Feature test package imports

.gitignore

@@ -24,6 +24,21 @@ var/
 .installed.cfg
 *.egg
 
+# pytest cache
+.pytest_cache
+
+# package and build testing
+build
+dist
+*.egg-info
+
+
+## development tools
+# pycharm
+.idea
+# visual studio code
+.vscode
+
 # PyInstaller
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.

LICENSE

@@ -1,28 +1,21 @@
-Copyright (c) 2018, University of California and The National Institues of Health. All rights reserved.
+Copyright (c) 2018, University of California and The National Institutes of Health.
 
-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.
-   * The name of the University of California may not be used to
-     endorse or promote products derived from this software without
-     specific prior written permission.
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
 
-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 THE COPYRIGHT HOLDERS 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.
+  1. Redistributions of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
 
-For portions of this code, copyright and license information differs from
-the above. In these cases, copyright and/or license information is inline.
+  2. 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.
 
+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
+THE COPYRIGHT HOLDER OR CONTRIBUTORS 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.

README.rst

@@ -1,15 +1,15 @@
 BIFS Module Repository
-========================
+======================
 
 This project is an implementation of Bayesian imaging in Fourier Space
 (BIFS). For details on the method see, e.g.:
 
-J. Kornak, K. Young, N. Schuff, A. Du, A. A. Maudsley, M. W. Weiner.
+ - J. Kornak, K. Young, N. Schuff, A. Du, A. A. Maudsley, M. W. Weiner.
    K-Bayes Reconstruction for Perfusion MRI I: Concepts and Application. Journal of Digital Imaging. (2009) Feb 10
-J. Kornak, K. Young.
+ - J. Kornak, K. Young.
    K-Bayes Reconstruction for Perfusion MRI II: Modeling Motivation
    and Technical Development. Journal of Digital Imaging. (2009) Mar 10
-J. Kornak , K. Young, B.J. Soher, A.A. Maudsley.
+ - J. Kornak , K. Young, B.J. Soher, A.A. Maudsley.
    Bayesian k -space-time reconstruction of MR spectroscopic imaging for enhanced resolution. IEEE Trans Med Imaging. 2010 Jul;29(7):1333-50.
 
 The basic idea is to perform something like a Bayesian image
@@ -19,6 +19,7 @@ efficient way by transforming to a space in which the degrees of
 freedom can, at least approximately, be treated independently.
 For a large class of images, transforming to a Fourier space
 representation appears to accomplish this quite well.
+
 And to emphasize a point that sometimes arises as a point of
 confusion regarding this method, the goal is NOT to find a transform
 that results in a parsimonious representation of the image, but one
@@ -41,14 +42,18 @@ Python, e.g. Enthought's Canopy environment).
 The currently required packages are (BIFS is known to work with
 the following versions but will typically work with many earlier
 versions as well):
-Python 3
-numpy 1.15.4-2
-scipy 1.2.1-1
-matplotlib 2.2.4-2
-imageio 2.4.1
-jsonpickle 1.2
-pyqt5 5.7.1-1 (note: some version of PyQT 5 is required, i.e.
-              (the package will not work with PyQT 4)
+
+::
+
+ Python 3
+ numpy 1.15.4-2
+ scipy 1.2.1-1
+ matplotlib 2.2.4-2
+ imageio 2.4.1
+ jsonpickle 1.2
+ pyqt5 5.7.1-1
+ (note: some version of PyQT 5 is required, i.e.
+  the package will not work with PyQT 4)
 
 
 Using the Package
@@ -70,10 +75,10 @@ py -3 bifs_gui.py
 This provides the easiest access to the BIFS package but provides
 limited access to BIFS class variables.
 
-To gain full access to the capabilities of BIFS one can: 1) run python,
-2) load the BIFS class (import bifs), and 3) e.g. modify the
-commands in one of the scripts (e.g. bifs_cl_2D.py) described below
-to suit one's particular project.
+To gain full access to the capabilities of BIFS one can:
+ 1) run python
+ 2) load the BIFS class (import bifs)
+ 3) e.g. modify the commands in one of the scripts (e.g. bifs_cl_2D.py) described below to suit one's particular project.
 
 Empirical Priors
 ~~~~~~~~~~~~~~~~
@@ -148,6 +153,8 @@ provided with the package (and mentioned above).
 
 Class variables available to constructor:
 
+::
+
     init_image - initial loaded image
     k_image - initial k-space image
     mod_image - initial modulus image in k-space
@@ -161,27 +168,30 @@ Class variables available to constructor:
 
     image_file_loaded - whether an image is loaded (True,False)
     initial_image_file_name - file name of initial image
+
     imdim - int image dimension (1,2 or 3)
     imdim1 - int specifying size of 1st dimension of image
     imdim2 - int specifying size of 2nd dimension of image
     imdim3 - int specifying size of possible 3rd dimension of "image"
+
     kdist = distance function on the shifted k-space lattice
 
-    view3Dslice - for 3D data this is a 2D array [a,b] where:
-                  a = axis perpendicular to slice
-                  b = fraction of maximum along that direction 
-                      for slice location
+    view3Dslice - for 3D data this is a 2D array [a,b] where::
+                   a = axis perpendicular to slice
+                   b = fraction of maximum along that direction
+                   for slice location
 
     prior - string specifying the prior distribution function to use
             current choices are:
-            'Gaussian'
+              'Gaussian'
 
     prior_choices - list of current prior distribution
                     function choices (see above)
     prior_mean_init - prior mean before parameter space function
                       is set up (used for tests)
-    prior_mean - the prior mean defined at each k-space point 
+    prior_mean - the prior mean defined at each k-space point
                  by the k-space parameter function
+
     prior_std - the prior std defined at each k-space point
     prior_scale - the overall scale of the prior variance
     prior_scale_orig - prior scale at the origin - generally set huge
@@ -197,15 +207,13 @@ Class variables available to constructor:
 
     bessel_approx_lims - limits for bessel approximation for rice
                          distribution - see paper referenced in code
-
     bessel_approx_array - array for bessel approximation for rice
                          distribution - see paper referenced in code
-    
+
     rice_denom_cutoff - cutoff for the denominator of the closed form
                         of the posterior with a Gaussian prior and
                         Rician likelihood derived from bessel approximation
                         see paper referenced in code
-
     param_func_type - string specifying the k-space BIFS parameter
                       function to use
                       current choices are:
@@ -218,30 +226,28 @@ Class variables available to constructor:
     decay - float decay exponent for the inverse power parameter function
     bvec - 2D float array specifying intercept and amplitude for parameter
            space functions 
-    banded_cutoff - cutoff for banded, inverse power k-space parameter function
 
+    banded_cutoff - cutoff for banded, inverse power k-space parameter function
     basis - string specifying the basis to use - currently only choice
             is "Fourier"
-
     basis_choices - list of current choices (see above)
 
     bumps - dictionary containing set of "bump filters" to implement
             note: these "bump filters" are elements that are added
-	    to the parameter function to increase (or decrease if the
-	    amplitude is specified as negative) the sensitivity of the
-	    analysis to frequency ranges known in advance to be important
-	    (or missing) in the analyzed images. E.g. if there is a
-	    predominance of features of a give size, adding filters at
-	    wavelengths corresponding to that size could enhance the
-	    sensitivity of the analysis. The scipy.signal package
-	    provides a number of filters meant to applied in
-	    the time (image) domain to characterize properties in the
-	    Fourier domain. Providing these shapes for application in
-	    the Fourier domain for BIFS was straightforward and might
-	    be interesting to experiment with re. effective image
-	    feature enhancement.
-
-
+            to the parameter function to increase (or decrease if the
+            amplitude is specified as negative) the sensitivity of the
+            analysis to frequency ranges known in advance to be important
+            (or missing) in the analyzed images. E.g. if there is a
+            predominance of features of a give size, adding filters at
+            wavelengths corresponding to that size could enhance the
+            sensitivity of the analysis. The scipy.signal package
+            provides a number of filters meant to applied in
+            the time (image) domain to characterize properties in the
+            Fourier domain. Providing these shapes for application in
+            the Fourier domain for BIFS was straightforward and might
+            be interesting to experiment with re. effective image
+            feature enhancement.
+
     bump_types - set of choices for "bump" filter types to add to k-space
                  parameter function; uses scipy.signal window types
                  so consult that documentation for available types - 
@@ -257,6 +263,7 @@ Class variables available to constructor:
                  "blackmanharris"
                  "nuttall"
                  "barthann"
+
     bump_default_type - the default window type used (currently "blackman")
 
 

setup.cfg

@@ -0,0 +1,13 @@
+[metadata]
+# This includes the license file(s) in the wheel.
+# https://wheel.readthedocs.io/en/stable/user_guide.html#including-license-files-in-the-generated-wheel-file
+license_files = 'LICENSE'
+
+[bdist_wheel]
+# This flag says to generate wheels that support both Python 2 and Python
+# 3. If your code will not run unchanged on both Python 2 and 3, you will
+# need to generate separate wheels for each Python version that you
+# support. Removing this line (or setting universal to 0) will prevent
+# bdist_wheel from trying to make a universal wheel. For more see:
+# https://packaging.python.org/guides/distributing-packages-using-setuptools/#wheels
+universal=1

setup.py

@@ -8,22 +8,61 @@
 with open('LICENSE') as f:
     license = f.read()
 
+packages_ = find_packages('src')
+
 setup(
     name='bifs',
-    version='0.2.0',
+    version='0.1.0a5',
     description='Implementation of Bayesian Imaging in Fourier Space (BIFS)',
     long_description=readme,
     author='John Kornak, Karl Young, Ross Boylan',
-    author_email='john.kornak@ucsf.edu,kyoung21b2000@gmail.com',
-    url='https://github.com/bifs',
-    license=license,
-    packages=['bifs'],
+    author_email='john.kornak@ucsf.edu',
+    url='https://github.com/bifs/bifs',
+    license='BSD',
+    package_dir={'': 'src'},
+    package_data={'bifs': ['images/*']},
+    include_package_data=True,
+    scripts=['src/bifs/gui/bifs_gui.py'],
+    entry_points = {
+        'console_scripts': ['bifs_gui=bifs.gui.bifs_gui:main'],
+    },
+    packages=packages_,
+    # packages=find_packages(where='src') # does not work for me.
+
     install_requires=['imageio',
                       'jsonpickle',
                       'matplotlib',
                       'nibabel',
                       'numpy',
                       'PyQt5',
-                      'scipy']
+                      'scipy'],
+    # Option items:
+    # extras_require={...},
+    # data_files=[(...)],
+
+    classifiers=[
+        # How mature is this project? Common values are
+        #   3 - Alpha; 4 - Beta; 5 - Production/Stable
+        'Development Status :: 3 - Alpha',
+
+        # Indicate who your project is intended for
+        'Intended Audience :: Developers',
+        'Topic :: Scientific/Engineering',
+        'Topic :: Scientific/Engineering :: Bio-Informatics',
+        'Topic :: Scientific/Engineering :: Image Recognition',
+
+        # Pick your license as you wish (should match "license" above)
+        'License :: OSI Approved :: BSD License',
+
+        # Specify the Python versions you support here.
+        # I assume we only support Python 3 and recent version.
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: 3.5',
+        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.8',
+        ],
+    keywords='Bayesian Fourier Image development',
+    python_requires='>=3.5',
 )
 

src/bifs/__init__.py

@@ -0,0 +1 @@
+

bifs/src/bifs_cl_1D.py → src/bifs/bifs_cl_1D.py

@@ -1,13 +1,10 @@
 # A command line type example for using BIFS example
 
 import numpy as np
-import scipy as sp
-from scipy import misc,stats
-import random
-from pylab import *
+from scipy import stats
 import matplotlib.pyplot as plt
-import bifs
-import bifs_util.util as bu
+from bifs.bifs_core import Bifs
+from bifs.bifs_util import util as bu
 
 # Make 1D "image"
 # Noise standard deviation in image space
@@ -18,7 +15,7 @@
 y = z + stats.norm.rvs(size=len(z),loc=0.0,scale=noiseSD)
 
 # Create mybifs BIFS object:
-mybifs = bifs.bifs()
+mybifs = Bifs()
 
 # Can take a look at what functions and variables are available with, e.g.:
 # dir(mybifs)