
 import string
+import sys
 from xml.sax.saxutils import escape as html_escape
 
+import numpy as np
+from numpy import log10
 
 clusteridstrings = ['%d' % i for i in range(10)] + list(string.ascii_uppercase) + list(string.ascii_lowercase)
 
@@ -249,8 +249,8 @@ def initialize(self, paramnames, width):
             number of html table columns.
 
         """
-        from ipywidgets import HTML, VBox, Layout, GridspecLayout
         from IPython.display import display
+        from ipywidgets import HTML, GridspecLayout, Layout, VBox
 
         grid = GridspecLayout(len(paramnames), width + 3)
         self.laststatus = []

From c2054efbab0d5ede72f0a6db7532783ecdd1e83d Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 28 May 2024 19:31:12 +0200
Subject: [PATCH 258/313] make flake8 happy

---
 setup.cfg                   |  5 +++-
 ultranest/calibrator.py     |  2 +-
 ultranest/integrator.py     | 53 +++++++++++++++++--------------------
 ultranest/netiter.py        | 16 +++++------
 ultranest/plot.py           |  7 ++---
 ultranest/popstepsampler.py |  6 ++---
 ultranest/samplingpath.py   |  9 +++----
 ultranest/stepsampler.py    | 14 +++++-----
 ultranest/store.py          | 17 ++++++------
 ultranest/utils.py          |  2 +-
 ultranest/viz.py            |  8 +++---
 11 files changed, 69 insertions(+), 70 deletions(-)

diff --git a/setup.cfg b/setup.cfg
index 1625a0fd..78dd7114 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,9 @@
 [flake8]
 exclude = docs
-ignore = E501,F401,E128,E231,E124
+extend-ignore = E501,F401,E128,E231,E124
+per-file-ignores =
+    ultranest/plot.py: B006
+    ultranest/integrator.py: B006
 
 [aliases]
 # Define setup.py command aliases here
diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index d4f03788..cd432386 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -135,7 +135,7 @@ def run(self, **kwargs):
                 nsteps=nsteps, generate_direction=self.stepsampler.generate_direction,
                 check_nsteps=self.stepsampler.check_nsteps,
                 adaptive_nsteps=self.stepsampler.adaptive_nsteps,
-                log=open(init_args['log_dir'] + '/stepsampler.log', 'w') if 'log_dir' in self.init_args else None)
+                log=open(init_args['log_dir'] + '/stepsampler.log', 'w') if 'log_dir' in self.init_args else None)  # noqa: SIM115
             self.sampler = sampler
             result = sampler.run(**self.run_args)
             print("Z=%(logz).2f +- %(logzerr).2f" % result)
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 5b52fc8a..f74560b0 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -24,11 +24,10 @@
 import numpy as np
 from numpy import exp, log, logaddexp
 
-from ultranest.mlfriends import (AffineLayer, LocalAffineLayer, MLFriends,
-                                 RobustEllipsoidRegion, ScalingLayer,
-                                 WrappingEllipsoid, find_nearby)
-
 from .hotstart import get_auxiliary_contbox_parameterization
+from .mlfriends import (AffineLayer, LocalAffineLayer, MLFriends,
+                        RobustEllipsoidRegion, ScalingLayer, WrappingEllipsoid,
+                        find_nearby)
 from .netiter import (BreadthFirstIterator, MultiCounter, PointPile,
                       SingleCounter, TreeNode, combine_results,
                       count_tree_between, dump_tree, find_nodes_before,
@@ -280,7 +279,7 @@ def pop(Lmin):
             logls_new = []
 
         j = 0
-        for Lmin, active_values, children in batch:
+        for _Lmin, active_values, children in batch:
 
             next_node2 = explorer2.next_node()
             rootid2, node2, (active_nodes2, _, active_values2, _) = next_node2
@@ -325,7 +324,7 @@ def pop(Lmin):
                 last_good_like = last_good_like * epsilon
                 break
 
-            for u, v, logl_old in children:
+            for u, v, _logl_old in children:
                 logl_new = logls_new[j]
                 j += 1
 
@@ -416,7 +415,7 @@ def _update_region_bootstrap(region, nbootstraps, minvol=0., comm=None, mpi_size
     return r, f
 
 
-class NestedSampler(object):
+class NestedSampler:
     """Simple Nested sampler for reference."""
 
     def __init__(self,
@@ -591,7 +590,7 @@ def run(
         if self.log:
             # try to resume:
             self.logger.info('Resuming...')
-            for i in range(self.num_live_points):
+            for _i in range(self.num_live_points):
                 _, row = self.pointstore.pop(-np.inf)
                 if row is not None:
                     prev_logl.append(row[1])
@@ -995,7 +994,7 @@ def warmstart_from_similar_file(
             old_param_names = f.readline().lstrip('#').strip().split()
             auxiliary_usamples = np.loadtxt(f)
     except IOError:
-        warnings.warn('not hot-resuming, could not load file "%s"' % usample_filename)
+        warnings.warn('not hot-resuming, could not load file "%s"' % usample_filename, stacklevel=2)
         return param_names, loglike, transform, vectorized
 
     ulogl = auxiliary_usamples[:,1]
@@ -1022,7 +1021,7 @@ def warmstart_from_similar_file(
     )
 
 
-class ReactiveNestedSampler(object):
+class ReactiveNestedSampler:
     """Nested sampler with reactive exploration strategy.
 
     Storage & resume capable, optionally MPI parallelised.
@@ -1491,7 +1490,7 @@ def _widen_roots(self, nroots):
         if self.log and self.use_point_stack:
             # try to resume:
             # self.logger.info('Resuming...')
-            for i in range(nnewroots):
+            for _i in range(nnewroots):
                 rowid, row = self.pointstore.pop(-np.inf)
                 if row is None:
                     break
@@ -1681,7 +1680,7 @@ def _find_strategy(self, saved_logl, main_iterator, dlogz, dKL, min_ess):
 
         Llo_KL = np.inf
         Lhi_KL = -np.inf
-        for i, (pi, dKLi, logwi) in enumerate(zip(p.transpose(), dKLtot, other_logw)):
+        for pi, dKLi, logwi in zip(p.transpose(), dKLtot, other_logw):
             if dKLi > dKL:
                 ilo, ihi = _get_cumsum_range(pi, 1. / 400)
                 # ilo and ihi are most likely missing in this iterator
@@ -1711,9 +1710,8 @@ def _find_strategy(self, saved_logl, main_iterator, dlogz, dKL, min_ess):
         logzerr_tail = logaddexp(log(tail_fraction) + main_iterator.logZ, main_iterator.logZ) - main_iterator.logZ
         maxlogzerr = max(main_iterator.logZerr, deltalogZ.max(), main_iterator.logZerr_bs)
         if maxlogzerr > dlogz:
-            if logzerr_tail > maxlogzerr:
-                if self.log:
-                    self.logger.info("logz error is dominated by tail. Decrease frac_remain to make progress.")
+            if self.log and logzerr_tail > maxlogzerr:
+                self.logger.info("logz error is dominated by tail. Decrease frac_remain to make progress.")
             # very convervative estimation using all iterations
             # this punishes short intervals with many live points
             niter_max = len(saved_logl)
@@ -1815,7 +1813,7 @@ def _refill_samples(self, Lmin, ndraw, nit):
                 warning_message = warning_message1 + (warning_message2 % (' (stored for you in %s.csv)' % debug_filename))
             else:
                 warning_message = warning_message1 + warning_message2 % ''
-            warnings.warn(warning_message)
+            warnings.warn(warning_message, stacklevel=2)
             logl_region = self.loglike(self.transform(self.region.u))
             if (logl_region == Lmin).all():
                 raise ValueError(
@@ -1850,12 +1848,12 @@ def _create_point(self, Lmin, ndraw, active_u, active_values):
             assert self.region.inside(active_u).any(), \
                 ("None of the live points satisfies the current region!",
                  self.region.maxradiussq, self.region.u, self.region.unormed, active_u,
-                 getattr(self.region, 'bbox_lo'),
-                 getattr(self.region, 'bbox_hi'),
-                 getattr(self.region, 'ellipsoid_cov'),
-                 getattr(self.region, 'ellipsoid_center'),
-                 getattr(self.region, 'ellipsoid_invcov'),
-                 getattr(self.region, 'ellipsoid_cov'),
+                 self.region.bbox_lo,
+                 self.region.bbox_hi,
+                 self.region.ellipsoid_cov,
+                 self.region.ellipsoid_center,
+                 self.region.ellipsoid_invcov,
+                 self.region.ellipsoid_cov,
                  )
 
         nit = 0
@@ -2074,11 +2072,10 @@ def _update_region(
                 nextregion = self.region_class(active_u, nextTransformLayer)
                 assert np.isfinite(nextregion.unormed).all()
 
-                if not nextTransformLayer.nclusters < 20:
-                    if self.log:
-                        self.logger.info(
-                            "Found a lot of clusters: %d (%d with >1 members)",
-                            nextTransformLayer.nclusters, (cluster_sizes > 1).sum())
+                if self.log and not nextTransformLayer.nclusters < 20:
+                    self.logger.info(
+                        "Found a lot of clusters: %d (%d with >1 members)",
+                        nextTransformLayer.nclusters, (cluster_sizes > 1).sum())
 
                 # if self.log:
                 #     self.logger.info("computing maxradius...")
@@ -2380,7 +2377,7 @@ def run(
             *min_num_live_points* live points remain, but not more than
             *widen_before_initial_plateau_num_warn*.
         """
-        for result in self.run_iter(
+        for _result in self.run_iter(
             update_interval_volume_fraction=update_interval_volume_fraction,
             update_interval_ncall=update_interval_ncall,
             log_interval=log_interval,
diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index 930cc6b1..3ad2b561 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -31,7 +31,7 @@
 from .utils import resample_equal
 
 
-class TreeNode(object):
+class TreeNode:
     """Simple tree node."""
 
     def __init__(self, value=None, id=None, children=None):
@@ -60,7 +60,7 @@ def __lt__(self, other):
         return self.value < other.value
 
 
-class BreadthFirstIterator(object):
+class BreadthFirstIterator:
     """Generator exploring the tree.
 
     Nodes are ordered by value and expanded in order.
@@ -204,14 +204,14 @@ def print_tree(roots, title='Tree:'):
             lanes[laneid] = node.children[0]
         else:
             # expand width:
-            for j, child in enumerate(node.children):
+            for j, _child in enumerate(node.children):
                 rightstr2 = _stringify_lanes(lanes[laneid + 1:], char='\\')
                 if len(rightstr2) != 0:
                     sys.stdout.write(leftstr + '║' + ' ' * j + rightstr2 + "\n")
             sys.stdout.write(leftstr + '╠' + '╦' * (nchildren - 2) + '╗' + rightstr + "\n")
 
             lanes.pop(laneid)
-            for j, child in enumerate(node.children):
+            for child in node.children:
                 lanes.insert(laneid, child)
         explorer.expand_children_of(rootid, node)
         lastlane = laneid
@@ -383,7 +383,7 @@ def find_nodes_before(root, value):
     return parents, parent_weights
 
 
-class PointPile(object):
+class PointPile:
     """A in-memory linearized storage of point coordinates.
 
     :py:class:`TreeNode` objects only store the logL value and id,
@@ -465,7 +465,7 @@ def make_node(self, value, u, p):
         return TreeNode(value=value, id=index)
 
 
-class SingleCounter(object):
+class SingleCounter:
     """Evidence log(Z) and posterior weight summation for a Nested Sampling tree."""
 
     def __init__(self, random=False):
@@ -568,7 +568,7 @@ def passing_node(self, node, parallel_nodes):
                 self.logVolremaining += log1p(-1.0 / nlive)
 
 
-class MultiCounter(object):
+class MultiCounter:
     """Like :py:class:`SingleCounter`, but bootstrap capable.
 
     **Attributes**:
@@ -612,7 +612,7 @@ def __init__(self, nroots, nbootstraps=10, random=False, check_insertion_order=F
         self.rootids = [allyes]
         self.insertion_order_sample = []
         # np.random.seed(1)
-        for i in range(nbootstraps):
+        for _i in range(nbootstraps):
             mask = ~allyes
             rootids = np.unique(np.random.randint(nroots, size=nroots))
             mask[rootids] = True
diff --git a/ultranest/plot.py b/ultranest/plot.py
index 9ed17743..6bd8e80b 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -215,7 +215,7 @@ def highest_density_interval_from_samples(xsamples, xlo=None, xhi=None, probabil
     return MAP, MAP - x_lo, x_hi - MAP
 
 
-class PredictionBand(object):
+class PredictionBand:
     """Plot bands of model predictions as calculated from a chain.
 
     call add(y) to add predictions from each chain point
@@ -418,7 +418,8 @@ def runplot(results, span=None, logplot=False, kde=True, nkde=1000,
         else:
             warnings.warn("The number of iterations and samples differ "
                           "by an amount that isn't the number of final "
-                          "live points. `mark_final_live` has been disabled.")
+                          "live points. `mark_final_live` has been disabled.",
+                          stacklevel=3)
             mark_final_live = False
 
     # Determine plotting bounds for each subplot.
@@ -786,7 +787,7 @@ def traceplot(results, span=None, quantiles=[0.025, 0.5, 0.975], smooth=0.02,
         labels = [r"$x_{%d}$" % (i + 1) for i in range(ndim)]
 
     # Setting up smoothing.
-    if (isinstance(smooth, int_type) or isinstance(smooth, float_type)):
+    if (isinstance(smooth, int_type) or isinstance(smooth, float_type)):  # noqa: SIM101
         smooth = [smooth for i in range(ndim)]
 
     # Setting up default plot layout.
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index df3c2706..b4b0b3b0 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -310,7 +310,7 @@ def __next__(
             nc = self.nsteps * self.popsize
             nrejects_expected = self.nrejects + self.nsteps * self.popsize * (1 - 0.234)
 
-            for i in range(self.nsteps):
+            for _i in range(self.nsteps):
                 # perturb walker population
                 v = self.generate_direction(allu, region, self.scale)
                 # compute intersection of u + t * v with unit cube
@@ -912,7 +912,7 @@ def __next__(
 
             interval_final = 0.
 
-            for k in range(self.nsteps):
+            for _k in range(self.nsteps):
                 # Defining scale jitter
                 factor_scale = self.scale_jitter_func()
                 # Defining slice direction
@@ -935,7 +935,7 @@ def __next__(
                 status = np.zeros(self.popsize, dtype=int_t)  # one for success, zero for running
 
                 # Loop until each points has found its next position or we reached 100 iterations
-                for it in range(self.max_it):
+                for _it in range(self.max_it):
                     # Sampling points on the slices
                     slice_position = np.random.uniform(size=(self.popsize,))
                     t = tleft_worker + (tright_worker - tleft_worker) * slice_position
diff --git a/ultranest/samplingpath.py b/ultranest/samplingpath.py
index 4fe85935..5f61d046 100644
--- a/ultranest/samplingpath.py
+++ b/ultranest/samplingpath.py
@@ -90,8 +90,7 @@ def box_line_intersection(ray_origin, ray_direction):
     """
     pF, tF, iF = nearest_box_intersection_line(ray_origin, ray_direction, fwd=True)
     pN, tN, iN = nearest_box_intersection_line(ray_origin, ray_direction, fwd=False)
-    if tN > tF or tF < 0:
-        assert False, "no intersection"
+    assert not (tN > tF or tF < 0), "no intersection"
     return (pN, tN, iN), (pF, tF, iF)
 
 
@@ -365,7 +364,7 @@ def interpolate(i, points, fwd_possible, rwd_possible, contourpath=None):
     if j == i:  # we have this exact point in the chain
         return xj, vj, Lj, True
 
-    assert not k == i  # otherwise the above would be true too
+    assert k != i  # otherwise the above would be true too
 
     # expand_to_step explores each reflection in detail, so
     # any points with change in v should have j == i
@@ -389,7 +388,7 @@ def interpolate(i, points, fwd_possible, rwd_possible, contourpath=None):
     return xl, vj, None, True
 
 
-class SamplingPath(object):
+class SamplingPath:
     """Path described by a (potentially sparse) sequence of points.
 
     Convention of the stored point tuple ``(i, x, v, L)``:
@@ -466,7 +465,7 @@ def extrapolate(self, i):
         return newpoint
 
 
-class ContourSamplingPath(object):
+class ContourSamplingPath:
     """Region-aware form of the sampling path.
 
     Uses region points to guess a likelihood contour gradient.
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index f9ca468e..7dc16400 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -468,7 +468,7 @@ def select_random_livepoint(us, Ls, Lmin):
     return np.random.randint(len(Ls))
 
 
-class IslandPopulationRandomLivepointSelector(object):
+class IslandPopulationRandomLivepointSelector:
     """Mutually isolated live point subsets.
 
     To replace dead points, chains are only started from the same
@@ -545,7 +545,7 @@ def __call__(self, us, Ls, Lmin):
             min(len(Ls), (island + 1) * self.island_size))
 
 
-class StepSampler(object):
+class StepSampler:
     """Base class for a simple step sampler, staggering around.
 
     Scales proposal towards a 50% acceptance rate.
@@ -1010,7 +1010,7 @@ def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=10, plot=Fals
 
         """
         # find most recent point in history conforming to current Lmin
-        for j, (uj, Lj) in enumerate(self.history):
+        for j, (_uj, Lj) in enumerate(self.history):
             if not Lj > Lmin:
                 self.history = self.history[:j]
                 # print("wandered out of L constraint; reverting", ui[0])
@@ -1256,7 +1256,7 @@ def RegionBallSliceSampler(*args, **kwargs):
     return SliceSampler(*args, **kwargs, generate_direction=generate_region_random_direction)
 
 
-class SequentialDirectionGenerator(object):
+class SequentialDirectionGenerator:
     """Sequentially proposes one parameter after the next."""
 
     def __init__(self):
@@ -1301,7 +1301,7 @@ def __str__(self):
         return type(self).__name__ + '()'
 
 
-class SequentialRegionDirectionGenerator(object):
+class SequentialRegionDirectionGenerator:
     """Sequentially proposes one region axes after the next."""
 
     def __init__(self):
@@ -1349,7 +1349,7 @@ def RegionSequentialSliceSampler(*args, **kwargs):
     return SliceSampler(*args, **kwargs, generate_direction=SequentialRegionDirectionGenerator())
 
 
-class OrthogonalDirectionGenerator(object):
+class OrthogonalDirectionGenerator:
     """Orthogonalizes proposal vectors.
 
     Samples N proposed vectors by a provided method, then orthogonalizes
@@ -1403,7 +1403,7 @@ def __call__(self, ui, region, scale=1):
         return v
 
 
-class SpeedVariableGenerator(object):
+class SpeedVariableGenerator:
     """Propose directions with only some parameters variable.
 
     Propose in region direction, but only include some dimensions at a time.
diff --git a/ultranest/store.py b/ultranest/store.py
index 0764c069..fdf8dd53 100644
--- a/ultranest/store.py
+++ b/ultranest/store.py
@@ -12,13 +12,14 @@
 
 from __future__ import division, print_function
 
+import contextlib
 import os
 import warnings
 
 import numpy as np
 
 
-class NullPointStore(object):
+class NullPointStore:
     """No storage."""
 
     def __init__(self, ncols):
@@ -51,7 +52,7 @@ def pop(self, Lmin):
         return None, None
 
 
-class FilePointStore(object):
+class FilePointStore:
     """Base class for storing points in a file."""
 
     def reset(self):
@@ -119,7 +120,7 @@ def __init__(self, filepath, ncols):
         self.nrows = 0
         self.stack_empty = True
         self._load(filepath)
-        self.fileobj = open(filepath, 'ab')
+        self.fileobj = open(filepath, 'ab')  # noqa: SIM115
         self.fmt = '%.18e'
         self.delimiter = '\t'
 
@@ -127,18 +128,16 @@ def _load(self, filepath):
         """Load from data file *filepath*."""
         stack = []
         if os.path.exists(filepath):
-            try:
-                for line in open(filepath):
+            with contextlib.suppress(IOError), open(filepath) as f:
+                for line in f:
                     try:
                         parts = [float(p) for p in line.split()]
                         if len(parts) != self.ncols:
-                            warnings.warn("skipping lines in '%s' with different number of columns" % (filepath))
+                            warnings.warn("skipping lines in '%s' with different number of columns" % (filepath), stacklevel=3)
                             continue
                         stack.append(parts)
                     except ValueError:
-                        warnings.warn("skipping unparsable line in '%s'" % (filepath))
-            except IOError:
-                pass
+                        warnings.warn("skipping unparsable line in '%s'" % (filepath), stacklevel=3)
 
         self.stack = list(enumerate(stack))
         self.ncalls = len(self.stack)
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 4e774269..2a398f4e 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -421,7 +421,7 @@ def verify_gradient(ndim, transform, loglike, gradient, verbose=False, combinati
 
     eps = 1e-6
     N = 10
-    for i in range(N):
+    for _i in range(N):
         u = np.random.uniform(2 * eps, 1 - 2 * eps, size=(1, ndim))
         theta = transform(u)
         if verbose:
diff --git a/ultranest/viz.py b/ultranest/viz.py
index 3c44e700..1f722ae0 100644
--- a/ultranest/viz.py
+++ b/ultranest/viz.py
@@ -215,9 +215,9 @@ def isnotebook():
     """Check if running in a Jupyter notebook."""
     try:
         shell = get_ipython().__class__.__name__
-        if shell == 'ZMQInteractiveShell':
+        if shell == 'ZMQInteractiveShell':  # noqa: SIM103
             return True   # Jupyter notebook or qtconsole
-        elif shell == 'TerminalInteractiveShell':
+        elif shell == 'TerminalInteractiveShell':  # noqa: SIM103
             return False  # Terminal running IPython
         else:
             return False  # Other type (?)
@@ -225,7 +225,7 @@ def isnotebook():
         return False      # Probably standard Python interpreter
 
 
-class LivePointsWidget(object):
+class LivePointsWidget:
     """
     Widget for ipython and jupyter notebooks.
 
@@ -357,7 +357,7 @@ def __call__(self, points, info, region, transformLayer, region_fresh=False):
                             'positive degeneracy' if rho[i,j] > 0 else 'negative degeneracy',
                             param2, param, rho[i,j]))
 
-        for i, (param, fmt) in enumerate(zip(paramnames, paramformats)):
+        for i, (_param, fmt) in enumerate(zip(paramnames, paramformats)):
             if nmodes == 1:
                 line = [' ' for _ in range(width)]
                 for j in np.unique(indices[:,i]):

From 188895a8d2d4db6dafed172c71019c7e6f3ac377 Mon Sep 17 00:00:00 2001
From: jacopok <jacopok@gmail.com>
Date: Thu, 23 May 2024 14:18:32 +0200
Subject: [PATCH 259/313] docs: draft of results dict explanation

---
 ultranest/integrator.py | 36 ++++++++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index f74560b0..54b38abe 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2438,6 +2438,42 @@ def run_iter(
         Yields
         ------
         results: dict
+            - niter (int): number of sampler iterations (not likelihood evaluations!)
+            - logz (float64): natural logarithm of the evidence $Z = \int p(d|\theta) p(\theta) \text{d}\theta$
+            - logzerr (float64): $1\sigma$ error on $\log Z$ ([can be safely assumed to be Gaussian](https://github.com/JohannesBuchner/UltraNest/issues/63))
+            - logz_bs (float64): estimate of $\log Z$ from bootstrapping
+            - logzerr_bs (float64): error on the estimate of $\log Z$ from bootstrapping
+            - logz_single (float64): (?)
+            - logzerr_single (float64): (?)
+            - logzerr_tail (float64): remainder integral contribution (?)
+            - ess (float64): effective sample size, i.e. number of samples divided by the estimated correlation length
+            - H (float64): [information gained](https://arxiv.org/abs/2205.00009)
+            - Herr (float64): (Gaussian) $1\sigma$ error on $H$
+            - posterior (dict): summary information on the posterior marginals - a dictionary of lists each with as many items as the fit parameters, indexed as $\theta_i$ in the following:
+                - mean (list): expectation value of $\theta_i$
+                - stdev (list): standard deviation of $\theta_i$
+                - median (list): median of $\theta_i$
+                - errlo (list): one-sigma lower quantile of the marginal for $\theta_i$, i.e. $15.8655$% quantile
+                - errup (list): one-sigma upper quantile of the marginal for $\theta_i$, i.e. $84.1345$% quantile
+                - information_gain_bits (list): information gain from the marginal prior on $\theta_i$ to the posterior
+        - weighted_samples (dict): weighted samples from the posterior, as computed during sampling, sorted by their log-likelihood value
+            - upoints (ndarray): sample locations in the unit cube $[0, 1]^{d}$, where $d$ is the number of parameters - the shape is  `n_iter` by $d$
+            - points (ndarray): sample locations in the physical, user-provided space (same shape as `upoints`)
+            - weights (ndarray): sample weights - shape `n_iter`, they add to 1
+            - logw (ndarray): ?
+            - bootstrapped_weights (ndarray): ?
+            - logl (ndarray): log-likelihood values at the sample points (?)
+        - samples (ndarray): re-weighted posterior samples: distributed according to $p(\theta | d)$ - these points are not sorted, and can be assumed to have been randomly shuffled (?)
+        - maximum_likelihood (dict): summary information on the maximum likelihood value $\theta_{ML}$ found by the posterior exploration
+            - logl (float64): value of the log-likelihood at this point: $p(d | \theta_{ML})$
+            - point (list): coordinates of $\theta_{ML}$ in the physical space
+            - point_untransformed (list): coordinates of $\theta_{ML}$ in the unit cube
+        - ncall (int): total number of likelihood evaluations (accepted and not)
+        - paramnames (list): input parameter names
+        - insertion_order_MWW_test (dict): results for the MWW test (?, what is [Buchner+21 in prep](https://johannesbuchner.github.io/UltraNest/performance.html#output-files)?)
+            - independent_iterations (float)
+            - converged (bool)
+
         """
         # frac_remain=1  means 1:1 -> dlogz=log(0.5)
         # frac_remain=0.1 means 1:10 -> dlogz=log(0.1)

From 81d87f17afb50603c69b24d2fd8865d2be2ada39 Mon Sep 17 00:00:00 2001
From: jacopok <jacopok@gmail.com>
Date: Tue, 28 May 2024 15:19:20 +0200
Subject: [PATCH 260/313] Update draft of results dictionary documentation

---
 ultranest/integrator.py | 88 +++++++++++++++++++++++------------------
 1 file changed, 49 insertions(+), 39 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 54b38abe..8a664ce6 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2292,7 +2292,7 @@ def run(
             widen_before_initial_plateau_num_warn=10000,
             widen_before_initial_plateau_num_max=50000,
     ):
-        """Run until target convergence criteria are fulfilled.
+        r"""Run until target convergence criteria are fulfilled.
 
         Parameters
         ----------
@@ -2376,6 +2376,48 @@ def run(
             of initial live points so that once the plateau is traversed,
             *min_num_live_points* live points remain, but not more than
             *widen_before_initial_plateau_num_warn*.
+            
+            
+        Returns
+        ------
+        results (dict): Results dictionary, with the following entries:
+        
+            - samples (ndarray): re-weighted posterior samples: distributed according to :math:`p(\theta | d)` - these points are not sorted, and can be assumed to have been randomly shuffled. See :py:func:`ultranest.utils.resample_equal` for more details.
+            - niter (int): number of sampler iterations
+            - ncall (int): total number of likelihood evaluations (accepted and not)
+            - logz (float64): natural logarithm of the evidence :math:`\log Z = \log \int p(d|\theta) p(\theta) \text{d}\theta`
+            - logzerr (float64): :math:`1\sigma` error on :math:`\log Z` (`can be safely assumed to be Gaussian <https://github.com/JohannesBuchner/UltraNest/issues/63>`_)
+            - logz_bs (float64): estimate of :math:`\log Z` from bootstrapping - for details, see the `ultranest paper <https://joss.theoj.org/papers/10.21105/joss.03001>`_
+            - logzerr_bs (float64): estimate of the error on the  of :math:`\log Z` from bootstrapping
+            - logz_single (float64): estimate of :math:`\log Z` from a single sampler
+            - logzerr_single (float64): estimate of the error :math:`\log Z` from a single sampler
+            - logzerr_tail (float64): contribution of the tail (i.e. the terminal leaves of the tree) to the error on :math:`\log Z` (?)
+            - ess (float64): effective sample size, i.e. number of samples divided by the estimated correlation length
+            - H (float64): `information gained <https://arxiv.org/abs/2205.00009>`_
+            - Herr (float64): (Gaussian) :math:`1\sigma` error on :math:`H`
+            - posterior (dict): summary information on the posterior marginal distributions for each parameter - a dictionary of lists each with as many items as the fit parameters, indexed as :math:`\theta_i` in the following:
+                - mean (list): expectation value of :math:`\theta_i`
+                - stdev (list): standard deviation of :math:`\theta_i`
+                - median (list): median of :math:`\theta_i`
+                - errlo (list): one-sigma lower quantile of the marginal for :math:`\theta_i`, i.e. 15.8655% quantile
+                - errup (list): one-sigma upper quantile of the marginal for :math:`\theta_i`, i.e. 84.1345% quantile
+                - information_gain_bits (list): information gain from the marginal prior on :math:`\theta_i` to the posterior
+            - weighted_samples (dict): weighted samples from the posterior, as computed during sampling, sorted by their log-likelihood value
+                - upoints (ndarray): sample locations in the unit cube :math:`[0, 1]^{d}`, where $d$ is the number of parameters - the shape is  `n_iter` by :math:`d`
+                - points (ndarray): sample locations in the physical, user-provided space (same shape as `upoints`)
+                - weights (ndarray): sample weights - shape `n_iter`, they add up to 1
+                - logw (ndarray): ?
+                - bootstrapped_weights (ndarray): ?
+                - logl (ndarray): log-likelihood values at the sample points (?)
+            - maximum_likelihood (dict): summary information on the maximum likelihood value :math:`\theta_{ML}` found by the posterior exploration
+                - logl (float64): value of the log-likelihood at this point: :math:`\log p(d | \theta_{ML})`
+                - point (list): coordinates of :math:`\theta_{ML}` in the physical space
+                - point_untransformed (list): coordinates of :math:`\theta_{ML}` in the unit cube :math:`[0, 1]^{d}`
+            - paramnames (list): input parameter names
+            - insertion_order_MWW_test (dict): results for the `Mann-Whitney U-test <https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test>`_; for more information, see the :py:class:`ultranest.netiter.MultiCounter` class
+                - independent_iterations (float): shortest insertion order test run length
+                - converged (bool): whether the run is converged according to the MWW test, at the given threshold
+
         """
         for _result in self.run_iter(
             update_interval_volume_fraction=update_interval_volume_fraction,
@@ -2426,7 +2468,7 @@ def run_iter(
             widen_before_initial_plateau_num_warn=10000,
             widen_before_initial_plateau_num_max=50000,
     ):
-        """Iterate towards convergence.
+        r"""Iterate towards convergence.
 
         Use as an iterator like so::
 
@@ -2437,43 +2479,11 @@ def run_iter(
 
         Yields
         ------
-        results: dict
-            - niter (int): number of sampler iterations (not likelihood evaluations!)
-            - logz (float64): natural logarithm of the evidence $Z = \int p(d|\theta) p(\theta) \text{d}\theta$
-            - logzerr (float64): $1\sigma$ error on $\log Z$ ([can be safely assumed to be Gaussian](https://github.com/JohannesBuchner/UltraNest/issues/63))
-            - logz_bs (float64): estimate of $\log Z$ from bootstrapping
-            - logzerr_bs (float64): error on the estimate of $\log Z$ from bootstrapping
-            - logz_single (float64): (?)
-            - logzerr_single (float64): (?)
-            - logzerr_tail (float64): remainder integral contribution (?)
-            - ess (float64): effective sample size, i.e. number of samples divided by the estimated correlation length
-            - H (float64): [information gained](https://arxiv.org/abs/2205.00009)
-            - Herr (float64): (Gaussian) $1\sigma$ error on $H$
-            - posterior (dict): summary information on the posterior marginals - a dictionary of lists each with as many items as the fit parameters, indexed as $\theta_i$ in the following:
-                - mean (list): expectation value of $\theta_i$
-                - stdev (list): standard deviation of $\theta_i$
-                - median (list): median of $\theta_i$
-                - errlo (list): one-sigma lower quantile of the marginal for $\theta_i$, i.e. $15.8655$% quantile
-                - errup (list): one-sigma upper quantile of the marginal for $\theta_i$, i.e. $84.1345$% quantile
-                - information_gain_bits (list): information gain from the marginal prior on $\theta_i$ to the posterior
-        - weighted_samples (dict): weighted samples from the posterior, as computed during sampling, sorted by their log-likelihood value
-            - upoints (ndarray): sample locations in the unit cube $[0, 1]^{d}$, where $d$ is the number of parameters - the shape is  `n_iter` by $d$
-            - points (ndarray): sample locations in the physical, user-provided space (same shape as `upoints`)
-            - weights (ndarray): sample weights - shape `n_iter`, they add to 1
-            - logw (ndarray): ?
-            - bootstrapped_weights (ndarray): ?
-            - logl (ndarray): log-likelihood values at the sample points (?)
-        - samples (ndarray): re-weighted posterior samples: distributed according to $p(\theta | d)$ - these points are not sorted, and can be assumed to have been randomly shuffled (?)
-        - maximum_likelihood (dict): summary information on the maximum likelihood value $\theta_{ML}$ found by the posterior exploration
-            - logl (float64): value of the log-likelihood at this point: $p(d | \theta_{ML})$
-            - point (list): coordinates of $\theta_{ML}$ in the physical space
-            - point_untransformed (list): coordinates of $\theta_{ML}$ in the unit cube
-        - ncall (int): total number of likelihood evaluations (accepted and not)
-        - paramnames (list): input parameter names
-        - insertion_order_MWW_test (dict): results for the MWW test (?, what is [Buchner+21 in prep](https://johannesbuchner.github.io/UltraNest/performance.html#output-files)?)
-            - independent_iterations (float)
-            - converged (bool)
-
+        
+        results (dict):
+        
+            Results dictionary computed at the current iteration, with the same
+            keys as discussed in the :py:meth:`run` method. 
         """
         # frac_remain=1  means 1:1 -> dlogz=log(0.5)
         # frac_remain=0.1 means 1:10 -> dlogz=log(0.1)

From d84ddfe0b8e2fd1d4c8750612954bb5f3a837421 Mon Sep 17 00:00:00 2001
From: jacopok <jacopok@gmail.com>
Date: Tue, 28 May 2024 15:43:28 +0200
Subject: [PATCH 261/313] docs: update information on logged files

---
 docs/performance.rst | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/docs/performance.rst b/docs/performance.rst
index e3ee853a..4e075531 100644
--- a/docs/performance.rst
+++ b/docs/performance.rst
@@ -98,16 +98,7 @@ If a `log_dir` directory was specified, you will find these files:
 * info folder: machine-readable summaries of the posterior
 
   * **post_summary.csv**: for each parameter: mean, std, median, upper and lower 1 sigma error. Can be read with `pandas.read_csv <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html>`_.
-  * **results.json**: Contains detailed output of the nested sampling run. Can be read with `json.load <https://docs.python.org/3/library/json.html>`_.
-
-    * paramnames: parameter names
-    * ncall, niter: Number of likelihood calls, nested sampling iterations
-    * maximum_likelihood: highest loglikelihood point found so far
-    * H, Herr: (global) information gain
-    * ess: effective sample size
-    * logz, logzerr: ln(Z) and its uncertainty. logzerr_tail is the remainder integral contribution, logzerr_bs is from bootstrapping
-    * posterior: for each parameter: mean, std, median, upper and lower 1 sigma error, and `information gain <https://arxiv.org/abs/2205.00009>`_.
-    * insertion_order_MWW_test: MWW test results (see Buchner+21 in prep)
+  * **results.json**: Contains detailed output of the nested sampling run, with all the same keys as the result dictionary in :py:meth:`ultranest.integrator.ReactiveNestedSampler.run`, except for ``samples`` and ``weighted_samples`` (as the sample information is saved in separate files - see the following entries in this list). Can be read with `json.load <https://docs.python.org/3/library/json.html>`_.
 
 * chains: machine-readable chains
 

From 65d5ca0951b1429752ef45764cc47ea396d28006 Mon Sep 17 00:00:00 2001
From: jacopok <jacopok@gmail.com>
Date: Tue, 28 May 2024 15:43:54 +0200
Subject: [PATCH 262/313] add details on the computation of logzerr and ess

---
 ultranest/integrator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 8a664ce6..4ad5d28e 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2386,13 +2386,13 @@ def run(
             - niter (int): number of sampler iterations
             - ncall (int): total number of likelihood evaluations (accepted and not)
             - logz (float64): natural logarithm of the evidence :math:`\log Z = \log \int p(d|\theta) p(\theta) \text{d}\theta`
-            - logzerr (float64): :math:`1\sigma` error on :math:`\log Z` (`can be safely assumed to be Gaussian <https://github.com/JohannesBuchner/UltraNest/issues/63>`_)
+            - logzerr (float64): global estimate of the :math:`1\sigma` error on :math:`\log Z` (`can be safely assumed to be Gaussian <https://github.com/JohannesBuchner/UltraNest/issues/63>`_); obtained as the quadratic mean of ``logz_bs`` and ``logz_tail``
             - logz_bs (float64): estimate of :math:`\log Z` from bootstrapping - for details, see the `ultranest paper <https://joss.theoj.org/papers/10.21105/joss.03001>`_
             - logzerr_bs (float64): estimate of the error on the  of :math:`\log Z` from bootstrapping
             - logz_single (float64): estimate of :math:`\log Z` from a single sampler
             - logzerr_single (float64): estimate of the error :math:`\log Z` from a single sampler
             - logzerr_tail (float64): contribution of the tail (i.e. the terminal leaves of the tree) to the error on :math:`\log Z` (?)
-            - ess (float64): effective sample size, i.e. number of samples divided by the estimated correlation length
+            - ess (float64): effective sample size, i.e. number of samples divided by the estimated correlation length, estimated as :math:`N / (1 + N^{-1} \sum_i (N w_i - 1)^2)`
             - H (float64): `information gained <https://arxiv.org/abs/2205.00009>`_
             - Herr (float64): (Gaussian) :math:`1\sigma` error on :math:`H`
             - posterior (dict): summary information on the posterior marginal distributions for each parameter - a dictionary of lists each with as many items as the fit parameters, indexed as :math:`\theta_i` in the following:

From 629900aa82a5ad363634d41a6cd4f5e611206453 Mon Sep 17 00:00:00 2001
From: jacopok <jacopok@gmail.com>
Date: Tue, 28 May 2024 15:45:12 +0200
Subject: [PATCH 263/313] docs: small formatting fixes

---
 ultranest/integrator.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 4ad5d28e..5117b9fb 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2392,7 +2392,7 @@ def run(
             - logz_single (float64): estimate of :math:`\log Z` from a single sampler
             - logzerr_single (float64): estimate of the error :math:`\log Z` from a single sampler
             - logzerr_tail (float64): contribution of the tail (i.e. the terminal leaves of the tree) to the error on :math:`\log Z` (?)
-            - ess (float64): effective sample size, i.e. number of samples divided by the estimated correlation length, estimated as :math:`N / (1 + N^{-1} \sum_i (N w_i - 1)^2)`
+            - ess (float64): effective sample size, i.e. number of samples divided by the estimated correlation length, estimated as :math:`N / (1 + N^{-1} \sum_i (N w_i - 1)^2)` where :math:`w_i` are the sample weights
             - H (float64): `information gained <https://arxiv.org/abs/2205.00009>`_
             - Herr (float64): (Gaussian) :math:`1\sigma` error on :math:`H`
             - posterior (dict): summary information on the posterior marginal distributions for each parameter - a dictionary of lists each with as many items as the fit parameters, indexed as :math:`\theta_i` in the following:
@@ -2403,9 +2403,9 @@ def run(
                 - errup (list): one-sigma upper quantile of the marginal for :math:`\theta_i`, i.e. 84.1345% quantile
                 - information_gain_bits (list): information gain from the marginal prior on :math:`\theta_i` to the posterior
             - weighted_samples (dict): weighted samples from the posterior, as computed during sampling, sorted by their log-likelihood value
-                - upoints (ndarray): sample locations in the unit cube :math:`[0, 1]^{d}`, where $d$ is the number of parameters - the shape is  `n_iter` by :math:`d`
-                - points (ndarray): sample locations in the physical, user-provided space (same shape as `upoints`)
-                - weights (ndarray): sample weights - shape `n_iter`, they add up to 1
+                - upoints (ndarray): sample locations in the unit cube :math:`[0, 1]^{d}`, where $d$ is the number of parameters - the shape is ``n_iter`` by :math:`d`
+                - points (ndarray): sample locations in the physical, user-provided space (same shape as ``upoints``)
+                - weights (ndarray): sample weights - shape ``n_iter``, they add up to 1
                 - logw (ndarray): ?
                 - bootstrapped_weights (ndarray): ?
                 - logl (ndarray): log-likelihood values at the sample points (?)

From c7e66aa031c9651b0dd9ca686bc5b39f9f98f3f9 Mon Sep 17 00:00:00 2001
From: jacopok <jacopok@gmail.com>
Date: Tue, 28 May 2024 16:24:49 +0200
Subject: [PATCH 264/313] docs: small tweaks to results explanation

---
 ultranest/integrator.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 5117b9fb..3430e5e8 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2381,16 +2381,16 @@ def run(
         Returns
         ------
         results (dict): Results dictionary, with the following entries:
-        
+                
             - samples (ndarray): re-weighted posterior samples: distributed according to :math:`p(\theta | d)` - these points are not sorted, and can be assumed to have been randomly shuffled. See :py:func:`ultranest.utils.resample_equal` for more details.
+            - logz (float64): natural logarithm of the evidence :math:`\log Z = \log \int p(d|\theta) p(\theta) \text{d}\theta`
+            - logzerr (float64): global estimate of the :math:`1\sigma` error on :math:`\log Z` (`can be safely assumed to be Gaussian <https://github.com/JohannesBuchner/UltraNest/issues/63>`_); obtained as the quadratic sum of ``logz_bs`` and ``logz_tail``. Users are advised to use ``logz`` :math:`\pm` ``logzerr`` as the best estimate for the evidence and its error.
             - niter (int): number of sampler iterations
             - ncall (int): total number of likelihood evaluations (accepted and not)
-            - logz (float64): natural logarithm of the evidence :math:`\log Z = \log \int p(d|\theta) p(\theta) \text{d}\theta`
-            - logzerr (float64): global estimate of the :math:`1\sigma` error on :math:`\log Z` (`can be safely assumed to be Gaussian <https://github.com/JohannesBuchner/UltraNest/issues/63>`_); obtained as the quadratic mean of ``logz_bs`` and ``logz_tail``
             - logz_bs (float64): estimate of :math:`\log Z` from bootstrapping - for details, see the `ultranest paper <https://joss.theoj.org/papers/10.21105/joss.03001>`_
             - logzerr_bs (float64): estimate of the error on the  of :math:`\log Z` from bootstrapping
             - logz_single (float64): estimate of :math:`\log Z` from a single sampler
-            - logzerr_single (float64): estimate of the error :math:`\log Z` from a single sampler
+            - logzerr_single (float64): estimate of the error :math:`\log Z` from a single sampler, obtained as :math:`\sqrt{H / n_{\text{live}}}`
             - logzerr_tail (float64): contribution of the tail (i.e. the terminal leaves of the tree) to the error on :math:`\log Z` (?)
             - ess (float64): effective sample size, i.e. number of samples divided by the estimated correlation length, estimated as :math:`N / (1 + N^{-1} \sum_i (N w_i - 1)^2)` where :math:`w_i` are the sample weights
             - H (float64): `information gained <https://arxiv.org/abs/2205.00009>`_
@@ -2405,19 +2405,18 @@ def run(
             - weighted_samples (dict): weighted samples from the posterior, as computed during sampling, sorted by their log-likelihood value
                 - upoints (ndarray): sample locations in the unit cube :math:`[0, 1]^{d}`, where $d$ is the number of parameters - the shape is ``n_iter`` by :math:`d`
                 - points (ndarray): sample locations in the physical, user-provided space (same shape as ``upoints``)
-                - weights (ndarray): sample weights - shape ``n_iter``, they add up to 1
-                - logw (ndarray): ?
-                - bootstrapped_weights (ndarray): ?
-                - logl (ndarray): log-likelihood values at the sample points (?)
+                - weights (ndarray): sample weights - shape ``n_iter``, they sum to 1
+                - logw (ndarray): logs of the sample weights (?)
+                - bootstrapped_weights (ndarray): bootstrapped estimate of the sample weights
+                - logl (ndarray): log-likelihood values at the sample points
             - maximum_likelihood (dict): summary information on the maximum likelihood value :math:`\theta_{ML}` found by the posterior exploration
                 - logl (float64): value of the log-likelihood at this point: :math:`\log p(d | \theta_{ML})`
                 - point (list): coordinates of :math:`\theta_{ML}` in the physical space
                 - point_untransformed (list): coordinates of :math:`\theta_{ML}` in the unit cube :math:`[0, 1]^{d}`
             - paramnames (list): input parameter names
-            - insertion_order_MWW_test (dict): results for the `Mann-Whitney U-test <https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test>`_; for more information, see the :py:class:`ultranest.netiter.MultiCounter` class
+            - insertion_order_MWW_test (dict): results for the Mann-Whitney U-test; for more information, see the :py:class:`ultranest.netiter.MultiCounter` class or `section 4.5.2 of Buchner 2023 <http://arxiv.org/abs/2101.09675>`_
                 - independent_iterations (float): shortest insertion order test run length
                 - converged (bool): whether the run is converged according to the MWW test, at the given threshold
-
         """
         for _result in self.run_iter(
             update_interval_volume_fraction=update_interval_volume_fraction,

From c0006be2d7b0b325f7d58237e1102150a05de8a4 Mon Sep 17 00:00:00 2001
From: jacopok <jacopok@gmail.com>
Date: Wed, 29 May 2024 16:32:25 +0200
Subject: [PATCH 265/313] fix whitespace

---
 ultranest/integrator.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 3430e5e8..982b4606 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2376,12 +2376,12 @@ def run(
             of initial live points so that once the plateau is traversed,
             *min_num_live_points* live points remain, but not more than
             *widen_before_initial_plateau_num_warn*.
-            
-            
+
+
         Returns
         ------
         results (dict): Results dictionary, with the following entries:
-                
+
             - samples (ndarray): re-weighted posterior samples: distributed according to :math:`p(\theta | d)` - these points are not sorted, and can be assumed to have been randomly shuffled. See :py:func:`ultranest.utils.resample_equal` for more details.
             - logz (float64): natural logarithm of the evidence :math:`\log Z = \log \int p(d|\theta) p(\theta) \text{d}\theta`
             - logzerr (float64): global estimate of the :math:`1\sigma` error on :math:`\log Z` (`can be safely assumed to be Gaussian <https://github.com/JohannesBuchner/UltraNest/issues/63>`_); obtained as the quadratic sum of ``logz_bs`` and ``logz_tail``. Users are advised to use ``logz`` :math:`\pm` ``logzerr`` as the best estimate for the evidence and its error.
@@ -2478,12 +2478,11 @@ def run_iter(
 
         Yields
         ------
-        
+
         results (dict):
-        
             Results dictionary computed at the current iteration, with the same
-            keys as discussed in the :py:meth:`run` method. 
-        """
+            keys as discussed in the :py:meth:`run` method.
+"""
         # frac_remain=1  means 1:1 -> dlogz=log(0.5)
         # frac_remain=0.1 means 1:10 -> dlogz=log(0.1)
         # dlogz_min = log(1./(1 + frac_remain))

From 6a61fe1a438b9657021b97658fbe779cdfe06a21 Mon Sep 17 00:00:00 2001
From: jacopok <jacopok@gmail.com>
Date: Wed, 29 May 2024 17:25:32 +0200
Subject: [PATCH 266/313] split long lines

---
 ultranest/integrator.py | 50 ++++++++++++++++++++++++++++++-----------
 1 file changed, 37 insertions(+), 13 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 982b4606..990abf60 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2382,39 +2382,63 @@ def run(
         ------
         results (dict): Results dictionary, with the following entries:
 
-            - samples (ndarray): re-weighted posterior samples: distributed according to :math:`p(\theta | d)` - these points are not sorted, and can be assumed to have been randomly shuffled. See :py:func:`ultranest.utils.resample_equal` for more details.
-            - logz (float64): natural logarithm of the evidence :math:`\log Z = \log \int p(d|\theta) p(\theta) \text{d}\theta`
-            - logzerr (float64): global estimate of the :math:`1\sigma` error on :math:`\log Z` (`can be safely assumed to be Gaussian <https://github.com/JohannesBuchner/UltraNest/issues/63>`_); obtained as the quadratic sum of ``logz_bs`` and ``logz_tail``. Users are advised to use ``logz`` :math:`\pm` ``logzerr`` as the best estimate for the evidence and its error.
+            - samples (ndarray): re-weighted posterior samples: distributed according
+            to :math:`p(\theta | d)` - these points are not sorted, and can be assumed
+            to have been randomly shuffled.
+            See :py:func:`ultranest.utils.resample_equal` for more details.
+            - logz (float64): natural logarithm of the evidence
+            :math:`\log Z = \log \int p(d|\theta) p(\theta) \text{d}\theta`
+            - logzerr (float64): global estimate of the :math:`1\sigma` error on
+            :math:`\log Z`
+            (`can be safely assumed to be Gaussian <https://github.com/JohannesBuchner/UltraNest/issues/63>`_);
+            obtained as the quadratic sum of ``logz_bs`` and ``logz_tail``.
+            Users are advised to use ``logz`` :math:`\pm` ``logzerr``
+            as the best estimate for the evidence and its error.
             - niter (int): number of sampler iterations
             - ncall (int): total number of likelihood evaluations (accepted and not)
-            - logz_bs (float64): estimate of :math:`\log Z` from bootstrapping - for details, see the `ultranest paper <https://joss.theoj.org/papers/10.21105/joss.03001>`_
-            - logzerr_bs (float64): estimate of the error on the  of :math:`\log Z` from bootstrapping
+            - logz_bs (float64): estimate of :math:`\log Z` from bootstrapping -
+            for details, see the
+            `ultranest paper <https://joss.theoj.org/papers/10.21105/joss.03001>`_
+            - logzerr_bs (float64): estimate of the error on the  of :math:`\log Z`
+            from bootstrapping
             - logz_single (float64): estimate of :math:`\log Z` from a single sampler
-            - logzerr_single (float64): estimate of the error :math:`\log Z` from a single sampler, obtained as :math:`\sqrt{H / n_{\text{live}}}`
-            - logzerr_tail (float64): contribution of the tail (i.e. the terminal leaves of the tree) to the error on :math:`\log Z` (?)
-            - ess (float64): effective sample size, i.e. number of samples divided by the estimated correlation length, estimated as :math:`N / (1 + N^{-1} \sum_i (N w_i - 1)^2)` where :math:`w_i` are the sample weights
+            - logzerr_single (float64): estimate of the error :math:`\log Z` from a
+            single sampler, obtained as :math:`\sqrt{H / n_{\text{live}}}`
+            - logzerr_tail (float64): contribution of the tail (i.e. the terminal
+            leaves of the tree) to the error on :math:`\log Z` (?)
+            - ess (float64): effective sample size, i.e. number of samples divided by
+            the estimated correlation length, estimated as
+            :math:`N / (1 + N^{-1} \sum_i (N w_i - 1)^2)` where :math:`w_i` are
+            the sample weights while :math:`N` is the number of samples
             - H (float64): `information gained <https://arxiv.org/abs/2205.00009>`_
             - Herr (float64): (Gaussian) :math:`1\sigma` error on :math:`H`
-            - posterior (dict): summary information on the posterior marginal distributions for each parameter - a dictionary of lists each with as many items as the fit parameters, indexed as :math:`\theta_i` in the following:
+            - posterior (dict): summary information on the posterior marginal distributions for each parameter -
+            a dictionary of lists each with as many items as the fit parameters,
+            indexed as :math:`\theta_i` in the following:
                 - mean (list): expectation value of :math:`\theta_i`
                 - stdev (list): standard deviation of :math:`\theta_i`
                 - median (list): median of :math:`\theta_i`
                 - errlo (list): one-sigma lower quantile of the marginal for :math:`\theta_i`, i.e. 15.8655% quantile
                 - errup (list): one-sigma upper quantile of the marginal for :math:`\theta_i`, i.e. 84.1345% quantile
                 - information_gain_bits (list): information gain from the marginal prior on :math:`\theta_i` to the posterior
-            - weighted_samples (dict): weighted samples from the posterior, as computed during sampling, sorted by their log-likelihood value
-                - upoints (ndarray): sample locations in the unit cube :math:`[0, 1]^{d}`, where $d$ is the number of parameters - the shape is ``n_iter`` by :math:`d`
+            - weighted_samples (dict): weighted samples from the posterior, as computed during sampling,
+            sorted by their log-likelihood value
+                - upoints (ndarray): sample locations in the unit cube :math:`[0, 1]^{d}`,
+                where :math:`d` is the number of parameters - the shape is ``n_iter`` by :math:`d`
                 - points (ndarray): sample locations in the physical, user-provided space (same shape as ``upoints``)
                 - weights (ndarray): sample weights - shape ``n_iter``, they sum to 1
                 - logw (ndarray): logs of the sample weights (?)
                 - bootstrapped_weights (ndarray): bootstrapped estimate of the sample weights
                 - logl (ndarray): log-likelihood values at the sample points
-            - maximum_likelihood (dict): summary information on the maximum likelihood value :math:`\theta_{ML}` found by the posterior exploration
+            - maximum_likelihood (dict): summary information on the maximum likelihood value
+            :math:`\theta_{ML}` found by the posterior exploration
                 - logl (float64): value of the log-likelihood at this point: :math:`\log p(d | \theta_{ML})`
                 - point (list): coordinates of :math:`\theta_{ML}` in the physical space
                 - point_untransformed (list): coordinates of :math:`\theta_{ML}` in the unit cube :math:`[0, 1]^{d}`
             - paramnames (list): input parameter names
-            - insertion_order_MWW_test (dict): results for the Mann-Whitney U-test; for more information, see the :py:class:`ultranest.netiter.MultiCounter` class or `section 4.5.2 of Buchner 2023 <http://arxiv.org/abs/2101.09675>`_
+            - insertion_order_MWW_test (dict): results for the Mann-Whitney U-test;
+            for more information, see the :py:class:`ultranest.netiter.MultiCounter` class
+            or `section 4.5.2 of Buchner 2023 <http://arxiv.org/abs/2101.09675>`_
                 - independent_iterations (float): shortest insertion order test run length
                 - converged (bool): whether the run is converged according to the MWW test, at the given threshold
         """

From af444ea760d47b3dc08d956a65d5275ec02056e2 Mon Sep 17 00:00:00 2001
From: jacopok <jacopok@gmail.com>
Date: Wed, 29 May 2024 17:55:04 +0200
Subject: [PATCH 267/313] Comply with pydocstyle

---
 ultranest/integrator.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 990abf60..2d4d1c19 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2502,11 +2502,10 @@ def run_iter(
 
         Yields
         ------
-
         results (dict):
             Results dictionary computed at the current iteration, with the same
             keys as discussed in the :py:meth:`run` method.
-"""
+        """
         # frac_remain=1  means 1:1 -> dlogz=log(0.5)
         # frac_remain=0.1 means 1:10 -> dlogz=log(0.1)
         # dlogz_min = log(1./(1 + frac_remain))

From 653d1340300be5455f0d70e2ac1c3971e54a4ad3 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 29 May 2024 22:04:43 +0200
Subject: [PATCH 268/313] rst formatting of lists

---
 ultranest/integrator.py | 55 +++++++++++++++++++++++------------------
 1 file changed, 31 insertions(+), 24 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 2d4d1c19..7d04fb86 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2383,62 +2383,69 @@ def run(
         results (dict): Results dictionary, with the following entries:
 
             - samples (ndarray): re-weighted posterior samples: distributed according
-            to :math:`p(\theta | d)` - these points are not sorted, and can be assumed
-            to have been randomly shuffled.
-            See :py:func:`ultranest.utils.resample_equal` for more details.
+              to :math:`p(\theta | d)` - these points are not sorted, and can be assumed
+              to have been randomly shuffled.
+              See :py:func:`ultranest.utils.resample_equal` for more details.
             - logz (float64): natural logarithm of the evidence
-            :math:`\log Z = \log \int p(d|\theta) p(\theta) \text{d}\theta`
+              :math:`\log Z = \log \int p(d|\theta) p(\theta) \text{d}\theta`
             - logzerr (float64): global estimate of the :math:`1\sigma` error on
-            :math:`\log Z`
-            (`can be safely assumed to be Gaussian <https://github.com/JohannesBuchner/UltraNest/issues/63>`_);
-            obtained as the quadratic sum of ``logz_bs`` and ``logz_tail``.
-            Users are advised to use ``logz`` :math:`\pm` ``logzerr``
-            as the best estimate for the evidence and its error.
+              :math:`\log Z`
+              (`can be safely assumed to be Gaussian <https://github.com/JohannesBuchner/UltraNest/issues/63>`_);
+              obtained as the quadratic sum of ``logz_bs`` and ``logz_tail``.
+              Users are advised to use ``logz`` :math:`\pm` ``logzerr``
+              as the best estimate for the evidence and its error.
             - niter (int): number of sampler iterations
             - ncall (int): total number of likelihood evaluations (accepted and not)
             - logz_bs (float64): estimate of :math:`\log Z` from bootstrapping -
-            for details, see the
-            `ultranest paper <https://joss.theoj.org/papers/10.21105/joss.03001>`_
+              for details, see the
+              `ultranest paper <https://joss.theoj.org/papers/10.21105/joss.03001>`_
             - logzerr_bs (float64): estimate of the error on the  of :math:`\log Z`
-            from bootstrapping
+              from bootstrapping
             - logz_single (float64): estimate of :math:`\log Z` from a single sampler
             - logzerr_single (float64): estimate of the error :math:`\log Z` from a
-            single sampler, obtained as :math:`\sqrt{H / n_{\text{live}}}`
+              single sampler, obtained as :math:`\sqrt{H / n_{\text{live}}}`
             - logzerr_tail (float64): contribution of the tail (i.e. the terminal
-            leaves of the tree) to the error on :math:`\log Z` (?)
+              leaves of the tree) to the error on :math:`\log Z` (?)
             - ess (float64): effective sample size, i.e. number of samples divided by
-            the estimated correlation length, estimated as
-            :math:`N / (1 + N^{-1} \sum_i (N w_i - 1)^2)` where :math:`w_i` are
-            the sample weights while :math:`N` is the number of samples
+              the estimated correlation length, estimated as
+              :math:`N / (1 + N^{-1} \sum_i (N w_i - 1)^2)` where :math:`w_i` are
+              the sample weights while :math:`N` is the number of samples
             - H (float64): `information gained <https://arxiv.org/abs/2205.00009>`_
             - Herr (float64): (Gaussian) :math:`1\sigma` error on :math:`H`
             - posterior (dict): summary information on the posterior marginal distributions for each parameter -
-            a dictionary of lists each with as many items as the fit parameters,
-            indexed as :math:`\theta_i` in the following:
+              a dictionary of lists each with as many items as the fit parameters,
+              indexed as :math:`\theta_i` in the following:
+
                 - mean (list): expectation value of :math:`\theta_i`
                 - stdev (list): standard deviation of :math:`\theta_i`
                 - median (list): median of :math:`\theta_i`
                 - errlo (list): one-sigma lower quantile of the marginal for :math:`\theta_i`, i.e. 15.8655% quantile
                 - errup (list): one-sigma upper quantile of the marginal for :math:`\theta_i`, i.e. 84.1345% quantile
                 - information_gain_bits (list): information gain from the marginal prior on :math:`\theta_i` to the posterior
+
             - weighted_samples (dict): weighted samples from the posterior, as computed during sampling,
-            sorted by their log-likelihood value
+              sorted by their log-likelihood value
+
                 - upoints (ndarray): sample locations in the unit cube :math:`[0, 1]^{d}`,
-                where :math:`d` is the number of parameters - the shape is ``n_iter`` by :math:`d`
+                  where :math:`d` is the number of parameters - the shape is ``n_iter`` by :math:`d`
                 - points (ndarray): sample locations in the physical, user-provided space (same shape as ``upoints``)
                 - weights (ndarray): sample weights - shape ``n_iter``, they sum to 1
                 - logw (ndarray): logs of the sample weights (?)
                 - bootstrapped_weights (ndarray): bootstrapped estimate of the sample weights
                 - logl (ndarray): log-likelihood values at the sample points
+
             - maximum_likelihood (dict): summary information on the maximum likelihood value
-            :math:`\theta_{ML}` found by the posterior exploration
+              :math:`\theta_{ML}` found by the posterior exploration
+
                 - logl (float64): value of the log-likelihood at this point: :math:`\log p(d | \theta_{ML})`
                 - point (list): coordinates of :math:`\theta_{ML}` in the physical space
                 - point_untransformed (list): coordinates of :math:`\theta_{ML}` in the unit cube :math:`[0, 1]^{d}`
+
             - paramnames (list): input parameter names
             - insertion_order_MWW_test (dict): results for the Mann-Whitney U-test;
-            for more information, see the :py:class:`ultranest.netiter.MultiCounter` class
-            or `section 4.5.2 of Buchner 2023 <http://arxiv.org/abs/2101.09675>`_
+              for more information, see the :py:class:`ultranest.netiter.MultiCounter` class
+              or `section 4.5.2 of Buchner 2023 <http://arxiv.org/abs/2101.09675>`_
+
                 - independent_iterations (float): shortest insertion order test run length
                 - converged (bool): whether the run is converged according to the MWW test, at the given threshold
         """

From 10e1320596ff9a4c2bdf752baa105aef4ecdc952 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 29 May 2024 22:16:26 +0200
Subject: [PATCH 269/313] prettify the bottom of the README, add contributors

---
 Makefile                     |  11 +-
 README.rst                   |  26 ++-
 docs/example-line.ipynb      | 322 +++++------------------------------
 docs/example-warmstart.ipynb | 294 ++++++--------------------------
 ultranest/__init__.py        |   8 +-
 5 files changed, 124 insertions(+), 537 deletions(-)

diff --git a/Makefile b/Makefile
index 628e7771..fc693b21 100644
--- a/Makefile
+++ b/Makefile
@@ -59,8 +59,12 @@ clean-doc:
 	rm -rf docs/build
 	nbstripout docs/*.ipynb
 
-lint: ## check style with flake8
-	flake8 ultranest tests
+SOURCES := $(shell ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc|pathsampler).py' | grep -v .pyx.py)
+
+lint: ${SOURCES} ## check style
+	flake8 ${SOURCES}
+	pycodestyle ${SOURCES}
+	pydocstyle ${SOURCES}
 
 test: ## run tests quickly with the default Python
 	PYTHONPATH=. pytest
@@ -68,6 +72,9 @@ test: ## run tests quickly with the default Python
 test-all: ## run tests on every Python version with tox
 	tox
 
+build:
+	$(PYTHON) setup.py build_ext --inplace
+
 coverage: ## check code coverage quickly with the default Python
 	PYTHONPATH=. coverage run --source ultranest -m pytest
 	coverage report -m
diff --git a/README.rst b/README.rst
index 4203749e..fdccaa4f 100644
--- a/README.rst
+++ b/README.rst
@@ -114,15 +114,15 @@ Features
 Usage
 ^^^^^
 
-`Get started! <https://johannesbuchner.github.io/UltraNest/using-ultranest.html>`_
+* `Get started! <https://johannesbuchner.github.io/UltraNest/using-ultranest.html>`_
 
 Read the full documentation with tutorials at:
 
-https://johannesbuchner.github.io/UltraNest/
+* https://johannesbuchner.github.io/UltraNest/
 
-`API Reference: <https://johannesbuchner.github.io/UltraNest/ultranest.html#ultranest.integrator.ReactiveNestedSampler>`_.
+* `API Reference: <https://johannesbuchner.github.io/UltraNest/ultranest.html#ultranest.integrator.ReactiveNestedSampler>`_.
 
-`Code repository: https://github.com/JohannesBuchner/UltraNest/ <https://github.com/JohannesBuchner/UltraNest/>`_
+* `Code repository: https://github.com/JohannesBuchner/UltraNest/ <https://github.com/JohannesBuchner/UltraNest/>`_
 
 Licence
 ^^^^^^^
@@ -134,3 +134,21 @@ GPLv3 (see LICENCE file). If you require another license, please contact me.
 The cute hedgehog icon was made by `Freepik <https://www.flaticon.com/authors/freepik>`_.
 It symbolises UltraNest's approach of carefully walking up a likelihood,
 ready to defend against any encountered danger.
+
+Contributors
+^^^^^^^^^^^^
+ * Nicholas Susemiehl
+ * QZ Gao
+ * Sigfried Vanaverbeke
+ * Warrick Ball
+ * Adipol Phosrisom
+ * Alexander Harvey Nitz
+ * Gregory David Martinez
+ * Grigorii Smirnov-Pinchukov
+ * Fabio F Acero
+ * Jacopo Tissino
+ * Benjamin Beauchesne
+ * Kyle Barbary (some ellipsoid code adopted from https://github.com/kbarbary/nestle)
+ * Adam Moss (some architecture and parallelisation adopted from https://github.com/adammoss/nnest)
+ * Josh Speagle (some visualisations adopted from https://github.com/joshspeagle/dynesty/)
+ * Johannes Buchner
diff --git a/docs/example-line.ipynb b/docs/example-line.ipynb
index 8bb5423b..74b33068 100644
--- a/docs/example-line.ipynb
+++ b/docs/example-line.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,20 +69,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -115,30 +104,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Velocity dispersion [log, km/s]')"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "samples = []\n",
     "\n",
@@ -169,20 +137,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(42, 2, 400)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "samples.shape"
    ]
@@ -208,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -286,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -304,79 +261,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] To achieve the desired logz accuracy, min_num_live_points was increased to 64\n",
-      "[ultranest] Sampling 64 live points from prior ...\n",
-      "[ultranest] Widening roots to 65 live points (have 64 already) ...\n",
-      "[ultranest] Sampling 1 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4f9f279059404b20acd5fbacf674a7ac",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=4e+01  41.4092..41.4608] | it/evals=1116/2721 eff=41.9804% N=64          \n",
-      "[ultranest] Likelihood function evaluations: 2734\n",
-      "[ultranest]   logZ = 28.69 +- 0.3423\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 313.8, need >100)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.43+-0.16 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy wants 62 minimum live points (dlogz from 0.27 to 0.81, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.42 bs:0.34 tail:0.01 total:0.34 required:<0.50\n",
-      "[ultranest] done iterating.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "result = sampler.run(min_num_live_points=50, min_ess=100) # you can increase these numbers later"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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pgHkR8+wMjYiIiHIJJsqU44T4hWCZwzLERsSqjq2tt1ZtWblCjqE+Q5ksExERUYYxUaYcJ+JDBGIjYtF2dVscGHAAANDvQr9kI8pBPkHY02sPIj5EMFEmIiKiDGOiTDmWZRlL1WubSjbQN9bXYjRERESU2zBRJp0S4heCiA8RqZYJ8gnKpmiIiIjoR8ZEmXSGurnHKZEr5FDkU2RDVERERPSjYqJMWqFnoIfO2zurXgNf5x67bHSBlYNVqvUVlgqYFjBN1kZ2xElEREQ/Bv7mJ62Q6klRrks5teesHKxgW8U2Xe2k1IampBYnERER5W7cwpqIiIiISA2OKJNWKOOU8NnjAwBwcHGAVC/j79k00YYu9EFERES6ib/1SSviouOws+tO7Oy6E3HRcVprQxf6ICIiIt3ERJmIiIiISA0mykREREREajBRJiIiIiJSg4kyEREREZEaTJSJiIiIiNRgokxEREREpAbXUSatkOnL0H5te9VrbbWhC30QERGRbmKiTFohk8tQya2S1tvQhT6IiIhIN3HqBRERERGRGhxRJq1Qxinx9NhTAEDJFiUzvYX197ahC30QERGRbuJvfdKKuOg4bHHegi3OW75rC+vvbUMX+iAiIiLdxESZiIiIiEgNJspERERERGowUSYiIiIiUoOJMhERERGRGkyUiYiIiIjUYKJMRERERKQG11EmrZDpy9BqaSvVa221oQt9EBERkW5iokxaIZPLUGNoDa23oQt9EBERkW7i1AsiIiIiIjU4okxaoYxXwu+8HwCgSP0ikMoysYW1BtrQhT6IiIhIN/G3PmlFXFQc1jdaj/WN1iMuKpNbWGugDV3og4iIiHQTE2UiIiIiIjWYKBMRERERqcFEmYiIiIhIDSbKRERERERqMFEmIiIiIlKDiTIRERERkRpcR5m0QiaXoemcpqrX2mpDF/ogIiIi3cREmbRCpi9D3bF1td6GLvRBREREuolTL4iIiIiI1OCIMmmFMl6JtzffAgBsq9hmegvr721DF/ogIiIi3cTf+qQVcVFxWF1jNVbXWP1dW1h/bxu60AcRERHpJibKRERERERqMFEmIiIiIlKDiTIRERERkRpMlImIiIiI1GCiTERERESkBhNlIiIiIiI1uI4yaYVMLkNDj4aq19pqQxf6ICIiIt3ERJm0QqYvg5Onk9bb0IU+iIiISDdx6gURERERkRocUSatEEqBIJ8gAICVgxUkUolW2tCFPoiIiEg3cUSZtCI2MhbLyy/H8vLLERsZq7U2dKEPIiIi0k1MlImIiIiI1GCiTERERESkBhNlIiIiIiI1mCgTEREREanBRJmIiIiISA0mykREREREanAdZdIKmVyG2mNqq15rqw1d6IOIiIh0ExNl0gqZvgzN5zbXehu60AcRERHpJk69ICIiIiJSgyPKpBVCKRDiFwIAMC9inuktrL+3DV3og4iIiHQTR5RJK2IjY7Go2CIsKrbou7aw/t42dKEPIiIi0k1MlImIiIiI1ODUC8r1gnyCMl3321Hkd7ffwbywOcyLmGsiLCIiItJxTJQp11JYKiBXyLGn1x6NtLe23lrIFXIM9RnKZJmIiOgHwESZci3zIuYY6jMUER8iMt1GbGQs1tZbCwBou7otDgw4gIgPEUyUiYiIfgBMlClXMy/yfVMlYsJjVK8ty1hqIiQiIiLKIfgwHxERERGRGhxRJq2Q6klRbUg11WtttZGhPmR8X0lERPQjYaJMWqFnoIc2y9povY2M9PH25tss7YuIiIh0C4fIiIiIiIjU4IgyaYUQQrUahcJSAYkkE1tYa6CNjPQhhNB4+0RERKS7mCiTVsRGxGKe9TwAwISwCdA31tdKGxnpo9+Ffhpvn4iIiHQXp14QEREREanBRJmIiIiISA0mykREREREajBRJiIiIiJSg4kyEREREZEaTJSJiIiIiNTg8nCkFVI9KSr2rah6ra02MtQHt7AmIiL6oTBRpmwR4hei2rgjUc1fagIAgh4EJfzrE5ShNvUM9NBhXQeNxJeePriFNRER0Y+FiTJluRC/ECxzWIbYiNg0y8oVcigsFdkQFREREVHqmChTlov4EIHYiFi4bHSBlYMVgITtoOOi4gAAeoZ6qu2nFZYKmBcxT1e7QghV8i1XyLNsC+vEPriFNRER0Y+FiTJlGysHK9hWsQUAxITHYKbJTADft4X197aRkT64hTUREdGPhU8nERERERGpwUSZiIiIiEgNJspERERERGowUSYiIiIiUoOJMhERERGRGkyUiYiIiIjU4PJwpBVSmRRlO5dVvdZWGxnpQyLV/DrNREREpLuYKJNW6BnqocuOLlpvIyN9cAtrIiKiHwunXhARERERqcFEmYiIiIhIDU69IK3QxBbWmmgjI31wC2siIqIfC0eUiYiIiIjUYKJMRERERKQGE2UiIiIiIjWYKBMRERERqcFEmYiIiIhIDSbKRERERERqcHk40gqpTAr71vaq19pqIyN9cAtrIiKiHwsTZdIKPUM9uB5y1XobGemDW1gTERH9WDj1goiIiIhIDSbKRERERERqcOoFaUVMeAzmWc8DAIx5PybTW1h/bxsZ6aPX8V4ab5+IiIh0FxNl0prYiFidaEMX+iAiIiLdw6kXRERERERqMFEmIiIiIlKDiTIRERERkRpMlImIiIiI1GCiTERERESkBle9IK2QSCWwa2ineq2tNjLUh4RbWBMREf1ImCiTVsiN5HDzdtN6Gxnpg1tYExER/Vg49YKIiIiISA0mykREREREanDqBWlFTHgMFhVdBAD49eWvmd7C+nvbyEgf3fZ2AwAE+QSlWU9hqYB5EXONx0NERETZh4kyaU3EhwidaCO9fSjyKSBXyLGn154068gVcgz1GcpkmYiIKAdjokyUTmaFzTDUZ2iayXmQTxD29NqDiA8RTJSJiIhyMCbKRBlgXsScyS8REdEPgg/zERERERGpwUSZiIiIiEgNJspERERERGpwjjJphUQqQYFqBVSvtdWGLvRBREREuomJMmmF3EiOgdcGar0NXeiDiIiIdBOnXhARERERqcFEmYiIiIhIDSbKpBWxEbFYWHQhFhZdiNiIWK21oQt9EBERkW7iHGXSCiEEQl6FqF5rqw1d6IOIiIh0ExNl+i4hfiHp2tKZiIiIKKdhokyZFuIXgmUOy9I1JUGukENhqciGqIiIiIg0g4kyZVrEhwjERsTCZaMLrBysUi2rsFTAvIh5NkVGRERE9P2YKNN3s3Kwgm0VW22HQURERKRRXPWCiIiIiEgNjiiTVkgkEliVtVK91lYbutAHERER6SYmyqQVcoUcQx4M0XobutAHERER6SZOvSAiIiIiUoOJMhERERGRGkyUSStiI2LhVc4LXuW8vmsL6+9tQxf6ICIiIt3EOcqkFUIIBD0MUr3WVhu60AcRERHpJo4oExERERGpwUSZiIiIiEgNJspERERERGowUSYiIiIiUoOJMhERERGRGlz1grRCIpHA3M5c9VpbbehCH0RERKSbmCiTVsgVcox4OULrbWRlH0E+QWmWUVgqYF7EPFPtExERUdZiokykYQpLBeQKOfb02pNmWblCjqE+Q5ksExER6SAmykQaZl7EHEN9hiLiQ0Sq5YJ8grCn1x5EfIhgokxERKSDmCiTVsRGxmJdg3UAALdzbpAbybXSRlb1YV7EnMkvERFRDsdEmbRCKAUCrgeoXmurDV3og4iIiHQTl4cjIiIiIlKDiTIRERERkRpMlImIiIiI1GCiTERERESkBhNlIiIiIiI1uOoFaY3CUqETbehCH0RERKR7mCiTVugb62Ns0Fitt6ELfRAREZFu4tQLIiIiIiI1OKL8gwnxC0lza2UgYboBd5YjIiKiHxkT5R9IiF8IljksQ2xEbJpl5Qo5hvoMzbJkOTYyFptabQIA9DzSM9NbWH9vG7rQBxEREekmJso/kIgPEYiNiIXLRhdYOVilWC7IJwh7eu3Bq/Ov0iyXWUIp8OrsK9VrbbWhC30QERGRbmKi/AOycrCCbRXbFM8rLBWQK+TY02tPmm3JFXKuCvGd0vOGg1NhiIiIsh8TZUrGvIg5hvoM5VzmLJbRNyRZORWGiIiIkmOiTGqZFzFnUpbF0vuGJHEqTMSHCH5PiFIRFBQENzc3eHt7o1ChQvDy8kKTJk0yVd7d3R0HDhxAeHg47OzsMGPGDLRt2za7LoWIdAQTZSIt4hsSooxzc3ODk5MT3NzckhwfOnQobGxsEBQUhJMnT6Jr167w9fVF3rx51baTWvlRo0ZhyZIlMDAwwLVr19C0aVM8f/4c+fLly4YrJCJdwXWUiYgoxwsLC8PevXsxefJkKBQKtGvXDo6Ojti3b1+mypcpUwYGBgYAAIlEgpiYGLx58ybbroeIdAMTZdIauUIOueL7llvTRBu60AeRrrh27RqGDRuGcuXKwdjYGEWKFEHXrl3x5MmTdNUPCwuDh4cHWrZsibx580IikWDdunWp1rl58ybatWuHvHnzQqFQoHz58li8eHGG4vb19YWJiQkKFSqkOubo6IgHDx5kuvyQIUNgZGSE6tWro3HjxnB0dMxQTESU83HqBWmFvrE+fg//Xett6EIflHPFxMSgV69eOHToEOLj43HmzBnUrl07xeOacPPmTezfvx9OTk5wcnLSSJvfmj17Ni5evIguXbqgQoUKePfuHZYuXYoqVargypUrKF++fKr1P3z4gClTpqBIkSKoWLEivL29Uy1//PhxtG3bFpUrV8akSZNgYmKCZ8+e4fXr1xmKOywsDGZmZkmOmZmZ4ePHj5ku7+XlhSVLlsDb2xv379+HRCLJUExElPMxUSYiyqSNGzdix44dKFOmDFq2bKmav5rScU24efMmJk+eDABZkiiPGjUKmzdvhr6+vupYt27d4OjoiFmzZmHjxo2p1re1tcXbt29hY2OD69evo3r16imWDQ0NRZ8+fdCmTRvs3LkTUmnKf+R0dnbGhQsXAAARERHYvn07RowYAQAYP348WrRogdDQ0GTtm5iYqG3PxMQkXeVlMhmaNGmChQsXwt7eHq1bt04xRiLKfZgoExFlUuKo56hRozBw4MA0j+cEderUSXbM3t4e5cqVg4+PT5r1DQwMYGNjk66+Nm/ejMDAQEyfPh1SqRTh4eEwMjJSmzAfPHhQ9Vrdw3xhYWEICwvDmzdvULBgQQDA/fv30adPH7V929vbZ6h8XFwcnj59mq7rIqLcg3OUSSviouKwuc1mbG6zGXFRcVprQxf6IN114MAB1K1bF8bGxjAzM0Pz5s1Vo5pFixaFh4cHgISlxBL/LJ/S8UePHqFr166wsbGBkZERHBwcMHv2bCiVSlV/R48eRe3atWFkZAQbGxsMHz4cwcHBqvNFixZVJd6TJ0+GRCLBy5cvs/rLACEEAgMDYWlpqdF2T548CTMzM7x58walS5eGiYkJzMzMMHjwYERFRWWoLRMTE7Rv3x4eHh6IjIzEwYMHcffuXbRv3z7D5UNCQrB582aEhYUhLi4OO3bswJkzZ9CgQQNNXDYR5SAcUSatUMYr4XvYV/VaW23oQh+km1atWgV3d3eYmZmhZ8+eiImJwdatW9GoUSPs27cPgwcPxoEDB3Dx4kW0atUKFSpUAAC1x4ODg1G3bl18+fIF3bt3h0KhwPHjxzF+/Hh8/PgRc+bMwe7du9G5c2fY2dmhX79+eP36NZYtW4Zz587hypUrMDIywuDBg3H27FkcOXIEdevWRb169WBunvXLC27atAlv3rzBlClTNNqur68v4uLi0L59e/z000+YOXMmvL29sWTJEgQHB2PLli0Zas/Lywt9+/ZFvnz5UKhQIWzbti3J0nCtWrVC/fr18fvvv6daPjQ0FKtWrcKQIUMghEDJkiWxefNmVKpUSZOXT0Q5gSCNCQkJEQBESEiItkNRK+BGgPCEpwi4EaDtUER0WLTwhKfwhKeIDovWWhu60EdadOn79j10/f741pcvX4S5ubnQ19cXd+/eVR0/efKkACBKlCghhBDCw8NDABCrVq1KUv+/x69duyYACCcnJ1WZgIAA0a1bNzF48GARGRkpChcuLEqVKiXCwsJUZRYsWCAAiDlz5qiOrVq1SgAQHh4eKcZ/5swZASDVjxcvXqTra+Hj4yPMzMxE7dq1RVxcXLrqJEq87rVr16o9X7x4cQFADBo0KMnxn3/+WQAQT548yVB/RESaxhHlXCLELyRdO7xRzpWe7x+3FNeMS5cuISQkBJ07d06yJFiTJk1Qs2ZNXL16FY8ePUp3e+XKlYOtrS28vb3RokULODs7o2bNmti0aRNkMhlOnz4Nf39/VK1aFVOnTlXVS3zYbP/+/Rg7dmy6+ytSpAjGjRuXapn0jES/e/cObdq0gbm5OXbu3AmZTJbuGNLDyMgIANCjR48kx11dXbFixQpcvnwZ9vb2Gu2TiCgjmCjnAiF+IVjmsAyxEbFplpUr5FBYKrIhKtIUhaUCcoUce3rtSbOsXCHHUJ+hTJa/U1BQwpsSOzu7ZOeKFi2Kq1ev4v379+luz8jICGfPnsW0adNw+PBhHD9+HEDCChGLFi1CTEwMAODGjRu4ceNGsvqBgYEZir948eKYNWtWhur8V0hICFq1aoXg4GCcP38eBQoU+K721ClQoAAePHiA/PnzJzlubW0NAPj8+bPG+yQiyggmyrlAxIcIxEbEwmWjC6wcrFItyxHHnMe8iDmG+gxN118M9vTag4gPEfwefycrq4T7SN2Dci9evADwNZlLL3t7e6xfvx4A8OTJExw4cACenp5wdXXF2rVrAQCDBg3C8uXLvyPyBM+fP8fKlStTLTNu3DjkyZNH7bmoqCi0bdsWT548wcmTJ1G2bNnvjkmdqlWr4sSJE6qH+RIFBAQA+Pp9SK+goCC4ubnB29sbhQoVgpeXF5o0afJd5S9fvoy6detiypQp+OOPPzIUDxHlfEyUdUx0dDRmzpyJCRMmqLZPTS8rByvYVrHNoshS9j0xa1NOitu8iDnMi5inK2ZNTrHRxBurxJiHDh2qoaiyXu3atWFmZoYDBw7g3r17qukXJ0+exL///otixYolSezS8s8//6Bv377o2bMnNm7ciFKlSmH06NE4duwYTpw4gcKFC0OhUGDPnj2YOnWqanWJvXv3YsCAARgxYoQqSUtcRSM+Pj7F/vz8/DB79uxUYxo0aJDaRDk+Ph7dunXD5cuXsW/fvlQ3SomIiICfnx8sLS0ztSJG165dMWvWLKxZswaNGzdWHV+9ejX09PQyvE700KFDYWNjg6CgIJw8eRJdu3aFr69vkgf6MlJeqVRi5MiRqa4FTUS5GxNlDRJCAACeXnwKE2P1i9ynJTw8HH9N/gvta7aHsbFxuup8ePwBUYjCl7AvMA5NXx1NCg0NxeTJkzFgwIBkO12lJCY8BlGIUtXXj9dPo4bm20hP3JqIU5NSiznOIA7xRvHY0itjKwWkRm4kh8tGFxhbZv7nKvFnuoljwkhd4n2iy0xNTTFr1iwMGTIE9erVQ/fu3RETE4MtW7ZAJpNh0aJFGdqlzdnZGYULF8amTZvw+fNnlClTBg8fPsSJEydQoUIF1K1bFx4eHhg3bhwqVqwIZ2dnREREYOfOnciTJ0+S9YITNy/ZtGkTYmNj8dtvvyVLBJ2cnDL9dR49ejT279+Ptm3b4tOnT8k2GOnVq5fq9b///otGjRrBw8MDnp6equNLly5FcHCwamT4wIEDqrWlhw8frpofXblyZfTv3x9///034uLi0LBhQ3h7e2PHjh2YMGFChqZ7hIWFYe/evXj+/DkUCgXatWsHR0dH7Nu3D/369ctU+ZUrV6JmzZoICQlJdxxElMto+WHCXMXf3z/NJ835wY8f/cPf31/bt2q67d69W9SqVUsYGRkJExMT0ahRI+Ht7a06n95VL4QQwtfXV/Ts2VMULFhQGBgYCDs7OzFs2DDx4cMHVZn169eLypUrCwMDA5E/f37h6uoqnj59mqTtyMhI0aZNG2FgYCCMjIyEn5+fRq+5YcOGqX7/vpW4usZ/V+Cws7NLsf5/V9uIiYkRnp6ews7OTsjlclGyZEmxYMGCDMd98+ZNkSdPniTHhg0bJkaPHp2p8h8+fBClS5cWnz9/Fn379hVTp07NcExElPNJhMgBwzs5hFKpREBAAExNTTM02vSt0NBQFC5cGP7+/ukendW2nBgzkDPjzskx+/n5QSKRoECBAqluVUyUGefPn0fv3r2TzCufOHEiPn78iL/++ivD5QcNGoRKlSph0KBBcHNzQ8mSJTlHmegHxKkXGiSVSlGoUKHvasPAwAAeHh6wsrLS+XmziXJizEDOjDsnx2xtbZ1jYibdU69ePVy8eFHtuYkTJ6JTp06q5fQShYaGwsRE/TQ4ExOTFMvfunUL165dw7JlyzQTPBHlWBxRJiKiHC8sLAx58+bFixcvULBgQQBAo0aN0KdPnxTnKKdUPiQkBH/88YcqyQ4JCYGenh46d+6sWqGEiH4MTJSJiChX6NKlC8zNzbFkyRKcOnUKffv2TXXVi5TKGxoaJhlt/vXXX1GsWDGMHz8eFhYW2XQ1RKQLOPWCiIhyBS8vL/Tt2xf58uVDoUKFsG3btiRJcqtWrVC/fn38/vvvaZZXKL5uzGRkZAQTExMmyUQ/II4oExERERGpwUfPiYiIiIjUYKKsY6Kjo+Hp6Yno6Ghth5JuOTFmIGfGzZhzhtx0zbwWIvqRceqFBnEd5ZwTM5Az487JMad3HWVN3EfalhO/TynhtegWIQS+fPnC9ciJsgkTZQ16/fo1ChcurO0wiHSav79/quuN8z4iSlta9xERaQZXvdAgU1NTAPiu0YqcOOKRE2MGcmbcOTnmhw8fomzZsqr7JCWauI+0Lau/T8p4Jfwv+QMACtcpDKlMcyOL/207LDws09eSlXFmRk68f/4r8RrSuo+ISDOYKGtQ4p+JzczMMv2fcE7eeS0nxQzkzLhzcsyWlpYAkOZ0Ck3cR9qWHd8nizYWWdLuf9s2Uhh917VkZZwZlRPvn5Tk1GlJRDkNp15oUGhoKMzNzRESEpJjf8ETZZX03h+8j4hSxvuDKHtxRJmIKIeJj43HjZU3AABV3atCJpfpZNtZGScRUXZgokxElMPEx8TjyLAjAIBKbpU0myhrsO2sjJOIKDtwbRkiIiIiIjWYKBMRERERqcFEmYiIiIhIDSbKRERERERqMFEmIiIiIlKDiTIRERERkRpcHo6IKIfRM9BDj4M9VK91te2sjJOIKDvwfy4iohxGqidFqTaldL7trIyTiCg7cOoFEREREZEaHFEmIsph4mPjcW/TPQCAY09HjW9hram2szJOIqLswESZiCiHiY+Jx75++wAAZbuU1fgW1ppqOyvjJCLKDkyUiYgoVSF+IYj4EJFmOYWlAuZFzLMhIiKi7MFEmYiIUhTiF4JlDssQGxGbZlm5Qo6hPkOZLBNRrsFEmYiIUhTxIQKxEbFw2egCKwerFMsF+QRhT689iPgQwUSZiHINJspERJQmKwcr2Fax1XYYRETZisvDERERERGpwUSZiIiIiEgNTr0gIsph9Az00Hl7Z9VrXW07K+MkIsoO/J+LiCiHkepJUa5LOZ1vOyvjJCLKDpx6QURERESkBkeUiYhyGGWcEj57fAAADi4OkOppbszjv21rsi1NxklElB2YKBMR5TBx0XHY2XUnAGBC2ATo6+lnWduabEuTcRIRZQe+vSciIiIiUoOJMhERERGRGkyUiYiIiIjUYKKcS1258hqXL/trOwwiIiKiHIuJci60det91KmzBnXq/I0lS65qOxwiIiKiHImJci5z+LAvevfeAyESPv/ll6OYPNkbIvEAEREREaULl4fLRZ4+/YROnbYjLk4JV1dHlCqVF56eZ+HpeRZ58hjhl19qajtEItIAmb4M7de2V73W1bazMk4iouzARDkXWbXqBqKi4lC/fhGsW9cecrkMenpS/PHHGXh5XcPw4TUgkUi0HSYRfSeZXIZKbpV0vu2sjJOIKDtw6kUuER+vxKZN9wAAv/5aE3J5wujN8OE1oa8vw+PHH/HgQZA2QyQiIiLKUZgo5xLe3i/x5s0XWFgYwtm5lOq4mZkBWrQoAQDYseOBtsIjIg1Sxinx5NATPDn0BMo4pc62nZVxEhFlBybKucSGDXcBAN26lYOBQdIZNV26lAUA7Nzpk+1xEZHmxUXHYYvzFmxx3oK46DidbTsr4yQiyg6co5wLhIfHYNeuhCS4d+8Kyc63bVsacrkUDx8G4eHDIJQta5XdIRKRDgrxC0HEh4gkx2IjY1Wv391+h+CXwdkcFRGR7mCinAvs3fsIYWExKF48D+rUKZzsvIWFIZo3L4FDh3yxc+dD/N//NdRClESkS0L8QrDMYRliI2JTLLO23loAgFwhh8JSkV2hERHpDCbKuUDitIvevSukuKpFly5lceiQL3bsYKJMREDEhwjERsTCZaMLrBy+/pUpNjJWlSD3u9APcqOEJNm8iLm2QiUi0homyjnc27dfcOLEcwBAr17Jp10katcuYfrF/fvv8ejRB5QpY5ldIRKRDrNysIJtFVvV5zHhMarXNpVsoG+sr42wiIh0Ah/my+G2bLkPpVKgdu1CKFkyb4rl8uQxQtOmxQEAO3c+zK7wiIiIiHIsjijncIlrJ/fpUzHNsp07l8WRI08xbdo57N//GOXKWaNcOSsUKGAKiQSQSiWQSCRJXif8C0gkElStaouCBc2y+pKIiIiIdAIT5RxMqRS4dy8QANCqVck0y3fs6IDJk8/Czy8E164F4Nq1gAz1Z2Skh6NHe6FBA7tMxUtEmiHTl6HV0laq17radlbGSUSUHX74RPn06dNo1KhRjtza+ePHCMTGJiziX6CAaZrlLSwM8ezZL/D1/YiHD4Pw4EHCx8ePERACEEJAqRQQAv/79+vn79+H4/nzz3B23ozTp/uiWrUCWX15RJQCmVyGGkNr6HzbWRknEVF2+KET5a1bt8LV1RUTJkzA1KlTIZXmrCnbb9+GAQCsrBSqLavToqcnhYODFRwcrNCpU/r7ioyMRatWm3D27Cu0bLkR587143rMRERElKvlrMxQwxSKhHVBZ86ciVGjRn1XW9HR0QgNDQUAhIaGIjo6+rvjS0tAwBcA6RtN/l5GRnIcONAD1asXwMePkWja9B88f/45y/ul3EEb90dupoxX4qX3S7z0fgllvIa3sNZg21kZJxFRdvihE2VHR0eUKVMGffr0wcqVKzFs2DAIITLV1syZM1G4cMJmH4ULF8bMmTM1GapaiYmyrW3GE+WoqKTbyc6ffwnt22/FuXOvUqxjamqAI0d6onx5a7x9G4amTf/BmzehGe6bfjzauD9ys7ioOKxvtB7rG61HXJSGt7DWYNtZGScRUXb4oRPlYsWKwcDAAIULF8bUqVPh5eWlGlk+deoUQkJC0t3WhAkT4O/vDwDw9/fHhAkTsiTmb719mziibJKhej4+QahceQVWr74JIGFu8qpVN7F//2M8ffpJVS4+XpnsjUO+fAocP94LJUrkwYsXwWjWbAOCgsK/80oot9PG/UFERPS9fqhEWd1ocaVKlaBUKvHLL79gxowZWLJkCQoWLIiRI0ciLCws3W0bGBjAzCxh6TQzMzMYGBhoLO6UZGbqxc6dD1Gjxmo8evQBs2ZdQExMPCQSCXbv7oZx4+qic+eyqrJLl/6LihX/wvz5l/Du3devha2tKU6e7INChczg4/MBLVtuQkhIlOYujHIdbdwfRERE3+uHSZTXr1+PMWPGYOzYsbh8+TKiohISuxo1amDbtm0AgAEDBqBs2bJ4//49ChUqBBsbG22GnKbEh/nSM/UiNjYeo0cfQ5cuOxAWFoNGjYri0qWfoP+/JZvKlrXCrFlNYWb2NYHZuvUB7t17jzFjTqBQoT/Rt+9eREcn/Pm0aFELnDjRG1ZWCty8+RbOzlsQERGbBVdJREREpB0/RKI8efJk9O/fH3v37sXixYvh7OyMZcuWITo6GuXKlUNcXBy+fPmC0aNH4/Xr1+jZsyfOnDkDd3d3bYeeqvSOKL97F4amTTfgzz+vAAB++60Ojh/vDWtr41TrHT7siuXL26BWrUKIjxf45587cHXdjbi4hIdyypSxxLFjvWBuboALF/zQseM2VSJNRERElNPl+kT5xYsXWLNmDUaNGoWzZ88iNDQUdnZ2WLJkCQIDA9GgQQOYmprCzs4OBw4cwPbt2zF79myMHTsW+/btw7t377R9CSlKHFHW00v52xgdHYeaNVfj3LlXMDXVx+7dXTF7drNU6yTKk8cIgwZVw+XLP+Ho0Z7Q15dh924fNG68Hhcv+gEAKle2xeHDPaFQyHHs2DM0aLAOmzffw/XrAfj0KTLTD0cSERERaVuuT5QjIiLw5s0bVK1aFYUKFYKBgQEWL14MPz8/HDhwAPHx8ejQoQNq1aqFDRs2oFGjRsifPz9+/fVXPHr0SKenX1SpYgsA6Ndvn2qHvv8KD4+Fn1/CQ4kXLvSHi4tDpvpq0aIktm/vDAMDGc6f90O9emvRqtUmXL8egDp1CmPv3m5QKOT499836NlzN6pXX4V8+ebAwmI2KlX6Cy4u2zB69DEsXfovDh16gocPgxATE5+5CyciIiLKBrl+wxF7e3s4ODhg6dKlcHFxSfJQUUREBGQyGcaNG4f379/Dzs4OMlnCnN18+fJpM+x0+fvvdvD3D8GNG2/RuPE/OH26Dxwd8ycpkyePIQwN9RAVFQcTE/3v6q99+zLw9R2OadPO4e+/b+Po0ac4evQp2rcvjSlTGuHp0+GYP/8yLl3yx4sXwXj3LgyhodG4cycQd+4kT+QtLAzh5lYRgwZVQ+nSlt8VG9GPRCaXoemcpqrXutp2VsZJRJQdcv2Isp6eHpo1a4YnT57g5s2E5dASn7jX00t4n2BiYgIzMzMolTlrQfw8eYxw4kRvVK1qiw8fItC48T/JRpYlEgkKFUp4Y/D69feveVy4sDlWrGiLx4+HoW/fipBKJdi37zEqVvwLz559xrx5zXHp0k94+3Y0wsN/x4MHQ3DwYA8sWdIKo0bVQocOZVCxYn6YmuojODgKCxdeRZkyy9CkyT/YufMhYmM5ykyUFpm+DHXH1kXdsXUh00+egIb4heDtzbepfgT5BGWqbU3GSfSjKFq0KJydnQEAnp6ekEgk+PDhA16+fAmJRIJ58+alWNfNzQ0mJhlbBjYreXt7QyKRYOfOnemu8+31ZyUnJyeUL19eo23m6hHl+Ph4yGQyTJkyBR07dkStWrUAAH5+CfNrbW0Tpi7cv38f8+fPR8mSJfH7779DIpFoLeaMSkyWmzXbkOLIcsGCpnj69JNGNwcpXjwP1q3rgPHj68HT0xs+Ph9Qp05h1XkhBBQKOcqWtVK71XV8vBLHjj3D8uXXcejQE5w+/QKnT7+Ara0JBgyoAnf3qqoEn4jSL8QvBMscliE2HavQyBVyKCwV2RAVESXq2LEjSpYsCVNT0wwtQ0vakasS5Rs3bkBPTw8ymQzly5eHTCZDXFwcTE1NUb9+fVW5t2/fAgDs7Ozg5+eHcePG4eTJk7h161aOSpITpZUsa3JE+b/KlLHE1q2dERkZC6k04WsXERGLVq02YfTo2mjXrrTaejKZFK1b26N1a3u8ehWMlStvYPXqW3j7NgxTp57D9Onn0bZtKQweXA3NmpVQtU1ECVtDv72Z8P+YbRVbSGVf/zgY8SECsRGxcNnoAiuH5G9Sv6WwVMC8iHm629ZknEQ5UVRUFAwNDb+rjQoVKqBChQoaikizYmNjIZPJIJXyXk2Ua74SHh4eaNmyJWrVqoWGDRuqtsjV09NLtvKCXC4HANy5cwejRo3C+fPncfXqVZQtWzZZuzlFatMwChZMWD7uzZsvWda/kZFc9XrRois4d+4Vhgw5lK61le3sLDB9ehP4+4/E1q2d0LChHZRKgX37HqNly02oVm0lt8om+kZcVBxW11iN1TVWp7g1tJWDFWyr2Kb68d8kOb1tazJOIm0LDQ2Fm5sbLCwskDdvXnTu3Fn1l+d169ZBIpFg7969qFy5MurUqQMA2Lp1K0qUKAFDQ0PUq1cPjx8/VrX37t07uLi4wMLCAsWKFcOKFSuS9Pft1ItEnz9/RqdOnaBQKFC8eHFs2bIlxXg/fvyILl26wNjYGDY2Npg2bVqKK0y5ubmhZMmSWLBgAWxsbGBsbIzOnTvj06eEXXgTp34sXLgQzs7OMDY2RmhoKG7duoV69erB0NAQtra2mDRpEuLikt7Dfn5+aNKkCYyMjFCuXDmcOnUq3V/zO3fuoGbNmjA0NETp0qWxb98+AMD27dshkUiwYcMGVdmdO3dCIpHg0KFDqdbNKrkiUd6yZQvmzJkDd3d3zJ8/H87Ozpg4cSKWLl0KAKpR4sQfJAcHB1hYWODXX3/F0aNHce7cOVSqVElb4WvMf5PlVq02ITY2HgMGVMH58/0wfny9bIlj5MjaGDeuLlaubAuFIiGBTs8ycfr6MnTrVh7e3m548GAIhg+vATMzA9y69Q716q1Nsr02ERGRJrRu3Rq7d+/G6NGjMXHiRJw7dw6tWrVK8ntrwIABcHJywqRJk7B9+3b07t0bbdu2xYIFCyCEQO3atfHq1SsAQJcuXbBv3z4MGDAAo0aNwvz58/HmzZtUY5g3bx6MjIwwZ84cWFlZwdXVFQcPHkxWLj4+Ho0aNcLjx48xZ84c9O/fH9OnT8fo0aNTbNvf3x8LFizAb7/9huHDh2P//v1o2bJlkjKTJ0+GTCbDsmXLEBgYiNq1a+PTp0+YNWsWXFxcMH36dAwaNChJnYkTJ8LBwQGzZs1CfHw8WrZsiTt37qT59X758iXq1KkDGxsbLFq0CDVq1EDHjh2xd+9etG7dGoaGhkmu/ciRIzA1NUXTpk1TrZtlRC7g4eEhihYtKl6/fi2EEMLHx0dUqFBBlCtXTrx7904olUoRFxenKh8bGyvq1asnJBKJePDggcbiCAkJEQBESEiIxtrMjE+fIoSZ2UwBeIrbt99qNZZEf/99U3TvvlMEBYVnqN7z559EyZKLBeAp8uefqzPXQxmX3vtDV+4jXRYdFi084Sk84Smiw6KTnAu4ESA84SkCbgRovO3UqOs3s21Rynh/aNa5c+cEAPHXX3+pju3cuVM0bNhQvHz5Uqxdu1YAEMOHD1edL126tOjTp48ICgoSQUFB4uXLl8LIyEiMHj1a3LlzRwAQv/76q6r8kydPhFQqFW3atBFCJOQsAERQUJB48eKFACBatGihKh8eHi6sra2Fk5OTEEKIvn37CmNjYyGEEFu2bBESiUTcu3dP1f/o0aOFXC4XHz9+THZ9ffv2FQDEtWvXVMfmzJkjAAhvb29V/xUqVBBKpVIIIUS/fv2EoaGhCAj4ei8PGzZMSKVS8fz5c3HmzBkBQPz888+q82/evBEGBgbCzc1N7dfZzs5Odf0///yzsLe3F+/fv1ddQ61atUTVqlWFEEJ06NBBWFhYiNjYWCGEEAUKFBDdu3dPV92GDRuKcuXKqY0hs3L0iLL437s9IQQiIyNVc4/LlCmDOnXqIDAwEEqlEhKJBDKZDEqlEv/3f/+H27dv48iRI3j27FmOnm6Rkjx5jFRrLN/83/xAbQoPj8HYsSewdet9lCvnhd27fdJdt1ixPLhwoR8qVsyPwMBwNGy4Dhcu+GVhtERE9KNIHAFt1aqV6linTp3g7e0NOzs71bHE81++fMHjx4/xzz//wMrKClZWVihatCgiIyNx584dPHz4EACSrPBgb2+PIkWKpBpH48aNVa8VCgWaNWuGe/fuJSt3/fp1CCHg6Oio6n/+/PmIjY2Fj4/6362mpqaoVq2a6nMXFxcASNJ+y5YtVX99v3PnDqpXr65a8AAA2rdvD6VSievXr6uNuUCBAqhWrZramNVdg6+vL6ytrVXXcOXKFdX3olOnTggODsaFCxdw584dBAQEoGPHjumqmxVy9MN8id/UTp06YerUqVi2bBkWL14MU1NT6OvrIyoqCsHBwapv9vLlyzFt2jTcvn0bu3btQrFixbQZfpaqUsUG3t4vce1aAFxdHbF69U08f/4Z48bVS3Prak0zNtbH0aO94Oa2Fw8eBKFTp+0oU8YSHTqUxsiRtdOMJ39+E3h7u6Ft2y24cMEPjRqtR926hdG0aXE0bVoc1aoVSNdOg0RERN+KjU14jubb5WFjY2MRHh6eZEk2KyurJOX69OmD3r17J2krb968eP36NYCvy88mSmv5WfGf6YlSqTTZnGAgYeqFVCrF0aNHky0+UK5cuVT7+LZtAEnaT7w+dbGk91hKMf9XfHw8KlasmGxJvMTradu2LfT19XHo0CHky5cPhoaGaN26dbrqZoVckV1UqFABixcvRuPGjaFQJCx19ObNG+TJkwcFChQAAPj4+CA4OBjz58/H3LlzVQ/05VYNGxYFAOzalfAOc+nSa/jzzyuYNOm0VuKpVq0Abtxwx8SJ9SGXS/Ho0QfMmnURxYotwrhxJ/DhQ0Sq9S0sDHHsWC906FAGcXFKnD37CpMmnUHt2muQL98ctG+/FTt3PsymqyEiotwg8a/KJ0+eVB2bNWsW8uTJg8DA5BtlmZubw9bWFu/evUPTpk3RtGlT1K1bFytXrsTTp09V7R07dkxV586dO6qHA1Ny9OhR1evo6GicOnUKDg7Jd9J1cHCAUqmEsbGxqv93795h1apVMDZWP+j05csXXLhwQfX54cOHVW2p4+joiGvXrqn+Sg8A+/fvh0QiQZUqVVTHjhw5onodFBSEa9eupdjmf6/B398ftWrVUl3DyZMncfbsWQAJX+OmTZvi4MGDOHLkCJo3b666trTqZoUcPaL8rcGDB6t21QMSvmnW1tYwNjaGj48PRo0ahWPHjiEgIECnt6XWlFatSsLa2hjv34fjyJGn2LOnG2bMOI8//2yhtZgMDPQwbVpjjB5dGydOPMfcuZdw/XoA5sy5BC+v6xg+vAZGj66NfPnUr+uqUMixe3dXPHv2GSdPPsfJk89x+vQLfP4chf37H2P//sf488/mGDmydjZfGRER5UTNmjVDpUqVMHLkSLx79w4SiQSzZ89G27ZtUbBgQbV1fvvtN4wcORK9evVCgwYNsH37dly5cgXTpk1DyZIl0aZNG8ydOxfR0dEoVKgQFi9erNroLCVXr15Fp06d0LhxY2zbtg0BAQGYM2dOsnK9evXC5MmT0bVrV4waNQqRkZGYMWMGevbsmeIAoFQqRdeuXTFy5EiEhoZi3rx5KF26NJo1a6Y2gR8/fjy2bNmCJk2awN3dHU+ePMFff/2F3r17o0SJEvD39weQsEKFXC6Ho6MjVq9ejejoaAwdOjStLznGjBmDbdu2oXnz5ujduzfu3LmDFStWYOPGjaoynTt3Rv/+/fHkyRP8/fffGaqrcRqd8axDateuLerVqyfu378v2rRpI8zMzMTNmzeztE9de8jit9+OC8BTtGmzSduhqKVUKsX+/Y9ElSorBOApAE9hajpDTJx4Snz8GJGuNuLi4sW1a2/EkCEHVW0sWXI1iyOnzODDfJoTFx0nznicEWc8zoi46Lgk5773Yb7U2k6Nun4z2xaljPeH5gUFBYlu3boJExMTkS9fPtG7d2/Vg3GJD/N9+zCcEEIsWrRIFC5cWBgaGopatWqJc+fOqc6FhoaKPn36CFNTU5EnTx4xceJEUbNmzVQf5ps2bZpo1qyZMDAwEFZWVuLPP/9Utfftw3xCCPHixQvRokULYWRkJPLnzy9GjhwpoqKi1F5bYt3169eLokWLCn19fdGgQQPh5+enaguAmDt3bpJ6V69eFbVq1RL6+vrC2tpajBs3TkRHJzyQm/gw39KlS0XNmjWFXC4XhQsXFps3b07xa/ztw3xCCHHq1ClRoUIFoa+vL0qWLClWrlyZpPzHjx+Fnp6e0NPTE58+fUpyLrW6WfEwn0SIdKzblYMkXk7Lli1x/fp1lC5dGnfv3sWFCxeyfAm40NBQmJubIyQkBGZm2t9V7vHjDyhTZhmkUgn8/EagYMGEmIQQmD79PKytjeHuXlXLUSbEs3//Y3h6nsXt2+8AAH/91QY//1wtjZpJ25g48TRmzryQqfqU9dJ7f+jafZTTvL35FiurroT7DXfYVrFNu0IO7/dHw/uDMsLNzQ07d+7kDoDfIVfMUf6WRCKBRCLBkCFD8PnzZ9y6dQuXLl3KFeskZ1Tp0paoX78IlEqBdetuq47v3/8YkyadwZAhh3D06FPtBfg/EokE7duXwY0b7ti9uyvatLFHv36VVefv3QvEly/RabYxfXrCtA4AGDToEP7++1aWxk1ERES5W65LlBM5OTlh9OjRuHXrls5uFZkdBgxImHj/99+3oVQmjLa3a1caffpURHy8QNeuO3D3bvIHFrRBKpXAxcUBBw+6Ql8/Yb55bGw8XFy2oXjxxbh69XWq9SUSCebObYZff60JABgwYD/++Sfrlowh0hahFHj/4D3eP3gPodTsHwU12XZWxklElB1ybaJsbm6OmTNnokyZMtoORas6dy4LMzMDPH/+Gd7eLwEkJJSrVrWFk1NRfPkSg1q1VsPFZRvWrr2F9+/DtRvwf/j5hUAmk0IiAcqWtUqzvEQiwYIFLTB4cDUIAfTrtw+bN6e9riNRThIbGYvl5ZdjefnliI1Me5t4bbWdlXESUdrWrVvHaRffKdcmykDydQx/RAqFHK6u5QEAa9Z8nYqgry/D7t1dUb16AURGxmHv3kfo338/bGzmoW7dvzF79gX4+ASla+vprFSiRF48eDAEZ870halpwlPDSqVAjx67sGvXQ7XxSSQSLF3aGgMGVIZSKdCnzx7s2PEgu0MnIiKiHC5XJ8qU4KefEqZf7Nr1EJ8/R6qO58ljhKtXB+DmTXd4ejZElSq2EAK4dMkf48efQtmyXihdeinGjz+p1YRZT0+KcuWsVZ/v3u2DrVvvo3PnHahRYzVOnHimZuFzCVasaAs3t0qIjxdwdd2NvXsfZXfoRERElIMxUf4BVK1qi4oV8yM6Oh4bNtxNck4ikaByZVt4eDjhxg13+PuPhJdXa7RsWRL6+jL4+n7CtWsBGt31JjIyFtWrr0KLFplb97BZs+KYNKkBjI3luH49AM2bb0S+fHPQt+9eREd/3RVIKpVg9eq26NnTEXFxSnTtugN37rzT1GUQERFRLsdE+QcgkUjw888Jy8AtXHgFcXEpb6VZqJAZBg+ujiNHeuLDh7HYubMLxo2rq9F4Vqy4gevXA1CwoKnqmBBC9bBhWszNDTFlSiM8f/4rfv21JgwMZPj8OQr//HMHffvuTdKOTCbFunUd0KxZccTGKrFpE+crExERUfowUf5B9O1bCZaWCrx4EYxdu9K31bOpqQE6dSqL5s1LQAiBsLAYxMenvl99egwcWAV//FEfTZsWVx07fNgXlSuvwOHDvulux9raGAsXtsTnz+Owe3dX6OlJsW3bA/z224kk5fT0pPjpp4Tl5g4cePLd8RMREdGPgYnyD0KhkGP48BoAgDlzLmV4zrGNzXyYms7Eo0cfvjsWY2N9TJ3aGK6ujqpjc+dewt27gTh79mWG2zMyksPFxQFr17YHAMyffxmLFl1JUqZly5LQ05Pi0aMPePr003fFT0QpC/IJwtubb/H25lu8u/11qtO72+9Ux0P8QrQYIZHuefz4MapXrw5jY2M0b94cr18nLIf68OFD1KtXDwqFAra2tvD09FRb/+zZs6hSpQoUCgUqVaqEEye+DhjNmjUL+fLlQ9OmTREcHAwAiImJQcWKFVWf6wKJRIKdO3dqrX5KmCj/QIYOrQ6FQo6bN9/i1KkXGaprbJywh/yXLzGZ7j8mJj7Fc7t3d8PEifXx229fp3m8ehWMf/99k+72e/WqgJkzmwAARo48hp07v46cm5sbokEDOwDAgQOPMxo6kU6RyWWoPaY2ao+pDZlcphNtKywVkCvk2NNrD1ZWXYmVVVdibb21qvNr661VHV/msIzJMtH/REdHo0WLFqhSpQqOHj2KL1++YPDgwQCAUaNGISYmBvv27cPo0aMxefJkXL58OUn9yMhIuLi4oFq1ajh69CgqV66MLl26IC4uDoGBgZgzZw42b94MPT09rFmzBgCwZs0auLi4wMLCItuuMyYm9fzh1q1baNasWTZFkwEa3RD7BxcSEiIAiJCQEG2HkqLhww8LwFM0a/ZPhuoFBYWLL1+ihVKpzHTfPXvuEm3bbhZPnnxIV/nevXcLwFNMnuyd7j6USqUYMuSgADyFgcFUce7cS9W5BQsuC8BTNGq0LsOx0/dL7/2RE+4jXRZwI0B4wlME3AjI9r6DXwWLgBsBqX7c2XhHa/HlBrw/slZ0WHSKH7GRsekqm1G7du0SZmZmIjo6oe7Lly+Ft3fC7722bduKDh06iA8fPogzZ84IAOLmzZtJ6n/69EksWbJEBAcHi7CwMDFu3DhhaWkpYmNjxaVLl0TdunWFEEKsWLFC/PzzzyI6OlqUL19efP78OdW4PDw8RP369UW/fv2EkZGRcHJyEhcuXBAlSpQQZmZmYvLkyUIIIWJjY8WwYcOEpaWlMDY2Fp06dVL9fAIQ06ZNE8WKFRPjx48Xr1+/FvXq1ROGhoaiS5cuom7dumLo0KGqsjt27BAvXrwQAMQ///wjChUqJKysrMTq1avT1deOHTsy/PVPCxca/sGMGlUbXl7XcOLEc9y69RaVK9umq56lpeK7+n3zJhTbtz9AbKwSnp5OaZZXKgX09WXQ05OiTRv7dPcjkUiweHErBASEYe/eR1i//g7q108YSW7bthRGjjyGs2dfYdu2++jWrXxmL4eI1DAvYg7zIubaDoMo02aazEzxnH1re7geclV9Ps96HmIjkm+k4yE8MtTntWvXYG9vj65du+LkyZOoXbu2auR3wYIFqF27NiwtLQEAI0eOROXKlZPUz5MnD4YNG4bg4GCYmJhAIpFgw4YN0NPTg7W1NXx9fREYGIjLly/Dzs4Oq1evRseOHdM1mnzx4kU0a9YMW7ZsQefOneHq6oo1a9Zg3759mDx5MoYPH46dO3di7dq1WL9+PfLmzYtevXph2bJlmDBhAgBg0aJFWLJkCWrXro3u3bsjJiYGBw4cwOHDh7Fz505UqlRJbd8rVqzAxo0bsXbtWgwbNgx9+vTBunXrUu0rK3DqxQ+maFELVYI4d+6lbOu3YEEz3Ls3GIsWtUSVKmkn5wlLu7WDn98IVK1aQHV89eqbmDPnIqKi4lKsK5NJsXlzRyxY0AIrVjirjpcokRfu7lWgVAr07Lkb27bd/76LItISoRQIfhmM4JfBWbKFtabazso4iXKLjx8/4ubNmyhTpgz279+PoKAg/PzzzwCAgQMHws7ODmfOnMH8+fOxaNEinD17Vm07pqamOH/+PHr06IHBgwfj48ePKFGiBBo0aAAbGxscPnwY3bt3h5eXF0aMGIHBgwfDwsICnTp1SnFaRP78+TFp0iS0b98eJUqUQKdOndC0aVMMGDAASqUSb9++RfPmzXH16lW4uLjA1tYWJiYmePPm67RJd3d3dOvWDRKJBBcuXMC8efPQtGlTzJ8/H7a2KecDf/zxBxo2bAh3d3dERUWlq6+swBHlH9DYsXWwefM9bNv2ANOnN0axYnnSrLNu3W2cPfsKXbuWRatW6R/h/Vbp0pYoXdoyQ3Vsbb8uIRccHIVx407i06dI5M9vjL59K6VYz8hIjhEjaqk+F0IgMjIOXl5tEBOjxLp1t9Gz524A4Mgy5TixkbFYVGwRAGBC2AToG+vrZNtZGSdRVpgQlvLIpFSWdGxxzPsxGunTxMQElpaWmDlzJiQSCf744w+4uroiMDAQZ86cwdGjR+Hk5AQnJyfs2LEDu3btQsOGDVX1nz59ijNnzmDgwIGoV68eKlWqhC1btuDs2bPo2LEjduzYgVevXsHGxkY1muzr64uDBw9i9+7dGDlyJPbu3YuuXbsmiy1v3ryq13p6ejAzMwMAGBgk7pSrREREBIYPH45nz56hTJkyiIqKStJGuXLlAECV0JYoUQJAwl+AixQpkuLXJXEUXV9fX9VXZGRkqn1lBY4o/4AqVbJB8+YloFQK/Pnn5bQrADh//hXWrbuNW7cytmFHfLwSgYGZ32deCKFaocPMzAALFrRAy5Yl0bNnBVWZCDV/+vpWTEw8+vTZi/bttyI+XmD16q879nFkmYiIEukb66f4oWeol66yGWVvbw+lUqn6XadUKiGVSmFiYgKpVIq4uK9/QY2Li4OpqWmS+q9fv4a7uzvevUv4/ZzYjomJiaqMnV3CFEQvLy+MHDkSz549Q8OGDdG4cWO0bt0az58/z3DcicaPHw+FQoEXL17g2LFjKFiwYJLzcnnCYgDW1gk77Cb2pVQqM9xvWn1lhUwlyn5+foiMjEx2PDw8PMuHwEkzEjcRWbPmFh4+DEqzvKlpwrvHJ08+ZqifgwefoESJxZg583yGY4z4GIFV1VZhZdWVCH0dCqlUgj59KuLIkZ7Q00v40RVCoHHj9Zg48VSKS949ffoJe/b44OTJ5/DyugaZTJokWXZ13Y2mTf/BsmX/4s2b0AzHSURElFkdOnTAly9f8Ntvv+H06dOYPn06WrdurVoqbtKkSThz5gzmzZuHGzduoG3btggLC8Pt27fx6dMn1KpVCwUKFMDw4cNx/vx5DBo0CLa2tqhVq1aSflatWoWOHTsiT548KFKkCJ49ewYAePLkCYoVK5bp+KOjoxEQEIDLly9j5cqVuH79Ol6/fp0sTyxevDgqVaqEsWPH4vTp0xg5ciQ+fcrYcq3p7UuTMpUoFytWDHv37k12fOfOnbC3z9yf5Sl7NWpUFA0a2CEyMg5OTutw4sSzVMs3bJjwbnT9+juYMSP9SW94eCzCw2OxbduDDMUXHxuPHV12JKzFeusdrv91XW25a9cCcPXqG8ybdxm+vupvuLJlrXD6dF/4+g5XTcdITJYHDkyYs3zq1AsMG3YEhQotQM2aqzFr1gW8ehWcoZiJiIgyytbWFhs3bsTu3bvRvn17FC9eHCtWrAAArF27FnZ2dujQoQOWL1+OtWvXolatWrh+/ToqV66M/fv3w9DQEPv378eTJ0/QvHlzvHz5EocOHVJNkwASEkwvLy+MGjUKAFC3bl04OjqiePHiUCqV6NChQ6bjnzp1KqKjo9GyZUscPHgQw4cPx8mTJ/Hy5ctkZbdt24aoqCi0adMGYWFhqF27NiQSSZb0pSkSkdIwnBr9+/cHAKxbtw4NGjRA8eLFk5y/ePEi3r17h5CQH3N9zNDQUJibmyMkJCTJD6iu+vgxAk2bbsDt2+8gkSSMMk+Z0gjyFNZOnTnzPH7//TQAYMaMxpgwoX6afYSEROHy5ddo0qRYiu2qc3j4YVxbeg0AUKZDGXTd1RUSqfqbafHiqyhXzgpNmhRXez4tiSPOe/c+xuXL/ki8I4yN5Th40BVOTkUz1S4lld77I6fdR9oQEx6jejr/v3N/3958i5VVV8L9hjts0/HgbEba1kRb3xvfj473B+VUQghs374dderUQeHChQEA5cuXh6urK37//XctR5eyDD3Mt337dkgkEkgkEly9ehU3btxIct7U1BQeHhlbFoW0J18+BS5d6o+RI49hxYobmDXrIry9X2HLlk4oWtQiWfnExPj330+rEua0kmVzc0O0bFkyQ3HdXH1TlSR329sNZdqXUZ1TxikR/SUaRnmMVMd++aVmhtqPiYmHvv7XpL1kybwYO7Yuxo6ti3fvwrB//2OsXn0T164FoHXrTThwoEemk3AiIiJKeHhv9uzZsLS0xJQpU3DlyhU8fPhQNzcZ+UaGpl6EhYXhy5cvEEJgzZo1+PLlS5KPgIAA1bA+5QxGRnL89Zcztm/vDHNzA1y58hqVKv2FXbseqi0/YUJ9TJ/eGEBCwjxr1gWNxuN3wQ+HhhwCADhNcUqSJMfHxmN3z91Y77QeER8j1NZ//z4cV6++Vnvu06dIuLhsg7n5LGzadFftnGYbGxO4u1fFuXP90Lq1PSIj4+DsvAXHjj3VwNURERH9uDZt2oSIiAg0atQIS5cuxcqVK1G9enVth5WqTM1RfvHiBVxcXPDw4UNs2bIF79+/x8ePGXvIi3RLly7lcOvWz6hZsyBCQqLRufMODB58EJGRyVeU+P33+pg2rREAYMKEU5g9O+1kec2am6hdew0uXvRLtVxYYBikMinKdi6LBn80SHrubRhenXuFwLuB2NBsAyI/J528f/Xqa9jbL0HnzjvUroRhYWGI0NBoREXFoVevPejadSc+fFCfcBsa6mH37q5o27YUoqLi0L79Vhw+7JvmdRJlB6meFNWGVEO1IdUg1dPs4kWabDsr4ySinMfBwQEXLlxAZGQknj59igEDBmg7pDRl6n+uggULonfv3ihfvjx69eqF+/fvY+DAgWjVqtUPOz85NyhWLA/On++H8eMTVsT4668bqFFjtdpVMSZObICpUxOS5fHjT2HOnIuptn3unB+uXHmNzZvvpVqubKey+OnyT2i/rn2yCf7mRczR51QfKKwUeHfrHTa22IiokK9rKFaokB958hjC2toY794lX5JOKpXg2LFemDLFCXp6Uuzc+RDly3vh4MEnamMxMNDDzp1d4eJSBtHR8XBx2YYDBx6nGj9RdtAz0EObZW3QZlkb6Blodjl8TbadlXESEWWHTCXKY8aMgbe3NxYtWqT68/WgQYNw8+ZNjB49WqMBUvaSy2WYObMpjh3rBWtrY9y//x7Vqq3EmjU3k01V+OOPBpgyxQkAMG7cSSxceCXFdgcProZFi1pi0qSGyc4JIRD56evosE0lmxQfILIqa4W+p/vCKJ8RAq4FYFPLTYgOjQaQMI3kzJm++PffASheXP0mKnp6Ukya1BBXrvyEsmWtEBgYjrZtt2DAgP0I/V8739LXl2Hbts7o3LksYmLi0bHjduzZ45PidRIREVHukalEecOGDRg9ejRcXb/ued68eXOMGzcO+/fv11hwpD3Nm5fAnTuD0KxZcURGxmHAgAPo3XsPYmPjk5SbNKkhpkxxgqmpPmrUSHnh71q1CuGXX2rCxsYk2bkLsy5geYXlCLgekK7YrMtbo8/JPjDMY4jXV15jU+tNiAlL2H6zWLE8kMnS/rGuWrUAbtxwx+jRtSGRJKwnXaHCcpw9+zJZWblchi1bOqF79/KIi1OiS5cd2LEjY8vdEWmSEALhQeEIDwpPcf1wXWg7K+MkIsoOmUqU4+PjYWxsnOx4dHQ0oqOTj8pRzmRjY4KjR3th9uym0NOTYtOme+jRY5faZPnhw6GoU6dwhvt4fOAxTk88jS9vviDgRvoSZSBh1Ln3id4wMDfA2xtvEXg3MMl5pVJg7dpbuHJF/YN9QMI85HnzmuPMmb4oWtQCr16FoFGj9Rg9+hiiouKSlNXTk2LDBhf06lUB8fECPXrswpYtqU8jIcoqsRGxmGc9D/Os5yE2jZ0ptdl2VsZJRJQdMpUod+vWDQsXLlQtDxcUFIStW7di7ty5aNOmjUYDJO2SSiX47be62L+/O/T1Zdi1ywcTJpxKVq5Qoa/reQYEfMHr18l3uFMqBY4de4r+/ffh9etQRIdGY3fP3YAAqv5cFdV+rpah2ApULYDex3ujx8EeKPyfJH369HPo338/Bg06mCyx/6+GDYvi7t1BGDCgMoQA/vzzCtq02ZysnJ6eFOvWtVft6Ner1x5cT+coOBEREeU8mUqUFy1ahDJlyqBly5YAAFdXV7i6uqJYsWJYunSpRgMk3dCqlT3Gjq0DAHj1KuUHNjdsuIOSJRdjzJjjas+PH38Ka9fexsiRxyCEUD0J/8n3E2LVrLCRloI1CqL4N2scRwUnPNzn7l4VtrYmGDy4Wro2OjE1NcCqVe1w4EAP6OlJcfr0C7x48TlZOZlMijVr2sHJqSiUSpHmKh5ERETp4eXlhWLFisHExATNmzfHixcv8PDhQzRu3BgmJiYoWbJkitNbz549iypVqkChUKBSpUo4ceKE6tysWbOQL18+NG3aFMHBwQCAmJgYVKxYUfU5pSxTibKhoSEOHz6My5cvY8mSJViwYAGOHTuGGzduIG/evJqOMdtwDl3q/PwSEuSKFfOnWKZiRRtERcUljBhHJ52+IJVKsGdPN3TuXBYrVjjD0NwQPQ/3hL6JPl6cfoHtHbcj7j91MuLtrbfwKueFK4uuIH9+E/j6DsfPGRyldnYuhYYN7dCkSTG1D/clXkeJEgkPC4aH88/JRET0fa5cuYLhw4dj9OjR2LNnD0JDQzF8+HB06tQJCoUCR48eRZ8+fdCjR49kyW1kZCRcXFxQrVo1HD16FJUrV0aXLl0QFxeHwMBAzJkzB5s3b4aenh7WrFkDAFizZg1cXFxgYWGR/Rebw2R6vZ6PHz+iaNGiqFmzJi5cuIAjR44gNDQUnTp10mR8We7NmzeIj49HgQIFoKfH5YtSc/v2OwCpJ8oVKuTH9evuqFzZRu3+7UWLWmDHji6qzwvVKgTXw67Y1HITnh59Cp/dPnDs4Zip+F6eeYkvAV9wbOQxWBS1SLJZSXR0HJ48+QhHx5RjT3TiRO809543NpYDAML+9xAhERHlfDHhKf+fLpVJoWeol2bZzGz7bmJigjVr1sDNzQ3v37+HsbExwsPD8ejRI6xcuRL16tVDvXr1sGTJEhw5cgQ9evRQ1Y2KisKUKVPQu3dv6Onp4fDhw5DLE35HPX/+HGXLlkWLFi3w6tUr3Lx5EzExMfDy8sL58+czHOePKFOZ4alTp9CuXTusXbsWJUqUgJOTE5RKJSQSCZYvXw53d3dNx5klpk2bhrVr1yIoKAhWVlY4cOAAypYtq+2wdFJ0dBx8fD4AACpVskm1bJUqtulu9+DBJ7h40Q8D9nXDu5vvMp0kA0CtkbXw4fEH3Fx5E7tdd8PtnBsKVC2ADx8i0K7dFjx69AHXr7unuHRcorSSZAAw/t9/hOGp/KdKREQ5y0yTmSmes29tD9dDX1f7SukhVQ/hkeF+y5cvj/Lly2Pv3r1wcXFBnjx5cOHCBdSpUwfbtm1D5cqVcebMGXz8+BFPnybdKTZPnjwYNmwYgoODYWJiAolEgg0bNkBPTw/W1tbw9fVFYGAgLl++DDs7O6xevRodO3bkaHI6ZWrqxYQJE2Bvb4+aNWti7dq1sLa2hp+fH1xdXbFo0SJNx5glduzYgWnTpqFz584YNmwY3NzckiTJGZ2GER0djdDQhAfYQkNDc93qHw8fBiEuTom8eY2SPLiXmuDgKCxb9m+KX8uXL4PRseM2zJp1ETc+RaHub3VV52IjYyGUGfseSCQStFnWBiValEBsRCy2OG9B8KtgmJjoIz5eQIiv00fS4927MAQFhas9Z2KSmChz6kV65Pb7g4hIExo1aoSjR4+iWLFiGDBgAFauXIl169bB1NQU48aNQ6lSpfDlyxe1dU1NTXH+/Hn06NEDgwcPxsePH1GiRAk0aNAANjY2OHz4MLp37w4vLy+MGDECgwcPhoWFBTp16oSYGA76pCRTI8r379/H3LlzYWdnh3PnzqFDhw4oVKgQmjRpgl27dmk6xizx8OFDFC1aFL///jvMzc0RFxeHkydP4t27d2jWrBny5cuXoakYM2fOxOTJkwEAhQsXhoeHBzw9PbMo+uz37bSL9Iy4xsTEw9FxOV6/DkWhQmZo/800iERFi1pgzpxmuHXrHVxcvpkm8SUam9tshmUZSzivcE5Xf4mkelJ02d4Ff9f7G+/vvccW5y3od6Efdu/uioiIWNjb50tXOyNHHsXChVcxbVojTJzYINn5xKkXTJTTJ7ffH9lNqidFxb4VVa91te2sjJMoK0wIm5DiOel/1ugf836Mxvo9d+4cvnz5gjZt2qBFixaIj49HmzZtcPDgQQQFBcHPzw8lSpRA0aJFkS9f0t9jT58+xZkzZzBw4EDUq1cPlSpVwpYtW3D27Fl07NgRO3bswKtXr2BjY6MaTfb19cXBgwexe/dujBw5Env37kXXrl01dj25Sab+58qXLx8CAwNx9+5dPHjwAI0aJWxlfP78eVhbW2s0wKwik8nw/v171TyeKlWqoHnz5ujTpw8qVqyI5cuXZ+hp0AkTJsDf3x8A4O/vjwkTUr7ZcqLERDmtaReJ9PVl6NOnAgDg999PIz5eqbbciBG1sG5d+yQrU/hf9If/RX/cXHUTR389muHRfQMzA7gecoWJrQne33+P03+cRsGCZkmS5C9fUh/RLF3aEkDCqLc6iVMvOEc5fXL7/ZHd9Az00GFdB3RY1yFLtrDWVNtZGSdRVtA31k/x49v5yamVzYzTp08n2dlYqVRCJpOhR48eOHDgAEqXLg0/Pz+8efMG1aolfUj99evXcHd3x7t3Cb+nE39nmph83eDLzs4OQMLKGiNHjsSzZ8/QsGFDNG7cGK1bt8bz588zFfePIFOJsqurK6ZNm4aqVavC0tISrVu3xqBBg7B27Vr06dNH0zFqVOIPUKVKlaBUKrF8+XIMHToUsbGx2Lt3L44cOYI6depg/PjxOHfuXJI6qTEwMICZWcKUBDMzMxgYGGTdRWjB7dsJG3r8N1GOjY3H3buBWLfuNn755Qjq11+LgQP3QwiBsWPrIk8eQzx8GIQNG+6m2Pa3I8Z//nkZ/gYytPu7HQDg3yX/4vjo4xmehmFe2Bw9DvSAQycHNJneJMk5H58gODoux+LFV1Os36NHeQQGjsGqVe3Unv86osxEOT1y+/2RXUL8QvD25ttUP4J8grQdJhFlkIuLC548eYKpU6fizJkz+OOPP9CqVSvkz58fU6dOxblz5zB8+HCULVsWjRo1QlhYGG7fvo1Pnz6hVq1aKFCgAIYPH47z589j0KBBsLW1Ra1atZL0sWrVKnTs2BF58uRBkSJF8OzZMwDAkydPUKxYMW1cdo6Qqbf4M2bMgIWFBT58+ID+/fvD2NgYdnZ2mDVrFsaM0dyfIjTp0aNHsLOzg5GREQDA2dkZDRs2xOzZs+Hg4IC2bduiXbuEpKhGjRpo3rw55syZg3bt2mXoT/+5UXh4DO7cSXinGhenxMqVN3Dz5lvcvPkWd+8GIjo66YYeFy74oUOHMmjTphQmTKiH3347CQ8Pb3TvXh6Ghin/yG3YcAejRx+Hqak+Hj4cCucVzjj480FcWXAFQQ+D0HFjRygsFemOu0DVAui6M/mfko4ceYpXr0Lg5XUN7u5V1cZkbm6Yatuco0zZLcQvBMsclqVrhzu5Qp6he+VbQghVH3KF/Lv+/9NkW0S5WcWKFbFu3TpMmDAB8+bNQ6tWrVT7UvTt2xetWrWCo6MjDh8+DKlUiuvXr6NRo0ZYu3Yt3NzcsH//fvTv3x/NmzdHlSpVcOjQIdXgBJDwnIiXlxcuXrwIAKhbty4cHR1RvHhxVKxYER06dNDGZecIEpHOv2s7Oztj2LBhaNmyJfr374+ff/4ZNWvWzOr4NGLbtm1wc3PDunXr0LZtWygUCb9AXr9+jV69euHcuXPo3LkzVq9eDTMzMyiVSnTp0gXPnz/HrVu30t1PaGgozM3NERISkuQHNKcbMGA/1qxJ+etgZmaAKlVsUaWKDV6+DMHu3T6oW7cwzp/vh6ioOJQsuQQBAV/g6dkQHh5OKbYTFRWH1q03oVWrkhg7NuHBvjsb7uDgzwcRFxkHi6IWcDvrBvMi5hm+BiEELs27hILVC8KuoR0WLryC3r0rwjKNZEIIgZMnn6Np0+JJfsmfPv0CTZr8Azs7czx9+gv0OP8yTem9P3LrffS93t58i5VVV8Jlowssilpgbb21AIB+F/pBbiRPUlZhqcjUfQIkLHmV+OT/hLAJmf5TckptJV6H+w132GZghRxKwPuDKHule0T55MmTyJs3LxQKBdatW4dChQql+OR6gwbJH37SplKlSiE6Ohrz5s2DVCqFs7MzjIyMYGtri5kzZ+KPP/7AoUOHsGLFCnTu3BmhoaF48+YN8ufPj+joaOjr6//QIyHfXnrevEaoVq0AqlSx+V9ybItixfJAKk0o9PbtFxw69AQXL/rD2/slGjUqhpkzm6Bv373w9DyLcuWs0bmz+iX4DA31cPx47yRJZ8XeFWFT0QbbOm5DTFgMolPYBCQt15dfx8nfTqJS/0oo6lQUI0fWTnL+yBFfNGhgp5p7nGj48CNYtuwa5s9vjlGjvtZxdLSGsbEcr16FYMyY41iwoMUP/TNC2cfKwQr5Sn+db29Tyea7klkiIkpZuhNlZ2dnbNy4EZs2bYJEIsH06dMxffr0JGWEEJBIJIiPj0+hFe148eIFAODGjRuYMGECJBIJWrduDYVCgZo1a2LNmjX49ddfMXnyZMyYMQNmZmYICwvDuXPnOJcSwOLFrQAkzCVetKgljP4zevUtW1tTDBxYBUuXXsPkyWfRqFEx9OlTEdevB2DJkn/Ru/ceFClijho1Cqqt/22SHBsbj0mTzmDkyFpw83ZDbGQs8qVz1Yr/ymufsGPk0yNPVT+niS5f9kfbtltQqlQ+XLjQH3nzGqnOlSqV0N/YsSdQpowlWre2BwBYWRlj/foO6Nx5BxYtugoTE31MndqIyTIREVEuku5EeefOnbhw4QICAwPRpUsX/PLLL6hfv35WxqYRsbGxqv3T+/bti7///ls1j7pNmzYwMjJC0aJFsW/fPmzatAnPnj2DTCZD9+7dUaJECS1HrxuMjOQpPtSmzrhx9bBy5U2cPfsKZ8++RMOGRbFgQQs8f/4Zhw75ol27Lbh6dQDs7CxSbefXX49i+fLrOHnyOa5eHQDZN0vzvPn3DSwdLGFgmr43MnYN7CBXyBH2NgyBdwJh881DifHxAtbWxqhY0QZ58iSdmzx8eA3cv/8eq1bdRPfuO3HlygCULWsFAOjUqSz+/LM5Ro06junTzyMuTomZM5swWSYiIsol0p0oDxs2DF27dkWnTp3g4eGBTp06oXz58lkZm0bExcWhXLlymDZtmmrnm9GjR2Ps2LEAvibLANCzZ09thpprFCpkhv79K+Gvv25g6tRzaNiwKGQyKbZs6YT69dfizp1AODtvwYUL/VJ9aG7kyFo4evQpPD2dkiTJT489xbYO21CodiG4HnJNNj9THT0DPRRrUgxPDjyB72HfJIlyvXpFcPv2IBga6qmS3JiYeERHx8HU1ABLl7bGkycfcfbsK7RtuwX//jsA+fIp/hdjbchkUvz661HMnn0RcXFKzJ3bjMkyERFRLpDuJ5BWrVqFvXv3ws/PD1OmTMGZM2fg5+en9kNXxMXFwcjICAsWLECNGjVgYmKCVq1aYe7cuQCAsWPH4tChQ4iKigLwdRm4jK7bS8mNH18PcrkUp069wMWLCT8TpqYGOHCgB2xtTXD//nt067YzxfWVAcDePh8ePRoGZ+dSSY4b5TWCVE+Kl2deYkeXHYiPTd9UH/v/TZt4vP8x4mOS1rG2NoaZ2dfR6fHjT6JmzdV4+/YL9PVl2LmzK4oVs8Dz55/RufMOxHxT/5dfamLZstYAgPnzL2PkyGP8GSIiIsoF0p0o16pVCwsXLkSxYsUghMCIESNQrFgxtR/acuPGDdy5cwf3798HAOjp6UEIAalUCqlUCiEEjIyM4OzsrEqWJ0yYgF27diEqKko1CsjRwO9nZ2cBN7dKAIApU86pjhcubI4DB3pAoZDD2toYsbEpJ8pAwsYliT5/jsTSpf+iQLUC6HGwB/QM9eB7yBd7eu+BMpWEO1HJViUBAG+uvsGOLjtSLBcSEoUdOx7Cx+cDTp9OmN9uaanAgQM9YGKiD2/vlxg+/HCSZHjIkOpYscIZALBo0VUMH34Eygyu/UxERES6Jd1TL44ePYpdu3YhMDAQY8eORffu3VGlSpWsjC1DPDw84OXlhbCwMCgUCowZM0b14F7iw1uJCbChoSGcnZ0hlUrRr18/1XrJhoapr51LGTNhQj38/fctHD/+DFeuvEatWoUAAFWrFsDt2z+jZMm86X5TEh0dh4YN1+HevfeIiYnHqFG10XV3V2xtvxUPtj2Avqk+2q5sm2p7FnYWaDqnKa4suILSHUqrjoe9C8P1FddRoVcF5C2RF+bmhjh71g0XL/qhZ88KqnLlylljy5ZOaNduC1auvAlHx/wYNqyG6ry7e1Xo6UkxYMB+LFt2DXFxSnh5tVGtCEKkKVKZFGX/t3rMf7fV1aW2szJOIqJsITLBzc1NXLlyRUyZMkU4OzsLZ2dn4enpKT59+pSZ5r7b5s2bhaGhofj999/FsmXLRJ8+fYREIhFLlixJtV5ERITYt2+f8PX11UgcISEhAoAICQnRSHu5Qb9+ewXgKVq12phimfh4pQgMDEuzrQULLosCBeaLu3ffqY7d335fTJZOFp7wFMfHHk9XTPFx8SIuOk71+aX5l4QnPIUnPMWaOmvEteXXRMTHiCR1oqJixadPCcfmzr0oAE8hk00Wx48/Tdb++vW3hUTiKQBP8dNP+0R8vDJdceV26b0/eB+pF3AjQHjCUwTcCNB2KN8lt1yHtvD+yL0ePXokqlWrJhQKhWjWrJnw9/cXDx48EHXr1hVGRkbCxsZGeHh4qK3r7e0tKleuLIyMjETFihXF8eNffx/OnDlT5M2bVzRp0kR8/vxZCCFEdHS0qFChgupzSlmm3uJ7eHigXbt28PDwwO3bt3Hr1i1MnjwZpUqV0sp+4Y8fP4aNjQ2GDBmCIUOGYMKECXB0dMRff/2FwMBACCHULllnZGSEdu3aoWTJktke849i4sT6kMkkOHLkKa5de5PsfEhIFDp02IqGDdfhy5fU10geMaIWHjwYAkfH/Kpj5bqUQ9vVbQEA0m+WlosJj8Htdbfx5e2XZO1IZVLIvpnSYelgiRLNS0AilcD/kj8ODT6EeTbzsH/AfsTHxCMqKg4uLtvQrNkGBAdHYfTo2ujbtyLi4wW6dNmBx48/JGm/T5+K2LixI6RSCdasuYWfftqf6lxsIiL6sUVHR6NFixaoUqUKjh49ii9fvmDw4MEYNWoUYmJisG/fPowePRqTJ0/G5cuXk9SNjIyEi4sLqlWrhqNHj6Jy5cro0qUL4uLiEBgYiDlz5mDz5s3Q09PDmjVrAABr1qyBi4sLLCwstHC1OUumEuWhQ4dCX18f169fh7+/P16/fo1r167B0NAQQ4cO1XSMKRLfPHwXGRmJt2/fAgDKlCmDOnXqIDAwEEqlEhKJBDKZDEqlEpMnT8bRo0ezLcYfXYkSeVXTF0aNOo6oqLgk56Oi4nDz5lu8ePEZ//6bPJH+LwuLr9Nj7t4NxNmzL1G5X2W4HnaFY09H1bmXZ15iX799+LPAn9jWcRve/W8LbnXsW9mj17FeGOk/Es3mNUP+CvmhjFXi1ppbODHuBF6/DsX16wF4+DAIDx8GQSKRYMUKZ9SpUxghIdHo0mVHsvnIrq6O2Ly5I2QyCdatu41Bgw7yAT8iIlLr0KFD+Pz5M5YsWYL69etj69atGDNmDPT19VGwYEFUqVIF1apVA4Bk00SjoqIwZcoUzJ07F1WrVkX+/PkhlyesBvX8+XOULVsWLVq0QMeOHeHr64uYmBh4eXlhxIgR2X2ZOVKmEmVvb2+MGDEiyRzlqlWr4pdffsH58+c1FlxaEuejdurUCe/fv8eyZcvw5UvCCKK+vj6ioqIQHBysKr98+XJMnjwZq1atUq10QVlv0qQGMDaW48IFP3TsuA3R0V+T5fz5TbBnTzdcvNgfTZoUT3ebz559QtOm/6BVq024eNEP9q3sYV3OOkmZAtUKAAAe7XmEFZVWYHun7akmzKYFTFFndB0MujMITWc3hZ6RHoLuB6Fkybw4ebIPjhzpiTp1CgMADAz0sHt3V9jb58Wff7ZQOw+5W7fy2LatM6RSCVavvoW5cy+l+/qIUhMTHoPJksmYLJmMmPAYnW07K+Mkygrh4TEID49JMrARExOP8PCYJL+7vi377UBJbDpXYfqva9euwd7eHl27doWJiQkGDBiAYsWKYcGCBbh48SIsLS3RqFEjjBw5EpUrV05SN0+ePBg2bBiEEDAxMcGcOXOwcOFC6OnpwdraGr6+vggMDMTly5dhY2OD1atXo2PHjhxNTqdMJcpGRkb49OlTsuOfPn3Syk52FSpUwOLFi9G4cWMoFAnr27558wZ58uRBgQIJyZKPjw+Cg4Mxf/58zJgxgw/uZaOSJfPi4EFXGBnp4ciRp+jSJenyatWrF0TVqgVUnwcGhqU5+lqwoBmqVy8IBwcrlPtPggwApZxLYeC1gRjyYAjKdy8PSACf3T5YUWkFdvfajdiI2FTbrzO2DiZGTETvE70BABUq5EfDhkWTlMmf3wQPHgxB06YpJ/idOpXFwoUtAADjxp3EsmX/ptovERFpj4nJTJiYzMSHDxGqY3PnXoSJyUwMG3Y4SVlr63kwMZkJP78Q1bFly65lqt+PHz/i5s2bKFOmDPbv34+goCD8/PPPGDhwIOzs7HDmzBnMnz8fixYtwtmzZ9W2YWpqivPnz6NHjx4YPHgwPn78iBIlSqBBgwawsbHB4cOH0b17d9Vo8uDBg2FhYYFOnTohJoZvZFOSqUS5W7du+PPPP7F48WK8fPkSL1++xKJFi7Bw4UJ06dJF0zGmy+DBg9G7d2/IZAlzT4OCgmBtbQ1jY2P4+Phg1KhRmDRpEnr06IHSpUun0RppmpNTURw40AOGhno4cOAJunffqfad9927gahY8S9MnHg61fYMDfWwZ083nDzZWzUdQ91ybFZlrdBpSycMuf81YX5//32ydZT/K72rccjlX+c6P3r0AX377k02vWTYsBoYO7bO/14fwaJFV9LVNhER/RhMTExgaWmJmTNnonHjxvjjjz9w6tQpnDlzBtOmTYOTkxNGjRqFGjVqYNeuXUnqPn36FKtWrYJMJkO9evWwYsUKhIWFqRLqHTt24OXLl/Dz88OpU6dUUzAOHjyI3bt34+nTp9i7d68WrjpnyFSiPHfuXNSpUwcjRoxAiRIlUKJECYwcORLVq1fHvHnzNB1juiQmyIliY2NhZGSEx48fY+zYsbh06RJu3LgBGxubFFqgrNakSXHs3dsN+voy7NnzCL1770k2cnz9egACA8Mxc+YFzJ59IdX29PVlyJPHSPX5nDkX0aLFRty69TZZ2cSEud/5fuh1tBcMLTL+F4ULF/zQpMk/6Nt3b7JzsbHxaNNmM/755w5+//1UknMSiQSzZzfF+PF1AQAjRhzD/PmchkFEpGvCwiYgLGwCLC0VqmNjx9ZFWNgELF3aOknZ9+/HICxsAooUMVcdGzq0eqb6tbe3h1KpVP1OTHy+CkjYPC1RXFwcTE1Nk9R9/fo13N3d8e5dwtTCxDZMTExUZezs7AAAXl5eGDlyJJ49e4aGDRuicePGaN26tVYWYsgpMpUoKxQKnDp1CmfPnlWNJJ8+fRrnzp1L8o3RBiEEhBAwNTXFw4cPMXDgQHh7e+Ps2bPJ5vVQ9mvRoiT27OkGuVyKbdseYOXKG0nO9+9fGXPmNAUAjB9/Cn/9dT1d7cbExGPBgis4fvwZ7t9/n2K5InWLwMTm68/orbW38PnF5+Tthcdgs/NmrG2wNsnOf6dPv8CJE8+SJfhyuQyrV7dF3bqF8fvv9ZO1J5FIMGNGE/zxR8K5MWNOpPlGgIiIspexsT6MjfWT/FVRX18GY2N9GBjoqS377TMq3/6VMSM6dOiAL1++4LfffsPp06cxffp0tGnTBi1btsSkSZNw5swZzJs3Dzdu3EDbtm0RFhaG27dv49OnT6hVqxYKFCiA4cOH4/z58xg0aBBsbW1Rq1atJH2sWrUKHTt2RJ48eVCkSBE8e/YMAPDkyROtbhan675rBfj69etj2LBhGD58OJycnDQU0vdJ3FhkyJAh+Pz5M27duoVLly6hUqVK2g6N/qd1a3vMnp2QDI8adRxPnyad7z52bF1MnJiQUA4ZcgibNt1Ns019fRkuX/4J48bVhavr19Uvbt9+l2T+2LcebH+A/f334++6f+P9f5Jrmb4Mvod84XfeDzFfEuZuValiixUrnHHkSE+17TVqVAznz/dLMhLxLYlEgqlTG8PTsyGAhDcC06efU1uWiIh+HLa2tti4cSN2796N9u3bo3jx4lixYgXWrl0LOzs7dOjQAcuXL8fatWtRq1YtXL9+HZUrV8b+/fthaGiI/fv348mTJ2jevDlevnyJQ4cOwczMTNV+dHQ0vLy8MGrUKABA3bp14ejoiOLFi0OpVKJDhw5aunLdl+6d+XIaJycnjB49Gj/99BPKlCmj7XDoP379tRb2738Cb++X6Nt3L86dc4Psm527pk5thJCQKCxdeg19+uxFcHAUhg6tkUqLQPHieTBrVlPV50qlQP/++/DgQRC2b++M9u2T/hwUqVcE1uWt8f7+e6xtsBauh1xRuHbCqhYyuQx6hnqIi4pDdGg0jPIaQaGQw929aqoxfDsKsWLFdTx4EIRFi1omOe7h4QQ9PSn++OMM/vjjDOLilPi//2vIrdN/YCF+IYj45uEhdYJ8grIpGiLShi5duqh9zmvPnj3Jjjk5OSX5y2bVqlVx586dFNs2MDDAw4cPkxxbuXLld0T748i1ibK5uTlmzpwJPb1ce4k5mlQqwbp17eHouByXLvlj7txLGD++nuq8RCLBokWtEBurxIoVNzBs2BG8ehWCWbOapntL6M+fI2FhYQgDAxnq1i2S7LxpAVO4nXXDZufNeH35NTY03YCuu7uiZIuEDWgMzAwSEuU0NkJR59GjDxgy5DCUSgEjIz3MmtU0SSI8cWID6OlJMX78KXh6nkVcnBJTpjRisvwDCvELwTKHZWmuxAIAcoUcCksFpDIp7FvbA8iaLaw11XZWxklElB0kgrsgaExoaCjMzc0REhKS5E8elLJ1626jX799kMuluHZtICpWTPqwpRACM2deUK2C0a1bOaxb1wGGhul7AySEgJ9fCOzsLFTH/v77FmrUKIjy5ROWlYsJj8H2Ttvx7NgzSOVSuGxwQflu5bHEfgk+Pf2Efhf6ocj/Eu0vX6Jx+vQLvHsXhp9/rpZq3ytX3sDPPx8EAEye7IT/+7+GycrMn38JY8acAACMH18XM2Y0ybXJcnrvjx/tPnp78y1WVl0Jl40usHKwSrWswlIB828eHMqJEq/X/YY7bKvYajucHOdHuz+ItI1v8Umr+vatiPbtSyM2Vonu3XchKCg8yXmJRILff6+PDRtcVA8ANm++AZ8/R6arfYlEkiRJ3rfvEX76aT8aNFirmrusb6yPHvt7oFy3clDGKrGrxy68u/11Y5Lo0K8jyv7+oejQYZvaXQb/y929KhYsSFhD2cPDGwsWXE5WZvToOqp1lmfNupjmsniUe1k5WMG2im2qHzk9SSYiymmYKJNWSSQSrFzZFra2Jnj06AMaN/4H79+HJyvXq1cFHDvWC+bmBjh/3g/1669FYGBYhvurX98OtWoVQq9eFVC48NfRGJm+DB03dUTVQVVRf2J9PDn4BJ+efoJULk0yylemjCUcHCwxZEi1JJumpGTEiFqYNq0RgIQHF9Wt4vHrr7WwdGkrAMDMmRcwbdo5xMcrM3xtREREpFlMlEnrrK2NceZMX9jamuD+/fdo3Hi92mQ5cVUJW1sTFChgmmQN5fTKm9cIp071wcKFLZNNcZDKpGjj1QaNpjSCQycH5CmRB21XtYVFUYuvZaQS3Ls3GHPnNoeZWfp2ofz99/r47beEDUcGDz6E1atvJiszdGgN1UogkyadQbVqq3D69IsMXx/9GGLCYzDDeAZmGM/Iki2sNdV2VsZJRJQdmCiTTihd2hLe3m4oUMAUDx4EoVGj9WpHjB0d8+PKlQHYtasr9PUzt16lQiFXPRAohMAvvxzB9u0PAHxdXtDKwQqD7w1Gpb6VktWXZfChJIlEglmzmmLkyIQ1Ld3dD2Dt2lvJyv32W10sWdIK5uYGuH37HZo0+Qft2m3B48cfMniF9COIjYhN1wOA2m47K+MkIspqTJRJZ5QqlQ/e3n1RsKApHj5MSJbfvUueLBcpYg5T04TRXCEEfvvtBPbvf5ypPrduvY8lS/5Fr1678epVcJJzciN5qnWfPfuE0aOPpWsKhkQiwfz5zTF8eA0IAfz0035s2JB8KZ9hw2rg6dNfMGxYdchkEhw48ATlyy/H8OGH8SGN5cOIiIhIs5gok06xt88Hb283FCpkBh+fDykmy4l27HiIuXMvoVOn7Xj5MjjD/XXtWg7u7lWwfHmbJA/9pSU+Xgknp/X4888rahNedRKWvGuJwYOrQQjAzW0ftmy5l6ycpaUCS5a0xv37Q9C2bSnExSmxdOk1lCy5GPPmXUJ0dOoPERIREZFmMFEmnVOyZF54e/dFoUJmePToA2rVWo2tW+9DqUy+kqGLSxn061cJ8+Y1Q9Fv5hKnl0wmxYoVbfHTT1VUx2Ji4pNtUa2u3siRtdCqVclkS9qlRiKRYOnS1hgwoDKUSoHevfekOLWiTBlL7N/fA6dO9UGlSjYICYnG2LEn4OCwDDt2PEgzRiIiyjmmTp0KOzs7GBsbo0ePHggLC8Pjx49VUwITP54+fZqs7ooVK5KUKVSokOrcrFmzkC9fPjRt2hTBwcEAgJiYGFSsWFH1OaWMiTLppBIlEpJlOztzvHoVgh49dqFatZU4duxpkgRRLpdhzZp2+PXXr3vav34dirCwzD04FBkZixYtNmLChFNpJqIjR9bC4cM9Ua1agQz1IZVKsGJFW/TrVwmzZzdF6dKWqZZv3LgYrl8fiL//bgdbWxO8eBGMrl13omnTDQgOjspQ30REpHu2bt2K6dOnY9q0adi7dy/u3r2LGTNm4N69eyhSpAhu3bql+ihSJPkGWvfu3UPHjh1VZY4fPw4ACAwMxJw5c7B582bo6elhzZo1AIA1a9bAxcUFFhYW2XmZORK3rSOdVaJEXty/PwQLF17B3LmXcOvWO7RsuQlOTkUxc2YT1KqV8I7529UrQkOj0bLlRshkUhw40ANFMrju7JEjT+Ht/RI3bgRg0KBqqY5S/3fVDG/vl6hduxAMDNK+raRSCdasaZekDSFEipuNyGRS9OtXGV26lMO8eZcwd+4lnD79Ak5O63D8eG9YWxun7wKJiEjnXLhwAXXr1kXv3r0BAEOGDMHixYshl8vh6OiISpUqpVr/3r17aNu2bbJyz58/R9myZdGiRQu8evUKN2/eRExMDLy8vHD+/PksuprchSPKpNNMTPTxxx8N8OzZLxg9ujYMDGT/S0jXwMVlGx4+DEpS3s8vBB8+RODu3UBUr74Kly/7Z6i/jh0dsGpVWxw86KpKkuPjldiy5R4iUnly/59/7qBJk3/Qo8cuxMWlbw3kb5PikJAoODmtx6FDT1KtY2KiD09PJ1y+/BPy5zfGnTuBaNBgLfz9Q9LVJ+UOEqkEdg3tYNfQDpJ0bumujbazMk6irBAeHpPhj2//z0/v////Vbx4cdy6dQu3b9/Gp0+fsHfvXjx//hz37t1DUFAQihcvDltbW0yfPl1t/Xv37uHEiROwtLRE6dKlcfjwYQCAtbU1fH19ERgYiMuXL8PGxgarV69Gx44dOZqcXoI0JiQkRAAQISEh2g4l1/LzCxb9++8VUulkAXgKqXSyWLLkapIyr14Fi4oVlwvAUxgYTBU7dz74rj6PHPEVgKcoUGC+iIiIUVvm1KnnwsBgqvjpp30iLi4+w31MmnRaAJ5CX3+qePQoKF11njz5IIoUWSAAT1Gu3DIRH6/McL/ZKb33x492HwXcCBCe8BQBNwK0HUq2+NGuV9N+tPsjuwGeGf7Yvv2+qv63rzMiPDxcNGzYUAAQUqlUtGvXTgAQ9vb2onXr1sLb21vMnDlTABCnTp1KUjcgIEAYGxuL0aNHi3Pnzonu3bsLMzMz8fnzZyGEEJ07dxYAhLW1tfDx8RHlypUTnz59EoMGDRLm5uaiY8eOIjo6OtNfs9yOUy8oRylc2Bxr1rTHmDF1MGHCKezb9xjDhx9BXJwSI0YkzFMuUsQcFy70R8+eu7F//2N07boTf/3VBgMHVs1Un5GRsShWzAKuro4wSmHJuMaNi+HatYEoX946xekTqZk0qQGKFrVApUo2KFYsT7rqCAE+0Ec5VpBPUJplFJYKbttNPwSFQgFvb2+8fv0acrkcBw4cwIkTJ/Dkyde/MjZs2BCbNm3C6dOn0bhxY9VxW1tbhIV9XR2qbNmysLS0xPXr19G0aVPs2LEDr169SjKa7Ovri4MHD2L37t0YOXIk9u7di65du2brNecUTJQpR3JwsMKePd3wxx+nMWPGBYwcmbCe8dixdSCRSGBioo/du7ti0KCDWL36FtzdDyIoKAITJtTLcCLr4uIAZ+dSSVbd8PMLweLFV/F//9cQJ08+R8OGdnB0zJ/p65HLZejfv3K6y9+9G4hmzTbg/ftwlC6dD0eP9lJtokKkyxSWCsgVcuzptSfNsnKFHEN9hjJZpmwTFjYhw3W+fS7FxcUhU/0ePHgQixcvVj2Ed+7cOVStWhX9+/fH8OHDUbny198PhoaGSeo+fPgQc+fOhZeXF4yMjNSWs7OzQ3R0NLy8vHDhwgUcPXoUDRs2ROPGjfH/7N11WFTZ48fx98zQoICCYoCB3b26FnZh4Nqta8euHetXwbVW13bt7lxbsbtr7cQCxQCR7pn7+2N+jCI1A0N6Xs8zjzhz77nnDlz4zLknmjdvzsuXL5NV7x+BCMpCpiWTyZg2rT4KhZypU88zbtxJLlzwZPXqluTObYFCIWflypbkymXOjBkXmTjxND4+Icyd20TnUGloGHsVwOHDj7J37xPmzr0CQKVKeTh3rhcWFkZ6O7+EREUpadZsC58+hVChgh3HjnUTg/l+MJEhkSwsuBCA31//jpG5/n7u9Fl2fGVZOlgy5PEQQpNYQMfnsQ97u+0l1DdUBGUhzZin8FoyMEje0K+iRYty5swZ5syZg729Pdu2bWPTpk3Mnz+fYcOGMWPGDG7cuMGjR49o27YtwcHBeHh44ODggK2tLbt27cLY2Jju3buzYsUKHBwcqFatWqxjrFq1irZt22JtbY2DgwMvXrwA4NmzZ6I1ORFiMJ+QqclkMv78sx4LFzbFyEjBoUPqlez27XuieX369AbMn98EgAULrtG27Q6uXPFKUbeFgQOrUKqULTt3tsPW1oxatewxN098Jb+kKJUqli27Qb9+B/DxCUlwOw8PP7y9gzA3N+TMmZ4iJP+gQn1DkwybGaHs+MqydLAkT6U8iT5sS9rq5fiCkBkUL16cNWvWsGDBAgYPHsz06dPp1KkTW7duxdTUlObNm7No0SJ27dpFqVKluHnzJhUrVuTAgQPY2tqyZ88eLl++TIMGDXj8+DHu7u4YGX0N/TGtySNHjgSgZs2alC1blsKFC6NSqWjTpk06nXnGJ1qUhSzht99+on79QnTrtoe7dz/i4rKDPn0qMH9+U7JnN2b48OrY2JjRu/d+9u9/yv79T6lQwY4bN/olqwWgcWNH7t8fhFwuo2ZNB/LksUhW3+RvKRRy1q27w40b3tjbWzJ5ct14t3v69DMAxYvbYGVlEu82giAIQubSo0cPevToEes5R0dHTpw4EWdbJyenWI09jRs35t69ewmWbWxszKNHj2I9t3LlyhTW+McgWpSFLKNMmVxcu9aXceNqIpPB2rV3KF9+ORcuvAGgW7dyXL36Kz17lsfMzJCiRXPECsmXLnmiVGo/tU9M9428ebOlOCTHGDmyBgBLltwgPDz+papjVvIrUSLxhUoEQRAEQUgZEZSFLMXY2IC//mrIuXO9KFjQitev/albdz0TJpwkMlJJ5cp5Wb++De/fj2Lu3Maa/Z488aVWrXUULrwowYCaFn75pST29tn59CmErVvvx7vNkycxLco507JqgiAIgvDDEUFZyJJq1y7A3bsD6d27ApIEf/11iWrVVvHgwScAsmc3xt7+6wCh588/kzOnKeXK5cbE5GuPpKgoZZrW29BQwW+//QTAvHlX4u1HLVqUBUEQBCFtiD7KQpaVPbsxa9e2pmXLYvTvf4i7dz9SpcpKevWqgLW1CUZGilgPV9e6REaq2Lz5HkZGCnLmNKVPnwPMnt2Qjh3LpFm9+/atxJQp53j40Ifjx1/QpEkRzWuSJPHkiQjKgiAIgpAWRFAWsjwXl5LUqGFP374HOHz4OStW3NJqP5lMvahHp07/cuXKW+bPb6K3vsiJsbIyoW/fiixYcI0FC67FCsq+vqF8+RKOTAZFi+ZI9boIGZNMLiNvlbyarzNq2alZT0EQhLQggrLwQ7Czs+Dgwc7s2vWIW7e8iYxUfvNQxfp/REQ03t5BPHz4deWwhQuvAaRZWB40qCoLFlzj5MmXBAVFkC2bMQDBwZEAmJgYJLhKoJD1GZoa0u9GvwxfdmrWUxAEIS2IoCz8MGQyGR06lKZDh9JJbitJEkOHHmHp0pua5xYuvIZcLmPu3MapHpaLFcuJo6M1L1584cyZ17RqVRz4Ohl+WFg0KpUkVuMTBEEQhFQkBvN9Q6XSfmowIWuTyWT8809zBg2qEuv5+fOvMmbMiRQtVqKtpk3VXS6OHvXQPPftoiahoVGpXgdBEARB+JH98EH5/fv3fPz4EZVKhVwuF2FZ0IgJywMGVP7//6ufnzv3CuPGnUz1sBwTlN3dPTTH+ra7RUhIZKoeX8i4okKjWFBwAQsKLiBKzx+Y9Fl2atZTELKaqVOnUqBAAczNzencuTPBwcE8evSI+vXrY2FhQZEiRThw4ECc/davX49MJovzOHv2LAB//fUXOXPmpGHDhvj7+wMQGRlJ+fLlNf8XEvZDB+XZs2dTr149KlSoQMOGDfH19RVhWYhFLpexdGkL+vatiCR9Dct//32ZP/44laph2cmpIIaGcl6/9uf5cz9NfczM1GE5JEQEjx+VJEkEvAkg4E2A3n8G9Vl2atZTELKS7du3M336dKZNm8a+ffu4d+8eM2bM4JdffsHMzIyjR4/So0cPOnfuHCfctmrViv/++0/zmDx5Mnny5KFcuXJ8/PiR2bNns3XrVgwMDFizZg0Aa9aswcXFBSsrq7Q/2Uzmhw3KS5cu5c8//8TJyYlWrVrx4sUL6tSpQ2hoKHK57m9LREQEgYGBAAQGBhIREaHvKgvpRC6XsWJFS/r0qRArLP/11yX+97/TqRYALCyMqF27ACYmBjx8+EnzfEz3i8zUoiyuD0EQhIRdvHiRmjVr0r17dxo1asTgwYPZsWMHT548Ydy4cdSqVYvJkydjZmaGu7t7rH1z5MhBhQoVqFChAnZ2dixdupR169aRI0cOXr58SalSpWjSpAlt27bl+fPnREZGsnTpUoYPH54+J5vJ/HBBWZIkwsPD2bt3L7Vr12bRokWsWLGC+fPn8+7dOy5fvpyscmfOnIm9vT0A9vb2zJw5U5/VFtKZXC5j1apW9OoVOyzPmHGRCRNSr2V57dpW+PmNxcWlpOa5mAF9malFWVwfgiBkBiEhkSl+JEfhwoX577//uHPnDn5+fuzbtw9PT08sLS3ZsWMHwcHBHDx4kM+fP+Ph4ZFgOZMnT6Zy5co0adIEgFy5cvH8+XM+fvzIlStXsLOzY/Xq1bRt21a0Jmvph5v1QiaTIZfLUSqVBAQEEBoaipGREdWqVcPY2Jg9e/awevVqfvnlF2rXro2dnZ1W5U6YMIG+fftib2+Pl5cXtra2qXwmQlqTy2WsXt0SSZLYsOGuZp7l58/9Um0WjAIFrOI8lxlblMX1IQhCZmBhkfIP8ZLkqvM+AwcO5MCBA1SsWBG5XI6zszPR0dEsWrSIwYMHs2TJEkqWLEmxYsUICgqKt4zXr1+zfv16zpw5o3nO0dGROnXqYGdnR65cuTh37hzt2rXjwoULDBo0iG3bttGgQQO2bduGkZFRss85K/vhWpQBjIyMqFevHv/99x/Tp0/n6NGjLF26FF9fX65cucK1a9fo2rUrmzdvBtCqtdDY2Jjs2bMDkD17doyNjVP1HIT0oVDIWbOmFd27l0OS1OG5aVNHzet+fmEEBaVOtwKlUt13PjO2KIvrQxAEIWFmZmacPXsWLy8vvL29admyJaampvTo0QMfHx+ePHnCvXv3CA4OJmfOnPGWsX79evLkyUPNmjVjPb9r1y5ev36Np6cnp06d0nTBOHToEHv27MHDw4N9+/alwVlmTj9Mi/L+/fv5/PkzUVFR9O3bl0mTJuHr68vmzZtZs2YN/v7+rFixAmdnZ/LkyYOzszPLly9nyJAhmJqapnf1hQxEoZCzbl1rVCqJLVvu07//IXx9Q5kwoTZDhx7h6tW3nDrVg0KFrPVyvPHjT3LkyHOaNSvCrFmNNC3KMYuPCIIgCPoRHDwhXY576NAhFi1axPHjxwE4f/48lStXpk2bNnTp0oUOHTrw8uVL3r17R5UqVeItY9euXbRs2TLe1woUKEBERARLly7l4sWLHD16lLp161K/fn2aN2/Oy5cvU+3cMrsfokXZ1dWVzp07M2LECAYNGkTlypXZvn07Cxcu5PHjx8yYMYPcuXNTvXp18uTJA6DpTxkZKcKIEJdCIWf9+jYMHqz+hfXHH6eZMOEkV668xcsrkEePfJIoQTuSJPHs2Wfu3/9E7twWABQqZAXArVveejmGkPnIZDJsS9liW8pW791+9Fl2atZTEFKDublRih/JUbRoUc6cOcOcOXPYsWMH27ZtY8iQIWTPnp2pU6dy/vx5hg0bRqlSpahXrx7BwcGa/swAQUFBPHr0iHLlyiV4jFWrVtG2bVusra1xcHDgxYsXADx79oxChQolq94/BCmLe/bsmWRnZydNnDhRunXrlnT27FmpZMmSkoWFhTRx4kQpJCREWrJkiSSTyaQbN25I4eHh0oMHD6Q6depITZs2lUJCQrQ+VkBAgARIAQEBqXhGQkYzc+YFCdwkcJNGjHCXDh9+qtfyIyOjpTVrbksqlUqSJEnaseOBBG5SsWKLpYiIaL0eKzVpe338aNeR9y1vyQ03yfuWd3pXJUMR70v8frTr40eyYcMGKV++fFKOHDmkWbNmSZIkST4+PlLz5s0lMzMz6aeffpJev34tSZIknTlzRgKkdevWSZIkSTdu3JAA6dSpU/GWHR4eLpUsWVLy8/PTPNevXz+pUKFCUps2baTw8PDUPblMLMt3vQgJCeHTp0+UKlWKSpUqAXDz5k1at27NokWLMDU1xcXFhXLlytGqVSuKFClCYGAgHz584PTp05iZmaXzGQgZ3fjxtTAwkDNmzAnmz7+GmZkRzZoVRSaT8erVF6KiVBQrFn+fsoRcuuRJjRr2yOUyDA0V9OlTUfNao0aFMTEx4Nmzz/z88xq2bfuFokV1K18QBEHIWHr06EGPHj1iPWdjY8Phw4fjbOvk5BRr/FSVKlUSHU9lbGzMo0ePYj23cuXKFNb4x5Dlu144OjpSrFgxNm7cqOlGYWZmxsGDB6lUqRJz587lzp07LFmyhNq1ayOXy6lSpQrnz5+nVKlS6Vx7IbMYPfpn5s1rDMD06ReYMOEUb974U6/eBurWXc+TJ75al7Vu3X/Urr2OQYMOoVLF/cVnbW3Kv/92IEcOU27dek/FiitYv/6OWNBBEARBEPQsy7com5mZ0aZNG1asWMHcuXMZM2YMBgYGmJiYcODAASpUqMDGjRs5evQoNWvWRJIkVCoVCoUivasuZDIjRtTAwEDOb78dZdasSwQGRmBpacK9ex+pU2cd//zTHEdHayIilERERBMeHq35Oubf16/9mTnzIpIEhoYKEurW2bx5Ue7dG0i3bns5e/Y1vXvv5/jxFyxb1gJLS5O0PXEhzUWFRrGq6ioA+t3oh6GZYRJ7pE/ZqVlPQRCEtJClg7JSqUShUDB+/HjOnDnDvHnzsLS0pH///hgYGGg6yXfv3p0TJ07QqFEjZDKZCMlCsg0b9hMKhZwhQ46wbNlNBgyohFwu486dD3TsuFvrcoYMqcrixc0SHQCVL192Tp7szqxZl5g8+Qzbtj3g6tW3bN36C9Wr59fH6QgZlCRJ+Pz/gFF930nQZ9mpWU9BEIS0kKWC8q1btzAwMEChUFCmTBkUCgVRUVFYWlpy6NAhfv75Z1xdXfn06ROTJk1CoVBgZGSEubm5WKFG0JvBg6uiUMgYOPAwK1bcpm/fikyYUIvffz+KgYEcY2MFxsYGmn9NTAxiPVerlgPDhlXTapYAhULOH3/Upl69gnTpsodXr/ypVWstU6fWY+zYmigUWb53lSAIgiCkmiwTlF1dXVm6dCnBwcGYmZkxevRoJkyYgKGhIZGRkdjY2HDlyhXatm3LwoULOXjwIPXq1ePGjRtYW1uTL1++9D4FIQsZMKAKBgZy+vU7yOrV/2FoqODdu5HI5akzRVaNGvbcuTOAAQMOsWPHQ/744zQnT75i0yYX8ubNlirHFARBEISsLksE5W3btjF79mxGjhxJvnz5uHbtGhMnTiRbtmwMHToUIyMjoqOjyZkzJwcPHmTt2rXs2rWL/fv3Y2dnx+HDh8mbN296n4aQxfz6ayUUCjl9+uxn2bKb3L//CRsbMyRJQpJI8N+8eS1o0qQIjRoVxtpa+8VuLC1N2LbtF5o0cWTYMHdOn35FuXLLWL++Dc7OxVLxTAVBEAQha8oSQfnp06fY2dkxePBg8uXLR/369blz5w7Lly+nffv25MqVS3MbO3v27AwfPpzhw4fz6dMnzM3NMTc3T+czELKqXr0qoFDI6NVrPxcvemq939q1dzA2VrB5c1vatdN+9hWZTEbv3hWpWdOBTp12899/H2jVahs3b/anUqU8yTkFQRAEQfhhZeqgLEkSMpkMSZIICwvj/fv35MuXjxIlSvDzzz+ze/duVCqVZoCeSqXizz//5KeffqJZs2bkypUrvU9B+AF0716e0qVzce3aW+RyGTKZDJmMOP/GdMu4f/8Thw8/58kTX7p23UOuXObUqVNAp2MWK5aTK1d+pVSppbx8+YWbN71FUBYEQRAEHWXqoBzTSvzLL78wdepUlixZwqJFi8iWLRtGRkaEh4fj7++vWZZ62bJl/Pnnn7i4uODk5ISpqfa3tQUhJSpVyqNTUJ01qyHt2+9i794ntG69nYsXe1O6tG4f7IyNDXB0tOblyy+YiWm5shSZTIZlAUvN1xm17NSspyAIQlrI1EE5Rrly5Vi0aBGWlpaalfTevXuHtbW1pu/x48eP8ff3Z+7cuTRv3lyEZCFDUyjkbNnSloYNN3H5shfNmm3h6tW+Og/MCw2NAhBBOYsxNDNk+OvhGb7s1KynIAhCWsgSQRlg0KBBseY/9vHxIVeuXJibm/P48WNGjhzJsWPH8Pb2xs7OLh1rKgjaMTU15MCBTtSsuZanTz/TrNkWLlzoTfbsxlqXIYKyIAiCICRflplk9ftFQqKiojA1NeXp06eMGTOGy5cvc+vWLRGShUwlZ04z3N27kju3OffufaRt2x1ERiq13j8sLBoAU9Ms85lYEARBENJMlgnKMdRTbElky5aNR48e0a9fP86ePcu5c+eoWLFieldPEHRWqJA1hw93wdzckFOnXvHrrwe0XuVMtChnTVFh6qWhV1VdRVRYVIYtOzXrKQiCkBayXDNTzICRwYMH4+Liwn///ce1a9coV65cOtdMEJKvcuW87N7dAWfnrWzefA97++zMmNEgyf1igrKpqQjKWYmkkvC+6a35OqOWrY+yfB77JLmNmY0Zlg6WySpfEAQhMVkuKMdwcnJi1KhR/Prrr5QoUSK9qyMIKda0aRFWrWpJnz4HmDnzIgqFjMaNHSlSJAd2dhbxzioQFpZ4i3JISCSvXvnz+rU/xYrlpFixnKl6DoKgLTMbMwzNDNnbbW+S2xqaGTLk8RARlgVB0LssG5QtLS2ZOXMmBgZZ9hSFH1Dv3hXx8grE1fUs06ZdYNq0C4A6CDs6WlOkSA7Nv0WK5CAkRB2Ur19/x9mzr3n16gsvX/rz8uUXXr36wsePIZqyFQoZy5c707dvpXQ5t6wowDOAUN/QRLfRpsX0R2TpYMmQx0O0ev/2dttLqG+oCMqCIOhdlk6RIiQLWdGkSXWwtDTm0KHnvHjhx5s3AYSGRnH//ifu3/8U7z6dO/+bYHnW1ibY2Jjx/Lkf/fodxMbGjDZtxF2YlArwDGBJySVEhSbdN9fQzBAzG7M0qFXmYulgKcKvIAjpSiRJQchkZDIZv/9end9/rw5AZKSS16/9efHCDw8PP168+IKHhx/Pn/vx7NnnJMuTJPVD0K9Q31CiQqNw2eyCbUnbRLcVfWwFQRAyJhGUBSGTMzJSJNi/WKlU4e0dxMuXX/6/u4V/rK8/fAjG3z8cf/9wTdcL0ZqsX7Ylbckjlg8XBEHIlERQFoQsTKGQY29vib29JXXrFozzekhIJK9f+/PqlRjMl9mkZlcNfZYtupQIgpCZiaAsCD8wc3MjSpfORenSudK7KoIOjMyNGOMzJsOXnZr1FARBSAtZbsERQRAEQRAEQdAHEZQFQRAEQRAEIR6i64UgCIKO0nt+5KiwKLY02wJAV/euGOpx5UV9lp2a9RQEQUgLIigLgiDoICPMjyypJN6ce6P5OqOWnZr1FARBSAsiKAuCIOhAzI8sCILw4xBBWRAEIRnE/MgZizZdXcQHF0EQdCWCsiAIgpBpmdmYYWhmyN5ue5Pc1tDMkCGPh4iwLAjJ4OPjQ69evTh79iz58+dn6dKlNGjQQOdtly1bxqpVq7h//z4TJ07Ezc0tDc9CdyIoC4IgCJmWpYMlQx4P0Wpw5d5uewn1DRVBWRAS0KtXL5ycnOjVq1ec14YMGYKdnR0+Pj6cPHmSDh068Pz5c3LkyKHTtnny5MHNzY2tW7emwRmlnAjKgiAIQqZm6WApwq8gpKLg4GD27dvHy5cvMTMzo1WrVpQtW5b9+/fTu3dvnbZt06YNAEeOHEmHM9GdmEdZEAQhEzI0M8TQLHWmW9Nn2alZT0HIjCIiIhg3bhx58+bF1NSUn376iRMnTuh9/1u3btG0aVOyZ89OtmzZaNy4MXfu3ElWnZ8/f46FhQX58+fXPFe2bFkePnyYom0zA9GiLAiCkMkYmRvxR8gfGb7s1KynINy+fZsDBw7g5OSEk5NTgs9lNL169WL37t0MHz6cokWLsn79epo3b86ZM2eoVauWXva/ffs2tWrVwt7eHldXV1QqFUuXLqVu3bpcv36d4sWL61Tn4OBgsmfPHuu57Nmz8/nz5xRtmxmIFmVBEARBEDKd27dvM2XKFM6ePZvocxnJ9evX2b59OzNnzuTvv/+mf//+nD59mgIFCjB27Fi97T9p0iRMTU25cuUKo0aNYsyYMVy+fBmVSsUff8T+8Ors7IyVlRVWVlZs3bqVwYMHa/7/119/AWBhYUFgYGCs/QIDA7GwsIhTR122zQxEUBYEQRAEQfhOZGQkKpVKr2Xu3r0bhUJB//79Nc+ZmJjw66+/cuXKFby8vPSy/4ULF2jYsCE5c+bUbJcnTx7q1q3LoUOHCA4O1jx/6NAh/P398ff3p0uXLixdulTz//HjxwNQtGhRgoODeffunWa/Bw8eULp06Th11GXbzEAEZUEQhEwmOjyarS22srXFVqLDozNs2alZTyFjevLkCR06dMDOzg5TU1NKlizJrFmzYgXOXbt2UbVqVczMzLCzs+OXX37h/v37mtdDQkIYM2YMjo6OmJqaYmdnR5cuXXj9+rVmm4IFC9KvXz8ApkyZgkwmi/e5b/c5evQoNWrU0JQ5bNgw/P39Na/LZDKqV6/O8ePHqVixIqampnFaRlPqv//+o1ixYnG6JlSrVg0gyT7E2u4fERGBqalpnP3NzMyIjIzkwYMHOtXbwsKC1q1b4+rqSlhYGIcOHeLevXu0bt1a522jo6MJDw9HqVTG+jqjEn2UBUEQMhmVUsXzI881X2fUslOznkLG4+/vT82aNQkKCqJTp06YmZlx/Phxxo8fz+fPn5k9ezYrV65kwIABWFtb06NHD4KCgti9ezenT5/m8uXLlCxZkrZt23L8+HGqVq1Kq1atePPmDdu2bePatWs8ffoUAwMDBg0axLlz53B3d6dmzZrUqlULa2vrOM9ZWqpnQ9mzZw/t2rWjQIEC9O7dm7dv37JkyRLOnz/P1atXNaHy7du3tGzZkrp16zJs2DCMjY31+h69f/+ePHniLlQU85y3t7de9i9evDhXr15FqVSiUCgAdQv5tWvXAGK19mpr6dKl9OzZk5w5c5I/f3527NihmRquWbNm1K5dW9OtI7Ftp02bxpQpUzTlTp8+nXXr1sU7JV1GIIKyIAiCIAgp5uHhgZ+fH05OTmzcuBFQB7sRI0YQHBxMaGgoY8eOJVu2bNy9exd7e3sA6taty4ABA5g3bx5Tp07lxYsXNGzYkKNHj2pCnrOzM4cPH+bt27cULFiQcePGkTNnTtzd3WnYsKFm0Yr4ngsPD9cMfLt9+zbm5uYALFiwgBEjRvDPP/8wZswYQB0gx4wZw+zZs+M9x7Nnz1KvXr1E34dXr15RsGDBeF8LCwuLN3ybmJhoXk+MtvsPHjyYQYMG8euvvzJ27FhUKhXTpk3j/fv3iR5n/fr1CR7b1tY2wSnd3N3dtd7Wzc0twy8y8i0RlAVBEARBSLHSpUuTJ08ezp49S5MmTXB2duann35iy5YtKBQKTp06RUBAAN26ddOEZIA+ffrQvHlzTExMsLGxwcPDAwA/Pz+ePn3KvXv3uHr1KqC+ba+ry5cv4+XlReXKlZk6darm+ZhuFQcOHNAEZUDTLzc+Dg4OjBs3LtHjxbRix8fU1JSIiIg4z4eHh2teT4y2+w8cOBAvLy/+/vtvNmzYAECVKlUYO3Ys06dPz7QD69KDCMqCIAj/L8AzQKsV3gRBiMvU1JRz584xbdo0jhw5wvHjxwF1t4CFCxcSGRkJQN68eWPtZ2BgEGvO3RUrVvD333/z4sULcubMSeXKlTE3N0/29GIxrai3bt3i1q1bcV7/+PGj5msbG5t4V5qLUbhwYc1MEMmRJ0+eeLs9xNTx+/cmJftPnz6d0aNH8/DhQywtLSlbtqyma0SxYsWSfQ4/GhGUBUHI8rQJwCE+Iexsu5Oo0KgkyzM0M8TMxkxf1RPSkDYfdMxszMRKf8lUtGhRTQvms2fPOHjwIG5ubnTp0oU1a9YA6n7A3/L29mbr1q0UL14chULBwIEDqVOnDqdOnaJAgQIAODk54enpmaw6xcz8MHDgQJYtW5botkn1SX758iUrV65MdJtx48ZhbW0d72sVKlTgzJkzBAYGxhqQF9N3uEKFComWrev+1tbWseZmPnnyJPnz56dEiRKJHic+Pj4+9OrVi7Nnz5I/f36WLl1KgwYNdN522bJlrFq1ivv37zNx4sQM3w1DBOUMJiIigpkzZzJhwgS9DyJILZmxzpA5652Z6zxkyJB0OX6AZwBLSi7ROgB3PdoVc1vzRLdLKkhlxu9TQrLKuZjZmGFoZsjebnuT3NbQzJAOezqk+OfgR7Nx40Z69uxJ165d2bx5M8WKFWPUqFEcO3aMEydOULJkSUxNTTlw4ACenp44ODgAsHjxYv766y/mzZtHVJT6Om3atKkmJH/58iXWrBgxZDIZQKwZE+J7rkaNGpiZmbF3716mTp2KjY0NAPv27aNv374MHz6c//3vf1qdo6enJ7NmzUp0m4EDByYYlNu1a8ecOXNYuXIlo0ePBtTX2Lp16/jpp59idUkJDQ3F09MTGxsbTZ112f97O3bs4MaNG8yZMwe5XPdJz4YMGYKdnR0+Pj6cPHmSDh068Pz583hb4BPbNk+ePLi5ubF161ad65AeZJIkSeldiawiICAAKysrvLy84kzdoq3AwEDs7e1TVEZay4x1hsxZ78xc50ePHlGqVCn8/f0T7cMXcx3dOnILC/OU96PzferLwf4HabmyJTbFbRLd1jSnKZb2KQ8+qf19igyJZG7euQCM8h6FkblRqpUdrgxP9rmkZj2T493jd9SrXg/3I+6aAV3fC/ENYW+3vUSFafHBytQQl80umNskHqj1KTgkmMrNKyd5HaUHPz8/KlSogJeXF82bN6dEiRI8evSIo0ePUq5cOW7dusWCBQsYM2YMuXPnpn379nh6enLgwAEKFy7M7du3uXPnDk5OTpiamtKxY0fMzc3Zv38/7969Q5IkOnbsyPr16zExMWHfvn24uLhQqFAhOnTowNixYzl//nyc53LkyMHs2bM1yz47OzsTGhrK7t27sba25vr16+TPnx+ZTEa+fPnitHjrW4cOHdi7dy8jRoygSJEibNiwgevXr3Pq1Cnq1Kmj2S5m4KCrq2usVldt9j9//jx//vknjRs3JmfOnFy9epV169bRqFEjDh48iIGBbu2kwcHB5MiRg5cvX2q6yTg5OdGzZ0969+6drG0HDhyInZ1dhm9RRhL0xsvLSwLEQzzEI5GHl5eXuI7EQzxS+EjqOkovz58/l7p27Srly5dPMjY2lgoUKCANHTpU8vX11WyzZ88eqXz58pKxsbGUJ08eqUuXLtKbN280r69cuVJydHSUjIyMpEKFCknjx4+Xtm/fLuXKlUuytbWVgoKCJEmSpLCwMKlFixaSsbGxZGpqKnl6esb7XIwNGzZIFStWlIyNjaXcuXNLXbp0kTw8PDSvA1K+fPlS/T0KCwuTRo8eLdnZ2UnGxsZS1apVpaNHj8bZ7syZMxIgubq66ry/h4eH1LhxY8nGxkYyNjaWSpQoIc2cOVOKiIhIVp1v374tWVtbx3pu6NCh0qhRo5K97YABA+KcW0YkWpT1SKVS4e3tTbZs2TS3f3SVmVsMM1OdIXPWOzPX2dPTE5lMRt68eRO97aeP6yi9ZcbvU0LEuWQskiQRFBSU5HUkCPp04cIFunfvHmsBl4kTJ/L582eWL1+erG0zS4uy6KOsR3K5PNbI3eQwNjbG1dUVW1vbTNMfMDPWGTJnvTNznXPlyqVVnfVxHaW3zPh9Sog4l4wno3W5EDK3WrVqcenSpXhfmzhxItOmTcPCwiLOKoWBgYHxTjOny7aZgWhRFgRBEARBEBIU0+/41atX5MuXD4B69erRo0ePBPsoJ7VtZmlRFvdtBEEQBEEQhARZWFjQunVrXF1dCQsL49ChQ9y7d4/WrVvrvG10dDTh4eEolcpYX2dUIigLgiAIgiAIiVq6dCne3t7kzJmTkSNHsmPHDs3UcM2aNWPGjBlabTtt2jRMTU1ZvXo106dPx9TUlE2bNqXLOWlDdL0QBEEQBEEQhHiIFmVBEARBEARBiIcIyhlMREQEbm5uREREpHdVtJYZ6wyZs96izplDVjpncS6CtjLz+yvqnj4yQ91F1ws9EvMoZ546Q+asd2aus5hHOXMS55KxaDuPcnpcR5n5/RV1Tx/pVXdd5iMXQVmP3r59m+g664IggJeXV6LzJIvrSBCSJq4jQUi5pK4jEAuO6FW2bNkAUvTJKDN+MsyMdYbMWe/MXOdHjx5RqlQpzXWSEH1cR+ktM36fEiLOJWOJOYeMeB0l9v6qlCq8LnsBYP+zPXJFynp+6ru8b+tuYW6h17JTW2b+uU6vumt7HYEIynoVc3sre/bsyf6GZ8aVozJjnSFz1jsz19nGxgYgydvA+riO0ltm/D4lRJxLxpQRr6Ok3l+rFlZ6PZ4+y/u+7vqua2rKzD/X6V13bbolia4XehQYGIilpSUBAQGZ9g+8IKQWba8PcR0JQsLEdSQIKafL9SFalAVBEARBSFXKKCW3Vt4CoHL/yigMFRmqvLQqW8h8RFAWBEEQBCFVKSOVuA91B6BCrwopD8p6Li+tyhYyn4zdQ10QBEEQBEEQ0okIyoIgCIIgCIIQDxGUBUEQBEEQBCEeIigLgiAIgiAIQjxEUBYEQRAEQRCEeIigLAiCIAiCIAjxENPDCYIgCIKQqgyMDeh8qLPm64xWXlqVLWQ+4idAEARBEIRUJTeQU6xFsQxbXlqVLWQ+ouuFIAiCIAiCIMRDtCgLgiAIgpCqlFFK7m+5D0DZrmX1soS1PstLq7KFzEcEZUEQBEEQUpUyUsn+3vsBKNW+lF6WsNZneWlVtpD5iKAsCIIgCEKGFeAZQKhvaKznosKiNF9/uPMBQ1NDzGzMsHSwTOvqCVmcCMqCIAiCIGRIAZ4BLCm5hKjQqAS3WVdrHQCGZoYMeTxEhGVBr0RQFgRBEAQhQwr1DSUqNAqXzS7YlrTVPB8VFqUJyL0v9sb/tT97u+0l1DdUBGVBr0RQFgRBEAQhQ7MtaUueSnk0/48MidR8bVfBDkNTw/SolvADENPDCYIgCIIgCEI8RFAWBEEQBEEQhHiIrheCIAiCIKQqA2MD2u1sp/k6o5WXVmULmY/4CRAEQRAEIVXJDeSUbl86w5aXVmULmY/oeiEIgiAIgiAI8RAtyt+RJAmZTJbe1RAEQRCELEMVreLx3scAlHQpidwgZe1035enT/quq5C5/fBB+dixY7x7945cuXJRsWJF8uXLJ8KyIAiCIOhRdEQ0uzvsBmBC8ASMDIz0Wp4+6buuQub2QwdlV1dXZs+ejaGhIZGRkRQtWpT58+fTsGHD9K6aIAiCIAiCkM5+2PsJt2/fZtmyZYwbN47bt28zd+5cTExMaNq0KatXr9a5vIiICAIDAwEIDAwkIiJC31UWhExL2+tDXEeCkDBxfQhC2vthg7KRkRGBgYHkzZuXIkWKMGTIEJYsWUKbNm0YOHAgGzduBEClUmlV3syZM7G3twfA3t6emTNnplrdBSGz0fb6ENeRICRMXB+CkPZ+uK4XISEhmJubI0kSRkZG3Llzh9DQUMzMzKhWrRp//vkn0dHR9OnTh0KFClG7dm2typ0wYQJ9+/bF3t4eLy8vbG1tk95JEH4Q2l4f4joShISJ60MQ0t4P1aI8dOhQ6tWrR1hYGGXLlmXkyJGsXr0ad3d3zTalSpVizJgxFC9eHDc3Nz5//qxV2cbGxmTPnh2A7NmzY2xsnCrnIAiZkbbXh7iOBCFh4voQhLT3wwTlCRMmsGrVKlq1akVkZCQA3bt3x8nJiR49enD8+HHNtjVr1qRt27Zcv34db2/v9KqyIAiCIAiCkI5+iK4X48ePZ/78+cyaNYuuXbtiaWkJgKOjI1OnTmX48OG0bt2a1atX4+zsjKWlJRUqVMDY2JigoKB0rr0gCIIgZG4KIwWt17XWfJ3RykursoXMJ0sHZUmSeP36NbNnz6ZRo0YMGDAAU1NTIiMjWbt2LVFRUdSuXZuJEyeyZcsWunfvTuvWrXF0dOS///4jW7ZsODo6pvdpCIIgCEKmpjBUUKFXhQxbXlqVLWQ+WTooy2QyChUqxKxZs/jjjz+4desWtWrVonz58jx9+hQACwsL+vXrx5gxY3BycmL27Nncvn2bXLlyceDAAXLnzp3OZyEIgiAIgiCkhywdlFUqFXK5nO7du3PlyhU6dOhA3bp1yZs3L/Pnz8fBwYGFCxcyf/58ChcuzJAhQ3B2dsbKyoro6GhNFw1BEARBEJJPFa3C45gHAEWaFNHLEtbflqdP+q6rkLllye/+tWvX8PPzQy5Xn56dnR0DBw7EwcGBgwcP0rBhQ5o2bUqpUqVYsWIFLVq04J9//gEgT548mJubi5AsCIIgCHoSHRHNNudtbHPeRnREdIYrL63KFjKfLBWUVSoVhw4dokaNGqxatQo/Pz/Na40bN6ZJkyYoFAqcnJxi7Ve4cGEMDNSN6zHhWhAEQRAEQfixZamuF3K5nEqVKmFgYMDatWuRy+X069cPKysrAKZMmUK7du0oW7Ysr1+/xsTEBF9fX27fvo2trS3h4eEYGxsjk8nS90QEQRAEQRCEdJelgjLAw4cPiY6O5uXLl8yaNQuZTEbfvn01Ybls2bLcvHmTGjVqkCNHDqysrPD39+fUqVOYmJikb+UFQRAEQRCEDCNLBWWlUsnKlStxcHBgwoQJLF++nKlTpwLECstVqlRh5MiRBAUFYW1tTZ8+fcQ0cIIgCIIgCEIsWSooR0dHU7VqVcaMGUOlSpWoWrUqffv2Zdq0aUDssDxr1qx0rKkgCIIgCIKQ0WWZkWtKpRJjY2NGjx5N1apVMTAwoEKFCqxcuZLChQszbdo0Vq9ejb+/P6BejOTbfwVBEARBEAThW5m2RdnLywtjY2MMDQ2xtrZGoVBo5k2OIZfLqVy5MitXrqR///7MmjWLsLAwfvvtN830b2LgniAIgiCkLoWRgmb/NNN8ndHKS6uyhcwnUwbladOmsX37dnx8fKhatSrdunWjU6dO8U7tJpPJqFy5MqtWraJ9+/asWbOGIUOGpEOtBUEQBOHHpDBUUG1ItQxbXlqVLWQ+mS4ob9y4kenTp9O7d28MDAw4c+YMXbp04cGDB7i6umJoaBhnH5lMRqVKlfj333+xsrIiR44c6VBzQRAEQRAEITPJdEH50aNH5M+fn8mTJ2NnZ8edO3dYvXo1M2fOxN/fn8WLFyNJUpzWZZlMRsWKFdOp1oIgCILw41IpVXhe8ATAobYDckUKl7D+rjx90nddhcwt03z3vx185+vry7t37wCoUKEC48ePZ8KECSxdupQ///wTuVyOJEkMGTKEtWvXpme1BUEQBOGHFx0ezYZ6G9hQbwPR4XpYwlrP5aVV2ULmk2mCcsygu27duhEcHMz69es14Tl//vz079+fX3/9lenTp3P48GGOHTvGsmXLWLNmDcHBwelZdUEQBEEQBCETyjRBOUbZsmWZOHEiS5YsYc6cOZrnHRwc6N+/P7lz5+bgwYNUrFiROXPmsHbtWiwsLNKxxoIgCIIgCEJmlOn6KAP8/vvvvHr1inHjxqFUKhkxYgTGxsZUrVqVkiVL8vDhQ3LlysXIkSPTu6qCIAiCIAhCJpUpg7K1tTV///03BgYGTJw4kSdPntC+fXusra35/PkzBQsWJCIiAhMTk/SuqiAQHa3izRt/8uXLjolJprzkBD05dswDgCZNiqRzTQRBEARtZNq/2rly5WLevHmULVuWSZMmsWXLFiwtLTEwMGDjxo0iJAsZQmSkkrJll/Hs2WdsbMx49ep3LCyM0rtaQjo4dsyDpk23AHD0aFcRlgVBEDKBTBuUASwtLRk+fDgtW7bk2rVrAPz8888ULFgwfSsmCIIgCIIgZHqZOijHcHR0xNHRMb2rIQhxGBkpePRoMG/eBJAvXzaMjbPEJSckQ5MmRTh6tKvma0H4kSgMFTSc3VDzdUYrL63KFjIf8VdbSFNRUUouX/bi+PEX2NlZMHRoNc3Uf1mVQiGncGHr9K6GkAH8SAE5KkrJn3+eA8DV1QkDg0w3yZKgRwojBTXH1Myw5aVV2ULmI4KykOrevQvE3d0Dd3cPTp58SWBghOa1ly+/MG9ekywflgXhRxIZqaRLl3/599/HADx/7sfmzW1FWBYEIdMRQVnQu5hW4yNHnuPu7sH9+59ivW5jY0b16vk5dOgZCxao+5aLsCwIWUNERDTt2+/i4MFnGBkpkCSJHTseAoiw/ANTKVW8v/0egDyV8uhlCetvy9MnfddVyNxEUBb0JjJSyfDhR9my5X6sVmOZDKpVy0ezZkVo3rwolSvnRS6XsWrVLfr3PyTCsiBkEWFhUbRtu5OjRz0wMTFg796OmuCc0rDs4xOCi8sOAgIi6NOnAj17ViBHDlN9n4KQSqLDo1ldbTUAE4InYGSestl/vi9Pn/RdVyFzE0FZ0IvISCXt2+/iwIGngLrVuGnTIjRrVoTGjR2xsTGLs0+/fpUBMmRY3rnzIUqlik6dymSI+ghCagsJiWTQoMNIEixd2pxs2Yx13r916+2cOvUKU1MDDh7sTIMGhQHYtat9isKyj08I9etv5MED9d2pkSOP88cfp+nYsTSDBlWhWrV84joVBCFViKAspNi3IdnYWMG2bb/QunUJ5PKk/3BlxLD855/ncHU9C8CRIx6sWtVSLBQiZGkhIZG0aLGVc+feAPDs2Wfc3btq3WIbFBSBs/M2zp9/g4WFEYcPd6FOnQKa11u3LpHssPxtSM6Tx4LRo39m06Z73LnzgQ0b7rJhw10qVLBj0KAqdOlSVsxTLgiCXomON0KKfB+SDxzojItLSa1Ccox+/SqzcqUzAAsWXGPkyGNIkpRaVU7UtyFZLpexefM96tZdj7d3ULrURxBS27chOXt2Y3LkMOX69XfUrbueDx+Ck9w/ICCcJk02c/68ev/jx7vFCskxYsKyoaGcHTse0q3bHqKjVYmW/X1IPnu2FyNH1uD27f5cufIrPXqUx9hYwZ07Hxgw4BB5885lyJDD3L//MdnvhyAIwrdEUBZSZPz4k7FCcuPGyZvP+vuw3Lnzv/j4hOizqvEKD4/m1KmXjBt3ggoVlmtC8qxZDTlxorsmNDg5rU/yj7ogZBafP4eyY8cD+vTZT9GiizUh+dixbpw714s8eSx48OATLVtuS/JDa6dO/3LlylusrEw4ebI7NWrYJ7jt92H5jz9OJVp2hw67Y4XkYsVyAiCTyahePT8bNrTB23sUc+c2pmjRHAQFRbJ06U3KlVvOqlW3dH9jBEEQviPuJwsZRkw3jIEDD7Njx0NOnXrF4sXN6NixtN66YkiSxOPHvhw//oLjx19w9uxrwsKiNa8rFDL++qsho0f/zKlTL/VyTEFIb9HRKq5ff8exYx4cPfqCGzfe8W3+tbEx4+DBzlSvnl/TD1gbAQHhHDvmAcCJE92pUiVvkvu0bl2C1atb0bPnPnbseMjs2Y20OlZCgV2SpHS7AyUIQtYngrKQIn/91ZAXL75w4MBTWrXalqJWZVCH5YoV89Cnz37u3/9E587quVg3bXJJcT/hjx+DcXbexs2b3rGez5PHgsaNHWnSxJGGDQtja2vOihU3GTLkCEqlRLVq+di7t6OY1krIlDw9A2jceBNPn36O9Xzp0rY0aeJIkyZFqF3bAVNTQ27ceEfTplvw8wujbNlcHDzYOdEPqdeuqQN3oUJWWoXkGG3blqRPn/14egbw5o0/BQpYxbvdzp3tNF0v6tXboGlVliSJq1ffsmzZTXbufEhEhBKAbNmM6NGjPAMGVKZs2dxa10cQBCEhIigLKWJkpNAM0okJy/v3d0rRCmRVquTl5s3+zJx5genTL7B79yOCgiLYt69TssPyhw/B1K+/gcePfTExMaBOnQI0blyYJk2KULq0baww8OlTiCYkd+9ejpUrxWA+IXPy9AzAyWk9r175Y2lpTJMmRWjSxJHGjR3Jnz97rG3DwqJo3nwrfn5hVKuWT6vBfFevvgVItLtFfCwsjKhcOS/Xr7/jwgXPBIOyra05p0/30IRlJ6f1jB79Mxs33uXu3a/9kCtWVA/m69xZDObLqBSGCuq61tV8ndHKS6uyhcxH/PUXUuz7sNyhw268vUdinoK5J42MFLi6OlG3bkFatNjKsWMvaN16O/v2dcTU1FCnst6/D6J+/Y08eeJL/vzZOXu2J46OORLc/sSJFyiVEuXK5WbDhjZi2ikhU/o2JDs6WnP2bK844fhb58+/wdc3lDx5LDh5srtW08NduaIOytWr59O5frVrO/x/UH5Dt27lEtzu+7A8atRxAExMDOjUqQyDBlWhatW84jrN4BRGCpzcnDJseWlVtpD5iHvJgl7EhOX8+bMTGBjBxYueeinXyakg7u5dMTc35PjxF7RqtZ3Q0Cit93//Poh69Tbw5Ikv9vZJh2SA48fVfZObNnXUyx9fSZL455/r/PPPddGXMgsKD49m8ODDDB58mPDw6KR3SAO6hmSAY8deANCsWRGtQrJKJXHtWvJalEEdlAEuXEj6d0VMWK5Z054yZXIxf34T3r0bybp1rcUcyoIgpCrRoizojZGRgkaNCrNu3R1OnXqVou4X36pTpwDu7l1p1mwLJ0++1PSFNjNLvGXZ21sdkp89+4yDgyVnzvSkcGHrRPeRJInjx9WBQR/1lySJYcPcWbLkBgBPnviyeHEz8Yc9iwgPj8bFZQdHj6oHtL165c/evR3TtatOckIyfA3K2v7cP3v2mS9fwjExMaBcOd37A9eqpQ7Kjx/74uMTgq2teaLb29qac/FiH52PI2QMkkrC57EPALYlbZHpMIWoNuXpk77rKmRuokVZ0KsGDQoBcPKkfmeMqF27AEePdsPCwohTp17h7LyVkJDIBLd/9y4QJ6f1PHv2mQIFLDl7NumQDHD//ic+fAjG1NSAmjV1byX71rchWSZTL+W9ZMkNhg1zFy3LWcC3IdnMzBAzM0OOHvXAxWVHurUsJzcke3kF8OiRD3K5jIYNC2t1rCtXvAD1mAIjI937cebMaUapUuqAo687UELGFRUWxbIyy1hWZhlRYdrfFUyr8tKqbCHzEUH5GyK8pFzMkrV37nzg8+dQvZZdq5YDx451I1s2I86ceY2z87Z4w/Lbt4E4OW3g+XM/Cha04uzZXhQqlHRIBjStyU5OBTE2Tn6r4PchefXqVqxe3UqE5Szi+5B8+HAXDh/ukq5hObkhGb7+3Fetmlfr1fi+DuTLH+/r0dEqfv11P337HkhwDnJdul8IgiCkBxGUga1bt/Ly5UtxO1wP7OwsKF3aFkmCM2de6738n3+214Tls2df07z5VoKDv4ZlLy91WPDwiAnJPSlY0Err8r/efk7+FHfxheQ+fSrSp09FEZazgPhCspNTQZycCqZbWE5JSIbk/dx/HcgXf1CePv08a9feYc2a/5gx40K824igLAhCRvfDB+WxY8cyaNAgnjx5IkKLnsR0v0itBTtq1LDn+PHuZM9uzPnzbxgw4BCgDqgtW27jxYsvFCpkxblzvRKcdio+ERHRXLjwBoCffor/j3/Mcby8Ajhx4gWLF19j9OjjdO78L3Xrrqdo0cVYWMyME5JjfB+W//nnevLeBCHdjBx5LE5IjvF9WB458liyjrFr10Pmz7/C5cteSYZtf/9w6tffkOyQDHDq1CsAredADw6O1CxMUrx4zjivP33qy59/ntf8f8qUczx96htnu5i5jv/7732iXakEQRDSyw89mG/ChAnMnz+fGTNmUL16dU2LsiRJonU5BerXL8SiRdc5d+5Nmh43LCxaM7fq8ePdcXCw1Gl/mUxG7twWeHoGMHDgIdzdu/L5cxhPnvjGeYSEJN5vzdTUgKVLW9CrV4U4r/30Uz5MTAwIC4tGqRQfzoSvJEnC1fUsU6d+DZmGhnIqVLCjevX8mkehQlbIZDIkSaJv3wO8ePFF081I15AMYGlpjJ9fGLduvadmTYcktzcwkJM3bzbevQuic+d/OX26JzY2ZprXZTIZCoUMlUr9861QyOL8TvX1DaVbtz2A+k6UQvHDt9v8cAI8Awj1TbyLXsygOkFILz9sUF64cCGzZs1ixYoVtGvXDmtrdR9WpVJJREQEZmZmOgXmiIgIAgMDAQgMDMTY2Bhj46SnWMosTp9+hUwG9eoVSnJbXUez6+rKFS+aNNlMUFAkdesWYMUKZwDk34xMzp1b92MaGSk4frwbdeuu5+7dj+TNOy/BbQ0M5BQpkoMSJWwoXNiKfPmyky9fNvLm/fqIb77ngIBwXFx2EBYWTf36hRg6tJrO9cyMtL0+MsN1NG9eE1698ufoUQ9atNgaq1X57NnXtGixldDQKJo2LcK8eU20LleSJEaNOs78+VcBqFu3AI8f+/LpUwg3bnhz44Y3ixer70DY2ppRvXp+cuQw5d9/H2NoKNdMz5gc48bVZODAw8yceZG+fSslOaOMiYkBJ0/2oF69Ddy//4n69TfECsvFiuVkz56OzJx5EYAJE2pRrNjXlmdf31Dq11fva2dnwcmTPbSaKWT37kcAtGtXKlnnmdllhutDWwGeASwpuYQoLab7NDQzxOybD2KCkJZ+yKAcGhrKp0+fsLGxQalUYm1tTWhoKF27duXt27coFArc3Nxo2rSp1mXOnDmTKVOmAGBvb4+rqytubm6pdAZpa8GCq4wYob6FvGRJcwYPrpro9jlzmlG6tC0PH/pw8aInLi4l9VaXS5c8adp0C8HBkTg5FeTQoc6ahU2+DcoxLVm6Kl7chlOn1IsbfPoUgqWlMSVK2FCihA0lS9povi5c2BpDHVdsUqkkevbcx/PnftjbZ2f79l9+mGWxtb0+MsN1ZGJiwN69HTX9lGPCMhArJOsyTZxSqWLgwEOsXv0fAIsWNWXYsJ+QJIk3bwK4evWt5nH79nt8fEI5ePCZZv9ZsxrqtIT093r3rshff13i9Wt/li27wahRPye5T4kSNpw50zPBsOzsXIwWLYoCxGpw+D4knznTkxIlbJI83tSp55g8+SwArq51cXWt+8Pd+csM14e2Qn1DiQqNwmWzS5LTu5nZmGGp4x1CQdAb6Qd17949qUmTJlK9evWka9euST/99JOUN29eqUGDBlKxYsUkuVwurV+/XpIkSVKpVEmWFx4eLnl5eUmA5OXlJYWHh6f2KSRIpVJJCxdelRYsuCIplUnXPTHTp5+XwC3WY8mS60nuN3DgQQncpBEjjqbo+N+6cOGNZGExQwI3qX79DVJISGSs1yMjozV19PMLTdGxAgPDpffvg7T63msr5r00MpoqXb/+Vm/lZgbaXh8Z6TpKSlhYlNS06WYJ3CQzs+mSmdl0Cdykpk03S2FhUVqXExkZLXXuvFsCN0kunyKtXXtbUqlUCf7shYVFSVeueEnz51+ROnXaLU2YcFIvP6dr1tyWwE2ytv5Lunv3g9b7PX7sI9nZzZHATSpbdqnk4xOS4LY+PiFS2bJLJXCT7OzmSI8f+2h1jD//PBvn99DEiadSdN5hYVHSsGFHpN9+OyKFh2v//UpPulwfAQEBEiAFBASkYQ0TFh0RLR0bfUw6NvqYFB0RLXnf8pbccJO8b3lniPISK1vIenS5Pn6ooPzw4UPp2rVrUlBQkCRJknT27FkpW7ZsUoMGDSQnJyfpxo0bkiRJ0v379yUXFxfJ3NxcevbsmdblZ4RfTCqVSho06JDmj0mPHnulqChlssr53/9OacpxdT0jjR59TOuwvGXLPQncpCpVVib3VGI5f/61ZG6uDiINGsQNyZIkSdHRSk39Pn9OWVDWRnS0Ulq58qYm2CTm2DEPSSZT123VqlupXreMSNvrIyNcR9r6NiwnJySHhUVJrVptk8BNMjD4U9q584H0+vUXqUKF5VK5csukR48+pWLtY4uMjJYqV16hCctXr3ppva82YVkfIXnmzAvSvHmXNf8fO/Z4ssJySEik1LjxJk05zs5bM01YzirXkT6DbWqUJ2RtulwfOt33jYiIoEqVKuzfv1+fjdppws3NjZo1a1K9enUcHR35+++/qVu3LosWLeL06dNERERQtKj6NmGZMmXo27cvkiTh4eGRzjXXniRJDBlyhGXLbiKTqQfQbNx4l/btdxERof00VZIkMWbMCaZNU0/pNGtWQ9zcnJg9uxGjR9cAYMiQIyxdeiPBMmKmfbp9+z1BQREpOCs4d+41zZptISQkikaNCnPwYPyr8umj64W23r8PokmTzfTvf4g+fQ4wbtzJBGdNiY5W0bPnPiQJ+vatSN++lVK1bkLaiemGMWhQFQYNqqJTd4vg4Eicnbdy4MBTTEwM2L+/E+XL21G79jru3PnAvXsfY/1MpzZDQwUnTnSnRo38fPkSTsOGmzhz5pVW+8Z0w7Czs9B0w/D9ZpCWPrpbzJzZgPHjazFiRA0WLVJ3i5s9+zKjRh3Xacai4OBIWrTYyvHjLzAzM8TExIBDh57Rrp1uvyeFjMnnsQ/vb79P9BHgGZDe1RQyEZ36KBsbG6NSqTh//jytW7dOrTrp3c6dO5k+fTqjRo2iVKlSuLu7M378eKytrenduzePHz+mdu3aWFp+7QNlbGyMubk5BgaZoxv39yF57drWWFub0KHDbvbte4Kz8zb27u2IhYVRouWoVBLDhh1h6dKbACxe3Ewz4EwmkzF7diMA5sy5wpAhRwDi7bNsb29JwYJWvH7tz5Urb7Wedup73w6OatzYkX37OsY7SC6mft+eR2o5fvwF3bvv5dOnEExMDAgPj+bvvy8TGalk/vwmcfpNfvoUwocPwcjlMhYvbp5q9RLSh4mJeoYTXfj7h9OixVYuX/bCwsKIgwc7kzOnKXXqrOPjxxBy5jRlyZLmFC+edJjUJ2trU44f706bNts5deoVzZptYffuDjg7F0ty34T6LAN6C8kxhg37CQMDOYMHH2H+/KtER6tYuLBpkn2WAwLCad5c/b5ny2bEkSNdCQ+PpmXLbZqwvHt3+xQtNiTET1JJmoCqj/7G35dnZmOGoZkhe7vtTXJfQzNDhjwekmA9vi9bLGH9Y9P5t4Grqyv9+vVDpVLRtGlTbG1jd8KvVCnjtZY9fPgQe3t7xo8fj5WVFR07duTVq1f8888/9O3blylTpmBiYsKJEyfw9fUlPDyc3bt3Y2xsTOnSpdO7+kmKLyTHTEt25EgXWrfezsmTL2nUaBNHjnTB2jr+lbeUShX9+h1k3bo7yGSwcmXLOK2fuoTl2rUdeP3anwsX3ugclD98CMbV9QyrV/+HSiVpPThKJgNJSp1VFqOilEyefIa//roEQNmyudixox0XLngyYMAhFi68RmSkkn/+aR6rJdDHJwQAGxszrVsbhazLxyeEJk02899/H7CyMuHo0a7I5TLq1l3Ply/hlC+fm+PHu5Mr19eZW06desmSJTdYv74N2bOn7iwHFhZGHDrUhU6ddrN//1NcXHawaZMLnTqVSXLf+MIyoNeQHGPQoKoYGMgZMOAQixdfJzpaFefa+5afXxhNmmzm5k1vrKxMOHasG9Wq5QPg4MHOIiynsqiwKBYWWgjAhOAJei/P0sGSIY+HaDXd3N5uewn1DU0wKH9ftpF54g1MQtam828CFxcXQD292qJFizTPS/8/lZpSqdRf7fTE0tKSt2/f8vHjR6ysrDA2NqZChQqsWbMGHx8fbG1t8fPzY968eRw7dgwrKyvs7Ow4cuQIefMmfyR5WkgsJIN6SelTp3rQrNkWrl59i5PTBo4d64adnUWscqKilPTsuY9t2x4gl8vYsKEN3bqVi/eY2obl2rUd2LTpHufPa7/qVkhIJHPnXmH27EuauYo7dy7D2rWttQqZcrkMpVLSe4tyUFAETZps1qxGNmhQFebObYypqSElS9piYCCnb98DLFt2k6goJStWtNT8wfbxUf/itrUV0xv96Ly9g2jYcCOPH/uSK5c5J050x98/HGfnrQQFRVK9ev44H2YjIqLp2XMf794FUaLERWbMaJDq9TQxMWDXrvb07r2fLVvu06XLvwQGRtC/f+Uk9/0+LAN6D8kx+vWrjKGhgj599sd77cXw8QmhcePN3LnzgZw5TTlxojsVK+bRvN6wYWERlrMASwdLMTuGoHc6/xZYt25datQjVcSE98qV1b/cJ0+ezPLly7G2tsbOzg6lUklwcDC2trbkyJGDjRs34uXlBUD+/PnJlStXelZfK/PnX00wJMf46af8nDvXi8aNN3Pv3kdq117HqVM9Yi3IMWTIEbZte4CBgZxt235Jcp7S+MJyyZI2seZZrl27AADXrr0lKkqZ5HRqSqWKGjXWaP64GhjIWbSoKX37VkKS1EsHK5UqVCopziMqSsXu3Y80C3joeyEPCwsj8ufPTvbsxqxZ0yrO+9OnT0UMDeX06rWfc+fe8OVLGDlzqoPxty3Kwo/LxyeEunXVy6vnz5+dkye7Y2ZmSPXqqzXzau/f3ylO9yhjY3U/6BkzLjJpUp00q6+hoYKNG13Ils2I5ctvMWDAIZYtu0n9+gWpX78QZcvmxtTUAGNjA0xMDDA0lGu6PsSE5SZNNgNw7Fi3REOySiVx7JgHCxde0yynnVRIjtGrVwUMDOT07LmP1av/o0iRHIwbF3u/tm13cufOB3LlMufUqR6UKRP3d/v3YXnUqOP884/oKiUIPzqdg3LPnuo+Z48ePeLu3bs0aNAAhUJBzpxxlzFNL9euXaNo0aLkyJEDgLp16zJy5EjKli2LlZUVAM+fP8fKyop8+fJpBu1dvnyZRo0aZfhW5G+dOKFeJnrKFKd4Q3KMsmVzc/Fibxo23ISHhx+1a6/j5MnuFC2akwsX3rBq1W1kMvj33w60alVcL3WLGcxjbGyg1aAkuVwWa3Wu6GgVgwcfYfDgIzod19HROtZt65SI+bAlk8lYtaolfn5hFCpkHe+23buXx8LCiKpV82lCMqBZcOXRIx98fUNFYP4BRURE07btTjw8/ChY0IozZ3pSsKAVnp4BREerAGjZsliCYwiqVs3H3r0d07LKgPqaXLq0BTlymDJz5kXu3PnAnTsfmDfvapxtZTJ1S3RMcDYxMcDAQI5CIaN16+1ER6sSfERFKYmKUmnKmTWrIWPG1NTLOURERHPxovqu1smT3eMNyTEaNizMmjWt6Np1DydPvtTL8QVByNx0Xu0gOjqadu3aUaZMGbp168aDBw/o168fzZo1IyAgfUeSqlQqDh06RI0aNVi1ahV+fn6a1/766y+6du2qafHw8fHB3t4eIyMjnj59yuDBg3F1dUUuz1wLQLx9q16lKaavXWIcHXNw4UJvihXLiadnAHXqrOfu3Q+arhN9+1bSOiRLksTYsSeYM+cKoF6I5PtV+7ZvfwCAi0sJrZanlclk3LjRj2vX+jJzZgMaN3bE1FT7z3LFiuVk2bIW3L8/CCMj3RYDic+8eVfo2XOfphuHpaVJgiH5zJlXKJUqXFxKxlkdrW7dAhQsaIWPTyjNmm0hMDBls4AImYskSfTvf4iLFz2xtDTmyJEuFCxoBYCDgyV//62+MzNu3Elu3fJOsrzAwAjmzbuCv394alZbQyaTMX16A96/H8W2bb/Qt29FChe2jnONSZJ6GXl//3A+fAjm9Wt/Xr78wvPnfjx79pmXL7/g6RmAt3cQnz6F4OcXRmBgBKGhUURFqbC0NGbkyOo8fz5Mp5C8bt1/9OixF5VKom/finH2jen6ZGAgTzQkx4hZuMXbO0jrOgiCkHXp3KI8evRozp49y8KFC/n9998BGDhwIN27d2fUqFGsXr1a75XUllwup1KlShgYGLB27Vrkcjn9+vXDysoqVh/q6Ohovnz5grm5Oc+ePeP333/n2rVrnDt3Djs7u3Srf3K8e6cOyvnyabd0bf782Tl//ms3jCpVVhEdrSJHDlOt+z7GF5K/758cHa1i1y71crPaDAKKYWAgp1q1fFSrlo/x42sRFaUkKCgShUKGXJ7wQx8rdKlUkqbl++lTX8aNO0l0tIp27Uol+gFi5Ur1bemePcuzdm3rOK3nhoYK3N27Urv2Om7e9KZVq224u3dNcPYOIWuZNesSGzfeRaGQsXNne0p+twrZb7/9xNmzb9i37wkdOuzm9u3+WFqaJFhe8+ZbuHTJi+hoFWPH6qfVFdTX9dGjHigUcho1Khznmsqd24JOncrEup5VKonISCXh4dGaR0TE16/DwtTTrRkYyJN82Nqa6dwneNWqW/TvfwhQjxuIbzDfp0/qrk+2tmZa/Z7ImzcbAEFBkQQFRZAtW+ZcIloQBP3QOShv2rSJ0aNH06VLF01Qbty4MePGjeOvv/7SewV19fDhQ6Kjo3n58iWzZs1CJpPRt29fTZcLuVyOsbExJUuWZP/+/QwZMoTLly9z+fJlypcvn76V11FYWBRfvqhblb5vxUxM7tzqgTXNmm3h+vV3AEyfXl+rLgHahGRQz3386ZN6mqsGDQrFeV1bhoYKcuSIf5aOlFAqVTx79plbt95z86Y3t2695969j7x7NxILCyOKF7dh9eqWfPoUQsuWiU+NlTOnKQqFDGtrExL6O1yihA1Hj3alXr0NnDv3hg4ddrNnTwedl8EWMpc9ex4zYcIpABYtahbv7C8ymYy1a1tx584HPnwI5r//PuDkVDDBMvv2rcSlS14sWnSN4cOr6+Xuydmzrxk37qTm90GbNiX4559mSX4Al8tlmm4WaW3ZshuablnDhlVLcHq4mDEC2nbHsrAwInt2YwIDI/D2DqJ4cRGUBeFHpvNvN6VSibl53F84ERERRESk7y1lpVLJypUrcXBwYMKECSxfvpypU6cCxArLADVq1GDz5s3cvn07U4ZkgHfv1LcGzcwMsbTU7Zd5jhymnDzZnWHD3DEyUtCvX9LT+mkbkuFrt4t27UrFCYNKpYqxY08QGalk0aJmemkN1sanTyEsX36TU6decfv2e4KDI+Ns899/7zWDEHv2rKBVub/8UoqbN/tTvnzuRM+lcuW8HDrUhSZNNnPo0DN69drPpk0uabqohJB2bt3yplu3PYA6yCV0rYB6/uJ//+2AqalBnBbn73XuXIYJE07x7l0QO3c+THB2Gm3cvfuBCRNO4e6uXljJzMyQyEgl+/Y94fTpV8ye3ZB+/SpnuJ/RxYuv8dtvRwEYObI6c+Y0TvDa+9qirP24hbx5s30TlNN2LuusSm4gp8rgKpqvM1p5aVW2kPnoHJQ7duzIggULKFGiBKDu67t9+3b+/vtvWrTQbdJ9fYuOjqZq1aqMGTOGSpUqUbVqVfr27cu0adOA2GF50KBB3Llzh6FDh1K2bNl0rHXyfe12kS1ZYTNbNmPWr2+j1ba6hOTISCX//vsYgI4d485DPXPmRebNu8rQoVXTJCR7ePgxd+5l1q+/S3j415W3TE0NqFgxD1Wq5KFKlbxUrpyX4sW1G5R6+vQrSpSw0dymrVBBuy47deoUYPfu9rRps4OtW+9jaWnMkiXN0+zDgpA23r0LpFWr7YSFRdOkiSPz5jVJcp9KlfIkuQ2oB8cOG1aNiRNPM3fuFbp2Lavzz4+vbygjRhxjy5Z7SBL/Px9xZSZNqsOnTyH063eQa9feMXDgYbZsuc+qVS0zTGCcN+8Ko0YdB2Ds2J/566+GiZ5/TB9lXQb45suXjSdPfDWNEULKGRgb0GKJ/jKCvstLq7KFzEfnoLxw4ULatm1L06bqJUS7dOmCJElUrFiRf/75R+8V1JZSqcTY2JjRo0drZimoUKECK1eupH///vGG5RUrVqRbffUhZiCftv2TkysiIpqxY0+waNF1IPGQDHDixAu+fAnHzs6COnUKxHl94MAq7NjxkLJlc2ueu3r1LX/+eY7//a8OP/9sr5d6X7v2lr//vsyePY+JWX+katW89O9fmerV81OihA0GyWgtuHTJE2fnreTKZc6FC72xt9dt3s4WLYqxaZMLXbr8y7JlN7G2NmH69NSfG1dIGyEhkbRqtR1v7yBKlbJlx452Ov+cnT//hoEDD/HrrxUZNernOK8PHFiF6dMvcOfOB06ffkWDBoV1Kt/MzJBTp14iSeoxBFOn1qNIEfUsQblzW3DpUh/++ec6Eyee5sIFT6pVW42n5/BE+06nhTlzLjNmzAkAJk6szdSp9ZL8kPBtH2VtxXwAFgP6kifAM0CrhT8EITPQOSibmJhw5MgRrl69yq1bt1AqlZQsWZJGjRqlRv0S5OXlhbGxMYaGhlhbW6NQKFCpVLFmrZDL5VSuXFkTlmfNmkVYWBi//fZbrOWqM6uY1g5d+ifr6tSplwwefIRnzz4DSYdkgO3bHwLQoUOpeGe7sLEx4+7dgbFu586YcQF3dw/c3T2oV68g//tfHerVK5iiltYpU85pbim3aFGUMWN+pk6dAikq88GDTzg7byMsLJoyZXLFWbhFW506lSEgIJyBAw8zY8ZFrKxM9DYdlpB+VCqJHj32cfv2e2xszDh0qLPO4TIqSsmQIUd4/NhXMwYB1ANkg4MjsbIyIUcOU3r3rsCSJTcYNeo4LVsWI1s2Y7JlM9L826JFMU1A//AhmD17HjNwYBXkchlmZoasXt2K3LnNqVw57nSYCoWc33+vTuvWJRg06DA//5w/3UOyh4cfY8eqQ7KbW10mT66r1bUcE5R1bVGGr3ftBO0FeAawpOQSokKjktzW0MwQs2ROlylJkiaMm9loN1AzI5QtZD46B+X69eszceJEGjRoQPXq1TXPHz9+nEWLFnHo0CG9VjA+06ZNY/v27fj4+FC1alW6detGp06d4p3aLWbBkVWrVtG+fXvWrFnDkCFDUr2OaeHbrhf69uFDMCNHHmPbNnVfYzs7CxYvbpbkQiRhYVHs2/cEiD3bxYMHn7h27S2//qruC/19n8d585qQO7c5Gzbc5cyZ15w585oaNfIzcWJtGjZUj8CPWZY6pnXYyEihKSc0NIodOx7QuLGjpoV93Lia5M5twejRNShdOuWLx7x540+TJpvx9w/n55/t2bGjHRs33sXHJ5S2bUtSrJhuc4kPGFAFf/9wxo8/xdixJ7GyMqFfv6RXPhMyrkmTTrNnz2OMjBTs3dsxwekEE2NoqODWrf4cOvQsVneMo0c9aNduJy4uJenduwK//fYTy5bd5O7dj9y9+zFOOZGR/9N8PWLEMbZvf4ClpTFdu6r7NDdvXjTJuhQsaMWRI11irXR5/fo7tm27z59/1kvTGSGWLLmOJEHTpkVwdXXSer/krIz5tUU5WKc6ChDqG0pUaBQum12w/aa/fVRYFOtqqRcs632xN4am6pCc3JX0okKjmJNrDpC8ZaYTa9H+tq5iCWtB66B8/vx5AM6ePUvNmjUxNIw9tdW2bds4ceKEfmsXj40bNzJ9+nR69+6NgYEBZ86coUuXLjx48ABXV9c49QJ1WK5UqRL//vsvVlZWmoVIMruYFmV9B+UvX8KoUGE5Hz+GIJfLGDKkKlOn1tOqRWnz5nsEB0dSoIAl1avnB9StbL/8spNnzz4THa1iwIAqcfYrUiQHq1a1YtKkuvz99yVWrbrNlStvcXbeluCx/vtvgKZv8Pz5V/jf/84wZszPmhUD69YtSN26BZPxDsT1/n0QjRptwts7iNKlbTl4sDPz519l0qQzAEyYcIrBg6uweHHc6akSM25cLb58CWfWrEsMGHCISpXyxNvCJ2R8Z8++ZsaMiwCsWtWSWrUckl2WkZGCtm1Lxnru5MmXREQo2b79Adu3P6BNmxIcPNiZCxfe/P9UZurpzIKCIomIiI41iDY0NIrixXNiZaV7q7BMJkOhUP9MK5Uq+vU7yL17H6lVy4FfflF/cD516iVLl97E1tYMW1szcuUyx9bWHFtbMxQKOZ8/h+LnFxbrYW5uxIIFTbWqQ0RENOvW3QHgt9+q6VT/mFkvdBETlB8+/ERkpFIvM4v8aGxL2pLnmw96kSFfB0/bVbBLt/BpZmOGoZkhe7vt1Wr7QK9AbLRYel3IurQOyk5OTpq+vzNmzGDGjBmxXpckCScnJ33XL45Hjx6RP39+Jk+ejJ2dHXfu3GH16tXMnDkTf39/Fi9ejCRJcVqXZTIZFStWTPX6paWQEPWtrezZ9duqY2Ag18x/WrlyHmbNapjonL8fPgSza9dDdux4yKVL6iXA+/atFOt2VUyL1NGjL+jfv3KCt7IcHCxZvLg5EyfWYd68KyxbdjPe2SkATQszqL+/+fJl0yzkoE/v3wfh5LSB58/9KFDAkmPHurF06Q1NSDYxMSAiIpqlS28SFaVi+XJnrcOySiXh5xemOQch8/r22/fff+/p3r2cXr+n8+c3oVu3cnTtuodnzz7j4xNC8+ZFtWoZ3revo17qolDImTWrIYMHH47V7ejhQx/27HmsU1l582ZjwYKmSJLEoEGHef7cj61b25I7d9zuTH5+YQQERCCTQZMmRXQ6TuXKebh27R3Dhx+jeHGbeMdNfK9GDXvMzAx5/NiXHj32smVLW60WTRIyPksHS4Y8HpJoH+pvW5RDPyfe11rI+rQOyrt27UKSJDp06MBvv/1G7dq1Y72eLVs26tSpo/cKxohZMESSJHx9fXn37h12dnZUqFCB8ePHY2VlxYwZM7C1tcXV1RVJkhg6dCiVK1emT58+qVav9BTTyvPtbVF9yJbNGHf3rjRpspkbN7xp3Xo7+/d3ihWWfX1D+fffR2zf/pBz517zTWalXbtSjBv3tb+tXC5j69a21Kq1jn37nvz/gJzE++Pa2Vkwe3Yjpk+vT3Bw5P9/SFO/FvO1mdnX+owe/TPjx9fS+zRWMSH52bPPODhYcuZMT9atu6MJyTNm1GfChNps3nyPnj33sWrVbQCtwrJKJTFw4CFWrbqNXC5jw4Y2ojU5E6tbtyDLl7dg4MDDLFhwDVB3KdJXWJbJZJiaGvDihXrF0T//rKf1vps23ePkyZeMGlWD8uVTtqhS06ZF8PD4LdYH1fr1C/HPP83w8QnFxyeET5/U//r4hKJSSeTMaUqOHLEfuXOba87L3d0DT88AXrz4Em9QjgmpkhT7A4k25s5twosXXzh27AXNmm3B3b1rkmE5b95s7NnTgZYtt7Fjx0MsLY1ZvtxZfJjNIiwdLBPt8vFt67cgaB2Uf/nlFwB69uzJr7/+GmdKtZCQED5//ky+fEkvpZwcMb+gunXrxrx581i/fj2VKqlbLfPnz0///v35+PEj06dPp0qVKigUCpYtW0aNGjXo0KEDFhbJG3SVkcUEMX0HZYCff7bH3b0rTZtu5sSJl7Rps4P161vj7u7Bjh0POXXqJUrl1+P+9FM+OnYsTfv2peMdXFi1aj4WLmzKoEGHmTDhFD/9lF+rlh1DQwXW1kkvOJIat0a/D8lnz/Zky5b7cUIyoJnLVtuwHF9I1nY+3MhIJRMnqhexmD69gbgtnIHEdCtKjbAsSRIjRx5HqZRo06YE9esnvZDPixd+WFmZsHPnQw4ffk6VKnlTHJQh5nfP13MqUyaXVstDJ2TKFCfkchmOjvH36f521hClUsLAQPv308TEgH37OtGmzXadwnKTJkXYsqUtnTr9y8qVt7GwMGL27EaiZVkQfjA6X/EbN27kwYMHcZ7fvXs3RYsmfQswpcqWLcvEiRNZsmQJc+bM0Tzv4OBA//79yZ07NwcPHqRixYrMmTOHtWvXZsmQDF+D8reBVZ9q1XLA3b0r5uaGHD/+grx55/Hrrwc4fvwFSqVEpUrqbhmvXv3O1at9GTGiRqIzcAwYUJnu3cuhVEp07Lib9+8z7tRLuoTkGN26lWPDhjbI5TJWrbrNwIGH4v0Qk9KQ3KnTbubMucKcOVfo1Gk3kZHKlJ+woDcDBlRh+XL1HKwLFlxj5MhjsVpfk+vIkeccP/4CIyMFc+YkPcuQJEn07LmPokUXkyuXOa6udaldO/n9plNTr14V6NGjfLytyfD17hmo+0nrKiYsN2niSGhoFM2abeH8+TdJ7te+fWlWrHAGYN68qzg5beDlyy86H18QhMxL6xblmO4LkiSxYsWKOAP3Ll26FO9AutTw+++/8+rVK8aNG4dSqWTEiBEYGxtTtWpVSpYsycOHD8mVKxcjR45Mk/qkl5iWjdRoUY5Ru3YBDh/uQvPmWwkNjaJs2Vx07FiaDh1KU7SobrM8yGQyli935r//PvDgwSe6ddvLyZPdtWptS8sBNWFhUTRosFGnkBwjqZZlfYTkvXufYGysfi/27n1Cp0672b69nWhZzkD03bIcGalk5Ej1IhvDh/+Eo2PSA5JjxgyYmRny55/1UnUaydT2bYtydLQK42QMy0huy3LfvpUwNlYwZMgRLl70pHz55cyf34Rff60oumIIwg9A66C8c+dOzWC+a9eucevWrVivZ8uWDVdXV71XMD7W1tb8/fffGBgYMHHiRJ48eUL79u2xtrbm8+fPFCxYkIiICExM0nfez9SWml0vvlW3bkGePBlCWFi0zlOgfc/MzJB//+1AqVJLOH36FW/fBia5YMfOnQ+ZNOkM69e3pkYN/SxGkpgPH4IxNFSQJ4+FTiE5RkJhGdBbSN63rxMAbdpsF2E5g9JnWF6y5DrPnn0mVy5zJk5MeixIaGiUZmGOCRNq6S0kx7SM6zsgBgSEc+3aOyIiomnZsnic17/t7pCSO2jJDcvdu5endu0C9Oy5j/Pn39Cv30EOHHjKqlUtE2wFF2KTG8gp37O85uuMVt73ZRdzLsazQ8+Qi642gqQjmUwmbdmyRdfdUoW/v780f/58ycLCQjIwMJBy5swp5c6dW3rw4EG61CcgIEACpICAgDQ5XocOuyRwkxYtupomx9OnUqWWSOAmHT78LN7XP38OlSRJklQqlVSu3DIJ3KRJk06nWf2iopTS06e+0tSp5yRwk8BNmjHjvE5lbNp0V5LLp0jgJvXrd0Dq1++ABG6SXD5F2rTprtblRERESy4u2yVwk4yNp0ru7s81r7m7P5eMjadK4Ca5uGyXIiKidapjWtL2+kjr6yi1LV9+Q/MzNHy4u6RSqXTa38cnRLK0nCmBm7Rq1S2t9nF1PSOBm1SgwHwpNDRSkiRJ+vAhSNq377H05ImPzucgSZL09m2A1LLlVmnx4mua565ffyvNn39F53P63oULbyRwkwoWXBDv61FRSs176OcXmqJjSZIkhYVFSU2abJLATTIzmy4dPfo86Z0kSYqOVkp//31JMjJSX3M2NrOlPXsepbg+usjo15H3LW/JDTfJ+5Z3mh5X37LKeQjx0+X60Pmj0qtXr3BxcYnz/JkzZ/j1119TGNt1Y2lpyfDhw7lz5w4bNmxg0aJFXL16ldKlS6dpPdJLas16kVwfPgTTqtU2nJ23JtmPr2xZ9cCf+/fjLpSwYcMdihRZxMWLnshkMs6e7cnEibX53/++tqSl9jkbGMg1LdmgXUvy977vs6yvluSmTb9Oj9W0aRH27euEsbFC07Is+ixnLN/3WR49+rhO+0+efIaAgAgqVLCjd+8KSW7v6RnA7NmXAJgzp7FmtprRo0/Qps0Otm+PO8ZEGwcPPuPgwWdMnnyG4OBIAgLCadt2JyNGHGPx4uvJKjNGoUJWAHh5BRAdHbcP8rd9lON7XVcxLctNmxYhNDSKli23sXv3oyT3UyjkjB79Mzdv9qN8+dz4+obStu1OevXaR0BAeJL7C4KQ+egclI2MjHBxccHR0ZHChQtrHm3atGHPnj2pUcckOTo60qVLF7p06ULBggXTpQ7pIa26Xmjj+vV3VKmykoMHn3H48HPKlVvGqlW3EhzEVK5cbgDu3/8U63mVSmL9+rt8+RLOkSPPAbC2NmXatPqabgWSpF7AZMKEk0REROvtHPbte8K0aeeJiIhmxowLKQrJMb4Ny/oOyTFEWNZNREQ0w4YdYdiwI3r9+UnMt2F53ryrrFlzW+t9f/21IrVqObBwYVOtZlwYN+4kYWHR1KlTgF9++bpoyU8/qWckunbtndbH/vb67d+/Mr/9Vo0LF3pjYWFE9uzGjBnzM2XK5KJXrwpalxmfPHmyYWSkQKmUePs27rLRMplM74OXTUwM2L+/E+3blyIqSkXHjru1/r6ULZuba9f6Mn58zf+/ru9Srtxyzp59rZe6ZUWSJBEZEklkSKReBrfqu7zvy44Ki9J8LfzYdF7CevDgwdy8eRMnJyf27t1L48aN+fz5M69fv2bBggWpUEUhITF/OGJW6Esvr1/7U6fOOiIilJQsaYONjRkXLnjSv/8h9ux5wooVzjh8N2dlzFRS3wdluVzG4cNdWLv2P4YMqRrv8S5c8GTfvicYGSno3r08pUrZxrudLiRJYvjwo7x5E8Dnz2EsWHBV89rx4y9xd/dAqZRQKlVx/jU2NiBnTlNsbMw0/6q/Vv9burQt//7bgXz5slG1qvbTJ44ffzLJkBwjJizH9FkeP/4k8+Y1SdF7klX163eQTZvuARAQEMHGjXHvkKWGAQOq8P59MFOmnGPIkCM0a1ZUswLc9x4+/ER0tIry5e2oXDkv58/30qpf8N27HzQtxl5eAXTq9C9FilhjZmbIrVvvAbhxw1ur+n76FELv3vsZMaI6tWs7YGxswMKFzTSvy2QyfvvtJwYNqpLiKdPkchkFCljy/LkfR448Z/DguNe+QiFDpZJwd39O4cLWGBsbYGSkiPUwNv76tYWFUZLvmZGRgm3bfsHKyoRVq27Tt+9BQkKi+O23n5Kss7GxATNnNsTZuRg9euzj5csv1K+/gcOHu9CsWerPAJXZRIVGMdNiJqCfZaH1Xd73ZccsOBIdnjYfpoWMS+egfPbsWVxdXRk+fDh169Zl7Nix1KtXj27dunHlyhV+++231KinEI+aNe3ZtOkec+deoXjxnPTrVzld6nHt2lsiIpQUL56Tq1f7YmFhxIIFV/njj1McPepB6dJL+euvBgwaVFUT7mvUyM+CBU34/DksTnlmZoYMHZrwMrV16hRg796OeHsH6SUkg3ohgxw51C3XpUrZ8s8/1zW3ePXVSlSsWE6OHu1KoULxzxX7vZhWYQcHS5ycCia5vZNTQRwc1GEjKkq0KMfn/v2PmpAM6oU4xo6tmaI5gLX16VOIZvU6g0QGH+3b94SOHXfj4lKC7dvbAdoPnjMwkJMtmxFBQZG8euXPq1f+cbYpWVK75XgVChlnz77m+PEXjBtXk2nT6se7naGhguHDjxIREc3Chc2SPaC0ZctizJt3lSFDjvD+fRBTptSLNQ95/fqFOHbsBX36HNCqvPLlc3PwYOckBwsrFHJWrHDGysqEv/++zO+/HwXQKiwDiAZHQcjadA7KkiRhZKT+5FauXDnu3btHvXr1qF+/PhMnTtR7BYWE9e9fmSdPfFmw4Br9+x8CSLewDOrbpzHLaY8cWYPmzYvSt+8BLl3yYuhQd+7e/cjKlS0BsLU15/ffq8dbTkBAOKamhon+wW3TpkSs/0v/v3JjcsnlMq5f76cJMPfuDeT69XcoFDKCg6P48iWMz5/D8PUNxdc39upj+fJlo2fPCpiaGuDrG6rZ7uu/ofj4hPLs2WecnDZw9mxPrcLylClO/PvvY54/92Py5DPMnp343LmTJp3m+XM/7OwsmDJF+1XbfiTTpl0AoH37UkgS7N79iGnTzmsCaWr59CmEBg028uDBJ+zsLDh8uEuCrcm5c5tjYCAnNDRK5+OULp2LT5/G8PLlFzw8/DQPP78wypXLTYUKdjRu7KhVWTlzmrFvX0fmzLkSa6XN7z165MOiRdeQJPWqnA0aFNa53gCzZzdCLpcxZ84Vpk27wMOHPmzc6IKFhfrvzc6d7Rk16hjXr3sTGamM9YiIiNZ8HdM14+7dj9Srt4EzZ3omGZZlMhmzZjXE0FDOjBkXtQrLERHRuLqeZfbsS0gSFChgyYYNbahbt2Cyzl8QhIxJ56BcrVo1/v77bypWrEiZMmVYvXo1LVq04MiRI0RERKRGHYUEyGQyze319AzL1tamlCuXO86qWiVK2HD+fG+WLr3B//53mn79KiVZVp0667hwwZMzZ3pq1YoK6kGE3brt4c8/6/Hzz8mfPu7bVj5DQwXjxp0kICCC8CRuvT175se0aedZtaolI0bUiHcbb+8g6tXboFNYzpnTjBUrnGndejtz5lymTZsSCZ7f5ctezJ17BYCVK53JkSPp1Qx/NI8e+bBr10MA/ve/OkiSxO7dj9i58yGurnUpWVI/dye+921IVk852CvRaRZr1LDn6tVfk323xMTEgFKlbPVyt6VRI0caNiyc6IfQUqVsOXy4C3fvfkx2SAZ1y+7ffzemTJlc9O9/iL17n1Cz5lr27+9EwYJWZM9uzKpVrZIsR6lU4ekZQMOGm3jx4otOYTmm1TypsHz//ke6ddvLvXvqwci9elVg4cKmmoYCQRCyDp07ls2ZM4fw8HD27t1LmzZt8PDwoHjx4uzZs4cuXbqkRh2FRMSE5eHD1b/M+/c/xKpVt5LYS78aN3bk7t2BrF4d94+YXC5j6NBqeHqOiNU/18VlBzt3Powzgj1muWoPDz+tjz916jlOnXqlt0FskZFKGjXaxMePIYSHRyOTQb582ahZ054uXcoyYUItVqxw5ujRrty+3Z+mTYsQFhZNzpxmCZaZN282zpzpSbFiOfH0DMDJaQOvXiW9wlerVsXp0aM8kgS9eu2Lt5UxNDSKXr32IUnQs2f5eOehTYpSqcoQg0JT0/TpF5AkcHEpQblyuSlf3o42bUogSerXUoM2IVmSJKZNO8/t2+81z5UtmzvDLJX8bUjeufMhEyacjDPAqVmzoowfX0svx+vZswJnz/Ykd25z7t37SNWqq7hwIelV9GIoFHIKFbLmzJmeFC5srQnLXl4BSe4bE5b/+EN9Lr//fpRFi65pXlcqVcyZc5kqVVZx795HbGzM2LOnA+vWtRYhWRCyquTMPxcWFia9e/dOkiRJev78uTR79mxpx44dKZ5LM7NLz/lfVSqVNHy4u2au0ZUrb+q1/Pfvg6QPH4L0UtbDh5809Xz3LjDWa2/e+Os8T2pQUIQ0YMBB6f79j3qpnyRJ0rNnvtKpUy8lD4/PSc5NrFSqpLNnX8V6LiwsKt5t370LlIoVWyyBm+TgMF96+dIvybr4+YVKefPOlcBNGjHiaJzXY77vefPOlb58CUuyvO95eHyWihZdJDk6LpQePNDfe/i99Jz/9ckTH82c1rdvf50X9dYtb83c1k+f+urteJIkSR8/BktlyiyVwE3Kk2dOvOUrlSppyJDDErhJtraz9TJHcHxUKlWKfz8/f/5ZUijU7+Hu3Q+lqVPPpVp9JUmSPD39pUqVVkjgJhka/imtWXNb5zLevPGXChdeKIGb5Oi4UPL09NdqP5VKJf3xx0nN76mFC69Kr159kerUWad5rmXLrXr7naiLzDqPckRwhOSGm+SGmxQRHJHi4+i7vITKfnPxjV7LFjKGVJ1HGeDDhw+alfns7e2xt7enefPmYjnPdJSaLcuhoVGULr2USpVW6mU6rRw5TOnbtyKjRtWI00/TwcFS06qsLQsLI5Yvd9ZqQJak5cibokVzUr9+IRwdcyQ5OEkul8Xql/jq1RccHRexdu1/cY6XnJZla2tTVq1S9+1esOBqrNa18+ffsHChusVr9eqWWFnpthrlixd+1Ku3gefP/Xjx4gu1aq3j3LnXOpWRGcyYcRGVSqJly2JUrJhH83ylSnlwdi6GSiUxY4b+WpW1aUmOilLSo8deliy5gUym7pOu68++Nh498qF06aWaVSKTq0iRHMyZ05j+/Stx//4nJk06Q5066/Uyr3F87O0tuXChNx06lCYqSpWs4zg4WOqtZbl06aWcP/8GCwsjVq1qyf79ncSqfILwA9A5KF+4cIFy5coxf/58AMLDw+nSpQulS5fm4cOHeq+goL3vw/KQIUfw9Q1NcblXr77Fzy8Mb+8gHj70ifP6sWMeFCu2mM6d/9WqPDs7C1atasWcOY1TXLf4JDTjQ3h4NDVqrKF8+eU8f/45VY4NsGTJDby9g1i16na8c74mJyw3b16UPn0qIEnQu/d+QkIiCQmJpHfv/UiSeq5dXaekignJXl6BlChhQ82a9vj7h9O48WZ27sw617KHhx9btqhnupg0Ke7yzzHPbd58jxcvtO/ykxBtQnJYWBS//LKTLVvuY2AgZ8uWtgwaFP90iCkRGBhBnTrryJ7dONHpBbU1fHh1li93pnXr4hQsaMXYsT8nOoNHSpmZGbJ9+y+4u3elf/+vYy8WLrzKoUPPtBrwqK+wHBoaRc2a9ty9O5C+fSuJhiEdyRVySrUrRal2pfSyLLS+y/u+7EINCgEgk4vv8w9P1+bqmjVrShUrVpRevHihee78+fOSo6Oj1LBhQ12Ly1IyytK7KpVKKltWfct348Y7KS4vZjlccJPWro17+3P37ocSuEm1aq1N8bEkSZIWLboq9ey5V/Ly0u19jIpSStOmnZPy5JkjeXsHxnn9n3+uac4jV66/Y92C1yelUiXNnn1Rev36S6Lb6doNw98/TLK3nyeBmzR06GHNLXt7+3lSQEC4TnX08PisKatEiX+k9++DpNDQSKlt2x0SuEkymZs0f/4VncpMSnrdMu7TZ58EblKzZpsT3KZp080SuEm//ro/RcfSprtFQEC4VLeu+ha+ick06dChpyk6ZlJWrLgp1a69VvLxCZEkSb0Mc0JLx+siJCQyxWUkR1BQhKYLyLe/I+7e/SA9eeKTYBeTlHTDWL/+P2nVqltSdLRSL+eQEpm160Vmk1XOQ4hfqna9uHPnDoMGDaJw4a+jm2vXrs3IkSO5fj1ly5gK+iGTyTTTpx048CzF5Z0///VW/507H+K8XrduQS5c6M2SJc1TfCyAVatus2HD3XiPlRiFQsaRIx68fx/M6tWxbzNHRETz11/qZX0tLY359CkEJ6cNsc5NF5IkJbhkrVwuY8yYmhQoYKV5bvLkM2zYcCfWdrq2LFtamrBmjXrA5D//3GDJkhsArF2r20Ci71uSz5zpiZ2dBaamhuzc2Y6hQ6siSTBixDFGjz6eqQf5vXr1hY0bE25NjjF5svq1DRvu8vq1f7KOpU1Lsq9vKPXrb+DcuTdkz27MsWPdaNGiWLKOl5DoaBX+/l9/Nvv3r8yZMz2xsVEPNh09+jgtWmzljz9Opeg4ZmaGKdo/uYKDI+nVqwL16xcif/7smuf/+OMUJUosIVeuObRuvZ2tW+/H2i8lLcs9e1agb99KGWaApSAIaUfnq97Kyoo7d+7Eef7BgweYmSU86l9IWy1aqG/DHz3qkWjQUamkRF+PjlZx5cpbzf+/X0kPwMbGjFq1HDTLUqdU376VmDLFiaJFc+i0n0wmY+7cxmzd2paJE2OHou3bH2iWxjUwkFO6tC2BgRE0abKZDx+Cda7j778fpVMn7bqaXLrkydSp5+nVa79mWe4Y34fl5s23Jvr9aNTIkQEDvt6CHjiwMg0baj8ll1KpolmzLXFCcgyFQs6iRc2YNashAHPnXuGvvy5qXX5Gs3DhNaKjVdSq5UCNGglPHVijhj01a9oTHa1i4cKrCW6XkIcPP1GnzrpEQ/Lr1/7UqrWWW7feY2NjxpkzPalTp4DOx0qMSiXRteseWrTYGmtaw5iAJ0kSOXKYIpNBxYp2ej12WrGzs2D16lacOtUj1vMGBnJMTNRzmR848JSuXfewYsXNWNt8H5ZbtEj8ehMEQdA5KA8ZMoRly5bRrVs3Nm/ezMaNG+nRowfLly+nf//+qVFHIRkOH1YHshw5TGOtbvWtqCglFSuuoHjxfwgOjox3G5mMWK02xYvH/uP/+LGPJoDqy2+//cTkyXUpXly7FcS+Vb16fjp3LhvnnI2N1VOGy2TqFt/Hj30B9RK2SqVug4Tevw9i6dIbHD3qwadPIYB6kZSE+kvWqGFP69bqKduOHvWI83revNk4fVr9R//JE1/8/OKuVvitv/9uRPnyuSlfPneSi5Akl5RFlhvLlk29WMXVq281q+LFZ8+ex1y79g5As8CFti5f9qJGjTU8ffqZfPmyxRuS9+9/QsWKK3j69DO5c5tz4UJvKlXKk0CJyffkiS+HDz/j5k1v7t6Ne0dGJpMxaVJdHj4cTPv2pfV+/PS0b18nAgLGc+XKr/Tvr56zffToE3h6xm41jgnLMpn6g7+PT0h6VPeHExkSyRTZFKbIphAZEv/fm/Qs7/uyV1ZeCUBUmO4L/whZi85BecKECUyYMIH9+/fTo0cPevXqxc6dO/n999+ZPHlyatRR0NGOHQ80I/jnzUt4wNzJky+5d+8jHz4EJxgOFAo558/3Yv361hw61DlWMPP2DqJJk81Ur76ax4/jDvLTVUhIpF5m1YgRGalk9erbSJJEx46l6dy5DJKkDsoqlfq5Bw8GkS9f9qQL+0auXOYcPtyFiRNrkyuXOf36HcDG5m/2738S7/ZyuYxmzdQDqV68iL9rxbezfyQV3LNlM+bOnYHcuTOQbNl0m7tVoZDj7t4Ve/vsPHniS716G2K1qCuVKn77zZ3x49W35UePrqG3+XHTg5ubE126lCU6WkXHjrvjDct79jymY8fdREer6NKlLG5uTlqXf/myF02bbiYoKJI6dQpw+/aAWCFZpZIYOfIYbdrswN8/nJ9+yse1a30pUUL3D4HaKFXKlitXfmXTJhd++il/gtt9u7hKVvlQBOoPvtWr52fZMmdq1XIgODiSQYMOxzlHBwdLzYwV3t5B6VFVQRAyiWR1uPr555+5dOkS9+7dY9q0aZQqVYqoqChUqtSZJkjQ3o4dD+jadQ9KpUSfPhVo27Zkgttu2/YAgO7dy2mee/8+iLDvPkHnyaNeorlFi2KaYBYYGEHz5upb+BYWRuTKZZ6iekuSxIABh/j557V6mZFCpZJo1GgT/fodZO7cK8hkMlataom7e1e8vEZw8WJvtm9vl+RqXfFRKOQ0aVJEs4pXjhymREeruHXrfYL7ODqqu5EkNKuCTCbTzB6QWtNtfVsX9UplscNyWFgU7dvv4p9/1NOVLVjQhL//bpzgHYnMQKGQs3FjmwTD8vcheePGNlr3Q/02JNerVxB3965xrgO5XKbpyz5qVA3On+8dq+96aihbNjcdOmjXWnz5shd16qznzJlXyT6ePhb50VZYWJRWH6blcvX1bmSk4MiR55rfdd+K+XAqgrIgCInROSgvXryYVq1a8eTJEzw8PJg0aRLBwcGsXr2a8ePHp0YdBS19G5J79arAypUtE5zCKCwsir171S2gXbuW1Tzn7LyNmjXXJjqgKSpKSbt2O7l79yO5c5vj7t410VXptOHtHcTRox7cvfuBjx91uxX65WXcVlq5XEanTqWxsjKhZEl16525uRFNmxbB0FBBzZoOKarvt4YN+4kXL35LdLq7mOW9X73yT7DFWKFQf6/im1JO3+ILy40abWLv3icYkc6ntgAAkQNJREFUGSnYsaMdv/9ePdXrkRYSCsv6DMmHDnWJNbjt2w87ixc359ixbsyZ0zjJObmTw98/HBeXHTqtZhlj69b7XLzoiZvbOa22f/78M5cve2n+L0kSefPOpWTJJbG6OOjanUkb3t5BlCixhAoVVmhVfokSNpoBnL//fjTOVJkiKAuCoA2dg/LChQtp2bIlLVu2ZNeuXZQuXZqnT58yduxYdu7cmRp1FLTwfUhevbplon/0jxx5TnBwJA4OlppBTh4efnh6BuDlFZhgK6IkSfTrd5ATJ15ibm7I4cNdKFTIOsX1z5cvO3fuDGTjRhdq1dIuxEqSxOW5l1lcbDFPvun2EHObdeDAKjx5MkSvswpMnnyGf/65HmtWgfz5s1O4cOLvgb29JQYGciIjlbx7F/8f5rRqUY7xfVi+dMkLKysTjh/vluX6r8YXllMjJEdGKhkx4iitWm3TDBIzMzOkcWPHVDu3kSOPsW/fE9q126lzN4rJk+sycGBltm5tm+S2J0++pGzZZXTp8i8h/98n1MsrkM+fw3jxwi/WoNA//zxHvnzzmD//im4nk4CoKCUdO+7G0zOAQYOqaP29Gju2JmXL5sLXN5QRI47Fei1fPhGUBUFIms5B+d27dzg7O2NqasqFCxdwdnZGJpNRoEAB/PxSPll/esjsffR0DcnwtdtFp06lNaG4bNnc3L7dn/37O+Hg8LVLwrfvj6vrWTZsuItCIWPnzvZUrpxXb+eRP392unQpq9W2KqWKo8OPcmL0CSSlhNdlL1RKFacnnWZ/7/1IkoRMJou1clZAQHiCi5Fow88vjNmzLzFsmLvOrXcGBnIKFrQC4GU8LeDwdWaCtArK8DUsFy2aA0dHay5e7B1rlcGs5PuwnBotyS9ffmHFilu4u3ukqDuDLqZPr0/9+oVYs6aVzotg5MplzrJlzlr1069RIz+5c1tQvLgNQUHqoOzgYMn796M4dapHrNbyO3c+4u0dpLfp1P744xQXL3qSLZuRpr+/NoyMFKxe3Qq5XMbmzfdiDaYVLcqCIGjDQNcdHB0dOX78OBEREbx7944GDRrw6dMnNm7ciKNj6rWapKaYPy4qlQq5PHPNk5mckBwYGMGhQ+r5lTt3jh1M7e0tY/Xb3b//CStX3mbTJhf27HnM1KnnAVi+3JnmzXVbCS4+06adp2nTIlSpon3gjgqLYk/XPTz5/64jjec2pvqI6ny484GLMy8iKSUcajlQqW8lzT53737gl1920rJlMebPb5qsuhoZKZgzpzGXL3tRuXLsGQvevPFn9uxL+PmFs23bL/Hu7+hojYeHHy9e+OHkVDDO6zEtyqlx2zoxjo45ePx4CDKZLFP3R9ZGTFguVkzdZ/x//6ujt5AM6tv9K1e2JHt2Yxo00H7aPm1FRirZtu0+Bw8+Y+vWXzAyUpAnTzZOnuyul5XiwsOjMTFR/1nw8Qlh165HDB6sXjHQ3NyIq1d/xc7OItax7OwsYrUmA2zZ0pa7dz/opT/23r2PmTNH3TK9fn0bihZVD5aM+TCclGrV8vH77z8xf/5VBg48xLNnwzAyUnwTlHWfHlIQhB+HzkF59OjR9OnTh927d1OlShXq1atHx44duXjxIlu3bk2NOqaaf//9lxcvXpAtWzYaNWpEkSIpX+I1LSUnJIM6/EZEKClePCflyyc893F4eDQDBx7mw4dgypdfzvv36paXSZPq0PebEJpc+/c/YdKkM0ybdh4Pj99iTUOXkFDfULa12sbbK29RGClw2eRC6f8fuJSnYh7qT6/PqfGncB/mTt6qebErr54r9vVrf168+MLevU9wdXXCyspE5/paWBgxdGg1hg6tFuc1uVzG0qU3kctlLFnSnBw5TONsE9NPOaGZL2L6KKdli/LXY2euD4gpoVDIcXV10mmfxELy9u0PKFs2F6VL5wKgW7dyiRWls28DoUIhY9y4k3z8GMKJEy803YpSGpKDgiKYOPE0e/c+4fHjIahUEqVLL8XHJ5TCha01y1/nyZMtiZLULCyMYo0DePXqCydPvqRfv8qJ7BWXh4cfvXrtB2DkyOq0bVsSd/fnzJhxkWbNivDHH7W1Kmfq1Ho8fOjDH3/U0rR8xwTld+/0O72lED+5Qk7R/29c0dcS1vos7/uy7Wva43XJSyxhLegelHv16kXx4sXx9fWlQYMGyOVyunfvzqhRo6hePfMM/nFzc+Ovv/7C2toaf39/LCwsmDNnDp06dcLYWLcptwAiIiIIDFT/wg0MDMTY2DhZ5WgruSEZvna76Ny5TKJ/YE1MDHB378ovv+zUdBfo2bM8U6Y4pbT6ANSpU4C2bUtSrFgOrUJyuH84a2uu5fOzz5hYmdBpfycKfLdgQ80xNfE878nzI8/Z1X4X/W/2xzi7Ma1bl2D9+tY4OxfThOTPn0PJls1YLwOs7O0tcXOrS6VKeRJcsezrzBfxB+WvLcqZuytQfLS9PtL6OtJGYiF506a79Oy5D1tbc27d6q/Vz7G23rzxZ/Lks7x9G6hZXEOhkDNqVA3CwqIpW1Y/C/xAzLXuwdu3gezZ85gePcrTrVs5Tp9+ha1tygbqvnsXSJkyywgLi6Js2dxUr57wtHXfCguLol27nQQGRlCzpj1//aVeBOfjxxAuXvTExyeECRNqafUhwdzciGPHusV6LjN2vciI14e2DEwM6HK4S4Yt7/uymy1qxsrKKzEw1jkmCVmN/lfQzviePHkiOTg4SBMmTJDevXsn7dmzR2rVqpUkl8ulSZMmSZ8+fdK5TFdXVwnQPFxdXfVf8f939+4HSaGYIoGb1KvXPik6Wqn1vn5+oZKBwZ8SuElPnvhovU/fvvulQYMOSZGR0cmtdrxUKpXW9VepVNLhIYel+QXmS58eJfw9CvENkebZz5PccJMODTqU4Ha9eu2TrKz+krZtu5/ocU+deim1bbtDOnr0uU7v9ff27XssgZuUM+cs6dChp3Fez59/ngRuUpkyS6WxY49LixZdlf7995F07dpb6e3bgBQdO71pe32k5XWkDQ+Pz1K2bDMkcJPq1VsvhYREal47f/61JJO5SeAmDRhwUFIqVXo99qdPwZrr/NkzX72WHZ8TJ15IJ0++0Pw/NDRSiorSz89cjx57JSen9dLz55+13mfy5NMSuEly+RTp/v2PmueDgiKkv/66IL17F6hzPR4/9pFu3/bWfA3q719gYLjOZaUHXa6PgIAACZACAgLSroKSJHnf8pbccJO8b3mn6XH1LauchxA/Xa6PH/KjkqmpKV++fMHMzIy8efPi4uJCxYoVKVSoENOnT8fIyIg//vhDp/7KEyZMoG/fvtjb2+Pl5YWtrW3SO+mBkZFcp36lHz+GEB2twtLSWOuV7ywsjJg1q1G83QlSSiaTabocaLNt04VNqetaF3PbhOdtNs1hinUhawK9AlEmMHgvOlrF+fNv8PcPx97+ayvgo0c+bNlyj1atimsWbHj58gt79jzm2DEPrlz5Nd6WvNu331O2bC4MDeNvnfb1DaVo0ZzY2Jjh6xsaq/Vf+v9b63XqFGDr1vs8ePCJBw/iLhWuUMiws7Mgf/7s5MuXnbJlc9G6dXEqVLDTS//U1KTt9ZFe19H3lEoVV6++ZeLE0wQFRfLzz/Zx+iRv2XIfSYJffinJ0qUt9N6/29bWnIULm1Khgp3mbkRq+n4pdFPT+O+MxFBGKZHJZVrd9l62rAUmJgY6vUcx77VKJVG79jomTKjFsGHVsLAwYtw43RfBefLEl7p116NUqtizpyNDhx4B1AMaM0vf/IxyfQjCj+SH6ZgofTNzQ3R0NLlz5+bKlSu8ffsWgIIFC/K///2PQYMG4erqyp49e+LslxhjY2OyZ1cHruzZs6fq7bBy5XKzcaMLMhmsXHmb335z16Ge6iCnyyIBo0Ydp2rVVfGGt+TaufMhf/11McnZI8L8wjg54STK/6+vXCFPNCQDhH8JJzoiGgMTA+pOqhvvNgYGcp49G8rly31irWC2Z89jZsy4yMyZFzXPde9ejilTnHj+fFi8Ifns2dfUrLmWdu12xbsYwoMHnyhadDHt2+/i+fNhrFnTKtZ0Yf/732maNt3M4MFVOHmyO7NnN+T333/il19KUr16fvLnz45CIUOplHj3Lohr196xZ89jpkw5R6VKKylceBEjRhzl/Pk3aT4QUFvaXh9peR19Lzg4kr17H9O7937s7OZSq9Y6zp17g4GBnFWrWsbpUnPixEtA3R0ptYJWvXqFkMvTf5Dll1dfuP7Pdc3/n+x/wtJSS7m3+V6i+6lUEi9e+GFmZqjzOYwdW5MjR7pQtmwu/P3DGTfuJGZmM5gz53KyziFv3mwUKGBJnjzZ6NNnP/fvf8LOzoLTp3tgbq7bsuXpJT2vj5SKDIlkhvkMZpjP0NsS1vos7/uy19ZcC4glrIVk9FHOrD5+/IiBgQE5cuSgcOHCjB49mkGDBrFjxw6GDh2KsbExNjY2/P777zx58oRx48ZRvXp18ufXrj9dWuvSpSxRUUp6997PP//c0MzIkFTLYsyI9vDwaK1Gjfv7h3P48HNevvzCixd+lCmTSy/1X7ToGpcueVGsWE6KFIm/tUylVLG9zXY8L3gS7B1Mmw1ttCrbNIcpv17+lU8PPmH5zTR331Mo5Jo5pGNUrZqXTp3K4Oz8dUYPY2MDJk+OP3CDui+lJEmoVPG/n25uZ/H3D2f+/CZYWZnQp09FzWvR0SpWr/6PT59CGDCgMi4uJeOdLUGpVPHxYwhv3wby7l0gnp4BnD37hmPHPHj92p8FC66xYME1bG3NaNWqOC4uJWjYsDDGon9dory9gzh48CkHDjzj1KmXRER8/QBpZWVCixZFGTiwCqVKxW65e/nyCy9ffsHAQB7vDCb6cPz4C1q23EaePBbcvz9I5+XKU0qlVOFx1IObS2/y3P05SOBQywG7CnZ8fvYZPw8/zk4+S5mOZTAwif/nbMqUs8yde4XNm9vSpk0JnY4vk8lo1qwojRs7smXLfSZNOoOnZwAzZlzAxaWEzq3s2bMbs2xZC1xcduDlFYiDgyWnTvVI8PePoH9RofoNnfou71vR4UmvACn8GH6Iv6LTp09n7dq1BAUF4ejoyJw5c3B2dubBgwdMmDCB7Nmz061bN0xNTSlatCjt2rXjt99+49mzZxk2KAP07FmByEgl/fsfYt68qxgbGzB9ev1Ew2/u3Bb4+IzRtCwnxcrKhOvX+3Ls2Atat9btD11imjYtwv+1d95hTWRdGH8nJPTexIIi2BBR7IoVFbti772uXVfXrmDv7urae9e1997bJyIKooINBUREioDSk5zvD8xIIEAoEqL39zw8wGRyc2aSm3nn3FPKlzfji/4rwm+/H4LvBEPTQBP1p9TP1ficgEOxqrlPdmrVqhxatcpd9ZM2bcrjzp3BcHQspjAxcM+ezihVyhAdO1bM9JhQKMD9+0OwZ48vOnT48biXVyjKlTOFiUlauIuGhgAlShh8T0AqCQCYMKEeEhJScenSG5w4EYAzZ14hIiIB27c/wfbtT6Cvr4m2bctj+PAamZbVf2eICFu2eGPbtid49Oij3GNlyxrDza0iOnasiIYNS2cZSnPlylsAQL16pX6agHV2tkaJEgaoXNkCiYniQhPK8RHxeLL9Cbw3eyMmXYdOu5Z2oO9NVOqMrYOHax8iNjgWXhu8UP/PzPMzMTEVBw8+Q3x8qlyDntyioSHAgAHV0KOHA3x9P6FiRXOlq9bcuvUeT558wsSJ9eDvH4GOHQ/h48evKF/eFFevDpCrF89gMBiK+OWF8oEDBzB37lxMmDABYrEYt27dQuvWrTF58mR06tQJX79+xejRoxEZGYmePXvC1tYW5cuXh4GBARITE1Vtfo4MH14TKSkSjB17AUuW3IWWlka2pa8EAg7m5jlnsUskUj6O1sxMF61bl0Pv3sfg7f0RL16M4Ss05JXZsxtn+7g4SYwbc24AABrPboxiSmT4x3+Ox8P1D+E8xRlahex9q127pNz/6b31uroi/PNPa357TEwSL4CBtGoY8+a58P+HhsahbdsD0NMT4fDh7qhTR37s9OjqitC5sz06d7ZHaqoEt24F4cQJf5w8+RIfP37F4cPPcfjwc2zb1gFDh+a/pJ+6Q0SYNes6H1rDcUDduqXQsWMFdOxYEZUrWygV7y0Lu3B1/Xk3IPr6mrh/f0imusU/E9+9vjgz7Awf6qRtog2nwU6o9UctmH2vXwwAIh0Rms5ritNDT+POojuoPqQ6tDOIVx0dEV6+HItnzz4XSIUObW2hXJhUTly+/BZuboeQlCSGWCzFsmX3EBmZgCpVLHHlSv9MtZ8ZDAZDEb98jLKfnx/KlSuHBQsWYO3atbh69So6deqE+fPn48SJExg6dCjGjRuHWbNmoXfv3hgwYADmz58PHR0dVKtWTdXmK8WYMXWwenVLAICHxy0sWXInX+N9+5aCevW2Y9u2x/w2IyMtXLjwGq9fR8Pb+2M2zy4YvDZ4ITY4FgYlDVBnXOa6xYq4MfcGbs+/jWO9jv1k6wB//wg0bboLnp4fMj1GRBg79jzmz78lFzt+504QKlfegAEDTmY7dkxMEgwNtRAUFIuGDXdgzZoHSsWgi0QaaNHCFuvXt0NIyCQ8eDCUr+k7bNgZbN/+OIcRfm0yimQPjyb4+HEy/ve/oZgxoxEcHCyVEqQSiRTXr6d13fuZQhlIq1uc3iZlcxHySuWulWHlZIUStUvAbacb/gz9E61WtZITyTKqDagGi8oWSIxOxL3l9xSOx3FcgZaxU5YzZ16iQ4eDSEoSw9nZGgsW3EJkZAJq1iyOmzcHMpHMYDCU5pcXyoaGhnj79i1CQkIAABYWFti7dy+GDx+ODRs24M6dO1i0aBH2798PHR0deHt7QygU4sKFC0U67CIjkybVx9KlzQEAM2dex+rV/8ty3/HjL2DIkFOIikrI9JhUShgw4AQePfqI2bOv80umGhoCNGtWFgBw9WrgTziCHyTFJuHOojSx7zLfBaIcsu8BINwvHI+3pgnBBtMb5Ov1I/wjkBSb/VLxnDk3cOtWkFzSn4xVq/6HDRsewcPjptzSfrFi+ggIiMS5c6+ybGMNAA4Olnj8eAS6drVHaqoUEydeQteuh3O1fC0QcKhbtxT27OmEcd9vNH5nsZxRJK9Z0xru7k3zJJgePw7Dly9JMDLSyrSS8LNISZHAw+Mmxo+/UOBjx32I4wW4SFeEQbcGYfjD4XAa5JTt3BMIBWi+JO0758E/DxCXrnFHSorkp4v6rNi//yk6d/4PKSkSNG5cGj4+YYiLS0HDhqVx7doAmJnlry40g8H4vfhlhbLsS7pevXowNjbGqlWrEB4ezj++efNm9OrVC8uWLUNgYCB69+6NEydOwNfXF6dOnYKjo2NWQxdZpk1ryDcDmTz5Mtavf6hwv127fLBzp49C4TV//i2cOBEATU0NnDjRUy4WUOY9u3r1Xb5t3bjRC8bGSzF06KlMj91bfg+J0YmwqGyBagNy9upLUiW4OOEiSEqo3K0yyjQqk+NzFPHt0zec/eMsNjpuxL1lij1kMtasaY3Bg52wfLmr3PYjR57jr7+uAABWr24lJ6QqVDBDq1Z2IEKW740MIyNtHDnSHf/+2wYikQAnTgSgRo3NmWJqc4LjOKxZ0/q3FsuKRPL48XXzPJ4s7MLFpWy+Q5CU5dGjj5g37xbWrfOCj8+nAhv33Y132Oi4ETc9bvLbskrMU0SFDhVg3cAa4kQxnv/3nN++ePEd2Nuvx6FDzwrMVmVYu9YT/fqdgERCcHGxgadnKBISxHB1tcXFi31hZJT7jpwMBuP35pcTyp6enoiOjuaXK11cXNC1a1fs2LEDu3btQnT0j3JkS5YsgampKWbMmAEgzfssFAqhr6++y3Jz5jTGzJlpNUYnTLiI9+mScWTMnt0YS5Y0z5QQc/ToC8ybdwsAsHlze74iRHx8Ctq1O4CTJ18CSOtUFp/PcjwcxyE2NhnR0ZnFutMgJzj0cEDzJc0hyEGIpMSn4JDbIby/8R4aWhposbxFrm1JiU/Brfm3sLbcWnhv9gZJCDHvY/ibrfjP8ZmeU7KkIXbscEOFCj+WpO/fD0H//icAAOPG1cGECWliLDExFb17H8PAgScxZkxtAMCOHT45nkOO4zB2bB3cvz8UZcsa4927GDg7b8e//3rmylv3O4vlghbJQOHEJ2fE2dkas2c3wqFDXbNtO58bfHb5YF/LfUiKScK7q+/4uOTcIKtt3u9yP7mEvpMnA/DyZVShtWMnIri738CECRcBAG3alMOdO0FITpagY8eKOH26t9qUgPtV4QQcyjQpgzJNyhRIW+iCHi/j2MVrFk/7u4jXqGcUAgXY6ESlSCQSOnPmDHEcR0uXLqWoKPkOUG5ubqStrU0LFy6kDx8+EBFRcnIytWzZkho1alQgNqiqE1JGpFIpNWu2mwAPmjHjqlLP+fTpK+nqLiLAg8qVW0teXqEklaZ1Ghs79hzfwcrQcAkBHnT58pt82RgZGU8BAREUGRmfr3EOuh0kD3iQBzzopYJud9khlUrJd58vrSqxih9ja52t9P72e/5xz3WetEh3EUXk0MUwPj6FLCyWE+BB5ubL5TqJTZhwgT9/ly69JiOjtHO4c+cTpW398iWROnc+xI+zevX9XB2r7HjGjz/Pj3HvXnCux8gPys6PgpxHu3f78Me7Zs2DfI+XmJhKItF8EonmF0q3vJ+BVCql63Ou85/5Iz2PUGpiaoG+RmxsEu3e7VNoHe/+/deTf5+HDz/Nd0zs3ftogXcTVTWqmEe54VfpaPerHAdDMb9lZz6BQIAaNWpAKBRix44dEAgEGD58OIyNjQGkVb/o378/5syZA19fX4wfPx4ikQjx8fEwNjZGSkoKRCLRL3H3yHEc2rcvj+vX32UbC5sRPT0REhJS8eZNNGrX3gobG2N062aP2Nhkfp+4uGRoaChXOSM7zMx0M8UKxgTFwLiMca7GEX6vE9xpdydUaFcBAPD8yHOEPQ5DyTolUbJOSRiWNFT43Fvzb+GWR5oH3bisMZovaQ6HHg5ynwG/fX5ITUhF4JVAmFc0h59fODZs8IKJiQ4WL24uN56sk1lkZAIcHTeiZs3iGDCgmpz3d8KES/z5zE2nQyKCikI+GengOGDv3s5o06Y8DAwK30OZlCTma6HnBXGyGGeGneEbhTSc0RDNFjYrMI/c149f8SXwC0o3LI0BSoRNFRTR0WkViurUKYkuXeyx9Xu+wvz5LlmW+GMwGAxl+GWEMgA8f/4cYrEYgYGBWLZsGTiOw7Bhw2BsbAxdXV0cO3YMkydPxokTJ9C4cWNYWFhAKpXi5s2b0NT8tZblUlPTljwzXlRv3w5CdHQiXF1t5ZYiixXTx7t3E3DhwhscOfICZ8++wvv3MVi5Uj4pUCDgcOBAV1SvXjxPdgUGfsG2bY+xcGEzuU5dUa+isKXmFlTuVhltN7RVKoEPALoe7Ip6f9ZDqXRlo14ceYEXR17w/xuUNOBFc8m6JVG6YWloiDRQfUh1eK33Qr2J9VD/z/qZYjM5jkPZFmXx4cEHfPoeFxoVlYhNm7xRtqyxnFDW1U0rhXXmzEvs2fMUFy++gbd3GLy9w6ChwUEoFEAsliIgIBJmZjrYvbsT2n0X9jkhlRJcXHbD1zccmpoaWLWqJR/CoSxEhIkTL2Lt2rTY6G3bOsDZ2TqHZ6k//ftXRUBAJJYsucsvy+cn9EJLS4iePasUlHm54vXrKNSpsw3Dh9fA0qUtct3pjohwsMNBBF4JBKfBof3m9qhRgCUDP3p/xJ7meyDUEmKM/xjomOogODgWxsbaMDT8ueUa+/evCnf3m3j4MBR2diZwcLDA8+cRaN58D27cGAhbW5Of+voMBuPX5ZcRyhKJBFu2bEHp0qUxY8YMbNq0CQsWLAAAXiwDwKpVqzB48GD4+/tDKpWifv36KF26tAot/znIWilnbCyycuV9nDnzCvPnN8WcDO2d9fQ00a1bZXTrVhkJCam4cOE1jh71x5kzLxEfnwpHR0tMn94QPXo45MmmpCQxmjffg/fvY+TqPYuTxDja8yhSvqUg5n0MNBQ07cgKTsDJiWQAcOjhAG1jbYQ+DMVnv8/4GvoVAScCEHAiAAKRADPiZgAiwMjaCBODJmYryq2crACAF8pOTlaYNq0BnJysMnU21NYWont3B3Tv7oCIiHgcOvQMe/c+hZfXRwBp7uCGDUvj4MGuKFVKsZdbEQIBh5kzG2H69Ks4fLg7atUqofRzAcUi+XepqcxxHBYtagYABSaWVcW+fU8RE5MEf//ITCI51CsU0lQpOA0OAg2B3G+RjggmtibgOA5V+1VFqGcouh/pDrt0bdQLAssqljAsaYiIFxG49OclJLayw4gRZ9GnTxVs3twBQFonykOHnkFXV4SOHSvyyZBRUQnQ19fMcyfJsmVN0KKFLa5eDcTevU9x5Up/NG26G69eRcHFZTcTy0WAlPgUrLFZAwCY8H4CNPMZM17Q42Uce0/zPQBYC2vGLySUxWIxateujb/++gs1atRA7dq1MWzYMCxcuBCAvFiuUqUKqlRRjVeosEhKkgnlH29xfHwKn4ikqEtcenR1RejatTK6dq2MxMRUvHgRgWrVrPKV5a+tLcSCBS5YuvQuRoyoyW+/POUyPvl8gq65Lrrs7wKBRv5yTCt3q4zK3SoDSPvCC3schlDPUIQ+DIUkRSLnOc7Jcy0Typ+ffYYkVQJjY20sXZpzwqCFhR7GjauLcePqIiAgEo8fh6FKFUtUrmyh1DmMjU1CYOAX3nPfo4cDOnasmOtl999ZJMv4GWJ51ar72L3bF7t2dUKNGnlbXckt7u5NUadOSYUl7Y50O4LY4FiFz7OobIHRz0cDSKt9XK5NOehZ6BW4fUItITpu74jtztvhu9sXjpUt8O1bCooX/9F989u3FD7hNTl5Nr992bJ72L3bF/v3d8lzF8nhw2vg6tVAHDvmj3nzmuLmzYFMLBcxEiIzlyQtSuOlJykf3SQZvxa/hFCWSCTQ0tLClClTwHEcOI6Dk5MTtmzZghEjRmQSyxk9gb8iyclpGezpPcpXrgQiKUmMMmWMUDUX7Z11dESoWTN3Xsys6NevKnr0cOBbPfsf94fXei8AQOe9nWFQIuuW1nlBU08TZRqVyXPJOJOyJtA00ETK1xREvYyCZRXLXI9RqZI5KlUyV3r/N2+i0arVPsTHp8DH5w9eGOVWJIeExMLd/SZ27vQB8HuKZBmKxPKXL4kYObJWnmop/+9/H+Dn9xlHjjwvNKEsEHBZhusY2xhDIBKAJASpWAqpRJr2t0QKbRP56jY/QyTLKFWvFOqOrwvPNZ4I3uCFQ3s6wSJdjoBUSnB1tUViohgi0Y8bxri4ZHz+HI8HDz7kWSi7uVXEnj2d0LVrZXAch+LFDZhYZuSbyIDIHB0quua6MGLt0H9Z1FIoh4SEQEtLCyKRCCYmJtDQ0IBUKoVA8OOLVyAQoGbNmrxYXrZsGRITEzF+/HgYGf36H2hZ6EV6cXXqVFp5Nze3ioV2o+DvH4G5c29i50436OunLY3JRPKXd19wakhaHWXnqc4o17pcodiUGzgBBysnKwTfCcYnn0+wrGIJiUSKV6+i8PVrSrbtpfNKyZIG0NMTQSKR4vPn+FwJOSLC9evvsG6dF06ffgmpNC3k43cWyTIyimUPj1vw8LiFunVLomPHiujYsSIcHJRrYT1+fF20a1cebm6VfrbZkEikEAg4Obve33yPNxffoPHsxtDU18SgW4N+uh3K0mxhM7w89RIx72NQ8dFHNOv/I6nP1FQHly/3z/Scv/9uBXt7c4wbl7/48f795RMImVhm5Jczw87kuI9IV4Qx/mOYWP5FUTuhvHDhQhw6dAgRERGoXbs2+vXrh169esmJZBkcx6FmzZrYunUrunfvju3bt2PMmDEqsLrw+eFRTnuLJRIpzp59BQCFcnGXvWbnzv/h5csomJvrYOPG9vxjRITjfY8jOTYZpeqVQrOFzQrFprxQql4piJPE0PjunT9xIgDdux9BrVol4OU1vMBfT0dHhFOnesHYWBsmJspVxoiNTcLu3b7YsMELL19G8dtdXGwwbVoDtGpV9G5CVIFMLJcpY4Tt25/Ay+sjPD1D4ekZilmzrqNsWWNeNDdqVDrLigmNG5dB48Z5W6XILfv2PcXy5ffh7t4EPXo4QJIqwfkx5xHxIgLggBZLcl87/Geiqa+J9lvaY1/LfXj470M49nZEqXrZdznV0RFhwoR6/P9SKWHYsNMYOrQ6GjTIfQ4JESE1VQpNTQ0mlhn5YvDdwdl6lCP8I3Ci3wkkRCYwofyLolZCec+ePVi0aBEGDx4MoVCIGzduoE+fPnj27Bnc3d0hEmX+MHMchxo1auDYsWMwNjaGqampCiwvfDKGXty/H4LIyAQYG2ujUaPCSV7U0BBgz57OmDbtKhZmEMIcx6GJexNcnHARXQ91hUYRLuHkmqH7npOTFXR1RTAw0CywMJ6QkFjcuxeCXr3SYufLllXuIu7nF471672wb99TxMenJZ3o62ti4MBqGD26NipXtsi3bb8aHMdh5MhaGDmyFj5+/IqzZ1/h9OmXuHo1EO/exWDNGk+sWeMJY2NttG1bHiNH1iw0UayIrVsf48WLCLx7l1bq0XOtJyJeREDXXBcNpuavXfvPws7VDtWHVoemgSYsHdPClVK+paSFgijRHW/DBi/s3OmDY8f8ERQ0MVNzpOw4cuQ53N1von//qpgxoxEA5llm5B0rJ6sCTRRkqB9qJZRfvHiBUqVKYe7cubCysoKPjw+2bduGJUuWICYmBv/++y+IKJN3meM4VK9eXUVWq4YfVS/S3mJZ2EW7duV/el3R9OKxTp2SuH59gEIxWa5VOYx+PjrfyXuFjZ2dCeLipkOjgOwWi6Xo3fsY7t0LgZdXKCpUMANRmleNiL7/lv9fLJbi/Pk3uH07iB+ncmULjBlTG/37V4WBwc8tx/WrUKKEAUaMqIkRI2ryya6nT7/E2bOvEBGRgAMH/PDff8/g7T0C1apZyT3369dkbN36GJ6eoTh0qOtPC2c6d64Ptm9/gsGDnRAXGsfX/m6xvAV0lFxxUAUdtnaQr0l+0A/nRp2DdX1r2LWyg10rO5SoWUJhDeeBA6vh3r0QtGxpmyuRDAAJCanw94+Ev3+k3HYmlhkMRl5QC6EsE15EhMjISISGhsLKygpOTk6YPn06jI2NsXjxYlhYWMDd3R1EhLFjx6JmzZoYMmSIqs1XCbKqFzHfM3fPnXsNAGja1Oanvm5sbBJatNiLw4e78V7R9BfLgJMBMCpjBKtqVuAEnFqIZCLC+5vvYdPUhk8W1dAoOFG0Zs0D3LsXAj09Edas8YREonxnEQ0NDp0722PMmNpo0qTML5+k+jPR09NEp06V0KlTJUgkUnh6hmLu3Bu4du0dpk69ikuX+sntz3EcZs68huRkCebObQwHh9wneiqDkZE2/vzeHvrM9KtI+ZaCErVLwGmg0095vYIi42cx3DccJCEE3w1G8N1g3JhzA2WblcWAawMyPdfAQAsHDnQBx3FITZVAKBQo/dnu3t0Bt24FYePGdpkeyyiW27U7gOfPR+e6JjUj93ACDiW+l7YsqBbWBTleYY3NUD/UQijLviD79euH1atXY9euXahRowY4jkOpUqUwYsQIhIeHY9GiRahVqxY0NDSwceNG1K9fHz169IC+fu6z2tWd+vVL4dSpl5g//xYqVDBDsWJ6CAiIxKxZ1+HsbP3TluTj41Px6NFH2NmtRULCrEyVGiJeROD00NNo4tEEdfORuFOYPDv0DMf7HEelzpXQ41iPAhejYnFacxgzM10Epyvx1bWrPTiO+57IBT6hS/Z/+fKmGDKkOkpm0XmQkXc0NARwdrbG2LF1cO3aO4SGxmXax9v7I5KTJRAIuJ/iwY+LS87UqEPWiTLSPxIfH31EyZ+QTPqzaLuuLepPro+3l97iyl9XkPItBclfk7PcXzbPFi++g8uXA/Hvv22UqjCiqyvCjh1uWT5evLgBLl/uBxubNQgIiMS3byk/vSEKI60U5/ACzOko6PEKa2yG+qEWQlmGo6MjZs2ahfnz56N06dL466+/AAClS5fGiBEjcPHiRZw5cwbz5s3DypUr0a5du99SJAPAX381wKtXUdixwwd9+x7Hhg1tERubDB+fT2jadBeuXx+IKnkodZYTxYvrw82tIuzsTJCYmJpJKJtVNENidCL89vmphVBOiU/B1alXAQDFaxbnL9537gRh/vzbKFPGCNu2dczXa4weXRurVz9AcHAsOneuhBMnAgAAVapYwsOjab7GZuQPX9+0RjMZyyNKJFK+HvOIETVQuoCTeMRiKZydt6NcOVNs2NAOJb6XTXRd7oqI5xF4d/0dAk4FqJVQBtLKLQp1hEj5lgKBSIAOWzpku39iYirWr/dCREQC3ryJVroUX3j4NxQrlvV3f/qupHp6ynUBZTAYvydFf907AxMmTED//v0xbdo0LF26FMnJaR6J2rVrw97eHs+fP4elpSX+/PNPVKyYfVONXxmBgMPWrR0xbFh1SKWE0aPPY+TIGqhe3QoREQno0OEgUlIkBf66HMfh5MleWLWqlcKKDebf6wlHBkSCSPkQA1Vxb/k9xH2Ig7GNMep/X/4G0oTM1auBuHnzfb5fw8BACx4eaV0S794Nxvz5TQEA8+bdgofHzXyN/fVrMr5m47VjZM+jR2EAgFq15AXatm2P4esbDmNjbSxYUPAVWx48+ICAgEjcuRMMHZ0fN5tCbSF6nuyJdpvaFelKMdlRsk5JFK9ZHC7zXfiGPlmhoyPCkycjsXChC7p3r5zj2O/fx+CPP87CymoVHBw2IDz8m8L94uNTAKQlOxdUrgGDwfg1USuPMgCYmJhgxYoVEAqFmDVrFgICAtC9e3eYmJggKioKNjY2SE5OhrZ27hJAfkUEAg6bN6cl1Gzd+hhjxlzAunVtcPDgM8yf78LXMy5MTMuZghNwSI5LxrdP32BQvGAbjBQkMUExuL/8PgDAdaWrXImg6tWLY/v2jgXmlR82rAb++ccTr15FITlZguXLW2Dq1KuYNy8tcSsvnuVHjz6iVat9AIBLl/rluvX1705qqgReXqEAIHfuvnxJxKxZ1wEA8+c3hbm5boG/dsOGpeHnNwpv337JdMOpZaCFWiNr8f8/++8ZHqx+gGLViqFYtWKwcrJCsarFoFVEEzot7C0w9H9DlY79LFnSELNmNc52n7dvo7FkyV3s3u3LhzJVq1YMlpaKm6skJKRViNHVZd7kwiI1IRXrK68HAIx5MQaifJ77gh6vsMZmqB9qJ5QBwNLSEqtXr4ajoyPmzJmD/fv3w8jICEKhEHv27GEiOR0CAYdNm9p/F83eGDPmPHbs6CiX1JeaKinwShhSKSEs7Gum+FmhlhAmtiaIfhONyIDIIi2Ur069CnGSGDZNbWDfxV7uMWNjbQwZUnCVVEQiDSxb1gKdO/+HFSvu49SpXvkSy48efYSr614+mdPVdS+uXOnPxLKSpKZK0Lv3MYSHx8PQUAtO6Tyf8+bdQlRUIipXtsAff9TKZpT8YW9vAXv7nHMJZO3ZQx+Gym03sTVBsWrF4LrcFablVF8WM/5zPPS+C9ecykFKpYT372NyrEjx6lUUFi26g/37n/JJsK6utpgzpzEaZdONU1ZKUY+V/So0iAixQbH830VtvMIam6F+qO2ak5GRESZOnAgfHx/s3r0ba9euxYMHD+Dg4KBq04ocAgGHDRvaYdSoWiAChgw5jV27fAAAz59/RunS/6BTp0MF9oXw6lUUDA2XwNFxo8Ix04dfFFWCbgfh+eHn4AQcWv3TqlCqSbi5VUT37pWRkiJBp06H4OhYDMuXpzWTyE0YRnqR7OxsDWdna8TEJMHVdS8ePfr4E4/g10Amko8d84empgYOHOgCne+rCS9eRGDduocAgDVrWhf4DeabN9H4+PFrrp5Td3xddD3UFQ1nNET5tuVhUDLt5vNL4BcEnAiAUEf1/pDwp+H4x+YfXJ9zHVKJNMf99+17iooV18Hd/YbCx1+8iEDfvsdhb78ee/b4QiIhtGlTDvfvD8Hly/2zFcnAj9ALFp/MYDByQvXfoPnEzs4OdnZ2qjajyCMQcFi/vi04Dtiw4RGGDDkFqZRw8+Z7fPr0DTo6Il4MEhH27n0KV1dbFM+Dx7dMGSMkJ0sgFqe1YM6YVGNWyQw4qxqhHPY4DBH+EUiOS5b7SYlLQXJcMtqsawPD715wswpmsGlmA6tqiuMow8O/4eHDUBgYaBVI2T2O47B/fxeIxVKcOBGATp0O4eTJ3HmWM4rkixf7AgBat96P+/dDmGc5BzKK5OPHe6BduwoA0ubFxIkXIZEQ3NwqokUL2wJ9bSLC4MGn4OPzCQcOdEGHDsrlWBjbGMPYxhhVelbhtyVEJiD8aTg+P/8Mg++JgFKxFFdnXEWdsXVgXMY4X7bGBMXg26dvsKpmBaF29pcRcbIYx/sdhzhRjHDfcKVCLu7cCYJYLM3k8fX3j4C7+00cPfoCsnvwjh0rYvbsRqhdW/nERuZRZjAYyqL2QpmhPBzHYd26thAIOKxb54WhQ09j69YO8PBoitTUH4l9L15EYODAk+C4tFjJ7t0ro0sXe6XLkGlpCREQMAZlyhhDKMy8aGHbwhbSVClsXQtWaChCKpaC0+D4mwCvDV54sv1Jlvs3ntsYhiUNUaZxGYzyGwXx98Ytijh2zB9jxpxHu3blC6w+tUikgf/+64aePY/ixIkAuLkdwqVL/ZQSy4pEsqxs2cWLfZlYzoHsRDIAnDnzCleuBEJTUwOrVrXM1djx8Sm8KJNKCdHRiZlim6OjEyEWSyGVklyoR1aEh3/Dy5dRCrsG6prromyzsijbrCy/7fJfl+H5jydenX6FwXcHQ89CcfxudkjFUtxZcge35t0CSQgamhooUasESjmXgrWzNSp1qpRp9eXGnBv47PcZuha6mZqQZMXWrR3Rs2cVuS6ib99Go06dbfj2Lc0b3KWLPWbPboTq1ZWrhJEe5lFmMBjKwoTybwbHcVi7tg0EAg5r1z7E8OFnsHRpcwwdWoPfJzY2GXXrloSnZyju3AnGnTvBGD/+Ilq3LodNm9qhjBLeKDu7rGMiy7Uqh3KtygEA3l5+i8QviRDpijL9aOpp8t6w3CBOFiPwaiD8j/vj5amX6Hu+L19Gq0rvKoh5FwNtY21oGWpB01ATWoZa/I9hupsBDU0NaGST8FitWjFUq1YMFSua5drG7Egvlt+/j0HVqsV4IZ6VWM5OJANplTWYWM6anERycrIYf/55CQAweXL9bD/fGQkOjkWzZrsxblwdmJnpYtas6yhXzhTXMjTaMDPTxb17Q/D8+WdYW2dfbs7LKxQuLruhp6eJt2/HQ18/TYTPm3cTnz/HY+TIWqhatZjcc5wnO8P/mD+iXkXhQLsDGHh9IDT1c+dRPT3sNHx3+wIAtIy0kBybjJD7IQi5H4IA2wDYd/4Ry//yzEskxybj/sq0hNgOWztAP5uSbRnJ6LF/8uQTUlMlqFzZAjt2dETduqVyZXt6ZB5llszHYDByggnl3xCO4/DPP63BcRzWrPHE9OnXMHfuTVy+3A9NmtjA2dkaDx4MQ3BwLI4de4GjR/1x/34ILl58g+rVN+Px45GwsTEuEFvuLLqDoHRtmNMj1BFiVsKsXI0nThJjfeX1iHkXw297eeYlL5Rtm9vCtnnBeLIbNCgNH58/CmSsjMjEcnx8Kt/C96+/GgD4IZYdHCzQvbsDvn5NRqtW+7IUyTIyiuVWrfbh/fsJrN01gDlzbmQpkgFg+/Yn+Pw5HjVqFMeMGQ2VHjcoKAZNm+7G+/cxWLnyfwgL+wqJhBAcHIvXr6NQvrz8TZZAwMHRsVgWo/3AyckKVlb6MDHRwadP31Due7Le7t2+ePcuBu3bV8gklA1LGaL/5f7Y0XAHPnp9xLnR59B5T2eljwUA6oyrg1dnX6H1P63h2NcRX95+4YWybjoPOUkJJ/qdQHJcWmlCpyFOqORWKduxpVLCvn1P0b17ZT4mPD2urrYgSlvxatZsD27cGIg6eawj/fZtNAAWesFgMHJGbZP5GPmD4zgsXtyc/z8lRZKpdXLp0kaYNKk+7t0bgpcvx6Jq1WL48iVJqaSy1FQJli+/ByenTYiNTcpyP6vqVrBpaoOSdUrCsoolTGxNoG+lz3t4c0tSbBIvkmuOrIkB1wegqXvTXI+THdHRiZg06SJevIgo0HEzIhJpQFdXxJe7AtLE8sCB1QCk1dplFA5GRlo4fbo3zpzprfSNRXqRXK6cKRYtcuHnmKfnsEw3mzExSXB3v8G3n8/Iq1dR/N8ikQZu3RqEhw+H8SKZiLB6dSuMH18HDRuWVjiGeSVzVPh+E5D63auaG0rULIGJQRNRtV9VcBwH03KmqDagGtpvai9X1znxSyJK1i0JTQNNWDpaovXfrXMce/bs6xg48CScnDYjMTGzbRERCXzt94SEVEileUs+3rXLB4sW3QEANG6s+DwxFBMbHIuwx2HZ/kT4K/5e5DgOFpUtYFHZokCSowt6vMIam6F+MI/yb0piYirc3A4BAHR0hNi/vwuapYtnzEiFCmbYurUD6tbdhr17n2LatAbZlq4SCgXYvdsXL15EYP9+P4weXVvhfq3/UXwBDbwWCKk45+z4jOhZ6kGkJ0JqfCrq/1kfZhUKNiwCACZMuIh9+54iICAKFy70LfDx03PwoB+mTr2KKVPq8x5lE5M0D7PW93bGBgZauHSpH1xd9+L+/RC0br1foVf569dk3ptsbKyNS5f6MW/ydxYscMGbN9E4dswfXboczuRV7tu3qtz+r19HwdbWJMtmFRlF8o0bA5H+elupkrlcxQwigovLbvj4fIJIpIHZs+XrBm/Y4IVx4y5g8+b2GDYsLUwqY84Ax3Ho1KkSGjYsjeRkCQwURC3FBsfC76AfAKD+lPqZd8gBcbIYL468QLUB1bJNytM100X/y/1BREoLjQ4dKmD79ieYO7cx71GWSKQYMuQ0Ro6sidq1S+D8+T6YMeMaFi5shnr1ch96sWuXD4YMOQUiYPToWhg/vuh3By0qxAbHYr39eqQm5HyDJdIVya0wyLaNfj66wOwp6PEKa2yG+sGE8m9IYmIqOnY8hKtXA6GnJ8KFC31zLKcEAHXqlISbW0WcOvUS7u43cfhw9yz35TgOHh5NEBeXjJ7psvGVsu9LIk4OOImvH7+i23/d4NBD+ZJ/HMfBxNYEn/0+I/ptdIEIZSKCVEq8KJo7tzFevIjAn3/Wy/fYOXH69Ct8/hyP5OQfyZayv9M3jKlVqwSuXOmfpVjOKJJZfLI8IpEGDh7syscpKxLLMp49+4zGjXeiRQtb7N3bmb9hkaFIJJcqlSZqidwVvj7HcZg2rQHmzbsFZ2frTI+Hh3+DVEp49OgjL5SzYu1aTyxZchdz5zbGnDlN5B57fuQ5pKlS2LjYwLp+5tfJDpISttXdhnDfcAhEAlTNcPOQ1XEpS/361nj9ehwM060kbdr0CHv2+OLUqQAEB09Cmzbl0aZN+VzZLSOjSF63ri3zFuaChMgEpCakovO+zrDIob63rrkujAq4rTuDoSqYUP7NyKtIlrFggQtOn36JI0de4MmTsGwzzrt3z1tN64vjL+Lrx68wq2CGCh0yC5WcMLUzxWe/z4j7EJen10/PixcRmDTpEpydS8H9ewhH+fJmePRoeKFcZA8d6orLl9+iWroSdbLl54ydFbMSywCYSFYCZcXy27fR+PYtBR8+xEEslkIrnVM+O5EsY/36h3j+PAKDBjnJxdj27OmALl3sFXbM9PBoiho1iqNjx5xLxr14EQGxWMqHZKSn/p/1UaJmiVwn8QEAJ+Dg0NMB4b7huD7zOip3rZxjabicCA2Ng0DA8WUoDTOEW/XtWxWPHoXB2blUpsdyAxPJBYeFvQWK18h9pREGQ11hMcq/EfkVyQDg6FgMvXqleYjnzFHcDCA/+B/3x9N9T8EJOHTa3UmubbSytNvYDjO+zkDN4TXzbc/z559x+fJb/POPJ19SCsidpyw/iEQaaNeugpzYykooAz/EsrGxNi+WmUhWHplY7trVHikpEnTpchgnTvjLNc5xc6uEq1cH4OzZPnLJYMqIZCBtlWDjxkcIyFBHnOM4/j2NikrAvHk3+ThcjuPg5pa59Joijh7tAX//MXBTkDzHcRxsmtqgRB4/A/Um1oNhKUPEBsfCc61nnsaQkZQkRufO/6FmzS3w9lbcCMfYWBs7d7pheD7mMhPJRYPUhFRscNiADQ4blArfKOzx8jt2hH9EjvHbscGxBWono3BgHuXfhIIQyTLmzWuKM2deoVIlc0gk0izjNIG0TPbTp19i925f7NnTKduY2PiIeJz94ywAoMG0BiiVhxhEANC3Ur4EVUa+fk1GcHAsHBwsAQDdulXG7NmNMGiQU6FnyEulBIGCOFBZ6IWWluLSdRk9ywCYSM4FijzLVasWw4gRNdC3b1UYG2tnql08c+Y1rFr1P6SkSLIVyWnjp82X9LXL00NEWLvWE/Pn30ZKigSLFjVXuF92VPre/VJGUmwSSELQMdXJ9VjpEemI0GxRM5wceBI35txAUmwSGs1olCcPdWRkAuLjU5GcLIFpBruePfsMB4f8J1IxkVx0ICJEfE+ALqgW1gU5Xl7H1jXXhUhXhBP9TuQ4rkhXhDH+Y1hYiprBhPJvQEGKZCAt9ODjxz+VSgTjOGD69Kt4+TIKhw49y9IzREQ498c5JEQkwNLREk3cmyjc72fyv/+FoGPHQzA314Wf3ygIhQJwHIcFC5rl/OQC5tmzz2jX7gAGDaqGefNc5B7LzqMsQyaWW7XaBwC4dKkfE8m5QCaW//zzErZte4KnT8MxduwF/PXXFfTo4YARI2qifv1S4DgO27Y9xpIldwEgR5EsGxsAUlMVJ6smJor5G9E+fRyVtlkqJRCRwhvX+yvvw/MfT7RY1gK1s0isVZaq/aoi4GQAAk4E4O7iu/Dd7YvBdwbDpKxJrsYpVcoQDx4MxevX0Sib7rkvXkSgZs0taNHCFocPd8vzDSoTyYzCwKi0Ecb4j0FCZEK2+0X4R+BEvxNIiExgQlnNYEL5F6egRPK1a4HQ0BDwjS+UrZbAcRz++ssZr19HZ9vyN/huMPyP+0MgFKDzns4QauX9o5kcl4wrU68gLiQOvc/2VvriWLmyBaTStMS9Dx/iCqxWdF7Yv/8pgoNj4esbnukxZYQykCaW37+fAED594vxA5FIA//+2xbz57tg376n2LLlMZ49+4zdu32xe7cvKle2wIgRNdC4cRk4OVmB44DTp3tnK5LTxs3eo6yrK4KX13AIBFyuhN21a4EYOPAk/vijFubO/XGjmRCZgIdrHyLlWwr0iuW+G19GOAGHHsd64OWpl7j05yXoWejlqiV2SoqE/+waGGihRrp4V7FYioEDTyIlRQKOy3tDkN27mUhmFB5GpY2Y+P2FYUL5F6agRPLx4/7o1u0wBAIOr1+Pk/P+PHjwAYcOPcPff7fK8kKUvutfVpRpVAbdDnfD149fYaVE+97sEOoI8XjbY5CE8C3sm8LuflIp4fDh53j06CNWrkxrR2xkpI1btwahUiVzha23CwuplLB/f1oJr379MlcWUFYoA0wgFwQmJjoYN64uxo6tA0/PUGzZ4o1Dh57hxYsITJx4CVpaGuja1R69elWBhkbOYiwnjzKAbMOZsuL4cX+EhX1DWNhXue3XZ19HclwyilUrJtc5Lz9wHIdKnSqhXOtyiP8cz5eKS/mWghtzb6Dh9IbQs8wsymVx3CtXuqJr18qZHl+27C4ePfoIY2NtbNmiXLvrjJw9+wpDhpxmIpnBYBQILJnvF2b8+Av5Fsl37gShX7/jIAIkEsL69V78YzExSWjefA/WrPHE2bOvlBovPj4FEoligVC5W2XUm5D/kmskJWh/72b3JfCLwn18fD6hd+9jWLXqf3KJRFWqWKpUJANpF/qQkDgYGmqhffvMVT+Sk9MaUigjlBkFB8dxqFevFHbscENY2GRs2NAWTk5WSE6W4MCBZ+jY8RBKlFiNoUNPZfkZB354lGU3PAXFmjVtcPp0L4wdW4ffFv0mGt5bvAEArde0zrb2cV4QagthVNoIklQJ3l1/hyUGS/Dg7wfYUGWDwqXokSPP4v37GCxZcjfTOXr37gvfnv3ff9ugRB7a10dExKNXr6OQSglDhjgxkcxgMPIN8ygzsuTOnSC0abMfiYk/OoWlDwUwNtbGpEn18PHjV7nyZVkRG5uENm32o2JFc2zf3lEuUY2IcHbkWZhVMEP9yfXzfHETJ4nxX5f/kBiVCKG2EGYVFddRrlGjOP76yxmGhlqo8BOakuSVoKAYDBlyCgAwZIgTtBWU35K1tA4Pjy9U2xg/MDLSxqhRtfHHH7Xg7R2Gbdse4/btIAQERGLHDh8kJUmwZ08nhZ5hq+/JpkFBMQVqk6amBjp0kC8fxwk4aIg0IEmR4NLES+iyvwvM7c0LTDy+vvAazw48w6uzr5AU86MDp4ZIA1DwEidO9MTp0y9Rr16pTOfm8uW3SE2VwtnZGn37Kh+bnZ5nzz4jPj4V1taG2LSpPRPJDAYj3zChnI7cdJFSB9aubYP372Nx9Wog2rTZnyuvskwkx8enwtXVFiNH1kS3bkcQFSXvJVqwwEXpc+bpGYqHD0MREBCJOXMaw9b2RwhH4NVAPN76GAAQ8SIC7Ta2y3WccmpiKv7r9B/eXn4LoY4Qfc72gZ5F2vKvRCLFsmX3MHZsHb4e6/Llrrka/2cTEhILN7dDiIpKRI0axeVajKenatViOHPmFZ4+zRy/zChcOI5DrVol+ETJ48f90bPnURw4kBY6o0gsO30PLXry5NNPsSkmJglHj77AsGE1YGJrgvab2+P67Ov45PMJGxw2QL+4Pmxb2KJs87KwbW4LwxxiqgFAKpEi+nU0wp6EoWKHinyVi4ATAXi67ymAtOz/Ch0roJJbJdi2sIVIQXyxjo4oywZEDx+GAgBcXGzy/D0saxFuYqIj1/mQoXo4joNRGSP+76I2XmGNzVA/mFAGsH79ejRt2hQODnlrkFFU0dER4fTpXnycsrJiOaNIPnWqFy/KoqIS5fbN+CWS3c1Gy5Z2OHiwK+zsTOVEMgDYtrBF6zWtcWnSJfjs9EH062j0ONZDYZyjIkhKvEgW6YnQ51wf2DSx4R8fP/4CNmx4hCtXAnH9+oAi9+V38eIb9Ot3HFFRiTAz08GxYz34Nr4ZqVq1GAAwoVwE6dLFHv/91y1bsVy9eppQfvo0HGKxtEBDfRITU1G//nYEBERCIOAwZEh1OA1ygrWzNc6POY/gu8H4FvYNT/c+xdO9aQLXvJI56k6oi1p/1AIASFIkiHjxvSbskzB8evwJn3w/ITU+rZ7soNuDUOb7d4hjX0doGWqhUqdKKFW/FAR5iK2W8fBhWghU+iYsuUX8ve29MrHijMJFpCvCxPcTi+x4hTU2Q/347WOUZ8yYgfHjx8PPz6/AazEWBWRiuUULW8THp6JNm/24cycoy/0ViWQdHRHMzHQBIJNHWcbLl5Ho1u0w/v33Ybb2dO/uIJflfu1aIO7eDQbHcag7vi76nO8DLSMtBN8NxtY6WxHul1kMJsUkIdQrFH4H/BD0/VhkXcO0DLXQ90JfOZEMAEOGVIe5uS7Gjq1dYCKZiDBt2hUMGXIKr19H5WkMsViK2bOvo23b/YiKSkT16lbw9ByWbcUNmVD28/uMiAgWfvGzEIulcHe/gTlzrvNx4cogE8tCoQAHDvhhwICTcvG45cubQU9PhMREMV6+jMxmpNyjoyNCr14OsLY25AU5AJhVMEP/K/0x7cs0DLg2AA1nNESJ2iXACThEBkQiJV0zHe8t3thcfTNODz0Nr3VeCLkfgtT4VAh1hChVrxSk4h/HYtPEBi1XtkTphqWzFcnnz79G8+Z7cOzYC4WPf/2ajOfPPwMAatfOexlDmVBWdZ4Bg8H4dfitPcozZszAypUrsXz5cri6uhY5L2NBoaxnOSuRDABmZmkNAdIaBIihlSEs4tatIBw75o/bt4MweLCTUtUWvnxJxIABJxEW9hUnT/ZCx44VUa5VOQx7MAwHOxxE9JtobKq6CWUal4GJrQmiXkUh6nUUEiJ+iHWnQU68d6v6kOqo2LEidM11M71WzZolEBg4vkCrQNy5E4zly+8DAPbufYqRI2ti7twmsFTSC/7p0zf07n0MN2++BwCMGlULq1e3UhiXnJ7y5U1hZ2eCt2+/oE2b/bh+fWC+2vsyMiMrUybzCt+8GYRjx3oo/d5m51kWCDg4OVnh3r0QPHnyiW9uU1DMndsE48bVzdTEA0hLvivbrCzKNiuL5oubI/FLIt7ffI/i6VrRF69RHNrG2rCqboXiNYrzv80qmOXZY7x7ty+uX3+HqlUtFVa7ePw4DESAtbUh3846LzChzGAwCprf9ttkw4YNWLZsGTZv3oyhQ4fCzCwtoSs+Ph6xsblvM5mcnIy4uDgAQFxcHJKTkwvU3vySk2c5O5EMpCUvyZLvMoZfAMDgwU4oX94UEREJWL36f0rZpKEhQOvWdqhQwQyurj9qLJtXMscwz2GwcbGBQChA0O0g+OzyQcj9EF4k61vpo0zjMjCrJJ+IJxPJSUliDBt2GoHpql4UdKk0WQUQS0s9iMVSrF/vhXLl1mLRottIUKLt6ZAhp3Dz5nvo6Ylw4EAXbNjQLkeRDKSdt7Nn+8DcXBfe3mHo2PEgEhMLtoVrQaPs/CgK8yi9SBYKBTAw0MTdu8GoU2cr/BSscGRFdp5lmbf3yZOwAref4zg5kfz+fQy+flV8HnVMdGDf2R7G6VYwStUrhanRUzHw+kC0XNkSVftWhYW9Rb7CKpYta4GFC12yLBUpi0/OT9gFAP785qW8njpQFOZHXklNTMXW2luxtfZWpBbA91VBj1dYYzPUj1/z20QJRCIRdHV18eHDBxgbGyM+Ph6urq6oVq0aqlatim3btiEmJkbp8ZYsWQJra2sAgLW1NZYsWfKTLM87WYnlnEQyAAgEPy6+0dGZhbJIpIFFi9I62C1ffh8PHnzI0R5DQy1s3+4GL6/hcq+3c+cTSLU00O9SPwz3Go66E+rCZYELuv3XDSMej8D0uOmYHDYZg24NQsNpDRWO/eefl7B9+xO0b38g21JdeeXjx684ftwfAHDlSn9cuzYANWoUx9evKZg9+wbKl/8XO3Y8yfa1165tgwYNrPHo0Qj07p27LP9Klcxx8WJfGBho4tatIPTseTTLBhZFAWXnh6rnUUaRfPhwN3h6DoOdnQmCgmLh7LwDZ868VHq8rMSyi0tZdO1qj5o18x5m8P59DOrX345GjXYiLk6xYLp7Nxi1am1B377H5T6LRCT3eZFKCe7uNzBkyCl8Cv/Gr66dO/cK7dsfwJIld+TG7dbtMPr1O45nzz4rZauNjTFmzWqMKlUUe8+9vPIfnwz8+h5lVc+P/EBSwsdHH/Hx0UeQtABaWBfweIU1NkMNod+Ma9euUVhYGEVGRtKwYcOocuXKdObMGWratCnZ2tpS9+7dqUmTJqShoUHz588nIiKpVJrjuElJSRQSEkIAKCQkhJKSkn72oeSZhIQUatFiDwEepKe3iPT0FhHgQa6ueyghISXL51Ws+C8BHnTz5juFj0skUmrVai8BHmRsvJR8fT/l2rYjR54T4EEVKvxLiYmpuX6+jI8f46hGjc1Z2ppf3N1vEOBBjRrt4LdJJFLav/8plSnzNwEeBHhQy5Z7+ccjIuLpwIGncuMo89nKjps335G29kICPKh//+MkkeRvvJ+FsvNDlfMoNVVCffocI8CDhML5dPz4C/6xyMh4cnHZRYAHcZwHLVt2N1fv3bFjL0gonE+ABw0ZcjLftj56FErFiq3gP2fdux9WaM+DByGkpbWAatXaQlFRCUREtH79Q9LXX5zJDmPjpQR4UEBABL9tw4aHBHhQly7/ye1bsuQqAjzIxyeM3xYcHEMBARF5+kzL5sz164G5fm569u3zJcCDWrTYk69xiiq5mR+xsbEEgGJjYwvktT96fyQPeNBH7495en7yt2TygAd5wIOSvyXn256CHu9nj53f88coWHIzP37N2+4smDt3Llq0aIHz58/DzMwMgwYNAgAsXrwYKSkpOHjwIA4fPowjR45gzJgxmDdvHry9vZWKXdbS0oKhYVqZJUNDQ2hpFd2Y0Yye5ew8yen5kdCX2aMMpHmdjx3rAWdna8TEJMHVdS9evcpdkpupqQ5KlzZC9+6VlQpDSA+lS8YsXtwAXl7D0SRDUl9BUbGiGapUsUS7duVx504QiAgCAYc+fRzx8uVYrFzpCmNjbXTvnhaPGRWVgOrVN6Nv3+O4ejWQHye/cfFNmtjgyJHu0NDgsHfvU0yceLFIJqUqOz9UNY8UeZI7p+tiZ2ami0uX+uGPP2qCCJg27SoGDTqldJKfzLPMccCOHT4IDY3Ls60XLrxGkya7EB4eD1tbE4hEAhw58gJnzmRu+lO3bilcvtwft24N4leEdHVF+PYtBR8+yHfwmzSpHhYscIGFxY847KZNbbB9e0eMGVNbbt8NG9phwQIXPrEUSAtFqlRpPSZNusRve/fuC0aNOov//S8ky+MRi6Xo1q0yJk2qx5fZyyu/ukdZna4zDMavwq/5baKAGTNmYMmSJbC0tMSZM2cAAA0aNMCCBQvw4MEDREdHo2zZsgAACwsLDBw4ELq6uvD19VWl2T8NmVgeNqw6hg+vkaNIBn4k9GUXp6mnp4lz5/rAyckKnz/Ho1Gjndi27TF/AcuJZs3Kws9vFObMacxv27nzCWxt12DmzGty+3p5hSI4OBYSiRTx8Slo1Wofbt16zz8uKOAuZOnp3dsR168PwPTp19C48S4sWXKXf0xLS4jJk53x9u14DBrkBCBNaLVpUw7ly5uhWDHlEsKUpX37Cti9uxMA4N9/H2Lf97q2DOVZuPB2liJZhkikgQ0b2uHff9tAQ4PDnj2++OOPc0q/Rpcu9nzogZfXRxARAgO/5Cru+cmTMHTocBDx8amoW7ckAgO/IDVVipkzG6FDh8xdHAGgceMy0E1X07hjx4p4+XIsjh/vIbff3LlNMHt2Y7n4Znt7CwwZUh3NmpWV27djx4qYPbux3I1eXFwyNDU1ULfuj/CJrVsfY9Mmb8ydezPLYxIKBVi5siVWr26V7zyC0NA08c/KwzEYjILit6h6MXPmTCxfvhwrVqzAx48fcevWLf6xLl26YM2aNShTpgwsLCz47UlJSdDR0YG+vr4qTC4UdHRE2Lq1o9L7N2tWFmfOvMK8ebdgY2OMgQOdFO5nbKyNy5f7oVmzPXj27DOGDz+Df/55gGXLWqBt2/I5elEzVnB48yYa797FyMVhEhGaNNmFxEQxXr8eh507n+DKlUAEBETizZvxP6298/Pnn/kqBbGxP+yJSdeVTEbGqgNr1rSGWCwt8KRCAIhIVwnkV63eUlTIj8feyckKfn6fERAQiefPP2P27BswMtKCt/cI2NmZ5vh8iYT4phr9+1dDdHQikpLEGDLESen33dRUR2FFjPyyYUM7LFvWQs6ba29vjvLlTTFhQl2Fzzl37hVq1y6pdDWR7Pjvv2eYO/cGAKBBA+t8j8dgMBjAbyCUx40bh40bN2LVqlWYOHEiHj16hHXr1uHKlStwdU3rzDZmzBgIBAJcvHgRvr6+0NDQwI0bNyAQCFCvXj0VH0HRYcKEunj1KgobNz7C4MFpbZazEssWFnp49Gg4Nm58hAULbuP58wi0b38QrVrZ4fz5vrny9k6cWA9t2pSXu7jHxSWjZElDhITEwtraELNnN8a7dzEYN67OTxPJ69Y9xPjxF7B+fVuMGlUbZcoYoUwZI6SmSjF1aoMcn5+Txz6v/PPPA365e9asRnlu//s7M3t2Y7x+HY0DB/zQo8dRhV7l1FQJxo27gM2bvQEAAwdWw6ZN7XL1OrJGO4GBX7BuXVucO/caFSqYoUQJ5Uqi1apVAsOGVce2bU+wevX/MGdOYxCRUiK7MMh4E+jqaofu3R0UhlFt3vwIf/xxDo0alcbVqwPyNW//++/Z94RFwqBBTkrNRwaDwVCGX1ooJyUl4dOnT5g3bx4GDx4MIC2uy9jYGE+ePIGrqyukUikEAgHi4+Nx9OhR7NixA+bm5rC2tsbly5dRunRpFR9F0YHjOKxf3xYAlBLLWlpCTJxYD4MGOWHp0rv4558HcHS0zHVIhIWFnlzcJJBWru7163GQSokf78CBrrk8otwRHBwLIiAoKK18oEikgefPR0MiIZXVMc4oknPTUpzxA6FQwIevKBLLUVEJ6NbtCG7efA+OSyt3NmWKc67PdXqhrKmpgStX+kNXV5SrcVataoVLl94iMPALrl9/h9OnX+LUqZfYtasTjI21c2XPz8bKKusVuSZNbGBoqAUnJyvk5yObUSRv29bhly0Pp+4oqnFflMYrrLEZasbPzCosCkgkEkpMTJTbNmzYMCpZsiSFhYVl2t/Pz4/evn1LkZGRuX6tgs4yLqpIpVIaNeosXwFg164nSj0vKCiGoqMT+P9DQmLp8uU3P8nKgkcqldK5c6/yXamioPj77//xVQ9mzbpWZOzKCmXnhyrnkaLKFy9efCY7uzUEeJC+/mI6fTogz+PfuRNEgAeVLftPpsckEindvx+s1DiXLr3h33vAg6pU2VDk338iylTJJiQkf+/xoUN+pKExjwAPGjToJInFknyNpw6oah6xqg35g52/osVvX/Xiv//+w7Jly7B48WIEBQVBQyNtSS8lJa1Nq6urK758+YKtW7ciNVW+mHiVKlVga2vLNyBhZEbmWR41qhaIgMGDT2H3bp8cn1e6tBFMTNLCJ+LiktG27X60abMfR48qbmtbFDh69AWSktIqG3Acp1SMdUFBRPj2LQXBwbHw8fmE69ff4ejRF9iyxRsTJlxgnuSfgMyz3KePI8RiKXr0OIq6dbfh7dsvsLExxv37Q9ChQ8U8jy/zKAcHx8rVMU5KEqNjx4No1GinXEJqVrRsaYfhw2vwf2/a1K5A3v/790MKvK22jAcPPqB8+X9x/fo7flupUoZ5Ho95khkMRmHwy4VezJ8/H4sXL4apqSk+ffqErVu3YsCAAZgyZQoMDNLiAHv06IF169Zh79696N27N8qVK8eHYDCUI7dhGBnR1haiWjUrREQkoHbt/JWE+lksX34P06ZdRevW5XDmTO+fVnKKiBAQEIkrVwJx9Wog3ryJRnR0IqKjE5Gamn21ECaSC56MYRhfv6agYcPSOH68R6YQoNxiZaUPbW0hkpLECAmJ44WzlpYGTE11IJEQevQ4Cm/vETmKyJUrW8LV1RbdulXO9fufkiLB/fshCAiIxB9/1OK3z517A/fuheDhw2FwdCyWzQi5Y88eXwwffgYpKRIsWHAbLi42+frMMpHMUFci/CNy3EfXXBdGpY0KwRqGMvxSQvnjx4/YuXMnxo0bh1GjRoHjOAwZMgRr167Fq1evsGnTJhgZpX34Fi5ciHbt2mHGjBk4cuQIE8l5ID9iWVNTA3v2dEJo6Fc5QUBEKhd9UVEJWLXqf3zJt3r1Sha4SA4P/4arVwN5cSwra6UITU0NvlJB+h8XFxv0719V5efrV0QmlsuVM4FUSpg9uzG0tPL/dSkQcChb1hj+/pEIDPzCC2WO47BpU3v4+X2Gj88ndO16GLdvD8r2NQ0NtdC9u4NSr/v0aThiYpLQuHEZAEB8fApcXHYDALp3r8zXSBeJNJCUJEbPnkfh5TUcenqa+TlcSCRSTJ9+FStXprW1d3OriL17OzOR/BuSmpiK/W32AwD6XugLUT6Tmwt6vJ89tq65LkS6IpzodyLHfUW6IozxH8PEchHhlxLKSUlJ+PjxI6ysrGBrawsAuHTpEsaOHYsjR45g/PjxWL9+PfT19eHg4IARI0bg77//xqxZs7Bo0SIVW1/4JCeLMWHCRcTGJmPIECc0b26b60Q7RWL5/fsYVKliCXNzXZiZ6X7/rQORSCPTc9OL5BMn/LFjhw8OHuwKff38XaBzS0qKBOfPv8bu3b44d+4V78mdP78p5sxpku/xExNTcft2EK5cSRPHT5/K187V1haiUaPScHW1Rc2aJWBm9kMQ5zbZi1EwCIUCzJvnUuDj2tqa8EI5Pbq6Ihw/3gM1a27Bw4ehGD78DDZtai9XAzk3EBEuXnyDlSv/h+vX36FpUxvcuDEQAGBiooOmTW1gaqqDr19TeKG8Z08nVKu2Cf7+kZg48WKuykdm5O3baIwadQ5XrqQ12Jk9uxHmzXPJU31zsViKN2+iceXKW0yadImJZDWEpISgW0H830VtvJ89tlFpI4zxH4OEyIRs94vwj8CJfieQEJnAhHIR4ZcSyra2tqhduzb27duHMWPGQFtbG5qamli3bh1SU1Nx/PhxVKlSBZMmTYKZmRmGDRuGZ8+eoXfv3qo2vdBJThajS5fDOH/+NQDg0KFnsLMzwciRNTFokFOulpgzimUPj1sK9zM01IKZmQ4voJs0KYO//nKGhoYA376lYPjwM4iKSkTTprtw7lwfFCv2c2tYExEePw7D7t2+OHjwGSLTfYHZ25ujSxd7jBhRM9+vsXfvU0yZclmu1jEAVK9uBVdXW7i62qFBA+ufVj6OUbRIX/kiI2XLmuDgwa5o02Y/9u59ilu3grBihSu6d1c+vCI5WYwDB/ywcuX/8OJF2jKvhgYHqZRw4oQ/UlIkSE2Von//qkhNleDMmZdITZV+3y7B2LG1MXv2DWzb9gQtWtiiZ88quTq++PgULFlyFytW3EdKigTa2kKsXt0SnTvbIyoqAUKhABoaAujpiTKJXKmU8PZtNJ4/j8Dz55+//45AQEAkUlJ+xHQzkcxQR4xKGzHxq4b8UkIZAPr3749p06Zh/Pjx+Pfff6GlpQVNTU1s2bIFAQEB2Lt3L/744w8YGBjA3t4ep0+f/i3bgE6Zchnnz7+Gjo4QvXpVwbFj/nj79gumTr2K+fNvw8trOCpVMld6PJlYrlDBDDduvEdkZAKiohIQGZmA6OhEEKUl8MXFJePduxgAwMWLbxAZmYCVK1tCXz+to1+HDgfh7R0GZ+cduH59AMqUMf4px5+SIkHfvsflEgmtrPTRr58jEhPFWL/eC4sW3cGiRXfg7t4EHh5N8/QavXodxYkTAQCAEiUM0Lq1HVxd7dC8edl8x7sy1BOZUL5/PwRisTRTWE+rVuVw4kRPjBt3AcHBsejZ8yi2bbPFjh1uOcYtBwXFoEWLvXjzJhoAYGCgicaNy+Dcude4fTsIt28H5WgfxwEDBlTD7t2+GDHiLBwcLPmOgjkRGhoHF5fdeP067fU1NdNCOUaPPo/Ro8/L7WtqqoO//nLGuHF1oKeniQ8f4tC69T48f644hlNXV4TKlS3QoUMFzJrViIlkBoNRKPwyQlmWjNevXz/cvHkTx44dg5GRERYuXAgtLS2IRCKsXLkSjRo1wqVLl9CtWzcA+C1FsjLkZaWf4zhMnFgPEyfKN2mRSKSIiUlCVFQiL6Bnz76Bp0/D+Qs6ANStWwr37w9F69b78PbtF7i47MaNGwMLXCynpEjQo8cRnDr1EiKRAF262GPgwGpwdbWDUCiAm9shuf29vcPy9Brdux/B6dMvoaWlgXnzmmLSpPo/rRkKQ31o3rwshEIB7twJxsCBJ7F7d6dMYtnNrRJcXe2wYsU9LF16D1euBKJKlQ1Yt64t+vZ1VOhdDgqKQdOmu/H+fQysrPTx55/1MGJETSxYcBvnzqWtHDk7W0NTUwMikeD7bw25/8+de43Pn+Px7VsKGjUqjTt3gtGy5V7cuzcEZcuaZHtckZEJcHXdi9evo1GqlCEaNrTGoUPPAaSFsWRsYx8dnYgZM67hn38eYPLk+ti82Rtv336BlpYGKle2gIODJRwcLODgYIEqVSxRpozxT21Lz2AwGIpQW6EcEhLCC2ATExMIBAKkpqZCT08PGzduRM+ePbFjxw6Eh4dj8+bN0NHRQUJCAvT19WFomPeSRL8KK1e2RGBgDM6ff42dO30AAHZ2Jvjjj1oYNMgJ5gVYbF1DQwAzs7RwiwoV0sruXbr0Fk+fhmfyWpcrZ4qbNwfBxWU33ryJhovLbly/PhA2NsYFZk9AQCSuXg2ElpYGTp3qhVatysk9nrG9b7Vqucv+zyiST5/ujZYt7fJtN+PXwNGxGA4f7oYePY7iwAE/AFAolnV1RXB3b4qePatg4MCTePgwFP37n8CJEwHYudNNrslNepFcrpwpbtwYyHufjYzS9hsxogY2b+6QrW1Pn4ajWrVNOHXqJXx9R6Jnz2N49uwzXF334u7dIVk2EImNTUKrVvvg7x+JUqUMcf36ADRpsgsAsH9/F/Tpk9YtUiolSCRSpKZKcezYC3h43EJgYNpKFgCULWv8U26OGdkTGxyrVOwsg/E7opZCeeHChTh06BAiIiJQu3Zt9OvXD7169YJIJIJEIoGxsTEOHz6MCRMm4PTp06hcuTIaNGiAd+/eQU9PD/b29jm/yC+OlpYQx4/34JP5hg6tjmbNyhaaxyYgIK1Wq6LwjlKlDHHjxkBeLJctuwbVqhVDlSqWcHS0/P67GKytDZWK2/TyCoWnZyjq1CmJOnVKomrVYrh4sR/i41MyiWQAMDOTF8pOTlZKHxcTyQxl6NzZXimxDKTNkXv3hmDZsrvw8LiF0NA4uQS/7EQykNbFEgBiYpJztKtq1WJwdrbG/fshOH48AJcu9UPDhjvw9u0XtG69DzdvDlLY/e/UqZd4/DgMFha6uHKlP7y9wxAW9g1WVvro1q0yv59AwEEgSPNk9+9fDb16VcGOHU+waNEd6Otr4sKFvkwkFzKxwbFYb78eqQmpOe4r0hWxjnWM3w61E8p79uzBokWLMHjwYAiFQty4cQN9+vTBs2fP4O7uzotlIyMjbNiwAW5ubti1axeeP3+OYsWK4cqVK7C2tlb1YRQJtLSE2LSpvUpeOzuhDPwQy1u2eGPBgtvw9Q2Hr698pQhDQy1UqWKJKlXSlmZFIg28fh2F16+jcfBgV7601fbtT7B5szdmz26EOnVKAgAaNsy6NXlePcpMJDNyQ27EslAowKxZjdG2bXno6Wny+7x6FYX69bcjOjpRoUgGfniUY2OTlLJr1KhauH8/BFu2eGPGjIa4fLk/GjbcAV/fcHTocBCXLvXLVIljwIBq+PYtBc7O1qhUyRxDhpzix8ou3Egk0sDIkbUwYkRNEIGFVqiAhMgEpCakovO+zrCwt8h23/zW9xXlsYJLYY1XWGMz1Au1E8ovXrxAqVKlMHfuXFhZWcHHxwfbtm3DkiVLEBMTg3///Zf3Murq6qJz587o3Lkzvn37BpFIxGKSiwAbN3ohNPQrBAIu24TBUqUM4eHRFIMHO8HP7zOePfvM/w4IiERcXDLu3w/B/fshmZ775k00qlVL8wQ7O1sjPDwe9jlcBGSk9yjr6YlgZ2ea43OYSGbkhdyIZQCoXr243P9OTpuQmCjOUiQDPzzKsbE5e5QBoFu3ypg48SJCQuJw/vxrdOhQEZcv90fjxjtx924wxow5j5073SAWS5GcLOZvSEePrg0gbQXnf//7AJFIgJEjlasaw3FcnvIiGAWHhb0FitconvOOeURTTxMz42cW2fEKa2yG+qE2QlnWiIKIEBkZidDQUFhZWcHJyQnTp0+HsbExFi9eDAsLC7i7u4OIMGbMGNSoUQPDhg2Dvv7PLTXGUI6NG7347Pdp0xooXMZNT1qDBhOULWuCjh1/tA5OSZHg1aso+PmF49mzz3j2LAISiRTly5uifHkzuVjKAQOqYcCAakrbKKspCwDVqlnl6OViIpmRH3IrlmUkJqZi4sR6KFnSAG5ulbKsiJFbj7K2thCDBzth5cr/YePGR+jQoSKqVi2Gs2f7YNSoc5g9uxGkUsKwYafx6lUUzp/vKzeP//33IQCgV68qP73EI4Pxq8I6+BUd1EYoy7zE/fr1w+rVq7Fr1y7UqFHje9OKUhgxYgTCw8OxaNEi1KpVCxoaGti0aROcnZ3Rq1cvJpSLAOlF8pQp9bFoUbM8j6WpqfE97EK5slW5Ib1H2ckp+7ALJpIZBUFexLKOjgiLFzfPcezcepQBYOTIWli58n+4ePEN3r37grJlTdCwYWn4+IyEhoYAgYFfcPr0S8TFJePhw1D+M//p0zccOvQMADBuXB2lX4/BYKTBOvgVPdRGKMtwdHTErFmzMH/+fJQuXRp//fUXAKB06dIYMWIELl68iDNnzmDevHlYuXIl2rVrx0RyESCjSF6+3LXIdptLH6MsC9+QIZUS3ryJhq/vJ/j4fMKVK4Hw8vrIRDIj32QUy0FBMahVqwRKlzaCtbXh999GsLLSz1Usb249ykBa9ZmWLe1w+fJbbN7sjaVLWwAAX7vY1tYEt24Ngr9/pNxnfssWb6SmSlG/finUrl1S6ddj/PqIk8Q43PUwAKDHsR4QaudPfhT0eIU1dk6wDn5FD7UTygAwYcIEvHv3DtOmTYNEIsGkSZOgpaWF2rVrw97eHs+fP4elpSX+/PNPVZvKQO5E8pcviQgIiEz3E4XY2CSIRBoQCgUQiQTff2f8XwCO4yAWS+V+JBLKtE1LSwNmZrowNdX+XrZOh/9taqqDpCQxb09yshibNz+Cr284fHw+4enTcMTHy2eHM5HMKCjSi+V790Jw717m+HuRSICSJQ158VysmB6MjLRhZKQFY2Nt/m/Zb1n94vj4VISFfUXx4gZK2TJqVC1cvvwW27c/wbx5TaGlJX+5cHQsBkfHHysuKSkSbNz4CAAwfnzdPJ4Bxq+KVCLF6++dYKUSaQ57F/54hTW2MrAOfkULtRTKJiYmWLFiBYRCIWbNmoWAgAB0794dJiYmiIqKgo2NDZKTk6GtnX38K+Pnc/Pme6VF8u7dPhgy5DSkUipME7Nl/PiLmbZpawvh6GgJJycrVKtWDK1alUO5cjkn/DEYytC5sz2ePBmJmzffIzg4FiEhcd9/xyI09CtSU6V4/z4G79/H5GHs//C//w1VajWnffsKKFXKEB8+xOHUqZfo0cMh2/1PnQrAp0/fUKKEAbp2ZSU4GQzGr4FaCmUAsLS0xOrVq+Ho6Ig5c+Zg//79MDIyglAoxJ49e5hILiI8ePABANC2bfkcwy1u3w6CVEowM9NBjRrFUamSOSpVMoe5uS7EYilSUyXff0sz/Z+aKgGQVkZLKBRAQ0OQ7m9ObntSkhhRUWmttaOiZD8JiIpKRHR0WvdAmVjX1RWhYcPScHIqBicnKzg5WaF8ebMcE60YjPyQVfy9WCxFWNhXOQEdERGP2Njk7z9JmX6nXwHx9AwFkXKdN4VCAVq0sMWuXT54+zY6x/1lXTZbtrSDSMQ6UDIYhQFL+vv5qK1QBgAjIyNMnDgRHTp0gKenJwDA2dkZNjY2qjWMkQkrKz2lY5KnTHHG9OkNf7JFWdOq1T5cvvwWQFq95UuX+qnMFgYjPUKhANbWabHKyiCVEjQ05uf59TQ0cp9HkJfnMBiM3MGS/goPtRbKMuzs7GBnx+JDGQwGg8Fg/PrkNukv6E7QT28o86vC1o+LGMnJyfDw8EBysvKlnFSNOtoMqKfdzGb14Fc75qtXr/0Sx/KrvS/K8MnnE8Ieh2X5o8zSvbKo8/lVR9uNShuheI3iMHUwxebTm2HqYIriNYrL/ZRpVIb3PG+puSXbn/X26xEbHFuox6AO5/2X8CgXFYjS4lrj4uLyPEZcXBzmzZuHYcOGwdBQcQOBokZ2NiclxQNIQkpKQo7nJSUlAUASkpLi83UOlSUru8XiRABJ/N+FYYuyqPPno2fPngB+zJOsKIh5pGqKyvuUFmsvXxYuLi5O6fJysjl5/fpNRERkfyy5meuqoqi8L/lBdm6VnUebmmyCFrLvSCvSEUGsJc73+5bd+U2JT0HS989iXFwcNCWa+Xqtgh4vve3aGtoFOvbPJrvzzhlz6P+wPxKjErMdI/JlJM6MOIPnl57DvGLWHXMLmvj4eGyatwludd2gp6dXaK/7Lf4bgJznEQBwpMxeDKX48OEDrK2tVW0Gg1GkCQkJQalSpbJ8nM0jBiNn2DxiMPJPTvMIYEK5QJFKpfj48SMMDAzy3EwjLi4O1tbWCAkJURuPhzraDKin3epsc3BwMDiOQ4kSJSAQZB31VRDzSNWo4/uUFexYihZEhK9fvxbJeaTO55fZrhpUZbuy8whgoRcFikAgyPHOJCe0tLTg7u4OCwsLaGllv1xWVFBHmwH1tFudbba0tFTK5oKYR6pGHd+nrGDHUvQwMso54UoV80idzy+zXTWo0nZl5hHAPMoMBoPBYDAYDIZCWNULBoPBYDAYDAZDAUwoMxgMBoPBYDAYCmBCmcFgMBgMBoPBUAATygwGg8FgMBgMhgKYUGYw1ByWj8tgMNQBdf2uUle7Zai7/aqGCeUiTPoPt1QqVaEluSM+Pl7VJuSK9OdZ3b5QpFIpOI5TG7vV+VwXBOo0j3Pid3z/GHlj/fr1eP78udrWRZfZrW7z98CBAwgMDFTb8y5D1d81TCgXYcLDwxEZGQkiyrEgdlFh7NixaNasGZKSknLeuYgQExODpKQkEJFaic6JEyeibdu2vN3qgLqe6/wQFhaG8PBwSKVSCAQCtbvYpic0NBTBwcEQi8Vq85nLiuvXr/8Wnz9VM2PGDIwfPx5+fn5qd76PHTuG5cuXY+PGjXjz5o3aXIcBYOrUqRg1ahQCAgLU7rxfunQJO3bswNmzZxEaGqr6awUxiiQLFy4kW1tbsrCwIGdnZ7p79y7FxMSo2qxsmT59OmlqatKCBQsoLi5O1eYoxdKlS6latWpUqVIl6tKlC71//55SU1NVbVaOTJ8+nUQiES1ZsoS+ffumanOUQl3PdX5YtmwZVaxYkaysrMjFxYUiIiKIiEgikajYstyzYMECsrW1JQMDA7K1taXnz5+r2qQ8c/DgQeI4jmbOnKmW74W6MH36dBIKhbRy5UqKjIxUtTm5wt3dnbS0tMjKyoq0tbXJ3Nycdu3aRUlJSao2LUdk53358uUUFRXFb5dKpSq0Sjnmzp1L2traZGBgQFpaWlSlShW6cuWKSm1iQrkIsn//fhIIBDRp0iQaN24cVa1alfT09Gju3LkUEhKiavMUMm3aNNLU1KS///6bPn/+rGpzlGLr1q0kEAhoxIgR1LlzZypRogQVK1aMduzYQdHR0ao2L0umTp1K2tratGrVKoXnuih+Garruc4P69evJz09PRo5ciSNGDGCSpcuTfb29hQfH69q03LN4cOHSUtLi6ZOnUozZsyg+fPnyz1eFD9z2XHq1CniOI44jqMJEyao2pxfkvXr1xPHcbR9+3b68uULv/3bt29F3ukTEBBApUuXphkzZlBoaCgdP36cOnbsSAKBgObMmVOkr3H//PMPcRxHW7ZskftuFYvF/HdPUZ2v3t7eZGFhQe7u7vT69Wtat24d1apVizQ0NGjr1q0qs4sJ5SLI9OnTqUKFCryn8PPnz9S/f3/iOI5Gjx5NoaGhKrbwB1KplAIDA4njOGrZsiUlJCQQEVFycjJt2bKFVq1aRd7e3kVSDA0ZMoTq1KlDYrGYiIgePXpErVu3Jh0dHVq+fHmR/DJ/+vQpcRxHvXv35relpKTQ4cOHadeuXRQSEkLJyckqtFAx6niu84pUKqXExERq0aIFtW7dmn8/jh07RoaGhir3juQFDw8PqlixIv8+paam0pUrV2jv3r306dMntVsZCAwMJHt7exo4cCDp6OjQmDFjiqx4UFe2bNlCenp6NG/ePCJKE8gtWrQgOzs7Kl26NG3dulVOQBclgoKCyMDAgBYsWMBve/fuHU2YMIEEAgEtWLCgSK5ExMfH08yZM8nCwoI2btzIb+vUqRPVqlWL6tatSxcuXFCxlVnj5+dHWlpatHnzZn6bp6cnde3alTQ0NGj37t1EVPgrckwoF0EWL15MGhoa5O/vL7f9jz/+II7jaOHChbwgLSosX76chEIh3blzh4iIKlWqxHtsdHR0aOLEifThwwcVWynP6NGjydzcXM5L/+XLF+ratSvp6enRvn37iKho3X0nJSXR2LFjSV9fn168eEFERA4ODvy5trS0pDVr1hQ54amO5zo/JCcnk4uLC9WuXZsXAyEhIWRhYUGjRo2inj170uHDhyksLEy1hirJggULyMTEhPdIOTo68p+5YsWK0dq1a4us6MkKJycnmj17Nq1cuZI4jqOJEycSEdHVq1eL3PxRJ65du0ZhYWEUGRlJw4YNo8qVK9OZM2eoadOmZGtrS927d6cmTZqQhoYGvzJRFOZ9ehvevn1L5cqVo7Zt28p9Z0VERNCYMWNIIBDQkSNHMj2vKPD06VNq1aoVubi4kKenJ9WtW5dKlChBzZs3pwoVKpBAIKBdu3YRUdGxXeYUfPr0KRkYGNCoUaPkVt6eP39Obm5upKGhQbdv3y50+5hQLkLIPrTXr18nMzMzGjZsGH369Elunz59+pCRkREfH6jqD7rszi4sLIw6d+5MxYsXp169elGzZs3owoUL9Pz5c17gr1+/Xu45qiC9t/Xw4cMkFApp4cKFFBsby2+PiIggV1dXKlmyZJHxhKe329/fn5ydnal+/frUvXt3atCgAR07dowuXrxIXbp0IR0dHTp37hwRqf7zIWP//v0kEonU4lwXFPPnzyehUEhTpkyhCxcu0IwZM4jjOHJyciIbGxsSiUS0YsUKIio671NGZHadOXOGjIyMaOXKlTR69GiqVKkSnTp1ii5evEidO3cmXV1dOnXqlNxzihKKbBo0aBDNnDmTUlJSaMmSJaShoUElSpQgR0fHIndTry7MmTOHD7cgIrp79y5VrlyZ6tevT87OzuTp6UlEaauk48ePJw0NDXr06JEqTeYJCwujiIgI/vq0adMm4jiOVq5cKReX/OrVK2revDnZ2toWmVDI58+fk6enJ339+pWIiG7evEkGBgbUvHlzatq0KXl5eRFRmse2c+fOpKenR69evVKlyTxjxoyh2rVr884/d3d3EolEdPToUbn9ZJ+lZs2aFXq8OxPKRYAHDx7IBdwTEY0YMYIEAgEtXbpU7rH379+TjY0Nubm5FbKV8iiy+dKlS1S3bl3S09OjxYsXyz3WoUMHqly5cmGamInNmzdT//79acCAAXTs2DGSSqU0cOBA0tPTo0OHDvETVSKR0O3bt8nY2JjGjBmjUpuJ5O0+fPgwpaam0rFjx8jGxoasrKxo3bp1/L6xsbFUt25datq0qQotJjp+/DitWLGCPDw86PXr10RE1LNnTzI0NCzS5zo/nDx5krZv306bNm3iQ0zGjx9PVlZWZGJiwscNfvz4kYiI2rVrR3Z2dkVudYgo7WYso10dO3YkCwsLaty4Mf3111/89ujoaKpVqxY1aNCgsM1Uil27dtGff/5JU6ZMofv371NiYiIREW3YsIHs7OwoJSWFIiIiyNHRkYRCIbVp04Z//xjKI0sgK1asGHXq1InffuzYMeI4jipVqiQX2+vt7U0GBga8qFYl6ZPn69WrR3fv3qUPHz7Q2LFjSSQS0ZYtW+Tmw8aNG0kkEtG1a9dUaHUa7u7uZGxszK8oLl++nIiIdu7cSRzHUf369eVWSM6dO0e6urp0/vx5VZnMk74AgMzGN2/ekKurK+nq6tKlS5fk9p89ezbp6+vT06dPC9VOJpRViEQioTNnzhDHcZkEMRGRm5sbaWtr08KFC3kPR3JyMrVs2ZIaNWqkCpNztHnu3LlkaGhI9+/fl9s+fvx4qlKlSmGaKseCBQtIIBBQtWrVyNzcnLS1tWny5Mn07t07atq0KVlaWtK+fft4r2Z8fDxVq1aNunTpojKbFdmtq6tLkydPJiKiUaNGEcdx5O3tze8vlUqpe/fu5OzsrCqTyd3dnfT19alUqVLEcRzZ2trSrVu3KCIigurVq0fFihUrkuc6P8ydO5d0dHTI0NCQOI6jatWq0cGDB4koLcRk48aNZGVlJfcF/8cff5CdnV2RW+Y/dOgQaWtr06FDh+SWP0NCQqhJkybEcRx1796dXxmQSCTUpUsXcnJyUpXJWeLh4UECgYBsbW1JU1OTTE1NeQ/hrVu3qEyZMhQVFUUDBgwgExMTGjhwIGlra9OQIUNUbbpaMWPGDBIIBLRq1SqaPHky1apVS+7xtWvX8isOMu7du0eWlpb033//FaapmVCUPK+vr0/u7u509epVGjhwIAmFQlq8eDG9ffuWiNLCc0xNTens2bMqtf2///4joVBI06ZNo927d1OvXr1IIBDQ1q1bSSwW09SpU+nMmTNyz7l69SpZWFjQ5cuXVWR1GtkVAHjw4AHVq1ePtLW1ad++ffx35NGjR8nMzIzu3btXqLYyoaxiQkNDSSQSUYUKFWj58uVycX7x8fHUpUsX/sJ0584devDgATVo0IDatWtHycnJKlnmzM5mIuLFwLt37ygsLIz8/PyoYcOG5OLiQomJiYVuc3h4OFWoUIHGjBnDizM3NzcyMjKiO3fu0KtXr6hRo0akq6tLf/31F/n6+tLTp0+pdu3a1LdvX0pNTVXJec7Kbn19fX658uHDh/y+MTExFBAQQPXr16dOnTpRSkpKodsdFBRExYsXp6lTp5K/vz/5+flR8eLFeRH88uVLatGiBWlpaRWpc50fXr16RVZWVjRr1izy9vammzdvkr29Penr69OsWbMoPj6erwDg5eVFSUlJ9OzZM2rcuDG1bt26yFXBePz4MXEcR7Vq1aLDhw/znjSxWEz379+nZs2aka6uLi1fvpwCAwPJx8eH6tatS61ataKkpKQi8/4FBgaStbU1TZkyhUJCQigpKYmqV69OZcqUoaCgICIiqlKlCunr65OJiQlduXKFPn36RHPmzCEzMzO1iR9XNWPHjiUNDQ36+++/iYjIy8uLtLS05ISYLJzhwoULtHTpUlqxYgW1bduWrKys+PdCVShKnu/Xrx9xHEdjxoyh27dv06RJk4jjOKpTpw7179+fGjduTCVLllR56MXcuXOpbNmy/DU4KSmJ6tatS9WqVSMi4ldPLl++TAcOHKAdO3ZQ27ZtqVSpUiorCpBdAYCNGzfS2rVr6cmTJ3TmzBnq1asXcRxHnTp1osmTJ1OzZs3IxsYmU0jqz4YJZRVz+fJl4jiOhEIhmZmZ0YoVKzIJzz///JPKli3LL62Ym5vTs2fPVGMwKWezl5cXCYVCsrS0pAoVKpClpSX5+fmpxN6goCC+nJqMN2/eEMdxtGjRIiJKC1no06cPGRsbk4aGBllbW5OpqalKa8VmZ7e7uzu/7f79+2RiYkKVK1cmR0dHlX4+/P39ieM4OnDgABGliat+/fqRnZ0dXbx4ka5cuUIfPnygcePGkb6+fpE51/nhyZMnJBAIaP/+/fy2+Ph4atGiBRkYGNDChQvp+fPnVK1aNSpevDg1atSIqlWrRsWKFSuSxyxbKuc4juzs7OjIkSO8mJdIJPTu3Tvq2LEj6enpkbGxMZUuXZpMTU1V+p2kiGfPnpFAIOA9+0REd+7cIY7jaN26dSQWi2n27NnUokULOnv2LB9uERkZyde7ZmRPYmIidevWjRYuXMh7/V6+fEnFihWjZcuWEdEPkfzt2zcaOnQocRxHFhYWVKNGjUJfQldEVsnzI0aM4K8RCQkJdODAAWrSpAkfJ1sUbF+1ahWJRCIKCAjgt40cOZKEQiHvpY2KiqLWrVsTx3FkYmJC9vb2RcL27AoAGBgY0J9//kne3t60adMmsrW1pdKlS1OtWrVUYjsTyipELBZTt27dqEyZMrRp0yZycnIiQ0NDhcLTz8+PDh8+TIcOHVLpHXhubJ46dSqNGjWKZs6cSW/evFGNwd9p2rQplStXjr97/fDhA59olZ4rV67Q2rVr6Z9//uGX2VSJIrtFIpFcjOjHjx9p4MCB1KFDBxo9ejS9fPlSVeZSYmIilStXTq4sWsOGDUlXV5f09fVJR0eH3NzcSCwW0+3bt2ndunVF5lznlbi4OKpUqRK1atVKLukyMTGRmjRpQiYmJrR//366e/cu9ejRg5o0aUJDhw5V6fuUFSkpKXyi0rx586hMmTJUpkwZOnLkSKaY5X379tG8efNo4cKFKp/fikhOTiYHBwdq0KABn4zl6+tLHMfxcZxfv36lt2/fspjkfCCRSHjPpYxhw4ZRyZIlFXrl/fz86O3btypvQKJM8nzv3r3J0NCQvwmMjo6m1NRUPmlOVchsv3nzJolEIurRowe/6uju7k4cx1FgYCC//+fPn8nb25u8vb0pPDxcJTbLUKYAgOwmRZZ/8+HDB5XW32ZCWYUkJSXRsmXLyNPTk1JTU8nb25uqV69ORkZGCoVnUUDdbJZ9oaxdu5ZMTU355cCwsDDiOI7mzp1LRGmTNyIiItMXvqpQ1m4ikosTV3VtT7FYTDNnzqS2bdtSaGgoBQUFUZMmTWjGjBl05coVGjt2rFxW/K+AWCym6dOnk4mJCS1evFiupnBsbCyVLVuWWrVqxW+TSqVFVpglJCTQ+PHj6X//+x99/fqVTxq1sbFRKJaLMhKJhCZOnEgWFhZ8zkRAQABxHEerV6/m94uIiKCUlBRVmamWHDp0iJYuXUqLFi2iwMBA/vzJbhT/++8/0tXVpfnz5xe5c5uX5PkOHToQEal83iqyfdq0abRv3z7+mtGnTx8yMTHhQzNfvXpFu3btUnn/hdwWAGjfvj1VqlSJiFR/XWNCWUXIJpxEIuE/4BKJhLy8vBQKz6IQ95dXm1Vpu2yCJSQk0OPHj/ntT548IY7jaMOGDUSUtkzbpUsXGjlypNzxqQpl7fbz86NOnToVCbtln4+EhAQ++TQ1NZXCw8P5pfvU1FQyMDCgadOmqczOgkR2zDExMVS3bl0yNzen9evXy4nlffv2EcdxKk+eyQmZzRKJRO7zd+TIETmxLLuZLArzOytk70tcXBzdvn2bt1EWNiYLx/Dz86NBgwbRwoULi+RxFEXmzZtHWlpaVLx4ceI4jmxsbGju3LkUFxcnt1+jRo2ofPnyfNUbVYsddUyel5GT7elxdXWlqlWrElFaKFyLFi2oTJkyKou5V9cCAOlhQrmQCA4OpvDwcLlasYq+OKRSKS88zc3Naf78+SpbbvhVbFbUNezChQvEcRydP3+eQkJCqG3btqSjo6Oy2C11tFtZm9OvMty7d49sbGzkStqpE48ePSIfHx+5eHuZxywiIoLKly9P5ubm5O7uzou1w4cPk76+Pp94WVRQdCzpxaLs78TERF4slytXjvbt21dkVl5kKDoWRZ/F3bt3E8dxdP/+fQoKCqK2bduSpqZmkYwVL4qEhoaSjY0NTZkyhd6+fUuBgYHUtGlTMjY2pl69esl979+6dYv09fWpW7duKrRYHnVMns/JdplNYrGYkpKSqFatWlS/fn16+fIltWzZkgwMDOScLapA3QoAZIQJ5UJgwYIF5ODgQJaWltSuXTu55BJFSKVSevToEZUtW5YvX1TY/Oo2X7lyhTiOIw8PD+rUqRPp6+vTkydPCs/YdKij3cra/PLlS5o4cSKNHj2aVq9eTc2bN6fixYvTu3fvCtfgAmDu3Ll8aUFTU1O5pULZknNkZCQ1btyYjI2NqUaNGjR58mRq3LgxWVtbq3zpMz3ZHYuii1JiYiLfgrtq1aqZvIeqJDfHcuDAAeI4jjZu3Ehdu3YlAwMDlc17deTt27ekqalJK1eu5LclJyfT8OHDydjYmAYMGMDH70ZGRtKff/5JHMfRzJkzVWWyHOqYPC8jJ9tln/X+/fuToaEhtWjRgnR1dcnHx0dFFv9A3QoAZIQJ5Z/M7t27SVtbm0aNGkXjxo2jKlWqEMdxNGvWrGxjt6RSKT1+/FguIL+w+JVtln2ZfPr0icqVK0ccx5Genp7KLpbqaHduPh/fvn2j1q1bk7a2NllaWlKdOnWKzJdfbjhw4ABpa2vTzJkzaf369TRgwADiOI7+/fdffh+ZBzM2Npb+/vtvcnZ2pnLlylHDhg2LRJa5DGWORREJCQl06tQpfim9KKDsscjmz5MnT8jExIQ0NTVVOu/VmQYNGpCTk5PcqkJycjINGjSIDA0Nafny5fxcePHiBbVs2bJIzHl1TJ6XoYztss/4hg0biOM4MjU1LRIiWV0LAKSHCeWfzLRp06hcuXJ8fNCTJ0/4XvFjxowhqVSq8titjPwuNvfv35+MjY1V6i1QR7tza3Nqaiq9efOGnjx5ovJM97zi7u5ONjY2fOyiv78/Va1alRwcHOjTp09ZJuiFh4fz9VmLCnk9lqJIbo8lNTWVGjZsSBzHsXCLPLJp0yYyMjKi4cOHy7V2TklJoXr16pGjo6PcikP6fVSJuiWipye3to8YMaLI3Jyr83mXwYTyT0J2dzd16lQyNjaW62cfEhJCs2bN4pfQZfuPHj1apdUAfheb//jjDzp27BglJSWprGC8OtqdF5tHjhxJu3btKnRbCwrZMc+dO5eKFStGXl5e/GN//PEHmZub8y2pidJi+N3d3YtEe9iM5OVYPDw86MKFC4Vua07k5VjmzJlDXl5e9PXrV5Wseqk76esh9+rVi0xNTWnKlClyQvju3bvEcRwdOXJEVWYqRB2T52Xk1vaihDoWAFAEE8o/madPn5JQKKSxY8fKvflBQUE0bNgwEolEdPbsWT5Jy9nZWeU1Gn8Hm+vUqVMkSl2po925tblBgwZFKqY1L8jq7w4aNIg/lvHjx5O+vj69ePGC32/dunXEcRx16dJF5e9TVuTlWIpa8p6M3B5Lhw4dily5sqKKomRd2bn78uULtWzZkkxNTal///78Z/3y5ctkYGBAly5dUonNMtQxEV0Gs73owYRyISArAC4rci/j4cOHVKpUKRo5ciR9+vSJVq1aJddhR5UwmwsPdbRbHW3OL//++y/t2bOH95J07dqVrK2t+S/4Fy9e0MKFC2n16tVF/pjZsTCyI7tk3fQlEQcOHEgmJiZkY2NDffv2JWdnZ7KysqLg4GBVma6WiegymO1FEyaUC4Ho6Gg+yWTJkiVyS1Wurq7UsGHDIrfUwGwuPNTRbnW0Ob9kjNtt3Lgx1axZk1JTU+nFixd8m1hV1SvNDexYGFmhTLKu7JzHx8fT8ePHqWPHjuTk5EStWrVSaeKeOiaiy2C2F12YUC4kwsPDaciQISQQCGjgwIF09uxZunfvHtWoUaPILm0ymwsPdbRbHW0uSOrXr08NGzakZ8+eUbt27cjQ0FDl9UrzCjsWhoy8JnN//fpV5Yl76piILoPZXnRhQrkQiYmJob///pv09fX5eoLFihUrEjUas4LZXHioo93qaHN+kUqlJJVK+RjN+vXrq22pMXYsDBl5SdYdNWoUbd26VSX2pkcdE9FlMNuLPkwoq4A3b97Q/v37af/+/WrTeIHZXHioo93qaHN+OXnyJHEcR9ra2uTr66tqc/IFOxaGjLwk66o6mVuGOiaiy2C2F12YUGYwGIw8EBMTQ1OmTCF/f39Vm5Jv2LEw0qPOybrMdtWgzrbnBBPKDAaDkUdkHch+BdixMGSoc7Ius101qLPtOSEEg8FgMPKEUPjrfIWyY2HIMDExwYoVKyAUCjFr1iwEBASge/fuMDExQVRUFGxsbJCcnAxtbW1Vm5oJZrtqUGfbc4IjIlK1EQwGg8FgMIoWsbGx2LlzJ+bMmYOkpCQYGRlBKBTi2rVrcHBwULV52cJsVw3qbHtWMKHMYDAYDAYjS96+fQtPT08AgLOzM2xsbFRrUC5gtqsGdbY9I0woMxgMBoPBYDAYChCo2gAGg8FgMBgMBqMowoQyg8FgMBgMBoOhACaUGQwGg8FgMBgMBTChzGAwGAwGg8FgKIAJZQaDwWAwGAwGQwFMKDMYDAaDwWAwGApgQpnBYDAYDAaDwVAAE8oMBoPBYDAYDIYCmFBmMBgMBoPBYDAUwIQyg8FgMBgMBoOhACaUGQwGg8FgMBgMBTChzGAwGAwGg8FgKIAJZQaDwWAwGAwGQwFMKDMYDAaDwWAwGApgQpnBYDAYDAaDwVAAE8oMBoPBYDAYDIYCmFBmMBgMBoPBYDAUwIQyg8FgMBgMBoOhACaUGQwGg8FgMBgMBTChzGAwGAwGg8FgKIAJZYZaYmNjg/bt26vaDAZDrUlISECTJk2gqamJ0aNHg4jQrVs3aGlpoW3btqo2j8FgMFQOE8oMBoPxm3Ls2DHcvn0bo0aNQo8ePXD37l0cO3YM3bp1w5gxY/I9vr6+PgYNGpR/QxmMX4SMc+Lo0aPgOA43b95UmU2M7GFCmcFgMH5TPn/+DACYNGkSmjZtyv8/fPhwtGvXTpWmMRgMJUhKSlK1Cb88TCgzijS7du2Cg4MDtLW1YWdnh7Vr1yrc78mTJ2jYsCG0tbVRvHhxzJkzB2KxGABw8+ZNcByH9evXo2bNmtDS0oKDgwOuXbsmN8ahQ4dgZ2cHbW1tNGzYEC9fvvzpx8dg/Gzi4+MxZswYmJmZQVdXF23btsW7d+8waNAgTJkyBQBQtmxZeHh4oFu3bgAAFxcXDBo0CGKxGNOmTUOpUqWgo6OD2rVr4+rVq/zYiYmJGDlyJIyMjGBqaooJEyYgNTUV79+/B8dxiI+Px+7du8FxHN6/f6+Kw2cw8kRW1x4iwpIlS1CyZEno6emhQYMGuH37Nv+8p0+fomXLljAyMoKxsTE6deqE0NBQhXNi0KBB6N69O4C0Ode0aVMAWc8rmV0cx+HkyZOoXr06nJ2dC/fE/I4Qg1FEuXfvHgGgrl270ubNm6lDhw4EgE6fPk1lypShdu3aERHRq1evSEtLi+zt7envv/+mUaNGEcdxNHToUCIiunHjBgEgHR0dGjduHP3zzz9UsWJFEgqF5OPjQ0RE//33HwmFQpowYQJt2LCBnJ2dycTEhN6/f6+y42cwCoJmzZqRpqYmTZs2jZYvX04lS5Ykc3Nzun//PvXu3ZsA0N9//02+vr40fvx4AkAzZ86k+/fv06JFi/j/165dS46OjqStrU2fP38mIqI2bdpQmTJlaPXq1bRw4UIyMTGhzp0707dv32jv3r2kpaVFjRo1or1799K3b99UfCYYDOXI7tozc+ZMAkAjR46kTZs2kZOTE+np6dHbt28pISGBihUrRqVLl6a1a9fSqlWryNTUlFxdXRXOCR8fH7k5d/nyZSLKel4REe3cuZMAkJmZGU2cOJGOHz+uylP1W8CEMqPIcurUKQJAa9euJSKi+Ph4OnjwIPn4+MgJ5cGDB5O2tjZ9/PiRf+7YsWNJIBBQYGAgL5THjBnDPx4SEkKampo0aNAgIiKqWLEiDRgwgCIiIigiIoLev39POjo6NHny5EI8YgajYLl16xYBoNWrV/PbHj16RADIw8ODVqxYQQDo3bt3RER05MgRAkA3btwgIqJJkyaRQCCgJ0+eEBHRu3fv6ODBg/Tx40f63//+RwDowoUL/LxZs2YNASA/Pz8iItLT06OBAwcW4hEzGPknu2uPnp4e9erVi9/3zZs31KRJEzp58iR9+PCBJk+eTHfv3uUf79KlC9nY2PD/Z5wTGedcTvNKJpTHjRv3c08Cg4eFXjCKLM2bN0fNmjUxfvx4WFtbY9iwYdDS0kLVqlXl9vP19UXt2rVRvHhxfpubmxukUikePXrEb2vWrBn/d6lSpVC7dm34+fnh69evePnyJfbs2QMLCwtYWFjAxsYGiYmJ8PX1/fkHymD8JGSfXzc3N35bzZo1UbJkSTx8+DDH5w8ZMgSmpqaoUaMGHB0dsWLFCpQrVw7Fixfn51abNm34eTNhwgS512Uw1JGsrj0CgQDx8fFo06YNv6+dnR1u3rwJNzc3lCxZElOmTIGfnx/Gjh2Lxo0b48SJEyAipV9b2XmV3gbGz0WoagMYjKzQ09ODp6cnrl+/jmvXruHs2bM4ePAgFi5cKLefoi8hZbYJBAKIxWJIpVIAwIABA9C/f3+5fUxNTfN7GAyGysjqAq3shbtKlSoIDAzE+fPncePGDRw7dgybNm3CjRs3IJFIAAD79++HpaVlpucxGOpKTtce2TUDSJtLsbGx0NbWRkhICOrUqQNbW1v069cPnTt3xooVKxAQEKD0a+c0ry5evAgAsLCwyO9hMpSEeZQZRZZTp06hZ8+ecHR0xNKlS/H06VOUK1cOZ8+eldvP0dERXl5eCAsL47edPn0aHMehRo0a/LYLFy7wf0dERMDLywv29vYwMjJC8eLF8enTJ7Ro0QItWrRAgwYNsGXLFrx58+bnHyiD8ZNwdHQEkDaXZHh7e+Pjx4+oVatWjs9fvHgxpkyZgp49e2LTpk3w8vKCVCrFhQsXYG9vz+8nmzdSqRTr16+HhoZGwR8Mg1FIZHXtOXr0KEQikVxC6927d2FiYoLjx4/jxIkTiImJwcGDBzFp0iQ0b94cUVFRuXptNq+KHsyjzCiyWFpa4vjx4/j8+TMGDBiA4OBgBAUFwdXVVU4UT58+HQcPHkTz5s0xYsQIvHr1Cps2bUL//v1hZ2eHkJAQAMCePXugoaEBBwcHbNu2DcnJyXyt2KlTp2LSpEno168fGjdujMOHD+PBgweZvNcMhjrh4uKChg0bYvr06QgPD4eZmRnWrFkDExMTjB8/Hjt37sz2+RoaGtiyZQtSU1PRqFEjXL9+HQBQvXp1uLq6onr16hg9ejTev38PbW1tLF26FE5OTry3y8DAAPfu3cOuXbvQtWtXGBgY/PRjZjDyS1bXnmHDhqFx48ZYu3YtDAwMUKVKFaxevRolS5ZE+/bteWfMjBkz0LZtW1y/fh2PHj2Cvr4+rl27hubNm2c5J3bt2oXk5GS0bNkyx3nFKGRUGB/NYOTIgQMHyMHBgbS1talEiRI0YcIESkpKkkvmIyLy9PSkevXqkaamJllaWtK0adMoOTmZiH5UvVi6dCk1bdqUtLS0yNramg4cOCD3WmvWrCFra2vS1tamevXq0e3btwv1WBmMn0FsbCwNHz6cjI2NSUdHh1xdXenVq1dERDkm84nFYpo1axZZW1uTlpYWVapUiTZs2MCPHRERQT179iR9fX0yMzOjQYMGUXR0NP/4ypUrSV9fn7S0tCg4OLjQjpnByC9ZXXtSU1Np9uzZZGlpSfr6+uTi4kJPnz4lIiKJREKjRo0iIyMjsrS0pB49etDff/9NhoaGfOJ4xjmRkJBATZo0IaFQSG3atCGi7OeVLJnPy8tLNSfmN4QjykWUOYOhhty8eRMuLi44cuQIXyeWwWAwGAwGIydYjDKDwWAwGAwGg6EAJpQZDAaDwWAwGAwFsNALBoPBYDAYDAZDAcyjzGAwGAwGg8FgKIAJZQaDwWAwGAwGQwFMKDMYDAaDwWAwGApgQpnBYDAYDAaDwVAAE8oMBoPBYDAYDIYCmFBmMBgMBoPBYDAUwIQyg8FgMBgMBoOhACaUGQwGg8FgMBgMBTChzGAwGAwGg8FgKOD/6DYiQi5R8XkAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 760x760 with 9 Axes>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 760x760 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from ultranest.plot import cornerplot\n",
     "cornerplot(sampler.results)"
@@ -384,20 +280,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.xlabel(xlabel)\n",
@@ -443,30 +328,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Posterior probability')"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x250 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "scatter_samples = result['weighted_samples']['points'][:,2]\n",
     "weights = result['weighted_samples']['weights']\n",
@@ -491,27 +355,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1186, 30)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "result['weighted_samples']['bootstrapped_weights'].shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,20 +373,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "bins=np.linspace(0.01, 0.2, 64+1)\n",
@@ -601,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -639,49 +481,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n",
-      "[ultranest] Widening roots to 457 live points (have 400 already) ...\n",
-      "[ultranest] Sampling 57 live points from prior ...\n",
-      "[ultranest] Widening roots to 520 live points (have 457 already) ...\n",
-      "[ultranest] Sampling 63 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "edbc65e2aed04b56b395d92f59895bde",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=2e+01  21.9772..21.9772]*| it/evals=4576/6329 eff=76.7086% N=400     0 0 \n",
-      "[ultranest] Likelihood function evaluations: 6374\n",
-      "[ultranest]   logZ = 15.1 +- 0.0855\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 1620.4, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.08 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.09, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.12 bs:0.09 tail:0.01 total:0.09 required:<0.50\n",
-      "[ultranest] done iterating.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sampler0 = ultranest.ReactiveNestedSampler(parameters0, log_likelihood0, prior_transform0)\n",
     "result0 = sampler0.run()"
@@ -701,18 +503,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AIC of constant model: -41\n",
-      "AIC of line model    : -80\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Lmax0 = result0['weighted_samples']['logl'].max()\n",
     "AIC0 = -2 * Lmax0 + len(parameters0)\n",
@@ -746,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -755,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -766,38 +559,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n",
-      "[ultranest] Widening roots to 413 live points (have 400 already) ...\n",
-      "[ultranest] Sampling 13 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "58fcef30127e463ab57f0bb4bb632a2a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Z=12.5(0.00%) | Like=22.41..40.30 [14.7169..22.4322] | it/evals=2993/6028 eff=53.0721% N=400 0   0 0   \r"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Kpredicts = []\n",
     "\n",
diff --git a/docs/example-warmstart.ipynb b/docs/example-warmstart.ipynb
index 054ecff6..9c34227d 100644
--- a/docs/example-warmstart.ipynb
+++ b/docs/example-warmstart.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,20 +98,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -139,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,20 +141,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.title(\"Prior predictive checks\")\n",
@@ -191,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,57 +181,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "74a0712aa182456fa12822d7251d85ef",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=2e+02  7 [173.9047..173.9128]*| it/evals=6080/46921 eff=13.0694% N=400    400  400 400 \n",
-      "[ultranest] Likelihood function evaluations: 46953\n",
-      "[ultranest] Writing samples and results to disk ...\n",
-      "[ultranest] Writing samples and results to disk ... done\n",
-      "[ultranest]   logZ = 159.8 +- 0.1527\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 981.1, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.06 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.43, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.19 bs:0.15 tail:0.41 total:0.43 required:<0.50\n",
-      "[ultranest] done iterating.\n",
-      "\n",
-      "logZ = 159.705 +- 0.538\n",
-      "  single instance: logZ = 159.705 +- 0.186\n",
-      "  bootstrapped   : logZ = 159.775 +- 0.353\n",
-      "  tail           : logZ = +- 0.406\n",
-      "insert order U test : converged: True correlation: inf iterations\n",
-      "\n",
-      "    Temperature         : 0.00945│ ▁▁▁▁▁▁▁▁▂▂▂▂▄▅▄▅▅▇▇▅▆▅▅▃▃▂▂▂▁▁▁▁▁▁▁ ▁ │0.01035    0.00989 +- 0.00012\n",
-      "    Amplitude           : 37.2  │ ▁▁▁▁▁▁▁▂▂▃▃▅▄▅▆▆▇▇▆▆▄▄▃▃▂▂▁▁▁▁▁▁▁▁▁▁▁ │59.7      47.4 +- 2.7\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from ultranest import ReactiveNestedSampler\n",
     "\n",
@@ -272,20 +202,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -310,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,45 +290,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Sampling 400 live points from prior ...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0eb4138742fd484683ad88f6385be5fa",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value=''), GridspecLayout(children=(HTML(value=\"<div style='background-color:#6E6BF4;'>&nb…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ultranest] Explored until L=2e+02  5 [169.4613..169.4636]*| it/evals=1600/5358 eff=32.2711% N=400 \n",
-      "[ultranest] Likelihood function evaluations: 5393\n",
-      "[ultranest]   logZ = 166.7 +- 0.0473\n",
-      "[ultranest] Effective samples strategy satisfied (ESS = 763.2, need >400)\n",
-      "[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.08 nat, need <0.50 nat)\n",
-      "[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.41, need <0.5)\n",
-      "[ultranest]   logZ error budget: single: 0.08 bs:0.05 tail:0.41 total:0.41 required:<0.50\n",
-      "[ultranest] done iterating.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized=vectorized)\n",
     "res = sampler.run(frac_remain=0.5)"
@@ -417,20 +300,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.errorbar(x=wavelength, y=y_obs, yerr=sigma, marker='x', ls=' ')\n",
@@ -460,17 +332,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Speed-up of warm-start: 770%\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"Speed-up of warm-start: %d%%\" % ((results_ref['ncall'] / res['ncall'] - 1)*100))"
    ]
@@ -530,20 +394,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "posterior_samples = results_ref['samples']\n",
     "\n",
@@ -563,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -573,20 +426,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.subplot(2, 1, 1)\n",
     "plt.plot(uguess, pguess[:,0])\n",
@@ -607,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -616,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -625,17 +467,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████| 6505/6505 [00:00<00:00, 12544.08it/s]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "nparams = len(parameters)\n",
     "u = np.ones(nparams) * 0.5\n",
@@ -670,20 +504,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "weights = results_ref['weighted_samples']['weights']\n",
     "i = np.random.choice(len(weights), p=weights, size=1000)\n",
@@ -719,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,26 +559,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# weight logl Temperature Amplitude\n",
-      "0.000154 0.000000 0.009828 0.584218\n",
-      "0.000154 0.000000 0.009933 0.582950\n",
-      "0.000154 0.000000 0.009911 0.583998\n",
-      "0.000154 0.000000 0.010134 0.580971\n",
-      "0.000154 0.000000 0.009717 0.585696\n",
-      "0.000154 0.000000 0.009823 0.584407\n",
-      "0.000154 0.000000 0.009729 0.585907\n",
-      "0.000154 0.000000 0.009938 0.582713\n",
-      "0.000154 0.000000 0.009849 0.582950\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "!head custom-weighted_post_untransformed.txt"
    ]
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 64bc2c06..7795170d 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -1,11 +1,5 @@
 # noqa: D400 D205
-"""
-Performs nested sampling to calculate the Bayesian evidence and posterior samples
-
-Some ellipsoid code is adopted from the Nestle library by Kyle Barbary (https://github.com/kbarbary/nestle)
-Some of the architecture and parallelisation is adopted from the nnest library by Adam Moss (https://github.com/adammoss/nnest)
-Some visualisations are adopted from the dynesty library by Josh Speagle (https://github.com/joshspeagle/dynesty/)
-"""
+"""UltraNets performs nested sampling to calculate the Bayesian evidence and posterior samples."""
 
 from .integrator import NestedSampler, ReactiveNestedSampler, read_file
 from .utils import vectorize

From f0fb12a26a6d7240c703d376c7cf9b151923d4ab Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 29 May 2024 23:27:20 +0200
Subject: [PATCH 270/313] rst formatting

---
 README.rst | 31 ++++++++++++++++---------------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/README.rst b/README.rst
index fdccaa4f..fc085893 100644
--- a/README.rst
+++ b/README.rst
@@ -137,18 +137,19 @@ ready to defend against any encountered danger.
 
 Contributors
 ^^^^^^^^^^^^
- * Nicholas Susemiehl
- * QZ Gao
- * Sigfried Vanaverbeke
- * Warrick Ball
- * Adipol Phosrisom
- * Alexander Harvey Nitz
- * Gregory David Martinez
- * Grigorii Smirnov-Pinchukov
- * Fabio F Acero
- * Jacopo Tissino
- * Benjamin Beauchesne
- * Kyle Barbary (some ellipsoid code adopted from https://github.com/kbarbary/nestle)
- * Adam Moss (some architecture and parallelisation adopted from https://github.com/adammoss/nnest)
- * Josh Speagle (some visualisations adopted from https://github.com/joshspeagle/dynesty/)
- * Johannes Buchner
+
+* Nicholas Susemiehl
+* QZ Gao
+* Sigfried Vanaverbeke
+* Warrick Ball
+* Adipol Phosrisom
+* Alexander Harvey Nitz
+* Gregory David Martinez
+* Grigorii Smirnov-Pinchukov
+* Fabio F Acero
+* Jacopo Tissino
+* Benjamin Beauchesne
+* Kyle Barbary (some ellipsoid code adopted from https://github.com/kbarbary/nestle)
+* Adam Moss (some architecture and parallelisation adopted from https://github.com/adammoss/nnest)
+* Josh Speagle (some visualisations adopted from https://github.com/joshspeagle/dynesty/)
+* Johannes Buchner

From c2178410eedfc7c9c6ae58bb8542292c8ab7d76e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 29 May 2024 23:34:15 +0200
Subject: [PATCH 271/313] rst formatting 2

---
 README.rst | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/README.rst b/README.rst
index fc085893..0189e309 100644
--- a/README.rst
+++ b/README.rst
@@ -116,13 +116,11 @@ Usage
 
 * `Get started! <https://johannesbuchner.github.io/UltraNest/using-ultranest.html>`_
 
-Read the full documentation with tutorials at:
+* Read the full documentation with tutorials at:
 
-* https://johannesbuchner.github.io/UltraNest/
-
-* `API Reference: <https://johannesbuchner.github.io/UltraNest/ultranest.html#ultranest.integrator.ReactiveNestedSampler>`_.
-
-* `Code repository: https://github.com/JohannesBuchner/UltraNest/ <https://github.com/JohannesBuchner/UltraNest/>`_
+  * https://johannesbuchner.github.io/UltraNest/
+  * `API Reference: <https://johannesbuchner.github.io/UltraNest/ultranest.html#ultranest.integrator.ReactiveNestedSampler>`_.
+  * `Code repository: https://github.com/JohannesBuchner/UltraNest/ <https://github.com/JohannesBuchner/UltraNest/>`_
 
 Licence
 ^^^^^^^
@@ -139,7 +137,7 @@ Contributors
 ^^^^^^^^^^^^
 
 * Nicholas Susemiehl
-* QZ Gao
+* Quinn Gao
 * Sigfried Vanaverbeke
 * Warrick Ball
 * Adipol Phosrisom

From 6c49a7e17ba0615c822bbbc62b294d402e1af1fa Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 29 May 2024 23:42:17 +0200
Subject: [PATCH 272/313] added one more contributor

---
 README.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.rst b/README.rst
index 0189e309..9cae47a8 100644
--- a/README.rst
+++ b/README.rst
@@ -141,6 +141,7 @@ Contributors
 * Sigfried Vanaverbeke
 * Warrick Ball
 * Adipol Phosrisom
+* Pieter Vuylsteke
 * Alexander Harvey Nitz
 * Gregory David Martinez
 * Grigorii Smirnov-Pinchukov

From 766392cba653ff2e6ce32a9bccbb1bfbb66f70ca Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 30 May 2024 00:05:05 +0200
Subject: [PATCH 273/313] doc typo

---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 9cae47a8..3b928ebd 100644
--- a/README.rst
+++ b/README.rst
@@ -119,7 +119,7 @@ Usage
 * Read the full documentation with tutorials at:
 
   * https://johannesbuchner.github.io/UltraNest/
-  * `API Reference: <https://johannesbuchner.github.io/UltraNest/ultranest.html#ultranest.integrator.ReactiveNestedSampler>`_.
+  * `API Reference <https://johannesbuchner.github.io/UltraNest/ultranest.html#ultranest.integrator.ReactiveNestedSampler>`_.
   * `Code repository: https://github.com/JohannesBuchner/UltraNest/ <https://github.com/JohannesBuchner/UltraNest/>`_
 
 Licence

From ed0dd461456bde341b368194ffc86a32bd10e227 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 20 Jun 2024 15:37:33 +0900
Subject: [PATCH 274/313] first step to support numpy 2.0

call np.import_array https://github.com/cython/cython/wiki/tutorials-numpy#c-api-initialization

to avoid the following error:

ultranest/mlfriends.pyx:1: in init ultranest.mlfriends
    # cython: language_level=3,annotate=True,profile=True,fast_fail=True,warning_errors=True
E   ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).
------------------------------- Captured stderr --------------------------------

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

also see
https://numpy.org/doc/stable//dev/depending_on_numpy.html#numpy-2-abi-handling
https://numpy.org/doc/stable//numpy_2_0_migration_guide.html
---
 ultranest/mlfriends.pyx | 1 +
 ultranest/stepfuncs.pyx | 1 +
 2 files changed, 2 insertions(+)

diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index eb1247b3..15605bbc 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -16,6 +16,7 @@ Includes
 
 import numpy as np
 cimport numpy as np
+np.import_array()
 from numpy import pi
 cimport cython
 from cython.cimports.libc.math import sqrt
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 44e7f2d5..51b72878 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -7,6 +7,7 @@ Efficient helper functions for vectorized step-samplers
 
 import numpy as np
 cimport numpy as np
+np.import_array()
 from numpy import nan as np_nan
 cimport cython
 from cython.parallel import prange

From bceff1a62dc27c40b2a32f94ebd276b791eca76f Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 20 Jun 2024 16:20:27 +0900
Subject: [PATCH 275/313] force numpy<2 for now, still errors:

ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject
---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 198ad016..c55dee9a 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,7 @@
         history_file.read())
     
 
-requirements = ['numpy', 'cython', 'matplotlib', 'corner']
+requirements = ['numpy<2', 'cython', 'matplotlib', 'corner']
 
 setup_requirements = ['pytest-runner', ]
 

From a77b9859f92e310fdcb8cdd20fee842973ecd262 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 20 Jun 2024 16:27:18 +0900
Subject: [PATCH 276/313] =?UTF-8?q?Bump=20version:=204.3.1=20=E2=86=92=204?=
 =?UTF-8?q?.3.2?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index c55dee9a..a815e16b 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.3.1',
+    version='4.3.2',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 7795170d..19f559eb 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -6,4 +6,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.3.1'
+__version__ = '4.3.2'

From aae89678772dca670224074d9f6e56bafbd65c8f Mon Sep 17 00:00:00 2001
From: Matthew Kirk <matthew.j.kirk@durham.ac.uk>
Date: Fri, 28 Jun 2024 18:01:32 +0100
Subject: [PATCH 277/313] Only import scipy when used

Allows importing and use of most ultranest functionality without needing
scipy
---
 ultranest/hotstart.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/ultranest/hotstart.py b/ultranest/hotstart.py
index e4aa4c50..a7e83e6c 100644
--- a/ultranest/hotstart.py
+++ b/ultranest/hotstart.py
@@ -11,7 +11,6 @@
 """
 
 import numpy as np
-import scipy.stats
 
 from .utils import resample_equal, vectorize
 
@@ -67,6 +66,7 @@ def get_auxiliary_problem(loglike, transform, ctr, invcov, enlargement_factor, d
         The first d return coordinates are identical to what ``transform`` would return.
         The final coordinate is the correction weight.
     """
+    import scipy.stats
     ndim, = ctr.shape
     assert invcov.shape == (ndim, ndim)
     assert df >= 1, ('Degrees of freedom must be above 1', df)
@@ -143,6 +143,7 @@ def get_extended_auxiliary_problem(loglike, transform, ctr, invcov, enlargement_
         The first d return coordinates are identical to what ``transform`` would return.
         The final coordinate is the correction weight.
     """
+    import scipy.stats
     ndim, = ctr.shape
     assert invcov.shape == (ndim, ndim)
     assert df >= 1, ('Degrees of freedom must be above 1', df)
@@ -224,6 +225,7 @@ def get_extended_auxiliary_independent_problem(loglike, transform, ctr, err, df=
         The first d return coordinates are identical to what ``transform`` would return.
         The final coordinate is the log of the correction weight.
     """
+    import scipy.stats
     ndim, = np.shape(ctr)
     assert np.shape(err) == (ndim,)
     assert df >= 1, ('Degrees of freedom must be above 1', df)

From 5387e8d4e99dd3c9014fbc559d87731696026072 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 23 Oct 2024 10:11:12 +0200
Subject: [PATCH 278/313] doc: make pydoclint happy

---
 setup.cfg                   |  4 ++-
 ultranest/calibrator.py     |  2 +-
 ultranest/integrator.py     | 49 +++++++++++++++++++++----------------
 ultranest/netiter.py        | 20 +++++++--------
 ultranest/ordertest.py      |  8 +++---
 ultranest/plot.py           | 32 +++++++++++++++---------
 ultranest/popstepsampler.py | 10 ++++++--
 ultranest/stepsampler.py    | 25 +++++++++++++++----
 ultranest/store.py          |  5 ++++
 ultranest/utils.py          |  9 +++++--
 ultranest/viz.py            |  3 +--
 11 files changed, 108 insertions(+), 59 deletions(-)

diff --git a/setup.cfg b/setup.cfg
index 78dd7114..ba2026a2 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,8 @@
 [flake8]
+style = numpy
+check-return-types = False
 exclude = docs
-extend-ignore = E501,F401,E128,E231,E124
+extend-ignore = E501,F401,E128,E231,E124,SIM114,DOC105,DOC106,DOC107,DOC301,DOC501,DOC503,DOC203,B006,SIM102,SIM113,DOC202
 per-file-ignores =
     ultranest/plot.py: B006
     ultranest/integrator.py: B006
diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index cd432386..9f82c111 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -15,7 +15,7 @@ def _substitute_log_dir(init_args, nsteps):
     """Append `nsteps` to `log_dir` argument, if set.
 
     Parameters
-    -----------
+    ----------
     init_args: dict
         arguments passed :py:class:`ReactiveNestedSampler`,
         may contain the key `'log_dir'`.
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 7d04fb86..80ad7433 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -166,10 +166,6 @@ def resume_from_similar_file(
         new likelihood function
     transform: function
         new transform function
-    verbose: bool
-        show progress
-    ndraw: int
-        set to >1 if functions can take advantage of vectorized computations
     max_tau: float
         Allowed dissimilarity in the live point ordering, quantified as
         normalised Kendall tau distance.
@@ -178,9 +174,13 @@ def resume_from_similar_file(
         when the live point order differs.
         Near 1 are completely different live point orderings.
         Values in between permit mild disorder.
+    verbose: bool
+        show progress
+    ndraw: int
+        set to >1 if functions can take advantage of vectorized computations
 
     Returns
-    ----------
+    -------
     sequence: dict
         contains arrays storing for each iteration estimates of:
 
@@ -554,17 +554,24 @@ def run(
 
         Parameters
         ----------
-        update_interval_iter:
+        update_interval_iter: None | int
             Update region after this many iterations.
-        update_interval_ncall:
+        update_interval_ncall: None | int
             Update region after update_interval_ncall likelihood calls.
-        log_interval:
+        log_interval: None | int
             Update stdout status line every log_interval iterations
-        dlogz:
+        dlogz: float
             Target evidence uncertainty.
-        max_iters:
+        max_iters: None | int
             maximum number of integration iterations.
 
+        Returns
+        -------
+        results: dict
+            dictionary with posterior *samples* and original *weighted_samples*,
+            number of likelihood calls *ncall*,
+            number of nested sampling iterations *niter*, evidence
+            estimate *logz* and uncertainty *logzerr*.
         """
         if update_interval_ncall is None:
             update_interval_ncall = max(1, round(self.num_live_points))
@@ -2190,6 +2197,14 @@ def _should_node_be_expanded(
         ----------
         it: int
             current iteration
+        Llo: float
+            lower loglikelihood bound for the strategy
+        Lhi: float
+            upper loglikelihood bound for the strategy
+        minimal_widths_sequence: list
+            list of likelihood intervals with minimum number of live points
+        target_min_num_children: int
+            minimum number of live points currently targeted
         node: node
             The node to consider
         parallel_values: array of floats
@@ -2198,14 +2213,6 @@ def _should_node_be_expanded(
             maximum number of likelihood function calls allowed
         max_iters: int
             maximum number of nested sampling iteration allowed
-        Llo: float
-            lower loglikelihood bound for the strategy
-        Lhi: float
-            upper loglikelihood bound for the strategy
-        minimal_widths_sequence: list
-            list of likelihood intervals with minimum number of live points
-        target_min_num_children:
-            minimum number of live points currently targeted
         live_points_healthy: bool
             indicates whether the live points have become
             linearly dependent (covariance not full rank)
@@ -2286,8 +2293,8 @@ def run(
             max_num_improvement_loops=-1,
             min_num_live_points=400,
             cluster_num_live_points=40,
-            insertion_test_window=10,
             insertion_test_zscore_threshold=4,
+            insertion_test_window=10,
             region_class=MLFriends,
             widen_before_initial_plateau_num_warn=10000,
             widen_before_initial_plateau_num_max=50000,
@@ -2379,7 +2386,7 @@ def run(
 
 
         Returns
-        ------
+        -------
         results (dict): Results dictionary, with the following entries:
 
             - samples (ndarray): re-weighted posterior samples: distributed according
@@ -3148,7 +3155,7 @@ def read_file(log_dir, x_dim, num_bootstraps=20, random=True, verbose=False, che
         whether to perform MWW insertion order test for assessing convergence
 
     Returns
-    ----------
+    -------
     sequence: dict
         contains arrays storing for each iteration estimates of:
 
diff --git a/ultranest/netiter.py b/ultranest/netiter.py
index 3ad2b561..d9407cdc 100644
--- a/ultranest/netiter.py
+++ b/ultranest/netiter.py
@@ -265,7 +265,7 @@ def count_tree(roots):
         list of :py:class:`TreeNode` specifying the roots of the tree.
 
     Returns
-    --------
+    -------
     count: int
         total number of nodes
     maxwidth: int
@@ -301,7 +301,7 @@ def count_tree_between(roots, lo, hi):
         upper value threshold
 
     Returns
-    --------
+    -------
     nnodes: int
         total number of nodes in the value interval lo .. hi (inclusive).
     maxwidth: int
@@ -343,7 +343,7 @@ def find_nodes_before(root, value):
         selection threshold
 
     Returns
-    --------
+    -------
     list_of_parents: list of nodes
         parents
     list_of_nforks: list of floats
@@ -415,14 +415,14 @@ def add(self, newpointu, newpointp):
         """Save point.
 
         Parameters
-        -----------
+        ----------
         newpointu: array
             point (in u-space)
         newpointp: array
             point (in p-space)
 
         Returns
-        ---------
+        -------
         index: int
             index of the new point in the pile
         """
@@ -448,7 +448,7 @@ def make_node(self, value, u, p):
         """Store point in pile, and create a new tree node that points to it.
 
         Parameters
-        -----------
+        ----------
         value: float
             value to store in node (loglikelihood)
         u: array
@@ -457,7 +457,7 @@ def make_node(self, value, u, p):
             point (in p-space)
 
         Returns
-        ---------
+        -------
         node: :py:class:`TreeNode`
             node
         """
@@ -868,11 +868,11 @@ def combine_results(saved_logl, saved_nodeids, pointpile, main_iterator, mpi_com
         Point pile.
     main_iterator: :py:class:`BreadthFirstIterator`
         iterator used
-    mpi_comm:
+    mpi_comm: None | object
         MPI communicator object, or None if MPI is not used.
 
     Returns
-    --------
+    -------
     results: dict
         All information of the run. Important keys:
         Number of nested sampling iterations (niter),
@@ -996,7 +996,7 @@ def logz_sequence(root, pointpile, nbootstraps=12, random=True, onNode=None, ver
         Whether to perform a rolling insertion order rank test
 
     Returns
-    --------
+    -------
     results: dict
         Run information, see :py:func:`combine_results`
     sequence: dict
diff --git a/ultranest/ordertest.py b/ultranest/ordertest.py
index 33d7f07d..866847bc 100644
--- a/ultranest/ordertest.py
+++ b/ultranest/ordertest.py
@@ -33,10 +33,10 @@ def infinite_U_zscore(sample, B):
 
     Parameters
     ----------
-    B: int
-        maximum rank allowed.
     sample: array of integers
         values between 0 and B (inclusive).
+    B: int
+        maximum rank allowed.
 
     Returns
     -------
@@ -72,10 +72,10 @@ def add(self, order, N):
 
         Parameters
         ----------
-        N: int
-            maximum rank allowed.
         order: int
             rank between 0 and N (inclusive).
+        N: int
+            maximum rank allowed.
         """
         if not 0 <= order <= N:
             raise ValueError("order %d out of %d invalid" % (order, N))
diff --git a/ultranest/plot.py b/ultranest/plot.py
index 6bd8e80b..2b5cc589 100644
--- a/ultranest/plot.py
+++ b/ultranest/plot.py
@@ -59,17 +59,21 @@ def cornerplot(
 
     Parameters
     ----------
+    results: dict
+        data dictionary
     min_weight: float
         cut off low-weight posterior points. Avoids meaningless
         stragglers when plot_datapoints is True.
     with_legend: bool
         whether to add a legend to show meaning of the lines.
-    color : str
-        ``matplotlib`` style color for all histograms.
+    logger: None | object
+        where to log
+    levels: list
+        list of credible interval levels
+    plot_datapoints : bool
+        Draw individual data points.
     plot_density : bool
         Draw the density colormap.
-    plot_contours : bool
-        Draw the contours.
     show_titles : bool
         Displays a title above each 1-D histogram showing the 0.5 quantile
         with the upper and lower errors supplied by the quantiles argument.
@@ -77,6 +81,8 @@ def cornerplot(
         If true, suppress warnings for small datasets.
     contour_kwargs : dict
         Any additional keyword arguments to pass to the `contour` method.
+    color : str
+        ``matplotlib`` style color for all histograms.
     quantiles: list
         fractional quantiles to show on the 1-D histograms as vertical dashed lines.
     **corner_kwargs: dict
@@ -240,16 +246,20 @@ class PredictionBand:
         plt.show()
 
     To plot onto a specific axis, use `band.line(..., ax=myaxis)`.
-
-    Parameters
-    ----------
-    x: array
-        The independent variable
-
     """
 
     def __init__(self, x, shadeargs={}, lineargs={}):
-        """Initialise with independent variable *x*."""
+        """Initialise.
+
+        Parameters
+        ----------
+        x: array
+            Independent variable.
+        shadeargs: dict
+            default arguments for shade function.
+        lineargs: dict
+            default arguments for line function.
+        """
         self.x = x
         self.ys = []
         self.shadeargs = shadeargs
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index b4b0b3b0..89b72e6e 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -484,10 +484,10 @@ def setup_brackets(self, mask_starting, region):
 
         Parameters
         ----------
-        region: MLFriends object
-            Region
         mask_starting: np.array(nwalkers, dtype=bool)
             which walkers to set up.
+        region: MLFriends object
+            Region
 
         """
         if self.log:
@@ -519,7 +519,13 @@ def advance(self, transform, loglike, Lmin, region):
             loglikelihood function
         Lmin: float
             current log-likelihood threshold
+        region: MLFriends object
+            Region
 
+        Returns
+        -------
+        nc: int
+            Number of likelihood function calls
         """
         movable = self.generation < self.nsteps
         all_movable = movable.all()
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index 7dc16400..dbaa538c 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -452,7 +452,7 @@ def select_random_livepoint(us, Ls, Lmin):
     """Select random live point as chain starting point.
 
     Parameters
-    -----------
+    ----------
     us: array
         positions of live points
     Ls: array
@@ -518,7 +518,7 @@ def __call__(self, us, Ls, Lmin):
         """Select live point as chain starting point.
 
         Parameters
-        -----------
+        ----------
         us: array
             positions of live points
         Ls: array
@@ -1008,6 +1008,16 @@ def __next__(self, region, Lmin, us, Ls, transform, loglike, ndraw=10, plot=Fals
         tregion: :py:class:`WrappingEllipsoid`
             optional ellipsoid in transformed space for rejecting proposals
 
+        Returns
+        -------
+        u: None | array
+            newly sampled untransformed point, or None if not successful yet
+        p: None | array
+            newly sampled transformed point, or None if not successful yet
+        L: None | float
+            log-likelihood value, or None if not successful yet
+        nc: int
+            number of likelihood function calls
         """
         # find most recent point in history conforming to current Lmin
         for j, (_uj, Lj) in enumerate(self.history):
@@ -1097,12 +1107,17 @@ def move(self, ui, region, ndraw=1, plot=False):
         ----------
         ui: array
             current point
+        region: object
+            ignored
         ndraw: int
             number of points to draw.
-        region:
-            ignored
-        plot:
+        plot: bool
             ignored
+
+        Returns
+        -------
+        unew: array
+            proposed point
         """
         # propose in that direction
         direction = self.generate_direction(ui, region, scale=self.scale)
diff --git a/ultranest/store.py b/ultranest/store.py
index fdf8dd53..f74623fb 100644
--- a/ultranest/store.py
+++ b/ultranest/store.py
@@ -75,6 +75,11 @@ def flush(self):
     def pop(self, Lmin):
         """Request from the storage a point sampled from <= Lmin with L > Lmin.
 
+        Parameters
+        ----------
+        Lmin: float
+            loglikelihood threshold
+
         Returns
         -------
         index: int
diff --git a/ultranest/utils.py b/ultranest/utils.py
index 2a398f4e..8156160c 100644
--- a/ultranest/utils.py
+++ b/ultranest/utils.py
@@ -221,7 +221,7 @@ def listify(*args):
 
     Parameters
     ----------
-    args: iterable
+    *args: iterable
         Lists to concatenate.
 
     Returns
@@ -468,6 +468,11 @@ def distributed_work_chunk_size(num_total_tasks, mpi_rank, mpi_size):
         process id
     mpi_size : int
         total number of processes
+
+    Returns
+    -------
+    chunk_size: int
+        number of tasks for process number `mpi_rank`
     """
     return (num_total_tasks + mpi_size - 1 - mpi_rank) // mpi_size
 
@@ -482,7 +487,7 @@ def submasks(mask, *masks):
     ----------
     mask : np.array(dtype=bool)
         selection of some array
-    masks : list of np.array(dtype=bool)
+    *masks : list of np.array(dtype=bool)
         each further mask is a subselection
 
     Returns
diff --git a/ultranest/viz.py b/ultranest/viz.py
index 1f722ae0..b2309903 100644
--- a/ultranest/viz.py
+++ b/ultranest/viz.py
@@ -33,7 +33,7 @@ def round_parameterlimits(plo, phi, paramlimitguess=None):
     """Guess the current parameter range.
 
     Parameters
-    -----------
+    ----------
     plo: array of floats
         for each parameter, current minimum value
     phi: array of floats
@@ -49,7 +49,6 @@ def round_parameterlimits(plo, phi, paramlimitguess=None):
         for each parameter, rounded maximum value
     formats: array of float tuples
         for each parameter, string format for representing it.
-
     """
     with np.errstate(divide='ignore'):
         expos = log10(np.abs([plo, phi]))

From 49401c3df7214de6b62f6bebf2ddb0f849fe9b00 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 25 Oct 2024 22:12:14 +0200
Subject: [PATCH 279/313] migrate to numpy>=2, using explicit int32 dtype

---
 evaluate/problems.py          |  2 +-
 tests/test_popstepsampling.py |  9 ++++----
 ultranest/integrator.py       |  2 +-
 ultranest/mlfriends.pyx       | 35 +++++++++++++++++-------------
 ultranest/popstepsampler.py   |  9 ++++----
 ultranest/stepfuncs.pyx       | 40 +++++++++++++++++++++++------------
 6 files changed, 58 insertions(+), 39 deletions(-)

diff --git a/evaluate/problems.py b/evaluate/problems.py
index 88681002..c2868874 100644
--- a/evaluate/problems.py
+++ b/evaluate/problems.py
@@ -57,7 +57,7 @@ def volume_asymgauss(loglike, ndim):
             return np.nan
         
         # compute volume of a n-sphere
-        return nsphere_volume(radius, ndim) * np.product(asym_sigma / asym_sigma_max)
+        return nsphere_volume(radius, ndim) * np.prod(asym_sigma / asym_sigma_max)
     
     gradient_asymgauss = gradient_to_center
     
diff --git a/tests/test_popstepsampling.py b/tests/test_popstepsampling.py
index 6995c9e0..01e9c865 100644
--- a/tests/test_popstepsampling.py
+++ b/tests/test_popstepsampling.py
@@ -8,6 +8,7 @@
 from ultranest.popstepsampler import generate_cube_oriented_direction, generate_random_direction, generate_cube_oriented_direction_scaled
 from ultranest.popstepsampler import generate_region_oriented_direction, generate_region_random_direction
 from ultranest.popstepsampler import slice_limit_to_unitcube,slice_limit_to_scale
+from ultranest.popstepsampler import int_dtype
 
 def make_region(ndim, us=None, nlive=400):
     if us is None:
@@ -175,9 +176,9 @@ def test_update_slice_sampler():
     The workers should be split among the 2 unfinished points at the end.
     """
     
-    worker_running = np.array([0,0,0,0,1,1,1,1,2,2,2,2])
+    worker_running = np.array([0,0,0,0,1,1,1,1,2,2,2,2], dtype=int_dtype)
     popsize = 12
-    status = np.zeros(12, dtype=int)
+    status = np.zeros(12, dtype=int_dtype)
     status[3:] = 1
     Lmin = 1.
     shrink = 1.0 
@@ -294,8 +295,8 @@ def test_direction_proposal_values():
     #test_stepsampler_cubegausswalk()
     #test_stepsampler_randomSimSlice()
     #test_direction_proposals()
-    #test_slice_limit()
+    test_slice_limit()
     #test_update_slice_sampler()
-    Test_SimpleSliceSampler(4)
+    #Test_SimpleSliceSampler(4)
     
     
diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index 80ad7433..dd89b201 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -43,7 +43,7 @@
 
 __all__ = ['ReactiveNestedSampler', 'NestedSampler', 'read_file', 'warmstart_from_similar_file']
 
-int_t = int
+int_t = np.int32
 
 
 def _get_cumsum_range(pi, dp):
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 15605bbc..b9f7edd0 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -22,13 +22,17 @@ cimport cython
 from cython.cimports.libc.math import sqrt
 
 
+ctypedef np.int32_t decl_int_t
+int_dtype = np.int32
+
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 cdef count_nearby(
     np.ndarray[np.float_t, ndim=2] apts,
     np.ndarray[np.float_t, ndim=2] bpts,
     np.float_t radiussq,
-    np.ndarray[np.int_t, ndim=1] nnearby
+    np.ndarray[decl_int_t, ndim=1] nnearby
 ):
     """Count the number of points in ``apts`` within square radius ``radiussq`` for each point ``b`` in `bpts``.
 
@@ -140,7 +144,7 @@ def find_nearby(
     np.ndarray[np.float_t, ndim=2] apts,
     np.ndarray[np.float_t, ndim=2] bpts,
     np.float_t radiussq,
-    np.ndarray[np.int_t, ndim=1] nnearby
+    np.ndarray[decl_int_t, ndim=1] nnearby
 ):
     """Gets the index of a point in `a` within square radius `radiussq`, for each point `b` in `bpts`.
 
@@ -224,7 +228,7 @@ cdef float compute_maxradiussq(np.ndarray[np.float_t, ndim=2] apts, np.ndarray[n
 @cython.wraparound(False)
 def compute_mean_pair_distance(
     np.ndarray[np.float_t, ndim=2] pts,
-    np.ndarray[np.int_t, ndim=1] clusterids
+    np.ndarray[decl_int_t, ndim=1] clusterids
 ):
     """Compute the average distance between pairs of points.
     Pairs from different clusters are excluded in the computation.
@@ -272,12 +276,12 @@ cdef _update_clusters(
     np.ndarray[np.float_t, ndim=2] upoints,
     np.ndarray[np.float_t, ndim=2] tpoints,
     np.float_t maxradiussq,
-    np.ndarray[np.int_t, ndim=1] clusterids,
+    np.ndarray[decl_int_t, ndim=1] clusterids,
 ):
     """same signature as ``update_clusters()``, see there."""
     assert upoints.shape[0] == tpoints.shape[0], ('different number of points', upoints.shape[0], tpoints.shape[0])
     assert upoints.shape[1] == tpoints.shape[1], ('different dimensionality of points', upoints.shape[1], tpoints.shape[1])
-    clusteridxs = np.zeros(len(tpoints), dtype=int)
+    clusteridxs = np.zeros(len(tpoints), dtype=int_dtype)
     currentclusterid = 1
     i = 0
     # avoid issues when old clusterids are from a longer array
@@ -295,7 +299,7 @@ cdef _update_clusters(
             break
 
         nonmembers = tpoints[nonmembermask,:]
-        idnearby = np.empty(len(nonmembers), dtype=int)
+        idnearby = np.empty(len(nonmembers), dtype=int_dtype)
         members = tpoints[clusteridxs == currentclusterid,:]
         find_nearby(members, nonmembers, maxradiussq, idnearby)
         # print('merging %d into cluster %d of size %d' % (np.count_nonzero(nnearby), currentclusterid, len(members)))
@@ -375,8 +379,9 @@ def update_clusters(
     Clustering is performed on a transformed coordinate space (`tpoints`).
     Returned values are based on upoints.
     """
+    print(clusterids)
     if clusterids is None:
-        clusterids = np.zeros(len(tpoints), dtype=int)
+        clusterids = np.zeros(len(tpoints), dtype=int_dtype)
     return _update_clusters(upoints, tpoints, maxradiussq, clusterids)
 
 
@@ -409,7 +414,7 @@ def make_eigvals_positive(
         raise e
     mask = w < max(1.e-10, 1e-300**(1. / len(a)))
     if np.any(mask):
-        nzprod = np.product(w[~mask])  # product of nonzero eigenvalues
+        nzprod = np.prod(w[~mask])  # product of nonzero eigenvalues
         nzeros = mask.sum()  # number of zero eigenvalues
         w[mask] = (targetprod / nzprod) ** (1. / nzeros)  # adjust zero eigvals
         a = np.dot(np.dot(v, np.diag(w)), np.linalg.inv(v))  # re-form cov
@@ -568,7 +573,7 @@ class ScalingLayer(object):
         """Updates the cluster id assigned to each point."""
         if clusterids is None and self.clusterids is None and npoints is not None:
             # for the beginning, set cluster ids to one for all points
-            clusterids = np.ones(npoints, dtype=int)
+            clusterids = np.ones(npoints, dtype=int_dtype)
         if clusterids is not None:
             # if we have a value, update
             self.clusterids = clusterids
@@ -846,7 +851,7 @@ class LocalAffineLayer(AffineLayer):
         return s
 
 
-def vol_prefactor(np.int_t n):
+def vol_prefactor(int n):
     """Volume constant for an ``n``-dimensional sphere.
 
     for ``n`` even:  $$    (2pi)^(n    /2) / (2 * 4 * ... * n)$$
@@ -1080,7 +1085,7 @@ class MLFriends(object):
         v = self.unormed[idx,:] + v * self.maxradiussq**0.5
 
         # count how many are around
-        nnearby = np.empty(nsamples, dtype=int)
+        nnearby = np.empty(nsamples, dtype=int_dtype)
         count_nearby(self.unormed, v, self.maxradiussq, nnearby)
         vmask = np.random.uniform(high=nnearby) < 1
         w = self.transformLayer.untransform(v[vmask,:])
@@ -1102,7 +1107,7 @@ class MLFriends(object):
         wmask = self.inside_ellipsoid(u)
         # check if inside region in transformed space
         v = self.transformLayer.transform(u[wmask,:])
-        idnearby = np.empty(len(v), dtype=int)
+        idnearby = np.empty(len(v), dtype=int_dtype)
         find_nearby(self.unormed, v, self.maxradiussq, idnearby)
         vmask = idnearby >= 0
         return u[wmask,:][vmask,:]
@@ -1117,7 +1122,7 @@ class MLFriends(object):
         N, ndim = self.u.shape
         # draw from rectangle in transformed space
         v = np.random.uniform(self.bbox_lo - self.maxradiussq, self.bbox_hi + self.maxradiussq, size=(nsamples, ndim))
-        idnearby = np.empty(nsamples, dtype=int)
+        idnearby = np.empty(nsamples, dtype=int_dtype)
         find_nearby(self.unormed, v, self.maxradiussq, idnearby)
         vmask = idnearby >= 0
 
@@ -1149,7 +1154,7 @@ class MLFriends(object):
 
         wmask = np.logical_and(w > 0, w < 1).all(axis=1)
         v = self.transformLayer.transform(w[wmask,:])
-        idnearby = np.empty(len(v), dtype=int)
+        idnearby = np.empty(len(v), dtype=int_dtype)
         find_nearby(self.unormed, v, self.maxradiussq, idnearby)
         vmask = idnearby >= 0
 
@@ -1200,7 +1205,7 @@ class MLFriends(object):
         if mask.any():
             # additionally require points to be near neighbours
             bpts = self.transformLayer.transform(pts[mask,:])
-            idnearby = np.empty(len(bpts), dtype=int)
+            idnearby = np.empty(len(bpts), dtype=int_dtype)
             find_nearby(self.unormed, bpts, self.maxradiussq, idnearby)
             mask[mask] = idnearby >= 0
 
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 89b72e6e..27700fc9 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -22,7 +22,7 @@
                                  update_vectorised_slice_sampler)
 from ultranest.utils import submasks
 
-int_t = int
+int_dtype = np.int32
 
 
 def unitcube_line_intersection(ray_origin, ray_direction):
@@ -432,7 +432,7 @@ def _setup(self, ndim):
         self.allL = np.zeros((self.popsize, self.nsteps + 1)) + np.nan
         self.currentt = np.zeros(self.popsize) + np.nan
         self.currentv = np.zeros((self.popsize, ndim)) + np.nan
-        self.generation = np.zeros(self.popsize, dtype=int_t) - 1
+        self.generation = np.zeros(self.popsize, dtype=int_dtype) - 1
         self.current_left = np.zeros(self.popsize)
         self.current_right = np.zeros(self.popsize)
         self.searching_left = np.zeros(self.popsize, dtype=bool)
@@ -936,9 +936,9 @@ def __next__(
                 # Slice bounds for each points
                 tleft, tright = self.slice_limit(tleft_unitcube,tright_unitcube)
                 # Index of the workers working concurrently
-                worker_running = np.arange(0, self.popsize, 1, dtype=int_t)
+                worker_running = np.arange(self.popsize, dtype=int_dtype)
                 # Status indicating if a points has already find its next position
-                status = np.zeros(self.popsize, dtype=int_t)  # one for success, zero for running
+                status = np.zeros(self.popsize, dtype=int_dtype)  # one for success, zero for running
 
                 # Loop until each points has found its next position or we reached 100 iterations
                 for _it in range(self.max_it):
@@ -955,6 +955,7 @@ def __next__(
                     nc += self.popsize
 
                     # Updating the pool of points based on the newly sampled points
+                    print(worker_running.dtype, status.dtype)
                     tleft, tright, worker_running, status, allu, allL, allp, n_discarded_it = update_vectorised_slice_sampler(
                         t, tleft, tright, proposed_L, proposed_u, proposed_p, worker_running, status, Lmin, self.shrink_factor,
                         allu, allL, allp, self.popsize)
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 51b72878..58f7d5b1 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -13,6 +13,10 @@ cimport cython
 from cython.parallel import prange
 
 
+ctypedef np.int32_t decl_int_t
+int_dtype = np.int32
+
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 cdef _within_unit_cube(
@@ -332,7 +336,7 @@ def step_back(Lmin, allL, generation, currentt, log=False):
 
 cdef _fill_directions(
     np.ndarray[np.float_t, ndim=2] v,
-    np.ndarray[np.int_t, ndim=1] indices,
+    np.ndarray[decl_int_t, ndim=1] indices,
     float scale
 ):
     cdef size_t nsamples = v.shape[0]
@@ -361,7 +365,7 @@ def generate_cube_oriented_direction(ui, region, scale=1):
     nsamples, ndim = ui.shape
     v = np.zeros((nsamples, ndim))
     # choose axis
-    j = np.random.randint(ndim, size=nsamples)
+    j = np.random.randint(ndim, size=nsamples, dtype=int_dtype)
     _fill_directions(v, j, scale)
     return v
 
@@ -388,7 +392,7 @@ def generate_cube_oriented_direction_scaled(ui, region, scale=1):
     v = np.zeros((nsamples, ndim))
     scales = region.u.std(axis=0)
     # choose axis
-    j = np.random.randint(ndim, size=nsamples)
+    j = np.random.randint(ndim, size=nsamples, dtype=int_dtype)
     _fill_directions(v, j, scale)
     v *= scales[j].reshape((-1, 1))
     return v
@@ -439,7 +443,7 @@ def generate_region_oriented_direction(ui, region, scale=1):
     """
     nsamples, ndim = ui.shape
     # choose axis in transformed space:
-    j = np.random.randint(ndim, size=nsamples)
+    j = np.random.randint(ndim, size=nsamples, dtype=int_dtype)
     v = region.transformLayer.axes[j] * scale
     return v
 
@@ -490,8 +494,8 @@ def generate_differential_direction(ui, region, scale=1):
     nsamples, ndim = ui.shape
     nlive, ndim = region.u.shape
     # choose pair
-    i = np.random.randint(nlive, size=nsamples)
-    i2 = np.random.randint(nlive - 1, size=nsamples)
+    i = np.random.randint(nlive, size=nsamples, dtype=int_dtype)
+    i2 = np.random.randint(nlive - 1, size=nsamples, dtype=int_dtype)
     i2[i2 >= i] += 1
 
     # compute difference vector
@@ -530,14 +534,22 @@ def generate_mixture_random_direction(ui, region, scale=1):
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-cpdef tuple update_vectorised_slice_sampler(\
-                          np.ndarray[np.float_t, ndim=1] t, np.ndarray[np.float_t, ndim=1] tleft,\
-                          np.ndarray[np.float_t, ndim=1] tright, np.ndarray[np.float_t, ndim=1] proposed_L,\
-                          np.ndarray[np.float_t, ndim=2] proposed_u, np.ndarray[np.float_t, ndim=2] proposed_p,\
-                          np.ndarray[np.int_t, ndim=1] worker_running, np.ndarray[np.int_t, ndim=1] status,\
-                          np.float_t Likelihood_threshold,np.float_t shrink_factor, np.ndarray[np.float_t, ndim=2] allu,\
-                          np.ndarray[np.float_t, ndim=1] allL, np.ndarray[np.float_t, ndim=2] allp, int popsize):
-
+cpdef tuple update_vectorised_slice_sampler(
+    np.ndarray[np.float_t, ndim=1] t,
+    np.ndarray[np.float_t, ndim=1] tleft,
+    np.ndarray[np.float_t, ndim=1] tright,
+    np.ndarray[np.float_t, ndim=1] proposed_L,
+    np.ndarray[np.float_t, ndim=2] proposed_u,
+    np.ndarray[np.float_t, ndim=2] proposed_p,
+    np.ndarray[decl_int_t, ndim=1] worker_running,
+    np.ndarray[decl_int_t, ndim=1] status,
+    np.float_t Likelihood_threshold,
+    np.float_t shrink_factor,
+    np.ndarray[np.float_t, ndim=2] allu,
+    np.ndarray[np.float_t, ndim=1] allL,
+    np.ndarray[np.float_t, ndim=2] allp,
+    int popsize
+):
     """Update the slice sampler state of each walker in the populations.
 
     Parameters

From 1d1df26e7b428da5481a1897aeedfb13269d4060 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 25 Oct 2024 22:42:26 +0200
Subject: [PATCH 280/313] switch to 64 bit integers

---
 ultranest/integrator.py     | 2 +-
 ultranest/mlfriends.pyx     | 4 ++--
 ultranest/popstepsampler.py | 6 ++----
 ultranest/stepfuncs.pyx     | 4 ++--
 4 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index dd89b201..f668a046 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -43,7 +43,7 @@
 
 __all__ = ['ReactiveNestedSampler', 'NestedSampler', 'read_file', 'warmstart_from_similar_file']
 
-int_t = np.int32
+int_t = np.int64
 
 
 def _get_cumsum_range(pi, dp):
diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index b9f7edd0..7ded1171 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -22,8 +22,8 @@ cimport cython
 from cython.cimports.libc.math import sqrt
 
 
-ctypedef np.int32_t decl_int_t
-int_dtype = np.int32
+ctypedef np.int64_t decl_int_t
+int_dtype = np.int64
 
 
 @cython.boundscheck(False)
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 27700fc9..8cd11bf4 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -18,12 +18,10 @@
                                  generate_mixture_random_direction,
                                  generate_random_direction,
                                  generate_region_oriented_direction,
-                                 generate_region_random_direction, step_back,
-                                 update_vectorised_slice_sampler)
+                                 generate_region_random_direction, int_dtype,
+                                 step_back, update_vectorised_slice_sampler)
 from ultranest.utils import submasks
 
-int_dtype = np.int32
-
 
 def unitcube_line_intersection(ray_origin, ray_direction):
     r"""Compute intersection of a line (ray) and a unit box (0:1 in all axes).
diff --git a/ultranest/stepfuncs.pyx b/ultranest/stepfuncs.pyx
index 58f7d5b1..c57fc932 100644
--- a/ultranest/stepfuncs.pyx
+++ b/ultranest/stepfuncs.pyx
@@ -13,8 +13,8 @@ cimport cython
 from cython.parallel import prange
 
 
-ctypedef np.int32_t decl_int_t
-int_dtype = np.int32
+ctypedef np.int64_t decl_int_t
+int_dtype = np.int64
 
 
 @cython.boundscheck(False)

From 215c1cc19cd65c328229979a389a3a4d568c999e Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 25 Oct 2024 22:54:52 +0200
Subject: [PATCH 281/313] doc: make pydocstyle happy

---
 ultranest/pathsampler.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ultranest/pathsampler.py b/ultranest/pathsampler.py
index 85b3c182..b0aaf553 100644
--- a/ultranest/pathsampler.py
+++ b/ultranest/pathsampler.py
@@ -1,4 +1,4 @@
-"""MCMC-like step sampling on a trajectory
+"""MCMC-like step sampling on a trajectory.
 
 These features are experimental.
 """
@@ -243,7 +243,7 @@ def set_gradient(self, grad_function):
         print("set gradient function to %s" % grad_function.__name__)
 
         def plot_gradient_wrapper(x, plot=False):
-            """wrapper that makes plots (when desired)"""
+            """Make plot while computing gradient (optionally)."""
             v = grad_function(x)
             if plot:
                 plt.plot(x[0], x[1], '+ ', color='k', ms=10)

From ebce8ec712a057992428bc080abe2b8cd05f66e9 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 25 Oct 2024 23:59:26 +0200
Subject: [PATCH 282/313] remove prints

---
 ultranest/mlfriends.pyx     | 1 -
 ultranest/popstepsampler.py | 1 -
 2 files changed, 2 deletions(-)

diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 7ded1171..3458e0ef 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -379,7 +379,6 @@ def update_clusters(
     Clustering is performed on a transformed coordinate space (`tpoints`).
     Returned values are based on upoints.
     """
-    print(clusterids)
     if clusterids is None:
         clusterids = np.zeros(len(tpoints), dtype=int_dtype)
     return _update_clusters(upoints, tpoints, maxradiussq, clusterids)
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index 8cd11bf4..e607b6ee 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -953,7 +953,6 @@ def __next__(
                     nc += self.popsize
 
                     # Updating the pool of points based on the newly sampled points
-                    print(worker_running.dtype, status.dtype)
                     tleft, tright, worker_running, status, allu, allL, allp, n_discarded_it = update_vectorised_slice_sampler(
                         t, tleft, tright, proposed_L, proposed_u, proposed_p, worker_running, status, Lmin, self.shrink_factor,
                         allu, allL, allp, self.popsize)

From 7b8141b31bfb086f0a17798863d850db18c680cf Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sat, 26 Oct 2024 00:42:45 +0200
Subject: [PATCH 283/313] compile also vol_prefactor

---
 ultranest/mlfriends.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 3458e0ef..3ae02b47 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -850,7 +850,7 @@ class LocalAffineLayer(AffineLayer):
         return s
 
 
-def vol_prefactor(int n):
+cpdef vol_prefactor(int n):
     """Volume constant for an ``n``-dimensional sphere.
 
     for ``n`` even:  $$    (2pi)^(n    /2) / (2 * 4 * ... * n)$$

From 0692ec6a9765978da54bc682aadc75cf2924c05c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 25 Oct 2024 22:54:52 +0200
Subject: [PATCH 284/313] doc: make pydocstyle happy

---
 ultranest/pathsampler.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ultranest/pathsampler.py b/ultranest/pathsampler.py
index 85b3c182..b0aaf553 100644
--- a/ultranest/pathsampler.py
+++ b/ultranest/pathsampler.py
@@ -1,4 +1,4 @@
-"""MCMC-like step sampling on a trajectory
+"""MCMC-like step sampling on a trajectory.
 
 These features are experimental.
 """
@@ -243,7 +243,7 @@ def set_gradient(self, grad_function):
         print("set gradient function to %s" % grad_function.__name__)
 
         def plot_gradient_wrapper(x, plot=False):
-            """wrapper that makes plots (when desired)"""
+            """Make plot while computing gradient (optionally)."""
             v = grad_function(x)
             if plot:
                 plt.plot(x[0], x[1], '+ ', color='k', ms=10)

From 4748f00ef208c0f5db55e33a4a995e3e0e648ec7 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sat, 26 Oct 2024 20:33:20 +0200
Subject: [PATCH 285/313] =?UTF-8?q?Bump=20version:=204.3.2=20=E2=86=92=204?=
 =?UTF-8?q?.3.3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index a815e16b..8ed7484d 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.3.2',
+    version='4.3.3',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 19f559eb..51db3981 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -6,4 +6,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.3.2'
+__version__ = '4.3.3'

From 760da825a15dccc214cdc4c2ec7228bbf4390d57 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sat, 26 Oct 2024 21:13:28 +0200
Subject: [PATCH 286/313] remove numpy<2 requirement

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 8ed7484d..06deeb0e 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,7 @@
         history_file.read())
     
 
-requirements = ['numpy<2', 'cython', 'matplotlib', 'corner']
+requirements = ['numpy', 'cython', 'matplotlib', 'corner']
 
 setup_requirements = ['pytest-runner', ]
 

From 53d611045d6dc776506cda4ccc5cb6b31d61d45c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sat, 26 Oct 2024 21:37:32 +0200
Subject: [PATCH 287/313] =?UTF-8?q?Bump=20version:=204.3.3=20=E2=86=92=204?=
 =?UTF-8?q?.3.4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 06deeb0e..90b619a4 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.3.3',
+    version='4.3.4',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 51db3981..47728f9f 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -6,4 +6,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.3.3'
+__version__ = '4.3.4'

From 7681e7d1a3ff09d83243c103bec76bc58c08469c Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sat, 26 Oct 2024 21:46:04 +0200
Subject: [PATCH 288/313] [ci] reinstall numpy to solve  "E   ValueError:
 numpy.dtype size changed, may indicate binary incompatibility. Expected 96
 from C header, got 88 from PyObject"

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 8cd5e1b9..4b061d8e 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -26,7 +26,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
 
     - name: Install dependencies
-      run: python -m pip install cython numpy scipy matplotlib corner getdist h5py pandas flake8 pycodestyle pydocstyle pytest-html pytest-xdist
+      run: python -m pip install --upgrade --force-reinstall cython numpy scipy matplotlib corner getdist h5py pandas flake8 pycodestyle pydocstyle pytest-html pytest-xdist
 
     - name: Lint with flake8
       run: flake8 $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc|pathsampler).py')

From fbde672781762ef01aa24b46bdd01c614e0b0005 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sat, 26 Oct 2024 22:04:12 +0200
Subject: [PATCH 289/313] reverting to force numpy<2

https://github.com/numpy/numpy/issues/26710
---
 .github/workflows/tests.yml | 2 +-
 setup.py                    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 4b061d8e..8cd5e1b9 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -26,7 +26,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
 
     - name: Install dependencies
-      run: python -m pip install --upgrade --force-reinstall cython numpy scipy matplotlib corner getdist h5py pandas flake8 pycodestyle pydocstyle pytest-html pytest-xdist
+      run: python -m pip install cython numpy scipy matplotlib corner getdist h5py pandas flake8 pycodestyle pydocstyle pytest-html pytest-xdist
 
     - name: Lint with flake8
       run: flake8 $(ls ultranest/*.py | grep -Ev '^ultranest/(flatnuts|dychmc|dyhmc|pathsampler).py')
diff --git a/setup.py b/setup.py
index 90b619a4..5cb8d449 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,7 @@
         history_file.read())
     
 
-requirements = ['numpy', 'cython', 'matplotlib', 'corner']
+requirements = ['numpy<2', 'cython', 'matplotlib', 'corner']
 
 setup_requirements = ['pytest-runner', ]
 

From 5fd2305a6983831f782e67df8dc993051373a824 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Sun, 27 Oct 2024 10:24:28 +0100
Subject: [PATCH 290/313] remove requiring old numpy

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 0aea3803..8a66457d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,5 +3,5 @@ requires = [
         "setuptools",
         "wheel",
         "cython",
-        "oldest-supported-numpy",
+        "numpy",
 ]

From 7ac7290099ecfe1d01af9b8d53f95e3045d5fa68 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 12 Dec 2024 02:49:47 +0100
Subject: [PATCH 291/313] [ci] upgrade base image & don't requiring old numpy
 version in setup.py

---
 .circleci/config.yml | 4 +++-
 setup.py             | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 1cf5aebf..cbbbb661 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,9 @@ jobs:
   build:
     
     docker:
-      - image: cimg/base:2021.04
+      - image: cimg/base:2024.12
+      #- image: cimg/base:2021.04
+      #- image: circleci/python:3.13.1
     
     steps: 
       
diff --git a/setup.py b/setup.py
index a815e16b..4f2cddd7 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,7 @@
         history_file.read())
     
 
-requirements = ['numpy<2', 'cython', 'matplotlib', 'corner']
+requirements = ['numpy', 'cython', 'matplotlib', 'corner']
 
 setup_requirements = ['pytest-runner', ]
 

From d27d4ae7af7522caf2497879c0534b8d03ddae04 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 12 Dec 2024 03:06:24 +0100
Subject: [PATCH 292/313] [ci] do not install python packages via apt

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index cbbbb661..b0bcb63d 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -15,7 +15,7 @@ jobs:
       - checkout
       
       - run: sudo apt-get update -y
-      - run: sudo apt-get install -y python3-pip python3-mpi4py python3-h5py python3-numpy python3-scipy python3-matplotlib python3-pandas openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
+      - run: sudo apt-get install -y python3-pip openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
       
       - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
 

From 6fe41b083d56e40f2346b24366694125765dd0bd Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 12 Dec 2024 03:20:32 +0100
Subject: [PATCH 293/313] [ci] sudo install pip

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index b0bcb63d..664a0a03 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -23,7 +23,7 @@ jobs:
 
       - run: mkdir -p test-reports
 
-      - run: python3 -m pip install -e .
+      - run: sudo python3 -m pip install -e .
       - run: for i in examples/test*.py; do python3 $i --help; done
       
       - run: coverage3 run --parallel-mode setup.py test

From 755f52b3b97ff9c491262ceed2dcde543ba897e3 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 12 Dec 2024 03:56:49 +0100
Subject: [PATCH 294/313] [ci] try installing to user directory instead

---
 .circleci/config.yml | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 664a0a03..d9497561 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -17,13 +17,11 @@ jobs:
       - run: sudo apt-get update -y
       - run: sudo apt-get install -y python3-pip openmpi-common libopenmpi-dev liblapack-dev libopenblas-dev libhdf5-dev 
       
-      - run: sudo ln -s /usr/lib/python3/dist-packages/numpy/core/include/numpy/ /usr/include/numpy
-
-      - run: sudo python3 -m pip install -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py pydocstyle pycodestyle flake8
+      - run: python3 -m pip install --user -r pip-requirements.txt pytest-html coveralls pyyaml mpi4py pydocstyle pycodestyle flake8
 
       - run: mkdir -p test-reports
 
-      - run: sudo python3 -m pip install -e .
+      - run: python3 -m pip install --user -e .
       - run: for i in examples/test*.py; do python3 $i --help; done
       
       - run: coverage3 run --parallel-mode setup.py test

From 83dd88f962894c933c2c55953c1b88dc2d524a5a Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 12 Dec 2024 04:09:36 +0100
Subject: [PATCH 295/313] [ci] remove editable install

---
 .circleci/config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index d9497561..acf11b08 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -21,7 +21,7 @@ jobs:
 
       - run: mkdir -p test-reports
 
-      - run: python3 -m pip install --user -e .
+      - run: python3 -m pip install --user .
       - run: for i in examples/test*.py; do python3 $i --help; done
       
       - run: coverage3 run --parallel-mode setup.py test

From 881d3f58a38960d4a5a88496417265349cd816ec Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 12 Dec 2024 11:54:12 +0100
Subject: [PATCH 296/313] make flaky test deterministic; fix error if nsteps is
 passed

---
 tests/test_clustering.py | 12 +++++++-----
 ultranest/stepsampler.py |  3 ++-
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/tests/test_clustering.py b/tests/test_clustering.py
index 790916c8..1f8c9a01 100644
--- a/tests/test_clustering.py
+++ b/tests/test_clustering.py
@@ -83,11 +83,13 @@ def test_clusteringcase_eggbox():
     points = np.loadtxt(os.path.join(here, "eggboxregion.txt"))
     transformLayer = ScalingLayer()
     transformLayer.optimize(points, points)
-    region = MLFriends(points, transformLayer)
-    maxr = region.compute_maxradiussq(nbootstraps=30)
-    assert 1e-10 < maxr < 6e-10
-    print('maxradius:', maxr)
-    nclusters, clusteridxs, overlapped_points = update_clusters(points, points, maxr)
+    for seed in range(10):
+        np.random.seed(seed)
+        region = MLFriends(points, transformLayer)
+        maxr = region.compute_maxradiussq(nbootstraps=30)
+        assert 1e-10 < maxr < 6e-10
+        print('maxradius:', maxr)
+        nclusters, clusteridxs, overlapped_points = update_clusters(points, points, maxr)
     # plt.title('nclusters: %d' % nclusters)
     # for i in np.unique(clusteridxs):
     #    x, y = points[clusteridxs == i].transpose()
diff --git a/ultranest/stepsampler.py b/ultranest/stepsampler.py
index dbaa538c..3b2c2b57 100644
--- a/ultranest/stepsampler.py
+++ b/ultranest/stepsampler.py
@@ -1505,9 +1505,10 @@ def SpeedVariableRegionSliceSampler(step_matrix, *args, **kwargs):
     Updates only some dimensions at a time, completely user-definable.
     """
     generate_direction = kwargs.pop('generate_direction', generate_region_random_direction)
+    nsteps = kwargs.pop('nsteps', len(step_matrix))
     return SliceSampler(
         *args, **kwargs,
-        nsteps=kwargs.pop('nsteps', len(step_matrix)),
+        nsteps=nsteps,
         generate_direction=SpeedVariableGenerator(
             step_matrix=step_matrix,
             generate_direction=generate_direction

From 99ebb6503ba93addca4476fd98ea275f27396ab9 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 12 Dec 2024 12:42:01 +0100
Subject: [PATCH 297/313] [ci] remove python 3.7 which does not work anymore

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 8cd5e1b9..df4aa89d 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.7, 3.8, 3.9, "3.10", 3.11, 3.12]
+        python-version: [3.8, 3.9, "3.10", 3.11, 3.12]
 
     steps:
     - uses: actions/checkout@v2

From 1a9fd91ce28d86c8076d4ede7b76b3183e0b30ac Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 13 Dec 2024 07:00:26 +0100
Subject: [PATCH 298/313] =?UTF-8?q?Bump=20version:=204.3.4=20=E2=86=92=204?=
 =?UTF-8?q?.4.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 90b619a4..e7a1cfbf 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.3.4',
+    version='4.4.0',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index 47728f9f..ec477891 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -6,4 +6,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.3.4'
+__version__ = '4.4.0'

From 50f9a58687e038eae2bc5d2911aced9631305c44 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Fri, 13 Dec 2024 09:51:09 +0100
Subject: [PATCH 299/313] add changelog until 4.3

---
 HISTORY.rst | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 2437597b..c3e9fdee 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,6 +2,18 @@
 Release Notes
 ==============
 
+4.4.0 (2024-12-13)
+------------------
+* Compatible with numpy version 2 and above. Any remaining errors like "ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject" are due to ultranest and numpy being installed with different versions. Reinstall numpy and ultranest in that case.
+
+4.3.0 (2024-04-12)
+------------------
+* added :py:class:`ultranest.popstepsampler.PopulationSimpleSliceSampler`: Vectorized, fixed-batch size slice sampler. (`PR <https://github.com/JohannesBuchner/UltraNest/pull/118>`_ by Benjamin Beauchesne) 
+* validation of passed parameter names (`PR <https://github.com/JohannesBuchner/UltraNest/pull/133>`_ by svaverbe)
+* documentation improvements, including documenting results dictionary (`PR <https://github.com/JohannesBuchner/UltraNest/pull/138>`_ by Jacopo Tissino)
+* make scipy actually optional (`PR <https://github.com/JohannesBuchner/UltraNest/pull/141>`_ by Matthew Kirk)
+* linting
+
 4.2.0 (2024-02-15)
 ------------------
 

From 90d7ac8f8cb847364b23643e744539fd32285553 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Wed, 22 Jan 2025 15:33:44 +0100
Subject: [PATCH 300/313] bug fix: LocalAffineLayer reverted back to
 AffineLayer after the first iteration.

Also, region of bounding box was computed incorrectly...
---
 ultranest/mlfriends.pyx | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ultranest/mlfriends.pyx b/ultranest/mlfriends.pyx
index 3ae02b47..dab7cc3a 100644
--- a/ultranest/mlfriends.pyx
+++ b/ultranest/mlfriends.pyx
@@ -598,7 +598,7 @@ class ScalingLayer(object):
         tpoints = self.transform(upoints)
         nclusters, clusteridxs, overlapped_uwpoints = update_clusters(uwpoints, tpoints, maxradiussq, self.clusterids)
         # clusteridxs = track_clusters(clusteridxs, self.clusterids)
-        s = ScalingLayer(nclusters=nclusters, wrapped_dims=self.wrapped_dims, clusterids=clusteridxs)
+        s = self.__class__(nclusters=nclusters, wrapped_dims=self.wrapped_dims, clusterids=clusteridxs)
         s.optimize(upoints, overlapped_uwpoints)
         return s
 
@@ -730,7 +730,7 @@ class AffineLayer(ScalingLayer):
         tpoints = self.transform(upoints)
         nclusters, clusteridxs, overlapped_uwpoints = update_clusters(uwpoints, tpoints, maxradiussq, self.clusterids)
         # clusteridxs = track_clusters(clusteridxs, self.clusterids)
-        s = AffineLayer(nclusters=nclusters, wrapped_dims=self.wrapped_dims, clusterids=clusteridxs)
+        s = self.__class__(nclusters=nclusters, wrapped_dims=self.wrapped_dims, clusterids=clusteridxs)
         s.optimize(upoints, overlapped_uwpoints, minvol=minvol)
         return s
 
@@ -844,7 +844,7 @@ class LocalAffineLayer(AffineLayer):
         uwpoints = self.wrap(upoints)
         tpoints = self.transform(upoints)
         nclusters, clusteridxs, overlapped_uwpoints = update_clusters(uwpoints, tpoints, maxradiussq, self.clusterids)
-        s = LocalAffineLayer(nclusters=nclusters, wrapped_dims=self.wrapped_dims, clusterids=clusteridxs)
+        s = self.__class__(nclusters=nclusters, wrapped_dims=self.wrapped_dims, clusterids=clusteridxs)
         local_overlapped_uwpoints = subtract_nearby(uwpoints, maxradiussq)
         s.optimize(upoints, local_overlapped_uwpoints, minvol=minvol)
         return s
@@ -1120,7 +1120,7 @@ class MLFriends(object):
         """
         N, ndim = self.u.shape
         # draw from rectangle in transformed space
-        v = np.random.uniform(self.bbox_lo - self.maxradiussq, self.bbox_hi + self.maxradiussq, size=(nsamples, ndim))
+        v = np.random.uniform(self.bbox_lo - self.maxradiussq**0.5, self.bbox_hi + self.maxradiussq**0.5, size=(nsamples, ndim))
         idnearby = np.empty(nsamples, dtype=int_dtype)
         find_nearby(self.unormed, v, self.maxradiussq, idnearby)
         vmask = idnearby >= 0

From 2ecc1c10083924a26f002747b5946c017668b163 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 23 Jan 2025 16:53:59 +0100
Subject: [PATCH 301/313] doc typos

---
 ultranest/calibrator.py | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index 9f82c111..8f017b26 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -52,11 +52,13 @@ class ReactiveNestedCalibrator():
     Usage is designed to be a drop-in replacement for ReactiveNestedSampler.
 
     If your code was::
+
         sampler = ReactiveNestedSampler(my_param_names, my_loglike, my_transform)
         sampler.stepsampler = SliceSampler(nsteps=10, generate_direction=region_oriented_direction)
         sampler.run(min_num_livepoints=400)
 
     You would change it to::
+
         sampler = ReactiveNestedCalibrator(my_param_names, my_loglike, my_transform)
         sampler.stepsampler = SliceSampler(nsteps=10, generate_direction=region_oriented_direction)
         sampler.run(min_num_livepoints=400)
@@ -77,19 +79,22 @@ def __init__(self,
         """Initialise nested sampler calibrator.
 
         Parameters
-        -----------
+        ----------
+
         param_names: list of str
             Names of the parameters.
             Length gives dimensionality of the sampling problem.
+
         loglike: function
             log-likelihood function.
+
         transform: function
             parameter transform from unit cube to physical parameters.
-        kwargs: dict
-            further arguments passed to ReactiveNestedSampler
 
-        if `log_dir` is set, then the suffix `-nsteps%d` is added for each
-        run where %d is replaced with the number of steps (2, 4, 8 etc).
+        kwargs: dict
+            further arguments passed to ReactiveNestedSampler.
+            if `log_dir` is set, then the suffix `-nsteps%d` is added for each
+            run, where %d is replaced with the number of steps (2, 4, 8 etc).
         """
         self.init_args = dict(param_names=param_names, loglike=loglike, transform=transform, **kwargs)
         self.stepsampler = None
@@ -107,12 +112,12 @@ def run(self, **kwargs):
         and 2) the consecutive log(Z) error bars must overlap.
 
         Parameters
-        -----------
+        ----------
         **kwargs: dict
             All arguments are passed to :py:meth:`ReactiveNestedSampler.run`.
 
         Yields
-        -------
+        ------
         nsteps: int
             number of steps for the current run
         result: dict

From b127a51342ca0d4894e1d6f344ce699be2eb35e4 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 23 Jan 2025 17:44:36 +0100
Subject: [PATCH 302/313] doc fixes

---
 setup.cfg               | 2 +-
 ultranest/calibrator.py | 4 ----
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/setup.cfg b/setup.cfg
index ba2026a2..2866e26b 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -2,7 +2,7 @@
 style = numpy
 check-return-types = False
 exclude = docs
-extend-ignore = E501,F401,E128,E231,E124,SIM114,DOC105,DOC106,DOC107,DOC301,DOC501,DOC503,DOC203,B006,SIM102,SIM113,DOC202
+extend-ignore = E501,F401,E128,E231,E124,SIM114,DOC105,DOC106,DOC107,DOC301,DOC501,DOC503,DOC203,B006,SIM102,SIM113,DOC202,DOC403,DOC404
 per-file-ignores =
     ultranest/plot.py: B006
     ultranest/integrator.py: B006
diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index 8f017b26..ef88d632 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -80,17 +80,13 @@ def __init__(self,
 
         Parameters
         ----------
-
         param_names: list of str
             Names of the parameters.
             Length gives dimensionality of the sampling problem.
-
         loglike: function
             log-likelihood function.
-
         transform: function
             parameter transform from unit cube to physical parameters.
-
         kwargs: dict
             further arguments passed to ReactiveNestedSampler.
             if `log_dir` is set, then the suffix `-nsteps%d` is added for each

From dbf7446ed53fd8e84f9b3587464c5e7ccb130040 Mon Sep 17 00:00:00 2001
From: Qize Liu <llqqzz1998@yeah.net>
Date: Thu, 11 Dec 2025 15:41:58 +0800
Subject: [PATCH 303/313] fix calibrator.py for issue#145

---
 ultranest/calibrator.py | 34 +++++++++++++++++++++++++++++++++-
 1 file changed, 33 insertions(+), 1 deletion(-)

diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index ef88d632..e05c88ab 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -95,7 +95,7 @@ def __init__(self,
         self.init_args = dict(param_names=param_names, loglike=loglike, transform=transform, **kwargs)
         self.stepsampler = None
 
-    def run(self, **kwargs):
+    def run_iter(self, **kwargs):
         """Run a sequence of ReactiveNestedSampler runs until convergence.
 
         The first run is made with the number of steps set to the number of parameters.
@@ -180,6 +180,35 @@ def run(self, **kwargs):
 
             nsteps *= 2
 
+    
+    def run(self, **kwargs):
+        """Run a sequence of ReactiveNestedSampler runs until convergence.
+
+        The first run is made with the number of steps set to the number of parameters.
+        Each subsequent run doubles the number of steps.
+        Runs are made until convergence is reached.
+        Then this function returns.
+
+        Convergence is defined as three consecutive runs which
+        1) are not ordered in their log(Z) results,
+        and 2) the consecutive log(Z) error bars must overlap.
+
+        Parameters
+        ----------
+        **kwargs: dict
+            All arguments are passed to :py:meth:`ReactiveNestedSampler.run`.
+
+        Returns
+        -------
+        result: dict
+            return value of :py:meth:`ReactiveNestedSampler.run` for the final run
+        """
+
+        for nsteps, results in self.run_iter(**kwargs):
+            print(f" {nsteps:d}", results)
+        return results
+
+
     def plot(self):
         """Visualise the convergence diagnostics.
 
@@ -188,6 +217,9 @@ def plot(self):
         * nsteps-calibration-jumps.pdf: distribution of relative jump distance
         * nsteps-calibration.pdf: evolution of ln(Z) with nsteps
         """
+        if not self.sampler.log_to_disk:
+            raise ValueError("Plotting requires log_dir to be set during initialization")
+ 
         self.sampler.stepsampler.plot(os.path.join(self.sampler.logs['plots'], 'stepsampler.pdf'))
 
         # plot U-test convergence run length (at 4 sigma) (or niter) vs nsteps

From f5ba3d7cbfa81447a5641905f86d137366be7b52 Mon Sep 17 00:00:00 2001
From: Qize Liu <llqqzz1998@yeah.net>
Date: Thu, 11 Dec 2025 16:08:08 +0800
Subject: [PATCH 304/313] add test for calibrator.py

---
 tests/test_calibrator.py | 77 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 tests/test_calibrator.py

diff --git a/tests/test_calibrator.py b/tests/test_calibrator.py
new file mode 100644
index 00000000..9053062d
--- /dev/null
+++ b/tests/test_calibrator.py
@@ -0,0 +1,77 @@
+import numpy as np
+import ultranest
+import ultranest.stepsampler
+import ultranest.calibrator
+import scipy.stats
+
+
+def test_calibrator(plot=False):
+    # velocity dispersions of dwarf galaxies by van Dokkum et al., Nature, 555, 629 https://arxiv.org/abs/1803.10237v1
+
+    values = np.array([15, 4, 2, 11, 1, -2, -1, -14, -39, -3])
+    values_lo = np.array([7, 16, 6, 3, 6, 5, 10, 6, 11, 13])
+    values_hi = np.array([7, 15, 8, 3, 6, 6, 10, 7, 14, 14])
+
+    n_data = len(values)
+
+    np.random.seed(42)
+
+    samples = []
+
+    for i in range(n_data):
+        # draw normal random points
+        u = np.random.normal(size=400)
+        v = values[i] + np.where(u < 0, u * values_lo[i], u * values_hi[i])
+
+        samples.append(v)
+
+    samples = np.array(samples)
+
+    # Define functions inside test_calibrator to access samples and n_data
+    def prior_transform(cube):
+        # the argument, cube, consists of values from 0 to 1
+        # we have to convert them to physical scales
+
+        params = cube.copy()
+        # let slope go from -3 to +3
+        lo = -100
+        hi = +100
+        params[0] = cube[0] * (hi - lo) + lo
+        # let scatter go from 1 to 1000
+        lo = np.log10(1)
+        hi = np.log10(1000)
+        params[1] = 10**(cube[1] * (hi - lo) + lo)
+        return params
+
+    def log_likelihood(params):
+        # unpack the current parameters:
+        mean, scatter = params
+
+        # compute the probability of each sample
+        probs_samples = scipy.stats.norm(mean, scatter).pdf(samples)
+        # average over each galaxy, because we assume one of the points is the correct one (logical OR)
+        probs_objects = probs_samples.mean(axis=1)
+        assert len(probs_objects) == n_data
+        # multiply over the galaxies, because we assume our model holds true for all objects (logical AND)
+        # for numerical stability, we work in log and avoid zeros
+        loglike = np.log(probs_objects + 1e-100).sum()
+        return loglike
+
+    parameters = ['mean', 'scatter']
+
+    sampler = ultranest.calibrator.ReactiveNestedCalibrator(
+        parameters, log_likelihood, prior_transform, log_dir="logs"
+    )
+
+    sampler.stepsampler = ultranest.stepsampler.SliceSampler(
+        nsteps=len(parameters),  
+        generate_direction=ultranest.stepsampler.generate_region_oriented_direction
+    )
+
+    sampler.run(min_num_live_points=400)
+
+    if plot:
+        sampler.plot()
+
+if __name__ == '__main__':
+    test_calibrator(plot=True)
\ No newline at end of file

From ebeb2f176af652a226df81d19969206f642b182e Mon Sep 17 00:00:00 2001
From: Qize Liu <llqqzz1998@yeah.net>
Date: Thu, 11 Dec 2025 17:52:38 +0800
Subject: [PATCH 305/313] Address requested changes for PR #174

---
 ultranest/calibrator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index e05c88ab..109da094 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -205,7 +205,7 @@ def run(self, **kwargs):
         """
 
         for nsteps, results in self.run_iter(**kwargs):
-            print(f" {nsteps:d}", results)
+            pass
         return results
 
 
@@ -218,7 +218,7 @@ def plot(self):
         * nsteps-calibration.pdf: evolution of ln(Z) with nsteps
         """
         if not self.sampler.log_to_disk:
-            raise ValueError("Plotting requires log_dir to be set during initialization")
+            return ValueError("Plotting requires log_dir to be set during initialization")
  
         self.sampler.stepsampler.plot(os.path.join(self.sampler.logs['plots'], 'stepsampler.pdf'))
 

From 547a1cfab9d82d8b4fd1dc2c78e91fbe91ef6f4d Mon Sep 17 00:00:00 2001
From: Qize Liu <llqqzz1998@yeah.net>
Date: Thu, 11 Dec 2025 18:09:17 +0800
Subject: [PATCH 306/313] Follow the style requirements of flake8

---
 ultranest/calibrator.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index 109da094..374b652c 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -180,7 +180,6 @@ def run_iter(self, **kwargs):
 
             nsteps *= 2
 
-    
     def run(self, **kwargs):
         """Run a sequence of ReactiveNestedSampler runs until convergence.
 
@@ -208,7 +207,6 @@ def run(self, **kwargs):
             pass
         return results
 
-
     def plot(self):
         """Visualise the convergence diagnostics.
 
@@ -219,7 +217,7 @@ def plot(self):
         """
         if not self.sampler.log_to_disk:
             return ValueError("Plotting requires log_dir to be set during initialization")
- 
+
         self.sampler.stepsampler.plot(os.path.join(self.sampler.logs['plots'], 'stepsampler.pdf'))
 
         # plot U-test convergence run length (at 4 sigma) (or niter) vs nsteps

From c7f011d810de29b012c0438b3b112eb78c90f564 Mon Sep 17 00:00:00 2001
From: Qize Liu <llqqzz1998@yeah.net>
Date: Fri, 12 Dec 2025 00:43:29 +0800
Subject: [PATCH 307/313] Restore the plot function

---
 ultranest/calibrator.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index 374b652c..f071083d 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -202,7 +202,6 @@ def run(self, **kwargs):
         result: dict
             return value of :py:meth:`ReactiveNestedSampler.run` for the final run
         """
-
         for nsteps, results in self.run_iter(**kwargs):
             pass
         return results
@@ -215,9 +214,6 @@ def plot(self):
         * nsteps-calibration-jumps.pdf: distribution of relative jump distance
         * nsteps-calibration.pdf: evolution of ln(Z) with nsteps
         """
-        if not self.sampler.log_to_disk:
-            return ValueError("Plotting requires log_dir to be set during initialization")
-
         self.sampler.stepsampler.plot(os.path.join(self.sampler.logs['plots'], 'stepsampler.pdf'))
 
         # plot U-test convergence run length (at 4 sigma) (or niter) vs nsteps

From f5ec27a4fe97ccf624f0a929aba5244c362444f8 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 20 Jan 2026 10:38:09 +0100
Subject: [PATCH 308/313] address numpy 2.4 removals

---
 tests/test_netiterintegrate.py |  2 +-
 tests/test_viz.py              |  4 +++-
 ultranest/calibrator.py        | 22 +++++++++++++++++++---
 3 files changed, 23 insertions(+), 5 deletions(-)

diff --git a/tests/test_netiterintegrate.py b/tests/test_netiterintegrate.py
index ec6bd974..8380aac4 100644
--- a/tests/test_netiterintegrate.py
+++ b/tests/test_netiterintegrate.py
@@ -186,7 +186,7 @@ def create_node(pointstore, Lmin):
 		main_iterator.passing_node(node, active_values)
 		for it, rootids in iterator_roots:
 			if rootid in rootids:
-				mask = np.in1d(active_rootids, rootids, assume_unique=True)
+				mask = np.isin(active_rootids, rootids, assume_unique=True)
 				#mask1 = np.array([rootid2 in rootids for rootid2 in active_rootids])
 				#assert (mask1 == mask).all(), (mask1, mask)
 				it.passing_node(node, active_values[mask])
diff --git a/tests/test_viz.py b/tests/test_viz.py
index 85f8b327..972bc689 100644
--- a/tests/test_viz.py
+++ b/tests/test_viz.py
@@ -13,8 +13,10 @@ def wrap_single_fmt_test(vlo, vhi, fmt_expected):
     assert vlo < vhi
     plo, phi, fmts = round_parameterlimits(np.asarray([vlo]), np.asarray([vhi]), [(vlo, vhi)])
     assert fmts[0] == fmt_expected, (fmts, fmt_expected)
+    assert len(plo) == 1, (plo)
+    assert len(phi) == 1, (phi)
     fmt = fmts[0]
-    assert fmt % plo != fmt % phi, (fmt, plo, phi, fmt % plo, fmt % phi)
+    assert fmt % plo[0] != fmt % phi[0], (fmt, plo, phi, fmt % plo, fmt % phi)
 
 def test_rounding_pos():
     wrap_single_test(0.00003, 0.001, 0, 0.001)
diff --git a/ultranest/calibrator.py b/ultranest/calibrator.py
index f071083d..e547e41d 100644
--- a/ultranest/calibrator.py
+++ b/ultranest/calibrator.py
@@ -5,12 +5,29 @@
 """
 
 import os
+from collections import deque
 
 import numpy as np
 
 from ultranest.integrator import ReactiveNestedSampler
 
 
+def _last_item_from_iterator(iterator):
+    """Get last item from iterator.
+
+    Parameters
+    ----------
+    iterator: iterator
+        Iterator or list of elements
+
+    Returns
+    -------
+    element: object
+        last item yielded by iterator.
+    """
+    return deque(iterator, maxlen=1).pop()
+
+
 def _substitute_log_dir(init_args, nsteps):
     """Append `nsteps` to `log_dir` argument, if set.
 
@@ -202,9 +219,8 @@ def run(self, **kwargs):
         result: dict
             return value of :py:meth:`ReactiveNestedSampler.run` for the final run
         """
-        for nsteps, results in self.run_iter(**kwargs):
-            pass
-        return results
+        _nsteps, result = _last_item_from_iterator(self.run_iter(**kwargs))
+        return result
 
     def plot(self):
         """Visualise the convergence diagnostics.

From 0a351030eb99e7c8c075be156fccd538110033ee Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 20 Jan 2026 11:16:58 +0100
Subject: [PATCH 309/313] doc formatting

---
 ultranest/integrator.py     |  4 ++--
 ultranest/popstepsampler.py | 30 ++++++++++++++++--------------
 2 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/ultranest/integrator.py b/ultranest/integrator.py
index f668a046..4f9ecaf6 100644
--- a/ultranest/integrator.py
+++ b/ultranest/integrator.py
@@ -2482,7 +2482,7 @@ def run(
 
         return self.results
 
-    def run_iter(
+    def run_iter(  # noqa: DOC101,DOC103
             self,
             update_interval_volume_fraction=0.8,
             update_interval_ncall=None,
@@ -2512,7 +2512,7 @@ def run_iter(
             for result in sampler.run_iter(...):
                 print('lnZ = %(logz).2f +- %(logzerr).2f' % result)
 
-        Parameters as described in run() method.
+        Parameters are described in the :py:func:`ReactiveNestedSampler.run` method.
 
         Yields
         ------
diff --git a/ultranest/popstepsampler.py b/ultranest/popstepsampler.py
index e607b6ee..420d46ae 100644
--- a/ultranest/popstepsampler.py
+++ b/ultranest/popstepsampler.py
@@ -705,16 +705,17 @@ def slice_limit_to_unitcube(tleft, tright):
     """
     Return the slice limits as of the intersection between the slice and the unit cube boundaries.
 
-    parameters
+    Parameters
     ----------
-        tleft: float
-                Intersection of the unit cube with the slice in the negative direction
-        tright: float
-                Intersection of the unit cube with the slice in the positive direction
+    tleft: float
+        Intersection of the unit cube with the slice in the negative direction
+    tright: float
+        Intersection of the unit cube with the slice in the positive direction
+
     Returns
     -------
-        (tleft_new,tright_new): tuple
-                Positive and negative slice limits
+    tnew: tuple
+        Positive and negative slice limits, `(tleft_new, tright_new) = tnew`
     """
     tleft_new, tright_new = tleft.copy(), tright.copy()
 
@@ -724,16 +725,17 @@ def slice_limit_to_unitcube(tleft, tright):
 def slice_limit_to_scale(tleft, tright):
     """Return -1..+1 or the intersection between slice and unit cube if that is shorter.
 
-    parameters
+    Parameters
     ----------
-        tleft: float
-                Intersection of the unit cube with the slice in the negative direction
-        tright: float
-                Intersection of the unit cube with the slice in the positive direction
+    tleft: float
+        Intersection of the unit cube with the slice in the negative direction
+    tright: float
+        Intersection of the unit cube with the slice in the positive direction
+
     Returns
     -------
-        (tleft_new,tright_new): tuple
-                Positive and negative slice limits
+    tnew: tuple
+        Positive and negative slice limits, `(tleft_new, tright_new) = tnew`
     """
     tleft_new = np.fmax(tleft, -1. + np.zeros_like(tleft))
     tright_new = np.fmin(tright, 1. + np.zeros_like(tright))

From 4ccfcfb6248cf995640ebfb708d35e95f592a9e5 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 20 Jan 2026 11:28:59 +0100
Subject: [PATCH 310/313] =?UTF-8?q?Bump=20version:=204.4.0=20=E2=86=92=204?=
 =?UTF-8?q?.5.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 setup.py              | 2 +-
 ultranest/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index e7a1cfbf..1e2d9157 100644
--- a/setup.py
+++ b/setup.py
@@ -74,7 +74,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JohannesBuchner/ultranest',
-    version='4.4.0',
+    version='4.5.0',
     zip_safe=False,
     cmdclass={'build_ext': build_ext},
 )
diff --git a/ultranest/__init__.py b/ultranest/__init__.py
index ec477891..6242f27f 100644
--- a/ultranest/__init__.py
+++ b/ultranest/__init__.py
@@ -6,4 +6,4 @@
 
 __author__ = """Johannes Buchner"""
 __email__ = 'johannes.buchner.acad@gmx.com'
-__version__ = '4.4.0'
+__version__ = '4.5.0'

From 46120edf849c0043ab9e0a903b69c11d2f162420 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Tue, 20 Jan 2026 13:18:25 +0100
Subject: [PATCH 311/313] [ci] give dirichlet fit more runtime

---
 .circleci/config.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index acf11b08..8296942d 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -29,7 +29,9 @@ jobs:
             echo "backend: Agg" > matplotlibrc
 
       - run: python3 examples/testfeatures.py
-      - run: python3 examples/rundirichlet.py
+      - run: 
+            no_output_timeout: 20m
+            command: python3 examples/rundirichlet.py
       - run: coverage3 run --parallel-mode docs/simple.py
       - run: coverage3 run --parallel-mode docs/gauss.py --x_dim=1 --log_dir=tmp
       - run: coverage3 combine

From c8a441c984ea75f0f5b7dbc028abf2580f6e5515 Mon Sep 17 00:00:00 2001
From: Johannes Buchner <johannes.buchner.acad@gmx.com>
Date: Thu, 22 Jan 2026 19:38:12 +0100
Subject: [PATCH 312/313] change quantile computation, print gave an error

---
 docs/API.rst                              | 2 +-
 docs/example-intrinsic-distribution.ipynb | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/API.rst b/docs/API.rst
index fb55a5a8..f85b1788 100644
--- a/docs/API.rst
+++ b/docs/API.rst
@@ -34,7 +34,7 @@ Experimental modules, no guarantees:
  * :py:mod:`ultranest.dychmc`: Constrained Hamiltanean Monte Carlo step sampling.
  * :py:mod:`ultranest.dyhmc`: Experimental constrained Hamiltanean Monte Carlo step sampling
  * :py:mod:`ultranest.flatnuts`: FLATNUTS is a implementation of No-U-turn sampler 
- * :py:mod:`ultranest.pathsampler`: MCMC-like step sampling on a trajectory
+ * :py:mod:`ultranest.pathsampler`: MCMC-like step sampling on a trajectory.
  * :py:mod:`ultranest.samplingpath`: Sparsely sampled, virtual sampling path.
 
 
diff --git a/docs/example-intrinsic-distribution.ipynb b/docs/example-intrinsic-distribution.ipynb
index 65e8dfbb..e5ca65b7 100644
--- a/docs/example-intrinsic-distribution.ipynb
+++ b/docs/example-intrinsic-distribution.ipynb
@@ -264,7 +264,7 @@
     "quantile = scipy.stats.norm().cdf(3)\n",
     "\n",
     "# look at the value:\n",
-    "print('scatter is < %.4f km/s at 3 sigma (%.3f%% quantile)' % (scipy.stats.mstats.mquantiles(scatter_samples, quantile), quantile*100))"
+    "print('scatter is < %.4f km/s at 3 sigma (%.3f%% quantile)' % (np.quantile(scatter_samples, quantile), quantile*100))"
    ]
   },
   {
@@ -326,9 +326,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From 29d1f8338cb4282e32625d4a56636d8f39304fd3 Mon Sep 17 00:00:00 2001
From: Thomas Wood <thomas.wood@stfc.ac.uk>
Date: Fri, 27 Feb 2026 17:46:57 +0000
Subject: [PATCH 313/313] Corrected comment that had slipped in from a
 different example

---
 docs/example-intrinsic-distribution.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/example-intrinsic-distribution.ipynb b/docs/example-intrinsic-distribution.ipynb
index e5ca65b7..c058cf87 100644
--- a/docs/example-intrinsic-distribution.ipynb
+++ b/docs/example-intrinsic-distribution.ipynb
@@ -139,7 +139,7 @@
     "    # we have to convert them to physical scales\n",
     "    \n",
     "    params = cube.copy()\n",
-    "    # let slope go from -3 to +3\n",
+    "    # let mean go from -100 to +100\n",
     "    lo = -100\n",
     "    hi = +100\n",
     "    params[0] = cube[0] * (hi - lo) + lo\n",