From 602a9b1eb5c963c6521b795a05faab99a41f510e Mon Sep 17 00:00:00 2001 From: claalmve Date: Wed, 4 Mar 2026 15:24:47 -0800 Subject: [PATCH 01/53] Upgrading drought metrics to new core --- work-in-progress/drought_metrics.ipynb | 264 ++----------------------- 1 file changed, 18 insertions(+), 246 deletions(-) diff --git a/work-in-progress/drought_metrics.ipynb b/work-in-progress/drought_metrics.ipynb index 71fd643e..d20b41eb 100644 --- a/work-in-progress/drought_metrics.ipynb +++ b/work-in-progress/drought_metrics.ipynb @@ -76,26 +76,21 @@ "### Imports" ] }, + { + "cell_type": "code", + "id": "xypsmbxjeoj", + "source": "# Install climate_indices at a specific commit compatible with the AE hub environment\n!pip install -qq git+https://github.com/monocongo/climate_indices.git@43c5451", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "execution_count": null, "id": "a7e19917", "metadata": {}, "outputs": [], - "source": [ - "import xclim\n", - "import os\n", - "import xarray as xr\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from pyproj import CRS\n", - "from climakitae.core.data_interface import get_data\n", - "from climakitae.core.data_load import load\n", - "from climakitae.core.data_export import export\n", - "from climakitae.util.utils import add_dummy_time_to_wl\n", - "from climakitae.util.utils import reproject_data" - ] + "source": "import climakitae as ck\nimport xclim\nimport os\nimport xarray as xr\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pyproj import CRS\nfrom climate_indices.palmer import pdsi as palmer_pdsi\nfrom xclim.indices.stats import standardized_index\n\nfrom climakitae.core.data_export import export\nfrom climakitae.util.utils import reproject_data\nfrom climakitae.new_core.derived_variables import register_user_function, list_derived_variables" }, { "cell_type": "markdown", @@ -128,76 +123,13 @@ "}" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "92a9173e", - "metadata": {}, - "outputs": [], - "source": [ - "# Making a `input_data` folder to hold intermediate data variables needed for PET\n", - "if not os.path.exists(\"input_data\"):\n", - " os.mkdir(\"input_data\")" - ] - }, - { - "cell_type": "markdown", - "id": "03173361", - "metadata": {}, - "source": [ - "**IMPORTANT NOTE**: The following cell saves out the intermediate data that is required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files." - ] - }, { "cell_type": "code", "execution_count": null, "id": "a0132b29-804a-4eef-893f-2f60adfefc62", "metadata": {}, "outputs": [], - "source": [ - "### Retrieving the different variables needed for PET\n", - "datas = []\n", - "\n", - "for _, (variable, var_long_name) in enumerate(variables_dict.items()):\n", - "\n", - " file_path = f\"input_data/{variable}_daily.nc\"\n", - "\n", - " if os.path.exists(file_path):\n", - " print(f\"Reading {variable} from file.\")\n", - " da = xr.open_dataarray(file_path)\n", - " \n", - " else:\n", - " print(f\"Computing {var_long_name}\")\n", - " ae_var_name = var_long_name\n", - " timescale = 'daily'\n", - " if variable == 'rlus' or variable == 'rsus':\n", - " timescale = 'hourly'\n", - " da = get_data(\n", - " variable=ae_var_name,\n", - " resolution='3 km',\n", - " timescale=timescale,\n", - " # Expanding the spatial extent to cover gridcells outside of WRF domain will throw an error!\n", - " # Please reduce the extent of your data retrieval if you do get an error like the `IndexError` mentioned above\n", - " latitude=(lat - 0.04, lat + 0.04),\n", - " longitude=(lon - 0.04, lon + 0.04),\n", - " approach=\"Warming Level\",\n", - " warming_level=[0.8, 1.5, 2.0, 3.0],\n", - " downscaling_method=\"Dynamical\"\n", - " )\n", - " da = load(add_dummy_time_to_wl(da), progress_bar=True)\n", - " if variable == 'tasmin':\n", - " agg_da = da.squeeze().resample(time='D').min()\n", - " elif variable == 'tasmax':\n", - " agg_da = da.squeeze().resample(time='D').max()\n", - " elif variable == 'precip':\n", - " agg_da = da.squeeze().resample(time='D').sum()\n", - " else:\n", - " agg_da = da.squeeze().resample(time='D').mean()\n", - " agg_da.to_netcdf(file_path) # Save for reuse\n", - " da = agg_da\n", - "\n", - " datas.append(da)" - ] + "source": "cd = ck.ClimateData(verbosity=-1)\nwarming_levels = [0.8, 1.5, 2.0, 3.0]\nprocesses = {\n \"clip\": [(lat - 0.04, lon - 0.04), (lat + 0.04, lon + 0.04)],\n \"warming_level\": {\"warming_levels\": warming_levels},\n}\n\ndef fetch(variable, table_id=\"1day\"):\n return (\n cd.catalog(\"cadcat\")\n .activity_id(\"WRF\")\n .table_id(table_id)\n .grid_label(\"d03\")\n .variable(variable)\n .processes(processes)\n .get()\n )\n\nprint(\"Fetching variables...\")\ntasmin = fetch(\"tasmin\")\ntasmax = fetch(\"tasmax\")\nhurs = fetch(\"hurs\")\nrsds = fetch(\"rsds\")\nrsus = fetch(\"rsus\", table_id=\"1hr\")\nrlds = fetch(\"rlds\")\nrlus = fetch(\"rlus\", table_id=\"1hr\")\nwspd10mean = fetch(\"wspd10mean\")\nprecip = fetch(\"precip\")\nprint(\"Done.\")" }, { "cell_type": "code", @@ -205,19 +137,7 @@ "id": "df54b41e", "metadata": {}, "outputs": [], - "source": [ - "# Creating daily variables for all hourly variables\n", - "tasmin = datas[0]\n", - "tasmax = datas[1]\n", - "hurs = datas[2] / 100 # Convert from % to fraction\n", - "new_hurs = hurs.assign_attrs(units='1')\n", - "rsds = datas[3]\n", - "rsus = datas[4]\n", - "rlds = datas[5]\n", - "rlus = datas[6]\n", - "sfcWind = datas[7]\n", - "precip = datas[8]" - ] + "source": "# Convert relative humidity from % to fraction as required by xclim\nhurs_frac = (hurs / 100).assign_attrs(units='1')\n\n# Resample hourly radiation fluxes to daily means\nrsus_daily = rsus.resample(time=\"1D\").mean()\nrlus_daily = rlus.resample(time=\"1D\").mean()" }, { "cell_type": "code", @@ -225,20 +145,7 @@ "id": "32f5bcba", "metadata": {}, "outputs": [], - "source": [ - "# Calculating PET from xclim\n", - "pet_calc = xclim.indices.potential_evapotranspiration(\n", - " tasmin=tasmin,\n", - " tasmax=tasmax,\n", - " hurs=new_hurs,\n", - " rsds=rsds,\n", - " rsus=rsus,\n", - " rlds=rlds,\n", - " rlus=rlus,\n", - " sfcWind=sfcWind,\n", - " method=\"FAO_PM98\"\n", - ")" - ] + "source": "# Calculating PET from xclim using the Penman-Monteith method\npet_calc = xclim.indices.potential_evapotranspiration(\n tasmin=tasmin,\n tasmax=tasmax,\n hurs=hurs_frac,\n rsds=rsds,\n rsus=rsus_daily,\n rlds=rlds,\n rlus=rlus_daily,\n sfcWind=wspd10mean,\n method=\"FAO_PM98\"\n)" }, { "cell_type": "code", @@ -264,38 +171,7 @@ "cell_type": "markdown", "id": "415dcfc7", "metadata": {}, - "source": [ - "Here, we will use the PDSI function from the `climate_indices` library, which is also what drought.gov has referenced. However, we will install a specific commit of the package that is compatible with the AE hub environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0eaac76-5c73-405d-817a-4c2bdf44f7fb", - "metadata": {}, - "outputs": [], - "source": [ - "# Pip install the specific commit of the package that supports AE package versions\n", - "!pip install -qq git+https://github.com/monocongo/climate_indices.git@43c5451" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0bc9fcf", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Making imports from `climate_indices` package\n", - "import climate_indices\n", - "from climate_indices.palmer import pdsi" - ] + "source": "PDSI is implemented as a derived variable using the new core `register_user_function` pattern. The underlying computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by drought.gov. The function is vectorized over spatial dimensions using `xr.apply_ufunc`." }, { "cell_type": "code", @@ -392,39 +268,7 @@ "id": "c6b5de48", "metadata": {}, "outputs": [], - "source": [ - "# Helper function to vectorize PDSI calculation across `combined_ds` dimensions.\n", - "def calc_pdsi(timeseries: xr.Dataset):\n", - " \"\"\"\n", - " Compute the Palmer Drought Severity Index (PDSI) from a Dataset.\n", - "\n", - " Parameters\n", - " ----------\n", - " timeseries : xarray.Dataset\n", - " Dataset containing 'precip' and 'pet' variables with a time dimension.\n", - "\n", - " Returns\n", - " -------\n", - " xarray.DataArray\n", - " PDSI values along the time dimension.\n", - " \"\"\"\n", - " # Extracting precip and PET by each timeseries and calculating PDSI\n", - " precip = timeseries['precip'].squeeze()\n", - " pet = timeseries['pet'].squeeze()\n", - " \n", - " pdsi_calc = pdsi(\n", - " precips=precip.values,\n", - " pet=pet.values,\n", - " awc=5,\n", - " data_start_year=2000,\n", - " calibration_year_initial=2000,\n", - " calibration_year_final=2030,\n", - " )\n", - " retval = xr.DataArray(pdsi_calc[0], coords={\"time\": precip.time.values}, dims=['time'])\n", - " \n", - " # Clipping PDSI to realistic values\n", - " return retval.clip(min=-10, max=10)" - ] + "source": "def pdsi_func(ds):\n \"\"\"Compute Palmer Drought Severity Index (PDSI) from a Dataset.\n\n Derived variable function: receives an xr.Dataset with 'precip' and 'pet'\n variables (both monthly, in inches) and adds 'pdsi' to it.\n\n Parameters\n ----------\n ds : xr.Dataset\n Dataset with 'precip' and 'pet' variables along a 'time' dimension.\n\n Returns\n -------\n xr.Dataset\n Input dataset with 'pdsi' variable added.\n \"\"\"\n def _pdsi_1d(precip_arr, pet_arr):\n result = palmer_pdsi(\n precips=precip_arr,\n pet=pet_arr,\n awc=5,\n data_start_year=2000,\n calibration_year_initial=2000,\n calibration_year_final=2030,\n )\n return result[0].clip(-10, 10)\n\n pdsi_values = xr.apply_ufunc(\n _pdsi_1d, ds[\"precip\"], ds[\"pet\"],\n input_core_dims=[[\"time\"], [\"time\"]],\n output_core_dims=[[\"time\"]],\n vectorize=True,\n output_dtypes=[float],\n )\n ds[\"pdsi\"] = pdsi_values.assign_coords(time=ds.time).assign_attrs({\n \"long_name\": \"Palmer Drought Severity Index\",\n \"units\": \"from -10 (dry) to +10 (wet)\",\n })\n return ds\n\nregister_user_function(\n name=\"pdsi\",\n depends_on=[\"precip\", \"pet\"],\n func=pdsi_func,\n description=\"Palmer Drought Severity Index computed from monthly precipitation and PET.\",\n units=\"unitless\",\n drop_dependencies=False,\n)" }, { "cell_type": "code", @@ -432,23 +276,7 @@ "id": "c625f3af", "metadata": {}, "outputs": [], - "source": [ - "# Applies the PDSI function across all dimensions so that a timeseries of PET/precip is always being passed into `pdsi`\n", - "pdsi_da = combined_ds.groupby([\n", - " 'combined_wl',\n", - " 'x',\n", - " 'y',\n", - " 'simulation'\n", - "]).apply(\n", - " lambda timeseries: calc_pdsi(timeseries)\n", - ")\n", - "\n", - "# Writing crs and reprojecting PDSI to lat/lon\n", - "pdsi_da = pdsi_da.rio.write_crs(crs.to_wkt())\n", - "pdsi_da = pdsi_da.transpose('time', 'combined_wl', 'simulation', 'y', 'x')\n", - "pdsi_latlon = reproject_data(pdsi_da, 'EPSG:4326')\n", - "del pdsi_latlon.attrs[\"_FillValue\"]" - ] + "source": "# Apply PDSI derived variable to the combined dataset\npdsi_ds = list_derived_variables()[\"pdsi\"].func(combined_ds)\npdsi_da = pdsi_ds[\"pdsi\"]\n\n# Writing crs and reprojecting PDSI to lat/lon\npdsi_da = pdsi_da.rio.write_crs(crs.to_wkt())\npdsi_da = pdsi_da.transpose('time', 'combined_wl', 'simulation', 'y', 'x')\npdsi_latlon = reproject_data(pdsi_da, 'EPSG:4326')\ndel pdsi_latlon.attrs[\"_FillValue\"]" }, { "cell_type": "markdown", @@ -524,53 +352,13 @@ "Now, we will calculate EDDI using PET." ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "d38a1969", - "metadata": {}, - "outputs": [], - "source": [ - "# Import `standardized_index` from xclim, which we will apply to our PET data object to generate EDDI\n", - "from xclim.indices.stats import standardized_index" - ] - }, { "cell_type": "code", "execution_count": null, "id": "3228b9fb", "metadata": {}, "outputs": [], - "source": [ - "def calc_eddi(timeseries: xr.DataArray):\n", - " \"\"\"\n", - " Compute the Evaporative Demand Drought Index (EDDI) for a time series.\n", - "\n", - " Parameters\n", - " ----------\n", - " timeseries : xarray.DataArray\n", - " 1D time series of ET₀. NaNs are skipped.\n", - "\n", - " Returns\n", - " -------\n", - " xarray.DataArray\n", - " EDDI values. Positive = dry, negative = wet.\n", - " \"\"\"\n", - " eddi = standardized_index(\n", - " da=timeseries,\n", - " freq='MS',\n", - " window=1,\n", - " dist=\"gamma\",\n", - " method=\"ML\",\n", - " zero_inflated=True,\n", - " fitkwargs={},\n", - " cal_start=\"2000-01-31\",\n", - " cal_end=\"2029-12-31\"\n", - " )\n", - " # Clipping EDDI to realistic values\n", - " retval = eddi.clip(min=-2.5, max=2.5)\n", - " return retval" - ] + "source": "def eddi_func(ds):\n \"\"\"Compute Evaporative Demand Drought Index (EDDI) from a Dataset.\n\n Derived variable function: receives an xr.Dataset with a 'pet' variable\n (monthly) and adds 'eddi' to it.\n\n Parameters\n ----------\n ds : xr.Dataset\n Dataset with 'pet' variable along a 'time' dimension.\n\n Returns\n -------\n xr.Dataset\n Input dataset with 'eddi' variable added.\n \"\"\"\n times = ds.time.values\n\n def _eddi_1d(pet_arr):\n pet_da = xr.DataArray(pet_arr, dims=[\"time\"], coords={\"time\": times})\n eddi = standardized_index(\n da=pet_da,\n freq=\"MS\",\n window=1,\n dist=\"gamma\",\n method=\"ML\",\n zero_inflated=True,\n fitkwargs={},\n cal_start=\"2000-01-31\",\n cal_end=\"2029-12-31\",\n )\n return eddi.clip(-2.5, 2.5).values\n\n eddi_values = xr.apply_ufunc(\n _eddi_1d, ds[\"pet\"],\n input_core_dims=[[\"time\"]],\n output_core_dims=[[\"time\"]],\n vectorize=True,\n output_dtypes=[float],\n )\n ds[\"eddi\"] = eddi_values.assign_coords(time=ds.time).assign_attrs({\n \"long_name\": \"Evaporative Demand Drought Index\",\n \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n })\n return ds\n\nregister_user_function(\n name=\"eddi\",\n depends_on=[\"pet\"],\n func=eddi_func,\n description=\"Evaporative Demand Drought Index computed from monthly PET.\",\n units=\"unitless\",\n drop_dependencies=False,\n)" }, { "cell_type": "code", @@ -578,23 +366,7 @@ "id": "1240839c", "metadata": {}, "outputs": [], - "source": [ - "# Applies the `calc_eddi` function across all dimensions so that a timeseries of PET is always being passed into `calc_eddi`\n", - "eddi_da = combined_ds['pet'].groupby([\n", - " 'combined_wl',\n", - " 'x',\n", - " 'y',\n", - " 'simulation'\n", - "]).apply(\n", - " lambda timeseries: calc_eddi(timeseries)\n", - ")\n", - "\n", - "# Writing crs and reprojecting EDDI to lat/lon\n", - "eddi_da = eddi_da.rio.write_crs(crs.to_wkt())\n", - "eddi_da = eddi_da.transpose('time', 'combined_wl', 'simulation', 'y', 'x')\n", - "eddi_da_latlon = reproject_data(eddi_da, 'EPSG:4326')\n", - "del eddi_da_latlon.attrs[\"_FillValue\"]" - ] + "source": "# Apply EDDI derived variable to the combined dataset\neddi_ds = list_derived_variables()[\"eddi\"].func(combined_ds)\neddi_da = eddi_ds[\"eddi\"]\n\n# Writing crs and reprojecting EDDI to lat/lon\neddi_da = eddi_da.rio.write_crs(crs.to_wkt())\neddi_da = eddi_da.transpose('time', 'combined_wl', 'simulation', 'y', 'x')\neddi_da_latlon = reproject_data(eddi_da, 'EPSG:4326')\ndel eddi_da_latlon.attrs[\"_FillValue\"]" }, { "cell_type": "markdown", @@ -676,4 +448,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file From 25d82182e28da51e26d1dff0a26f10bffd8ed967 Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Wed, 25 Feb 2026 16:45:39 -0300 Subject: [PATCH 02/53] getting started: removing plot calls, improving workflow --- analysis/warming_level_methods.ipynb | 194 ++++++++++++--------------- 1 file changed, 89 insertions(+), 105 deletions(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index 6678bd13..a223b272 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -155,18 +155,24 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "#from climakitae.core.data_load import load\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "from dask.diagnostics import ProgressBar\n", + "\n", + "import climakitae as ck\n", "from climakitae.util.utils import add_dummy_time_to_wl\n", - "from climakitae.util.unit_conversions import convert_units" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook, we'll be demonstrating two methods to grab Global Warming Level data from `climakitae`: `get_data` and `ClimateData`. The `ClimateData` method is a newer method that offers some additional flexibility and performance enhancements, but is still actively under development. Both methods are valid for retrieving our climate data. For more information about how both of these methods work, check out this notebook.\n" + "from climakitae.util.unit_conversions import convert_units\n", + "\n", + "# Set default plotting parameters for readability\n", + "plt.rcParams.update({\n", + " 'font.size': 15, # default font size\n", + " 'figure.titlesize': 20, # suptitle\n", + " 'axes.titlesize': 18, # title\n", + " 'axes.labelsize': 15, # axis labels\n", + " 'xtick.labelsize': 15, # x tick labels\n", + " 'ytick.labelsize': 15, # y tick labels\n", + " 'legend.fontsize': 15, # legend\n", + "})" ] }, { @@ -183,65 +189,13 @@ "- Warming Levels: 0.8 and 2.0, with a warming level window of 30 years." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Method 1: `get_data`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we'll go through an example that retrieves data using the `get_data` function. The syntax is nearly identical to loading SSP-based timeseries data, but with the addition of ```approach = \"Warming Level\"```, and a list of desired warming levels rather than SSP scenarios.\n", - "\n", - "The appropriate data will be pulled for each climate model, using a combination of climate model data run in the historical period and climate model data run for future SSP data.\n", - "\n", - "This example is included here as an example in markdown, and we will use the second method to load data to work with in this notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "```python \n", - "from climakitae.core.data_interface import get_data\n", - "\n", - "tasmax_gwl_data = get_data(\n", - " variable=\"Maximum air temperature at 2m\",\n", - " resolution=\"3 km\",\n", - " timescale=\"monthly\",\n", - " downscaling_method=\"Statistical\",\n", - " approach=\"Warming Level\",\n", - " warming_level=[0.8, 2.0]\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Method 2: `ClimateData`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A new method for loading data through climakitae is currently in development. This approach offers more flexibility and faster performance, but is still actively in development and may continue to undergo changes. Here we demonstrate the equivalent data loading operation with the new ClimateData object." - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from climakitae.new_core.user_interface import ClimateData\n", - "cd = ClimateData()" + "cd = ck.ClimateData(verbosity=-1)" ] }, { @@ -265,6 +219,7 @@ " .grid_label(\"d03\") # Looking at domain 03, which is `3 km` data over California\n", " .variable(\"tasmax\") # Grabbing the maximum temperature variable\n", " .processes({ # Centering our dataset around GWLs\n", + " \"convert_units\": 'degF',\n", " \"warming_level\": {\n", " \"warming_levels\": [0.8, 2.0], # Standard GWLs include: 0.8, 1.0, 1.5, 2.0, 2.5, 3.0, but any number can be requested\n", " \"warming_level_window\": 15, # Default is 15 years on either side of GWL crossing time, for total of 30 years of data\n", @@ -279,7 +234,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You'll notice we did not have to specify an SSP to load the data because simulations from any SSP can be used to measure climate impacts at a given GWL. This **ability to utilize data from all SSPs equivalently is one of the benefits of the GWL approach**. \n", + "You'll notice we did not have to specify an SSP to load the data because simulations from any SSP can be used to measure climate impacts at a given GWL. **This ability to utilize data from all SSPs equivalently is one of the benefits of the GWL approach**. \n", "\n", "As you will see below, we receive a data object with up to 129 simulations, which includes data from simulations of SSP 2-4.5, SSP 3-7.0, and SSP 5-8.5" ] @@ -304,7 +259,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Looking at the `tasmax_gwl_data` variable\n", "tasmax_gwl_data" ] }, @@ -312,14 +266,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**NOTE: One small difference between the `tasmax_gwl_data` object returned by `ClimateData` and `get_data` is that `ClimateData` will return a xr.Dataset, while `get_data` will return a xr.DataArray. You will just need to dot index (i.e. `tasmax_gwl_data.tasmax`) into the xr.Dataset to get the DataArray information from above.**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You'll notice that there are two dimensions and one coordinate that are not common in climate data retrieved using an SSP-approach:\n", + "There are 3 coordinates in our dataset that are unique to using a Global Warming Levels approach:\n", "1. `warming_level`: This dimension denotes which warming levels the data is centered around, and in this case, we can see the values as 0.8 and 2.0. These are the same as the WLs we chose above.\n", "2. `time_delta`: This dimension represents the offset in timesteps from the `centered_year` of a given simulation. Since each simulation spans 30 years, we can align and stack them along a common `time_delta` axis. Negative values indicate timesteps before the `centered_year`, while positive values indicate timesteps after it.\n", "3. `centered_year`: The year that a given simulation reaches a certain `warming_level`, which can be different for each simulation" @@ -336,7 +283,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, let's take a quick glance at what this data looks like in a couple of figures:" + "Now, let's take a quick glance at what this data looks like in a couple of figures. First, we'll load in a subset of data: one simulation, averaged across the entire time window." ] }, { @@ -345,26 +292,34 @@ "metadata": {}, "outputs": [], "source": [ - "# select just one simulation for this illustration\n", - "tasmax_gwl_data.isel(sim=0)" + "# Load in a small subset of the data to plot\n", + "# One simulation and grab the mean of the time delta simulation \n", + "sim = \"LOCA2_UCSD_EC-Earth3_ssp245_mon_d03_r1i1p1f1\"\n", + "arr = tasmax_gwl_data.tasmax.sel(sim=sim).mean(dim='time_delta')\n", + "\n", + "# Read data into memory for quicker plotting\n", + "with ProgressBar(): \n", + " arr = arr.compute()\n", + "\n", + "# Find the difference between WL 2.0 and WL 0.8\n", + "# Difference between highest and lowest warming levels\n", + "wls = arr.warming_level.values\n", + "diff = arr.sel(warming_level=wls[-1]) - arr.sel(warming_level=wls[0])" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-25T19:25:36.620353Z", + "iopub.status.busy": "2026-02-25T19:25:36.620120Z", + "iopub.status.idle": "2026-02-25T19:25:36.625350Z", + "shell.execute_reply": "2026-02-25T19:25:36.624465Z", + "shell.execute_reply.started": "2026-02-25T19:25:36.620331Z" + } + }, "source": [ - "# Plotting the first timestep of GWL max temp data in a side-by-side plot\n", - "from support_functions.warming_level_approach_helpers import fig1\n", - "\n", - "# Selecting the first simulation, then converting temperature to degF and averaging across the WL data\n", - "arr = convert_units(tasmax_gwl_data.tasmax.isel(sim=0), 'degF').mean(dim='time_delta').compute()\n", - "\n", - "# Finding the difference between WL 2.0 and WL 0.8\n", - "diff = arr.sel(warming_level=arr.warming_level.values[-1]) - arr.sel(\n", - " warming_level=arr.warming_level.values[0]\n", - ")" + "Now, we will plot the difference between two different warming levels. " ] }, { @@ -373,8 +328,20 @@ "metadata": {}, "outputs": [], "source": [ - "# Using a plotting helper to plot the average warming and a delta plot\n", - "fig1(arr, diff)" + "fig, axes = plt.subplots(1, 3, figsize=(17, 6), constrained_layout=True)\n", + "\n", + "sim_plotname = \"EC-Earth3\" # simulation name used for plot title\n", + "for ax, wl in zip(axes[:2], wls):\n", + " im = arr.sel(warming_level=wl).plot(ax=ax, cmap='magma_r', vmin=35, vmax=95, add_colorbar=False)\n", + " ax.set_title(f\"WL {wl} Avg Max Temp\")\n", + "\n", + "fig.colorbar(im, ax=axes[:2], label='Max Temp (°F)')\n", + "diff.plot(ax=axes[2], cmap='RdBu_r', vmin=-6, vmax=6, cbar_kwargs={'label': 'Δ Max Temp (°F)'})\n", + "axes[2].set_title(f\"Difference: WL {wls[-1]} - WL {wls[0]}\")\n", + "axes[1].set_ylabel('')\n", + "fig.suptitle(f\"Warming levels comparison for LOCA2 simulation {sim_plotname}\", y=1.08, fontweight=\"bold\")\n", + "\n", + "plt.show()" ] }, { @@ -398,9 +365,6 @@ "outputs": [], "source": [ "# Import precipitation data from WRF\n", - "\n", - "from support_functions.warming_level_approach_helpers import fig2\n", - "\n", "prec_gwl_data = (cd\n", " .catalog(\"cadcat\") # Same catalog of data as before\n", " .activity_id(\"WRF\") # Dynamically-downscaled data\n", @@ -424,15 +388,17 @@ "metadata": {}, "outputs": [], "source": [ - "# TAKE THE DIFFERENCE between warming levels before aggregating across simulations.\n", + "# Take the difference between warming levels before aggregating across simulations.\n", "wl_diff = prec_gwl_data.sel(warming_level=3.0) - prec_gwl_data.sel(warming_level=1.0)\n", "\n", - "# We average the differences across simulations and time.\n", - "avg_diff = wl_diff.mean(dim=['sim', 'time_delta'])\n", + "# Load data into memory using dask progress bar, averaging across time first\n", + "with ProgressBar():\n", + " diff_mean_time_delta = wl_diff.mean(dim='time_delta').compute()\n", "\n", - "# To understand the range of differences across simulations, we can also compute the max and min difference across simulations\n", - "max_diff = wl_diff.mean(dim=['time_delta']).max('sim')\n", - "min_diff = wl_diff.mean(dim=['time_delta']).min('sim')" + "# Average, max, and min differences across simulations (in-memory ops)\n", + "avg_diff = diff_mean_time_delta.mean(dim='sim')\n", + "max_diff = diff_mean_time_delta.max('sim')\n", + "min_diff = diff_mean_time_delta.min('sim')" ] }, { @@ -448,8 +414,20 @@ "metadata": {}, "outputs": [], "source": [ - "arrs = [min_diff, avg_diff, max_diff]\n", - "fig2(arrs)" + "titles = ['Multi-model minimum change', 'Multi-model mean change', 'Multi-model maximum change']\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(13, 4), subplot_kw={'projection': ccrs.PlateCarree()}, constrained_layout=True)\n", + "\n", + "for ax, arr, title in zip(axes, [min_diff, avg_diff, max_diff], titles):\n", + " pcm = arr.prec.plot(ax=ax, x=\"lon\", y=\"lat\", transform=ccrs.PlateCarree(),\n", + " cmap=\"BrBG\", vmin=-20, vmax=20, add_colorbar=False)\n", + " ax.set_title(title)\n", + " ax.set_extent([-125, -105, 28, 50])\n", + " ax.add_feature(cfeature.STATES.with_scale(\"10m\"), edgecolor=\"black\", linewidth=0.1)\n", + " ax.add_feature(cfeature.COASTLINE.with_scale(\"10m\"), edgecolor=\"black\")\n", + "\n", + "fig.colorbar(pcm, ax=axes, label=\"Δ Precipitation (mm/month)\", shrink=0.8)\n", + "plt.show()" ] }, { @@ -802,7 +780,6 @@ "**2. Using the Analytics Engine to load climate data on GWLs**\n", "\n", "* The Analytics Engine provides tools for loading data on GWLs using the same syntax as loading time-based data\n", - "* Either the `get_data` function or the `ClimateData` tool can be used to load equivalent data on GWLs\n", "* Historical reference GWLs can be used to calculate climate change signals\n", " \n", "**3. Translating between target-year and GWL planning on AE**\n", @@ -811,6 +788,13 @@ "* When loading LOCA2 data on GWLs, more data is available because simulations from three different SSPs can be used simultaneously\n", "* Compared to the equivalent target year, regional climate change at GWL targets will be *slightly less* due to avoiding the influence of overly hot models" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From ab17d8fab1c657175ed477d97bf587cd16f7d3b1 Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Wed, 25 Feb 2026 22:29:08 -0300 Subject: [PATCH 03/53] rename figs path, delete support function --- .../gwl_figure1.png | Bin .../gwl_figure2.png | Bin .../warming_level_approach_helpers.py | 412 ------------------ 3 files changed, 412 deletions(-) rename analysis/{static_figures => figures}/gwl_figure1.png (100%) rename analysis/{static_figures => figures}/gwl_figure2.png (100%) delete mode 100644 analysis/support_functions/warming_level_approach_helpers.py diff --git a/analysis/static_figures/gwl_figure1.png b/analysis/figures/gwl_figure1.png similarity index 100% rename from analysis/static_figures/gwl_figure1.png rename to analysis/figures/gwl_figure1.png diff --git a/analysis/static_figures/gwl_figure2.png b/analysis/figures/gwl_figure2.png similarity index 100% rename from analysis/static_figures/gwl_figure2.png rename to analysis/figures/gwl_figure2.png diff --git a/analysis/support_functions/warming_level_approach_helpers.py b/analysis/support_functions/warming_level_approach_helpers.py deleted file mode 100644 index 3cbc294b..00000000 --- a/analysis/support_functions/warming_level_approach_helpers.py +++ /dev/null @@ -1,412 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from matplotlib import gridspec -from matplotlib.colors import Colormap, ListedColormap -from matplotlib import cm -import cartopy.crs as ccrs -import cartopy.feature as cfeature - -import matplotlib.pyplot as plt -import seaborn as sns -import pandas as pd - -from climakitae.core.data_interface import ( - DataInterface, -) -from climakitae.util.utils import read_csv_file - -from climakitae.core.paths import ( - HIST_FILE, - SSP119_FILE, - SSP126_FILE, - SSP245_FILE, - SSP370_FILE, - SSP585_FILE, - GWL_1850_1900_TIMEIDX_FILE -) - -def lighten_cmap(cmap: str, factor: float = 0.3) -> ListedColormap: - """ - Lightens a colormap by blending the colors with white. - - Parameters:i wa - cmap (str): The colormap name to lighten (e.g., 'inferno'). - factor (float): The factor by which to lighten the colormap (default is 0.3). - - Returns: - ListedColormap: A new colormap that is lighter than the original. - """ - cmap = cm.get_cmap(cmap, 256) # Get the existing colormap - new_colors = cmap(np.linspace(0, 1, 256)) - - # Lighten the colors by blending with white - white = np.array([1, 1, 1, 1]) - new_colors = (1 - factor) * new_colors + factor * white - - return ListedColormap(new_colors) - -lighter_r_rev = lighten_cmap("inferno_r", factor=0.3) - - -def fig1(arr, diff): - - fig = plt.figure(figsize=(13, 4)) - - # Grid layout: - # [ WL1 | WL2 | cbar1 | spacer | diff | cbar2 ] - gs = gridspec.GridSpec( - 1, 6, - width_ratios=[1, 1, 0.03, 0.2, 1, 0.03], # spacer + two colorbars - wspace=0.15, - figure=fig - ) - - # Axes - ax1 = fig.add_subplot(gs[0, 0]) # first warming level - ax2 = fig.add_subplot(gs[0, 1], sharey=ax1) # second warming level - cax1 = fig.add_subplot(gs[0, 2]) # colorbar for WL plots - ax_diff = fig.add_subplot(gs[0, 4]) # difference plot - cax2 = fig.add_subplot(gs[0, 5]) # colorbar for diff plot - - # --- Plot first two warming levels --- - sim_name = arr.sim.item().split('_')[2] - for i, (ax, wl) in enumerate(zip([ax1, ax2], arr.warming_level.values[:2])): - pcm = arr.sel(warming_level=wl).plot(ax=ax, add_colorbar=False, cmap=lighter_r_rev) - ax.set_title(f"Avg Max Temp for Sim\n{sim_name} at WL {wl}") - if i > 0: - ax.set_ylabel("") - ax.tick_params(labelleft=False) - if i != 1: - # ax.set_xlabel("") - pass - - # Shared colorbar hugging the 2nd plot - cb1 = fig.colorbar(pcm, cax=cax1) - cb1.set_label("Max Temp (°F)") - - # --- Difference plot --- - pcm_diff = diff.plot(ax=ax_diff, add_colorbar=False, cmap='RdBu_r', vmin=-6, vmax=6) - ax_diff.set_title( - f"Difference Plot:\nWL {arr.warming_level.values[-1]} - WL {arr.warming_level.values[0]}" - ) - ax_diff.tick_params(labelleft=False) - ax_diff.set_ylabel("") - # ax_diff.set_xlabel("") - - # Second colorbar for the difference plot - cb2 = fig.colorbar(pcm_diff, cax=cax2) - cb2.set_label("Δ Max Temp (°F)") - - fig.tight_layout() - plt.show() - - - -def fig2(arrs): - fig = plt.figure(figsize=(13, 4)) - - # Grid layout: - # [ min | mean | max | cbar ] - - shared_vmin = -20 - shared_vmax = 20 - gs = gridspec.GridSpec( - 1, 4, - width_ratios=[1, 1, 1, 0.03], # spacer + two colorbars - wspace=0.15, - figure=fig - ) - - # Axes - ax1 = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree()) - ax2 = fig.add_subplot(gs[0, 1], projection=ccrs.PlateCarree()) - ax3 = fig.add_subplot(gs[0, 2], projection=ccrs.PlateCarree()) - cax1 = fig.add_subplot(gs[0, 3]) # colorbar - - - pcm = arrs[0].prec.plot( - ax=ax1, - x="lon", - y="lat", - transform=ccrs.PlateCarree(), - cmap="BrBG", - vmin=shared_vmin, - vmax=shared_vmax, - add_colorbar=False, - ) - - pcm = arrs[1].prec.plot( - ax=ax2, - x="lon", - y="lat", - transform=ccrs.PlateCarree(), - cmap="BrBG", - vmin=shared_vmin, - vmax=shared_vmax, - add_colorbar=False - ) - - pcm = arrs[2].prec.plot( - ax=ax3, - x="lon", - y="lat", - transform=ccrs.PlateCarree(), - cmap="BrBG", - vmin=shared_vmin, - vmax=shared_vmax, - add_colorbar=False - ) - - ax1.set_title('Multi-model minumum change') - ax2.set_title('Multi-model mean change') - ax3.set_title('Multi-model maximum change') - - # Shared colorbar hugging the 2nd plot - cb1 = fig.colorbar(pcm, cax=cax1) - cb1.set_label("Δ Precipitation (mm/month)") - - for ax in [ax1, ax2, ax3]: - ax.set_extent([-125, -105, 28, 50]) - ax.add_feature(cfeature.STATES.with_scale("10m"), edgecolor="black", - linewidth=0.1) - ax.add_feature(cfeature.COASTLINE.with_scale("10m"), edgecolor="black") - - - fig.tight_layout() - plt.show() - - - - - - -### GWL illustration figures -- run to re-generate static images ### - -# --- setup and helper functions --- - -colors = {'ssp119': "#00a9cf", 'ssp126': "#003466", 'ssp245': "#f69320", 'ssp370': "#df0000", 'ssp585': "#980002", - 'hist':'#222222'} - -scenarios = ['ssp585','ssp370', 'ssp245'] # Order matters for stacking - -def _load_gwl_illustration_data(): - ssp119_data = read_csv_file(SSP119_FILE, index_col="Year") - ssp126_data = read_csv_file(SSP126_FILE, index_col="Year") - ssp245_data = read_csv_file(SSP245_FILE, index_col="Year") - ssp370_data = read_csv_file(SSP370_FILE, index_col="Year") - ssp585_data = read_csv_file(SSP585_FILE, index_col="Year") - hist_data = read_csv_file(HIST_FILE, index_col="Year") - - ssp_data = { - 'ssp119': ssp119_data, - 'ssp126': ssp126_data, - 'ssp245': ssp245_data, - 'ssp370': ssp370_data, - 'ssp585': ssp585_data} - return hist_data, ssp_data - - -def _load_warming_trajectories(): - data_interface = DataInterface() - df = data_interface.data_catalog.df - - columns_of_interest = ['activity_id', 'source_id', 'experiment_id', 'member_id'] - unique_combinations = df[columns_of_interest].drop_duplicates() - simulations_df = unique_combinations.reset_index(drop=True) - - warming_trajectories = read_csv_file( - GWL_1850_1900_TIMEIDX_FILE, index_col="time", parse_dates=True - ) - return warming_trajectories, simulations_df - -def _filter_warming_trajectories(simulations_df, warming_trajectories, activity): - columns_to_keep = [] - - # Filter simulations_df for the specific activity - activity_simulations = simulations_df[simulations_df['activity_id'] == activity] - - # Iterate through each row in the filtered simulations_df - for _, row in activity_simulations.iterrows(): - # Construct the column name pattern - column_pattern = f"{row['source_id']}_{row['member_id']}_{row['experiment_id']}" - - # Find matching columns in warming_trajectories - matching_columns = [col for col in warming_trajectories.columns if column_pattern in col] - - # Add matching columns to our list - columns_to_keep.extend(matching_columns) - - # Create a new DataFrame with only the relevant columns - filtered_trajectories = warming_trajectories[columns_to_keep] - - return filtered_trajectories - - -def plot_trajectories(ax, df, linestyle, scenario =None): - if scenario: - cols_to_plot = [col for col in df.columns if scenario in col] - else: - cols_to_plot = df.columns - - - for column in cols_to_plot: - scenario = next((s for s in scenarios if s in column), None) - if scenario: - ax.plot(df.index, df[column], color=colors[scenario], linestyle=linestyle, alpha=0.3) - - -def plot_warming_level_period(ax,df,sim,wl): - scenario=sim.split('_')[2] - ts = df[df[sim]>2.0].index.min() - start_ts = df.index.get_loc(ts)-(12*15) - end_ts = df.index.get_loc(ts)+(12*14) - subset = df.iloc[start_ts:end_ts] - ax.plot(subset.index, subset[sim], color=colors[scenario], linestyle='-', alpha=1,linewidth=4) - -# --- figures --- - -def gwl_fig1(): - warming_trajectories, simulations_df = _load_warming_trajectories() - - loca2_warming_trajectories = _filter_warming_trajectories(simulations_df, warming_trajectories, "LOCA2") - - # Set the style for the plots - plt.style.use('seaborn-v0_8-whitegrid') - # Define colors and scenarios - - - fig = plt.figure(figsize=(15, 8)) - gs = gridspec.GridSpec( - 2, 2, - width_ratios=[1, 1,], # spacer + two colorbars - height_ratios=[1, 1,], - wspace=0.15, - figure=fig - ) - - ax1 = fig.add_subplot(gs[0, 0]) - ax2 = fig.add_subplot(gs[0, 1], sharey=ax1, sharex=ax1) - ax3 = fig.add_subplot(gs[1, 0], sharey=ax1, sharex=ax1) - ax4 = fig.add_subplot(gs[1, 1], sharey=ax1, sharex=ax1) - ax1.set_xlim(np.datetime64("1950", 'Y'), np.datetime64("2100", 'Y')) - - ax1.set_title("Target-year planning") - plot_trajectories(ax1,loca2_warming_trajectories,linestyle='-',scenario='ssp370') - ax1.axvline(x=np.datetime64("2050", 'Y'), color='m', linestyle='--', alpha=0.7) - legend_elements = [ - plt.Line2D([0], [0], color=colors['ssp370'], label='SSP3-7.0'), - ] - ax1.legend(handles=legend_elements, loc='upper left', fontsize=10) - - ax2.set_title("GWL planning") - plot_trajectories(ax2,loca2_warming_trajectories,linestyle='-') - ax2.axhline(y=2, color='m', linestyle='--', alpha=0.7) - legend_elements = [ - plt.Line2D([0], [0], color=colors['ssp245'], label='SSP2-4.5'), - plt.Line2D([0], [0], color=colors['ssp370'], label='SSP3-7.0'), - plt.Line2D([0], [0], color=colors['ssp585'], label='SSP5-8.5'), - ] - ax2.legend(handles=legend_elements, loc='upper left', fontsize=10) - - ax3.set_title("Example target-year climatologies") - plot_trajectories(ax3,loca2_warming_trajectories[['MIROC6_r2i1p1f1_ssp370','ACCESS-CM2_r2i1p1f1_ssp370']], linestyle='-') - subset = loca2_warming_trajectories.loc[np.datetime64("2035", 'Y'):np.datetime64("2065", 'Y')] - ax3.plot(subset.index, subset['MIROC6_r2i1p1f1_ssp370'], color=colors['ssp370'], linestyle='-', alpha=1,linewidth=4) - ax3.plot(subset.index, subset['ACCESS-CM2_r2i1p1f1_ssp370'], color=colors['ssp370'], linestyle='-', alpha=1,linewidth=4) - - ax3.axvline(x=np.datetime64("2050", 'Y'), color='m', linestyle='--', alpha=0.7) - - ax4.set_title("Example GWL climatologies") - plot_trajectories(ax4,loca2_warming_trajectories[['MIROC6_r2i1p1f1_ssp370','EC-Earth3_r3i1p1f1_ssp585']], linestyle='-') - plot_warming_level_period(ax4,loca2_warming_trajectories,'MIROC6_r2i1p1f1_ssp370',2.0) - plot_warming_level_period(ax4,loca2_warming_trajectories,'EC-Earth3_r3i1p1f1_ssp585',2.0) - ax4.axhline(y=2, color='m', linestyle='--', alpha=0.7) - - - - #set title for figure - fig.suptitle("LOCA2 and WRF Global Warming Trajectories", fontsize=16) - plt.savefig("./gwl_figure1.png", dpi=300) - -def gwl_fig2(): - warming_trajectories, simulations_df = _load_warming_trajectories() - loca2_warming_trajectories = _filter_warming_trajectories(simulations_df, warming_trajectories, "LOCA2") - - hist_data, ssp_data = _load_gwl_illustration_data() - - # Set the style for the plots - plt.style.use('seaborn-v0_8-whitegrid') - - df = loca2_warming_trajectories - selected_scenario = 'ssp370' - plot_ipcc=True - plt.figure(figsize=(6,5)) - - scenarios_to_plot = [selected_scenario] if selected_scenario else scenarios - - - if plot_ipcc: - context_data = hist_data - context_data.index = pd.to_datetime(context_data.index, format='%Y') - # plt.fill_between(context_data.index, context_data['5%'], context_data['95%'], - # color='#0000AA', alpha=0.5, zorder=8) - plt.plot(context_data.index, context_data['Mean'], color='k', - linewidth=2, linestyle='--',zorder=8) - - - for scenario in ['ssp370']: - # Plot context data - if scenario in ssp_data: - context_data = ssp_data[scenario] - context_data.index = context_data.index.astype('int') - context_data.index = pd.to_datetime(context_data.index, format='%Y') - plt.fill_between(context_data.index, context_data['5%'], context_data['95%'], - color=colors[scenario], alpha=0.4, zorder=4, label='SSP3-7.0 90% Range') - plt.plot(context_data.index, context_data['Mean'], color=colors[scenario], - linewidth=2, linestyle='--', label=f'{scenario.upper()} (IPCC Best Estimate)', zorder=8) - - - for i, scenario in enumerate(scenarios_to_plot): - if selected_scenario: - plot_color='k' - else: - plot_color = colors[scenario] - - - # Filter columns for the current scenario - scenario_cols = [col for col in df.columns if scenario in col] - if scenario_cols: - # Plot each trajectory - for col in scenario_cols: - plt.plot(df.index, df[col], color='k', alpha=0.4, zorder=5+i, linewidth=0.8) - - - - # Customize the plot - plt.title(f"LOCA2 & WRF Warming Trajectories", fontsize=16) - plt.xlabel("Year", fontsize=12) - plt.ylabel("Global Warming Level (°C)", fontsize=12) - plt.grid(True, linestyle=':', alpha=0.6) - - for i, scenario in enumerate(scenarios_to_plot): - plt.plot([0], [0], color='k', label=f'{scenario} simulations'), - - plt.legend(loc='upper left', fontsize=10) - - # Set x-axis limits - plt.xlim(np.datetime64("1950", 'Y'), np.datetime64("2100", 'Y')) - - #plt.axvline(x=np.datetime64("2047", 'Y'), color='m', linestyle='dotted') - #plt.axhspan(ymin=1.6,ymax=3.3, color='m', alpha=0.1, zorder=2) - - plt.axhline(y=2, color='m', linestyle='dotted') - #plt.axvline(x=np.datetime64("2037", 'Y'), color='m', linestyle='dashed',alpha=0.2) - #plt.axvline(x=np.datetime64("2061", 'Y'), color='m', linestyle='dashed',alpha=0.2) - - plt.axvspan(xmin=np.datetime64("2037", 'Y'), xmax=np.datetime64("2061", 'Y'), color="m", alpha=0.1) - plt.axvline(x=np.datetime64("2047", 'Y'), color='m', linestyle='dotted') - - plt.tight_layout() - plt.savefig("./gwl_figure2.png", dpi=200) - - From 63984c5c656537ecaacee220ee4656679c2edded Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Wed, 25 Feb 2026 22:56:44 -0300 Subject: [PATCH 04/53] cleaned up notebook; --- analysis/warming_level_methods.ipynb | 329 +++++++++++++-------------- 1 file changed, 164 insertions(+), 165 deletions(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index a223b272..22be8541 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -77,7 +77,7 @@ "source": [ "In the figure below, the timeseries of the average global surface temperature it plotted for each of the global climate simulations that were downscaled by LOCA2 and WRF to produce the data used in the Analytics Engine.\n", "\n", - "![GWL warming trajectories](static_figures/gwl_figure1.png)" + "![GWL warming trajectories](figures/gwl_figure1.png)" ] }, { @@ -116,7 +116,7 @@ "\n", "The red shaded area in the plot above represents the IPCC's 90% confidence interval for the rate of future global warming under the SSP3-7.0 scenario, with the best estimate warming trajectory shown as the red dashed line.\n", "\n", - "![GWL timing](static_figures/gwl_figure2.png)" + "![GWL timing](figures/gwl_figure2.png)" ] }, { @@ -161,18 +161,20 @@ "\n", "import climakitae as ck\n", "from climakitae.util.utils import add_dummy_time_to_wl\n", - "from climakitae.util.unit_conversions import convert_units\n", + "from climakitae.util.warming_levels import get_year_at_gwl\n", "\n", "# Set default plotting parameters for readability\n", - "plt.rcParams.update({\n", - " 'font.size': 15, # default font size\n", - " 'figure.titlesize': 20, # suptitle\n", - " 'axes.titlesize': 18, # title\n", - " 'axes.labelsize': 15, # axis labels\n", - " 'xtick.labelsize': 15, # x tick labels\n", - " 'ytick.labelsize': 15, # y tick labels\n", - " 'legend.fontsize': 15, # legend\n", - "})" + "plt.rcParams.update(\n", + " {\n", + " \"font.size\": 15, # default font size\n", + " \"figure.titlesize\": 20, # suptitle\n", + " \"axes.titlesize\": 18, # title\n", + " \"axes.labelsize\": 15, # axis labels\n", + " \"xtick.labelsize\": 15, # x tick labels\n", + " \"ytick.labelsize\": 15, # y tick labels\n", + " \"legend.fontsize\": 15, # legend\n", + " }\n", + ")" ] }, { @@ -195,7 +197,7 @@ "metadata": {}, "outputs": [], "source": [ - "cd = ck.ClimateData(verbosity=-1)" + "cd = ck.ClimateData(verbosity=-2) # Silence output" ] }, { @@ -212,20 +214,25 @@ "outputs": [], "source": [ "# Here is an example of loading data at 2 specific warming levels (0.8 and 2.0) with a 15-year window for Max Temp.\n", - "tasmax_gwl_data = (cd\n", - " .catalog(\"cadcat\") \n", - " .activity_id(\"LOCA2\") # Statistical downscaling\n", - " .table_id(\"mon\") # Looking at `monthly` data\n", - " .grid_label(\"d03\") # Looking at domain 03, which is `3 km` data over California\n", - " .variable(\"tasmax\") # Grabbing the maximum temperature variable\n", - " .processes({ # Centering our dataset around GWLs\n", - " \"convert_units\": 'degF',\n", - " \"warming_level\": {\n", - " \"warming_levels\": [0.8, 2.0], # Standard GWLs include: 0.8, 1.0, 1.5, 2.0, 2.5, 3.0, but any number can be requested\n", - " \"warming_level_window\": 15, # Default is 15 years on either side of GWL crossing time, for total of 30 years of data\n", - " # \"warming_level_months\": [6, 7, 8], # Optional: specify months for seasonal averages, still WIP development\n", - " },\n", - " })\n", + "tasmax_gwl_data = (\n", + " cd.catalog(\"cadcat\")\n", + " .activity_id(\"LOCA2\") # Statistical downscaling\n", + " .table_id(\"mon\") # Looking at `monthly` data\n", + " .grid_label(\"d03\") # Looking at domain 03, which is `3 km` data over California\n", + " .variable(\"tasmax\") # Grabbing the maximum temperature variable\n", + " .processes(\n", + " { # Centering our dataset around GWLs\n", + " \"convert_units\": \"degF\",\n", + " \"warming_level\": {\n", + " \"warming_levels\": [\n", + " 0.8,\n", + " 2.0,\n", + " ], # Standard GWLs include: 0.8, 1.0, 1.5, 2.0, 2.5, 3.0, but any number can be requested\n", + " \"warming_level_window\": 15, # Default is 15 years on either side of GWL crossing time, for total of 30 years of data\n", + " # \"warming_level_months\": [6, 7, 8], # Optional: specify months for seasonal averages, still WIP development\n", + " },\n", + " }\n", + " )\n", " .get()\n", ")" ] @@ -293,13 +300,13 @@ "outputs": [], "source": [ "# Load in a small subset of the data to plot\n", - "# One simulation and grab the mean of the time delta simulation \n", + "# One simulation and grab the mean of the time delta simulation\n", "sim = \"LOCA2_UCSD_EC-Earth3_ssp245_mon_d03_r1i1p1f1\"\n", - "arr = tasmax_gwl_data.tasmax.sel(sim=sim).mean(dim='time_delta')\n", + "arr = tasmax_gwl_data.tasmax.sel(sim=sim).mean(dim=\"time_delta\")\n", "\n", "# Read data into memory for quicker plotting\n", - "with ProgressBar(): \n", - " arr = arr.compute()\n", + "with ProgressBar():\n", + " arr.load()\n", "\n", "# Find the difference between WL 2.0 and WL 0.8\n", "# Difference between highest and lowest warming levels\n", @@ -330,16 +337,24 @@ "source": [ "fig, axes = plt.subplots(1, 3, figsize=(17, 6), constrained_layout=True)\n", "\n", - "sim_plotname = \"EC-Earth3\" # simulation name used for plot title\n", + "sim_plotname = \"EC-Earth3\" # simulation name used for plot title\n", "for ax, wl in zip(axes[:2], wls):\n", - " im = arr.sel(warming_level=wl).plot(ax=ax, cmap='magma_r', vmin=35, vmax=95, add_colorbar=False)\n", + " im = arr.sel(warming_level=wl).plot(\n", + " ax=ax, cmap=\"magma_r\", vmin=35, vmax=95, add_colorbar=False\n", + " )\n", " ax.set_title(f\"WL {wl} Avg Max Temp\")\n", "\n", - "fig.colorbar(im, ax=axes[:2], label='Max Temp (°F)')\n", - "diff.plot(ax=axes[2], cmap='RdBu_r', vmin=-6, vmax=6, cbar_kwargs={'label': 'Δ Max Temp (°F)'})\n", + "fig.colorbar(im, ax=axes[:2], label=\"Max Temp (°F)\")\n", + "diff.plot(\n", + " ax=axes[2], cmap=\"RdBu_r\", vmin=-6, vmax=6, cbar_kwargs={\"label\": \"Δ Max Temp (°F)\"}\n", + ")\n", "axes[2].set_title(f\"Difference: WL {wls[-1]} - WL {wls[0]}\")\n", - "axes[1].set_ylabel('')\n", - "fig.suptitle(f\"Warming levels comparison for LOCA2 simulation {sim_plotname}\", y=1.08, fontweight=\"bold\")\n", + "axes[1].set_ylabel(\"\")\n", + "fig.suptitle(\n", + " f\"Warming levels comparison for LOCA2 simulation {sim_plotname}\",\n", + " y=1.08,\n", + " fontweight=\"bold\",\n", + ")\n", "\n", "plt.show()" ] @@ -365,19 +380,25 @@ "outputs": [], "source": [ "# Import precipitation data from WRF\n", - "prec_gwl_data = (cd\n", - " .catalog(\"cadcat\") # Same catalog of data as before\n", - " .activity_id(\"WRF\") # Dynamically-downscaled data\n", + "prec_gwl_data = (\n", + " cd.catalog(\"cadcat\") # Same catalog of data as before\n", + " .activity_id(\"WRF\") # Dynamically-downscaled data\n", " .institution_id(\"UCLA\")\n", - " .table_id(\"mon\") # Monthly data\n", - " .grid_label(\"d02\") # Domain 02, which is 9 km data\n", - " .variable(\"prec\") # Precipitation data\n", - " .processes({ \n", - " \"warming_level\": {\n", - " \"warming_levels\": [1.0, 2.0, 3.0], # Standard GWLs include: 0.8, 1.0, 1.5, 2.0, 2.5, 3.0, but any number can be requested\n", - " \"warming_level_window\": 10, # Number of years (+/-) around warming level to retrieve\n", - " },\n", - " })\n", + " .table_id(\"mon\") # Monthly data\n", + " .grid_label(\"d02\") # Domain 02, which is 9 km data\n", + " .variable(\"prec\") # Precipitation data\n", + " .processes(\n", + " {\n", + " \"warming_level\": {\n", + " \"warming_levels\": [\n", + " 1.0,\n", + " 2.0,\n", + " 3.0,\n", + " ], # Standard GWLs include: 0.8, 1.0, 1.5, 2.0, 2.5, 3.0, but any number can be requested\n", + " \"warming_level_window\": 10, # Number of years (+/-) around warming level to retrieve\n", + " },\n", + " }\n", + " )\n", " .get()\n", ")" ] @@ -393,12 +414,12 @@ "\n", "# Load data into memory using dask progress bar, averaging across time first\n", "with ProgressBar():\n", - " diff_mean_time_delta = wl_diff.mean(dim='time_delta').compute()\n", + " diff_mean_time_delta = wl_diff.mean(dim=\"time_delta\").compute()\n", "\n", "# Average, max, and min differences across simulations (in-memory ops)\n", - "avg_diff = diff_mean_time_delta.mean(dim='sim')\n", - "max_diff = diff_mean_time_delta.max('sim')\n", - "min_diff = diff_mean_time_delta.min('sim')" + "avg_diff = diff_mean_time_delta.mean(dim=\"sim\")\n", + "max_diff = diff_mean_time_delta.max(\"sim\")\n", + "min_diff = diff_mean_time_delta.min(\"sim\")" ] }, { @@ -414,13 +435,31 @@ "metadata": {}, "outputs": [], "source": [ - "titles = ['Multi-model minimum change', 'Multi-model mean change', 'Multi-model maximum change']\n", + "titles = [\n", + " \"Multi-model minimum change\",\n", + " \"Multi-model mean change\",\n", + " \"Multi-model maximum change\",\n", + "]\n", "\n", - "fig, axes = plt.subplots(1, 3, figsize=(13, 4), subplot_kw={'projection': ccrs.PlateCarree()}, constrained_layout=True)\n", + "fig, axes = plt.subplots(\n", + " 1,\n", + " 3,\n", + " figsize=(13, 4),\n", + " subplot_kw={\"projection\": ccrs.PlateCarree()},\n", + " constrained_layout=True,\n", + ")\n", "\n", "for ax, arr, title in zip(axes, [min_diff, avg_diff, max_diff], titles):\n", - " pcm = arr.prec.plot(ax=ax, x=\"lon\", y=\"lat\", transform=ccrs.PlateCarree(),\n", - " cmap=\"BrBG\", vmin=-20, vmax=20, add_colorbar=False)\n", + " pcm = arr.prec.plot(\n", + " ax=ax,\n", + " x=\"lon\",\n", + " y=\"lat\",\n", + " transform=ccrs.PlateCarree(),\n", + " cmap=\"BrBG\",\n", + " vmin=-20,\n", + " vmax=20,\n", + " add_colorbar=False,\n", + " )\n", " ax.set_title(title)\n", " ax.set_extent([-125, -105, 28, 50])\n", " ax.add_feature(cfeature.STATES.with_scale(\"10m\"), edgecolor=\"black\", linewidth=0.1)\n", @@ -449,37 +488,6 @@ "* Illustrate how GWL planning avoids the influence of the \"hot model problem\"" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading data for GWL planning" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We will again use the 0.8 and 2.0 warming levels for max temp as an example - this data was loaded above as `tasmax_gwl_data`, but here is the code snippet again for reference.\n", - "\n", - "# tasmax_gwl_data = (cd\n", - "# .catalog(\"cadcat\") # Same catalog of data as before\n", - "# .activity_id(\"LOCA2\") # Statistically-downscaled data\n", - "# .table_id(\"mon\") # Monthly data\n", - "# .grid_label(\"d03\") # 3 km data\n", - "# .variable(\"tasmax\") # Maximum daily temperature data\n", - "# .processes({ \n", - "# \"warming_level\": {\n", - "# \"warming_levels\": [0.8, 2.0], # Available warming levels include: 0.8, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0\n", - "# \"warming_level_window\": 15, # Number of years (+/-) around warming level to retrieve},\n", - "# },\n", - "# })\n", - "# .get()\n", - "# )" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -498,15 +506,6 @@ "The `get_year_at_gwl` tool translates GWLs to years using IPCC trajectories. If you're interested in learning more about how these tools work, check out the [warming level timing explorer notebook](https://github.com/cal-adapt/cae-notebooks/blob/main/analysis/warming_level_timing_explorer.ipynb)." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from climakitae.util.warming_levels import get_year_at_gwl" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -540,16 +539,24 @@ "outputs": [], "source": [ "# Here, we'll use the `ClimateData` object again now to retrieve SSP data.\n", - "time_data_08 = (cd\n", - " .catalog(\"cadcat\")\n", - " .activity_id(\"LOCA2\") # Statistical downscaling\n", - " .table_id(\"mon\") # Looking at `monthly` data\n", - " .grid_label(\"d03\") # Looking at `45 km` data\n", - " .variable(\"tasmax\") # Grabbing the maximum daily temperature variable (will be used later)\n", - " .experiment_id([\"historical\", \"ssp370\"]) # Grabbing historical + SSP 3-7.0 data\n", - " .processes({\n", - " \"time_slice\": ('1987-01-01', '2016-12-31'), # Only selecting data from 1987 to 2017 (a 30-year slice around the year 2002).\n", - " })\n", + "time_data_08 = (\n", + " cd.catalog(\"cadcat\")\n", + " .activity_id(\"LOCA2\") # Statistical downscaling\n", + " .table_id(\"mon\") # Looking at `monthly` data\n", + " .grid_label(\"d03\") # Looking at `45 km` data\n", + " .variable(\n", + " \"tasmax\"\n", + " ) # Grabbing the maximum daily temperature variable (will be used later)\n", + " .experiment_id([\"historical\", \"ssp370\"]) # Grabbing historical + SSP 3-7.0 data\n", + " .processes(\n", + " {\n", + " \"convert_units\": \"degF\",\n", + " \"time_slice\": (\n", + " \"1987-01-01\",\n", + " \"2016-12-31\",\n", + " ), # Only selecting data from 1987 to 2017 (a 30-year slice around the year 2002).\n", + " }\n", + " )\n", " .get()\n", ")" ] @@ -568,7 +575,7 @@ "outputs": [], "source": [ "# Let's see what year the world would reach 2.0 degrees celsius of warming above pre-industrial levels at different SSP trajectories\n", - "get_year_at_gwl(2.0, 'all')" + "get_year_at_gwl(2.0, \"all\")" ] }, { @@ -585,16 +592,24 @@ "outputs": [], "source": [ "# Here, we'll use the `ClimateData` object again now to retrieve SSP data.\n", - "time_data_20 = (cd\n", - " .catalog(\"cadcat\")\n", - " .activity_id(\"LOCA2\") # Statistical downscaling\n", - " .table_id(\"mon\") # Looking at `monthly` data\n", - " .grid_label(\"d03\") # Looking at `45 km` data\n", - " .variable(\"tasmax\") # Grabbing the maximum daily temperature variable variable (will be used later)\n", - " .experiment_id([\"historical\", \"ssp370\"]) # Grabbing historical + SSP 3-7.0 data\n", - " .processes({\n", - " \"time_slice\": ('2033-01-01', '2062-12-31'), # Only selecting data from 2033 to 2062 (a 30-year slice around the year 2047).\n", - " })\n", + "time_data_20 = (\n", + " cd.catalog(\"cadcat\")\n", + " .activity_id(\"LOCA2\") # Statistical downscaling\n", + " .table_id(\"mon\") # Looking at `monthly` data\n", + " .grid_label(\"d03\") # Looking at `45 km` data\n", + " .variable(\n", + " \"tasmax\"\n", + " ) # Grabbing the maximum daily temperature variable variable (will be used later)\n", + " .experiment_id([\"historical\", \"ssp370\"]) # Grabbing historical + SSP 3-7.0 data\n", + " .processes(\n", + " {\n", + " \"convert_units\": \"degF\",\n", + " \"time_slice\": (\n", + " \"2033-01-01\",\n", + " \"2062-12-31\",\n", + " ), # Only selecting data from 2033 to 2062 (a 30-year slice around the year 2047).\n", + " }\n", + " )\n", " .get()\n", ")" ] @@ -603,34 +618,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Perform analysis on the data we loaded" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "### Perform analysis on the data we loaded\n", "For the next two sections, we are going to conduct a comparison of the two datasets by analyzing *changes in temperature during the hottest months of the year.* More specifically, we will try to answer the following question:\n", "
\n", "\n", - "

 How much will the temperature of the hottest month of the year in Sacramento increase under climate change by the middle of the century?

\n", + "#### How much will the temperature of the hottest month of the year in Sacramento increase under climate change by the middle of the century?\n", "\n", "\n", "We will conduct a comparison between the 0.8°C and 2.0°C datasets, and do a corresponding time-based component comparison, to further illustrate the differences between a time-based and GWL approach." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert to degF for analysis\n", - "tasmax_gwl_data = convert_units(tasmax_gwl_data.tasmax, 'degF')\n", - "time_data_08 = convert_units(time_data_08.tasmax, 'degF')\n", - "time_data_20 = convert_units(time_data_20.tasmax, 'degF')" - ] - }, { "cell_type": "code", "execution_count": null, @@ -638,13 +635,12 @@ "outputs": [], "source": [ "# To simplify the analysis for this example, we will choose a single gridcell in Sacramento\n", - "\n", "lat = 38.617\n", "lon = -121.445\n", "\n", - "gwl_sac = tasmax_gwl_data.sel(lat=lat, lon=lon, method='nearest')\n", - "time_08_sac = time_data_08.sel(lat=lat, lon=lon, method='nearest')\n", - "time_20_sac = time_data_20.sel(lat=lat, lon=lon, method='nearest')" + "gwl_sac = tasmax_gwl_data.sel(lat=lat, lon=lon, method=\"nearest\").tasmax\n", + "time_08_sac = time_data_08.sel(lat=lat, lon=lon, method=\"nearest\").tasmax\n", + "time_20_sac = time_data_20.sel(lat=lat, lon=lon, method=\"nearest\").tasmax" ] }, { @@ -694,10 +690,14 @@ "# to make the following steps faster and easier to iterate on, we will load the data into memory\n", "# This step will take a few minutes to run\n", "# NOTE: this is only recommended once the data has been subset to a small region of interest like in this example\n", - "\n", - "gwl_sac.load()\n", - "time_08_sac.load()\n", - "time_20_sac.load()" + "with ProgressBar():\n", + " print(\"Loading gwl_sac...\")\n", + " gwl_sac.load()\n", + " print(\"Loading time_08_sac...\")\n", + " time_08_sac.load()\n", + " print(\"Loading time_20_sac...\")\n", + " time_20_sac.load()\n", + " print(\"Complete\")" ] }, { @@ -707,10 +707,12 @@ "outputs": [], "source": [ "# calculate the maximum monthly temperature for each year in the GWL data, then average across years\n", - "gwl_sac_temp_mean = gwl_sac.groupby('time.year').max().mean(dim='year')\n", + "gwl_sac_temp_mean = gwl_sac.groupby(\"time.year\").max().mean(dim=\"year\")\n", "\n", "# now calculate the change signal between 0.8 and 2.0 degC warming levels\n", - "gwl_sac_temp_delta = gwl_sac_temp_mean.sel(warming_level=2.0) - gwl_sac_temp_mean.sel(warming_level=0.8)" + "gwl_sac_temp_delta = gwl_sac_temp_mean.sel(warming_level=2.0) - gwl_sac_temp_mean.sel(\n", + " warming_level=0.8\n", + ")" ] }, { @@ -727,9 +729,9 @@ "outputs": [], "source": [ "# perform the same calculation for the time-based data\n", - "time_08_sac_summer_mean = time_08_sac.groupby('time.year').max().mean(dim='year')\n", - "time_20_sac_summer_mean = time_20_sac.groupby('time.year').max().mean(dim='year')\n", - "time_20_sac_summer_delta = time_20_sac_summer_mean - time_08_sac_summer_mean\n" + "time_08_sac_summer_mean = time_08_sac.groupby(\"time.year\").max().mean(dim=\"year\")\n", + "time_20_sac_summer_mean = time_20_sac.groupby(\"time.year\").max().mean(dim=\"year\")\n", + "time_20_sac_summer_delta = time_20_sac_summer_mean - time_08_sac_summer_mean" ] }, { @@ -739,13 +741,17 @@ "outputs": [], "source": [ "# Plotting the differences between GWL and target-year planning\n", - "plt.figure(figsize=(6, 3))\n", - "gwl_sac_temp_delta.plot.hist(width = .2, bins=np.arange(0,6,0.25), alpha=0.7, label = \"Warming Level 2.0 - 0.8\")\n", - "time_20_sac_summer_delta.plot.hist(width = .2, bins=np.arange(0,6,0.25), alpha=0.5, label = \"Target year 2047 - 2002\")\n", - "\n", - "plt.title('Comparing regional warming \\n by GWL vs Time-based Approach')\n", - "plt.xlabel('Change in average annual maximum monthly temperature (degF)')\n", - "plt.ylabel('Count of simulations')\n", + "plt.figure(figsize=(12, 7))\n", + "gwl_sac_temp_delta.plot.hist(\n", + " width=0.2, bins=np.arange(0, 6, 0.25), alpha=0.7, label=\"Warming Level 2.0 - 0.8\"\n", + ")\n", + "time_20_sac_summer_delta.plot.hist(\n", + " width=0.2, bins=np.arange(0, 6, 0.25), alpha=0.5, label=\"Target year 2047 - 2002\"\n", + ")\n", + "\n", + "plt.title(\"Comparing Regional Warming by GWL vs Time-Based Approach\")\n", + "plt.xlabel(\"Change in average annual maximum monthly temperature (degF)\")\n", + "plt.ylabel(\"Count of simulations\")\n", "plt.legend()\n", "plt.show()" ] @@ -788,13 +794,6 @@ "* When loading LOCA2 data on GWLs, more data is available because simulations from three different SSPs can be used simultaneously\n", "* Compared to the equivalent target year, regional climate change at GWL targets will be *slightly less* due to avoiding the influence of overly hot models" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 537b8dc766b52eedae4c42aad45a752121cf3393 Mon Sep 17 00:00:00 2001 From: Calvin Chen <33679281+claalmve@users.noreply.github.com> Date: Wed, 4 Mar 2026 14:22:13 -0800 Subject: [PATCH 05/53] adding hyphens --- analysis/warming_level_methods.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index 22be8541..9246f8e6 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -15,11 +15,11 @@ "\n", "For more general information about using GWLs from climate planning, see the section titled [\"How should a user choose between Global Warming Levels and a time-based approach to planning?\"](https://analytics.cal-adapt.org/guidance/using_in_decision_making/#how-should-a-user-choose-between-global-warming-levels-and-a-time-based-approach-to-planning?) in our guidance material. \n", "\n", - "For more in-depth information about the methods for how GWL methods are implemented on the CalAdapt: Analytics Engine, please refer to the information on the \n", + "For more in-depth information about the methods for how GWL methods are implemented on the Cal-Adapt: Analytics Engine, please refer to the information on the \n", "[Analytics Engine Methods page](https://analytics.cal-adapt.org/analytics/methods/\n", ").\n", "\n", - "In this notebook, we will demonstrate the tools that the CalAdapt: Analytics Engine provides to enable climate planning with the Global Warming Level approach.\n", + "In this notebook, we will demonstrate the tools that the Cal-Adapt: Analytics Engine provides to enable climate planning with the Global Warming Level approach.\n", "\n", "**Intended Application:** As a user, I want to **learn to work with climate data on Global Warming Levels in the Analytics Engine**, including: \n", "* Gaining a conceptual understanding of the GWL approach to climate planning\n", From b820bead24c12b8709bec7aabe1d2dff1f5e2194 Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Thu, 5 Mar 2026 21:27:24 -0300 Subject: [PATCH 06/53] Update analysis/warming_level_methods.ipynb Co-authored-by: Calvin Chen <33679281+claalmve@users.noreply.github.com> --- analysis/warming_level_methods.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index 9246f8e6..6cd86f6c 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -664,7 +664,7 @@ "We will be using a function called `add_dummy_time_to_wl`. This function replaces the `time_delta` axis in GWL data with a dummy time axis (starting from 2000), in order to do time-based operations in python.\n", "\n", "We will take a look at:\n", - "1. The 99th percentile of historical precipitation events.\n", + "1. Calculating the average of the annual maximum monthly temperatures across all years in the 30-year GWL window (for 0.8°C and 2.0°C).\n", "2. Use that as a threshold for determining the future extreme precipitation events." ] }, From 3ce7cff645345c48bcb677bde0d6fc8ac5d612de Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Thu, 5 Mar 2026 21:28:43 -0300 Subject: [PATCH 07/53] Update analysis/warming_level_methods.ipynb Co-authored-by: Calvin Chen <33679281+claalmve@users.noreply.github.com> --- analysis/warming_level_methods.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index 6cd86f6c..4b7f5361 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -134,7 +134,7 @@ "\n", "**The GWL approach in the Analytics Engine avoids this bias by breaking the estimation of climate impacts into a two step process:**\n", "\n", - "1) Aggregate data from all models to estimate the climate impacts at a given GWL - as illustrated in the first figure, data is taken from each simulation where it intersects the horizontal magenta like at 2.0 °C GWL.s\n", + "1) Aggregate data from all models to estimate the climate impacts at a given GWL - as illustrated in the first figure, data is taken from each simulation where it intersects the horizontal magenta line at 2.0 °C GWL.", "\n", "2) Use the IPCC's refined warming trajectories to estimate *when* that GWL will most likely be reached -- In this case the year 2047, for SSP 3-7.0\n", "\n" From 59c460b608338e7522bab6e534027f4256cafdfe Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Thu, 5 Mar 2026 21:28:52 -0300 Subject: [PATCH 08/53] Update analysis/warming_level_methods.ipynb Co-authored-by: Calvin Chen <33679281+claalmve@users.noreply.github.com> --- analysis/warming_level_methods.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index 4b7f5361..a4faf2f4 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -61,7 +61,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This section will give a conceptual explaination of the GWL approach, and illustrate some key concepts to understand how it differs from a more traditional target-year planning approach.\n" + "This section will give a conceptual explanation of the GWL approach, and illustrate some key concepts to understand how it differs from a more traditional target-year planning approach.\n" ] }, { From 79ebc38b222305b649c1c17f0903d1f68d3f7210 Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Thu, 5 Mar 2026 21:29:00 -0300 Subject: [PATCH 09/53] Update analysis/warming_level_methods.ipynb Co-authored-by: Calvin Chen <33679281+claalmve@users.noreply.github.com> --- analysis/warming_level_methods.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index a4faf2f4..cf0406e5 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -127,7 +127,7 @@ "\n", "The horizontal magenta line represents the 2.0 °C GWL, which is estimated to be reached between 2037 and 2061 under SSP3-7.0, with the best estimate being 2047, as indicated by the magenta lines and shaded area.\n", "\n", - "**Exagerated warming in target-year approach**\n", + "**Exaggerated warming in target-year approach**\n", "\n", "When using a traditional target-year based planning approach, the downscaled estimates of future impacts will be biased towards a warmer world by these models. For example, in the year 2047 (vertical magenta line), the IPCC's estimates suggest global warming will be between 1.7 and 2.5 °C, but the model runs contain warming up to 3.3 °C. These global warming levels will correspond to exaggerated regional climate impacts \n", "\n", From edaf42bac5762c203c1e658e65606cbc1af2cd06 Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Thu, 5 Mar 2026 21:29:10 -0300 Subject: [PATCH 10/53] Update analysis/warming_level_methods.ipynb Co-authored-by: Calvin Chen <33679281+claalmve@users.noreply.github.com> --- analysis/warming_level_methods.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index cf0406e5..810e67ec 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -129,7 +129,7 @@ "\n", "**Exaggerated warming in target-year approach**\n", "\n", - "When using a traditional target-year based planning approach, the downscaled estimates of future impacts will be biased towards a warmer world by these models. For example, in the year 2047 (vertical magenta line), the IPCC's estimates suggest global warming will be between 1.7 and 2.5 °C, but the model runs contain warming up to 3.3 °C. These global warming levels will correspond to exaggerated regional climate impacts \n", + "When using a traditional target-year based planning approach, the downscaled estimates of future impacts will be biased towards a warmer world by these models. For example, in the year 2047 (vertical magenta line), the IPCC's estimates suggest global warming will be between 1.7 and 2.5 °C, but the model runs contain warming up to 3.3 °C. These global warming levels will correspond to exaggerated regional climate impacts. \n", "\n", "\n", "**The GWL approach in the Analytics Engine avoids this bias by breaking the estimation of climate impacts into a two step process:**\n", From 4611b15e7e0cd2ae3d719c0f69430d06e4ff55ab Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Thu, 5 Mar 2026 21:29:36 -0300 Subject: [PATCH 11/53] Update analysis/warming_level_methods.ipynb Co-authored-by: Calvin Chen <33679281+claalmve@users.noreply.github.com> --- analysis/warming_level_methods.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index 810e67ec..73898496 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -665,7 +665,7 @@ "\n", "We will take a look at:\n", "1. Calculating the average of the annual maximum monthly temperatures across all years in the 30-year GWL window (for 0.8°C and 2.0°C).\n", - "2. Use that as a threshold for determining the future extreme precipitation events." + "2. Take the difference between WL 2.0 and WL 0.8 to get the change signal." ] }, { From fb4b01aebbced3de700a7a3d1c646cb3626d1c05 Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Thu, 5 Mar 2026 21:29:48 -0300 Subject: [PATCH 12/53] Update analysis/warming_level_methods.ipynb Co-authored-by: Calvin Chen <33679281+claalmve@users.noreply.github.com> --- analysis/warming_level_methods.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index 73898496..795a7021 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -229,7 +229,7 @@ " 2.0,\n", " ], # Standard GWLs include: 0.8, 1.0, 1.5, 2.0, 2.5, 3.0, but any number can be requested\n", " \"warming_level_window\": 15, # Default is 15 years on either side of GWL crossing time, for total of 30 years of data\n", - " # \"warming_level_months\": [6, 7, 8], # Optional: specify months for seasonal averages, still WIP development\n", + " # \"warming_level_months\": [6, 7, 8], # Optional: specify months for seasonal averages\n", " },\n", " }\n", " )\n", From 6afcfd115c52a1ed93be4b0afb35f5a4acf3a564 Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Thu, 5 Mar 2026 21:42:37 -0300 Subject: [PATCH 13/53] responding to reviewer comments --- analysis/warming_level_methods.ipynb | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index 795a7021..9f04b753 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -134,7 +134,7 @@ "\n", "**The GWL approach in the Analytics Engine avoids this bias by breaking the estimation of climate impacts into a two step process:**\n", "\n", - "1) Aggregate data from all models to estimate the climate impacts at a given GWL - as illustrated in the first figure, data is taken from each simulation where it intersects the horizontal magenta line at 2.0 °C GWL.", + "1) Aggregate data from all models to estimate the climate impacts at a given GWL - as illustrated in the first figure, data is taken from each simulation where it intersects the horizontal magenta line at 2.0 °C GWL.\n", "\n", "2) Use the IPCC's refined warming trajectories to estimate *when* that GWL will most likely be reached -- In this case the year 2047, for SSP 3-7.0\n", "\n" @@ -204,7 +204,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Above, we've created a `ClimateData` object called `cd`, which we will use to grab GWL (Global Warming Level approach) data below." + "Above, we've created a `ClimateData` object called `cd`, which we will use to grab GWL (Global Warming Level approach) data below.\n", + "\n", + "**NOTE**: Because of the different rates of warming within each climate model, some simulations may not have complete data for particularly low or high warming levels. Any time a simulation does not have the full user-requested time window centered around a requested warming level, that warming level is skipped for that simulation and NaNs will be returned. A warning will be printed any time this occurs during a data request." ] }, { @@ -729,9 +731,9 @@ "outputs": [], "source": [ "# perform the same calculation for the time-based data\n", - "time_08_sac_summer_mean = time_08_sac.groupby(\"time.year\").max().mean(dim=\"year\")\n", - "time_20_sac_summer_mean = time_20_sac.groupby(\"time.year\").max().mean(dim=\"year\")\n", - "time_20_sac_summer_delta = time_20_sac_summer_mean - time_08_sac_summer_mean" + "time_08_sac_mean = time_08_sac.groupby(\"time.year\").max().mean(dim=\"year\")\n", + "time_20_sac_mean = time_20_sac.groupby(\"time.year\").max().mean(dim=\"year\")\n", + "time_20_sac_delta = time_20_sac_mean - time_08_sac_mean" ] }, { @@ -745,7 +747,7 @@ "gwl_sac_temp_delta.plot.hist(\n", " width=0.2, bins=np.arange(0, 6, 0.25), alpha=0.7, label=\"Warming Level 2.0 - 0.8\"\n", ")\n", - "time_20_sac_summer_delta.plot.hist(\n", + "time_20_sac_delta.plot.hist(\n", " width=0.2, bins=np.arange(0, 6, 0.25), alpha=0.5, label=\"Target year 2047 - 2002\"\n", ")\n", "\n", From fcbbc4822475a71fe45b633aad27b9fdba86064e Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Thu, 5 Mar 2026 21:45:43 -0300 Subject: [PATCH 14/53] fix resolution --- analysis/warming_level_methods.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/analysis/warming_level_methods.ipynb b/analysis/warming_level_methods.ipynb index 9f04b753..d8fef4b9 100644 --- a/analysis/warming_level_methods.ipynb +++ b/analysis/warming_level_methods.ipynb @@ -545,7 +545,7 @@ " cd.catalog(\"cadcat\")\n", " .activity_id(\"LOCA2\") # Statistical downscaling\n", " .table_id(\"mon\") # Looking at `monthly` data\n", - " .grid_label(\"d03\") # Looking at `45 km` data\n", + " .grid_label(\"d03\") # Looking at `3 km` data\n", " .variable(\n", " \"tasmax\"\n", " ) # Grabbing the maximum daily temperature variable (will be used later)\n", @@ -598,7 +598,7 @@ " cd.catalog(\"cadcat\")\n", " .activity_id(\"LOCA2\") # Statistical downscaling\n", " .table_id(\"mon\") # Looking at `monthly` data\n", - " .grid_label(\"d03\") # Looking at `45 km` data\n", + " .grid_label(\"d03\") # Looking at `3 km` data\n", " .variable(\n", " \"tasmax\"\n", " ) # Grabbing the maximum daily temperature variable variable (will be used later)\n", From ee0e6e7e41376242455090b0c445333d09ed8160 Mon Sep 17 00:00:00 2001 From: Nicole Keeney Date: Mon, 9 Mar 2026 23:00:27 -0300 Subject: [PATCH 15/53] categorize notebook --- AE_navigation_guide.ipynb | 57 ++++++++++++++++++++++----------------- 1 file changed, 33 insertions(+), 24 deletions(-) diff --git a/AE_navigation_guide.ipynb b/AE_navigation_guide.ipynb index 9955d9cc..2e6a99b6 100644 --- a/AE_navigation_guide.ipynb +++ b/AE_navigation_guide.ipynb @@ -35,73 +35,82 @@ "source": [ "## Table of Contents \n", "-------------------------------------------------------------------\n", + "\n", + "Each notebook is labeled with a type to help you find the right resource for your needs:\n", + "\n", + "| Type | Description |\n", + "|------|-------------|\n", + "| Tool | Execute a specific workflow to produce an analysis, data product, or visualization |\n", + "| Education | Learn about a climate science topic or concept |\n", + "| Methods | Walk through a methodology used in our analyses |\n", + "\n", "### data-access\n", "AE catalog data can be accessed in **three different ways**, each with distinct benefits. The appropriate data access method will depend on your unique use case and workflow. \n", "\n", - "1. [basic_data_access.ipynb](data-access/basic_data_access.ipynb): Overview to accessing, spatially and temporally subsetting, and exporting climate data from the AE data catalog using helper functions in `climakitae`. \n", + "1. [basic_data_access.ipynb](data-access/basic_data_access.ipynb): Overview to accessing, spatially and temporally subsetting, and exporting climate data from the AE data catalog using helper functions in `climakitae`. Notebook type: Tool\n", "\n", - "2. [interactive_data_access_and_viz.ipynb](data-access/interactive_data_access_and_viz.ipynb): Retrieve, subset, and visualize data options using a simple and intuitive interactive graphical user interface (GUI). This notebook leverages functionality from both `climakitae` and our visualizations library `climakitaegui`. \n", + "2. [interactive_data_access_and_viz.ipynb](data-access/interactive_data_access_and_viz.ipynb): Retrieve, subset, and visualize data options using a simple and intuitive interactive graphical user interface (GUI). This notebook leverages functionality from both `climakitae` and our visualizations library `climakitaegui`. Notebook type: Tool\n", "\n", - "3. [outside_AE_data_access.ipynb](data-access/intake_direct_data_download.ipynb): Access and export AE catalog data without using the helper functions from AE's python libraries. This notebook instead leverages the python library `intake` for interfacing with the data catalog. This method may be useful for users accessing the data outside of the Analytics Engine and who don't want to set up `climakitae` in their computational environment.\n", + "3. [outside_AE_data_access.ipynb](data-access/intake_direct_data_download.ipynb): Access and export AE catalog data without using the helper functions from AE's python libraries. This notebook instead leverages the python library `intake` for interfacing with the data catalog. This method may be useful for users accessing the data outside of the Analytics Engine and who don't want to set up `climakitae` in their computational environment. Notebook type: Tool\n", "\n", "4. [renewables_data_access](data-access/renewables_data_access.ipynb\n", - "): Access and plot our derived renewables data products. This notebook leverages the Python library `intake` for interfacing with the renewables data catalog, which is separate from the larger AE catalog. Eventually, data access will be fully integrated into `climakitae`, but for now, this is the best way to access the renewables data.\n", + "): Access and plot our derived renewables data products. This notebook leverages the Python library `intake` for interfacing with the renewables data catalog, which is separate from the larger AE catalog. Eventually, data access will be fully integrated into `climakitae`, but for now, this is the best way to access the renewables data. Notebook type: Tool\n", "\n", "5. [weather_station_data_access](data-access/weather_station_data_access\n", - "): Access and plot quality controlled, standardized historical weather station data. This notebook leverages the Python library `intake` for interfacing with the historical weather station data catalog, which is separate from the larger AE catalog. Eventually, data access will be fully integrated into `climakitae`, but for now, this is the best way to access the data.\n", + "): Access and plot quality controlled, standardized historical weather station data. This notebook leverages the Python library `intake` for interfacing with the historical weather station data catalog, which is separate from the larger AE catalog. Eventually, data access will be fully integrated into `climakitae`, but for now, this is the best way to access the data. Notebook type: Tool\n", "\n", "------------------------------------------\n", "### exploratory\n", "Explore our data and fundamental climate science topics with these notebooks. \n", "\n", - "- [historical_climate_data_comparisons.ipynb](exploratory/climate_data_comparisons): Learn how to compare datasets across the historical period, using observations, reanalysis, and model output.\n", + "- [historical_climate_data_comparisons.ipynb](exploratory/climate_data_comparisons): Learn how to compare datasets across the historical period, using observations, reanalysis, and model output. Notebook type: Education\n", "\n", - "- [internal_variability.ipynb](exploratory/internal_variability.ipynb): Explore uncertainty within climate models due to internal variability in the climate system, using projected changes in extreme precipitation across different climate model simulations.\n", + "- [internal_variability.ipynb](exploratory/internal_variability.ipynb): Explore uncertainty within climate models due to internal variability in the climate system, using projected changes in extreme precipitation across different climate model simulations. Notebook type: Education\n", "\n", - "- [model_uncertainty.ipynb](exploratory/model_uncertainty.ipynb): Explore uncertainty across climate models, using projected variations in air temperature trends across different climate model simulations.\n", + "- [model_uncertainty.ipynb](exploratory/model_uncertainty.ipynb): Explore uncertainty across climate models, using projected variations in air temperature trends across different climate model simulations. Notebook type: Education\n", "\n", - "- [warming_levels.ipynb](exploratory/warming_levels.ipynb): Explore the concept of Global Warming Levels, which can be used to compare possible climate outcomes across multiple scenarios or model simulations, with respect to extreme event planning.\n", + "- [warming_levels.ipynb](exploratory/warming_levels.ipynb): Explore the concept of Global Warming Levels, which can be used to compare possible climate outcomes across multiple scenarios or model simulations, with respect to extreme event planning. Notebook type: Education\n", "\n", - "- [warming_levels_approach.ipynb](exploratory/warming_levels_approach.ipynb): A comparison notebook that walks through the differences between a time-based approach and a Global Warming Levels approach.\n", + "- [warming_levels_approach.ipynb](exploratory/warming_levels_approach.ipynb): A comparison notebook that walks through the differences between a time-based approach and a Global Warming Levels approach. Notebook type: Education\n", "\n", - "- [warming_level_timing_tools.ipynb](analysis/warming_level_timing_tools.ipynb): Demonstrates tools for etimating the timing of Global Warming Levels, and translating between time-based and GWL planning targets.\n", + "- [warming_level_timing_tools.ipynb](analysis/warming_level_timing_tools.ipynb): Demonstrates tools for etimating the timing of Global Warming Levels, and translating between time-based and GWL planning targets. Notebook type: Methods\n", "\n", - "- [timeseries_tranformations.ipynb](exploratory/timeseries_transformations.ipynb): Explore data transformation and analysis options for working with climate timeseries data using a graphical user interface (GUI).\n", + "- [timeseries_tranformations.ipynb](exploratory/timeseries_transformations.ipynb): Explore data transformation and analysis options for working with climate timeseries data using a graphical user interface (GUI). Notebook type: Methods\n", "\n", "---------------------------------\n", "### threshold-tools\n", "Notebooks for understanding extreme weather. \n", "\n", - "- [threshold_exceedance.ipynb](tools/threshold_exceedance.ipynb): Perform calculations and explore visualizations of threshold exceedance events using an interactive graphical user interface (GUI). An extension of the topics introduced in threshold_basics.\n", + "- [threshold_exceedance.ipynb](tools/threshold_exceedance.ipynb): Perform calculations and explore visualizations of threshold exceedance events using an interactive graphical user interface (GUI). An extension of the topics introduced in threshold_basics. Notebook type: Tool\n", "\n", - "- [threshold_event_types.ipynb](tools/threshold_event_types.ipynb): Define different types of extreme events and explore their likelihood of occurence using extreme value theory.\n", + "- [threshold_event_types.ipynb](tools/threshold_event_types.ipynb): Define different types of extreme events and explore their likelihood of occurence using extreme value theory. Notebook type: Education\n", "\n", "---------------------------------\n", "### climate-profiles\n", "Notebooks for generating climate profiles. \n", "\n", - "- [custom_climate_profiles.ipynb](climate-profiles/custom_climate_profiles.ipynb): Learn how to generate Standard Year (SY) and Typical Meteorological Year (TMY) progiles for custom locations, quantiles, and warming levels.\n", + "- [custom_climate_profiles.ipynb](climate-profiles/custom_climate_profiles.ipynb): Learn how to generate Standard Year (SY) and Typical Meteorological Year (TMY) progiles for custom locations, quantiles, and warming levels. Notebook type: Tool\n", "\n", - "- [typical_meteorological_year_methodology.ipynb](climate-profiles/typical_meteorological_year_methodology.ipynb): Explore the concept and methodology of a Typical Meteorological Year to represent the typical climatological conditions over one year of hourly data.\n", + "- [typical_meteorological_year_methodology.ipynb](climate-profiles/typical_meteorological_year_methodology.ipynb): Explore the concept and methodology of a Typical Meteorological Year to represent the typical climatological conditions over one year of hourly data. Notebook type: Methods\n", "\n", "-------------------------------------\n", "### collaborative\n", "Notebooks we have developed with industry partners. \n", "#### Demand Forecast Unit\n", - "- [degree_days.ipynb](collaborative/DFU/degree_days.ipynb): Perform calculations and explore visualizations of generated weather and climate information for an annual consumption model by producing heating and cooling degree days.\n", + "- [degree_days.ipynb](collaborative/DFU/degree_days.ipynb): Perform calculations and explore visualizations of generated weather and climate information for an annual consumption model by producing heating and cooling degree days. Notebook type: Tool\n", "\n", - "- [download_localized_stations.ipynb](collaborative/DFU/download_localized_stations.ipynb): Download bias-corrected air temperature and dewpoint temperature model timeseries localized for any of 71 weather stations in the WECC.\n", + "- [download_localized_stations.ipynb](collaborative/DFU/download_localized_stations.ipynb): Download bias-corrected air temperature and dewpoint temperature model timeseries localized for any of 71 weather stations in the WECC. Notebook type: Tool\n", "\n", - "- [localization_methodology.ipynb](collaborative/DFU/localization_methodology.ipynb): Introduction to the localization method for examining climate projections at a location.\n", + "- [localization_methodology.ipynb](collaborative/DFU/localization_methodology.ipynb): Introduction to the localization method for examining climate projections at a location. Notebook type: Methods\n", "\n", - "- [station_hourly_profiles.ipynb](collaborative/DFU/station_hourly_profiles.ipynb): Explore the process to produce hourly profiles of localized data.\n", + "- [station_hourly_profiles.ipynb](collaborative/DFU/station_hourly_profiles.ipynb): Explore the process to produce hourly profiles of localized data. Notebook type: Methods\n", "\n", "#### Investor-Owned Utilities\n", - "- [heat_index.ipynb](collaborative/IOU/heat_index.ipynb): Generate historical and projected trends of Heat Index at a location.\n", + "- [heat_index.ipynb](collaborative/IOU/heat_index.ipynb): Generate historical and projected trends of Heat Index at a location. Notebook type: Tool\n", " \n", - "- [vulnerability_assessment.ipynb](collaborative/IOU/vulnerability_assessment/vulnerability_assessment.ipynb): Generate data-informed answers for vulnerability assessments through a customizeable metric builder.\n", + "- [vulnerability_assessment.ipynb](collaborative/IOU/vulnerability_assessment/vulnerability_assessment.ipynb): Generate data-informed answers for vulnerability assessments through a customizeable metric builder. Notebook type: Tool\n", "\n", - "- [one_in_x_in_8760.ipynb](collaborative/IOU/8760/one_in_x_in_8760.ipynb): Generate 8760 hourly climate profiles with 1-in-X events inserted in them.\n", + "- [one_in_x_in_8760.ipynb](collaborative/IOU/8760/one_in_x_in_8760.ipynb): Generate 8760 hourly climate profiles with 1-in-X events inserted in them. Notebook type: Tool\n", "\n", "--------------------------------------\n", "### work-in-progress\n", @@ -121,7 +130,7 @@ "\n", "- [generalized_climate_signal_selector.ipynb](work-in-progress/generalized_climate_signal_selector.ipynb): Tool to estimate the climate signal for a given variable for all WRF and LOCA2 simulations.\n", "\n", - "- [compute_santa_ana_wind_indicators.ipynb](work-in-progress/compute_santa_ana_wind_indicators.ipynb): Tool to generate timeseries of sea level pressure and upper level wind and temperature indicators for Santa Ana winds.\n" + "- [compute_santa_ana_wind_indicators.ipynb](work-in-progress/compute_santa_ana_wind_indicators.ipynb): Tool to generate timeseries of sea level pressure and upper level wind and temperature indicators for Santa Ana winds." ] }, { From b005c4991e0e119018045dabfa2269c9d817f781 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Thu, 29 Jan 2026 15:11:55 -0600 Subject: [PATCH 16/53] adding notebook on derived variables --- analysis/derived_variables_demo.ipynb | 1888 +++++++++++++++++++++++++ 1 file changed, 1888 insertions(+) create mode 100644 analysis/derived_variables_demo.ipynb diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb new file mode 100644 index 00000000..fb4d1343 --- /dev/null +++ b/analysis/derived_variables_demo.ipynb @@ -0,0 +1,1888 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "4d750b3f", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "a97eee28", + "metadata": {}, + "source": [ + "# Derived Variables Demo: Heating & Cooling Degree Days\n", + "\n", + "This notebook demonstrates how to use derived variables in ClimakitAE, focusing on **heating degree days (HDD)** and **cooling degree days (CDD)** for energy demand analysis in Los Angeles.\n", + "\n", + "## What are Degree Days?\n", + "\n", + "- **Heating Degree Days (HDD)**: Measure of energy demand for heating when outdoor temperature falls below 65°F\n", + "- **Cooling Degree Days (CDD)**: Measure of energy demand for cooling when outdoor temperature rises above 65°F\n", + "\n", + "These metrics are essential for:\n", + "- Building energy modeling\n", + "- Utility demand forecasting\n", + "- Climate adaptation planning\n", + "- Energy efficiency assessments" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2f59f965", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from dask.diagnostics import ProgressBar\n", + "\n", + "import climakitae as ck\n", + "\n", + "# Initialize ClimateData\n", + "cd = ck.ClimateData(verbosity=-1)" + ] + }, + { + "cell_type": "markdown", + "id": "e42bba08", + "metadata": {}, + "source": [ + "## Why Derived Variables? Old vs New Approach\n", + "\n", + "### ❌ Previous Approach (Legacy Interface)\n", + "\n", + "```mermaid\n", + "flowchart TD\n", + " A[Step 1: Fetch first variable
tasmax_data = get_data] --> B[Step 2: Fetch second variable
tasmin_data = get_data]\n", + " B --> C[Step 3: Define calculation function
def calc_cdd]\n", + " C --> D[Step 4: Manually compute
cdd = calc_cdd]\n", + " D --> E[Step 5: Manual post-processing
cdd_clipped = cdd.sel
cdd_annual = cdd.resample]\n", + " E --> F[⏱️ RESULT]\n", + " \n", + " style A fill:#ffcccc,stroke:#cc0000,stroke-width:2px\n", + " style B fill:#ffcccc,stroke:#cc0000,stroke-width:2px\n", + " style C fill:#ffcccc,stroke:#cc0000,stroke-width:2px\n", + " style D fill:#ffcccc,stroke:#cc0000,stroke-width:2px\n", + " style E fill:#ffcccc,stroke:#cc0000,stroke-width:2px\n", + " style F fill:#ffeeee,stroke:#cc0000,stroke-width:2px\n", + "```\n", + "\n", + "**Pain Points:**\n", + "- 💥 Multiple `get_data()` calls might load full datasets into memory (lazy dataset handling was inconsistent)\n", + "- 🔧 Manual coordination of multiple variables\n", + "- 📦 Post-processing happens after data is loaded\n", + "- 🔁 No reusability - repeat for each analysis\n", + "- ⚠️ Error-prone parameter matching\n", + "\n", + "---\n", + "\n", + "### ✅ New Approach (Derived Variables System)\n", + "\n", + "```mermaid\n", + "flowchart TD\n", + " A[Step 1: Define function ONCE and register
@register_derived] --> B[Step 2: Single call with processing
ClimateData.variable.processes.get]\n", + " B --> C[⚡ LAZY RESULT
computed on demand]\n", + " \n", + " style A fill:#ccffcc,stroke:#00cc00,stroke-width:2px\n", + " style B fill:#ccffcc,stroke:#00cc00,stroke-width:2px\n", + " style C fill:#eeffee,stroke:#00cc00,stroke-width:3px\n", + "```\n", + "\n", + "**Benefits:**\n", + "- ✨ **Single `.get()` call** - All processing in one query\n", + "- 🔄 **Automatic variable fetching** - System knows CDD needs t2max & t2min\n", + "- 💾 **Lazy evaluation** - Data not loaded until needed\n", + "- 🎯 **Processing pipeline** - Clipping, time slicing happen efficiently\n", + "- ♻️ **Reusable** - Define once, use everywhere\n", + "- 🛡️ **Type-safe** - Registry validates dependencies\n", + "- 📦 **Composable** - Chain with other processors seamlessly\n", + "\n", + "---\n", + "\n", + "### Code Comparison\n", + "\n", + "**Old Approach:**\n", + "```python\n", + "# Step 1 & 2: Multiple data fetches\n", + "tasmax_data = get_data({'variable': 'tasmax', 'area': 'Los Angeles', ...})\n", + "tasmin_data = get_data({'variable': 'tasmin', 'area': 'Los Angeles', ...})\n", + "\n", + "# Step 3 & 4: Manual calculation\n", + "def calc_cdd(tasmax, tasmin):\n", + " t_avg = (tasmax + tasmin) / 2\n", + " return np.maximum(0, t_avg - 291.48) # 65°F threshold\n", + "\n", + "cdd = calc_cdd(tasmax_data, tasmin_data)\n", + "\n", + "# Step 5: Manual post-processing\n", + "cdd_clipped = cdd.sel(lat=..., lon=...)\n", + "cdd_annual = cdd_clipped.resample(time='Y').sum()\n", + "```\n", + "\n", + "**New Approach:**\n", + "```python\n", + "# Step 1: Register once (already done in builtin!)\n", + "@register_derived(variable='CDD_wrf', query={'variable_id': ['t2max', 't2min']})\n", + "def calc_cdd_wrf(ds): ...\n", + "\n", + "# Step 2: Single call - that's it!\n", + "cdd_data = (\n", + " ClimateData()\n", + " .variable('CDD_wrf') # Derived variable!\n", + " .activity_id('WRF')\n", + " .processes({\n", + " 'clip': 'Los Angeles County',\n", + " 'time_slice': (2020, 2050)\n", + " })\n", + " .get()\n", + ")\n", + "# Returns lazy dataset with all processing applied\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5356d830", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - Derived Variables (computed from source variables during loading):\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - ------------------------------------------------------------------\n", + "Derived Variables (computed from source variables during loading):\n", + "------------------------------------------------------------------\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - CDD_loca depends on: tasmax, tasmin \n", + " └─ Cooling degree days from LOCA2 data (base 65°F)\n", + "CDD_loca depends on: tasmax, tasmin \n", + " └─ Cooling degree days from LOCA2 data (base 65°F)\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - CDD_wrf depends on: t2 \n", + " └─ Cooling degree days from WRF data (base 65°F)\n", + "CDD_wrf depends on: t2 \n", + " └─ Cooling degree days from WRF data (base 65°F)\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - HDD_loca depends on: tasmax, tasmin \n", + " └─ Heating degree days from LOCA2 data (base 65°F)\n", + "HDD_loca depends on: tasmax, tasmin \n", + " └─ Heating degree days from LOCA2 data (base 65°F)\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - HDD_wrf depends on: t2 \n", + " └─ Heating degree days from WRF data (base 65°F)\n", + "HDD_wrf depends on: t2 \n", + " └─ Heating degree days from WRF data (base 65°F)\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - dew_point_2m depends on: t2, rh \n", + " └─ Dew point temperature at 2m from temperature and relative humidity\n", + "dew_point_2m depends on: t2, rh \n", + " └─ Dew point temperature at 2m from temperature and relative humidity\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - diurnal_temperature_range_loca depends on: tasmax, tasmin \n", + " └─ Daily temperature range (maximum minus minimum)\n", + "diurnal_temperature_range_loca depends on: tasmax, tasmin \n", + " └─ Daily temperature range (maximum minus minimum)\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - diurnal_temperature_range_wrf depends on: t2max, t2min \n", + " └─ Daily temperature range from WRF data (maximum minus minimum)\n", + "diurnal_temperature_range_wrf depends on: t2max, t2min \n", + " └─ Daily temperature range from WRF data (maximum minus minimum)\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - heat_index depends on: t2, rh \n", + " └─ Heat index (feels-like temperature accounting for humidity effects)\n", + "heat_index depends on: t2, rh \n", + " └─ Heat index (feels-like temperature accounting for humidity effects)\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - relative_humidity_2m depends on: t2, q2, psfc \n", + " └─ Relative humidity at 2m computed from temperature, specific humidity, and surface pressure\n", + "relative_humidity_2m depends on: t2, q2, psfc \n", + " └─ Relative humidity at 2m computed from temperature, specific humidity, and surface pressure\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - specific_humidity_2m depends on: t2, rh, psfc \n", + " └─ Specific humidity at 2m from temperature, relative humidity, and pressure\n", + "specific_humidity_2m depends on: t2, rh, psfc \n", + " └─ Specific humidity at 2m from temperature, relative humidity, and pressure\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - wind_chill depends on: t2, u10, v10 \n", + " └─ Wind chill (feels-like temperature accounting for wind effects)\n", + "wind_chill depends on: t2, u10, v10 \n", + " └─ Wind chill (feels-like temperature accounting for wind effects)\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - wind_direction_10m depends on: u10, v10 \n", + " └─ Wind direction at 10m height (meteorological convention: direction wind comes FROM)\n", + "wind_direction_10m depends on: u10, v10 \n", + " └─ Wind direction at 10m height (meteorological convention: direction wind comes FROM)\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - wind_speed_10m depends on: u10, v10 \n", + " └─ Wind speed at 10m height computed from U and V components\n", + "wind_speed_10m depends on: u10, v10 \n", + " └─ Wind speed at 10m height computed from U and V components\n", + "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Show all available derived variables\n", + "# Notice the HDD and CDD variables for both WRF and LOCA2 data\n", + "cd.show_derived_variables()" + ] + }, + { + "cell_type": "markdown", + "id": "7db2357a", + "metadata": {}, + "source": [ + "## 2. Understanding the Degree Days Variables\n", + "\n", + "The degree days metrics are built-in derived variables that automatically fetch and compute from temperature data:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "41b921f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "HDD_wrf:\n", + " Depends on: ['t2']\n", + " Description: Heating degree days from WRF data (base 65°F)\n", + "\n", + "CDD_wrf:\n", + " Depends on: ['t2']\n", + " Description: Cooling degree days from WRF data (base 65°F)\n", + "\n", + "HDD_loca:\n", + " Depends on: ['tasmax', 'tasmin']\n", + " Description: Heating degree days from LOCA2 data (base 65°F)\n", + "\n", + "CDD_loca:\n", + " Depends on: ['tasmax', 'tasmin']\n", + " Description: Cooling degree days from LOCA2 data (base 65°F)\n" + ] + } + ], + "source": [ + "from climakitae.new_core.derived_variables import list_derived_variables\n", + "\n", + "# Get info about the degree days variables\n", + "derived_vars = list_derived_variables()\n", + "\n", + "degree_days_vars = ['HDD_wrf', 'CDD_wrf', 'HDD_loca', 'CDD_loca']\n", + "\n", + "for var_name in degree_days_vars:\n", + " if var_name in derived_vars:\n", + " info = derived_vars[var_name]\n", + " print(f\"\\n{var_name}:\")\n", + " print(f\" Depends on: {info.depends_on}\")\n", + " print(f\" Description: {info.description}\")" + ] + }, + { + "cell_type": "markdown", + "id": "fff155f9", + "metadata": {}, + "source": [ + "## 3. Fetching Cooling Degree Days for Los Angeles\n", + "\n", + "Let's fetch CDD data for Los Angeles County to analyze cooling energy demand under future warming:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b979b365", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nschroed/Work/climakitae/climakitae/util/utils.py:1981: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 1.2C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "cd = ck.ClimateData(verbosity=-2)\n", + "\n", + "# Fetch cooling degree days for Los Angeles County\n", + "cdd_data = (\n", + " cd\n", + " .catalog(\"cadcat\")\n", + " .variable(\"CDD_wrf\") # Cooling degree days from WRF\n", + " .activity_id(\"WRF\")\n", + " .institution_id(\"UCLA\")\n", + " .table_id(\"day\")\n", + " .grid_label(\"d03\") # 3km resolution\n", + " # .experiment_id(\"ssp370\") # High emissions scenario\n", + " .processes({\n", + " \"warming_level\": {\n", + " \"warming_levels\": [1.2, 2.0, 3.0]\n", + " },\n", + " \"clip\": \"Los Angeles County\",\n", + " })\n", + " .get()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a70213f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 6GB\n",
+       "Dimensions:            (warming_level: 3, x: 64, y: 72, sim: 5, time: 10950)\n",
+       "Coordinates:\n",
+       "  * warming_level      (warming_level) float64 24B 1.2 2.0 3.0\n",
+       "  * x                  (x) float64 512B -4.215e+06 -4.212e+06 ... -4.026e+06\n",
+       "  * y                  (y) float64 576B 7.369e+05 7.399e+05 ... 9.499e+05\n",
+       "  * sim                (sim) object 40B 'WRF_UCLA_EC-Earth3-Veg_ssp370_day_d0...\n",
+       "  * time               (time) datetime64[ns] 88kB 2010-01-05 ... 2039-12-28\n",
+       "    lakemask           (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
+       "    landmask           (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
+       "    lat                (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
+       "    lon                (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
+       "    centered_year      (sim, warming_level) float64 120B 2.025e+03 ... 2.058e+03\n",
+       "    Lambert_Conformal  int64 8B 0\n",
+       "Data variables:\n",
+       "    CDD_wrf            (sim, warming_level, time, y, x) float64 6GB dask.array<chunksize=(1, 1, 2433, 44, 2), meta=np.ndarray>\n",
+       "Attributes: (12/119)\n",
+       "    AERCU_FCT:                        1.0\n",
+       "    AERCU_OPT:                        0\n",
+       "    AUTO_LEVELS_OPT:                  2\n",
+       "    BL_PBL_PHYSICS:                   1\n",
+       "    BOTTOM_TOP_GRID_DIMENSION:        40\n",
+       "    BOTTOM_TOP_PATCH_END_STAG:        40\n",
+       "    ...                               ...\n",
+       "    warming_level:                    {'warming_levels': [1.2, 2.0, 3.0]}\n",
+       "    clip:                             Process 'clip' applied to the data. Cli...\n",
+       "    update_attributes:                Process 'update_attributes' applied to ...\n",
+       "    filter_unadjusted_models:         yes\n",
+       "    concat:                           Process 'concat' applied to the data. T...\n",
+       "    warming_level_simple:             Process 'warming_level_simple' applied ...
" + ], + "text/plain": [ + " Size: 6GB\n", + "Dimensions: (warming_level: 3, x: 64, y: 72, sim: 5, time: 10950)\n", + "Coordinates:\n", + " * warming_level (warming_level) float64 24B 1.2 2.0 3.0\n", + " * x (x) float64 512B -4.215e+06 -4.212e+06 ... -4.026e+06\n", + " * y (y) float64 576B 7.369e+05 7.399e+05 ... 9.499e+05\n", + " * sim (sim) object 40B 'WRF_UCLA_EC-Earth3-Veg_ssp370_day_d0...\n", + " * time (time) datetime64[ns] 88kB 2010-01-05 ... 2039-12-28\n", + " lakemask (y, x) float32 18kB dask.array\n", + " landmask (y, x) float32 18kB dask.array\n", + " lat (y, x) float32 18kB dask.array\n", + " lon (y, x) float32 18kB dask.array\n", + " centered_year (sim, warming_level) float64 120B 2.025e+03 ... 2.058e+03\n", + " Lambert_Conformal int64 8B 0\n", + "Data variables:\n", + " CDD_wrf (sim, warming_level, time, y, x) float64 6GB dask.array\n", + "Attributes: (12/119)\n", + " AERCU_FCT: 1.0\n", + " AERCU_OPT: 0\n", + " AUTO_LEVELS_OPT: 2\n", + " BL_PBL_PHYSICS: 1\n", + " BOTTOM_TOP_GRID_DIMENSION: 40\n", + " BOTTOM_TOP_PATCH_END_STAG: 40\n", + " ... ...\n", + " warming_level: {'warming_levels': [1.2, 2.0, 3.0]}\n", + " clip: Process 'clip' applied to the data. Cli...\n", + " update_attributes: Process 'update_attributes' applied to ...\n", + " filter_unadjusted_models: yes\n", + " concat: Process 'concat' applied to the data. T...\n", + " warming_level_simple: Process 'warming_level_simple' applied ..." + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add dummy time dimension to warming level data for easier handling\n", + "def add_dummy_time_to_wl(data):\n", + " \"\"\"\n", + " Convert time_delta dimension to a dummy time dimension for easier handling.\n", + " \"\"\"\n", + " import xarray as xr\n", + " import pandas as pd\n", + "\n", + " if 'time_delta' in data.dims:\n", + " wl_values = data['time_delta'].values\n", + " time_values = pd.to_datetime(wl_values, unit='D', origin='2025-01-01')\n", + " data = data.rename({'time_delta': 'time'})\n", + " data = data.assign_coords(time=time_values)\n", + " return data\n", + "\n", + "cdd_data = add_dummy_time_to_wl(cdd_data)\n", + "cdd_data" + ] + }, + { + "cell_type": "markdown", + "id": "d245123a", + "metadata": {}, + "source": [ + "## 4. Visualizing Cooling Degree Days: Spatial Based\n", + "\n", + "Let's calculate and visualize annual cooling degree days:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24589b10", + "metadata": {}, + "outputs": [], + "source": [ + "# Count days with cooling degree days > 0 per year\n", + "cooling_days_per_year = cdd_data[\"CDD_wrf\"].resample(time=\"Y\").sum()\n", + "\n", + "# Average years for spatial plot\n", + "spt = cooling_days_per_year.mean(dim=\"time\") \n", + "\n", + "# Compute with progress bar\n", + "with ProgressBar():\n", + " spt = spt.median(dim='sim').compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e655325", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Create figure with 3 subplots (one per warming level)\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "\n", + "warming_levels = spt.warming_level.values\n", + "\n", + "for idx, (ax, wl) in enumerate(zip(axes, warming_levels)):\n", + " # Select data for this warming level\n", + " wl_data = spt.sel(warming_level=wl)\n", + " \n", + " # Create contourf plot\n", + " im = wl_data.plot.contourf(\n", + " ax=ax, x='lon', y='lat',\n", + " cmap='YlOrRd', levels=100,\n", + " add_colorbar=False,\n", + " vmin=1,\n", + " vmax=300\n", + " )\n", + " \n", + " ax.set_title(f'{wl}°C Warming Level', fontsize=13, fontweight='bold')\n", + " ax.set_xlabel('Longitude', fontsize=11)\n", + " ax.set_ylabel('Latitude', fontsize=11)\n", + "\n", + "# Add shared colorbar\n", + "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", + "\n", + "fig.suptitle('Cooling Days (65 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", + " fontsize=15, fontweight='bold', y=1.02)" + ] + }, + { + "cell_type": "markdown", + "id": "78da135d", + "metadata": {}, + "source": [ + "## 5. Temporal Trends by Warming Level\n", + "\n", + "Let's visualize how cooling days evolve over time for each warming level:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df7c9f7b", + "metadata": {}, + "outputs": [], + "source": [ + "# Average over spatial dimensions for county-wide average\n", + "from dask.diagnostics import ProgressBar\n", + "with ProgressBar():\n", + " cooling_days_per_year = cooling_days_per_year.mean(dim=[\"y\", \"x\"]).compute()\n", + "\n", + "# Plot time series for each warming level\n", + "fig, ax = plt.subplots(figsize=(14, 7))\n", + "\n", + "# Define colors for each warming level\n", + "colors = ['#2E86AB', '#A23B72', '#F18F01'] # Blue, Purple, Orange\n", + "warming_levels = cooling_days_per_year.warming_level.values\n", + "\n", + "# Plot ensemble mean for each warming level\n", + "for idx, wl in enumerate(warming_levels):\n", + " wl_data = cooling_days_per_year.sel(warming_level=wl)\n", + " \n", + " # Plot individual simulations with low alpha\n", + " for sim in range(wl_data.sizes['sim']):\n", + " sim_data = wl_data.isel(sim=sim)\n", + " ax.plot(sim_data.time.dt.year, sim_data.values, \n", + " alpha=0.15, color=colors[idx], linewidth=0.8)\n", + " \n", + " # Plot ensemble mean with solid line\n", + " ensemble_mean = wl_data.mean(dim='sim')\n", + " ax.plot(ensemble_mean.time.dt.year, ensemble_mean.values,\n", + " color=colors[idx], linewidth=3, label=f'{wl}°C Warming', \n", + " marker='o', markersize=4, markevery=5)\n", + "\n", + "ax.set_xlabel('Year', fontsize=13, fontweight='bold')\n", + "ax.set_ylabel('Annual Cooling Days', fontsize=13, fontweight='bold')\n", + "ax.set_title('Annual Cooling Days by Global Warming Level\\nLos Angeles County (UCLA WRF)', \n", + " fontsize=15, fontweight='bold', pad=15)\n", + "ax.legend(loc='upper left', fontsize=11, framealpha=0.95)\n", + "ax.grid(True, alpha=0.3, linestyle='--')\n", + "\n", + "# Add annotation\n", + "ax.text(0.98, 0.02, 'Higher warming → More cooling days', \n", + " transform=ax.transAxes, fontsize=10, \n", + " verticalalignment='bottom', horizontalalignment='right',\n", + " bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.3))\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e73ae1a4", + "metadata": {}, + "outputs": [], + "source": [ + "# Summary statistics for cooling days by warming level\n", + "for wl in warming_levels:\n", + " wl_data = cooling_days_per_year.sel(warming_level=wl).mean(dim='sim')\n", + " mean_days = wl_data.mean().values\n", + " print(f\"\\n{wl}°C Warming Level:\")\n", + " print(f\" Average cooling days per year: {mean_days:.0f} days\")\n", + " print(f\" Range: {wl_data.min().values:.0f} - {wl_data.max().values:.0f} days\")" + ] + }, + { + "cell_type": "markdown", + "id": "7337c751", + "metadata": {}, + "source": [ + "## 6. Customizing CDD\n", + "\n", + "There are two ways to customize the calculation of Cooling Degree Days:\n", + "1. Use the built-in methods to pass your own threshold and register it\n", + "2. Define your own Cooling Degree Days method and register it" + ] + }, + { + "cell_type": "markdown", + "id": "99060031", + "metadata": {}, + "source": [ + "### 6.1 Customizing CDD: Using Built-in Functions with Custom Thresholds\n", + "\n", + "The built-in `calc_cdd_wrf` and `calc_hdd_wrf` functions accept optional threshold arguments:\n", + "- `threshold_f` - threshold in Fahrenheit\n", + "- `threshold_c` - threshold in Celsius \n", + "- `threshold_k` - threshold in Kelvin\n", + "\n", + "**Best Practice:** Wrap the builtin function with your threshold and register it as a new variable. This:\n", + "- ✅ Creates a distinct, named variable (`CDD_wrf_75f`)\n", + "- ✅ Is reusable across sessions (if added to your project)\n", + "- ✅ Doesn't mutate global state\n", + "- ✅ Keeps the variable name meaningful" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e6398c0", + "metadata": {}, + "outputs": [], + "source": [ + "from climakitae.new_core.derived_variables.registry import register_user_function\n", + "from climakitae.new_core.derived_variables.builtin.temperature import calc_cdd_wrf\n", + "\n", + "# The builtin calc_cdd_wrf accepts threshold_f as an argument\n", + "# We can wrap it with a custom threshold and register as a new variable\n", + "\n", + "def calc_cdd_wrf_75f(ds):\n", + " \"\"\"CDD with 75°F threshold - wraps builtin with custom threshold.\"\"\"\n", + " return calc_cdd_wrf(ds, threshold_f=75.0)\n", + "\n", + "register_user_function(\n", + " name=\"CDD_wrf_75f\",\n", + " depends_on=[\"t2\"], # Same dependency as CDD_wrf\n", + " func=calc_cdd_wrf_75f,\n", + " description=\"Cooling Degree Days from WRF with 75°F base\",\n", + " units=\"K\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51ef713c", + "metadata": {}, + "outputs": [], + "source": [ + "cd = ck.ClimateData(verbosity=-2)\n", + "\n", + "# Fetch cooling degree days with 75°F threshold\n", + "cdd_data_75f = (\n", + " cd\n", + " .catalog(\"cadcat\")\n", + " .variable(\"CDD_wrf_75f\") # Now using our custom 75°F variable\n", + " .activity_id(\"WRF\")\n", + " .institution_id(\"UCLA\")\n", + " .table_id(\"day\")\n", + " .grid_label(\"d03\")\n", + " .processes({\n", + " \"warming_level\": {\n", + " \"warming_levels\": [1.2, 2.0, 3.0]\n", + " },\n", + " \"clip\": \"Los Angeles County\",\n", + " })\n", + " .get()\n", + ")\n", + "\n", + "cdd_data_75f = add_dummy_time_to_wl(cdd_data_75f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fee2320", + "metadata": {}, + "outputs": [], + "source": [ + "# Count days with cooling degree days > 0 per year\n", + "cooling_days_per_year = cdd_data_75f[\"CDD_wrf_75f\"].resample(time=\"Y\").sum()\n", + "\n", + "# Average years for spatial plot\n", + "spt = cooling_days_per_year.mean(dim=\"time\") \n", + "\n", + "# Compute with progress bar\n", + "with ProgressBar():\n", + " spt = spt.median(dim='sim').compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aca87b14", + "metadata": {}, + "outputs": [], + "source": [ + "# Create figure with 3 subplots (one per warming level)\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "\n", + "warming_levels = spt.warming_level.values\n", + "\n", + "for idx, (ax, wl) in enumerate(zip(axes, warming_levels)):\n", + " # Select data for this warming level\n", + " wl_data = spt.sel(warming_level=wl)\n", + " \n", + " # Create contourf plot\n", + " im = wl_data.plot.contourf(\n", + " ax=ax, x='lon', y='lat',\n", + " cmap='YlOrRd', levels=100,\n", + " add_colorbar=False,\n", + " vmin=1,\n", + " vmax=300\n", + " )\n", + " \n", + " ax.set_title(f'{wl}°C Warming Level', fontsize=13, fontweight='bold')\n", + " ax.set_xlabel('Longitude', fontsize=11)\n", + " ax.set_ylabel('Latitude', fontsize=11)\n", + "\n", + "# Add shared colorbar\n", + "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", + "\n", + "fig.suptitle('Cooling Days (75 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", + " fontsize=15, fontweight='bold', y=1.02)" + ] + }, + { + "cell_type": "markdown", + "id": "50ee9391", + "metadata": {}, + "source": [ + "#### Factory Pattern: Creating Multiple Threshold Variables\n", + "\n", + "For projects needing multiple thresholds, use a factory function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31226884", + "metadata": {}, + "outputs": [], + "source": [ + "# Factory function to create CDD/HDD variables with arbitrary thresholds\n", + "def create_degree_day_variable(\n", + " name: str,\n", + " threshold_f: float,\n", + " metric: str = \"CDD\", # \"CDD\" or \"HDD\"\n", + " data_source: str = \"wrf\", # \"wrf\" or \"loca\"\n", + "):\n", + " \"\"\"\n", + " Factory to create degree day variables with custom thresholds.\n", + " \n", + " Parameters\n", + " ----------\n", + " name : str\n", + " Name for the new derived variable (e.g., \"CDD_wrf_80f\")\n", + " threshold_f : float\n", + " Threshold temperature in Fahrenheit\n", + " metric : str\n", + " \"CDD\" for cooling degree days, \"HDD\" for heating degree days\n", + " data_source : str\n", + " \"wrf\" for WRF data (depends on t2), \"loca\" for LOCA2 (depends on tasmax, tasmin)\n", + " \"\"\"\n", + " from climakitae.new_core.derived_variables.builtin.temperature import (\n", + " calc_cdd_wrf, calc_hdd_wrf, calc_cdd_loca, calc_hdd_loca\n", + " )\n", + " \n", + " # Select the appropriate function and dependencies\n", + " func_map = {\n", + " (\"CDD\", \"wrf\"): (calc_cdd_wrf, [\"t2\"]),\n", + " (\"HDD\", \"wrf\"): (calc_hdd_wrf, [\"t2\"]),\n", + " (\"CDD\", \"loca\"): (calc_cdd_loca, [\"tasmax\", \"tasmin\"]),\n", + " (\"HDD\", \"loca\"): (calc_hdd_loca, [\"tasmax\", \"tasmin\"]),\n", + " }\n", + " \n", + " base_func, depends_on = func_map[(metric, data_source)]\n", + " \n", + " def custom_func(ds):\n", + " return base_func(ds, threshold_f=threshold_f)\n", + " \n", + " register_user_function(\n", + " name=name,\n", + " depends_on=depends_on,\n", + " func=custom_func,\n", + " description=f\"{metric} ({data_source.upper()}) with {threshold_f}°F base\",\n", + " units=\"K\",\n", + " )\n", + " print(f\"✓ Registered '{name}' ({metric} at {threshold_f}°F)\")\n", + "\n", + "# Example: Create variables for multiple thresholds\n", + "create_degree_day_variable(\"CDD_wrf_70f\", threshold_f=70.0, metric=\"CDD\", data_source=\"wrf\")\n", + "create_degree_day_variable(\"CDD_wrf_80f\", threshold_f=80.0, metric=\"CDD\", data_source=\"wrf\")\n", + "create_degree_day_variable(\"HDD_wrf_60f\", threshold_f=60.0, metric=\"HDD\", data_source=\"wrf\")" + ] + }, + { + "cell_type": "markdown", + "id": "935475dc", + "metadata": {}, + "source": [ + "### 6.2: Define your own cooling degree days function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9fe3aa8", + "metadata": {}, + "outputs": [], + "source": [ + "from climakitae.new_core.derived_variables.registry import register_user_function\n", + "from climakitae.new_core.derived_variables.utils import f_to_k\n", + "\n", + "THRESHOLD_F = 75.0\n", + "threshold_k = f_to_k(THRESHOLD_F)\n", + "\n", + "def calc_custom_cdd(ds):\n", + " \"\"\"\n", + " A custom CDD function that runs on a different variable. \n", + "\n", + " In this example we look at days where the daily minimum temperature exceeds 75°F.\n", + " \n", + " IMPORTANT: The output variable name must match the registered name!\n", + " \"\"\"\n", + " import xarray as xr\n", + " \n", + " # Calculate degree days above threshold\n", + " cdd_values = np.maximum(0, ds.t2min - threshold_k)\n", + " \n", + " # Replace zeros with NaN (days below threshold become NaN)\n", + " cdd_values = xr.where(cdd_values > 0, cdd_values, np.nan)\n", + " \n", + " ds[\"CDD_min_wrf_75f\"] = cdd_values\n", + " ds[\"CDD_min_wrf_75f\"].attrs = {\n", + " \"units\": \"K\",\n", + " \"long_name\": \"Cooling Degree Days (WRF) - Min Temp > 75°F\",\n", + " \"comment\": f\"Cooling degree days calculated from daily minimum temperature with base {threshold_k} K. Days below threshold are NaN.\",\n", + " \"derived_from\": \"t2min\",\n", + " \"derived_by\": \"climakitae\",\n", + " \"threshold\": f\"{threshold_k} K\",\n", + " }\n", + " return ds\n", + "\n", + "register_user_function(\n", + " name=\"CDD_min_wrf_75f\",\n", + " depends_on=[\"t2min\"],\n", + " func=calc_custom_cdd,\n", + " description=\"Cooling Degree Days from WRF with 75°F base using daily minimum temperature\",\n", + " units=\"K\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "76ffca59", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2983f13b", + "metadata": {}, + "outputs": [], + "source": [ + "cd = ck.ClimateData(verbosity=-2)\n", + "\n", + "# Fetch cooling degree days for Los Angeles County\n", + "cdd_data_75f = (\n", + " cd\n", + " .catalog(\"cadcat\")\n", + " .variable(\"CDD_min_wrf_75f\") # Cooling degree days from WRF with 75°F base\n", + " .activity_id(\"WRF\")\n", + " .institution_id(\"UCLA\")\n", + " .table_id(\"day\")\n", + " .grid_label(\"d03\") # 3km resolution\n", + " # .experiment_id(\"ssp370\") # High emissions scenario\n", + " .processes({\n", + " \"warming_level\": {\n", + " \"warming_levels\": [1.2, 2.0, 3.0]\n", + " },\n", + " \"clip\": \"Los Angeles County\",\n", + " })\n", + " .get()\n", + ")\n", + "\n", + "cdd_data_75f = add_dummy_time_to_wl(cdd_data_75f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78a28dd0", + "metadata": {}, + "outputs": [], + "source": [ + "# Count days with cooling degree days > 0 per year\n", + "cooling_days_per_year = cdd_data_75f[\"CDD_min_wrf_75f\"].resample(time=\"Y\").sum()\n", + "\n", + "# Average years for spatial plot\n", + "spt = cooling_days_per_year.mean(dim=\"time\") \n", + "\n", + "# Compute with progress bar\n", + "with ProgressBar():\n", + " spt = spt.median(dim='sim').compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c1ddb25", + "metadata": {}, + "outputs": [], + "source": [ + "# Create figure with 3 subplots (one per warming level)\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "\n", + "warming_levels = spt.warming_level.values\n", + "\n", + "for idx, (ax, wl) in enumerate(zip(axes, warming_levels)):\n", + " # Select data for this warming level\n", + " wl_data = spt.sel(warming_level=wl)\n", + " \n", + " # Create contourf plot\n", + " im = wl_data.plot.contourf(\n", + " ax=ax, x='lon', y='lat',\n", + " cmap='YlOrRd', levels=100,\n", + " add_colorbar=False,\n", + " vmin=1,\n", + " vmax=300\n", + " )\n", + " \n", + " ax.set_title(f'{wl}°C Warming Level', fontsize=13, fontweight='bold')\n", + " ax.set_xlabel('Longitude', fontsize=11)\n", + " ax.set_ylabel('Latitude', fontsize=11)\n", + "\n", + "# Add shared colorbar\n", + "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", + "\n", + "fig.suptitle('Cooling Days (75 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", + " fontsize=15, fontweight='bold', y=1.02)" + ] + }, + { + "cell_type": "markdown", + "id": "9350277e", + "metadata": {}, + "source": [ + "## 7. Summary: Key Takeaways" + ] + }, + { + "cell_type": "markdown", + "id": "f8c1cc86", + "metadata": {}, + "source": [ + "**What We've Learned:**\n", + "\n", + "1. **Derived variables simplify analysis** - Single `.get()` call automatically fetches and computes HDD/CDD from temperature data\n", + "\n", + "2. **Warming levels provide climate-relevant framing** - Analyzing by 1.2°C, 2.0°C, and 3.0°C warming shows progressive impacts\n", + "\n", + "3. **Los Angeles shows clear cooling demand increase** - CDD rises significantly with warming while HDD decreases\n", + "\n", + "4. **Spatial patterns matter** - Inland areas experience more extreme cooling demands than coastal regions\n", + "\n", + "5. **Energy costs will increase** - Higher warming levels translate directly to higher HVAC energy costs\n", + "\n", + "6. **Customizing Functions** - Metrics can be customized and added to the catalog (locally, this does not change the catalog other users see). " + ] + }, + { + "cell_type": "markdown", + "id": "8e93d8ff", + "metadata": {}, + "source": [ + "## 8. Available Degree Days Variables" + ] + }, + { + "cell_type": "markdown", + "id": "9f9e064a", + "metadata": {}, + "source": [ + "| Variable | Data Source | Input Variables | Description |\n", + "|----------|-------------|-----------------|-------------|\n", + "| `HDD_wrf` | WRF | t2 | Heating degree days (base 65°F) |\n", + "| `CDD_wrf` | WRF | t2 | Cooling degree days (base 65°F) |\n", + "| `HDD_loca` | LOCA2 | tasmax, tasmin | Heating degree days (base 65°F) |\n", + "| `CDD_loca` | LOCA2 | tasmax, tasmin | Cooling degree days (base 65°F) |\n", + "\n", + "All variables automatically:\n", + "- Fetch required temperature data\n", + "- Calculate daily average: (max + min) / 2\n", + "- Apply 65°F (291.48K) threshold\n", + "- Return lazy-evaluated xarray datasets" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "climakitae", + "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": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d0708a1095e1b6af1bf3aefeefd8d61bc32f83fb Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Thu, 29 Jan 2026 22:10:02 +0000 Subject: [PATCH 17/53] updating notebook --- analysis/derived_variables_demo.ipynb | 1267 ++----------------------- 1 file changed, 92 insertions(+), 1175 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index fb4d1343..a807469d 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4d750b3f", + "id": "78cd4487", "metadata": {}, "outputs": [], "source": [ @@ -18,11 +18,32 @@ "source": [ "# Derived Variables Demo: Heating & Cooling Degree Days\n", "\n", - "This notebook demonstrates how to use derived variables in ClimakitAE, focusing on **heating degree days (HDD)** and **cooling degree days (CDD)** for energy demand analysis in Los Angeles.\n", + "This notebook demonstrates how to use derived variables in ClimakitAE, focusing on **cooling degree days (CDD)** for energy demand analysis in Los Angeles County.\n", + "\n", + "### Purpose\n", + "\n", + "This notebook uses (and serves as a living example) for the following tools in ClimakitAE:\n", + "- ClimateData(): user interface\n", + "- Climakitae custom metrics over the Cal-Adapt Data Catalog (cadcat)\n", + " - Built in functions like Cooling Degree Days\n", + " - Modifying the built in functions\n", + " - Defining **your own** metric for notebook specific custom metric analysis\n", + "- Spatial subsetting using climakitae\n", + "- Warming level approaches using climakitae\n", + "\n", + "> [NOTE]\n", + "> Custom metrics defined by users DO NOT show up in the cadcat catalog and ARE NOT available to other users.\n", + "> Custom metrics are defined locally and exist in the users environment only at run time and do not persist between sessions.\n", + "\n", + "### Outcomes\n", + "\n", + "Users can expect to take away visualizations \n", + "\n", + "\n", + "**RUNTIME**: 7 minutes\n", "\n", "## What are Degree Days?\n", "\n", - "- **Heating Degree Days (HDD)**: Measure of energy demand for heating when outdoor temperature falls below 65°F\n", "- **Cooling Degree Days (CDD)**: Measure of energy demand for cooling when outdoor temperature rises above 65°F\n", "\n", "These metrics are essential for:\n", @@ -34,13 +55,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "2f59f965", "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", "from dask.diagnostics import ProgressBar\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr\n", "\n", "import climakitae as ck\n", "\n", @@ -148,76 +171,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "5356d830", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - Derived Variables (computed from source variables during loading):\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - ------------------------------------------------------------------\n", - "Derived Variables (computed from source variables during loading):\n", - "------------------------------------------------------------------\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - CDD_loca depends on: tasmax, tasmin \n", - " └─ Cooling degree days from LOCA2 data (base 65°F)\n", - "CDD_loca depends on: tasmax, tasmin \n", - " └─ Cooling degree days from LOCA2 data (base 65°F)\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - CDD_wrf depends on: t2 \n", - " └─ Cooling degree days from WRF data (base 65°F)\n", - "CDD_wrf depends on: t2 \n", - " └─ Cooling degree days from WRF data (base 65°F)\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - HDD_loca depends on: tasmax, tasmin \n", - " └─ Heating degree days from LOCA2 data (base 65°F)\n", - "HDD_loca depends on: tasmax, tasmin \n", - " └─ Heating degree days from LOCA2 data (base 65°F)\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - HDD_wrf depends on: t2 \n", - " └─ Heating degree days from WRF data (base 65°F)\n", - "HDD_wrf depends on: t2 \n", - " └─ Heating degree days from WRF data (base 65°F)\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - dew_point_2m depends on: t2, rh \n", - " └─ Dew point temperature at 2m from temperature and relative humidity\n", - "dew_point_2m depends on: t2, rh \n", - " └─ Dew point temperature at 2m from temperature and relative humidity\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - diurnal_temperature_range_loca depends on: tasmax, tasmin \n", - " └─ Daily temperature range (maximum minus minimum)\n", - "diurnal_temperature_range_loca depends on: tasmax, tasmin \n", - " └─ Daily temperature range (maximum minus minimum)\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - diurnal_temperature_range_wrf depends on: t2max, t2min \n", - " └─ Daily temperature range from WRF data (maximum minus minimum)\n", - "diurnal_temperature_range_wrf depends on: t2max, t2min \n", - " └─ Daily temperature range from WRF data (maximum minus minimum)\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - heat_index depends on: t2, rh \n", - " └─ Heat index (feels-like temperature accounting for humidity effects)\n", - "heat_index depends on: t2, rh \n", - " └─ Heat index (feels-like temperature accounting for humidity effects)\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - relative_humidity_2m depends on: t2, q2, psfc \n", - " └─ Relative humidity at 2m computed from temperature, specific humidity, and surface pressure\n", - "relative_humidity_2m depends on: t2, q2, psfc \n", - " └─ Relative humidity at 2m computed from temperature, specific humidity, and surface pressure\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - specific_humidity_2m depends on: t2, rh, psfc \n", - " └─ Specific humidity at 2m from temperature, relative humidity, and pressure\n", - "specific_humidity_2m depends on: t2, rh, psfc \n", - " └─ Specific humidity at 2m from temperature, relative humidity, and pressure\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - wind_chill depends on: t2, u10, v10 \n", - " └─ Wind chill (feels-like temperature accounting for wind effects)\n", - "wind_chill depends on: t2, u10, v10 \n", - " └─ Wind chill (feels-like temperature accounting for wind effects)\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - wind_direction_10m depends on: u10, v10 \n", - " └─ Wind direction at 10m height (meteorological convention: direction wind comes FROM)\n", - "wind_direction_10m depends on: u10, v10 \n", - " └─ Wind direction at 10m height (meteorological convention: direction wind comes FROM)\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - wind_speed_10m depends on: u10, v10 \n", - " └─ Wind speed at 10m height computed from U and V components\n", - "wind_speed_10m depends on: u10, v10 \n", - " └─ Wind speed at 10m height computed from U and V components\n", - "2026-01-22 07:51:00 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Show all available derived variables\n", "# Notice the HDD and CDD variables for both WRF and LOCA2 data\n", @@ -236,33 +193,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "41b921f2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "HDD_wrf:\n", - " Depends on: ['t2']\n", - " Description: Heating degree days from WRF data (base 65°F)\n", - "\n", - "CDD_wrf:\n", - " Depends on: ['t2']\n", - " Description: Cooling degree days from WRF data (base 65°F)\n", - "\n", - "HDD_loca:\n", - " Depends on: ['tasmax', 'tasmin']\n", - " Description: Heating degree days from LOCA2 data (base 65°F)\n", - "\n", - "CDD_loca:\n", - " Depends on: ['tasmax', 'tasmin']\n", - " Description: Cooling degree days from LOCA2 data (base 65°F)\n" - ] - } - ], + "outputs": [], "source": [ "from climakitae.new_core.derived_variables import list_derived_variables\n", "\n", @@ -294,19 +228,7 @@ "execution_count": null, "id": "b979b365", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nschroed/Work/climakitae/climakitae/util/utils.py:1981: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 1.2C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "cd = ck.ClimateData(verbosity=-2)\n", "\n", @@ -332,955 +254,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "a70213f9", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Cli...\n", - " update_attributes: Process 'update_attributes' applied to ...\n", - " filter_unadjusted_models: yes\n", - " concat: Process 'concat' applied to the data. T...\n", - " warming_level_simple: Process 'warming_level_simple' applied ..." - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Add dummy time dimension to warming level data for easier handling\n", "def add_dummy_time_to_wl(data):\n", @@ -1292,12 +269,22 @@ "\n", " if 'time_delta' in data.dims:\n", " wl_values = data['time_delta'].values\n", - " time_values = pd.to_datetime(wl_values, unit='D', origin='2025-01-01')\n", + " time_values = pd.to_datetime(wl_values, unit='D', origin='2050-01-01')\n", " data = data.rename({'time_delta': 'time'})\n", " data = data.assign_coords(time=time_values)\n", - " return data\n", + " return data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b2546b0", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "cdd_data = add_dummy_time_to_wl(cdd_data).drop_sel(sim=\"WRF_UCLA_EC-Earth3-Veg_ssp370_day_d03_r1i1p1f1\") \n", "\n", - "cdd_data = add_dummy_time_to_wl(cdd_data)\n", "cdd_data" ] }, @@ -1320,6 +307,7 @@ "source": [ "# Count days with cooling degree days > 0 per year\n", "cooling_days_per_year = cdd_data[\"CDD_wrf\"].resample(time=\"Y\").sum()\n", + "cooling_days_per_year = xr.where(cooling_days_per_year > 0, cooling_days_per_year, np.nan)\n", "\n", "# Average years for spatial plot\n", "spt = cooling_days_per_year.mean(dim=\"time\") \n", @@ -1350,7 +338,7 @@ " # Create contourf plot\n", " im = wl_data.plot.contourf(\n", " ax=ax, x='lon', y='lat',\n", - " cmap='YlOrRd', levels=100,\n", + " cmap='plasma', levels=100,\n", " add_colorbar=False,\n", " vmin=1,\n", " vmax=300\n", @@ -1367,6 +355,14 @@ " fontsize=15, fontweight='bold', y=1.02)" ] }, + { + "cell_type": "markdown", + "id": "cf08ad93-8e95-430f-9d3c-5d29255cc269", + "metadata": {}, + "source": [ + "The plot above shows the median number of cooling days (65 °F) per year across Los Angeles County. Note the increase in the number of cooling days as the targeted Global Warming Level increases." + ] + }, { "cell_type": "markdown", "id": "78da135d", @@ -1428,6 +424,14 @@ "plt.tight_layout()" ] }, + { + "cell_type": "markdown", + "id": "086ad63a-7070-4e12-a423-fe6a2d1757de", + "metadata": {}, + "source": [ + "The plot above shows the median number of cooling degree days (65 °F) as a function of time averaged over Los Angeles County. As the Global warming level increases so too does the number of cooling days. Please note that the time axis is a \"dummy\" sequence, and the true timescale for the data can be found in the dataset's metadata." + ] + }, { "cell_type": "code", "execution_count": null, @@ -1461,18 +465,7 @@ "id": "99060031", "metadata": {}, "source": [ - "### 6.1 Customizing CDD: Using Built-in Functions with Custom Thresholds\n", - "\n", - "The built-in `calc_cdd_wrf` and `calc_hdd_wrf` functions accept optional threshold arguments:\n", - "- `threshold_f` - threshold in Fahrenheit\n", - "- `threshold_c` - threshold in Celsius \n", - "- `threshold_k` - threshold in Kelvin\n", - "\n", - "**Best Practice:** Wrap the builtin function with your threshold and register it as a new variable. This:\n", - "- ✅ Creates a distinct, named variable (`CDD_wrf_75f`)\n", - "- ✅ Is reusable across sessions (if added to your project)\n", - "- ✅ Doesn't mutate global state\n", - "- ✅ Keeps the variable name meaningful" + "### 6.1 Customizing CDD: Built-in Methods" ] }, { @@ -1528,7 +521,7 @@ " .get()\n", ")\n", "\n", - "cdd_data_75f = add_dummy_time_to_wl(cdd_data_75f)" + "cdd_data_75f = add_dummy_time_to_wl(cdd_data_75f).drop_sel(sim=\"WRF_UCLA_EC-Earth3-Veg_ssp370_day_d03_r1i1p1f1\") " ] }, { @@ -1539,7 +532,8 @@ "outputs": [], "source": [ "# Count days with cooling degree days > 0 per year\n", - "cooling_days_per_year = cdd_data_75f[\"CDD_wrf_75f\"].resample(time=\"Y\").sum()\n", + "cooling_days_per_year = cdd_data_75f[\"CDD_wrf\"].resample(time=\"Y\").sum()\n", + "cooling_days_per_year = xr.where(cooling_days_per_year > 0, cooling_days_per_year, np.nan)\n", "\n", "# Average years for spatial plot\n", "spt = cooling_days_per_year.mean(dim=\"time\") \n", @@ -1568,10 +562,10 @@ " # Create contourf plot\n", " im = wl_data.plot.contourf(\n", " ax=ax, x='lon', y='lat',\n", - " cmap='YlOrRd', levels=100,\n", + " cmap='plasma', levels=100,\n", " add_colorbar=False,\n", - " vmin=1,\n", - " vmax=300\n", + " vmin=0,\n", + " vmax=340\n", " )\n", " \n", " ax.set_title(f'{wl}°C Warming Level', fontsize=13, fontweight='bold')\n", @@ -1587,72 +581,10 @@ }, { "cell_type": "markdown", - "id": "50ee9391", - "metadata": {}, - "source": [ - "#### Factory Pattern: Creating Multiple Threshold Variables\n", - "\n", - "For projects needing multiple thresholds, use a factory function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31226884", + "id": "c367d24b-38f2-4ac8-b436-57fff6d331a9", "metadata": {}, - "outputs": [], "source": [ - "# Factory function to create CDD/HDD variables with arbitrary thresholds\n", - "def create_degree_day_variable(\n", - " name: str,\n", - " threshold_f: float,\n", - " metric: str = \"CDD\", # \"CDD\" or \"HDD\"\n", - " data_source: str = \"wrf\", # \"wrf\" or \"loca\"\n", - "):\n", - " \"\"\"\n", - " Factory to create degree day variables with custom thresholds.\n", - " \n", - " Parameters\n", - " ----------\n", - " name : str\n", - " Name for the new derived variable (e.g., \"CDD_wrf_80f\")\n", - " threshold_f : float\n", - " Threshold temperature in Fahrenheit\n", - " metric : str\n", - " \"CDD\" for cooling degree days, \"HDD\" for heating degree days\n", - " data_source : str\n", - " \"wrf\" for WRF data (depends on t2), \"loca\" for LOCA2 (depends on tasmax, tasmin)\n", - " \"\"\"\n", - " from climakitae.new_core.derived_variables.builtin.temperature import (\n", - " calc_cdd_wrf, calc_hdd_wrf, calc_cdd_loca, calc_hdd_loca\n", - " )\n", - " \n", - " # Select the appropriate function and dependencies\n", - " func_map = {\n", - " (\"CDD\", \"wrf\"): (calc_cdd_wrf, [\"t2\"]),\n", - " (\"HDD\", \"wrf\"): (calc_hdd_wrf, [\"t2\"]),\n", - " (\"CDD\", \"loca\"): (calc_cdd_loca, [\"tasmax\", \"tasmin\"]),\n", - " (\"HDD\", \"loca\"): (calc_hdd_loca, [\"tasmax\", \"tasmin\"]),\n", - " }\n", - " \n", - " base_func, depends_on = func_map[(metric, data_source)]\n", - " \n", - " def custom_func(ds):\n", - " return base_func(ds, threshold_f=threshold_f)\n", - " \n", - " register_user_function(\n", - " name=name,\n", - " depends_on=depends_on,\n", - " func=custom_func,\n", - " description=f\"{metric} ({data_source.upper()}) with {threshold_f}°F base\",\n", - " units=\"K\",\n", - " )\n", - " print(f\"✓ Registered '{name}' ({metric} at {threshold_f}°F)\")\n", - "\n", - "# Example: Create variables for multiple thresholds\n", - "create_degree_day_variable(\"CDD_wrf_70f\", threshold_f=70.0, metric=\"CDD\", data_source=\"wrf\")\n", - "create_degree_day_variable(\"CDD_wrf_80f\", threshold_f=80.0, metric=\"CDD\", data_source=\"wrf\")\n", - "create_degree_day_variable(\"HDD_wrf_60f\", threshold_f=60.0, metric=\"HDD\", data_source=\"wrf\")" + "The plot above shows the median number of cooling days (75 °F) per year across Los Angeles County. Note the increase in the number of cooling days as the targeted Global Warming Level increases. " ] }, { @@ -1671,9 +603,10 @@ "outputs": [], "source": [ "from climakitae.new_core.derived_variables.registry import register_user_function\n", - "from climakitae.new_core.derived_variables.utils import f_to_k\n", + "from climakitae.util.utils import f_to_k\n", + "import xarray as xr\n", "\n", - "THRESHOLD_F = 75.0\n", + "THRESHOLD_F = 85.0\n", "threshold_k = f_to_k(THRESHOLD_F)\n", "\n", "def calc_custom_cdd(ds):\n", @@ -1681,22 +614,16 @@ " A custom CDD function that runs on a different variable. \n", "\n", " In this example we look at days where the daily minimum temperature exceeds 75°F.\n", - " \n", - " IMPORTANT: The output variable name must match the registered name!\n", " \"\"\"\n", - " import xarray as xr\n", - " \n", - " # Calculate degree days above threshold\n", " cdd_values = np.maximum(0, ds.t2min - threshold_k)\n", " \n", " # Replace zeros with NaN (days below threshold become NaN)\n", " cdd_values = xr.where(cdd_values > 0, cdd_values, np.nan)\n", - " \n", " ds[\"CDD_min_wrf_75f\"] = cdd_values\n", " ds[\"CDD_min_wrf_75f\"].attrs = {\n", " \"units\": \"K\",\n", - " \"long_name\": \"Cooling Degree Days (WRF) - Min Temp > 75°F\",\n", - " \"comment\": f\"Cooling degree days calculated from daily minimum temperature with base {threshold_k} K. Days below threshold are NaN.\",\n", + " \"long_name\": \"Cooling Degree Days (WRF)\",\n", + " \"comment\": f\"Cooling degree days calculated from daily minimum temperature with base {threshold_k} K\",\n", " \"derived_from\": \"t2min\",\n", " \"derived_by\": \"climakitae\",\n", " \"threshold\": f\"{threshold_k} K\",\n", @@ -1709,20 +636,10 @@ " func=calc_custom_cdd,\n", " description=\"Cooling Degree Days from WRF with 75°F base using daily minimum temperature\",\n", " units=\"K\",\n", - ")" + ")\n", + " " ] }, - { - "cell_type": "markdown", - "id": "76ffca59", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -1733,7 +650,7 @@ "cd = ck.ClimateData(verbosity=-2)\n", "\n", "# Fetch cooling degree days for Los Angeles County\n", - "cdd_data_75f = (\n", + "cdd_data_min_75f = (\n", " cd\n", " .catalog(\"cadcat\")\n", " .variable(\"CDD_min_wrf_75f\") # Cooling degree days from WRF with 75°F base\n", @@ -1741,7 +658,6 @@ " .institution_id(\"UCLA\")\n", " .table_id(\"day\")\n", " .grid_label(\"d03\") # 3km resolution\n", - " # .experiment_id(\"ssp370\") # High emissions scenario\n", " .processes({\n", " \"warming_level\": {\n", " \"warming_levels\": [1.2, 2.0, 3.0]\n", @@ -1751,7 +667,7 @@ " .get()\n", ")\n", "\n", - "cdd_data_75f = add_dummy_time_to_wl(cdd_data_75f)" + "cdd_data_min_75f = add_dummy_time_to_wl(cdd_data_min_75f).drop_sel(sim=\"WRF_UCLA_EC-Earth3-Veg_ssp370_day_d03_r1i1p1f1\") " ] }, { @@ -1762,7 +678,8 @@ "outputs": [], "source": [ "# Count days with cooling degree days > 0 per year\n", - "cooling_days_per_year = cdd_data_75f[\"CDD_min_wrf_75f\"].resample(time=\"Y\").sum()\n", + "cooling_days_per_year = cdd_data_min_75f[\"CDD_min_wrf_75f\"].resample(time=\"Y\").sum()\n", + "cooling_days_per_year = xr.where(cooling_days_per_year > 0, cooling_days_per_year, np.nan)\n", "\n", "# Average years for spatial plot\n", "spt = cooling_days_per_year.mean(dim=\"time\") \n", @@ -1791,10 +708,10 @@ " # Create contourf plot\n", " im = wl_data.plot.contourf(\n", " ax=ax, x='lon', y='lat',\n", - " cmap='YlOrRd', levels=100,\n", + " cmap='plasma', levels=100,\n", " add_colorbar=False,\n", - " vmin=1,\n", - " vmax=300\n", + " vmin=0,\n", + " vmax=30\n", " )\n", " \n", " ax.set_title(f'{wl}°C Warming Level', fontsize=13, fontweight='bold')\n", @@ -1804,7 +721,7 @@ "# Add shared colorbar\n", "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", "\n", - "fig.suptitle('Cooling Days (75 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", + "fig.suptitle('Cooling Days (Min Temp > 85 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", " fontsize=15, fontweight='bold', y=1.02)" ] }, @@ -1866,7 +783,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climakitae", + "display_name": "Python 3 (climakitae)", "language": "python", "name": "python3" }, @@ -1880,7 +797,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.11" + "version": "3.12.12" } }, "nbformat": 4, From 070021522c4169097566d06fb41c4c97ee38ccdb Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Wed, 11 Feb 2026 16:28:16 +0000 Subject: [PATCH 18/53] updating notebook --- analysis/derived_variables_demo.ipynb | 67 +- analysis/internal_variability.ipynb | 1748 ++- data-access/basic_data_access.ipynb | 15790 +++++++++++++++++++++++- 3 files changed, 17436 insertions(+), 169 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index a807469d..9ff81d74 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "78cd4487", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, { "cell_type": "markdown", "id": "a97eee28", @@ -37,7 +26,7 @@ "\n", "### Outcomes\n", "\n", - "Users can expect to take away visualizations \n", + "Users can expect to take away visualizations of cooling degree days for various thresholds across 3 global warming levels.\n", "\n", "\n", "**RUNTIME**: 7 minutes\n", @@ -57,7 +46,12 @@ "cell_type": "code", "execution_count": null, "id": "2f59f965", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-06T22:45:35.572679Z", + "iopub.status.busy": "2026-02-06T22:45:35.572414Z" + } + }, "outputs": [], "source": [ "from dask.diagnostics import ProgressBar\n", @@ -71,6 +65,29 @@ "cd = ck.ClimateData(verbosity=-1)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "a70213f9", + "metadata": {}, + "outputs": [], + "source": [ + "# Add dummy time dimension to warming level data for easier handling\n", + "def add_dummy_time_to_wl(data):\n", + " \"\"\"\n", + " Convert time_delta dimension to a dummy time dimension for easier handling.\n", + " \"\"\"\n", + " import xarray as xr\n", + " import pandas as pd\n", + "\n", + " if 'time_delta' in data.dims:\n", + " wl_values = data['time_delta'].values\n", + " time_values = pd.to_datetime(wl_values, unit='D', origin='2050-01-01')\n", + " data = data.rename({'time_delta': 'time'})\n", + " data = data.assign_coords(time=time_values)\n", + " return data\n" + ] + }, { "cell_type": "markdown", "id": "e42bba08", @@ -252,29 +269,6 @@ ")" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "a70213f9", - "metadata": {}, - "outputs": [], - "source": [ - "# Add dummy time dimension to warming level data for easier handling\n", - "def add_dummy_time_to_wl(data):\n", - " \"\"\"\n", - " Convert time_delta dimension to a dummy time dimension for easier handling.\n", - " \"\"\"\n", - " import xarray as xr\n", - " import pandas as pd\n", - "\n", - " if 'time_delta' in data.dims:\n", - " wl_values = data['time_delta'].values\n", - " time_values = pd.to_datetime(wl_values, unit='D', origin='2050-01-01')\n", - " data = data.rename({'time_delta': 'time'})\n", - " data = data.assign_coords(time=time_values)\n", - " return data\n" - ] - }, { "cell_type": "code", "execution_count": null, @@ -282,7 +276,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "cdd_data = add_dummy_time_to_wl(cdd_data).drop_sel(sim=\"WRF_UCLA_EC-Earth3-Veg_ssp370_day_d03_r1i1p1f1\") \n", "\n", "cdd_data" diff --git a/analysis/internal_variability.ipynb b/analysis/internal_variability.ipynb index d045d3b7..92500172 100644 --- a/analysis/internal_variability.ipynb +++ b/analysis/internal_variability.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e0f320a0-fcc6-48e7-bc56-2d8895209127", + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T18:17:17.151218Z", + "iopub.status.busy": "2026-02-09T18:17:17.150972Z", + "iopub.status.idle": "2026-02-09T18:17:17.191691Z", + "shell.execute_reply": "2026-02-09T18:17:17.190897Z", + "shell.execute_reply.started": "2026-02-09T18:17:17.151185Z" + } + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, { "cell_type": "markdown", "id": "39c7ab89-d6db-4fa2-a4ec-08dfae9b20d9", @@ -69,12 +88,1195 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "25f3706b-fb81-4ae5-b3fe-04f71ac4fc34", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T18:17:20.521181Z", + "iopub.status.busy": "2026-02-09T18:17:20.520952Z", + "iopub.status.idle": "2026-02-09T18:17:29.319394Z", + "shell.execute_reply": "2026-02-09T18:17:29.318474Z", + "shell.execute_reply.started": "2026-02-09T18:17:20.521157Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " 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ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false;\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true;\n", + " root._bokeh_onload_callbacks = [];\n", + " const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version)?.Panel));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " if (Bokeh != undefined && !reloading) {\n", + " const NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh[NewBokeh.version] = NewBokeh;\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh);\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const version = '3.8.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n const missingOrBroken = Bokeh == null || Bokeh.Panel == null || (Bokeh.version != version && !Bokeh.versions?.has(version)) || Bokeh.versions?.get(version)?.Panel == null;\n if (shouldSkip && !(isBokehOrPanel && missingOrBroken)) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.8.7/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.2.min.js\", \"https://cdn.holoviz.org/panel/1.8.7/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false;\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true;\n root._bokeh_onload_callbacks = [];\n const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version)?.Panel));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n if (Bokeh != undefined && !reloading) {\n const NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh[NewBokeh.version] = NewBokeh;\n Bokeh.versions.set(NewBokeh.version, NewBokeh);\n }\n root.Bokeh = Bokeh;\n }\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " let retries = 0;\n", + " const open = () => {\n", + " if (comm.active) {\n", + " comm.open();\n", + " } else if (retries > 3) {\n", + " console.warn('Comm target never activated')\n", + " } else {\n", + " retries += 1\n", + " setTimeout(open, 500)\n", + " }\n", + " }\n", + " if (comm.active) {\n", + " comm.open();\n", + " } else {\n", + " setTimeout(open, 500)\n", + " }\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " })\n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " 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undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true;\n", + " root._bokeh_onload_callbacks = [];\n", + " const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version)?.Panel));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " if (Bokeh != undefined && !reloading) {\n", + " const NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh[NewBokeh.version] = NewBokeh;\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh);\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const version = '3.8.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n 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!== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n const missingOrBroken = Bokeh == null || Bokeh.Panel == null || (Bokeh.version != version && !Bokeh.versions?.has(version)) || Bokeh.versions?.get(version)?.Panel 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url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.8.7/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false;\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true;\n root._bokeh_onload_callbacks = [];\n const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version)?.Panel));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n if (Bokeh != undefined && !reloading) {\n const NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh[NewBokeh.version] = NewBokeh;\n Bokeh.versions.set(NewBokeh.version, NewBokeh);\n }\n root.Bokeh = Bokeh;\n }\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + 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comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " let retries = 0;\n", + " const open = () => {\n", + " if (comm.active) {\n", + " comm.open();\n", + " } else if (retries > 3) {\n", + " console.warn('Comm target never activated')\n", + " } else {\n", + " retries += 1\n", + " setTimeout(open, 500)\n", + " }\n", + " }\n", + " if (comm.active) {\n", + " comm.open();\n", + " } else {\n", + " setTimeout(open, 500)\n", + " }\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " 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JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, 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comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import climakitae as ck\n", "from climakitae.core.data_interface import DataParameters\n", @@ -110,9 +1312,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "cca191af-ce6e-4f77-b1e6-c8fc189c9014", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T18:17:45.372218Z", + "iopub.status.busy": "2026-02-09T18:17:45.371974Z", + "iopub.status.idle": "2026-02-09T18:18:02.484893Z", + "shell.execute_reply": "2026-02-09T18:18:02.483979Z", + "shell.execute_reply.started": "2026-02-09T18:17:45.372192Z" + }, "tags": [] }, "outputs": [], @@ -164,9 +1373,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "eb4a30bf-e17b-4a94-822c-6703aa0dfec7", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T18:18:02.486053Z", + "iopub.status.busy": "2026-02-09T18:18:02.485785Z", + "iopub.status.idle": "2026-02-09T18:18:02.527361Z", + "shell.execute_reply": "2026-02-09T18:18:02.526616Z", + "shell.execute_reply.started": "2026-02-09T18:18:02.486022Z" + }, "tags": [] }, "outputs": [], @@ -184,12 +1400,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "fde659c8-575f-4206-a182-9f989c0edd84", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T18:23:19.064955Z", + "iopub.status.busy": "2026-02-09T18:23:19.064709Z", + "iopub.status.idle": "2026-02-09T18:23:37.085725Z", + "shell.execute_reply": "2026-02-09T18:23:37.084847Z", + "shell.execute_reply.started": "2026-02-09T18:23:19.064931Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model FGOALS-g3\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MIROC6\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-HR\n" + ] + } + ], "source": [ "hist_wrf = wrf_ds.sel(\n", " time=slice('1981','2010'))\n", @@ -231,9 +1464,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "c6a86c4b-199f-4e27-9467-b36097e02e11", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T19:05:00.175476Z", + "iopub.status.busy": "2026-02-09T19:05:00.175252Z", + "iopub.status.idle": "2026-02-09T19:05:00.181194Z", + "shell.execute_reply": "2026-02-09T19:05:00.180141Z", + "shell.execute_reply.started": "2026-02-09T19:05:00.175453Z" + }, "tags": [] }, "outputs": [], @@ -298,12 +1538,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "c8e8171f-897a-4d12-9a7f-079f312408ef", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T19:05:00.215554Z", + "iopub.status.busy": "2026-02-09T19:05:00.215344Z", + "iopub.status.idle": "2026-02-09T19:05:00.264886Z", + "shell.execute_reply": "2026-02-09T19:05:00.263951Z", + "shell.execute_reply.started": "2026-02-09T19:05:00.215533Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'cads_delta_percentile' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m delta_vmax = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 5\u001b[39m ncols = \u001b[32m3\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m num_simulations = \u001b[38;5;28mlen\u001b[39m(\u001b[43mcads_delta_percentile\u001b[49m.simulation.values)\n\u001b[32m 8\u001b[39m cads_hist_plots = make_precip_unc_map(\n\u001b[32m 9\u001b[39m data=cads_hist_percentile.isel(simulation=\u001b[32m0\u001b[39m),\n\u001b[32m 10\u001b[39m title=(cads_hist_percentile.isel(simulation=\u001b[32m0\u001b[39m\n\u001b[32m 11\u001b[39m ).simulation.item()), vmin=vmin, \n\u001b[32m 12\u001b[39m vmax=vmax, sopt=\u001b[38;5;28;01mFalse\u001b[39;00m, width=\u001b[32m300\u001b[39m, height=\u001b[32m300\u001b[39m)\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sim_i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[32m1\u001b[39m, num_simulations): \u001b[38;5;66;03m# plot remaining simulations\u001b[39;00m\n", + "\u001b[31mNameError\u001b[39m: name 'cads_delta_percentile' is not defined" + ] + } + ], "source": [ "vmin = 0\n", "vmax = 2000\n", @@ -426,9 +1685,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "3efbf741-92bc-4634-900b-c6671e2a6cec", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T16:36:56.213760Z", + "iopub.status.busy": "2026-02-09T16:36:56.213465Z", + "iopub.status.idle": "2026-02-09T16:36:56.223141Z", + "shell.execute_reply": "2026-02-09T16:36:56.222245Z", + "shell.execute_reply.started": "2026-02-09T16:36:56.213728Z" + }, "tags": [] }, "outputs": [], @@ -452,12 +1718,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "761cf95b-b710-47e3-8d14-9822654ed521", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T16:36:56.224795Z", + "iopub.status.busy": "2026-02-09T16:36:56.224377Z", + "iopub.status.idle": "2026-02-09T16:37:14.637288Z", + "shell.execute_reply": "2026-02-09T16:37:14.636278Z", + "shell.execute_reply.started": "2026-02-09T16:36:56.224762Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model FGOALS-g3\n", + "3.0°C warming level not reached for ensemble member r2i1p1f1 of model FGOALS-g3\n", + "3.0°C warming level not reached for ensemble member r3i1p1f1 of model FGOALS-g3\n", + "3.0°C warming level not reached for ensemble member r4i1p1f1 of model FGOALS-g3\n", + "3.0°C warming level not reached for ensemble member r5i1p1f1 of model FGOALS-g3\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MIROC6\n", + "3.0°C warming level not reached for ensemble member r2i1p1f1 of model MIROC6\n", + "3.0°C warming level not reached for ensemble member r3i1p1f1 of model MIROC6\n", + "3.0°C warming level not reached for ensemble member r10i1p1f1 of model MPI-ESM1-2-HR\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-HR\n", + "3.0°C warming level not reached for ensemble member r2i1p1f1 of model MPI-ESM1-2-HR\n", + "3.0°C warming level not reached for ensemble member r3i1p1f1 of model MPI-ESM1-2-HR\n", + "3.0°C warming level not reached for ensemble member r4i1p1f1 of model MPI-ESM1-2-HR\n", + "3.0°C warming level not reached for ensemble member r5i1p1f1 of model MPI-ESM1-2-HR\n", + "3.0°C warming level not reached for ensemble member r6i1p1f1 of model MPI-ESM1-2-HR\n", + "3.0°C warming level not reached for ensemble member r7i1p1f1 of model MPI-ESM1-2-HR\n", + "3.0°C warming level not reached for ensemble member r8i1p1f1 of model MPI-ESM1-2-HR\n", + "3.0°C warming level not reached for ensemble member r9i1p1f1 of model MPI-ESM1-2-HR\n" + ] + } + ], "source": [ "hist_cae_ds, warm_cae_ds = get_ensemble_data(\n", " variable=copt.variable, selections=selections, \n", @@ -475,9 +1773,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "16e10ef0-1bba-41d6-a617-9eb4af510d44", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T16:37:14.638336Z", + "iopub.status.busy": "2026-02-09T16:37:14.638004Z", + "iopub.status.idle": "2026-02-09T16:37:14.663798Z", + "shell.execute_reply": "2026-02-09T16:37:14.662348Z", + "shell.execute_reply.started": "2026-02-09T16:37:14.638306Z" + }, "tags": [] }, "outputs": [], @@ -503,9 +1808,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "36d2ac31-ae62-47fd-8fe2-0de2f535ac12", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T16:37:14.664523Z", + "iopub.status.busy": "2026-02-09T16:37:14.664339Z", + "iopub.status.idle": "2026-02-09T16:37:14.674754Z", + "shell.execute_reply": "2026-02-09T16:37:14.673807Z", + "shell.execute_reply.started": "2026-02-09T16:37:14.664503Z" + }, "tags": [] }, "outputs": [], @@ -553,12 +1865,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "db16eeb1-0866-48fe-9788-9982cac07f5b", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T16:37:14.675729Z", + "iopub.status.busy": "2026-02-09T16:37:14.675493Z", + "iopub.status.idle": "2026-02-09T16:56:00.131345Z", + "shell.execute_reply": "2026-02-09T16:56:00.129842Z", + "shell.execute_reply.started": "2026-02-09T16:37:14.675701Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m 1\u001b[39m all_hist_ens = hvplot_percentile_column(\n\u001b[32m 2\u001b[39m data=hist_cae_percentile.pr, \n\u001b[32m 3\u001b[39m col_title=\u001b[33m\"\u001b[39m\u001b[33m### 99th percentile monthly precipitation accumulation across model ensemble members, 1981-2010\u001b[39m\u001b[33m\"\u001b[39m, \n\u001b[32m 4\u001b[39m vmin=\u001b[32m0\u001b[39m, vmax=\u001b[32m900\u001b[39m, \n\u001b[32m 5\u001b[39m sopt=\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 6\u001b[39m ) \n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m all_ssp_ens = \u001b[43mhvplot_percentile_column\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 9\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mssp_cae_percentile\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 10\u001b[39m \u001b[43m \u001b[49m\u001b[43mcol_title\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m### 99th percentile monthly precipitation accumulation across model ensemble members, 2071-2100\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 11\u001b[39m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m900\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 12\u001b[39m \u001b[43m \u001b[49m\u001b[43msopt\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[32m 13\u001b[39m \u001b[43m)\u001b[49m \n\u001b[32m 15\u001b[39m all_diff_ens = hvplot_percentile_column(\n\u001b[32m 16\u001b[39m data=cae_delta_percentile.pr, \n\u001b[32m 17\u001b[39m col_title=\u001b[33m\"\u001b[39m\u001b[33m### Change in 99th percentile monthly precipitation accumulation across ensemble members by end-of-century\u001b[39m\u001b[33m\"\u001b[39m, \n\u001b[32m 18\u001b[39m vmin=-\u001b[32m150\u001b[39m, vmax=\u001b[32m150\u001b[39m, \n\u001b[32m 19\u001b[39m sopt=\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 20\u001b[39m ) \n\u001b[32m 22\u001b[39m pn.Tabs((\u001b[33m'\u001b[39m\u001b[33mPresent-day\u001b[39m\u001b[33m'\u001b[39m,all_hist_ens),\n\u001b[32m 23\u001b[39m (\u001b[33m'\u001b[39m\u001b[33m+ \u001b[39m\u001b[33m'\u001b[39m+ \u001b[38;5;28mstr\u001b[39m(warm_level)+\u001b[33m'\u001b[39m\u001b[33m C\u001b[39m\u001b[33m'\u001b[39m,all_ssp_ens),\n\u001b[32m 24\u001b[39m (\u001b[33m'\u001b[39m\u001b[33mDifference\u001b[39m\u001b[33m'\u001b[39m,all_diff_ens))\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 35\u001b[39m, in \u001b[36mhvplot_percentile_column\u001b[39m\u001b[34m(data, col_title, vmin, vmax, sopt, num_cols)\u001b[39m\n\u001b[32m 33\u001b[39m col = pn.Column(col_title)\n\u001b[32m 34\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sim \u001b[38;5;129;01min\u001b[39;00m np.unique(data.simulation.values):\n\u001b[32m---> \u001b[39m\u001b[32m35\u001b[39m pl_by_sim = \u001b[43m_hvplot_one_sim\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 36\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msim_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43msim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msopt\u001b[49m\u001b[43m=\u001b[49m\u001b[43msopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_cols\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m6\u001b[39;49m\n\u001b[32m 37\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 38\u001b[39m col += pn.Row(pl_by_sim)\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m col\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 15\u001b[39m, in \u001b[36m_hvplot_one_sim\u001b[39m\u001b[34m(data, sim_name, vmin, vmax, sopt, num_cols)\u001b[39m\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# Make a plot for each individual member_id\u001b[39;00m\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m member_id_i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(to_plot_by_sim.member_id.values)):\n\u001b[32m---> \u001b[39m\u001b[32m15\u001b[39m plot_i = \u001b[43mmake_precip_unc_map\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 16\u001b[39m \u001b[43m \u001b[49m\u001b[43mto_plot_by_sim\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msimulation\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43misel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmember_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmember_id_i\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 17\u001b[39m \u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{0}\u001b[39;49;00m\u001b[33;43m member \u001b[39;49m\u001b[38;5;132;43;01m{1}\u001b[39;49;00m\u001b[33;43m\"\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43msim_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmember_id_i\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 18\u001b[39m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 19\u001b[39m \u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 20\u001b[39m \u001b[43m \u001b[49m\u001b[43msopt\u001b[49m\u001b[43m=\u001b[49m\u001b[43msopt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 21\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 22\u001b[39m plots_by_sim = plot_i \u001b[38;5;28;01mif\u001b[39;00m plots_by_sim \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m plots_by_sim + plot_i\n\u001b[32m 23\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (plots_by_sim + hv.Layout()).cols(\u001b[32m6\u001b[39m)\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 41\u001b[39m, in \u001b[36mmake_precip_unc_map\u001b[39m\u001b[34m(data, title, vmin, vmax, sopt, width, height, xlim, ylim, xticks, yticks, xaxis, yaxis, absolute)\u001b[39m\n\u001b[32m 38\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 39\u001b[39m cmap = read_ae_colormap(cmap=\u001b[33m\"\u001b[39m\u001b[33mae_blue\u001b[39m\u001b[33m\"\u001b[39m, cmap_hex=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m _plot = \u001b[43mhv\u001b[49m\u001b[43m.\u001b[49m\u001b[43mQuadMesh\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlon\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlat\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m.opts(\n\u001b[32m 42\u001b[39m tools=[hover],\n\u001b[32m 43\u001b[39m colorbar=\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[32m 44\u001b[39m cmap=cmap,\n\u001b[32m 45\u001b[39m symmetric=sopt,\n\u001b[32m 46\u001b[39m clim=(vmin, vmax),\n\u001b[32m 47\u001b[39m xaxis=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 48\u001b[39m yaxis=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 49\u001b[39m clabel=\u001b[33m\"\u001b[39m\u001b[33mPrecipitation (mm/month)\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 50\u001b[39m title=title,\n\u001b[32m 51\u001b[39m width=width,\n\u001b[32m 52\u001b[39m height=height,\n\u001b[32m 53\u001b[39m xlim=xlim,\n\u001b[32m 54\u001b[39m ylim=ylim,\n\u001b[32m 55\u001b[39m )\n\u001b[32m 56\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _plot\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/holoviews/element/raster.py:830\u001b[39m, in \u001b[36mQuadMesh.__init__\u001b[39m\u001b[34m(self, data, kdims, vdims, **params)\u001b[39m\n\u001b[32m 828\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mlist\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m data == []:\n\u001b[32m 829\u001b[39m data = ([], [], np.zeros((\u001b[32m0\u001b[39m, \u001b[32m0\u001b[39m)))\n\u001b[32m--> \u001b[39m\u001b[32m830\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 831\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.interface.gridded:\n\u001b[32m 832\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m DataError(\n\u001b[32m 833\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m).\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m type expects gridded data, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 834\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.interface.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m is columnar. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 835\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mTo display columnar data as gridded use the HeatMap \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 836\u001b[39m \u001b[33m\"\u001b[39m\u001b[33melement or aggregate the data (e.g. using np.histogram2d).\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 837\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/holoviews/element/selection.py:25\u001b[39m, in \u001b[36mSelectionIndexExpr.__init__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m25\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 26\u001b[39m \u001b[38;5;28mself\u001b[39m._index_skip = \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/holoviews/core/data/__init__.py:337\u001b[39m, in \u001b[36mDataset.__init__\u001b[39m\u001b[34m(self, data, kdims, vdims, **kwargs)\u001b[39m\n\u001b[32m 334\u001b[39m kdims, vdims = kwargs.get(\u001b[33m'\u001b[39m\u001b[33mkdims\u001b[39m\u001b[33m'\u001b[39m), kwargs.get(\u001b[33m'\u001b[39m\u001b[33mvdims\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 336\u001b[39m validate_vdims = kwargs.pop(\u001b[33m'\u001b[39m\u001b[33m_validate_vdims\u001b[39m\u001b[33m'\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m337\u001b[39m initialized = \u001b[43mInterface\u001b[49m\u001b[43m.\u001b[49m\u001b[43minitialize\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mdatatype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mdatatype\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 339\u001b[39m (data, \u001b[38;5;28mself\u001b[39m.interface, dims, extra_kws) = initialized\n\u001b[32m 340\u001b[39m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m(data, **\u001b[38;5;28mdict\u001b[39m(kwargs, **\u001b[38;5;28mdict\u001b[39m(dims, **extra_kws)))\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/holoviews/core/data/interface.py:257\u001b[39m, in \u001b[36mInterface.initialize\u001b[39m\u001b[34m(cls, eltype, data, kdims, vdims, datatype)\u001b[39m\n\u001b[32m 255\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m257\u001b[39m (data, dims, extra_kws) = \u001b[43minterface\u001b[49m\u001b[43m.\u001b[49m\u001b[43minit\u001b[49m\u001b[43m(\u001b[49m\u001b[43meltype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvdims\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 258\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m DataError:\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/holoviews/core/data/grid.py:107\u001b[39m, in \u001b[36mGridInterface.init\u001b[39m\u001b[34m(cls, eltype, data, kdims, vdims)\u001b[39m\n\u001b[32m 105\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mValues for dimension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdim\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not found\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 106\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data[name], get_array_types()):\n\u001b[32m--> \u001b[39m\u001b[32m107\u001b[39m data[name] = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 109\u001b[39m kdim_names = [dimension_name(d) \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m kdims]\n\u001b[32m 110\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m vdim_tuple \u001b[38;5;129;01min\u001b[39;00m data:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/xarray/core/common.py:181\u001b[39m, in \u001b[36mAbstractArray.__array__\u001b[39m\u001b[34m(self, dtype, copy)\u001b[39m\n\u001b[32m 179\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m 180\u001b[39m copy = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m181\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m np.array(\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m, dtype=dtype, copy=copy)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/xarray/core/dataarray.py:798\u001b[39m, in \u001b[36mDataArray.values\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 785\u001b[39m \u001b[38;5;129m@property\u001b[39m\n\u001b[32m 786\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mvalues\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> np.ndarray:\n\u001b[32m 787\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 788\u001b[39m \u001b[33;03m The array's data converted to numpy.ndarray.\u001b[39;00m\n\u001b[32m 789\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 796\u001b[39m \u001b[33;03m to this array may be reflected in the DataArray as well.\u001b[39;00m\n\u001b[32m 797\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m798\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/xarray/core/variable.py:556\u001b[39m, in \u001b[36mVariable.values\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 553\u001b[39m \u001b[38;5;129m@property\u001b[39m\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mvalues\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> np.ndarray:\n\u001b[32m 555\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"The variable's data as a numpy.ndarray\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m556\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_as_array_or_item\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_data\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/xarray/core/variable.py:336\u001b[39m, in \u001b[36m_as_array_or_item\u001b[39m\u001b[34m(data)\u001b[39m\n\u001b[32m 322\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_as_array_or_item\u001b[39m(data):\n\u001b[32m 323\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Return the given values as a numpy array, or as an individual item if\u001b[39;00m\n\u001b[32m 324\u001b[39m \u001b[33;03m it's a 0d datetime64 or timedelta64 array.\u001b[39;00m\n\u001b[32m 325\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 334\u001b[39m \u001b[33;03m TODO: remove this (replace with np.asarray) once these issues are fixed\u001b[39;00m\n\u001b[32m 335\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m336\u001b[39m data = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data.ndim == \u001b[32m0\u001b[39m:\n\u001b[32m 338\u001b[39m kind = data.dtype.kind\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/dask/array/core.py:1718\u001b[39m, in \u001b[36mArray.__array__\u001b[39m\u001b[34m(self, dtype, copy, **kwargs)\u001b[39m\n\u001b[32m 1711\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[32m 1712\u001b[39m warnings.warn(\n\u001b[32m 1713\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mCan\u001b[39m\u001b[33m'\u001b[39m\u001b[33mt acquire a memory view of a Dask array. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1714\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mThis will raise in the future.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 1715\u001b[39m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[32m 1716\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1718\u001b[39m x = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1720\u001b[39m \u001b[38;5;66;03m# Apply requested dtype and convert non-numpy backends to numpy.\u001b[39;00m\n\u001b[32m 1721\u001b[39m \u001b[38;5;66;03m# If copy is True, numpy is going to perform its own deep copy\u001b[39;00m\n\u001b[32m 1722\u001b[39m \u001b[38;5;66;03m# after this method returns.\u001b[39;00m\n\u001b[32m 1723\u001b[39m \u001b[38;5;66;03m# If copy is None, finalize() ensures that the returned object\u001b[39;00m\n\u001b[32m 1724\u001b[39m \u001b[38;5;66;03m# does not share memory with an object stored in the graph or on a\u001b[39;00m\n\u001b[32m 1725\u001b[39m \u001b[38;5;66;03m# process-local Worker.\u001b[39;00m\n\u001b[32m 1726\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m np.asarray(x, dtype=dtype)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/dask/base.py:378\u001b[39m, in \u001b[36mDaskMethodsMixin.compute\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 354\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute\u001b[39m(\u001b[38;5;28mself\u001b[39m, **kwargs):\n\u001b[32m 355\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[32m 356\u001b[39m \n\u001b[32m 357\u001b[39m \u001b[33;03m This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 376\u001b[39m \u001b[33;03m dask.compute\u001b[39;00m\n\u001b[32m 377\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m378\u001b[39m (result,) = \u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraverse\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 379\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/dask/base.py:686\u001b[39m, in \u001b[36mcompute\u001b[39m\u001b[34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[39m\n\u001b[32m 683\u001b[39m expr = expr.optimize()\n\u001b[32m 684\u001b[39m keys = \u001b[38;5;28mlist\u001b[39m(flatten(expr.__dask_keys__()))\n\u001b[32m--> \u001b[39m\u001b[32m686\u001b[39m results = \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 688\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m repack(results)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/queue.py:171\u001b[39m, in \u001b[36mQueue.get\u001b[39m\u001b[34m(self, block, timeout)\u001b[39m\n\u001b[32m 169\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 170\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._qsize():\n\u001b[32m--> \u001b[39m\u001b[32m171\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mnot_empty\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 172\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m timeout < \u001b[32m0\u001b[39m:\n\u001b[32m 173\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m\u001b[33mtimeout\u001b[39m\u001b[33m'\u001b[39m\u001b[33m must be a non-negative number\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/threading.py:355\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[32m 354\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m355\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 356\u001b[39m gotit = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 357\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[31mKeyboardInterrupt\u001b[39m: " + ] + } + ], "source": [ "all_hist_ens = hvplot_percentile_column(\n", " data=hist_cae_percentile.pr, \n", @@ -616,12 +1964,129 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "96160b74-abd5-47e2-97fe-da1c1b2a7a20", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T16:56:12.703405Z", + "iopub.status.busy": "2026-02-09T16:56:12.703168Z", + "iopub.status.idle": "2026-02-09T16:56:25.776020Z", + "shell.execute_reply": "2026-02-09T16:56:25.775256Z", + "shell.execute_reply.started": "2026-02-09T16:56:12.703379Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--> The keys in the returned dictionary of datasets are constructed as follows:\n", + "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/markdown": [ + "```html\n", + "
\n", + " 100.00% [62/62 00:09<00:00]
\n", + "\n", + "```" + ], + "text/plain": [ + "div((progress((),{'max': 62, 'value': 62}), ' ', '100.00% [62/62 00:09<00:00]'),{})" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--> The keys in the returned dictionary of datasets are constructed as follows:\n", + "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/markdown": [ + "```html\n", + "
\n", + " 100.00% [6/6 00:00<00:00]
\n", + "\n", + "```" + ], + "text/plain": [ + "div((progress((),{'max': 6, 'value': 6}), ' ', '100.00% [6/6 00:00<00:00]'),{})" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model BCC-ESM1\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model CAMS-CSM1-0\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model FGOALS-g3\n", + "End year for SSP time slice occurs after 2100; skipping ensemble member r1i1p1f1 of model GFDL-ESM4\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model IITM-ESM\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model INM-CM4-8\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model INM-CM5-0\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MIROC6\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM-1-2-HAM\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-HR\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-LR\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model NorESM2-LM\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model NorESM2-MM\n" + ] + } + ], "source": [ "mdls_ds = grab_multimodel_data(copt,alpha_sort=True)\n", "\n", @@ -647,9 +2112,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "c1735845-4ef3-419c-80d3-b7def064e15d", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T16:56:25.776986Z", + "iopub.status.busy": "2026-02-09T16:56:25.776799Z", + "iopub.status.idle": "2026-02-09T16:56:35.209133Z", + "shell.execute_reply": "2026-02-09T16:56:35.207858Z", + "shell.execute_reply.started": "2026-02-09T16:56:25.776967Z" + }, "tags": [] }, "outputs": [], @@ -683,6 +2155,12 @@ "execution_count": null, "id": "177a3cd3-a380-4ee9-8bf1-3630119a5950", "metadata": { + "execution": { + "iopub.status.busy": "2026-02-09T16:56:00.135017Z", + "iopub.status.idle": "2026-02-09T16:56:00.135755Z", + "shell.execute_reply": "2026-02-09T16:56:00.135160Z", + "shell.execute_reply.started": "2026-02-09T16:56:00.135143Z" + }, "tags": [] }, "outputs": [], @@ -812,9 +2290,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "2d359a58-0665-4598-8266-0c499810f1ed", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T16:56:35.210193Z", + "iopub.status.busy": "2026-02-09T16:56:35.209914Z", + "iopub.status.idle": "2026-02-09T16:56:43.303954Z", + "shell.execute_reply": "2026-02-09T16:56:43.303022Z", + "shell.execute_reply.started": "2026-02-09T16:56:35.210161Z" + }, "tags": [] }, "outputs": [], @@ -842,12 +2327,127 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "4737e327-7f07-40e7-8225-a0aaebad5389", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T16:56:43.304799Z", + "iopub.status.busy": "2026-02-09T16:56:43.304542Z", + "iopub.status.idle": "2026-02-09T16:56:47.187202Z", + "shell.execute_reply": "2026-02-09T16:56:47.185217Z", + "shell.execute_reply.started": "2026-02-09T16:56:43.304778Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ], + "text/plain": [ + ":Layout\n", + " .Overlay.I :Overlay\n", + " .QuadMesh.I :QuadMesh [x,y] (z)\n", + " .Bounds.I :Bounds [x,y]\n", + " .Overlay.II :Overlay\n", + " .QuadMesh.I :QuadMesh [x,y] (z)\n", + " .Bounds.I :Bounds [x,y]\n", + " .Overlay.III :Overlay\n", + " .QuadMesh.I :QuadMesh [x,y] (z)\n", + " .Bounds.I :Bounds [x,y]\n", + " .Overlay.IV :Overlay\n", + " .QuadMesh.I :QuadMesh [x,y] (z)\n", + " .Bounds.I :Bounds [x,y]\n", + " .Overlay.V :Overlay\n", + " .QuadMesh.I :QuadMesh [x,y] (z)\n", + " .Bounds.I :Bounds [x,y]" + ] + }, + "execution_count": 15, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "27ebd242-15c1-4bd5-9970-8a8b44afa3d4" + } + }, + "output_type": "execute_result" + } + ], "source": [ "lat0 = selections.latitude[0]\n", "lat1 = selections.latitude[1]\n", @@ -905,12 +2505,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "6bedd466-e867-46d0-9a17-20ffbe7b010e", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T16:56:47.188005Z", + "iopub.status.busy": "2026-02-09T16:56:47.187745Z", + "iopub.status.idle": "2026-02-09T16:57:29.078575Z", + "shell.execute_reply": "2026-02-09T16:57:29.077592Z", + "shell.execute_reply.started": "2026-02-09T16:56:47.187970Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model FGOALS-g3\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MIROC6\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-HR\n" + ] + } + ], "source": [ "# first for the downscaled results\n", "reg_hist_wrf = reg_wrf_ds.sel(\n", @@ -960,6 +2577,12 @@ "execution_count": null, "id": "55622c8b-dcde-4031-922a-aa0cf6052360", "metadata": { + "execution": { + "iopub.status.busy": "2026-02-09T16:56:00.139241Z", + "iopub.status.idle": "2026-02-09T16:56:00.139516Z", + "shell.execute_reply": "2026-02-09T16:56:00.139368Z", + "shell.execute_reply.started": "2026-02-09T16:56:00.139352Z" + }, "tags": [] }, "outputs": [], @@ -1029,9 +2652,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "16e3b462-3eae-4b42-a592-beb6b24eb8f0", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T18:18:20.129245Z", + "iopub.status.busy": "2026-02-09T18:18:20.129002Z", + "iopub.status.idle": "2026-02-09T18:18:30.504247Z", + "shell.execute_reply": "2026-02-09T18:18:30.503326Z", + "shell.execute_reply.started": "2026-02-09T18:18:20.129222Z" + }, "tags": [] }, "outputs": [], @@ -1049,12 +2679,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "dbed3ea6-b9d2-40b2-ad21-68f3f7587772", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T18:24:33.027293Z", + "iopub.status.busy": "2026-02-09T18:24:33.027049Z", + "iopub.status.idle": "2026-02-09T18:24:52.337546Z", + "shell.execute_reply": "2026-02-09T18:24:52.336627Z", + "shell.execute_reply.started": "2026-02-09T18:24:33.027270Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model FGOALS-g3\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MIROC6\n", + "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-HR\n" + ] + } + ], "source": [ "box_hist_wrf = box_wrf_ds.sel(\n", " time=slice('1981','2010'))\n", @@ -1086,9 +2733,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "ce7a9586-7590-4c22-947e-3d7d160ab50f", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T18:24:52.338267Z", + "iopub.status.busy": "2026-02-09T18:24:52.338036Z", + "iopub.status.idle": "2026-02-09T18:24:57.895414Z", + "shell.execute_reply": "2026-02-09T18:24:57.894599Z", + "shell.execute_reply.started": "2026-02-09T18:24:52.338246Z" + }, "tags": [] }, "outputs": [], @@ -1118,9 +2772,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "f6f6ecb9-98f5-4cc8-be8b-673e3d27556e", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T18:24:57.896377Z", + "iopub.status.busy": "2026-02-09T18:24:57.896180Z", + "iopub.status.idle": "2026-02-09T18:25:41.598172Z", + "shell.execute_reply": "2026-02-09T18:25:41.597292Z", + "shell.execute_reply.started": "2026-02-09T18:24:57.896357Z" + }, "tags": [] }, "outputs": [], @@ -1156,12 +2817,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "28dfeb20-d40a-4895-9777-6e742489f58b", "metadata": { + "execution": { + "iopub.execute_input": "2026-02-09T19:04:41.216787Z", + "iopub.status.busy": "2026-02-09T19:04:41.216531Z", + "iopub.status.idle": "2026-02-09T19:04:41.404137Z", + "shell.execute_reply": "2026-02-09T19:04:41.402796Z", + "shell.execute_reply.started": "2026-02-09T19:04:41.216764Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'make_precip_unc_map' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m delta_vmin = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 4\u001b[39m delta_vmax = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m mmm_hist_plot = \u001b[43mmake_precip_unc_map\u001b[49m(\n\u001b[32m 7\u001b[39m data=hist_percentile_mmm, title=(\u001b[33m\"\u001b[39m\u001b[33mMulti-model mean\u001b[39m\u001b[33m\"\u001b[39m),\n\u001b[32m 8\u001b[39m vmin=vmin, vmax=vmax, sopt=\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m 9\u001b[39m height=\u001b[32m300\u001b[39m, width=\u001b[32m300\u001b[39m)\n\u001b[32m 11\u001b[39m mmm_ssp_plot = make_precip_unc_map(\n\u001b[32m 12\u001b[39m data=ssp_percentile_mmm, title=(\u001b[33m\"\u001b[39m\u001b[33mMulti-model mean\u001b[39m\u001b[33m\"\u001b[39m),\n\u001b[32m 13\u001b[39m vmin=vmin, vmax=vmax, sopt=\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m 14\u001b[39m height=\u001b[32m300\u001b[39m, width=\u001b[32m300\u001b[39m)\n\u001b[32m 16\u001b[39m mmm_diff_plot = make_precip_unc_map(\n\u001b[32m 17\u001b[39m data=delta_percentile_mmm, title=(\u001b[33m\"\u001b[39m\u001b[33mMulti-model mean\u001b[39m\u001b[33m\"\u001b[39m),\n\u001b[32m 18\u001b[39m vmin=delta_vmin, vmax=delta_vmax, sopt=\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[32m 19\u001b[39m height=\u001b[32m300\u001b[39m, width=\u001b[32m300\u001b[39m)\n", + "\u001b[31mNameError\u001b[39m: name 'make_precip_unc_map' is not defined" + ] + } + ], "source": [ "vmin = 0\n", "vmax = 2000\n", @@ -1237,6 +2917,12 @@ "execution_count": null, "id": "1969c1c5-ead1-4fae-ab7c-e8f6e4815c6b", "metadata": { + "execution": { + "iopub.status.busy": "2026-02-09T16:56:00.144288Z", + "iopub.status.idle": "2026-02-09T16:56:00.144526Z", + "shell.execute_reply": "2026-02-09T16:56:00.144413Z", + "shell.execute_reply.started": "2026-02-09T16:56:00.144398Z" + }, "tags": [] }, "outputs": [], diff --git a/data-access/basic_data_access.ipynb b/data-access/basic_data_access.ipynb index 18925990..ba93fb4c 100644 --- a/data-access/basic_data_access.ipynb +++ b/data-access/basic_data_access.ipynb @@ -34,9 +34,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "9d4ff9a3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:11:20.412931Z", + "iopub.status.busy": "2026-01-02T23:11:20.412754Z", + "iopub.status.idle": "2026-01-02T23:11:25.229645Z", + "shell.execute_reply": "2026-01-02T23:11:25.228729Z", + "shell.execute_reply.started": "2026-01-02T23:11:20.412912Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -79,9 +87,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "8a4da135-f64c-415e-a03b-0863cd67bd99", "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:11:25.230500Z", + "iopub.status.busy": "2026-01-02T23:11:25.230256Z", + "iopub.status.idle": "2026-01-02T23:11:25.236454Z", + "shell.execute_reply": "2026-01-02T23:11:25.235598Z", + "shell.execute_reply.started": "2026-01-02T23:11:25.230479Z" + }, "tags": [] }, "outputs": [], @@ -92,10 +107,388 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "0a0cd8f7", + "execution_count": 12, + "id": "d61e8e1b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-05 19:18:05 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n" + ] + } + ], + "source": [ + "pressure_data = (cd\n", + " .catalog(\"cadcat\")\n", + " .variable(\"psfc\")\n", + " .activity_id(\"WRF\")\n", + " .institution_id(\"UCLA\")\n", + " .grid_label(\"d03\")\n", + " .table_id('1hr')\n", + " .processes({\n", + " \"warming_level\": {\n", + " \"warming_levels\": [2.0]\n", + " },\n", + " \"clip\": \"Southern California Edison\",\n", + " \"convert_units\": \"mb\"\n", + " }).get()\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e8f83638", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "pressure_data.isel(warming_level=0, sim=0, time_delta=10).psfc.plot.contourf(x='lon', y='lat', levels=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d8e10216", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - Variables:\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - ----------\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - cape: Convective Available Potential Energy\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - cf: Capacity factor\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - cin: Convective Inhibition\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - dew_point: No description available\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - effective_temp_index: No description available\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - etrans_sfc: Evapotranspiration\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - evap_sfc: Evaporation\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - ffwi: Fosberg fire weather index\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - gen: Power generation\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - gh_sfc: Ground heat flux\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - hursmax: Maximum relative humidity\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - hursmin: Minimum relative humidity\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - huss: Specific humidity at 2m\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - iwp: Ice water path\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lcl: Lifting Condensation Level\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lfc: Level of Free Convection\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lh_sfc: Latent heat flux at the surface\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lw_dwn: Instantaneous downwelling longwave flux at bottom\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lw_sfc: Longwave flux at the surface\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lwdnb: Instantaneous downwelling longwave flux at bottom\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lwdnbc: Instantaneous downwelling clear sky longwave flux at bottom\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lwp: Liquid water path\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lwupb: Instantaneous upwelling longwave flux at bottom\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lwupbc: Instantaneous upwelling clear sky longwave flux at bottom\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - noaa_heat_index: No description available\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - pblh: Planetary boundary layer height\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - pr: Precipitation (total)\n", + "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - prec: Precipitation (total)\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - prec_c: Precipitation (convective only)\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - prec_max: Maximum precipitation\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - prec_snow: Snowfall\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - psfc: Surface Pressure\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - q2: Water Vapor Mixing Ratio at 2m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rainc: Precipitation (cumulus portion only)\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rainnc: Precipitation (grid-scale portion only)\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rh: Relative humidity\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rh_max: Maximum relative humidity\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rh_min: Minimum relative humidity\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rsds: Shortwave flux at the surface\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - runsb: Subsurface runoff\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - runsf: Surface runoff\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - sfc_runoff: Surface runoff\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - sh_sfc: Sensible heat flux at the surface\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - snow: Snow water equivalent\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - snownc: Snowfall (snow and ice)\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - subsfc_runoff: Subsurface runoff\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - sw_dwn: Instantaneous downwelling shortwave flux at bottom\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - sw_sfc: Shortwave flux at the surface\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swddif: Shortwave surface downward diffuse irradiance\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swddir: Shortwave surface downward direct irradiance\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swddni: Shortwave surface downward direct normal irradiance\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swdnb: Instantaneous downwelling shortwave flux at bottom\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swdnbc: Instantaneous downwelling clear sky shortwave flux at bottom\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swupb: Instantaneous upwelling shortwave flux at bottom\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swupbc: Instantaneous upwelling clear sky shortwave flux at bottom\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - t2: Air Temperature at 2m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - t2max: Maximum air temperature at 2m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - t2min: Minimum air temperature at 2m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - tasmax: Maximum air temperature at 2m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - tasmin: Minimum air temperature at 2m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - tsk: Surface skin temperature\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - tskin: Surface skin temperature\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - u10: West-East component of Wind at 10m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - uas: West-East component of Wind at 10m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - v10: North-South component of Wind at 10m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - vas: North-South component of Wind at 10m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - wspd10max: Maximum wind speed at 10m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - wspd10mean: Mean wind speed at 10m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - wspeed: Wind speed at 10m\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - znt: Surface roughness length\n", + "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - \n", + "\n" + ] + } + ], + "source": [ + "cd.show_variable_options()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0a0cd8f7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:11:25.237308Z", + "iopub.status.busy": "2026-01-02T23:11:25.237103Z", + "iopub.status.idle": "2026-01-02T23:11:25.421141Z", + "shell.execute_reply": "2026-01-02T23:11:25.420530Z", + "shell.execute_reply.started": "2026-01-02T23:11:25.237290Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ========================================================\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CAL ADAPT DATA -- ALL AVAILABLE OPTIONS USING CLIMAKITAE\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ========================================================\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - catalog options (Cloud data collections):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -----------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cadcat\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - hdp\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - renewable energy generation\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - activity_id options (Downscaling methods):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - LOCA2\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - WRF\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - institution_id options (Data producers):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ----------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CAE\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ERA\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - UCLA\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - UCSD\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_institution_id_options() to see all institution ids.\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - source_id options (Climate model simulations):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ----------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 10 of 17 total options\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ACCESS-CM2\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CESM2\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CESM2-LENS\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CNRM-ESM2-1\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - EC-Earth3\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - EC-Earth3-Veg\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ERA5\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - FGOALS-g3\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - GFDL-ESM4\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - HadGEM3-GC31-LL\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_source_id_options() to see all source ids.\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - experiment_id options (Simulation runs):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ----------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - historical\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - reanalysis\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ssp245\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ssp370\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ssp585\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - table_id options (Temporal resolutions):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ----------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - 1hr\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - day\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - mon\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - yrmax\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - grid_label options (Spatial resolutions):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -----------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - d01 (45 km)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - d02 ( 9 km)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - d03 ( 3 km)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Variables:\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ----------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 15 of 70 total options\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cape: Convective Available Potential Energy\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cf: Capacity factor\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cin: Convective Inhibition\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - dew_point: No description available\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - effective_temp_index: No description available\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - etrans_sfc: Evapotranspiration\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - evap_sfc: Evaporation\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ffwi: Fosberg fire weather index\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - gen: Power generation\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - gh_sfc: Ground heat flux\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - hursmax: Maximum relative humidity\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - hursmin: Minimum relative humidity\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - huss: Specific humidity at 2m\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - iwp: Ice water path\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lcl: Lifting Condensation Level\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_variable_options() to see all variables.\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - installation options (Renewable energy generation types):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ---------------------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - pv_distributed\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - pv_utility\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - windpower_offshore\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - windpower_onshore\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - station_id options (Weather station names):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 15 of 14982 total options\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_69007093217\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72012200114\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72019300117\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72020200118\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72025400119\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72026723224\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72027294282\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72032204129\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72033353175\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72033900121\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72034594086\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72036524267\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72036904135\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72038500419\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72038800469\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_station_id_options() to see all station ids.\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - network_id options (Weather network names):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CAHYDRO\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CDEC\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CIMIS\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CNRFC\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CRN\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CW3E\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CWOP\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - HADS\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - HNXWFO\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - HOLFUY\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - HPWREN\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - LOXWFO\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - MAP\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - MARITIME\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - MTRWFO\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - NCAWOS\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - NDBC\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - NOS-NWLON\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - NOS-PORTS\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - OtherISD\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - RAWS\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - SCAN\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - SGXWFO\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - SHASAVAL\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - SNOTEL\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - VALLEYWATER\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - VCAPCD\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Processors (Methods for transforming raw catalog data):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -------------------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing all processors\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - bias_adjust_model_to_station\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - clip\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - concat\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - convert_units\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - export\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - filter_unadjusted_models\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - metric_calc\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - time_slice\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - update_attributes\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - warming_level\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_processors() to see all processors.\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Stations (Available weather stations for localization):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -------------------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 15 of 33 total stations\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Arcata Eureka Airport (KACV)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Bakersfield Meadows Field (KBFL)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Blythe Asos (KBLH)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Burbank-Glendale-Pasadena Airport (KBUR)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Desert Resorts Regional Airport (KTRM)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Downtown Los Angeles USC Campus (KCQT)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Fresno Yosemite International Airport (KFAT)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Gillespie Field Airport (KSEE)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Imperial County Airport (KIPL)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Lancaster William J Fox Field (KWJF)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Long Beach Daugherty Field (KLGB)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Los Angeles International Airport (KLAX)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - McCarran International Airport (KLAS)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Merced Municipal Airport MacReady Field (KMCE)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Modesto City-County Airport (KMOD)\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_station_options() to see all stations.\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "============================================================\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Current Query Status:\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ============================================================\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Current Query:\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - --------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - catalog: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - installation: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - activity_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - institution_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - source_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - experiment_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - table_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - grid_label: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - variable_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - station_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - network_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - processes: UNSET\n" + ] + } + ], "source": [ "# Get a comprehensive overview of all available options\n", "cd.show_all_options()\n", @@ -128,12 +521,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "0c193f9f-c154-413f-92dc-084d2ea05ae7", "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:11:25.422419Z", + "iopub.status.busy": "2026-01-02T23:11:25.422152Z", + "iopub.status.idle": "2026-01-02T23:11:25.466224Z", + "shell.execute_reply": "2026-01-02T23:11:25.465347Z", + "shell.execute_reply.started": "2026-01-02T23:11:25.422398Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Available Catalogs ===\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - catalog options (Cloud data collections):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -----------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cadcat\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - hdp\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - renewable energy generation\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "\n", + "=== Choose 'renewable energy generation' catalog and explore installations ===\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - installation options (Renewable energy generation types):\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ---------------------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - pv_distributed\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - pv_utility\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - windpower_offshore\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - windpower_onshore\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n", + "\n", + "=== Choose 'pv_utility' installation and explore variables ===\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Variables (constrained by current query)::\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cf: Capacity factor\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - gen: Power generation\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n" + ] + } + ], "source": [ "# Explore options step by step\n", "print(\"=== Available Catalogs ===\")\n", @@ -159,12 +592,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "6bf820b0-9940-4ec9-b8d4-ad9e8cd4cb9c", "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:11:25.467098Z", + "iopub.status.busy": "2026-01-02T23:11:25.466907Z", + "iopub.status.idle": "2026-01-02T23:11:25.504914Z", + "shell.execute_reply": "2026-01-02T23:11:25.503793Z", + "shell.execute_reply.started": "2026-01-02T23:11:25.467080Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Climate Data Catalog ===\n", + "\n", + "=== WRF (Dynamical Downscaling) Variables ===\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Variables (constrained by current query)::\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 15 of 58 total options\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cape: Convective Available Potential Energy\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cin: Convective Inhibition\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - dew_point: No description available\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - effective_temp_index: No description available\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - etrans_sfc: Evapotranspiration\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - evap_sfc: Evaporation\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ffwi: Fosberg fire weather index\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - gh_sfc: Ground heat flux\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - iwp: Ice water path\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lcl: Lifting Condensation Level\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lfc: Level of Free Convection\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lh_sfc: Latent heat flux at the surface\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lw_dwn: Instantaneous downwelling longwave flux at bottom\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lw_sfc: Longwave flux at the surface\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lwdnb: Instantaneous downwelling longwave flux at bottom\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n" + ] + } + ], "source": [ "print(\"=== Climate Data Catalog ===\")\n", "cd.reset()\n", @@ -185,12 +655,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "7fae3993-ead4-49d8-87ad-066ec5c3181f", "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:11:25.505894Z", + "iopub.status.busy": "2026-01-02T23:11:25.505610Z", + "iopub.status.idle": "2026-01-02T23:11:25.548127Z", + "shell.execute_reply": "2026-01-02T23:11:25.547290Z", + "shell.execute_reply.started": "2026-01-02T23:11:25.505866Z" + }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Current Query:\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - --------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - catalog: cadcat\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - installation: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - activity_id: WRF\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - institution_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - source_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - experiment_id: ['historical']\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - table_id: mon\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - grid_label: d01\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - variable_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - station_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - network_id: UNSET\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - processes: UNSET\n", + "\n", + "Available variables for this query:\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Variables (constrained by current query)::\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ------------------------------------------\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 5 of 35 total options\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cape: Convective Available Potential Energy\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cin: Convective Inhibition\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - dew_point: No description available\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - effective_temp_index: No description available\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - etrans_sfc: Evapotranspiration\n", + "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", + "\n" + ] + } + ], "source": [ "# Build a partial query and check its state\n", "partial_query = (cd\n", @@ -219,10 +729,947 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "52e1efe9-553f-4ac8-8bfe-749923318492", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:11:25.548850Z", + "iopub.status.busy": "2026-01-02T23:11:25.548662Z", + "iopub.status.idle": "2026-01-02T23:11:26.964493Z", + "shell.execute_reply": "2026-01-02T23:11:26.963543Z", + "shell.execute_reply.started": "2026-01-02T23:11:25.548832Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:11:26 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n", + "2026-01-02 23:11:26 - climakitae.new_core.processors.processor_utils - WARNING - \n", + "\n", + "No historical data found for WRF.UCLA.MIROC6.ssp370.mon.d02 with key WRF.UCLA.MIROC6.historical.mon.d02. \n", + "Historical data is required for time domain extension. \n", + "Keeping original SSP data without historical extension.\n", + "\n", + "2026-01-02 23:11:26 - climakitae.new_core.processors.processor_utils - WARNING - \n", + "\n", + "No historical data found for WRF.UCLA.TaiESM1.ssp370.mon.d02 with key WRF.UCLA.TaiESM1.historical.mon.d02. \n", + "Historical data is required for time domain extension. \n", + "Keeping original SSP data without historical extension.\n", + "\n", + "2026-01-02 23:11:26 - climakitae.new_core.processors.processor_utils - WARNING - \n", + "\n", + "No historical data found for WRF.UCLA.EC-Earth3.ssp370.mon.d02 with key WRF.UCLA.EC-Earth3.historical.mon.d02. \n", + "Historical data is required for time domain extension. \n", + "Keeping original SSP data without historical extension.\n", + "\n", + "2026-01-02 23:11:26 - climakitae.new_core.processors.processor_utils - WARNING - \n", + "\n", + "No historical data found for WRF.UCLA.EC-Earth3-Veg.ssp370.mon.d02 with key WRF.UCLA.EC-Earth3-Veg.historical.mon.d02. \n", + "Historical data is required for time domain extension. \n", + "Keeping original SSP data without historical extension.\n", + "\n", + "2026-01-02 23:11:26 - climakitae.new_core.processors.processor_utils - WARNING - \n", + "\n", + "No historical data found for WRF.UCLA.MPI-ESM1-2-HR.ssp370.mon.d02 with key WRF.UCLA.MPI-ESM1-2-HR.historical.mon.d02. \n", + "Historical data is required for time domain extension. \n", + "Keeping original SSP data without historical extension.\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 2GB\n",
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<xarray.Dataset> Size: 2GB\n",
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+       "Data variables:\n",
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+       "    ...                               ...\n",
+       "    intake_esm_attrs:_data_format_:   zarr\n",
+       "    intake_esm_dataset_key:           WRF.UCLA.MIROC6.ssp370.mon.d02\n",
+       "    resolution:                       9 km\n",
+       "    update_attributes:                Process 'update_attributes' applied to ...\n",
+       "    filter_unadjusted_models:         yes\n",
+       "    concat:                           Process 'concat' applied to the data. T...
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<xarray.Dataset> Size: 2GB\n",
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+       "Attributes: (12/116)\n",
+       "    AERCU_FCT:                        1.0\n",
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+       "    ...                               ...\n",
+       "    intake_esm_attrs:_data_format_:   zarr\n",
+       "    intake_esm_dataset_key:           WRF.UCLA.EC-Earth3.ssp370.mon.d02\n",
+       "    resolution:                       9 km\n",
+       "    update_attributes:                Process 'update_attributes' applied to ...\n",
+       "    filter_unadjusted_models:         yes\n",
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<xarray.Dataset> Size: 327MB\n",
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+       "    prec               (sim, time, y, x) float32 326MB dask.array<chunksize=(1, 408, 104, 109), meta=np.ndarray>\n",
+       "Attributes: (12/188)\n",
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+       "    historical_prepended:             True\n",
+       "    resolution:                       45 km\n",
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<xarray.Dataset> Size: 653MB\n",
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+       "    AER_ANGEXP_OPT:                   1\n",
+       "    ...                               ...\n",
+       "    intake_esm_attrs:_data_format_:   zarr\n",
+       "    intake_esm_dataset_key:           WRF.UCLA.EC-Earth3.ssp370.mon.d01\n",
+       "    resolution:                       45 km\n",
+       "    concat:                           Process 'concat' applied to the data. M...\n",
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<xarray.Dataset> Size: 168GB\n",
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+       "    ...                               ...\n",
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+       "    historical_prepended:             True\n",
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<xarray.Dataset> Size: 1TB\n",
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+       "    CMIP6_CV_version:                    cv=6.2.3.0-7-g2019642\n",
+       "    Conventions:                         CF-1.7 CMIP-6.2\n",
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+       "    update_attributes:                   Process 'update_attributes' applied ...\n",
+       "    filter_unadjusted_models:            yes\n",
+       "    concat:                              Process 'concat' applied to the data...
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T...\n", + " warming_level_simple: Process 'warming_level_simple' applied ..." + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "wrf_wl_data = (cd\n", " .catalog(\"cadcat\")\n", @@ -626,10 +8617,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "60be13cc", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:12:00.390942Z", + "iopub.status.busy": "2026-01-02T23:12:00.390730Z", + "iopub.status.idle": "2026-01-02T23:12:00.416242Z", + "shell.execute_reply": "2026-01-02T23:12:00.415482Z", + "shell.execute_reply.started": "2026-01-02T23:12:00.390924Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Stations (Available weather stations for localization):\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - -------------------------------------------------------\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Arcata Eureka Airport (KACV)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Bakersfield Meadows Field (KBFL)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Blythe Asos (KBLH)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Burbank-Glendale-Pasadena Airport (KBUR)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Desert Resorts Regional Airport (KTRM)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Downtown Los Angeles USC Campus (KCQT)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Fresno Yosemite International Airport (KFAT)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Gillespie Field Airport (KSEE)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Imperial County Airport (KIPL)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Lancaster William J Fox Field (KWJF)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Long Beach Daugherty Field (KLGB)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Los Angeles International Airport (KLAX)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - McCarran International Airport (KLAS)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Merced Municipal Airport MacReady Field (KMCE)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Modesto City-County Airport (KMOD)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Needles Airport (KEED)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Oakland Metro International Airport (KOAK)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Ontario International Airport (KONT)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Oxnard Ventura County Airport (KOXR)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Palm Springs Regional Airport (KPSP)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Red Bluff Municipal Airport (KRBL)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Redding Airport (KRDD)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Riverside Municipal Airport (KRAL)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Sacramento Executive Airport (KSAC)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - San Diego Lindbergh Field (KSAN)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - San Diego Miramar Wscmo (KNKX)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - San Francisco International Airport (KSFO)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - San Jose International Airport (KSJC)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - San Luis Obispo Airport (KSBP)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Santa Ana John Wayne Airport (KSNA)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Santa Barbara Municipal Airport (KSBA)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Stockton Airport (KSCK)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Ukiah Municipal Airport (KUKI)\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - \n", + "\n" + ] + } + ], "source": [ "# Let's take a look at all HADISD station options\n", "cd.show_station_options()" @@ -637,10 +8680,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "97841200", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:12:00.417193Z", + "iopub.status.busy": "2026-01-02T23:12:00.417001Z", + "iopub.status.idle": "2026-01-02T23:12:00.436395Z", + "shell.execute_reply": "2026-01-02T23:12:00.435638Z", + "shell.execute_reply.started": "2026-01-02T23:12:00.417176Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Boundary Types (call again with boundary_type='...' to see options):\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - --------------------------------------------------------------------\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - No boundaries available with current parameters\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Available Ca Counties Boundaries:\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - ---------------------------------\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - No boundaries available with current parameters\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Available Ious Pous Boundaries:\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - -------------------------------\n", + "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - No boundaries available with current parameters\n" + ] + } + ], "source": [ "# Now, let's look at other boundaries that `climakitae` supports\n", "cd.show_boundary_options()\n", @@ -658,10 +8725,847 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "57df3126", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:12:00.437372Z", + "iopub.status.busy": "2026-01-02T23:12:00.437171Z", + "iopub.status.idle": "2026-01-02T23:12:08.672462Z", + "shell.execute_reply": "2026-01-02T23:12:08.671586Z", + "shell.execute_reply.started": "2026-01-02T23:12:00.437354Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:12:03 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "    centered_year      (sim, warming_level) float64 120B 2.018e+03 ... 2.083e+03\n",
+       "    Lambert_Conformal  int64 8B 0\n",
+       "Data variables:\n",
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+       "Attributes: (12/120)\n",
+       "    AERCU_FCT:                        1.0\n",
+       "    AERCU_OPT:                        0\n",
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+       "    BL_PBL_PHYSICS:                   1\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:        40\n",
+       "    BOTTOM-TOP_PATCH_END_STAG:        40\n",
+       "    ...                               ...\n",
+       "    warming_level:                    {'warming_levels': [1.5, 2.0, 3.0]}\n",
+       "    clip:                             Process 'clip' applied to the data. Cli...\n",
+       "    update_attributes:                Process 'update_attributes' applied to ...\n",
+       "    filter_unadjusted_models:         yes\n",
+       "    concat:                           Process 'concat' applied to the data. T...\n",
+       "    warming_level_simple:             Process 'warming_level_simple' applied ...
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Cli...\n", + " update_attributes: Process 'update_attributes' applied to ...\n", + " filter_unadjusted_models: yes\n", + " concat: Process 'concat' applied to the data. T...\n", + " warming_level_simple: Process 'warming_level_simple' applied ..." + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "box_lat_lons = (\n", " (34.05, 37.77),\n", @@ -772,10 +11419,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "62e37ada", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:12:39.027715Z", + "iopub.status.busy": "2026-01-02T23:12:39.027488Z", + "iopub.status.idle": "2026-01-02T23:12:53.305141Z", + "shell.execute_reply": "2026-01-02T23:12:53.304158Z", + "shell.execute_reply.started": "2026-01-02T23:12:39.027696Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:12:45 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "clip_counties = (cd\n", " .catalog(\"cadcat\")\n", @@ -806,10 +11495,887 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "6156519a", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:12:53.306032Z", + "iopub.status.busy": "2026-01-02T23:12:53.305817Z", + "iopub.status.idle": "2026-01-02T23:13:00.178678Z", + "shell.execute_reply": "2026-01-02T23:13:00.177730Z", + "shell.execute_reply.started": "2026-01-02T23:12:53.306014Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:12:56 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 8GB\n",
+       "Dimensions:            (county: 2, sim: 5, warming_level: 3, time_delta: 10950,\n",
+       "                        y: 100, x: 64)\n",
+       "Coordinates:\n",
+       "  * county             (county) <U18 144B 'Alameda County' 'Los Angeles County'\n",
+       "  * sim                (sim) object 40B 'WRF_UCLA_EC-Earth3_ssp370_day_d03_r1...\n",
+       "  * warming_level      (warming_level) float64 24B 1.5 2.0 3.0\n",
+       "  * time_delta         (time_delta) int64 88kB -5475 -5474 -5473 ... 5473 5474\n",
+       "  * y                  (y) float64 800B 7.369e+05 7.399e+05 ... 1.4e+06\n",
+       "  * x                  (x) float64 512B -4.215e+06 -4.212e+06 ... -4.026e+06\n",
+       "    lakemask           (county, y, x) float32 51kB nan nan nan ... nan nan nan\n",
+       "    landmask           (county, y, x) float32 51kB nan nan nan ... nan nan nan\n",
+       "    lat                (county, y, x) float32 51kB nan nan nan ... nan nan nan\n",
+       "    lon                (county, y, x) float32 51kB nan nan nan ... nan nan nan\n",
+       "    centered_year      (sim, warming_level) float64 120B 2.018e+03 ... 2.083e+03\n",
+       "    Lambert_Conformal  int64 8B 0\n",
+       "Data variables:\n",
+       "    t2                 (county, sim, warming_level, time_delta, y, x) float32 8GB dask.array<chunksize=(1, 1, 1, 3652, 28, 2), meta=np.ndarray>\n",
+       "Attributes: (12/120)\n",
+       "    AERCU_FCT:                        1.0\n",
+       "    AERCU_OPT:                        0\n",
+       "    AUTO_LEVELS_OPT:                  2\n",
+       "    BL_PBL_PHYSICS:                   1\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:        40\n",
+       "    BOTTOM-TOP_PATCH_END_STAG:        40\n",
+       "    ...                               ...\n",
+       "    warming_level:                    {'warming_levels': [1.5, 2.0, 3.0]}\n",
+       "    clip:                             Process 'clip' applied to the data. Sep...\n",
+       "    update_attributes:                Process 'update_attributes' applied to ...\n",
+       "    filter_unadjusted_models:         yes\n",
+       "    concat:                           Process 'concat' applied to the data. T...\n",
+       "    warming_level_simple:             Process 'warming_level_simple' applied ...
" + ], + "text/plain": [ + " Size: 8GB\n", + "Dimensions: (county: 2, sim: 5, warming_level: 3, time_delta: 10950,\n", + " y: 100, x: 64)\n", + "Coordinates:\n", + " * county (county) \n", + "Attributes: (12/120)\n", + " AERCU_FCT: 1.0\n", + " AERCU_OPT: 0\n", + " AUTO_LEVELS_OPT: 2\n", + " BL_PBL_PHYSICS: 1\n", + " BOTTOM-TOP_GRID_DIMENSION: 40\n", + " BOTTOM-TOP_PATCH_END_STAG: 40\n", + " ... ...\n", + " warming_level: {'warming_levels': [1.5, 2.0, 3.0]}\n", + " clip: Process 'clip' applied to the data. Sep...\n", + " update_attributes: Process 'update_attributes' applied to ...\n", + " filter_unadjusted_models: yes\n", + " concat: Process 'concat' applied to the data. T...\n", + " warming_level_simple: Process 'warming_level_simple' applied ..." + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "clip_counties_separated = (cd\n", " .catalog(\"cadcat\")\n", @@ -845,10 +12411,973 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "a6246068", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:13:00.179519Z", + "iopub.status.busy": "2026-01-02T23:13:00.179293Z", + "iopub.status.idle": "2026-01-02T23:13:08.605372Z", + "shell.execute_reply": "2026-01-02T23:13:08.604472Z", + "shell.execute_reply.started": "2026-01-02T23:13:00.179499Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:13:03 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 3GB\n",
+       "Dimensions:            (sim: 5, warming_level: 3, y: 72, x: 64,\n",
+       "                        time_delta: 10950)\n",
+       "Coordinates:\n",
+       "  * sim                (sim) object 40B 'WRF_UCLA_EC-Earth3_ssp370_day_d03_r1...\n",
+       "  * warming_level      (warming_level) float64 24B 1.5 2.0 3.0\n",
+       "  * y                  (y) float64 576B 7.369e+05 7.399e+05 ... 9.499e+05\n",
+       "  * x                  (x) float64 512B -4.215e+06 -4.212e+06 ... -4.026e+06\n",
+       "  * time_delta         (time_delta) int64 88kB -5475 -5474 -5473 ... 5473 5474\n",
+       "    lakemask           (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
+       "    landmask           (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
+       "    lat                (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
+       "    lon                (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
+       "    centered_year      (sim, warming_level) float64 120B 2.018e+03 ... 2.083e+03\n",
+       "    Lambert_Conformal  int64 8B 0\n",
+       "Data variables:\n",
+       "    t2                 (sim, warming_level, time_delta, y, x) float32 3GB dask.array<chunksize=(1, 1, 3652, 44, 2), meta=np.ndarray>\n",
+       "Attributes: (12/121)\n",
+       "    AERCU_FCT:                        1.0\n",
+       "    AERCU_OPT:                        0\n",
+       "    AUTO_LEVELS_OPT:                  2\n",
+       "    BL_PBL_PHYSICS:                   1\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:        40\n",
+       "    BOTTOM-TOP_PATCH_END_STAG:        40\n",
+       "    ...                               ...\n",
+       "    clip:                             Process 'clip' applied to the data. Cli...\n",
+       "    convert_units:                    Process 'convert_units' applied to the ...\n",
+       "    update_attributes:                Process 'update_attributes' applied to ...\n",
+       "    filter_unadjusted_models:         yes\n",
+       "    concat:                           Process 'concat' applied to the data. T...\n",
+       "    warming_level_simple:             Process 'warming_level_simple' applied ...
" + ], + "text/plain": [ + " Size: 3GB\n", + "Dimensions: (sim: 5, warming_level: 3, y: 72, x: 64,\n", + " time_delta: 10950)\n", + "Coordinates:\n", + " * sim (sim) object 40B 'WRF_UCLA_EC-Earth3_ssp370_day_d03_r1...\n", + " * warming_level (warming_level) float64 24B 1.5 2.0 3.0\n", + " * y (y) float64 576B 7.369e+05 7.399e+05 ... 9.499e+05\n", + " * x (x) float64 512B -4.215e+06 -4.212e+06 ... -4.026e+06\n", + " * time_delta (time_delta) int64 88kB -5475 -5474 -5473 ... 5473 5474\n", + " lakemask (y, x) float32 18kB dask.array\n", + " landmask (y, x) float32 18kB dask.array\n", + " lat (y, x) float32 18kB dask.array\n", + " lon (y, x) float32 18kB dask.array\n", + " centered_year (sim, warming_level) float64 120B 2.018e+03 ... 2.083e+03\n", + " Lambert_Conformal int64 8B 0\n", + "Data variables:\n", + " t2 (sim, warming_level, time_delta, y, x) float32 3GB dask.array\n", + "Attributes: (12/121)\n", + " AERCU_FCT: 1.0\n", + " AERCU_OPT: 0\n", + " AUTO_LEVELS_OPT: 2\n", + " BL_PBL_PHYSICS: 1\n", + " BOTTOM-TOP_GRID_DIMENSION: 40\n", + " BOTTOM-TOP_PATCH_END_STAG: 40\n", + " ... ...\n", + " clip: Process 'clip' applied to the data. Cli...\n", + " convert_units: Process 'convert_units' applied to the ...\n", + " update_attributes: Process 'update_attributes' applied to ...\n", + " filter_unadjusted_models: yes\n", + " concat: Process 'concat' applied to the data. T...\n", + " warming_level_simple: Process 'warming_level_simple' applied ..." + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Here, we'll take the temperature data that's natively in Kelvin to be returned in Fahrenheit instead\n", "degF_data = (cd\n", @@ -872,10 +13401,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "cbf697e4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:13:08.606124Z", + "iopub.status.busy": "2026-01-02T23:13:08.605925Z", + "iopub.status.idle": "2026-01-02T23:13:12.306141Z", + "shell.execute_reply": "2026-01-02T23:13:12.305330Z", + "shell.execute_reply.started": "2026-01-02T23:13:08.606107Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Here we'll see that the values are within the reasonable ranges of we'd see for Fahrenheit for Los Angeles County\n", "degF_data.mean(dim='time_delta').isel(sim=0, warming_level=0).t2.plot(x=\"lon\", y=\"lat\")" @@ -907,10 +13465,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "3c8ed48e-0666-43c3-adc3-35369beab38e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:13:12.307185Z", + "iopub.status.busy": "2026-01-02T23:13:12.306903Z", + "iopub.status.idle": "2026-01-02T23:13:29.622530Z", + "shell.execute_reply": "2026-01-02T23:13:29.621641Z", + "shell.execute_reply.started": "2026-01-02T23:13:12.307156Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:13:15 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n", + "2026-01-02 23:13:21 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n", + "2026-01-02 23:13:26 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n" + ] + } + ], "source": [ "metrics = ['min', 'mean', 'max']\n", "data = []\n", @@ -942,10 +13536,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "cb78c222-caf1-46af-a44b-959a3d4cf0c6", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:13:29.623540Z", + "iopub.status.busy": "2026-01-02T23:13:29.623339Z", + "iopub.status.idle": "2026-01-02T23:15:57.780003Z", + "shell.execute_reply": "2026-01-02T23:15:57.779125Z", + "shell.execute_reply.started": "2026-01-02T23:13:29.623524Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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5z3+Y3W5nQ4YMYQsXLmTjxo1jUVFRrGHDhiw9PV0eZ+W9Ky4ujpUtW5aNHDmSLViwgLVq1YoBYN98843P59KwYUM2cOBA9vbbb7N58+axtm3bMgDsvffek8d8+eWXrEyZMiwxMVGOQb5iCWOMOZ1O9s8//7CLFy+y7777jiUmJrLo6Gh2/fp1r+dNnTqVAWBXrlxR7U9LS2M8z7MxY8bI++7cucOaNGkix6F+/foxURTl402bNmWRkZEsLS3N53qNmDBhAgOgev+qUKECe/PNN9kPP/zAOI6T3ytFUWSFCxdmHTp0kMeWKVOGPffcc+y9995jc+bMYQ888AADwL7++mvVdQCwqlWrsuLFi7MpU6aw+fPns59//ln+fatTpw5r3749mz9/PnvyySflzxrNmjVj/fr1YwsWLGCPPvooA8A+/vhjj7n9/fzSu3dvBoA9+eSTbP78+ax3796sdu3aHnPqIcXQ//73v4ZjBEFgbdu2ZQUKFGCjRo1iixYtYsOHD2d2u5117dpVHvf6668znufZpUuXVOdv376dAWBr166V95n9O9PGZ4LI7WQl/ph9L3r00UdZbGwsO3/+PGOMsV9++YWFhYWxwYMH+1yfmdj1/fffszp16rBixYrJMeXLL7/0Obcoiuyff/5hly5dYjt27GBNmzZlNpuNHT9+3Ot5y5cvZwDYjz/+6HGsTJkyrEePHvJjQRBYt27d5JjSsmVLdu/ePfl4v379GAD5tfGHRYsWMQBs69at8r5WrVqxoUOHstOnTzMA7MiRI/KxOnXqsKpVq8qPzcRtxtz3APfffz8rXLgwGz9+PFu4cCHbunWrfB9Sp04d1qRJE9U9TN++fVm/fv1Yhw4dVLFmypQpHnMPGDBAfiz9XtatW5e1atWKzZs3j73wwgvMZrOx3r17q8596aWXGADWuXNn9t5777EhQ4awMmXKsGLFiqnmNAKA6r5DDzMx4JNPPmEA2P79+1Xnnjt3ziNumblXYoyxAQMGsPj4eJ/PgQh96A43F6IMoO+99x6Ljo5md+/eZYwx1qtXL9ayZUvGmL6AYvRB+umnn1aN6969OytatKjPtUjXVTJ9+nTGcRz7888/5X1JSUl+CSbh4eFyICtatCh79913fZ6zdu1aQ/GuV69erFSpUh77z5w5ww4ePMicTqdqf/fu3RkA1RukVZKTk5nNZlN9+KhSpYockB544AH24osvyseKFy/OHnnkEfmx3mv8zDPPsAIFCrDU1FR5X/PmzRkAtnDhQtVY6SamePHi7ObNm/J+6cawdu3aquf9+OOPs7CwMI+59YTAqlWrqm5O33nnHQaAHT16lDHmvuktWrQoa9iwoeoaH330EQMQECHw3LlzzGazsWnTpqn2Hz16lNntdnm/KIrsvvvuYz179lSNW7Nmjer35fbt26xQoUJsyJAhqnGXL19msbGxqv0kBBJ5FX/jyM6dOxkAtmLFCtV8mzZt8thv9b3rk08+kfelpaWxUqVKefy96qF3nXbt2rEKFSqo9lWvXt2yYLJ37145BgFgVapUUd34GJGUlMRsNpvuseLFi7O+ffuq9gmCwA4fPsx+//13j/GFCxdmtWvXtrRuLRs3bpS/jGOMsUuXLjEAbPv27ez27dvMZrOxjRs3MsYY+/XXXxkA1Xuq9jVOT09nNWrUYK1atVLtB8B4nme//fabar/0+9auXTuVyNmkSRPGcRwbNmyYvM/lcrEyZcp4/F/5+/nl4MGDDAAbNWqUatzAgQMDJgR++umnjOd5tnPnTtX+hQsXMgBs9+7djDHGTpw4wQCwefPmqcY999xzrGDBgvLrbOXvjIRAIq+RlfsYs+9Fly5dYkWKFGGPPPIIS0tLY3Xr1mXlypVjt27d8rk+s7GrU6dOlgUT6b1X2sqUKaNrXNDy3//+11C8a9iwIWvcuLHH/mPHjrEjR46o3nMZY6xu3bosNjbW0rq1/PbbbwwAe+ONNxhj7i/NoqKi5C9wSpYsyebPn88Yy7wPUn5+Nhu34+PjGQC2adMm1X7pPqRGjRqqL0Yef/xxxnGc6ossxtyxRvt/ZSQEtmnTRvWajR49mtlsNvke6vLly8xut7Nu3bqp5ps8eTIDEBAh0GwMuHXrFgsPD2cvvPCCatysWbNU9+Jm75UYIyGQyIRSg3M5vXv3xr179/D111/j9u3b+Prrr/3q8jds2DDV44ceegjXr19HcnKy1/OUNRru3LmDa9euoWnTpmCMeXQo9Idvv/0W33zzDd566y2UK1cOd+7c8XmOZFPXq/0QERGhsrFLVKhQAfXq1fMoRC49/+joaH+WL59bq1YtuT7GtWvXcOLECblzljIt6+TJk/jnn39UVn3la3z79m1cu3YNDz30EO7evYvff/9dda3w8HBV4xElvXr1QmxsrPxYqj/1xBNPqJ53o0aNkJ6ejosXL/p8boMGDVLVD3zooYcAuIsQA+4ajNevX8eQIUNU1+jfv79cHyurrFu3DqIoonfv3rh27Zq8lSpVCpUqVZI7l3Ech169euGbb75Rdf1cvXo17rvvPvk137x5M27evInHH39cNZ/NZkOjRo10O6ERRF7GShxZu3YtYmNj8cgjj6j+PurXr4+CBQuq/j6svHcVLFhQVaMuLCwMDzzwgPxe4g3ldW7duoVr166hefPm+OOPP3Dr1i3Tr4Me1apVw+bNm7F+/Xq89NJLiIqKMtU1+N69e4a1VfXiEM/zqF27NqpUqeIxPjk5OUsxCACaNm0KnuflOLR79244HA40bNgQBQsWRK1ateQ4pC1PAahf4xs3buDWrVt46KGHcOjQIY9rNW/eHNWqVdNdx+DBg1WlQRo1agTGGAYPHizvs9lsaNCggan/e8D355dNmzYBgEeatrJeYlZZu3YtqlatisTERNXfRatWrQBA/ruoXLky6tSpg9WrV8vnCoKAzz//HJ07d5ZfZyt/ZwSRl7F6H2P2vahUqVKYP38+Nm/ejIceegiHDx/Ghx9+qCoZZOYavmKXVYoUKYLNmzfjq6++wuuvv45ixYqZjimAtXubqlWrolatWh7lmAIRU6pWrYqiRYvKMeXIkSO4c+eOfG/TtGlTOZbs3bsXgiAYxhRfcTshIQHt2rXTXcdTTz0Fh8MhP5ZiytNPP60a16hRI1y4cEG3w7KWoUOHql6zhx56CIIg4M8//wQAbNmyBS6XK+gxxUwMiImJQYcOHbBmzRpVOY3Vq1ejcePGKFeuHADz90oEoYSaheRyihcvjjZt2mDlypW4e/cuBEFQ1Zgxi/RGISGJNDdu3PAaNM+fP4/XXnsNGzZs8Khbk9UbMAByLZ0OHTqga9euqFGjBgoWLOi15boUXLS1IABYLjArPffbt2+jUKFCFlauplmzZpg3bx6uXbuGPXv2wGazyYXmmzZtigULFiAtLU33Buy3337DxIkT8X//938ewqz2Nb7vvvsMbz61/8eSKFi2bFnd/WbqEHn7vQEgB837779fNc5ut/tVS0WPU6dOgTGmqn+iRPkBoU+fPpg7dy42bNiAfv36ISUlBd988w2eeeYZOeifOnUKAOQbOC1mPkQSRF7CShw5deoUbt26hRIlSugev3r1qvyzlfeuMmXKeNysFC5cGL/88ovP9e/evRuTJk3C3r17PWrD3bp1S/UFiFViYmLQpk0bAEDXrl2xcuVKdO3aFYcOHULt2rUNz4uMjDTsJu9PHLp9+7a1hWsoVKgQqlevrhL76tatK69DedO2e/duWYiV+PrrrzF16lQcPnxYFVv16v0quw9rsRKHzNbC8/X55c8//wTP8x7r0salrHDq1CkcP34cxYsX1z2u/Lvo06cPXn75ZVy8eBH33Xcftm3bhqtXr6JPnz6q+cz+nRFEXsbqfYyV96K+ffti+fLl2LhxI4YOHYrWrVubWpOV2GWVsLAwOaY8+uijaN26NR588EGUKFECjz76qOF5gb63MftFixEcx6Fp06bYsWMHRFHE7t27UaJECfl9tWnTpnjvvfcA6H+5ZCVuByqmiKKIW7duoWjRol6fm7/3NkWKFAmYycFKDOjTpw/Wr1+PvXv3omnTpjhz5gwOHjyIuXPnquYze69EEBIkBOYB+vXrhyFDhuDy5cvo0KGDX4KVzWbT3a/8dkGLIAh45JFH8O+//2LcuHFITExEVFQULl68iIEDB6qKxAaCihUrom7dulixYoVXIbB06dIAgEuXLnkcu3TpEuLi4kxfUyrMfvToUdnt5g+SELh7927s2bMHNWvWRMGCBQG4g2VaWhoOHDiAXbt2wW63yyLhzZs30bx5c8TExOD1119HxYoVERERgUOHDmHcuHEer7G3DwJG/8f+/N8H4txAIYoiOI7Dt99+q7se6XUGgMaNG6N8+fJYs2YN+vXrh6+++gr37t1T3YBJr+mnn36KUqVKecyndY0SRChgNo6IoogSJUpgxYoVusclIcTqe5e/7yVnzpxB69atkZiYiDlz5qBs2bIICwvDN998g7fffjvgcahHjx548sknsWrVKq9CYOnSpSEIAq5evar6IJ+eno7r169bjkOHDx9Genp6ljq4N2vWDAsXLsTNmzexe/du2bkBuOPQhx9+CKfTiV27dqF+/fpyl96dO3eiS5cuePjhh7FgwQKULl0aDocDy5Ytw8qVKz2uE6g4ZDaO5JY4VLNmTcyZM0f3uPKmtE+fPpgwYQLWrl2LUaNGYc2aNYiNjUX79u1V85n5OyOIUMBs/LH6XnT9+nX89NNPANzNNkRRBM97T3azGruyStOmTVG6dGmsWLHCqxCovLfRilyXLl1SfXHji8TERPz888+4cOGCx1xWaNasGb766iscPXpUN6a8+OKLuHjxInbt2oW4uDi5qYXVuJ1f723MxoDOnTujQIECWLNmDZo2bYo1a9aA53n06tVLNZ/ZeyWCkKA73jxA9+7d8cwzz2Dfvn2qdJNgc/ToUZw8eRIff/wxnnrqKXm/svuXhD9dgvW4d++e7rdhSmrUqAG73Y6ffvoJvXv3lvenp6fj8OHDqn2+6Ny5M6ZPn47ly5dnWQgEgF27dmHv3r1y1ywAiIuLQ3x8PHbv3i27NAoUKADA3Z33+vXrWLduHR5++GH5nLNnz/q9luwkPj4eAHD69GlVp0SXy4Vz586hVq1aWb5GxYoVwRhDQkICKleu7HN879698c477yA5ORmrV69G+fLlZeFVmg8ASpQoIX9rSxChjtk4UrFiRfzwww948MEHvX44z673rq+++gppaWnYsGGD6lt8vTSXQMShtLQ02VXgjTp16gBwl0fo2LGjvP+nn36CKIrycTN07twZe/fuxRdffIHHH3/cn2UDcMeh999/Hz/88AN+/vlnvPjii/Kxpk2b4t69e9i4cSP++OMP9OzZUz72xRdfICIiAt99950qLW3ZsmV+ryU7iY+PhyiKOHv2rMoNodfh3l8qVqyII0eOoHXr1j5/zxISEvDAAw9g9erVGD58ONatW4du3bqpXluzf2cEEQqYjT9W34uSkpJw+/ZtTJ8+HRMmTMDcuXMxZswYr2uxErsCdW+TmppqKaYoRb+///4bf/31F4YOHWr6ep07d8Znn32G5cuXY8KECX6tGVDf2+zevRujRo2Sj9WvXx/h4eHYtm0bfvzxR1UctBK3cyPKexulW/H69esB6+puJQZERUXh0Ucfxdq1azFnzhysXr0aDz30kOoLR6v3SgQBAFQjMA9QsGBBvP/++5g8eTI6d+6cbdeVvlFQfkPCGMM777zjMTYqKgqA+5s2X7hcLt030v379+Po0aMerdB///13nD9/Xn4cGxuLNm3aYPny5ap0qk8//RQpKSmqb0h80aRJE7Rv3x4ffPAB1q9f73E8PT0dY8eO9TlPXFwcEhISsGXLFvz000+qb80A903Y+vXrceLECZV1Xu81Tk9Px4IFC0w/h5ykQYMGKFq0KJYsWaKqy7FixYqABcsePXrAZrNhypQpHt/WMcZw/fp11b4+ffogLS0NH3/8MTZt2uQhDLdr1w4xMTF488034XQ6Pa73zz//BGTdBJGbMBtHevfuDUEQ8MYbb3gcc7lc8nt8dr136V3n1q1bujeGUVFRpmIQ4I5Ven//H3zwAQCo4pBUN+ratWvyvlatWqFIkSJ4//33Vee///77KFCgADp16mRqHYC7Bl7p0qXxwgsv4OTJkx7Hr169iqlTp/qcR4otc+bMgdPpVMWh8uXLo3Tp0pg1a5ZqLOB+jTmOgyAI8r5z587pxsTciFRbSvu7N2/evIBdo3fv3rh48SKWLFnicezevXse9Y379OmDffv24cMPP8S1a9dUrnRpPjN/ZwQRCpiNP1beiz7//HOsXr0aM2bMwPjx49G3b19MnDhR9z1Uew3AXOyKiooynSp8584djxRYwC1u3rhxQxVTnE4nfv/9d1VmU/Xq1ZGYmIjFixernv/7778PjuMslYV67LHHULNmTUybNg179+71OH779m288sorPudp0KABIiIisGLFCly8eFEVU8LDw1GvXj3Mnz8fd+7c8XlvYxS3cyOtW7eG3W73iO9SKnQgsBoD+vTpg7///hsffPABjhw54hFTrN4rEQRAjsA8w4ABA7L9momJiahYsSLGjh2LixcvIiYmRg5oWurXrw8AGDFiBNq1awebzYa+ffvqzpuSkoKyZcuiT58+qF69OqKionD06FEsW7YMsbGxePXVV1Xjq1atiubNm2Pbtm3yvmnTpqFp06Zo3rw5hg4dir/++gtvvfUW2rZtq0q/McMnn3yCtm3bokePHujcuTNat26NqKgonDp1CqtWrcKlS5cwe/Zsn/M0a9YMn376KQCoHIGAWwj87LPP5HHK/YULF8aAAQMwYsQIcByHTz/9NFvt6VkhLCwMkydPxvPPP49WrVqhd+/eOHfuHD766CNUrFjR9Lepp0+f1r3RrVu3Ljp16oSpU6diwoQJOHfuHLp164bo6GicPXsWX375JYYOHaoSa+vVq4f7778fr7zyCtLS0jyCZUxMDN5//308+eSTqFevHvr27YvixYvj/Pnz2LhxIx588MGABnuCyC2YiSPNmzfHM888g+nTp+Pw4cNo27YtHA4HTp06hbVr1+Kdd97BY489lm3vXW3btkVYWBg6d+6MZ555BikpKViyZAlKlCjhUR6ifv36eP/99zF16lTcf//9KFGihGEt0G3btmHEiBF47LHHUKlSJaSnp2Pnzp1Yt24dGjRooGpssn//frRs2RKTJk3C5MmTAbhTmd544w0kJSWhV69eaNeuHXbu3Inly5dj2rRpKFKkiOnnWLhwYXz55Zfo2LEj6tSpgyeeeEKOqYcOHcJnn32GJk2a+JynXLlyKFu2LPbu3Yvy5ct7pCc3bdoUX3zxBTiOU8WoTp06Yc6cOWjfvj369euHq1evYv78+bj//vtN1XDMaerXr4+ePXti7ty5uH79Oho3bozt27fLgoDZOLRlyxakpqZ67O/WrRuefPJJrFmzBsOGDcPWrVvx4IMPQhAE/P7771izZg2+++471Y1+7969MXbsWIwdOxZFihTxcJ+b/TsjiFDBTPwx+1509epVPPvss2jZsqVcSui9997D1q1bMXDgQOzatcswRdhK7Kpfvz5Wr16NMWPGyI2XjITMU6dOoU2bNujTpw8SExPB8zx++uknLF++HOXLl8fIkSPlsRcvXkTVqlUxYMAAfPTRR/L+//73v+jSpQvatm2Lvn374tdff8V7772H//znP6hatarP10/C4XBg3bp1aNOmDR5++GH07t0bDz74IBwOB3777TesXLkShQsXxrRp07zOExYWhoYNG2Lnzp0IDw+X45LytXzrrbcAqO9trMTt3EjJkiUxcuRIvPXWW+jSpQvat2+PI0eO4Ntvv0WxYsVMx5SffvpJ996mRYsWlmNAx44dER0djbFjx8Jms6lc/YDbEWjlXokgAADZ0ZqYsIbU3vzAgQNex8XHx7NOnTqp9gFgkyZNkh9PmjSJAWD//POP7jXOnj3r9RrHjh1jbdq0YQULFmTFihVjQ4YMYUeOHGEA2LJly+RxLpeLPf/886x48eKM4zjm7VcrLS2NjRw5ktWqVYvFxMQwh8PB4uPj2eDBg3XXA4A1b97cY//OnTtZ06ZNWUREBCtevDhLSkpiycnJXp+PEXfv3mWzZ89mDRs2ZAULFmRhYWGsUqVK7Pnnn2enT582NceiRYsYAHbfffd5HDt06BADwACwK1euqI7t3r2bNW7cmEVGRrK4uDj20ksvse+++44BYFu3bpXHNW/enFWvXt1j7rNnzzIA7L///a9q/9atWxkAtnbtWtV+vd+v5s2bq15jo3Olayn/7xlj7N1332Xx8fEsPDycPfDAA2z37t2sfv36rH379rqvlZL4+Hj5tdFugwcPlsd98cUXrFmzZiwqKopFRUWxxMRElpSUxE6cOOEx5yuvvMIAsPvvv9/wulu3bmXt2rVjsbGxLCIiglWsWJENHDiQ/fTTT/IY6e+HIPIaWYkjjDG2ePFiVr9+fRYZGcmio6NZzZo12UsvvcT+/vtveUxW37sGDBjA4uPjfT6XDRs2sFq1arGIiAhWvnx5NnPmTPbhhx96xLDLly+zTp06sejoaMO4IXH69Gn21FNPsQoVKrDIyEgWERHBqlevziZNmsRSUlJUY6X3Q2VsVb5OVapUYWFhYaxixYrs7bffZqIo+nxOevz9999s9OjRrHLlyiwiIoIVKFCA1a9fn02bNo3dunXL1ByPP/44A8D69evncWzOnDkMAKtatarHsaVLl7JKlSqx8PBwlpiYyJYtW6b7/geAJSUleZxv9Ptm9BlkwIABLCoqymNufz+/3LlzhyUlJbEiRYqwggULsm7durETJ04wAGzGjBke61UixTWj7dNPP2WMMZaens5mzpzJqlevzsLDw1nhwoVZ/fr12ZQpU3T/fx588EEGgP3nP/8xvLaZvzNtfCaI3E5W4o+Z96IePXqw6Ohodu7cOdW5//vf/xgANnPmTK/XNRu7UlJSWL9+/VihQoUYAK/x6p9//mFDhw5liYmJLCoqSr6PGDVqlMd7mPSeM2DAAI95vvzyS1anTh0WHh7OypQpwyZOnMjS09O9Ph8jbty4wV577TVWs2ZNVqBAARYREcFq1KjBJkyYwC5dumRqjgkTJjAArGnTph7H1q1bxwCw6Oho5nK5VMfMxm2jzyBW7mEY048X8fHxqtfY6FzpWsr/e5fLxV599VVWqlQpFhkZyVq1asWOHz/OihYtyoYNG2b4ekl4iylvvPGGPM5MDJDo378/A8DatGljeF0z90pmP3sRoQ/HWB6xHhEEkWcQRRHFixdHjx49dFOpCIIgCCKYHD58GHXr1sXy5cvRv3//nF4OQRAEkYe5efMmChcujKlTp5pKrSaI3A7VCCQIIkukpqZ6pFR88skn+Pfff9GiRYucWRRBEASRb7h3757Hvrlz54LneVVDAIIgCILwhVFMAUD3NkTIQDUCCYLIEvv27cPo0aPRq1cvFC1aFIcOHcLSpUtRo0YNS41bCIIgCMIfZs2ahYMHD6Jly5aw2+349ttv8e2332Lo0KEoW7ZsTi+PIAiCyEOsXr0aH330ETp27IiCBQti165d+Oyzz9C2bVuPOvAEkVchIZAgiCxRvnx5lC1bFu+++y7+/fdfFClSBE899RRmzJiBsLCwnF4eQRAEEeI0bdoUmzdvxhtvvIGUlBSUK1cOkydPpvQtgiAIwjK1atWC3W7HrFmzkJycLDcQ0Wv+QRB5FaoRSBAEQRAEQRAEQRAEQRD5AKoRSBAEQRAEQRAEQRAEQRD5ABICCYIgCIIgCIIgCIIgCCIfQDUC/UQURfz999+Ijo4Gx3E5vRyCIPIYjDHcvn0bcXFx4Hn/vpMRRRG1atXCqVOnLJ/74osvUq2TXA7FGYIgskogYg0ApKamIj093dI5YWFhiIiI8PuaRPZAsYYgiKyQk3EGoFjjL1Qj0E/++usv6kRHEESWuXDhAsqUKePXuYIgwG634/vtpVG8uM30eSuXp+C7r8vB6XQCAJKSkpCUlOTXGojgQXGGIIhAkZVYk5qaivIJBXHlsmDpvJiYGJQuXRo8z1OcycVQrCEIIhDkRJwBKNb4CzkC/SQ6OhqA+xc+JiYmh1dDEEReIzk5GWXLlpXfS7JCuXg7SpU2/3ZerBiPhIQErF+/PsvXJoIHxRmCILJKIGJNeno6rlwW8NuZsoiOMef2uJ0sonrFC/T+lQegWEMQRFbIqTgDUKzJCiQE+olknY+JiaFfOoIg/CYQaTicyw7OZf7tnBNtOHv2LKpVqwaAHIG5FYozBEEEikDEmpgCDsQUMOc+51xuV0fDhg1hs9kozuRiKNYQBBEIsjvOABRrsgIJgQRBEPkQcgQSBEEQwebAgQMkLhEEQRBBhWKNdahrMEEQBEEQBEEQBEEQBEHkA0gIJAiCyOPwLh680/zGCZycGlytWjXMnz8/p58CQRAEkcuxEmd4p/sWo2HDhhRnCIIgCFNYjTMUa/yHUoMJgiDyIZQaTBAEQQQbStciCIIggg3FGuuQI5AgCCKPwwnWNoggRyBBEARBEARBEEQ+hByBBEEQ+RByBBIEQRBWkL9MMjkWoE6OBEEQhHmsxBlpPECxxh9ICCQIgiAIgiAIIuBQuhZBEAQRbCjWWIdSgwmCIPI4nIuBd1rYBEapwQRBEARBEARBEPkQcgQSBEHkQyg1mCAIgrACn/Glk9mxAKVrEQRBEOaxEmek8QDFGn8gIZAgCIIgCIIgiIBD6VoEQRBEsKFYYx1KDSYIgsjjcC5maaOuwQRBEIRVuHRmaQPcLg2KMwRBEIQZrMYZijX+Q45AgiCIfAilBhMEQRDBhlwaBEEQRLChWGMdcgQSBEHkcTgXwDktbAI5AgmCIAiCIAiCIPIj5AgkCILIh5AjkCAIgrACJ7g3s2MBKuBOEARBmMdKnJHGAxRr/IGEQIIgCIIgCIIgAg6laxEEQRDBhmKNdSg1mCAIIo/DO5mljRMYpQYTBEEQBEEQBEHkQ0gIJAiCyIckJCTg2LFjOHbsmE8L/Y4dO9C5c2fExcWB4ziPlGLGGF577TWULl0akZGRaNOmDU6dOqUa8++//6J///6IiYlBoUKFMHjwYKSkpHi9bmpqKpKSklC0aFEULFgQPXv2xJUrV/x6vgRBEETW4F0Wvnhyuc+hTo4EQRCEWSzFGYo1WYKEQIIgiDyOVE/D7AZmrVnInTt3ULt2bcNxs2bNwrvvvouFCxfixx9/RFRUFNq1a4fU1FR5TP/+/fHbb79h8+bN+Prrr7Fjxw4MHTrU63VHjx6Nr776CmvXrsX27dvx999/o0ePHpZfH4IgCCJnOHDggKkvnAiCIAjCXyjWWIeEQIIgiHyIFUdghw4dMHXqVHTv3t3jGGMMc+fOxcSJE9G1a1fUqlULn3zyCf7++2/ZOXj8+HFs2rQJH3zwARo1aoRmzZph3rx5WLVqFf7++2/da966dQtLly7FnDlz0KpVK9SvXx/Lli3Dnj17sG/fviw/f4IgCCJ3Qe5zgiAIIphQnMkk1wiBM2bMAMdxGDVqlLxv8eLFaNGiBWJiYsBxHG7evOlznsmTJ4PjONWWmJioGpMb/yMIgiCyE1EUkZycrNrS0tIsz3P27FlcvnwZbdq0kffFxsaiUaNG2Lt3LwBg7969KFSoEBo0aCCPadOmDXiex48//qg778GDB+F0OlXzJiYmoly5cvK8BEEQRPZh2X0Oa+la5D4nCILI31iNM1ZjDcWZTHJF1+ADBw5g0aJFqFWrlmr/3bt30b59e7Rv3x4TJkwwPV/16tXxww8/yI/tdvXTHD16NDZu3Ii1a9ciNjYWw4cPR48ePbB79+6sPRGCIIgcgHNx4Jyc+fECh5MnTyI2Nla1f9KkSZg8ebKla1++fBkAULJkSdX+kiVLyscuX76MEiVKqI7b7XYUKVJEHqM3b1hYGAoVKmQ4L0EQBJG72bJli9zJMTk5GeHh4QgPD9cd26FDB3To0EH3mNZ9DgCffPIJSpYsifXr16Nv376y+/zAgQPyF0/z5s1Dx44dMXv2bMTFxXnMK7nPV65ciVatWgEAli1bhqpVq2Lfvn1o3Lhxll8DgiAIIriYjTUUZzLJcUdgSkoK+vfvjyVLlqBw4cKqY6NGjcL48eMtvzh2ux2lSpWSt2LFisnHKN2MIAgCqFy5Mm7duqXarHzhQhAEQeQvOCfAOTmTm/ucsmXLIjY2Vt6mT5/u17XJfU4QBBH6WIszgY01+S3O5LgjMCkpCZ06dUKbNm0wderUgMx56tQpxMXFISIiAk2aNMH06dNRrlw5AL7/I4xEx7S0NFXaXHJyckDWShAEkRPwPC9/c5YVSpUqBQC4cuUKSpcuLe+/cuUK6tSpI4+5evWq6jyXy4V///1XPl9v3vT0dNy8eVPlCrxy5YrhOXkdijMEQYQaFy5cUMUaIzegL8h9Hjgo1hAEEWoEItbktziTo47AVatW4dChQ35/O6hHo0aN8NFHH2HTpk14//33cfbsWTz00EO4ffs2AP//I6ZPn65SmcuWLRuwNRMEQWQJl8VNtNY12BsJCQkoVaoUtmzZIu9LTk7Gjz/+iCZNmgAAmjRpgps3b+LgwYPymP/7v/+DKIpo1KiR7rz169eHw+FQzXvixAmcP39enjfUoDhDEESoERMTo9r8FQKJwEGxhiCIUINijXVyTAi8cOECRo4ciRUrViAiIiJg83bo0AG9evVCrVq10K5dO3zzzTe4efMm1qxZk6V5J0yYoEqhu3DhQoBWTBAEkf1Y6RqckpKCw4cP4/DhwwDcIuLhw4dx/vx5ucnT1KlTsWHDBhw9ehRPPfUU4uLi0K1bNwBA1apV0b59ewwZMgT79+/H7t27MXz4cPTt21eupXHx4kUkJiZi//79ANxW/MGDB2PMmDHYunUrDh48iEGDBqFJkyYhW7OJ4gxBELkap8UN1pqFeEPpPleidIln1X1uNG+oQbGGIIhci9U4E8BYk9/iTI6lBh88eBBXr15FvXr15H2CIGDHjh147733kJaWBpvNluXrFCpUCJUrV8bp06cB+J9u5q24MZH/6Gr7FAAgcAwA8LXrqZxcDpHP4QQOnMtKs5BMRyDgLtHgTQz86aef0LJlS/nxmDFjAAADBgzARx99hJdeegl37tzB0KFDcfPmTTRr1gybNm1SfcmzYsUKDB8+HK1btwbP8+jZsyfeffdd+bjT6cSJEydw9+5ded/bb78tj01LS0O7du2wYMEC088zr0FxhlBypMtY1ePaG2bn0EoIwn8OHDgQkDIUSve5VHZCcp8/++yzANTu8/r16wOw5j7v2bMngNB3n1OsISTEPYmAw30vAycHvunvObsggvCTQMSa/BZnckwIbN26NY4eParaN2jQICQmJmLcuHEBEQEBt5PlzJkzePLJJwHk3v8IIri8wK9WPRZY5s9zWR9Lc0kiIADYmFt8edT+iWqMEyKcnAgBDOkQIUDEfuczFldNEMEjISEB69evNzW2RYsWYIwZHuc4Dq+//jpef/11wzFFihTBypUrDY+XL1/e4xoRERGYP39+lp0kBJEdSMKdIHh+fqm3caZfc/nap4XEQiIvk5KSIn9xD2S6z4sUKYJy5crJ7vNKlSohISEBr776qqH7fOHChXA6nbru89atW+OTTz7BAw88oHKfFylSBDExMXj++edD2n1O5F2SX+8C3i4CADhbxr92Qd4XMXKz6bnEndUAZbhyMLcw6AsHA9/whOnrEERuguJMJjkmBEZHR6NGjRqqfVFRUShatKi8//Lly7h8+bL8n3X06FFER0ejXLlyKFKkCAC3oNi9e3cMHz4cADB27Fh07twZ8fHx+PvvvzFp0iTYbDY8/vjjAJBr/yOI7MXGZYqBSdxqCADuZQh3TjDZ6eeE+9//CU+qBECP+Rgnn6N7HDyaOJYAAMI0GfnbnYOz8EwIgiCI3ITNJniIgYc6jfM6XqL2htmmBD8jjM4lgZAIBJzAgRPMuc+lcQ0bNoTNZvPpPAfIfU4QZhBdvCz8ydhE2KLSIKxsDK5gGrhwF1iBzNjCOTP/brlWv4H9X3XAoTO5gwFO33/j4oEqYLaM+56MeWw1T1p9KgThgZU4I40HzMcaijOZcMybzSObadGiBerUqYO5c+cCACZPnowpU6Z4jFu2bBkGDhwIwO0iGThwICZPngwA6Nu3L3bs2IHr16+jePHiaNasGaZNm4aKFSvK56empuKFF17AZ599pvqPsJKjnZycjNjYWNy6dSsgKQ9E8FC6AR2QSwkAcIuBTkBXCJREQCsoxUClK9AbJATmTwLxHiIIAux2O/76X0WULmb+e5331t7A0v8rBafT/ddg5gaNyH4ozuQttCKcnjNQD6UQGExIDMyfBOJ9RJrjxv9VRExBc7/XySkCCrc6Q+9feQCKNXmH5Ne7AIDKFcjZBfDhLjiK3AEX7gIineAiXGCOzPsPTkfcYw4GZLgK4dDcqyjHC7zuOK0QCJAYmF/JqTgDUKzJCjnmCNRj27ZtqseTJ0+WBT4jzp07p3q8atUqn9ehdLP8h0Pzs1NvDOMAKe4xABxUYqBS5JNSgjPn5OAE03UG2jIm1RME0yGiiWMJ9jqHWHg2BJF1rKQGEwQRPATBli1i4JEuY0kMJAiCCAG0rkDenhFDIp2ZKcMmnH2yyOfkPMVAszgh32gJRyuTGEgQeYQc6xpMENmFnvPdKlpxT+CYxz4HODjAqURCB8v8E7NBHZDT4Q7UAkQ84FgUgFUSBEEQeREz7kGeF8Hzos9xBBE0XJxbMDCzuTLTtQLRNZggCJM4zMUJr0KhUhS0mZhP0cFVOFTZ1PUJQhcrcYZiTZbIVY5AgsiNaAU/J0Q4MjR0gWMe7kAJB3g4oR88lSIgAKRzIqqHvYff0ocHatlEfkLg3N/qmoVxlroGEwThnazU9bMKz4sQRf++xz3UaZzlxiUEkRUC1TWYIAhPmI/PflJ6sFfRz6wr0IJjUDxQhRqKENkKxRrrkCOQIDTYMpx9ekjCnpHAp4fSFahHOpc5V5Wwd72MJIjAkZCQgGPHjuHYsWMkAhJEgDFbH9DKOf6Kf9r5vTUuIQivCLy1DeTSIIhgwindelJ6sJMHczBVjUDTmEknhqI+IEEEGqtxhmKN35AjkCBMohX/lM5AqZagJCDq1QqUagRq3YDu8wW4IKpEQYIwC+fizNWCkRDIEUgQuRFf9QL9FQOVIqPosuGndi+jwXdv+jUXQViBXBoEERikRiFaOLsAaIQ5zsn5JwQCPsVAXyKg1MWVXIFEdkKxxjokBBIEAJcfHYKBDHGQU9yYWZwmnRNJBCRyBGoWQhD5A0kEFF0KMVC08MUBQRAEkWtRNg0xgjmYtS+MtZgQFSUBUCYr1yMIIuhQajCR71F6LxwG9f5Mz5XRRETrBpRQugElEVCJlZRjgiAIIvTwJ63YaB4SAYlAwjk5SxtA6VoEEQy04h9nD37necBkSrDUxIEg/MBqnKFY4z/kCCRChvH8agDADLFPtl5XmSJsBkEj9pEbkMgyihoZphApNZgg/EFqClJ7w+wcXol3tKnAgFoAfGDztGxfE5E/oXQtgrDGrcndAACxk9ebOyGjTiBnMT1YdUxqGOInshuQBEAih6BYYx0SAok8jyQAGj32hjPjXzs4j/RgG+PgNHD2ec6jFgN9Ofv0hD8zbsAy4XN09/+VNsbnuQShhFKDCcIays7Awe4SLAl53uoFejsvq/zU7mWPfVRTkCAIIrhIIqD2Z473vEdQNgrx5QjUSwv2EAqNxMCMMYFuECIeqOIxr63eyYBegyAIY0gIJPI0VkQ/CafvITIOcACDKtXXqRHxpK7AVtN6tWnBAJDKuQzHG4mA0jGlEBmm6VR8In2EpbUReQvm4sGcFhyB1CyEIEwTbNHPG2YFQSMBUJkSbIb9j7wCnte/2fup3cvgNTeb2nXldqckkUWcvHszNdb9e9SwYUPYbDaKMwThA6XwZxZ/BMBAo6oN6GCZrkAvmSrigSq6wqJwqDLgcP/MZAFSPcZR6VSW1kvkcqzEGYBiTRYgIZDItwhevtiygZM7ARueD+YeF+C03mIRswAAEcz6n6dWAJSoEvau7n53nUL3+slVmL8gRyBBBIdAufL05tQKb9r9Zq+9/5FXPPbxPANv8NnblwDoPl/E0W7uOGLTKV4vuNyT11xv/KUWEXpQuhZB+MaXCMhEXtcVCCjenx3q92Wl208lCDp5wCHK+726Ak00CWE25tkoBABsIsQ9ibpzeYiADs91KwVA0ZE5f9q5yrrr4J2Zc5JYmP+gWGMdahZC5Fmyww1o0zQP0Yp+ApjpzXN+7zds3tyBnnNl/inbwXvdjCgWMQsxkTNMX5MgCILIXqQGIEoBUCnKWU0lBiQBUP9mj7cLKhFQez33+SJ4xQ2qngioRBILCYIgiMCgrQ+oRSX2OTSNRgLgGmQ2linu6YmHRiKgA15FQNHBqURAszhPVbJ8DkHkN0gIJAgN2s7BDngGIEnYS4eou+kh7beBVzn37JrHeqRyLkNhUBIBw5h3oY8IYQTO2sYgpwZThy2CCC2UQp1SxJMEP+2mh1YA1M7rnk8tAAK+RUAib8NEHkwwuYnuzyPUyZEgfCO6gv/53VvjEBU6Kb264p3DY5haDFRuRvN4WZ8VAVDpBgQALnuaKBNBwFKcoViTJSg1mCAy0GsYosXBeN1UYAEibF5EOD1xMIzxAKdfK1BCK/5Jj43Shl0QsyQGJt8b7/e5RN6CUoMJwjc5WR8wK9hsgt8pyloBUJqPIPyB0rUIIsgYuAH1ugYzBzPXOMQbTmSKeA7FPukaBqnCRiKgxxozQhfvZH65AYn8CcUa65B9iCBMYGOcKv1WD0Eh9mmFvzDwCFOcbyQa+lMXUIvLS9MSo2NW0pCJ/IcgCHj11VeRkJCAyMhIVKxYEW+88QYYy/zwlpKSguHDh6NMmTKIjIxEtWrVsHDhQq/zOp1OvP7666hYsSIiIiJQu3ZtbNq0KdhPhyDyFXrCXiDGEvkQF29tA7k0CCLbcJr78se04BcsTIiAElqnnxnIDZjHsRpnKNb4DTkCiXyLlXqBZlE6A9MhIgw8bODkVOIwxbFAk86JXlOMtSJgOuf9MZGHcNncm1kE3lLX4JkzZ+L999/Hxx9/jOrVq+Onn37CoEGDEBsbixEj3B2px4wZg//7v//D8uXLUb58eXz//fd47rnnEBcXhy5duujOO3HiRCxfvhxLlixBYmIivvvuO3Tv3h179uxB3bp1zT8fgshnmHHoWXUFGomARo1B9BBcvNdGIQA1C8lvkEuDIMwhunjwJsorKMd4jHdpmn3kEHodgT3GmBQkJTGQ3IGENyjWWIccgUS+Rnl7o00LtunUBjQ3p2cAtoGTNyMkx2EEs8ub9rFyv4RT6UTMEPO0op83lyCRP4mPj8e+ffuwb98+PPnkk0hOTkZaWpru2D179qBr167o1KkTypcvj8ceewxt27bF/v37VWMGDBiAFi1aoHz58hg6dChq166tGqPl008/xcsvv4yOHTuiQoUKePbZZ9GxY0e89dZbAX++BEEYY0UEtIqQDbWviLwPOc+J/MqNiT3kn7OjVqAell2C/ropdOoKKuEEY0cf72Typt1PEGagOKOGPp0RIYWdy9ysoBUBtQ1DfOGtPqB7Ph4OHw1BwhjvkX5sNlXYCVEWBJVioLSZhboG502YiwNz8uY3gcPJkycRGxur2qZPn647f9OmTbFlyxacPHkSAHDkyBHs2rULHTp0UI3ZsGEDLl68CMYYtm7dipMnT6Jt27aG605LS0NERIRqX2RkJHbt2hWAV4UgCDMEKx1YcPEeIqAo8nm27iIBd+qhlQ3m07Uk5/l7772H48ePY+bMmZg1axbmzZsnjxkzZgw2bdqE5cuX4/jx4xg1ahSGDx+ODRs2GM47ceJELFq0CPPmzcOxY8cwbNgwdO/eHT///HNgXhOCCDA5JQbKmHUUOhWbGbykBGvxld5rJApK53FODsLRyiYXRuQqrMYZC7GG4owaSg0mQgI94U/a59LUpg1ESrAy3dfzmH4A12sy4l6T+w1MEuwc4FUuP/VY42OAWxD0VcvQFzGRM6hpSD6gcuXKHm698PBw3bHjx49HcnIyEhMTYbPZIAgCpk2bhv79+8tj5s2bh6FDh6JMmTKw2+3geR5LlizBww8/bLiGdu3aYc6cOXj44YdRsWJFbNmyBevWrYMgUIEXIu+SlUYdZue3iiT2iRZKCPjrBjTjADzUaRzqbZzp1/xE3sJsupbSeQ4A5cuXx2effWboPAeAoUOHYtGiRdi/f79hCYpPP/0Ur7zyCjp27AgAePbZZ/HDDz/grbfewvLly7P47Agil6FIFTbr8lONy4a0YrPrkkQ9bd1AgtDDTKyhOKOGHIFEnsYf958ZnJy5IKVtAKInAgpgsggogKnqA0o1/RywwQ5e8dhznqwKfL5Q1hckZ2Dow/M8YmJiVJuRELhmzRqsWLECK1euxKFDh/Dxxx9j9uzZ+Pjjj+Ux8+bNw759+7BhwwYcPHgQb731FpKSkvDDDz8YruGdd95BpUqVkJiYiLCwMAwfPhyDBg0Cz1NoIohgYNb9F4wuwaLo/ruWRNJDncYF/BpE7iM5OVm1GZWgIOc5QfiBTQRnFwBf7+3+CHyBbCriIyXYG1abfyi7IguHyBWYXzATayjOqCFHIJEnmWhbbVoAtHNqV6DuGHByerAkAho5/rSEgTds/iHtD/Mi4oUxHumcKDsDwZDxONP9Z1UE9NU4xBfkCMxjCLx7MwvjLDULefHFFzF+/Hj07dsXAFCzZk38+eefmD59OgYMGIB79+7h5Zdfxpdffil/y1arVi0cPnwYs2fPRps2bXTnLV68ONavX4/U1FRcv34dcXFxGD9+PCpUqGD+uRBEPiIQAl1OdAbWioCANXcikTtgAgcmmPvwJY0rW7asav+kSZMwefJkj/HkPCeITMw2DlHhyPiddmjOk0RA6V8rnxcDgYWUYDPoNQ3RrRMYjK6QRNCxEmek8YC5WENxRg0JgQShg54I6IRomN6rJ/QpxUE9oVByDwoQdcVAAACnrv8XSDKvyQPMjlTOFfBrELmXhIQErF+/3tTYu3fverj0bDYbRNH9e+l0OuF0Or2O8UZERATuu+8+OJ1OfPHFF+jdu7e5J0EQRJ6BRMD8yYULF1TpWmac59WrV8fhw4cxatQoxMXFYcCAAQDUzvP4+Hjs2LEDSUlJiIuLM/zC6Z133sGQIUOQmJgIjuNQsWJFDBo0CB9++GHgnyxBZCOcXQBvF8DZBXAZXXq5QKX2OrS19zhTnYCDga/UYEkEtOocJEILM7GG4owaEgKJPMlUoQ8m2lYHbD6tGxAAnGAQJHegyVRhf7CBl8VAAJAaC5tp8qEU76TGIr7qBNozjrkUAiSRt2ECD2ahuDQTeEuOwM6dO2PatGkoV64cqlevjp9//hlz5szB008/DQCIiYlB8+bN8eKLLyIyMhLx8fHYvn07PvnkE8yZM0ee56mnnsJ9990nNyX58ccfcfHiRdSpUwcXL17E5MmTIYoiXnrpJX9eBoIgciGSGxAgATDP4+IBs/+HGakYrVu3hs1mI+c5QQQQWfCzMVkEhBkHoTc3oA8RkctwXmVFEJTSdk3XL9S83ei5AfXml9yAnAVnGZFLsBJnAEuxhuKMGhICiTxLVsRAQc9BrkgJdvpICzabNuwNo5TiMMbLYiBYZpMRX469VM7ls8uwXSEQ2sFb6ihMhBZWHIHz5s3Dq6++iueeew5Xr15FXFwcnnnmGbz22mvymFWrVmHChAno378//v33X8THx2PatGkYNmyYPOb8+fMq12BqaiomTpyIP/74AwULFkTHjh3x6aefolChQoF6mgSRJWpvmG25y20wG4VYIVjr4Pmsxw1R5PDA5mkBWA2R2zHbLISc50R+pfDUdbgxsYfl81Spww61Hc5DaDMQAP1J1bUkCBrUBjQjCFoVAT2uQSJgvsJMrKE4o4aEQCLf4wLzWhfQKbsFRcMxWUEwEAOdGR53B3jc5tI9xvgS/bR1Au06LkG7l/qGBCERHR2NuXPnYu7cuYZjSpUqhWXLlnmdZ9u2barHzZs3x7FjxwKwQoIIHv6IgTlNbhEj9RBFujkjPCHnOZGfsSoGcnZBv1GItj4g4CECWnXlGa7BiiCordfnyPpalHUBKS2YMAPFGTWmhMAePax/S7Fw4UKUKFHC8nkEYQUzrkBfjUIkAu0E1Dr+tIKfp7SnxgGb7NiLyKjhpxT3zDgATa2T8Ya1D4k8QpCbhRD6UGzMP+QWMVAQbD4bhuRmEVDJ3lavocn/vZ7TyyAswEQLzUIyBN+GDRuaSg0m57k+FGfyD2bFQGUdQLk+oF00rg+oaRKiK7ppz3VyprsGe60faNSwQ9pvUhDUugF1m4PoXoe+eMprWIkz0njAXKyhOKOGY4z5/EvieR69e/dGZGSkqUlXrlyJ48ePW8qLnjFjBiZMmICRI0fKrpPFixdj5cqVOHToEG7fvo0bN274fEGnT5+OdevW4ffff0dkZCSaNm2KmTNnokqVKvKYFi1aYPv27arznnnmGSxcuND0epOTkxEbG4tbt26ZSnkggouRGKgnAjrhTg12AkjLcAMaiYACx+RagXc5l64QaMZRJ4mAynp8el19tfX67sGJdM7dLEQrBAL6rkBlfUBvjkBlWnA6J+I2l666PnUODi6BeA8RBAF2ux3n326A0oXCTJ/33g+XsC2llunUYEKfYMdGijO5CzNCYHaJcN7EwECswdv8VlKDtd2CRZdNdgQKgo2EwGwgEO8j0hzXFzVATKS5LyCT77lQ9Jmf6P0ri2THPRjFmtyFJAZquwZzGe+9tnAXbBFO8OFO2GJSwRVMyxQCMxyBKkFNKfLpfWnsrTagkRgoCWya437VD9SkD0tr99UkRELpBuScnLo+YMY6+aa/W18XYZqcijMAxZqsYPpVfvfdd01/u/T5559bWsSBAwewaNEi1KpVS7X/7t27aN++Pdq3b48JEyaYmmv79u1ISkpCw4YN4XK58PLLL6Nt27Y4duwYoqKi5HFDhgzB669nfgAtUKCApTUTuQuz9QIlERAAfLnIBVXjEK2bL+sOOl9NOpwQPFJ3peYeZl2BWuFQQisC6hETOYPEQILwQTBjI5G7yC2uQG8ESog04zwMBOQKJAjfUJzJX3hzBvJ2dzowH+4EF+6S04KVIqAHAu+zEYghvpyBmuNmuwurxjmhW0uQE8yLgYCiUQioPiBBmMFULtnWrVtRpEgR05N+++23uO+++0yNTUlJQf/+/bFkyRIULlxYdWzUqFEYP348GjdubPramzZtwsCBA1G9enXUrl0bH330Ec6fP4+DBw+qxhUoUAClSpWSN18KclpaGpKTk1UbkbvRugGVIqCRU11Cr0twoGoDOiGoNm+4FN2EJcFPT9gzgwuivAFuAVASAbVCJ5G3YC6bpQ1CZmpwtWrVMH/+/Jx+CnmSQMdGijO5n9obZuf0EgC4hTppy+vsbfWa70FErsByrIE7XYvijP8E4x6MYk3ehbOJ4MNdgI2BD3eBszG3IOgLoxIyZgRCX+m1muPeRDhO4OTjyp+VN2ZqQc/7paXjnI81ijureZ+IyDVYjTMUa/zHlCOwefPmliZt1qyZ6bFJSUno1KkT2rRpg6lTp1q6jhlu3boFAB5BdMWKFVi+fDlKlSqFzp0749VXX/XqCpw+fTqmTJkS8PURwcGXCKiNK0a1AaW0YC1GHX/1Gn9IGDnvnBDggM1jnxIHeL/FOm/OQxIA8y9WugYT+gQ6NlKcyf3kRkdgbhUDpbRgIn9jtmswoU8w7sEo1uRdOLsA3i6Aj0h3uwEdnkpZVpuABAKtM9CUQ0/hDOScnPw89JyBHunAyjmIfAnFGuv41WlAFEWcPn0aV69e9Wil/PDDD5ueZ9WqVTh06BAOHDjgzzJ8IooiRo0ahQcffBA1atSQ9/fr1w/x8fGIi4vDL7/8gnHjxuHEiRNYt26d4VwTJkzAmDFj5MfJyckoW7ZsUNZNBA+lCOhS1AdUoif8OTkRNnCqsUZioH/r8hQD9ZCahijxp2kICYChBRM4MJf5m24m8tQsJAhkNTZSnCFyE0bpwaLI+6wTaEUEpNRggjBPIO7BKNbkDUQX71EnkLeLgI25nYAZTUL8xkq6sEFNQG+YTc81ShPWioGAWxA0FAEJgrCEZSFw37596NevH/78809o+4xwHAdBMFdT5sKFCxg5ciQ2b96MiIgIq8swRVJSEn799Vfs2rVLtX/o0KHyzzVr1kTp0qXRunVrnDlzBhUrVtSdKzw8HOHh4UFZJ5E96ImAWvREQABwZHTW1YqBZvFVD9C9Pv2/HelcyRUoiX6SIKgVAZ0QVQ1DlPsJQoIcgYElELGR4gyRV5CEPj1BkJyAoYsyDcv3WPf7oNmuwYRvAnUPRrEm78HxoiwKcnZBrg0oo6gPqBTQAo6e8BaAa3kTAwF4CILKYz7RS4kmci1W4ox7PMUaf7EsBA4bNgwNGjTAxo0bUbp0aXCcf0r8wYMHcfXqVdSrV0/eJwgCduzYgffeew9paWmw2fxPdxk+fDi+/vpr7NixA2XKlPE6tlGjRgCA06dPGwqBRN5FmRYMuEVACWW3YK0IqE0XVoqB0rlmXIFmREB/8NUx2IzwJ40nkZAgskagYiNB5CZ8NQ0h0Y/wBaVrBQ6KM/kDo67BADJdfC4bEJFmOIdPMdDf5iFaAig4eoiBgIcgSBBGUKyxjmUh8NSpU/j8889x//33Z+nCrVu3xtGjR1X7Bg0ahMTERIwbN85vEZAxhueffx5ffvkltm3bhoSEBJ/nHD58GABQunRpv65J5H5sHCDpei7Aww0oiYBGtQIlN54kBgKQ3YHexECtCOirOYgSl86c3moFZkXUc4AHmB2AK2jCJRE8mMCDWfnGU+QoNTjABCo2EkRuQ6pB6G8XYW0NQ55nEEW6qcuTCJx7MzsW5NIIJBRnCAi82zFlF8DBe7dgU85A6bg/QlsQXIceXYeVNf90OgubhX/omP8nE9mLlTgjjQfFGn+wLAQ2atQIp0+fznIQio6OVtXtA4CoqCgULVpU3n/58mVcvnwZp0+fBgAcPXoU0dHRKFeunNz8o3Xr1ujevTuGDx8OwH1Du3LlSvzvf/9DdHQ0Ll++DACIjY1FZGQkzpw5g5UrV6Jjx44oWrQofvnlF4wePRoPP/wwatWqlaXnROQN7ODcTUAUbkDAWAS0MQ4Cx3TFQCt4EwH1RD8j9NJ+M6/hfR5tOrFRGjER+lBqcGAJVGwkiNxKVgVBLTabAEGwYXfzyXhw++SAzEnkPsilETgozuQflG5ATlGCQXTx4OVOuzbAR8dgbWqtId6OG4mETs4vMVAS+oxqCEr7VYIg4F0U1DQJYTamml/cWY3EwBCHYo11TAmBv/zyi/zz888/jxdeeAGXL19GzZo14XCo/xIDKaYtXLhQ1dVKKoK7bNkyDBw4EABw5swZXLt2TR7z/vvvAwBatGihmks6JywsDD/88APmzp2LO3fuoGzZsujZsycmTpwYsHUTOY+d8+wcbOMAZ8Y+R4a4B6hTgo1qBBqhdQXawOt2Dg6UCBjG9EU7Xy4+vQYjgFsQVIqHERpXYEzkDCTfG296fQSRn8ip2EgQOYnS4edLFMytHY0JIq9AcYZQwlw2iC4bOJcNTHDBrG/KQxAUePPpwX64Bj1EPD8wFAQBa92BbaLbSfl/1cG1+i3L6yKIUIFj2mqzOvA8D47jPArTypNkHLNSqDavk5ycjNjYWNy6dYvU51zARNtqj30uTZkJwF0rMBXAPU6UHYECZ1wnUItSNFO6AqUGIlKKsCQGSoKakRDoTQRUintaAdCucPG5ICquk3mOVvxTzivNp1dnMJXzTA8mMTDwBOI9RBAE2O12/DHlIZSONV/4e8GO8/j4dAScTvdfB9no/SPYsZHiTO7jSJexhsfys+glCYK+XgMxowC4lBosCDaIGWUNyBUYHALxPiLNcWXWw4iJNJdMlHzPhZIv7UDlypUpXSsLZMc9GMWa3MWNiT10HYG8XQRnE2GLcIIvkA5b7F3wsalyarDcUEMS7JyKewdF+rAsBkpCoFlXn5EQqHO+GSHQbFdhK3N6zC+tWeDdr4uTB9fuqPcTCcvkVJwBKNZkBVOv8tmzZ4O9DoLINeil1wYifdas88+bu89uYh1GAqCvub1BzsDQg1KDsw7FRiK7kNJofSG6bOC13SSzgUCIoDuavYGHd70agNUQuQlK18oaFGcIJvKq9GAVBvUBPZBEQYeYWTtQ4wrUE9qsinVm8TmvUnTMSi1CB/Ov/iGR56BYYx1TQmB8fLz8844dO9C0aVPY7epTXS4X9uzZoxpLEDmFNi0YcLsBvTnJPboGc+56gICBOOijcYgvN6Ae/oqAYYwPaJMPvflIDMy9MJEDs/JhjZqFBASKjfkLb27A7MCK6y4rYqAy3Te7XY5bm7yJlntfztZrEkRuhuJM/kO3W3AGtggn+HAn+Ih0cN4cchnCn9RIjtOkAasaiZit9acU1ZQios75Hk0/NMf8wducVq4hflMbfMcjfq2BIEIJyzanli1b4t9///XYf+vWLbRs2TIgiyIIK2jTgvVEQLNoBT8nJ8qbVfTq+aVzomrTHgsEUrqv9lre5k/lXPJG5A8SEhJw7NgxHDt2jETAAECxMbTxJQIGUzDT1uELVLMOM9cKJFJasC+2NnkzaGsgsojIuwUAM5vo/gzUsGFDVKtWDfPnz8/hxed9KM6ENjcm9sCNiT0gujzvH3i7CD7cCdhEcOEutwjoUL9fc04uMy1YgyQIqtKFAfffqnS+VYFO8C4jaOfjBM4/EVDxnMycb2aMuKGu9XUQ2YOVOEOxJktYFgKlOhRarl+/jqioqIAsiiDM4ksElByAkhtQAODKqOdnAwcHONiY+1/VeTqimS8x0KaYw6b501KmBSvFRjMinTe06cZWU5i14p+v9cREzrC+SCLPU758eXAc57ElJSXh3Llzusc4jsPatWsN50xJScHw4cNRpkwZREZGolq1ali4cGHA1+50OnHhwgWcOHFC9wYqUFBsDF0CLQKaFcS8oSfYKef15xp6c/oSBsWMwvW+UK1N9Pw7yc/1FUOdAwcO0BdOAYLiTOhyY2IPw2O2MBc4m5ghBrrA2QVw4S7Ai3NQgrOJ8uYPPh14ssBo3AHYsgCoN5dGDDQ9H6UF5xso1ljHdCXGHj3cb1Acx2HgwIEID88sTC8IAn755Rc0bdo08CskCB28NQeR0IqAvrAxziNeSE1AALfQp0wX1iKNVaYHK5HENQd4OCH6FO0kwVAaJ4l+ZuoEAsbpwpJT0R/xkVKDcyfMxVu68RdFHmfPmU8NPnDggKoI+a+//opHHnkEvXr1QtmyZXHp0iXV+MWLF+O///0vOnToYDjnmDFj8H//939Yvnw5ypcvj++//x7PPfcc4uLi0KVLF9PPRY/bt29j+fLlWLVqFfbv34/09HT5BqpMmTJo27Ythg4dioYNG2bpOgDFxlDHSATMinBlJW03mA69rJJV4dHoNaTUYIJQQ3EmtPEmAnK8okFIuDNTBHQIsrjHtGm5Ts5dO1Dr/vNGRnqvlfTboGFU10+TgmypzqAGvsvP/q6OIEIG00JgbGwsAPe3UdHR0YiMjJSPhYWFoXHjxhgyZEjgV0gQGvwVAZVuQCMkkU4PAUwWAwEYCoJmsOLcc0IEuEwBzwURdvAqN6BW1ItgdqRyLsP0ZKuQABh6xMfHY/ny5fLj5ORkhIeHq24wJIoXL656PGPGDFSsWBHNmzcHx3EoVaqU6viXX36J3r17o2DBgobX37NnDwYMGIAWLVoAAIYOHYpFixZh//79WRIC58yZg2nTpqFixYro3LkzXn75ZcTFxSEyMhL//vsvfv31V+zcuRNt27ZFo0aNMG/ePFSqVMnv61FsDE1yuh5gIPBWKzC7BUY9FyAAuWOwBImAuRvRxeumLRqNBdzpWtTJMWtQnAldfImAtgwHIGwiYGPgww1K+EiOP4EHczDDFGFth2HpHGXTEEMxUFkfUIl0volag9K8fjchMVPP0MPVoV4v1QfM3ViJM9J4gGKNP5gWApctWya3rp83b57XGzyCCBZmRUCBZf4MQG7XYQcHFxgcjIOTs/aNVzpEhGUIeEpB0D1/YL890xMj0zlRJQYaIYuMGbUCtam/ViERMPfDRB5MtCBMMw4nT56Uby4kJk2ahMmTJ3s9NT09HcuXL8eYMWN0U5QOHjyIw4cP+6zR0bRpU2zYsAFPP/004uLisG3bNpw8eRJvv/22+eehw4EDB7Bjxw5Ur15d9/gDDzyAp59+GgsXLsSyZcuwc+fOLAmBFBtDj5ysB6h3LTOinRVHnlUR0KhjMW8XDK9rJPxJKOeTfqaOwaGJ2U6O5cuXx59//umx/7nnnsOLL76IhIQE3fPWrFmDXr166R5LSUnB+PHjsX79ely/fh0JCQkYMWIEhg0bZu1J5DAUZ0KP6+Pdv7O8iTtxPtwF3i6Aj0gHAO9NQmxiphgI/1yBgLXGHGbQzqV8bFkUtJLuKzVLcXKAkwfX7qi1axF5Boo11rFkaWKMYcWKFR5pYASRW3Dq/OyP78HI7Sel/ApghuKfXlpwoNAT8ozEPUkQlJqHEISSypUr49atW6ptwoQJPs9bv349bt68iYEDB+oeX7p0KapWreozTWnevHmoVq0aypQpg7CwMLRv3x7z58/Hww8/7M/Tkfnss88MRUAl4eHhGDZsGJ5++uksXQ+g2EjkHQLtBNRzHCpFQEGw6W6ApxuQyP0wFw/mspncrBVwP3DgAC5duiRvmzdvBgBVCQrlNmXKFBQsWNBnCYpNmzZh+fLlOH78OEaNGoXhw4djw4YNgXtRsgmKM/kTTnqPlYQz6bG3+oDKeoAO/+9J/G7uoYDZmE9BMcuCo7dGEkCmOzILrwWRfViLMxRrsoIlhYDneVSqVAnXr1/PkoOCIAKFrw7B2lsUX6nBgDo92AbOq9vP6JigIwYa1ezztQYAqpqCZuaQruUt1ZnI3/A8b+qbMy1Lly5Fhw4dEBcX53Hs3r17WLlyJV591bezZ968edi3bx82bNiA+Ph47NixA0lJSYiLi0ObNm0sr0vJH3/8gYSEBF3HYjCg2Jh/yImmFmZcgd7ceYHAyBWoRSsCAiT45XfMujTyUgmKnIDiTP6Ftwvu2oA2ptstGIBHei+AzBRhq/UCTcI5OY/6hAHDqE6gEk1sMUyHJvIFFGusY/ldYcaMGXjxxRfx66+/BmM9BJGj2FhmENG6AsPAy6nBVtE2+MgOcU5ZH5BcgaGNKPByTQ0zGxM5nD3rbhZi5tsziT///BM//PAD/vOf/+ge//zzz3H37l089dRTXue5d+8eXn75ZcyZMwedO3dGrVq1MHz4cPTp0wezZ8+2/Py1VKpUCf/884/8uE+fPrhy5UqW5/UGxUYiN6IUB7OzLqAVEdBmEygtOIRJTk5WbWlpaT7PkUpQPP30015LUAwePNjrPFIJiosXL4Ixhq1bt+LkyZNo27at389Hj+zqTk9xJrTw1TiKt7s7BcPG3M5AhRtQtwuw8v1W20jEIQbFEccZ1Q20iGVXoOJ6nJPLXIeTz9yIfEWox5pgxBnL6sBTTz2Fu3fvonbt2ggLC1MVrAUQ1ABI5G/06gMq0TYI8RZe9eoDOsDBqePw8+UK1MMGXtcVqFqDic7BRvhyFyqPkSuQ0CMhIQHr16+3dM6yZctQokQJdOrUSff40qVL0aVLF49v27Q4nU44nU7wvPr332azQRSz/rsq1VKS+OabbzB9+vQsz+sNio2hT064Ab1h1qUnYeQs5PnMvznRSq1RL9cB1CKg3jpzc0dkQh8m8GAmb/ilcWXLllXtN1OLNpAlKIYOHYoyZcrAbreD53ksWbIkyyUogOztTi9BcSZ/wdlEwCYauwFNCnt6zUMM3XwZdQL9ruFnpplHAFEJgBkwgXe/dk6e0oHzIFbijDQeCM1YE+w4Y1kInDt3rl8XIoicxltasI1xEBTioCSeORivagpilTDGAxzgzJAltQKeGTEwlXMhgtmzJBwSRFYRRRHLli3DgAEDYLd7ho7Tp09jx44d+Oabb3TPT0xMxPTp09G9e3fExMSgefPmePHFFxEZGYn4+Hhs374dn3zyCebMmRPspxIUKDYSwcRs0xCrKEVA5eNACIISRmJlsJ4Tkbu4cOGCKl1LrzO9ltxegiK7u9NLUJzJX3B2AbzUNdgu6KcEeyOjcQigLwaq0EktNr1OSVTUzBHIZiNeyRABfYlHXKvfsmM1RA4RarEmO+KMZSFwwIABVk8hiGzBAXWzECMkN6Dk8lM6AR3gAAYIHLNUK1BJGHikQzTlCgSMxUDp+nppvXpuQKXrz0gwtFKnkMg7SAVzTY8XeJy94E4NBoCkpCQkJSV5PeeHH37A+fPnDZtrfPjhh/K3U3qcOHECt27dkh+vWrUKEyZMQP/+/fHvv/8iPj4e06ZNC0iHLY7jPCz+wa4XSLExtMltbkAJpSswkHUCeV70WwxUrim3vm5E9hETE2OpHq1UgmLdunW6x62WoPjyyy9lF3utWrVw+PBhzJ49O0tCYHZ3p5egOBNaSO/X2hRh3i6C491pwZxdABfuyuwUbLZJiAm81fjz2jVYITDqks2uQMCLCEiuwHxDqMWa7IgzfhUOEwQB69evx/HjxwEA1atXR5cuXWCz0Qc+InhMFfr4TA82wkyTEK0rUMIfV6AkBgKZrkD3Otz7rKbrWnUF6s0dwexI5Vymr0mENlZTg9u2beuRcqvkzTffxJtvvml4XHtuqVKlsGzZMtPXtwJjDAMHDpS/DUxNTcWwYcMQFRWlGmcU/P2FYiORGxFdNt1aVFo3oN5xpRhoNRWZCD2kOrNmxwLuTo42m83UF05A3ihB8dlnn5kaJ3WnDyQUZ0IPI0EQgJwWDIcgi4C69QGN8OEKVImBGkeflZTgoDYO0cOXGCnhyKyVSK1E8gZW4ow0Hgi9WJMdccayEHj69Gl07NgRFy9eRJUqVQAA06dPR9myZbFx40ZUrFjRr4UQhBm0YqCdM+4cbINnnUC92oC652aIgv66AjPXYOwKVM5txRVIKcKEFqv1NMAym4UA5hyBeQmta+KJJ54I+jUpNoYOtTfMxpEuY+XHuV38suIKzO50XG/iIaUF5w/MdnIE8lYJiuzuTg9QnAklis5Yi+vje8mPJRGQzxD73E1CFPcPWhHQisvNSoowYOzoM9vJN2ONXl2FAcIt8IngoHYFynUCiXxDKMaaYMcZy0LgiBEjULFiRezbtw9FihQBAFy/fh1PPPEERowYgY0bNwZ8kQRhBRsHgGWmCZtxA/pCcgUaiYG2jO+ZlMeMXIFgmam9ZsRAPbRjg9UMJPne+KDMS+Q8/jQLySsEy2noDYqNoYVWDMzr6LkCRZH36QrMCrxN9GgYQuIf4Y28VIKiUqVKuHTpEkqUKAHA3Z3+3XffRcmSJbM8txEUZ0ILSQz01j2YuWxgggscjEVAU248L2Kg7vkm0nu9iooZ5wdVDDTpCsxWpyKRJ8grsSbYcYZj3nK9dIiKisK+fftQs2ZN1f4jR47gwQcfREpKSkAWlttJTk5GbGwsbt26ZSkfnQgMSleg1hHohLtzsPwz1GKgtkage1zGPo6pOgdLqcJOiHJ6sJErUE8MlIRAASLSORFOCHJ6sCQGakU8rRiYFZFPm4IspQabrRNIQmDwCMR7iCAIsNvtOD6yA0pFR5g+b/GBM9gTEbpCYE4QjNhIcSbnOdRpXE4vQcaXiKZ03um5ApU3m9JcZoRAZXqwkbtPup4ocqqxkhDoq2vwg9sn+1wH4R+BeB+R5jg/viNiwh3mzklzotyMb1C5cmVL6Vp5BZ7ncfnyZfkGLTo6GkeOHEGFChWCds1g3YNRrMlZbkzsIf+srA9oj0oDH+4EH5UGvoATXME0QyegLHbJYqF0I6QQ6jSimVLEU4llSiedUWdhxXzSPLpzaDoQe8MjFdmX+1BC4N1rcKqzYzibCDhEeV38Q8fMzUdYJqfiDBDasSbYccZyfmF4eDhu377tsT8lJQVhYWEBWRRB+GKq0Ef+2a4TJ2yafXadyhA2xT6Hwc9KHIz3OE8P5fEwSOfwCGM8HLDBnk1pvZKgqBQW9RqPeCMmckZA10QEB1HkIAq8+U3MTA2uVq0a5s+fn9NPIaCkpqZixowZGD9+PC5dupQt16TYSFjBZhPkLZBzSnhzmCgx0xBEKRYarVdOa+PVN3x8xs2gt+fJU/pWSHPgwAEcO3YsZG7MchKKM6FJ4ame9YpFFw8h1QExoxkcEzhrJWAkjAQ+s+iJcVrRMQPOQHQ0W2vQQzB0sMzNG7YMsc8hGqcD20SIexJNrYPIm1CssY7ld5RHH30UQ4cOxY8//gjGGBhj2LdvH4YNG4YuXboEY40EoYtSDFQifYdg49w/Z6W6k41xGXP6/lNROgH1xEKtGBjG9Od0QlRtgUC7fqNr60FiYGiSkJCAY8eOhWTQHDx4ME6dOoWiRYtmqTOkFSg2hib1Ns4M+JxaUSyQgqA3MdCodqDV7sBG69WKgbLr0IQYuLfVa5bWQOQMUhF3sxvgLuAeil845UR3eoozoYueGMgEHhB4MJcNcNmAew63683BPDYZSYBTinImxECvdQN9iIFeU28zzrXSeEQXM4JgBlQbMG9jNc6EcqwJdpyxXCPw3XffxYABA9CkSRM4HG7JxeVyoUuXLnjnnXcCtjCCCCTu2x8OLjA4GGfYNCSzSQinShGW8FUrUADz6RjMKZSNR6x2D46JnEFpwrkYyelnFiaGdrOQrVu3YvPmzahevTpeeeUVXL16VbbVBwuKjURW8dWV12yNPTPdfbVz+aoXqO0gbHQdqVkJzzOIIme607DNJmD/I6/ggc3TfI4l8hZWCrjnJXKiOz3FmfyHkOoAbCI4u+D+siXNDs7hQ3zTw0yjD/mivKdgqFczUGdO3S7EinqBgI7zTwGzMe+ioV7KM+AWJMGDgwg43Y1CmMC7ayvCfQw2EeKBKuAbnjCen8izhGKsCXacsSwEFipUCP/73/9w6tQp/P777wCAqlWr4v777/drAQQRCIy6B2sbh3gcVwh6kvinFQP1OggrkRuCaFx30tzKpiFuVyDkxiHpQWryAeg3H1E+hzDGm64VSIQeodwspHnz5njnnXdQuXJllCtXLugiIECxkTCHLyHPrHBm5jqCYPPoIqzXNEQ+ZkIMlMZ5W69e52KpcYj0/I2eI4mBRF4hJ7rTU5zJn4hpdvB2AcwugLMxsFQ74DC6s4GHACcjCXdmmmzoiYFeUDYO8dW8xFcDEZ9iIGAsCALuOopO467BJAYSeYVgxxnLQqBEpUqVUKlSpUCuhSByHK34pzvGwBWYDhFh4A2bieghdRM2mwIsOfnM1voz6kRMrkAilFm6dCnmzp2LK1euYMuWLdl6bYqNhBFm038DJQZK6AlzgL7D0EwnYa070Ejc8+YK1F5XuRYSA3MvzGWDaDP3u8lc7t+jhg0bhlwBdyBnutNLUJwJbZjIg9O8D4tpDsDGwNkYuEinuU7BgKnuv6YxmsuMsKg514wYKI3zisaVKAuSGWKg0TpIDMy9WIkz7vGhG2uCHWcsC4GCIOCjjz7Cli1bcPXqVYii+o3q//7v/wK2OILIDozSfAE9l6CnK1Dp+JPEQC3eXIHI+FlPDDRyIVpBKwZmZU4SA3MnTFEjw9T4EE8NLlCgAF5++eVsvSbFRsIbgWwKklW8uQIB82KgNFZCKfhpU4SBzFqBvsoYaBuOEHmbUEzXyikozoQuys7BWpjL5v7UnmYHH5Huvn1w8oDDS1xROvqUIpxCOFO6+AAdJ59RirA0j8580jxe55Cu50MMBEy6AyUyBEkPMdDJq9KDJUgMDC0o1ljHshA4cuRIfPTRR+jUqRNq1KgR9MK4BGEGvbRgfzGqD6gao+MKFCDCphD89ARBJWGMh5MzDuLarr9SfT9/0BMDwewAXJQenE8J5dTgnIBiI2GEPyKgkSvQbJ1Ab3P4O58eeu5APTEQUOzPSBMmiLxMamoq5s6di5s3b2LkyJEoXbp00K9JcSb/wQSdFFeXW9Ay7QoEPN18WtHMCCMhT6dGn+QKlOa0XMfQX3RqFarEQC2BdEkSRBAJdpyxrCqsWrUKa9asQceOHQO6kBkzZmDChAkYOXIk5s6dCwBYvHgxVq5ciUOHDuH27du4ceMGChUq5HOu+fPn47///S8uX76M2rVrY968eXjggQfk46mpqXjhhRewatUqpKWloV27dliwYAFKliwZ0OdE5F60DUOsuALNonUHKl2BmXNn3KR5cQVmjs2aO1AvTTjCohhIbkAitzNs2DBMnDgRZcqU8Tl29erVcLlc6N+/f5avG6zYSBDBwJcrMFAo05JVIqGX2lMNvnsz6Osi/IOJPJjJLtPSuFBM1wLc3ekjIiKQmJiINm3a4Lfffgv6NSnO5C9EFw/ebvJzv/SeqvySRSviGYh3PsVALUoRTXmeRgz0wE8Bzh9XoLQGj+elqJ/IN/3d8lqI4GMlzkjjgdCMNcGOM5a/kg0LCwt4UdoDBw5g0aJFqFWrlmr/3bt30b59e0spXqtXr8aYMWMwadIkHDp0CLVr10a7du1w9epVeczo0aPx1VdfYe3atdi+fTv+/vtv9OhhbMkmci8u5t0NKAThCx/Zqcc8/3wEjVBn1AzEljFHGOPhgA128Bk/87o1/ZTX9nbcF0ohUTlPmM5z0UIiYO5FFG0QBfMbE3k5NbhatWqYP39+Tj+FgFG8eHFUr14dHTt2xPvvv48DBw7g4sWLuH79Ok6fPo0NGzbgpZdeQrly5fD222+jZs2aAbluMGIjkTuot3Fmjlw3kOnEeqKfJNBltR6hNo1Yb91Suq+v50S1AUOPAwcO4NixYyFzYyaxdetWjBkzBi+++CJOnTqlus8IFhRnQpfCU91dP/XKvDAvDmpZ6JLG2ET1ZhLmYOYcfHqdg5VYuaaPtGBLaN2O0jWUdQkzXiu+6e8kAoYgoRhrgh1nLCsKL7zwAt555x0wFpg/3pSUFPTv3x9LlixB4cKFVcdGjRqF8ePHo3HjxqbnmzNnDoYMGYJBgwahWrVqWLhwIQoUKIAPP/wQAHDr1i0sXboUc+bMQatWrVC/fn0sW7YMe/bswb59+wLynIjsY4bYx2Oflz5aXrFB/a2RjWU+dmQcU+4zwpsYaCZd2H097+OsioHKxiBOiPIGZDYe8SYGkggYeiQkJODYsWMhFzTfeOMNnDx5Eg8++CAWLFiAxo0by52Dq1Spgqeeegp//PEHFi9ejH379nl8AeUvgY6NRO4ip8TA/ILNJpAISOQppO70s2fPzrbu9BRnQhtJDAQyXU6SMMiUDZ+cNq/ioCUsCHeG5PI0W0nkzLZUZYIIEMGOM5ZTg3ft2oWtW7fi22+/RfXq1eFwOFTH161bZ3CmPklJSejUqRPatGmDqVOnWl2OivT0dBw8eBATJkyQ9/E8jzZt2mDv3r0AgIMHD8LpdKJNmzbymMTERJQrVw579+41FB3T0tKQlpYmP05OTs7SWonAMUPsg/H86oDMpU0RNkoH9pWmK9ULlDByBmbO5w7wLogIY3xA6/ZJImAq5/JZY1Dv2mbcgkTOwgTO0ofCUG8WUrJkSbzyyit45ZVXcOPGDZw/fx737t1DsWLFULFixaDUVQpEbKQ4Q5hBcvAFwjEopQhntVZgVshNjVQI74guHqLNXKyRBIxQTNcCcqY7faDuwSjW5F4KT12HW5O7AcjsHiy6ePcdhfZznpPXr4GXEyjr9HnrIhzM+nw6tQK18A8dC861iYBhJc5I44HQjDXBjjOWhcBChQqhe/fuAbn4qlWrcOjQIRw4cCAg8127dg2CIHjU+itZsiR+/91tAb58+TLCwsI8ag2WLFkSly9fNpx7+vTpmDJlSkDWSQQPPTegE4AAwKWpAaitE6g6ptMwxFcTERt4lRtQKwYajfdX+FOKkWZEPrPjSPjLH+SXZiGFCxf2cJsHg0DERoozhBUCJd7lpBhIImDoE6qdHHOiO32g7sEo1uRulLUBlWIg4HYGMsEFy19nGtXss4o/Qp6XzsEBxZ/nQ4QMoRhrgh1nLAuBy5YtMzVu9+7daNCgAcLDw3WPX7hwASNHjsTmzZsRERFhdRnZzoQJEzBmzBj5cXJyMsqWLZuDKyJ8YaY+oCMj1ddIEAQ8XYEej8HpNgIxg1I8VLoCJfQafOjhTdyTjllxBvqak8h/XLx4EePGjcO3336Lu3fv4v7778eyZcvQoEEDeczx48cxbtw4bN++HS6XC9WqVcMXX3yBcuXK6c7ZokULbN++3WN/x44dsXHjxqA9l2AQiNhIcYawii/xTq+Lb05D4l/eRaoza26s+7NMKLo0copA3YNRrMn9+GwU4gqAuCY56Lw5+PyZT49gi4G+RMDsECKJgGAlzrjHU6zxl6BZfzp06ICLFy8aHj948CCuXr2KevXqwW63w263Y/v27Xj33Xdht9shCNY/KBYrVgw2mw1XrlxR7b9y5QpKlSoFAChVqhTS09Nx8+ZNwzF6hIeHIyYmRrURuRubpQZYmYOlWoEOxfdterUBHeBVDUPCwKscgN7cgGHg5XqBtoxGIbrrymKnYCURzG5a3JPGOcDjr7QxPkYTOY0o8JY2xjhLzUJu3LiBBx98EA6HA99++y2OHTuGt956S+W0O3PmDJo1a4bExERs27YNv/zyC1599VWvX/SsW7cOly5dkrdff/0VNpsNvXr1Cthrk9vwFhspzhDBwIzwphUIRQsd+7IKzzO5mQgReoRiAfdhw4bhr7/+MjV29erVWLFiRZBXpMbXPRjFmtzL9fGZn39kF6DB+zETeHd6sC9yS228QNU11EJOQAKhF2uyI84EzfLjq5Bt69atcfToUdW+QYMGITExEePGjYPNZv1b67CwMNSvXx9btmxBt27dAACiKGLLli0YPnw4AKB+/fpwOBzYsmULevbsCQA4ceIEzp8/jyZNmli+JhE66KUKa9OBpcdKV6CD8QAnQgDLcAYao20W4q+TMCuQ048ArKUGz5w5E2XLllW5ERISElRjXnnlFXTs2BGzZs2S91WsWNHrvEWKFFE9XrVqFQoUKBDSQiAVeSfMktVuvt4QRU5XfMvJWoEEkVeQutM/+OCD6Ny5Mxo0aIC4uDhERETgxo0bOHbsGHbt2oVVq1YhLi4Oixcvztb1UZzJ20jlGtw/GzgDnRnxwSaCc3K5pxGGiTp9EpzAme4czGwMnGBS8AuW4EgQ2Uh2xJkc+0uJjo5GjRo1VFtUVBSKFi2KGjVqAHDX8zt8+DBOnz4NADh69CgOHz6Mf//9V56ndevWeO+99+THY8aMwZIlS/Dxxx/j+PHjePbZZ3Hnzh0MGjQIABAbG4vBgwdjzJgx2Lp1Kw4ePIhBgwahSZMmlroTE6GJ5AzUdhA2HJ/xJ+RgvM9zfHUM1iNQbkBfSG5BK65BIm8jiiKSk5NVm7J4uJINGzagQYMG6NWrF0qUKIG6detiyZIlqrk2btyIypUro127dihRogQaNWpkuQbh0qVL0bdvX0RFRWXlqRFEvsKMYKgV93ieQRSD46JQrsdXKjI5AfMWosiZd59n/H41bNjQlPM8L5FT3emJ/IPReydz2QCXDSxDFJNcgZyTcwtguUUEy0jF5Zyce20BQFc0VM5t9PwpLThPYSnOhHCsyY44k6vv+BcuXKgqZvvwww8DcNfIGDhwIAB3Otq1a9fkMX369ME///yD1157DZcvX0adOnWwadMmVQORt99+GzzPo2fPnkhLS0O7du2wYMGC7HlSRK7EDs6jmYiE1gWo5wpUkl0uP2WjEAkS8fInougOhmZhIo+TJ08iNjZWtX/SpEmYPHmyx/g//vgD77//PsaMGYOXX34ZBw4cwIgRIxAWFoYBAwbg6tWrSElJwYwZMzB16lTMnDkTmzZtQo8ePbB161Y0b97c55r279+PX3/9FUuXLjX9PMzgcrmwbds2nDlzBv369UN0dDT+/vtvxMTEoGDBggG9FkHkdpR1AgMlwvmbRixdn7cLqLdxZkDWQuQ+QrGAO5Az3emJ/IXkDBRdPGxhGd2DBQ5M4MDBBjjd76EMcFsRHBlf+Gjr8UlimZ5r0IKDTzVfbnEgmiWvrZewTCjGmmDHmVzytYGbbdu2Ye7cufLjyZMngzHmsUkiIACcO3fO48Z1+PDh+PPPP5GWloYff/wRjRo1Uh2PiIjA/Pnz8e+//+LOnTtYt26d1/qARO5mPL86IPPYrffgAgDdZh7+uP/CGA8HbLDr1AzMLmeglpy6LhF8KleujFu3bqm2CRMm6I4VRRH16tXDm2++ibp162Lo0KEYMmQIFi5cKB8HgK5du2L06NGoU6cOxo8fj0cffVQe44ulS5eiZs2aeOCBBwLzBAH8+eefqFmzJrp27YqkpCT8888/ANypzmPHjg3YdQjCG/6m+IZyii45AQk9Ll68iCeeeAJFixZFZGQkatasiZ9++kk15vjx4+jSpQtiY2MRFRWFhg0b4vz584ZztmjRAhzHeWydOnUK2LoLFy6M2rVro3Hjxrj//vtJBCQChvTFjbJOIJP2CZw7RdjFgwm82nmn9+WwN8EvkK45heimm7KcsQ6zacGmsYnq56F9TBCgOKMkaEIgBUEiJ3D4eZ4LzNAR6J43I2WYcbqPJcymFEvoOQddBuKbU9NNWEJyAZIbMP/CMjpsmd2YyOHPP/9E48aN0bhxY3z66aeIiYkx7DBYunRpVKtWTbWvatWqclAsVqwY7Ha71zHeuHPnDlatWoXBgwf7+QroM3LkSDRo0AA3btxAZGSkvL979+7YsmVLQK9lFoqNRH5HKQJKdbCIvIHVxlSA+XQtakoVOCjOhB4cL4K5bBBdNjApNVj6V6oX6NQRA7WCoJPz3HxhNI+V9QcgPdijRqDRnFoBMEOM5BueyPIaiOBjNc5YiTUUZ9TkWLMQggg1HIyHkwvuN09GDj0SAQmrWGkW8uCDD+LECfUHqJMnTyI+Ph6Au1FTw4YNvY7xxtq1a5GWloYnnnjC3OJNsnPnTuzZswdhYWGq/eXLl/faUTGYUGzMmxzqNC7br2nFSZgXmnxoXYAkAuYPzKZrUVOqwEFxJvRxuwIFcLCBQQDn4uUUYQ6i2okniXi+3HE2USX4eTQh0c6jTRE2KypK9QMtNAvRxUxaM6UE5xvMxBqKM2r8cgS6XC788MMPWLRoEW7fvg0A+Pvvv5GSkiKPuX37NipUqBCYVRJELkcvPVhLOkR5yyv8lTYmp5dA5AJGjx6Nffv24c0338Tp06excuVKLF68GElJSfKYF198EatXr8aSJUtw+vRpvPfee/jqq6/w3HPPyWOeeuop3fTjpUuXolu3bihatGhA1y2KIgTBU2z466+/EB0dHdBrARQbicARzI7BZgU4s7X/eN44phldi0TA/AM1pQosFGfyL5z0vqnXPVeRIuzhDJQw00xEIxbqNvtQzmHCWcgcLCBdjS2LhiQC5ivMxBqKM2osC4FUc4kIZZw6zT+0BCI92JsgaM8lpTtJBMw7iC7e0sZEDmfPnkW1atVMpWw1bNgQX375JT777DPUqFEDb7zxBubOnYv+/fvLY7p3746FCxdi1qxZqFmzJj744AN88cUXaNasmTzm/PnzuHTpkmruEydOYNeuXQFPCwaAtm3bqurOchyHlJQUTJo0CR07dgzotSg2EoHCXxEwmOKhGWpvmK27n7cLHptEvY0zqVFIHoIJvKUNAMqWLYvY2Fh5mz59uu7cUlOqSpUq4bvvvsOzzz6LESNG4OOPPwYAVVOq9u3b4/vvv0f37t3Ro0cPbN++3dT6paZU//nPfwLzgmQzFGdCH2byCxhmlCLsSwwETHcXVop3ger8K5Mxn0e6rw9Mpfc6mIcISGnBeQerccZKrKE4o8ZyPqFUc+nIkSMq90b37t0xZMiQgC6OIAKFt/p/gcIBHmDwKz3YBh6CjjDoAJ/lhh1Ggl6Z8DnyNZQ4IZIImA+wkhoMAI8++igeffRRr2OefvppPP3004bHt23b5rGvSpUqQUtjmj17Ntq3b49q1aohNTUV/fr1w6lTp1CsWDF89tlnAb0WxUYiFBFF3qvjT0ISAfXEQKO0ahIA8wcXLlxQpWsZ1aIVRRENGjTAm2++CQCoW7cufv31VyxcuBADBgzwaEoFAHXq1MGePXuwcOFCU93pg9GUCsi+7vQUZ0Kbay/1AZApBnIm3nuVyN2EAeM0YSVGKcOaFOFgYzZF2FbvJAB9UU88UEX3HBIA8w9mYg3FGTWWhcDcWHOJIPQQMmKK2QQkpRtQ0AiHTgMh0QEOTjDYGAdB4yYMA+/h+pPEPls2uP58iXl/pY1BQvhc1b6zaaOCtyAiaIgiZzqNDwAY42VHIAAkJSWp0nxDhbJly+LIkSNYvXo1jhw5gpSUFAwePBj9+/dXNQ8JBBQbiUCQ064+PXyJgTXXz7E8J4mAeRNB4CGYFAikca1bt4bNZvMZZ4yaUn3xxRcAvDel2rVrl8/1SE2pXn/9dVPrN8uff/6J9u3b4/z580hLS8MjjzyC6OhozJw5E2lpaVi4cGHArkVxJv/hdlGL4O2a92CBA4MUL9y1AmFQcsGj1p8WLzUEmYP5Pj8A+BIDJRHQCL7hCQ8xkETAvImVOCONB8zFGoozaiwLgdldc4kg/EESAZ0mxmrTgbUioB56wh+Q6QoEJ3qdR+n+0xMF7RnzpHOi7NjLqjNQj7Npo2QxkETA/IVVR2Bew+l0IjExEV9//TX69++vSmMOBhQbiVDBSuMRMyJgvY0zZVcgCYD5D7PNQvJqU6rsdOlRnMk/cLwoi4AcL4Lz1ejDCCcPOEQ5tdenIChdR+EK9DjH7FoUc5gRE43EQF8ioB4kAuY/zMQaijNqLAuBUs2lxYsXAwhuzSWCsIoTahHQ262MXj1AM05AgXM7ACUx0MgVaAOncgXqpf5q94cxHuAAp87KfaUJp3IuANY7CJMASIQiDocDqamp2XY9io1EVgmEGzAr3YNFly1oTTxIACR8MXr0aDRt2hRvvvkmevfujf3792Px4sXyeyrgbkrVp08fPPzww2jZsiU2bdqEr776SlV24qmnnsJ9993nUR8qWE2pstOlR3EmfyGJgLZwF7gMURAw+V4tdRHWaf6hxEOcU4qBeugdk+bQqyOYIQaadRRmpZMwiX+ELyjOqLGcn/jWW29h9+7dqppL0iJmzqQPekT2Mp5frbtfKQK6wGAHJ28SDsbBwTyDlhNM3pQIHPNwAWobh7h/5uFgwUn9dYD32CQkATCVc8miIJE/EEUbRMHCZrFZSF4lKSkJM2fOhMsV/L8Hio1EoLHZBI8tUFgV/KyUHiBCF9Fls7QB7mZTodyUKjtdehRn8h+8XczsFmwAc/n4Eslp8f3bZDMRAOqmHFbTh7WipI35LQISoYPVOGMl1lCcUcMxP6q0u1wurFq1Cr/88gtSUlJQr169oNRcys0kJycjNjYWt27dMpXyQASeiTa3COhS/AZLjkDJByQ1CbFrOvlqm4dI7kBBRwAEoJsGrOwWLHDu86RxTohwZqQHe3ME6jUJSedEOCHABRHpJhuPSE5BSQC8lvqSqfOInCMQ7yGCIMBut2NX9wEoEWm+Rf0nJ37B0TJFQzo1GHAH8y1btqBgwYKoWbMmoqLUr9G6desCer1Ax0aKMzmPUaMLq5gR8ZSOQKPxZlyDZs8VDW4eJZFQOY9RjUB/6gMS2Usg3kekOQ73HYhojSPBiNvp6aiz6qOQf//q06cPYmNjsXjxYkRHR+OXX35B8eLF0bVrV5QrVw7Lli0L6PWCcQ9GsSbnkRqFSHC8CEeBNNkNCLhFQdjEzC9ybCzzWLgLsAvgbAxwCICipqDsCnTo1wC0hNIRaHSu1hmoJyrKa3LPkZX6gETOk1NxBsgfsSZYccZyajAA2O32gOc+E4QVJBFQiVPzrxUcjNNPFdbZ5w2j2oFez8lw9RmlDptBShuOYHZyAxJEBoUKFULPnj2z7XoUG0OLQImAZgmU489MerCRCEgQhDWyszs9QHEm1NAKgBJa17ZHsxCT+F1b0AxOTl8MdDD9NGGCIPwiWHHGLyHw008/xaJFi/DHH39g7969iI+Px9tvv40KFSqga9eufi+GIMygFAFd2tIWisda11+gsSr4afHVOdisG1BCKQYWi5hFrsB8hChwEAXzH7qYIjUYCN2uwYF2YviCYmPokN0iYKCR3H9KQdBmE3QdhaLIgecz45lUfyor9QaJ0EQUedNp4tK4hg0bmuoanFfJzu70AMUZQg1z2dSpwwo3YFBFQF9kRQx0qB8KRyvDVpNcgfkFK3FGGg+EdqwJVpyxXPTl/fffx5gxY9ChQwfcuHFDzlcuXLgw5s6d6/dCCCJYaNOCtTg5FhA3YKDrBIaZPD+M8fJYqWagJAYShBEJCQk4duwYjh07FnIBMyeg2EjkRgTBptq0iCKn+tcqR7uNydL6iNDnwIEDIRtnnE4nKlasiFOnTqF///6YNWsWFixYgP/85z9BEQEpzuQPOINSDFI6rezo1nwBzCnSaz1EQCtpwTbRfGfgQDv/HL6HEIQeoRprghlnLCsV8+bNw5IlS/DKK6/Abs80FDZo0ABHjx7N0mIIwl+M0oHt4GADYJQEpScAGl9Dv36gEgc4Ve1AJVoHoABR3ozwJQYqj5MYmH8RNTf7vjbG+HzRLCQhIQEVKlQw3AIJxcb8TTAbe2QXSjFQutGUxEOjb+dtdhHHHhsV9LURRG4ku7vTU5wJfTjeXQNQNxVY0cTDVHkHh5i5KWAOZq42oCQI+hIGjcRAb9eQ6gaaFBKFo5VNjSOIUCOYccZyavDZs2dRt25dj/3h4eG4c+dOQBZFEGbQSwtWCoKSCCh9uST96x6TEXjkjveeHYIhj/c/BdgGDmHg5YYhes1BgKzVB1QiC4NSXGV+Zf8T+YCEhISQbxYyatQo1WOn04mff/4ZmzZtwosvvhjQa1FsJLRIYqCZBh9W5wzUvDzP/HYDKjnabQw1DskHMIGH6DLnIWBC6KdrAZnd6T/44AOVOBcMKM6EFnoNQiQR0P1z5r2B9HcnpQLzdhGiwIMvkO6zq7CEafHPn2OSoKd3DZvoswsxJ3C6DUOUa6YU4fyBlTgjjQdCO9YEK85YnikhIQGHDx9GfHy8av+mTZtQtWrVgC2MIMwidQqWREABmQ5ABwCb5h7HqYgzdnBwgRk2C/EHBzi3wMjxGf+KKjHQLHbwcEFEGON16wXquQW15zjAIyF8Ls6mjfLvyRBEHmbkyJG6++fPn4+ffvopoNei2Jh/8eX+C5Y70KjmX1ZQ1gvU1goURd6je7Cg+LBOYiChx4EDB0K2kyPgfn5btmzB999/H/Tu9BRnQhutCCil97IM958keDCBh5gG2KPSMk+WxEDpPIUL0HJn4Kxg1EDE9PkAHJ5r5jKERhIDCSNCOdYEK85YFgLHjBmDpKQkpKamgjGG/fv347PPPsP06dPxwQcf+LUIgggUkgioFADVTkBPJDFQj6y4Af3BaoMQPZRioDQniYGhDRN5MAuFdRnLH81CjOjQoQMmTJgQ0GYiFBvzJ/6KfFoBz995AiEGal2B2uYhBCFBzUI8yc7u9BRnQhveLsIW5tJt8sHklGBeJRKqcHjGEUsiYKCai+il++q5AgXevV9HPOScnOHaSQwMbahZiCfBijOWhUCpMOHEiRNx9+5d9OvXD3FxcXjnnXfQt2/fgC+QICSU3YKVKN2AShFQr96soBNTgtVd2MY4XVegUXqwVREwnRNVrkA71D+7Mq5h5Cgk8jf5ITXYiM8//xxFihQJ6JwUG0MHsx2DAyUCSvuyq66gqdpSirG+XIFKyBVIaAlllwaQvd3pKc6EDnppwb5QioC2CKdP4c6nCJjdXYV9pAjL6cEZrkCCsEIox5pgxRlLQqDL5cLKlSvRrl079O/fH3fv3kVKSgpKlCgRlMURhIRWBNTWB3TAe9xQpg9Lt1reBEAb4wy7BjvBVB2C9Y6r1+Z/irBS0HPPLWbOafFcInQRBE6VoucLUcgfjsC6deuC4zL/VhljuHz5Mv755x8sWLAgYNeh2Bg6mBEBsyLYeXPv+SsGWnEFmhUB/XUFkghIEMGB4kzooBUBtShr/jHFe7ZWBOTtAji7oOoWDABwiNmTDqwU9fwVFX24AgmCCB6WhEC73Y5hw4bh+PHjAIACBQqgQIECQVkYQUgYOQEBdbqvJAJq3YBaEdCXA9ABzmdKsC8xUEIpKDoYL4uB9xQCndat54TnjaDW1eeEu/6f1hWohcRAwoj84Ajs2rWrSgjkeR7FixdHixYtkJiYGLDrUGwMDcw6AbOCL9EumGKgFScgkCkGWnUFEqGLKPAQeZNicj4o4A64Y6kyzmj5448/AnIdijOhgU8RMENQkxuF2EWILl4WB3m7u4MvH+5SCYaSGKibMmyEJMIFArNzeUsRhklXoFGtJyIksBJnpPFAaMeaYMUZy6nBDzzwAH7++WePQrUEESxsnH5Kr95xqS6g3DiEZT7WioB2hZBnJA5KoqDAMXeqrwKlWCiJgnoCopQi7DSRDqwnAmYFEgGJQDB58mRMmTJFta9KlSr4/fffAQAtWrTA9u3bVcefeeYZLFy40Ou8x48fx7hx47B9+3a4XC5Uq1YNX3zxBcqVKxewdWcXFBsJiaym+QZaDLQqAJqBxEDCLKGcrgVkb3d6ijOhjUd6sCQKyuJgRlywMVkEVHUMtmfje7Jeiq+0z6S4KDn+mINlnCuqXYGUIkxYIJRjTbDijGUh8LnnnsMLL7yAv/76C/Xr1/foWlKrVi2/F0MQWUHZHVgpAALwkNfsBm4+GzgIGWKeNj1Y+lkrCLqv491BqCcGSiKgN/HPm5AnuQL9OZcILQTBZqlhABN5y6nB1atXxw8//CA/1ravHzJkCF5//XX5sS+nwpkzZ9CsWTMMHjwYU6ZMQUxMDH777TdERESYfh6+sNlsuHTpkkfq1PXr11GiRAkIQuCEd4qNRKA7+PqDVgzMqgiodQWqj5EYSBDZ2Z2e4kzo4e4QLGQ6ADWo3ncl159CBORsLLNjsHJepcjmDSMnn0qQs5iia9FpqG0MIrsCAV33n60eNQoh8hfBijOWhUCpGO2IESPkfRzHgTEGjuMCemNFEN7Qc4YbCYCBbAii5w70F6sioAOezkIpPdgFUW4Y4vKSekwQgPXUYLvdjlKlShkeL1CggNfjWl555RV07NgRs2bNkvdVrFjR9PlmYEz/7z4tLQ1hYWEBvRbFxrxNVtOCAy0CBqp5CG8XAuoI1K7LSmc/Iu8juHgInLn/c6lubSina3kjGN3pKc6EFkoR0P2z4vN6Rg1A98+eAiAAWQTkbEy3YzCQKbLp1dszXUdQGqecQxL6vDT/0CVjvGH9P51agUT+wkqckcYD+TPWZDXOWBYCz54969eFCCJQuJhaBJTEv6yKgA7GwSnV8zOoEyi58HyJgd5ShbVoBb+s1PXzdl5C+FycTRvl17xE6CGKIpKTk1X7wsPDER4erjv+1KlTiIuLQ0REBJo0aYLp06erUnhXrFiB5cuXo1SpUujcuTNeffVVQ1egKIrYuHEjXnrpJbRr1w4///wzEhISMGHCBHTr1i3Lz+3dd98F4L5B+uCDD1CwYEH5mCAI2LFjR0BrBAIUG/MTucH5FyyUz00S/Kw0DjnSZSxqb5gdlLUReZNQTtfyRjC601OcCS20IiBnE8Fp3YFmREAfGIlushNP6+AzEuKyKgj6GqNXK1Bv2KHK5AokPMiPsSarccayEEh1KYjcgF7NQJU4mPGvkQho1SGoTBM2KwYCxoKiDRm1MHTX5ruWoISv9GByA+YPmMhbcuaIjMPJkycRGxur2j9p0iTdunqNGjXCRx99hCpVquDSpUuYMmUKHnroIfz666+Ijo5Gv379EB8fj7i4OPzyyy8YN24cTpw4gXXr1ule/+rVq0hJScGMGTMwdepUzJw5E5s2bUKPHj2wdetWNG/e3NLz1/L2228DcDsCFy5cCJstU9wICwtD+fLlfdYvtArFRkIiEE4+IHCuQF+IYmA7M5IYSOQnsqs7PUBxJq9zdczj4DV33kYiIG8XVCKgSgAEdNOBsw0Hs54urEBPmNSmB/uCxEAiPxGsOGNZCNywYYPufo7jEBERgfvvvx8JCQl+LWbGjBmYMGECRo4ciblz5wIAUlNT8cILL2DVqlVIS0tDu3btsGDBApQsWdJwHqOuKrNmzZILKpYvXx5//vmn6vj06dMxfvx4v9ZOBIdJds+OwU7Nv1qymg6srBMIqMU8KTXXlxhopqtwGOMhDXFB9NkB2D1v1oS9MuFz8FfamCzNQYQGlStXxv79+1X7jNyAHTp0kH+uVasWGjVqhPj4eKxZswaDBw/G0KFD5eM1a9ZE6dKl0bp1a5w5c0Y33VcU3b/HXbt2xejRowEAderUwZ49e7Bw4cIsC4GSa6Jly5ZYt24dChcunKX5zBDM2EgEl0B2CzYj3Jnp8BsovKUFWxUAtd2DifyHaOFLJ2lcqKdrZVd3eoDiTF7GLQLqf4aXREAZhTvPQwTUCoKAYVqwGQxdgb7wJQZaTBnWEwG9uQIBEgNDFStxRhoPhHasCVacsSwEduvWTa5HoURZo6JZs2ZYv369pZuvAwcOYNGiRR6FbkePHo2NGzdi7dq1iI2NxfDhw9GjRw/s3r3bcK5Lly6pHn/77bcYPHgwevbsqdr/+uuvY8iQIfLj6Oho0+slgo+eCKjFKPT5IwI6OR3nnqZhiGq8STHQyBWohzcx0JsIaEZEJEIXUbBZqgPGRA5//vknGjduDMBcsxAlhQoVQuXKlXH69Gnd440aNQIAnD59WlcILFasGOx2u9ysRKJq1arYtWuX6XX4YuvWrQGbyxfBio1EcAmkCJgX8Cb+GYmTZtOCCUIPs+la1J3eNxRn8ibXXupj6ARUwtvFzNqAis7AgAkRMBs6BkuiHCfoxBGbaEr8U7kBnRnjHWLmHAThJ6Eca4IVZywrB5s3b0bDhg2xefNm3Lp1C7du3cLmzZvRqFEjfP3119ixYweuX7+OsWPHmp4zJSUF/fv3x5IlS1SB69atW1i6dCnmzJmDVq1aoX79+li2bBn27NmDffv2Gc5XqlQp1fa///0PLVu2RIUKFVTjoqOjVeO03beInMOMCBjIjvJaEdCWIeD5PC9DnDMWCzNrDkqk+3D1pXOix+YP2vOy6iYkQouEhAQcO3YMx44ds/zNWUpKCs6cOYPSpUvrHj98+DAAGB4PCwtDw4YNceLECdX+kydPBjz16a+//sKCBQswfvx4jBkzRrUFkmDERiK45AURMJDOwaymAGu7BhP5D6lDvdkNcLs0qlWrhvnz5/ucv3r16rh06ZK8ab8YGjJkiOq4stmUHlJ3+sTERGzbtg2//PILXn311YB3p7969arH/uvXr6vKUgQCijN5j2sv9TE1TisCSmg7A1txAjIHkzdvyOKcJORlIe3XNE7fEoSeG1BXhCRCCqtxJj/EmmDFGcuOwJEjR2Lx4sVo2rSpvK9169aIiIjA0KFD8dtvv2Hu3Ll4+umnTc+ZlJSETp06oU2bNpg6daq8/+DBg3A6nWjTpo28LzExEeXKlcPevXtlN4s3rly5go0bN+Ljjz/2ODZjxgy88cYbKFeuHPr164fRo0fDbtd/SdLS0pCWliY/1hbZJ7IPBwBw7jqBNrhdgYHsCmwVM85AaZyEYEGUU56XyrkAABHM2p8uiYBEVhg7diw6d+6M+Ph4/P3335g0aRJsNhsef/xxnDlzBitXrkTHjh1RtGhR/PLLLxg9ejQefvhhlcM7MTER06dPR/fu3QEAL774Ivr06YOHH34YLVu2xKZNm/DVV19h27ZtAVv3li1b0KVLF1SoUAG///47atSogXPnzoExhnr16gXsOkBgYiPFmbxPbk2b9ccJSBCBwEoBd+pO751A3YNRrMkdiC4etjARTOAhunjwOinBpkTAYLgBDbr3+ivEGXYJdohqodKHaKkUByktmFASyrEmWHHGshB45swZ3Rc5JiYGf/zxBwCgUqVKuHbtmqn5Vq1ahUOHDuHAgQMexy5fvoywsDAUKlRItb9kyZK4fPmyqfk//vhjREdHo0ePHqr9I0aMQL169VCkSBHs2bMHEyZMwKVLlzBnzhzdeaZPn+5hIyWCg9YNqG0MYucAMLjr6zG3EGgHF7B0YACqGoGq8RpBzcmJcDBeJQZ6w2heX9eRBEBvUHpw/kUUrbl9GONw9uxZOTXXV2rwX3/9hccffxzXr19H8eLF0axZM+zbtw/FixdHamoqfvjhB8ydOxd37txB2bJl0bNnT0ycOFE1x4kTJ3Dr1i35cffu3bFw4UJMnz4dI0aMQJUqVfDFF1+gWbNmFp+9MRMmTMDYsWMxZcoUREdH44svvkCJEiXQv39/tG/fPmDXAQITGynOEHoEQ1z0Jf5J19NLC86NQieRe6Hu9IEjUPdgFGtyFo7P/IzPxMzmgcqUYE7jwDYjAnKBSK31UivQighoKPwB+m5AzTW91QYkCD1CMdYEO85YFgLr16+PF198EZ988gmKFy8OAPjnn3/w0ksvoWHDhgDcL27ZsmV9znXhwgWMHDkSmzdvDqhNX8mHH36I/v37e8yvTAurVasWwsLC8Mwzz2D69Om6vzQTJkxQnZOcnGzqORLBwa4QAf1BTwA0LdJpU24VYiAAVRdfpaCnPU+JMo1XzwFoFuoSTJglISEB69evNzV21apVhsfKli3rUUtDD71vs55++mlL7nGrHD9+HJ999hkA97d/9+7dQ8GCBfH666+ja9euePbZZwN2rUDERoozuYtgNvUI1ty+5vT3mpQWTAAZ9Wg5c79DYsbvmvY9jLrT+0+g7sEo1mQfost9TyDVA1SKgFJtZ47nwevoFUo3oIwFJ6Akxknpwd7EOVXXXkkMNHAFGpJRJ9DwOloB0EH3LIQnVuKMNB4IzVgT7DhjWQhcunQpunbtijJlysgv+IULF1ChQgX873//A+CuH6V1g+hx8OBBXL16VZWiJamb7733Hr777jukp6fj5s2bKlfglStXTNk1d+7ciRMnTmD1at/15ho1agSXy4Vz586hSpUqHse9qcpE7sCsK1ArApoVAG2Mk8tmCGCwKer+SSKfUhDUHhPA5PqANvCq9OAwxnuIeFZFQDNQx2AiPxEVFYX09HQA7nqFZ86cQfXq1QHAtGvdLIGIjRRnch/Z2eHXG4FwBZp9HuT4IwLJhQsXVC426k7vP4G6B6NYkzO4G4QIuk1CZKFQmRIsnSf97EUE9OYGVAqCvsRAaZwpMVDab6amoImagASRFUIx1gQ7zlgWAqtUqYJjx47h+++/x8mTJ+V9jzzyCHje/Udu1gbZunVrHD16VLVv0KBBSExMxLhx41C2bFk4HA5s2bJF7vh74sQJnD9/Hk2aNPE5/9KlS1G/fn3Url3b59jDhw+D53mUKFHC1NqJ7EFKC74XAIe4UgA0I/4pO/2a7fxr5PqzmhIsiYCBSvd1WO8LROQhlMVyzSCKvKXU4LxK48aNsWvXLlStWhUdO3bECy+8gKNHj2LdunWmasxaIZCxkQg+ZhqF+CMABlNE8yUGmkn3zaqoSSIhYZWYmBjTdZuUUHd6TyjO5D2Uoh9vF2ALc6n2qQQ8bTqsUhAMUHdgvcYhWnFQdgeadQZqBEEPwdGiCEhpwYQ/hHKsCVacsSwEAgDP82jfvj1atGiB8PBwcJx/hUOjo6NRo0YN1b6oqCgULVpU3j948GCMGTMGRYoUQUxMDJ5//nk0adJEdROnLUIPuG3ua9euxVtvveVx3b179+LHH39Ey5YtER0djb1792L06NF44oknsuUbPcIYvW7BLsmpnoW4YFUENMIBPqM+oflAbHQ9G3iEMcDJGd9YGYmAqZzLcsMQglBiJTU4rzJnzhykpKQAAKZMmYKUlBSsXr0alSpVMqwHmxUCFRuJ4JIXugUb4XdqL8+y3DWYIESRM/17JI1r2LAhbDab5S+cpO70Tz75pO7x3NadfsOGDTh//rzsQpcIdKyhOJN3MOoYzNlEuQag1C1YfVyTEuxFBPRwAvqRbqsVBzknZ0kMZDbmrh/oYADEzO7DADkBCctYiTPSeCD0Y00w4oxlJUEURUybNg0LFy7ElStXcPLkSVSoUAGvvvoqypcvj8GDB/u1ECPefvtt8DyPnj17Ii0tDe3atcOCBQtUY7RF6AF3TSvGGB5//HGPOcPDw7Fq1SpMnjwZaWlpSEhIwOjRo1X1Mojsx1uTEIEBTmTWBLRyKxQoEVCJg/Fea/5Zmkt6NizTURjB7KZTg6VGJUT+RRBsEFzm/yqYaK1ZSF5EEAT89ddfcufiqKiogNdrUpLdsZHwj7wsAvoDbxfkOlRA1l2BRq5EXlH3qub6wIvsRN7FbCdH6k7vG4ozeYOrYx73SP8FMlOAOSk92Oa9SQhnY4YiYEAagxggOfrMiIGSe08rBjJk1AqUhElf9QEVTUo4gdN3BToy/nVm8QkSIUkox5pgxRnL6sHUqVPx0UcfYdasWap2xTVq1MAHH3zg90Iktm3bhrlz58qPIyIiMH/+fPz777+4c+cO1q1b51EfkDGGgQMHqvYNHToUd+/eRWxsrMc16tWrh3379uHmzZu4d+8ejh07hgkTJlC9jBxkaphxMwI9BMUmoa0P6OSYLAIKYFkSAaW0YBvjZNHNYSJl19s1BXgG8ShmhwM8HOARwezypodZoTCM8fJGEBIJCQk4duwYjh07FnIiIADYbDa0bdsWN27cyJbrBTs2Elkn2CKglbTZYDYh0a5DavQhdQDWG2OE9E27UkzUW7soUnwhsobUnb5KlSro3bs3ihYtKnenDwsLww8//IC2bdsiMTERL7zwAnr27ImvvvpKNYdRd/pZs2ahZs2a+OCDD4LWnf7o0aOIiIjAF198gQsXLqB58+bo1atXwK4DUJzJCxiJgFKTEFnAs2XUCwx3eYqAPpozGYqAAWy+ITUY8YWyk7BKvLOJ8hzMwdxrkzYT6HYolgRABzJFQYKwSF6MNcGKM5YdgZ988gkWL16M1q1bY9iwYfL+2rVr4/fff/d7IUT+xEgAVLoBlV/82GC9U7BVAdBMLUAH3E1BAuUMdCg9jtLlOWk9oqFDUEoRJlcgQXhSo0YN/PHHH0hISAj6tSg25m7ymxNQ6/yTnIGSGCiKnOV6f6LLJouKgWheQuQ9RMEGwWLXYLPpWtSd3jcUZ3I33kRA3i7IYqDbGSjINQGVIqBUE1DPDejVBRjsDrwm6wUyG1O0UsxcE1Pco3DK/dq5fNUjdAJwALaaJ82vnchTWIkz0nggtGNNsOKMZSHw4sWLuP/++z32i6IIp5O8uoR5lCKgU1ELQCkCuvw08SmdgPI+HYHPbBMQfzArQMpOPe2XXwpBMFBiYJWwd3EifYSpdRF5Bybylhw5jOWPZiFTp07F2LFj8cYbb6B+/fqIiopSHfenqLARFBtzJ9klAOYGN6AWPTEQgEoQBOCzFo8ocpkCohcxUBR58LyIo93GUHowIWM2XSuvkp3d6SnO5F58iYDKtGBlTUAPEdDADRgsF6Cc/msGC2IgoLitcXKZz1ng9bsX6zw/yRWolyYsHK1MYiChIpRjTbDijGUhsFq1ati5c6dH8cPPP/8cdevW9XshRP7Fmwho9mONMi1YKwJ6E/q8HRM474HRmytQTwRM10kFVqIUBJ0QYAcPFzK6BgdQDCQIIH80C+nYsSMAoEuXLqqC6owxcBwHQQico4liY/7GSNyThLLsEv+Mrq9cg1IQBKASBQF9YVArBkrzSHNK1yExMLQRBR4iZ+4zhpjRMMDfAu55hezsTk9xJndiRgS0hbnA2cRMN6B2rEIE5JTCl7cuwd5EQCcffJdgBkY1/dR1AyXca1I6BPVEQK/zZ9wcCocqw1aPxMBQw0qckcYDoR1rghVnLAuBr732GgYMGICLFy9CFEWsW7cOJ06cwCeffIKvv/7a74UQ+ROngRNBKQKqmob4ms+CCKjFl/Bneh4TIqCyPqBNI9yFMRiKgQDgYGFwauZL5VyGYmA6J6rqA5IrMPQQBN6S0CDmg2YhALB169ZsuxbFRkKPnBIA9dCKgtqbUa0wqBUEpcdGgqBWDCQIILRdGkD2dqenOJM70RMBlcckEdAW4cwUvRTClq4T0CEojvvZGdiHGOjhyjODoqmHX8iioM4cDuZ2Dzo5zVhjdyCJgYREKMeaYMUZy0Jg165d8dVXX+H1119HVFQUXnvtNdSrVw9fffUVHnnkEb8XQhCS4CeJgNJjX65AF5hhZ2Czqb96IqDeeb7EQrNOQK34p4cDNlkMBADV1IrYrXQKKsVA9xzuc9MzHIvUMISQyA+OwObNm2fbtSg2Enkdb05BpSioJwhqxUAA5AokQp7s7k5PcSbvoKwL6CECSmM0XYI98OYGzGk06cE+XYFazKQjWxAECSJUCWacsSwEAsBDDz2EzZs3B2QBRP5kkn01HD40KdkRqHPMWyqwFl9ioFbcs1ozUEoL9icd2GMtXsZL7kAgU9BL50Q5RVkvbdiXO5AgQp2dO3di0aJF+OOPP7B27Vrcd999+PTTT5GQkBDQzpEAxUYiNNAKgoBb9PPmENSKgaLIo/aG2dm3aCJbEATedBF3IR+ka0nd6Y8fP45ChQplyzUpzuR+OIUjWq4LKD82EP5cNp/dgi2h4wSUHIC6NfoCgJEYqER53FAg1K5N+ThDFDRzLSJvYiXOSOOB0I01wYwzpAgQOYZRbUDlYwFu0U+7aRHA4FRsfq9Jca7AMY/NiECLgJJYp+omDLcYaFf82WaOc/+rdAZKOCF6pBIToYUo8JY2pkgNrlatGubPn5/TTyEofPHFF2jXrh0iIyNx6NAhpKWlAQBu3bqFN998M4dXR+R3RJdN3rILQbCZTlfW3rBqawlKSILg/7N33uFRlNsf/86WNFLohJ4gJRRpASGAiFJCuTSRLggixUtHELEBAgKKFAVBUcEfwgVRUGxwsdAD0sJF0ShKkyogCQSS7O68vz82M5mZndmd2ZKyOZ/n2YfszDvvvLMke3a++z3nCNdRmNKhiYLn8OHDOHXqVFDdmEkRutMTBOAqAqqiELCYRBCT1Qe0B+Y2XSkCGhIFddTzEx5a+wWYmbkX8xwm+QOQpw4ThIRgjjWBijO6HIGlSpWSFVp3x82bN31aEFG80WoQIrj+rEz999CT+KflCpSKe0oR0Bs8NgSRuvMkY905AT0RwkyqzkAhTdhljRyJgkTxSA2eO3cuVq1ahaFDh2Ljxrwu5a1bt8bcuXN9np9iY+EmvzoGG0FL9JN24w0E3opzJotDc83yuoCcizOQIIoDge5OT3Gm6MApaqMKacEmC68poDG7WeYYZA7OKQbazM46gXYTGNx0DdaDLffew9fmIQbXoCUGGjqfQyKGCj/nrsPn+QmiiBCoOKNLCFy6dKn4840bNzB37lwkJycjKSkJAJCSkoIdO3bgpZde8moRRPFibshGyIrcKbBCng5s01GXTyn02XK3yeflxH1u5/NT0xC9qImAnsQ6aZowkCcGKtESAwmiOJCWloa2bdu6bI+JicGtW7d8np9iY+HlRI+pAIqXGCWtz+dPpGKgMkVY2jVY2lmYCE4cDrOB1GDnuGBN1xIIdHd6ijOFl+vP9hd/VoqAal/sePyyJzc92EUMVCJpAsKk9fPUnHI2k/rPClGQs3GyuVzwRYj0gEsdQUV6sPS6mJXlCoI8YGUwNU8L2LqIgsFInBHGA8EdawIVZ3QpBE888YT4c58+ffDKK69g3Lhx4rYJEyZg+fLl+PbbbzF58mSvFkIEP04B0BVlWjAAmDnAlrvdyjhNMdDGMZjBuYiBShFQCzPjZMKf3CHoGvSsKtn0VmYCOB4OMITApOkKDNGZia/XsacUA4X1KesFanUTJoIHB2+C3aH//9bBTMWia3BsbCxOnz6NuLg42fZ9+/ahRo0aPs9PsbFw4hQBixfuREB/uPPUxECzOc/5J3MHBtjdSBQtgrmTIxD47vQUZwonggioFAABwBJmy20Wklsf0MznvSdqpMKK4p+aGAjocgUKQp4onNnknwtZ7udEzsyrdhT2KAbqQaXBhxK1+n6aTUWUx/pjjURQEsyxJlBxxrBVaMeOHVi4cKHL9s6dO+O5557zy6KI4old0SVYcAZawEFv2T89AqCeTsJaNfWUYpogvAEQBUl3YiDgPiVYTQQUugeroWwgIk0RlkLOQEKJL6nBCxYswIwZMzBx4kSZWwFwfjvVtWtXbN++HVu3bkWvXr10zTlmzBi88847WLJkCSZNmuTVupSMHDkSEydOxAcffACO43Dp0iWkpKRg6tSpfndPUGwsHBR2EdClM28RF8ykYiAgTxEmiOJAfnanpzhTeFATAE0Wh1P8yxUBTaE2eZ1AT400tMRAQDNFWK8wxnR+WezivgO03YDuavUp9ynW6FEMlLgCxeYmSvGSagUSxYRAxRnD9qAyZcrg888/d9n++eefo0yZMn5ZFBHc2HjXN26lCCi4BIVbCYtOh58SMzjxoWu8Rg1CKUqRzQqT0xWYez4tcsBrioA5HO8iAtrgEAVAK8ziQ4lW8xBB+JPOawOPM9mT3F4fUfTgeZOhhy/NQg4fPox33nlHbGOvZOnSpbrrGQls3boVBw8eRKVKlQwd54nnnnsOgwYNQvv27XHnzh20bdsWTz31FEaPHo3x48f79VwUGwkjOG8Y/SMCarkBjTQG0YN0vdIUYOH8VBcw+OF5A42p+LxOjsHclApwdqd//PHH0apVK1y8eBEAsG7dOuzbt8+v56E4Uzi48Vxfl23C+yNn4mEOteeJgFI3oAbMbgbL/fJEbBwifW7Le2/VK+jJ5tc6xpaPWUIqTT7UHIAuzUOkIqTV6WTkbJy8diARVBiKM8Uo1gQizhi2CM2ePRtPPfUUdu3ahRYtWgAADh06hO3bt2P16tVeL4QglCKggBl5zkC1jsHOMf7/VsjG8aLA5wkrTABzHgPArStQKQCqnlvDAeg8l/rNltIZCCBXDLTnPidXIJGHN47AO3fuYPDgwVi9erVqs43U1FS88cYbOHLkCCpWrKhrzosXL2L8+PHYsWMHunXrZmg9nuA4Di+88AKmTZuG06dP486dO6hXrx4iIyP9eh6AYmNxRumKK+hz+Hst0nO7qxcoQLUCCSnBnK4FOLvTDxkyBIMHD1btTv/111/77VwUZwongtBnsuSlA8tEQIm4xbkRBYXGIUpnIIA8MVBwGKqk9upBb9MRj25Ab7FxbtOGZQiuQDMPBpNrDUQSAwkJwRxrAhVnDP8FDRs2DPv370d0dDS2bNmCLVu2IDo6Gvv27cOwYcO8WgRRfJGKfg7mfNiQ91CidAZqdRFWw5NYKE0rFlx+No4XxT2j51GrCehOBBQcgEoR0J0oKEXpDBRSmMOYRXQKCnUDCYLneWRkZMgeQmDRYuzYsejWrRs6dOjgsu/u3bsYNGgQVqxYgdjYWN1rGDJkCKZNm4b69et7dR16CAkJQVRUFCpWrBgQERCg2FgYCVTzDGFu4aF8Lt3u73O622bUAcjbzeLDyLnVnIHCNQvnVxMIiaKP3WEy9ACC36UhdKdfvXo1rFaruL1169Y4duyYX89FcabwoXT7CV2CnY7APBGQszg0RUBhn9Z+JnXO2U0uDj/OxskeAHxy+/m1Bp+aWCdtBOLJFSgRJJmVieKnamMUIigwGmeKQ6wJVJzxyh7UokULrF+/3uuTEsUXtbRg2X6N7YIrEHB1BrprJqKFuzqBQgMRweXnL7REQL1CnzvU6giGMBPAadc7JIIHh91kqMMW4zn89ttviImJkW2fOXMmZs2apXrMxo0bcezYMRw+fFh1/+TJk9GqVSv07NlT9zoWLlwIi8WCCRMm6D7GCHa7HbNnz8abb76JO3fuAAAiIyMxfvx4zJw5UxZM/QHFxsKHv916RgQ+f55bax5v5lcT/qTbtNLYlM5A4TipM1B4fR7YOc/wuojgJJhdGkDgu9MroThTeHARAYW6gWYeMDOZsOfOCaiGar1AKLoI63QFcmYezGHS7QYU8cUNKBUAhZ+l83lwBnqsFwgSAwk5wRxrAhVndAmBGRkZhl7Y27dvIyoqyutFEcGHVsdgAWmDEC0x0B2CM9CTICg09NCcJ1cgVHYTztvv/hs2wUUIaDcF8UYEtMGhmRLsCaegaSE3ICGjdu3a+PHHH2XbQkNDVcdeuHABEydOxM6dOxEWFuayf9u2bfj+++9x/Phx3ec/evQoli1bhmPHjhmuKaiX8ePHY8uWLXjttdeQlJQEAEhJScGsWbNw48YNrFy50qf5KTYWDfIjdbcwnttblF1/pR2Bta5HK02YIIKdQHenpzhT+HBX888c5nQCii4/qbtN6zidzZVEMRB5PgW977qqIqCKiOizG9Bduq7DpCouqjUOAVTEQPCAwyQTAwmiOBCoOKPLN1yqVClcu3ZN96SVK1fGn3/+6fWiCEINaZi0gBMfUoykCmshpAibGedMEZY8/IlSBHTXEERtvHS7P1yFRNHFzpthd+h/OHgTzp07h5YtW6Jly5ZYt24doqOjNYXAo0eP4tq1a2jatCksFgssFgt2796NN998ExaLBTt37sQff/yBkiVLivsBoE+fPmjXrp3qnHv37sW1a9dQrVo18Zhz587hmWeecQl03rJhwwasXbsWo0ePRsOGDdGwYUOMHj0a77//PjZs2ODz/BQbiw7+SNX1do5Apih7g54mJUrXoFT885QmTAQvzFBTquBP1wLyutMfOnRI7E6/fv16TJ06FU8//bTP81OcKRqYLM4mITDz4ELteSKgxQFYHM5tZqb6EMZoohQKbWbA7vz7Yg6T7OELARUB1dAh5rmkCecKiczKwD3ys7HzEUUCI3GmuMSaQMUZXY5Axhjee+893bWVbDZvPF0E4UTLFagVIgUxUJouLLj+1OoCenIFOteQ5wwEoOoO1EI5t8NNaq6W6Cds16oXqJYKrESrEQlBAMaahbRv3x4nT56UbRs+fDgSEhIwffp0lC1bFqNHj5btv//++7FkyRJ0795ddc4hQ4a41BpMTk7GkCFDMHz4cP0X4obQ0FBVUTE+Ph4hISE+z0+xsehQ1Fx5gUaa2qsXLWegngYiRPElmNO1AGd3ep7n0b59e9y9exdt27ZFaGgopk6d6pfu9BRnCj/mEDvMoXanG1AQAXOFPwDydF4gr+FHrpjHWeEU98x2RT1A1/dnMWVY0klYnA95HYI9pgHraTSi4eDTxMx7FgP1zilUbrEpnIF6z0MUO4I51gQqzugSAqtVq2aoG1VsbKzfay8RRR9lfUChUYhdobEZEQE94QDTbBLirk6gcr9UEDSruA4FoVCZFqxXBAxRdCcWRDwtwU+5za6zDiCJg4Q3REVFoUGDBrJtJUqUQJkyZcTtag1CqlWrhvj4ePF5QkIC5s+fj969e6NMmTIoU6aMbLzVakVsbCzq1Knjl3WPGzcOc+bMwZo1a0S3Y3Z2NubNm4dx48b5PD/FxsLFiR5TVbcXBhGwsKYIS0U8o2iJgQThDxYsWIAZM2Zg4sSJWLp0qWwfYwxdu3bF9u3bsXXrVvTq1UvXnGPGjME777yDJUuWYNKkSX5ZZ6C701OcKdzIRMCIHJjCcvJEQEEAzBXqXMS53Np9UsTUXweX5xK0m+UdhAVsuTUD7QpRzMLrFwThwQnoTrhTq/2nHKsm2BkRGHPdIaIYKKkXSBC+UtzjjC4h8OzZsz6dhCDUREBBALQhTxRUioBGBEChiYiVcQDn6sxzu77csVZlqrFCLFQTAY2iFP20xngSAwFtAVBL8Mu495zOVRJFCcFGr3s843DmzBnUq1cPgLMb8NixYwO1PJG0tDSkp6cH/DwCx48fx3fffYcqVaqgUaNGAIATJ04gJycH7du3x6OPPiqO3bJli+H5KTYWfgqj+BZolDX+1FKTpa+Lv8RAIvhx2M1wMH3/38LvRfPmzWE2mw3FmcOHD+Odd95Bw4YNVfcvXbrUcG3ZrVu34uDBg6hUqZKh4/QidKePiorya3d6ijOFFyEd2FwiG6ZQO0xhOTBF2NQFQA0HnvhbrBAFXQRByXu06AoE5M5AQC4MWtw3CdGdCqwm3Ol15Hnr3rMhzxWolirmSyMTolBjJM4A3scaijNedg0mCH8gvKdriYDeIO0oLKQAu3MFuq6JqYqB0v16CIEJOeBhVtQVVDoElfulY6RioFEEsTFPTDQhy6uZiGDFSGqwGrt27XK7nzGVws8q26T4+4anZMmS6NOnj2xb1apV/XoOovggTY31hvwSzTyJgGprUaYKa9UR9PU1IIofRtO17ty5g8GDB2P16tWYO3euy/7U1FS88cYbOHLkCCpWrKhrzosXL2L8+PHYsWMHunXrpnstesjv7vRE4cBk4cGZeHAWh6YIKApw7tJwhX22PPEOdpNzHptZFPyYXmuEIAwKgqDFS8FMr9AmHScVFm3+T+V1SREmCAlGYg3FGSckBBIBx9kxWP7GLbgBHUxbABRCnt2N+KZsFqJErwAoRU0MFNAjCgrnDJGIfEKqsJrw53q8c4wDvCgGqrkC9aQDS8XEMGZB2bDXcD3rWY/HEUQwsGbNmoJeApFPqKUFF3enmifBTk2Y1NNIRGsewVloMjH82PEFPLBznuG5iOAjIyND9jw0NFSzMRXgdKh369YNHTp0cLlBu3v3LgYNGoQVK1aolqNQg+d5DBkyBNOmTUP9+vWNX4AHAt2dnihc/PPio6IIaLLwspqAIoIIqKcOn4CVB4fcGn9SMRAQBUFp/UDmQRDjkJc2zOAmRdhbZ52fHXmqnYOlrkApuenB/N56MD14yq/rIIouRmINxRknJAQSAcUpAqqjVwS05dbfU2sAYhNSgRVYGSce5w3uxEDxHIq0YStMEJ7aOF5cpwNMFAVzVMS7EBVxUHATuqsxKMWSO4dSHLTkronEwODGbjfBbqCrNc8XTGowQQQCrdqA7lBLhdUjguW3K9DTudTmMrI+b1yKwnhyBhY/HA4zHBpNztTGAq4u7JkzZ2LWrFmqx2zcuBHHjh3D4cOHVfdPnjwZrVq1Qs+ePXWveeHChbBYLJgwYYLuY4ywYcMGbNy4EV26dBG3NWzYEFWrVsXAgQNJCAwS/nnxUZdtnJmHyeLI6xBsdXgnAqohbSiSKwjK0oU9wBycOB6AthhotCGIOwQXoKeUY+n5bJw4XlMMlCBzBZp5EgODECNxRhgP6I81FGfyICGQCBjuREABtbIPShFQEAC1HHgOLi/1V00UVENP52CpGKjVPVjaXdjBMacYCLgIgsK5hJRh4WctZOPcuAKluHMIhjATwAE28CQGEgB8Tw0uCty4cQMvv/wyfvjhB1y7dg08L/8buXnzZgGtjPAX3oiAWkjFQW+ccQWBN2KcyeT8OxDqinqbsuxJECRXIAEAFy5ckKVraTk0Lly4gIkTJ2Lnzp0ICwtz2b9t2zZ8//33OH78uO5zHz16FMuWLcOxY8cM13rSS6C70xMFj1QENOUKdCaLMy0YZuZ0AypEQGZl4PQ2tbDl3Q9wygYibgRBKWrioKyWoCdnoD8x2szDkxioBjUNIRToiTUUZ+RQ720i3xFqAprd/K24EwFtYLIHkOcWtHF5DkIr41Qfwng99f6UY9SahUjFQmG/1YPIpxQBzeDEh3x73jhvRECt7WGMvgMIJvjcZiF6H4yZREdgvXr1sGLFioK+hIAwZMgQ7Ny5E0888QQWLVqEJUuWyB5E8URPYwzebtYc52vKcUE66QQRUIkva1KrNWgyee/IJwovDp6DgzfpfDg/z7Rv3x4tW7bEunXrEB0drSkEHj16FNeuXUPTpk1hsVhgsViwe/duvPnmm7BYLNi5cyf++OMPlCxZUtwPAH369EG7du1U59y7dy+uXbuGatWqicecO3cOzzzzjOpNlTcI3emzs7PFbf7sTk8UHgQRkDMpxEDARQQEdDTjsJlkIqBbLHyeKGh15KUN58KZWZ7oJ4E5uLy6gXaTvCGJIKT5oX6fIdTOJxH1qA5g8cZYnDEWayjOyPFKDdi7dy/eeecd/PHHH/jkk09QuXJlrFu3DvHx8WjTpo3Xi1Fr4ZyVlYVnnnkGGzduRHZ2NpKTk/H222+jQoUKmvMMGzYMH374oWxbcnIytm/fLj6/efMmxo8fjy+++AImkwl9+vTBsmXL/Nrpi9CH1BXogDwdGMgT46RuPKkgJzj3pKnDNk49ZViJ0uEn3S49h/S54P6TX0NemrDafjUHolb9QmGs0hXodPQ5A7+e+oBqCAKlzcvjieChODgC9+7di3379okdg/PjfIGIjUTBoezAK5AfTTMCMb+77uLUAZgIBHoLuLdv3x4nT56UbRs+fDgSEhIwffp0lC1bFqNHj5btv//++7FkyRJ0795ddc4hQ4agQ4cOsm3JyckYMmQIhg8fbvBK1Al0d3olFGcKEWZeTAtWioACqs5AD+KfiytQilA/EJCLgblin1QMFFyCYppw7njmMDnvQKTpy546A/vbSag2t05noDI9mCAAfbGG4owcw0Lgp59+iiFDhmDw4ME4fvy4qEymp6fj1Vdfxddff210SgDaLZwnT56Mr776Cps3b0ZMTAzGjRuHRx99FPv373c7X+fOnWWF4pWq8ODBg3H58mXs3LkTNpsNw4cPx6hRo7Bhwwav1k/I0ZMWrER5y+POtScIbYI4J4wTBEEtkU3qMhS3qZxDEPPMjMsTGhXioBJlzUAAsDITbJzQKCTPjahcn3ScdKxTDHTtNuwNvnQhJoiiSEJCAu7du5cv5wpUbCQKnoISA4tqh15p0xCC0EtUVBQaNGgg21aiRAmUKVNG3K5WuL1atWqIj48XnyckJGD+/Pno3bs3ypQpgzJlysjGW61WxMbGok6dOn5Zd352p6c4U3BI3YAmSUdgzsyAcK2K5yqoiIBK0c9j+q60E7BOUVC6XXo8Z+PyxEt3zsBAi4IGxEBRBKT0YMIgFGfkGBYC586di1WrVmHo0KHYuDFP7GndurVq+2U9aLVwTk9Px/vvv48NGzbgkUceAeDsAlm3bl0cPHgQLVu21JwzNDRUs9PLL7/8gu3bt+Pw4cNo1qwZAOCtt95C165dsWjRIlSqVMnlmOzsbJkdU9mZhvAeafiUpgRLRTV3zj1ALgiK7kBOXjNQ6TIUjhVSe9UEQTUx0FfUREDpv8615gVZQQwMYYCNc3jtBgRynYUAzuRM8noOovDh4DnYDaR2OIpJs5C3334bzz33HF5++WU0aNAAVqu8/Zwel4pe/BEbKc4UHwqr+66wrosoHDgcJjiYvljjyHWfNm/eHGazOd/iTFpaGtLT0wN+HoH87E7vr3swijX+QfiCyFNzEHf1AtWcf1puQEEgVK0hCLgVBd0hrM1jKrOANw1GzLyL0CgTIaUYqRmod81EkcFInAEKJtYES5wxLASmpaWhbdu2LttjYmJw69Ytrxah1cL56NGjsNlsMrtlQkICqlWrhpSUFLdC4K5du1C+fHmUKlUKjzzyCObOnSuqtSkpKShZsqQoAgJAhw4dYDKZcOjQIfTu3dtlvvnz52P27NleXR8BWE0MNl77Wxt3ngc94ptUEFSKgfLzaLsMlYKg4AqUioHScZ6wwmQoBVeatis4BJUpxdbcAsF6xECtTsIEARSP1OCSJUsiIyND/CJJgDEGjuPgcPjPbeWP2EhxJn8QHGtG8NYV6Kurr6i7AglCb2qwGrt27XK7nzGVmmgq26ScPXvWq7UUBvx1D0axxhgmi8bnaDMDCqCxlKogCMgbi2iRKwwySG6RckVMTWEOKmKhH8RA3cKjdB1UP5DQwNtYU5zjjGEhMDY2FqdPn3Ypfrhv3z7UqFHD8ALctXC+cuUKQkJCULJkSdn2ChUq4MqVK5pzdu7cGY8++iji4+Pxxx9/4Pnnn0eXLl2QkpICs9mMK1euoHz58rJjLBYLSpcurTnvjBkzMGXKFPF5RkZGwKz/xRW7hiNPiUv6rUSYE8czuNQNdOhwGGohTQvW02REtj5F2q/R48zgEAIT7kkEPQtMXgt8FuoRRBQTBg8eDKvVig0bNqBChQoB6+YF+Cc2Upwxhi8dgwVRz4hYZVQMFJx1UoeddJxe950eMbCwCYa83YxmO14t6GUQRMDJz+70/roHo1jjO1wgBEC7Se7w87QGXwTB3C7CgFwQdCcGAgpBUDivEUFQOtZgkxISAYniSqDijGEhcOTIkZg4cSI++OADcByHS5cuISUlBVOnTsVLL71kaC5PLZy9ZcCAAeLP999/Pxo2bIj77rsPu3btQvv27b2aMzQ0VLPTGeE/hPRdaequ8FzzGJljLrcOH8egdognEU+tS7CwDk81AtWO8+QKtKpYn7WOMcOEkNyv8Nx1EBYw2kmYKLrY7WbYmQFBgzcVi9Tgn376CcePH/dbjQ53+CM2UpzJf4w619yJgUCe0Kcl8CkFO3+k4ioFx4IQBMn9Vzxw8CY4dH6ZWFCpwfnNkCFDcPr0aYwYMSLgXzj56x6MYo13SOsDijUD3aWtCsfprWNnQASUzS8R1zSbjEix5TYOyRUemcPknMNmEsVAIM+xJ1t/bo1DLvdeQiYICugVBnWOIwGweGEkzgjjgeCONYGKM4aFwOeeew48z6N9+/a4e/cu2rZti9DQUEydOhXjx483NJe0hbOAw+HAnj17sHz5cuzYsQM5OTm4deuWzBV49epVzfp/atSoUQNly5bF6dOn0b59e8TGxuLatWuyMXa7HTdv3jQ0L2Ech0EXuFH3naxpiCJd2B94IwYCxl2BetKK9aT+UnMQQovikBrcrFkzXLhwIV+EQH/GRsI/6BXZvHEHauGNw08p3Cnn8JR67Gn+QEMiIOEOX1KDiwL52Z2e4kz+kz6rl8s2c5gNplAbTGE5Lvt0i37CeHcdghWIgp2R+awOwGYWOwiL4yB531aKgYBMEFRFIRoC3qX6qqI1DzUGIdwQzLEmUHHGsBDIcRxeeOEFTJs2DadPn8adO3dQr149REZGGj65pxbOVatWhdVqxXfffSd2SklLS8P58+eRlJSk+zx//fUXbty4gYoVKwIAkpKScOvWLRw9ehSJiYkAgO+//x48z6NFixaGr4PwjLQ+oN1AnFDrxKtM61UT5oQ6gdLxegU8TxgRAwHjtQL1zWkWXYFqgqCaAGgDL9YhJIILnufAG/h2iDEUC0fg+PHjMXHiREybNg3333+/S7MQZZd6X/BnbCT8g1GnnV5BUMsVaAR3Yp0/RDxfxEAjLkXpa8XnxnnqFkwUJ/KzOz3FmYJDcAOaQm2AmQcXane6Aa2+v18bEQM9od1ohMnEQObg8tyMEmegsB5B6JOh7HisGKOaViw891bEUztOUp/Q1OpX7+YliCJEoOKMYSFQICQkRLyJ9BY9LZxHjBiBKVOmoHTp0oiOjsb48eORlJQkaxQibeF8584dzJ49G3369EFsbCz++OMPPPvss6hZsyaSk5MBAHXr1kXnzp0xcuRIrFq1CjabDePGjcOAAQNUOwYT/sHB8kRAm8YYK+OQpRCx1MRA2bwawqBUDFQbp8SIa1CvuCh1BQYaad3AEGaSiYGCEOlvQZIouhQHR2D//v0BAE8++aS4jeO4gDQLEfBHbCR8w9dU2/xodJEfjj1vU4W9EQFl2900BiOKNg6H/jIUDt45LpjTtYD87U4vQHGm4DCF2mEKtYOzOMCF2o2l8yqFNAma9f58wcI7Rb5cV6AS5uCczkBJmrCwBqU7UBOJGOipA7AhtARAtZ+JoMJInAGKR6wJVJzRJQQ++uijuifcsmWLVwvRYsmSJTCZTOjTpw+ys7ORnJyMt99+WzZG2sLZbDbjf//7Hz788EPcunULlSpVQqdOnTBnzhxZPYz169dj3LhxaN++vTj/m2++6de1E3noEQHdoSYGaopanCQ4qMQeqTjoa8qwUXegEhvHq9YJdIdS5PN4DsnrlMXZDZ2LIIoyZ86cCej8BRkbCXV8FQH14osrML9r+AUiVVgpApL4R2gRzOlaQOC701OcKTwIYp0oAlodHlN1AbgVALXOoSYI6jqXEokYyME1Rdi5PlcxUL7fR9HNqBvQkwhIECoEc6wJVJzRJQTGxMTITrh161bExMSgWbNmAJy1/m7dumUoWGmhbOEcFhaGFStWYMWKFZrHSFs4h4eHY8eOHR7PU7p0aWzYsMHrdRLqzA3Z6Ha/VARUqxdo09El2BN3Obsortk4uKbDFlDWkrfdgzXnk6QH60EQAa9nPeu3NRCFAwdvgt1A2jfPc8UiNbh69eoBnT8/YyMhx5eOwf7CHynC+YU/xUASAYsvDrsJDhM1C5ES6O70FGcKDqE+IGfK++xusjicQqCOJiEAvBbRvBH9lOKhKCoCMjFQFa0GIoUBNVGU6gUGLUbiDFA8Yk2g4owuIXDNmjXiz9OnT0e/fv2watUqmM1CpzoH/v3vfwetCkvoQ0sEtKncJOhtGuJOBFSrvacmtLk4Bzn1NxfB2Sc4BoVze0rv9dUV6A6j4qGQHiw4BoVrJycgocRIavDKlSuxcuVKnD17FgBQv359vPzyy+jSpQsAYPTo0fj2229x6dIlREZGolWrVli4cCESEhI055w1axY2btyICxcuICQkBImJiZg3b57f67SuW7cOq1atwpkzZ5CSkoLq1atj6dKliI+PR8+ePX2am2Jj/lMYBEApUlGsqIiCBJGfBLNLAwh8d3qKMwWDWpMQTvkeL7jn3KXOWnnfHXUGUQp4nJmX302ppAmr4SlNWbdQaGXe1whUipskABIaBHOsCVScMfzO9MEHH2Dq1KliAAKc6bhTpkzBBx984NfFEUUHT05AAavnIYrxnCyVF5DX5VO6/azM5DHV1gZefHg6t9o5vcWToKenfp/ZwJ8s1QMsPjgcnKGH1BFYr149t45rAKhSpQoWLFiAo0eP4siRI3jkkUfQs2dP/PzzzwCAxMRErFmzBr/88gt27NgBxhg6derk1qpeu3ZtLF++HCdPnsS+ffsQFxeHTp064e+///bb67Jy5UpMmTIFXbt2xa1bt8T1lCxZEkuXLvXbeQCKjYUFs9nhF+ebN6IebzeLj/zAaBq03o7G7iA3IEHIEbrT5wcUZ/IHNRFQCrNLUmwlIiCzMvFREHBmXlOcE7dbeKfzz12TE7ufhEsry6sPKPws3eYJEgEJAkDg4ozhv3S73Y5ff3Xt0PPrr7+C50l4KK68mDNAc5/VxGDmAIvk/duc+7O7eoFmxslcekphLm+fvl9jG8fLHs7zqzkI1QOUOzFQyzGop1GIN+nCIblip1Vh8RfcgFInILkBCTXi4+Nx6tQpnDp1yqOFvnv37ujatStq1aqF2rVrY968eYiMjMTBgwcBAKNGjULbtm0RFxeHpk2bYu7cubhw4YLoIFRj0KBB6NChA2rUqIH69etj8eLFyMjIwP/+9z+/XeNbb72F1atX44UXXpDdODVr1sylY72vUGwsXAiCoBFRUCrk+SrmuZsjv+oYeoOh18uNCPjAznn+WA5RyHDwnKEH4EzX0vOFU1FF6E6/du1aHD16FP/73/9kD39CcSZ/iJn1mep2lvu+zuxmQPH+rhT/ZIKglXfvGgw0uecXhULRyViALnaDYimJgMUHo3GmOMSaQMUZw12Dhw8fjhEjRuCPP/7AAw88AAA4dOgQFixYgOHDh3u9EKLo82LOAI/OQAsHgBlrGOIuNVfYp5YmLMWo2OZODHQn+rkTC4U0X4eQcgzXscI1qImbITAhx4DLjwRAwh08zyMjI0O2LTQ0VNZUSQ2Hw4HNmzcjMzMTSUlJLvszMzOxZs0axMfHo2rVqrrWkpOTg3fffRcxMTFo1KiR/ovwwJkzZ9CkSROX7aGhocjMzPTbeQCKjflFo22LDKcHm80Ow043f5HfdQQ9pSrrqRWo5/UiCL0Ec7oWkL/d6SnOFCzMYQKfbYHJ4nB23M11zul2AApioJ5UYW+EQ0/z5qYpi6nC/nL+aa7HQ8dgnSnDaiIgszISBwkZwRxrAhVnDAuBixYtQmxsLN544w1cvnwZAFCxYkVMmzYNzzzzjFeLIIIHd2KgmVOvDegAYAdTbRSiB6kYCLi6/LREQKFjrw08rDDprvUnjFETBI3UC3SAiWKgsnuw8hrM4CQCogkO8Ia7BxPBi91hgt1A92mecfjtt99kRcgBYObMmZg1a5bqMSdPnkRSUhKysrIQGRmJrVu3is1GAGdr+2effRaZmZmoU6cOdu7ciZCQELfr+PLLLzFgwADcvXsXFStWxM6dO1G2bFnd1+GJ+Ph4pKamujQN2b59O+rWreu38wAUG/MTb8TAgkRNDAxEJ19/QmIgQegj0N3ppVCcyT9iZn2mmiIsuAI5uxksy5KbaptrbxDSbwPY4VYQHVVFMKl4qCUKSmsWCs5AnTUDpfitkYgP9QO5R372zxoIopATqDhjWAg0mUx49tln8eyzz4pukmBVXwn/I6QEgwHgAJtCSxPELis4Q92CtZx6UhHQIZlPzY0H+Kfxh6d0YKmo5w+0ugeHMQu5AglNateujR9//FG2zZ0bsE6dOkhNTUV6ejo++eQTPPHEE9i9e7coBg4ePBgdO3bE5cuXsWjRIvTr1w/79+9HWFiY5pwPP/wwUlNTcf36daxevRr9+vXDoUOHUL58eZ+u7ZVXXsHUqVMxZcoUjB07FllZWWCM4ccff8R//vMfzJ8/H++9955P51BCsTF/MSoGCqKblrglCHX5Vd9PWEtBiIGFXYQkCi9GvnSyF4NOjkDgu9NLoTiTv2iJgXCYnHUCsy3gzMzpsIuS5DqZeVEM9JdzTS31WA3xXO6alFh5cJA0A7E6nGKg0DkYELsHq57DkwjoMOWJop5cgYBXYiC5AYMXo+aG4hBrAhVnfPrKIjo6mgIQ4YJavUCrST0IGGke4kkYVAp4WiKg1hhxrJfORHcoU321hEjVYyVvhiE6/mRDmEl33USi+GIymcT3cOHhTggMCQlBzZo1kZiYiPnz56NRo0ZYtmyZuD8mJga1atVC27Zt8cknn+DXX3/F1q1b3a6hRIkSqFmzJlq2bIn3338fFosF77//vs/XNnv2bNy5cwdPPfUUFi5ciBdffBF3797FoEGDsHLlSixbtgwDBmjXNfUVio35Q6Ntiwwf46luYH53/nU4zIXWeaf2OklfH5NGXCcIKYcPH9ZVi7Yos27dOrRu3RqVKlXCuXPnAABLly7F559/HrBzUpzJH9TqBfJ2Z4qwIAaybAu4LMX7uC+OOZUGJHpxqU3oBlm9QJ34zQmopIAarBDBQ7DHmkDEGcOOwPj4eHCctojx559/er0YInjwlCIMuLoBtfAkzHlyD+px3wnpwdJz+qNTsDuUYqAyPdgbLDDBnps27Py6j1yBxQGHgzP07ZlD0jUYgFffnvE8j+zsbNV9jDEwxjT3ezOnERjL+5sfPHgwBg8ejLt37+LOnTs+uw21oNhYtHDnEHQnBgbKMShdhy9uPZPF4dc1ekoRNpkYdQ4mijUrV67Eyy+/jEmTJmHevHku3el79uzpt3NRnCkYol/ehoxXesg3OkxOEdDiAGczO913VqYqALq4At259RTHeYt4TuFcnmoOCq5AN6iKgJJ5ORuXt2apK1C6LnPeNXEOD7Ej11lJ9QCJ4k6g4oxhIXDSpEmy5zabDcePH8f27dsxbdo0rxZBBCdKMdBqYrCp3DCYAeiRqtQEP6GTsHSftHGILym4+SEGBgJBDBSQpghfz3q2oJZFFDLi4+Px2Wef6Ro7Y8YMdOnSBdWqVcPt27exYcMG7Nq1Czt27MCff/6JTZs2oVOnTihXrhz++usvLFiwAOHh4ejatas4R0JCAubPn4/evXsjMzMT8+bNQ48ePVCxYkVcv34dK1aswMWLF9G3b1+/XJ/yZikiIgIRERF+mVsNio0Fg6/1AqWimx5nXkGkEHuDOzFTS2g0mfLiBs/n3aQqxUB/i41E0cHuMIlpWB7HsuBP1wLyutP36tULCxYsELc3a9YMU6f6t5YpxZmCR0inFVr7wczAhdoBhwmcjQeTvjW6SxEORBdhqfAmrVMoFR7dnddoF2G916CRHszMTC4G+lAvkAgejMQZoHjEmkDFGcNC4MSJE1W3r1ixAkeOHPF6IURw4s4ZaAVkle2sjAM41zqB0vp/gvBnAxN/doev9fj0iIFKN6E7hM7BWvsA1zRiG3KdgpKOw56wwJRXh9FAp2GiaOJggMPAfzNjMOQIvHbtGoYOHYrLly8jJiYGDRs2xI4dO9CxY0dcunQJe/fuxdKlS/HPP/+gQoUKaNu2LQ4cOCBz36WlpSE9PR0AYDab8euvv+LDDz/E9evXUaZMGTRv3hx79+5F/fr1vXsRFNSuXdutcwIAbt686ZdzARQbCxKjYqDJxMuELgGlQObWCWdAEPRGlPMFb9KbpSKg8JzEQMIf6O3kuHLlSqxcuRJnz54FANSvXx8vv/wyunTpAgAYPXo0vv32W1y6dAmRkZFo1aoVFi5ciISEBM05Z82ahY0bN+LChQsICQlBYmIi5s2bhxYtWvjl2oD87U5PcabgEFyBJk+ptEonnJf1Ag25ATXSdWXnE8RANSeihffYQdhwSrDO8R7FQMnrRxDuCOZYE6g4Y1gI1KJLly6YMWMG1qxZ468piSDBnRgoYAEHu9gVl5OJgYBTJJS5ATXiqEw0dCO6qaEl6AVCDBSPU6xPbQ6pw1GJ0DlYq2GIkujwBci495yudRLBjRFHoLu6fZUqVcLXX3/tcQ5pum5YWBi2bNmi69zeMnv2bJeuyAUBxcb8Qa8YKAheWu43KWoinVIc9CSIqYlyBdmoQ7Xun0k9vgjbhdeHOgkTDt4EB6fvs47DoEujSpUqWLBgAWrVqgXGGD788EP07NkTx48fR/369ZGYmIjBgwejWrVquHnzJmbNmoVOnTrhzJkzMJvVfy9r166N5cuXo0aNGrh37x6WLFmCTp064fTp0yhXrpzxF0CF/OxOrwXFmfxBFAOFDUpR0GYCBx4M7sVAAS1R0B8iYEGgZ92cg5OlBwPkDCTkGIkzQPGINYGKM34TAj/55BOULl3aX9MRQYoyPdjMAXn6HgcwwKZRE1DmAJQMkboF1dKDla5AaW0+vTX5hHO4EwQ9iYHCPqmo5+n8grBpFR1+wjWZ4MidRxAD3SFNDyYxkCgODBgwIGD1AI1AsbHw4EnwEtASBgH1VGItd2BRFgGVY9TEQHci6I8dX8ADO+f5sFoiWNDr0ujevbvs+bx587By5UocPHgQ9evXx6hRo8R9cXFxmDt3Lho1aoSzZ8/ivvvuU51z0KBBsueLFy/G+++/j//9739o3769F1eTR0F0p9eC4kwB484d7UbUUnMI+lIX0CjMk9POTedgf6c2exIDVV+r7+uDe+Rnv66DKLoEY6wJdJwxLAQ2adJElm7FGMOVK1fw999/4+233/Z6IUTxxArkNrUQtnBiSquaeKd0CgJ5x0rFOqUrUBADjXTrVcNT4xKbWDXEsyCoF+n1AEA4zHCAIUdYk7Q5CKDqDMzi7B7FQqLoYndwsHtIg5Xi4Dmc97FZSGHGU0pwIKDYWLjRI3hJx7oTAwWUDUc8peQWlAiopyagJ7TEQHeQGEgAQEZGhux5aGio2w71AOBwOLB582ZkZmYiKSnJZX9mZibWrFmD+Ph4VK1aVdc6cnJy8O677yImJgaNGjXSfwEazJ49G2PGjMFTTz2F8PBwWXf6SpUqBaQ7PcWZwgNzmMDbTXmf6O0m5+2LIJAJKcK+OtvMkvm09uUDzGFymx4sEzA9rEvNFQjkNRIRBUFBDFRLD85NcWY77geXfFLHFRDBTjDGmkDHGcNCYM+ePWVByGQyoVy5cmjXrp3b3GmCELCaGJDrCrQzbTFQTXRTq/kndwKquwKFYz2hR8jTg95UYaXDUEtoNDMO4Ex5rxHHIwQm5IAX3YHKNGFL7n41yBVIGEkNLmpI05DzC4qNwYWaGOiutqAnYSy/RUB359MrAJolThCH3aR5/dQ5uPjgcED3l06O3Ldh5c3TzJkzMWvWLNVjTp48iaSkJGRlZSEyMhJbt24Vv7ACgLfffhvPPvssMjMzUadOHezcuRMhISFu1/Hll19iwIABuHv3LipWrIidO3eibNmyuq7BHQXRnZ7iTCHEwYHPtsBkcYCLzMlLD7Yyj7XtDHXCVYpr/q6bp1Un0J0r0AgaDUMA5N4IArA5/3FxB8rmoXqBwY6ROAMEd6wJdJwxLARqvaAEYRRz7t+4UgwUbl+kop+7JhmC8Cd1znmqFegpJVePIOipCYdSDPS1A7EgBkqbh6iJgQBym4Q4EGKwTiJRNOEZB4cBt6vRZiFFDZ7P/995io3BjbS2oLtGI1JBMNDinzfzG3EBah3P8yZR/JSmB+fntRNFhwsXLsjStdw5NOrUqYPU1FSkp6fjk08+wRNPPIHdu3eLsWrw4MHo2LEjLl++jEWLFqFfv37Yv38/wsLCNOd8+OGHkZqaiuvXr2P16tXo168fDh065JebqPzuTk9xpnDBZ1vBW3iYzAws2wLOzIDIbAAGRT5vMOoGVBHQODPvOT3YKDrWpeYG1ITqBRI6CdZYE8g4Y1gINJvNuHz5sstF3bhxA+XLl4fDQR/+CDkeG4VwTjFQwHkroWgOooFyjDm387DSFai3FqDr/K7NPIx04TXSREQPesRAPZAbkAhmR2BBQLGx8OIP8UvtuZ7Ow4UFX18Ds4WHw0NXSSnUVIQQiI6O1lW3CQBCQkJQs2ZNAEBiYiIOHz6MZcuW4Z133gEAxMTEICYmBrVq1ULLli1RqlQpbN26FQMHDtScs0SJEqhZsyZq1qyJli1bolatWnj//fcxY8YMn68tv7vTU5wpfDiyrKL4xZkZEG7T/lrWaF09qaimdNPpEMf0iJEyMdCDK9BTerDPqcpW6HIFSsVLw92MiaAlWGNNIOOMYSFQK+UqOzvbo2WSIJSYOael1ybZZoXTFWhV1MZT4k4otLpJL/YFIyKgEk/dh9XWqnaMVAwUcCcGWmFCFvQ1FSEIwjsoNgYfnmoF6uk8HEwIYqDSFUgUHxw8B4fu1GDnOL2dHNXgeR7Z2dmq+xhjYIxp7vdmTqPkd3d6ijOFD+Ywgc923k6bS2Tn1QpUClS+NNdQS6lV2yYIf2ouP+H8npyBWmKgy7lMrtek5S4UXovc9GCXGoE29cP0wBwmH6u/E4UNI3EGCP5YE8g4o1sIfPPNNwE47YnvvfceIiMjxX0OhwN79uyh+hSEYRxM7gbUgyenYF6nXaHxiPfpvf5A6gr0JAaq4U4MFDoJO8BEMRBAbkpw3jfDQtdgEgODExvPwWYwaF4I4tTg/IRiY+FHEOm8ccXpPUZrXFEWCAUHoFlnfSilMJj0/SsBWRdRtNDbyXHGjBno0qULqlWrhtu3b2PDhg3YtWsXduzYgT///BObNm1Cp06dUK5cOfz1119YsGABwsPD0bVrV3GOhIQEzJ8/H71790ZmZibmzZuHHj16oGLFirh+/TpWrFiBixcvom/fvn65tvzqTk9xpnDD7GYg1C7f5sk95wmNY9XSajXr6amhIQh6FAPVXIESMZCzcdodj4XGKYD7WoEKZK5AMw8GZ/1FDjo6HhPFjmCNNYGMM7qFwCVLlgBwqqKrVq2C2Zz3YS8kJARxcXFYtWqV/1dIFBscKnFB2RxET7ow4FovEFB33AkCnVIQVKur5216sTC/VAw0ilIMlImcLmKgs5OwNTfJWhAHBTGQIABKDfYXFBsLByd6TPU4xhdB0Fv0diBWw58193je5PV1q6UEK69LaBhCLsHgxkiHertBl8a1a9cwdOhQXL58GTExMWjYsCF27NiBjh074tKlS9i7dy+WLl2Kf/75BxUqVEDbtm1x4MAB2Q1SWloa0tPTATjTaH/99Vd8+OGHuH79OsqUKYPmzZtj7969qF+/vg+vgpP87E5PcabwIohiyq7xMhHQ325Af2DljYuBubgTAwGoC4LCvLmdlDkYqBWoUidQXKuBkhVE0cBInAGCO9YEOs7oFgLPnDkDwFkIccuWLShVqlTAFkUQWlh11g4EXOsFSoU0pRinJQhKsXHe1xrUi9b5rTCpioGCSCrUC5SKgSEMsHHOhiHO1yHPLUgQhH+g2Fj0KAhB0BNFWTyTNgwhCCV6XRrvv/++5r5KlSrh66+/9jiHNHU2LCwMW7Zs0bdIL8jP7vQUZwo3nMUBaIlaBkVAt8462bzQ12XX7Ryu7kC3DUQkHYRdxEDJfFq1CcVOykJNRWWKsNoxatemImISRDDGmkDHGcM1An/44YdArIMgdGNEDNQa704UdIdSDNRqBKIm6HnqROxWhMx1FCrFQGW9QKWDklyBxQOHpNu2HngEd9fggoBiY9FD7mgLnCjozhVYpAXA3NdMuAZyBRLFiYLoTk9xpuAxuSuVYHEAFj5PIPMgAkoFPxfxTBDM3KXSSsRAn1AIa6IY6KFeoCAYugiCwpwKRJHTYQLAG3M7WhmA3PTg3NeKM/Pgup7QPwdBFDECHWd0CYFTpkzBnDlzUKJECUyZMsXt2MWLF/tlYUTwYOaYWMizoHAnHkrTiI12BfYG6fxqTkS1tGQAEJfPmeCAa91AKzPBxvEwg3PrCgxjhvV/Igih1GDfodgYPBRGl6CAvzoRK8VIf12rsD7ebpaJgUTwYecDlxpMqENxpvCQ8UoPmFQ+QpssvNMVKMWgE9CtC1BNDFQTAFVSaHXjpRgIqAiCgKtjT1lLMFcM1JMirHQFMisDB3IFBitG4gxAscYXdCkCx48fh83mfMc5duxYvtbFIIITq4nBxqv/Hrn7cktLzNPj6tMrBgJ5opq3KAVF5VyCq9Cd6Ch19pnB5c0hEQRdzqsiBgquQGctwfxpkELkLw4GGPF58owcgf6AYmPw4UstPXdouQI9uecCKaj541ql1yWkCAtiIEEA+tO1CHUozhQueLtJ0xXI6a15pxeVJht60mm9xp0YCDgFQaUoKHktVAVBAa3GIjrrBTIzy+0OzAMOU54YSBC5UKwxji4hUGpF37VrV6DWQhCauEsFNpLaa1U0mZfOm9dt2CniqYmB7moEKsVEQNvdJ2xXzicdL03zFf4VBEErM8lSjdWcjIIY6Mh1AwIAGHAmZ5LmNRDFB3IE+g7FxuCksLgDi5KrThA0pWIgQRC+Q3Gm8OEiBppVHIHwkPoLnfUAdYqBomsuN4VWEMu0zq2Jom6g25qBQJ4wqBAEdYmBMFYv0Lk++fWRLE4Q3mPYU/vkk0/i9u3bLtszMzPx5JNP+rSYBQsWgOM4TJo0SdyWlZWFsWPHokyZMoiMjESfPn1w9epVzTlsNhumT5+O+++/HyVKlEClSpUwdOhQXLp0STYuLi4OHMfJHgsWLPBp/YQ2Zh1indmLd3NvOvBKsYJzEQed251/Gkabg5gZ55Ky6wBzeQjYOF4m/nk6n3Cs9BgbeHm6sWIOM0wIYSZYYYbF+J88QRA6CGRsJAoGnjd53fFXDS1hUU3wK0oioHBdwpqV3TOJ4MHBOEMPwJmuVa9ePaxYsaKAV1/0oThTeODV0mWtee99SoGPWZmq6GdIpJMep9IcRCakSYQ44dxaa1DFh07HqiKgAtk6dL4GWtdHBBdG4wzFGu8x/An3ww8/xL1791y237t3D//3f//n9UIOHz6Md955Bw0bNpRtnzx5Mr744gts3rwZu3fvxqVLl/Doo49qznP37l0cO3YML730Eo4dO4YtW7YgLS0NPXr0cBn7yiuv4PLly+Jj/PjxXq+fUGd+6H/8PqeDY+IjkEjFQOHhLTngxQcgT/t1Oa/kPGZwMMNVWATc1BJUEAKTKAYSwYnN4MOBvNRgCpr+IVCxkQgu3ImB0kdRo6Ddk0Th5fDhwzh16hSVn/ADFGcKP5yZl4toZj7v4S1SR563NQAlKIVBTYHQBzFQFbX5JNemu/OxsFYSAwkJFGuMo1sZyMjIQHp6OhhjuH37NjIyMsTHP//8g6+//hrly5f3ahF37tzB4MGDsXr1apQqVUrcnp6ejvfffx+LFy/GI488gsTERKxZswYHDhzAwYMHVeeKiYnBzp070a9fP9SpUwctW7bE8uXLcfToUZw/f142NioqCrGxseKjRIkSXq2fyD8CLf4pEdJu/UmOWkdhDynIgiCoFAaNiIGApF4gUeyJj4/HqVOndAXN+fPno3nz5oiKikL58uXRq1cvpKWliftv3ryJ8ePHo06dOggPD0e1atUwYcIEpKena86p171d2AlkbCQKB/50BQJFRzQzW3jxofuYXBGz2Y5XA7Usgih2UJwpItg8f8bOF1egl0KZIcegEncNRaxycVT1HEInYA9ioOgK9HadBEGI6G4fWrJkSTGFtnbt2i77OY7D7NmzvVrE2LFj0a1bN3To0AFz584Vtx89ehQ2mw0dOnQQtyUkJKBatWpISUlBy5Ytdc2fnp4OjuNQsmRJ2fYFCxZgzpw5qFatGgYNGoTJkyfDYlF/SbKzs5GdnS0+z8jIMHCFhCescN8kJD9QioxGuwl7Eikd4GGWiIoOMBe3nxSpGOhO8BNqBnrC6Qr0OIwgXNi9ezfGjh2L5s2bw2634/nnn0enTp1w6tQplChRApcuXcKlS5ewaNEi1KtXD+fOncOYMWNw6dIlfPLJJ6pzSt3bjRo1wj///IOJEyeiR48eOHLkSD5foff4MzZSnCk+CGKgv0XGQCGIgQ43N3tC45Ci6Ggk9GHn9RuS7LmfN6iTo+/4+x6MYo3/YXYzmMMOOEzgvHDSeawXKNQK9CCAuQhpghjors6fAmZlxsVJrS+MFK+F7Bo1hEpP9QKVXYSJ4MJInAEo1viCbiHwhx9+AGMMjzzyCD799FOULl1a3BcSEoLq1aujUqVKhhewceNGHDt2DIcPH3bZd+XKFYSEhLgIeBUqVMCVK1d0zZ+VlYXp06dj4MCBsk4yEyZMQNOmTVG6dGkcOHAAM2bMwOXLl7F48WLVeebPn++10FmcmZE90G16sFAX0C55v7eKP3EAkwtsag05jOKu8YjL+gyKgc75nWNsHO+SAuzI3ZeDPJeeGoILUWwI4qapiNpzNYQuwkTwwcPZOdjIeCNdg7dv3y57vnbtWpQvXx5Hjx5F27Zt0aBBA3z66afi/vvuuw/z5s3D448/DrvdrvoFi+DelrJ8+XI88MADOH/+PKpVq6b/ggoQf8ZGijPFD6k7sDCJglouQLOF1yUGEoQAdXL0HX/fg1Gs8R/MYXI2rsi2gFkczs7BYfa8ASoCnCGhTaerz6MwpjaPhjiouTYL7975p8SdC1C1mQgnCp16xEBz8zTN/UTxg2KNcXQLgQ899BAA581j1apVYTL5/kHvwoULmDhxInbu3ImwsDCf51Nis9nQr18/MMawcuVK2b4pU6aIPzds2BAhISEYPXo05s+fj9DQUJe5ZsyYITsmIyMDVatW9fuagxFPYiAAWDgADADnKmjIUmHBRHHOBqba6MPfuBMfPQmD0s6/3qAUBMXtOt2CBKFF9erV8dFHH4nPMzIyEBoaqvr+p0RI+ZXejKiNiY6O1nRZax2j5t4uzPgzNlKc8Z5G2xbhRI+pPs1R0CJWQZ9fL3rEQCI4cTBnjVm9YwFyafgDf9+DUazxjuiXtyHjFdea844sq7NzsOAKzLIAVvd5TmodfWWuQDWhTC2tWEsAtDLPtirpOQw4Bl2QfHGk1ihEUwTU4W7U1UmYCCqMxBlhPECxxhv036HlUr16dQDOtK7z588jJ0fuMVI2+3DH0aNHce3aNTRt2lTc5nA4sGfPHixfvhw7duxATk4Obt26JbsxvHr1KmJjY93OLYiA586dw/fff+9RIW7RogXsdjvOnj2LOnXquOzXe4NMqONg2sHIrBD/zIIoCEBwBYJzptJawYlioD81QE8uQ0EMFFyBgvimlZKrJs6Z/VBrUFqvUCkOCoKjVDhVCpHuXIhE0UVoAqIXB4DffvsNMTExsu0zZ87ErFmz3B7L8zwmTZqE1q1bo0GDBqpjrl+/jjlz5mDUqFG616Tl3i4q+CM2UpzxDW/FQKlwpSZiCd2D80PgKkpiIOA+VZggAHJp+BN/3YNRrPEeNTGQOUzgsy0wWRx5rkAAiMr7ZKaV+utVGq5e9IiB7rA53985M+90Pho6tzNGaKYC526nNF/CX1CsMY5hIfDvv//G8OHD8c0336judzj0a7jt27fHyZMnZduGDx+OhIQETJ8+HVWrVoXVasV3332HPn36AADS0tJw/vx5JCUlac4riIC///47fvjhB5QpU8bjWlJTU2EymajYboB4MWcA5oZslG2zmhhsvPPNX0gRBssTNJQpwmpiYCCbhwhzm1VETCszwcbxMsFPKgoK+wWUApyyYYg3HYkFUVJ6Lnc1BwlCSu3atfHjjz/Ktum5MRg7dix++ukn7Nu3T3V/RkYGunXrhnr16nkUFQXcubeLCv6MjYT3GBUD9Yh7+S3OuTufv2sL+ipwenIHEgThPyjOFA5UxUC7GXx23p2LCa4dhAtEDNSLmXd1BVp5UQwUUaYHG2gmpYXg+lMTBMkVSBCBw/Cnt0mTJuHWrVs4dOgQwsPDsX37dnz44YeoVasWtm3bZmiuqKgoNGjQQPYoUaIEypQpgwYNGiAmJgYjRozAlClT8MMPP+Do0aMYPnw4kpKSZI1CEhISsHXrVgDOG8rHHnsMR44cwfr16+FwOHDlyhVcuXJF/OYsJSUFS5cuxYkTJ/Dnn39i/fr1mDx5Mh5//HFZ12LCv7yYM8DjGAvnFADNnPNhBWAGYAGHcGYSu+YKKcFmxqkKdUZxN4eDYzLBUXDlaYl3QqdhK3M+1MQ5T848PbUIpWsBtEVA6fbdthG65iWCH5PJhOjoaNnDkxA4btw4fPnll/jhhx9QpUoVl/23b99G586dERUVha1bt8JqtarMIkfq3t65c2eR/TbPn7GRCDwmE29IACsIMdCX/fmJ0c7CRNHFbvABONO16tWrhxUrVuT/goMMijOFh+iXXV9vR5YVfLYVfFaIs2bgnVCwLItMTBMFPzMvc8ippQrL8LZLrt7jJCKgTKzMFTJlKb863+/1NAaRjSfBj4DxOEOxxnsMOwK///57fP7552jWrBlMJhOqV6+Ojh07Ijo6GvPnz0e3bt38usAlS5bAZDKhT58+yM7ORnJyMt5++23ZmLS0NLFm1cWLF8Vg2LhxY9m4H374Ae3atUNoaCg2btyIWbNmITs7G/Hx8Zg8ebKsXgYRGJTOQKUr0MEk9QIBgHMKgY5cp6CVcbBxTLNuoBShKUgg6gjqaRwiHeOuVqBmerERMVAytXpaMjkFgxkHjNXTYDDWLIQxhvHjx2Pr1q3YtWsX4uPjXcZkZGQgOTkZoaGh2LZtm666r964twsr+R0bCW3cuQJ9EdAEMTC/RDhlIxHlef0lTubnNRHFD0rX8h8UZwovzGECZ+bFeoEAYHJw4KRNRKwOwMKDs/FgEZCLgQ6TXDgTugQXZvR+AWTgOty5AwnCHRRrjGNYCMzMzBTTZ0uVKoW///4btWvXxv33349jx475vKBdu3bJnoeFhWHFihVu1V3G8t444+LiZM/VaNq0KQ4ePOjTOgnvUUsTFpCJgYBMEAQDHLlpwoIY6FI3EK71/vR2CdaTaqw8hzIFWA1hjFIM9Eacs4GXuQDFc0hER2okQughPj4en332ma6xY8eOxYYNG/D5558jKipK7NoeExOD8PBwZGRkoFOnTrh79y4++ugjZGRkICMjAwBQrlw5mM1mAE739vz589G7d2/RvX3s2DF8+eWXonsbcDYhCQkJ8f9FB5BAx0bCGP5oHqKW9prfYqD0vIFEeU0Ou8lrl9/9ny3217IIgpBAcaZwEfn8l7jz6r9ctjsyQwFJ3UCYGTiLw/kwMyAy23lbEyV5j1VLzZUiOAW9dQb6AVmtQHfxwaq4Li+g2oEEEXgMf5Vcp04dpKU523U3atQI77zzDi5evIhVq1ahYsWKfl8gEfxYTfKgJqQFA3mCoJAuHAZnmrBVkcordf35I1VYQMuVZ2acpiCn9tybGoBCDUK9Yp6QkkwUPxxgsBt4OMBER6AeG/3KlSuRnp6Odu3aoWLFiuJj06ZNAIBjx47h0KFDOHnyJGrWrCkbc+HCBXEeNff2X3/9hcaNG8uOOXDgQOBerABBsbHw0WjbIp/n0BLDhAYiwqO4YDI7xIcWJ3tRdkWwYgNgYzofucdQupb/oDhT+Ih8/kvxZ2lDDd5ucoqBdjP4bAtYtgV8ZiiYgwPLcj7nsszyyQSHoDvxzMb51gBEJ2rpwS64qQ+rVgvR8BrIHVgsMRRnKNb4hGFH4MSJE3H58mUAzg6TnTt3xvr16xESEoK1a9f6e31EkOIuRVhA6g60s9zmIRzgjAvyBiIARGegEl8bikhdeA6O+VVolOIu3djG8aKYqOUKVJ1T4Vh8xPoBvrc96ftiiSKPEUegJ5d1u3btPI5RzqPHvV2UoNhYOPHWGSgVAJXOQLWUXOF5UU6x9eR0dCf+AdQ4hFCH0rX8B8WZwomaM5DZzc5P9HYTTBYePACTxQE+KyS3kQgDy7I4nYFhKu+tWg5BQST0t0PQkyMxF0MdhP2Y2iyIgdI6go5jtWFu+pvfzkEUbSjWGMfwJ7bHH38cw4YNAwAkJibi3LlzOHz4MC5cuID+/fv7e31EEKNsHmI1MVV3ICB3BkobiFgZ5zHF1hfhThDcpAKdS/OQ3KYg7sQ5G8dr1ghUnk86j/QYQdDz1fX3iPUDn44nCMIVio1FA73uPaWgpWyIoSWY5bdDsLAKj+QKJIwyf/58NG/eHFFRUShfvjx69eolut8A4ObNmxg/fjzq1KmD8PBwVKtWDRMmTBBd5mrYbDZMnz4d999/P0qUKIFKlSph6NChuHTpUn5ckt+hOFN4EZyBUpGM2eWOP95uBhwcmN0Mlm0B7lmdjUS0hDU1Ic2hGC84BI06BfWKeZ4I4Jc/epyAjmO1A3Z+IvigOCPH57/eiIgING3aFGXLlvXHeohihlonYaUgqBQDwxTdhIG8enuBaAwiYAPv8lAidDH2tZuxVDh05KZy6l0jUfywgcHG6X84AEOpwYRxKDYWHvyRIiygRwwE9AuOhQ3puo26+5TjSQwMPnjkNafy9BD+OvSma+3evRtjx47FwYMHsXPnTthsNnTq1AmZmZkAgEuXLuHSpUtYtGgRfvrpJ6xduxbbt2/HiBEjNOe8e/cujh07hpdeegnHjh3Dli1bkJaWhh49evj0OhQWKM4ULrTEQCFNWNyWbRHThVm2Bdxdc95+pcNPy1WnJeTpEQWFY5Wioj8JUKMTShMOfozEGaOxhuKMHI7pyM0y0k138eLiUSQ6IyMDMTExSE9PJxuqH9BqHgJATBl25P6m2nPrAQidhB0A7uW65RxgYnqw4NpTPjeKHnFNcOk5awdy4nkdHHOKhiqOQFG8zE35lboPpeNzwCMEJpjBeXYeStaqVVvQAYbdNu03NCJ/8Md7iMPhgMViwWNYgQiulO7jfmE7UKnnLd2pwYQ6gY6NFGf8i5AirNdF565ZhlL0UhP+8tOt5054FNahV5yUrlv5GkjTg3mH3O2iJhxS45CCxx/vI8Ico/ABQhCh65gc3MW7eBIXLlyQnTc0NBShoaEej//7779Rvnx57N69G23btlUds3nzZjz++OPIzMyExaKv2tHhw4fxwAMP4Ny5c6hWrZquYwqS/LgHo1jjP6QpwlyuGMZZHDBZ8mr/mSzO91FTiWxwoXZwYXZwkTnOFGFBCFQT8tylChtBTwqw8vw25zGiyKl8v8+NFZyZB6y8erqzlNzrlKb6uqzBjegnPY7SgwuegoozgG+xprjHGV1Xc/z4cV2TcRyp9IR3uOskLNQPlHUUzq0PKGhr4cyEe7mdeQGIXYQdnOeuwkZwJ8JJHYDKWoUOMORIRLoQmOAAk6U1CyKeVMDL8SBCqnU6VhMA9ToKCYLQD8XGooVQL9AfXX/V6gYChcsJqLxGf6yRd5g91gokghcboDvvQijgXrVqVdn2mTNnYtasWR6PF1KxSpcu7XZMdHS07psz4RiO41CyZEndxxQkFGeKFlqdhHm7yXkHYebB282yeoEAAAufe1uTKwZamasYqBT91AQ9h8m9OBgoB6AEeaMRyc/50OSEKPoYiTPCeMC7WFPc44yuK/rhhx8CvQ6CcIseMdDKONg4ubgGBlEMBCATBAHfG4kIqImAUjegeD7wMMMkuvykyMcxjyKg9Nxa10ECYPGAN5A+DgBM0jUYAMaOHYuxY8cGanlBC8XGooeR5iEOu8mtK1DYpyYI5jfKBia+rEMqlKq9BkonoBbkBiQAqLo0PMHzPCZNmoTWrVujQYMGqmOuX7+OOXPmYNSoUbrXkpWVhenTp2PgwIFFxvlGcaboIYiBzGESXYGAihgIRfMQIFcEzP2yxZOIpib4BSgl1wUL71t9QBsHWBk4B+fWFaiFt8cRwYvRWENxxouuwQQRKNy5AqVoi4EcLIyDHUzsJmwVugvDVRAE4BeXoLIWoKe57nF2hDATcuB0Blpzt4tdgTVSegXUuhiriYEkAhLuMNI1mCCCDX+4AgXUBMGCQO/1qHU99pWCvnai8BIdHW34Zmjs2LH46aefsG/fPtX9GRkZ6NatG+rVq6fLXQg4C7r369cPjDGsXLnS0HoIwihSZ6DQNISzODTFQM7M5C4oZWqtsnagN+46nW5Al7TgAEOiHuEPjMYaijMkBBKFDGnzEKUoKLgCAXdiIJxPhG1SpDGGk6fvunPVSZGKcFLUjnVXWzCH4xHCAMAEG8eLIqBetNYhrodEwGKFDQwWA//n0mYhADkCieKFtHmIp4YWWq5AIT1W6o6TjjMqjPnLzacXT2KgJ1egksIihhKBxUjzMmFc8+bNYTabdceZcePG4csvv8SePXtQpUoVl/23b99G586dERUVha1bt8JqtarMIke4OTt37hy+//77Qu/SIIIDoXlI5sKuAJyCoKYYmG1xOgPhrLPHzLy6+CdsU+5TjpOiEAA5GydP31Xsc53PwPu6VRErfBAVmZlRc5BiiJE4I4wHjMUaijNOSAgkCi3KjsJzQza6iIEiKmKg4A60wpkyLCCmB3spBioxKgJKyQGPcDhvJK0wuT3OAQZIRENBDPRXejNRvCBHIEG4pq+qCYPuhDCT2aGaKuuLMKbHrSiIePmViqxHDARcaycSxOHDh3XdEDHGMH78eGzduhW7du1CfHy8y5iMjAwkJycjNDQU27ZtQ1hYmMd5hZuz33//HT/88APKlCnj1XUQhLeUmP61y7a7bySLX+fzAEySf2F1OG9pomyuzUOkYqACUTiT1hdUEQGl/2oJglpwZl7WFVnEbhIbhuhGci3euAI5BwdT8zRj5ySCFj2xhuKMHPq0RhQZBGHQalJ03+WAUBNgzf05jAPCAJgBhIKDBRzCmQlhLLfzbq5VUNrhV5yLef7myZPIp6dzrxQHGGwcj7ucXXOMUC9QGCvMawMvf+g4H0EQBKGOv+va6RHPjOKvtF69YqNRzBY+INdNBDdjx47FRx99hA0bNiAqKgpXrlzBlStXcO/ePQDOm7NOnTohMzMT77//PjIyMsQxDkdeGmVCQgK2bt0KwHlz9thjj+HIkSNYv349HA6HeExOTk6BXCdBCPDClyYOE3i7GY7MUPDZFrAsC1i2BVyW2SmW6XDVCSKaTEzTEAGV26QPF4y4AaXobUoiOaea+09LHGRmRunEhGEozsghIZAoUmiJgYBTDAznnIJgGCd5wCkKWsDByjjDYqBaCq6aGCiIcb4gFfKUzUSkYqAwVvnQwgxOfBDBh4NzOlP1Pngur1lIvXr1sGLFioK+BIIoNKiJgUqXG+8wiw9PeBLF1AQ3LRFOud1XUdCdGKjcR04/wg4GO6fzIUnX0hNnVq5cifT0dLRr1w4VK1YUH5s2bQIAHDt2DIcOHcLJkydRs2ZN2ZgLFy6I86SlpYmdIC9evIht27bhr7/+QuPGjWXHHDhwIECvEkF4JuKZHWB2s0wMBACWbQGfGeoUA/8JB66HA7dCwd22Oh9ZZiBLX8MmKV7V/fMkAhbQFz4kAAY3huKMwVhDcUYOpQYTRRZpmrCAS7owAKub+oFCarAVnCxNWA96RT+1OgchOmsCCmKg3g7CaudTin+7bSN0z0UEL5QaTBDa3P/ZYpc0Yb3psWp4kzLr74YeWgSieQhBCBhJDXZHu3btPI5RzhMXF6frGIIoCEpM/xp330h21gy08E5nIJwuHUduPUHO4nB2FLY43UhcmB1caK70oWgoIjrqbJzMkefW6aes6ecN0rhoM+V1PdZCWJvODsfSWoEkAhJa6E0NdkdxizP0yY8ockhrB1pNTDVV2Mw5G4lYcmOf4A5UcwYKDkCpM1CPK9ATgkPPGxee8hilIKhVRJWahBRP7GCwGXg4QI5AgvAGI2Ke0FCkKKCn1iCl+xIEQfiXiGd2AHBNE+aznenBfFaIM2U4MxRMmjZ816yeVivdpifd12aSP9TGuMNdXPAiPdgdJAIShH8hIZAokigbiQiCoFQUFNyBgiAopAwLYqAUPbUB/YEZJoQwE3I4QdDLC6DKzsFqYqA0XVgp+qmJgMo5jHYnJoKX+Ph4nDp1CqdOnaKOwQShgla9QIfdpEsQ1JM27A8nXn7VDNQ9TxESQAljGPnSyWhqMEEUV9TEQEEQzK394kwjzgqRi4FZEjFQWkvQYdIWAD0JfDrGcO6cfEYchnqFQpAIWJwwam6gWOM9lBpMFFlezBmAuSEbXbYLYqCN50Qx0MFy3YEMEG5RrCqdg92lCHvq6uuJEJg8pvhamUlW608Q8vzhACQRkCAIwhhqKcICghjorVMuGNNxBRGw3idLC3YhRKFBb2owQRAKhFRhi8MpCJoZ+Cx5BXHOZgeLcOSl2WqJa942/fACzsbldSN2mHSnAAPedQ8mCIBijTcE36dQolihdAZK0XIHhuW6A4UUYWnjEAAuz/2BWnpwjkZzDyszqboDjaYYU2OQ4oORRiEOjoEHKDWYIHTiqZOwXodgUUqt9cYdSE7A4MeRW1pC7wMglwZB6MHFFSggpApL3IEuzsC7ZtFFKCAKcUDgRUC7FynFBKGB0ThDscZ7yBFIBDVKd6DgDDQDMDPAjjzBzAYGM+Pg4LS/iVK6Am0cr8tpp3TuSdODc8A7U34VcyndgchdK9UBJPwBNQshCP24cwYKKMVAT8IfNekgigPk0iAIfUQ8s0PePEQg11XH282iO9DFGWjl5eKfFCvvd4GOM/NggvBo4Z1ioFbMM5ACbARz098CMi9RNKFYYxz6BEoUeV7MGSB7qCEIgoIz0Ko2RuIENOIKtHG86kMLc+6fnZ7OwVruQKNQWjDhC3v27EH37t1RqVIlcBznIiBu2bIFnTp1QpkyZcBxHFJTUz3O+fPPP6NPnz6Ii4sDx3FYunRpQNZOEP7i/s8Wiw89CE5BqUCoFAf9VZeP501uH97MRxAEQeQvEc/sQMQzOxA2cSfCJu7M25ErpvH23NqzEmcgAFURULbNH92B3aGIbao1CgmCKFTQJz0i6NASBJXdhaUI4pq0c7BVh+DmrcBmhkl86EEpCFLaLyHFDh42Aw/eYNfgzMxMNGrUSHNcZmYm2rRpg4ULF+pe8927d1GjRg0sWLAAsbGxhq6XIAoab0RBgYJIEfaHsGekYzIRnDj7ETCdD+cxlK5FEN4jEwRVxEBPaLoE/Qxn5t03ETEyl47rUnV0EEGBsThDscYXKDWYKHaYOcCuEheFtFsrOGdTEU7+sxrunH9qhBjQ3q25Y22KzsLCOSlNmPCF6tWr46OPPhKfZ2RkIDQ0FKGhoS5ju3Tpgi5dumjONWTIEADA2bNndZ+/efPmaN68OQDgueee030cQRRVHHaTqgiYXynCPG/ymwNRCdUHJLSgdC2C8COKNGHOzLRTcnNhVuZ06Pk5Rdid8KdLgJQeb+MAvaIliYCEChRrjENf7xLFCneuQEDutJOmBUt/tvrxz0ZoAqLl8FPrUqzmQvTUjVgQD3fYhxlfJFHo0UpP13o4OIbffvsNMTExssf8+fML+lIIokjhqW6gEq1Ow4ES6JRQyi9BEEQRx5eae4FMEbby7uc38/KHG3S5AgmC8AlyBBJBjVni5HMo6v05v1DiYGEc7LnOOhvHXJqHgNPXSEQ8j/yGu38AAQAASURBVE6XnlL8Uwp8aiKgO8SmIxoYdS8SwU3t2rXx448/yrapuQEJgvAvgjPQbOELdbqtmoNQy9VIFA9sYIDOzzg2SSdHs9mMsWPHYuzYsQFcHUEQWoiuQMD/zUP8JS7qdQXa/HM6onBiJM7kjadY4w0kBBJBi1lDtLOaGByMQxic3YMdDAA42ACEMg7ZYBA0OuHNxQpO/Nkd/hIB3eFO0JOKgQ4wqiVIaGIymchCTxAFjFQMLIwpwnrFQN7hrFllMjuQsOkt/yyUCAooXYsgfMelk7CUe1ZZ114xZVfRSVgmBvoLFRHQX3UJOQcHZqYSSIQ+KNYYp/B+FU0QAcTMARYOCOeAMMnDCsACDlbmTNe1ghPTguU/O/90vGkWIhXn1LoCu4NcfYQaDjBDD6PNQgiC8C9aTsD8TBH2pbuw1voFQZAgCIIIPHy2BY6MMPDpYWC3wsFuhTt/vhMKlmVxEf5EkS7QXYTV0JPSrFgvpQgTROAoVELgggULwHEcJk2aJG7LysrC2LFjUaZMGURGRqJPnz64evWq23kYY3j55ZdRsWJFhIeHo0OHDvj9999lY27evInBgwcjOjoaJUuWxIgRI3Dnzp1AXBZRQMzIHqi5z2piMHN5gqAlVwQ0c06XoCAGCmjVCwSMiYFKEdAIShFQcB8q6wNKn1MzEUKL+Ph4nDp1CqdOnSILPUF4id6uwWoUdL1AvWiJhIU5rZkIDA6OGXoA1MmRIPxBxDM7XLbxdjMcmaFg2RbwmaFwZIQ5RcHcbbhnBctyTf7zmxho1A2ot6uwv12LRJHCaJyhWOM9heZT3OHDh/HOO++gYcOGsu2TJ0/GF198gc2bN2P37t24dOkSHn30Ubdzvfbaa3jzzTexatUqHDp0CCVKlEBycjKysrLEMYMHD8bPP/+MnTt34ssvv8SePXswatSogFwbUXBIxUBlqrDQOMScG28EMTBMIgYKrkDAKQBapUIeTLobh0gbghh1AQLGnYAkBhYvbOCRY+DhMOgIvHPnDlJTU5GamgoAOHPmDFJTU3H+/HkAzi9WUlNTcerUKQBAWloaUlNTceXKFXGOoUOHYsaMGeLznJwccc6cnBxcvHgRqampOH36tJ9fHYIILMVBDATUG42QGEh44vDhw/SFE0H4AV76fuswiQ473m4WHwCcwmC2xSkG2k3g7rpxaheEM1APEjFQzRVobvpbfq6GKAJQrDFOofgEd+fOHQwePBirV69GqVKlxO3p6el4//33sXjxYjzyyCNITEzEmjVrcODAARw8eFB1LsYYli5dihdffBE9e/ZEw4YN8X//93+4dOkSPvvsMwDAL7/8gu3bt+O9995DixYt0KZNG7z11lvYuHEjLl26pDpvdnY2MjIyZA+iaGBEDAScYqAVTjEwnJlkYqBzvzNFWJomLAh7ajX5tFyAvnQf1nIDavG97Ul8b3vS6/MRwYcRR+CRI0fQpEkTNGnSBAAwZcoUNGnSBC+//DIAYNu2bWjSpAm6desGABgwYACaNGmCVatWiXOcP38ely9fFp9funRJnPPy5ctYtGgRmjRpgqeeesrfl1pkoDhTdCnKYqDJxBuqGahEKgbW+2Spv5ZFFELs4GHT+bDnfj4hl0bhg2JN0UTNFSgVBAHIxEDm4MCynIKgUgz0Vx0/3XMq3YAGzy8VA0kEDG6MxBmKNb5RKJqFjB07Ft26dUOHDh0wd+5ccfvRo0dhs9nQoUMHcVtCQgKqVauGlJQUtGzZ0mWuM2fO4MqVK7JjYmJi0KJFC6SkpGDAgAFISUlByZIl0axZM3FMhw4dYDKZcOjQIfTu3dtl3vnz52P27Nn+umSiADFzTNZB2GpisPGcKAbamVMYtAnO+dzOwQDymhhJnjs45hT1mNy5565Rh1hjUEUMVHYL1uMGdOQeY5bMJzQO2W0b4fF4gnBHu3btwJj2h7Zhw4Zh2LBhbufYtWuX7HlcXJzbOYsjFGeKNvd/thgne03xaQ5lJ2F/NxBxJ/gJ+7w5HwmAhBZUwL3wQbGm6BI2cSfuvpHs2jjEYRLFNt5uhsniAJ8VkndXYOHB2XKbh5h5wGHKax6i0UWYSQRGTm9arxrSY30QIDkHB1PzNO/XQQQ1FGuMU+BC4MaNG3Hs2DEcPnzYZd+VK1cQEhKCkiVLyrZXqFBBlnKmPEYYo3XMlStXUL58edl+i8WC0qVLa847Y8YMTJmS9wE/IyMDVatWdX9xRKFhRvZAzA/9j/hcSwwEgAV8f815+pvWu2wzM06sT2BlJkBDuJO6Ab+0D9U8R7JlreY+wL0b0AFeJgaSCFg8cDYB0f8hTdosBHB+GUNW+oKH4kzxwZOD0FdBEQAabVsU8HMAvrkhCULKnj178Prrr+Po0aO4fPkytm7dil69eon7t2zZglWrVuHo0aO4efMmjh8/jsaNG7ud8+eff8bLL7+Mo0eP4ty5c1iyZImsFnlxhGJN0SbimR26xMCwMd+5HCu1KPB763m/CElKMffIzy5zezyHsg6gIDpqCI4kABL+guJMHgUqBF64cAETJ07Ezp07ERYWVpBL8UhoaChCQ0MLehmED3gSA2fbtQVAgU38YI9jHrF+4LJNKgLusA9ze7yn/QCQZF0te26GSSYEpdhGepyDKN7Ex8eL5RKIwgHFmaKPHlegHuFMz5gTPaZq7vMkAuo5h9b8PG8S3YMkAhYvHGDgdNYdFr60bN68Ocxms64vnDIzM9GoUSM8+eSTqvXAMzMz0aZNG/Tr1w8jR+r7nHP37l3UqFEDffv2xeTJk3UdE+xQrCn6aIqBuYSNcxUBlZgePOV2PweA/7qR9v5cAdCXcwASsVApAto4wMpIBCxmGIkzwnhAf6yhOJNHgQqBR48exbVr19C0aVNxm8PhwJ49e7B8+XLs2LEDOTk5uHXrlswVePXqVcTGxqrOKWy/evUqKlasKDtGUHNjY2Nx7do12XF2ux03b97UnJcIDtx1EvYX+VGLj4Q+giCIwkl+iWN6xL7CPD9RPDCSrtWlSxd06dJFc/+QIUMAAGfPntV9/ubNm6N58+YAgOeee073cQRR2FGtGehnTF1PBP4cOsRCgvCE3lhDcSaPAm0W0r59e5w8eVLsHJmamopmzZph8ODB4s9WqxXffZf3rUZaWhrOnz+PpKQk1Tnj4+MRGxsrOyYjIwOHDh0Sj0lKSsKtW7dw9OhRccz3338PnufRokWLAF0tQRBEYLBxPHIMPOzgDXUNJgiCIAhvUDalyM7OLuglEQRBEEEGxRrjFKgjMCoqCg0aNJBtK1GiBMqUKSNuHzFiBKZMmYLSpUsjOjoa48ePR1JSkqxRSEJCAubPn4/evXuD4zhMmjQJc+fORa1atRAfH4+XXnoJlSpVEvO/69ati86dO2PkyJFYtWoVbDYbxo0bhwEDBqBSpUr5dv0EQRAFBaUGEwRBEEawcTyYjgZmAMROjsraczNnzsSsWbP8vTSCIAgiCDASZwCKNb5Q4M1CPLFkyRKYTCb06dMH2dnZSE5Oxttvvy0bk5aWhvT0dPH5s88+i8zMTIwaNQq3bt1CmzZtsH37dlkdwvXr12PcuHFo3769OP+bb76Zb9dFEAThL2xwAHDoHk/NQgiCIIj84MKFC7J0LapNRxAEQfgbijXGKXRC4K5du2TPw8LCsGLFCrepa4zJC0pyHIdXXnkFr7zyiuYxpUuXxoYNG3xaK0EQRFGFHIEEQRBEoImOjtZdI5AgCIIgvIFijXEKtEYgQRAEQRAEQRCFnxzwhh6As4g61aIlCIIg9GA0zlCs8Z5C5wgkCIIgjOEADw7662k4KDWYIAiCyAeMdA2+c+cOTp8+LT4/c+YMUlNTUbp0aVSrVg03b97E+fPncenSJQDO0kAAEBsbi9jYWADA0KFDUblyZcyfPx8AkJOTg1OnTok/X7x4EampqYiMjETNmjX9dp0EQRBEwaE31lCcyYMcgQRBEMWQ+Ph4nDp1CqdOnSIRkCAIgvAIDx4OnQ/eC5fGkSNH0KRJEzRp0gQAMGXKFDRp0gQvv/wyAGDbtm1o0qQJunXrBgAYMGAAmjRpglWrVolznD9/HpcvXxafX7p0SZzz8uXLWLRoEZo0aYKnnnrKb68LQRAE4R+MxBlvYg3FmTzIEUgQBEEQBEEQhN8x4ghs166dS91vKcOGDcOwYcPczqGsNR4XF+d2ToIgCKLoozfWUJzJgxyBBEEQRZwcjjf0cHC8mBpM9TQIgiAIgiAIgiCKD+QIJAiCKIZQ12CCIAjCCDkcDzOnrx6tQ5KuZTabqRYtQRAE4REjcQagWOMLJAQSBEEUcexgYNQshCAIgihkGEkNJgiCIAhvoFhjHBICCYIgiiHkCCQIgiAIgiAIgih+UI1AgiAIQhcrVqxAXFwcwsLC0KJFC/z4449ux2/evBkJCQkICwvD/fffj6+//jqfVkoQBEH4GyOdHB1edA0mCIIgijdG4wzFGu8hRyBBEEQRJ5uzw8TZdY+3cTzOnDlrKDV406ZNmDJlClatWoUWLVpg6dKlSE5ORlpaGsqXL+8y/sCBAxg4cCDmz5+Pf/3rX9iwYQN69eqFY8eOoUGDBsYukCAIgiiSULoWQRAEEWgo1hiHHIEEQRDFkOrVq+PgwYM4ePAghgwZgoyMDGRnZ2uOX7x4MUaOHInhw4ejXr16WLVqFSIiIvDBBx+ojl+2bBk6d+6MadOmoW7dupgzZw6aNm2K5cuXB+qSCIIgCIIgCIIgCA+QEEgQBFFE4TgOQAiy8DdyOF73w8b+xldfHkZMTIzsMX/+fNXz5OTk4OjRo+jQoYO4zWQyoUOHDkhJSVE9JiUlRTYeAJKTkzXHEwRBEIWbHPDIgUPng9K1CIIgCGMYizMUa3yBUoMJgiCKKCaTCSHm1sixfwuz9alcYdA9PEuHzXEEBw/uRd26dWX7QkNDVY+5fv06HA4HKlSoINteoUIF/Prrr6rHXLlyRXX8lStXPK6RIAiCCA4oXYsgCIIINBRrjEOOQIIgiCLM1etb4OAvw8H/oWt8jv17WEwJaNGiBaKjo2UPLSGQIAiCIGxGnOccuTQIgiAIYxiJMxRrfIMcgQRBEEWYkiVLIsTyELLtO2A23efWFcjz12FzHENa2s+GzlG2bFmYzWZcvXpVtv3q1auIjY1VPSY2NtbQeIIgCCL4IJcGQRAEEWgo1hiHHIEEQRBFnH/SPwVj6bDzp9yOy7Z/C6u5MWrXrm1o/pCQECQmJuK7774Tt/E8j++++w5JSUmqxyQlJcnGA8DOnTs1xxMEQRAEQRAEQRCBh4RAgiCIIk5ERASWr1iAHPt/wRivOsbBX4ad/xl/nNni1TmmTJmC1atX48MPP8Qvv/yCp59+GpmZmRg+fDgAYOjQoZgxY4Y4fuLEidi+fTveeOMN/Prrr5g1axaOHDmCcePGeXV+giAIomCxgTf0AChdiyAIgtCP0ThDscZ7KDWYIAgiCHjqqacwbtyLsPOpsJqbuuzPtv8XVnMLVK1a1av5+/fvj7///hsvv/wyrly5gsaNG2P79u1iQ5Dz58/DZMr7bqlVq1bYsGEDXnzxRTz//POoVasWPvvsMzRo0MC7CyQIgiCKHJSuRRAEQQQaijXGISGQIAgiCAgJCcGHHy7DE09MhMXUEByX9/bu4M/Bwf+Jy9f2+HSOcePGaTr6du3a5bKtb9++6Nu3r0/nJAiCIAiCIAiCIPwHpQYTBEEECYMGDQKHENgch8VtjDFk23YgxNwG5cqVK8DVEQRBEEWZLM5u6AFQuhZBEAShH6NxhmKN95AjkCAIIkgwm834dMsqPNp7CKzmRHBcCBz873CwK7hx43hBL48gCIIoZlC6FkEQBBFoKNYYhxyBBEEQQUTPnj3BcSWR4zjgdAPa/4tQSzvExMQU9NIIgiAIwi0rVqxAXFwcwsLC0KJFC/z4449ux2/evBkJCQkICwvD/fffj6+//jqfVkoQBEEURSjOOCEhkCAIIojgOA47/rsGOfbdsDmOgLEM3Ez/pKCXRRAEQRRxbByPHJ0PG2e8k+OmTZswZcoUzJw5E8eOHUOjRo2QnJyMa9euqY4/cOAABg4ciBEjRuD48ePo1asXevXqhZ9++smv100QBEHkD0bijDexhuJMHhxjjBX0Iooi6enpKFmyJC5cuEA2VIIgDJORkYGqVavi1q1bAXHrWcw14eD/xMqVb2PMmDF+n58IPBRnCILwFX/EmoyMDMTExKBE6HPgEKrrGIZsZGYvcHn/Cg0NRWio+hwtWrRA8+bNsXz5cgAAz/OoWrUqxo8fj+eee85lfP/+/ZGZmYkvv/xS3NayZUs0btwYq1atMnKJxRqKNQRB+EJBxRnAeKyhOJMH1Qj0ktu3bwMAqlatWsArIQiiKHP79u2ACIE/Hv4YLR8YiyeffNLvcxP5A8UZgiD8hS+xJiQkBLGxsbhyZYGh4yIjI13ev2bOnIlZs2a5jM3JycHRo0cxY8YMcZvJZEKHDh2QkpKiOn9KSgqmTJki25acnIzPPvvM0DqLOxRrCILwBwURZwD9sYbijBwSAr2kUqVKuHDhAqKiosBxnK5jBLU8GL9xo2sregTrdQFF49oYY7h9+zYqVaoUkPmbNm2KHLt6UCOKBhRn5NC1FU3o2goWf8SasLAwnDlzBjk5OYbPrXzv0nIDXr9+HQ6HAxUqVJBtr1ChAn799VfVY65cuaI6/sqVK4bWWdwxGmuKwu+9t9C1FU3o2gqWgowzwvn1xBqKM3JICPQSk8mEKlWqeHVsdHR0of1D9hW6tqJHsF4XUPivjRp4EO6gOKMOXVvRhK6t4PBHrAkLC0NYWJgfVkMUNryNNYX9994X6NqKJnRtBQfFmaIHNQshCIIgCIIgCKLAKFu2LMxmM65evSrbfvXqVcTGxqoeExsba2g8QRAEUXyhOCOHhECCIAiCIAiCIAqMkJAQJCYm4rvvvhO38TyP7777DklJSarHJCUlycYDwM6dOzXHEwRBEMUXijNyKDU4HwkNDcXMmTM166MUZejaih7Bel1AcF8bQbgjmH/36dqKJnRthF6mTJmCJ554As2aNcMDDzyApUuXIjMzE8OHDwcADB06FJUrV8b8+fMBABMnTsRDDz2EN954A926dcPGjRtx5MgRvPvuuwV5GUFPMP/e07UVTejaCL1QnMmDY4yxgl4EQRAEQRAEQRDFm+XLl+P111/HlStX0LhxY7z55pto0aIFAKBdu3aIi4vD2rVrxfGbN2/Giy++iLNnz6JWrVp47bXX0LVr1wJaPUEQBFHYoTjjhIRAgiAIgiAIgiAIgiAIgigGUI1AgiAIgiAIgiAIgiAIgigGkBBIEARBEARBEARBEARBEMUAEgIJgiAIgiAIgiAIgiAIohhAQiBBEARBEARBEARBEARBFANICCRcaNeuHSZNmhRU5x02bBh69erl0xxxcXHgOA4cx+HWrVua49auXYuSJUv6dK7iTrt27cTXOjU1taCXQxBEAKBYow7FmvyDYg1BBDcUZ9ShOJN/UJwhCiskBBKFhi1btmDOnDni87i4OCxdurTgFqTCK6+8gsuXLyMmJqaglxIUaH3A2LJlC3788cf8XxBBEEEPxZriB8UagiDyE4ozxQ+KM0RRw1LQCyAIgdKlSxf0EjwSFRWF2NjYgl4GAMBms8FqtRb0MgJC6dKlkZGRUdDLIAgiCKFYYwyKNQRBEMagOGMMijMEkf+QI5DwyD///IOhQ4eiVKlSiIiIQJcuXfD777+L+4VvQHbs2IG6desiMjISnTt3xuXLl8UxdrsdEyZMQMmSJVGmTBlMnz4dTzzxhMzaLrXRt2vXDufOncPkyZNFOzUAzJo1C40bN5atb+nSpYiLixOfOxwOTJkyRTzXs88+C8aY7Bie5zF//nzEx8cjPDwcjRo1wieffOLV67N27VpUq1YNERER6N27N27cuOEy5vPPP0fTpk0RFhaGGjVqYPbs2bDb7eL+X3/9FW3atEFYWBjq1auHb7/9FhzH4bPPPgMAnD17FhzHYdOmTXjooYcQFhaG9evXAwDee+891K1bF2FhYUhISMDbb78tO/eFCxfQr18/lCxZEqVLl0bPnj1x9uxZ3dfnaf7p06ejdu3aiIiIQI0aNfDSSy/BZrOJ+0+cOIGHH34YUVFRiI6ORmJiIo4cOYJdu3Zh+PDhSE9PF/+PZ82apXtdBEEEFxRr3EOxhmINQRC+QXHGPRRnKM4QxQhGEAoeeughNnHiRPF5jx49WN26ddmePXtYamoqS05OZjVr1mQ5OTmMMcbWrFnDrFYr69ChAzt8+DA7evQoq1u3Lhs0aJA4x9y5c1np0qXZli1b2C+//MLGjBnDoqOjWc+ePVXPe+PGDValShX2yiuvsMuXL7PLly8zxhibOXMma9SokWy9S5YsYdWrVxefL1y4kJUqVYp9+umn7NSpU2zEiBEsKipKdq65c+eyhIQEtn37dvbHH3+wNWvWsNDQULZr1y7N16V69epsyZIlsm0HDx5kJpOJLVy4kKWlpbFly5axkiVLspiYGHHMnj17WHR0NFu7di37448/2H//+18WFxfHZs2axRhjzG63szp16rCOHTuy1NRUtnfvXvbAAw8wAGzr1q2MMcbOnDnDALC4uDj26aefsj///JNdunSJffTRR6xixYritk8//ZSVLl2arV27ljHGWE5ODqtbty578skn2f/+9z926tQpNmjQIFanTh2WnZ2tea0CnuZnjLE5c+aw/fv3szNnzrBt27axChUqsIULF4r769evzx5//HH2yy+/sN9++419/PHHLDU1lWVnZ7OlS5ey6Oho8f/49u3b4nHCNR8/ftzjOgmCKHpQrFGHYg3FGoIg/APFGXUozlCcIQgSAgkXpMHrt99+YwDY/v37xf3Xr19n4eHh7OOPP2aMOYMmAHb69GlxzIoVK1iFChXE5xUqVGCvv/66+Nxut7Nq1appBk3G1IOUnqBZsWJF9tprr4nPbTYbq1KliniurKwsFhERwQ4cOCCbZ8SIEWzgwIGar4vaegYOHMi6du0q29a/f39Z0Gzfvj179dVXZWPWrVvHKlasyBhj7JtvvmEWi0X8YMAYYzt37lQNmkuXLpXNc99997ENGzbIts2ZM4clJSWJ56lTpw7jeV7cn52dzcLDw9mOHTs0r1Xv/Gq8/vrrLDExUXweFRUlC7JS1qxZI3utpFDQJIjghmKNOhRrXOdXg2INQRCeoDijDsUZ1/nVoDhDBDNUI5Bwyy+//AKLxYIWLVqI28qUKYM6dergl19+EbdFRETgvvvuE59XrFgR165dAwCkp6fj6tWreOCBB8T9ZrMZiYmJ4Hner+tNT0/H5cuXZeu1WCxo1qyZaKU/ffo07t69i44dO8qOzcnJQZMmTQyd75dffkHv3r1l25KSkrB9+3bx+YkTJ7B//37MmzdP3OZwOJCVlYW7d+8iLS0NVatWldXpkL5WUpo1ayb+nJmZiT/++AMjRozAyJEjxe12u10s/HvixAmcPn0aUVFRsnmysrLwxx9/uL02PfMDwKZNm/Dmm2/ijz/+wJ07d2C32xEdHS3unzJlCp566imsW7cOHTp0QN++fWW/KwRBEBRr3EOxhmINQRC+QXHGPRRnKM4QxQsSAgm/oCzwynGcSw0Lf2AymVzmldZu0MOdO3cAAF999RUqV64s2xcaGurbAjXON3v2bDz66KMu+8LCwgzNVaJECdm8ALB69WrZhwTA+aFEGJOYmCjW3pBSrlw5j+v2NH9KSgoGDx6M2bNnIzk5GTExMdi4cSPeeOMNceysWbMwaNAgfPXVV/jmm28wc+ZMbNy40eXDBkEQhCco1rg/H8UaijUEQfgGxRn356M4Q3GGCA5ICCTcUrduXdjtdhw6dAitWrUCANy4cQNpaWmoV6+erjliYmJQoUIFHD58GG3btgXg/Pbo2LFjLkVypYSEhMDhcMi2lStXDleuXAFjTCy2m5qaKjtXxYoVcejQIfFcdrsdR48eRdOmTQEA9erVQ2hoKM6fP4+HHnpI1zVoUbduXRw6dEi27eDBg7LnTZs2RVpaGmrWrKk6R506dXDhwgVcvXoVFSpUAAAcPnzY47krVKiASpUq4c8//8TgwYNVxzRt2hSbNm1C+fLlZd9o6UHP/AcOHED16tXxwgsviNvOnTvnMq527dqoXbs2Jk+ejIEDB2LNmjXo3bu36v8xQRDFD4o17qFYQ7GGIAjfoDjjHoozFGeI4gUJgYRbatWqhZ49e2LkyJF45513EBUVheeeew6VK1dGz549dc8zfvx4zJ8/HzVr1kRCQgLeeust/PPPP2LgUyMuLg579uzBgAEDEBoairJly6Jdu3b4+++/8dprr+Gxxx7D9u3b8c0338gCwsSJE7FgwQLUqlULCQkJWLx4MW7duiXuj4qKwtSpUzF58mTwPI82bdogPT0d+/fvR3R0NJ544gnd1zVhwgS0bt0aixYtQs+ePbFjxw6ZhR4AXn75ZfzrX/9CtWrV8Nhjj8FkMuHEiRP46aefMHfuXHTs2BH33XcfnnjiCbz22mu4ffs2XnzxRQBw+/oAwOzZszFhwgTExMSgc+fOyM7OxpEjR/DPP/9gypQpGDx4MF5//XX07NkTr7zyCqpUqYJz585hy5YtePbZZ1GlShWf5q9VqxbOnz+PjRs3onnz5vjqq6+wdetW8fh79+5h2rRpeOyxxxAfH4+//voLhw8fRp8+fQA4/4/v3LmD7777Do0aNUJERAQiIiJ0v/4EQQQHFGvcQ7GGYg1BEL5BccY9FGcozhDFjIIpTUgUZpQFbm/evMmGDBnCYmJiWHh4OEtOTma//fabuF+tOOrWrVuZ9NfLZrOxcePGsejoaFaqVCk2ffp01rdvXzZgwADN86akpLCGDRuy0NBQ2VwrV65kVatWZSVKlGBDhw5l8+bNkxXWtdlsbOLEiSw6OpqVLFmSTZkyhQ0dOlRWxJfnebZ06VJWp04dZrVaWbly5VhycjLbvXu35uuiVliXMcbef/99VqVKFRYeHs66d+/OFi1a5PJ6bN++nbVq1YqFh4ez6Oho9sADD7B3331X3P/LL7+w1q1bs5CQEJaQkMC++OILBoBt376dMea+yOz69etZ48aNWUhICCtVqhRr27Yt27Jli7j/8uXLbOjQoaxs2bIsNDSU1ahRg40cOZKlp6drXquR+adNm8bKlCnDIiMjWf/+/dmSJUvE68/OzmYDBgxgVatWZSEhIaxSpUps3Lhx7N69e+LxY8aMYWXKlGEA2MyZM8XtVFiXIIIbijXqUKyhWEMQhH+gOKMOxRmKMwTBMRaAogcE4QGe51G3bl3069cPc+bMKejl6CIuLg6TJk3CpEmTAn6u/fv3o02bNjh9+nSxLUJ79uxZxMfH4/jx427TLQiCILSgWOMeijUUawiC8A2KM+6hOENxhiicmAp6AUTx4Ny5c1i9ejV+++03nDx5Ek8//TTOnDmDQYMGFfTSDDF9+nRERkYiPT3dr/Nu3boVO3fuxNmzZ/Htt99i1KhRaN26dbENmF26dEH9+vULehkEQRQxKNa4h2KNHIo1BEEYheKMeyjOyKE4QxRWqEYgkS+YTCasXbsWU6dOBWMMDRo0wLfffou6desW9NJ0s3v3brGbl7J1va/cvn0b06dPx/nz51G2bFl06NBB1qUqUERGRmru++abb/Dggw8GfA1qvPfee7h37x4AoFq1agWyBoIgih4Ua9xDsUYOxRqCIIxCccY9FGfkUJwhCiuUGkwQxZjTp09r7qtcuTLCw8PzcTUEQRBEMEKxhiAIgggkFGcIwhgkBBIEQRAEQRAEQRAEQRBEMYBqBBIEQRAEQRAEQRAEQRBEMYCEQIIgCIIgCIIgCIIgCIIoBpAQSBAEQRAEQRAEQRAEQRDFABICCYIgCIIgCIIgCIIgCKIYQEIgQRAEQRAEQRAEQRAEQRQDSAgMMjiOw6xZswp6GYUOxhhycnIKehmFlnbt2qFdu3YFvYxCx6xZs8BxXEEvgyCIIMBms4Hn+YJeRqGFPr+oQ/GZIAg1eJ6H3W4v6GUUWuLi4jBs2LCCXkahY9iwYYiLiyvoZRCFABICCyFr164Fx3HgOA779u1z2c8YQ9WqVcFxHP71r38VwApd+frrr3V/gOd5HmvXrkWPHj1QtWpVlChRAg0aNMDcuXORlZWl+5wHDhxAmzZtEBERgdjYWEyYMAF37txxGffRRx+hbNmyiIqKwvDhw1UFwaysLCxZsgQtWrRATEwMwsLCULt2bYwbNw6//fab5hp+/PFHcByHJUuWuOzr2bMnOI7DmjVrXPa1bdsWlStX1n2twUxcXFyh+T0miGChKMYRX3n77bexdu1aXWNv3LiB119/HW3btkW5cuVQsmRJtGzZEps2bTJ0zvfffx9169ZFWFgYatWqhbfeestlDGMMkyZNQlRUFEqVKoU333xTda6rV69i6tSpSEhIQEREBEqUKIHExETMnTsXt27d0lzDa6+9Bo7jcPz4cZfzlipVChzH4cyZM7J9WVlZCA0NxaBBgwxdbzBy9uxZcByHRYsWFfRSCCIoCJb4s2HDBixdulTX2Lt372LFihXo1KkTKlasiKioKDRp0gQrV66Ew+HQfc5t27ahadOmCAsLQ7Vq1TBz5kxVse+1115DdHQ0oqOjMX36dNW5MjIyMHv2bDRq1AiRkZEIDw9HgwYNMH36dFy6dElzDR9//DE4jsPWrVtd9jVq1Agcx+GHH35w2VetWjW0atVK97UGMxzHYdy4cQW9DIJwCwmBhZiwsDBs2LDBZfvu3bvx119/ITQ01GXfvXv38OKLL+bH8mR8/fXXmD17tq6xd+/exfDhw/H3339jzJgxWLp0KR544AHMnDkTXbp0AWPM4xypqalo37497t69i8WLF+Opp57Cu+++i759+8rGnT17Fk8//TSef/55rF+/HkeOHHEJ6tevX0ebNm0wZcoUlC9fHq+88gpWrFiBXr16Ydu2bWjQoIHmOpo2bYqIiAjVDzoHDhyAxWLB/v37ZdtzcnJw+PBhtG7d2uN15hf//e9/8d///regl0EQhJ/xJo4UVYwIgSkpKXjhhRdQunRpvPjii5g3bx4iIiIwYMAAzJw5U9cc77zzDp566inUr18fb731FpKSkjBhwgQsXLhQNm7Dhg3YsmUL3nvvPbz++uuYNWsWDh06JBtz+PBhNGjQACtWrMCDDz6IxYsX44033kCTJk2wYMEC9OvXT3Mdbdq0AQCXOPTzzz/j1q1bqnHo8OHDyMnJEY8tDBTU5xeCIAJDUY8/RoTAP//8E+PHjwdjDFOmTMGiRYsQHx+Pf//733jyySd1zfHNN9+gV69eKFmyJN566y306tULc+fOxfjx42Xj9u/fj/nz52Px4sV499138Z///Acff/yxy3oaN26MOXPmoF69eli4cCHefPNNPPzww3j//ffduoy1YkpGRgZ++ukn1Zhy4cIFXLhwoVDFlLS0NKxevbqgl0EQhRZLQS+A0KZr167YvHkz3nzzTVgsef9VGzZsQGJiIq5fv+5yTFhYWH4u0StCQkKwf/9+2bdGI0eORFxcHGbOnInvvvsOHTp0cDvH888/j1KlSmHXrl2Ijo4G4HSWjRw5Ev/973/RqVMnAMCRI0fQoUMHPPPMMwAAq9WK9957D88++6w417Bhw3D8+HF88skn6NOnj+w8c+bMwQsvvKC5DovFghYtWrgExLS0NFy/fh2DBg1yCaRHjx5FVlaWX4Ll3bt3ERER4fM8ISEhPs9BEEThw5s4UhyoX78+fv/9d1SvXl3c9u9//xsdOnTAwoUL8eyzz6JEiRKax9+7dw8vvPACunXrhk8++QSAM47xPI85c+Zg1KhRKFWqFACn6PjMM8/g8ccfBwD88ssv2LdvH1q0aAEAuHXrFnr37g2z2Yzjx48jISFBdq558+a5vZlp1qwZwsLCsG/fPtkN4/79+1GmTBk0a9YM+/btE88P5N3g+RqHeJ5HTk6OXz57FIXPLwRB6Kc4xZ/Y2FicPHkS9evXF7eNHj0aTz75JNasWYOXXnoJNWvWdDvH1KlT0bBhQ/z3v/8VX6/o6Gi8+uqrmDhxohgbUlJSMGzYMIwaNQqA0+G+d+9e8Qsju92ORx99FFevXsWuXbtc3ufnzZvn8oWVlEqVKiE+Pt7l/iUlJQWMMfTt29dln79iCmMMWVlZCA8P92keAIVeaCaIgoYcgYWYgQMH4saNG9i5c6e4LScnB5988olmOo+yxo5Q4+z06dMYNmwYSpYsiZiYGAwfPhx37971uIa9e/eib9++qFatGkJDQ1G1alVMnjwZ9+7dE8cMGzYMK1asEM8vPLQICQlRtY737t0bgPMmyR0ZGRnYuXMnHn/8cVEEBIChQ4ciMjJS9q1YjRo1sGfPHuzcuRNpaWl49913UatWLXH/oUOH8NVXX2HEiBEuIiDgDCKe0oXatGmDq1ev4vTp0+K2/fv3Izo6GqNGjRJFQek+4TgA+Pzzz9GtWzdUqlQJoaGhuO+++zBnzhyXVIJ27dqhQYMGOHr0KNq2bYuIiAg8//zzsrSmFStWoEaNGoiIiECnTp1w4cIFMMYwZ84cVKlSBeHh4ejZsydu3rzpMrf028Fdu3aB4zh8/PHHmDdvHqpUqYKwsDC0b99edp0CwnnDw8PxwAMPYO/evX6va/TRRx8hMTER4eHhKF26NAYMGIALFy6I+8eNG4fIyEjV3+uBAwciNjZW9pp+8803ePDBB1GiRAlERUWhW7du+Pnnn/22XoIoDHgTR3iex9KlS1G/fn2EhYWhQoUKGD16NP755x/ZOKPvXadOncLDDz+MiIgIVK5cGa+99pqua1izZg0eeeQRlC9fHqGhoahXrx5WrlwpGxMXF4eff/4Zu3fvFmOQu/ef+Ph4mQgIOONXr169kJ2djT///NPtmn744QfcuHED//73v2Xbx44di8zMTHz11Vfitho1amD9+vU4ceIEDh48iG3btsni0DvvvIOLFy9i8eLFLiIgAFSoUMGtUy4kJATNmzd3+UJq//79SEpKQuvWrVX3lSxZUnS8L1q0CK1atUKZMmUQHh6OxMREUeCUIqQ7rV+/HvXr10doaCi2b98upgLu27cPEyZMENOtR48ejZycHNy6dQtDhw5FqVKlUKpUKTz77LMu7n9fPr/cu3cPEyZMEMuA9OjRAxcvXvRr3cHs7GzMnDkTNWvWFD8PPfvss8jOzhbHNGjQAA8//LDLsTzPo3Llynjsscdk2/T8nRFEUcWb+KPnvWjNmjXgOA4ffPCBbPurr74KjuPw9ddfu12XntjVrl07fPXVVzh37pwYU9zVVStbtqxMBBTQe29z6tQpnDp1CqNGjZKJpv/+97/BGJO9BjVq1MC2bdtw8OBBnDhxAuvXr5fFlE8//RQnTpzACy+8oCrMRUdHY968eW7X06ZNGxw/flx2v7d//37Ur18fXbp0wcGDB2U1b/fv3w+O48RsJz1xG8grD7Rjxw40a9YM4eHheOedd2T3IbNnz0blypURFRWFxx57DOnp6cjOzsakSZNQvnx5REZGYvjw4bL3YmFuaY1AIU7t378fU6ZMQbly5VCiRAn07t0bf//9t+xYnucxa9YsVKpUCREREXj44Ydx6tQpv9Yd1BMD/vWvf6FGjRqqxyclJaFZs2aybZ7ulQhCCgmBhZi4uDgkJSXhP//5j7jtm2++QXp6OgYMGGBorn79+uH27duYP38++vXrh7Vr1+pK5d28eTPu3r2Lp59+Gm+99RaSk5Px1ltvYejQoeKY0aNHo2PHjgCAdevWiQ+jXLlyBYAzmLrj5MmTsNvtLm9+ISEhaNy4saxOUtOmTTF48GB06tQJCQkJ+OuvvzBjxgxx/7Zt2wAAQ4YMMbxeATUL/f79+9GyZUu0aNECVqsVBw4ckO2LiopCo0aNADgDU2RkJKZMmYJly5YhMTERL7/8Mp577jmXc924cQNdunRB48aNsXTpUtkNx/r16/H2229j/PjxeOaZZ7B7927069cPL774IrZv347p06dj1KhR+OKLLzB16lRd17ZgwQJs3boVU6dOxYwZM3Dw4EEMHjxYNmblypUYN24cqlSpgtdeew0PPvggevXqhb/++kv/i+iBefPmYejQoahVqxYWL16MSZMm4bvvvkPbtm3F2ln9+/d3uQEHnK7JL774Ao899hjMZjMA5+9pt27dEBkZiYULF+Kll17CqVOn0KZNG5w9e9Zv6yaIgsabODJ69GhMmzYNrVu3xrJlyzB8+HCsX78eycnJsNls4jgj713//PMPOnfujEaNGuGNN95AQkICpk+fjm+++cbjNaxcuRLVq1fH888/jzfeeANVq1bFv//9b/ELKABYunQpqlSpgoSEBDEGuXNza6E3DglxRhmHEhMTYTKZZHFozJgxsFgsaNy4MZKSktCyZUt0795d3L9t2zaEh4fLRCKjtGnTBhcvXpS9fwnO+1atWolpwoDTcXHgwAEkJSXBZHJ+DFy2bBmaNGmCV155Ba+++iosFgv69u3r8n4KAN9//z0mT56M/v37Y9myZbKb4/Hjx+P333/H7Nmz0aNHD7z77rt46aWX0L17dzgcDrz66qto06YNXn/9dd2fE/R8fhk2bBjeeustdO3aFQsXLkR4eDi6detm7EV0A8/z6NGjBxYtWoTu3buLaXtLlixB//79xXH9+/fHnj17xN8jgX379uHSpUuyvzm9f2cEUVTxJv7oeS8aPnw4/vWvf2HKlCmiyHHy5EnMnj0bI0aMQNeuXd2uS0/seuGFF9C4cWOULVtWjCl604Sl+BpTKlWqhCpVqshiSq9evdCwYUMkJSWhcePGiIyMFN2BgP/ubWw2m6yMhTSmpKen46effpLtS0hIQJkyZQDoi9sCaWlpGDhwIDp27Ihly5ahcePG4r758+djx44deO655/Dkk09iy5YtGDNmDJ588kn89ttvmDVrFh599FGsXbvWrctRyvjx43HixAnMnDkTTz/9NL744guXen4zZszA7Nmz0axZM7z++uuoVasWkpOTkZmZaeRldIueGNC/f3+cOXMGhw8flh177tw5HDx4UPZ3pOdeiSBkMKLQsWbNGgaAHT58mC1fvpxFRUWxu3fvMsYY69u3L3v44YcZY4xVr16ddevWTXYsADZz5kzx+cyZMxkA9uSTT8rG9e7dm5UpU8bjWoTzSpk/fz7jOI6dO3dO3DZ27Fjm669Thw4dWHR0NPvnn3/cjtu8eTMDwPbs2eOyr2/fviw2NtZl+x9//MGOHj3KbDabbHvv3r0ZAI/ndEdGRgYzm81sxIgR4rY6deqw2bNnM8YYe+CBB9i0adPEfeXKlWMdO3YUn6u9xqNHj2YREREsKytL3PbQQw8xAGzVqlWysWfOnGEAWLly5ditW7fE7TNmzGAAWKNGjWTXPXDgQBYSEuIy90MPPSQ+/+GHHxgAVrduXZadnS1uX7ZsGQPATp48yRhjLDs7m5UpU4Y1b95cdo61a9cyALI5tVD7PZZy9uxZZjab2bx582TbT548ySwWi7id53lWuXJl1qdPH9m4jz/+WPb7cvv2bVayZEk2cuRI2bgrV66wmJgY2Xbh74cgihrexpG9e/cyAGz9+vWy+bZv3+6y3eh71//93/+J27Kzs1lsbKzL36saaudJTk5mNWrUkG2rX7++rvccLW7cuMHKly/PHnzwQY9jx44dy8xms+q+cuXKsQEDBsi2ORwOlpqayn799VeX8aVKlWKNGjXyas0CX331FQPA1q1bxxhj7PLlywwA2717N7t9+zYzm83sq6++Yowx9tNPPzEAsvdU5Wuck5PDGjRowB555BHZdgDMZDKxn3/+WbZd+H1LTk5mPM+L25OSkhjHcWzMmDHiNrvdzqpUqeLyf+Xt55ejR48yAGzSpEmyccOGDXOZUw0hhr7++uuaY9atW8dMJhPbu3evbPuqVasYALZ//37GGGNpaWkMAHvrrbdk4/7973+zyMhI8XU28nemjM8EUdjx5T5G73vR5cuXWenSpVnHjh1ZdnY2a9KkCatWrRpLT0/3uD69satbt26sevXquq5ZjezsbFavXj0WHx/vcv+h5PXXX2cA2Pnz5132NW/enLVs2dJl+6lTp9iJEydk77mMMdakSRMWExPj9boZY+znn39mANicOXMYY4zZbDZWokQJ9uGHHzLGGKtQoQJbsWIFYyzvPkj6+Vlv3K5evToDwLZv3y7bLtyHNGjQgOXk5IjbBw4cyDiOY126dJGNT0pKcvm/ql69OnviiSfE58LvZYcOHWSv2eTJk5nZbBbvoa5cucIsFgvr1auXbL5Zs2YxALI5tQDAxo4dq7lfbwxIT09noaGh7JlnnpGNe+2112T34nrvlRhj7IknnvDp95oIHsgRWMjp168f7t27hy+//BK3b9/Gl19+6VWXvzFjxsieP/jgg7hx4wYyMjLcHiet0ZCZmYnr16+jVatWYIy5dCj0hVdffRXffvstFixYgJIlS7odK9jU1Wo/hIWFyWzsAjVq1EDTpk1ldnsA4vVHRUV5uXLnsQ0bNhQdgdevX0daWpqY/ixNy/rtt9/w999/y6z60tf49u3buH79Oh588EHcvXsXv/76q+xcoaGhGD58uOo6+vbti5iYGPG5UH/q8ccfl113ixYtkJOTg4sXL3q8tuHDh8vqBz744IMAIKbNHTlyBDdu3MDIkSNl5xg8eLBYH8tXtmzZAp7n0a9fP1y/fl18xMbGolatWmLnMo7j0LdvX3z99dey7tGbNm1C5cqVxdd8586duHXrFgYOHCibz2w2o0WLFqqd0AiiKGMkjmzevBkxMTHo2LGj7O8jMTERkZGRsr8PI+9dkZGRshp1ISEheOCBBzym4CrPk56ejuvXr+Ohhx7Cn3/+ifT0dN2vgzt4nsfgwYNx69Yt1c6/Su7du6dZW1UtDplMJjRq1Ah16tRxGZ+RkeFTDAKAVq1awWQyiXFo//79sFqtaN68OSIjI9GwYUMxDinLUwDy1/iff/5Beno6HnzwQRw7dszlXA899BDq1aunuo4RI0bISoO0aNECjDGMGDFC3GY2m9GsWTNd//eA588v27dvBwCXNG1lgX1f2Lx5M+rWrYuEhATZ38UjjzwCAOLfRe3atdG4cWNZ92mHw4FPPvkE3bt3F19nI39nBFGUMXofo/e9KDY2FitWrMDOnTvx4IMPIjU1FR988IGsZJCec3iKXb4wbtw4nDp1CsuXL3e5/1Dizb1N3bp10bBhQ5dyTP6IKXXr1kWZMmXEmHLixAlkZmaK9zatWrUSY0lKSgocDodmTPEUt+Pj45GcnKy6jqFDh8JqtYrPhZiibMDSokULXLhwQbXDspJRo0bJXrMHH3wQDocD586dAwB89913sNvtAY8pemJAdHQ0unTpgo8//lhWTmPTpk1o2bIlqlWrBkD/vRJBSKFmIYWccuXKoUOHDtiwYQPu3r0Lh8PhVfqQ8EYhIIg0//zzj9ugef78ebz88svYtm2bS90af92Abdq0CS+++CJGjBiBp59+2uN4Ibgoa0EAMFxgVrj227dvexQg3dGmTRu89dZbuH79Og4cOACz2YyWLVsCcAbLt99+G9nZ2ao3YD///DNefPFFfP/99y7CrPI1rly5subNp/L/WBAFq1atqrpdTx0id783AMSgqSyAbLFY3NZSMcLvv/8Oxpis/okU6QeE/v37Y+nSpdi2bRsGDRqEO3fu4Ouvv8bo0aPFoP/7778DgHgDp0TPh0iCKEoYiSO///470tPTUb58edX9165dE3828t5VpUoVl5uVUqVK4X//+5/H9e/fvx8zZ85ESkqKS2249PR02Rcg3jJ+/Hhs374d//d//yeWbXBHeHg4cnJyVPd5E4du376te7waJUuWRP369WViX5MmTcR1SG/a9u/fLwqxAl9++SXmzp2L1NRUWWxVq/cbHx+vuQ4jcUhvLTxPn1/OnTsHk8nksi5PhfmN8Pvvv+OXX35BuXLlVPdL/y769++P559/HhcvXkTlypWxa9cuXLt2TZZCbOTvjCCKMkbvY4y8Fw0YMAAfffQRvvrqK4waNQrt27fXtSYjsctbXn/9daxevRpz5szxmKoM+P/eRu8XLVpwHIdWrVphz5494Hke+/fvR/ny5cX31VatWmH58uUA1L9cMhK3/RVTeJ5Henq6mJ6sd0699zalS5f2m8nBSAzo378/PvvsM6SkpKBVq1b4448/cPToUVmqupF7JYIQICGwCDBo0CCMHDkSV65cQZcuXbwSrITaaEqYoli3FIfDgY4dO+LmzZuYPn06EhISUKJECVy8eBHDhg2TFYn1lp07d2Lo0KHo1q0bVq1apeuYihUrAgAuX77ssu/y5cuoVKmS7vMLhdlPnjwput28QRAC9+/fjwMHDuD+++9HZGQkAGewzM7OxuHDh7Fv3z5YLBZRJLx16xYeeughREdH45VXXsF9992HsLAwHDt2DNOnT3d5jd19END6P/bm/94fx/oLnufBcRy++eYb1fUIrzMAtGzZEnFxcfj4448xaNAgfPHFF7h3757sBkx4TdetW4fY2FiX+Tx9a0sQRRG9cYTneZQvXx7r169X3S8IIUbfu7x9L/njjz/Qvn17JCQkYPHixahatSpCQkLw9ddfY8mSJX6JQ7Nnz8bbb7+NBQsW6K6pVLFiRTgcDly7dk32QT4nJwc3btwwHIdSU1ORk5PjUwf3Nm3aYNWqVbh165ZYy0mgVatW+OCDD2Cz2bBv3z4kJiaKXXr37t2LHj16oG3btnj77bdRsWJFWK1WrFmzBhs2bHA5j7/ikN44Ulji0P3334/Fixer7pfelPbv3x8zZszA5s2bMWnSJHz88ceIiYlB586dZfPp+TsjiGBAb/wx+l5048YNHDlyBICz2QbP82LdUy2Mxi5vWLt2LaZPn44xY8a4bfQkRXpvoxS5Ll++LPvixhMJCQk4fvw4Lly44DKXEdq0aYMvvvgCJ0+eVI0p06ZNw8WLF7Fv3z5UqlRJbGphNG4X13sbvTGge/fuiIiIwMcff4xWrVrh448/hslkQt++fWXz6b1XIggBuuMtAvTu3RujR4/GwYMHZekmgebkyZP47bff8OGHH8qag0i7fwm46xKsxaFDh9C7d280a9YMH3/8sW4BpkGDBrBYLDhy5Aj69esnbs/JyUFqaqpsmye6d++O+fPn46OPPvJZCAScBcFTUlLErlmAs9Bv9erVsX//ftGlERERAcDZnffGjRvYsmUL2rZtKx5z5swZr9eSnwhdN0+fPi1rXGK323H27Fk0bNjQ53Pcd999YIwhPj4etWvX9ji+X79+WLZsGTIyMrBp0ybExcWJwqswHwCUL18eHTp08Hl9BFEU0BtH7rvvPnz77bdo3bq12w/n+fXe9cUXXyA7Oxvbtm2TfYuvlubiTRxasWIFZs2ahUmTJmH69Om6jxOKmR85ckTm9jhy5Ah4npcVO/dE9+7dkZKSgk8//RQDBw7UfZySNm3aYOXKlfj2229x/PhxTJs2TdzXqlUr3Lt3D1999RX+/PNP9OnTR9z36aefIiwsDDt27JClpa1Zs8brteQn1atXB8/zOHPmjMwNodbh3lvuu+8+nDhxAu3bt/f4exYfH48HHngAmzZtwrhx47Blyxb06tVL9trq/TsjiGBAb/wx+l40duxYsZHQjBkzsHTpUkyZMsXtWozELm9iyueff46nnnoKjz76qGpjDC2kMUUq+l26dAl//fWXrBmIJ7p3747//Oc/+Oijj2QNEo0ivbfZv38/Jk2aJO5LTExEaGgodu3ahUOHDsnioJG4XRiR3ttI3Yo3btzwW1d3IzGgRIkS+Ne//oXNmzdj8eLF2LRpEx588EHZF45G75UIAqCuwUWCyMhIrFy5ErNmzZJ1Ggw0wjcK0m9IGGNYtmyZy9gSJUoAgO6uRL/88gu6deuGuLg4fPnll27fBH/99VecP39efB4TE4MOHTrgo48+kqVTrVu3Dnfu3JF9Q+KJpKQkdO7cGe+99x4+++wzl/05OTm6OuxWqlQJ8fHx+O6773DkyBHZt2aA8ybss88+Q1pamsw6r/Ya5+Tk4O2339Z9DQVJs2bNUKZMGaxevVpWl2P9+vV+C5aPPvoozGYzZs+e7fJtHWMMN27ckG3r378/srOz8eGHH2L79u0uwnBycjKio6Px6quvqnZm/Pvvv/2yboIoTOiNI/369YPD4cCcOXNc9tntdvE9Pr/eu9TOk56ernpjWKJECUOd8TZt2oQJEyZg8ODBmk4vAGLdqOvXr4vbHnnkEZQuXRorV66UjV25ciUiIiIMdawdM2YMKlasiGeeeQa//faby/5r165h7ty5HucRYsvixYths9lkcSguLg4VK1bEa6+9JhsLOF9jjuPgcDjEbWfPnlWNiYURobaU8ndPT61HvfTr1w8XL17E6tWrXfbdu3fPpZNk//79cfDgQXzwwQe4fv26zJUuzKfn74wgggG98cfIe9Enn3yCTZs2YcGCBXjuuecwYMAAvPjii6rvocpzAPpiV4kSJQylCu/ZswcDBgxA27ZtsX79ek13os1mw6+//irLbKpfvz4SEhLw7rvvyq5/5cqV4DjOUFmoxx57DPfffz/mzZuHlJQUl/23b9/GCy+84HGeZs2aISwsDOvXr8fFixdlMSU0NBRNmzbFihUrkJmZ6fHeRituF0bat28Pi8XiEt+FVGh/YDQG9O/fH5cuXcJ7772HEydOuMQUo/dKBAGQI7DI8MQTT+T7ORMSEnDfffdh6tSpuHjxIqKjo/Hpp5+qCjyJiYkAgAkTJiA5ORlms1nW0lzK7du3kZycjH/++QfTpk3DV199Jdt/3333ISkpSXxet25dPPTQQ9i1a5e4bd68eWjVqhUeeughjBo1Cn/99RfeeOMNdOrUSZZ+o4f/+7//Q6dOnfDoo4+ie/fuaN++PUqUKIHff/8dGzduxOXLl7Fo0SKP87Rp0wbr1q0DAJkjEHAKgf/5z3/EcdLtpUqVwhNPPIEJEyaA4zisW7cuX+3pvhASEoJZs2Zh/PjxeOSRR9CvXz+cPXsWa9euxX333af729TTp0+r3ug2adIE3bp1w9y5czFjxgycPXsWvXr1QlRUFM6cOYOtW7di1KhRMrG2adOmqFmzJl544QVkZ2e7BMvo6GisXLkSQ4YMQdOmTTFgwACUK1cO58+fx1dffYXWrVv7NdgTRGFBTxx56KGHMHr0aMyfPx+pqano1KkTrFYrfv/9d2zevBnLli3DY489lm/vXZ06dUJISAi6d++O0aNH486dO1i9ejXKly/vUh4iMTERK1euxNy5c1GzZk2UL19esxbojz/+iKFDh6JMmTJo3769S3pOq1atxDSnH3/8EQ8//DBmzpyJWbNmAXCmMs2ZMwdjx45F3759kZycjL179+Kjjz7CvHnzULp0ad3XWKpUKWzduhVdu3ZF48aN8fjjj4sx9dixY/jPf/4ji4laVKtWDVWrVkVKSgri4uJc0pNbtWqFTz/9FBzHyWJUt27dsHjxYnTu3BmDBg3CtWvXsGLFCtSsWVNXDceCJjExEX369MHSpUtx48YNtGzZErt37xYFAb1x6LvvvkNWVpbL9l69emHIkCH4+OOPMWbMGPzwww9o3bo1HA4Hfv31V3z88cfYsWMHmjVrJh7Tr18/TJ06FVOnTkXp0qVd3Od6/84IIljQE3/0vhddu3YNTz/9NB5++GGMGzcOgFOk+eGHHzBs2DDs27dPU4QzErsSExOxadMmTJkyRWy8pCVknjt3Dj169BBFu82bN8v2N2zYUMySuXjxIurWrYsnnngCa9euFce8/vrr6NGjBzp16oQBAwbgp59+wvLly/HUU0+hbt26Hl8/AavVii1btqBDhw5o27Yt+vXrh9atW8NqteLnn3/Ghg0bUKpUKcybN8/tPCEhIWjevDn27t2L0NBQMS4JtGrVCm+88QYA+b2NkbhdGKlQoQImTpyIN954Az169EDnzp1x4sQJfPPNNyhbtqzumHLkyBHVe5t27doZjgFdu3ZFVFQUpk6dCrPZLHP1A857ZyP3SgQBAMiP1sSEMYT25ocPH3Y7rnr16qxbt26ybQDYzJkzxeczZ85kANjff/+teo4zZ864PcepU6dYhw4dWGRkJCtbtiwbOXIkO3HiBAPA1qxZI46z2+1s/PjxrFy5cozjOObuV+vMmTMMgOZD2ZYdAHvooYdc5tm7dy9r1aoVCwsLY+XKlWNj/5+9s4+zoe7//2tmztldsdZNCblbwrqvEKovisu6uUr4dbtJcpFCIkS6ocjNVYjUStKttqtSl+qKSm6LbG5KFxEpSrgitk12z5n5/P44Z2Zn5szMmTlnzu65eT8fj3m0Z+Yzn8/niPPeeZ3X+/0eNYoVFRVZvh8zzp49y5588knWsWNHVqVKFZaWlsaaNm3KxowZww4cOGBrjiVLljAA7KKLLgq5tmPHDuX9HT9+XHPt888/Z507d2aVKlVidevWZZMmTWJr1qxhANi6deuUcd26dWOtWrUKmVv+8/znP/+pOb9u3ToGgL311lua80Z/v7p166b5Mza7V15L/f+eMcYWLlzIGjZsyNLT09nll1/OPv/8c9a+fXvWu3dvwz8rNQ0bNjT9uzBs2DBl3DvvvMOuuuoqVrlyZVa5cmWWk5PDRo0axfbt2xcy59SpUxkAdvHFF5uuu27dOpabm8uysrJYRkYGa9KkCbvjjjvYV199pYyR//0QRKIRTRxhjLHnn3+etW/fnlWqVIllZmayNm3asEmTJrGjR48qY6L97BoyZAhr2LBh2PeyatUq1rZtW5aRkcEaNWrE5syZw1588cWQGHbs2DHWr18/lpmZaRo3ZOQ/H7ND/Rknfx6qY6v6z6l58+YsLS2NNWnShM2fP59JkhT2PRlx9OhRNm7cONasWTOWkZHBzjvvPNa+fXs2c+ZMdubMGVtz3HLLLQwAu/XWW0OuzZs3jwFgLVq0CLm2bNky1rRpU5aens5ycnLY8uXLDT//ALBRo0aF3G/2983sd5AhQ4awypUrh8wd6e8vf/75Jxs1ahSrUaMGq1KlCrv++uvZvn37GAA2e/bskP2qCfc7yauvvsoYY6y0tJTNmTOHtWrViqWnp7Pq1auz9u3bs+nTpxv+/7nyyisZAPaPf/zDdG07/8708Zkg4p1o4o+dz6KBAweyzMxM9uOPP2ru/fe//80AsDlz5liuazd2FRcXs1tvvZVVq1aNAbCMV3KcMDvUn23yZ47+eYcxxt599112ySWXsPT0dFavXj320EMPsdLSUsv3Y8bvv//OHnnkEdamTRt23nnnsYyMDNa6dWs2ZcoU9uuvv9qaY8qUKQwAu+KKK0KurVy5kgFgmZmZzO/3a67Zjdtmv4M4eYZhzDheNGzYUPNnbHavvJb6/73f72cPP/wwq127NqtUqRK75ppr2N69e1nNmjXZyJEjTf+8ZKz+Ljz++OPKODsxQCYvL48BYD179jRd186zkt3fvYjkh2MsQaxHBEEkDJIk4YILLsDAgQMNU6kIgiAIIpbs2rULl156KV577TXk5eVV9HYIgiCIBOb06dOoXr06ZsyYYSu1miDiHaoRSBBEVJw7dy4kpeKVV17BqVOn0L1794rZFEEQBJEy/PXXXyHnFixYAJ7nNQ0BCIIgCCIcZjEFAD3bEEkD1QgkCCIqtm7dinHjxuGGG25AzZo1sWPHDixbtgytW7d21LiFIAiCICJh7ty52L59O66++mp4PB589NFH+OijjzBixAjUr1+/ordHEARBJBBvvvkmXnrpJfTt2xdVqlTB5s2b8cYbb6BXr14hdeAJIlEhIZAgiKho1KgR6tevj4ULF+LUqVOoUaMGbr/9dsyePRtpaWkVvT2CIAgiybniiivwySef4PHHH0dxcTEaNGiAadOmUfoWQRAE4Zi2bdvC4/Fg7ty5KCoqUhqIGDX/IIhEhWoEEgRBEARBEARBEARBEEQKQDUCCYIgCIIgCIIgCIIgCCIFICGQIAiCIAiCIAiCIAiCIFIAqhEYIZIk4ejRo8jMzATHcRW9HYIgEgzGGP744w/UrVsXPB/ZdzKMMfTr1w/ff/+943tHjRqF++67L6J1ifKB4gxBENHiRqwBgHPnzqG0tNTRPWlpacjIyIh4TaJ8oFhDEEQ0VGScASjWRArVCIyQn3/+mTrREQQRNUeOHEG9evUiulcURXg8Hrz8Ri1Ur2E/8L7/3ll8+Xkj+Hw+AAFRcNSoURHtgYgdFGcIgnCLaGLNuXPn0Ci7Co4fEx3dV7VqVdSpUwc8z1OciWMo1hAE4QYVEWcAijWRQo7ACMnMzAQQ+AtftWrVCt4NQRCJRlFREerXr698lkRDpy7pqF3H/sf53v+W4sTRbLz33ntRr03EDoozBEFEixuxprS0FMePifjvwfrIrGrvS6c/iiS0anKEPr8SAIo1BEFEQ0XFGYBiTTSQEBghsnW+atWq9JeOIIiIcSMNh/N7wPntf5xzkoBDhw6hZcuWAMgRGK9QnCEIwi3ciDVVz/Oi6nmCvfX8AVdHx44dIQgCxZk4hmINQRBuUN5xBqBYEw0kBBIEQaQg2dnkCCQIgiBiS2FhIYlLBEEQREyhWOMc6hpMEARBEARBEARBEARBECkACYEEQRAJDu/nwfvsH5zIKanBLVu2xOLFiyv6LRAEQRBxjpM4w/sCjxgdO3akOEMQBEHYwmmcoVgTOZQaTBAEkYJQajBBEAQRayhdiyAIgog1FGucQ45AgiAIgiAIgiAIgiAIgkgBSAgkCIJIcDjR2QEJlBpMEARBOMJxrAGlaxEEQRD2cRpnKNZEDqUGEwRBpCCUGkwQBEHEGkrXIgiCIGINxRrnkCOQIAgiweH8DLzPwSEycgQSBEEQBEEQBEGkIOQIJAiCSEHIEUgQBEE4gQ9+6WR3LBBI1xIEAaNGjcKoUaNiuT2CIAgiwXESZ+TxAMWaSCAhkCAIgiAIgiAI16F0LYIgCCLWUKxxDqUGEwRBJDicnzk6qFkIQRAE4RSulDk6ACrgThAEQdjHaZyhWBM55AgkCIJIQSg1mCAIgog15NIgCIIgYg3FGueQI5AgCCLB4fwA53NwiOQIJAiCIAiCIAiCSEXIEUgQBJGCkCOQIAiCcAInBg67YwEq4E4QBEHYx0mckccDFGsigYRAgiAIgiAIgiBch9K1CIIgiFhDscY5lBpMEASR4PA+5ujgREapwQRBEARBEARBECkICYEEQRApSHZ2Nvbs2YM9e/aEtdBv3LgR1157LerWrQuO40JSihljeOSRR1CnTh1UqlQJPXv2xPfff68Zc+rUKeTl5aFq1aqoVq0ahg0bhuLiYst1z507h1GjRqFmzZqoUqUKBg0ahOPHj0f0fgmCIIjo4P0OvnjyB+6hTo4EQRCEXRzFGYo1UUFCIEEQBGHJn3/+iXbt2pkG17lz52LhwoXIz8/Hl19+icqVKyM3Nxfnzp1TxuTl5eG///0vPvnkE3zwwQfYuHEjRowYYbnuuHHj8P777+Ott97Chg0bcPToUQwcONDV90YQBEHEjsLCQltfOBEEQRBEpFCscQ4JgQRBEAmOXFjX7gHmrGtwnz59MGPGDAwYMCDkGmMMCxYswEMPPYT+/fujbdu2eOWVV3D06FHFObh3716sXr0aL7zwAjp16oSrrroKixYtQkFBAY4ePWq45pkzZ7Bs2TLMmzcP11xzDdq3b4/ly5fjiy++wNatW6P9IyMIgiDiDHKfEwRBELGE4kwZcSMEzp49GxzH4b777lPOPf/88+jevTuqVq0KjuNw+vTpsPNMmzYNHMdpjpycHM2YePwfQRAEUZ40bNgQW7duxdatWzF48GAUFRWhpKTE8TyHDh3CsWPH0LNnT+VcVlYWOnXqhC1btgAAtmzZgmrVqqFDhw7KmJ49e4LneXz55ZeG827fvh0+n08zb05ODho0aKDMSxAEQZQfjr90grN0LXKfEwRBpDZO44zTWENxpoy46BpcWFiIJUuWoG3btprzZ8+eRe/evdG7d29MmTLF9nytWrXCp59+qrz2eLRvc9y4cfjwww/x1ltvISsrC6NHj8bAgQPx+eefR/dGCIIgKgDOz4HzcfbHixz279+PrKwszflHH30U06ZNc7T2sWPHAAAXXnih5vyFF16oXDt27Bhq1aqlue7xeFCjRg1ljNG8aWlpqFatmum8BEEQRHyzdu1apZNjUVER0tPTkZ6ebji2T58+6NOnj+E1vfscAF555RVceOGFeO+993DzzTcr7vPCwkLli6dFixahb9++ePLJJ1G3bt2QeWX3+YoVK3DNNdcAAJYvX44WLVpg69at6Ny5c9R/BgRBEERssRtrKM6UUeGOwOLiYuTl5WHp0qWoXr265tp9992HyZMnO/7D8Xg8qF27tnKcf/75yjVKNyMIggCaNWuGM2fOaA4nX7gQBEEQqQXnAzgfZ/MI3FO/fn1kZWUpx6xZsyJam9znBEEQyY+zOONurEm1OFPhjsBRo0ahX79+6NmzJ2bMmOHKnN9//z3q1q2LjIwMdOnSBbNmzUKDBg0AhP8fYSY6lpSUaNLmioqKXNkrQRBERcDzvPLNWTTUrl0bAHD8+HHUqVNHOX/8+HFccsklypgTJ05o7vP7/Th16pRyv9G8paWlOH36tMYVePz4cdN7Eh2KMwRBJBtHjhzRxBozN2A4yH3uHhRrCIJINtyINakWZyrUEVhQUIAdO3ZE/O2gEZ06dcJLL72E1atX47nnnsOhQ4fwf//3f/jjjz8ARP4/YtasWRqVuX79+q7tmSAIIir8Dg/JWbMQK7Kzs1G7dm2sXbtWOVdUVIQvv/wSXbp0AQB06dIFp0+fxvbt25Uxn332GSRJQqdOnQznbd++Pbxer2beffv24fDhw8q8yQbFGYIgko2qVatqjkiFQMI9KNYQBJFsUKxxToUJgUeOHMHYsWPx+uuvIyMjw7V5+/TpgxtuuAFt27ZFbm4u/vOf/+D06dP417/+FdW8U6ZM0aTQHTlyxKUdEwRBlD/Z2dnYs2cP9uzZg1GjRlmOLS4uxq5du7Br1y4AARFx165dOHz4sNLkacaMGVi1ahV2796N22+/HXXr1sX1118PAGjRogV69+6N4cOHY9u2bfj8888xevRo3HzzzUotjV9++QU5OTnYtm0bgIAVf9iwYRg/fjzWrVuH7du3Y+jQoejSpUvS1myiOEMQRFzjc3jAWbMQK9TuczVql3i07nOzeZMNijUEQcQtTuOMi7Em1eJMhaUGb9++HSdOnMBll12mnBNFERs3bsQzzzyDkpISCIIQ9TrVqlVDs2bNcODAAQCRp5tZFTcmUo8h/BvKzy9Lt1TgTggi0PyD8ztpFlLmCAQCJRqsxMCvvvoKV199tfJ6/PjxAIAhQ4bgpZdewqRJk/Dnn39ixIgROH36NK666iqsXr1a8yXP66+/jtGjR6NHjx7geR6DBg3CwoULles+nw/79u3D2bNnlXPz589XxpaUlCA3NxfPPvus7feZaFCcIdTs6PeA8vNlH86pwJ0QROQUFha6UoZC7T6Xy07I7vO7774bgNZ93r59ewDO3OeDBg0CkPzuc4o1hBq2pg3glcBd89+K3gpBRIwbsSbV4kyFCYE9evTA7t27NeeGDh2KnJwcPPDAA66IgEDAyXLw4EEMHjwYQPz+jyBiy0PCm8rPflZ2frZ0k+O51CKg/FpEYFIfGEQu9OcP/Lc7XocgYkl2djbee+89W2O7d+8OxpjpdY7j8Nhjj+Gxxx4zHVOjRg2sWLHC9HqjRo1C1sjIyMDixYujdpIQRHnwVe6Dhuc7rHnC8VxqEVD/WvIb/34UyToEEU8UFxcrX9wDZe7zGjVqoEGDBor7vGnTpsjOzsbDDz9s6j7Pz8+Hz+czdJ/36NEDr7zyCi6//HKN+7xGjRqoWrUqxowZk9TucyJxOTn5BvAeUXnNeyQAQNa09xzPJf2nHThBUl6zz1qBeYO/h6nO6+Gv+M7xWgQRL1CcKaPChMDMzEy0bt1ac65y5cqoWbOmcv7YsWM4duyY8j9r9+7dyMzMRIMGDVCjRg0AAUFxwIABGD16NABgwoQJuPbaa9GwYUMcPXoUjz76KARBwC23BFxb8fo/gig/PFyZGDiZfxM+AGLwtQ+AHF79QXHvBXYzgFABEAC8jIOPYxDAKWKgwDhFAJR//rvnFeW1ci8CP78t3ubiuyMIgiDiCTOBUI1axPsq90HwFr+d8R7RUAxUr6N+UATIUUi4Aydy4ER77nN5XMeOHSEIQljnOUDuc4IIh5EICADFT/wdfLoPfLo/cGSUgkv3a+7lBAZ+0FcAAGnVpRGrANIXOYA39MthvuO+yCYkCBVO4ow8HrAfayjOlMExK5tHOdO9e3dccsklWLBgAQBg2rRpmD59esi45cuX44477gAQcJHccccdmDZtGgDg5ptvxsaNG3Hy5ElccMEFuOqqqzBz5kw0adJEuf/cuXO4//778cYbb2j+RzjJ0S4qKkJWVhbOnDnjSsoDETvUbkA1frX4x5QSAyFCoIyPC/2n4g0Kez6OaVyBACByTPk55L6gCOgDw7/FwTbfCZFMuPEZIooiPB4Pfv53E9Q53/5vdM+89TuWfVYbPl/gb72dBzSi/KE4k1jYEfycoBfz9Jg5A83uJTEwNXHjc0Se4/fPmqBqFXsZO0XFIqpfc5A+vxIAijWJw+8PDVR+VguBQpofnsol4M8rDYiAAgNUcYATdM8jXhHwSGWOQG/gv0wt8Fm4Ao2EQIDEwFSlouIMQLEmGirMEWjE+vXrNa+nTZumCHxm/Pjjj5rXBQUFYdehdDPCDOvHLnO8jAM4QASDF5wiAKoFPzVqsfDvnlcodZgod5ykBhMEEX+YOQPNBMSvch+k9GGCIIgkQC0CKggSOIPPf0MRUPPaRPATVT1F1aKgiQgIAFJhcxIDCSJBqLCuwQRR0Xjsu44N3YDyefkAAAFlkxqlAasJOAYl+CDhGu+L9jdDEARBJByS5CDo2MSOa1A+CCJq/Bzgs3n4y9K13OgaTBBEACMRUF3rL0T4UyOLgEZCoktIhc1jNjeRAjiJMxRroiKuHIEEkczITsGyZiISfFwgEItg6OJdii2+4RW5RSJRETntN7fhYJyjrsEEQVhjJy2Y58unEouV6EeuQKK8catrMEEQDgh+SaQRBXUioCIe+nhzVyBgnSJMEHECxRrnkCOQSHoELnBY4bO6pnMDimDK4QQrEbAUgZ8v9y5xNCdBREp2djb27NmDPXv2kAhIECmCJHHY9repFb0NIlEReWcHyKVBEOUNs2q0oBcBdXA+3b1G42QnlhHBa9IXOXa2ShChOI0zFGsihhyBRMogcGXdgSPBSPgTwTTpwCFrBrsGq2sEyiKgPJ8sAoqgb9yIyOD8XOgvb1aI5AgkiHhH8gthU38dzadKTd5yzSPo8tljrs1NEGaQS4MgEgAjV2A4J6DZ751BYYZ91grcNf91YXMEER6KNc4hIZBIKYzEwGjEQcBYIFSuqdyESk1ACxGwlCMxkCgfqFkIQaQOahFQFKleIEEQRKKh7hgcFr8AeEQwkTOsGchE3tAVyCwagYQlKAA6+mKaIIgKg1KDiZQjXJpwpPjAlMP4elk6MKAVAUs5iURAgiCIJEWSuIiahbjR5ENeVxQFEgGJqOB8nKMDoHQtgog1HF/2/MAMYoYmVdhv89HfaV1AvQjo4wMHQTjEaZyhWBM55AgkkoZHPW9iuv+mqOYQAJglYgngQtx/ZqKfFeo5ZBGQIKJCVSPDFhKlBhNEJETabIPnWcRdg6MRA9UioHLOyWcFQUQJpWsRhDNOTr4BNWe/5egeziOGLyXhE8oahriFKp5wPo7EP6LCoFjjHBICiaTgUc+bmv+GQ58i7EVowxAPOPjB4GVcSMMQwFoEFPUNRnSvSw3qAfpMJcgyWqU9E9xv2UPdrtK7w95HEHooNZggnCF3BrbTIdiI8uoaLGMkPMoi4JUbplneu+WaRzSvqZ4gQRBE7Plt0k3g+IAYKONGrVhNirCfV5qG2EadMmyQ+htJOjD7rJV2DqonSBDlCgmBREJjV/hzgpUrUMaJCOgDg8A4gOMBBohcYHanzUEuSXtOIwCqzwNAGgv9Fk4Ajy2+4Y7WIQiCILREKv7FA07SgfUCoIxRp2G9sBmJU5JIMJyk+/kCfz86duwIQRDIeU4QYfhtUmRZTbwTUU/lCjSrExgWLzNvFOKVbH1GyCKguiYh29SybIB6X6oxfMd9zvZKJB5O08op1kQMCYEEEcTIFWgXvfhnhQAOaeBRioAYmMZ4TXqw3vW3q/RuReyTMRL9AnNrz6eBRzfvMgjg4GU8vOADoqRqzz5IWOO/w/b+ifiD+XkwJ0GTugYThG0SWQQ04/Nu05SfeZMHQUEw/kpMLwDKbpUd/R6wvK/dqiedbpNIAihdiyDCE6kIqIez6x4M5woU+TIxzqiBiCwGChIg8mBeVuYKVImBbE0bg3st1jURAAGACQzijmaBBzb5XHCM2pEotNlvPj+RtFCscQ4JgUTC4pYb0KiTsF2cCIDhkIU/H0SN808tApq5/pTrqp8FlAVFWQQ0I9fzktLI5DPfnRHsnkg0KDWYIOIPSeJcTyEWBNHQFWgmAMr3hIxX7cssVc1MBASAr6+boPxMoiBBEIR7uOUK5Hyc/c7BNsVA7T2qxibyOkaxSO0UFHS1nPT36xB3NwN8gHAZCYIEYQVV9CQIA6wSqSJpEBI6f8AVKIt4aYxXRD6j9F95jHYO1f3gFRFQAKeIgF7Gh4iAIsc0bkAAigiob4ZCJAgi5+xgUByB1GGLIOILucNwpJ2GreAFSTmMEAQxRMzjeRa1CKiGOhcnLkziwUSbhxT4vYM6ORJEbFELeXLXYCZyQLhGUyYdhBUxT+RDBDn5UNCJeRpxziuVCX/qn/Xj1HiZMmfoWmX3qu+PpD4hEb84ijMUa6KCHIFEwuINPpj4onxQsmoaIjcMscJIGPTCeE9eBOoE+lSpwAJ4pV6gWuxTpwsbiYDKNZUAqKyjGm/lBAS0IuAG3zDLsUTyQI5AgghPPKQFy2KgU6eg007FdoS8aEVAIvWgdC2CiB0cH/qlDvML4VOEjVyBPt4wbVcR41QPSUxg4ETZ/WfhDARC5tSIgBapwGXnjU+bCoCR1nkiEhqKNc4hRyCR8HjLoROjl6lENhORT42ROCgwrVAnuwKB0Np+gHkdQM0YAxGwbJ/W6cA+SOQEJGwhiiIefvhhZGdno1KlSmjSpAkef/xxMFb296a4uBijR49GvXr1UKlSJbRs2RL5+fmW8/p8Pjz22GNo0qQJMjIy0K5dO6xevTrWb4cgEpJI3IGyeGgl1Bm5APX3W+FEBCQ3YILj550dIJcGQcQDnKD7LA+KgGauQAA6kU73X+hSdnWYOf7sioBWc3M+jlyAyYzTOEOxJmLIEUgkBV6eReQMdONLIy84y3RhWThUdw/2GXQMlsVAdTdhfSMR9biy1+4Ew1KHXYyJOMIvhE8BUSPyjpqFzJkzB8899xxefvlltGrVCl999RWGDh2KrKws3HvvvQCA8ePH47PPPsNrr72GRo0a4eOPP8Y999yDunXr4rrrrjOc96GHHsJrr72GpUuXIicnB2vWrMGAAQPwxRdf4NJLL7X/fggiBYi2dqBRrUArEc+sKQhBOIFcGgQR/zjqIGzmDNTPaVVr0GIts5qAtgjuy2xPRPJCscY55AgkkganzkC/iyY4K5egwDgIjDMdkxYi7Ok6/6qcgUYpwSJYxI4+rw3XIZGcNGzYEFu3bsXWrVsxePBgFBUVoaSkxHDsF198gf79+6Nfv35o1KgR/t//+3/o1asXtm3bphkzZMgQdO/eHY0aNcKIESPQrl07zRg9r776Kh588EH07dsXjRs3xt13342+ffviqaeecv39EkQyw3tE5bCLExHQDWQR8rIP57g+N5H4kPOcSFXCdQyWnHzRa4CpGxCw7hxshVOBTo1eBDRrDEIQLkNxRgupAERSUR5pwmoEBynDshgY+K/1Pz11IxD1OSv0gqAPkqHz0IrLvUscjSfiA+bnwHy8/UPksH//fmRlZWmOWbNmGc5/xRVXYO3atdi/P9CB7euvv8bmzZvRp08fzZhVq1bhl19+AWMM69atw/79+9GrVy/TfZeUlCAjI0NzrlKlSti8ebMLfyoEkTxYCXN68c9KDJTTgCtKBASAHf0ecH1+opzwCc4O2E/Xkp3nzzzzDPbu3Ys5c+Zg7ty5WLRokTJm/PjxWL16NV577TXs3bsX9913H0aPHo1Vq1aZzvvQQw9hyZIlWLRoEfbs2YORI0diwIAB2Llzpzt/JgQRZ3AeMVT8kzERAcO6Ae2mUIWbR58ObOUajNANSCQ4TuOMg1hDcUYLpQYTSYdaDIy2kYjjtR2k6XoZD3ASRDDF3adPzw0n/tnBB8lQePTCOEX5krTnsKv07qjXJeKbZs2ahbj10tPTDcdOnjwZRUVFyMnJgSAIEEURM2fORF5enjJm0aJFGDFiBOrVqwePxwOe57F06VJ07drVdA+5ubmYN28eunbtiiZNmmDt2rVYuXIlRJFSEAlCJhbCnJtriaLgqE6g5BfwVe6D6LDmCcdrEYmH3XQttfMcABo1aoQ33njD1HkOACNGjMCSJUuwbds20xIUr776KqZOnYq+ffsCAO6++258+umneOqpp/Daa69F+e4IovyR/Dx4vZPPSIDTC4LhRECvQedfHwcODp168nwiH3pO2UvofCFr+BCR85ATOcDHQfoiB/wV3zmfgEhI7MQaijNayBFIJDVOHYL6L5PkjsE+znwetSswHGpXoBFp4JUjEsyahthBhIRSToIPJMKkAjzPo2rVqprDTAj817/+hddffx0rVqzAjh078PLLL+PJJ5/Eyy+/rIxZtGgRtm7dilWrVmH79u146qmnMGrUKHz66aeme3j66afRtGlT5OTkIC0tDaNHj8bQoUPB8xSaCMIO5VW3L5p11G5AdYrbtr9NjWpPRGJQVFSkOcxKUJDznCACMCn870CSqsmH0iFYYObdgp2mAyvioFoUhLnrzsjZJ0hlh3qcQWMQt1KCZREQQKCD8WetXJmXiH/sxBqKM1rIEUgkJDPSCmyPNWokIoaJN04feWRhTzQQDI3OAWWOPAGcYY0/vRgYrpmHLAKq6/7pRUCjfYpgIXM3T1uIfaX3Wq5HxBEir/3mNRyMc9QsZOLEiZg8eTJuvvlmAECbNm3w008/YdasWRgyZAj++usvPPjgg3j33XeVb9natm2LXbt24cknn0TPnj0N573gggvw3nvv4dy5czh58iTq1q2LyZMno3HjxvbfC0EkMZE49KKtJxUr5H1F0v2YiA+YyIHZLMIvj6tfv77m/KOPPopp06aFjCfnOUE4Q/Lz8J7nB++RlC9rLNOCgRAR0NINaIaVGGjVzddBZ+BwyPs07R4s8tRZOEFxEmfk8YC9WENxRgsJgURKEGlXYadYCYJGeBkPH2cuBqpRpw+rRUK1C1AWAc0EQDPUbkA/JBIBU4Ds7Gy89957tsaePXs2xKUnCAIkKfCLo8/ng8/nsxxjRUZGBi666CL4fD688847uPHGG+29CYIgNBiJgDzPohLf7LgBzdKD9V2K1fvQXyOSkyNHjmjStew4z1u1aoVdu3bhvvvuQ926dTFkyBAAWud5w4YNsXHjRowaNQp169Y1/cLp6aefxvDhw5GTkwOO49CkSRMMHToUL774ovtvliBihOQXDOrBSgEhT5BC3YDqn73Gn+Fh6wLaEQWN7lELcCZzuCECyj9rBD8fRyJgimIn1lCc0UJCIJGQPFR6syNXIBBeDJS/4JLDpT4tONLOvHaxIwYCMBQB7bgA9fggwcdJqtfx/a0FUXFce+21mDlzJho0aIBWrVph586dmDdvHu68804AQNWqVdGtWzdMnDgRlSpVQsOGDbFhwwa88sormDdvnjLP7bffjosuukhpSvLll1/il19+wSWXXIJffvkF06ZNgyRJmDRpUoW8T4KoaJyKdkYPh/GIXqAkETB1kEtPhIOc5wRRBpN4cLxWqDP7vOc9YsAJqLsW4gw0aw6idwOGEwj1exVYICVXmc/h85IXxi5DXX1AW25F6FyCPio1kyrYiTUUZ7TQvw4iYXmo9OYKWdcXpSCov18t4hnV+DNDLwJ6wTt2AgLalGO/wy7DRHzARB7M7+AQeSU12E43x0WLFuH//b//h3vuuQctWrTAhAkTcNddd+Hxxx9XxhQUFKBjx47Iy8tDy5YtMXv2bMycORMjR45Uxhw+fBi//vqr8vrcuXN46KGH0LJlSwwYMAAXXXQRNm/ejGrVqrn+Z0QQkZAIDS0kv6AcZkTacMSJyKgW90RRMHUDyue7fPZYRHsiKhA/D/gFm0fg9xG7XYPLy3nu9/vxzjvvoH///k7eOUHEjPPnvhnxvWHFP5kwIqAVdtx7Thx+kbgB7YqAGkgETEwcxRlnsYbijBZyBBJEDBAYZ5geLIuA+muymGc3TVh/n9kenOCFAH8wRZhIfpykBmdmZmLBggVYsGCB6ZjatWtj+fLllvOsX79e87pbt27Ys2ePrT0QREXRYc0T+Cr3wQpZW5K4cu0aHO16dp1+kpOapkRCY7drMDnPiVTm/Llv4rdJN4Udp3YFKo7BEOefeVqwmQgYqRtQjV7g4wzqvJmKgGpXoAMnoNr9J68Xki5MpAR2Yg3FGS22hMCBAwc6njg/Px+1atVyfB9BOCGSFGEj9GnB5uMif0DSi4A+SBoHnw+So5qBatTzOBUA1XjAh21KQsQhMW4WQhhDsZFwm1g20oi2ViBBMMlBs5Dg37WOHTtCEISwcWbRokV4+OGHcc899+DEiROoW7cu7rrrLjzyyCPKmIKCAkyZMgV5eXk4deoUGjZsaOg8V7s5ZOf5Dz/8gCpVqqBv37549dVXE8Z5TnEmddCLgUbpwXr4oNPP0hkojwknAqoJ48DTi3xGAp98ThHozERAdVpwhOnAhnglcgUmIE7ijDwesBdrKM5o4RhjYf+F8TyPG2+8EZUqVbI16YoVK7B3715HedGzZ8/GlClTMHbsWMV18vzzz2PFihXYsWMH/vjjD/z+++9h/0BnzZqFlStX4rvvvkOlSpVwxRVXYM6cOWjevLkypnv37tiwYYPmvrvuugv5+fm291tUVISsrCycOXPG1jedRGyxKwbKNQJFBvhZIPaIzLw+IBBaI1AvBtptDOID04iAQGg9P/m8XLvPTAw0SgtWroVrDMIxpT6gPP9f8GuahZRyEg6V3GfrfRGR4cZniCiK8Hg8ODy/A+pUS7N93zOf/or1xW1tOwIJY2IdGynOxBfl4Qo0E+rcdAWGEwP1a0Vbg1DdLVh2DEoiD1EU0HXzw1HNTYTHjc8ReY6TSzqgaiV7yURFf/lR866v6PMrSsrjGYxiTXyhFgONhEDeI4L3SBDS/PBULgF/Xin4jNIy8U/dOdgrAnJTETVGXYI1QqGqIYcTp58s5Jl1FrbCG3rKrhsQALhzXGizEF+gHA7f9+sINkTYpaLiDECxJhps/ykvXLjQ9rdLb7/9tqNNFBYWYsmSJWjbtq3m/NmzZ9G7d2/07t0bU6ZMsTXXhg0bMGrUKHTs2BF+vx8PPvggevXqhT179qBy5crKuOHDh+Oxx8rq05x33nmO9kzEF06cgWIwpvhUP0eKExGw7GdJ87NaxPOCj8oZGIkb0MwBmJ2+gMRAgghDLGMjEV+UR4qwmWvPzRTheHEGCoKIjVc9TmIgQYSB4kzqYuQKlEVAPt0HCJJ5t2AzwomAOjiRC20IYjKmbI3gf00EQUOXoA8aMdCRGzAS4ZEgUhxbftl169ahRo0atif96KOPcNFFF9kaW1xcjLy8PCxduhTVq1fXXLvvvvswefJkdO7c2fbaq1evxh133IFWrVqhXbt2eOmll3D48GFs375dM+68885D7dq1lYMU5OTHJ3GK8Od34XlKLwL6wCxTh+2KhvFAmkXtQSL+YH7B0QGRc9QshDAmlrGRiE8iaR4iSZzp4XSeSNfVU551B8Ox8arHww8i4gLHsQb2m4UQxlCcST30zUOYVPY7uSfDB44POPx4j+TcsW3UHCTELRgaHwxFQKM6fHpBTufy40ROM5eluOhinT9p1aWuzUXEFqdxhmJN5NhyBHbr1s3RpFdddZXtsaNGjUK/fv3Qs2dPzJgxw9E6djhz5gwAhATR119/Ha+99hpq166Na6+9Fg8//LClK7CkpAQlJSXK66KiItf3SsQOWQT0q9yAeqJLflKt5bCOoJuuQFlsNHIGqtOS5bRgqgeYujhpFkIY43ZspDiTfLjtvLPrDFSvazberjNQXZyeIJxit1kIYUwsnsEo1iQeamegkO4PuAAFCRBYSH1AW/OZue3suvCU1FvO2X0O4Xyc4V6pGQihh2KNcyLqGixJEg4cOIATJ06EtFLu2rWr7XkKCgqwY8cOFBYWRrKNsEiShPvuuw9XXnklWrdurZy/9dZb0bBhQ9StWxfffPMNHnjgAezbtw8rV640nWvWrFmYPn16TPZJlA9yTUBAmxJs100uC3JecPCBaToDR9NExAhZDDTagx1EjmnEQH1tQiK5YCIH5rfv4mQST81CYkC0sZHiTPxTHnUCZeHOTKSLdSdho/kjFQPl+oBWUGowQdjHjWcwijWJiywG8ul+8B7RtgjIRD60TiBgWhfQEhMRTkkP1qX4OsKgcYgs+smCYIgISGnBBBERjoXArVu34tZbb8VPP/0EfZ8RjuMgivY+kI4cOYKxY8fik08+QUZGhtNt2GLUqFH49ttvsXnzZs35ESNGKD+3adMGderUQY8ePXDw4EE0adLEcK4pU6Zg/PjxyuuioiLUr18/JvsmYoedmoB+F0Q9p2nAelegjNoVaHTN6n6zPdhxAwYah5BomMyQI9Bd3IiNFGcINVaOPSsx0I4bMBLIGUio07DCjw383bPbNZgIj1vPYBRrEhtF/FPV1+PMOvJaYVEX0DE+DvCy0FqBFoQdJwt8OkFQc41IOpzEmcB4ijWR4lgIHDlyJDp06IAPP/wQderUAcdFZs3dvn07Tpw4gcsuu0w5J4oiNm7ciGeeeQYlJSUQBPt/CfSMHj0aH3zwATZu3Ih69epZju3UqRMA4MCBA6ZCYHp6OtLT0yPeD1GxyAKgwEXXIMTKFRgtZp2EjfAa1PAzExONBD1KCSYId3EjNlKcIfSEEwPD3RspZkKjEzHQjhuQSH4oXcs93HoGo1gT36i7BkeFNwZf3MhinBh83lCLiUExMPCz8e12RULtmqGnrGoLEqkJxRrnOBYCv//+e7z99tu4+OKLo1q4R48e2L17t+bc0KFDkZOTgwceeCBiEZAxhjFjxuDdd9/F+vXrkZ2dHfaeXbt2AQDq1KkT0ZpE/CPI8YIBMBAD5VAZqRtQFgaNcOqs04+XRT8fp+o2zIXO6WXG6cT6sXbrDZ7j/LbGERUPE3kw0UGDF4mj1GCXcSs2EsmDW/UBw6UKlzeywGclCJqJgIIgQhRJICSISKA4QwABxxQ8gd/tOY9Y5gaMxrHtYo0/w47ADu+1hYM6gfx1Ox3vhSCSHcetQTt16oQDBw5EvXBmZiZat26tOSpXroyaNWsq9fyOHTuGXbt2Kevt3r0bu3btwqlTp5R5evTogWeeeUZ5PWrUKLz22mtYsWIFMjMzcezYMRw7dgx//fUXAODgwYN4/PHHsX37dvz4449YtWoVbr/9dnTt2hVt27aN+n0R8Y8XZcKglbPcxzH4LNx+3mC6rlFjDmUOF513Ri5AzVqcpBznOFE5RDDNoXYDipAgql57QQ9oqUJ2djb27NmDPXv2kAjoAm7FRiJ5cNrUw435IhkbKZJfMD2csK6L807MRAUhcs4OUCdHN6E4QzAp8MWv5OeVf2N6LNOEfY4f/VX3Gqxn8SW06649H6c9jDDZD3UNTiCcxhmKNRFjyxH4zTffKD+PGTMG999/P44dO4Y2bdrA69VWA3VTTMvPz9cUs5WL4C5fvhx33HEHgICw99tvvyljnnvuOQBA9+7dNXPJ96SlpeHTTz/FggUL8Oeff6J+/foYNGgQHnroIdf2TcQfXp4BwQcuv0l8lN2AVuJfyLwqJ6CRK9Cs6UekyDUD9ehdfnqxz4jS4DxpQYExjfHKOQDIYB6cnzEXv52bFPW+CSIZqajYSKQudt2BdhuKmKUeuy0k6tfgBQlS8IFNEKjuYDJD6VrRQXGGUCP5eXA8D04IfOmiyF5BN2BEtQJjiJOagYbCoR3Xn52MGI8EadWl5AxMYijWOMeWEHjJJZeA4zhNYdo777xT+Vm+5qRQrRHr16/XvJ42bRqmTZtmec+PP/6oea0vnqunfv362LBhQwS7I+KZGWkFEd/rxiOIXCswFrUD9RilCssYCYClBuM09+iue8Br5qlaaTaK/poc8X6J2OO4sC6lBrtCecVGIjGJZSqvm+nCshjoxMHotlC4rssTuHpL7DsyE9HhxPEpUQF3V6A4Q6gJ1GmV7JWD8QmBOoF+Xkkl1iDykTUMcVKKJlIcOv6I5MFpZgHFmsixJQQeOnQo1vsgiHJFZMZpwbIbUO+wk7v22q2vFw2ywBcuFViPLN6pHYB6kc+nkj2t0oDTGA9wwLngaxIDkw/qGhw9FBsJN7ArwsVyXqdpzLHoTLz28tnosY3iTLJBLo3ooDhD2CXe3IAyTlyBABynIIesp7ufE8qEU+mdDuAHfWV/L0TCQLHGObaEwIYNGyo/b9y4EVdccQU8Hu2tfr8fX3zxhWYsQSQK4ZqE2BEAzVyA6vTgcCKfUUMQs7FymrDR3tQCoM/E8+iDGCIG6sdmMA9KuVIAJAbGM0ziwJzUYiFHoCtQbEwtvsolx5oRZi7BSNyKJAYShBaKM4QaT4YPQpoffLovfAd3XddgJvLggg5AzseBeVmZK1Dd8dcpNpyFjsVA9dzqeXQiH4tgzyQGEkQAx/7aq6++WtOsQ+bMmTO4+uqrXdkUQThhVvobEDimOfT4JA4iC9QHtGoQApSJfj4w007AVpg1EdGLfPJhdD1S9PUAzURAPT6ItscSyQE1C3EXio3JjVMR0KkIFmmKbzg3Xnl2GpYkTnMYoe8YbFQfcE37uTHZH+ECEh94MLdzSIFHDCrg7h4UZ5Kf3ybdZHie4yV4MnzgeAmcIIFP9wNWwprX4nf6aBqGhMNBJ1+oSlwq9QHV9wdFQM7HKYeeEPef+rXF+xT/1dH+PonyxUmcoVgTFY4/CeQ6FHpOnjyJypUru7IpgrDLrPQ3DM+rxUCfzQchsyYhVoKgHaFQ3yxE7t5btq4UtQhYatAQxI6wZyQA+lVzneP8mmtVK82OcIdEItOoUSNwHBdyjBo1Cj/++KPhNY7j8NZbb5nOWVxcjNGjR6NevXqoVKkSWrZsifz8fNf37vP5cOTIEezbt8/wAcotKDYmL+QEdB+5UYheGCSSj8LCQvrCySUoziQvJ8bfghPjbwl0A9bB8RJ4jwiOlyCk+yFkBNyAnMoRqEkLNhIBDeYNwUrEcyLwmXT1DWkGEs6ZoRnLmwp7hkJhLMVOIi6hWOMcW6nBADBw4EAAgaK0d9xxB9LT05Vroijim2++wRVXXOH+DgnCADMBUI8sAqrdgKKJdudlnGXHYHV3YDP0TUP0RFJj0MdJjusFWuGHBE+Y7wBKOcmw2zGlBscnzM87K6wr8Tj0o/3U4MLCQk0R8m+//RZ/+9vfcMMNN6B+/fr49ddfNeOff/55/POf/0SfPn1M5xw/fjw+++wzvPbaa2jUqBE+/vhj3HPPPahbty6uu+462+/FiD/++AOvvfYaCgoKsG3bNpSWlioPUPXq1UOvXr0wYsQIdOwY/TfCFBuTm/IUAWNVKzAesBL81Nf8Io/c7dSpniDUUJxJbk6Mv8X0miwCCmn+MhEw3QcIDJxHDAiA6hRhMyegUbMQFwhJMVYTbbqxET4e8Dp7L7YaqxBECmJbCMzKygIQ+DYqMzMTlSpVUq6lpaWhc+fOGD58uPs7JAgddkRAkXGGIqAZHnAhdQLNxDy1IGg2JuQeF9J+w6HvEuyFYOoK9OtEPlkY9ENSRMBznF9Ta5BEwOTCSbOQCy64QPN69uzZaNKkCbp16waO41C7dm3N9XfffRc33ngjqlSpYjrnF198gSFDhqB79+4AgBEjRmDJkiXYtm1bVELgvHnzMHPmTDRp0gTXXnstHnzwQdStWxeVKlXCqVOn8O2332LTpk3o1asXOnXqhEWLFqFp06YRr0exMTmJVgBUC3rlmZ5rRHkIjJE4+9T3yA5BEgHjG8nPGzqWzMYC1MnRDSjOJC9GIqDk58EHhTtZBOQ9UsABKEiKCBiCVTpwNFi4ATU1+mJZK9ArhXf5GVw3EgGpPmB84yTOyOMBijWRYFsIXL58udK6ftGiRZYPeAQRK5w4AWXnn14EFLiAOOiFsTgogDN07okcC6n7BxgLhpHUFrTCiSswjfEaMbBsT2W/IOi7CRulFqshETD5kCQJRUVFmnPp6ekap4ERpaWleO211zB+/HjDFKXt27dj165dYWt0XHHFFVi1ahXuvPNO1K1bF+vXr8f+/fsxf/58529GRWFhITZu3IhWrVoZXr/88stx5513Ij8/H8uXL8emTZuiEgIpNiYfbrsAZRGuPARBURQM6+65Ac8z0/cgCKKhGCiFcWKo76EmIckJdXKMHoozycexsbeBD/NZHXADBg5OkH/W3ROuYQjgzA0YjYsvQgzrA6pgXhZI+5XFQNkVGEYY1IiAQbGIRMDkhWKNcxx5ZRljeP3110PSwAgiHvHbbA6ixmsg9KlRdwUOJ/YZdRB2EyuXYRrjlcP0fkj4gyvFH1wpfJA0h9oNSCJg/MMk3tEBxmH//v3IysrSHLNmzQq71nvvvYfTp0/jjjvuMLy+bNkytGjRImya0qJFi9CyZUvUq1cPaWlp6N27NxYvXoyuXbtG8keg8MYbb5iKgGrS09MxcuRI3HnnnVGtB1BsJOxhx5XnhlgYTd093iOG70RpA0nkNXUAjQ6CUJPItWjLA4ozyUU4EVANJ6jcgEAgJVhOC4auPqAelQgodwy2lVor1/nTi3NRptlG5AaEynko710nAjKRDzkUHLjLiOSHYk0Zth2BAMDzPJo2bYqTJ09G5aAgiFgiqsQ8JyKgGtkVqHb7yfX/1M5AX3CMeRqxebAVwSBY1BsMmctA+BPBUAoppFuwAF45p3YI6pHPl3Klyjmr8UTy0KxZM2zbtk1zLpwbEAgIfX369EHdunVDrv31119YsWIFHn744bDzLFq0CFu3bsWqVavQsGFDbNy4EaNGjULdunXRs2dP+2/EgB9++AHZ2dmGjsVYQLGRsIuVq84NZGeeXWegmejHe0TDuqN29q92AcqCHwl/yQHz82A269Eyf+D3CLvpWolWi7a8oTiTmnC8nCIcdAMaCGlM5ELFQBsuQNP6fk4ag8QCm2Jj2Np/JAAmJE7iTGA8xZpIcfwvZPbs2Zg4cSK+/fbbWOyHIFzBrCGIFZ6gKBfOFQiEOgPN3IHe4D8xdVpvuDRcZQ1dd2FlTgOXn6D6pyz/LIT5563vCKzsj0TAhEMSeaWmhp2DSRx++ukndO7cGZ07d8arr76KqlWrhhUCf/rpJ3z66af4xz/+YXj97bffxtmzZ3H77bdbzvPXX3/hwQcfxLx583Dttdeibdu2GD16NG666SY8+eSTEf85yDRt2hT/+9//lNc33XQTjh8/HvW8VlBsJOzC8ywumoKEc/5F4wxUu/7siICUFpy8rF27Flu3bsXgwYNRVFSEkpISw3EXXHABateurRwffPCBUotWEATNtdq1azuuRduoUSOMGDEC7dq1C/kSLFGgOJMaqOsDArB0A2rwBT9rDURALkztPg1e5k56sG4OMzdgJC7BEBHQz4ceREpBscY5jv+V3H777di2bRvatWuHSpUqoUaNGpqDIGKF3fqAbiG79dRdgo1qBEaCLAba6SIcTgxMUwl/4cQ/GSunIpEaZGdnY8+ePdizZ4/torrLly9HrVq10K9fP8Pry5Ytw3XXXRfSXESPz+eDz+cDz2v/vgqCAEmK/u+mXEtJ5j//+Q/+/PPPqOe1gmIj4RQzQdAtx6CVAGdX5Is2TVjfEESdMkwkHkbpd1YHANSvX99xCQq5Fu2dd95pWYt22LBhlvPItWh/+eUXMMawbt067N+/H7169YrsD8AEn8+HI0eOYN++fTh16pSrc6uhOJN6yDUCAZg3CdGjEsE4QXImAqpxSxCETbFPXsvJfvWin08oO4iExGmcSZVYE4s44yg1GAAWLFjgysIEUVHo3YICENJb18s4+Exq/NkVA+VUYi94jfCWBl7jClSnCHsZr6QAq5uWmKURmzY2sSH0kfOPcIIkSVi+fDmGDBkCjyc0dBw4cAAbN27Ef/7zH8P7c3JyMGvWLAwYMABVq1ZFt27dMHHiRFSqVAkNGzbEhg0b8Morr2DevHmxfisxgWIjESnRpgvbSteNcedgWfAzSgvWnydSiyNHjmgKuNspQeFmLdoRI0agXr168Hg84HkeS5cujboWLQD88ccfeO2111BQUIBt27ahtLQUjDFwHId69eqhV69eGDFiBDp27Bj1WjIUZ1ILuUlI4GfRngioImIB0ApBCkndVVKMgbI043JuOGJH+OOv21kOGyEqimSMNbGOM46FwCFDhkS0EEGUN0b1AdUioJ36gUa1Au1gZ3wpJMXNp7nXRAxU9m0g4KnFRTMR0AtB0zmYSB6YX3BWT0PkcejIIbRs2RIAwtbTAIBPP/0Uhw8fNm2u8eKLLypByYh9+/bhzJkzyuuCggJMmTIFeXl5OHXqFBo2bIiZM2di5MiRtt+HGXJhX/25WEKxkYgGNzsLy3UCzWoERuvycypcGomAksiDj8VDKhF3VK1a1XEnx3ivRTtv3jzMnDkTTZo0wbXXXosHH3wQdevWRaVKlXDq1Cl8++232LRpE3r16oVOnTph0aJFrtT1oziTeihpwWEcdYZ1As2Qu+7axctM6wZywfMaMdANDARHzbqCVJYerBIBNX8OPgHw0nNPqpBssaY84oxjIRAARFHEe++9h7179wIAWrVqheuuuw6CQDZcInZMKbnFVnqwz8YDSqRNRNxCFu5kMVDv+DMSA502FwHMXX9m9QGJ1CE7Oxvvvfee7fG9evUKSblV88QTT+CJJ54wva6/t3bt2li+fLnt9Z3AGMMdd9yhfBt47tw5jBw5EpUrV9aMW7lypavrUmxMPfSCmNp1F4kLL1J3oJP7JL/gSmdgw30IErn/khi5zqzdsYD9Au4yci1as89np7Vo3333XaWcRdu2bbFr1y48+eSTUQmBhYWF2Lhxo2mH+ssvvxx33nkn8vPzsXz5cmzatMm1Bh8UZ1KHskYhDj6vK1D80jQfQZkr0FENQLXoaCYGeqWyrsFy2rRPABMD90XaPIWID5zEGXk8kHyxpjzijGMh8MCBA+jbty9++eUXNG/eHAAwa9Ys1K9fHx9++CGaNGnidEqCsI1dMRAAvNAKfmYNRNTh0gMOftPGH85cgYA2PRgMACdZ1gX0cZJhMxDLNYJCodoVaJX2W8pJyGAeAH7LcdQ9OHFQ18iwdwOHQ4ecOQITCb1r4rbbbov5mhQbk4cOa57AV7kPRnSvWoyLNBVXFvXcTuWNdXqwHtmZSKQ2hYWFjlwaiVCL9o037P0emp6e7orLXYbiTPJQa94bODH+FtPrsggYDUzkY5MeXJ6oxEDmZYoDEdC5Aq0gETAlSLZYUx5xxrEQeO+996JJkybYunWrUpj25MmTuO2223Dvvffiww8/jGgjBBFvyHUCzerwuYXeFWiU+muF0d6sRLw0xgMcAAsxMC0oRhb9RZ0ckxWnjsBEIlZOQysoNiYXdsXAaOv7Wc0byT3yXsKlBztxBfIeEVKY0gN60U/9mlyChF0SqRbtDz/8gOzs7JiXnVBDcSa5CCcGAoHSL5JfAC+YZPL4BcDos9zP2xLANC6+CETDEHFOnSLs49ypFSjvS+TL1jNwBXIocwUShBWJEmtiHWcc/2a2YcMGzJ07V9OdqmbNmpg9ezY2bNjg6uYIwogpJdZB08szCBzg4QKuQKd4LNJvvTZSc+VmIvJYdXMRL+Mt03vDCY7660bj5c7BaSbOQg94pDEeXvDIYB7TcQRB2IdiY+qiF+3MugFXxF4qGjMhUn093BgifmCMB5NsHqwsXatly5ZYvHhx2PljUYu2Y8eOyMvLQ8uWLTF79mzXatE2bdoU//vf/5TXN910E44fPx71vFZQnEk+as0zd/0wKZjtIfKADYFLEcE0NfPK/3d8zsdpUno5t8S5oCCoCI1eXUdkr2i/TiIRtziKM0kea2IdZxw7AtPT0/HHH3+EnC8uLkZaWpormyKIcIRLEfbyDJBdGsy8JqDZ44ecIhypK7AsJTiQTiwwDuC03YPVmDUOMcNsL3J6sAAeIqQQZ6DcMMSjpCoD5yzWqZc+Dz+XjLe9L6JikCTOkeNGkpI7NfjcuXNYsGABTp8+jbFjx6JOnToxX5NiY/LhJEU43gQ4wNgVGGl6sN4VaNTcRF5PdgBauQKpUUjq4CRdK9Fq0ar5z3/+g1mzZsVkLRmKM8mJkTOQSYE6aRzPQzznBQQJvMCMOwebuQLlucKkCDt2Bepq9+ldgRrccgXq1lY7AzkEHmlgUVcu4VOkibAkY6yJdZxx/DXB3//+d4wYMQJffvklGGNgjGHr1q0YOXIkrrvuOtc2RhDhCOcMVOMFIBjEKDvVi7xBR5/s5AvnCtQ7AdWvveAVV6CZ8Cc3BlEf+utWyPOqnYFq1583+K49JuvLYwN1BANiIJF8ZGdnY8+ePdizZ09SiYAAMGzYMHz//feoWbNmVAXhnUCxMTnpsMb8F8GKhPeIyhFyTSX0WdXpC5fua7Sm1Vqa86qHLlmItBL/Pu82zdFeCCIVoTiTWkh+IcQVyMJ8bmtSY1XCWIgz0Kd9rRHyzEQ9NWGEtahcgV5mLR6aOQM9kuIKNEoRZmva2N8DQaQAjoXAhQsXokmTJujSpQsyMjKQkZGBK6+8EhdffDGefvrpWOyRICJCnSIcck31s1lItUoRlhE588YiQKgYaEWpiVswEtRioHzIgmAgLdj+A6AXPLLTF7i2N4KINevWrcP48eMxceJEfP/99zhx4kTM16TYSFQUTgQ6QOvic1sM1Kf5yq9JDEwO5G6Odg/AWbpWIsFxXEjdpljXC6Q4k7yYpQjL/5bEc15IJV5FCAwRuvSf5b7Qz3ZDMVAnCCrinV4MNBLm7LrsgnM5ThGWBUH9oVqbmQiGhinCXgnsM+MOrET84DTOJHOsiXWccZwaXK1aNfz73//G999/j++++w4A0KJFC1x88cWubYogokHgGERVXT7FCcjMU4EFGF8zSxFWdxAWOaapA2i5t2CKsNxBGADEoAAoBNN6naQIW6HuIizPr15PMzaYQkz1AhMTSeQdpQazJE8N7tatG55++mk0a9YMDRo0QK1atWK+JsVGoiIxaughNw9xs3GIvJZ8n34twDpFGAgvBl65YZrtvRDxj9NOjokCYwx33HEH0tPTAQRKUowcORKVK1fWjFu5cqVra1KcSU3EUg94jxQQPEoCj+58RmnowGCKMBO5MiFM1zREFgM1qbI+PuCq06cI69N6vczaLagWFb1lDT7Uc3EiBxZtHT9lT6o0YahShIEyITT432A1JEUM5K75b3R7IOKOZIw1sY4zjoVAmaZNm6Jp06aR3k4Q5YKXZ/DZ7OhoJgY6xeeglmAaePyFMkEwnBioFvbsCIZ6MRBA0B0I+DixrIMwzJuLyDRPW4h9pfeGXZNIDJK5a/CyZcuwYMECHD9+HGvXri3XtSk2EhWFVXdfvRjotFagfJ9G0DOoG2gmBqrPqdELlFQ7ML5hfgGSYM9FyvyB/5cdO3aEIAhJ94XTkCFDNK9vu+22club4kzqIH9RI5aWPbLzHjHoDDRojqEWAyEAXtGwg3CIIGhXDNSjqxeoRtNBWH3eQAxUv7btHPQyyGKgBvV7NXBGAiAxMI5xEmcC45M31sQ6zjgWAkVRxEsvvYS1a9fixIkTkCTtB8tnn33m2uYIwk30DUO8Bufkjx21IKh3BQbmct44RLt2wBUocqLS2COwrqQ499zCSAwM7EGAHxIymAfnOL+tuUgMjE+Yyhpva3ySOwLPO+88PPigvSYPbkGxkShP7Lr41OKcG4S4+2yIgeqfjQRFPVuueQRdPnvMtT0TFUsyujQAxKwJiRUUZ5IXfbMQPXLjED5gDALzC+CCgh+gS4VVNw/xBcVAEzSNRIKOPg6SuRho5Qr0SmWuwKCwGAlMYI7FQAa+zBUoBoVPPx/63lX7IjEwuUjGWBPrOONYCBw7dixeeukl9OvXD61bt455PQyCiAR9erDfRLczEgMBe+5AtbhnVCvQKF1YP05Q1Q6Uu/umMaAU9hx/kRAQBhF0BfKoHGwKIouBcpMQIrlJZkdgRUCxkShvjMTASF2BVsKiUd0/J2IggJAUYbM0ZYIgzKE4k9owiQ+4pfwCeMGviIEAtOnA8niLFOFwhLj5woiBZp2DOR8HhlBB0ZUUYRmvqvaTVycGAoH37ucD6cHkOicIBcdP/AUFBfjXv/6Fvn37urqR2bNnY8qUKRg7diwWLFgAAHj++eexYsUK7NixA3/88Qd+//13VKtWLexcixcvxj//+U8cO3YM7dq1w6JFi3D55Zcr18+dO4f7778fBQUFKCkpQW5uLp599llceOGFrr4nIr4QHYqBamRXoBGm7kBOO8assYgszEHn2jNKEZZfl0KyXU/QyA0oryk3DQk4FMN/HJAbkIh3Ro4ciYceegj16tULO/bNN9+E3+9HXl5e1OvGKjYShBVO6/s5FeDMxjsRAwGY1gvUI4k81QiMY5jEg0n2vqSUxyVjuhYQeJZYsGABTp8+jbFjx6JOnToxX5PiDKEgcmDB3+E5oxhg4QqUxTA5PVjjCgxZhzduCqIWA9XpwWauQLN5THDkCgzugYFXxEhOkMqao5gIoOQGjE+cxBl5PJCcsSbWccax5SgtLc31orSFhYVYsmQJ2rZtqzl/9uxZ9O7d21GK15tvvonx48fj0UcfxY4dO9CuXTvk5uZqukaOGzcO77//Pt566y1s2LABR48excCBA117P0R8IBgIb2aCn9fgnNGjipdxGhefESIXEP3OcZJyiEpKsaQceuQafaWcFNLQQ4B23TTwth2D+nGyiCiv6YUQ/G/ofOpzJALGL5IkQBLtH0zildTgZOuwdcEFF6BVq1bo27cvnnvuORQWFuKXX37ByZMnceDAAaxatQqTJk1CgwYNMH/+fLRp08aVdWMRG4n4oMOaJyp6C5aENAkJIwyqhbho0obtiopOxEcSAZOPwsJC7NmzJ2kezGSGDRuG77//HjVr1kTPnj3LZU2KM8mLWddgIHx3d9NOwibnZDhBUg7D61aNQYCQ2oGmHXwN5tG4Ab0wfhCLlKD4aPq+rvkviYBJSDLGmljHGcdC4P3334+nn34ajLlj5y0uLkZeXh6WLl2K6tWra67dd999mDx5Mjp37mx7vnnz5mH48OEYOnQoWrZsifz8fJx33nl48cUXAQBnzpzBsmXLMG/ePFxzzTVo3749li9fji+++AJbt2515T0R5ceUEuuaGnrcijNecLY7BcsYiX9AQORLAx9s4lH2T1JUCXbqseGESCfI9Qg9wbWNxMA0xuNQyX2urUnEB9nZ2dizZ0/SBc3HH38c+/fvx5VXXolnn30WnTt3VjoHN2/eHLfffjt++OEHPP/889i6dWvIF1CR4nZsJOKLWIiBvEd05OazItyDoh4jV57TOfSEey9yIxCjVGOAREAisVi3bh3Gjx+PiRMn4vvvv9cYDmIFxZnkRhYD5ZrPdlxRzMnntoNa0rYxEv/UtQF9ujWtxMVIHtJ8nGZOvRipFwOZSWMTgohHYh1nHKcGb968GevWrcNHH32EVq1awevV/qt12r541KhR6NevH3r27IkZM2Y43Y6G0tJSbN++HVOmTFHO8TyPnj17YsuWLQCA7du3w+fzaVTVnJwcNGjQAFu2bDEVHUtKSlBSUqK8LioqimqvhHtMKbkFs9LNv0mzi50UYeP7OCU92Cz910wENCKN8SjlpDKHYDAF2E0BEIDiOpQ7B/uDr73g4YMEr06YJOIXJnKOfrlJ9mYhF154IaZOnYqpU6fi999/x+HDh/HXX3/h/PPPR5MmTWJSV8mN2EhxhogGszRhdZquWgCU6wU67SDsJlQrMLGQ/DwkwV6skcWMZEzXAoBu3brh6aefRrNmzZQvm2KNW89gFGvil1rz3tA0DmESD46Xgp/vqjRbjwiIHBB01qnrBRqiSg+2TAU2wk4HYYvuwfAFGnkotQJDrsNQBAybHhzOsahCfr9c7m7b9xAVg5M4I48HkjPWxDrOOBYCq1WrhgEDBriyeEFBAXbs2IHCwkJX5vvtt98gimJIrb8LL7wQ3333HQDg2LFjSEtLC6k1eOGFF+LYsWOmc8+aNQvTp093ZZ9E/BKpGChj1CE4MG/gQyqcIKjuIBwQA+FqF2F9yrEaT7CTcSlXJgJ6YtSwhKh4UqVZSPXq1UPc5rHAjdhIcSa1iNaB58acejFQLyaqm4u4CQmAqUMydnIEgGXLlmHBggU4fvw41q5dWy5ruvUMRrEm/pH8vCL8yWKg9rqz+rAK+qYhsntP7dwz6/hrJgKqMGsaEhaThy9LMVDej2o9zsdp3otj0ZNIWJIx1sQ6zjgWAu22Mf7888/RoUMHpKenG14/cuQIxo4di08++QQZGRlOt1HuTJkyBePHj1deFxUVoX79+hW4I6I8CNc9WBb+1K5AK/SCoJfx8HGSkh6srt1XykmKGCh3EZZdgQI4iDbWM0PuUKzHAx4elQvQa1gpkUhFfvnlFzzwwAP46KOPcPbsWVx88cVYvnw5OnTooIzZu3cvHnjgAWzYsAF+vx8tW7bEO++8gwYNGhjO2b17d2zYsCHkfN++ffHhhx/G7L3EAjdiI8UZIpnQNwwxHUcPaUQCct555zmqYe4Gbj2DUaxJDNRioCvomoZoMBL+7CI3DrFyBULVQRjBzr76OoGAoSDouHGIfB+JgESCE+s4EzO7T58+ffDLL7+YXt++fTtOnDiByy67DB6PBx6PBxs2bMDChQvh8Xggis6/5Tj//PMhCAKOHz+uOX/8+HHUrl0bAFC7dm2Ulpbi9OnTpmOMSE9PR9WqVTUHkZy4Wa/WfA37//Rk0a4UUsTin53GIl4IIUca47Gr9O6I1iTKD0nkHR2McY6ahfz++++48sor4fV68dFHH2HPnj146qmnNE67gwcP4qqrrkJOTg7Wr1+Pb775Bg8//LDlFz0rV67Er7/+qhzffvstBEHADTfc4NqfTbxhFRspzhB2kCTOcZMPOfXXyIknpwvLc+pdhWZdfvXn9feRCJh8OGlKJQX/fnTs2DHpmlLFO+GewSjWxC/qtGBbRCCQxQS9W1AvLOprBWrGur+dkHI5Xik6sZMoN5zGGYo1kePYEWiXcIVse/Togd27tXn6Q4cORU5ODh544AEIgnMnUlpaGtq3b4+1a9fi+uuvBwBIkoS1a9di9OjRAID27dvD6/Vi7dq1GDRoEABg3759OHz4MLp06eJ4TSL+EZnzdF87KcJ6F6D82qyJiD5lWBEDGeAzcegp90JSUoSjcQI6geoDJjdOUoPnzJmD+vXra9wI2dnZmjFTp05F3759MXfuXOVckyZNLOetUaOG5nVBQQHOO++8pBYCqcg7kYhEkyJsJiQSqUEypmuNHDkSDz30EOrVqxd27Jtvvgm/34+8vLxy2FkAijPJgx1XYNj6gC5j6dATArUA3UwPjgROkKgxSIqRbLGmPOJMzITAcGRmZqJ169aac5UrV0bNmjWV88eOHcOxY8dw4MABAMDu3buRmZmJBg0aKA+QPXr0wIABAxShb/z48RgyZAg6dOiAyy+/HAsWLMCff/6JoUOHAgCysrIwbNgwjB8/HjVq1EDVqlUxZswYdOnSxVF3YiK+8AgM/hh9KyanB3vAwR8U4dSpueq6gHZThM0IpAeb1/KTG4dEilyDUE49JlIXSZJCCoSnp6cbphKtWrUKubm5uOGGG7BhwwZcdNFFuOeeezB8+HBlrg8//BCTJk1Cbm4udu7ciezsbEyZMkX5UsYOy5Ytw80334zKlStH9d4IoiKQHXCxbrxhNb9cMyoW9QdlQc/t+n6CIJJYmCBIEgfJ5sO1/O8hGQu4X3DBBWjVqhWuvPJKXHvttejQoQPq1q2LjIwM/P7779izZw82b96MgoIC1K1bF88//3xFb5lIECRRAG/zM9ayTqBdYdCsHmAQzseVdeJVNQuR03o1gqCcIuyUSARAG+uEdAz2MpfbLhKxwEmckccDyRdryiPOVJgQaIf8/HxNMduuXbsCCNTIuOOOOwAE0tF+++03ZcxNN92E//3vf3jkkUdw7NgxXHLJJVi9erWmgcj8+fPB8zwGDRqEkpIS5Obm4tlnny2fN0WUG6LOmSdwAXdgtHgZB5+F8Gd0zk43Ybt1/0otGn5YiYTqGoThkN2AbjYqIWKHJPGOgiaTeOzfvx9ZWVma848++iimTZsWMv6HH37Ac889h/Hjx+PBBx9EYWEh7r33XqSlpWHIkCE4ceIEiouLMXv2bMyYMQNz5szB6tWrMXDgQKxbtw7dunULu6dt27bh22+/xbJly2y/Dzv4/X6sX78eBw8exK233orMzEwcPXoUVatWRZUqVVxdi0hN9CmwFdmFV0b9cCiLgnLNvmiFNzv3Ok0L7vLZYxHvh4hvks2lAQCPP/44Ro8ejRdeeAHPPvss9uzZo7memZmJnj174vnnn0fv3r0raJdEoqIXAwOuQN0guZOvDZjIgROijEmq9TiRU4RA62YeknVKcKSEEwH163qlMjGTSFqSLdaUR5yJq6f89evXY8GCBcrradOmgTEWcsgiIAD8+OOPIQ+uo0ePxk8//YSSkhJ8+eWX6NSpk+Z6RkYGFi9ejFOnTuHPP//EypUrLesDEvHNP89bEXZMJOUnrO7xMk5p3OEFF5IO7LXxnZNaBPRWQBpuGuMN039JBEwNmjVrhjNnzmiOKVOmGI6VJAmXXXYZnnjiCVx66aUYMWIEhg8fjvz8fOU6APTv3x/jxo3DJZdcgsmTJ+Pvf/+7MiYcy5YtQ5s2bXD55Ze78wYB/PTTT2jTpg369++PUaNG4X//+x+AQKrzhAkTXFuHIPQ4reHn2roxcALGAlkEpO7BhJpffvkFt912G2rWrIlKlSqhTZs2+OqrrzRj9u7di+uuuw5ZWVmoXLkyOnbsiMOHD5vO2b17d3AcF3L069cvqr1eeOGFmDp1Knbv3o3ffvsNO3bswOeff459+/bh999/x9tvv00iIBExksUXLpI/9Pdz5hcCh83sKE3abJRiHdOLjIJKeHOpLl/IGmbj1IJfsC4g87KAiEk1aQlQnFETM0cgx5H5lihf7KQHR+oKNEoPDpw3ThFWY3TOp3PmhasRKGPk+DNz+clCpd5lKKcHK3MaiIEkAiYWTFUs19Z4icNPP/2klEMIZ6OvU6cOWrZsqTnXokULvPPOOwACjZo8Ho/hmM2bN4fdz59//omCggI89pi7rqCxY8eiQ4cO+Prrr1GzZk3l/IABA5S05vKGYmNyUVGCX6KidgGSCJh4yA2n7I4F7KdryU2prr76anz00Ue44IIL8P333xs2pRo2bBimT5+OqlWr4r///W/YplSlpaXK65MnT6Jdu3au1qKtXr26Zp8VDcWZ5EDtDOR449/1JX/gt3rohTK/oEkP1rgC/TzgkbRddWUx0EC4M0oPVrsCw+K2M9BOCnLwfaiFQf6K79zbAxEznMQZeTxgL9ZQnNFSYc1CCKI88Bk8pMUyRVgWA+XUYLUIqBf/lPMmIqBcxy+N8baFOaMagkYpx2WNRyTD84C9TsNE4uKkWciVV16Jffv2ac7t378fDRs2BBBo1NSxY0fLMVa89dZbKCkpwW233WZv8zbZtGkTvvjiC6SlpWnON2rUyLKjYiyh2JiYfJX7oKPxFZkabFk3qgIwSkWWRcCKTqEmYo/ddC1qSuUeFGdSBHV6sMiBIdA0RBH9jMRACIBXVMTAEPSCnVoYlNeLVAwsRzTOQEEK7WpMJB12Yg3FGS0RPen7/X58+umnWLJkCf744w8AwNGjR1FcXKyM+eOPP9C4cWN3dkkQLqBO9RVsfllqlB7s0aX9qlOEzTASAX2cpBEBRY3TMPBPU+3WMxPm7Ah28h7TwGvGC+A1h37ODb5hYecmkp9x48Zh69ateOKJJ3DgwAGsWLECzz//vOYbt4kTJ+LNN9/E0qVLceDAATzzzDN4//33cc899yhjbr/9dsP042XLluH666/XuPbcQJIkiGKoIPLzzz8jMzPT1bUAio1EgHgQt9QpwhUhCur/DARB1BxGY4jkpKioSHOUlJQYjlu1ahU6dOiAG264AbVq1cKll16KpUuXKtflplTNmjVDbm4uatWqhU6dOtn+Qksm0ZtSUZxJLdTZHmpXoDo9WP15z+yUh/AFx/h5MLHsMB4bOG/WBZgTOfMagbHEQthTREBKB04p7MQaijNaHAuBVHOJiFfsdA3Wi4F2BUHT+YK1AQUDEVB2AxqlAetdgFZNQiJN07XTeMQIEgETD8nPOzqYxOHQoUNo2bIlWrZsicWLF1vO37FjR7z77rt444030Lp1azz++ONYsGCBpk39gAEDkJ+fj7lz56JNmzZ44YUX8M477+Cqq65Sxhw+fBi//vqrZu59+/Zh8+bNGDbM/b9vvXr10tSd5TgOxcXFePTRR9G3b19X16LYmHrEe1qw/HBoVDcwXFpuedUa7LDmCXRY80S5rEVEj1o0sHMAQP369ZGVlaUcs2bNMpxbbkrVtGlTrFmzBnfffTfuvfdevPzyywCgaUrVu3dvfPzxxxgwYAAGDhyIDRs22Nq/3JTqH//4hzt/IOUMxRkC0Il9JgKeUitQ91munFeJgWXXTERBtUvQSWO6MC68qAVE/fyy8GcgAPIdtRkrRPziNM44iTUUZ7Q4Tg2Ox5pLBGGFXxcnvNB2qleLgW6kDKtRi4BmKcBmgp2+ll8kiGCmgl4X79KQcyQCpg5OUoMB4O9//zv+/ve/W4658847ceedd5peX79+fci55s2bxyyN6cknn0Tv3r3RsmVLnDt3Drfeeiu+//57nH/++XjjjTdcXYtiIxGP6AU9uXOw21z24RzD89v+NtX0HhIAU4MjR45o0rXS09MNx0mShA4dOuCJJwJ/Ly699FJ8++23yM/Px5AhQ0KaUgHAJZdcgi+++AL5+fm2utPHoilVeUJxJrk5Ntad8ijMb5AiDChpwsp5nypNGNCkCsviChcrR10E6cVWYp5U2LxsXvU9VBcwZbATayjOaHEsBMZjzSWC0CMyDj6JMxT2fKGnHKNuGiLXCjRrzmG5T91YfeMPvRtQ7zyU708DH1IfMJyYt8U3XCMGpoEnAZBIKurXr4+vv/4ab775Jr7++msUFxdj2LBhyMvLQ6VKlVxdi2IjASRXuqvdWoNmIiAAXP7JTGz729SQPxcSAVOHqlWr2qoRmKhNqYBAuu769etx8OBB3HrrrcjMzMTRo0dRtWpVVKlSxbV1KM6kHrwgQkjz2y7xIIuAyuug604vCIaIgYB53cAgmqYh0RBBrUHhsv2W1/mO+wJioKqpCYmAqYWdWENxRotjIbC8ay4RhFPUIqCfuSP8OUFgHMABUDW1MnIDRpq6q1nLoBGIE9RiIImAiYskcZAkB+kajFdSg4HwXYMTEZ/Ph5ycHHzwwQfIy8vTpDHHAoqNqUW8pwXbwaiJhyRxtsVMQRDRbtWTYcdd/slMTbMVEgETF1HkIdpMDZTH2e0anKhNqX766Sf07t0bhw8fRklJCf72t78hMzMTc+bMQUlJCfLz811bi+JM6iB3C+Y9EniPGPxv4FmCsyEKGgmCZV2DBedioI+31/DDrKNv8H5FTFQ3OgEsxcBwIqARJAImLk7ijDwesBdrKM5ocSwEyjWXnn/+eQCxrblEEJGgFwH1rsBo6wLKGLkC9XjBm3YLjgYv401TjZ2yxUfpJKmI09TgRMPr9eLcuXPlth7FRoIwh8S/1MVu1+Bx48bhiiuuwBNPPIEbb7wR27Ztw/PPP698pgKBplQ33XQTunbtiquvvhqrV6/G+++/ryk7cfvtt+Oiiy4KqQ8Vq6ZU5ZmuS3EmteA9EjheUv7rlLDuQDNBMSgGatKC9SJg0M0ni3ch9f4EKbSeoF4MVM3jBlQHMLWxE2sozmhxLAQ+9dRTyM3NLZeaSwQRjn+etwJAWaMQkVmLgFCd0wuCkaQRq8VAQJW6y2AoDLqJLAbKrkA5PZhIPSRJ0HSWCz+eS3pHIBB4X3PmzMELL7wAj8dxuHMExUYiHtOCeY9oq/GHKAphG4iocTKWSB4kvwCJtxdr5L93dh2BclOqKVOm4LHHHkN2drZpU6pZs2bh3nvvRfPmzQ2bUvG8VoCQm1J9/PHHTt6uLcozXZfiTGoREAHLnIFqYY63SOFVIzcWMXMHapyCZlg4AWUnHxOYveYfamehA1cgkTo4iTPyeMBerKE4o8Xxk1G9evXw9ddfo6CgAN98801May4RhBWyCGjEOVVKsNdkjMjsuwMjeeTxggMYAI4PCoPWQduOiCeCaeoEmomB5PIjwpHsjkAg8O3g2rVr8fHHH6NNmzaoXLmy5vrKlStdW4tiY/KiTmsFjNOC41EEBCLr/uskPZggwmHXEQgkZlOq8kzXpTiTvJg1CtG7Ae2kBRuhdwcCsHYFythJBw6HL9QZyEGyV2/Q7CGOIHTYjTUUZ8qIyCLh8Xhcz30mCCdYiYD6LsFWOBEDzdC7As0wS+eNxMXn4yR4Ga+ZN9p6gQSRbFSrVg2DBg0qt/UoNiYXegHQjEQVzfT1Ae26AskNSBBllHe6LsWZ5MOtbsFOCHECqusE2nQbythyAirr8tqUY5EHIBmnB5MISBAAYhdnIhICX331VSxZsgQ//PADtmzZgoYNG2L+/Plo3Lgx+vfvH/FmCMIOViKgL+jUEORmHQgcVrHEKCUY0KYFCzB3BYarE+gWcldgvSswsHaZyJgGHl28S8kVmEJIIgfJyS9iKZIavHz58nJdj2IjkejIYqDsCrTbOZhIDSSJt92YSh5nNzU4UXnyySfRu3fvckvXpTiTXFg5ASPBiWOQiRw4CGAQQ9KDNWKdPD6KWn7MoF6g3hVolhYs7m4GoY3zhiFEYuIkzsjjgeSONbGKM/b/lIM899xzGD9+PPr06YPff/9dsSlWr14dCxYsiHgjBBEp6vqAerwo/y+UBHDwggt0D1b2Uebes4sICaLOLSi7B0UwQ3ehLBCmgUc37zLHeydSh+zsbOzZswd79uxJuoBZEVBsTC6S3Q3oFl9fN6Git0DEOYWFhUkdZ+rXr4+vv/4aU6dOxbhx43DppZdi9uzZ2LlzJ2rVquXqWhRnUgPewHXNCZJG5JP81s8TnEcMOZwQItwZYdQdOFJ8nCNnIUHoSeZYE6s441gIXLRoEZYuXYqpU6dqiq936NABu3fvjngjBOEWnmAcEbjAkaE6BNURC7w6MdKL6BfSC4LqVGK1GCiLjAI4RRAkMTA1kEQBooODMV5xBLZs2RKLFy+u6LcQE7Kzs9G4cWPTw00oNqYGRvUBkw05ZVh+r+HqDJIYSKQqPp8PTZo0wffff4+8vDzMnTsXzz77LP7xj3/EpGYfxZnkhxdExQ2od2Mzv6A0/wBUYqBOtLMj+nEC07gAmcgF0oP16Ov7uYmPB+fjQrsLGyDubha7fRBEHBPLOOM4NfjQoUO49NJLQ86np6fjzz//jGozBBEpIuPgkzglzVd2AapFQQBIRyAV+K9gbUCztGAjrNKD1XgZB3CAL5gyLDAOIsfgBQ8fJNNagQAsO/+KkCAEtXs5TVgAF1IvEADVDCTCkgrNQu677z7Na5/Ph507d2L16tWYOHGiq2tRbCTiHSsRUxJ58AapYHb5+roJaLfqyYjvJxIDJvJhnUjqsUByp2t5vV6cO3eu3NajOJNc6NOCZRFQ3RxERv1vjw/+2+IECRIAPt1vOH/YbsAydpqGqNF1+1WwcAhygmTPZWi1LKUIpwRO4ow8HkjeWBPLOONYCMzOzsauXbvQsGFDzfnVq1ejRYsWrm2MIOyiFgHlRiEelevPyzMIwdp9IuMgihy8CNQA1DsDwwmDRmKgWbOQsq7BABgg2qgfGK5xiFoMDLwOrRcIaBuIXON9EZ/5zDsfEUSyMnbsWMPzixcvxldffeXqWhQbUw99WjCvSdty3q03lhiJgIIgKu4/XpA0YiDVCiTcwknX4ERk1KhRmDNnDl544QWNSy8WUJxJbtQiIO8pcwYyiYdYqvrdP/izkOYPuP9EPkTIsy0CGn2u+3n7DUPMxD+16OeVAu6/oBioaRgi1wpEoGkIB/NagQCJgYQ5yRxrYhVnHM80fvx4jBo1CufOnQNjDNu2bcMbb7yBWbNm4YUXXnBtYwRhF5/EoUSOJwi4AQVOKwB6BAa/yEHgGASOc9RZOBJkVyCAEFeej5OUc1bCnwA+pEZgYL6AGCi7AuU5FVegynkoQ2JgcsMkHsxBYV3GUqNZiBl9+vTBlClTXG0mQrEx+VGLaVYiYDxRnqnM5ApMfqhZSCiFhYVYu3YtPv74Y7Rp0waVK1fWXF+5cqVra1GcSV7kuoByLNE3C9E7pCJtJqJQwTErpHswoHQQthQDfYC4oxmEy0gMTFaoWUgosYozjoVAOR/5oYcewtmzZ3Hrrbeibt26ePrpp3HzzTdHtAmCsIu+Y7DcKMSHgJtP7fATOAaPLpCImgYe2s7AdnHiCgQCqcHgQgXBcO4/K9TOQL0r0Gcwr5MmJURqkAqpwWa8/fbbqFGjhqtzUmxMHqwahRg1B4lHETBSAdCOK1A+LyO7CgFgR78HcNmHc6LbPJFUJLNLAwCqVauGQYMGlctaFGeSB7NuwYCxyCe7zGWnoFH6MBBsEqJ+9rFZL9AxsuPPYUkJwxRhgw7CgfPOt0WkLskca2IVZxwJgX6/HytWrEBubi7y8vJw9uxZFBcXu94ViyD06AVANep0XpEBXoPnH7/IhdQRdBsjMVAW6EQwCIyDz0AQtEtpsK5gmkrUM3MFEgQBXHrppeC4sg8ExhiOHTuG//3vf3j22WddW4diY/IQrQgYbynBTpDTg+3UC1SLf2pIBCRSDTed5VZQnEkezERAM3EP0DoFOV4K/Ff3OR3SJET1OhKxz9C1p8esXmAE83M+znaKMEGkErGKM46EQI/Hg5EjR2Lv3r0AgPPOOw/nnXdeTDZGEDJWIqCa0Hp/nGLdM6ojaIX6Syg3vB4BETDQMOQcRFu1AM0o5SSkMd7QFWgmBpo1JyGSA1HkIDoorCuJqZEa3L9/f40QyPM8LrjgAnTv3h05OTmurUOxMTmwEgH1JJMAaIQsBlKtQEKNJPKQeHt/16UkL+Be3lCcSQ6snIBq1KIgk3jFJagWARXhz0iIMxIBvarPbn2HYG8Un+tqh5+ZKBisExjYT5krUBEDfXxgjApO5EgMTEGcxBl5PECxJhIcpwZffvnl2LlzZ0ihWoKoCNSpvtrzAXFEUIkAehHQjuPcaVhUuwK9QfFPxhvs4mvU3COwln2xTi0GlgKKK5Ag7OIkNXjatGmYPn265lzz5s3x3XffAQC6d++ODRs2aK7fddddyM/Pt5x37969eOCBB7Bhwwb4/X60bNkS77zzDho0aGD/jYTZd3lBsTF1SHYRUEYvBhJEJCRzuhYQiKXqL5z0/PDDD66tRXEmOeFNPl/LUoR1AplKbFPGOBXMohH+rNCl/TIvA6drKGLVPVjtCoSXGYqBnFh+tW+JxCGZY02s4oxjIfCee+7B/fffj59//hnt27cPKVbYtm3biDZCEE6R6wPqHX4+AD75yy+DuBhOADS67geDx0TA06NPEVanB+uxK/6VGjj6ZDEQKEsRll2BRGohioJpqp4RTOIdOwJbtWqFTz/9VHmt71o1fPhwPPbYY8rrcE6FgwcP4qqrrsKwYcMwffp0VK1aFf/973+RkZFh+32EQxAE/PrrryGpUydPnkStWrUgiu79IkyxkShPeI9oKUDyPHO9UQi5AgkilPvuu0/z2ufzYefOnVi9ejUmTpzo6loUZ5KPsiYh1r+7G6UB8x4JECTLz2PDlGCPFOgMbITRPtRuPXkfFmKeU8KlIOuFP77jPtfWJohEIFZxxrEQKBejvffee5VzHMeBMQaO41x9sCIIABB4QNTFB7nm3zlR6/jT3MdFV2dW/pssi3pqcS+cKCiLgXpXYGBeZ9/a6UVAX3BnXghBMRBKijBB2MVpsxCPx4PatWubXj/vvPMsr+uZOnUq+vbti7lz5yrnmjRpYvt+OzBm/G+tpKQEaWlprq5FsTGxsUoLlgU1uUZgeTsAzR7y5PNu7UcyeLCzUy+QSB1EPw+Rs/f7hlyuItnTtcaOHWt4fvHixfjqq69cXYviTHKhFgFNm3+Y1AF03DVY7wCMtutwLNCnB/s4Y0cHkdQ4iTPyeCC5Y02s4oxjIfDQoUMRL0YQkaIWA9UioJ8B51io4OdFmTCorx3oBFn806T4Ms6WQ9Cqk7Bd1CKgT5eo7IOoEQNhIQaKYOjmXYYNvmFR7YdIHiRJQlFRkeZceno60tPTDcd///33qFu3LjIyMtClSxfMmjVLk8L7+uuv47XXXkPt2rVx7bXX4uGHHzZ1BUqShA8//BCTJk1Cbm4udu7ciezsbEyZMgXXX3991O9t4cKFAAIPSC+88AKqVKmiXBNFERs3bnS1RiBAsTFZUbvqZEec2/NHO6cdQTDa9F79/WauQOoaTOhJ5nQtK/r06YMpU6a4WuSd4kzyYOQEVHcE1gt9hgKg7AaMYR29ELdemLReAKHdf3XzRYtU2JxcgUQIqRhroo0zjoVAqktBVDThREAgcM7r0nqyCKg4+eTnQhbeGWhGGniUBpt9mKUHW4mA6vNeBB7+1OnBRGrBJB6S5KBZCOOwf/9+ZGVlac4/+uijhnX1OnXqhJdeegnNmzfHr7/+iunTp+P//u//8O233yIzMxO33norGjZsiLp16+Kbb77BAw88gH379mHlypWG6584cQLFxcWYPXs2ZsyYgTlz5mD16tUYOHAg1q1bh27dujl6/3rmz58PIOAIzM/PhyCUCSRpaWlo1KhR2PqFTqHYmHwYpda6LQbqU3iN5nYrFdeqfICRGzAcRvuS/AK+yn0QHdY84Xg+gkgm3n77bdSoUcPVOSnOJDZW3YJlEVBzPijAcfprwfPqz9+QjsFmqOYxSseVhTqja5yPMxX51HUA9TUBASiNQtxC+iIH/BXfuTonQSQa0cYZx0LgqlWrDM9zHIeMjAxcfPHFyM7Ojmgzs2fPxpQpUzB27FgsWLAAAHDu3Dncf//9KCgoQElJCXJzc/Hss8/iwgsvNJ3HrJji3LlzlTzqRo0a4aefftJcnzVrFiZPnhzR3onYMK9KoGNwSGqwhQgIRCcCelE2r49jIam8ZR16WURioBBsGmKGUT1AGb9ONPSYuADlzsH6eoHkCiRkmjVrhm3btmnOmbkB+/Tpo/zctm1bdOrUCQ0bNsS//vUvDBs2DCNGjFCut2nTBnXq1EGPHj1w8OBBw3RfSQr8vezfvz/GjRsHALjkkkvwxRdfID8/P2ohUHZNXH311Vi5ciWqV68e1Xx2iGVsJGKLk27BQGycgbGc20n90HDo95esjVIIYyQHXzrJ45I5XQsALr30Us1zB2MMx44dw//+9z88++yzrq5FcSa5KHP/qQQ9VVdgQCcCGgiAajiPaFwT0AGmtfqCabtGYqCh8Ke/F+64AdWQGJicOIkz8ngguWNNrOKMYyHw+uuvV+pRqFHXqLjqqqvw3nvvOXr4KiwsxJIlS0IK3Y4bNw4ffvgh3nrrLWRlZWH06NEYOHAgPv/8c9O5fv31V83rjz76CMOGDcOgQYM05x977DEMHz5ceZ2ZmWl7v0TskUVANX6Rg8/lAuiRYEcMVHcPNtIKjVyBRiKgmRvQCmoYklpIouDogZxJHH766Sd07twZgL1mIWqqVauGZs2a4cCBA4bXO3XqBAA4cOCAoRB4/vnnw+PxKM1KZFq0aIHNmzfb3kc41q1b59pc4YhVbCRii1MRsDyIpdAYspbLD2YEocduulaidqfv37+/5gGN53lccMEF6N69u+slKCjOJCZ6J6BRl2CjOoFhRUAD0Y+JXNRiYDhkMdBUAHTD/SfyZY1JCMIGyRxrYhVnHAuBn3zyCaZOnYqZM2fi8ssvBwBs27YNDz/8MB566CFkZWXhrrvuwoQJE7Bs2TJbcxYXFyMvLw9Lly7FjBkzlPNnzpzBsmXLsGLFClxzzTUAgOXLl6NFixbYunWr8hCrR1+w/t///jeuvvpqNG7cWHM+MzPTUXF7ovwwEgHVeDggAwB0rkC9EzCa+oB6fAYuPjkN16pmoL5ZiBluiYCy29CqWzFBOG0Woqa4uBgHDx7E4MGDDa/v2rULAFCnTh3D62lpaejYsSP27dPWeNm/f7/rqU8///wzVq1ahcOHD6O0tFRzbd68ea6tE4vYSMSWcCKgWefd8hLp1LjZqdeJ+Cc3CommviCRPIiiAJG396WT7ER14tJIxO70RuU0YgXFmcTDLB3YDNkNqEn11YuAOqHPdlqwizhJ/7XtBPSS8Ec4izPyeCC5Y02s4oxjIXDs2LF4/vnnccUVVyjnevTogYyMDIwYMQL//e9/sWDBAtx555225xw1ahT69euHnj17aoTA7du3w+fzoWfPnsq5nJwcNGjQAFu2bDEVAtUcP34cH374IV5++eWQa7Nnz8bjjz+OBg0a4NZbb8W4ceNC/iLIlJSUoKSkRHmtL7JPxA59WnCGIOFcMKgIgCIGxkIElN18ZmKaCAZw4RuIWIlx+hqBZuKf3bRgO2sShBMmTJiAa6+9Fg0bNsTRo0fx6KOPQhAE3HLLLTh48CBWrFiBvn37ombNmvjmm28wbtw4dO3aVePwzsnJwaxZszBgwAAAwMSJE3HTTTeha9euuPrqq7F69Wq8//77WL9+vWv7Xrt2La677jo0btwY3333HVq3bo0ff/wRjDFcdtllrq0DuBMbKc7EH2ZiYKyJlSuQHIBEeeOkgHsidqcXBAG//voratWqpTl/8uRJ1KpVy9VOvm49g1GsiR+YxEPyG3cB5j2SuyJgmPqA8Eq23HxyarBGDDS4z1AA9OvOqdKgnUJpwYSaZI41sYozjn8jPHjwoOEfctWqVfHDDz8AAJo2bYrffvvN1nwFBQXYsWMHZs2aFXLt2LFjSEtLQ7Vq1TTnL7zwQhw7dszW/C+//DIyMzMxcOBAzfl7770XBQUFWLduHe666y488cQTmDRpkuk8s2bNQlZWlnLUr1/f1vqEc8zcgH6xLOB4eQaBAypxAcFPLQIKnLtOQDPUDkHZ9ecHUw4rzBp6OHUAyo1C9HOrDyL5kaSAcGD3YIzDoUOH0LJlS7Rs2RKLFy+2nP/nn3/GLbfcgubNm+PGG29EzZo1sXXrVlxwwQVIS0vDp59+il69eiEnJwf3338/Bg0ahPfff18zx759+3DmzBnl9YABA5Cfn4+5c+eiTZs2eOGFF/DOO+/gqquucu3PZcqUKZgwYQJ2796NjIwMvPPOOzhy5Ai6deuGG264wbV1AHdiI8WZ+ITnmSLKVYQbUCbaenzhREBRFMLWE9S//0CB+9CDIGSKioo0h1qA0iN3p2/cuDHy8vJw+PBhzfXXX38d559/Plq3bo0pU6bg7NmzpnPJ3embNWuG3Nxc1KpVC506dYrYCW+GPk1XpqSkBGlpaa6u5dYzGMWaioPXdF/XfiYbugFhXRPQFHW88Nr4TJbdeF7J3JmnE/uYl5XVC9TdY0sEjAaLzsREapLMsSZWccaxI7B9+/aYOHEiXnnlFVxwwQUAgP/973+YNGkSOnbsCCDwh2snqBw5cgRjx47FJ5984qpNX82LL76IvLy8kPnHjx+v/Ny2bVukpaXhrrvuwqxZswwL5k+ZMkVzT1FREQXOCsIjMHjAAD8PMSgOlofwBwBiUPATWGBBTbpwcA9eVrYZudmIL3iYYdUgBAh1A4bDy3iqE0hY4iQ1uKCgwPRa/fr1Q2ppGGEUxO68805H7nGn7N27F2+88QaAwLd/f/31F6pUqYLHHnsM/fv3x9133+3aWm7ERooz8U15i4BGrkCzFOFwIqGVCKgW/9QpwHwYlwYJfqmHJAqQOHuCtBT8e6X/DEuW7vQLFy4EEKjP98ILL6BKlSrKNVEUsXHjRtdrBLr1DEaxpmIwqg1oOlblBlRQuQHDOgGj/XwO5w6U9ybyIfUCQ0RAMwHQwAUZMj+RcjiJM/J4IDljTazjjGMhcNmyZejfvz/q1aun/IEfOXIEjRs3xr///W8AgfpRDz30UNi5tm/fjhMnTmhStOQ39cwzz2DNmjUoLS3F6dOnNa7A48eP27Jrbtq0Cfv27cObb74ZdmynTp3g9/vx448/onnz5iHX09PTTTtqErFH7Qb0ixw8wWDor4AvhHxg8HEMXnAQGAcfAj9rGogEkUVAWUD0ORT0CIKInMqVKyt1AevUqYODBw+iVatWAGDbtW4XN2IjxZnUxGn6sV4MdOIUdLODMEHY4ciRIxoXW7J0p58/fz6AwJdc+fn5EISyf1tpaWlo1KhR2OLyTnHrGYxiTcVhnAIshjQKUV8D4EwEdAs7qcKCFGjsYXc8QcSIZIw1sY4zjoXA5s2bY8+ePfj444+xf/9+5dzf/vY38HzgH//1119va64ePXpg9+7dmnNDhw5FTk4OHnjgAdSvXx9erxdr165VOv7u27cPhw8fRpcuXcLOv2zZMrRv3x7t2rULO3bXrl3geT4k95qoWEQpIPyJTPuQJPrtPzSJwdgZC9egyLEQMdBqLGDs1ktjvKkr0KkbEDDvGrzBN8zxXET8YyelT40k8UpqMOC8a3Ci0LlzZ2zevBktWrRA3759cf/992P37t1YuXKlrRqzTnAzNhKxJx67BRthVivQaZowL0iQRB6CIEYsBlZkWjSRuFStWtV23SY18d6d/tChQwCAq6++GitXriyXLr0UZxIfteAni3yyOCinBRu6AeX7I0kTjgWy+GewT06QylyBbqYDE4QFyRhrYh1nHAuBQKBlce/evdG9e3ekp6dr2hk7ITMzE61bt9acq1y5MmrWrKmcHzZsGMaPH48aNWqgatWqGDNmDLp06aJ5iNMXoQcCNve33noLTz31VMi6W7ZswZdffomrr74amZmZ2LJlC8aNG4fbbrutXAI5YY5ZfUCfxCmCnpELUGTGQp9o87nF7jgA8IJTUn294DRioOHcXFlasBe8xhWYBh6lCG0YEimlkJAGuZFK2R8I1Qok9ETTNThRmDdvHoqLiwEA06dPR3FxMd588000bdrU1Y7BMm7FRqJikF158SZ4xapxSLS42cmYSAzkOrN2xwLOOjmqSZTu9OvWrXNtLjtQnEkczDoG87LYhzJh0MwpCEBxA9oR+zghhrHCK5XVBJQJCn5KerBbrkCRD4iMPo7qAaYYTuKMPB5I7lgTqzjjWAiUJAkzZ85Efn4+jh8/jv3796Nx48Z4+OGH0ahRIwwb5q7jaP78+eB5HoMGDUJJSQlyc3Px7LPPasboi9ADgZpWjDHccsstIXOmp6ejoKAA06ZNQ0lJCbKzszFu3DhNvQyi/NGLgOpuwSILCIA+g/vOsdCOwTJ2XIBmImDAM8EBDEr9P7CAsCeLgcocQTEwZG5VmrDAOM1r7Vo80oLrGDUMUbsF05hxgBUhQQCviIGyO5FEwORHFAWIDhxCTOKS3hEoiiJ+/vlnpXNx5cqVXU/TUlPesZGIDDtOwHgV3iJFdgGGcwUKYepX2f1zuezDORHvlUg+7HZyTNTu9ECgodaqVatw+PBhpRyFjJtfOlGcSRyMREBZ7OM9UogAaOoGNBH2HDkAfUKgYYifN6/L5+PNm4QA2msmbkUGXhEDOQRrBXqksK5AJvLWnYNJDCRskOyxJhZxxrEQOGPGDLz88suYO3cuhg8frpxv3bo1FixYEHUQ0v+BZWRkYPHixZZdLY2K0I8YMUKT463msssuw9atW6PaJ+Eu1iKgqlswtGKgaCEChsOJC9AIWQz0BoU2fSMRM9FPRgimEqcFxTsgKPIF365aEDRLHfZBhBcCSjkJaYwPEQMJwoxkdwQKgoBevXph7969IZ3nY0GsYyMRPYmSDmxEJOKkuvagkRjoxj7IFUi4hdyd/uTJk7jgggtw1VVXKd3pz507h08//RQLFizAn3/+ifr162PQoEEhtfDMutPPmjUL9957L5o3b+56d/q1a9fiuuuuQ+PGjfHdd9+hdevW+PHHH8EY09RAdwOKM/GPmQtQRv15qe8SrHYF6j9X1cJfTNKAbYiAzMsCIqBalFM1CYEgmYuBasyEwaAYyfm4wFqyK1Beh8RAwgUSMdbEKs44FgJfeeUVPP/88+jRowdGjhypnG/Xrh2+++67iDdCpCZGqcCiQSySnX36tGD5vBNRL9xYI9ehjDfoEFSLfGoxEAgVAK26BcvIKcIAUCko6KVBJ/6xMmegx4bIR2Igkeq0bt0aP/zwA7Kzs2O+FsXG+MaJCBivbkA3xUDAupOwHlEUTMVDEgNTB0kUIDrsGmw3XStRu9NPmTIFEyZMwPTp05GZmYl33nkHtWrVQl5eHnr37u3qWhRn4hsrEZAXyhqCcLxkLgIKkmVKsJUIGHFasF0R0PB68LwsCAbFQADgIIXkJIV1CdoUA/mO+4zvJxIeJ3FGHg8kd6yJVZxxrBL88ssvuPjii0POS5IEn89KQiEILWb1ANX4RQ6CTlgzcgDabQTiRARUh1ov4wxTbGUB0I7YZ4QATjnSwCuHEDzSGA8vBHghwBN8HbrnwE5lkVBfb9CqgQmRHDCJh+TgYKysWUjLli0tHdeJzIwZMzBhwgR88MEH+PXXX1FUVKQ53IRiY/xiVwSMVwFQjZO6OTLq9yUIoiLo8YKkOcIhpxMb1e9RNy/Z0e8Bx3skkpfCwkLs2bMn6cpPyOzduxe33347AMDj8eCvv/5ClSpV8Nhjj2HOHHfT5CnOxC/hREB1WnDIdTkdOM5EQOZlyhHYk2TuylOfF1TioVfSrKGkAOvEQGb1xZT6WlBwlAqbm48nUpJkjjWxijOOhcCWLVti06ZNIefffvttXHrppRFvhCAArRvQLwZTbA1q70U0t0WM9MFcBPTYrLEnNxDRH3YxEgVlMVAWBD3gLd2AajFQhKSkHMti4DXeF23vh0husrOzsWfPnqQNmgDQt29ffP3117juuutQr149VK9eHdWrV0e1atVcbwxFsTE+cZoOXBFiYCTinlN4nikHUCYIql1+doRBdW1BvSBIYmDyI4m8owMIuDSS+QunypUrK/Wa6tSpg4MHDyrXfvvtN1fXojgTn9gRAQMuQFHjBgSgqQmoFwHV2BYBPWLg0OMLfj4HxTe18KYW/ELEP0ARKcNiIgbqBUFOCL5/nSiq7CnYbITzcYE0Y4DEwBTCaZxJhVgTqzjjODX4kUcewZAhQ/DLL79AkiSsXLkS+/btwyuvvIIPPvgg4o0QhBq/yCkCoM+FhyQzEdDo+1M7CU5WjT+c4tW5/HycpKkfCLnmn6ppiVwb0AhZDJRrBpYCSvMQARyu8b6Iz3yxS5Mhyh9R5A2L/5shpUCzEKB8uzlSbCScYkcAjIUwKc+pThlWE+6zRL4u36dOWaY0YUKP3QLuiUrnzp2xefNmtGjRAn379sX999+P3bt3Y+XKlejcubOra1GcSWz0bkBOUy8wVASUr2tcgWbOv2g/d+0IfXbwMk2aMABA5BVhkZOzlnxlDUIMm4WompcYpgkHkQqbU5owASC5Y02s4oxjIbB///54//338dhjj6Fy5cp45JFHcNlll+H999/H3/72t4g3QhBqRMYpAqBexHMr+cHMAQgAfgsnn75jcCToxT/9NVkMDOyNKTUETesHoixFWBYI1Q1E5PqDVDOQkEn2ZiEA0K1bt3Jbi2Jj4mEkxJWHI7A8HIB20AuCMmZdhfWoBUF5Dp5nihi4o98D1EGYSHrmzZuH4uJiAMD06dNRXFyMN998E02bNnW1YzBAcSbR0LsBwxJGBDRE31QkkhRht0RAGYO6gQDCCoIh6MRAAEr9QcAiTZkgkoxYxRnHQiAA/N///R8++eSTiBclCKv6gH4xIAKWuByX1MgioDp8Gol/Pp3rT3bqxRpZDFSvKXcXFoJBMC24DVkQ9EKAD6JGECxzBwbHgsRAIrXYtGkTlixZgh9++AFvvfUWLrroIrz66qvIzs52tXMkQLGRMCfa2n6xxEgQlN1+RoKgnIYjpw+rG4mo3YEkAiYfosjbLuIuqtK17BRwT0REUcTPP/+Mtm3bAgikb+Xn58d0TYoziYucFuwKdlyCdjFw2rmCQ0HQEPm8kTuQxMCkxEmckccDyRtrYhlnSBEg4gajbsE+gyNa9CKgH0wjAvo4phxWCC7VLrSDvnZg4JwsCBr/M5ZFQR9EjXuwFDFUWIkKwWktDaZKDU7WehoA8M477yA3NxeVKlXCjh07UFJSAgA4c+YMnnjiiQreHRFvxEp4ixcHYDj0dQSB0LRhNeraPKIoaOsH+u3/Ek8kN8lcwF0QBPTq1Qu///57RW+FiGN4j6gRAE0dcMHa6GoXIFN9luoFP05g0YuAytoxlAT0Yp3q/Su1COUagrrmIgo+XlM7sOx8YsRXIvYka6yJZZyx5QisXr06OM7eP7RTp05FtSGCUOOFO+KfwNnoGKwS/py6/tyqF6hG7QqU0TsS1e7AUk5SXIF+SJqmIj6IAFfmDCSIVEgNnjFjBvLz83H77bejoKBAOX/llVdixowZUc9PsTG+cdooxG0SRQA0gueZpoagVaqwJPKa5iJqVyBBJDutW7fGDz/8gOzs7JjMT3EmseF4G1++izwklNUJZH4hfEqwi1jV4HMNde1AILBOUHxU3IHq614J8PGapiacIGnShQkiVYhVnLElBC5YsED5+eTJk5gxYwZyc3PRpUsXAMCWLVuwZs0aPPzww65ujkhOjNKCjdyAMm6JgZr1dK9lEdCuAGinTqDPhvNOL/RFSqC7cFmasAc8/Cj7ObAfWQwkIzCRGuzbtw9du3YNOZ+VlYXTp09HPT/FxvjFjggYz2JVPOzNTAzkBUnjBBQEUREDldcJLIIS5oii4CA1ODAuWdO1ZGbMmIEJEybg8ccfR/v27VG5cmXN9WiL11OciV/sdAuW3YBmacGSnzc870gM9NoY57Pp0q4gMRAwFgQ5QQIrCcgVDGViIAcJDDz4/9sTm70SFYaTOCOPB5I71sQqztgSAocMGaL8PGjQIDz22GMYPXq0cu7ee+/FM888g08//RTjxo2LaCNEamBVGxAI1AeU8XCAP0bPQgLsdQe2gxccZE1Q7wy0I/QZiY9yoxCzcXK9QD1pjAe4ssYhAELcgfomI0TiI0o8/A7SOkTGp0TX4Nq1a+PAgQNo1KiR5vzmzZvRuHHjqOen2BifVLQTUEYtpCUDZs5AvRhIEGqSuZMjAPTt2xcAcN1112mce4wxcBwHUYzut02KM/GJHRFQFv/UIqCcFmwk8kl+ATxETdMQwEAUjMQtaCYWBh12iisQKBPnjD7PfVz51eULugLhkQB/0D1o1GGYIJDcsSZWccZxs5A1a9ZgzpzQAtC9e/fG5MmTI9oEkRrYFQHFGNXek9ODjRyGXsbBxzHHzUAExkHkmOIQlF+HQ7+GLOrJ9f8iaUgS+PWhrHEIAMUV6Ke6gISOaFKDZ8+ejSlTpmDs2LEatwIQCEp9+/bF6tWr8e677+L666+3NefIkSOxZMkSzJ8/H/fdd19E+9IzfPhwjB07Fi+++CI4jsPRo0exZcsWTJgwwXX3BMXG+CBeRECZSMTAWLsBw+1Hvb5+/7IYKLsCjcRBdfMQgkh21q1bV25rUZyJf9QioCfDVyYGmohXihsw6MRTi4HlnSJc7ph8gc28TJsmDJSJgfKfV9AVSBCpQKzijGMhsGbNmvj3v/+N+++/X3P+3//+N2rWrOnaxojUQZS0IqAvhg4Kda1AvStQFgMdz6kTA40wE/b0rr5SSGG7+qpFQzNXoKY5iNI5ODDvf0tHh9xDJDaSxEOS7DsCA81CfojIEVhYWIglS5Yo3av0LFiwwHY9I5l3330XW7duRd26dR3dF47JkydDkiT06NEDZ8+eRdeuXZGeno4JEyZgzJgxrq5FsZFIBOyIkvoaf2Zipl4M1KcIE8mHJJU1iLEzFkjudC0A6NatW7mtRXEmfuGDX34Iaf6QdGC1CGgp7KnTckUu1BkocprmIMprn2AvPVhGLahFQnk26PBK4ICyWoF+vixFmEhKnMQZeTyQ3LEmVnHGsRA4ffp0/OMf/8D69evRqVMnAMCXX36J1atXY+nSpa5vkEgdZBFQFupilRYMlLkCA48qgYDmB4OXcQAX3pGnF/zCOQGNnIZOuveajZVFQ/m6unmIkl0crB1YykmIwGhIJCmROAKLi4uRl5eHpUuXGjbb2LVrF5566il89dVXqFOnjq05f/nlF4wZMwZr1qxBv379HO0nHBzHYerUqZg4cSIOHDiA4uJitGzZElWqVHF1HYBiI2GOE1dgLN2ATpyJVmKgUb1AtRhIEGqSOV1LZtOmTViyZAl++OEHvPXWW7jooovw6quvIjs7G1dddZVr61CciQ/0acGyCGhUE5ATJEPxj/dIkPzGYofkF6wbh/iFyNKDZeyIgJEIbeHEG/WcYcZqXIGyGAgoKcIAlC7CBAEkf6yJRZxx/C/ojjvuwOeff46qVati5cqVWLlyJapWrYrNmzfjjjvuiGgTBCEyDufEgAjoZ7EVAWW8wf/KvgVPUDnzMs6wRl9Ea0TQmKMUUsgRjjTwGidhoHkIDy8EeII/E4QaSZJQVFSkOUpKSizvGTVqFPr164eePXuGXDt79ixuvfVWLF68GLVr17a9h8GDB2PixIlo1apVRO/DDmlpacjMzESdOnViIgICFBsJa3iehRX54kUENEO9P0EQFdFPrgtIImDy4xd5RwcQcGm0bNkSixcvruDdx4Z33nkHubm5qFSpEnbs2KHE0TNnzuCJJ55wdS2KM/EBL4iaQw/HSxDS/RoRMCASlh3yOQ168U1VN535A08rTD6nf223GYhqTWYmxrkoAmpSfEW+7LAB09Ui5ARJSRFmIm++fyKhcRpnUiHWxCrOOHYEAkCnTp3w+uuvR7woQcjouwUbCYBudwxWpwcr5xBIE/aAc+QMtIOX8fBxksYV6MQN6AR1urDcSdjHlf2SQo1CkhPRzzvqsMUkDvv370dWVpbm/KOPPopp06YZ3lNQUIAdO3agsLDQ8Pq4ceNwxRVXoH///rb3MWfOHHg8Htx7772273GC3+/H9OnTsXDhQhQXFwMAqlSpgjFjxuDRRx+F1+sNM4MzKDYS4VA768qjM3A0AqDeFQiErxkIAJLI48oN0yJel0gukt2lMWPGDOTn5+P2229HQUGBcv7KK680dM5HC8WZ+ETtAuQ9AQFQEfoiENaMXIHKf+WU4KAzMOIUYZlgwxC30Nf3U7/Wi3vhUDoJB59tFGcgQehI5lgTqzhjSwgsKipy9Af7xx9/IDMzM+JNEclHuEYhRqgFQKMGH26gntdNMdAXRugzq+/nBvq55cYhcoowQQBAs2bNsG3bNs259PR0w7FHjhzB2LFj8cknnyAjIyPk+qpVq/DZZ59h586dttffvn07nn76aezYscNxTUG7jBkzBitXrsTcuXPRpUsXAMCWLVswbdo0nDx5Es8991xU81NsJCKhopuB6JHTfO06+qzEQACOavsQRKKzb98+dO3aNeR8VlYWTp8+HfX8FGfiG72rj+MD6cC8RwIESRHz9PX+1G4/ICD8GRKsFRhzMbCckEXBSARBzsdpawaapFYTRLIRqzhj619Q9erVceLECduTXnTRRfjhhx8i3hSRGshuQL8Y2iBELwLK//WqXruFej6zNGH5KLvH2YOWnCKsnkPfFERO7w3XLMQO8hyUIkyYwfM8qlatqjnMhMDt27fjxIkTuOyyy+DxeODxeLBhwwYsXLgQHo8Hn3zyCQ4ePIhq1aop1wFg0KBB6N69u+GcmzZtwokTJ9CgQQPlnp9++gn3338/GjVq5Mp7XLFiBV566SXcddddaNu2Ldq2bYu77roLy5Ytw4oVzr+c0EOxkQAQrAlVdlQEksQpR6QYNfgwm6883IxE/MGCjansHExK/nQtAKhduzYOHDgQcn7z5s1o3Lhx1PNTnIlP1Cm+AJQuwUK6H0KGLyACpvvByYdH1B7B8xAYIDDw6X7zWBIUDZX0YKM0Yb8AJnKBc3bThGME8zLl0KCq6RfSFdjmvAACYqAggb/O/pfPROLgJM6kSqyJVZyx5QhkjOGFF16wXVvJ54uFd4tIJWSnntuinx0MnYEAfBxThDwR2i7BIsfgA7NsGGKEmeinbwJihJ0Ow2oUZyCRdPglAX4HXTpFicehQwdtdw3u0aMHdu/erTk3dOhQ5OTk4IEHHsD555+Pu+66S3O9TZs2mD9/Pq699lrDOQcPHhxSazA3NxeDBw/G0KFDbb8XK9LT0w1FxezsbKSlpUU9P8XG1MZM9OM9orm7w2XcqP2nbv7hpOGHWQMRglCTzOlaADB8+HCMHTsWL774IjiOw9GjR7FlyxZMmDABDz/8cNTzU5yJP4xcgJ4MH4S0gAjIp/sAgSn1AS07Batg6t/TRePPdiNnIIBQd6A8V3m4AzXdjrXPJUpqb9DNp05D5nxc5M5AgtCRzLEmVnHGlhDYoEEDR92oateu7XrtJSI58YscRGb8gW73b5A8zs1ffcpCsa6jMMoEQb0YGHafulqBVg1J5HRkpynEaqFSvleuFUhpwYQaJ12DMzMz0bp1a825ypUro2bNmsp5owYhDRo0QHZ2tvI6JycHs2bNwoABA1CzZk3UrFlTM97r9aJ27dpo3ry5w3djzOjRo/H4449j+fLlituxpKQEM2fOxOjRo6Oen2JjfPFV7oMVvQWF8hADo+lErL/XSsgzqhWonls/Fx9JoXmC0DF79mxMmTIFY8eOxYIFCzTXGGPo27cvVq9ejXfffRfXX3+9rTlHjhyJJUuWYP78+bjvvvtc2efkyZMhSRJ69OiBs2fPomvXrkhPT8eECRMwZsyYqOenOBPfCGkBAZD3SPBULgGf7lMcgGo4fWqwQmh3YOYXylKJZUEwmCKsRnOPLAgGn2AYxLJUYaBMEPTzoV2D9fUB9eUdrD7T1SKg0VijUhFGNQnt3EcQLpPqccaWEPjjjz9GvABBGCFKARFQTaSdgmXnYDSCoFkNQr07EAikC6vFwND9BIKZz0J4C9eV2EjQc4K8N/W9aYzXNA4hkgfZRm97PONw6NAh245At9i3bx/OnDkT83Vkdu7cibVr16JevXpo164dAODrr79GaWkpevTogYEDBypjV65c6Xh+io2EFeXpDDTdgwMBL1pXH7kCkx/RL0Bk9v4fy38XOnbsCEEQHMWZwsJCLFmyBG3btjW8vmDBAse1Zd99911s3boVdevWdXRfODiOw9SpUzFx4kQcOHAAxcXFaNmypWsd6inOxC8cLwVcgOllTkC1CGgu/qnmUMaENgYBEBD/dGJgiGgoz6WrHaiIgUB0tQPNhEG1CGjk7PNxgesir6nxp04RNiV4H5F6OIkzQOSxhuJMhF2DCcItRBaoD6jv4hsNalHP7rxW4qGVGGi5j6AD0Oya6V5U9xgJemr06cFqp6FaqBTAQ4SE/5ZG74IikgMnjkAj1q9fb3mdsdB/H0bn1Lj9wFOtWjUMGjRIc65+/fqurkEkDm7Xu5NFvoqqC2hFSLdf1R6VfQfH6Bt/RLKWGynKRHLiNF2ruLgYeXl5WLp0qWE3xF27duGpp57CV199hTp16tia85dffsGYMWOwZs0a9OvXz/ZenJCWlobMzExkZma6JgIS8QvHB34n5z1SeBHQKkYEP485gQVEPFXaL6ByB+rME+ox6nMBgntAQAxU9iOLgUauQBkj959ekJNf68Yy1XvmRC4gDhql8drtUExiIOEAJ7GG4kwAEgKJmGPWMdiuCBiraidO5jUSAwPny5x7Ml7wiivQSvBT4w2KeT5ImnvUqcSRiIGANr34cu8SbPPdFTIHQSQjy5cvr+gtEHGClVAVktrqUBhUu/70omAsXYFGQp7R3sPtyY6QZ5UerJ6DXIGEnqKiIs3r9PR008ZUQMCh3q9fP/Ts2TPkAe3s2bO49dZbsXjxYsNyFEZIkoTBgwdj4sSJaNWqlfM3EAa/34/p06dj4cKFKC4uBgBUqVIFY8aMwaOPPkppuknGb5NuUomAckdgSZO2a1sEVF/3CxoxENCl/spFi1QpwkwXW/T3KbUC1U5BO8juPlnE09f/04uF4er8qVyBmv1GWeuPrWkDLnd3+IFESuAk1lCcCUBCIBFTFma9Bo8A+FXf6NhNC463csdGTURkV6CgcwiqxUArvLpmH2pBULt2dGJgYI6AK/CStOewq/TusHsjEge/n4ffQeMYSaqY1GCCiAXhagM6dauFE9biDdP0X4sH0EjEQCdsueYRdPnsMdfmI+IDURQg2mw8JgvCehf2o48+imnTphneU1BQgB07dqCwsNDw+rhx43DFFVegf//+tvc8Z84ceDwe3HvvvbbvccKYMWOwcuVKzJ07F126dAEAbNmyBdOmTcPJkyfx3HPPxWRdovz5bdJNIee4oCgmdwPWEKZOIFM/C3lERQyUr4UIe+ragQbIwqC+mYgsBgIwdgXqa/b5gm4+vaPPyC3o1boAZVcgE5jWFWjg7tMIg/pagyboxUPpP+3A9/067H1E4uAkzsjjAfuxhuJMGSQEEjFjYdZrYceYuQF9NsYIqligHu/VjXEz7VhGLQbKnYK94ALiYLB7sF7ks0JQNUyR75XdgXKqsFlNQhmlFmBwXauxJAYS0aYGJwInT57EI488gnXr1uHEiROQJO0vmadOnaqgnRFuEesGIeGccGriKUXYzl6MxEDAnU7EAPB5t2m4csM0V+YiEpcjR45o0rXMHBpHjhzB2LFj8cknnyAjIyPk+qpVq/DZZ59h586dttfevn07nn76aezYscNxrSe7rFixAgUFBejTp49yrm3btqhfvz5uueUWEgKTALUAKLsBgbLuwerPW0XsMzqnQ3YAKgTFQPU9akFQRu8E1GNYNxC6moHyWJFXxEwAoYKcWXqvCZxJt2MApp2FTdc2wHGtQSJlsBNrKM5oISGQqDB8wYcN2Q2odwDqBTz5uld3XeCMx8nor0cjDMquQO25wAKyCAgOsNlIOHCfyrln1IHYqtagEXp3IOC8+zCRWEiOm4XwKeEIHDx4MA4cOIBhw4bhwgsvjFmAJpIbJ2JgyL0xbBZilnbsRJB0M3VZ7yqk7sHJhyhxEDl7sUYM/l3o0aOHrQLu27dvx4kTJ3DZZZeVzSGK2LhxI5555hncfffdOHjwIKpVq6a5b9CgQfi///s/w7q1mzZtwokTJ9CgQQPNnPfffz8WLFjgSl3a9PR0NGrUKOR8dnY20tLSop6fiB/UIqByziOGuvRsiIDq62ZioPp+I0FQxkwYVDsEAZQ1EIFxrUDOxwVceiIPQApN+bXp2osKs/RjImVwEmfk8YC9WENxRktEQuCmTZuwZMkSHDx4EG+//TYuuugivPrqq8jOzsZVV10V8WaMWjifO3cO999/PwoKClBSUoLc3Fw8++yzuPDCC03nueOOO/Dyyy9rzuXm5mL16tXK61OnTmHMmDF4//33wfM8Bg0ahKeffpoK/MYAv8E3PyIL1AY0EgHNBECj116YC4JmyC5Bs07BYe9HaIqwJguXAWGaAmvnC7oBRVWasRimEUk41KJfiChos24hkdykgiNw06ZN2Lx5s9IxuDzWi0VsJGKLKAphm2REIwbGAjOxzw1XolmacLg/A/keqhNIqLFbwL1Hjx7YvVtb72vo0KHIycnBAw88gPPPPx933aWtcdymTRvMnz8f1157reGcgwcPRs+ePTXncnNzMXjwYAwdOtThOzFm9OjRePzxx7F8+XLFgVJSUoKZM2di9Gj3G7RRnKkYDEVAXlJcgZwn1G1ntyafWuwDoKkbaDRG3zHYqIOwjLqBiGY/qi7CTOQDjy1eybkYKKcRW+HQVaisQxAOsBNrKM5ocSwEvvPOOxg8eDDy8vKwc+dOlJSUAADOnDmDJ554Av/5z38i2ohZC+dx48bhww8/xFtvvYWsrCyMHj0aAwcOxOeff245X+/evTWF4vX20Ly8PPz666/45JNP4PP5MHToUIwYMQIrVhg3tiCcoU4L9ghSiBjok9QuuABWAqDRo40QHBPOIRhLNGIgUCYABvdiJeip04FlvOAMXYFOECFBUIl/alFQUNKG6Zs2IvnJycnBX3/9VS5rxSo2EuWDXrgyEgadioGxdAO6OXe0rkDqGEy4QWZmJlq3bq05V7lyZdSsWVM5b1S4vUGDBsjOzlZe5+TkYNasWRgwYABq1qyJmjVrasZ7vV7Url0bzZs3d2XfO3fuxNq1a1GvXj3lS6evv/4apaWl6NGjBwYOHKiMXblyZVRrUZyJHzTpwOovYIy6BtvEriAoX7crCGrEQHWKsC943iNZi4GAtTAni3zhBEH9+zUQB/UNRcLOQc5BwgEUZ7Q4FgJnzJiB/Px83H777SgoKFDOX3nllYbtl+1g1sL5zJkzWLZsGVasWIFrrrkGQKALZIsWLbB161Z07tzZdM709HTTTi979+7F6tWrUVhYiA4dOgAAFi1ahL59++LJJ59E3bp1Q+4pKSlRAi4Q2pmGsI/cKMTPjEVAKwFQ7tjrAQcRoWKgPJddMdDKFaheW/+IpE4R9gTVP1N3oH5ejhmKgLIT0Asu6Cq013DEeO9aMVAPdQ5OLkSJM3TeWo1PhdTgZ599FpMnT8YjjzyC1q1bh3TVsuNSsYsbsZHiTMVg5GAzcwlaiYGxFP7sIvmFmNYqlN9/OPGPXIHJiSjyEG1mFYjBchUdO3a0lRrsFvv27cOZM2divo5MtWrVMGjQIM05fdF6t3DrGYxijTvwHgkQJEM3oG28qiYeQQzThQHD+oGAuSCoJ3Ddvhgo7ytEnDP6fVOQytyBUXQDltc1EwQ14iHVCUxKnMQZoGJiTbLEGcdC4L59+9C1a9eQ81lZWTh9+nREmzBr4bx9+3b4fD6N3TInJwcNGjTAli1bLIXA9evXo1atWqhevTquueYazJgxQ1Frt2zZgmrVqikiIAD07NkTPM/jyy+/xIABA0LmmzVrFqZPnx7R+yPKEA10LTsioF+nqJW9LgsIdsVAvfPQTAxUi32y6Gh2HTBPFdY7/IxEQPW1aNOCZWTXn1oQ1KcJE6lLKqQGV6tWDUVFRcoXSTKMMXAcB1F0TzBxIzZSnIkNdppg6IUrq1TheEsT1hOJGOikcYhdB2C4dGsiNbCbGmyEUT0mNYyF/js0OqfGjXpNatTZR7HGrWcwijUxQu8G9Dr4DNQJgiFioGp+ACEdhgHrFGE1Ic1DjMRAFZwP2q7CRnOCLxMDXUBxJYYjzL6I1CHSWJPKccaxEFi7dm0cOHAgpGDh5s2b0bhxY8cbsGrhfOzYMaSlpYUUbLzwwgtx7Ngx0zl79+6NgQMHIjs7GwcPHsSDDz6IPn36YMuWLRAEAceOHUOtWrU093g8HtSoUcN03ilTpmD8+PHK66Kioph945ds2HUq2RUBobsmuwNlIkkVtlMvUF7DKryqxUAfx5RGIjJ6UdAb9npo9+BwjT8E8JrUX7U70KiRCEEkK3l5efB6vVixYkXMm4W4ERspzjjDacfgcE42J8JVMoqBRqjfo9MU4Ms/mRn1+gRBlOHWMxjFGnfgBCnkczZiZ6CMV7QWA2VMOgwD4WsGhjQPUYuBCE1m4oQwzjuvKn3YxVRdvRholEpMEETkOBYChw8fjrFjx+LFF18Ex3E4evQotmzZggkTJuDhhx92NFe4Fs6RcvPNNys/t2nTBm3btkWTJk2wfv169OjRI6I509PTDdtQE+5gJMLpBUCfgVPOyziNGGiWKgyUNQlxglGXYLUgaHTdSAwUg+9FL/xFitMuwGp3IHUPTj78fgF+Zj8NT5JSo2vwt99+i507d7pWo8MKN2IjxZnYYyet1S6pIgYShIwo8RBtfplYUanB5c3JkyfxyCOPYN26dThx4gQkSfs71qlTp1xby61nMIo10cF7RAhpfutBajeg3JnXb/OLeJ0YKGPqEPTbEA5VKCnCAgsVA+Vuwqp9MxNDh7o+n6auYDgxUJBsNwMh8S/1cBJn5PFAcseaWMUZx0Lg5MmTIUkSevTogbNnz6Jr165IT0/HhAkTMGbMGEdzhWvhvGbNGpSWluL06dMaV+Dx48dN6/8Z0bhxY5x//vk4cOAAevTogdq1a+PEiROaMX6/H6dOnXI0L+EeTjr4iihz2vk4ZioGyvMq94VrauVgD4F9mGMlBpY3pZyqg3D8PrMS5UwqpAZ36NABR44cKRch0M3YSCQOiSAGAu50E7aC6gESZkSTGpwIDB48GAcOHMCwYcNi7jynOFP+/DbpJs1r3iOC9wS6BQsZPkBgARFN/RnrJCXYDJPagWqMmorom4lYpQkzkdOIgQDAyXlPakHQCE+o2Gc7nTdSqB4gYUEyx5pYxRnHQiDHcZg6dSomTpyIAwcOoLi4GC1btkSVKlUcLx6uhXP9+vXh9Xqxdu1apUDivn37cPjwYXTp0sX2Oj///DNOnjyJOnXqAAC6dOmC06dPY/v27Wjfvj0A4LPPPoMkSejUqZPj90HYQ5QCjUJEixp5tucKimoCOFMxEDAWBOMVq27BPs65+087d/CXBPpiLSmRJA6Sg6DAGFLCEThmzBiMHTsWEydORJs2bUKahei71EeDm7GRiC1uugKB+BcDgfDuwGganZAISKQymzZtwubNm5VOjrGE4kzFw3skCGl+8Ok+pVGIJQaCmSPUoqJP+1lr2GXYphgopwirxUCoX8vuQAuYyJelDTup1eeguR0AjQCodidyggQud7fRHQSRVMQqzjgWAmXS0tKUh8hIsdPCediwYRg/fjxq1KiBqlWrYsyYMejSpYumUYi6hXNxcTGmT5+OQYMGoXbt2jh48CAmTZqEiy++GLm5uQCAFi1aoHfv3hg+fDjy8/Ph8/kwevRo3HzzzYYdg4noUYuAPolznKJrOm/QHagWAwMEAqBeEFRjJA46dQW6jb5RiFnXYKP0XiMBUO0G9EOCn9KCiSCp4Ai86abAN/l33nmnco7juJg0C5FxIzYSiUcyiIEEEQ5RtF+GQpQC45I5XQsIPIP89ddf5bomxZmKQU4J5tN94NP94NP94YXAcOnA4YRC9f0moqBGEIxGDNTjU7kDKwoTEVB+Tf6G5MNJnAFSI9bEKs7YEgIHDhxoe8KVK1dGvBkj5s+fD57nMWjQIJSUlCA3NxfPPvusZoy6hbMgCPjmm2/w8ssv4/Tp06hbty569eqFxx9/XFMP4/XXX8fo0aPRo0cPZf6FCxe6uneirFGIXgT0G8SbcCKcnGarRy0GyuP0TUSMPk7Ua1k5Bo3qALqJ3glo5Qw0w0gELJuvbPelDpyFBJHoHDp0KKbzV2RsJOIPtcswXkXBSFKFrdyTejeg5NTpQSQ9yZyuBQDPPvssJk+ejEceeQStW7cOcZ5H+94pzsQfgdRgMbwIGA47bkGz+oJ6cc6n6iKsT/MFID/J6AVBffMQGAmDNtyBANxNC/aFin4a7NZbJFKGZI41sYoztoTArKws5WfGGN59911kZWWhQ4cOAAK1/k6fPu0oWJmhb+GckZGBxYsXY/Hixab3qFs4V6pUCWvWrAm7To0aNbBixYqI90mYszDrNcPzViKgmnChxqjenlXdQP2cVqKgF8aCpNtioJXY5wXnWAzUdwoGzEW/QyX3OZqbiH9EiYffQWFdSeJSIjW4YcOGMZ2/PGMjocVpx+DypiIcgnrBzmp9u6nATkRAIvkR/TxEnpqFqKlWrRqKiopwzTXXaM675TynOFNx6OsDKggSEBTKIu4S7DRlWD3eSART1RTUC4KAWhQ0/vto6gpUz10eGNQBNGtWQiQnTuIMkBqxJlZxxpYQuHz5cuXnBx54ADfeeCPy8/MhCIEPFVEUcc899yStCkvYQy8Aym5AUYKmLqCHMxYD1eKbvmOwHczEQACKIBgYV4bZY0wsxUAjkU+fEhwJQlAIMnIHUkowocdJavBzzz2H5557Dj/++CMAoFWrVnjkkUfQp08fAMBdd92FTz/9FEePHkWVKlVwxRVXYM6cOcjJyTGdc9q0aSgoKMCRI0eQlpaG9u3bY+bMma7XaX311VeRn5+PQ4cOYcuWLWjYsCEWLFiA7Oxs9O/fP6q5KTZWDPEuAspUdLpwtOs7raNIbkDCiGR2aQBAXl4evF4vVqxYEZNmIRRnKgYjEZD3SIGaeMHPOsVJF/yvLVEw2pqBZnPI4qDXSPwzdgkadhg2cwU6wU7nYDVOm4CQG5AwIJljTazijOMagS+++CI2b96sBCAgkI47fvx4XHHFFfjnP//pysaIxMLMBSgjcAzgAageLPwsILipxTW90GaUCmyFvokIAF3twAB6p6C6lmAs0oTlfelFwHACoBe8YZ3ANPCGdQJlZDegT7fbNEbBMxkRHTbhceoIrFevHmbPno2mTZuCMYaXX34Z/fv3x86dO9GqVSu0b98eeXl5aNCgAU6dOoVp06ahV69eOHTokCZWqGnWrBmeeeYZNG7cGH/99Rfmz5+PXr164cCBA7jggguc/QGY8Nxzz+GRRx7Bfffdh5kzZyrfmFWrVg0LFiyIWghUQ7GxfEgUEVDGSozjPaKlM88ofddsvJloFysxklKCCSLAt99+i507d5ZLd3qKM+WDkQjIgq4jxZ2mE9EU0czKPRehCMgJUnhXnJFj0EAUVFyCfkEj9DGRi1z8c9IoxOx+EzGQUoIJInZxxvG/Jr/fj++++y7k/HfffQdJItdRqnLvmdtMrwk84BEYBI7BG3wgEbiAMzBWjTnUqcM+joUcf3ES/GCKQCiiTODzwXpfkSRDCbpytiLHwoqAgkrY8QYFPPU8aeCRZvFPWBYByQ1IGJGdnY09e/Zgz549YS301157Lfr27YumTZuiWbNmmDlzJqpUqYKtW7cCAEaMGIGuXbuiUaNGuOyyyzBjxgwcOXJEcRAaceutt6Jnz55o3LgxWrVqhXnz5qGoqAjffPONa+9x0aJFWLp0KaZOnap5cOrQoUNIx/poodhImGEq0pmIerxHNK3hF0mzj0g7JNsVEI1EwCs3TItoTSK+ESXO0QEE0rVatmxpWeInkenQoQOOHDlSLmtRnKlYJD+vOoRAvT2PGBDPvGKZ8OaRjA+bcIKkOdTnbGG0nlfnWtTFEk5gkTsAI0RTU9COmOjnI/4zJRIHp3EmFWJNrOKMY0fg0KFDMWzYMBw8eBCXX345AODLL7/E7NmzMXToUNc3SCQO9565TXEG+k2+uZKdgb4IH0z0tQGjGsvBsJZgrKseOU0DNnMFyoRzB8p4wJMoSChIkoSioiLNufT0dE1TJSNEUcRbb72FP//8E126dAm5/ueff2L58uXIzs5G/fr1be2ltLQUzz//PLKystCuXTv7byIMhw4dwqWXXhpyPj09HX/++adr6wAUG8uLDmueSDhXIODMmVee3X3DuRLDYSQC8k5SwoikJ5nTtQBgzJgxGDt2LCZOnIg2bdqEFHFv27ata2tRnCkfzp/7pqErMPDZLAUFQKms8YZX1IhSsmAXzsVnW9gzuMdW3TyPFNp12KfqKBzsMFzuqNKrNVg4A8kJSIQjmWNNrOKMYyHwySefRO3atfHUU0/h119/BQDUqVMHEydOxP333x/RJojUwCMw+I3qUagwS711IgBaIafmeuWGI/J25C/IwnQaDrdPt5AbhgiMU4RDL+Ph4yTDZilm6IU/j3MTMJEA+EUefgdp3xLjsH//fk0RcgB49NFHMW3aNMN7du/ejS5duuDcuXOoUqUK3n33XSW1GAh0tJo0aRL+/PNPNG/eHJ988gnS0tIs9/HBBx/g5ptvxtmzZ1GnTh188sknOP/8822/j3BkZ2dj165dIU1DVq9ejRYtWri2DkCxsTxxQwzkeRaxUy4coihAEAzSeiu4ZqAZsuhoJAjG8s+JIJKBm24KCEZ33nmnco7jONeahaihOFN+mIuBPMQSDziPCF7kwEo84DL8ZaKe7Gzz8aZCHxPNr9nFVrowENp1uLzEQJt1ApmXgfNF6Frv+3VE9xFEohGrOONYCOR5HpMmTcKkSZMUN0myqq+Ec9SuQCM8AgsoaHygrpm6RqBRgw630Nfm84EpYqBcT7Ai3IFq1KKfHrdcgQQh06xZM2zbtk1zzsoN2Lx5c+zatQtnzpzB22+/jSFDhmDDhg2KGJiXl4e//e1v+PXXX/Hkk0/ixhtvxOeff46MjAzTOa+++mrs2rULv/32G5YuXYobb7wRX375JWrVqhXVe3vssccwYcIEjB8/HqNGjcK5c+fAGMO2bdvwxhtvYNasWXjhhReiWkMPxcbyxUwMjNbhFg3qunnyz0aCoB2MXIGxfl9mf3YkBhIyTr508qdAJ0cg4DwvLyjOlC9GYqDaFSj5BXB+AfAJQJVSJdWV83HGqa5Bt1u0ImBE6N2BdolVx2AbrkCN0Bnp/omEw6m5IRViTazijGMhUA0FH8KIe8/chnlVVmjOCXyge7DmXLB7cEawVqDI1I06Qht8RIpRl175vJkYCBi7A6MNh3brAooci8oVmMZ4xe1I6cCEETzPO/oMT0tLw8UXXwwAaN++PQoLC/H0009jyZIlAICsrCxkZWWhadOm6Ny5M6pXr453330Xt9xyi+mclStXxsUXX4yLL74YnTt3RtOmTbFs2TJMmTIlqvc2ffp0jBw5Ev/4xz9QqVIlPPTQQzh79ixuvfVW1K1bF08//TRuvvnmqNawgmJjxWLlcNOMC7rzohW59I0z9NdkMdCpG7AiBE2nQiovSNQohLAkmdO1AIQ4zsuLZP4zjSeMxUCVKzDdH3AF+nhFNFPXvtO43VRuwf/P3n2HR1F9fQD/zpYUQhJ6CSUJNRRpoQgiIL2IgEpXivzAElBABVGUICBFhEhHVFCKFCmiIohIkVCEQHhRMIjSBAICkkAgye7Mff/YzGRmd3Z3ZrMpm5zP8+xDdsqdmSXZs3P23Hu9zVX1oYMCWBUIyF6rrGSgQ9UjjQlIXCjMsSa34ozuRGBkZKTLKYv//vvvHJ0QKRzG3x/kNBkoVgVawMHEZc8eDM4xGQhmm+xDnviyn3hDTjFJiIZEorNkoGO7+uhNYho1zPhqXxVonwwUqwKNMICHQMnAIoTnOV3fnvE6Zw1WIwgCMjIyVNcxxsAYc7rekzb1YCz772Lw4MEYPHgwHjx4gPv37+e42tAZio15z74q0L6Cztk4e/aJLnmCTm9S0FUSEPC8ItDb9HRLVksG2lcFGo28dO32yUBKDJKiZvXq1Vi2bBkuXLiAw4cPIzw8HHFxcYiMjPTq7PQUZ/KHWjKQCQYIGWYIGVYYAjJtyUCzoJwAA3B4DgAccp4QdNot2G6cPUUyLZer6jgLp3q9TtlVBap1E9bcBZqQQi434ozuRODYsWMVzy0WC06ePImdO3fizTff9OgkSOGklgxUo5YMVGOfABQTdhZZlZ1R6tabXVEnsq/GE5Nv9slAkbybcF4TqwDVqgIV26kkAwEgEwAvS/6JYwP+njk6d0+c+IzIyEhs27ZN07aTJk1Ct27dULVqVdy7dw/r1q3Dvn37sGvXLvz999/YsGEDOnfujLJly+Kff/7BrFmzEBgYiO7du0ttREVFYebMmejTpw/S0tIwY8YMPPXUU6hYsSJu3bqFxYsX4+rVq+jbt69Xrs/+ZqlYsWIoVqyYV9pWQ7Exf4jJQD2Ta7gbE0+NswShPCGWXzyeFdjFa6b2GlEXYWLlDVI3LLfbssLfXQsAli5divfeew9jx47FjBkzpLGaSpQogbi4OK8mAinOFCx8pgmGDJNt0hCL0ZaAc9aVVqyM4w3ZCS8tM+UCbhOGDuMTytu1r6zL7y62ZgbIk32y1wWA8rXJhcpJUvDpiTNA0Yg1uRVndCcCX3vtNdXlixcvxvHjxz06CVJ42ScD5V2EzQYG3m7yEDEZmJ3b4uCuuM6+gs++ghDQP1NvbnE1DqCzbe0TmmL3YFf8YMDDrESgGUZYcnVqE5LfeObY9d4VxqCrIvDmzZsYMmQIrl+/jtDQUDRo0AC7du1Cp06dcO3aNfzyyy+Ii4vDf//9h/Lly6NNmzY4dOiQovouKSkJKSkpAACj0Yg//vgDX3zxBW7duoXSpUujWbNm+OWXX1CvXj3PXgQ7tWrVclk5AQB37tzxyrEAio35qemuD3Cix0Td+8kTYVq7EUvbO6mOk8urasDcTNDZVwfKj1UQkqCk4NPaXWvp0qVYunQpLl68CACoV68e3nvvPXTr1g0A8OKLL+Knn37CtWvXULx4cbRq1QqzZ89GVFSU0zZjY2Oxfv16XLlyBX5+foiOjsaMGTPQokULr1wbACxcuBArVqxA7969MWvWLGl506ZN8cYbb3jtOADFmfxUZs4G3H7L9ReVjDeAszhWBSqoVME5o+gqC6gmxuyTgA7dbGXbaa6ssxpt3YW9xf4axef2CUEnyUCqCiRaFOZYk1txJkdjBMp169YNkyZNwsqVK73VJClCTGIXVuaYDDTKxupzReyOa2acokowJ0lAT6oC7bsF25+Plm7A8m3FZCAYAM5x0hBn4wUaYQDstq3ttwBJma9qPj4pvPRUBH722WdO14WFhWHHjh1u25B31w0ICMCWLVs0HdtTU6dOdZgVOT9QbMwbTb6f7VEyUKR3Yg5XXWXVEoD5PVuw/fH1VlDm1wQspGDhBQN4TtsNOa+zSqNy5cqYNWsWatasCcYYvvjiC/Tq1QsnT55EvXr1EB0djcGDB6Nq1aq4c+cOYmNj0blzZ1y4cAFGo/rvZ61atbBo0SJUq1YNDx8+xPz589G5c2ecP38eZcuW1f8CqLhw4QIaN27ssNzf3x9paWleOYY7FGfyRulZm6RkoGA1wJA1Zh2zGsF4DpzVYEtcwU0yMIu7bZyNnQd4YcKRnIwT6K2x+lxUB1JlYNGlJ84ARSPW5Fac8Voi8Ouvv0apUqW81RwpRFx1ETbadQWWdxPmkT1eoFm2j3xmYfmtjEllghExgaaWDOQ5ppqUczZOYE7Jux2LFX5mLUlGBkUy0MI5VgU6Swb6MQMy3VQPElIYDRgwINfGA9SDYmPBJCbrnFW0qSXKnI0tKK+QK2jUEpCukoD21yCNAyhLBmqpQIxvG4vH9sfqPFtSGGmt0ujZs6fi+YwZM7B06VIcOXIE9erVw6hRo6R1ERERmD59Oho2bIiLFy+ievXqqm0OGjRI8XzevHn47LPP8H//93/o0KGDB1fjKDIyEomJiQ6Due/cuRN16tTxyjHcoTiTt9RmdQcAWIxgJgEc4JgM9KSiLSsxxrKG99HSndi+ElBXIk1MBrr6okiWBLRPRuoaH1A6P+fVgWpjBsoJOxrC0P2U/mOSQqkwx5rcijO6E4GNGzdWdLdijCE5ORn//vsvlixZ4vGJkKLDaABs2S3AVrSWNa5fViwQk4H2SUKRGdnJQPltGY/sZKCZcbbxBl30K7ZPArqahCSnxKpAMWGnKQEo7ivvHs0BYEZbYlOKndoSfeKEIZH+cbiQMVb7yZMCz8pzsLrpBivHCxwu53CykILMXZfg3ECx0XfIk116urc660rsLjGWF9WAeiYCUeMsiamYFEQlGejq9aNkIAGA1NRUxXN/f3/4+/u73IfneWzatAlpaWlo2bKlw/q0tDSsXLkSkZGRqFKliqbzyMzMxCeffILQ0FA0bNhQ+wU48f777+ONN97A+PHjERMTg/T0dDDG8Ouvv+Krr77CzJkz8emnn+b4OHIUZwowqxEwWgGrwdaBB7JJQWTsZxTWlDyTJcbE/bxJqgoEHJOAFqPquIc5rki0J38dLJyyC7U562dxbENZMpKSgURUGGNNbscZ3YnAXr16KYKQwWBA2bJl0a5dO5d9pwlRY+QYYAAsAuc08adGrBC0uNxKdhwXY/PpSco5o2WWYHkyEHBMUsoTkfJ1RnDZFX/ipwtZN2GzOFtsVkLQVfJTrA6s7D8P/2SM13BlpLDS0zXY18i7IecVio2+wVtVe/aTaajNPJzf3YHl9FQBOlvP80bqJlzE8Tw0f+kkfp6zv3maMmUKYmNjVfc5ffo0WrZsifT0dBQvXhxbt26VvrACgCVLlmDChAlIS0tD7dq1sXv3bvj5+bk8j++++w4DBgzAgwcPULFiRezevRtlypTRdA2uTJ06FS+99BL+97//ITAwEJMnT8aDBw8waNAghIWF4eOPP8aAAQNyfBw5ijMFh60q0PaZWsgwgTPxMJh4cFklCuLHdXv2yUFdyUBAkRC0b0e3rO7BeulOAtq6MjldzYxZCU4xGSl2GTYKYDCAs3C2cQIBWzLQavBe92RS4OiJM0DhjjW5HWd0JwKdvaCEuGIyCrDKyuLFqkArzymSgTlhhLIqUKQ2866ecfrsxwnUkvRzRj5eoLPuvHLy5KD0s10yEICUELRwgmq7VtkHD/sxBgkpbAQh73/HKTYWfGoJr5xOduFsdl1n2+Z1Ek3PWIB62hSsRodKSJ43Fsju0SR/XblyRdFdy1WFRu3atZGYmIiUlBR8/fXXGDp0KPbv3y/doA0ePBidOnXC9evXMXfuXPTr1w/x8fEICAhw2uYTTzyBxMRE3Lp1CytWrEC/fv1w9OjRHA8dIf/CafDgwRg8eDAePHiA+/fv59qwFBRnChbBaoCQYStNMJh4MBMPrlhWiYLVAKaWsOINykSaWZCSeIoEn7Pla92GAAEAAElEQVRkm90suyL75KCUGHTXLdh+rEBv4w22c3aSDBSTgOLPzpOBdjuqVAeSoq0wxprcjjO6BywwGo24efOmw/Lbt287HUCRFG0LQteoLjcaAJPRTSIsBzHJzDiH7r5GximSgFqrAa1g0iOn5OMP2lcBig9njOBghu0axH8BwJz1pyxVB6qQjxVI1YCFi8A48Doe8lmD69ati8WLF+f3Jfg8io0Fnzzpx/NGr854a8iqBtG7LrdoPaaexJ2Wbb39uhLfFxISoni4ujnz8/NDjRo1EB0djZkzZ6Jhw4b4+OOPpfWhoaGoWbMm2rRpg6+//hp//PEHtm7d6vL4QUFBqFGjBh599FF89tlnMJlMLifA0sN+GIpixYrl6ti0FGcKFsFqBJ9pgpBhtj3S/cAyTLYqO/Hx0JxdxZaVvGK8QXrAYtA2hp+ZKRNpRkH9AfXqQMWsu/bJs6yuv5yzezJ3VYPuzl88tt15MZXjKZbJr1c+NqL8fKy6UxmkkCqssSY344zuikBnXa4yMjLclkySos2+KlAr+2Sgq+7DrqoCgexJOvKb/UzCeqhVBtomE8meUdg+AWqCAWC2ZKBZf/6fFEKFuWtwfqDY6BtyO0mV1wm/vD4HcUxAtepG+XiBlAwsnHiBA6+5a7BtO60zOaoRBAEZGRmq6xhjYIw5Xe9Jm3rVqlXL7Zi0d+7c8cqxAIozBQETDOAM2Ukpa7qtIpAzCjD4W2xVdRkmxVh7HNTH2QNsCTrOKGifbdjZer1dg02CMonmrpuwbKxAMamoq4uwm8pALaTuwXbnQwoXPXEGKPyxJjfjjOZE4IIFCwDYspKffvopihcvLq3jeR4HDhyg8SmIx3LaLVhOTAYCkCYNAaBrko7cmjlYLifJQHvybs9iF2E/GJAJANQVuNCzCBwsOoPmlUI8WUheothIfJk3uvOK+wt2X/S1OfhujtolhYPWmRwnTZqEbt26oWrVqrh37x7WrVuHffv2YdeuXfj777+xYcMGdO7cGWXLlsU///yDWbNmITAwEN27d5faiIqKwsyZM9GnTx+kpaVhxowZeOqpp1CxYkXcunULixcvxtWrV9G3b1+vXNvUqVMRGhrqlbZcoThTsFnTzTD6WaXnjOeksQIdJt9Q6S7MZN2FnY4Z6EkCzV2lnvw8rAbAbBvjUOwizHhOWSUoTxSaBGVC0CLrvqsyszEzM83JQEUXYWfExCUlA0mWwhprcjPOaE4Ezp8/H4AtK7ps2TJFCbqfnx8iIiKwbNky758hKVTUqgLFTL59pZ98dmC9bL+dshmEbU+zj2k3GYcaeZIuN5KC9klAi45EpUNVoPRz9gQiPGcLjn7MoNgmU+Msw6Rwo4pA76DYWDCc6DExT48nn0BDj8Iw2YZiJmG7cQL1zMJMiNzNmzcxZMgQXL9+HaGhoWjQoAF27dqFTp064dq1a/jll18QFxeH//77D+XLl0ebNm1w6NAhRReppKQkpKSkALB1o/3jjz/wxRdf4NatWyhdujSaNWuGX375BfXq1fPKOQ8YMCBXuwKLKM4ULPZVgfKxYjmrEZynVdkWQ3YSTUyaKdZ7Xk3nllghqCcZmMXhjFQqBsVJUpjUK0mwdWpyM0SUglmQhkgXz9WTyU5I0eZrsSY344zmROCFCxcA2AZC3LJlC0qWLJkrJ0SIt4jJQACKhKB85l6tLFK1nXcSgmJ79uMBepoMtHUN5hTJQCM4h6pAsYswIcQ7KDYWPfIKOvvEl6cJwvzkaVWgfVLTYBQg8AZKBhZiVp7TPJujVWd3LVdjKYWFhWHHjh1ujynvOhsQEIAtW7ZoOldPuOuq5U0UZwo+1W6yaglBWRWe6j7yLsJqycDc5CIZKMcZWXYCzsyrj9MnqxgEsusRNHV/tiebPdjhXEmhoyfOAIU71uR2nNE9RuDevXtz4zwI8YhZ9rOr6kH5mIHyhKBeehKCetp3NnahlspFILtrsDwZaPvX9gFCrAq0gLclA0mhwrPs7vBaCMieLASgrsHeQLGxaNCaMPO1ZJjeZKB9VSCgnhAlRGt3LV/jbLy+3ERxpmAzmHhwJt6WKDO5noDD3fh6Uhdh+2SgWlWg/SQcZmZLmpltXXaZXU8s+2M7TCRiFSv61GcSFpcpEoJy9slBeTdiZCUDYYCrqkCpe7A4e7B4XeI+vK2LteGpk47HJ0VSYYw1uR1nNCUCx48fj2nTpiEoKAjjx7uebXTevHleOTFSNFh1TlXvaqIQta7E8vECHSYQcTFGn7sEnLsxBF0lAdWqAG3n7/q1cDabsBkcLGCqyUALsscK9MvqRmzRlTIihRV1Dc45io2+RbAaczSRhrvklq8nvzypDHRWFUgKJ6uQexWBvkYQ8qZSi+JMwXFrQn/V5Qb7WXizSElAcRy7rO3cTrKR1UVYkQwEshOC8mSgPAno5L2XMwouJ/iQr5c463ZrzR77ULVSEFDuJ08KZiUEtSYDs9tgAATF9ameMykU9MQZoHDHmtyOM5oSgSdPnoTFYkuxnDhxIk/L4UnRYeScJ/pcJQDdtiu2AX3JQOnYTpKCeicUkbcjrwB0lwC0rxbU2nUYsE0cAs4xGUgKF54BVvebSQRGFYHeQLHRd4jJqpwmA/VwVhWYW+MEeuO6tCYD7a9LXhVoyMuubKTAK4xVGnmJ4oxv4Ew8IEtmeZwEtG9XPnmIvDpQ5yzB7hJn4nmJlXYOXYTtY5bV6ND12WE8QcCxC7GTZCBcVQXKl2VVBcJi0P1aksKNYo1+mhKB8lL0ffv25da5EJKrxOpAk2zcQMB9MlA+bh8PpjsZ6CwB6I6zbdXGEXRVFSh2ERaTgYmZL2o+B1J4UUVgzlFs9A3eSro5S5LlZSVgXiUx9RATm2pdhAkhOUNxpuCRTxTikqdJQLtZdx2SgYD2sQOzugc7Pb54LNk2imSgyMSrJwPFdVlUk4GKfVSSgUaA4znNVYFSMpAQkiO6a2pfeOEF3Lt3z2F5WloaXnjhhRydzKxZs8BxHMaOHSstS09PR0xMDEqXLo3ixYvjmWeewY0bN5y2YbFYMHHiRDzyyCMICgpCWFgYhgwZgmvXrim2i4iIAMdxisesWbNydP4kf2mZYVgewkyyZJp9Ms++O7CeBJ68PR4MFtlDVxsuSvfU2hO3N8quxcg4mGGAmRl0T5BCCNEuN2Mj8V3OkoQGE687qVdQkoDOqhxFvt5FmjjHM07XA7B116pbty4WL16cz2fv+yjOFEycQVB2D5a/V2tNApqF7IcWbrrFMjPLTh5qaVfrcT2lNtuweA28AbBwDtV/gF2XYTOTEqC6JxwhPkNvnKFY4zndicAvvvgCDx8+dFj+8OFDfPnllx6fyLFjx7B8+XI0aNBAsXzcuHH49ttvsWnTJuzfvx/Xrl3D008/7bSdBw8e4MSJE3j33Xdx4sQJbNmyBUlJSXjqqacctn3//fdx/fp16TFmzBiPz5+oWxC6xqvtWaCe8NOSBBTZJwPFhKCzyj77hJzDGH8q1YTyZTzHHB65RX6uaolBP5oopFCy6HzwyO4aTEHTO3IrNhLf5yoxpjW5V1CSgO7YqgIpGUiyHTt2DGfOnKHhJ7yA4kzBxnn6Pq0hCcepdQXmDcqEoEqyUZEwsz+Ok+dS0tIk2LoHu6rU08ose23klYaWrNmAtSYDAUoGElUUa/TTnBVITU1FSkoKGGO4d+8eUlNTpcd///2HHTt2oFy5ch6dxP379zF48GCsWLECJUuWlJanpKTgs88+w7x589C+fXtER0dj5cqVOHToEI4cOaLaVmhoKHbv3o1+/fqhdu3aePTRR7Fo0SIkJCTg8uXLim2Dg4NRoUIF6REUFOTR+RPP8FlxxsgxmMWbB41Fa/aJDb2cdVySJwON4KQqOj3j8ol4WdVeblQFiu2q7SMm/8zS+VNVIFGKjIzEmTNnNAXNmTNnolmzZggODka5cuXQu3dvJCUlSevv3LmDMWPGoHbt2ggMDETVqlXx6quvIiUlxWmbWqu3C7rcjI3E+3KaUMuNbq/uqgMLWhLQVZUjYEsGNt89Iy9PiZBCjeKMb2BOhqHI9bHsxISgkypBh2Sgq8SjfTJQTkMsUiQNzXz2wxWLjiIF8VpofEBCckzTGIEAUKJECakLba1atRzWcxyHqVOnenQSMTEx6NGjBzp27Ijp06dLyxMSEmCxWNCxY0dpWVRUFKpWrYrDhw/j0Ucf1dR+SkoKOI5DiRIlFMtnzZqFadOmoWrVqhg0aBDGjRsHk0n9JcnIyEBGRob0PDU1VccVEjVGAwAw1ZmDTRxgzeUvepzNKGw/ZqARnEMVoNpYgXrwHFNU6inPK7tbMZA9/l9OGBmXPV4gITrt378fMTExaNasGaxWK95++2107twZZ86cQVBQEK5du4Zr165h7ty5qFu3Li5duoSXXnoJ165dw9dff63aprx6u2HDhvjvv//w2muv4amnnsLx48fz+Ao9583YSHEmdxSERJqziUPk1CYRyetz11rJl9cToZCCwSpon6NA/AxXGGdyzGvevgejWONdBpMAwWqAQX4/Y5/8Miur2FQr/HIRMzPlMd0lA50l5+zHCsxBjJInGzkL53IWYcXEIWame7IU4jv0xBmAYk1OaE4E7t27F4wxtG/fHps3b0apUqWkdX5+fggPD0dYWJjuE1i/fj1OnDiBY8eOOaxLTk6Gn5+fQwKvfPnySE5O1tR+eno6Jk6ciIEDBypmknn11VfRpEkTlCpVCocOHcKkSZNw/fp1zJs3T7WdmTNnepzoLMpeTXnObfdgk5HZMnIGwCLYT4Jhq/gTZxQWn+c2tWQg4Ngt2BlX26lN9mF/HPHnnCQD5ROHiBOJgKOuwYWRAH0zawvQN2vwzp07Fc9XrVqFcuXKISEhAW3atEH9+vWxefNmaX316tUxY8YMPPfcc7BarapfsIjV23KLFi1C8+bNcfnyZVStWlX7BeUjb8ZGijO+QevMup4oCElLrVwlAwkR0UyOOeftezCKNTnDBINiwhA+0wTOKECwGsFZjeD8rer7BWS9N4oTXuRyMksxyQicJyHtu9i6PS8t7/F2E6U4nJu8ok+elOTtkoHmrOUWx1mEDY+fcX8epMigWKOf5kRg27ZtAdhuHqtUqQKDIecJhStXruC1117D7t27ERAQkOP27FksFvTr1w+MMSxdulSxbvz48dLPDRo0gJ+fH1588UXMnDkT/v7+Dm1NmjRJsU9qaiqqVKni9XMuCkxGAdas8nWjIbuLcEHjbjZh+6pALVxVAqpxlwx013XYPhlIiCg8PBxr1mQn6VNTU+Hv76/6/mdP7PIrvxlR2yYkJMRplbWzfdSqtwsyb8ZGijOea/L9bJzoMTG/T6PIEROiNFMwIbnH2/dgFGs8U2bOBtya0N9huWA1gM8wgROHefC3AhbmvEusUdCcDPRkHLzcaNMjriZKUalItE9cKreHMhlI4wMSkmPa79CyhIeHA7B167p8+TIyMzMV6+0n+3AlISEBN2/eRJMmTaRlPM/jwIEDWLRoEXbt2oXMzEzcvXtXcWN448YNVKhQwWXbYhLw0qVL+Pnnn91miFu0aAGr1YqLFy+idu3aDuu13iATR+6qAsUuwmJVIAROUd2UX1WBgLZuwnq4SgI6Syp6UhnobDs9SUjiO/SOlckDOHfuHEJDQxXLp0yZgtjYWJf7CoKAsWPH4rHHHkP9+vVVt7l16xamTZuGUaNGaT4nZ9XbvsIbsZHiTM7kZTLQk6pALd2Dc5NgNeZatV5+XxvJGzzLHlJFy7YAddfyJm/dg1Gs8ZyYDJRXBYrDIRhMAgSTAC7DCoOJB2e225k3ZI9tpyEZWGAmwzDz4GAEUxnKydn2jsvcTGSSxaGLMGRVgeJ+dpWBpHDRE2fE7QGKNZ7QnQj8999/MXz4cPzwww+q63le+39dhw4dcPr0acWy4cOHIyoqChMnTkSVKlVgNpuxZ88ePPPMMwCApKQkXL58GS1btnTarpgE/PPPP7F3716ULl3a7bkkJibCYDDQYLu5xD4ZKK8KzF5m+8u3qCTE8iIZKB8nUHFsD7oJ2ycMPZlsxFl7YpJPa5tUFUjU1KpVC7/++qtimZYbg5iYGPz22284ePCg6vrU1FT06NEDdevWdZtUFLmq3vYV3oyNJP/Ik3vuEluUDFTK72sjBRN11/IeijMFg1ploGA1Sl2ExWSfAQAXYLU9txjAQbAluXQkAz0hb89llZ0K+XlwRsF212DVVoGqmCjERTWgq/NRXWeB7cYvLytBiM+hWKOf7trysWPH4u7duzh69CgCAwOxc+dOfPHFF6hZsya2b9+uq63g4GDUr19f8QgKCkLp0qVRv359hIaGYsSIERg/fjz27t2LhIQEDB8+HC1btlRMFBIVFYWtW7cCsN1QPvvsszh+/DjWrl0LnueRnJyM5ORk6Zuzw4cPIy4uDqdOncLff/+NtWvXYty4cXjuuecUsxYT73o15Tmn64yy30SzgcHIAf4G26QhJnFsWHFbu+f5RUu3YPkMvs7Wa21Lvo2WJKB8G/EcvuGfd7sfKRoMBgNCQkIUD3eJwNGjR+O7777D3r17UblyZYf19+7dQ9euXREcHIytW7fCbHb/Vyqv3t69e7fPBnFvxkbiuSbfz/ZoP6ORd0jqaUnyeZL4yq3xBbXKyWQeBoMgPdTk97WR3GXV+QBsVRp169bF4sWL8/6ECxmKMwVHmTkbHJZZ083gM0wQMkxgWQ9YjLafeYMtGWjhlLP7GpWTiMi5TA7KEo5qcqOakDPqa1NKAsqqARXnJV6D2rXwBsDCOVb+ZX2stJ9MhBQeeuMMxRrP6a4I/Pnnn/HNN9+gadOmMBgMCA8PR6dOnRASEoKZM2eiR48eXj3B+fPnw2Aw4JlnnkFGRga6dOmCJUuWKLZJSkqSxqy6evWqFAwbNWqk2G7v3r1o164d/P39sX79esTGxiIjIwORkZEYN26cYrwMkjvklYH2VYHyLsJGIwPPONuXPwInVQKCOa8MdHb74eyWR8/tirvxAtW2B5ddNShW5Unn5GEXXb1dk+VVgNQtuPDioe/3mUHfZCGMMYwZMwZbt27Fvn37EBkZ6bBNamoqunTpAn9/f2zfvl3TuK+eVG8XVHkdG4lzersIu0peaRkDLzcnD/Em+Yy+eisD1RJ/4jJBoEmoiHNUpeE9FGcKFvtJQwDA8sD2haohqyKOWY0w+FttE2wEWAFeAGcRwIK1DZDutqIvq6pQ9fy8mQw084BFw5dIzsZFtD8ftSSmfJnsmjieU+0iTIgcxRr9dCcC09LSpO6zJUuWxL///otatWrhkUcewYkTJ3J8Qvv27VM8DwgIwOLFi11mdxnLfmOJiIhQPFfTpEkTHDlyJEfnSTynKRkIZGc2xNXijMJM/3hogDIhmFe3bGIVnzhrL6+STNQ74YgnyUBC7EVGRmLbtm2ato2JicG6devwzTffIDg4WJq1PTQ0FIGBgUhNTUXnzp3x4MEDrFmzBqmpqUhNTQUAlC1bFkaj7a8vKioKM2fORJ8+faTq7RMnTuC7776TqrcB2yQkfn5+3r/oXJTbsZHoozUZqDWB567bq95kYEHoRquWDFS7DmfVf854WpVJCHGN4kzBUmbOBtx+q6/Dcj7TBGuaPwxWAwxWW0Ug528Fl/UvAqy28e+Cs+5mZF2EAcdKwJwkA/WSuhWbbd2ZAdi6+WZ1D+aMTHWsQLVuwQCkakC3SUB74jYW2cQg1EWYEK/S/a5Ru3ZtJCUlAQAaNmyI5cuX4+rVq1i2bBkqVqzo9RMkRY/YTdiUFVSMWckzscuwibPFAC1dhOVj/vHQXzklZ9ZRUWdfPSh1AWauuwrL8WBOE35GcLoTiKTw4sFg1fHgwaSKQC1l9EuXLkVKSgratWuHihUrSo8NG2xdY06cOIGjR4/i9OnTqFGjhmKbK1euSO2oVW//888/aNSokWKfQ4cO5d6LlUsoNhY83k5IuUv08bwx35N7euWkmzApeiywTYaq6ZG1D3XX8h6KMwVP6VmbHJYJViMsD/wgZJjBp/ln/5v1YOm2bsNcuuz91+ik+2wWb44hmFNaugirzhRsz8yUD1fticlHvbPjEZ+jK85QrMkR3RWBr732Gq5fvw7ANsNk165dsXbtWvj5+WHVqlXePj9SSLmqCgRsyUBesCUDrTwnJQPFWYUB2AoHxS7DsFX88VAm/2D33OQmeeZswhCR3i7CtvNSr+DTWqnHg7mcUVjcRov+hrXYIAzWtC0p3PRUBLqrsm7Xrp3bbezb0VK97UsoNvoeT7rzaqnkE9e7az8/qgLl3YNFOZ1AxGAQFN2DT/SYSFWBRELdtbyH4kzBVHrWJofKQGnykKyKas7EA7wBBgBMfL81Zc2MG5D1XFbZpzaBiKIyULxv0pJw08HhmEbBNr4hoL17sB2HxKZ89mQ5qfLP7p4nqyqQ4znF2ID8iVowNjmn+3xI4USxRj/dFYHPPfcchg0bBgCIjo7GpUuXcOzYMVy5cgX9+/d3vTMhMq4mDwGUlYHy6kCzQS2pls1Vsk9LtVRBIo7v56o6ENDXvbi/YW2Oz4sQokSxsWDKjYSU1gSiWCFY0CoF1ZJ+8uSg/bl6MgagnjEaCQGAmTNnolmzZggODka5cuXQu3dvqfoNAO7cuYMxY8agdu3aCAwMRNWqVfHqq69KVeZqLBYLJk6ciEceeQRBQUEICwvDkCFDcO3atby4JK+jOFNwqVcGGsAEA/gME5jVCMFqgJBhtlUFZpjA7vuBpZtUJw8BbAk0+4cD3uA8sZbfsroFcxZOX0Wji0pBh8lDCNGB4oxSjgcUKFasGJo0aYIyZcp443xIESMmA01OAph8NmGT7FsgZ12EXd1qWTgmPXJCTxdhVywak44WMEVC0BnqKlx0WcAUv9/uHjygq2sw0Y9iY8HhLBmYk+ScJ9WEvpQM9AZKBhY+ApTDrLh6iJ/qtHbX2r9/P2JiYnDkyBHs3r0bFosFnTt3RlpaGgDg2rVruHbtGubOnYvffvsNq1atws6dOzFixAinbT548AAnTpzAu+++ixMnTmDLli1ISkrCU089laPXoaCgOFPwiclAxhuyk4FWI4R0P9uswg/NwD2zYzLQRWLPaWLN1Sy8rvbLI86O7XYGYJVkoPgQjtX21umRAkJPnNEbayjOKHFMQ98sPbPpzps3L0cn5CtSU1MRGhqKlJQUKkP1ggWhaxy6B8vxsnhm5TnwjINF4MAzwJo1PoDYRdgC9S7CagnAnCT13LUnX8/LknmK7cWxA+2SeGLCzwImTTBiZBzMsrEBxWPZH8flOWetp+7B+c8b7yE8z8NkMuFZLEYxrqTm/c6yXQjrdVdz12CiLrdjI8UZ71JLTGlN6KnNkFuQEnueUEv8iQlCPROGuHpNqItw/vPG+4jYxih8Dj8U07RPJh7gE7zg8XH//fdflCtXDvv370ebNm1Ut9m0aROee+45pKWlwWTSNtrRsWPH0Lx5c1y6dAlVq1bVfV55LS/uwSjWeI99F2HpPdXPCoNJAGcUwJl4GPytMPhbYQzKsE0kUjwje/IQeyr3R2JSjZmZ86Sh3X72iTi3YxFmTRbCeIM0WYjYNdh+whBp3EAzb+vybBSkikB70jlnHV+eCPSk4s/QLMn9RiRX5VecAXIWa4p6nNF0NSdPntTUGMdRRRLJHeKYgUBWZSAPp+MFZncTtq1z1d1XTKJ5khCUJ+K07i9P6nnKfsxAtXELLWA0WzAhuYxio29Rm0lY62y/gmCAwSAoEoLifr6aEFQbL1CkdxZkQpwRZ5AX+fv7w9/f3+1+YlesUqVKudwmJCRE882ZuA/HcShRooTmffITxRnfojZeIAAwwQDBart1EccLFDJMtqQgAJh5cGbBNl6g/Vh59ok+2QzDWqlV4+muDnQxPiDjOSkZ6CoJKOENAASHaj8xKeg0ISifRZgQGU9iTVGPM5quaO/evbl9HqSIezXlOcwrvs7lNp4lAwExIQgprtrN6JvDrr6e7M9zDEbG6UrY8RwDWHYVoTwB6SwZCGiflIT4LsHNGJL2mGzWYACIiYlBTExMbp1eoUWx0feoJQM9IZ8gw9cTgnLOJg4RE6F6UDVg4WMBNH+iEGubqlSpolg+ZcoUxMbGutxXEASMHTsWjz32GOrXr6+6za1btzBt2jSMGjVK4xkB6enpmDhxIgYOHOgzlW8UZ3yP+uQhBhhMgGAFOKMRAmy3MHyaLVFhMPHZk4eYreoNSwkw2Xuxs95ULnpZ6cH0tmPSP1ah/SQggC0hqJoMpCRgoacnzojbA/pjDcUZD2YNJiS3jL8/yGvJQOnnLDyyZwSWJ828Nd6fPbVuwTxn1z2Yc10ZKO8WbM/VTMJq7Sj2zeEYiaRw0DNrMCGFmafVb/bdheVt+GpSUJ4EtH9dtCQD82MmZFKwXblyRXEzpKUaMCYmBr/99hsOHjyouj41NRU9evRA3bp13SYVRRaLBf369QNjDEuXLtW0DyGeclYZCGQn18RkIMswZc8kDNiSXQEuYpIiGVYAJwnRw8mMwITopTfWUJzxwmQhhHjT+PuD3G5jP4GIOJOwfAIRZ5OImGTj62lNAuZ0chF7PKeSFNRBvp+rc6OEX9EhTiij9UGThZCirMn3sx2q1XKSvJJ3GRYZjbz00LI8v6hV/7kiCAaHh72CdH3Eu/is6nOtDwDo0KEDHn30UaxevRohISFub85Gjx6N7777Dnv37kXlypUd1t+7dw9du3ZFcHAwtm7dCrPZrNKKknhzdunSJezevbvAV2mQwqH0rE0Owy/YugjbTR4im0lYmjwkXT0mMSOTHgDUK+ScjQ1oMSgfrsjXW3WmC9x1C9aAZgcuuvTGGU9iDcUZG6oIJAWOs2SgvFrQbWUg4LQ6UF5w7Gr8QDk9FYR6Eodid19X1X3pnJBdOahSRejseGrVhpQcJCKqCCRFnVrX1VNPveF0e3cVcfJ18gSZWlKsoFbN2XcNprECSU4dO3ZM0w0RYwxjxozB1q1bsW/fPkRGRjpsk5qaii5dusDf3x/bt29HQECA23bFm7M///wTe/fuRenSpT26DkI8UXrWJodlKbG9pfECAbGmzwxkJfek6BFsUY4PaOFsXYeNtjGQGLK6z5pZ9niCbiYIUbAYHJN2sgSg7m7BevEGxfWJyT89lYE0UQgRaYk1FGeUqCKQ+Az7BKGrykCxOjCQy64OFHP5RigrBMWHFhaOeVwh6KoC0H58N4dZhFWOqfZtiDtGxuVoohJCCCnMGm6f65V29I6nl5/0VgUSkhtiYmKwZs0arFu3DsHBwUhOTkZycjIePnwIwHZz1rlzZ6SlpeGzzz5DamqqtA3PZ/8OR0VFYevWrQBsN2fPPvssjh8/jrVr14LneWmfzMzMfLlOQkJjt6lXBj7wg5DmD+GBWaoM5NKNyuSePLFnhuvKQDnZDMCKBJ+TKkFpG73VgPJjmZn0cL293QzEVA1IcgnFGSVKBBKfZjRkJwTFZGCAUYDZwBBgVHYXFpOBaglBLeQJN73JQHeVeGoVgeIkH2LiTuxOLP7rMPafhmQgTRxSOPFcdpdzLQ+BY9Q1mBAnXCUD1brCOqPWZRgomNWAlAwkWljBYOU0PrI+kzRr1kxTnFm6dClSUlLQrl07VKxYUXps2LABAHDixAkcPXoUp0+fRo0aNRTbXLlyRWonKSlJmgny6tWr2L59O/755x80atRIsc+hQ4dy6VUixD21ZCCfbpa6CQsPzGD3/cD+CwT+8wfu+oPLSgxy6co45KyCTtEl2I5DQtBunbdpTgrqaZPGFCyUdMUZnbGG4owSdQ0mPsXZhCJiV2GTkcHKczByDDzjYDYwmAFYBA4mBliRNbuQOIMwbMlAZ7dAagk/caIO+ay9rrYHspJ5nOuqQFf7gSmTifJZh+WcJfrkyzcIg3WdAymcqGswIc413D7XaTdhT2bQFRXEJKAop8lAX6qCJHlHT9dgV9q1a+d2G/t2IiIiNO1DSH4Ijd3m0E2YT7eVK0jDNBgZDAGZ4IwMzMSDC7CCg63KiAU4f89V6xKsluDTlfQz84DFFsM4IwNTq9yzGqSRmTgI2hN/WZOGaEVJQGJPa9dgV4panKGKQOJznI0hKFYHmoxMqg40imP7GRyrA+1p7R4MQFEZ6O3JRERGcDAjuyuvkXFuKwvtKwXNWW2I7WmdaZgQQohzribLsCdPkBXG8facVT4SQghxTV4ZCNgSc9Y0f/Bp/rbqwAwTrCnFwKcG2CYUSTeB3fezVQZq6UIr6xLswGpw3fXXTbdgzhvJuNweh5AQ4hT99RGf5Gp2YXlXYQBSQlAtGSjvJmzPXYJP3hVXS0LQyJSJPT0CmAFmcAhg2X+y7pKC8ipASgAWblbdswZT12BC3NE6XqDWhGBhRAnAokVPrNHbNZiQok6wKhOCtq7CJoA3QLAawTJM2cnAdBO4eybPxtOzTwCKzz0ZD1CG8ZytYjCruzMsBteTlYjEZKCWbUmhp/eehmKN56hrMPFZzroJA45dhQFbQlAxs7BYu86yuwgDnNOZhI3gHMbhE7sJu2OGYzdeh22yEoTyhKLYtgVMU0WgeCz787Y/BiHUNZgQ91x1EbYnJgPdJceMRr5AdxHWytV1PrJtXh6eCSnItHYNJqSoErsIAwBnEKRkIKwGGHgDmFWAMcACAVm3MVn/wmqwfcIP4BXdau3HBlRUA7pL9rlab/EsbnEWTrWLsLPlhHiCYo1+RfMrbFJoaK0MlFcHyisDRWa7ffV099U6Y6/i3OySejyyKwrNjHNI2MknDnFVUWhfBUhJwKJBz0QhPMcgAFQRSIhGemcSlncbLqzVglQJSAgh3hMauw0AwAQDmCxmiJOJiNWBDpWB9/3A3TMDstmFpeSaOXffp1XHCLSXlYyUVwZyFk56Li2nqkBC8lzh+3RKiIxR9htunwwEHMcLNMI2VqCzpJmz6j9nyUD75WL3YPFnV1WC4jmIx1Qk+VQSgjQWINEjMjISZ86cwZkzZxATE5Pfp0NIgaY3GViY2FcvUhKw6OKzhpbQ+gCouxYhWonJQCA7IWg/s7CQYYKQYc5OBt73B8swgXuQt1XmLpOA8u7BMvIEoDcYm5zzWluk4NAbZyjWeI4SgcTnjb8/SHqoUUsGAo4ThthXBXqSDNRaHSifxENLd1/5fq5ISUOVqkJCPHXgwAH07NkTYWFh4DjOoUvxli1b0LlzZ5QuXRocxyExMdFtm7///jueeeYZREREgOM4xMXF5cq5E+ItDbfP9VpC0JcnDSmMFY4k9xw7doy+cCJEo9DYbdJD5JAMtBogZJizJhSxjRfIeOfj8XHGvPvyRlOVoDtUFUg8QLFGP/o0RwoVZ0lBo5vfdDEpaEZ2VaDL7V2s15sQBCBN4OCw3q4qUL6P0zZVEoCUFCzcrBBg0fEQdE4WkpaWhoYNGzrdLi0tDa1bt8bs2bM1n/ODBw9QrVo1zJo1CxUqVNB1vYTkJzEhmNPEoC8nA0nRZOGyJ0dz/7DtQ1UahHhGnhRUTwaawDJMgNVWgSefnCOvugd7lY4ZhD2aJIX4BH1xhmJNTtBkIaTIkE8gAh6w2E+qwQG8LBdnAmebSMRJxZ7a5CFyztZ5MmuwPS2Tj8hZOEbJQKIQHh6ONWvWSM9TU1Ph7+8Pf39/h227deuGbt26OW3r+eefBwBcvHhR8/GbNWuGZs2aAQDeeustzfsRQgjxHTSAOyHeYUsGZlfxcCbZl0kWI5hJAAeAgwBmZmBmZksMmm3L7bvquuThxCByUiWixeA0Iel0shALp5gAhRB3KNboRxWBpEhRqwyUdwk2ctlVgdJ6LyXQ5N2B7ZeZXYzrp1YV6Ir8WxJ7n7IBOs+a+AILJ+h68BzDuXPnEBoaqnjMnDkzvy+FEJ/ibkZhd7MD+2pVIHUPJoSQvCevDARgmzxErI4Tl1uy35/FhCDMgi0xZ/JOhSBnZE4fMPOeH0dPspIQkiNUEUiKNGnm4KycmQVZ3YSlHBoHK2zVdHpmEnZFrWuvtyb3cFqFCO+dPykcatWqhV9//VWxTK0akBCSMzxvdJnwMxp5twnD/KZ2DYJgoIlDihhbTwRtnyUssgHcjUYjYmJiaOwmQrxArAzkjEbA35q9IquKjwHgLLaqQBgFgDfYqgORVRkI2LoSe5vZSZxTqQYUqxU5C6e5KpAZGXUJLgL0xJns7SnWeIISgaTIMRoAKw8EGAXwjINFkAUVZksGStsC8CQZaAFzO5Zf9jFcb6d2TLVuwXqOSYjBYKASekJymdaKP19IBmr1yLZ5+X0KpACh7lqE5C4h3Q9AVpVe1jKWbrLdvRghJQMBZHcTBrybDJQnAV1UA8qTfvJkoGIdb7CdM0DJQKIZxRr9qP6WFEkmI4PJyGDkGMwGhgAjg5GzVQiakd1FWNpeNhuvnKskngXM5Th+RrvuwGpdkL1Vxad18hLim8QJarQ+9E4WQgghaolKQTBQN2FCCMljTLB1AxYyTLZH1gzCwgMzWLoJeJj1r5gANAqKCUSkbsLyh0jv+IAqSUCtMxXLE4OKWY9VughzPEdJQEK8qEB9eps1axY4jsPYsWOlZenp6YiJiUHp0qVRvHhxPPPMM7hx44bLdhhjeO+991CxYkUEBgaiY8eO+PPPPxXb3LlzB4MHD0ZISAhKlCiBESNG4P79+7lxWSSf2M8cLCeOFSgmA8WEoFoyUAyH8mSgPGmnlgyUV+apJQPV9rEf189ZEtBVctHVOkoGErnIyEicOXMGZ86coRJ6QjyUk1mD7fnCeIHOqhYpGVg08BzT9QBoJkdCcgufaYKQYc5KBprBp/nbEoJp/lmzCRvAPTC6TgbKeTKun7tKQFm3YGddgJ0mA0Vqy0ihpTfOUKzxXIH55Hbs2DEsX74cDRo0UCwfN24cvv32W2zatAn79+/HtWvX8PTTT7tsa86cOViwYAGWLVuGo0ePIigoCF26dEF6erq0zeDBg/H7779j9+7d+O6773DgwAGMGjUqV66N5B+tyUCRq2SgEbZkoFp1oNvuvbIknJauwGpJQN6uwlDtTdD+WIR4w/3795GYmIjExEQAwIULF5CYmIjLly8DsH2xkpiYiDNnzgAAkpKSkJiYiOTkZKmNIUOGYNKkSdLzzMxMqc3MzExcvXoViYmJOH/+fN5dGCFekJfJQMFqhGDN3y7ErpKBlBAk9o4dO0ZfOBGSQ6Gx21SXi8lAPt0M8Abb5CFZVYIs3QSWYVIm11SSgQ4JQZWx/hjPZU9KoradLAmoVg3odBxAFdL5yqsC7ZKBhmZJmtsjRQPFGv0KxCe2+/fvY/DgwVixYgVKliwpLU9JScFnn32GefPmoX379oiOjsbKlStx6NAhHDlyRLUtxhji4uIwefJk9OrVCw0aNMCXX36Ja9euYdu2bQCAs2fPYufOnfj000/RokULtG7dGgsXLsT69etx7dq1vLhkUkDYVwYCzpOBgOvqQG9N+OGOfeJPbbnYLVl8rBEGYY3gPClKfJsFAjJ1PHidXYOPHz+Oxo0bo3HjxgCA8ePHo3HjxnjvvfcAANu3b0fjxo3Ro0cPAMCAAQPQuHFjLFu2TGrj8uXLuH79uvT82rVrUpvXr1/H3Llz0bhxY/zvf//z9stDSK7Lq2SgwVQwqgadJQO9+ToQQgjJ5iwZKM4ibJ8MZGIyMN0ELl35ni1PBgKy5J1KVZ88AeiQDPQC1SpAkUoykJKAhHhHgUgExsTEoEePHujYsaNieUJCAiwWi2J5VFQUqlatisOHD6u2deHCBSQnJyv2CQ0NRYsWLaR9Dh8+jBIlSqBp06bSNh07doTBYMDRo0dV283IyEBqaqriQXzD+PuDYDIK0sOeWjJQjTwZ6Kw6UDHmn8rPRifdiz1ln/AD4FAluEEYnOPjkMJHT9fgdu3agTHm8Fi1ahUAYNiwYarrY2NjpTb27dsnbQ8AERERqvvs27fP+xfrIyjO+LainAxsuH0uJQGLACsEWDQ+rLB93qLuWgUPxRrfpZYMtM0inDVmoNUgJQOFdD9bMvC+nzIZmHUvxMzMlhA0C8quwiZBqvZzmfhzN5agOfs40nHFhxvOxgukJGDhpyfOUKzJmXyfNXj9+vU4ceIEjh075rAuOTkZfn5+KFGihGJ5+fLlFV3O7PcRt3G2T3JyMsqVK6dYbzKZUKpUKaftzpw5E1OnTtV0TaTgeTXlOSwIXQMAUjLQKgssRgPAZ8WlyZkDVNsYy21QXW6ym1XYCE4aj8/ZLL6fMvVjDDV85fZajIwDz6lPRGI/czAlAYsG2yQg2sd2kU8WAti+jKFS+vxHcabocJY0O/XUG149TpPvZ6suP9Fjoqb95d2Q1RKQPG90egxCAH0zOR44cAAffvghEhIScP36dWzduhW9e/eW1m/ZsgXLli1DQkIC7ty5g5MnT6JRo0Yu2/z999/x3nvvISEhAZcuXcL8+fMVY5EXRRRrfFto7DakxPZWLLMlAwHOaIQAW6WP38j9DvuKdwjCL3VV2+aMApjKRB2KY/EcONmwSoanTqpv93M9l+0AbqoB5XgDDI+f0bYtKZK0xhqKM9nyNRF45coVvPbaa9i9ezcCAgLy81TcmjRpEsaPHy89T01NRZUqVfLxjIhe8mSgmjcfuO46G8f6uz2Gu0Qen9VN15kvhIFuj/Gs0fk1ANljBH7DP++2LVJ0RUZGSsMlkIKB4ozva7h9rttknqvKOS1VdVqSeO6Sc+7Wa00UUhKwaOHBwGkch1j8QrRZs2YwGo2avnBKS0tDw4YN8cILL6iOB56WlobWrVujX79+GDlypKbzePDgAapVq4a+ffti3LhxmvYp7CjWFF6Mt1UGFnv9O5fbuUqqScnCzU2dbiO14yQJCABc+98V7amRkoUWg2JiEcUxKAFYpOiJM+L2gPZYQ3EmW74mAhMSEnDz5k00adJEWsbzPA4cOIBFixZh165dyMzMxN27dxVVgTdu3ECFChVU2xSX37hxAxUrVlTsI2ZzK1SogJs3byr2s1qtuHPnjtN2/f394e/v78llkgLk1ZTncrV9LYm8nPqaz91rIITkD4ozhUNud5HNi+QbJfiIt+ipCOzWrRu6devmdP3zz9u+4Lx48aLm4zdr1gzNmjUDALz11lua9yvMKNb4PmfjBXqT4ZnjuX4MMVmoui7Xj04KE62xhuJMtnwdI7BDhw44ffq0NHNkYmIimjZtisGDB0s/m81m7NmzR9onKSkJly9fRsuWLVXbjIyMRIUKFRT7pKam4ujRo9I+LVu2xN27d5GQkCBt8/PPP0MQBLRo0SKXrpYQQnKHhROQqeNhhaBrshBCCCHEE/Zj0WVkZOT3KRFCCClkKNbol68VgcHBwahfv75iWVBQEEqXLi0tHzFiBMaPH49SpUohJCQEY8aMQcuWLfHoo49K+0RFRWHmzJno06cPOI7D2LFjMX36dNSsWRORkZF49913ERYWJvX/rlOnDrp27YqRI0di2bJlsFgsGD16NAYMGICwsLA8u35CCMkv1DWYEEKIHhZOAOO0jUcrDuBu3+V0ypQpiomkCCGEEJGeOANQrMmJfJ8sxJ358+fDYDDgmWeeQUZGBrp06YIlS5YotklKSkJKSor0fMKECUhLS8OoUaNw9+5dtG7dGjt37lSMQ7h27VqMHj0aHTp0kNpfsGBBnl0XIYR4iwU8AO0zidJkIYQQQvLClStXFN21qEsqIYQQb6NYo1+BSwTu27dP8TwgIACLFy922XWNMeWAkhzH4f3338f777/vdJ9SpUph3bp1OTpXQgjxVVQRSAghJLeFhIRoHiOQEEII8QTFGv3ydYxAQgghhBBCSMGXCUHXA7ANok5j0RJCCNFCb5yhWOO5AlcRSAghRB8eAjhoH0+Dp67BhBBC8oCeWYPv37+P8+fPS88vXLiAxMRElCpVClWrVsWdO3dw+fJlXLt2DYBtaCAAqFChAipUqAAAGDJkCCpVqoSZM2cCADIzM3HmzBnp56tXryIxMRHFixdHjRo1vHadhBBC8o/WWENxJhslAgkhpAiirsGEEEIKkuPHj+OJJ56Qno8fPx4AMHToUKxatQrbt2/H8OHDpfUDBgwAoBwU/vLlyzAYsjs8Xbt2DY0bN5aez507F3PnzkXbtm0dhiMihBBSuFGcyUaJQEIIIYQQQohLAgTwGqvPBVl3LaPRqKnyvF27dg7jfssNGzYMw4YNc9mG/U1XRESEyzYJIYQUHHrijLg9oD3WUJzJRolAQgjxcZmcAIHT0TWYE6hrMCGEkFynp2swIYQQ4gmKNfpRIpAQQoog6hpMCCFEj0xOgFHjl068BxWBhBBCijY9cQagWJMTlAgkhBAfZwUDo8lCCCGEFDBUpUEIISS3UazRjxKBhBBSBFFFICGEEEIIIYQUPQb3mxBCCCHA4sWLERERgYCAALRo0QK//vqry+03bdqEqKgoBAQE4JFHHsGOHTvy6EwJIYR4G581iLvWB2DrrlW3bl0sXrw4n8+eEEJIQac3zlCs8RxVBBJCiI/L4KwwcFbN21s4ARcuXNTVNXjDhg0YP348li1bhhYtWiAuLg5dunRBUlISypUr57D9oUOHMHDgQMycORNPPvkk1q1bh969e+PEiROoX7++vgskhBDik6i7FiGEkNxGsUY/qggkhJAiKDIyEmfOnMGZM2c0jQ84b948jBw5EsOHD0fdunWxbNkyFCtWDJ9//rnq9h9//DG6du2KN998E3Xq1MG0adPQpEkTLFq0yNuXQgghhBBCCCFEI0oEEkKITzNCYKm69hBYKr7/9i+kpqYqHhkZGarbZ2ZmIiEhAR07dpSWGQwGdOzYEYcPH1bd5/Dhw4rtAaBLly5OtyeEEFKwZUJAJniND+quRQghRB99cYZiTU5Q12BCCPFRRqMRZmNzpPE/I9D4nKZ9GEtDOn8YQ4cOQGhoqGLdlClTEBsb67DPrVu3wPM8ypcvr1hevnx5/PHHH6rHSU5OVt0+OTlZ03kSQgjxfdRdixBCSG6jWKMfVQQSQogPu/zPFliFP8ELVzRtn2E9AKOhKpYvX46UlBTFY9KkSbl8toQQQnyVhROQqfFh4ahKgxBCiD564gzFmpyhikBCCPFhFSpUgJ+xFTKsP6KY3wiX2wosFRb+MI4fPwx/f3/4+/trOkaZMmVgNBpx48YNxfIbN26gQoUKTs9Lz/aEEEIKH6rSIIQQktso1uhHFYGEEOLjkv/dAl74B1b+L5fbZVp/hslQG9HR0bra9/PzQ3R0NPbs2SMtEwQBe/bsQcuWLVX3admypWJ7ANi9e7fT7QkhhBBCCCGE5D5KBBJCiI8rWbIk/ExtkWHdBcaY6jaCcBsWPgH/99tGj44xfvx4rFixAl988QXOnj2Ll19+GWlpaRg+fDgAYMiQIYquxa+99hp27tyJjz76CH/88QdiY2Nx/PhxjB492qPjE0IIyV8WCLoeAHXXIoQQop3eOEOxxnPUNZgQQgqBO3c3I7h4BfDCWZiMdR3WZ1h/gsnYEHXq1PGo/f79++Pff//Fe++9h+TkZDRq1Ag7d+6UJgS5fPkyDIbs75ZatWqFdevWYfLkyXj77bdRs2ZNbNu2DfXr1/fsAgkhhPgc6q5FCCEkt1Gs0Y8SgYQQUggEBQXh4wUfYOxrM2A0RIHjspNyvJAMq/AbLl48n6NjjB492mlF3759+xyW9e3bF3379s3RMQkhhBBCCCGEeA91DSaEkEJi1KhRYCwDVuGUYnmmdTfMxmYIDw/PpzMjhBDi69I5q64HQN21CCGEaKc3zlCs8RxVBBJCSCHh7++Pz1fOxwsvvA6ToQE4zgheuAyrcB7JyZfz+/QIIYQUMdRdixBCSG6jWKMfVQQSQkgh8txzz4GDERb+OAAgw/oj/IytpLH8CCGEkIJq8eLFiIiIQEBAAFq0aIFff/3V5fabNm1CVFQUAgIC8Mgjj2DHjh15dKaEEEJ8EcUZG0oEEkJIIWIymbBx01JkWvfAyp8FL1xF8r9b8vu0CCGE+DgLJyBT48PC6Z/JccOGDRg/fjymTJmCEydOoGHDhujSpQtu3rypuv2hQ4cwcOBAjBgxAidPnkTv3r3Ru3dv/Pbbb169bkIIIXlDT5zxJNZQnMnGMcZYfp+EL0pJSUGJEiVw5coVKkMlhOiWmpqKKlWq4O7duwgNDfVq24wxmIxVILAb8DN1QIZlp1fbJ3mD4gwhJKe8EWtSU1MRGhqKIP+3wMFf0z4MGUjLmOXw/uXv7w9/f/U2WrRogWbNmmHRokUAAEEQUKVKFYwZMwZvvfWWw/b9+/dHWloavvvuO2nZo48+ikaNGmHZsmV6LrFIo1hDCMmJ/IozgP5YQ3EmG40R6KF79+4BAKpUqZLPZ0II8WX37t3zeiKQ4zj8tGc1OnWMwZ27m73aNsk7FGcIId6Sk1jj5+eHChUqIDl5lq79ihcv7vD+NWXKFMTGxjpsm5mZiYSEBEyaNElaZjAY0LFjRxw+fFi1/cOHD2P8+PGKZV26dMG2bdt0nWdRR7GGEOIN+RFnAO2xhuKMEiUCPRQWFoYrV64gODgYHMdp2kfMlhfGb9zo2nxPYb0uwDeujTGGe/fuISwsLFfaf+KJJ2Dlz+RK2yRvUJxRomvzTXRt+csbsSYgIAAXLlxAZmam7mPbv3c5qwa8desWeJ53GM+2fPny+OOPP1T3SU5OVt0+OTlZ13kWdXpjjS/83nuKrs030bXlr/yMM+LxtcQaijNKlAj0kMFgQOXKlT3aNyQkpMD+IecUXZvvKazXBRT8a/N2JSApXCjOqKNr8010bfnHG7EmICAAAQEBXjgbUtB4GmsK+u99TtC1+Sa6tvxDccb30GQhhBBCCCGEkHxTpkwZGI1G3LhxQ7H8xo0bqFChguo+FSpU0LU9IYSQoovijBIlAgkhhBBCCCH5xs/PD9HR0dizZ4+0TBAE7NmzBy1btlTdp2XLlortAWD37t1OtyeEEFJ0UZxRoq7Becjf3x9TpkxxOj6KL6Nr8z2F9bqAwn1thLhSmH/36dp8E10b0Wr8+PEYOnQomjZtiubNmyMuLg5paWkYPnw4AGDIkCGoVKkSZs6cCQB47bXX0LZtW3z00Ufo0aMH1q9fj+PHj+OTTz7Jz8so9Arz7z1dm2+iayNaUZzJxjHGWH6fBCGEEEIIIaRoW7RoET788EMkJyejUaNGWLBgAVq0aAEAaNeuHSIiIrBq1Spp+02bNmHy5Mm4ePEiatasiTlz5qB79+75dPaEEEIKOoozNpQIJIQQQgghhBBCCCGkCKAxAgkhhBBCCCGEEEIIKQIoEUgIIYQQQgghhBBCSBFAiUBCCCGEEEIIIYQQQooASgQSQgghhBBCCCGEEFIEUCKQOGjXrh3Gjh1bqI47bNgw9O7dO0dtREREgOM4cByHu3fvOt1u1apVKFGiRI6OVdS1a9dOeq0TExPz+3QIIbmAYo06ijV5h2INIYUbxRl1FGfyDsUZUlBRIpAUGFu2bMG0adOk5xEREYiLi8u/E1Lx/vvv4/r16wgNDc3vUykUnH3A2LJlC3799de8PyFCSKFHsabooVhDCMlLFGeKHoozxNeY8vsECBGVKlUqv0/BreDgYFSoUCG/TwMAYLFYYDab8/s0ckWpUqWQmpqa36dBCCmEKNboQ7GGEEL0oTijD8UZQvIeVQQSt/777z8MGTIEJUuWRLFixdCtWzf8+eef0nrxG5Bdu3ahTp06KF68OLp27Yrr169L21itVrz66qsoUaIESpcujYkTJ2Lo0KGK0nZ5GX27du1w6dIljBs3TiqnBoDY2Fg0atRIcX5xcXGIiIiQnvM8j/Hjx0vHmjBhAhhjin0EQcDMmTMRGRmJwMBANGzYEF9//bVHr8+qVatQtWpVFCtWDH369MHt27cdtvnmm2/QpEkTBAQEoFq1apg6dSqsVqu0/o8//kDr1q0REBCAunXr4qeffgLHcdi2bRsA4OLFi+A4Dhs2bEDbtm0REBCAtWvXAgA+/fRT1KlTBwEBAYiKisKSJUsUx75y5Qr69euHEiVKoFSpUujVqxcuXryo+frctT9x4kTUqlULxYoVQ7Vq1fDuu+/CYrFI60+dOoUnnngCwcHBCAkJQXR0NI4fP459+/Zh+PDhSElJkf6PY2NjNZ8XIaRwoVjjGsUaijWEkJyhOOMaxRmKM6QIYYTYadu2LXvttdek50899RSrU6cOO3DgAEtMTGRdunRhNWrUYJmZmYwxxlauXMnMZjPr2LEjO3bsGEtISGB16tRhgwYNktqYPn06K1WqFNuyZQs7e/Yse+mll1hISAjr1auX6nFv377NKleuzN5//312/fp1dv36dcYYY1OmTGENGzZUnO/8+fNZeHi49Hz27NmsZMmSbPPmzezMmTNsxIgRLDg4WHGs6dOns6ioKLZz5072119/sZUrVzJ/f3+2b98+p69LeHg4mz9/vmLZkSNHmMFgYLNnz2ZJSUns448/ZiVKlGChoaHSNgcOHGAhISFs1apV7K+//mI//vgji4iIYLGxsYwxxqxWK6tduzbr1KkTS0xMZL/88gtr3rw5A8C2bt3KGGPswoULDACLiIhgmzdvZn///Te7du0aW7NmDatYsaK0bPPmzaxUqVJs1apVjDHGMjMzWZ06ddgLL7zA/u///o+dOXOGDRo0iNWuXZtlZGQ4vVaRu/YZY2zatGksPj6eXbhwgW3fvp2VL1+ezZ49W1pfr1499txzz7GzZ8+yc+fOsY0bN7LExESWkZHB4uLiWEhIiPR/fO/ePWk/8ZpPnjzp9jwJIb6HYo06ijUUawgh3kFxRh3FGYozhFAikDiQB69z584xACw+Pl5af+vWLRYYGMg2btzIGLMFTQDs/Pnz0jaLFy9m5cuXl56XL1+effjhh9Jzq9XKqlat6jRoMqYepLQEzYoVK7I5c+ZIzy0WC6tcubJ0rPT0dFasWDF26NAhRTsjRoxgAwcOdPq6qJ3PwIEDWffu3RXL+vfvrwiaHTp0YB988IFim9WrV7OKFSsyxhj74YcfmMlkkj4YMMbY7t27VYNmXFycop3q1auzdevWKZZNmzaNtWzZUjpO7dq1mSAI0vqMjAwWGBjIdu3a5fRatbav5sMPP2TR0dHS8+DgYEWQlVu5cqXitZKjoElI4UaxRh3FGsf21VCsIYS4Q3FGHcUZx/bVUJwhhRmNEUhcOnv2LEwmE1q0aCEtK126NGrXro2zZ89Ky4oVK4bq1atLzytWrIibN28CAFJSUnDjxg00b95cWm80GhEdHQ1BELx6vikpKbh+/brifE0mE5o2bSqV0p8/fx4PHjxAp06dFPtmZmaicePGuo539uxZ9OnTR7GsZcuW2Llzp/T81KlTiI+Px4wZM6RlPM8jPT0dDx48QFJSEqpUqaIYp0P+Wsk1bdpU+jktLQ1//fUXRowYgZEjR0rLrVarNPDvqVOncP78eQQHByvaSU9Px19//eXy2rS0DwAbNmzAggUL8Ndff+H+/fuwWq0ICQmR1o8fPx7/+9//sHr1anTs2BF9+/ZV/K4QQgjFGtco1lCsIYTkDMUZ1yjOUJwhRQslAolX2A/wynGcwxgW3mAwGBzalY/doMX9+/cBAN9//z0qVaqkWOfv75+zE3RyvKlTp+Lpp592WBcQEKCrraCgIEW7ALBixQrFhwTA9qFE3CY6Oloae0OubNmybs/bXfuHDx/G4MGDMXXqVHTp0gWhoaFYv349PvroI2nb2NhYDBo0CN9//z1++OEHTJkyBevXr3f4sEEIIe5QrHF9PIo1FGsIITlDccb18SjOUJwhhQMlAolLderUgdVqxdGjR9GqVSsAwO3bt5GUlIS6detqaiM0NBTly5fHsWPH0KZNGwC2b49OnDjhMEiunJ+fH3ieVywrW7YskpOTwRiTBttNTExUHKtixYo4evSodCyr1YqEhAQ0adIEAFC3bl34+/vj8uXLaNu2raZrcKZOnTo4evSoYtmRI0cUz5s0aYKkpCTUqFFDtY3atWvjypUruHHjBsqXLw8AOHbsmNtjly9fHmFhYfj7778xePBg1W2aNGmCDRs2oFy5copvtLTQ0v6hQ4cQHh6Od955R1p26dIlh+1q1aqFWrVqYdy4cRg4cCBWrlyJPn36qP4fE0KKHoo1rlGsoVhDCMkZijOuUZyhOEOKFkoEEpdq1qyJXr16YeTIkVi+fDmCg4Px1ltvoVKlSujVq5fmdsaMGYOZM2eiRo0aiIqKwsKFC/Hff/9JgU9NREQEDhw4gAEDBsDf3x9lypRBu3bt8O+//2LOnDl49tlnsXPnTvzwww+KgPDaa69h1qxZqFmzJqKiojBv3jzcvXtXWh8cHIw33ngD48aNgyAIaN26NVJSUhAfH4+QkBAMHTpU83W9+uqreOyxxzB37lz06tULu3btUpTQA8B7772HJ598ElWrVsWzzz4Lg8GAU6dO4bfffsP06dPRqVMnVK9eHUOHDsWcOXNw7949TJ48GQBcvj4AMHXqVLz66qsIDQ1F165dkZGRgePHj+O///7D+PHjMXjwYHz44Yfo1asX3n//fVSuXBmXLl3Cli1bMGHCBFSuXDlH7desWROXL1/G+vXr0axZM3z//ffYunWrtP/Dhw/x5ptv4tlnn0VkZCT++ecfHDt2DM888wwA2//x/fv3sWfPHjRs2BDFihVDsWLFNL/+hJDCgWKNaxRrKNYQQnKG4oxrFGcozpAiJn+GJiQFmf0At3fu3GHPP/88Cw0NZYGBgaxLly7s3Llz0nq1wVG3bt3K5L9eFouFjR49moWEhLCSJUuyiRMnsr59+7IBAwY4Pe7hw4dZgwYNmL+/v6KtpUuXsipVqrCgoCA2ZMgQNmPGDMXAuhaLhb322mssJCSElShRgo0fP54NGTJEMYivIAgsLi6O1a5dm5nNZla2bFnWpUsXtn//fqevi9rAuowx9tlnn7HKlSuzwMBA1rNnTzZ37lyH12Pnzp2sVatWLDAwkIWEhLDmzZuzTz75RFp/9uxZ9thjjzE/Pz8WFRXFvv32WwaA7dy5kzHmepDZtWvXskaNGjE/Pz9WsmRJ1qZNG7ZlyxZp/fXr19mQIUNYmTJlmL+/P6tWrRobOXIkS0lJcXqtetp/8803WenSpVnx4sVZ//792fz586Xrz8jIYAMGDGBVqlRhfn5+LCwsjI0ePZo9fPhQ2v+ll15ipUuXZgDYlClTpOU0sC4hhRvFGnUUayjWEEK8g+KMOoozFGcI4RjLhUEPCHFDEATUqVMH/fr1w7Rp0/L7dDSJiIjA2LFjMXbs2Fw/Vnx8PFq3bo3z588X2UFoL168iMjISJw8edJldwtCCHGGYo1rFGso1hBCcobijGsUZyjOkILJkN8nQIqGS5cuYcWKFTh37hxOnz6Nl19+GRcuXMCgQYPy+9R0mThxIooXL46UlBSvtrt161bs3r0bFy9exE8//YRRo0bhscceK7IBs1u3bqhXr15+nwYhxMdQrHGNYo0SxRpCiF4UZ1yjOKNEcYYUVDRGIMkTBoMBq1atwhtvvAHGGOrXr4+ffvoJderUye9T02z//v3SbF72U9fn1L179zBx4kRcvnwZZcqUQceOHRWzVOWW4sWLO133ww8/4PHHH8/1c1Dz6aef4uHDhwCAqlWr5ss5EEJ8D8Ua1yjWKFGsIYToRXHGNYozShRnSEFFXYMJKcLOnz/vdF2lSpUQGBiYh2dDCCGkMKJYQwghJDdRnCFEH0oEEkIIIYQQQgghhBBSBNAYgYQQQgghhBBCCCGEFAGUCCSEEEIIIYQQQgghpAigRCAhhBBCCCGEEEIIIUUAJQIJIYQQQgghhBBCCCkCKBFICCGEEEIIIYQQQkgRQInAQobjOMTGxub3aRQ4jDFkZmbm92kUWO3atUO7du3y+zQKnNjYWHAcl9+nQQgpBCwWCwRByO/TKLDo84s6is+EEDWCIMBqteb3aRRYERERGDZsWH6fRoEzbNgwRERE5PdpkAKAEoEF0KpVq8BxHDiOw8GDBx3WM8ZQpUoVcByHJ598Mh/O0NGOHTt0fYD/9ddf8corryA6Ohpms1l3sqVdu3bSayR/dO3a1WHbNWvWoEyZMggODsbw4cNVE4Lp6emYP38+WrRogdDQUAQEBKBWrVoYPXo0zp075/I6OI7D/PnzHdb16tULHMdh5cqVDuvatGmDSpUq6brmwioiIqLA/B4TUlj4YhzJqSVLlmDVqlWat9+wYQOee+451KxZExzH6U62qMUgjuMwa9YsxXaMMYwdOxbBwcEoWbIkFixYoNrejRs38MYbbyAqKgrFihVDUFAQoqOjMX36dNy9e9fpecyZMwccx+HkyZMOxy1ZsiQ4jsOFCxcU69LT0+Hv749BgwbpuubC6OLFi+A4DnPnzs3vUyGkUCgs8WfdunWIi4vTvP2PP/6IESNGoH79+jAajbqTLREREaox5aWXXnLYds6cOQgJCUFISAgmTpyo2l5qaiqmTp2Khg0bonjx4ggMDET9+vUxceJEXLt2zel5bNy4ERzHYevWrQ7rGjZsCI7jsHfvXod1VatWRatWrXRcceHFcRxGjx6d36dBiEum/D4B4lxAQADWrVuH1q1bK5bv378f//zzD/z9/R32efjwIUymvP9v3bFjBxYvXqw5Gbhjxw58+umnaNCgAapVq+Yy2eZM5cqVMXPmTMWysLAwxfOLFy/i5ZdfRmxsLMLDwzF16lTExcVhwoQJ0ja3bt1C165dkZCQgCeffBKDBg1C8eLFkZSUhPXr1+OTTz5xWk3YpEkTFCtWDAcPHsS4ceMU6w4dOgSTyYT4+HgMHz5cWp6ZmYljx46hZ8+euq85t/z444/5fQqEkFzgSRzxVUuWLEGZMmU0VwAsXboUCQkJaNasGW7fvu3RMTt16oQhQ4YoljVu3FjxfN26ddiyZQs+/fRTPHjwAG+99RZatGiBFi1aSNscO3YM3bt3x/379/Hcc88hOjoaAHD8+HHMmjULBw4ccPo+Lf7fHjx4UHHs33//HXfv3pXiUGRkpOJ4mZmZDr8X+Sm/Pr8QQnKHr8efdevW4bfffsPYsWM1b79hwwY0adLE4X5Eq0aNGuH1119XLKtVq5bieXx8PGbOnIl58+ahWLFiePvttxEdHY1+/fpJ2/z999/o2LEjLl++jL59+2LUqFHw8/PD//3f/+Gzzz7D1q1bnd57yWNKnz59pOWpqan47bffpJjyxBNPSOuuXLmCK1euYMCAAR5dd25ISkqCwUA1T4Q4Q5+4CrDu3btj06ZNWLBggeLD8bp16xAdHY1bt2457BMQEJCXp+ixl19+GRMnTkRgYKDbqjtnQkND8dxzz7nc5vjx4+jYsaMUVM1mMz799FNFInDYsGE4efIkvv76azzzzDOK/adNm4Z33nnHafsmkwktWrRAfHy8YnlSUhJu3bqFQYMGOXwbmpCQgPT0dK/cgD148ADFihXLcTt+fn45boMQUvB4EkeKitWrV6NSpUowGAyoX7++R23UqlXLbRw6fPgwXn/9dWm7s2fP4uDBg1Ii8O7du+jTpw+MRiNOnjyJqKgoxf4zZszAihUrnLbftGlTBAQE4ODBgxgzZoy0PD4+HqVLl0bTpk1x8OBBxXmKcSmncUgQBGRmZnrls4evfH4hhGhT1OLPBx98gBUrVsBsNuPJJ5/Eb7/9pruNSpUqaYopw4YNw6hRowAAt2/fxi+//CIlAq1WK55++mncuHED+/btc3ifnzFjBmbPnu20/bCwMERGRjrcvxw+fBiMMfTt29dhnbdiCmMM6enpCAwMzFE7AAp8opmQ/EZp8gJs4MCBuH37Nnbv3i0ty8zMxNdff+20O4/9GDviGGfnz5/HsGHDUKJECYSGhmL48OF48OCB23P45Zdf0LdvX1StWhX+/v6oUqUKxo0bh4cPH0rbDBs2DIsXL5aOLz5cKV++vFfe5K1WK+7fv+90fbVq1XDgwAHs3r0bSUlJ+OSTT1CzZk1p/dGjR/H9999jxIgRDklAwBZE3HUXat26NW7cuIHz589Ly+Lj4xESEoJRo0ZJSUH5OnE/APjmm2/Qo0cPhIWFwd/fH9WrV8e0adPA87ziOO3atUP9+vWRkJCANm3aSN8Cyrs1LV68GNWqVUOxYsXQuXNnXLlyBYwxTJs2DZUrV0ZgYCB69eqFO3fuOLQt7xa3b98+cByHjRs3YsaMGahcuTICAgLQoUMHxXWKxOMGBgaiefPm+OWXX7w+rtGaNWsQHR2NwMBAlCpVCgMGDMCVK1ek9aNHj0bx4sVVf68HDhyIChUqKF7TH374AY8//jiCgoIQHByMHj164Pfff/fa+RJSEHgSRwRBQFxcHOrVq4eAgACUL18eL774Iv777z/Fdnrfu86cOYMnnngCxYoVQ6VKlTBnzhxN17By5Uq0b98e5cqVg7+/P+rWrYulS5cqtomIiMDvv/+O/fv3SzHI3ftPlSpVvFIt8PDhQ6SnpztdX61aNaxduxanTp3CkSNHsH37dkUcWr58Oa5evYp58+Y5JAEBW7ycPHmy0/b9/PzQrFkzhy+k4uPj0bJlSzz22GOq60qUKCElQOfOnYtWrVqhdOnSCAwMRHR0NL7++muHY4ndndauXYt69erB398fO3fulLoCHjx4EK+++irKli2LEiVK4MUXX0RmZibu3r2LIUOGoGTJkihZsiQmTJgAxphD255+fnn48CFeffVVaRiQp556ClevXvXquIMZGRmYMmUKatSoIX0emjBhAjIyMqRt6tevr6iSEQmCgEqVKuHZZ59VLNPyd0aIr/Ik/mh5L1q5ciU4jsPnn3+uWP7BBx+A4zjs2LHD5XlpiV3t2rXD999/j0uXLkkxxV1X37CwMJjNZpfbaJGZmYm0tDSn66tVq4bt27fjyJEjOHXqFNauXauIKZs3b8apU6fwzjvvqCbmQkJCMGPGDJfn0Lp1a5w8eVJxvxcfH4969eqhW7duOHLkiGLM2/j4eHAch8ceewyAtrgNZA8PtGvXLjRt2hSBgYFYvny54j5k6tSpqFSpEoKDg/Hss88iJSUFGRkZGDt2LMqVK4fixYtj+PDhivdisW15DwExTsXHx2P8+PEoW7YsgoKC0KdPH/z777+KfQVBQGxsLMLCwlCsWDE88cQTOHPmjFfHHdQSA5588klUq1ZNdf+WLVuiadOmimXu7pUIUWCkwFm5ciUDwI4dO8ZatWrFnn/+eWndtm3bmMFgYFevXmXh4eGsR48ein0BsClTpkjPp0yZwgCwxo0bs6effpotWbKE/e9//2MA2IQJE9yey5gxY1j37t3ZBx98wJYvX85GjBjBjEYje/bZZ6VtDh06xDp16sQAsNWrV0sPrWJiYpjeX8W2bdsys9nM/Pz8GABWvnx5NnnyZJaZmal6DQAYANagQQN28+ZNad3bb7/NALADBw7oOr7crl27GAC2cuVKadkLL7zAOnfuzB4+fMjMZjP75ptvpHW9e/dmwcHBzGq1Ss/79evHPvzwQ7Z06VLWt29fBoC98cYbDtdcoUIFVrZsWTZmzBi2fPlytm3bNnbhwgUGgDVq1IjVrVuXzZs3j02ePJn5+fmxRx99lL399tusVatWbMGCBezVV19lHMex4cOHO7Tdtm1b6fnevXul35vo6Gg2f/58Fhsby4oVK8aaN2+u2HfJkiUMAHv88cfZggUL2Pjx41mpUqVY9erVFW06o/Z7bG/69OmM4zjWv39/tmTJEjZ16lRWpkwZFhERwf777z/GGGMHDhxgANjGjRsV+6alpbGgoCAWExMjLfvyyy8Zx3Gsa9eubOHChWz27NksIiKClShRgl24cEHaTvz7IcTX5CSO/O9//2Mmk4mNHDmSLVu2jE2cOJEFBQWxZs2aKd5j9bx3hYWFsSpVqrDXXnuNLVmyhLVv354BYDt27HB7Lc2aNWPDhg1j8+fPZwsXLmSdO3dmANiiRYukbbZu3coqV67MoqKipBj0448/an696tWrp+n9Sg4ACwoKYhzHMQCsTp06bO3atQ7bpaWlsZYtW0pxaNCgQUwQBGl9q1atWGBgIMvIyNB1fLlJkyYxAIr3r2rVqrEPPviA/fTTT4zjOOm9UhAEVrJkSdatWzdp28qVK7NXXnmFLVq0iM2bN481b96cAWDfffedwzXXqVOHlS1blk2dOpUtXryYnTx5Uvp9a9SoEevatStbvHgxe/7556XPGq1bt2aDBg1iS5YsYU8++SQDwL744guHtj39/NKvXz8GgD3//PNs8eLFrF+/fqxhw4YObaoRY+iHH37odBue51nnzp1ZsWLF2NixY9ny5cvZ6NGjmclkYr169ZK2e//995nBYGDXr19X7L9//34GgG3atElapvXvzD4+E1LQ5ST+aH0vevLJJ1loaCi7fPkyY4yx//u//2N+fn5sxIgRbs9PS+z68ccfWaNGjViZMmWkmLJ161bNr0GPHj1YeHi45u0Zs30eDgwMZEajkQFg4eHhLC4uzmE7nudZ7969pZjyxBNPsIcPH0rrBw0axABIr40nli9fzgCwvXv3Ssvat2/PRo0axc6fP88AsFOnTknrGjVqxOrUqSM91xK3xWuuUaMGK1myJHvrrbfYsmXL2N69e6X7kEaNGrGWLVsq7mEGDBjABg0axLp166aINVOnTnVoe+jQodJz8feycePGrH379mzhwoXs9ddfZ0ajkfXr10+x74QJExgA1rNnT7Zo0SI2cuRIVrlyZVamTBlFm84AUNx3qNESA7788ksGgP3666+KfS9evOgQt7TcKzHG2NChQ3X/bpLCie5wCyB5AF20aBELDg5mDx48YIwx1rdvX/bEE08wxtQTKM4+SL/wwguK7fr06cNKly7t9lzE48rNnDmTcRzHLl26JC3zJJmXk31feOEFFhsbyzZv3sy+/PJL9tRTTzEADm/kor/++oslJCQwi8WiWN6nTx8GQPEGqVdqaiozGo2KDx+1a9eWAlLz5s3Zm2++Ka0rW7Ys69Spk/Rc7TV+8cUXWbFixVh6erq0rG3btgwAW7ZsmWJb8SambNmy7O7du9Jy8cawYcOGiuseOHAg8/Pzc2hbLRFYp04dxc3pxx9/zACw06dPM8YYy8jIYKVLl2bNmjVTHGPVqlUMgFcSgRcvXmRGo5HNmDFDsfz06dPMZDJJywVBYJUqVWLPPPOMYruNGzcqkr337t1jJUqUYCNHjlRsl5yczEJDQxXLKRFIfJWnceSXX35hABwSWjt37nRYrve968svv5SWZWRksAoVKjj8vapRO06XLl1YtWrVFMs8SeblZN9WrVqxuLg49s0337ClS5ey+vXrMwBsyZIlDtvyPM8SExPZH3/84bCuZMmSrGHDhh6dt+j777+XvoxjjLHr168zAGz//v3s3r17zGg0su+//54xxthvv/3GACjeU+1f48zMTFa/fn3Wvn17xXIAzGAwsN9//12xXPx969KliyLJ2bJlS8ZxHHvppZekZVarlVWuXNnh9fb080tCQgIDwMaOHavYbtiwYV5LBK5evZoZDAb2yy+/KJYvW7aMAWDx8fGMMcaSkpIYALZw4ULFdq+88gorXry49Drr+TujRCDxNTm5j9H6XnT9+nVWqlQp1qlTJ5aRkcEaN27MqlatylJSUtyen9bY5UkyLyf79uzZk82ePZtt27aNffbZZ+zxxx93Wbhx5swZdurUKcV7LmOMNW7cmIWGhnp03qLff/+dAWDTpk1jjDFmsVhYUFCQ9AVO+fLl2eLFixlj2fdB8s/PWuN2eHg4A8B27typWC7eh9SvX1/xxcjAgQMZx3GKL7IYs8Ua+9fbWSKwY8eOitds3LhxzGg0SvdQycnJzGQysd69eyvai42NZQC8kgjUGgNSUlKYv78/e/311xXbzZkzR3EvrvVeiTFKBJJs1DW4gOvXrx8ePnyI7777Dvfu3cN3333n0Sx/9jNOPf7447h9+zZSU1Nd7ifvvpuWloZbt26hVatWYIw5zFCYlz777DNMmTIFTz/9NJ5//nl88803GDlyJDZu3IgjR444bF+tWjU0adLEYSBy8fqDg4M9Ppfg4GA0aNBAGh/j1q1bSEpKkmbOknfLOnfuHP79919Fqb78Nb537x5u3bqFxx9/HA8ePMAff/yhOJa/v79i4hG5vn37IjQ0VHoujj/13HPPKa67RYsWyMzMxNWrV91e2/DhwxXjBz7++OMAbIMQA7YxGG/fvo2RI0cqjjF48GCULFnSbftabNmyBYIgoF+/frh165b0qFChAmrWrCnNXMZxHPr27YsdO3Youotv2LABlSpVkl7z3bt34+7duxg4cKCiPaPRiBYtWqjOhEaIL9MTRzZt2oTQ0FB06tRJ8fcRHR2N4sWLK/4+9Lx3FS9eXDHukZ+fH5o3by69l7giP05KSgpu3bqFtm3b4u+//0ZKSorm18Hb4uPj8dprr+Gpp57CSy+9hISEBNSvXx9vv/22ojsVABgMBjRs2BC1a9d2aCc1NTVHMQgAWrVqBYPBIMWh+Ph4mM1mNGvWDMWLF0eDBg2kOGQ/PAWgfI3/++8/pKSk4PHHH8eJEyccjtW2bVvUrVtX9TxGjBihGBqkRYsWYIxhxIgR0jKj0YimTZtq+r8H3H9+2blzJwDglVdeUWwnHy8xpzZt2oQ6deogKipK8XfRvn17AJD+LmrVqoVGjRphw4YN0r48z+Prr79Gz549pddZz98ZIb5M732M1veiChUqYPHixdi9ezcef/xxJCYm4vPPP0dISIjbc9ITu/LS9u3bMWHCBPTq1QsvvPAC9u/fjy5dumDevHn4559/HLavU6cOGjRo4DAckzdiSp06dVC6dGkpppw6dQppaWnSvU2rVq2kWHL48GHwPO80priL25GRkejSpYvqeQwZMkTR3VqMKS+88IJiuxYtWuDKlSuwWq1ur23UqFGK1+zxxx8Hz/O4dOkSAGDPnj2wWq25HlO0xICQkBB069YNGzduVAynsWHDBjz66KOoWrUqAO33SoTI0WQhBVzZsmXRsWNHrFu3Dg8ePADP84oxZrQS3yhEYpLmv//+cxk0L1++jPfeew/bt293GLcmP2/A1Lz++utYsWIFfvrpJzz66KOa9hGv/d69eyhRooTHx27dujUWLlyIW7du4dChQzAajdI5tGrVCkuWLEFGRobqDdjvv/+OyZMn4+eff3ZIzNq/xpUqVXI6sYf9/7GYFKxSpYrqci3jELn6vQEgBc0aNWootjOZTG7HUtHqzz//BGNMMf6JnPwDQv/+/REXF4ft27dj0KBBuH//Pnbs2IEXX3xRCvp//vknAEg3cPa0fIgkxJfoiSN//vknUlJSUK5cOdX1N2/elH7W895VuXJlh5uVkiVL4v/+7//cnn98fDymTJmCw4cPO4wNl5KSovgCJD/5+flh9OjRUlJQ66DpISEhuHfvXo6OXaJECdSrV0+R7GvcuLF0Mya/aYuPj5cSsaLvvvsO06dPR2JiomKcJbXxfuWzD9vTE4e0joXn7vPLpUuXYDAYHM7LPi7lxJ9//omzZ8+ibNmyquvlfxf9+/fH22+/jatXr6JSpUrYt28fbt68if79+yva0/p3Rogv03sfo+e9aMCAAVizZg2+//57jBo1Ch06dNB0TnpiV37iOA7jxo3Drl27sG/fPreTiIhCQkI0f9Hi6titWrXCgQMHIAgC4uPjUa5cOel9tVWrVli0aBEA9S+X9MRtb8UUQRCQkpKC0qVLu7w2T+9tSpUq5bUiBz0xoH///ti2bRsOHz6MVq1a4a+//kJCQgLi4uIU7Wm9VyJERIlAHzBo0CCMHDkSycnJ6Natm0cJK6PRqLpc/u2CPZ7n0alTJ9y5cwcTJ05EVFQUgoKCcPXqVQwbNkwxSGxBIAYF+4kwXBEHZj99+rRU7eYJMREYHx+PQ4cO4ZFHHkHx4sUB2IJlRkYGjh07hoMHD8JkMklJwrt376Jt27YICQnB+++/j+rVqyMgIAAnTpzAxIkTHV5jVxOsOPs/9uT/3hv7eosgCOA4Dj/88IPq+YivMwA8+uijiIiIwMaNGzFo0CB8++23ePjwoeIGTHxNV69ejQoVKji0Z181SkhhoDWOCIKAcuXKYe3atarrxUSI3vcuT99L/vrrL3To0AFRUVGYN28eqlSpAj8/P+zYsQPz588vNHEoMTERmZmZOZrBvXXr1li2bBnu3r2L+Ph4qXIDsMWhzz//HBaLBQcPHkR0dLQ0S+8vv/yCp556Cm3atMGSJUtQsWJFmM1mrFy5EuvWrXM4jrfikNY4UlDi0COPPIJ58+aprpfflPbv3x+TJk3Cpk2bMHbsWGzcuBGhoaHo2rWroj0tf2eEFAZa44/e96Lbt2/j+PHjAIAzZ85AEAS3E0DpjV35zdOYcvLkSVy5csUhYaZH69at8e233+L06dOqMeXNN9/E1atXcfDgQYSFhUmTWuiN20X13kZrDOjZsyeKFSuGjRs3olWrVti4cSMMBgP69u2raE/rvRIhIrrj9QF9+vTBiy++iCNHjii6m+S206dP49y5c/jiiy8wZMgQabl89i+Ru1mC84L47ZeeD9A9e/bEzJkzsWbNmhwnAgHg4MGDOHz4sDRrFmCbRSw8PBzx8fFSlUaxYsUA2GbnvX37NrZs2YI2bdpI+1y4cMHjc8lL4eHhAIDz588rZkq0Wq24ePEiGjRokONjVK9eHYwxREZGolatWm6379evHz7++GOkpqZiw4YNiIiIUFSIVq9eHQBQrlw5dOzYMcfnR4gv0BpHqlevjp9++gmPPfaYyw/nefXe9e233yIjIwPbt29XfIuv1s3Fl+PQ4cOHsXnzZgwcONDjY7du3RpLly7FTz/9hJMnT+LNN9+U1rVq1QoPHz7E999/j7///hvPPPOMtG7z5s0ICAjArl274O/vLy1fuXKlx+eSl8LDwyEIAi5cuKCohlCb4d5T1atXx6lTp9ChQwe3v2eRkZFo3rw5NmzYgNGjR2PLli3o3bu34rXV+ndGSGGgNf7ofS+KiYnBvXv3MHPmTEyaNAlxcXEYP368y3PRE7t8OaZ89dVXWLNmDSZNmuTxseX3NvHx8Rg7dqy0Ljo6Gv7+/ti3bx+OHj2K7t27S+v0xO2CSH5vI69WvH37ttdmddcTA4KCgvDkk09i06ZNmDdvHjZs2IDHH38cYWFhivb03CsRAgA0RqAPKF68OJYuXYrY2Fj07Nkzz44rfqMg/4aEMYaPP/7YYdugoCAAtm/avO2PP/7A5cuXpeepqakOU8QzxjB9+nQAcDrOhJqWLVuia9eu+PTTT7Ft2zaH9ZmZmXjjjTfcthMWFobIyEjs2bMHx48fV3xrBthuwrZt24akpCRF6bzaa5yZmYklS5Zovob81LRpU5QuXRorVqxQjMuxdu1arwXLp59+GkajEVOnTnX4to4xhtu3byuW9e/fHxkZGfjiiy+wc+dO9OvXT7G+S5cuCAkJwQcffACLxeJwvH///dcr501IQaI1jvTr1w88z2PatGkO66xWq/Qen1fvXWrHSUlJUb0xDAoKypUYJI4bdevWLWmZ2vvEvXv3EBcXhzJlyiA6Olpz+y+99BIqVqyI119/HefOnXNYf/PmTSm+uSLGlnnz5sFisSjiUEREBCpWrIg5c+YotgVsrzHHceB5Xlp28eJF1ZhYEIkx3/53b+HChV47Rr9+/XD16lWsWLHCYd3Dhw+RlpamWNa/f38cOXIEn3/+OW7duqWoShfb0/J3RkhhoDX+6Hkv+vrrr7FhwwbMmjULb731FgYMGIDJkyervofaHwPQFruCgoJypauwxWLBH3/8gevXr0vL7ty5o7hucbtZs2bBz89P8WW7O88++yweeeQRzJgxA4cPH3ZYf+/ePbzzzjtu22natCkCAgKwdu1aXL16VRFT/P390aRJEyxevBhpaWlu722cxe2CqEOHDjCZTFi6dKliudgV2hv0xoD+/fvj2rVr+PTTT3Hq1CmHmKL3XokQgCoCfcbQoUPz/JhRUVGoXr063njjDVy9ehUhISHYvHmzaoJHvOl59dVX0aVLFxiNRgwYMMBp25cuXcLq1asBQCrrF290wsPD8fzzz0vb1qlTB23btsW+ffsAACdOnMDAgQMxcOBA1KhRAw8fPsTWrVsRHx+PUaNGoUmTJrqu88svv0Tnzp3x9NNPo2fPnujQoQOCgoLw559/Yv369bh+/Trmzp3rtp3WrVtL1ySvCARsicCvvvpK2k6+vGTJkhg6dCheffVVcByH1atX52l5ek74+fkhNjYWY8aMQfv27dGvXz9cvHgRq1atQvXq1TV/m3r+/HnVG93GjRujR48emD59OiZNmoSLFy+id+/eCA4OxoULF7B161aMGjVKkaxt0qQJatSogXfeeQcZGRkOwTIkJARLly7F888/jyZNmmDAgAEoW7YsLl++jO+//x6PPfaYV4M9IQWFljjStm1bvPjii5g5cyYSExPRuXNnmM1m/Pnnn9i0aRM+/vhjPPvss3n23tW5c2f4+fmhZ8+eePHFF3H//n2sWLEC5cqVU9xEAbY4tHTpUkyfPh01atRAuXLlnI4FCgAHDhzAgQMHANgSe2lpadL7UJs2baRqkV9//RVPPPEEpkyZgtjYWADA4sWLsW3bNvTs2RNVq1bF9evX8fnnn+Py5ctYvXq1ri6+JUuWxNatW9G9e3c0atQIzz33nBRTT5w4ga+++gotW7Z0207VqlVRpUoVHD58GBEREYpqAcAWbzZv3gyO4xQxqkePHpg3bx66du2KQYMG4ebNm1i8eDFq1KihaQzH/BYdHY1nnnkGcXFxuH37Nh599FHs379fSghojUN79uxBenq6w/LevXvj+eefx8aNG/HSSy9h7969eOyxx8DzPP744w9s3LgRu3btQtOmTaV9+vXrhzfeeANvvPEGSpUq5VB9rvXvjJDCQkv80fpedPPmTbz88st44oknMHr0aAC2JM3evXsxbNgwHDx40GkXYT2xKzo6Ghs2bMD48eOliZdcJTL/7//+D9u3bwdg+1ybkpIixZSGDRtK+169ehV16tTB0KFDsWrVKgC2iUKmT5+OZ599FpGRkbhz5w7WrVuH3377DR988IHqUDbOmM1mbNmyBR07dkSbNm3Qr18/PPbYYzCbzfj999+xbt06lCxZEjNmzHDZjp+fH5o1a4ZffvkF/v7+Dl9wtWrVCh999BEA5b2NnrhdEJUvXx6vvfYaPvroIzz11FPo2rUrTp06hR9++AFlypTRHFOOHz+uem/Trl073TGge/fuCA4OxhtvvAGj0aio6gdsFYF67pUIAQDkwczERCdxevNjx4653C48PJz16NFDsQwAmzJlivR8ypQpDAD7999/VY9x4cIFl8c4c+YM69ixIytevDgrU6YMGzlyJDt16hQDwFauXCltZ7Va2ZgxY1jZsmUZx3HM3a+WOC282qNt27YO1yRf9vfff7O+ffuyiIgIFhAQwIoVK8aio6PZsmXLFNPB6/HgwQM2d+5c1qxZM1a8eHHm5+fHatasycaMGcPOnz+vqY3ly5czAKxSpUoO606cOCFd340bNxTr4uPj2aOPPsoCAwNZWFgYmzBhAtu1axcDwPbu3Stt17ZtW1avXj2Hti9cuMAAsA8//FCxXHyNN23apFiu9vvVtm1bxWvsbF/xWPL/e8YYW7BgAQsPD2f+/v6sefPmLD4+nkVHR7OuXbuqvlZy4eHhTn8XRowYIW23efNm1rp1axYUFMSCgoJYVFQUi4mJYUlJSQ5tvvPOOwwAq1GjhtPj7t27l3Xp0oWFhoaygIAAVr16dTZs2DB2/PhxaRvx74cQX5OTOMIYY5988gmLjo5mgYGBLDg4mD3yyCNswoQJ7Nq1a9I2OX3vGjp0KAsPD3d7Ldu3b2cNGjRgAQEBLCIigs2ePZt9/vnnDjEsOTmZ9ejRgwUHB6vGEnvi37faQx5HxfdD+bIff/yRderUiVWoUIGZzWZWokQJ1rlzZ7Znzx631+PMtWvX2Lhx41itWrUUsW3GjBksJSVFUxsDBw5kANigQYMc1s2bN48BYHXq1HFY99lnn7GaNWsyf39/FhUVxVauXKn6/geAxcTEOOzv7PfN2WeQoUOHsqCgIIe2Pf38kpaWxmJiYlipUqVY8eLFWe/evVlSUhIDwGbNmuVwvnJiXHP2WL16NWOMsczMTDZ79mxWr1495u/vz0qWLMmio6PZ1KlTVf9/HnvsMQaA/e9//3N6bC1/Z/bxmZCCLifxR8t70dNPP82Cg4PZxYsXFft+8803DACbPXu2y+NqjV33799ngwYNYiVKlGAA3MYr8brVHkOHDpW2E99z5MuOHz/OevbsySpVqsT8/PxY8eLFWevWrdnGjRtdHtOV//77j7333nvskUceYcWKFWMBAQGsfv36bNKkSez69eua2pg0aRIDwFq1auWwbsuWLQwACw4OZlarVbFOa9x29hlEzz0MY+rxIjw8XPEaO9tXPJb8/95qtbJ3332XVahQgQUGBrL27duzs2fPstKlS7OXXnrJ6eslchVTpk2bJm2nJQaIBg8ezACwjh07Oj2ulnslrZ+9SOHHMeYjpUeEEJ8hCALKli2Lp59+WrUrFSGEEJKbEhMT0bhxY6xZswaDBw/O79MhhBDiw+7evYuSJUti+vTpmrpWE1LQ0RiBhJAcSU9Pd+hS8eWXX+LOnTto165d/pwUIYSQIuPhw4cOy+Li4mAwGBQTAhBCCCHuOIspAOjehhQaNEYgISRHjhw5gnHjxqFv374oXbo0Tpw4gc8++wz169dXTG1PCCGE5IY5c+YgISEBTzzxBEwmE3744Qf88MMPGDVqFKpUqZLfp0cIIcSHbNiwAatWrUL37t1RvHhxHDx4EF999RU6d+7sMA48Ib6KEoGEkByJiIhAlSpVsGDBAty5cwelSpXCkCFDpJnOCCGEkNzUqlUr7N69G9OmTcP9+/dRtWpVxMbGUvctQgghujVo0AAmkwlz5sxBamqqNIGI2uQfhPgqGiOQEEIIIYQQQgghhJAigMYIJIQQQgghhBBCCCGkCKBEICGEEEIIIYQQQgghRQCNEeghQRBw7do1BAcHg+O4/D4dQoiPYYzh3r17CAsLg8Hg2XcyjDG8/PLL+PPPP3Xv+8ILL2Dw4MEeHZfkDYozhJCc8kasAYD09HRkZmbq2sfPzw8BAQEeH5PkDYo1hJCcyM84A1Cs8RSNEeihf/75h2aiI4Tk2JUrV1C5cmWP9uV5HiaTCTPnlkJoCe2Bd9+eh/jj9+qwWCwAgJiYGMTExHh0DiT3UJwhhHhLTmJNeno6IiKL40Yyr2u/kJAQVKxYEQaDgeJMAUaxhhDiDfkRZwCKNZ6iikAPBQcHA7D9woeEhOTz2RBCfE1qaiqqVKkivZfkRJ9ng1Chova383upAjIeRGLbtm05PjbJPRRnCCE55Y1Yk5mZiRvJPH7/qwqCQ7R96XQvVUC96lfo/csHUKwhhOREfsUZgGJNTlAi0ENi6XxISAj90hFCPOaNbjic1QTOqv3tnBOMuHDhAurWrQuAKgILKoozhBBv8UasCQ4xIETHDRrxDRRrCCHeQHHGt1AikBBCiqDISKoIJIQQoh1nNYKzGjVua+ve1axZMxiNRvrCiRBCiFt64oxte4o1nqJEICGEEEIIIcTrjh07RlVmhBBCchXFGv2o7pIQQnycwWqAwaL9wfGc1DW4bt26WLx4cX5fAiGEkAJOT5wxWGy3GM2aNaM4QwghRBO9cYZijeeoIpAQQoog6hpMCCEkt1GVBiGEkNxGsUY/qggkhBBCCCGEEEIIIaQIoEQgIYT4OI7X94AA6hpMCCFEF92xBtRdixBCiHZ64wzFGs9R12BCCCmCqGswIYSQ3EbdtQghhOQ2ijX6UUUgIYT4OM7KYLDoePCMKgIJIYQQQgghpAiiikBCCCmCqCKQEEKIHoasL520bgvYumsZjUbExMQgJiYmN0+PEEKIj9MTZ8TtAYo1nqBEICGEEEIIIcTrqLsWIYSQ3EaxRj/qGkwIIT6OszJdD5oshBBCCCGEEEKKJqoIJISQIoi6BhNCCNGDy2TgMrV12RK3o+5ahBBCtNITZ8TtAYo1nqBEICGEEEIIIcTrqLsWIYSQ3EaxRj/qGkwIIT6OswKcRceDp67BhBBC9OF4fQ/AVqVBcYYQQogWeuMMxRrPUUUgIYQUQdQ1mBBCSG6jKg1CCCG5jWKNflQRSAghPs5gYboeHM+oIpAQQgghhBBCiiCqCCSEkCKIKgIJIYToYbDavnjSui1AA7gTQgjRTk+cEbcHKNZ4gioCCSGEuHTgwAH07NkTYWFh4DjOIYHIGMN7772HihUrIjAwEB07dsSff/6p2ObOnTsYPHgwQkJCUKJECYwYMQL37993edz09HTExMSgdOnSKF68OJ555hncuHHD25dHCCEklxw7dgxnzpyhGzNCCCG5hmKNfpQIJIQQH6d7UF2mb7KQtLQ0NGzY0Ol2c+bMwYIFC7Bs2TIcPXoUQUFB6NKlC9LT06VtBg8ejN9//x27d+/Gd999hwMHDmDUqFEujztu3Dh8++232LRpE/bv349r167h6aef1v36EEIIKfjoSydCCCG5ieJMtgKTCJw1axY4jsPYsWOlZZ988gnatWuHkJAQcByHu3fvum0nNjYWHMcpHlFRUYptCuJ/BCGE5KXw8HAcOXIER44cwfPPP4/U1FRkZGSobtutWzdMnz4dffr0cVjHGENcXBwmT56MXr16oUGDBvjyyy9x7do1KbiePXsWO3fuxKeffooWLVqgdevWWLhwIdavX49r166pHjMlJQWfffYZ5s2bh/bt2yM6OhorV67EoUOHcOTIEa+9DoQQQrTJ7Zkc6UsnQggp2nJ71mCKM9kKxBiBx44dw/Lly9GgQQPF8gcPHqBr167o2rUrJk2apLm9evXq4aeffpKem0zKyxw3bhy+//57bNq0CaGhoRg9ejSefvppxMfH5+xCCCEkH3BWDpyF0749z+HcuXMIDQ1VLJ8yZQpiY2N1HfvChQtITk5Gx44dpWWhoaFo0aIFDh8+jAEDBuDw4cMoUaIEmjZtKm3TsWNHGAwGHD16VDXBmJCQAIvFomg3KioKVatWxeHDh/Hoo4/qOk9CCCF5T89Mjt26dUO3bt1U19l/6QQAX375JcqXL49t27ZhwIAB0pdOx44dk+LNwoUL0b17d8ydOxdhYWEO7YpfOq1btw7t27cHAKxcuRJ16tTBkSNHKNYQQogP0BprKM5ky/eKwPv372Pw4MFYsWIFSpYsqVg3duxYvPXWW7pfHJPJhAoVKkiPMmXKSOuoyoQQQoBatWohJSVF8dDzhYsoOTkZAFC+fHnF8vLly0vrkpOTUa5cOcV6k8mEUqVKSduotevn54cSJUo4bZcQQkjBlpqaqng4qzx3x92XTgDcfumkxt2XToQQQgo+b8SaohZn8j0RGBMTgx49eihemJz6888/ERYWhmrVqmHw4MG4fPmytM7T/4iMjAyHXzBCCPFVBoMBISEhioe/v39+n1aRRnGGEFKQcRaAs3AaH7Z9qlSpgtDQUOkxc+ZMj45NXzp5D8UaQkhBpS/OeDfWFLU4k69dg9evX48TJ07g2LFjXmuzRYsWWLVqFWrXro3r169j6tSpePzxx/Hbb78hODjY4/+ImTNnYurUqV47T0II8Rpr1kMrIXuyEMD2hYyns2xVqFABAHDjxg1UrFhRWn7jxg00atRI2ubmzZvKU7ZacefOHWl/tXYzMzNx9+5dxfv1jRs3nO7j6yjOEEIKmytXrii6a9EXTvmPYg0hpLChWKNfvlUEXrlyBa+99hrWrl2LgIAAr7XbrVs39O3bFw0aNECXLl2wY8cO3L17Fxs3bsxRu5MmTVJ0obty5YqXzpgQQvJeZGQkzpw5gzNnznicBBTbqVChAvbs2SMtS01NxdGjR9GyZUsAQMuWLXH37l0kJCRI2/z8888QBAEtWrRQbTc6Ohpms1nRblJSEi5fviy1W9hQnCGEFGgWnQ8AHTp0wKOPPorVq1fnqPJc/qWTnPzLoZx+6eSs3cKGYg0hpMDSG2e8GGuKWpzJt0RgQkICbt68iSZNmsBkMsFkMmH//v1YsGABTCYTeJ73ynFKlCiBWrVq4fz58wA8/4/w9/d36EZHiq7/cesx1PBVfp8GIXni/v37SExMRGJiIgBbNWFiYiIuX74szfY+ffp0bN++HadPn8aQIUMQFhaG3r17AwDq1KmDrl27YuTIkfj1118RHx+P0aNHY8CAAdKgulevXkVUVBR+/fVXALYxOUaMGIHx48dj7969SEhIwPDhw9GyZctCO3g7xRkid7zL2zje5e38Pg1CcuTYsWM5/sIJoC+dvIliDZFjux7J71MgJMe8EWuKWpzJt67BHTp0wOnTpxXLhg8fjqioKEycOBFGo9Erx7l//z7++usvPP/88wCU/xHPPPMMgILxH0Fy12TjBulnK7P9O0vo71FbQw1fwQwOZsZJyUAeTFpvAQPPMennb/jnPTxrQrTheA6cVc+swfq6Bh8/fhxPPPGE9Hz8+PEAgKFDh2LVqlWYMGEC0tLSMGrUKNy9exetW7fGzp07FdXea9euxejRo9GhQwcYDAY888wzWLBggbTeYrEgKSkJDx48kJbNnz9f2jYjIwNdunTBkiVLNF8nIXlJTNoJQvbfYvPdM3LUlv3PzjTd9YFHxyGkILl//770xT2Q/aVTqVKlULVqVelLp5o1ayIyMhLvvvuu0y+dli1bBovFovqlU4cOHfDll1+iefPmii+dSpUqhZCQEIwZM6ZQf+lEfNftt/rCYMouljGYBITGbvOoLWFHQ3BGAQDAfq5n+9ecdT+TtVzO0OoPj45DSEFCcSZbviUCg4ODUb9+fcWyoKAglC5dWlqenJyM5ORk6T/r9OnTCA4ORtWqVVGqVCkAtoRinz59MHr0aADAG2+8gZ49eyI8PBzXrl3DlClTYDQaMXDgQAAosP8RJO+YOFsy8C2DLTloAcBnxT0LADG8WsHwKRug2Ne+CtDMOFg4BiM4KRloBgcwgOcYzODQy7haSgwCgJFl3yRSkpDkl8jISGzbtk3Ttu3atQNjzOl6juPw/vvv4/3333e6TalSpbBu3Tqn6yMiIhyOERAQgMWLF2Px4sWazpOQgsBgYFIy8NdO77jd3j5Z6EkVoLt9KFFIvIHjOXC8ti+dxO2aNWsGo9GoaSxa+tKJENfsk4AAkBLbG0Y/Kwz+Fhj8rbZHQCY4f+Xg0cZ+2WPyC9sb684CCIeisp+YHT8TGpol6WuQEBV64oy4PaA91lCcycYxV3d3eaxdu3Zo1KgR4uLiAACxsbGqg9muXLkSw4YNA2C7eRw2bBhiY2MBAAMGDMCBAwdw+/ZtlC1bFq1bt8aMGTNQvXp1af/09HS8/vrr+OqrrxT/EXr6aKempiI0NBQpKSlUUu8D5BWBIqs8+cekIQYUiUCRhXP8MzHLEnriejEZaMn6l+eY9LMzlAwsmrzxHsLzPEwmE/75pjoqltH+iW7Rpv/w2c8VYLHYfutzMlkIyT0UZ3yLfTJOXhnoisGQNx/DKBlYNHnjfURs47+fqyOkuLYeO6n3eZRs/xe9f/kAijW+47/JT0s/i4lAADD6WWEKyoDB3wIuKxkIWdKQM9rFGTMPmASpIhDmrMpAeYJPpSowe3/1uEXJwKIpv+IMQLEmJ/J11mB7+/btUzyPjY2VEnzOXLx4UfF8/fr1bo9DVSZFi1oSUE4tCaiFhWNSMtC+MtAMTkoAyn92ODbH0MW0Crusw3QcmZCc01MRSAjJPYLA5VkykBBCiO+TJwElRgGcvxWcSXk3o5oEVDx3kvDjZVMJyJOCTpKAACAcq03JQEJ8RL5NFkJIfjNlFWsYs/7VkgTkwZTjAapUCsqJ3YDNUFaG8BzLqha0Bdb25s+1nTQhhJBCR2v1YE7QxCMkx6wcYNH4sGZ316pbty59+U6Il6gmAbUSk4A5acMN4VjtXGubFAF64gzFmhwpUBWBhOQnI9STgfbdfuU/G8G5TQbay55IRICFE6T2WppX4LBlpP4TJ4TnlN/cusM4XZOFEEJc80aSTUtloDg+lGD1bEK1Xzu94/EEJoR44tixY9Rdi5BcxrnqxiuySwJK+1gMzqsCAdddhAkpICjW6EcVgaTQM3LZVX/OmN20YT8rsDQGoJvx/xzacZIEzMyqDGxuXq6rPUI8FRkZiTNnzuDMmTOUBCSkCBCrDg+3fy+fz4QQQki+sE8C2uEsdjdMatuJlVhqstYpJhYhhBRIlAgkRYa7ZCBgqwp0Rp4AFJ8Djt2Fne1rnwQU98uEAD7rkcnRt25EP87KgbNof4DPrgikMnpCCg53XYQFq9GjakCxXZ637RvfNlZ3G4SAN+h7gLprEeITLCopAXeVgPKEoPznrL9/9nM9754jKRr0xhmKNR6jrsGkSDFytslBvMWSNTEIoF4dyNt1G5ZXAgKQkoAAKAlI8hRNFkJI7snJmH/enDxEfh5iElDQM4wAITlE3bUI8Q75jMHOMKvRYbIQxnMOE4Yw3qBaFchcTATilDwBmMWhspCQXEaxRj/6NEiKHC2VgXqIlYL2D8ftHLsDi1WAmZwAC3hYdM1bTAghhCgJAic9RJQEJN6gp/JcTARQlQYhuYsz6CgksGqMAXrHBcyKLdLfvsWgXmVIiBt64wzFGs9RRSAhOvEck2YD1rOP9LNqkpCHFVQRSDwkK43XRKDJQggpyDypCnRWhUhJQJKfqEqDkNzHmXhpMimJ1QjYL/M2+ypASv6RfEKxRj9KBJJCYYppg67t1boImwFYkD17sAkcrLKknRkcLLAlAT1JBsplyroD660CrO23QPo5KfNVj8+BFG3UNZgQfbwxM7AeepKBepKAPG9Em4PvumxPPobgY/tjnW5HCCHEe25N6O9Q4eeQ4POUxWibOdhqkCYN0UzeZVil268nXYHZrkek2Yq59r/r3p8QkjOUCCQ+TW8C0FNGcIpKPj1JQDF5CM4Ai6zqj4cAP2YAOGhKBkb6x8GPGWCS9eiv57fI5T6/Z47WfJ6EEELU5XUSUCRP8KklBV2NRSgmAd0tsydPAhqMguosw0ajMmY13z3DbbukENDT3c9i+31t1qwZjEYjVZ4T4satCf092s+gIamnZ5xAt8zM+azBsrYBwNlW8iQgAMXEIopxCmXnZ2j1h/5zJb5Hb7dyijUeo0QgIR7ypCrQzAwAJ8APBmQC0kQhcpH+cQBsYwr+kzEeAFDZfx7MLob0NDuZ77i5eTkAwAgD/GCAMSskm5lBGq/wZ8sLuq6BFDzMagDTEzR56hpMiFb5lQS0Jyb9DAbm8WQkRiMPnjfiQOtp0nPAVvVnnwB0tr+cmJwUXyNnlStNvp/t0fkS30fdtQhxz9MkoFv23YO1VgXyBtfjBGpIBgKAsKOhlGzkupwGkJX0Mztu6ywBKFYjCsdqg4nJTLNyH7Ei0fjIObfnRAonijX6USKQ+CxvVgO6mk3YzDhYssb4E7sHA3DbRdh+xmBnxKpAAA7jBFb2n+d0P7Xknx/LTgYZsxKHfln/GsHZEpF22po/A5DdXfmwZaSm8ya+jboGE+KbtCYBxaSf2nJ77pKAzhKAimUuuq+d6DFR8ZwSg4QQ4j3yqkBmtQ1yZF8BqMa+KpCzcI4zB7uaSdgoALwBzMwU3YNVZyS2qwKUlrtJADL767BLAsrxp2vZfrAAxiaUFCTEFRrRk5Ac4jkmPbQygsuq0HP8E/RTSdbJqwHVJhXxYwb4MVt74sNPVgUYwIyqSUBSSPCcvgeDVBFIM2wRQgBbAjA3koCk8GCCAYzX+BBsnzloJkdCcpfu7r2WrC+I3M0gzBsckoDMyGRVeVn/KpJ3gmqyT1onb8vM1JOAZgaYmfJYgC0BqJIE9GR8QlJw6YozFGtyhCoCic8yZ92MWDzsIqXaJpQThsjZjxMoEisEzeCcJgN5qaLQADBI3XJFzqoC1boDm2BQVAPaJw7lFYAAXCYAxeuhasCihyoCCfF97sYNFKsCDUbB6azBzroBi/u7Ox4lAYkr1F2LkAJI7CIMWVWgxQCYBYeqQIeKvKxlHM8puggrqgKdJQOhUsmnUgXoQNaV2L47sOO1OT00KcQo1uhHJULE55k1zqroirNuwZr35xgsyH6I5N2I7alVBZphhAm26j77JKBapaBaV2CxbTMz6KoCVBuvkBAA4Hke7777LiIjIxEYGIjq1atj2rRpYCz7d/3+/fsYPXo0KleujMDAQNStWxfLli1z2a7FYsH777+P6tWrIyAgAA0bNsTOnTtz+3II0aygjA+oh7OZhu0Tfs4qAEW5lQQUrO4nLCEFlNWg7wGq0iAkN9nPMKyZWcN7tyJBB0VFHgBlZaDGqkStSUCHSkAZzsJRFWBhpjfOUKzxGFUEkkLBbGAeVQY6+9JI762NOF6gROU+TNzGVVVgZtYyU9Y29uQzBrsaD1AvHoJ0bOKDrEbbQyveoGuykNmzZ2Pp0qX44osvUK9ePRw/fhzDhw9HaGgoXn31VQDA+PHj8fPPP2PNmjWIiIjAjz/+iFdeeQVhYWF46qmnVNudPHky1qxZgxUrViAqKgq7du1Cnz59cOjQITRu3Fj79RBCNBErA10lAAFtSUBCtKAqDULykbsva7ImDdE1g7DYfQqyysAs9mMFynkjCehW1nnJz4kUDRRr9KOKQFJo6K0MtGZtrrca0AznwcWSVVUnTwqawUn7OJtYRErkMVu3X7EyUEz8iT+L61wxujg/QkTh4eE4cuQIjhw5gueffx6pqanIyMhQ3fbQoUPo1asXevTogYiICDz77LPo3Lkzfv31V8U2Q4cORbt27RAREYFRo0ahYcOGim3srV69Gm+//Ta6d++OatWq4eWXX0b37t3x0Ucfef16CSlsXCXnXK7TmQT0JsFqRNNdH+Ra+8R3UeU5KarczRjsjSpqxcQh8mpAVzMHu6KWoMuKLfYJP9WxALV0BybEyyjOKFEikBQqWpOBask/CzwbVkKeGDTDICUD3W1nT54MtG1jC/xiMlB8Lk4M4rCfh3/OmbLzre23wKM2SP5iVg7MYtD+4DmcO3cOoaGhisfMmTNV22/VqhX27NmDc+dsM7CdOnUKBw8eRLdu3RTbbN++HVevXgVjDHv37sW5c+fQuXNnp+edkZGBgIAAxbLAwEAcPHjQC68KIQTQl9jL7SQg4JvdrUkWi1HfA9q7a4mV54sWLcLZs2cxe/ZszJkzBwsXLpS2GT9+PHbu3Ik1a9bg7NmzGDt2LEaPHo3t27c7bXfy5MlYvnw5Fi5ciDNnzuCll15Cnz59cPLkSe+8JoQUMJxJOWOwliSg22pAnTdIYvLPZRUgoDoZSfY6fceksQELCb1xRkesoTijRF2DSaHjySQi3ogdiq6/Tphhm7GV55ht/D5OgB8MUjLOCAN4CFKizw/Z3YXVxghUm3XYU5asDtGR/nG4kDHWa+2SgqlWrVoO1Xr+/v6q27711ltITU1FVFQUjEYjeJ7HjBkzMHjwYGmbhQsXYtSoUahcuTJMJhMMBgNWrFiBNm3aOD2HLl26YN68eWjTpg2qV6+OPXv2YMuWLeB5mnyAEFdyo6tubiYB7f3a6R003z0jz45H8o/W7lryynMAiIiIwFdffeW08hwARo0aheXLl+PXX391OgTF6tWr8c4776B79+4AgJdffhk//fQTPvroI6xZsyaHV0dI3hOsBhjsK/nUEnl2CUG3SUCzekUfx3OqE4aoEtuyn5zKTQIQUJmUxAL9yUBkdQu2cBAORcHQ6g/9DRCfpCXWUJxRoopAUmiZDcxthSDPHJOAntwKybv+it1/xe7BFpXB/uy7CPvBscJPWmdXAajG02pAUnQZDAaEhIQoHs4SgRs3bsTatWuxbt06nDhxAl988QXmzp2LL774Qtpm4cKFOHLkCLZv346EhAR89NFHiImJwU8//eT0HD7++GPUrFkTUVFR8PPzw+jRozF8+HAYDPT7TEhB4Y2ko7xrmzir8eH27+W4XVLwpaamKh7OhqCgynNCbJjg/jOQYM3ehhPHATSy7J/teaM7sKuqCbXKP/nDxbbMyLQnGt1QjA3IG8B+rueVdknBpyXWUJxRoopA4pOm+63XvK2nE4noZQanmCVYMXmIynZiN2IjOPBgDpWB9uxn9VXbxtPxAeUThVggoEzAHNxKn+BRWyQf8AbHb19dYZyuyULefPNNvPXWWxgwYAAA4JFHHsGlS5cwc+ZMDB06FA8fPsTbb7+NrVu3St+yNWjQAImJiZg7dy46duyo2m7ZsmWxbds2pKen4/bt2wgLC8Nbb72FatWqab8WQooYTxJzRiMPntc/zpS3Kw/FJKAn50LyH+M5MI2D8IvbValSRbF8ypQpiI2NddieKs8J0UewGmAuZgWgnMGd05FUc1oN6KyrsLtkoKvZfF11A9ZJPE/ViUksHMAbaGZhH6UnzojbA9piDcUZJUoEEiLj6s+Vd1LZZz8xiDwZKLIl/hzf1MzMoJg9WKzsy3QyzqAatWpAs5sKQmfEhCAlAQu/yMhIbNu2TdO2Dx48cKjSMxqNEATb74vFYoHFYnG5jSsBAQGoVKkSLBYLNm/ejH79+mm7CEKIZq6SgWrdgr2VBBSrASkJWDRduXJF0V1LS+V5vXr1kJiYiLFjxyIsLAxDhw4FoKw8Dw8Px4EDBxATE4OwsDCnXzh9/PHHGDlyJKKiosBxHKpXr47hw4fj888/9/7FEpJLBKtRkewDIHUPFv/lTHx2RaB8W0/HBfRkEg+1ZKCWbsA6yLsuO8xSbJ8EtFAPk6JCS6yhOKNEiUDikyZnDshxVaC7cQGtWQk9i11ln1pXX8WxZMlANapdhbOqAkWeJAQVx8hK6OlJCFqzjuVqjENSNPXs2RMzZsxA1apVUa9ePZw8eRLz5s3DCy+8AAAICQlB27Zt8eabbyIwMBDh4eHYv38/vvzyS8ybN09qZ8iQIahUqZI0KcnRo0dx9epVNGrUCFevXkVsbCwEQcCECZSIJiQnBCdV8J5WBnqL/NiCnipm4rPEoSfcocpzQrIxwQDOoLwHUEsGclL3W8d7C4fKQGdJQK3VgM7O1ciUXXL1JhDN0DxYu8PkIyooCVg0aYk1FGeU6C+EkFygVv1nAVNNAorJOiM4p1177bsFa2XhBEXFoSsmuB+LkBRMjDeAWXU8eIPUNVjLbI4LFy7Es88+i1deeQV16tTBG2+8gRdffBHTpk2Ttlm/fj2aNWuGwYMHo27dupg1axZmzJiBl156Sdrm8uXLuH79uvQ8PT0dkydPRt26ddGnTx9UqlQJBw8eRIkSJbz+GhHiiaa7PsjvU/A6teo/j7oNOxuLSsa+GhCwJQAF3oDH9sfqPibJZ1YDYDVqfNg+T2idNTivKs+tVis2b96MXr166blyQnJNmTkbvNaW027BbpKArmip3tNT4ad5ZmDZOtUZiF2hJKDv0hVn9MUaijNKVBFIfJbeqsC8plYZ6HTcQFkXYXl1oHzcwJywcILL6kA/ZoCFK9jjGBDv0tM1ODg4GHFxcYiLi3O6TYUKFbBy5UqX7ezbt0/xvG3btjhz5oymcyAkvzTd9QGOd3k7v09DM2fVgHL5XRlIig6tswZT5TkpysrM2YBbE/q73U5eFcgZBHAmHoasLsFuuwXDeRLQ02pAOXmCj3MyxpvTmYHlVYF2yUFXCUB5t2D7Y7KsqnMaKbBo0BJrKM4oaUoEPv3007obXrZsGcqVK6d7P0L08DQZaFWJKVrSYO66BduTJwPlSUCLSnLPWTLQFeVsw/pDnXwfM4xS92DiY3J5shCijmIjUaMlEZfT9p2N35eTY/O8UbVaMDdQEtI3MUHHZCFZv4vNmjWD0Wh0G2cWLlyId999F6+88gpu3ryJsLAwvPjii3jvvezZpdevX49Jkyb9P3tnHh5Flb3/t6q6O4EQwiIIDFtQ9m3YBNRBFGQdQeA3LgRRYURmWAIRRNxYBAOOA7ggQUR0RMSvCjO4wSCyKkhkURg0yKIgiwgIMSxJd1X9/ui+lVvVtXZXJ53O/TxPPXTXcu/tJumTevs95yAjIwPnz59HgwYNdJ3ntJuDOM+PHDmCSpUqoV+/fnjrrbfKjPOcxZnyg1YM1EsPtovKGUhqCFqJgDQW7rsw0U0j8OmJgoauQTot2IEIyEhMnMQZcj5gL9awOKOGk2XZ8jeM53ncddddqFChgq1BV6xYge+++85RXvScOXMwdepUZGZmKq6TV199FStWrMDu3bvx+++/47fffrN8Q7Ozs7Fq1Sp8//33qFChAm688UbMnTsXTZs2Vc7p3r07Nm/erLru4YcfRk5Oju315ufnIy0tDRcvXrT1TScjttgRA+kagYVSMO6IcnH8oW9/ApQIR2oEEmGOFgONHH6qeTVCoJ4IqDqfSuUlcxZBUqUHk47BdoRAIyegn5OU8a8ggCJOgh8iApBQxEk4WjjBdJ2M6HDjM0QURXg8Hhyb3xG1q/hsX/fyZ6ewqaCNbUcgQ59Yx0YWZ+ILu67AWAuBBK0YGMm8WkFOKwSaNQyxSg0macFkbWQuSeSVx922PeVovQznuPE5QsY4t7gjKlewl0yUfyWA6g9/zT6/oqQk7sFYrIkvaDFQTwjkPSJ4jwTBF4AnpRB8xSLwyUXF4p9HLH7sFU2FwDChTekiTDXk0Ah4em4/5Rwi5Nms+adCJ03YrhsQALirHODnwF0VAD+vOAL5ft9EsBiGE0orzgAs1kSD7Xf5xRdftP3t0vvvv+9oEbm5uVi8eDHatGmj2n/58mX06dMHffr0wdSpU22NtXnzZowZMwadOnVCIBDA448/jl69euHAgQNISUlRznvooYcwc+ZM5XnFihUdrZkRXzhxBopUTIkkTinj2BABgfAUYS94lRhIN+fwQzJ0BgrgHdcKNBMBCUapx3WT5uHnwixH8zEY5Y1YxkZGfBFvKcK0M7CkxEe32XLzM0wMZDAsYHGm/KLnCiQiIJ/kBwSpOCUYUKcFU1iKgCYpwZzIhTcEMTineA6Y3mTpugRJmnAIZzUBjQ9Ja9qBH7DH/lgMRjnBVi7Zxo0bUa1aNduDfvrpp/jDH/5g69yCggJkZGRgyZIlqFq1qurYhAkT8Nhjj6FLly625167di0eeOABtGzZEm3btsUbb7yBY8eOYdeuXarzKlasiFq1aikbU5DLB7QIKLrsNifNQIyagtB4wSubdj8QLuAR55/goL+PVcdgO6nHjLKBHBAcbRA5R81CGPrEMjYy4hOr5iElLchJEhfVnCWVCmzGxq6J15AlUXEca2C/WQhDHxZnyh/a5iGyVPz3PO8Rg7UBBQm8R7LVtMkSrQioI8CZiYAqtGm+GpcfJ3KqsUzFRb978VRa0861sRixxWmcYbEmcmw5Am+55RZHg9588822zx0zZgz69++Pnj17YtasWY7mscPFixcBICyIvv3221i+fDlq1aqFO+64A0899ZSpK7CwsBCFhYXK8/z8fNfXyogddFpwQA7/4sgqLdhyfB1RzQ9Zt3uwFVrHIIE0DrErBuo1CNF2EDZrRKIVKRmJhZNmIQx93I6NLM4w4g3TeoRU0Xrtfu0YjPKL3WYhDH1icQ/GYk3ZgzgDg+JfyAUoSIAgqx2BVhilBCvHnbjwqM/2GNXy4/yc7lrdFAkZiQGLNc6JqGuwJEk4dOgQzpw5E9ZKuVu3brbHWblyJXbv3o3c3NxIlmGJJEmYMGECbrrpJrRq1UrZP3ToUDRo0AB16tTBt99+iylTpiAvLw+rVq0yHCs7OxszZsyIyToZJYMoF4uAbroBzdx/TpuLWOEzEedEyKYNQ4gIqHUCGqUbW9UyZDAYaqKNjSzOxD/xlBocC5w2DNGKgVoRkMFguIsb92As1pRtaDcgEQHp5iCcTlMOWeTV6cEEVcpwhCIgmZekB2tSfK1QpRXrdA4moh8RBK1EQNkrg4um9hODUU5wLATu2LEDQ4cOxU8//QRtnxGO4yCK9v6APH78ODIzM7F+/XokJyc7XYYtxowZg/3792Pbtm2q/aNGjVIet27dGrVr10aPHj1w+PBhXHfddbpjTZ06FVlZxfXS8vPzUa9evZism1GyGLkBjdDW/Asbz1YDEXUwNnLfOU3f1YqBWgcgPZ6ZG5BRtpBFDnLAvoNTlnjWNdhl3IiNLM4w4hEzVyAQnfgnigJu3Z7Y4moiQadhWZ8b/Jmx2zWYYY1b92As1pRdOF7tBlThNE3YpC6gKXpCnJ8DvHJ4rUATTM/T6SSsEgCZ0JewOIkzwfNZrIkUx0Lg6NGj0bFjR3z88ceoXbs2OC4ya+6uXbtw5swZtG/fXtkniiK2bNmCl19+GYWFhRCEyP+4HDt2LD766CNs2bIFdevWNT23c+fOAIBDhw4ZCoFJSUlISkqKeD2M+MNpDKEbdwCAIHMQOdlSGFTPGXv3nZV4SAuAxA1IOgYzyg8sNdhd3IiNLM4w4hUrMdDsOkb5hqVruYdb92As1sQ3Z7LuBe/gDl3PAQgg2DHYbYgYF+rIaygm6jj7AAvxz3BO/d1WdQs5QQp2DvZIgIMvyxllExZrnONYCPzhhx/w/vvv4/rrr49q4h49emDfvn2qfQ8++CCaNWuGKVOmRCwCyrKMcePGYfXq1di0aRPS09Mtr9m7dy8AoHbt2hHNyYhv/DG4ESHiHxEDTed3KPJpXXxuYeUA9EJQiYFXuUBM1sFwH1nkg3/s2EXimCPQZdyKjQxGSWM3FZiIepEIgmQeURTCHjMYDHuwOMMAgo4peIJ/0zuqD2iGGzX+KFcggOI0YQfYbkpCz6mHVwL8VJMV1jWYwQjDsTzeuXNnHDp0KOqJU1NT0apVK9WWkpKC6tWrK/X8Tp8+jb179yrz7du3D3v37sX58+eVcXr06IGXX35ZeT5mzBgsX74cK1asQGpqKk6fPo3Tp0/jypUrAIDDhw/jmWeewa5du/Djjz9izZo1GD58OLp164Y2bdpE/boYZRdtWrDdRiEADJuCRCMCGjn7BHCmtQAJRZBUmxbaDcgof6Snp+PAgQM4cOAAEwFdwK3YyGDEO3Y7FTM3YAIics42sE6ObsLiDEOWgl/8SgFe+R0DoKQFG7oDCf4onHF6opvJl9CORT27a6A3m+thXYPLEE7jDIs1EWPLEfjtt98qj8eNG4dHHnkEp0+fRuvWreH1qj2/boppOTk5qmK2pAjusmXL8MADDwAICntnz55Vzlm0aBEAoHv37qqxyDU+nw+fffYZFixYgEuXLqFevXoYMmQInnzySdfWzYhPhFC88NvQ9+yIgEauQPq5UQdg/TmdCXK0GOi09p+RCOiFgEDoWLLswTXJz+Hs1UcdrYvBKC+UVmxklG2cNuSIV5wKfcQByAsSJOpmbWPXZ1mdwASGpWtFB4szDIIsBQVAjufBCUKwWVNpL8oCJzUDdYVDu92BDQRAJT0YQTGQOQMTFxZrnGNLCPzjH/8IjuNUhWlHjBihPCbHnBSq1WPTpk2q59OnT8f06dNNr/nxxx9Vz7XFc7XUq1cPmzdvjmB1jHhmlm+la2M5dQKS+oD0Yzspw9HglYsDHi0gmomARt2BtXjAq8apXGEO8q88FsEqGSWF48K6LDXYFUoqNjISj1iIgWVZYNxwwxz02MniTLwjBQTbzWEkVsDdFVicYdAEO7VLuuVgwtyAfiFYJzDAK6nEKkQ+soYhTkrRRIqZAGgxv1VXYUZ84yTOBM9nsSZSbAmBR48ejfU6GIxSwexPJm1qrlk6rp3GIbRgRwt52mPRYlfwM0sJ9sk8wAFXQ8+ZGJh4sGYh0cNiIyPeoGvwlQXoWoHrOjyH3ruYAz3RYC6N6GBxhkHg+LJZyseJKxCA4xRkJ0gfdAQ/5GtXxmLEFyzWOMeWENigQQPl8ZYtW3DjjTfC41FfGggE8OWXX6rOZTDiCdEkBpH6gMQNqFefz6obrxMXIBH+tIKgXfycFPG1QLEIaNUpOFn2oIgringeRskgSxxkJ7VYmCPQFVhsZNDYSZWlG2S46eDTNt6IpTuQniuSOSSTGzomBjIYalicYdDwHhEcL4FP8oO3ahSi6Rosizy4kAOQ83OQvXKxKzDU6CMibDgLHYuB9Nj0OBqRUDZas7YWItU5WPy/ThDuynW+FgYjwXCsJNx6662qZh2Eixcv4tZbb3VlUQyGE5ykBQdMYpBWBPRDNnT3mWHUOER/TsmRG5B2JZpdW8RJYRu9H7AWAbVUrjDH0fmM+IY1C3EXFhsTm697P46ve7M6dnqCI9nMrnHSIfjTds9HvD5GjJH44I25nU0K3mKwAu7uweJM4nMm617d/RwvgfeIEHwBCEkB8B4JMBPWvKXkCneSlkuVuFTqA9LXh0RAzs8pmxbtfrtpweKKLvbXyShZnMQZFmuiwrEQSOpQaDl37hxSUlJcWRSDYRe7IqAoB0VAf+ixUTd7PddfpIKgIKtFOzI+2UzXG8F8hCJOgh8irsCPK/DDDxF+iCoBkIiAAYs04qtcQPWciYHlk4YNG4LjuLBtzJgx+PHHH3WPcRyH9957z3DMgoICjB07FnXr1kWFChXQokUL5OTkuL52v9+P48ePIy8vT/cGyi1YbExc7AiAdhtnlGTKrhPxza359DYaMzcgIzHJzc1lXzi5BIszicuZrHtti4BCsh98kh8c5QhU1QfUEwED1GevUedgt2rrGXT0DWsGYnQzpjsmH77RYxsIhUotxQAP+AVn2TOMMgWLNc6xlRoMAIMHDwYQLEr7wAMPICkpSTkmiiK+/fZb3Hjjje6vkMEwIDvpHcDEfecP3ZjRImA0+CHruv30moTo1QqMRtwLm1OnWUgRJNP6gLQDkBYAA5Dgob4TCEAKiYnhY7E6gfGJHOCdFdaVeBz90X5qcG5urqoI+f79+3H77bfjL3/5C+rVq4dTp06pzn/11Vfxj3/8A3379jUcMysrC59//jmWL1+Ohg0b4r///S/+/ve/o06dOhgwYIDt16LH77//juXLl2PlypXYuXMnioqKlBuounXrolevXhg1ahQ6deoU1TwAi42JjpUI6LRzLiMIEQj1xMq+eyaV9HIYjLiGxZnExkgABNQiIO+RguKfEHQDch4xKADSKcJGTkC9ZiFOMBAJw1KMtddEk25suSadc7ySsdBJcPD3MoORyNgWAtPS0gAEv41KTU1FhQoVlGM+nw9dunTBQw895P4KGQwNQQHQHK0ISDCrEwgEU2/NBDsjMdB0LZwUncMPEnzOzbsqcY9GzwVIxEBaBLzKBVQNRZgImFg4aRZSo0YN1fM5c+bguuuuwy233AKO41CrVi3V8dWrV+Ouu+5CpUqVDMf88ssvcf/996N79+4AgFGjRmHx4sXYuXNnVELgvHnzMHv2bFx33XW444478Pjjj6NOnTqoUKECzp8/j/3792Pr1q3o1asXOnfujJdeegmNGzeOeD4WGxMXMxEwGgGQuAJL2rHnBnRzDyvsuv/o81h9wPhGCvCQAjb/XwPF6Vqsk2N0sDiTuBiJgFKAB+8JioDBf0N1AZMCwTqBevUBSyEd2LBGn9vYEfgA1TlaNyBBGLrD7dUxXMRJnCHnAyzWRIJtIXDZsmVK6/qXXnrJ9AaPwYgVtAgoysY3YkTwo52AViKgV+aUOoFm2BED9VKJi0ICnJWopxUNIxEBvSgOeHq1AHU7BsvGnYSZCJh4SJKE/Px81b6kpCSV00CPoqIiLF++HFlZWbopSrt27cLevXsta3TceOONWLNmDUaMGIE6depg06ZNOHjwIObPn+/8xVDk5uZiy5YtaNmype7xG264ASNGjEBOTg6WLVuGrVu3RiUEstiYeJRULcCSSBN20jSE58PjViSCJy3sWdUOJAREnomACQrr5Bg9LM4kJmZOQN4jhdyAEgRfAJwgKcKgqjagVcMQwJkbUM/FZzdlOMKmIbr1AU2QnZSYCNDCIMeahCQwLNY4x5HCIMsy3n777bA0MAYjniBuQCciII1gIPKJnKx0BbZTN1DkZN30WiIIRuMSNGsw4tN0E/ZCUAmDyhiQ8DtXBD+CDsBLXEB5TLsBmQgY/8gS72iDzOHgwYNIS0tTbdnZ2ZZz/fvf/8aFCxfwwAMP6B5funQpmjdvbpmm9NJLL6FFixaoW7cufD4f+vTpg4ULF6Jbt26RvAUK77zzjqEISJOUlITRo0djxIgRUc0HsNhY3kikdGCel5XN6LgWM2HRSAQ0EgRZzUAGoSzXoi0JWJxJLE5nDrN9LidQKcFkH0kLhqY+oBZKBCQdg+G1KQzq1fqL8jM7ks7BiuvQaN0BXn9jMHRgsaYY245AAOB5Ho0bN8a5c+eiclAwGLHCT92gkcYgenihrhnoAYeARpjTq/MHBAU+0giEuAONznUbEbIiVGrFQB94FAEQIcEn84buPppgF+Gi4jFCIqKdaxllmyZNmmDnzp2qfVZuQCAo9PXt2xd16tQJO3blyhWsWLECTz31lOU4L730Enbs2IE1a9agQYMG2LJlC8aMGYM6deqgZ8+e9l+IDkeOHEF6erquYzEWsNjIKAl4XnZNhDQS/tzASAA0cwd6LFwkjPhADvCQbdbXkgPB/1O76VplrRZtScPiTGLB23Rqc3zw94gngl6oNqAlNlyAhvX93GoaEik6YqPslZVmIJwg6bsCSfovSZEmYqC/7JXhKM84iTPB81msiRTHcvmcOXMwefJk7N+/PxbrYTBsYZYWDKjrApqh/ZjxhsY1cgXqEUlX4WhcgXpdh8l6feAh2Py11nYEBogwyG7IyhqSyCs1NexsssThp59+QpcuXdClSxe89dZbqFy5sqUQ+NNPP+Gzzz7DX//6V93j77//Pi5fvozhw4ebjnPlyhU8/vjjmDdvHu644w60adMGY8eOxd13343nn38+4veB0LhxY/z666/K87vvvhu//PJL1OOawWJj+aC03YBm7j0nY0SLnitQ6+6jhT9J5HU3Qo+dzHmeqGzYsAE7duzAfffdh/z8fBQWFuqeV6NGDdSqVUvZPvroI6UWrSAIqmO1atVyXIu2YcOGGDVqFNq2bRv2JVhZgcWZ8gkR/vjQv7QbEEB4J1wdEZBz8mWLV468yYd2HAojN2AkLkEFPbHPL6g3RrmAxRrnOBYChw8fjp07d6Jt27aoUKECqlWrptoYjFhhp0mIXcw6CHspkZGuBShQ+0UbtQStsCMGFkFSztNiJAYGH5v/auulLDPKF+np6Thw4AAOHDhgu6jusmXLULNmTfTv31/3+NKlSzFgwICw5iJa/H4//H4/eF79cyoIAiQp+p9NUkuJ8Mknn+DSpUtRj2sGi42MksSJmBdtU5JIhEOtCEgIsFTgMo0s8o42AKhXr57jEhSkFu2IESNMa9GOHDnSdBxSi/bEiROQZRkbN27EwYMH0atXr8jeAAP8fj+OHz+OvLw8nD9/3tWxaVicKV+QRiEAVJ2CDdERvThBciYC0rjYCMSW2EfmM1uvRVqzLHKqjVH2cBpnykusiUWccZQaDAALFixwZWIGI1KM3IB0p2A3IB2E6bRfQeYUEZAWAwULh6IWERIE8EpHYDrll+5cbKexiFoA5OADjysId/tpYc4/hhMkScKyZctw//33w+MJDx2HDh3Cli1b8Mknn+he36xZM2RnZ2PQoEGoXLkybrnlFkyePBkVKlRAgwYNsHnzZvzrX//CvHnzYv1SYgKLjYzSxG4331imBOtBRMCARgxkqcDlh+PHj6sKuNspQeFmLdpRo0ahbt268Hg84HkeS5YsiboWLQD8/vvvWL58OVauXImdO3eiqKgIsiyD4zjUrVsXvXr1wqhRo9CpU6eo5yKwOFO+4D2SukmIQyIWAGm8sjpVWJDCUneVFGOgOM24pLoJUyjCX0BQmqiQfaZ1FBkJQSLGmljHGcdC4P333x/RRAxGvGDmBiTY7SBMiMQhSMTA4ueyo5Rk+jqg2BFo5CD0QtDtIMwo+8gBwVk9DZHH0eNH0aJFCwCwrKcBAJ999hmOHTtm2Fzj9ddfV4KSHnl5ebh48aLyfOXKlZg6dSoyMjJw/vx5NGjQALNnz8bo0aNtvw4jSGFf7b5YwmJj4lPaacGSxKmEvEhqBmrHKAmYC7B8U7lyZcedHOO9Fu28efMwe/ZsXHfddbjjjjvw+OOPo06dOqhQoQLOnz+P/fv3Y+vWrejVqxc6d+6Ml156yZW6fizOlD+UJiF2awPawc/bbxhigVK3jxYD3UBHcFTNS9cJpJ2Q5G9hSgxUhmQdgxOaRIs1JRFnHAuBACCKIv7973/ju+++AwC0bNkSAwYMgCCwPHxG7JhaeK+j9GA9Z6AdEZDGyhVoeq3MARwPyAA4KdTMQ7+LMHH+ETGQiHpOagg6OVevPiCjfJGeno5///vfts/v1atXWMotzbPPPotnn33W8Lj22lq1amHZsmW253eCLMt44IEHlG8Dr169itGjRyMlJUV13qpVq1ydl8VGRqwxE/LMXIGiKJh2+wWgcpxIDr5YiATmCiybkDqzds8F7BdwJ5BatEafz05r0a5evVopZ9GmTRvs3bsXzz//fFRCYG5uLrZs2WLYof6GG27AiBEjkJOTg2XLlmHr1q2uNfhgcab8UNwopPiz2TUx0AlaV6ABKjHQzymuQEc1AOm5LMRAS0JiIHMDli2cxBlyPpB4saYk4oxjIfDQoUPo168fTpw4gaZNmwIAsrOzUa9ePXz88ce47rrrnA7JYNjGqkkIjcC5lybsNnYbelgJgkbuP7O03yJOQrLsARBg6cEJAl0jw94FHI4edeYILEtoXRPDhg2L+ZwsNiYOHdc9i697P67aV9puQBqyFjedfZGknZlhlapMi4A8EwQTmtzcXEcujbJQi/add+x9KZ2UlOSKy53A4kziUHPeOzidOcx29+BIkEXenfRgp2i7ELuE0jnYKwVdjQSvqN8UpDREU0apkWixpiTijGMhcPz48bjuuuuwY8cOpTDtuXPnMGzYMIwfPx4ff/xxRAthMOzwZNE9mOVbaft8J2KgBxwClOBG0oP1XIFh84QESuIUJOfadQXSRJoiXHx9cHyjNGCfzAMcANmDIq7I+BwAZ68+GvE6GPGNU0dgWSJWTkMzWGxMLPTEwHjBLD3YTq3AsBRjnZsl3iOqXIGRpCETPIJkmB7MREAGTVmqRXvkyBGkp6fHvOwEDYsziUWtF5YbioHEDSgHBEgBAbzgIJMnwOt2DtZDcfG5JN4ZuQIjhqwpFEMUMZDgkYo7B+uth7kBGTqUlVgT6zjj2G+7efNmPPfcc6ruVNWrV8ecOXOwefNmVxfHYOjxZNE9pscFDvBwgJd6TvDqXlGMBxw8JiKc10KgE2ROEQXJuYLMwQseXplXmnlosRIHVXNQa9Ady8Ll5wEPn8zDG/qXwWBED4uNiUs8uQH1XIB2nYFOuwdbuQS1qcZE1KP3k30sDTgxkGUesmRzk4vTtVq0aIGFCxdajh+LWrSdOnVCRkYGWrRogTlz5rhWi7Zx48b49ddfled33303fvnll6jHNYPFmcSj1gvLDY/JUijbQ+SBOO2Aa1gXkPryh3Nj7doY4tV0Q/ZapAB7mTuwrOAoziR4rIl1nHHsCExKSsLvv/8etr+goAA+n8+VRTEYVthxBno4ALJ1XUABCPPOEXeg1hXoFK2L0Cvztp2BkSBCgk/mUcRJps1BPKH5rVKE6ybNw8+FWa6vk+EuksQp3Tntnp/IqcFXr17FggULcOHCBWRmZqJ27doxn5PFxsSj47pnsfP2J0p7GbagHXtOXYHE+Wcl/Om5As3mIsd4QXL0+cRILJyka5W1WrQ0n3zyCbKzs2MyF4HFmcSk5rx3cCbrXtU+WeIRuOoFx0sQr3oBQYKQpOMKDNXBk0UuKIT5haDoFXIF2kkPduwK1NTu07r0XHcF0vMCkMErKcKhW71iV6BeHGMiYLkgEWNNrOOM47/M/vznP2PUqFH46quvIMsyZFnGjh07MHr0aAwYMMC1hTEYVpg5A7UuQCtXoNsllgVNLUPiCjSDFgZFyKqNRvtczxVoB+IK1NsPICQSBsVARuKRnp6OAwcO4MCBAwklAgLAyJEj8cMPP6B69epRFYR3AouNickN62e7PibPy2GbW+MSjBqDmAmEUigFjWxAuDiot1Y9ByDZRx8zcwVuufkZw2MMBiMIizPlC1niIRZ5FFegXOiBHEEjJ90a0n6TewcbjUFsCYaRugKthENBUjkROUFSUqF1XYGh+oHSmnb218BglAMcKwgvvvgirrvuOnTt2hXJyclITk7GTTfdhOuvvx4vvPBCLNbIYNjCq7lBISnC9HMztKGVpAh7Q6KeWd0+vS7CVinCsUDbhMQLQbVFgpWAyWDEGxs3bkRWVhYmT56MH374AWfOnIn5nCw2Ji6xEAO1uCUI2hEDCVYpz26JgfQ+JgaWbUg3R7sb4CxdqyzBcVxY3aZY1wtkcSZxqTlPvzGAFBAgFnkgXvVCKvQqQqBMC2vafaRxBlU7z1AMDAmCiqOPnKcVA/XEOZPPc1Udv9Bjx2Kg0RaaW/bKwcYhyjXWzj/pk7b218AoFZzGmUSONbGOM45Tg6tUqYL//Oc/+OGHH/D9998DAJo3b47rr7/etUUxGG5iN0VY91pNAxEzRE7WdQKKnH6jEZIeTJp7CKHnkTj8tKnGJD1YCxED/RCV9GCz8wnpSQtwtHCC43UxSgZJ5B2l3skJnhp8yy234IUXXkCTJk1Qv3591KxZM+ZzstiYuLiZHhyt2Kdt9GE0h1t1DaWAAN4j6jYPIesh0GnCdDqwNkXYTAz84pbpuGnzdFfWzogPnHZyLCvIsowHHngASUlJAIIlKUaPHo2UlBTVeatWrXJtThZnyidSgIdYGLplD31+8sn6zf6MUoSDxyiHHv057OeDabbaFGGnab2qTr5Ukw9qLk7kIEfbwENZkxRME0YoRdikaQiN9Elb8P2+iW4NjLgjEWNNrOOMYyGQ0LhxYzRu3DjSyxmMmODlZfjpGxODrsFe6AuDevUCzSDinlUTETuIkCzFQJXYpznHqu4gSfk1qh9o1TiEiYGJRSJ3DV66dCkWLFiAX375BRs2bCjRuVlsZESDmyIeoF/DTxQFxa1nJSzSbkDy2KybsHY+7XNWL7BsIwcESIK97AI5EPx7pFOnThAEIeG+cLr//vtVz4cNG1Zic7M4U36QJR5SqDQg75EgFXrAe8SQM5BqkBGqFQiYi4HKuKHPYUUQtCMGeuVwp6CmXiCNUa1APTGQfu7IOShIxQ5Icr0NoZGJgfGLkzgTPD9xY02s44xjIVAURbzxxhvYsGEDzpw5A0lSf7B8/vnnri2OwSgN9MRA0jTECpIiTDsDiSvQDkQMjAYBvKppiBbt/mTZg6ucTgFiHZgYGJ/IlDXe1vkJ7gisWLEiHn/88RKdk8VGhhVu1QIsbfTcgXpiIBH9iOhIuwUBGAqCzBWYWCSiSwNAzJqQmMHiTOJyOnMYeEr7kAI8eEq8IynCnCAFndqCDI5uEgIoKcK6zUMMUDUTIWnCkJyJgQSvVOwKDAmLkSALsj0xMLQW2SuDgwSI5HW4XfmdURZIxFgT6zjjWAjMzMzEG2+8gf79+6NVq1Yxr4fBYMQKI1egHbTintYZqJcmrCcGBl18QQGQiHM+GShCZE1AiCtQKwZq3X4+mQc4wCdLCPrp1WIgaRTCSFwS2RFYGrDYyDDDiQjohivQqosw7Qo0HMOki7BTMVBvHbydYvMMBkOBxZnyjfYLXzkghIuBQHgnYUDXFWiGys2nRUcM1HYOBgD4g2m7MsIFRVdShMlaSIqwXyouwmRDDJTXtQbXe1/0a2AwyiiO7/ZXrlyJ//u//0O/fv1isR4Go0RxmiJMmnxo6/0RVPu58P1+KnVXABfWAZhGL0WYPC+CZJhCrCcGGuEBD8hQxEArmBuQEe+MHj0aTz75JOrWrWt57rvvvotAIICMjIyo52WxkWGE205AO3UCS2I8u2Ig/VhPlGSUHWSJhyzZ+5KSnJeI6VpAsFbTggULcOHCBWRmZqJ27doxn5PFmcRGEgXwOl/QyBIPjtfU+RM5yKG632ZiIABnrkAtRvUCaTGQTg8OuQJNx7SBbVcgWYsYnDtUFt4WTASMT5zEGXI+kJixJtZxxrHlyOfzxaQo7Zw5c8BxHCZMmKDse/XVV9G9e3dUrlwZHMfhwoULtsZauHAhGjZsiOTkZHTu3Bk7d+5UHb969SrGjBmD6tWro1KlShgyZAh++eUXF18NI17R6xzstbhG2z3YCpGTIXIyrnKSsqncg5p0XR94COBVNfxETa0/AZyq07CVW9COm9An8/BCgCc0t153YHofEwHjF0kSIIn2N1nildTgROuwVaNGDbRs2RL9+vXDokWLkJubixMnTuDcuXM4dOgQ1qxZg0cffRT169fH/Pnz0bp1a1fmjVVsZJQ+JdE1WEtJpBG7IciZuQaVczRdhM2ciDdtZmnBiUZubi4OHDiQMDdmhJEjR+KHH35A9erV0bNnzxKZk8WZxKXWC8vD9jkp+QJoOgmb7OMEKWyLCI1jkHYQ0unGnJ8LqyOocgN6YX0zZgXpImxF6D1l9QETj0SMNbGOM46FwEceeQQvvPACZNm9P1Jzc3OxePFitGnTRrX/8uXL6NOnj6NaT++++y6ysrIwbdo07N69G23btkXv3r1x5swZ5ZyJEyfiww8/xHvvvYfNmzfj5MmTGDx4sGuvh1FyPFl0jyvj6MUfo1skARy84CDIXFiTELNagMQN6KUceoLmeq17T9v8w4kYGDzfQjC0IQYCTARMRNLT03HgwIGEC5rPPPMMDh48iJtuugmvvPIKunTponQObtq0KYYPH44jR47g1VdfxY4dO8LiTqTEIjYyGEa41VCEiIFujWdXwKTFQPK427anXFkDg1ESbNy4EVlZWZg8eTJ++OEH1X1GrGBxJrGxLQZSopocEEKNQ2zgUFgMS/V1A7MxIxUD6TG9IWHTJA2aiYCMskKs44zj1OBt27Zh48aN+PTTT9GyZUt4verfWqftiwsKCpCRkYElS5Zg1qxZqmPEHbhp0ybb482bNw8PPfQQHnzwQQBATk4OPv74Y7z++ut47LHHcPHiRSxduhQrVqzAbbfdBiBYiLF58+bYsWMHunTpojtuYWEhCgsLlef5+fkOXiUjljxZdA9m+VbaOlevgzDBTs1AvaYhXnCGqcIEv0bQ88q8yhlI1wosrutXLOSJkBUR0Cql2C2IKGjVTZjBiDeuvfZaPPHEE3jiiSfw22+/4dixY7hy5QquueYaXHfddTGpq+RGbGRxJn65Yf1s7Lz9idJehgoi3jl1D5KafcSpR1J23U45ptHrFmxVo5ARf0gBHpJg728CImAkYroWANxyyy144YUX0KRJE+XLpljj1j0YizXxS8157+BM1r2qfcHGIQBdtEgKBAsAIeSsI/UCDaHSgx2n7RqlBwPFKcI66cHFc2tqBYatDboioGV6sBOhMiQMMhEw/nESZ8j5QGLGmljHGcdCYJUqVTBo0CDXFjBmzBj0798fPXv2DBMCnVJUVIRdu3Zh6tSpyj6e59GzZ09s374dALBr1y74/X6VvbJZs2aoX78+tm/fbigEZmdnY8aMGVGtj1F28YBDwIb4ZtQhmIhqtCBIxEBa2NPW9RMhRdw4RFlTaEwzvMT/SOoFhmAiYNlAFrniujF2zk/wrsE0VatWRdWqVWM+jxuxkcUZRiQYCXhWDUMkkY+bhh1MFExcErGTIwAsXboUCxYswC+//IINGzaUyJxu3YOxWBPf6NUKlAI8BJ/++ZYiII22aYi3OIVXwajjr43UW92mIXYwcGKYioFmHYyB4Ot02CSFUXZJxFgT6zjjWAi028b4iy++QMeOHZGUlGR4zsqVK7F7927k5uY6XYYuZ8+ehSiKuPbaa1X7r732Wnz//fcAgNOnT8Pn86FKlSph55w+fdpw7KlTpyIrK0t5np+fj3r16rmybkb8oHUFGjUNUY6HhD87rsDg+OGCoBGk2y8tBkbqCrQjBhKY+Fc+cNo1+MSJE5gyZQo+/fRTXL58Gddffz2WLVuGjh07Kud89913mDJlCjZv3oxAIIAWLVrggw8+QP369XXH7N69OzZv3hy2v1+/fvj4448dv6bSxI3YyOIMo6QhYqCeK1AKCLZqAKrGiyDFmImAjLJIxYoVHZUucgO37sFYrElcwpqG0Jg1DdET/uyi5wo0QuQBhJp6aOsEAtapWUZzG0GJgNE0L2EwSoNYx5mY3fH37dsXJ06cMDx+/PhxZGZm4u2330ZycnKsluEaSUlJqFy5smpjJCbR1qt1NFdIdBPA6br+ijSNReyKf9ragsE5rMfX4gGPvKLxtuZklB6SyDvaZJlz1Czkt99+w0033QSv14tPP/0UBw4cwD//+U+V0+7w4cO4+eab0axZM2zatAnffvstnnrqKdPP91WrVuHUqVPKtn//fgiCgL/85S+uvTfxhllsZHGG4TYl0XTEDtq0YAITAcsWTppSSSEHaqdOnRKuKVW8Y3UPxmJN/HI6cxgAKL8/ekgBHhD5YNd2u511Y42VW5A0DdG91vxS2UjYdAATAcsOTuMMizWR49gRaBerQra7du3CmTNn0L59e2WfKIrYsmULXn75ZRQWFkIQnHW0u+aaayAIQlgH4F9++QW1atUCANSqVQtFRUW4cOGCyhVIn8Ng6NUL1KYHG7kABYPuwkaNRLT1AmmIOy+a1GA7+EO+Rw94BEIioifGczJKFyeOwLlz56JevXoqN0J6errqnCeeeAL9+vXDc889p+y77rrrTMetVq2a6vnKlStRsWLFhBYCWZF3RklDUoT10oOB+EgRLu35GbEjEdO1Ro8ejSeffBJ169a1PPfdd99FIBBARkZGCawsCIsziQGdIsyXlfRWIVgLkIOmTqAdnLoBTeAESSmZo4iA0bgeGXFPosWakogzpXan36NHD+zbtw979+5Vto4dOyIjIwN79+51LAICgM/nQ4cOHVQ51JIkYcOGDejatSsAoEOHDvB6vapz8vLycOzYMeUcRtnG67IDQvuT6JU5VfdeIvxpOwibYdSdl0aboiuAC+sybHitjfHN3IBMBEx8JElCfn6+aqOLh9OsWbMGHTt2xF/+8hfUrFkT7dq1w5IlS1Rjffzxx2jSpAl69+6NmjVronPnzo5Sj4FgLYx77rkHKSkp0bw0BqPM41YXXz2MRDddkdCiG6XZcb3x9LoFMxGw7CBJnH33eehnOBFdGjVq1EDLli3Rr18/LFq0CLm5uThx4gTOnTuHQ4cOYc2aNXj00UdRv359zJ8/H61bty7tJTPKKLQz0EoMJN2DZScuQQuxTuXiCz2WBVnZVNioIai/BkQmAvo507RglQuQiYBlBkdxJoFjTUnEmZg5Aq1ITU1Fq1atVPtSUlJQvXp1Zf/p06dx+vRpHDp0CACwb98+pKamon79+oqTpEePHhg0aBDGjh0LAMjKysL999+Pjh074oYbbsCCBQtw6dIlpYtwWloaRo4ciaysLFSrVg2VK1fGuHHj0LVrV8NGIQyGEVpXIBEDtU5BIzegHtp6fnopvTR0GrCZAGhUI9BvUgXRGyaDMuIRSeINU+/0kCUeBw8eRFpammr/tGnTMH369LDzjxw5gkWLFiErKwuPP/44cnNzMX78ePh8Ptx///04c+YMCgoKMGfOHMyaNQtz587F2rVrMXjwYGzcuBG33HKL5Zp27tyJ/fv3Y+nSpbZfhx0CgQA2bdqEw4cPY+jQoUhNTcXJkydRuXJlVKpUydW5GIzSws2Ov9qxIqkVqBrP5LOJiIBdP58Z8fiM+CbRXBoA8Mwzz2Ds2LF47bXX8Morr+DAgQOq46mpqejZsydeffVV9OnTp5RWyUgUJFEA75GUhiFyQAhrgGHWMMS0bqBdSOdg7dimzTwicAXawaoZCTUvJ0iAV4IcEinjJJGaEQMSLdaURJyJa9tPTk4O2rVrh4ceeggA0K1bN7Rr1w5r1qxRzjl8+DDOnj2rPL/77rvx/PPP4+mnn8Yf//hH7N27F2vXrlU1EJk/fz7+/Oc/Y8iQIejWrRtq1aqFVatWldwLY7jKLN/KsH1GrkAhygjgCYUQ2hXoBReWDuzEHUhjNwWY1ArU1gIsgqRbHxBQC4p23IBMBExsmjRpgosXL6o2uuM6jSRJaN++PZ599lm0a9cOo0aNwkMPPYScnBzlOAAMHDgQEydOxB//+Ec89thj+POf/6ycY8XSpUvRunVr3HDDDe68QAA//fQTWrdujYEDB2LMmDH49ddfAQRTnSdNmuTaPAyGGbF090WKUxeeFBB0t0jmEgSR1QZk6HLixAkMGzYM1atXR4UKFdC6dWt8/fXXqnO+++47DBgwAGlpaUhJSUGnTp1w7NgxwzG7d+8OjuPCtv79+0e11muvvRZPPPEE9u3bh7Nnz2L37t344osvkJeXh99++w3vv/8+EwEZriMF+OJ/reoEmnxGy/QXNA7FOq3wF+YMFIqFN7eceHbrBMpaVyIlAjIYAIszNDFzBHKc8z98N23apHo+ffp0XXcKzY8//hi2b+zYsYpDUI/k5GQsXLgwYayjjNhA1wk06h5Md+8lHYS16O2juwYb1QcMGwey7dTgIkjwgTcUBRmJhUwVy7V1vsThp59+UlzQY8aMwZgxYwzPr127Nlq0aKHa17x5c3zwwQcAgvVZPR6P7jnbtm2zXM+lS5ewcuVKzJzprisoMzMTHTt2xDfffIPq1asr+wcNGqR8wVTSRBIbGQy3MKoT6DZ25iACIRMFyw4kFcvuuUAwXUsQBMs4Q5pS3Xrrrfj0009Ro0YN/PDDD7pNqUaOHIkZM2agcuXK+N///mfZlKqoqEh5fu7cObRt29bVWrRVq1ZVrbO0YXEmsZFFXtf9R1KDOY9Y7AIMCAD9HAACfJijEH7eULTj/FyxmObnAG/QBWi7iYfbzkCrTsFkTlDCoCCBv/F799bAiBlO4gw5H7AXa1icUVNqzUIYjNKCuALFKH9EvTIHf0jkIynCWjFQKwL6NcKckQioTQ9WxrPZNZgQqRjI3ICJj5NmITfddBPy8vJU+w4ePIgGDRoACNZn7dSpk+k5Zrz33nsoLCzEsGHD7C3eJlu3bsWXX34Jn8+n2t+wYUPTjoqxhMVGhhWROgjdTA+OFjPBkXYIMhEw8bGbrsWaUrkHizOJjxwQICGY2hf8VwTsCHN+AfAGP3dlkVfX0dMT62hxUJMe7FgMLEFULkBBirx+IaPMYCfWsDijJiJ5PhAI4LPPPsPixYvx+++/AwBOnjyJgoIC5Zzff/8djRo1cmeVDIYLaLvTR5om7NG48ugUYYK2RqAfkm0RUItoku5rlEpM7491x2FG4jNx4kTs2LEDzz77LA4dOoQVK1bg1VdfVX3jNnnyZLz77rtYsmQJDh06hJdffhkffvgh/v73vyvnDB8+XDf9eOnSpbjzzjtVrj03kCQJohguNPz8889ITU11dS6AxcZEZuftT5T2EmKG0zThSOoF8oKkbAQmApYPWFMqd2FxhmFFWNMQ7XO/EHQFIigGymbuK1JrT6dpCBAUAw1rBMYSE2EvTARklAvsxBoWZ9Q4VghYzSVGWYHUCQxQ8cCLcEHQdAyr46HagHopu8QNqCcAakVA2umnFe6smoVEixeCspHnALC36G/YW/S3mM7NcAcpwDvaZInD0aNH0aJFC1sdtjp16oTVq1fjnXfeQatWrfDMM89gwYIFqjb1gwYNQk5ODp577jm0bt0ar732Gj744APcfPPNyjnHjh3DqVOnVGPn5eVh27ZtGDlypLtvCoBevXphwYIFynOO41BQUIBp06ahX79+rs7FYiPDjHisExgNvEe0FATNhD762A3rZ7u2LkZsIaKB3Q0A6tWrh7S0NGXLzs7WHZs0pWrcuDHWrVuHv/3tbxg/fjzefPNNAFA1perTpw/++9//YtCgQRg8eDA2b95sa/2kKdVf//pXd96QEobFGQZQXCcwElQdhQN8mCBoKQw6aUxn4cKLWkDUji9I4VsIvlMeGGUDp3HGSaxhcUaN49TgeKy5xGCUBEZ1As2wUwtQm+5L3H9G6cFO2ezXF1hu8C4O3xmKyUwATHycpAYDwJ///Gf8+c9/Nj1nxIgRGDFihOFxbR1YAGjatGnM0pief/559OnTBy1atMDVq1cxdOhQ/PDDD7jmmmvwzjvvuDoXi42MeMAoPZjn5TAxkhckR3V49OA9IqSAgI7rntU9vv22pw0FQSYAlg+OHz+uStdKSkrSPU+SJHTs2BHPPhv8WWrXrh3279+PnJwc3H///WFNqQDgj3/8I7788kvk5OTY6k4fi6ZUJQmLM4nNybHDwVtU5pElHhwvKeKHKj1YCISfr6kVqOAPTeQVdWsGkvE5QTKtHxgxEdQaNBPzpC+bGV/HagOWC+zEGhZn1DgWAuOx5hKDoYdf4qKuA2iEBxwCIQGP1AokrkAndfyszo3WDWgkAgLATv/D6OpdojgQiyDBJwf3MxiJQL169fDNN9/g3XffxTfffIOCggKMHDkSGRkZqFChgqtzsdjIcINYOQdj6Ug0EgEBoOvnM3XTqpkIWH6oXLmyrRqBZbUpFRBM1920aRMOHz6MoUOHIjU1FSdPnkTlypVRqVIl1+Zhcab8wTspoSBywbaCHhFyQFAaipg2DiE1A4nLUNtEhELVNCQaSK1BB2Kg0P6g+Zjadfk5JgCWM+zEGhZn1DgWAku65hKDYYXAyRBl9U2OmyIg3T3Y1npkDuDUIp+eG1BPBIyksYdeQ5AiSNjut/52eLv/IdzmfV15zCibSBIHSXKQriHzSmowYN01uCzi9/vRrFkzfPTRR8jIyFClMccCFhsZZRGj+oB2G4+0/3iu5Tk3rJ+tEgOZCFh2EUUeok0XKTnPbtfgstqU6qeffkKfPn1w7NgxFBYW4vbbb0dqairmzp2LwsJC5OTkuDYXizPlC14QwYeEOY6n0lw9krrJhxMoMRBAUBCkGojodhQG7LsCjTr6hq43EhPNxEBLERBBt6CU27T4ORMByyxO4gw5H7AXa1icUeNYCCQ1l1599VUAsa25xGDYhRYDaRGQ1Ad0IuTZRc8VqFqTzAEcH1Yj0AkiJMUVWAQJPvAROQ/N+NxvnMrJSFycpgaXNbxeL65evVpi87HYyIgWt1x7ViKetqOvJPKOG4Y4hYl/5Re7XYMnTpyIG2+8Ec8++yzuuusu7Ny5E6+++qrymQoEm1Ldfffd6NatG2699VasXbsWH374oarsxPDhw/GHP/whrD5UrJpSlWS6Losz5QveIykCYLAuq6QSBK3QugIB6KcJawmJgSqxUSsChtx8RLwLq/cnSOH1BGkxEGpXoBuwOoDlGzuxhsUZNY6FwH/+85/o3bt3idRcYjCsyE4K/5mjRUAiANJNP5yKgmbn02IgANdFuuBYkm6KMD2XniuQUX6QJAGSaFFYRnU+l/COQCD4uubOnYvXXnsNHo/jcOcIFhsZsYAW9Eqi4YgoCqybL8MQKSBAsipiRp0L2HcEkqZUU6dOxcyZM5Genm7YlCo7Oxvjx49H06ZNdZtS8bz6bybSlOq///2vk5dri5JM12VxJvEhX8yQlGDSlInXcelxNjq402IgYJEmbIQNJ6AsyPaaf9DOQpIiHMJJvUBG4uIkzpDzAXuxhsUZNY7vjOrWrYtvvvkGK1euxLfffhvTmksMhhl6IiCBFgG1OE31pbHbMISkB0MGwPGAbNwshKAV8owahYiQVV2KBXAq4dGNBiOMxCfRHYFA8NvBDRs24L///S9at26NlJQU1fFVq1a5NheLjYmLXo07t9ET+eyk5kY9Lyk4b9MVaNUpmMHQYtcRCJTNplQlma7L4kzicnLscMNjeunBTpBDQolRzUBTLERAW+KdP9zM4Fq9QQYjhN1Yw+JMMRFZJDwej+u5zwyGE8xEQMCe0BdpDUGtGKh1BRrhlXlDMdDKzUdcgSQ9mIwHBAVGAZzKFcgafjAYQJUqVTBkyJASm4/FxsTCTQHQqagXrQhIhMVIxiGuQLt1AhmM8kxJp+uyOJN4GImAtPBXnCLszpf9YU5Auk6gwzlsOQFBdSIG1K5ASPrpwd7wXQxGeSRWcSYiIfCtt97C4sWLceTIEWzfvh0NGjTA/Pnz0ahRIwwcODDixTAYdjATAf0lkDoFqMVAszqBdrCb0hsmBnISvDKvEhiDYiBwg3cxEwPLEZLIQbL5hxgAyOUkNXjZsmUlOh+LjYxIiZXg5iSduCRqBTLKNpLE225MRc6zmxpcVnn++efRp0+fEkvXZXEmsTBzAgLuCX+GBATIEMPSg/WakUTj4JN16gVykFRjsrRgBuAszpDzgcSONbGKM/bf5RCLFi1CVlYW+vbti99++02xKVatWhULFiyIeCEMRqyw84WSX7NFPycXTA9WnqtdfJGiTfslAqBXLm4iEmwowqOrd0lUczESm/T0dBw4cAAHDhxIuIBZGrDYmFiURDpwPEOaidBCol5a8O7+U0psTYyySW5ubkLHmXr16uGbb77BE088gYkTJ6Jdu3aYM2cO9uzZg5o1a7o6F4sz5RutKEhSfhW0Yp5HVG1aZAdfIOui1x3YBrQoyPm5oCvQZCxxX5OI5mGULxI51sQqzjh2BL700ktYsmQJ7rzzTsyZM0fZ37FjR0yaNCnihTAYscBIBPTCvNZfpGKgN1QbkNTs82rq97mBCAlFgKqDcHDuoEuQNQ8pf0iioOoCaoUs8+XCEZieng6OM/7j8siRI67NxWIjg7j6SqKpRzQoIp/WoYHIXIG7+09B+4/nurI2BqMs4ff70axZM3z00UfIyMhQFZyPBSzOJD6kUQgt+pG0YCnAK/tlkdd17SnX2KjnSrsAZZEDh+L0YKvxXYFuHALzeoPiviYQWh+M7XoYjDgklnHGsRB49OhRtGvXLmx/UlISLl265MqiGAyniDIXlhbsBeAJ7RI4+txgM5Go5rM4LoCDPyQACjIHkZPhBQ8/JNNagbSAJ4APcwAWcRJ8Mq8SA0mKMFAsBpKxbvEuxWb/yMhfKCNhKQ/NQiZMmKB67vf7sWfPHqxduxaTJ092dS4WG8s3dGpvSXf6jRRekEzFQG2tQCkgGDYLYWJg+UAWeUgBe5kNxPWTyOlaXq8XV69eLbH5WJxJLLRpwbQIyPGS7uetWOQJHZOCYp1H3V3YTAA07A5s1jREI9YFF6Hu9lt8roMSNURoJCnC4ItrFBrAxMDygZM4Q84HEjfWxDLOOBYC09PTsXfvXjRo0EC1f+3atWjevLlrC2Mw7CJSKbhE5CMiIBEAvdSNmRiywgtc8PxoU4G1zUJIrUAvuFDXYAAyINqsH2jk5isKCXxEDAQQVi8wOD+vNBARITMxkFFuyczM1N2/cOFCfP31167OxWJj+cWsvp+btf/KiuOQwaBx0jW4LDJmzBjMnTsXr732GjyeiEqv24bFmcRFTwRU3H9UvbSgQMJDliQIvoDuWEQMNBT+aPQEwABvv2GInws2+tCKgJovmTghKFzSY4e5Dv1c8JbJZN1MDGQYkcixJlZxxvFIWVlZGDNmDK5evQpZlrFz50688847yM7OxmuvvebawhgMJ/glThEBaRegt5Q6HpIUYb9OWrA/lL4LWDcK0XMFAkQMDB6nx9XWICSpw0wMTGxkiVf9oWh5vlw+moUY0bdvX0ydOtXVZiIsNjLsoifkOREMeV6OSAx0Uj6AuQIZDHvk5uZiw4YN+O9//4vWrVsjJSVFdXzVqlWuzcXiTGKjJwISnDikbGEjddg2Vk5Ab9D5F5xXChcaaVcgJGsxcHcTCO2ZGMgoP8QqzjgWAv/617+iQoUKePLJJ3H58mUMHToUderUwQsvvIB77rknokUwGHb5R8UVAMIDjhiKF1oRUKBceHrpw7GGdgXaqRVIHH52avwRMVCvXqAWs2OM8kl5SA024v3330e1atVcHZPFxsTBbqOQSNx+RgIeEdrs4lQMNBIBjdKDI4WJgYkN6xocTpUqVTBkyJASmYvFmcTBqFuwnggIAFKoMQg5TuoGGqFyA1qIfracg1pI3Ii0jmBIDDStRehG90ZGmYN1DQ4nVnHGkRAYCASwYsUK9O7dGxkZGbh8+TIKCgpc74rFYOgRFAHD8UuckuYL6DsBIxUBrb4vCxiIe/6QAEkEOBEyBJmDnxIEjYQ+Jd3XAUWQUAGCYe1BtxuWMBhlgXbt2qmahciyjNOnT+PXX3/FK6+84to8LDYmDrEUAa2IRAwk15nhxAmoWk8ErkAmAjK0JHK6FgBXneVmsDiTOBiJgGbwSi1ASakRaKuZh5M0YU2NPlsNQ4zqBeqgpAcTVyDtDHToCmQwtCRyrIlVnHEkBHo8HowePRrfffcdAKBixYqoWLFiTBbGYBCMBEBAXR9Q0LkXIsdJ6jBgv1FIpCKgHoJSN5DHVYjWbj/quFF6MKCfIqyFiYCJjyhyEB2kjUhi+UgNHjhwoEoI5HkeNWrUQPfu3dGsWTPX5mGxMTEoTRGQ4FQMBIzdgZEKgET4M0NPDCSOFUbiIok8JN7e/7OU4AXcSxoWZxIDQyeg5jOXOP5I2RfiEtQVAUOPOY8Y3IiApv3Chhb6/ILxMadE6Q40EhzNuggzEhcncYacD7BYEwmOU4NvuOEG7NmzJ6xQLYNREgREa1cf7QYkLkC6U7BTp7kTwc90XaHmHdGk6fo18qQX5h+UTARkGOEkNXj69OmYMWOGal/Tpk3x/fffAwC6d++OzZs3q44//PDDyMnJMR33u+++w5QpU7B582YEAgG0aNECH3zwAerXr2//hVisu6RgsZFBIKJcpIKhm2JgpCguQANXIKBOVWMiIMOIRHZpAMFYSn/hpOXIkSOuzcXiTGLCC8Uin2q/RwJ0jAAcJfwpacQmgpmuGzAa4c8InTITslcG5+eUOoGKK5BApwgDKlcgvLKuGMjZuBdklD8SOdbEKs44FgL//ve/45FHHsHPP/+MDh06hBUrbNOmTUQLYTDcgk4Bpl2AWgFQNLnPcjs8EhEwfB79b8/MHH4B7TUcUEHTMZhRvhBFwZH7R5Z4x47Ali1b4rPPPlOea7tWPfTQQ5g5c6by3MqpcPjwYdx8880YOXIkZsyYgcqVK+N///sfkpOTbb8OKwRBwKlTp8JSp86dO4eaNWtCFN37TWexkWFHiLMr2LkhBgqC6NgVSJ+vFQMN18pEQEY5ZsKECarnfr8fe/bswdq1azF58mRX52JxJvEg3YKt0DrmFBFQkAxLNQSvc/iFlF63YD8fFPKAYtdfhLVlZe11xJlo0qWYCH+yEBQG+U55Ec3NYJRVYhVnHAuBpBjt+PHjlX0cx0GWZXAc5+qNFYNhBp0WrN5f/FjPBWgmAJrhpxqPeA3mpo/TdQKdOvOMBEJnYzA3IMMYp81CPB4PatWqZXi8YsWKpse1PPHEE+jXrx+ee+45Zd91111n+3o7yLL+70BhYSF8Pp+rc7HYWLaxmxZshJtuvHjFTsowI7ERAzxEzp4AQMpVJHq6VmZmpu7+hQsX4uuvv3Z1LhZnEgtaBNRrEALoC4Bm5xtCOwCdXhtLHDgTmROwfOAkzpDzgcSONbGKM46FwKNHj0Y8GYPhFtrmH1pxz64AaJQmLCDoCjRrBuJEDKRxKtAVhRx+dFow2UeG8oGHCB5gbkCGTSRJQn5+vmpfUlISkpKSdM//4YcfUKdOHSQnJ6Nr167Izs5WpfC+/fbbWL58OWrVqoU77rgDTz31lKErUJIkfPzxx3j00UfRu3dv7NmzB+np6Zg6dSruvPPOqF/biy++CCB4g/Taa6+hUqVKyjFRFLFlyxZXawQCLDaWJ+w6/9yYJ5b1CLXouQf1xD+zdX3d+3F0XPdsTNbHKJskcrqWGX379sXUqVNdLfLO4kzioBUBg7X/RFUzEBpdAZC4AWNYRy+sfp9JcxDOH3LueSNYT4CHDASLJxFx0M8BOmNJuU2ZK5ARRnmMNdHGGcdCIKtLwShtaBFQz/1HMBMBndYJ1BP07IiBRvjAowiSZSMQLdq04CJOgkcu/tbErAbhLd6l2OwfGdF6GfGNLPGQJAfNQmQOBw8eRFpammr/tGnTdOvqde7cGW+88QaaNm2KU6dOYcaMGfjTn/6E/fv3IzU1FUOHDkWDBg1Qp04dfPvtt5gyZQry8vKwatUq3fnPnDmDgoICzJkzB7NmzcLcuXOxdu1aDB48GBs3bsQtt9zi6PVrmT9/PoCgIzAnJweCUCxw+Hw+NGzY0LJ+oVNYbGTEA05rBUo207us0oNpmBjIYADvv/8+qlWr5uqYLM4kHrQIqEW3FiCgCHH0NZxJerAReg066NTdMDeinwuKfDpxg4iA2scKfk1dQLN1+blgnUCzkhRfNgN/4/em4zAYiU60ccaxELhmzRrd/RzHITk5Gddffz3S09MjWsycOXMwdepUZGZmYsGCBQCAq1ev4pFHHsHKlStRWFiI3r1745VXXsG1115rOI5RMcXnnntOyaNu2LAhfvrpJ9Xx7OxsPPbYYxGtnREb6I7BdKMQsw7AVrUAnYqAZliJgXquQKtUYT0BUNcNaIDZ2EwMZBCaNGmCnTt3qvYZuQH79u2rPG7Tpg06d+6MBg0a4P/+7/8wcuRIjBo1SjneunVr1K5dGz169MDhw4d1030lKfgzPHDgQEycOBEA8Mc//hFffvklcnJyohYCiWvi1ltvxapVq1C1atWoxrNDLGMjI7ZYpQUnSkqsmejntJZgSbsVGfGB5OBLJ3JeIqdrAUC7du1U9x2yLOP06dP49ddf8corr7g6F4szZRvSMVj7pQrv0ToDJZUIqKAjADrGIi1YTxwEoNQJVMRA+ho94U97LXTqA0YJEwMTEydxhpwPJHasiVWccSwE3nnnnUo9Chq6RsXNN9+Mf//7345uvnJzc7F48eKwQrcTJ07Exx9/jPfeew9paWkYO3YsBg8ejC+++MJwrFOnTqmef/rppxg5ciSGDBmi2j9z5kw89NBDyvPU1FTb62XEHloE1MPMAUhwWwQkIhvtujMTA/WchFpoV6CZCBjWJASAT3Y3qDLKDzzPR2yhr1KlCpo0aYJDhw7pHu/cuTMA4NChQ7pC4DXXXAOPx6M0KyE0b94c27Zti2hNemzcuNG1sayIVWxkxBY7tQHdFgGduPbcEtzsOv+0kNdu1w0IlI96iQz7JHq61sCBA1U3aDzPo0aNGujevbvrJShYnCmbEAFQD9IpWK/mnyod2EgEpNKCI3EFRoKeGKjCbxJvaDegXyhOA46nuoWMMkkix5pYxRnHfxmuX78enTp1wvr163Hx4kVcvHgR69evR+fOnfHRRx9hy5YtOHfuHCZNmmR7zIKCAmRkZGDJkiWqwHXx4kUsXboU8+bNw2233YYOHTpg2bJl+PLLL7Fjxw7D8WrVqqXa/vOf/+DWW29Fo0aNVOelpqaqztN232KUHloRMKApECtwgCe0yw99F2CkTUGMoJ12ImTVc63g5+dk1T69dF2f5tdPKwL6IaqcgEZ4IUAADwFc2DxFkFQbIzGRRAFSwP4mS5zSNbhFixZYuHCho/kKCgpw+PBh1K5dW/f43r17AcDwuM/nQ6dOnZCXp67xcvDgQddTn37++We88soreOyxx5CVlaXa3CQWsZERW6JtEBLv2HH5Oe04bgfmFGREwvTp08FxnGqjb3C6d+8ednz06NGW43733XcYMGAA0tLSkJKSgk6dOuHYsWOurnvatGnK9tRTT2H06NGui4AAizNlEa0IqP1SRaacT1o3IGAiAgpyTGsDWsH5OWVT8PO6IqAs8sbdghmMEqYsxppYxRnHjsDMzEy8+uqruPHGG5V9PXr0QHJyMkaNGoX//e9/WLBgAUaMGGF7zDFjxqB///7o2bMnZs2apezftWsX/H4/evbsqexr1qwZ6tevj+3bt6NLly6WY//yyy/4+OOP8eabb4YdmzNnDp555hnUr18fQ4cOxcSJE+Hx6L8lhYWFKCwsVJ5ri+wzYodWBKTxcIBfEweNBMBo3IBE9PNDhpcS2+iafGbuP7N0XW2NQD3xL2DiGLRCDNUiBMDEQIaCk67BkyZNwh133IEGDRrg5MmTmDZtGgRBwL333ovDhw9jxYoV6NevH6pXr45vv/0WEydORLdu3VQO72bNmiE7OxuDBg0CAEyePBl33303unXrhltvvRVr167Fhx9+iE2bNrn2Gjds2IABAwagUaNG+P7779GqVSv8+OOPkGUZ7du3d20ewJ3YyOIMQ49oXYF6bkC3xT9G+UAUBYi8vZ8d8jPmJF2rZcuW+Oyzz5Tn2r/JH3roIcycOVN5btSQinD48GHcfPPNGDlyJGbMmIHKlSvjf//7H5KTk229BjsIgoBTp06hZs2aqv3nzp1DzZo1Xe3k69Y9GIs18YUUEHSbg5iKgJpznaKbAuyVzN18ofRg2SuHpwMbCIAqiBvQQAQ0TEs2gKUFJyZO4gw5H0jsWBOrOONYCDx8+LCu7bJy5co4cuQIAKBx48Y4e/asrfFWrlyJ3bt3Izc3N+zY6dOn4fP5UKVKFdX+a6+9FqdPn7Y1/ptvvonU1FQMHjxYtX/8+PFo3749qlWrhi+//BJTp07FqVOnMG/ePN1xsrOzMWPGDFtzMqLDKiVY4OSgl1XiIMqAF8UiXyxEwOIxZNW/RBDUSxemESEr1xCM6gRqRUC9dGCCT+bhAa+kBxuJjYJz4y+jjCFJztLxZLnYEQjAMmj+/PPPuPfee3Hu3DnUqFEDN998M3bs2IEaNWrg6tWr+Oyzz7BgwQJcunQJ9erVw5AhQ/Dkk0+qxsjLy8PFixeV54MGDUJOTg6ys7Mxfvx4NG3aFB988AFuvvlmh6/emKlTp2LSpEmYMWMGUlNT8cEHH6BmzZrIyMhAnz59XJsHcCc2sjjDiLYeodXngBMB0CwtmLn+GHbZsGGD8tmYn59v2p3e4/GgVq1ahmNVrFjR9LiWJ554Av369cNzzz2n7NMrVxEN2jRdQmFhIXw+n6tzuXUPxmJN6WBVYoG4AbXCnpkIaAs76bdeqfhfMzEwBJ0azPk51XWGAiBg7QT02hQDI+lMzEhoEjnWxCrOOBYCO3TogMmTJ+Nf//oXatSoAQD49ddf8eijj6JTp04AgB9++AH16tWzHOv48ePIzMzE+vXrXf12jub1119HRkZG2Ph0WlibNm3g8/nw8MMPIzs7W/eHZurUqapr8vPzbb1GRnQYuQGJGCiauAUJkYiAJOR6wCFAuf6uhhx5gszpugP151cLiHponX5mAiCD4QZOHIErV640PFavXj1s3rzZcgy9IDZixAhH7nGnfPfdd3jnnXcABIP+lStXUKlSJcycORMDBw7E3/72N9fmciM2sjgTXzjtwOsWemKgG7UCrURAKxEyERqmMKJDEgVInD0xWQr9vGk/w4y60wPBz8g6deogOTkZXbt2RXZ2NurXr68cf/vtt7F8+XLUqlULd9xxB5566ilDp4YkSfj444/x6KOPonfv3tizZw/S09MxdepU3HnnnbZegxkvvvgigGB9vtdeew2VKlVSjomiiC1btrieHuzWPRiLNaULb+OzlHYDKuiIgDGpC6gRA2WRD3frkeca0S8iEZDVB2RQOIkz5HwgMWNNrOOMYyFw6dKlGDhwIOrWrau84cePH0ejRo3wn//8B0CwfpTWDaLHrl27cObMGVWKFnlRL7/8MtatW4eioiJcuHBB5Qr85ZdfbKm0W7duRV5eHt59913Lczt37oxAIIAff/wRTZs2DTtupiozYoNZSjBB4PQ7B8cKQeZwlZOCDULAgWh7XgM3IBH/RI6IgZEHu0jSghmM8kxKSgqKiooABOsVHj58GC1btgQA2651u7gRG1mciT8iEQPtCHZW40YiBkYqWhKBkAl9jFhw/PhxlYvN6DOuc+fOeOONN9C0aVOcOnUKM2bMwJ/+9Cfs378fqampGDp0KBo0aIA6derg22+/xZQpU5CXl4dVq1bpjnfmzBkUFBRgzpw5mDVrFubOnYu1a9di8ODB2LhxY9Td6efPnw8g+CVXTk4OBKH4xtXn86Fhw4bIycmJag4tbt2DsVhTOmgFQDodmDQNCbuGCH0lJQISKDHQKmVXN1XYjgDo1V+/aSMSBsOARIw1sY4zjoXApk2b4sCBA/jvf/+LgwcPKvtuv/128Hzwl96u+tmjRw/s27dPte/BBx9Es2bNMGXKFNSrVw9erxcbNmxQOv7m5eXh2LFj6Nq1q+X4S5cuRYcOHdC2bVvLc/fu3Que58Nyrxmljyhz8FM3OF4HrohoU4IDJi4+ICjw6bkDafcfEQHJv16Zh99md+BoxT8feKUuoA88NvtHRjUeIz5xWvBfknhHqcFllS5dumDbtm1o3rw5+vXrh0ceeQT79u3DqlWrbNWYdYKbsZERe+w0CnGrY2+kOBEDnYiAdj4rmCjIcIvKlSvb6uTYt29f5XGbNm3QuXNnNGjQAP/3f/+HkSNHYtSoUcrx1q1bo3bt2ujRowcOHz6sm4IlScG/fQYOHIiJEycCAP74xz/iyy+/RE5OTtRC4NGjRwEAt956K1atWlUiXXpZnCn78B5JJfrRab8kLVjXDUihJwCWhDNQhd1O9CERUA6ZOzi7qc0O6wUyGIkYa2IdZxwLgUCwZXGfPn3QvXt3JCUlqdoZOyE1NRWtWrVS7UtJSUH16tWV/SNHjkRWVhaqVauGypUrY9y4cejatavqJk5bhB4I2tzfe+89/POf/wybd/v27fjqq69w6623IjU1Fdu3b8fEiRMxbNiwEgnkDGOyk96BRwh3A4pysfNPlDkImh85uk4gIVIR0CqUehEU/szEPy3FdQX5MFegAB4+GQAHXNGs2ifztsRAbQdiu8cY5RcnqcFllXnz5qGgoAAAMGPGDBQUFODdd99F48aNDevBRoNbsZERP5RGarAVdtckCCJEUQAvSJBEXnludC6DYYUkcbZ//sh5Tgq401SpUgVNmjTBoUOHdI937twZAHDo0CHdm7NrrrkGHo9H+cKL0Lx5c2zbts32OqzYuHGja2PZgcWZsoO2YzBAOgOH6q+GHIGkW7AWPTegbcEvRsKglVuPE6Ti9GDKCWhbACSIfFAM9HOsHmA5w0mcIecDiR1rYhVnHAuBkiRh9uzZyMnJwS+//IKDBw+iUaNGeOqpp9CwYUOMHOmu42j+/PngeR5DhgxBYWEhevfujVdeeUV1jrYIPRCsaSXLMu69996wMZOSkrBy5UpMnz4dhYWFSE9Px8SJE1X1MhglT3ZSsJaXVgT0SxwCclDY8yIoCAbkYMdgtzAKl6RGIBBs8EE3CtGm/Qpy+IJEqpOwIHOq54DasRecjw+rD2hHDCyCxAS/cowoChAD9h2BsuSsWUhZRBRF/Pzzz0rn4pSUFNfTtGhKOjYyIsOOEzAaonUQloQgRwuCkc5n5pbsuO7ZiNfGSDxyc3NtuTS0FBQU4PDhw7jvvvt0j+/duxdAsOyDHj6fD506dUJeXp5q/8GDB9GgQQPH6zHj559/xpo1a3Ds2DGlHAXBzS+dWJwpO2hFQLpRCO0KVMRAIzeggQhoJAjqCm5+IZiGG+CN6/GFOgIboneMqhMoe2VwsNdoJAzNujg/FxQcmRjIcECix5pYxBnHQuCsWbPw5ptv4rnnnsNDDz2k7G/VqhUWLFgQdRDatGmT6nlycjIWLlyIhQsXGl6jV4R+1KhRKmsnTfv27bFjx46o1slwFyICGuHhAK3hjq4NGCv3nxZSF1AM1QikXYEkTVgZmzMPWqRzcFAMhOIKJNCCIOkMrEcRJ8EnIziGiRho1NWYUT5JdEegIAjo1asXvvvuu7DO87Eg1rGRET2xFgGdUFJOQyNXYCQCoPa60k6dZiQWkyZNwh133IEGDRrg5MmTmDZtGgRBwL333ovDhw9jxYoV6NevH6pXr45vv/0WEydORLdu3ZQve4DwDKHJkyfj7rvvRrdu3XDrrbdi7dq1+PDDD8PuNaJhw4YNGDBgABo1aoTvv/8erVq1wo8//ghZllU10N2AxZn4R88FSOA9kioVmHQJBsKFPV7z3I4IGBU2REDZKxeLf0SY09YG9EpQbtk8krpWIGDdNVgrSDIxkOEyZTHWxCrOOJbt//Wvf+HVV19FRkaGqmBh27Zt8f3330e8EEb5xUwEFHVcdm4gwrkIaIS2HiDZ9I4b4QMfShHm4YUALwR4wKs2OxQZNCNhIiCjPNKqVSscOXKkROZisTG+KQkRMF5FMSLeEUeKIIjKZgeJqgWlTS2Ox9RpRuyQQvVo7Wykk2OnTp3QokUL0y/0gaDb4d5770XTpk1x1113oXr16tixYwdq1KgBn8+Hzz77DL169UKzZs3wyCOPYMiQIfjwww9VY2gzhAYNGoScnBw899xzaN26NV577TV88MEHuPnmm117T6ZOnYpJkyZh3759SE5OxgcffIDjx4/jlltuwV/+8hfX5gFYnIl3zERAGpIKTFyAxAlo5Aa0KwI6Tr8l2BUBlX2aeQTNOd7QaxOkoBhIb14xvElIyA1IdxxWNR9RUo1ZvCkPOIkz5SXWxCrOOHYEnjhxAtdff33YfkmS4PdH25qBUZ7QEwDNhD+PRYdgUXNMr24gEJkA6JU5+HUcfkb1AiNB5QwEVO5AP0RDMdAPEeCC1wmhVGOWJly+kCUekmT//1yWy0ezkFmzZmHSpEl45pln0KFDB6SkpKiOR5JCYASLjfFLPDkBgdiLZ0SQpOehnYHKOkIOQRqjGoKSyCvXWjkDv+79OEsPZijYTddauXKl4bF69eph8+bNlmPoZQiNGDECI0aMsLw2Ur777ju8807w71mPx4MrV66gUqVKmDlzJgYOHIi//e1vrs3F4kz88vPfHgBvYHbjBQm8IJrUAgztE6SwuoClKgJCIwAKUrgI6JWDAp0gKSnCAIJpwlDdygSFPuIS9IrFKctaQq5AIgayNGGGHRI51sQqzjgWAlu0aIGtW7eG5Ty///77aNeuXUSLYDCM8Lt8w6QXQq06A9tF6/yj6wgaoXXqETEQKBYEizgJXhT/deE3kTLFUOClKwf4wEOEDAEcbvO+js/9sfuDmFF2SPTUYADo168fAGDAgAGqguqyLIPjOIiie+k1LDbGJyUpAuoJfHqiXEnB83KYGAgUi31E2KMdf2YuQa0YSJ/P0oQZ5ZWUlBSlXlPt2rVx+PBhtGzZEgBw9uxZV+dicSY++flvDxgeIyIgnRZMpwQDUBx12nRgGkcioFnqcKgenyzyqjVYNQEx7eJLi4FkvJAhgaMylTjlmDHKukitwZAgqCcGSrlNwXfKMxmNwUgMYhVnHAuBTz/9NO6//36cOHECkiRh1apVyMvLw7/+9S989NFHES+EwSBuQFr807r89LD7Hag2LEYjAOo1/tBiJyU4bFxwiptPKwgC4aJg8Vyi6ms31TVgYiCjfFKS3RxZbGToUdqps1oxEFCLfVqXIKAWBrWQY3qCIC0GMldgYiKJPCTOnvuc/KxE2smxrNClSxds27YNzZs3R79+/fDII49g3759WLVqFbp06eLqXCzOlC1oEVBJB+YpYYw0B4F5h2CVK9DM+WcmABq570CJgFTzD9VzO2hrBpJ0YTpDySuC83PgvEGhz+iVkBRhWhAkgqIMXrUuJgYmHk7iDDkfSOxYE6s441gIHDhwID788EPMnDkTKSkpePrpp9G+fXt8+OGHuP322yNeCINBU+gg9gD2BEMaIxFQm/7r1UlVtuP0s8Ibav7hD3UD9sq88pi4BElHYQG84vSjm4bQnYS9EBSnoBeCcswnB69VREXwrF5gAiKKvGFKnx5SOegaDAC33HJLic3FYiMjXnDiztOKgoC+UzBsDh1BkImBDD0i7eRYVpg3bx4KCgoAADNmzEBBQQHeffddNG7c2NWOwQCLM/EO7ZqmCYqANjIQLERAXbRNRagxZNHm3/vaNTsRALUYCIIAwlKHFYegQaxR1QxU/pVC4qJOmjKjXJPIsSZWccaxEAgAf/rTn7B+/fqIJ2UwtPUB9WoDXiVfUIUOeTXHo6mG4gEXJgbq1QDU2+cmXk03YFog1HMHAggTBYnoR8RAOnXYz4nwQihOMwZQQcdRyCh/lIfUYADYunUrFi9ejCNHjuC9997DH/7wB7z11ltIT093tWA8wGIjIzK0abZuQFyATtJ17aQOk+NKSjAlCGrFQCYCJh6iyEPk7P0NIZYDl4Yoivj555+VbpIpKSnIycmJ6ZwszpQt6JqARjUCI8JEADTbV2LQIp1WFAwJgto6gmGCIOk4TKcy+3m1GMhIOJzEGXI+kLixJpZxhnUUYMQdohwU+cgmyuH7/JrzCU7EQQ/ljLMr+NFuOiEGHY1ph6BX5lXuQCIKCqEOwwSfRkwMQFJtV+DHJc6vCIaiSzURGfGDJPKONplyBNrpsFVW+eCDD9C7d29UqFABu3fvRmFhIQDg4sWLePZZJlIwSg+64x29z20iSU3WCpJ6Dhft2okgSPaVdko0I37Izc3FgQMHEurGjCAIAnr16oXffvuttJfCKGWCacD6whRPpQADUNcHpAk5+GgXoBwo/pw1EvuiFvxEvjgdOBbY6DIMmLwvIUFQFvmgWOjn1V2FGQwkbqyJZZyx5QisWrWqqtC6GefPn49qQYzyDV0fkHT9VcQ9yiGoTQX2GzymEWCvY3AkQplVvUAr/Jy9b7VIujAARQwUIcEn86r6gQHNt2QBFB8rYt+gMVA+HIGzZs1CTk4Ohg8fruoSdtNNN2HWrFlRj89iI8MpsRD7YgHpMkzgBUkR++hjqhqBBmlxDEYi06pVKxw5cgTp6ekxGZ/FmfjHUAAMdQq2ROQhwbxZiB6uO/5EPrq0YDNIQxEC1WWY83PFYmDosMod6KfiJu2ojKV4yWDEEbGKM7aEwAULFiiPz507h1mzZqF3797o2rUrAGD79u1Yt24dnnrqKVcXx0hM/lFxBejOFnppwV6ETpGBqwiKeFpBkODEBWglBtoVAe3UCfTbENz0BEA/J4WlDNPQYiAApYYgEQMJ9GOfzMMPMVQjkAVORvkgLy8P3bp1C9uflpaGCxcuRD0+i43xy9e9Hw89Yq6BSDETA7XQ6cL0Y0ZiIYqCg9Tg4HmJmq5FmDVrFiZNmoRnnnkGHTp0QEpKiup4tDWrWJyJb06OHa67n4iDxA1olBYsBfiw/XJAsK4NSGPQBETBb/47q3TlBWIrBmrRioFAUBD0B9OAZaA4PZg0OwkEm4xwCNUZvO1/JbNWRonhJM6Q84HEjjWxijO2hMD7779feTxkyBDMnDkTY8eOVfaNHz8eL7/8Mj777DNMnDgxooUwygdBEdAcUhPQTxyACIp35CMhmtqA9Hhu4QWniJNaZ6Adp59WfBTA2XYIFl9T3FDEej7m2Eg0RIlHwME3o6LMl4tmIbVq1cKhQ4fQsGFD1f5t27ahUaNGUY/PYmN8UiwCxhdlxQ1IoxUDrfYTV2BZfK2M2JDIBdwBoF+/fgCAAQMGqJx7siyD4ziIYnR/cbI4E58YCYCAulswoK4NSNJf9YQ+KRD8ax6CrBIDw4RBh85BU6HQzwNeKVwMBNwXBLWuQAo9MTB8rcadjxmMRI41sYozjpuFrFu3DnPnzg3b36dPHzz22GMRLYJRPrASAUlaMEn7pV2BgFoMLAlox59X4yohxwSZg8jJikOQPLdCK/4Rh58PvOqY0w6/PpmnfPVqVyAQ/pxRfokmNXjOnDmYOnUqMjMzVW4FIBiU+vXrh7Vr12L16tW48847bY05evRoLF68GPPnz8eECRMiWpeWhx56CJmZmXj99dfBcRxOnjyJ7du3Y9KkSa67J1hsjA/iVQS0Q7y66GjRz64rkMEoL2zcuLHE5mJxJv7hBQmeJL/SKZj3hIuAqvOJG5B24olcmBgYN/g597r1amKJVgykbgHDXI2yyDO/P6PcEKs441gIrF69Ov7zn//gkUceUe3/z3/+g+rVq7u2MEb5RFv7j0ZPBIxUHCSuQNI92Ctz8HMyBHAQIZuKgGFjacRAPYxSjq3q9RkJhk7wQ1IJqv8rGmt6PqPsIUk8JMm+IzDYLORIRI7A3NxcLF68WOlepWXBggW26xkRVq9ejR07dqBOnTqOrrPisccegyRJ6NGjBy5fvoxu3bohKSkJkyZNwrhx41ydi8VGhhW0SBaLTsFOMHL5GaEnBmprBdKdhFmtwMREknhDIVjvXCCx07UA4JZbbimxuViciW+0IqDgCwTTgjWfh4biHl0rMCQG0sgip6oLqDwvaadcDBt1WIqBRBB0q/syI+5wEmfI+UBix5pYxRnHQuCMGTPw17/+FZs2bULnzp0BAF999RXWrl2LJUuWuL5ARvnAL3EqETCg0c1I4xAgPK2Xfu6WY5CIelYioDKvhROQCIw0eqJeESSlO7DeMRpyHt08hKT9ksYgkIPpyVoxkMGIxBFYUFCAjIwMLFmyRLfZxt69e/HPf/4TX3/9NWrXrm1rzBMnTmDcuHFYt24d+vfv72g9VnAchyeeeAKTJ0/GoUOHUFBQgBYtWqBSpUquzgOw2MhwRmk654zSdq3ESTMxUFsf0Mkf8YzEJpHTtQhbt27F4sWLceTIEbz33nv4wx/+gLfeegvp6em4+eabXZuHxZn4QC8tmKQDc7wET7JfSQc2SwU2QgoISuMQXVdgQHCeHuyUSL7Msfrcp8e0GyO0YiAFcwUyaBI91sQizjj+S+2BBx7AF198gcqVK2PVqlVYtWoVKleujG3btuGBBx6IaBEMBiEgh4uApGaglzx3aS4yjicURryhpiUkHdeuCGiGWdMPI4og6W5GaIVDn2ZOb+i4HxJLDWYoSJKE/Px81VZYWGh6zZgxY9C/f3/07Nkz7Njly5cxdOhQLFy4ELVq1bK9hvvuuw+TJ09Gy5YtI3oddvD5fEhNTUXt2rVjIgICLDaWR3he1t3iGTu1+8zOoUVC4vpj6cCM8swHH3yA3r17o0KFCti9e7cSRy9evIhnn33W1blYnIlPaAc07xFVIiDnEcFRKcLaTYVWfBOpWmCB4OeyTPZpn1s0BFGg5pTd/MLGYCyOdg+KfPFmguyVi+sVAkExUJCCa2dOQEY5JFZxxrEjEAA6d+6Mt99+O+JJGQyPICMgGgttRg1BiDPQ7YYfyviaFGE3oceMJMVXC+0e1HYSBtSuQHBBIdBOJ2NG2UMM8I46bMkSh4MHDyItLU21f9q0aZg+fbruNStXrsTu3buRm5ure3zixIm48cYbMXDgQNvrmDt3LjweD8aPH2/7GicEAgHMmDEDL774IgoKCgAAlSpVwrhx4zBt2jR4vV6LEZzBYmP8wfMyJMldz4CV2Ecfd3vuaHDSwMOs3p+RM5Ce46bN06NbLCMuCYg8AjY9BKSBVSKnawHBbo45OTkYPnw4Vq5cqey/6aabdJ3z0cLiTHyhdAemmoPwHgl8kl95DMCxw452BRKIO1BJCQ45A6NOEQ41DFFwobQDLQDSj2UH9QXJuZyfK3YGinxQDAww13mi4iTOkPOBxI41sYoztoTA/Px8R1bL33//HampqREvilG+seoKbCYGamsGWqUNa2sFqo/pi4FGdQDV5xQHUa/Mh3UB1hPuzKA7/QomH44kPdgn8yjiJHghIEBde5UL2J6Tkdg0adIEO3fuVO1LSkrSPff48ePIzMzE+vXrkZycHHZ8zZo1+Pzzz7Fnzx7b8+/atQsvvPACdu/e7bimoF3GjRuHVatW4bnnnkPXrl0BANu3b8f06dNx7tw5LFq0KKrxWWwsG7gpBsa7488It7v4GjUQMeomzCi/JHq6Vl5eHrp16xa2Py0tDRcuXIh6fBZn4hOVCzD0RQjHSxB8AXBCyO0nSGFinhFSwOBzU+Qgh+5gOI8YOzHQRTiTGoKq7sQ2UQRBSHQ/RCYGMlQkcqyJVZyx9RtUtWpVnDlzxvagf/jDH3DkyJGIF8VIPKw6BgPhKcE0AlecIqzaT20Ekdqgs98KkiKshx0RUH/M0E0SlW5sVAtQi6gRDOnndlKHPeDhk3l4wSNZjsgEzEhAeJ5H5cqVVZuRELhr1y6cOXMG7du3h8fjgcfjwebNm/Hiiy/C4/Fg/fr1OHz4MKpUqaIcB4AhQ4age/fuumNu3boVZ86cQf369ZVrfvrpJzzyyCNo2LChK69xxYoVeOONN/Dwww+jTZs2aNOmDR5++GEsXboUK1ZYfyZZwWJj2aG0BLx4EA4jFeasrtNLE9buZyQWcqgxlZ1Npgq4t2jRAgsXLizl1ceGWrVq4dChQ2H7t23bhkaNGkU9Posz8YeeCBhM9Q26AoVkf1AETAoAggwuKWC4QZABQQ5dq960KOnBbqUJxwiS2hsm+PmDnwlmQqHVuLRzkR9g/8tnRtnBSZwpL7EmVnHGliogyzJee+0127WV/H4rTxeDERkCZ9xZmHYIat19HkqAi7TTsBkiF+w0bNYwRA8rMVBP4DNzBJLjWlcgACVFmJF4BCQBASdpfxKPo0cP2+4a3KNHD+zbt0+178EHH0SzZs0wZcoUXHPNNXj44YdVx1u3bo358+fjjjvu0B3zvvvuC6s12Lt3b9x333148MEHbb8WM5KSknRFxfT0dPh8vqjHZ7Gx7OCGIzBSUS8W6clGlLQbz8gZyGAQEtmlAQAPPfQQMjMz8frrr4PjOJw8eRLbt2/HpEmT8NRTT0U9Posz8QlPfxHikSD4/PBWLAqmBIdEQNLkw06jEFl7Z0KXT9LrIEw5AwGAC10vh+6EyPMScwcKkm7tP1UXYEBJQ47EGaiMRzkDGQxCIseaWMUZW0Jg/fr1HXWjqlWrluu1lxiJiShz8Lt0g2QkAtL7iCAYaVikG4g4dQeSFGE65VgwCGV0SjLdDZhGKyK6UXeQUX5w0jU4NTUVrVq1Uu1LSUlB9erVlf16DULq16+P9PR05XmzZs2QnZ2NQYMGoXr16qhevbrqfK/Xi1q1aqFp06YOX40+Y8eOxTPPPINly5YpbsfCwkLMnj0bY8eOjXp8Fhvji697P667P57q9MUCu809nFxHn8McfozSYM6cOZg6dSoyMzOxYMEC1TFZltGvXz+sXbsWq1evxp133mlrzNGjR2Px4sWYP38+JkyY4Mo6H3vsMUiShB49euDy5cvo1q0bkpKSMGnSJIwbNy7q8VmciW8EXwCeZD8EXwB8kh98UiC4JReFncsJevcN4XUAgwPLumIg3UVY9ZgWBLWpwkCxIBjgO9X+VAAAfGZJREFUwxtueDXPtYKeWc1AkS8+rneeyBeLgV4pKATqiYGRdBRmMKKkvMcZW0Lgjz/+GPEEDIYeAZFTiYBGLj8as3P0blP8nByW5huArHIHOkFQuQplRRTUCoJ0fUBtbUC9sYyOi5CVWoJEDBSo5iDm1+u7AplYmJgQG73t82UOR48ete0IdIu8vDxcvHgx5vMQ9uzZgw0bNqBu3bpo27YtAOCbb75BUVERevTogcGDByvnrlq1yvH4LDbGP/EiAsbKFRipCEiOu+EgZHUByw9iQIAo2/u/Jj8TkRRwz83NxeLFi9GmTRvd4wsWLHBcW3b16tXYsWMH6tSp4+g6KziOwxNPPIHJkyfj0KFDKCgoQIsWLVzrUM/iTPzCe6QwEVBIKVQ5APXFP+gc1xEEyTEiCBqIgQBUgiDtDlTGj6Z2oJEwSIuAeu4+P6c4BcPEQDPo8RnlDidxBog81rA4E2HXYAYjWogISMQ9Uh/QKKGBFgG159BhjXYDGtX603MMGqEn2HllTkmxjaazMKkbqIWIh7QYGMRZQNSKgQBwtHBCpMtlJBhOHIF6bNq0yfS4LIf/bujto3H7hqdKlSoYMmSIal+9evVcnYNRfpAkLi5q/tEYiXCq+n2aNdOCpNsiHksPZmhxmq5VUFCAjIwMLFmyRLcb4t69e/HPf/4TX3/9NWrXrm1rzBMnTmDcuHFYt24d+vfvb3stTvD5fEhNTUVqaqprIiAjPqGbg3C8pCsChgmARunBRMgT5KCIRzUEAShBUDQXJGRVs5Hi9GBdMVDPFUgwcPXpPtecK1OvmTNbr9aBaIRBujGDoYeTWMPiTBAmBDJijlGjEFEOCoBm1Uy0LsDSrHxCC4temYOfkxWh0EgQpDsGGwl/etcoGDgKzeakodOKvRDQ0vcy/lcUfUokg1EWWLZsWWkvgcGIOWbOPz3hkuwjgiC5PhpBkLkCGUbk5+erniclJRk2pgKCDvX+/fujZ8+eYTdoly9fxtChQ7Fw4ULdchR6SJKE++67D5MnT0bLli2dvwALAoEAZsyYgRdffBEFBQUAgEqVKmHcuHGYNm0aS9NNME5nDgMvBN2AQKhJSCgdmKASAa3qA5LjAUElBgJUHUCtOzDkCtQSdl3IHagSA+1A3H2ktp/WoacVC41q/XnlMFegstYIG4bQ18nrWoPrvc/kbEZ5wkmsYXEmCBMCGTFlXqUVEHhApGJGgPqWSE/Y00sBps+jm31owytJ+7Xj+tNLHTZC7zwiBobtDzn3SIqwmQDoNXD5+S1SeLUioFnKrwAePhmKK5CJgYlHIMAj4MAxKkmlkxrMYMSCSGoDRlr7LhpXYEk2DbGzRu163BAECV/cMh03bZ4e9TiM+EIUBYg2262RnyOtC3vatGmYPn267jUrV67E7t27kZubq3t84sSJuPHGGzFw4EDba547dy48Hg/Gjx9v+xonjBs3DqtWrcJzzz2Hrl27AgC2b9+O6dOn49y5c1i0aFFM5mWUPEERUKREQBFcSBTjPGJwI4KbgQBIC3Iy7ZrziIoYSI5pm4yoBEEd6DRhQzFQL0U4VLOv+DkXFPGIkEcwqxWoXYsgq12BGndfWKMQOs2YUa5xEmfI+YD9WMPiTDFMCGTEjHmV9J2AAOCXOCUd2PAczXPR4LEeRnUA7aQOa7FznkDShZUv7GRDkc90DOpaPySVo9ANvBDgh8jEQEbUqcFlgXPnzuHpp5/Gxo0bcebMGUiS+nfp/PnzpbQyhlsYiYB2iKa+XklhJejpCYtOhEo9cTJSdx+5jqQHb7n5GXTbFn3XVEbZ5vjx46p0LSOHxvHjx5GZmYn169cjOTk57PiaNWvw+eefY8+ePbbn3rVrF1544QXs3r3bca0nu6xYsQIrV65E3759lX1t2rRBvXr1cO+99zIhMEEgIiCB4yWVIAjAUAQ0cuPRop/qOh1BUK8uoBH0cUNnYCg9WBZ5RcwEEC7IacVALbTLTy8dmL7eKtU3AjFQ+qQt+H7fOLqGkZjYiTUszqhhQiAjriBuQK0D0C2cOAbtQNcLhByq68fJcDq8UeOR4BxqMdCJG5CGrhXIYJQH7rvvPhw6dAgjR47EtddeG7MAzUg8zByD8VQrUCvkRbIuN8VA7RgMRuXKlW3Vbdq1axfOnDmD9u3bK/tEUcSWLVvw8ssv429/+xsOHz6MKlWqqK4bMmQI/vSnP+nWrd26dSvOnDmD+vXrq8Z85JFHsGDBAlfq0iYlJaFhw4Zh+9PT0+Hz+aIen1H6nM4cZniM84iAIBe79zwO03BRXB9QQUcQJHdDtCBIMBIGtY1EFAxqBaq6+BJXII0brr0oxUBHTUcY5Qo7sYbFGTUR/QZt3boVw4YNQ9euXXHixAkAwFtvvYVt27ZFvBAg2MKZ4zhVq+WrV69izJgxqF69OipVqoQhQ4bgl19+MR3ngQceAMdxqq1Pnz6qc86fP4+MjAxUrlwZVapUwciRI5Wca0Z8ICK8EQi9RYMHnGn3YOIC1LoB9a7xyhwEcPCGNkEufmxnS5Z5xQ0IQPXYiki6AHsd2K0ZZQMp1DXY7ibLvJIa3KJFCyxcuLC0X0JM2Lp1K9577z1MmTIFDzzwAO6//37VFov5YhEbGe5iV+ASRcHw3HjpRkzjtjjJhDyGFlHiIEq8zS34O9KpUydbcaZHjx7Yt28f9u7dq2wdO3ZERkYG9u7diyeeeALffvut6jgAzJ8/37Ae7H333Rd2TZ06dTB58mSsW7fOlfdk7NixeOaZZ1BYWKjsKywsxOzZszF2rPuZFyzOlA7EDch7gg1CgGJXoJISHIEISOAEWbUBCAqCGrehkoJMdybWPKeRA0JwEzmA/AsExUByjsgrohrn54qFOj0noFbEi7DWnykiX7wxyh3O4oyzWMPijBrHjsAPPvgA9913HzIyMrBnzx5lQRcvXsSzzz6LTz75JKKFGLVwnjhxIj7++GO89957SEtLw9ixYzF48GB88cUXpuP16dNH9R+mtYdmZGTg1KlTWL9+Pfx+Px588EGMGjUKK1YYp7My7GOWFmyEnhvQTPAjx8wEPSs84Ezn0KsjqOcqVDkDAVWKsBF6gp8XnK4rkGC3S7FoIBASVyATAxnlITW4WbNmuHLlSonMFavYyCh9jNyBTp2BkYiHduZwo/ag0RiRioGsezCDYLeTY2pqKlq1aqXal5KSgurVqyv79Qq3169fH+np6crzZs2aITs7G4MGDUL16tVRvXp11flerxe1atVC06ZNI3k5YezZswcbNmxA3bp10bZtWwDAN998g6KiIvTo0QODBw9Wzl21alVUc7E4E1/wHgkQJEMRDkBxPT6/s7+7VWnDOvUDgeJagMo1BqnDxc/F8HqBQHGKMAB4paAzUPEKaRqF6EHEQKumIXqvU7M/rHagxfkcqyvICGEn1rA4o8axEDhr1izk5ORg+PDhWLlypbL/pptu0m2/bAejFs4XL17E0qVLsWLFCtx2220Agl0gmzdvjh07dqBLly6GYyYlJRl2evnuu++wdu1a5ObmomPHjgCAl156Cf369cPzzz+POnXqRPQ6GJGj1yDELgHIpmKgAPP0Yisx0C508xAi6OmJfSKnv1+QuVB9QC4oJHK8adMQIzegkQhI8Mk8dvofNj2HUbYQJQ4BBzfdYjlpFvLKK6/gsccew9NPP41WrVqFddWyc3Nql1jERkbiEGsHYTykKmvdkzy7QUs4RJGHaNIATXWuFDyvU6dOEAShxOJMXl4eLl68GPN5CFWqVMGQIUNU+7RF692CxZnSgXYDKvtoR17IDahC25DDTBA0OaakDZvUDyQYpgKrjtsXAwFQgiCF9u9N8llPpxPbcArqdQ5W5rUQBBFaJ0sPTjycxBmgdGJNosQZx0JgXl4eunXrFrY/LS0NFy5ciGgRRi2cd+3aBb/fj549eyr7mjVrhvr162P79u2mQuCmTZtQs2ZNVK1aFbfddhtmzZqlqLXbt29HlSpVFBEQAHr27Ame5/HVV19h0KBBYeMVFhaq7JjaFtWMyKFFQOIGFKF23Ol256XENDMx0I6fwUwMpF2BTgRDI3efWeovEQPDxtLUCYwkJZjBoCkPjsAqVaogPz9f+SKJIMsyOI6DKLqX+uhGbGRxpmSIpP5dpK5ANwRAu3NEKwZG4yx0o9MwIzGx6wjUQ68eE40sh//M6+2jcaNeE41RulgscOsejMUad+AEKVwQBMJFQBq7xyhRUFVDMOQOpOejHYKAdTMRAME0YTMxkMzth7qjsB5+ISjcCZJrqcKqeoVmWK2NUW6INNaU5zjjWAisVasWDh06FFawcNu2bWjUqJHjBZi1cD59+jR8Pl9YwcZrr70Wp0+fNhyzT58+GDx4MNLT03H48GE8/vjj6Nu3L7Zv3w5BEHD69GnUrFlTdY3H40G1atUMx83OzsaMGTMcvz5GOKKOEKbtEKw6ZpBeq03btXIGlhQCOCWF16wJCMFLrVl7Huke7Gx+3tIVyGCUBzIyMuD1erFixYqYNwtxIzayOOOMaDoGl4QY6KYL0E6KcGk1MTF6H7t+PrOEV8JgJDZu3YOxWOMc4gYk9QENMRP6nKBxCYalCgMqQZBuNmImCCquQEHWFwOh7nfICTZcd14qfdhFJ7htMZDBYESEYyHwoYceQmZmJl5//XVwHIeTJ09i+/btmDRpEp566ilHY1m1cI6Ue+65R3ncunVrtGnTBtdddx02bdqEHj16RDTm1KlTkZWVpTzPz8+PmfW/PGCWCkyHUCMRUHtc69hzWxDUroMWIK3WCOgLgt4o1ugDb9sVWMRJ8DmwWDPKHoGAgIBsX9SQJL5cpAbv378fe/bsca1GhxluxEYWZ0oWIuo5EQTNxECg2JUXi1RgPaFPO49b7kAGQw9R4iHa7DNYWqnBJc25c+fw9NNPY+PGjThz5gwkSf232fnz512by617MBZrokdJE7bTHIScG4jgb3GvaO4OBHTrBwLhNQQJpmIg6SYcWrdsUnaGrs+niHZ2xECrzsH0HLFoRsKIa5zEGXI+kNixJlZxxrEQ+Nhjj0GSJPTo0QOXL19Gt27dkJSUhEmTJmHcuHGOxrJq4bxu3ToUFRXhwoULKlfgL7/8Ylj/T49GjRrhmmuuwaFDh9CjRw/UqlULZ86cUZ0TCARw/vx5w3GTkpLCGo4wnBMQ7X2gByDrCmx0swwBajGuJN2BVuIfWZu2uYdZMxAz12C0FHHMIchQUx5Sgzt27Ijjx4+XiBDoRmxkccZd7Ka6OnUHGomBQOxrAdJioNlcJeUOpN830hyE1QZk0ESTGlwWuO+++3Do0CGMHDky5s5zt+7BWKyJDk+yH4IvYN4kRDnZhc9DA3cgEO4Q1BMEjZBFztgZSAuCepA0YuIYLIkUXVYPkGFCIseaWMUZx0Igx3F44oknMHnyZBw6dAgFBQVo0aIFKlWq5Hhy0sKZ5sEHH0SzZs0wZcoU1KtXD16vFxs2bFAKJObl5eHYsWPo2rWr7Xl+/vlnnDt3DrVr1wYAdO3aFRcuXMCuXbvQoUMHAMDnn38OSZLQuXNnx6+DYY+AyEGUOfht3ijRzTf0ECFbioFAZO5AOy4/vfUAaoHSTBB0th77AZZOCyYCoJ/yWbKOwYmHJHGQHAQFWUa5cASOGzcOmZmZmDx5Mlq3bh3WLETbpT4a3IyNjJInEndgaWFXbIy1GKgnAmofMxiJztatW7Ft2zalk2MsYXGm5DmTda/ymOMlRQTkk/zgk2yKgW6hcQcCxinDxDlo5grkPGKxGAiq1qBWELSJ7VRepzGCEgBphyInSOB679O7gsFIKGIVZxwLgQSfz6fcREaKnRbOI0eORFZWFqpVq4bKlStj3Lhx6Nq1q6pRCN3CuaCgADNmzMCQIUNQq1YtHD58GI8++iiuv/569O7dGwDQvHlz9OnTBw899BBycnLg9/sxduxY3HPPPaxjcIygRUBRBgKaOOGFfp1AIuwRYY6uvwfoi4H0dYCzBh/0vFYipJsQN6C2UYhRfUC9tGC7dQH9ttqnMBKd8uAIvPvuuwEAI0aMUPZxHBeTZiEEN2IjI3oidebZdQeauQKjxU4tQLvESgw0EgEZiY0o2i9DIUrB8xI5XQsI3oNcuXKlROdkcab04Hgp1CREsicEWqUDax2DAd48ldikoQigFgQ55Yt//ZqBKjEQQnjnY3oOt2ofOsVABCTPWeJw4uEkzgDlI9bEKs7YEgIHDx5se8BVq1ZFvBg95s+fD57nMWTIEBQWFqJ379545ZVXVOfQLZwFQcC3336LN998ExcuXECdOnXQq1cvPPPMMyob/Ntvv42xY8eiR48eyvgvvviiq2tnFKMVAc2ag+hhJszppQvrCYJOsRIDjdCKk0a4mSKsJwJq3YAB1kCEUc44evRoTMcvzdjI0CfWqbk0bouBdgU7p9197YqBdscsC65JRvyQyOlaAPDKK6/gsccew9NPP41WrVqFOc+jfe0szsQPvEcE75EgJPvBJ/n1RUBSZ88OemnD9D76sZUoGJo7TBC0cAcSMTA4R1AMpF2CTl6Xq409/OGin4pI6i0yEppEjjWxijO2hMC0tDTlsSzLWL16NdLS0tCxY0cAwVp/Fy5ccBSsjNC2cE5OTsbChQuxcOFCw2voFs4VKlTAunXrLOepVq0aVqxYEfE6GcbMq6R+X0XSTCpCERBQp+pqXYGquTTpuXpCnhNxMFIxUG9NNFZdhN2uF0iLgHlF410dm1H6iBKPgIPCupLElYvU4AYNGsR0/JKMjQw10XQMdhMiijkRBN1w6MVKDDRDTwDUcwMyoZBRnqhSpQry8/Nx2223qfa75Txncab0OJ05DLzex5kgKU1CwgQzuzitHWglCgKqWoKKIAh9d6AWXfGvNNCpA8hEQEZ5J1ZxxpYQuGzZMuXxlClTcNdddyEnJweCEPxQEUURf//73xNWhWXYQysARoLej7Fb9fro8WItBhoJlUYCnzYlOHwNPPycZCiCCpQIRLsDWRowwwgnqcGLFi3CokWL8OOPPwIAWrZsiaeffhp9+/YFADz88MP47LPPcPLkSVSqVAk33ngj5s6di2bNmhmOOX36dKxcuRLHjx+Hz+dDhw4dMHv2bNfrtL711lvIycnB0aNHsX37djRo0AALFixAeno6Bg4cGNXYLDaWDvEiAtJEIgiWNGZioJmoaCTsMRGw/CEGeIg86xpMk5GRAa/XixUrVsSkWQiLM6XD6cxhxgdDn32Kk85JnUA3mod4JHMxzIYgaNhMxMgVGMvUYJMmIGZdixmJiZM4A5SPWBOrOOO4RuDrr7+Obdu2KQEICKbjZmVl4cYbb8Q//vEPVxbGKFu8mLYcHgEImHxgC+RnloorVu5AN9x4dMMOu6nDHnCq2oJaMZAW5PyQbTX/0BMBLQVA8Lp1An3gdesEEuguwbQb0OPANcZgEOrWrYs5c+agcePGkGUZb775JgYOHIg9e/agZcuW6NChAzIyMlC/fn2cP38e06dPR69evXD06FFVrKBp0qQJXn75ZTRq1AhXrlzB/Pnz0atXLxw6dAg1atRwZd2LFi3C008/jQkTJmD27NnKN2ZVqlTBggULohYCaVhsLBmsREA7HXWNcEPEsiMIxrKJh1W6stHcTh2GZjjtwsxIbBI5XQsA9u/fjz179pRId3oWZ0oGUxGQoBHSFNHMTDBzQwSkx9KKgdragtRaFAEwzCFYLApG7ArUdgwW+aBr0sn1dpyAAHMDMgxJ5FgTqzjj+LcpEAjg+++/D9v//fffQ5JYDbLyyviLwyxEQBleXobAAZ4SruwqQlZEO/ox4ExoNHMR+iErm9Fx1Zo42VIEFELzeUO/pl45+C8RM306v75mbkAmAiYuosg52ujU4BYtWpiWXgCAO+64A/369UPjxo3RpEkTzJ49G5UqVcKOHTsAAKNGjUK3bt3QsGFDtG/fHrNmzcLx48cVB6EeQ4cORc+ePdGoUSO0bNkS8+bNQ35+Pr799lvX3peXXnoJS5YswRNPPKG6cerYsWNYx/poYbExvuB5Wdns4LZwJYpCqYlhVnO7Ifhp3YBaAbTbtqeinoMRf4gS52gDgi4NO3GmrNKxY0ccP368ROZicab0kAI8pIAAKcBDvOqFRGrtecSgeOYVi4U3j6S/2YQTJMPNEKPagoTQ2hShj3IxcoJcKmnBjmoKBvjiRioRvKeMsoPTOFMeYk2s4oxjR+CDDz6IkSNH4vDhw7jhhhsAAF999RXmzJmDBx980PUFMsoOWQVDDdODPYIczPvlAYR+YbWdg+3gVrderTtQK/JF0mmYYFbfz0r8s4KkCBOsnIGAWgD0grk0GEEaNGiA5cuXK8/z8/ORlJSkaqqkhyiKeO+993Dp0iV07do17PilS5ewbNkypKeno169erbWUlRUhFdffRVpaWlo27atsxdiwtGjR9GuXbuw/UlJSbh06ZJr8wAsNpYUHdc96zg12E23m1OMHHqxdAVazW2E0ftk5fBj7j+GGYns0gCAcePGITMzE5MnT0br1q3Diri3adPGtblYnCkZar2wXHEFSqIAPvQ5Kkt8cBN5QOSDjTaSAkGRjRKlOEGyndJqKu5ZYZYirNd52Csq6cJ0M5HitZSQGChISnp18drUrkDV+8dcgAwbJHKsiVWccSwEPv/886hVqxb++c9/4tSpUwCA2rVrY/LkyXjkkUciWgSDoU0R1qbmuiUAEpHOG0rttdPdl8aN5iG25tE0DNGmCJs1TKHTgtVjspu1RCUg8gjIDpqFyBwOHjyoKkIOANOmTcP06dN1r9m3bx+6du2Kq1evolKlSli9erXSbAQIdrR69NFHcenSJTRt2hTr16+Hz+czXcdHH32Ee+65B5cvX0bt2rWxfv16XHPNNbZfhxXp6enYu3dvWNOQtWvXonnz5q7NA7DYWJJEIgaWJqUpBhrh1txMBGSUd+6++24AwIgRI5R9HMe51iyEhsWZkoMWA2kCV73geAlSgAdf6AGSAgACxYJeKE3W7O5CFvnoBEAaK1ecXrqwiRioSyT1AW2mB8teGZzf5N0yEQH5ft84XxeDUQaJVZxxLATyPI9HH30Ujz76KPLz8wFE3rKYkXg4dQX6SUkNBMVAAeqGIW4Jb1qXHqnrR8RAeg67jUTMxLhIEGQuzDGot0+LHVcgg6GlSZMm2Llzp2qfmRuwadOm2Lt3Ly5evIj3338f999/PzZv3qyIgRkZGbj99ttx6tQpPP/887jrrrvwxRdfIDk52XDMW2+9FXv37sXZs2exZMkS3HXXXfjqq69Qs2bNqF7bzJkzMWnSJGRlZWHMmDG4evUqZFnGzp078c477yA7OxuvvfZaVHNoYbGxZHEqBkZTO9ANSkIMdEOUc+oKZPUAyxdOvnQKlIMC7kDQeV5SsDhTshiJgWKRB0JSAFJAAFcYfIxKRWpRS1s3D1Acb66JgE6wajBS0thwBQKIv3UzYo5Tc0N5iDWxijOOhUAaFnwYetgVA0WRgxfFwp+RGGiF1tWnFefM6vZ5KTHPbiORWEILf1pXYNi5BkKkT+Z1XYF+iMwVyFDged7RZ7jP58P1118PAOjQoQNyc3PxwgsvYPHixQCAtLQ0pKWloXHjxujSpQuqVq2K1atX49577zUcMyUlBddffz2uv/56dOnSBY0bN8bSpUsxderUqF7bjBkzMHr0aPz1r39FhQoV8OSTT+Ly5csYOnQo6tSpgxdeeAH33HNPVHOYwWJj/GImdAGl43Aj6yktd6AWO6nUvCApdQKJGOg0FZlRPkjkdC0AYY7zkiKR39N4otYLy3Emq/jvGFniIQUAsdADziOCTwoEm2z4ecArhtW9U7ndiDho0iXXCidpx4Y4cQVG0y04ElegkRgIMEGQYUoix5pYxRnHQmB6erppy+IjR45EtSBGYkCLgQIPiDpxQODM6wTS6cFa0ctuSq+ZkEaOe6muwvTYerUDgdikBwvUPFoXIHlO0oO1dQKN8EKAHyICkJQ6gdoGIozEQBQ5R9+eiVSzEAARfXsmSRIKCwt1j8myDFmWDY9HMqYTZLn49ycjIwMZGRm4fPkyCgoKonYbGsFiY8kTaYqwmTuQFrIiFQUlkQcfgeMjFoJgpOKc3ntk5v5jzkBGeeatt95CTk4Ojh49iu3bt6NBgwZYsGAB0tPTXe1Oz+JM/CAWecB7JEiFAfCFnpA4F/5ZqysMuiAIajFzGSrCYby56yhXoDZFWFfwZM1BGOWYWMQZx0LghAkTVM/9fj/27NmDtWvXYvLkyREtgpGYGDkDPYIMMcBBlMNrA5q5Au2Kf0ZOOa24RqDFwEiwcu5FQrH4FxzbKkWYpAcL4CFCMnQF/q9orKvrZJRd0tPT8e9//9vWuVOnTkXfvn1Rv359/P7771ixYgU2bdqEdevW4ciRI3j33XfRq1cv1KhRAz///DPmzJmDChUqoF+/fsoYzZo1Q3Z2NgYNGoRLly5h9uzZGDBgAGrXro2zZ89i4cKFOHHiBP7yl7+48vq0N0sVK1ZExYoVXRlbDxYbS4do6gVapQsbCWh2BC+tGOhEjLOTLmw3fTdah56ZO5B2BboxFyP+CYi8koZlea6c+OlaALBo0SI8/fTTmDBhAmbPnq3UaqpSpQoWLFjgqhDI4kzpUHPeOziTdW+wLiAlRolFHnjIZ2CAB+fnjDvhhuKBHPpiXiUIGmEgFGpFMm19Qu0YrrgIbWD6+gleGaBdkmTtIl8sBoZcgSW1bkZ84STOAOUj1sQqzjgWAjMzM3X3L1y4EF9//XVEi2AkPkauQD2IGKja5yBN18/JYWKgVkQTOdmWGBiNK5CMF6lIqBUDbV8XEgMJHvAqVyAj8RBl+79fACDLcOQIPHPmDIYPH45Tp04hLS0Nbdq0wbp163D77bfj5MmT2Lp1KxYsWIDffvsN1157Lbp164Yvv/xS5b7Ly8vDxYsXAQCCIOD777/Hm2++ibNnz6J69ero1KkTtm7dipYtW0b2Jmho0qSJqXMCAM6fP+/KXACLjaVJtM1DtKKbVVqsnTTiSByB2jVE6ww0E+acjE2Lgcz5x3CK3XStRYsWYdGiRfjxxx8BAC1btsTTTz+Nvn37AgAefvhhfPbZZzh58iQqVaqEG2+8EXPnzkWzZs0Mx5w+fTpWrlyJ48ePw+fzoUOHDpg9ezY6d+7symsDgJdeeglLlizBnXfeiTlz5ij7O3bsiEmTJrk2D8DiTGlCxEAtEu2w8/PgIJmLYZraeLZSiUNj66F1ApLxtA04FFHNjiswIATThd1C+36Q51pB0EoMDPDMFcgwJJFjTaziTFQ1Amn69u2LqVOnYtmyZW4NyUgA7KYIiwYx0wMOHlndQVjbUViLVqSzarYBwNARaFeANBPraEFQcFh3kBYDIQPgjLsHWzUNCUBCU9+LyCsa72gNjMTEiSNw6dKlhsfq1KmDTz75xHIMOl03OTkZq1atsjV3pMyYMSOsK3JpwGJjyWBXDOQ1NzeSTl0ku8KgkSimFQHtCHJuNzGJRAQ0e2+YGMgAAFHiIXL2vlQUHbo06tatizlz5qBx48aQZRlvvvkmBg4ciD179qBly5bo0KEDMjIyUL9+fZw/fx7Tp09Hr169cPToUQiC/s9jkyZN8PLLL6NRo0a4cuUK5s+fj169euHQoUOoUaOG8zdAh6NHj6Jdu3Zh+5OSknDp0iVX5rCCxZmSgYiBvAcwq2ZuyxlngN51eunEYanAVu5CLZF0Dya4JcYZuANl8CoxEEB8pTQzYoqTOAOUj1gTqzjjmhD4/vvvo1q1am4Nx0ggzJqHGOEN/UvCrEcj1GmfE7QCIRHQ9MRArSsQsE4/thIhtRiNR4uGlmnJOuun6wQ6EQMZjPLCPffcE7N6gE5gsTF+0Apd2n16oiCgL5zRohjgvJ6gdky36gJapefqzaP3vtD7yfuiJwZq04MJX9wyHTdtnu5k6YwExa5L44477lA9nz17NhYtWoQdO3agZcuWGDVqlHKsYcOGmDVrFtq2bYsff/wR1113ne6YQ4cOVT2fN28eli5dim+//RY9evSI4NWEk56ejr1794YVc1+7di2aN2/uyhxWsDhTctSc9w7OPno3pIAA3iOpUoVlkVf+og9zBup8TlqKhZRLDkC4OKYj/mmdgLrNN4wgYqCZG5B6vUZuREcYuANpMZADIAfCXYHSJ23B9/vG+ZyMhCSRY02s4oxjIbBdu3aqdCtZlnH69Gn8+uuveOWVVyJeCKN8opcGrIfVLRYtGAYQSuflwjsIq8Z04M4zEh6dQERB0WFNwmI3IBRXIBB6DMDPSTrNVHj4ZMDPqYN5ESehbtI8/FyYFcUrYcQbAZFDwCINlkaUOByLsllIPGOVEhwLWGyMb4zELqNzjERB5VyNk89ufb7S7AzsRATUnqMnBmphLkGGHvn5+arnSUlJSEpKMr1GFEW89957uHTpErp27Rp2/NKlS1i2bBnS09NRr149W+soKirCq6++irS0NLRt29b+CzBg5syZmDRpErKysjBmzBhcvXoVsixj586deOedd5CdnY3XXnst6nloWJwpfYodgRr8wc8/GSGBLJQmrIUWy2w5BzU19ADojusa2pjgF3Q7B5s1J4kI+n3wc2oxEFCnNDMxkKFDIsaaWMcZx0LgwIEDVUGI53nUqFED3bt3N82dZjBoBE6GQH6OQiKXUXqwnVsLveYiquMmzTaciHJO3ICqOeTiTsR2m57QCOAgyjIEyBAhBx2FnLqLMBmXdgV6IeBKSGqlm4dck/wczl59NKLXwkgMnKQGlzXoNOSSgsXG8onWJWd1bmngxry0GEigxU89EXDLzc+g27anop6bET+IImx/6UT+ptPePE2bNg3Tp0/XvWbfvn3o2rUrrl69ikqVKmH16tXKF1YA8Morr+DRRx/FpUuX0LRpU6xfvx4+n890HR999BHuueceXL58GbVr18b69etxzTXX2HoNZsyYMQOjR4/GX//6V1SoUAFPPvkkLl++jKFDh6JOnTp44YUXcM8990Q9Dw2LM/GDHGpmIAV4SJeCYgOfJgIBXiUGKoTce2G1++ymEesIgtpxIkVJD44Vfi68TiCFLIQETrIGkjJMxEBoXIEAqxeYwDiJM0Bix5pYxxnHQqDRG8pgOMEjyPDKMhC6gfJr4oMdl6Bd9DrvOq3VF6kAGLYWG01GjFAERI07kIiBQLE7MCgGAgg1CbnMBYLHIeFq6DGDkahIUsn/cchiY/nFqs5fSQqAjpqAOCwGT8RAM1cgg6Hl+PHjqnQtM4dG06ZNsXfvXly8eBHvv/8+7r//fmzevFm5QcvIyMDtt9+OU6dO4fnnn8ddd92FL774AsnJyYZj3nrrrdi7dy/Onj2LJUuW4K677sJXX30VdekI+gunjIwMZGRk4PLlyygoKIhZWQoWZ+ILKcBDKvRC8kiAIIMTZHCVChUxkBAmChI04qBK4DNy3FGCII1h0xGztOBQnUCnOHIDinxwzQZiIBEByWM9MVBZo7bRCasbyKBIxFgT6zjj+DdIEAScOXMmbP+5c+cMCygyyjd0fUCB+okTOBleXobAFdcE1MNp3yo6jVfrvhNkTiUCat2A2lRiI9EuUjEvWgRw8CL4Gor/LX5TiSBI8FHPiQiYLHuYGzDBkGQOooON7hrcokULLFy4sLRfQpmHxcb4xangZXQN7xFNx+J5Wdno5yVFLEVA7XXauehaiSxFmEFTuXJl1WZ2c+bz+XD99dejQ4cOyM7ORtu2bfHCCy8ox9PS0tC4cWN069YN77//Pr7//nusXr3adP6UlBRcf/316NKlC5YuXQqPx2PaAMsJ2jIUFStWjGltWhZn4gspIMB/2YfApSRIhR5IhR7IV0MeG0qkkkVed4Of2qzwymohTZDCtxB67kBZp0ZhcNzgZzcnmMQPK7HQav1kbhuuRVoYJK83zAHpFyISMBmJTaLGmljGGceOQKOUq8LCQkvLJINhBukeTLsBScqvCOMUYaPbGdp9R3fudQot+tntImxGNK5APQSZU+oGko7CWgHUF0ofTpZd6w/EKOMkcmpwacBiY2JDC2eRNhhxCz03XqnWHjRoGMJIPESJg2g7NTh4nt1OjnpIkoTCwkLdY7IsQ5Zlw+ORjOmUJk2aWNakPX/+vCtzASzOxBuyxEMKAGKRB9ylpGBsSAoE05y8YrEYaJDCSsQ54rCzlSasd5wIbKHmIsX7bX4uazsI60HVCtSu2xY6zkDZTHwMW6OkJEMpIiATAxMSJ3EGSPxYE8s4Y1sVePHFFwEEVcnXXnsNlSpVUo6JoogtW7aw+hQMR3gEGWLA+hddKwba8THQ3X3pRhq0AzBaUTCS69wQErUpwtrXQWoG+sDjCiR4ISAACV7wilDISCz8Ege/w6B5PIGbhZQkLDbGP0qjiwhSYenrIz0nEmixz0rkcyoCRuoGpK8nr1fbJIU8F0UBt25/PKp5GImB3U6OU6dORd++fVG/fn38/vvvWLFiBTZt2oR169bhyJEjePfdd9GrVy/UqFEDP//8M+bMmYMKFSqgX79+yhjNmjVDdnY2Bg0ahEuXLmH27NkYMGAAateujbNnz2LhwoU4ceIE/vKXv7jy2mbMmIG0tDRXxjKDxZn4Qgrwqm7BgateCL5g2R1Z5ACRA0dSnWzUsVO6DXslYzHQrM4eSaM1GV+FNsUW0BUDZZELdwpS4qZKEPTz4Ggnh6ajseyVLdOElXPpFGE9IkxpZiQ2iRprYhlnbAuB8+fPBxBURXNyclQWdJ/Ph4YNGyInJ8f9FTLKHSR2ap2BgPM0YUV408QTs8692vRgu809SC1C7Vj09U6FRJESM3XXRNcLBJSagcXn8SB1AouFQyYGMpgj0C1YbIwPvu5tLTpFKwg6PceOOGin1l681uPT1grU65zMYNjlzJkzGD58OE6dOoW0tDS0adMG69atw+23346TJ09i69atWLBgAX777Tdce+216NatG7788ktVilReXh4uXrwIIJhG+/333+PNN9/E2bNnUb16dXTq1Albt25Fy5YtXVnzPffcE9NUYAKLM2UQg2675tfwxQIaEc1iRSRioJ74RgmCKsLcjgC8weYfQUIOv0hdgaH1xLTJCSMhKWuxJpZxxrYQePToUQDBQoirVq1C1apVY7IgRuIj8IBoVAOX6h5sJAjaDau0K5B24pHOvVrBzwgzYdDJOJHMpbdfT5ikG6KAI6Jf0BUoyjz8XPBd88l8mCjKYDAih8XGskekgqBT9DrtqtbhgsAXSUqwFBBcf+10ejATBBOXgMjZ7uYYcJiuZVZLqU6dOvjkk08s56RTZ5OTk7Fq1Spba40Eq1QtN2FxJr7hPSI4QQp+tgJAQAAE88Z8Zmm1EbkCDTCsDQio3YpEFDQQA1XrE2TbjjyZ7u4r8uD8UrEgaFcoJd2Do+iMzCg7OIkzQGLHmljHGccFwzZu3BiLdTAY8CIo+gmhn3nRINY5FQMBdddfUqOPCGpOhTytS48ehwhyXs0xozEigXYZesEBMiBysq4YKHIiBPDwhjyVAeYGTEhE2ZlbVkJxsxCApQa7AYuNZQ9apIuVKGglBkY1dhR1AaMVQ43Sg8lj0jyEwbCbrlXWMKrXF0tYnIlvlM9T5d+QG86Jsy/kCuT8XFAs016rJwaaCGScoO/YC9tPxLoAb1kzkOyzJQjStRJRnMjEQYIscIauQCU9WJv2rHEFCkP2mM/PKDckYqyJdZyxJQRmZWXhmWeeQUpKCrKyskzPnTdvnisLYyQ2Rq5AIga6De0OBNQNO6J19WmFRTP05nFaq9Crs15B5sLEQD9XLESqnIClV1OeEUew1ODoYbExcYiFS85tYtEQJJrXzXtEICAozkbiCmSOwMQlIMXOEVjWkKSS+WKVxZn44UzWvbr7eb06gE7Tgmm0KcKAWhCkxUBaJAudK3vloHvOK+k2DFHSdakxFVGQpAzr1eGjv9TyiCqR0LDrMBmDvB8BPigGekljExspwl4ZgNoVyAkSu51JUJzEGSCxY02s44wtIXDPnj3w+4PyzO7du0vUDs8of9DOQD1XYKShVesOtCMG0u4+Gq3oR1KPzQRBenwr8U/UqSUokDloQdMkTdgLXnELFgFAyA3ogUmaAIPBsA2LjYlFrFKG3XAFlmZXYCuCawuKf7QYyGAAienSKElYnIlPtA1DOI8IOKl3ZwNVirBWEHSQJku7/4zciSqHIFU/UHEFamNYQCh2PiI8fVg9tqyumRjgg81FQGoGSrrvnV7TENkrg0OoOUksaygyyhws1jjHlhBIW9E3bdoUq7UwGKa4dVuhdQcSzMRAQF9000ILgkZEIgKS/YKm6zA9FnEF0s9J8xBSKxAcsLfob5avg1G2EGXAvBqNGklmqcFuwGJjYlJiNQQ1zTbMzisLECcgXS+QwWBEB4szZR9ZjFy0Ig44Q0HQCq+FaEa5Bsk5skjV9TNL/SXioEmsNHIKkk7JwRRhgBM5W65AiHyxGMhgMKLC8V9qI0aMwO+//x62/9KlSxgxYkRUi5kzZw44jsOECROUfVevXsWYMWNQvXp1VKpUCUOGDMEvv/xiOIbf78eUKVPQunVrpKSkoE6dOhg+fDhOnjypOq9hw4bgOE61zZkzJ6r1M2KHXojRE/Oc4tUIa2FOvwg7a2jHdauhiJFIGDY/qVEoc/DKPARwEMAHU4QZDARTgw8cOIADBw4wEdAFYhkbGaWDFBBcq+9nJCpaiXxlRQQk6yROQJ45NRISUeYcbUAwXatFixZYuHBhKa++7MPiTOkixbLkgVdSb1ZYfNmiajZiNZ7dOY0wiJNhIqBGVJTFoDMQIg/4uTD3H6CTMkzESodNUxhlB6dxhsWayHGsCrz55pu4cuVK2P4rV67gX//6V8QLyc3NxeLFi9GmTRvV/okTJ+LDDz/Ee++9h82bN+PkyZMYPHiw4TiXL1/G7t278dRTT2H37t1YtWoV8vLyMGDAgLBzZ86ciVOnTinbuHHjIl4/Q595lVZYniPKgMeB3haArIiA+s4+443goQQ+PTGQFgStxEAz91/YuTYEQa3rj95vdMzsWi94eGUePvAQWFpwQuJ3uIkodgSyoOkOsYqNjNKnJMRAPcGvrIiABK0YyGAAwb/v2RdO7sDiTOljJgZyHtG4Vp4ZNkQ43Y65Iq8WBDVfwMheuVgw0xP7DJ4r7kG92odGaOKkk/eB83OKGKiHIgaS18LEQIYOLNY4x3bX4Pz8fMiyDFmW8fvvvyM5OVk5JooiPvnkE9SsWTOiRRQUFCAjIwNLlizBrFmzlP0XL17E0qVLsWLFCtx2220AgGXLlqF58+bYsWMHunTpEjZWWloa1q9fr9r38ssv44YbbsCxY8dQv359ZX9qaipq1aoV0ZoZ7kFqAXo4IGDxma4n/JF9KnFPc45VAxK6XqCyLipVOFJnYKRYCX5G6yGNQvS6CJMUYQYDYM1C3CKWsZFRviBCmiRxZUYENKp/2PXzmaWwGgYjMWFxJj4J1gkMPuY9EuSAACSFF2qJeS07G+5ARUgk4p9OExHleCiVWK/bsB3CRECrxil+XjnHVoqwnwuKgawEBYMRFbZ/g6pUqYJq1aqB4zg0adIEVatWVbZrrrkGI0aMiFiBHTNmDPr374+ePXuq9u/atQt+v1+1v1mzZqhfvz62b99ue/yLFy+C4zhUqVJFtX/OnDmoXr062rVrh3/84x8IBIyrbBUWFiI/P1+1MazxWAQ/gZPh5WUIMdDZBA5h4xq5AoGgGGjlDiREkurrtDuwHl5wxWm/obXRazQTLEmKMIPhlOzsbHTq1AmpqamoWbMm7rzzTuTl5SnHz58/j3HjxqFp06aoUKEC6tevj/Hjx+PixYuGY9ot4xDvuBkbWZyJX2LtClSdE0cioCCIymaHeFo7w30CEuC3uQVCf/6xdK3ocfsejMUad+F4CVKAB0QOssgVC2EB6jZbm/obbTquQ8Lcc2Zz08fsdj/Wi21e0fR6WiClXYG2UoQZCYuTOMNiTXTYdgRu3LgRsizjtttuwwcffIBq1aopx3w+Hxo0aIA6deo4XsDKlSuxe/du5Obmhh07ffo0fD5fmIB37bXX4vTp07bGv3r1KqZMmYJ7771X1Ulm/PjxaN++PapVq4Yvv/wSU6dOxalTpzBv3jzdcbKzszFjxgz7L4wBABh/cZit9GAvLwOhoulmrkCjRh9A0BmoFff0EFBcc1BvPCt3oB5m6cH0ddoaf0bOPysHoiL8aboVk7mMXIEulSpkxBkS9Dtsm53vpFnI5s2bMWbMGHTq1AmBQACPP/44evXqhQMHDiAlJQUnT57EyZMn8fzzz6NFixb46aefMHr0aJw8eRLvv/++7ph0GYe2bdvit99+Q2ZmJgYMGICvv/7awasvXdyMjSzOlA/c6CJcGpBmIFq0r4eJgQwa1skxety+B2Oxxl3EIg94jwTJI4FLCsAoD0n2yionG+fnYioGqroOQ+MM1FsbdZ1tNAJgmBvQIL1Y5ZLUeQ8UZ6AXytupdBEOuQL5Px2wv05GwsNijXNsC4G33HILgODNY7169cDz0dtxjx8/jszMTKxfv15lc3cLv9+Pu+66C7IsY9GiRapjWVlZyuM2bdrA5/Ph4YcfRnZ2NpKSksLGmjp1quqa/Px81KtXz/U1lycEHgBkQIRS6JNAfe6HYSYG0tgVRozEQEAt8GnFQBFy1A47u8IgvQbtGsljIgYC5inCDAYANGjQAMuXL1ee5+fnIykpSffzb+3atarnb7zxBmrWrIldu3ahW7duaNWqFT744APl+HXXXYfZs2dj2LBhCAQC8HjCQ42TMg7xjJuxkcWZyOm47ll83fvx0l6GbfScgfEmDuq5AM3EQCD+XgODkQi4fQ/GYk1k1HphOU5nDlPtkwI8OF6AFOCDqcKFHsiFHnB6TjgifglU91sbopvTWnhmY0ZSV48TZMgQ1XUArdzt1Os37VhMz+PnIIMHIBXXAwSMxUAGgxEVtoVAQoMGDQAE3RzHjh1DUVGR6ri22YcZu3btwpkzZ9C+fXtlnyiK2LJlC15++WWsW7cORUVFuHDhgsoV+Msvv1jW9iMi4E8//YTPP//cUiHu3LkzAoEAfvzxRzRt2jTsuNENMsOarIKhpq5AjxAUA4Of/eGuQLren5mTT3sOfa2ZsGg2ntYdqCcGkv3h6yg+L5K0YDKmnTRkD7hitx9XPKeuGMhIOEgTELuIAA4ePIi0tDTV/mnTpmH69OmW15OUX9qVoHdO5cqVdUVAs2v0yjiUBdyIjSzOlB5GKbu0sCUFBFupvYmAWSqwkRgIlF23I8Meoqz+G8vqXCCYriUIgqXznGGNW/dgLNZETq0XluNM1r2qfbLEq12BhQHwHhFcsqbklMg7EgPjqRmGSgwkcZB+TENEQK0bUCP86b0+WgzkAMgIuQL1xEBGQuIkzpDzARZrIsGxEPjrr7/iwQcfxKeffqp7XBTt/9f16NED+/btU+178MEH0axZM0yZMgX16tWD1+vFhg0bMGTIEABAXl4ejh07hq5duxqOS0TAH374ARs3bkT16tUt17J3717wPM+K7cYIIzFQ4AFRChcDiRCo1PjTxAoRxTX+6GYhJD1YRDAF2Ik4oodeyq9emjAtCEZSP1APp45D8vq9MgfVZTJUYiCDAQBNmjTBzp07Vfvs3BhIkoQJEybgpptuQqtWrXTPOXv2LJ555hmMGjXK9nqMyjiUFdyMjYzIiMQVaCXslQdhKxKB00wMZDBoWLqWe7A4Ex/UnPdOmBgoFnnBe6Sg+02QijsIa8Uwh2JgtGjTg+2crzwWpOK7Bn/o854WAcm/evHDo+lATImAZuvRPeZHmBjIYGhhscY5jr3lEyZMwIULF/DVV1+hQoUKWLt2Ld588000btwYa9ascTRWamoqWrVqpdpSUlJQvXp1tGrVCmlpaRg5ciSysrKwceNG7Nq1Cw8++CC6du2q6hjcrFkzrF69GkBQBPx//+//4euvv8bbb78NURRx+vRpnD59WvnmbPv27ViwYAG++eYbHDlyBG+//TYmTpyIYcOGoWrVqk7fEoZNsgqG6u4X+ODmEWQIIeEtiQcqcMWf+6TxB3H4CShu/OEBp4iCdI3AWP45ZCbQGTUYAaxTf7U4rUtI3guvrG4gQub9j3ifo/kZiQvP86hcubJqsyMEjhkzBvv378fKlSt1j+fn56N///5o0aKFLXchYF7GoazgZmxkRE7Hdc9ansN7RGWzA31eoouCBDsin1ETkfLimiyPBBxuACvg7iYszsQ3gateiIUeSIUeSJeSIBd6gAAf7L7r58FdFsIFv5BQZuSOMyQkOOohe+WYuAmLm6Bo4kPoubY+oC0RkLwO7WsxahwSuhFkzUMSF6dxhsWayHHsCPz888/xn//8Bx07dgTP82jQoAFuv/12VK5cGdnZ2ejfv7+rC5w/fz54nseQIUNQWFiI3r1745VXXlGdk5eXp6SqnThxQgmGf/zjH1Xnbdy4Ed27d0dSUhJWrlyJ6dOno7CwEOnp6Zg4caKqXgYjNpilCQs8EBCBZEGCKHPBL35CqcIeOZQuHPSJAwh+KUQ3/tCDHLNz66ZNDzZrABIcU+3+04p/5LjWhUfSdcPGo0RCcg3dNMSOO7D4dYZShbmgmOilU4cZCYcIZ8K3DGfNQghjx47FRx99hC1btqBu3bphx3///Xf06dMHqampWL16Nbxer84oapyWcYhXSjo2MowxcgZGI1DRzkDybyIJXtGkPTN3IMMM5tJwDxZn4gc9VyAA+C8nKSnCuCSDD3URlkmqsCiB8/OQU+1Z2ywdfVTzES2uioFeUe0KNKoXaNIlWLUePRGT3qd9TcQVyGAYwGKNcxwLgZcuXVLSZ6tWrYpff/0VTZo0QevWrbF79+6oF7Rp0ybV8+TkZCxcuNBU3ZXl4g+Whg0bqp7r0b59e+zYsSOqdTJiQ5JXhihBUTUEQQ42EgkJgoq4JevXDjTCiSDohEhTgc2cgSJXXMtPTxDUQlyQ9GsTUPwNCcGqEzGjfJGeno5///vfts6VZRnjxo3D6tWrsWnTJqSnp4edk5+fj969eyMpKQlr1qyx1QAqkjIO8UqsYyPDGbFoHqJNE040QVArBoqiYFor0Ij2H891c1kMBiMEizPxhVYMlCUeHC/Bf9kHAOADPPjLPvBJfkCQwV0SwScFgEqFQV8DEQMtUoSjEQOdoqzBKwH+0JgeCQgYjG+SFgxAcQNaioBayDl+LvheCTJLEWYwXMbxp0bTpk2Rl5cHAGjbti0WL16MEydOICcnB7Vr13Z9gYzEwyhFmEDShD0h27fAyfDyMgQO8ITSg5M5qn4gwgW+AGTVRnDqnLIj8pmlAmvxglM203k5Wdc1CAQbmJCteA0MRuwYM2YMli9fjhUrViA1NVUpt3DlyhUAQRGwV69euHTpEpYuXYr8/HzlHLpmkdMyDmUJFhvjDztpwmbopb4adfstLynDRkQiGDLKHn4AftnmFrqGpWu5B4sz8UfNee+E7ZMCAvyXfQhcSoJ41YvApSQE8pMhXkqCeCkJckEwbZi7SsUNKkXYcZpwaeARVSKgYVqwGV5ZvZmgpAg77Y7HKHM4ijMs1kSFY0dgZmYmTp06BSDYYbJPnz54++234fP58MYbb7i9PkaCYtVJmG4iEhC5YO3AUCMRgQMK6S+boE4T1uv+SzcU0TtG1xgMmKT7RoNW/KOfGzXxIO5Af6hxiFfjJNR7PYCx2DmMX4HlkrkQyyh7iBrB2875TlKDSd2+7t27q/YvW7YMDzzwAHbv3o2vvvoKAHD99derzjl69CgaNmwIwHkZh7IEi43xj5P0V1rY0qa+GjUQKU9dhc3Y3X8KcwUyFFi6lnuwOBOfXPPcuzj76N2qfcF4IEEs9IAXeXCCBHLrwlE1ajkUQU4maVDFzj49d6DKGUgcgHYENweEzSlIwRqHynMZssOOvWHCJt0whYacpxU9/RzgDXYKpmsDirubQGh/0NFaGIkLizXOcewIHDZsGB544AEAQIcOHfDTTz8hNzcXx48fx913321+MYNBYccZCCDMGQgUOwOJK5BOEzYSx4BwpyART/Tcg26irfVHb9rjWogz0MidaOZD0V7jh4y7+bdtrpqRyKSnp+PAgQM4cOCAZX1AWZZ1NxILunfvbngOEQHJOOQaUsZBbytrIiDAYmO8Eq0rkGDHGQhE7g6MF0ehW+vY3X+KK+Mwyg/Z2dno1KkTUlNTUbNmTdx5552K+w0Azp8/j3HjxqFp06aoUKEC6tevj/HjxytfLunh9/sxZcoUtG7dGikpKahTpw6GDx+OkydPlsRLch0WZ+KXa557F0AwPZggBXjIEh/8V+QhBwRIhR6Il5KCzUSueiBf9ajTeimBjLgD6S3MGSjyrqUFu04oLZjzc84cjSYuwbDmIQyGA1icURP1J0fFihXRvn17XHPNNW6sh1HOcCIGkq7C2jRhWgw0u4Xxc7Ky2UHrvosUq4YfZmKg1iloZ+3k9lR7rpHrkFH28UNW/XxbbSKKm4UwG31sYLExfoiVGGhGoqQLR9MExO0ajYzSh5RwtrMROcNuutbmzZsxZswY7NixA+vXr4ff71dKTgDAyZMncfLkSTz//PPYv38/3njjDaxduxYjR440HPPy5cvYvXs3nnrqKezevRurVq1CXl4eBgwYENX7EC+wOBP/GImBMtkKfMDv3nAx0KQjsK6oRnffNbm+NFOMI5pbRxDkRE7ZpNymLq2OES84iTNOYw2LM2o42aqzBuCom+68efOiWlBZIT8/H2lpabh48SKzobqAWZowEEwTJgREDqLMwS/9//buPb6JKu8f+GeStGkLbaEF2nKzRZEWUe5ghRUUtPK4K3hBQBRUhGf3AQX6rIIrS4vo4gUFLygqLuyj8oNFgWV1BRFFVkDkVkSLBZTbCq0XoIUiaZI5vz/SmWaSSTJp06ZpPu/XKy+bycyZOZ2aL/nme86R4KxeTdgO1wIiQPXP8B4irJdEC5TsM5o01Gsv0LHKvna3ij87auYGdE/cxUCCWUiIq87dxwgpYOWjK+Hj3RYArJTHGu0S1ZNQvIc4nU5YLBbcgUVIkFoaPu6A2IC2w88aXiyE9NV3bGScCS0lMWVk+K6/pJ9ncixQws/I+WqTNPQ1RLk25OoFuUzVVffu12wkAeorYcghwuEXivcRpY1J+CtikWDomCpcwOu4v9bn/emnn9CmTRt89tlnuPbaa3X3WbVqFe6++25UVlbCYjE229HOnTvRr18/HDt2DB07dgz6uhpaQ3wGY6wJnZ8fGQXJVPOhRXkvNcc6YLLI6tx5ksUJSzMbTM1sMCXYITW3+V5JWKfiT0mqiRjhe3iwx3GeibiAcxFWLxYinCbXYiHVqwbrDQ1W5wiMcQJKP2N8JzNhltUEn/tw39pU/Jn6lgTeiepVuOIMULdYE+1xxlBv9u7da6gxSWK5LtUPZc5AoHqosBPqnIEANKsJA0ploOs1f8N9lSScr4RgjJBqXUHomegLeJxyuIDPhUKc1XMFGlXbVY2JKDDGxsgSzErC/lbMVbYryS/lg56vpFyg1YUbonIwUNJQSQAShVpFRYXmudVqhdVqDXicMhQrJSXF7z5JSUmGP5wpx0iShBYtWhg+JpwYZyKLMl+gezIQQHVVoOuji2SWqysDY1yrCZsFEOOEFCO75guMEdp58jwTfdUrDNeVe9Kvru0Jp6QmA/0lATWq5/7TtGMWHP5LtVKbWBPtccZQjz799NP6vg6KcoEWDwGMJwO9815ur0M/MRcoIaiozXDhYJKJvtjdEoBOCLWfvhY/8boGSGpVoK8kI0UuGSKohK8IcrEQ0sfYGJlCtaiH3iIiSvue6mMREaMJROXcta0g9JcU9YfVgE2PHTD8VaRS29ShQwfN9oKCAhQWFvo9VpZlTJs2DQMGDEC3bt109/n5558xd+5cTJo0yeAVARcvXsSMGTMwZsyYiKl8Y5yJPK2eWYlfZo7UbJMdJpgs0CQDnRdrZjhX3pklVEHEOLyTgUDNNvfEoK/5AQNUA3ry9boIdv5Bi8HFS9wWDPFcBET5mQnB6BRMnFH2B4KPNYwztVg1mKi+hDQZWP2z+2rCyorA/hJzdkkYruQLht453c+l7KO5do/9nZIAhHYuQfcVj42ck0iRlZXFocEUdYKpCjTCszoQ0Cb9wr2SsOe5jSYDw33d1HScOHFC82HISDXg5MmT8fXXX+Pzzz/Xfb2iogI333wzunbtGjCpqLDb7bjzzjshhMCrr75q6Bii2kp9ahV+mTnS673UszLQKxloqR4yG+fj/de9gs4zKVhfHPW4GImPFYGJghVsrGGcCcFiIUShFGjxEKBmAREAPhcQURYRUX9W9lcW5vBT2edvQZH6TqxpEoP+VhKG/jXqVQMaWZmYIpu9en5Jow8uFkLRrM+GvxhKhvlbKMNkkmFyG/plNjvVh2a/OibTTBan+giV2rRVl0VDqOlwVlefG30AwJAhQ3D11VfjrbfeQlJSUsAPZ1OmTMH777+PTz/9FO3bt/d6/dy5c7jpppuQmJiINWvWICYmRqcVLeXD2bFjx7Bx48ZGX6VBTZPsllBTFw9xmuC8GAPZZnEtIqIsHnJR/z1XmIX68LWyrie12s9u0j78CfS6P0aGBQfAasDoFWycqU2sYZxxYUUgNTq+koHu1YKBKgMdwpUAdK+wA2oqA923KZV5RpN8npV87ts9+Uo4KnP96bWlGQIMVyWgWVQP7a0eEuxZFeiL0i/OFUieWBFI0czXSsJ7bp6heR5oWKzJJEOWtR+Y9KoEG1qwyT5ZlrzmCfSsZAk0byKThaRn586dhj4QCSHw4IMPYs2aNdi8eTOysrK89qmoqEBeXh6sVivWrVuHuLi4gO0qH84OHTqETz/9FKmpqbXqB1GwUp9apbu9vHAEJJNcPVxYdiUDK2uSFiZUf3xRFg9Rqv7skutjgLlmkQ3JKekPI67md0iw3aSftHNfJETdVg/v727DgwHvIcJGcKEQUhiJNYwzWqwIpIjhmSD0VxloNelXB5oRXHUgAM23DQq9Yb569LYHaitGSDBXrxKsJPx8zetnJHmp9M8MSV19mIiIvIVyXju9CkFPRufsC2ZuPyNJQK8hwwYXC3E6zerDU23mESRSTJ48GW+//TaWL1+OxMRElJaWorS0FL/++isA14ezG2+8EZWVlXjzzTdRUVGh7uN01vztZWdnY82aNQBcH87uuOMO7Nq1C++88w6cTqd6TFVVVVj6SZRcuBZCNlUPEzap1YHOSiuclVbIlVa1MlCyS66EmZKUUxJ71R9w1MRZoMpAt+SeNsHnVh3o9rO6T22GBSttxAj14X//ms8lklNiNSDVG8YZLSYCKaL5SgbqJQSVZCDgnQz0ew6PCj2FMoQ4UDLO1z7u7elWE1afV0ncOaXqYZ3Kf6uPN3INMUJSE4wcItz0OCXX34fRhywJDg0m8sEzGaiX9JJlk/oIpC4JMtlhDnkSMBT8VQYaSYBSZHJAwCEZfFT/G6Vv376G4syrr76K8vJyDB48GBkZGepj5cqVAIA9e/Zgx44d2L9/Py677DLNPidOnFDbKSkpUVeC/OGHH7Bu3Tr85z//QY8ePTTHbNu2rZ5+S0SBJReuBeCaMxCAfjLwbDzEmXjgjNWVFDwXAzhNkC5q445XMrA6iacZEuxBNyHo9lqoGU4KBtMm5xRskoKKM0HGGsYZLQ4Npoiit6CIkgx0ym7DhIGaNLfHQiJmAE5N7JD8Dq91pyTfzAaTacEsPOK5crFSFagMBw7Far8xQsLfxJg6t0ORj0ODiYyr7cq5RNEumKHB/gwePDjgPp7tZGZmGjqGKBySC9eivHAEhGyqGSoMQLa5Pp4LmwUwC0gWJ0xWB4TFCcnmgNS8CogR3omwACsD6yX4/Cb9alMN6DCp6zZKkI0n/qoXDTGKSUDyZHRosD/RFmdYEUgRx9ccgmaT62ExC7U6UKkQjDMLWE1AvE51IOCqDAw0RNid5wSlnpQKvNpwTxq6D+U1C6lOyUAj1Y9ERKQ/RDjYOfA8FxPxFEylX32o7SIkTIgSEYWGUhnoTrbFuKoCbTGQL8TCWWmF/XQzOMsTIM67hg1L5yz6Q2h9VAPqJvxCvBqwcErBzyVYD9WHRGQM/++jiORvdWGlQtBS/W2Re0LQfWVh93kDFb6Sd/4qAN2Tgb4SgP4WDTEixsf8fsrxgZKOShKQycCmyRH0qsEcGkwUiK9koNGEoJFhw6FWm+RiQw0npsgXTKwJdmgwUTRzHyIMoGY1YYdJnSOwZmXhAMnAQBymmiSg8rP7trqqHuYMu8n/YiUKz/kPKaoF+5mGsab2ODSYIpbeMGGFsqqwxSzgqA6SZkloVha2K1NqqEf5HiLsNT8gRMjn2nNP5ilVgWoCsvr0ZtRUBRq9Bib/SA+HBhMF1uuDp71WEgaa3lBhk8VpKIlotM/d182v6yVRE2F0aDBRtNIdImxxVZQLhxky4Hrutrqw8gWOBABxTs2wWr/VgIGSfaFIBtrNgMVtNWC7pDtE2Nd2otpgrAkeKwKpyXKvDHSvDlQqA5WqQHV/GBsibEdNIk752Whln1HulYXKAh+ei4cYwSRgdAhmoRCnJCADrAgkMsjXSsLBVAcCDTekVllgRO9BRESNj6/FQ4TT5EoGVq8urCQDXSsLK5WBMa5qOmfNar0AgBhZ71QNQ0ko6ixUItklNVmpJi1ZFUjU4JgIpIjmb4gw4L2qMFBdGQj91YQB/8lAu07Cz32bv0VB3BN7vtpSzu/Vj+ptMR5JQX/nZRKQ/MnKykJxcTGKi4sxefLkcF8OUaPmKxkIBJ8QbOrc50akpsV9fmQjD4DDtYiM8pwv0H2YsHCY1YSgmgxUhgnbLK5kYCMg3IYqu1ciKsk/Q0OFDTL3OhiytqjxCDbOMNbUHhOBFPHyz99laM5AoCYZqFQFujMyX2BdhgN7Juvc21LO5TmXn/s1eM5T6O9amASkUNqyZQt+97vfoW3btpAkyWtI8erVq3HjjTciNTUVkiShqKgoYJvffPMNbr/9dmRmZkKSJCxcuLBerp0oVPwlA4GahKDnw517VWBTnJuPSUDytHPnTn7hRGRQcuFaCNmkqQzUrQ60WWqSgRctEE7f8/FJ5gh7X2ZVINUCY03wOEcgNRmeyUD3+QOVOQN9MUuAUxibLzAQuyS8koieSUBfi4/U9pwU3RyQYYfxf+jJbouFAMDkyZP9Bs7Kykp0794d999/P2677Tbd1wcOHIg777wTEydONHQNFy5cQKdOnTBy5EhMnz7d8LUThZNeMlBvDkF3nvMJms1ONUFodG6+UJEdZt0EpNFraGpzI1Jw7BIAPyMfNPsCgHBVaZjN5oBxhohc9FYSrnj8FpgsslplJ1mckG0W1/t5M5trJ7sJEmSIGAERI1yJwRhZd3iuIcGuAFxXThNgMGlZq0VSKCIEE2cAxpq6YCKQoob7AiJwAnaPZJySDFRYIAHClcQzQ6pZoReSOqzXV1WevyHCwYoRkt/2nHBdn14CksiXSy65BG+//bb6vKKiAlarFVar1WvfYcOGYdiwYT7buueeewAAR48eNXz+vn37om/fvgCAmTNnGj6OKBIpib/GkETzlQwkqg+cwJ0oNJRFRNyTgQAAjy9ylEU43JOBEjwWDfEnRAlAtRLRbvI5X6HPxULskmYBFKJAGGuCx6HBFFXMOn/x7rNqqIuIuL/uMbef6xjtPH2+KvyMMpo4DHQepR1H9XLqnhWGi8Wo2l0gNWp2SQ7q4ZQEDh48iOTkZM1j3rx54e4KUUQJVA3oSS8hGI6knGcFoK9r4AIjRESNh+xrVd/q4cOe1X/uC4dIZlmzmq9PMbWPSZK5jsk7o8lKIqozVgRSVLNIgMNtSLC9+r+eQ4SVqjz3ysC6UJKLoaocVKoC3dtUzqEkAzlvILm7/PLL8eWXX2q26VUDElFoKcNrwzlEGKhJ8gWbiPQ3PFiW+SGuKXONhjA6NLhmAncO1yKqGyGbIFXPwSo7TDDBrSLQfT+nCRKgDhGGWYaASVsZCNSs6hsimgRgjFObcNSpBlSqFZXqRV0eVYHCLDgkOAoEE2dq9mesqQ0mAinqmE2Aw+laMCQGgF2WaoYECwBuQ4RdH5O8k4EAQpIQNMJoslC5nkAJQSIAMJlMLKEnakTCkQwEAlf96Q0l9pcM7L5ufsiujSIfh2sRhYZ7MhAARPV7t3BKwHkrYHFCinNAOFxzIUlwQJgRsmSg8JGEU5OABioJ3ZN+PpOBfuYKZDKQfGGsCR6/uqWoZDELmCXXI8YkEGd2rSJsUYYGS9ohw75W8a3tkGC7JAwl+Dz3UZJ9dgjNQ28fX21Q0+OECOrhvlhI165dsWjRonB3gSiqNKY5A43QSxZ6rohMRET1y301YQCQbTFwlCfAWREHudIKuTwO4rwV4nwsxEVLzVBbs6xJtqnDhC06w4U95ggUTim4JKCR4cdK29XXpCQEVcp1u22TnBKTgEQh1KgSgU899RQkScK0adPUbRcvXsTkyZORmpqK5s2b4/bbb0dZWZnfdoQQmD17NjIyMhAfH4+hQ4fi0KFDmn1Onz6NsWPHIikpCS1atMCECRNw/vz5+ugWhYnnKsLuzCZXMlBJCAKuCkG9ZKASDvWSgUDd5wdUeCYHfSXwPBN/yjb37UrCJ1BbFL2ysrJQXFyM4uJiltAT1ZLeKsJGOZ1mr2RaY17Eg8lAckoiqAfgGq7FL5yIQs9ps8B5MQayzQJHpRXOinhXUrDSCmGzAL/GQLrg9h6tJAOrh+pK7lV3QSTvFLrzAVo82nYbFuxrCLAmQWnX+Uylt42arGDjDGNN7TWaRODOnTvx2muv4aqrrtJsnz59Ov75z39i1apV+Oyzz3Dy5Encdtttftt65pln8OKLL2Lx4sXYsWMHmjVrhry8PFy8eFHdZ+zYsfjmm2+wceNGvP/++9iyZQsmTZpUL32j8AmUDARQ62Sg8gg1X9WCTs9kn8cbIOCdJGQykELh/PnzKCoqQlFREQDgyJEjKCoqwvHjxwG4vlgpKipCcXExAKCkpARFRUUoLS1V2xg3bhweffRR9XlVVZXaZlVVFX744QcUFRXh8OHDDdcxohCoSzJQEe7FQ4xiMpCCtXPnTn7hRFRHyYVr1Z+F21ysTpsFsi0GwmGG7DBBtlkgX4iFfDEWwmaBsFkgXQwyGehW3eerElBD2d9PMtHnPIA61GSg+8IhHslAU98Sw+1RdGCsCV6jSASeP38eY8eOxRtvvIGWLVuq28vLy/Hmm2/i+eefx/XXX4/evXtj6dKl2LZtG7744gvdtoQQWLhwIWbNmoXhw4fjqquuwv/93//h5MmTWLt2LQDgwIEDWL9+PZYsWYL+/ftj4MCBeOmll7BixQqcPHmyIbpMjUSwyUAzXMlA94U36ish6I/TI6nnnhT0HDKsVAf+TR7ToNdIDccOGVVBPJxBDg3etWsXevbsiZ49ewIA8vPz0bNnT8yePRsAsG7dOvTs2RM333wzAGD06NHo2bMnFi9erLZx/PhxnDp1Sn1+8uRJtc1Tp05h/vz56NmzJx544IFQ/3qI6h2Tga7qRs4PSERUP3wlA+XqFYOVOQPVhKDNAnHRAnE+VpsMBGqSgdWrCasJwVpUBnqSfMzv53N/fxV/OslAJgGJQqNRJAInT56Mm2++GUOHDtVs3717N+x2u2Z7dnY2OnbsiO3bt+u2deTIEZSWlmqOSU5ORv/+/dVjtm/fjhYtWqBPnz7qPkOHDoXJZMKOHTt027XZbKioqNA8KDLkn78LFrOsPjzpJQP1KHMGulcHeiYEjapL8tCzAtAr8edWJWiHwNvyXXhb9l0ZSdEpmKHBgwcPhhDC67Fs2TIAwL333qv7emFhodrG5s2b1f0BIDMzU/eYzZs3h76zEYJxhiIlGagnFMlQatwckGE3+HDA9e8tDtdqfBhrIpfRZCCcJgibBXKl1ZUMvOhWGVj9WUit0tOrDgyw8IehBUJiPM6jnNtAolB3vkAwCRgNgokzjDV1E/ZVg1esWIE9e/Zg586dXq+VlpYiNjYWLVq00GxPS0vTDDnzPEbZx9cxpaWlaNOmjeZ1i8WClJQUn+3OmzcPc+bMMdQnanweKr8bLya/DQBqMtDhFljMrgW2AACzqkZ7HT9NWql5bgaghD5L9arCANSVhQNZIrzPMd70/wIeBwBmIamVf57sEIhRVjWWBN513m2oTYpsrqpP49/Aui8WAri+jGEpffgxzkS2Xh88jT03zzC8r6d9t/zRa1tdVxLus+EvXtt25f3J6xwKo+dyX0mYCUDyJ5iVHLds2YJnn30Wu3fvxqlTp7BmzRqMGDFCfX316tVYvHgxdu/ejdOnT2Pv3r3o0aOH3za/+eYbzJ49G7t378axY8ewYMECzVzk0YixJrIlF65FeeEIADXJQMkkQ3aY1AofGUDc1M1ex0oA5G3Z6nNl5V7EyIDdBMksB7eisN0M0+27vDaLDVd676uTAPRbDejOaYLpN8XG9qWoZDTWMM7UCGsi8MSJE5g6dSo2btyIuLi4cF5KQI8++ijy8/PV5xUVFejQoUMYr4iC5Z4M9OVRm/7w2YViVMD2H5BWGLoOX0N0jQzdHWV6J+A+SoLwH857DF0PRaesrCx1ugRqHBhnIp+RZKCvxJmRYbWeSTxf9BKARl4L5hyywxywLWpanBCQdL6E9LUv4KrSMJvNhr5wqqysRPfu3XH//ffrzgdeWVmJgQMH4s4778TEiRMNXceFCxfQqVMnjBw5EtOnTzd0TFPHWBP5lFWDTdVDeYVs0iQDm834l89jTdd867dtCYC8rqeh69BLAgKAlLdf054e+d9da57YTZqFRTTnYAIwqgQTZ5T9AeOxhnGmRlgTgbt378aPP/6IXr16qducTie2bNmCl19+GRs2bEBVVRXOnj2rqQosKytDenq6bpvK9rKyMmRkZGiOUbK56enp+PHHHzXHORwOnD592me7VqsVVqu1Nt2kRuSh8vqrkNOr8gu1lfLYej8HEYUH40zTUJ8Vcg2ReGNyj0IpmIrAYcOGYdiwYT5fv+ce1xecR48eNXz+vn37om/fvgCAmTNnGj6uKWOsiXwtn1hdr+2bbtlbr+0DgRN8XCuYgmE01jDO1AjrHIFDhgzB/v371ZUji4qK0KdPH4wdO1b9OSYmBps2bVKPKSkpwfHjx5Gbm6vbZlZWFtLT0zXHVFRUYMeOHeoxubm5OHv2LHbv3q3u88knn0CWZfTv37+eektEVD/skoyqIB4OyEEtFkJERFQbnnPR2Wy2cF8SERE1MYw1wQtrRWBiYiK6deum2dasWTOkpqaq2ydMmID8/HykpKQgKSkJDz74IHJzc3H11Verx2RnZ2PevHm49dZbIUkSpk2bhieeeAKdO3dGVlYW/vznP6Nt27bq+O+cnBzcdNNNmDhxIhYvXgy73Y4pU6Zg9OjRaNu2bYP1n4goXDg0mIiIgmGXZAjJ2Hy0ygTunkNOCwoKNAtJERERKYKJMwBjTV2EfbGQQBYsWACTyYTbb78dNpsNeXl5eOWVVzT7lJSUoLy8XH3+yCOPoLKyEpMmTcLZs2cxcOBArF+/XjMP4TvvvIMpU6ZgyJAhavsvvvhig/WLiIiIiKgpO3HihGa4FoekEhFRqDHWBK/RJQI3b96seR4XF4dFixb5HbomhHZCSUmS8Pjjj+Pxxx/3eUxKSgqWL19ep2slImoM7HCiZh3rwLhqMBERNYSkpCTDcwQSERHVBmNN8BpdIpCIiOofhwYTEVEwqiBDRnBDg4NZNZiIiKJbMHEGYKypCyYCiYginBMypCCCppMVgURE1ACCWTX4/PnzOHz4sPr8yJEjKCoqQkpKCjp27IjTp0/j+PHjOHnyJADX1EAAkJ6ejvT0dADAuHHj0K5dO8ybNw8AUFVVheLiYvXnH374AUVFRWjevDkuu+yykPWTiIjCx2isYZypwUQgEVEUYkUgERE1Jrt27cJ1112nPs/PzwcAjB8/HsuWLcO6detw3333qa+PHj0agHZS+OPHj8NkMqn7nDx5Ej179lSfz58/H/Pnz8egQYO8piMiIqKmjXGmBhOBRERERETklwwZToPV53IthmsNHjzYa95vd/feey/uvfdev214fujKzMz02yYRETUewcQZZX/AeKxhnKnBRCARUYSrkmTIUhBDgyWZQ4OJiKjeBTM0mIiIqDYYa4LHRCARURTi0GAiIgpGlSTDbPBLJycncCcioiAFE2cAxpq6YCKQiCjCOSAguFgIERE1MqzSICKi+sZYEzwmAomIohArAomIiIiIiKKPKfAuREREwKJFi5CZmYm4uDj0798fX375pd/9V61ahezsbMTFxeHKK6/Ev/71rwa6UiIiCjVn9STuRh+Aa7hW165dsWjRojBfPRERNXbBxhnGmtpjRSARUYSzSQ6YJIfh/e2SjCNHjgY1NHjlypXIz8/H4sWL0b9/fyxcuBB5eXkoKSlBmzZtvPbftm0bxowZg3nz5uG3v/0tli9fjhEjRmDPnj3o1q1bcB0kIqKIxOFaRERU3xhrgseKQCKiKJSVlYXi4mIUFxcbmh/w+eefx8SJE3Hfffeha9euWLx4MRISEvDXv/5Vd/8XXngBN910Ex5++GHk5ORg7ty56NWrF15++eVQd4WIiIiIiIgMYiKQiCiiSZDFr0EdIcSveP+fh1BRUaF52Gw23f2rqqqwe/duDB06VN1mMpkwdOhQbN++XfeY7du3a/YHgLy8PJ/7ExFR41YFGVVwGnxwuBYREQUnuDjDWFMXHBpMRBShzGYzLKbuqJQ/Q7z5DkPHCGHDRed23DnqZiQnJ2teKygoQGFhodcxP//8M5xOJ9LS0jTb09LS8O233+qep7S0VHf/0tJSQ9dJRESRj8O1iIiovjHWBI+JQCKiCFZy6D1cemkXOM3XwmzynqvPU5VzGySpJZYtW4bXX39d85rVaq2vyyQiIiIiIqJGgEODiYgiWKdOnRBj7o0qx8aA+wrxK6ocW7Dx42WIi4tDUlKS5uErEdiqVSuYzWaUlZVptpeVlSE9PV33mPT09KD2JyKixs0uyagy+LBLHK5FRETBCSbOMNbUDSsCiYgi3NHjq9GuXSac8g8wm9r53K/K8RnMpva4/vrrg2o/NjYWvXv3xqZNmzBixAgAgCzL2LRpE6ZMmaJ7TG5uLjZt2oRp06ap2zZu3Ijc3Nygzk1ERJGLw7WIiKi+MdYEjxWBREQRrm3btog158Lm2OBzH1mcQ5VzG7Ztf6dW58jPz8cbb7yBv/3tbzhw4AD+8Ic/oLKyEvfddx8AYNy4cXj00UfV/adOnYr169fjueeew7fffovCwkLs2rXLZ+KQiIgaNzvkoB4AqzSIiMi4YOMMY03tsSKQiKgJOFm2Gq1atYND/h4WUyev16scn8Biuhz9+vWrVfujRo3CTz/9hNmzZ6O0tBQ9evTA+vXr1QVBjh8/DpOp5rula665BsuXL8esWbPwpz/9CZ07d8batWvRrVu32nWQiIgiDqs0iIiovjHWBI+JQCKiJiA1NRWxlmths38Ec+x/Q5Ik9TVZPg27cxe+/rqoTueYMmWKz4q+zZs3e20bOXIkRo4cWadzEhERERERUehwaDARURPx8+n3IMTPcMolmu02xyZYTFfiiiuuCNOVERFRpLsoOYJ6AByuRURExgUbZxhrao8VgURETURiYiJiLYNhc3wEs+lySJIJTrkMDvkrfP/9wXBfHhERRRkO1yIiovrGWBM8VgQSETUhZ8+9CyEuwCHvBwBUOTYixtwHWVlZYb4yIiIi/xYtWoTMzEzExcWhf//++PLLL/3uv2rVKmRnZyMuLg5XXnkl/vWvfzXQlRIRUSRinHFhIpCIqAmJi4vDG0ueg82xEU75GBzyQRw7sTrcl0VERBHOLsmoMviwS8Gv5Lhy5Urk5+ejoKAAe/bsQffu3ZGXl4cff/xRd/9t27ZhzJgxmDBhAvbu3YsRI0ZgxIgR+Prrr0PabyIiahjBxJnaxBrGmRqSEEKE+yIiUUVFBZKTk1FeXs4yVCIKWn2+hzgcDsTGZkCISsSa+8Hm2BzS9qlhMM4QUV2F4n1EaaO5tQCSFGfoGCEu4rxtTlDn7d+/P/r27YuXX34ZACDLMjp06IAHH3wQM2fO9Np/1KhRqKysxPvvv69uu/rqq9GjRw8sXrzY0DmJsYaI6iZccQYIPtYwztTgHIG1pORPKyoqwnwlRBSJlPeO+vguxmKx4N13X8PIO6bg1I+sBoxUjDNEVFehjDUCNsBgMwI2zfkVVqsVVqvVa/+qqirs3r0bjz76qLrNZDJh6NCh2L59u+45tm/fjvz8fM22vLw8rF271thFEgDGGiKqm3DFGXV/GIs1jDNaTATW0rlz5wAAHTp0CPOVEFEkO3fuHJKTk0Pe7m233QanfFvI26WGwzhDRKFSl1gTGxuL9PR0lJY+FdRxzZs393r/KigoQGFhode+P//8M5xOJ9LS0jTb09LS8O233+q2X1paqrt/aWlpUNcZ7RhriCgUwhFnAOOxhnFGi4nAWmrbti1OnDiBxMRESJJk6JiKigp06NABJ06caHKl9+xb5Gmq/QIio29CCJw7dw5t27YN96VQI8U4o8W+RSb2LbxCEWvi4uJw5MgRVFVVBX1uz/cuvWpACq9gY00k/N3XFvsWmdi38ApnnFHOz1gTPCYCa8lkMqF9+/a1OjYpKanR/o9cV+xb5Gmq/QIaf9/qoxKQmg7GGX3sW2Ri38InFLEmLi4OcXHG520KVqtWrWA2m1FWVqbZXlZWhvT0dN1j0tPTg9qf9NU21jT2v/u6YN8iE/sWPowzkYerBhMRERERUdjExsaid+/e2LRpk7pNlmVs2rQJubm5usfk5uZq9geAjRs3+tyfiIiiF+OMFisCiYiIiIgorPLz8zF+/Hj06dMH/fr1w8KFC1FZWYn77rsPADBu3Di0a9cO8+bNAwBMnToVgwYNwnPPPYebb74ZK1aswK5du/D666+HsxtERNRIMc7UYCKwAVmtVhQUFDTJMevsW+Rpqv0CmnbfiPxpyn/77FtkYt/IqFGjRuGnn37C7NmzUVpaih49emD9+vXqRO3Hjx+HyVQzmOmaa67B8uXLMWvWLPzpT39C586dsXbtWnTr1i1cXYgKTfnvnn2LTOwbGcU4U0MSoVjnmYiIiIiIiIiIiBo1zhFIREREREREREQUBZgIJCIiIiIiIiIiigJMBBIREREREREREUUBJgKJiIiIiIiIiIiiABOBdfDkk0/immuuQUJCAlq0aOH1+r59+zBmzBh06NAB8fHxyMnJwQsvvKDZZ/PmzZAkyetRWlrq99xfffUVfvOb3yAuLg4dOnTAM888E8quhaRv7rZu3QqLxYIePXr4Pe/Ro0d1fx9ffPFFHXvkEq5+AZFxzz7//HMMGDAAqampiI+PR3Z2NhYsWOD3vPV9z8LZN6D+7xuRP4wzkRdnAMYaxhrGGoocjDOMMwDjDBA5941xhkLBEu4LiGRVVVUYOXIkcnNz8eabb3q9vnv3brRp0wZvv/02OnTogG3btmHSpEkwm82YMmWKZt+SkhIkJSWpz9u0aePzvBUVFbjxxhsxdOhQLF68GPv378f999+PFi1aYNKkSY2ub2fPnsW4ceMwZMgQlJWVGTr/xx9/jCuuuEJ9npqaWrcOVQtXvyLlnjVr1gxTpkzBVVddhWbNmuHzzz/Hf//3f6NZs2YBr7O+7lk4+9YQ943IH8aZyIszAGMNYw1jDUUOxhnGGcYZrcZ+3xhnKCQE1dnSpUtFcnKyoX3/53/+R1x33XXq808//VQAEGfOnDF8vldeeUW0bNlS2Gw2dduMGTNEly5dDLdhVF36phg1apSYNWuWKCgoEN27d/fbxpEjRwQAsXfv3uAvNggN3a9Iu2fubr31VnH33Xf7fL2h7pkQDd+3hrxvRP4wzrhEUpwRgrFGwVjDWEONH+OMC+NMd79tRNp9c8c4wzhDNTg0uIGVl5cjJSXFa3uPHj2QkZGBG264AVu3bvXbxvbt23HttdciNjZW3ZaXl4eSkhKcOXMm5NdslF7fli5diu+//x4FBQVBtXXLLbegTZs2GDhwINatWxfKywxaKPoVSffM3d69e7Ft2zYMGjQoYFuN6Z4BoelbY71vRP4wzhgTCe9ZjDXeIuG+uWOsoaaIccaYSHi/YpzxFgn3zR3jDNUGhwY3oG3btmHlypX44IMP1G0ZGRlYvHgx+vTpA5vNhiVLlmDw4MHYsWMHevXqpdtOaWkpsrKyNNvS0tLU11q2bFl/nfBBr2+HDh3CzJkz8e9//xsWi7E/tebNm+O5557DgAEDYDKZ8N5772HEiBFYu3Ytbrnllvq6fJ9C1a9IuWeK9u3b46effoLD4UBhYSEeeOABn+00tnsGhK5vjfG+EfnDOBNYpLxnMdZoRcp9UzDWUFPFOBNYpLxfMc5oRcp9UzDOUJ2EuySxsZkxY4YA4Pdx4MABzTFGSnv3798vWrVqJebOnRvwGq699lq/pb033HCDmDRpkmbbN998IwCI4uLiRtE3h8Mh+vTpI1599VV1m5Fycz333HOPGDhwYET3KxLumbvvv/9efPXVV+L1118XKSkpYvny5X7b8xTonkVK32p734j8YZyJvDgTKX2LhPvmjrHGhbGGQo1xhnGmvvoWCffNHeOMC+MMeWJFoIf//d//xb333ut3n06dOgXVZnFxMYYMGYJJkyZh1qxZAffv168fPv/8c5+vp6ene03kqjxPT0/3eVxD9u3cuXPYtWsX9u7dq05sKssyhBCwWCz46KOPcP311xs6R//+/bFx40afr0dCvyLhnrlTvjG68sorUVZWhsLCQowZM8bwOQLdMyAy+lbb+0bkD+NM5MUZIDL6Fgn3zR1jjQtjDYUa4wzjjIJxhnEGYJwhb0wEemjdujVat24dsva++eYbXH/99Rg/fjyefPJJQ8cUFRUhIyPD5+u5ubl47LHHYLfbERMTAwDYuHEjunTp4restyH7lpSUhP3792u2vfLKK/jkk0/w7rvvepUm+xPo9xEJ/YqEe+aLLMuw2WxBnSfQPQMio2+1vW9E/jSGv33GGa3G9p7FWGNMY7tvvjDWUENrDH/3jDNaje39inHGmMZ233xhnKGghbMcMdIdO3ZM7N27V8yZM0c0b95c7N27V+zdu1ecO3dOCOEq523durW4++67xalTp9THjz/+qLaxYMECsXbtWnHo0CGxf/9+MXXqVGEymcTHH3+s7vPSSy+J66+/Xn1+9uxZkZaWJu655x7x9ddfixUrVoiEhATx2muvNaq+edIrN/fs27Jly8Ty5cvFgQMHxIEDB8STTz4pTCaT+Otf/xrR/YqUe/byyy+LdevWiYMHD4qDBw+KJUuWiMTERPHYY4/57Ft937Nw9q0h7huRP4wzkRdnwtm3SLlvjDWMNdR4MM4wzgTTt0i5b4wzjDPkHxOBdTB+/HjdOQA+/fRTIYTrzVTv9UsuuURt4+mnnxaXXnqpiIuLEykpKWLw4MHik08+0ZynoKBAc4wQQuzbt08MHDhQWK1W0a5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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Taking a look at the 3 warming levels\n", "fig, axes = plt.subplots(3, 3, figsize=(15, 12), sharex=True, sharey=True)\n", @@ -985,10 +13598,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "f8363e54-c20c-4bb3-8af6-60726ddb09eb", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:15:57.780815Z", + "iopub.status.busy": "2026-01-02T23:15:57.780572Z", + "iopub.status.idle": "2026-01-02T23:16:02.164796Z", + "shell.execute_reply": "2026-01-02T23:16:02.163951Z", + "shell.execute_reply.started": "2026-01-02T23:15:57.780796Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:16:00 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n", + "2026-01-02 23:16:01 - climakitae.new_core.processors.warming_level - WARNING - \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 1.2C. \n", + "Skipping this warming level.\n", + "2026-01-02 23:16:01 - climakitae.new_core.processors.warming_level - WARNING - No valid slices found for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1. Ensure the warming level times table is correctly configured.\n" + ] + } + ], "source": [ "# Let's first find the 90th, 95th, and 98th percentiles over Humboldt County for a 1.2°C reference warming level period.\n", "percentiles = [90, 95, 98]\n", @@ -1017,10 +13657,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "f482fc76-2e5b-423d-b4fe-023616d62934", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:16:02.166088Z", + "iopub.status.busy": "2026-01-02T23:16:02.165583Z", + "iopub.status.idle": "2026-01-02T23:16:08.007029Z", + "shell.execute_reply": "2026-01-02T23:16:08.006069Z", + "shell.execute_reply.started": "2026-01-02T23:16:02.166057Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:16:05 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n" + ] + } + ], "source": [ "# Now, we'll get all the raw data from the future WLs and count the number of days each simulation is above the different 1.2°C WL percentiles.\n", "wrf_wl_data = (\n", @@ -1043,10 +13705,2163 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "56c0d85b-4d4e-4887-8eba-35ae6ed1db5f", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:16:08.007940Z", + "iopub.status.busy": "2026-01-02T23:16:08.007664Z", + "iopub.status.idle": "2026-01-02T23:16:08.043239Z", + "shell.execute_reply": "2026-01-02T23:16:08.042487Z", + "shell.execute_reply.started": "2026-01-02T23:16:08.007914Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Size: 2GB\n", + "Dimensions: (sim: 5, warming_level: 3, y: 52, x: 47,\n", + " time_delta: 10950)\n", + "Coordinates:\n", + " * sim (sim) object 40B 'WRF_UCLA_EC-Earth3_ssp370_day_d03_r1...\n", + " * warming_level (warming_level) float64 24B 1.5 2.0 3.0\n", + " * y (y) float64 416B 1.643e+06 1.646e+06 ... 1.796e+06\n", + " * x (x) float64 376B -4.143e+06 -4.14e+06 ... -4.005e+06\n", + " * time_delta (time_delta) int64 88kB -5475 -5474 -5473 ... 5473 5474\n", + " lakemask (y, x) float32 10kB dask.array\n", + " landmask (y, x) float32 10kB dask.array\n", + " lat (y, x) float32 10kB dask.array\n", + " lon (y, x) float32 10kB dask.array\n", + " centered_year (sim, warming_level) float64 120B 2.018e+03 ... 2.083e+03\n", + " Lambert_Conformal int64 8B 0\n", + "Data variables:\n", + " t2 (sim, warming_level, time_delta, y, x) float32 2GB dask.array\n", + "Attributes: (12/121)\n", + " AERCU_FCT: 1.0\n", + " AERCU_OPT: 0\n", + " AUTO_LEVELS_OPT: 2\n", + " BL_PBL_PHYSICS: 1\n", + " BOTTOM-TOP_GRID_DIMENSION: 40\n", + " BOTTOM-TOP_PATCH_END_STAG: 40\n", + " ... ...\n", + " clip: Process 'clip' applied to the data. Cli...\n", + " convert_units: Process 'convert_units' applied to the ...\n", + " update_attributes: Process 'update_attributes' applied to ...\n", + " filter_unadjusted_models: yes\n", + " concat: Process 'concat' applied to the data. T...\n", + " warming_level_simple: Process 'warming_level_simple' applied ..." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display(ref_percentiles)\n", "display(wrf_wl_data)" @@ -1062,9 +15877,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "5d21461c-246b-4068-9ec2-5bb2b0ff5f64", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:16:08.044203Z", + "iopub.status.busy": "2026-01-02T23:16:08.043990Z", + "iopub.status.idle": "2026-01-02T23:16:08.075643Z", + "shell.execute_reply": "2026-01-02T23:16:08.074692Z", + "shell.execute_reply.started": "2026-01-02T23:16:08.044185Z" + } + }, "outputs": [], "source": [ "# Dropping `EC-Earth3-Veg` by only keeping the other sims\n", @@ -1085,10 +15908,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "11bfc1c2-1ccc-45e4-9e9e-ec674b19cbc2", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:16:08.076551Z", + "iopub.status.busy": "2026-01-02T23:16:08.076306Z", + "iopub.status.idle": "2026-01-02T23:19:04.995037Z", + "shell.execute_reply": "2026-01-02T23:19:04.993933Z", + "shell.execute_reply.started": "2026-01-02T23:16:08.076525Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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q1auLkiVLirfeekvMmTNHREZGCn9/f3HixAmnfc7uNZOUlCTCwsJEUFCQmDhxonj//fdFrVq1hEqlUjzfLf2PjY0VtWvXFu+//76YNWuWSEtLc3id3377rQAgWrZsKRYvXiwWL14sRo0aJXr06CG3cfVx8dbPFbbPM0cmTJggAIh9+/Zl286a5fW8evVqeduuXbuERqMRVapUEXPnzhXTp08XJUuWFMWKFVO8piyvhxo1aoiOHTuKRYsWiWeffVb+/GOtXr16YtCgQeKDDz4QCxcuFK1btxYAxKJFixTtmjVrJpo1a5ZjvyMiIhTtLDHm8uXL4tlnnxVdu3aV923btk0AEBs2bBBCCLFp0yZRq1YtMWXKFLFixQrx5ptvimLFionIyEjFc8zyvhsbGyuaNWsmFi5cKGbPni33MyIiQpQrV06MHz9eLFy4UMTGxgq1Wi3Wr18vSpcuLaZNmybmz58vypQpI0JCQkRKSordua3vz8jISPHYY4+JsLAw8eabb4pFixaJxx9/XEiSJP744w+53cWLF0Xx4sVFiRIlxPTp08V7770nYmJi5M+ZOcXJnD7HCCFcjk0VK1YU7dq1szt+8ODBolixYvJnAFffuxw9H+Pi4kRISIiYNGmSWLVqlXjnnXdEixYtxA8//JDt7SSigsWEHdEj5pdffhEAxK5du4QQQphMJlG2bFnFB+udO3cKAOLLL79UHNuuXTtRsWJF+e+EhAShUqnETz/9pGi3bNkyAUDs379f3gZAqFQq8X//9392fUpPT1f8nZWVJapXry4nU4QQ4ujRowKAGDNmjKLtoEGD7BJ2Q4cOFeHh4eLGjRuKtr179xYhISF212fLkrATwvzhyM/PT1y+fFkI4ZmEna+vr+KD3/LlywUAUbp0acUHT8uXBOu2zZo1EwDEvHnz5G06nU7Url1blCpVSv4Q56nHxta1a9eEVqsVrVu3FkajUd6+aNEiAUB8/PHHdrc/uw+vrra9ffu2/GXfwtHjOGvWLCFJkrhw4YK8rU+fPiIiIkLR319//VXx4fW3335zmJBy1XvvvSf8/f3lx+/UqVMCgNi6dauineXLRN26deXHSggh5s6dKwCIL774Qgjh+v1sMplEmTJlRLdu3RTXs3HjRgFA/Pjjj0IIIe7evSuKFi0qhg0bpmiXlJQkQkJC7LbbsjzvP/74Y3H9+nVx+fJlsWPHDlGpUiUhSZIi4e/O6w+AIqkohBAzZswQAQEB4tSpU4rtb7zxhlCr1eLff/8VQtz/AhIcHCyuXbumaNuyZUtRo0YNkZmZKW8zmUwiLi5OVK5cWd5meTxatWqlSJa+8sorQq1Wizt37gghhLhz544ICgoS9evXFxkZGYrrshxnMplE5cqVRXx8vOJc6enpIioqSjz99NPO7l7F7Zk+fbq4fv26SEpKEnv37hV16tQRAMSWLVvcug7La6pPnz6K6zl9+rRQqVSia9euiueW9W1x5/kycOBAAUC89dZbirZ16tQRdevWlf++fv263Xu1bV+t2SZSXH1euMI2Yefqc9byOqhevbri9dunTx8hSZJo27at4viGDRvaxQYAAoD45Zdf5G0XLlwQfn5+ivc3Zxy9ZsaMGSMAKN7v7969K6KiokSFChXkx9nS/4oVK+YYB4UQ4uWXXxbBwcHCYDA4bePq4+KNnyuEcC1hZ/nxyfJ+YJGRkSGuX78uX27fvi3vc5QgscTpmzdvytuOHz8uVCqVGDBggLzN8nro1KmT4vpefPFFAUDx45ejxzE+Pl5xnwrhesKuR48ewt/fX35+z5o1S0RFRQkhhFiyZIkoVaqU3HbcuHECgLh06ZLTvhw8eFAAEJ999pm8zfK+27hxY7vnluUzztq1a+Vtf/31l/xYHzp0SN5ueU5Z38fOEnbW8VAIc4z19fVV/Jg3evRoIUmS4gfLmzdviuLFi3ssYedqbJowYYLw8fERt27dkrfpdDpRtGhRMWTIEHmbq+9dts9Hy2cq2x+IiMj7cEgs0SMmMTERYWFhaNGiBQDzkJJevXph/fr18vClp556CiVLlsSGDRvk427fvo1du3ahV69e8rZNmzahatWqiImJwY0bN+SLZQjX999/r7juZs2aITY21q5P/v7+iutJTk5GkyZN8Ouvv8rbLcNcXnzxRcWxtiu5CiGwZcsWdOzYEUIIRb/i4+ORnJysOG9OJk2aBIPBgNmzZ7t8TE5atmypGEJrWam3W7duinlgLNv/+ecfxfEajQYjRoyQ/9ZqtRgxYgSuXbuGo0ePAvDcY2Pru+++Q1ZWFsaMGaOY82jYsGEIDg62G4LlKZYJ4e/evStvs37epKWl4caNG4iLi4MQQjEEdsCAAbh8+bLiNicmJsLf3x/dunUDAHmC7Z07dzocXpqTxMREtG/fXn78KleujLp16zodFjt8+HDFxN0vvPACNBoNvv76awCu38+SJKFHjx74+uuvkZqaKrfbsGEDypQpIw9V2rVrF+7cuYM+ffoong9qtRr169e3ez44M2TIEISGhiIiIgJt2rRBcnIyEhISUK9ePQCeef1t2rQJTZo0QbFixRTHt2rVCkaj0W7oYLdu3eThlgBw69Yt7NmzBz179sTdu3fl42/evIn4+HicPn1aHgZt/XhYDy9s0qQJjEYjLly4IN9/d+/exRtvvGE3P5bluGPHjuH06dPo27cvbt68KV9vWloaWrZsiR9//NGloZtTp05FaGgoSpcujebNm+Ps2bOYM2cOnnnmmVxdx/PPP6/4e9u2bTCZTJgyZYrdvGWW25Kb54vt9TRp0sTuvSsv3H1euCo3z9kBAwYoXr/169eHEAJDhgxRtKtfvz7+++8/GAwGxfaGDRuibt268t/ly5dH586dsXPnzlytNPr111/jySefVAx5DgwMxPDhw3H+/Hl5mLjFwIEDFe+fzhQtWhRpaWmKqQhsufq4eOPnCldZhgzbLkyybNkyhIaGyhfr+9/WlStXcOzYMQwaNAjFixeXt9esWRNPP/20/N5vbeTIkYq/LZ93rNta387k5GTcuHEDzZo1wz///OPydA7WGjdujIyMDPmzxP79+xEXFwfAPJ/mtWvXcPr0aXlfVFSUvDCXdV/0ej1u3ryJSpUqoWjRog7v92HDhjmcby4wMBC9e/eW/37sscdQtGhRVK1aVf5cBDj/jORIbGysPIUDYJ6q47HHHlMcu2PHDjRs2FCxuE/x4sVdGursCndiU69evaDX6/H555/Lx3/77be4c+eO/HrJS7z19/eHVqvF3r17HU7HQUTeg4tOED1CjEYj1q9fjxYtWijmQ6pfvz7mzZuH3bt3o3Xr1tBoNOjWrRvWrl0LnU4HX19ffP7559Dr9YoP1qdPn8aff/6p+LJszbLogEVUVJTDdl999RVmzpyJY8eOKeaosf4CfeHCBahUKrtz2K4+ev36ddy5cwcrVqzAihUrXOpXdipWrIj+/ftjxYoVHlvFsHz58oq/Lcki2xVQLdttP0xFRETYTc5cpUoVAOa5oho0aOCxx8aWJYHx2GOPKbZrtVpUrFhR3u9plmSUdULz33//xZQpU7B9+3a7+8j6i8rTTz+N8PBwJCYmomXLljCZTFi3bh06d+4sny8qKgqvvvoq3n//fSQmJqJJkybo1KkTnn32WcVqeY78+eef+O233zBgwACcOXNG3t68eXMsXrwYKSkpCA4OVhxTuXJlxd+BgYEIDw+X545x537u1asX5s+fj+3bt6Nv375ITU3F119/jREjRsivIcsXLMuXXlu2/XNmypQpaNKkCVJTU7F161asX79ekfTxxOvv9OnT+P3333P93D1z5gyEEJg8ebLTFRuvXbuGMmXKyH/bviaLFSsG4P5rzzJHX/Xq1bPtN2BOhjiTnJwsn9uZ4cOHo0ePHlCpVChatKg8N1dur8P2/jl79ixUKlW2CXp3ny9+fn52j1exYsU8+kXQ3eeFq3LznHXnPdxkMiE5ORklSpSQt9u+/gHze3h6ejquX7/u9orLFy5cUCQyLKpWrSrvt37uuvp+/+KLL2Ljxo1o27YtypQpg9atW6Nnz55o06aN3MbVx8UbP1e4yhInUlNTFfGgW7du8v06duzYbJOtzt7TAfPjtHPnTruFF2yfJ9HR0VCpVIo5xvbv34+pU6fi4MGDdj822a726grreezq16+PAwcOYObMmQDM73/BwcHYv38/ypUrh6NHjyoeu4yMDMyaNQurV6/GpUuXIIRQ9MWWs8eubNmydo9TSEiIy5+RHLF9zQL271EXLlxwuNqtq6vc58Sd2FSrVi3ExMRgw4YNGDp0KADzD3ElS5aU35fzEm99fX0xZ84cjB07FmFhYWjQoAE6dOiAAQMG5GrFdyLKP0zYET1C9uzZgytXrmD9+vVYv3693f7ExES0bt0aANC7d28sX74c33zzDbp06YKNGzciJiYGtWrVktubTCbUqFED77//vsPrs/1w5egX/Z9++gmdOnVC06ZNsWTJEoSHh8PHxwerV6+2m8zdFZbqkmeffdbpl9qaNWu6dc6JEyciISEBc+bMQZcuXez2O/sC4OzDu7MVzJxtt/7Q6ypPPDbe5I8//gBw/4Oz0WjE008/jVu3buH1119HTEwMAgICcOnSJQwaNEhRZaRWq9G3b1+sXLkSS5Yswf79+3H58mW7FSLnzZuHQYMG4YsvvsC3336Ll156CbNmzcKhQ4fkCbEdWbNmDQDglVdewSuvvGK3f8uWLQ4nW/eUBg0aoEKFCti4cSP69u2LL7/8EhkZGYovUpb7IyEhweGHcVdX6KxRowZatWoFAOjSpQvS09MxbNgwNG7cGOXKlfPI689kMuHpp592uuqsJTltYfvctfRh3LhxiI+Pd3gO2y9gnnjtWa733XffVVRoWLOt0HGkcuXK8n3sievIzWvb3edLQazK6O7zwp3zAu49ZwviPTw/ufqcKFWqFI4dO4adO3fim2++wTfffIPVq1djwIAB8oT47jwuD+rnipiYGADmONSoUSNFXyz9sVQY5ifbzxpnz55Fy5YtERMTg/fffx/lypWDVqvF119/jQ8++CBXi7HUqlULQUFB2LdvH9q1a4dbt27JFXYqlQr169fHvn37EB0djaysLEVV4ejRo7F69WqMGTMGDRs2REhICCRJQu/evR32xdnzMD9eX97w2nQ3NvXq1Qtvv/02bty4gaCgIGzfvh19+vSR33/zGm/HjBmDjh07Ytu2bdi5cycmT56MWbNmYc+ePahTp06ubiMReR4TdkSPkMTERJQqVQqLFy+22/f5559j69atWLZsGfz9/dG0aVOEh4djw4YNaNy4Mfbs2YOJEycqjomOjsbx48fRsmXLXP1qDZiTGX5+fti5c6dcRQKYVz6zFhkZCZPJhHPnzil+dbauaALMwxyCgoJgNBqdful1V3R0NJ599lksX77cYRVDsWLFFCt3WuRXtdnly5ftfok/deoUAMhDbT3x2DgSGRkJAPj7779RsWJFeXtWVhbOnTvnsfvcVkJCAgDIH3JPnDiBU6dO4dNPP8WAAQPkds6Gbg0YMADz5s3Dl19+iW+++QahoaEOPzDXqFEDNWrUwKRJk3DgwAE0atQIy5YtkysMbAkhsHbtWrRo0cJuuDYAzJgxA4mJiXYJu9OnT8vD0gFz5caVK1fQrl07AO7fzz179sSHH36IlJQUbNiwARUqVECDBg3k/dHR0QDMX8A9+RjNnj0bW7duxdtvvy0PD8vr6y86Ohqpqam5Pt5yf/n4+Hj0PQAwf2F3Vm1haRMcHJxvrwNPXEd0dDRMJhNOnjzpNOmXH8+XvL4P5fV54Ux+xIycWCoYrZ06dQpFihRxWlmWncjISPz999922//66y95f25ptVp07NgRHTt2hMlkwosvvojly5dj8uTJqFSpkluPi7d9rnBVhw4dMHv2bCQmJioSdu6wfk+39ddff6FkyZJ2lfOnT59WVKGdOXMGJpNJjvNffvkldDodtm/frqggc3WKA0fUajUaNGiA/fv3Y9++ffLKwBZxcXHYsGGD/D5onbDbvHkzBg4ciHnz5snbMjMzHX4+8kaRkZF2nykB+8+ZueVubOrVqxemT5+OLVu2ICwsDCkpKYqhwp6Kt2PHjsXYsWNx+vRp1K5dG/PmzZN/iCSiwsc57IgeERkZGfj888/RoUMHdO/e3e4yatQo3L17V15aXqVSoXv37vjyyy+RkJAAg8GgqNgBzEmCS5cuYeXKlQ6vLy0tLcd+qdVqSJKkqEY7f/48tm3bpmhnSa4sWbJEsX3hwoV25+vWrRu2bNkiV2VZu379eo59cmTSpEnQ6/WYO3eu3b7o6GgkJyfj999/l7dduXIFW7duzdV15cRgMGD58uXy31lZWVi+fDlCQ0PleZE88dg40qpVK2i1WixYsEDxy/RHH32E5ORktG/fPlfnzc7atWuxatUqNGzYEC1btgRw/9dy6z4IIfDhhx86PEfNmjVRs2ZNrFq1Clu2bEHv3r0VVUIpKSl280zVqFEDKpVKMZzK1v79+3H+/HkMHjzY4euqV69e+P7773H58mXFcStWrIBer5f/Xrp0KQwGA9q2bQvA/fu5V69e0Ol0+PTTT7Fjxw707NlTsT8+Ph7BwcF45513FNdrkdvXRXR0NLp164ZPPvkESUlJHnn99ezZEwcPHsTOnTvt9t25c8fucbJVqlQpNG/eHMuXL8eVK1dy1QdbrVu3RlBQEGbNmoXMzEzFPsvjU7duXURHR+O9995TzCeYl+u15Ynr6NKlC1QqFd566y27qhfLbcmP50uRIkUAINdf3vP6vHAmv2JGdg4ePKiYW+q///7DF198gdatW+eqWrFdu3b4+eefcfDgQXlbWloaVqxYgQoVKrg0P6kjN2/eVPytUqnkih3L+6I7j4u3fa5wVaNGjfD0009jxYoV+OKLLxy2yalSKzw8HLVr18ann36qeA388ccf+Pbbb+Ufa6zZ/rhq+bxjiROO4mBycnKuE5MWjRs3xvXr17F69WrUr19fMe1BXFwc/v77b3zxxRcoUaKEPOza0h/b+2HhwoW5mpexMMTHx+PgwYM4duyYvO3WrVtO56J1l7uxqWrVqqhRowY2bNiADRs2IDw8HE2bNpX35+W9Kz093S6WRUdHIygoKNvPPERU8FhhR/SI2L59O+7evYtOnTo53N+gQQOEhoYiMTFR/gDdq1cvLFy4EFOnTkWNGjUUH8wAoH///ti4cSOef/55fP/992jUqBGMRiP++usvbNy4ETt37sQTTzyRbb/at2+P999/H23atEHfvn1x7do1LF68GJUqVVIkwOrWrYtu3bph/vz5uHnzJho0aIAffvhBriyz/iV+9uzZ+P7771G/fn0MGzYMsbGxuHXrFn799Vd89913uHXrltv3n6XKzjIMyFrv3r3x+uuvo2vXrnjppZeQnp6OpUuXokqVKrma4DonERERmDNnDs6fP48qVapgw4YNOHbsGFasWCFPhO6Jx8aR0NBQTJgwAdOnT0ebNm3QqVMn/P3331iyZAnq1atnN8zUXZs3b0ZgYCCysrJw6dIl7Ny5E/v370etWrWwadMmuV1MTAyio6Mxbtw4XLp0CcHBwdiyZUu2c9kMGDAA48aNAwC7fu7ZswejRo1Cjx49UKVKFRgMBiQkJMgfiJ1JTEyEWq12mqjs1KkTJk6ciPXr1+PVV1+Vt2dlZaFly5bo2bOnfP81btxYfn26ez8//vjjqFSpEiZOnAidTmf3JTg4OBhLly5F//798fjjj6N3794IDQ3Fv//+i//9739o1KgRFi1a5PR2Zmf8+PHYuHEj5s+fj9mzZ+f59Td+/Hhs374dHTp0wKBBg1C3bl2kpaXhxIkT2Lx5M86fP4+SJUtme47FixejcePGqFGjBoYNG4aKFSvi6tWrOHjwIC5evIjjx4+7dRuDg4PxwQcf4LnnnkO9evXQt29fFCtWDMePH0d6ejo+/fRTqFQqrFq1Cm3btkW1atUwePBglClTBpcuXcL333+P4OBgfPnll25dry1PXIfleTJjxgw0adIEzzzzDHx9fXHkyBFERERg1qxZ+fJ88ff3R2xsLDZs2IAqVaqgePHiqF69erbzAlrzxPPCmfyIGdmpXr064uPj8dJLL8HX11f+IWr69Om5Ot8bb7yBdevWoW3btnjppZdQvHhxfPrppzh37hy2bNlit7iIq5577jncunULTz31FMqWLYsLFy5g4cKFqF27tvx5wN3HxZs+V7hjzZo1aNOmDbp06YK2bduiVatWKFasGJKSkvDdd9/hxx9/lBNpzrz77rto27YtGjZsiKFDhyIjIwMLFy5ESEgIpk2bZtf+3Llz6NSpE9q0aYODBw9izZo16Nu3rzyEuHXr1nIF5IgRI5CamoqVK1eiVKlSDhNCrrJUzR08eNCuXw0aNIAkSTh06BA6duyo+OzVoUMHJCQkICQkBLGxsTh48CC+++47xfyN3uy1117DmjVr8PTTT2P06NEICAjAqlWrUL58edy6dcvlis/3339f/oHCQqVS4c0333Q7NvXq1QtTpkyBn58fhg4davdazu1716lTp+TPILGxsdBoNNi6dSuuXr2qqOIjIi9QMIvRElFh69ixo/Dz8xNpaWlO2wwaNEj4+PjIy8ObTCZRrlw5AUDMnDnT4TFZWVlizpw5olq1asLX11cUK1ZM1K1bV0yfPl0kJyfL7QCIkSNHOjzHRx99JCpXrix8fX1FTEyMWL16tZg6daqwfYtKS0sTI0eOFMWLFxeBgYGiS5cu4u+//xYAxOzZsxVtr169KkaOHCnKlSsnfHx8ROnSpUXLli3FihUrcryvIiMjRfv27e22nz59WqjVagFAbNq0SbHv22+/FdWrVxdarVY89thjYs2aNQ5vg6P74dy5cwKAePfddxXbv//+e7vratasmahWrZr45ZdfRMOGDYWfn5+IjIwUixYtsuuvJx4bZxYtWiRiYmKEj4+PCAsLEy+88IK4ffu2oo3l9l+/fj3H81naWi5+fn6ibNmyokOHDuLjjz8WmZmZdsecPHlStGrVSgQGBoqSJUuKYcOGiePHjwsAYvXq1Xbtr1y5ItRqtahSpYrdvn/++UcMGTJEREdHCz8/P1G8eHHRokUL8d133zntc1ZWlihRooRo0qRJtrctKipK1KlTRwghxOrVqwUA8cMPP4jhw4eLYsWKicDAQNGvXz9x8+ZNu2NduZ8tJk6cKACISpUqOe3L999/L+Lj40VISIjw8/MT0dHRYtCgQeKXX37J9jY4ei5aa968uQgODhZ37twRQrj++nP23Lt7966YMGGCqFSpktBqtaJkyZIiLi5OvPfeeyIrK0sI4fx1Y3H27FkxYMAAUbp0aeHj4yPKlCkjOnToIDZv3iy3sTweR44ccXh7v//+e8X27du3i7i4OOHv7y+Cg4PFk08+KdatW6do89tvv4lnnnlGlChRQvj6+orIyEjRs2dPsXv3bof9tMjp9rh7HTm9/j7++GNRp04d+b2hWbNmYteuXXb3Q07Pl4EDB4qAgAC78zt6/ztw4ICoW7eu0Gq1AoCYOnWq07aRkZFi4MCBim2uPC9cERAQYHduV56zzl4Hzp5Hjh4Dy3N+zZo1ctyrU6eO3XPNGWevmbNnz4ru3buLokWLCj8/P/Hkk0+Kr776StEmp9exrc2bN4vWrVuLUqVKCa1WK8qXLy9GjBghrly5omjnzuPibZ8rHD3PnMnIyBDz588XDRs2FMHBwUKj0YjSpUuLDh06iMTERGEwGOS2ltezbSz67rvvRKNGjeT3kI4dO4qTJ08q2lj6efLkSdG9e3cRFBQkihUrJkaNGiUyMjIUbbdv3y5q1qwp/Pz8RIUKFcScOXPExx9/LACIc+fOye2aNWsmmjVr5tLtTEtLExqNRgAQ3377rd3+mjVrCgBizpw5iu23b98WgwcPFiVLlhSBgYEiPj5e/PXXX3b3sbPXi6Wf1apVs9vu7HOZ7XPAcm7r2+7sWEf3yW+//SaaNGkifH19RdmyZcWsWbPEggULBACRlJRkdw5rtp9jrC9qtVpu50pssjh9+rR8jn379jm8Xlfeu2yfjzdu3BAjR44UMTExIiAgQISEhIj69euLjRs3ZnsbiajgSUJ42Uy4RERuOHbsGOrUqYM1a9agX79+hd0d8mI3btxAeHg4pkyZ4nSFNiKi/CRJEkaOHJnrilYiKlhjxozB8uXLkZqaWiAL7BARWeMcdkT0wMjIyLDbNn/+fKhUKsW8HkSOfPLJJzAajejfv39hd4WIiIi8jO3nzJs3byIhIQGNGzdmso6ICgXnsCOiB8bcuXNx9OhRtGjRAhqNBt988w2++eYbDB8+HOXKlSvs7pGX2rNnD06ePIm3334bXbp0kVfYIyIiIrJo2LAhmjdvjqpVq+Lq1av46KOPkJKSwqp8Iio0TNgR0QMjLi4Ou3btwowZM5Camory5ctj2rRpmDhxYmF3jbzYW2+9hQMHDqBRo0Z2qwoTERERAeYVlzdv3owVK1ZAkiQ8/vjj+OijjziKg4gKDeewIyIiIiIiIiIi8iKcw46IiIiIiIiIiMiLMGFHRERERERERETkRZiwIyIiIiIiIiIi8iJM2BEREREREREREXkRJuyIiIiIiIiIiIi8CBN2REREREREREREXoQJOyIiIiIiIiIiIi/ChB0REREREREREZEXYcKOiIiIiIiIiIjIizBhR0RERERERERE5EWYsCMiIiIiIiIiIvIiTNgRERERERERERF5ESbsiIiIiIiIiIiIvAgTdkRERERERERERF6ECTsiIiIiIiIiIiIvwoQdERERERERERGRF2HCjoiIiIiIiIiIyIswYUdERERERERERORFmLAjIiIiIiIiIiLyIkzYEREREREREREReREm7IiIiIiIiIiIiLwIE3ZERERERERERERehAk7IiIiIiIiIiIiL8KEHRERERERERERkRdhwo6IiIiIiIiIiMiLMGFHRERERERERETkRZiwIyIiIiIiIiIi8iJM2BEREREREREREXkRJuyIiIiIiIiIiIi8CBN2REREREREREREXoQJOyIiIiIiIiIiIi/ChB0REREREREREZEXYcKOiIiIiIiIiIjIizBhR0RERERERERE5EWYsCMiIiIiIiIiIvIiTNgRERERERERERF5ESbsiIiIiIiIiIiIvAgTdkRERERERERERF6ECTsiIiIiIiIiIiIvwoQdERERERERERGRF2HCjoiIiIiIiIiIyIswYUdERERERERERORFmLAjIiIiIiIiIiLyIkzYEREREREREREReREm7IiIiIiIiIiIiLwIE3ZERERERERERERehAk7IiIiIiIiIiIiL8KEHRERERERERERkRdhwo6IiIiIiIiIiMiLMGFHRERERERERETkRZiwIyIiIiIiIiIi8iJM2BEREREREREREXkRJuyIiIiIiIiIiIi8CBN2REREREREREREXoQJOyIiIiIiIiIiIi/ChB0REREREREREZEXYcKOiIiIiIiIiIjIizBhR0RERERERERE5EWYsCMiIiIiIiIiIvIiTNgRERERERERERF5ESbsiIiIiIiIiIiIvAgTdkRERERERERERF6ECTsiIiIiIiIiIiIvwoQdERERERERERGRF2HCjoiIiIiIiIiIyIswYUdERERERERERORFmLAjIiIiIiIiIiLyIkzYEREREREREREReREm7IiIiIiIiIiIiLwIE3ZERERERERERERehAk7IiIiIiIiIiIiL8KEHRERERERERERkRdhwo6IiIiIiIiIiMiLMGFHRERERERERETkRZiwIyIiIiIiIiIi8iJM2BEREREREREREXkRJuyIiIiIiIiIiIi8CBN2REREREREREREXoQJOyIiIiIiIiIiIi/ChB2RBw0aNAiBgYGF3Y0cDRo0CBUqVCjsbjx0hBDIysoq7G4Q0UOOsebRxlhDRPmNcebRxjjjPbwmYbdkyRJIkoT69esXWh8++eQTSJIkX/z8/BAREYH4+HgsWLAAd+/eLbS+uer27dvQaDTYuHGj3b47d+4gPDwcjRo1ghDCbv+hQ4egUqkwfvz4guhqgUlNTcWYMWNQtmxZ+Pr6omrVqli6dKnDtnfu3MHw4cMRGhqKgIAAtGjRAr/++quiTXp6OqZNm4a9e/cWQO9z7/Lly5g2bRqOHTvm9rG2rwVnF0uA3L17N4YMGYIqVaqgSJEiqFixIp577jlcuXLFo7fp66+/xrRp01xuv3LlSjRr1gxhYWHw9fVFVFQUBg8ejPPnz7t8jgMHDqBx48YoUqQISpcujZdeegmpqal27dasWYOSJUsiKCgIgwcPdhjkMjMz8cEHH6B+/foICQmBn58fqlSpglGjRuHUqVMu9efatWt44403UKNGDQQGBsLPzw+VKlXC4MGDsW/fPrndxo0bIUkStm7daneOWrVqQZIkfP/993b7ypcvj7i4OPnvChUqoEOHDi717UHAWOMZjDX2XI012b2/JiUlye0YaxhrGGseTIwznsE4Y8+d7zRHjx5Fhw4dULp0aQQGBqJmzZpYsGABjEaj3IZxhnGGceYBILxEXFycqFChggAgTp8+XSh9WL16tQAg3nrrLZGQkCA+/vhj8c4774jWrVsLSZJEZGSkOH78eKH0zVXr1q0TGo1G3L592+H+9evXCwBi+fLliu16vV7UqlVLVKhQQaSlpRVATwuGwWAQcXFxQqvVildeeUUsWbJEdO7cWQAQb7/9tqKt0WgUcXFxIiAgQEybNk0sWrRIxMbGiqCgIHHq1Cm53fXr1wUAMXXqVLvrGzhwoAgICMjvm+WSI0eOCABi9erVdvsGDhwoIiMjnR579uxZkZCQoLj4+vqKJk2aKLZt3bpVCCFE3bp1RVRUlHjttdfEypUrxYQJE0RQUJAICwsTV65c8dhtGjlypHDnbeuFF14QAwcOFO+995746KOPxKRJk0RYWJgoWbKkuHTpUo7H//bbb8LPz0/UqVNHLF26VEycOFH4+vqKNm3aKNqdO3dOBAYGivfee09s2rRJVK9eXcyZM0fR5vr166Ju3boCgOjQoYOYP3++WLVqlRg/frwoV66c8PHxybE/hw8fFiVLlhS+vr5i4MCBYtGiRWLlypXizTffFLGxsQKA+OGHH4QQQly6dEkAEK+++qriHMnJyUKlUgmNRiNmzJih2Pfvv/8KAGL8+PHytsjISNG+ffsc+/agYKzxDMYaJXdije3jb33JyMiQ2zHWMNYw1jyYGGc8g3FGyZ0488svvwitViuqVasm3n//fbFs2TK57UsvvSS3Y5xhnGGc8X5ekbD7559/BADx+eefi9DQUDFt2rRC6YcluB05csRu3+7du4W/v7+IjIwU6enphdA71/Tv3180a9Ys2zZt27YVxYoVE0lJSfK29957TwAQX3/9dT730Cw1NTXbfWfPnvXI9WzcuFEAEB999JFie7du3YSfn5+4evWqvG3Dhg0CgNi0aZO87dq1a6Jo0aKiT58+8rZHIbg5EhAQIAYOHOhw3w8//CCMRqPdNgBi4sSJbl1PdtwNbo788ssvAoCYNWtWjm3btm0rwsPDRXJysrxt5cqVAoDYuXOnvG3Tpk2iS5cu8t/btm0THTp0UJyrffv2QqVSic2bN9tdT2Zmphg7dmy2fbl165YIDw8XpUuXFn/++afdfpPJJNauXSt+/vlneVtUVJR48sknFe127NghJEkSffr0EfHx8Yp9a9euFQDEF198IW97UIObI4w1nsNYo+ROrMnu8bfGWGOPsYaxxtsxzngO44ySO3Fm2LBhQqvVips3byraNm3aVAQHB8t/M87YY5xhnPE2XpGwmzFjhihWrJjQ6XTihRdeEJUrV5b3ZWVliWLFiolBgwbZHZecnCx8fX0VT4rz58+Ljh07iiJFiojQ0FAxZswYsWPHDgFAfP/999n2I6cP0e+8844AIFasWCFvO378uBg4cKCIiooSvr6+IiwsTAwePFjcuHFDbrNnzx45eNtKTEwUAMSBAweEEEJcuXJFDBo0SJQpU0ZotVpRunRp0alTJ3Hu3Lls+y6EuUIsNDRUzJ07N9t2586dE0WKFBF9+/YVQpgz0IGBgaJXr15ym6+//lo0btxYFClSRAQGBop27dqJP/74Q3EeV267EEJMnTpVABD/93//J/r06SOKFi0qateunW3/JEkSLVq0EImJiYqKA3eNHj1aALD7hW3Tpk12j2WPHj1EWFiY3Zv08OHDRZEiRURmZqY4d+6cAGB3sQQ6S3C7ePGi6Ny5swgICBAlS5YUY8eOFQaDIcf+Wt5Idu7cKWrVqiV8fX1F1apVxZYtW+za3r59W4wZM0ZERkYKrVYrypQpI/r37y+uX78uvv/+e4f9tAQ6Twc3Z4oXLy6eeeaZHNv9+OOPonv37qJcuXJCq9WKsmXLijFjxig+SA4cONDhbXLXjRs3BADx+uuvZ9suOTlZaDQaxS8zQgih0+lEYGCgGDp0qLzt6NGjonjx4uLbb78Vf/31l2jXrp145ZVX5P2HDh0SAMSwYcPc7q+F5f1n/fr1Lh/Tv39/4ePjo7gfJ0+eLKpXry4+++wzERISoni+jxw5UkiSpHgNP6jBzRHGGsYa2/4VRqyxfvxTUlIcxgbGmoFuHcNYY8ZYU/gYZxhnbPtXGHGmV69eIjg42O47Ta9evURYWJjcN8YZ1zHOmDHOFDyvmMMuMTERzzzzDLRaLfr06YPTp0/jyJEjAAAfHx907doV27Ztsxs/vW3bNuh0OvTu3RsAkJaWhqeeegrfffcdXnrpJUycOBEHDhzA66+/7pF+9u/fHwDw7bffytt27dqFf/75B4MHD8bChQvRu3dvrF+/Hu3atZPnVGjevDnKlSuHxMREh7c9OjoaDRs2BAB069YNW7duxeDBg7FkyRK89NJLuHv3Lv79998c+3fkyBFcv34d7dq1y7ZdhQoVMH36dKxduxa7du3CSy+9BI1Gg/nz5wMAEhIS0L59ewQGBmLOnDmYPHkyTp48icaNGyvGybty26316NED6enpeOeddzBs2DCn/QsPD8d7772H69evo1+/fggPD8eoUaPw22+/5Xgf2NLpdFCr1dBqtYrtRYoUAWCe38Hit99+w+OPPw6VSvmyePLJJ5Geno5Tp04hNDRUniuia9euSEhIQEJCAp555hm5vdFoRHx8PEqUKIH33nsPzZo1w7x587BixQqX+nz69Gn06tULbdu2xaxZs6DRaNCjRw/s2rVLbpOamoomTZpg4cKFaN26NT788EM8//zz+Ouvv3Dx4kVUrVoVb731FgBg+PDhcj+bNm3qxr2XN6mpqUhNTUXJkiVzbLtp0yakp6fjhRdewMKFCxEfH4+FCxdiwIABcpsRI0bg6aefBgD59iQkJLjUl5s3b+LatWv45ZdfMHjwYABAy5Ytsz3mxIkTMBgMeOKJJxTbtVotateurXg+Pv744+jXrx9at26NmJgYXLx4ERMmTJD3b9++HcD995Dc+PLLL+Hv7694ruWkcePG0Ov1OHz4sLxt//79iIuLQ1xcHJKTk/HHH38o9sXExKBEiRK57qc3Y6xhrLFWWLHGokWLFggODkaRIkXQqVMnnD59Wt7HWOM6xhrGGm/COMM4Y62w4kzz5s2RkpKCESNG4M8//8SFCxewbNkyfP755/J7BuOM6xhnGGcKVWFmC4W4X8q5a9cuIYS5BLJs2bLi5Zdfltvs3LlTABBffvml4th27dqJihUryn/PmzdPABDbtm2Tt2VkZIiYmBiP/BolhBAhISGiTp068t+OSsnXrVsnAIgff/xR3jZhwgTh6+sr7ty5I2+7du2a0Gg08i8Zt2/fFgDEu+++m20/nZk8ebLLvzDo9XpRu3ZtUbx4cQHcn//h7t27omjRonZZ86SkJBESEqLY7uptt/waZT2s1FU///yzeP7550XRokUFAFGnTh2xePFip/NZ2LI8J3766SfF9jfeeEMAUJT4BgQEiCFDhtid43//+58AIHbs2CGEyLl8HDDPGWKtTp06om7dujn2NzIyUgBQ/PqUnJwswsPDFc+7KVOmOP2F02QyCSEKtnzckRkzZggAYvfu3Tm2dfRcmjVrlpAkSVy4cEHeltvycV9fX/nXqxIlSogFCxbkeIzlF0vr57JFjx49ROnSpe22nz17Vhw9elTo9XrF9q5duwoALj9vHSlWrJjDX3FTUlLE9evX5Yv10Iz/+7//EwDkeR30er0ICAgQn376qRBCiLCwMLF48WL5PGq12u61/6D+GmWLsYaxJjsFGWs2bNggBg0aJD799FOxdetWMWnSJFGkSBFRsmRJ8e+//8rtGGtcw1hzH2NN4WKcYZzJTkHGGYPBIEaNGiV8fHzk9yS1Wi2WLl2qOJZxxjWMM/cxzhS8Qq+wS0xMRFhYGFq0aAEAkCQJvXr1wvr16+VVbJ566imULFkSGzZskI+7ffs2du3ahV69esnbduzYgTJlyqBTp07yNj8/v2x/+XBXYGCgYmUlf39/+f+ZmZm4ceMGGjRoAACK1UUHDBgAnU6HzZs3y9s2bNgAg8GAZ599Vj6XVqvF3r17cfv2bbf79vXXX6N9+/YutdVoNFixYgVu3bqFBg0ayPfRrl27cOfOHfTp0wc3btyQL2q1GvXr11eswOLqbbd4/vnn3b5N9erVw9KlS3HlyhUkJiaiePHiGDVqFMLDw/Hss8/m+Ctd3759ERISgiFDhmDXrl04f/48VqxYgSVLlgAAMjIy5LYZGRnw9fW1O4efn59d25zY3tYmTZrgn3/+cenYiIgIdO3aVf47ODgYAwYMwG+//SavILhlyxbUqlVL0c5CkiSX+5lffvzxR0yfPh09e/bEU089lWN76+dSWloabty4gbi4OAghcvUrpK1vvvkGX3/9NebNm4fy5csjLS0tx2Msj7ez54Sj50PFihXx+OOPQ6PRKLanpKQAAIKCgnLTffkcgYGBdtv79++P0NBQ+WL963vVqlVRokQJeaWl48ePIy0tTV4xKS4uDvv37wcAHDx4EEajEY0bN851H70ZYw1jTXYKMtb07NkTq1evxoABA9ClSxfMmDEDO3fuxM2bN/H222+71W/GGsYaa4w1hYtxhnEmOwUZZ9RqNaKjoxEfH49PP/0UGzZsQMeOHTF69Ghs27bNrX4zzjDOWGOcKXiFmrAzGo1Yv349WrRogXPnzuHMmTM4c+YM6tevj6tXr2L37t0AzG/E3bp1wxdffAGdTgcA+Pzzz6HX6xXB7cKFC4iOjrZ7cVeqVMljfU5NTVU8QW/duoWXX34ZYWFh8Pf3R2hoKKKiogAAycnJcruYmBjUq1dPUUKemJiIBg0ayP3z9fXFnDlz8M033yAsLAxNmzbF3Llz5Te07CQlJeHXX391ObgB5sABAHXr1pXvM8uQnKeeekrxggkNDcW3336La9euuX3bLSz7csPPzw99+/bFjh078OGHH8JkMiExMdFhELVWunRpbN++HTqdDq1bt0ZUVBTGjx+PhQsXAoDizcLf319+flnLzMyU97va19DQUMW2YsWKufyBpVKlSnbP4SpVqgCAXL5/9uxZVK9e3aXzFbS//voLXbt2RfXq1bFq1SqXjvn3338xaNAgFC9eHIGBgQgNDUWzZs0AOH4uuatFixZo27YtXn31VWzatAnTp0/HokWLsj3G8ng7e064+nwAzB9QACg+GLsrKCjI4dLrb731Fnbt2qUYXmAhSRLi4uJw6NAhmEwm7N+/H6VKlZLfc6yDm+XfhyW4WWOsYaxxVUHEGkcaN26M+vXr47vvvnOrr4w1jDXWGGsKD+MM44yrCiLOzJ49G3PmzMG6deswYMAA9OzZE1u3bkXjxo0xcuRIGAwGl/vKOMM4Y41xpuBpcm6Sf/bs2YMrV65g/fr1WL9+vd3+xMREtG7dGgDQu3dvLF++HN988w26dOmCjRs3IiYmBrVq1Sqw/l68eBHJycmKYNmzZ08cOHAA48ePR+3atREYGAiTyYQ2bdrAZDIpjh8wYABefvllXLx4ETqdDocOHbJ7gY0ZMwYdO3bEtm3bsHPnTkyePBmzZs3Cnj17UKdOHad9++abb+Dn5yf/qpdblj4nJCSgdOnSdvuts+zu3HbA9YSXI3/++SdWr16NhIQEJCUloVq1ahg6dKhLt7dp06b4559/cOLECaSlpaFWrVq4fPkygPtBAzDPM3HlyhW74y3bIiIiXOqrWq12qd3D6L///kPr1q0REhKCr7/+2qVfX4xGI55++mncunULr7/+OmJiYhAQEIBLly5h0KBBDp9LeREdHY06deogMTERo0aNctouPDwcAJw+J1x9PgDmD7eAeQ6JJk2auNnj++c4fvw49Ho9fHx85O01a9bM9rjGjRvjyy+/xIkTJ+S5Hizi4uIwfvx4XLp0Cfv27UNERAQqVqyYq/55M8YaxhpXFUSscaZcuXL4+++/Xe4rYw1jjS3GmsLDOMM446qCiDNLlizBU089ZfdjUadOnfDqq6/i/PnzLiV/GWcYZ2wxzhS8Qk3YJSYmolSpUli8eLHdvs8//xxbt27FsmXL4O/vj6ZNmyI8PBwbNmxA48aNsWfPHkycOFFxTGRkJE6ePAkhhCKbf+bMGY/01zIZZHx8PABzCfvu3bsxffp0TJkyRW5nPXG0td69e+PVV1/FunXrkJGRAR8fH8WvaRbR0dEYO3Ysxo4di9OnT6N27dqYN28e1qxZ47Rv//vf/9CiRYs8BRDLdQNAqVKl0KpVK6ft3L3tuZGcnIwNGzbg448/xuHDhxEYGIhevXrhueeek8vUXaVWq1G7dm35b0sVg/VtrF27Nn766SeYTCbFwhOHDx9GkSJF5ECY3+XZZ86csXsOnzp1CoB5cl3A/DhZT6zpSEGXkd+8eROtW7eGTqfD7t275eCQkxMnTuDUqVP49NNPFROyOvt1xRMyMjIc/spkrXr16tBoNPjll1/Qs2dPeXtWVhaOHTum2JaTjh07YtasWVizZk2ug1uHDh1w6NAhbN261a3rtvy6tG/fPuzfvx9jxoyR99WtWxe+vr7Yu3cvDh8+nOPkzg8qxhrGmuwUdKxx5p9//lFUMjDWOMZY4xxjTeFhnGGcyU5Bx5mrV6/Kw7Ct6fV6AJAr7BhnHGOccY5xpuAV2pDYjIwMfP755+jQoQO6d+9udxk1ahTu3r0rr0SiUqnQvXt3fPnll0hISIDBYLALDPHx8bh06ZJ8DGAu81y5cmWe+7tnzx7MmDEDUVFR6NevH4D7vzoIm9WDLCsT2SpZsiTatm2LNWvWIDExEW3atFGsNpOeni4Pv7SIjo5GUFBQti9EvV6PXbt2uVU67kx8fDyCg4PxzjvvyG/q1q5fvw7A/dvujrt37+LZZ59FeHg4RowYAUmSsGrVKly5cgWrVq1yO7DZun79OubMmYOaNWsqglv37t1x9epVfP755/K2GzduYNOmTejYsaM87t+yGtOdO3fy1A9nLl++jK1bt8p/p6Sk4LPPPkPt2rXlXwi7deuG48ePK9pZWB6TgICAfO2ntbS0NLRr1w6XLl3C119/jcqVK7t8rKPnkhACH374oV1bd26TwWBwWLL/888/48SJE3YrJf3111+K+UNCQkLQqlUrrFmzRlH2nZCQgNTUVPTo0SPHPlg0bNgQbdq0wapVqxzOHZKVlYVx48Zle44XXngBYWFheOWVV+QPO9ZsX4sWTzzxBPz8/JCYmIhLly4pfo3y9fXF448/jsWLFyMtLe2hKR23xljDWONMYcUay22z9vXXX+Po0aNo06aNvI2xxh5jTfYYawoH4wzjjDOFFWeqVKmCXbt24ebNm/I2o9GIjRs3IigoSE5mMs7YY5zJHuNMwSu0Crvt27fj7t27islUrTVo0AChoaFITEyUg1ivXr2wcOFCTJ06FTVq1EDVqlUVx4wYMQKLFi1Cnz598PLLLyM8PByJiYnyogGuZrK/+eYb/PXXXzAYDLh69Sr27NmDXbt2ITIyEtu3b5fPFxwcLM/JoNfrUaZMGXz77bc4d+6c03MPGDAA3bt3BwDMmDFDse/UqVNo2bIlevbsidjYWGg0GmzduhVXr16Vl3l3ZN++fUhJSfFIcAsODsbSpUvRv39/PP744+jduzdCQ0Px77//4n//+x8aNWqERYsW5eq2u+rmzZvYuXMnnn/+eQwdOhTVqlXL0/maNWuGhg0bolKlSkhKSsKKFSuQmpqKr776SlFJ1717dzRo0ACDBw/GyZMnUbJkSSxZsgRGoxHTp0+X2/n7+yM2NhYbNmxAlSpVULx4cVSvXt1j8y9UqVIFQ4cOxZEjRxAWFoaPP/4YV69exerVq+U248ePx+bNm9GjRw8MGTIEdevWxa1bt7B9+3YsW7YMtWrVQnR0NIoWLYply5YhKCgIAQEBqF+/fp7m3XCmX79++PnnnzFkyBD8+eef+PPPP+V9gYGB6NKli9NjY2JiEB0djXHjxuHSpUsIDg7Gli1bHAamunXrAgBeeuklxMfHQ61WO31tpKamoly5cujVqxeqVauGgIAAnDhxAqtXr0ZISAgmT56saF+1alU0a9YMe/fulbe9/fbbiIuLQ7NmzTB8+HBcvHgR8+bNQ+vWrRVfrF3x2WefoXXr1njmmWfQsWNHtGzZEgEBATh9+jTWr1+PK1eu4L333nN6fPHixbF161Z07NgRtWrVQu/evVGvXj34+Pjgv//+w6ZNmwAA5cuXVxyn1WpRr149/PTTT/D19ZXvQ4u4uDjMmzcPgPO5Hs6cOYOZM2faba9Tp45H3nfyE2MNY40zhRVr4uLiUKdOHTzxxBMICQnBr7/+io8//hjlypXDm2++KbdjrLHHWJMzxpqCxzjDOONMYcWZN954A88++yzq16+P4cOHw9/fH+vWrcPRo0cxc+ZMeRgi44w9xpmcMc4UsAJajdZOx44dhZ+fn0hLS3PaZtCgQcLHx0fcuHFDCGFe2rlcuXICgJg5c6bDY/755x/Rvn174e/vL0JDQ8XYsWPFli1bBABx6NChbPtkWQLdctFqtaJ06dLi6aefFh9++KFISUmxO+bixYuia9euomjRoiIkJET06NFDXL582ekS2TqdThQrVkyEhISIjIwMxb4bN26IkSNHipiYGBEQECBCQkJE/fr1xcaNG7Pt97hx40RsbGy2bZwBIEaOHGm3/fvvvxfx8fEiJCRE+Pn5iejoaDFo0CDxyy+/uH3bLUugX79+3aU+ZWVlCZ1Ol6vb48grr7wiKlasKHx9fUVoaKjo27evOHv2rMO2t27dEkOHDhUlSpQQRYoUEc2aNRNHjhyxa3fgwAFRt25dodVqFbd34MCBIiAgwK695T7IiWW56Z07d4qaNWsKX19fERMTIzZt2mTX9ubNm2LUqFGiTJkyQqvVirJly4qBAwfKrxchhPjiiy9EbGys0Gg0iuXQPb0EumXpdkcXV67n5MmTolWrViIwMFCULFlSDBs2TBw/ftxuCXeDwSBGjx4tQkNDhSRJ2d6nOp1OvPzyy6JmzZoiODhY+Pj4iMjISDF06FBx7tw5u/YARLNmzey2//TTTyIuLk74+fmJ0NBQMXLkSIfvBa5IT08X7733nqhXr54IDAwUWq1WVK5cWYwePVqcOXPGpXNcuXJFjB8/XsTGxgp/f3/h6+srKlasKAYMGOBwuXYhhJgwYYIAIOLi4uz2ff755wKACAoKEgaDwW5/do/t0KFD3bsDCgFjDWONM4UVayZOnChq164tQkJChI+Pjyhfvrx44YUXRFJSkl1bxhr7fjPW5IyxpmAxzjDOOFOY32l27NghmjVrJkqWLCm0Wq2oUaOGWLZsmV07xhn7fjPO5IxxpuBIQjipOXyIzJ8/H6+88gouXryIMmXKFGpfDAYDIiIi0LFjR3z00UceOWdsbCw6dOiAuXPneuR8VHgqVKiA6tWr46uvvirsrhCRmxhr6EHBWEP0YGKcoQcF4wyRZxTqohP5ISMjQzFJaWZmJpYvX47KlSsXemADgG3btuH69euKiSjzIisrC7169XJrwkYiIsobxhoiIspPjDNERPTQJeyeeeYZlC9fHrVr10ZycjLWrFmDv/76C4mJiYXar8OHD+P333/HjBkzUKdOHTRr1swj59VqtZg6dapHzkVERK5hrCEiovzEOENERA9dwi4+Ph6rVq1CYmIijEYjYmNjsX79eodLjRekpUuXYs2aNahduzY++eSTQu0LERHlDWMNERHlJ8YZIiJ6JOawIyIiIiIiIiIielCocm5CREREREREREREBYUJOyIiIiIiIiIiIi/y0M1hV1BMJhMuX76MoKAgSJJU2N0hogeMEAJ3795FREQEVCrP/3Zy4MABfLCgNRI+uQE/Pz+Pn5/yH+MMEeVVfsaa1NRUDBhcGhNe+x716tXz6Lmp4DDWEFFeeCrOZGZmIisry+3jtFrtQ/1dhwm7XLp8+TLKlStX2N0gogfcf//9h7Jly3r0nEIIjHn1KRz7VYd5j5fBxNduevT8VDAYZ4jIU/Ij1sx5vzy++iINN240xQ970pnseUAx1hCRJ+QlzmRmZqJCVCCuJhndPjY4OBjh4eFQqVQYOXIkRo4cmas+eCsm7HIpKCgIgPmJGRwcXMi9IaIHTUpKCsqVKye/l3jSV199hfPn9FjxSSjGvnQTo59P4fvUA4hxhojyKr9iza1bt7Dg/WR8nFgKo0fcwO7du9GqVSuPXgcVDMYaIsoLT8SZrKwsXE0y4v/OlkNQsOtVendTTKgW/d9D/f7FhF0uWX5FDA4OfmifHESU/zxdkWAymfDGm90w9vWi6NItACuWpGDWe5GY9dZtj14P5T/GGSLyFE/HmhmzKqLuE1p07ByAs6f1eH1Ce/zSMpNVdg8gxhoi8gRPvP8HF/FBcBG169dpMFfk1atXD2q1+qGssOOiE0RED5H169cjJcWEwcOCIUkSJr9VHIvnJ+PGjRuF3TUiInoIXLlyBSuWpGDyW8UBAMNfDMali0Zs27atcDtGRESPpCNHjuDkyZMPXbIOYMKOiOihodfrMWnyQLwxqRh8fc2/csU19kODOD+89U6lQu4dERE9DKa+9RhatvbH40/4AgCKFFFh/ISieHNiLxiN7s8/RERERI4xYUdE9JBYsjIcGo2E3v0CFdsnTi+GVctScOnSpULqGRERPQzOnTuHhE9SMXFaMcX2AUOCkJ4usHbt2kLqGRERPehUepXbF8A8JDY2NhaLFy8u5FvgeUzYERE9BDIyMjDn7TuYOK0YNBrlHBK16/givl0RTJ5WtZB6R0RED4M3p9REl+4BiKmqVWzXaiVMmFIUk6cMQVZWViH1joiIHkUcEktERF5t3oKyKBWmRqeuRRzunzi1KNYmpOLMmTMF3DMiInoY/N///R+2bU7DG5OKOtzfs08g/P0lLFwWUbAdIyIiekgxYUdE9IBLSUnBB+/eweTpxaBSOV6hqfJjWvToE4A3J9cu2M4REdFD4Y2JT6L/oEBUiPJxuF+tljBxejG8O+sO0tPTC7h3RET0oJOM7l8ADoklIiIv9s67kYiJ1aJla/9s270+sRi+3JaO33//vYB6RkRED4Off/4Ze3ZlYNyEotm269CpCMqUVePd+WULpmNERPTI45BYIiLySjdu3MCSD5MxeXoxSJLj6jqLcuU1GPRcEF5/s2EB9Y6IiB4Gr09ohhEjgxFWWpNtO0mSMOWt4pj/XjLu3LlTMJ0jIiJ6SDFhR0T0AJs2MxoNG/mhYSM/l9qPfb0oftqbgQMHDuRzz4iI6GGwZ88e/Ho0Cy+PDXGpfbOn/FCjlhZvz6mQvx0jIqKHisogoNK7cTEIAA/3kNjsfyYjIiKvZTKZsGrZXfzvu9IuHxNaSo0hw4PxwYLWiItLzcfeERHRw+D9Dzti+IvBKFpM7VJ7SZLw+sSi6NvtKt6dlc+dIyKiR96RI0cQHBxc2N3IF6ywIyJ6QAkhoNMJlAv3g2TQuHwpV0aLU39GIzY29qH9NYqIiDzDoGuCcmW07sWZCD+kpkqMM0RE5DIpS7h9AVhhR0RED5moqChs27atsLtBREQPKZVKhZMnTxZ2N4iI6CHnTRV2P/30E5YvX46zZ89i8+bNKFOmDBISEhAVFYXGjRu7fT5W2BERPeBUBhVUetcvklHCuXPnWPlAREQukYySW3FGpZdgMpkYZ4iI6JGxZcsWxMfHw9/fH7/99ht0Oh0AIDk5Ge+8806uzskKOyKiRxAr7IiIKD+xwo6IiNwhGc0Xd9oD5iGxarUaI0eOxMiRI/Oncy6YOXMmli1bhgEDBmD9+vXy9kaNGmHmzJm5OicTdkRERERERERE9MDxliGxf//9N5o2bWq3PSQkBHfu3MnVOTkklojoAWf5NcrVC0zgkFgiInKZZHIz1pjAIbFERPRIKV26NM6cOWO3fd++fahYsWKuzskKOyKiRxCHxBIRUX7ikFgiInKHygCo9MKt9oD3DIkdNmwYXn75ZXz88ceQJAmXL1/GwYMHMW7cOEyePDlX52SFHRHRA04yCKj0blyMwusq7H766Sc8++yzaNiwIS5dugQASEhIwL59+wq5Z0REJBndjDN676uwY5whIno4HTlyBCdPnswxWXf37l2MGTMGkZGR8Pf3R1xcHI4cOSLvF0JgypQpCA8Ph7+/P1q1aoXTp0+73I833ngDffv2RcuWLZGamoqmTZviueeew4gRIzB69Ohc3TYm7IiIHkFRUVE4efKkS8Etv+XHikpERFS4LBV2jDNEROQNnnvuOezatQsJCQk4ceIEWrdujVatWsk/4sydOxcLFizAsmXLcPjwYQQEBCA+Ph6ZmZkunV+SJEycOBG3bt3CH3/8gUOHDuH69euYMWNGrvvsNQm72bNnQ5IkjBkzRt62YsUKNG/eHMHBwZAkyaWJ+qZNmwZJkhSXmJgYRZvMzEyMHDkSJUqUQGBgILp164arV696+BYREZErLCsqrVy5Ej4+PvL2Ro0a4ddffy3EnhER0cOAcYaIyPu5Oy+39SqxOVVzZ2RkYMuWLZg7dy6aNm2KSpUqYdq0aahUqRKWLl0KIQTmz5+PSZMmoXPnzqhZsyY+++wzXL582eVphJKTk3Hr1i1otVrExsbiySefRGBgIG7duoWUlJRc3SdekbA7cuQIli9fjpo1ayq2p6eno02bNnjzzTfdOl+1atVw5coV+WJb6v7KK6/gyy+/xKZNm/DDDz/g8uXLeOaZZ/J8O4iICoNkEG5dvG3RifxYUYmIiDxHMroZa4zCq4bEMs4QET28du/ejUOHDqF///5ISUmRq6itGQwGGI1G+Pn5Kbb7+/tj3759OHfuHJKSktCqVSt5X0hICOrXr4+DBw+61I/evXtj/fr1dts3btyI3r17u3mrzAp90YnU1FT069cPK1euxMyZMxX7LNV2e/fudeucGo0GpUuXdrgvOTkZH330EdauXYunnnoKALB69WpUrVoVhw4dQoMGDdy+DUREDxpvWnTCsqJShQoVFNvzsqISEREVLm9adIJxhojI+0l6QNJLbrUHgHLlyim2T506FdOmTVNsCwoKQsOGDTFjxgxUrVoVYWFhWLduHQ4ePIhKlSohKSkJABAWFqY4LiwsTN6Xk8OHD+P999+32968eXNMnDjRxVulVOgVdiNHjkT79u0Vmcy8On36NCIiIlCxYkX069cP//77r7zv6NGj0Ov1iuuLiYlB+fLls82c6nQ6pKSkKC5ERJR3lhWVDh8+LK+olJiYiHHjxuGFF14o7O4VGMYZIqL8wThzH2MNET1s/vvvPyQnJ8uXCRMmOGyXkJAAIQTKlCkDX19fLFiwAH369IFK5Zm0mE6ng8FgsNuu1+uRkZGRq3MWasJu/fr1+PXXXzFr1iyPnbN+/fr45JNPsGPHDixduhTnzp1DkyZNcPfuXQBAUlIStFotihYtqjgup8zprFmzEBISIl9ss7hERIVFMlh+kXLxYvSuIbH5saLSg4hxhoi8ltG9OAODd60SyzhzH2MNET1sgoODFRdfX1+H7aKjo/HDDz8gNTUV//33H37++Wfo9XpUrFhRHqFpu7bB1atXnY7etPXkk09ixYoVdtuXLVuGunXrunmrzAptSOx///2Hl19+Gbt27bIbR5wXbdu2lf9fs2ZN1K9fH5GRkdi4cSOGDh2a6/NOmDABr776qvx3SkoKAxwRPbC8aUisZUWl8ePH48yZM0hNTUVsbCwCAwMLu2sFinGGiB4m3jQklnHmPsYaIvJa+nsXd9rDvOiEWq3GyJEjXVqVPCAgAAEBAbh9+zZ27tyJuXPnIioqCqVLl8bu3btRu3ZtAOb3x8OHD7tciT1z5ky0atUKx48fR8uWLQGY59c7cuQIvv32Wzdu2H2FlrA7evQorl27hscff1zeZjQa8eOPP2LRokXQ6XRQq9V5vp6iRYuiSpUqOHPmDADzHBZZWVm4c+eOosoup8ypr6+v00wtPXpSZnaEMKihz9DCoPNB6fmJhd0leoSp9AIqvXC5vWQUcoUdAJeDW35JTk6G0WhE8eLF5T4BwK1bt6DRaBAcHFxofStIjDNk7aPSqwEABqN5MMSI6wMLszv0iFMZ3YszKv39RScAxhlvwlhDFh00n0Et7s8X9oWxfyH2hij3jhw54tL7+M6dOyGEwGOPPYYzZ85g/PjxiImJweDBgyFJEsaMGYOZM2eicuXKiIqKwuTJkxEREYEuXbq41I9GjRrh4MGDePfdd7Fx40b4+/ujZs2a+Oijj1C5cuVc3bZCS9i1bNkSJ06cUGwbPHgwYmJi8Prrr3skWQeYF7U4e/Ys+vc3vwHVrVsXPj4+2L17N7p16wbAvHLUv//+i4YNG3rkOsn7JGc5n1A4RPuPW+fKeL81VBofmABotOYx6ldG94chSwODzgcGvY/cVqU2yv+P/myZe50mykfeVGHXu3dvdOzYES+++KJi+8aNG7F9+3Z8/fXXhdQzIteJXTXM/+pVgE4DoVdB6DSAUQXNoP1unWt1xMdQqQCTSYJGbYLBqMLy0E9hNJq/WFmSeBq1SXHci7cGeOCWEHmGN1XYMc7Qw+DgU1MAAPosHxiNamRlaWAyqZCl0yIjU4ve/zzv8rk6qxOghmS3zcLHap+PULZbZ+qXm+4TFTrL/HYXL15E8eLF0a1bN7z99tvw8TF/f3/ttdeQlpaG4cOH486dO2jcuDF27Njh1ojQ2rVrIzHRc8U8hZawCwoKQvXq1RXbAgICUKJECXl7UlISkpKS5Oq4EydOICgoCOXLl0fx4sUBmBN/Xbt2xahRowAA48aNQ8eOHREZGYnLly9j6tSpUKvV6NOnDwDz0rxDhw7Fq6++iuLFiyM4OBijR49Gw4YNuULsI8qSzJMMGvhfElAn+QA3/M1fuowqCIMKmv4HkbWsOYRRBUADSWUCoIKkNkGlNkGt1cNkVEHja67LNZnsp4c8O+B5mIz2iejKiYU7rwtRYcuPFZWICoXaBAmAgAGSxvwFR6RrkTa3LTJuBuHu1aK4ezsIt28Uw61bIXKsMBjM//Y6+wLWRS2HRqOCwaCGSiUUSTu1WsBovP+3rSXFP5P/b73/peRn8/FGE3k/xhl6mPho9VAbzUUBWVkaqO8VCKwo9QlupfgiOUuF2wK4odJDD2Vl7BfG/orEnDN6CEXSzloflTIZoZcENhsZZyjvJKMEyejGKrH32ro6JLZnz57o2bOn8/NJEt566y289dZbrnfahslkwpkzZ3Dt2jWYTMofVps2ber2+QotYeeKZcuWYfr06fLflhu4evVqDBo0CABw9uxZ3LhxQ25z8eJF9OnTBzdv3kRoaCgaN26MQ4cOITQ0VG7zwQcfQKVSoVu3btDpdIiPj8eSJUsK5kZRgcuuus6aSq+CZDRC0kswWSXrYFTB8EkjSBpzsk1SC8AAqDQmmACojCqoNCaotQYgSwONrx4G3f0qO8sXMkfJOiJPkIzmi8sEvGpIbH6sqERU4CzVbmoTJLUJIlMDyccEAUCYVNCn+8Kg94Feb66MsLAk6wBgXdRy+f8ajVFO2gGABsoknW11nTXbCrwlxT9j9R3ljcm9OCOZ4FVDYhln6GGjUpug1WbBZFRBD8BgVCMlTYtUvQqpAkh18oJ1JVlnYUna6SVzHLKttLPe3keVyMo7KjSuDonNb4cOHULfvn1x4cIFCKFMlkuSBKPRnS9sZoW6SqytvXv3Yv78+fLf06ZNgxDC7mJJ1gHA+fPnMW3aNPnv9evX4/Lly9DpdLh48SLWr1+P6OhoxfX4+flh8eLFuHXrFtLS0vD555+7vPIHPbwkIyDp7wciodNA6HxgyvSBMc0XJp05vy1pjFBpjPeq7CBX2anUxnv/mqDSmPc5StaZbH41ON2v8D7A0qMrKioKJ0+exMmTJ136EnX37l2MGTMGkZGR8Pf3R1xcHI4cOSLvF0JgypQpCA8Ph7+/P1q1aoXTp0+71Jf8WFGJqLBJPvcTeABgyNIgK1MLXYavPIzJYFDBZLp/sbAk2jQa+w92arX9PGIGo0pxsT6HWi2gVgssD/3Uo7ePKCeWIbGMM0SeoVYbFRfAnLhT34sVmXoVUk1AqmSCEcKuui43rM+hl4TiAiiTeM+q1ub5+ogeZM8//zyeeOIJ/PHHH7h16xZu374tX27dupWrc3p1hR1RQZInU7aqYDCmuT4pr0pjgsloggrmuetMBvt8uG2yDvfanh3wPOe4o1yTDJIi2Zxje6PkdoXdc889hz/++AMJCQmIiIjAmjVr0KpVK5w8eRJlypTB3LlzsWDBAnz66afyJK3x8fE4efJkjvM+5MeKSkSFTm0CfA1AqlbeZLSJCyaTym5oa3aVc7ZDYwE4HBprnazL6ZxErpCMbsYZveR2hR3jDJF7VGqTnLizyIQ5yWZJqHmCs+GxlmSd9Vx4z6rWYo2pr8eumx4xBglwI9bA4N6Q2Px2+vRpbN68GZUqVfLYOb2qwo6oMJl8JMBqtIQwmKvihFG6fzGoIQxq87BYQK6kU5zHqHI6/FWlFlA5qI5QqUw4N2i4B24FkWvcqbDLyMjAli1bMHfuXDRt2hSVKlXCtGnTUKlSJSxduhRCCMyfPx+TJk1C586dUbNmTXz22We4fPmySwtbWFZUKleuHDZu3Igvv/wSlSpVwu+//44mTZp46BYT5R/TT7FO90n34oRGa4BGa4DRoLab59RgUMNwL+ZYV8nlJKdknUZtkv9WqQRWR3zs0nmJPMGdCjvGGaLcUauN0PqYv8BoVAI+ObTPLb1NxZ6jZJ0PJPhAwmDVunzqBZFjR44ccbmaOz/Vr19fXn/BU1hhR2TNwSvCZFBDmFTmIbD3Enoqm2FKKrUJJidfsLKbu06lNkJ1b2itSmPCheeeQ+SqVbnrO5EbTCYTUlJSFNt8fX3h62tfVWowGGA0Gu0qGPz9/bFv3z6cO3cOSUlJaNWqlbwvJCQE9evXx8GDB9G7d+8c++PpFZWICprwuf9FxlKJJPmYIO4tTCTdq4TQ+mYBdwPkuessiTrr/zsaCmvN6GRCZttkHQB5DjzLeRPKr0T/f4e5e/OIcoVxhshzLO/nJpMyBqisqqjVMCfOMvPh+i1Vds6SdQCgEW5URxHZMqoUo91ybm9+TXhLhd3o0aMxduxYJCUloUaNGvLqsxY1a9Z0+5xM2BHdI9T3vnA5GDpkHt5qfvNQaUwwZqnkOexyyzZZZ9lG5DYDFNWhOTIBp06dQkhIiGLz1KlTFXOCWgQFBaFhw4aYMWMGqlatirCwMKxbtw4HDx5EpUqVkJSUBAAICwtTHBcWFibvy7FLHl5RiajAWcUOcS9eWOb8ltQCKrURGq0BKpWQ3/strL98qVRCkcRzle0wWMu5AGUCUKM2YV3UcvQ5N8Lt66BHmAnuxRmj+X2dcYbIM36Jf1P+v2UFcbXaKBcMaKy+Q2iEpFgsIieWZFtu5ryzTdRZknjPSxuwTPRy+3xEueEti05069YNADBkyBB5myRJEELketEJJuyI7jH5mMxJOz+jPFm4sKpisAyRNRrUkDRGWI8oNxlVMBlUEFa/CNgOebLGZB0VtipVquDnn39WbHNU9WCRkJCAIUOGoEyZMlCr1Xj88cfRp08fHD16NM99yY8VlYgKlCVZJ1fZmex+Idb4GqBSmaDSGKHVGqDVGpCaZj/vlm3yzlpOQ2UdzVlnSdZxHjsqaCqVCrdv31ZsY5whyhvLKB8VzIsZ2fKRAEvezQeSRxaesJwLcFxdZ5usy69huUTe7ty5cx4/JxN2RPcIjQHGIhqofQTgYDiScLE815K8c8Y6MafSmJR/8wsVFRCVSuXWL1HR0dH44YcfkJaWhpSUFISHh6NXr16oWLGivMr21atXER4eLh9z9epV1K5dO8dzW1ZU+t///ofw8HBIEodT0APIakis+f8mQG0zbEljgkplHhqrURvNi0hYD4m1WeHVdtiTM4oEnaOhsFb7bav7iPIT4wxR/lGphDwtj8FqCh7boao5Je2cLSrhzP3EnMRkHXmUpHd/gSPAe4bERkZGevycXHSCHhrJWRWRnFUxT+cQGgnCT0DyM0Ly1UNSCwgHlXLCaK6mM2ZpYMzSyAtNOJvHzsKSnDNXWZhs9vFLFOWOZJTMK8W6ejFCXiU2NjYWixcvdvm6AgICEB4ejtu3b2Pnzp3o3LkzoqKiULp0aezevVtul5KSgsOHD6Nhw4Y5nvP06dN45513ULVqVRQtWhQhISGKC5G3MB2IgelAjP0OHwdfhqzntNMYoVKbf6BR36uw9tHqodU6HmNoWXgip4q6nKrmrIfCqlQmOVnX6+wL2R5HZMcE9+KM4f4qsYwzRK77tf3r+LX96w732c6hDUCxSqxGLXJdjeNuJZ51go/JOips3rLoBGCuFm/UqBEiIiJw4cIFAMD8+fPxxRdf5Op8rLCjB55tki4vSTujnwThYwLUJki+Bqh89UCGVh4OC0CRlLP8P7uKOluOqhtcTdZdenGgw2PCFya4fP1EgHmVWFdW1rPYuXMnhBB47LHHcObMGYwfPx4xMTEYPHgwJEnCmDFjMHPmTFSuXBlRUVGYPHkyIiIi0KVLlxzPbVlRyZNLoBN5mnWizi5p5+RbimWKBUltvqjUJqg1Jqg15uo6lcoEP1890jO0TpNzttttF5xwlLRzVl0HABoHq5vb2vKYfXKl29+F/yGYHiyWVWJdxThDjzrrRJ1t0k7l5Fu7PFep2gi1SpgLuy1DYoXr89g5k13lnUZIimo+C1dmYR0jbbDbNp9z3tEDbunSpZgyZQrGjBmDt99+W55uoWjRopg/fz46d+7s9jmZsKMHWl4r6myZfEww+klQ+Rkh+euh8tNDrdXDmKn8NmZbSZfdSrCuyilpZ52ss3VldH+rvtj2TYVyy1fnrXPk3fSS+eIqoyRX2AFwqXw8OTkZEyZMwMWLF1G8eHF069YNb7/9trz60WuvvYa0tDQMHz4cd+7cQePGjbFjxw67Ff8cyY8VlYg8xWFFnYWjyjpbGiMkjUmuspPuDYkFzF+wzJVvAvDwFFqOVprNKVnnKFHnbJ/BYD9nKxeyeIgZ3YwzBsgVdgDjDFFOjncaB/W9rxNGF79XqDRGmAxq849BKhM0agEfFeBjMle9GfNQNecud6rrHCXrsttuHc0WMqn3cNOrzBeX23vXKrELFy7EypUr0aVLF8yePVve/sQTT2DcuHG5OqckbGdeJZekpKQgJCQEycnJXrEiyaPK0wk7ANDe9oH2soDqhi9MN4rAcCsA+rv+MKSZJ0o2D3+1+qJy70uLyaiWh8cadOawZTKpFMk8Z4tNqO5VXmTH2XBby3GO9iurAdX3+2pS2fUjctWqbK+fPMsT7yFGoxEajQYXv4hGeEnXf39ZtOk29pxr6FaFXX5Sqeyfu3ldUelhwDjjHeyr6Rx/bBIam4nsDfcSHDf9YLoZgMykokhNKoq714si5XYQkm+FIC3VH1l6H6RnaJGVpcmxmi4narW4V7l3f3XY+3PamVyqrgOUyTjzeUxO91kvsGTdf8vcfNaJw/7/DnPp+slzPPE+0qZNG3StfQzDOhV1+Zhzl7MQ0+c/6PX6XF2npzHOOMdYU/iOd7L/Im+btFPbLE5n2W8yqHE3JRC3bwcj6VowbqVqcUMvIQ0CqZIReknkONzVnUSdz72qOsv8dbbDYfNewqBk+8rUy9vtb5OlL1ydtmB54j3Eco47W6oiOMD1Z1FKmhFFu/3pNe9f/v7++OuvvxAZGYmgoCAcP34cFStWxOnTp1GzZk1kZGS4fU5W2NEDKz+SdQBg8pEg/E3moUx+Bqh8DZDSzMOZjA5WY1JpTHIizDLxq3Kb0SMVeE7766CiTvm3JaDfT9Y58t+IwQDAajwqcPmxohKRx7mYqHNGUpmg8dVD66+DT6offHz0UGu0gN4HGrUJWVZt3U3UWTMYVdDAZLfCrMvHO5jiIbskneU6le0dx7x1UcsBsBKPCh7jDD1obBN0jvY7qsTTqAT8JQmZ95JpnkzW5RdX0+XOknW2w3KflzYwaUeFIioqCseOHbNbfGLHjh2oWrVqrs7JhB2RDaE2X6A2yavFqjRG83Amm+o6R8xJO3MAValM5mo2q6SddXWbNZNR5fbCE9n1xTZRZ7lueggZJcDFVYwBAML9IbH5KT9WVCLymGyGvbqarAMAta8BGq0BGq0evv46+PnroMv0hU6nfN93lKxTq0W2+633Wbe1ZTCoXK6ys+Yodjiac89Zoo4eAiY344xR5faQ2PzEOEOPAo3GPCxWLZlDlw8kGD0wj52Fj7gff/QQwL358qwTZka4V2WXU7LOtkbXkqwzSEJeodaun25cP3kXYTIv7Oh6e+8aEvvqq69i5MiRyMzMhBACP//8M9atW4dZs2ZhVS5HszFhR2TD5GOCyUeC2kcAagFJY4SkFlBpTBAGE0xOquwAwATz0suW1WBNBpWctAPMSTTFPs394axydV4eV4u1rubLqarOFqvrHh3uLjqR3xISErBs2TKcO3cOBw8eRGRkJObPn4+oqKhcTdBK5AmmI4853ZdTsk5oBCTLnF9q8xx2mgAdfNK10Gj9odHqHb7fq9Ui26RcTvvlvpskGAxqu0UnskvaOaquc9jOxYo6azmtaEsPH3cXnchvjDPkray/K7hzjEplgs7q/VejNkGtMs9jpzYBvkKFTMkEHxcq7dxhhHC42MT9/XkfGussUQeYk3XOMFn3aDpy5IhXDIl97rnn4O/vj0mTJiE9PR19+/ZFREQEPvzwQ/Tu3TtX52S5DZENoTFA+Nz7smVZMVZjhOSgKs6aSmOCSm2EpDZBozWY56Zz8KXIZFQrgrJcCWdZcTaHueicMRnVuU7WqXIouyfKT0uXLsWrr76Kdu3a4c6dO3YrKhF5E6ER9sk6jdUlB5p7lXZqtRFqtREaq/dfV5NaarVwWklnNErKueSMrsUCZ8m63A5/JfImjDP0ILMk56wv8j6NOZaoVeZknbnCTsAnH0a6WlfqGWGeG88giXv/V3Llm4WjNnook3VGCEVVHZN1DzmDyv0LzBV2sbGxWLzY+eJZ+d51gwGfffYZWrVqhdOnTyM1NRVJSUm4ePEihg4dmuvzMmFH5IDQSPIXL8kq6WYp0TUZVIqLhXXSzryQRPbhymQ7L1A2STu7Y432ST/b83II7KNBMkiQ9K5frFeJLezgBtxfUWnixIlQq+8/l5944gmcOHGiEHtGpOSwqs42SWebuLNKrEkqZWxQqUxQa4wOp0mwZl1RZ53Uy274q4XBoLZL2hkMKpeq6fIjWcf56x5MktG9OCMZJHlILOMMUc4cJuNs/nbG8gOQWmNeLdYRT81V52x4rbtJO2fJOmeyS9QRHTlyBCdPnizU4bAajQbPP/88MjMzAQBFihRBqVKl8nxefpsncsDkI0H4CHM9+T3C5Hz+OutFJyxJO8tQJ3eq7Mz/v5+0s12R1qW+M1lHLoiKisLJkycLPbgB5snA69SpY7fd19cXaWlphdAjIqVsq+osbXyEOW44IGnMCxcB99/j1ZaVwq2+XBmMKpeGu2rUphyr8QxGFUwmSfE3cG/1cpvEnatDYXPLeqVaenRYhsQyzhA5d6LLq3bbHCXq1BqT3UW5X1mxLW+HpJh7zhMsSTvrKjt5n01b2x4ZHWxzdJzl/ACTdeQZRqMRkydPRlRUFPz9/REdHY0ZM2ZAiPvPLyEEpkyZgvDwcPj7+8vVcq568skn8dtvv3m03/xGT+SAvPAEAGFQQRjU5mo64/2KOtshqBaWRJ2lyg7I/kuK9fnkbdkuJqHKtrout1RqEy69ONAj56ICZlS5dzF5V4WdZUUlW3lZUYnIExwm6gC7qjpniTrbSfotP/xY3rfVmrxPR2BdZWdJytkm/SyVcNYVcjn9qOOJ6jqNg9u3IXppjseRF7IsOuHGxZsq7Bhn6EFmm5xzRKW6X11n9FCCzt1EnyXBZj+s1XmiLqdz5dZoaUOejqdCole7f4FrQ2LnzJmDpUuXYtGiRfjzzz8xZ84czJ07FwsXLpTbzJ07FwsWLMCyZctw+PBhBAQEID4+Xq6ay8mLL76IsWPHYtGiRTh48CB+//13xSU3uOgEkROSEYBRBaHzgVGngTBY5poz/ysPj3VwrEpjurfAhLnazmRQrhRry7IAhWJRCuP9qj3rv7OjGJ6bmwls1SZcGd0f4QsT3DqOHjzetOhEfqyoRJQvHHxqsk3WCR+rxSYAwCiZf/gxOp5KAXCtItqyAqzBqMrVAg4GgxoajVFxvOV6rX9UcjVuaDTGXA2J1WhM2PLYYnT7u3Arrij/edOiE4wz9CByJVFnYTKpYLx3AQC9h4rSfFxYZdayYqxGSIoFKfTIeV657CrrLDRCcrvKjrOrPlpcWXTiwIED6Ny5M9q3bw8AqFChAtatW4eff/4ZgLm6bv78+Zg0aZK8ENFnn32GsLAwbNu2zaVFIyxtXnrpJXmbJEkQQkCSJHn+VHcwYUfkgGQEJL0Ekak2f9G6t8S0Qedjt9S0deLOOuGmUhthtCTdNCaYshwsJmFS2VXfWZ/D/Hd21XZ5C0fWw3etKwOTxvRD6fmJeTo3kavyY0UlIo9y8mnJaWWdNYPa/OOPQW2u1r43JUKWzgdGg/n/BqPKroINuF/VZrfS672km0ZtgsGokleOtfxt3d5kkqBSCYfH329T8AMumLSjgsQ4Qw8CdxJ0lvZ6PWA0qpGl08Jw73uB/t5pLAs26CXhcJVYV+e1c5S000vCrgLPIAmHSTt35LWyDrifrBsjbcB80SvP5yPvl5KSovjb19cXvr6+im1xcXFYsWIFTp06hSpVquD48ePYt28f3n//fQDmqROSkpLQqlUr+ZiQkBDUr18fBw8edClWnDt3zgO3RokJO3ogJWdVzLdzSwYNNCkmSHdVEBk+MOl8YDKoYMjSmCsknCTQLFutQ61lWKzJqJYTc3YLRNxL2lmq7By1KWhM1j1YhEEFoXfjC7fVohMAMHLkyEKbX8hgMGDt2rWIj49Hv379kJ6ejtTUVI9M0kqUZ3lJ1MH8w49Jfy9ZZ5QgjPcq7YxqGLJ8oNebk3aWSjXLUFZHyTvLfssQWGdtrBNxlsScJWlnqbKzPt6Vaj2HyUQXquusr4/z1z3YhFFyK84Iw/0hsQDjDFF2ckrUOVvEzhxLNMjI8EVWlgZZWWoYrKZEyOvcb9ZJOWeVdpbknB4CPpAUSTsAcuLOFe4m6yzXBXCF2IeF+bOS688ZS9ty5coptk+dOhXTpk1TbHvjjTeQkpKCmJgYqNVqGI1GvP322+jXrx8AICkpCQAQFhamOC4sLEzel5PIyEiX++4qJuyIbKgzVVBnGiFlqmHSaeThsPfnmlMpEmq2QTSnj7OOhsZaJ+3kdm7+ymY7xApwfVisdXUdPRq8ZUisZUWlP//8E4B5RaUiRYoUcq+IHHM1UQeYV28WmRrzXF46DYxZPjAZzT/+6LM00Os15i9Yeh/F4hC2bBNq1km77Ni2s07aAbBL3Dm6PmdJwdwMhaVHj7cMiWWcIW/mKFnnLEFny2hQQZ/lA32WD3R6H2TqNNDpVTAK9+aLc5UleedsiKx10g6AInFn4SyBl9fKOleG39LD67///lMMibWtrgOAjRs3IjExEWvXrkW1atVw7NgxjBkzBhERERg40HPzuCckJGDZsmU4d+4cDh48iMjISMyfPx9RUVHyUFt3cNEJeiCFaP/Jl/NKBg3UGSZIGeYvWkLnA2FSwZilubfIhDlZZz0HkWXxCctiEIYsjcMVXlWa+ys+OQrEtok167mOHCXjbNs6w6qGR4BRcu8i4FWLTuTHikpEnpTdCrAOGQDoza83YfWjT1a6Lww6H+gyfOUvWVlZGsXqsNZJMmfVb0ajZLewhLMknnKhCavKC6vKPtv2zobo0iPM5GacMcKrFp1gnKEHgXmaGuepNsV8o/emVzAa1cjS+yArSw2dXg2dUWUOQTAn0PIyHNbZohM+QrnyrHWyTY/712eQhEtVfjkl69ypFOTPSQ84g8o8lYjLF/NnlZYtW6JBgwZISEhAcHCww4Td+PHj8cYbb6B3796oUaMG+vfvj1deeQWzZs0CAJQuXRoAcPXqVcVxV69elfflZOnSpXj11VfRrl073LlzR56zrmjRopg/f36u7hJW2NEDK0T7j8eHxqozVVCnGyFlqCFStTDpNPLqsJbhsNbJMcv/LQtGyNvv/d+u+k5jAgzO5wtyNAG47XUReYK3VNgB91dUunjxIurWrYuAgADF/po1axZSz+hRp65xCoa/Krt1jLzghNH8oVMY1DDqNDCk+0KfoUVWptY8HDbLB1l6H0VizJVknTXrpJ1aLVyqvLOd08562KorPFVdx/nrHn7eUmEHMM6Q94rdPB8nu49xOVEHmL9nGA3mZJ0+ywdGg0qurss0ApnCav46D8wJlxuWajtAOXTVXXkd1kuPBlcWnUhPT4dKpfw+rVarYTKZX19RUVEoXbo0du/ejdq1awMwz413+PBhvPDCCy71Y+HChVi5ciW6dOmC2bNny9ufeOIJjBs3zo1bdB8TdkRWVHoBVaYkD4c1X3xg0PnAkKWBQWcutrZOuFkPZbUdxupsLjp5PrtsEncFWRlnMqrkYbFEBS0/VlQi8hRNzGn3k3YGSZ6/znQvWZeV7ousDF/oMnyRmeGLjAxfGAwqjyTAXEnUWXO0EAXRw4xxhrxZ7Ob5+KvXaLvtOX0XMBnUMBhV0Ol97KrrMiST06GrttV1zirp8so6aeeMJxaZsF7kwghW2ZFjHTt2xNtvv43y5cujWrVq+O233/D+++9jyJAhAMwxYcyYMZg5cyYqV66MqKgoTJ48GREREejSpYtL13Hu3DnUqVPHbruvry/S0tJy1W+XEnbPPPOM2ydetmyZW5O5zp49GxMmTMDLL78slwuuWLECa9euxa+//oq7d+/i9u3bKFq0aLbnmTVrFj7//HP89ddf8Pf3R1xcHObMmYPHHntMbtO8eXP88MMPiuNGjBiBZcuWudxf8g6erLKTDBqoMgSkzPvDYY1ZPlaLTZjf/u2Grlol12yr4BzNQycvLGFQuTzHHFG2LGXhrjKqvGbRCSB/VlQqCAURG8k7uJW0M9z716iCuPfDjzFLA32GFroMX+gy/KDL9JVX8zOZzMNbs6uuc5SQc3UuO8B+VVjL9VqSdq5W2Xmquk6lMmFr1YXo+qf9F1TyUiaVm3FG7TWLTgCMM+T9YjYsxF+9RueYpFOpTPJwWJNJuje1ghpGkwS9yVxdp5NMLlfX5SZZ585CEnmRU3Vdbqv2yHsJk5uLTtyb6qNevXpQq9XZxpqFCxdi8uTJePHFF3Ht2jVERERgxIgRmDJlitzmtddeQ1paGoYPH447d+6gcePG2LFjB/z8/FzqT1RUFI4dO2a3+MSOHTtQtWpVl2+XNZcSdtu2bUPPnj3h7+/v0knXrl3r1upLR44cwfLly+3K0dPT09GmTRu0adMGEyZMcOlcP/zwA0aOHIl69erBYDDgzTffROvWrXHy5ElF+fuwYcPw1ltvyX9z8tkHl6eSdpbFJpCpASxfsjJ95CGx2XFWEWe98itwf4isyag2D6M1KI+zTt7lZ5VdTue9NrY3Ss1bny/XTd7BnSGxRqMR06ZNw5o1a5CUlISIiAgMGjQIkyZNgiSZA6UQAlOnTsXKlStx584dNGrUCEuXLkXlyjknOfJjRaWCkN+xkR5gegkiU33/hx+dRq6u0+vNc9aZTCpk6c0fw7KbL85ZUs7ZdleG0jri7tBYV3j6fPRgcWdILOOMY4wzj5aYDQtxqo/riW3L1AqZOg0y9SroBJB5L1HnrLour9xN1jlaPfZ+NZxn+phdn8ZIGzBf9PLI9ZD3cmVIbFBQEObPn5/tXHKSJOGtt95S5Inc8eqrr2LkyJHIzMyEEAI///wz1q1bh1mzZmHVqlW5OqfLQ2IXLFjg8pv/5s2bXe5Aamoq+vXrh5UrV2LmzJmKfWPGjAEA7N271+Xz7dixQ/H3J598glKlSuHo0aNo2rSpvL1IkSIuTx5I3s8TSTvLYhNSphrGe/PXWRabAHKeQ87VpB1gP7edPKTWSfLOk+SFL3JYhZZJO7KYM2cOli5dik8//RTVqlXDL7/8gsGDByMkJEQeXjR37lwsWLAAn376qVxCHh8fj5MnT7r0q5SnV1QqKPkVG8n7WKrshJMiI8lonr9OMtybViHDR/7hx1KtbTLdX9HPMhxWl+W5wTvuJuvyc2hsdsk6S7xklR1ZMM44xzjzaKmybrGctHP0Wd3yncE8vYIfsrI00OnVyDRKyBQ5V6VZD1F1VF1nm/zyVFItv8/pjA+L8KgAPffcc/D398ekSZOQnp6Ovn37IiIiAh9++KE8NYO7XMoIfP/99yhevLjLJ/3mm29QpkwZl9qOHDkS7du3R6tWrVw+vzuSk5MBwK7/iYmJKFmyJKpXr44JEyYgPT092/PodDqkpKQoLvTwUGdooU4XkDJUclWEyaCW53WzVNjllETLcb+8GIVytViHQ2c9VF1nO9+e4jpsEoeCqwI+kIRBgtCrXL8YJbdWiT1w4AA6d+6M9u3bo0KFCujevTtat26Nn3/+2Xz9QmD+/PmYNGkSOnfujJo1a+Kzzz7D5cuXXariy48VlQqCp2Mj44z308ScdrpPqGEeDpuphkjzUQyHNWRpYNCbk3b6LB8YjGpk6TWKVVvl67BKurk7N5030GiMimSd5fY4io8atRFfVp9fUF2jvDC6GWcMKrdWiWWccSw/voMx1ng/lcbk9Id1k0mFzHsrjWdk+CI9Qwvdveo6y8qwnkyIqSEpLrnh6YUvNEKSh8O60qfxqg0evX7KP+LeYl3uXADzkNjCWpF8+/bt0Ov18t/9+vXD6dOnkZqaiqSkJFy8eBFDhw7N9fld+nberFkzaDSur0/RuHFjh0vp2lq/fj1+/fVXeSldTzOZTBgzZgwaNWqE6tWry9v79u2LNWvW4Pvvv8eECROQkJCAZ599NttzzZo1CyEhIfKlXLly+dJnKhzqTGGujshUQ2T4QFhV02W3apOrHFXnqdSmexfnSTtPUKlM8oXIIjIyEocOHcKhQ4fQv39/pKSkQKfTOWwbFxeH3bt349SpUwCA48ePY9++fWjbti0A89xASUlJih9eQkJCUL9+fRw8eDDHvlhWVJo4cSLU6vvVRk888QROnDiRl5uZrzwdGxlnvJ/+tPOhd5IRkDJUkDLNc0paD4c16HyQpfNBlk4Lg1GFrCyNYj643A5j9RaWJB2HwJI1lUrFOJNH+fEdjLHmwaaTFy/yQ0aGLzJ1GqTqVdCLvFeu5dfcdDktPOGMJTFnSc5Z/5/I2pEjR3Dy5MlCmSu1a9euuHPnDgDzqrPXrl0DYB7R6YnpCXK1SqzJZMKZM2dw7do1eRlcC+thp9n577//8PLLL2PXrl0uT+LnrpEjR+KPP/7Avn37FNuHDx8u/79GjRoIDw9Hy5YtcfbsWURHRzs814QJE/Dqq6/Kf6ekpDDAPSQkgwaSQUDKkACjynyx4WgFVZNR7TCZ58rcc7arsqrURsW8dvktv5KDVEicPG+dEhJOnTqFkJAQxeapU6di2rRpds3feOMNpKSkICYmBmq1GkajEW+//Tb69esHAEhKSgIAhIWFKY4LCwuT92UnP1ZUKgx5jY2MMw8Wk48Elf7+lyNJb44hIlMNkamBMc33/nBYvQ8MWT7Q683DYbOrxna0QIQjrrSxHe7qqKLPVc4WnHA3SWcwqKCxikEd/xiT6z5RATJJ7sUZo7nCjnHGszzxHYyxxvvZfo+wjhlGo/re3HUa6PQ+SNdpYBTm1VENkrm6Ti+5tuCEXhKKYbHW88t5G9tEnbf2kx49oaGhOHToEDp27CivPO5JbifsDh06hL59++LChQsQQvlG4M6y6EePHsW1a9fw+OOPy9uMRiN+/PFHLFq0CDqdTvELmLtGjRqFr776Cj/++CPKli2bbdv69esDAM6cOeM0Yefr6+tS1SA9eFR6FdTpRkgZankYh10btQkaXz0Muuy/aGXHMpedbaJPpTbdS+DlX9KOCTqyVaVKFXmokYWz97iNGzciMTERa9euRbVq1XDs2DGMGTMGERERGDhwYJ77kh8rKhU0T8RGxpkHgymnCXEMagidBkar4bBZmVro9RpkZWmQpffJdpEJa+6sBOsKSwLPWeLONimnGNqqMWa7n8iWSqXC7du3FdsYZ3LPU9/BGGseXJbvB0ajGlk6LbKy1DAYJRhwfzhsTsk6yyIQnmZ9Tk8PgbXGRN3DzXqYq2vtzc81V1aJzS/PP/88OnfuDEmSIElStuskuPo+bc3thN3zzz+PJ554Av/73/8QHh6e6wxiy5Yt7UrQBw8ejJiYGLz++uu5TtYJITB69Ghs3boVe/fuRVRUVI7HHDt2DAAQHh6eq+ukB5tkNF/MVUr3KiTufZmS1KZ7SbZ7w1c1JpiyVPcXovBQlV1eknaWZJx1e1cSdJ4Y6ksPLpVKleNqShbjx4/HG2+8IU+WWqNGDVy4cAGzZs3CwIED5cB09epVxfvo1atXUbt27RzPnx8rKhU0T8VG8m7ZJusM5io7AcCU6WNO1lkNh7WtrDOZJKeJO1er7HJLpRIuVdvZrhzrboIup9tgMHpuwQ3yTowznsM482j4Z+CIbPeb7iUzjCYVMnUa6Iz3h8N6Yu663FTZ2SYAnSXvLAtiuDOslQk6coUrq8Tml2nTpqF37944c+YMOnXqhNWrV6No0aIeO7/bCbvTp09j8+bNqFSpUp6uOCgoSDGvHAAEBASgRIkS8vakpCQkJSXhzJkzAIATJ04gKCgI5cuXlydgbdmyJbp27YpRo0YBMA+DXbt2Lb744gsEBQXJZfIhISHw9/fH2bNnsXbtWrRr1w4lSpTA77//jldeeQVNmzZFzZo183Sb6MEkrw6rl+SMvjCoIe59sbLMM2eEBiq1ESrV/YRddhwl7Wyr7GyTdnK7XFTa5TVJZ90XZ4lI8k7C6LgyNLv2lkUnAOT4a1R6ejpUKuX51Wq1PBwnKioKpUuXxu7du+UvTikpKTh8+DBeeOGFHPuTHysqFTRPxUZ6eJh0PtBn+CIrU2seDpvlA6NBLa8OmxNL0s7TVXYWlqRdTivF2ibtXPWgz8tHSsIouRlnJHnRCYBxxhMYZ8h2lI/RpILeBGTCnAzT57BCrCO2w2LdlVO1nqP91ivZOkveeTJR966pl8fORfnMKJkv7rRH4VbYAUBMTAxiYmIwdepU9OjRA0WKFPHYud1O2NWvXx9nzpwpkGCxbNkyTJ8+Xf7bMjfD6tWrMWjQIADA2bNncePGDbnN0qVLAQDNmzdXnMtyjFarxXfffYf58+cjLS0N5cqVQ7du3TBp0qT8vTHklSSDxrw6rME8B5h5SKzjL1JyFZzGBJXJmGOVnTOWpJ0zjubLsz7W3fOZz+m8f9ldHz28oqKiXFpZDwA6duyIt99+G+XLl0e1atXw22+/4f3338eQIUMAmIfijBkzBjNnzkTlypURFRWFyZMnIyIiAl26dHF4zu3bt6Nt27bw8fEBYF5RqV+/fkhPT0dqaqpHJmktSAUZG8l7CR8BWCW3DPfmr9PrNTAYVbmuKMtN0i67JJw7bdzhbpLOskos57F7OKlUKpw8edKltowzOWOcIUd096rrPDkM1VGVXX4Mo3XG/Qq/7I1XbWDS7iFXmBV21qZOnQqDwYDvvvsOZ8+eRd++fREUFITLly8jODgYgYGBbp/TpYTd77//Lv9/9OjRGDt2LJKSklCjRg05CFrkpUpt7969ir+nTZvmcGJaa+fPn1f8bTung61y5crhhx9+yEXv6GGkzlRBlWkCMtXmicJ1Gsdz2FkNiwUg/wpsnbQD7BNjjqrs5CGsNlV296vbVHZDZBX9cHWobA5JRFcSddfG9kapeetduj4qRLlYdMKdCruFCxdi8uTJePHFF3Ht2jVERERgxIgRmDJlitzmtddeQ1paGoYPH447d+6gcePG2LFjh9NFhbp27YqkpCSEhoZCrVbjypUrKFWqFIoUKeLRX6XyU0HFRnpwmQwqxXBYk0mFLL3rv5VmNzTWdp+ryTdHc9Hl2I8cquxcWwCDPw490ISbcebeohOMM3nDOPPoUWmM8rBXW5bvBOpsPuPnJnGX1yq7vDBIQlFl50qyLqcEnSNM2lFBuHDhAtq0aYN///0XOp0OTz/9NIKCgjBnzhzodDosW7bM7XNKIqcMF8zJCUmSnCbDLPvcmfD0QZeSkoKQkBAkJyd7RTb3UZecVTFXx/le18DnGqC64QvTbT+IdC1MmT4w6XxgyNDeW+FPA5NRBZNBBWOWD0xGFQxZPjAZzF++bIfH2ibKrL+k2FbCWdraDos1GVU2/96/DkcJO+vz5iVRp1KbIMl9McrnLTlnY7bnJPd54j3EaDRCo9Hg3w+eQHhRrcvHLfruCvam1nS5wi4/lC5dGitXrkTHjh2hUqlw9epVhIaGFlp/ciO/YyPjjPfRna9it82yUqwqVYKUqgHuaKH/tzjSLhXH7YslcedGUdy+Xgx3U4sgM1OLTJ2PPH+cwaiC0Wroh6M57ayTYbZVdrlN2AHOV33N6Thn158d22SdRmOSt2nURqg1RrQ7Ntat/pBrPPE+0qZNG3QJP4PnmoXl3Piec9czEfvm79Dr9bm6Tk9gnHENY413OT/0ObuEnWUobFaGL9LvFkF6mj9u3CiGC5eK4UKqBmkQSJWMLq8OCziumLNN2lknz/K7ws6StHOWsHMnSWd971mmnrX8VDaLSTuP88R7iOUcV+c2RbC/6z9spmQYEPbaj6hSpUqhDom16NKlC4KCgvDRRx+hRIkSOH78OCpWrIi9e/di2LBhOH36tNvndOneOHfunNsnJnoQSHpAStVAZKrNQ2INavN8YE7GzktqE3Cv8g0AYIBdws7ZEFlHw1Ztq+ysq+2yq7RzJrdDX633CaNKTtpZ3Hi9J5N25FH5vaJSQWBsJMC8EIUlaafYbvnBxaBG1r0VYh0l5NRqISftNGqTXRvrSjpPzmeXm0o7+dhcJuqy83XteUzakUcxztCDKrsqOwujQQWjSYIR9+eEc5SssyTa8nPl1vyW22QdPTq8ZUjsTz/9hAMHDkCrVRZSVKhQAZcuXcrVOV1K2Fkvgf7jjz8iLi4OGo3yUIPBgAMHDtgtl070IBAZPhA6DUw6DYRBnW2QVCwOYXA9mVbQXJ2bzpV2KrUJt97sjuLvbM5rtygfuLsEOozuDYnND/m9olJBYGx8tDiqrnNEZPjciyPmCmxLNZ2lQsK6ui6v8rqarKOknaV/uZnfzpUEncYyLYSDKSOYtPNiRsmtOCMMareGxOYHxhl62BgNKphMknkRI5MKBjcm57dduVUPYVc15+rQWOshrIZsFrpwtZ0z7g59deUdaoJqA6vsKN+YTCaHPwBdvHgRQUFBuTqn24tOtGjRQp4DwlpycjJatGjhtb9Q0cMrt8NhAfNQJkkvQRhVMGWav2QZdRoIkyrbueKsF6CAwbxNMWzVjYUobNvmpcrOci5Xq+lcwUUpHk7uLDqRX/JzRaWCxtj4cNOdrwKR3TcHPSAZJEiZ5ipto05jTtYZJRiNahjvJTosybHsknWOquzcZT18NacqOtuknWX1WHfllKzTuLKSucqEHY+/iza/jnf7+sn7uLPoRH5hnKEHxfmhz2W73zIVjz7LB1l6DXQ6HxhMEnIz6NyTw1stSTnbhJzt6q8aIeUqaecKd8smmLTzYiY350u992NoYa8Sa9G6dWvMnz8fK1asAGCetiA1NRVTp05Fu3btcnVOtxN2lnkSbN28eRMBAQG56gRRbuUlWScZNJCMQn5TsHzJMmaZXxaW4bHOKBJZDpJ2zo8zKtre/1tlt/CEbdIOcLzwhGsrxeb8Zcl2KKwtVtlRfsiPFZUKGmPjwyunZJ2kByQjAL1kXmn8XhwRRhWMRkuC7n5syGlYLJB90s7dYbGuDH21ndfO3eo6R3PUudOeKL8xzpA3yylZB5irkvU6LbKytMjSaaHTq2G0SorpPZQMs66ys14t1lFFnjVXEnLO2tgm9+S+IPsqO+8b30SFxVuGxM6bNw/x8fGIjY1FZmYm+vbti9OnT6NkyZJYt25drs7pcsLumWeeAWDOEg4aNAi+vr7yPqPRiN9//x1xcXG56gSRu/KSqLMm6c0BQhjM89cZszSKRJ3JhYSdu8Nh3W2vSNY5qbLLLlnnLFGXXXLOWXUgk3XeSRglh6sbO21vUhX6kFhr+bGiUkFhbHy4OUrWCY05bkgG85cOdaaAlHGvuk6nub9wUZa5WtvoIFlmdGEYk3XSLj+GvuYXR8k6Zwk6y4IT1lhd552ESeVenDFKhT4k1hrjDHkrZ8k6yzx2JpMKhiwNdBm+0OvNC+FlZWmgy9Ig897bp7fMT5eXpJ278hLRWF1H+aVs2bI4fvw4NmzYgOPHjyM1NRVDhw5Fv3794O/vn6tzupywCwkJAWD+dScoKEhxhVqtFg0aNMCwYcNy1Yn/Z+/M4+SoyvX/nKWqezJZIAgJS4CwExAuBIRcuXovoAFREKIixisggguEHZX7U0BkM4pEMOwhoBKDaESRC15AiIoBQxBFw6IIJCwBJNss3V11lt8fVae6uqZ6qZ7umZ7J+X4+9ZmZ6urqGsj02+ep530fiyULrRLrqE8BKGi/8gOoljRIhY0Ghtf+gBoPoACyC3LViDvugtep7bJrlHouuuj1YosuK9SNPjqhJdZw1lln4YADDsCf//xnbLHFFtH+Y489tuPriq2No5NIqIuJdUaoi8N7QrGuQKGLHNqnUCUnrCEsnDdEK+bXNSLWRedvQXtsdK6UlNe0Y1ol7DXjorNi3eiiE1piDbbOWDqRuFjHmIJMeb9XgsIvOfBLbrkd1ncgJIFA4ILrJKq55ZLHDEa0a7RKJRNiASvWdTpK1B5LlXY80DktsQDAOcfs2bMxe/bsaN8bb7yBCy64AN///vezn6/RAxcuXBhFil933XUjwjpuGX20SqwDwhYmMXC/khSixGM/l8tCvEW2mvsuSwhFo6muaS47I9qlp8/SVGddI2Jd8pqsWNf5BMnGWeY9DH/oRJx2JCoNFbY2jj6Kr+1WV6gDANYfzEAlBRq563SJB86i8O9RShZri21OeGulaNfQ6w0mPTasR0as44l6Em8NTj5mwyY6G61IpjqjJe0oh52tM5ZOIinUJb83wp1SFMJ34PtOFDbhldwoIXYk04iwl0ayOjkpp0kJbgdgxbrRTKMtsTvuuCNeeeWVAfu/9KUvYf78+SgWizjvvPOwePFilEolzJw5E9dffz0mTZpU99x/+9vf8Mgjj8B1XXziE5/AZptthn/961+4/PLLceONN2KnnZrTMTJ9AtRa484778Qbb7zR1ItZLJ0EK8TEK0mhE66HYGB4UBaSgkitVlmgthBnHm9UrKu1v97cujhJsc5cQ3KzbBpMnToVK1euxMqVK4f9TlQ7EpWGElsbNz1MOywpkqB+FDl0kUP15SA9J7pDbJx1WUibT5elHbYRJ91gaV58lNEGYEA7rGV0YRx2ts4MHltnRh+MqQqxLokK08aloFCCRe66QjGYXyckiYSprC47R5OG0mArntPCoIp245B0Ic/S+ZgxVY1vZYfdtGnTMH/+/JrnX758Od54441oe/DBBwEAH//4xwEA55xzDu69917cfffdWLp0KV5//fVoLEEtfvnLX2K//fbDmWeeiS984Qs44IAD8Mgjj2DPPffEs88+i5///Of429/+1tR/k0yfuCil2HXXXfHOO+809WIWSydRzTURPR6KdMk7yiqDcJdkMKJYpeOu8XOkXaN5PuWqYrNYhgOTqGRoRaLSUGJr4+ilVp0g4dswKTIgdNepkgNZdIIbPjGxjlKVySmXJVSiHWQR/ZKtr0l3HeOyYrNYhgNbZyydRC2hLkkwVoGgWMijUMijVHLQV3BQkhQSA9NZs5BVtBsOaqXgWlHOYli+fHlDN4e23HJLTJ48Odp+9atfYeedd8b73/9+bNiwAQsWLMB3v/tdHHrooZg+fToWLlyIP/zhD3j88cdrnveyyy7D6aefjo0bN+K73/0u/vnPf+LMM8/E//7v/+KBBx7AEUcc0fTvlvkW6VVXXYULLrgAf/3rX5t+UYulkyCOAqq0j5pZdvHNkFWsy3xdTEVbnEbSXoHaLbtAujvPincjk2x3ohh0rCW2kbtR7ebqq6/GY489VpGoZNqUvvWtbw3rtTWKrY2jj3o3dQCUk2GLQdiESRqXHk8djdCIU87MuBsq0a5R914zbbJpAl2aeGfbYUcAKpvrAZJFLbG2zrQGW2c2PcyNH+Ou8zweiHU+RVHWFrMapRm3XSfCq2yWTYONGzdWbKVSqe5zPM/Dj370I3z2s58FIQQrVqyA7/s4/PDDo2P22GMPbL/99li2bFnNcz3//PM4/fTTMXbsWMyZMweUUlxzzTU48MADB/27Zf53/JnPfAb9/f3Yd9994brugLSLtWvXDvqiLJY0Wjm/LiL2F0BzAujPVTxMmYLwBvd2rxRtavh2HMIUdDiXrl5ibCPEBbk0AbDdYqRl+Omk0Il2JCoNNbY2ji4aEeuIH7TDVrjrPA5R4hCeAxUbs2DcZpRqMKYbDp4wol0jxytFQKmGECxzW6wR7eIOwKyz7OLpsMn5dMka2EyrsGXk0UmhE7bOWEYSJh1WSYZSIRe56/oLLvpLHL0+RUG3L3DCJ3rYRDwJDdZA+23cXVdrpWZFu5FF1rnc5tgpU6ZU7L/44otxySWX1HzuPffcg/Xr1+Okk04CAKxZswau62KzzTarOG7SpElYs2ZNzXP19PREM/QYY+jq6mp6Zl2SzP+G43Zyi2WkQgSPZhCBKRAeONkoVw072JpBSdZ0S6wR7YDKEIosJF+70Vl5ls4mGAae4YNVh4VOAK1PVBpqbG0cPcTFOpXSb0N9DeIH8+tIkUH5NHLXBaKdA+FxCN+BigleSdFKSNqwu62WyJflPPVICneDCaAwDrq0G1bxfVa8GxlohUx1RkvSUaETgK0zlpGH7/EKd12xxFH0KaQGzCd6v45o50MPmD83HIJcXIRrpdDYqJhhAydGN6tXr64IncjlcjWODliwYAGOPPJIbLPNNi25hl//+tdRqrdSCg8//PAAR/TRRx+d+byZBbsTTzwx84tYLJ0G9SmIH1tEhIsUQitbUJOiWD3BjVJVdfFRLdG1GlrSQEQMX09JFvwcu65BueysKLdJ0ykOu3YlKg01tjaOPuJinQ7fZs3cOuprwKdBO2yJB3eEFQ2EurAdVgoK3+cD6ghnqiI5Nk1sk5IMaImNi3Zpjw8WI6IpRVPn7TUTZtGIu3ywDnRL59IpDjtbZyydTHL0jbk5rwSFX3LheS68kotSyUHJZ5CaQCBohxVEQ0LDH8Qcu3o06njLgjlfuxyCLHa5cnhHwlqGiPHjxzeUEmt45ZVX8NBDD2HJkiXRvsmTJ8PzPKxfv77CZffmm29i8uTJdc+ZfI/+/Oc/X/EzISQ1/KgeTblEpZS455578OyzzwIA9tprLxx99NFgrDnhwGJphAnuP1vWFmsWXRDhz07gsqNcgnAJnhPw+tP/PRuBrJZwV0vYy+qyM8cnxbmmXXZVRENaQ6i0WNrBL3/5S3zsYx+DEMEf4ty5c3HLLbfgE5/4BKZPn46f//zngxrSOtTY2jg60bH/fdTXZXd22A4LGcz2ipJhJQtcEb4DmXCncS7heTwU2wJRrJUOOUMzbbEGc+PJiHZDkTprsbQLW2csnUxSrAMAGZ+XrQg8r5wMKxWBrwA/bIet564zpLnssjLYc6QJfwykpaIdG/mj+DZ5zGepLMcDQUosY6xhN/fChQux1VZb4aijjor2TZ8+HY7j4OGHH8asWbMABLPpVq1ahRkzZtS+DtW+m4+ZBbt//OMf+NCHPoTXXnsNu+++OwDgyiuvxJQpU3Dfffdh5513bvlFWiyGVoh2RHBQXwcOiVjhIFyC5gSYYFAlB5RJSPCqc+OSzjbKVfSmYY6L3HGJOXb1RDsVzqtLvf4aLrus7bbWZTc60IJWtN7VQymKl14e/pZYk6j0zW9+E7feeivOPffcKFGpFUNahxJbG0cPXZOeR9/aPaKfScrbanyfFgxakkC0k4Fwp1X1v0lKy71MRhRLE+3quejSHjdz7IaSRua0Jn+3RtNyLZ2DlhnrjKQd0RJr64ylE5ly82149YsnDdgfuesUhVI0cmMDgBAUQpKoHda46zqJZgS4Vol2VqzbtFm+fHnDDjulFBYuXIgTTzwRnJflsAkTJuCUU07Bueeei4kTJ2L8+PGYM2cOZsyYgYMPPrhdl16XzJ+YzjzzTOy8885YvXo1nnrqKTz11FNYtWoVpk6dijPPPLMd12ixtB8uQXIinGMnQXM+eE6AucEd2biwVU8UMwuXtHbV+B2DtFbWas42JVnq8ea6jAOvERq5a2HOO/GKnzZ0TsvIY+rUqVi5cmVDEeg77rgjCCEDNvO8YrGI008/HVtssQXGjh2LWbNm4c0336x7De1MVBpqbG0cXXRPfK7m48QnwSiF0JWtBIsGJVcbi8C5ioQrzlRDLa1SkoqtGmkCWNbZc2aB2CiNiG5V/1vYm0WbBKYl1taZ1mDrzOhiuxtur/i53ApbNgYwJoN6Ea4thCq3w2alUTdeGkZQM+cQNVpwm2mfrfecVqThWiyGhx56CKtWrcJnP/vZAY9dc801+PCHP4xZs2bhfe97HyZPnlzRNjscZBbsli5dirlz52LixInRvi222AJXXXUVli5d2tKLs1jSmOD+s2XnIoJAOxpgGmAKNO+D5gQoV2CuAM/5MVGsUrTLIpK1g7TrSpI2g6/W4xZLGsuXL8cbb7wRbQ8++CAA4OMf/zgA4JxzzsG9996Lu+++G0uXLsXrr7+O4447ru5525moNNTY2jj6qCXaqbwG4rWDy8BRFHtPpbFWUsYlKFWgVFW0mDKmUxNaW0Ujol3ydeMiWxZhTSRuBiXbgS2WWtg6Ux9bZ0YfRrQzYp2Mza+jTIK7Ao7rw3UFOFeQmsCPaWVJoWuwba9xmpmLV88pV+vxdoVSWEYWWgddCg1vutwSO23aNMyfP7/ua3zwgx+E1hq77bbbgMfy+Tzmz5+PtWvXoq+vD0uWLGlofl07ydwSm8vl0NPTM2B/b28vXNdtyUVZLPVo1Tw7ndcgkIFbQhJAUpCiA97lVRzn9+dqtqkCYZtr2BZrZgDFW2NN21CW8AkTPJHEJMZmmWMXP1e5dbf2daz9n49Zl90IwBStxp9AMrXEbrnllhU/X3XVVdh5553x/ve/Hxs2bMCCBQuwaNEiHHrooQCCuRB77rknHn/88boW8nYlKg01tjZuemiuQfMiTBnXkSM7DuMSjjvQGyAEAw9n2MXn2WUl3hZr2mpb3RabFkBRC6UoBAAeu6GV1vKb9byW4UVrkqnOaJWtJdbWmfrYOjM62e6G27H6tLLTx9w0MQ47s+UciW5XolcyFGu8xTsgVd108Tl0RpBzNGlInDMz6Mw5BNHgKUmzgxHbsrTH+hpwSDCOnCMIl6jVFnshvcsmxY5isrTEjjQyC3Yf/vCHcdppp2HBggV4z3veAwB44okn8IUvfGFEFDvL6KFZ0Y76FIAOEwB1YDP1JYikgBCgogSSIqj5/ZXx0OUZconZdLFZdhX7Y6JdLeoJg6m/Ux3hLn7O5Py8eq9nRbvRyQ477IAf/ehH0c8bN25ELperG4PueR5+9KMf4dxzzwUhBCtWrIDv+zj88MOjY/bYYw9sv/32WLZsWd2FVLsSlYYaWxtHJ90Tn4vm2VE/sYjggM5LkLwA6y6BFVw4XSUUe7tAQjed4wwU8ZSiyOd8FEtOqmiXtV10KES7epjaJgQFj9VPKRjAg1ERtX63B/b/No546oKhulzLEEEpxeOPPx79bOvM4LB1ZvQy5ebbItGOUgUVa4LL5T10dZUwpquEMQUHYz2GnlhpYSCAbs4RB9R+nk80HF3pfIuLds1QL3W2nmgnARj/thHtosfqiHaWzkcJCsXaHzoxksh8a/Paa6/FzjvvjBkzZiCfzyOfz+O9730vdtllF3zve99rxzVaLC1FOQoyT6C6CJQTtMTqLhUsvMZ6oGM90O4SWHcJvMsDH1OC0+UhN64AwlS0MVeAMlXRmprWIpu2r5E5cvWccyTREhu/lrTzqHC+Uvwa4o/V4l9f+UTd67UMH0rSckJlA5tWBC+88AImTJhQsV155ZV1X+uee+7B+vXrcdJJJwEA1qxZA9d1K+LPAWDSpElYs2ZN7etWqu42EhZRgK2NmwLKSVkFODqoG6Ze5AS4K8CYhJvzwV0fjhO0NDmuD9cR6OoqIp/3kM/5oDRoiTUz7Tp1tlu164o75IwrRIhgFp6IjWBoZDbe//7b1YO8Sks70YpkqjMmdMLWmdZh68zoRtb4LJ7vKqKrq4TuLh/drsS4sBwZ0YyBVAhrtcS0rLPsagl6tWbZDYY0QS/uVU/+xcZvjckal3QhvWswl2XpYJYvX97QvNShYP369bj11ltx4YUXYu3atQCAp556Cq+99lpT58vssNtss83wi1/8An//+9/x3HPBfJc999wTu+yyS1MXYLEMNZoLKHDE9WrKNCgEwFRQIrhMVbNdAKIU/NkoycqiGRIutlhbrGGwLrsocTa2CIq3xsafN3B2XXk/BaAqzhd7rEMXi5bWs9tuu+GPf/xjxb56rgcAWLBgAY488khss8027bq0EYmtjZsmmmsgdNnRnA8+poTc2AIAwCtoEKrAmATlTvCVDryxUiw5UIqEohitGS5RjcG47OJiXEWaeUJkqzZrL/4zj53DtMYCQWtw/NrSzgMEot2Hnj6v7jVbRgaUUqxbt65in60zzWPrzKYJ5RKUqQEuO19Q9EGjQtuKOe1qtcZmJe60q+WySwptaU65ZkIpousIvzooO+2SrbFA2WmXJt7Z1lhLO/nLX/6Cww8/HBMmTMDLL7+MU089FRMnTsSSJUuwatUq/OAHP8h8zsyCnWHXXXfFrrvu2uzTLZZhxYh2JFwwaAcAdDhbzq8Q4uKQsGgKjw8Qzoxol4aZZTewHZWlOvDKjwcCW3yOXTJ91jymU4S3+PWkiXYAghbeBkS7f33lE3jXt35S9VotIwtKaeZZD6+88goeeuihirSkyZMnw/M8rF+/vsL98Oabbw77kNbhwNbG0UuyJVY7GkQEabFkrAfaWwLb2AWe8yvczNHzqQKlOhKv4uJYq0W7OEKwiqCLAY9XaVVN3nQy1Jo9JyQFByAEUltjgUonhCH+Ola0G13YOtN6bJ3ZNKBMglIKGc6yc1wfXV0l5HN5dJc4ipJBalKeZ2fKRvizT2Lz6hLC2WBaWpNUm2UHlMW5em2wWfFRKdqlUc9pZ0W7zkQLBsUaD63SIvis0Sktseeeey5OOukkzJ07F+PGjYv2f+hDH8KnPvWpps6ZWbCTUuL222/Hww8/jLfeegtKVX7I+81vftPUhVgsQ43mAjIfOO0oADkGUA7AQynLzLQjgkbFQCsKVUpvaSVMAQ0O0FaKAgI1wyfi4pmWtMIVlxTtzOunue3i50sT5EwIRjV3Xhwr2nUmWjDoDImMWlK8tLrx0AnDwoULsdVWW+Goo46K9k2fPh2O4+Dhhx/GrFmzAADPP/88Vq1ahRkzZmT8TUYutjZuWiiHgMVXA0yB5AQoD1L9lKDg0ZzTYGC48J0Ktx0r5KGUaR+lEIINWrQzNDMLD8AAB3gt0c68Tuprw4h2iAIopGBgvDGXuaUDkTRbnRHZQicMts5Ux9aZ0cvLp3wOAKAEC94juQIEwLiCUhICPBLtXFeCUY08A4QEoEngPNOo6rbLQry1Nv785Dy7LLRSrEsj7rIDGhM4rGg3uuiU0Inly5fjpptuGrB/2223rTvCoRqZBbuzzjoLt99+O4466ijsvffeIMROdrSMXIxopxkFKwKAhsprUKEAKQFfgPjBgoRErbDpglaaoyKNLC67eLBFmmhnnm8wop15Ttr1RudEuTU2LtrFn2sZvUydOhX33HNPw8crpbBw4UKceOKJ4LxcOiZMmIBTTjkF5557LiZOnIjx48djzpw5mDFjRt1B4KMJWxs3PTQDCAfgaKAIEEeB5oLWWOrxAamxlGn4Xvlvx5E+XM+BFEGTT3/BhRAMns8GJdbFabQttlmBr5bbTilaU7QDYIW7UQ6lFCtXrmz4eFtnamPrzKZB2o0S8z7OmULO8cGZRo4p+OY4DSAMn+BhgquEjhJg09pjG3HZVRPvWhE+MViSLrt4AEW8PdZiGUpyuRw2btw4YP8LL7wwIA29UTL/W168eDF+8pOf4EMf+lBTL1iNq666ChdeeCHOOusszJs3DwBw8803Y9GiRXjqqafQ09ODdevWDRg4m8b8+fPx7W9/G2vWrMG+++6L6667LkpTAoBisYjzzjsPixcvRqlUwsyZM3H99ddj0qRJLf2dLCODeHssReCyI10aREgQnwCSQJfS/1QqQhwaFOzilIWyxkS7NNJaZFPbsWLuuWqiHVDZIpv8vay7btPloYcewqpVq/DZz352wGPXXHMNKKWYNWtWxXvqpkS7aqOls9GODlYGTrCQIUyBcgUeE+tMIJEoOdG+eIuTkEFAA2cKnteeJUa9ttg4jbrsar5eQgCMz7MzVHPb2XbYTRdbZ2pj68zoh3IJFXOymrZYM8eOMgXGFRhV4IwirzRgbvBoM7cOEKGgVittNY1aDrpaj5m22Fa3vtbDiHbRzxlEO+uu60y0otAZPnOYYzulJfboo4/GpZdeip/8JFgzE0KwatUqfOUrX4kc4lnJrDC4rtvy4abGOrjPPvtU7O/v78cRRxyB//mf/2n4XHfddRfOPfdcXHzxxXjqqaew7777YubMmXjrrbeiY8455xzce++9uPvuu7F06VK8/vrrOO6441r2+1hGHpoLaIYwNRaQeQKd19B5CaTMBIpjWk3LW2MtI1kWQObcOsP5zfPqPZ48pxHuzGsarFjXuejw/2OjGzTBSy8FLbHTpk3D/Pnz677GBz/4QWitsdtuuw14LJ/PY/78+Vi7di36+vqwZMmSzHOFWp2oNNS0ozZaOoPuic+l7lcOgWYIVgRMAVyCcAXm+kEAhSvgjimBucH3POdH7bGMBa2zQXuTAK8xy7QWjOmKrVFMMm1cVEsmvsZrVDNOOCHDdl9TU0yKbFhvZLgojb+OFes6F61Itjojyy2xts60BltnRi87Lri16mOMKzhOkDgOAIwq5HMCOUch7yjkmUaOAGNJeZ4b16Qh51urQimGm3gFjY+bTZuZeqU63op1o5BOSYm9+uqr0dvbi6222gqFQgHvf//7scsuu2DcuHG4/PLLmzpn5lu55513Hr73ve/h+9//fkus2L29vZg9ezZuueUWXHbZZRWPnX322QCARx99tOHzffe738Wpp56Kk08+GQBw44034r777sNtt92Gr371q9iwYQMWLFiARYsW4dBDDwUQzMvYc8898fjjj29S1vrRwAT3n9jg7dSScylHgUkKzQmIr4OB4sljYrPrdEykA8qtqfFjGnEmNOKyi45NSXkF0mfa1aPWjDxzTQYr1o0+srbEtpN2JCoNNa2ujZbOonvicyi8uXv0s3YA4pvvNYijAaaDYKKcCNp0uIIslu/9K0nDlFgNxhWkDBJkDdVaS9PIIs7VIz6PLs0ZZ8S6Zpx2BiFoRQhFGlasG31kbYltJ7bOWDqdHRfcGs2yM1S4nbmE63pgTGJMlxfudc2jKEoS1J6wPda47JqdZdcsw+WyqxZAYdtjLUPJhAkT8OCDD+L3v/89/vKXv6C3txf7778/Dj/88KbPmfnf7+9//3s88sgjuP/++7HXXnvBcZyKx+OpTo1w+umn46ijjsLhhx8+QLDLiud5WLFiBS688MJoH6UUhx9+OJYtWwYAWLFiBXzfr/iPtscee2D77bfHsmXLqgp2pVIJpVIp+jmtN9kyPLRStNMMgB8sxrTJDk/MEoq76Cq+TwmiiNOMqJZ83coQCTZo0S553vg54y2yls5GKZKpJVupssMOaHwYeLtoR6LSUNOK2mjrzMhAcwIiEomxPLzBwxQIUyBMg7lBEdEyuAFCmQLP+VCKgjs+ZKxmiMR7txHv0ubKtVKsi2Pm0dWaZ9eMaBcX/cz3QQuwjFx2rMF2XcvwoTXNWGeaC51oF7bOlLG1pnPZccGt+OeJnw/eM3nl2iLfVUKpkAuc2b4DIRmELMtRUjNIGc5zQ+CyA0Hmttg0mmmvHU7irbFAWbSzzrrORwkKxTLUGtFZLbGrV6/GlClTcMghh+CQQw5pyTkzC3abbbYZjj322Ja8+OLFi/HUU09h+fLlLTnfv/71L0gpB8yimzRpEp57LmhpWbNmDVzXHTALb9KkSTWTO6688kp84xvfaMl1WjqT+Cw7JgYWpWQ6WvyDaz2xrhnSZsjFZ88R1pgrLysDQjHa8LtZhp9Octi1I1FpqGlFbbR1ZuShHAKKQKwjTpgwzhWIDO71ExoIeJSVv2ZpL02KZ+0S6wxxt535vtlU1ywhFjJD+qhl5NBJDjtbZ8rYWjOyiG54SAbHEeDMzLCTyDnB2AFGCRjRYKTssmvV6sC45ZKi3VA76arh1D8EQHp7rGX00CkpsTvuuCMOOeQQfPrTn8bHPvYxbL755oM+Z2bBbuHChQ0d99hjj+GAAw5ALpdLfXz16tU466yz8OCDDyKfz2e9jCHnwgsvxLnnnhv9vHHjRkyZMmUYr8gynJTbYVnbBa1qaa9a0grRzrjrqqXVZroz3gYh0GKpRjsSlYaaVtRGW2dGDpoTADpqizVz7AgPEsaJcShLErTJSgrKJDSjmV3LzSa4mufVS4qNu98Mydczol2zLbHAwLbYuKuw2Rl+Fkuj2DpTxtaazsYET6S57IBgXcCYhOsKCMmQzwlIRcFpkAprXHYsbIs1pCXFZiHpsDNinUmKNcETaVQEQzR9BfWJt8UmXXaWkYGSLOO89uBzRac47J588kksWrQIl156KebMmYMjjjgCn/70p/GRj3yk6ntyPdqmNBx55JE1h7iuWLECb731Fvbff39wzsE5x9KlS3HttdeCcw4ps394e9e73gXGGN58882K/W+++WY0mHby5MnwPA/r16+vekwauVwO48ePr9gsow8TPhEsxkJijoa4y64ZsY4y2RIhzIhvRpxLe2NLc+bVIin0mTfMra5ePJhLtQwBA4NPam9aZQ+daCcmUcn3g49xrUhU6lRq1UZbZzob5RCoep/+mYqEO9MaS8Pvo69MgoZ1xQhpnMlIJJOyM1cYzYp1SXde2nmSLcGWzkOHoxcybRlDJ9qJrTNlbK3pXJIz7IBgNE38fdQkjDOqkHN8MKpDx50OXXatFaok9Ihph01WEn9kXLalBXRK6MR+++2Hb3/721i1ahXuv/9+bLnlljjttNMwadKk1AT0RmibYKd17b+Qww47DM888wyefvrpaDvggAMwe/ZsPP3002As+4c313Uxffp0PPzww9E+pRQefvhhzJgxAwAwffp0OI5Tcczzzz+PVatWRcdYLNGizNEgTuiaiEGYAnf9Qc93G4xbwVCRypb2GlX2G+cebcK5YRn5TJ06FStXruyI4taORKVOpV5ttIwcNA9SxeMinqkXJEyMJVwGbbFcBu2wrPyVJeoHpaqmE26wiCptp8lE2EznzODctmx6mJZYW2eGFltnRj40nOuZNo6AhTd43JwHxhVyOR/5nACjOnDZ0cD07QCR482p4nzbFPC1Fe4sQw8hBP/1X/+FW265BQ899BCmTp2KO+64o6lzDVtoyrhx47D33ntX7Ovu7sYWW2wR7V+zZg3WrFmDf/zjHwCAZ555BuPGjcP222+PiRMnAgiEv2OPPRZnnHEGgGCo7IknnogDDjgA73nPezBv3jz09fVFqbETJkzAKaecgnPPPRcTJ07E+PHjMWfOHMyYMcMmxFoAlNNilUNAuQaJHBNh8QwXW5oFLU61XHaNLoKSqaxAthZWEkt7tWx6aEEzuT11h4VOtCNRyWIZCkxrLBCkxSInAJ+C5IJpOURQUC6hFQVzRRTyQ6kCoQqUSzAmwbgE5wqcSzDGIKoYsaUkbZ9jZ2i2FRfINr8OaM3NK0t7UZJkqjNKko4KnbB1xjISYJHTmg5ojaVKgoY3f5ww1EiKIMCIUY2cIyEVgVDBLDumg7ZVBoIiaf3N+Swz7NrVBtvo/DrAinYjhWaC9IDOaYk1vPrqq1i0aBEWLVqEv/71r5gxY0bTTvOOTjm+8cYbK4aivu997wMQzHA46aSTAAAvvvgi/vWvf0XHHH/88Xj77bdx0UUXYc2aNfi3f/s3PPDAAxVBFNdccw0opZg1axZKpRJmzpyJ66+/fmh+KUvHE7TFukFSbJeG7g1cdjQnwMeUosQ/JcMB4lwNWzADqbIoMm90zYh+cawAOHrppNCJdiQqWSztQjkENPbJP3DaqWjZQrp86IITtcbSnIASLHLZmfZYxmToqiu3O3GmwJmCZKSqey2LaNfoHLu05xkaaWkdDEpRzHp++D9cW1pPJ4VO2DpjGcmYOsFYcJNHMQrJGBzXh5QMUlFIRSAVhZAEjiZwdBA+4YBAagKf1K8BPtEtceM1E0hhxLfBiHvxGXaWTYtOCZ246aabsGjRIjz22GPYY489MHv2bPziF7/ADjvs0PQ5O2o1/uijj2LevHnRz5dccgm01gM2I9YBwMsvv4xLLrmk4jxnnHEGXnnlFZRKJTzxxBM46KCDKh7P5/OYP38+1q5di76+PixZsqTm/DpLZ7PB26nl51SOChZhDEBeAjkBkvPBu0vg3aWK1ibu+uC5oD02vgGoWIhF564xq6eRIZtZRLjBYMU6y1Cx44474v3vfz9uueUWrFu3brgvx2KpS+osO6bKSbGmNTbngzAFlhOBSOcK8Jwf1A1XwM35cHMegHK9cF2BnFueaZfmVhvsnLtqYqCQtG1trjzpIh9EK67FkhVbZywjCeO0M62xBjM6wQRPmNZY1xVwXYmcK5BzFBjRyJFAvMppWlM8G0wQRfIcogFRsB5ZXHMWS1Zee+01fPrTn8YWW2yBrq4uvPvd78aTTz4ZPa61xkUXXYStt94aXV1dOPzww/H3v/+94fNfdtllOOigg7BixQr89a9/xYUXXjgosQ5oo2BHyKbbK28Z+WguogWZzmtgnA861gPrLoF3eWCuWXz5wQLMFcFMu1ioRDXhrpXpq/VEtWbm09Wah2fpTJRiUUhII5tWtKNCJ5588km85z3vwaWXXoqtt94aH/3oR/HTn/4UpVJpWK+rHdjaOIpxdBg6ES5YzIIr74NwCeb6kcsuqhuOD0o1urpKyIWLLs6CBRljumZrqZQkdatFfI5dXJyrJdTVEtXSrq+Rdtj4OcUwOdQt2dCaZqszknVU6IStM5aRRppox3hslIKZiUoVXMdHzvGRcyRyjkTeUXBoED7hIHDZNeqca8SJZ2hlGEU702MBYJ4+vs2vYGkFmcONws8uBx54YN1as27dOrz3ve+F4zi4//77sXLlSlx99dXYfPPNo2Pmzp2La6+9FjfeeCOeeOIJdHd3Y+bMmSgWiw1d/6pVqzB37lzsu+++g/sPEaNtLbF24KllpKMZIMcQEKlB8hJkrAfiUzDB4EoCwiVUyYHwOJSgIJKBSgrh8UiUU5JlbplVkjUk6plZSFrSqq2xWUgT6YbKzWcZejqpJXa//fbDfvvth7lz5+LRRx/FokWLcNppp0EpheOOOw633XbbcF9iy7C1cWTSt3aPAfuSrbFpEK6ggYrEWOYKcEkhPQ7ucuS6ilCKwPccSMHguiJ6vgeW2U2Xdc5dI446pWj5phNVFYIbZyrTvDsr0G06dFJLrK0zltEKZwqKS7iuiFpjSz4DIxocBAxB+AQjBGM0RX8b5tml0UxbrA8jMDYv4LXOFmEZSTTSEvutb30LU6ZMwcKFC6N9U6dOjb7XWmPevHn42te+hmOOOQYA8IMf/ACTJk3CPffcg09+8pN1r4MQgvXr12PBggV49tlnAQDTpk3DKaecggkTJjTzqzXnsBNC4KGHHsJNN92Enp4eAMDrr7+O3t7e6Jienh7stFPrWxUtlqHCtMXKPIHOa+huH2SsB9pdAh9fhDuuCN5dgtPlwR3jgYetT9wVsQTWstsOSE97il5vEIuYpCsu7qxrNgVWSYptr28uzcZiaYZWJioNB7Y2jl50jWkFRISLY7/6wiTpsuOugNNVAncFcuZrvgTH9SNHdj7nw3Vky0MmqqXF1iOr0w6orHlCUCvWWYYdW2csnUzyBjxLeW8N5p/q6HsA5fl2VEUBFHlHIc8Clx0DgRMGUDgpIlqyLTaLy65d2NZYSxY2btxYsaW5p3/5y1/igAMOwMc//nFstdVW2G+//XDLLbdEj7/00ktYs2ZNRRjRhAkTcNBBB2HZsmUNXceTTz6JnXfeGddccw3Wrl2LtWvX4pprrsHOO++Mp556qqnfLfMnp1deeQXvfve7ccwxx+D000/H22+/DSBQLM8///ymLsJi6UQ0F4FY5wTpf7pLAZuVQCYUQccXwCf0wxlXAB9TigpsJNAx1bBQ1swMn0ZCJRp+/SrnsGLdyEFLEom2DW2xlNhOaFUyvPrqq5g7dy7+7d/+De95z3swduzYjrm2etjaOPrRbKBwlzrLDgD4wHv8NCdAmAZzRRQ84XaV4OY95LtKyHeVKlpjXVe0TbRrlKT7rtbcuRNe+nzm8x//4hdx/ItfbOraLEOMylpnaEe1xBpsnbF0OmmiHeUyuvlvKJsDgq8sdNnlcj7yOQFGdeiyC8QvrknUGpsm2nUKcWddUrS7scm21utsO+yIIVOdiRlWpkyZggkTJkTblVdeOeDc//znP3HDDTdg1113xa9//Wt88YtfxJlnnhndtFmzZg0AVISVmp/NY/U455xzcPTRR+Pll1/GkiVLsGTJErz00kv48Ic/jLPPPrup/yaZW2LPOussHHDAAfjzn/+MLbbYItp/7LHH4tRTT23qIiyWTkU5CrKLgvgKDBqAAkEpKnMMgBYM0uOgLGh9JWGbajWS7UTtpprwtvrzJ0dF3oh25uetr/vh0FycZdjopJbYdiQqDTW2Nm46GNGO1Om7IU7YDgtEX2nOhw5HKpjEWJ7z4foepKBQqryIkjEnnFIEsoFQolpkTYrNghHr0kS7H0+9KdVdboW60U8ntcTaOmPpdF794knR9/XWEgbGJKRk4EzBC9cWcZddyadghIDpwGUnTJuqKUoxfOgKIa9VibFZSYp0pj3WiHVpot0cclfquaxQt+mwevXqipbYXC434BilFA444ABcccUVAIJRCX/9619x44034sQTT2zJdTz55JO45ZZbwHlZZuOc48tf/jIOOOCAps6ZWbD73e9+hz/84Q9wXbdi/4477ojXXnutqYuwWDoVzQU0c6G6goI1QLSTNFh48dbOg2h0jl0t6oluU25aWCHaNfIcS2cSH7raCFqXHXYAcPrpp+P0009v1+XV5bLLLsMJJ5yAa6+9tqVDWocSWxs3PTQDWFGDZBi0Q3h5lp1JG+eugNtVgpKVolyhEHzYFIJGIRSNzrPLOsduMDTirDM3qYxwZ8W6kYdSJFOdUZJEDjvA1plWYOvMpoUR7aSkUOENnGBtULl8j9piZdgWyykYV+BcgTMNRwaZSFID0BQwM+x0Z7S+NkI9Z911+vgBop0V60YmMvw3n+V4ADjssMPAGKtZa7beeuuoJhn23HNP/OxnPwMATJ48GQDw5ptvYuutt46OefPNN/Fv//ZvDV3P+PHjsWrVKuyxR+Xs49WrV2PcuHENnSNJZpuPUgpSDhQSXn311aYvwmLpZGSXB5knUF3hPLuuoD2WOApgCjQnwFw/WHxVEe7qzaczi5nBzLFrhik3LYzEHtsCu2kxdepUrFy5EitXrmxoEdXOGPR2JCoNNbY2bnqwogYtaFBfg1SZX2fqBAlrgxbBTR5CVdQWG8w+9eHmPYwZ2x+2yBYrwic4l+BMpYpwjOloSyMtVKLROXb1giQaEevix9gW2E0L47CzdaY12Dozuqk1TkcpGq0RKC0nxQJlwS4OZ0HNyDkKOaaQI0A+TIzNaYouTYN5dnUcdM0IeiL2nFakyDYqvPkot9NasW7TY/ny5XVrzXvf+148//zzFfteeOGFyGk9depUTJ48GQ8//HD0+MaNG/HEE09gxowZDV3H8ccfj1NOOQV33XUXVq9ejdWrV2Px4sX43Oc+hxNOOKGJ36wJh90HP/hBzJs3DzfffDOAYHhrb28vLr74YnzoQx9q6iIslk5HdnkAgjua1NdBi1NegnT5IP1u4JjI+VD9wTG1HE/tbofNmhg75aaF9Q+ybNKYGPT/+q//wv33348tt9wSf//731Nj0O+44w5MnToVX//61zFz5kysXLkS+Xy+5vnbkag01NjaOLpRTjgryA9bjkKxLnDYEZBitrYhymUUPqEEhWYU7pgSlKLIdQVfHceH43Jw34FSCoLquvF3zbrqjDDXSGJsMzQz386yaWHrTH1sndk0oExBhbO54u46pShUYjSCOZYlnHciPI5RBYDBoYCUwSifgNgcO41BJcc2kwbbDpqdb2fZdDjnnHPw7//+77jiiivwiU98An/84x9x8803V7ynnn322bjsssuw6667RnVmm222wUc/+tGGXuM73/kOCCH4zGc+AyGCG6+O4+CLX/wirrrqqqauO7Ngd/XVV2PmzJmYNm0aisUiPvWpT+Hvf/873vWud+HHP/5xUxdhsTTLBm/oUrBklwciXSiHgHIATAFMg+RE1BZLuYL0ytMXkoW1nWQV6iyjh+BDXIaWWEUztcS2Owb9ySefxMyZM9HV1YX3vOc9AIBrrrkGV1xxBf7v//4P+++/f8O/23Bha+OmgXIUqE9BRCjWFQiIIECx9nu9TnFPU66gRVA3lFSgQNgayyA8H27Og+87KBSC93bOJTx/4OsMtv017qKr56iruP4aqeeW0YfOWGeUpJlaYm2dqY+tM5seSrAKd10jyMSxnGmUJMBCh50xvUlNABIIbg4IfOgBc+yAwc2y6wQhzzKyUIJB0cbXz0bQPvDAA+u2xB544IH4+c9/jgsvvBCXXnoppk6dinnz5mH27NnRMV/+8pfR19eH0047DevXr8chhxyCBx54oO5NIYPruvje976HK6+8Ei+++CIAYOedd8aYMWMa/p2SZBbstttuO/z5z3/G4sWL8Ze//AW9vb045ZRTMHv2bHR1dTV9IRZLVoZSrDNoBqguAl1UII4OhoqHg8MBREPElccjsa5dba5K0ujOGg3nXJjrmDzvzra8pmX0sMMOO+BHP/pR9PPGjRuRy+VSh7T+8pe/xMyZM/Hxj38cS5cuxbbbbosvfelL0ZDrejHo9RZSJlEpPqRVCIHPfe5zOPvss/Hb3/62Fb9yW7G1cXSysX+3qo8Rn4AUKBCKGMQn0fe10IJFHzBN+ATP+RCl4GYPd3xwl4P7Ao7jI5fz4Hm8oXPXo1XBE1asszQCpRSPP/549LOtM4PD1pnRy+tnfKbqY0rQCnddtU4dzhTSRqqatFgfBBxB2ygDovAJRsohFH4TLaytFuV8DAyesFjqsXz58orQiWp8+MMfxoc//OGqjxNCcOmll+LSSy8d1PWMGTMGm222WfT9YMgs2AFB0sWnP/3pQb2wxTIYhkOsAwJ3BZEUMk9AChqEhbPsABA6cPZEUqwbTDtsMtHVYjFoyTK5ObUieOGFFwa0AV188cW45JJLBhxvYtDPPfdc/M///A+WL1+OM888E67r4sQTTxx0DHo7EpWGA1sbRxe1xToAourDAVwCkTBXPfEv3v4EBM47xxWRy84t5uC6Ap7f1Ee29Evjgws1smx6aEUy1hkGpZStMy3G1pnRxZqzgv+XlFX/fJ+2dlCChTdfyu/lfkLmEpJCxp5r2mKjnwHEW2ONWJfmsksjKdQ1+jxzlbXymqxoZxmpCCHwjW98A9deey16e3sBAGPHjsWcOXNw8cUXw3Gy/8tuauX/wx/+EIcccgi22WYbvPLKKwACW/kvfvGLZk5nsWRiuMQ6IoIPeZoB2kEQPpGXAJfRQHESpv5VGxrbKLUSYs1QWrPIAwYW+TVnz057qsUSsdtuu2HDhg0V24UXXph6rFIK+++/P6644grst99+OO2003DqqafixhtvbMm1mESlJINJVBoObG0cPdQS69IwoRPar/2xSksKLUnQXhje0ImPM1CSQQkapcdSquG4fjRAvBlqPa/Zc1osjUAptXWmxdg6M3pJrh2kpAPEOtPuSrkMNqrLKbGx50tBIQSFkARSEfCEs9qsMhwAPGx3dfRA8S4LWcQ6830j0sUXEumvltGNUjTzBgTtrtOmTcP8+fOH9frnzJmDm2++GXPnzsWf/vQn/OlPf8LcuXOxYMECnHnmmU2dM7NgZ+5+HXnkkVi3bl2UVrT55ptj3rx5TV2ExTJSMAPHZReFZghy0pkGyfmgMccCDYW7lr1ulUVVUrTTMZfGG3P+u2Wvbxl9UEoxfvz4ii2tTQmoHoNuFj/xGPQ4b775ZvRYLdqRqDTU2NpoARA5rsGC2gAuy/tCVCyhVQsG6fGg5Slx44UxCe76cMLNdcWgBbZG22HrtbyaD8k/nnrToK7HMvqxdaZ12Doz+ql3w59xBUoVGAtSYmkYXlQh1ikKqUj0VUgCoQh8Vd0YHhfbGhHeaj0//fHq4ly1/fHUV4ulHo2kxA4FixYtwu23347Pf/7z2GeffbDPPvvg85//PBYsWIBFixY1dc7Mgt11112HW265Bf/v//2/Clv5AQccgGeeeaapi7BYOh3jrgMAEupwcgyBzmuQvADJCdBY+AQQinZ8aNwLSaedFe02LZSgmTatSBQ60cjdqHbHoH/nO9/Bcccdh8985jPYcccdseOOO+Kkk07Cxz72MXzrW99q4r/I0GNr46YB9SlYsbbolRTotE+hBYUWLNhCd50qOZAehyhVzjyNzyoyizLXFaBUYUyXlylgohmBz4h1jc6ps6LdpkH077bhjUShE7bOtAZbZzYdqo1QMJj3ZxYzByhJISWDkAxCBu46IQlKkqIoCWRK6YgLYgyk4XAJVkPg400GVNTCuuwsI4VcLocdd9xxwP6pU6fCdd2mzpl5IMpLL72E/fbbL/Xi+vr6mroIi2UkQH0KIgHqlyueymuQvATp8sG6S2B9wZ1jwhQgaeSyqxc8kVwYKcmi59a722ZcdvEQikaeZ9m0mTp1Ku65556Gjm13DHo7EpWGGlsbRyeal/0IrOCCFTVoQYd1oLwo0Y4O2mLD937iqJrtsarkQHgcouRAh+/f5RsvDL7HocM2D+4KMCaj8AnXkSjI+h/fWtHuSqmqaMkS4TUmz/3jqTfhhJc+P+jXs4wuKKVYuXJlQ8faOlMfW2dGF2Z+XRxTC6SkFW7s+PxIxhWkoKnvz2ZmnRAUJZ+iJCl8BUgduOt8XW6H9REkxKZhUmOjFtkqSbHNuPHSX6++m+4L5C7cqI9vyetZOhcts6UiG3G7kZTYoeCMM87AN7/5TSxcuDBylJdKJVx++eU444wzmjpnZsFu6tSpePrpp6M7XoYHHngAe+65Z1MXYbF0MkTwCrGOiJhg5xDQcQq0T4LkBFhOgLsCouRUiGdA+tDYViXtJUU7oOy2e+1LJ2Lb6+9oyetYNk2GIgYdaG2i0lBja+PoxdQAI9axYiDOEVFloRKKdtVQgkJ4HKWervB9m1U463yPQ3gcUrKobjiuD9dz4LoibEcl6Cs4kXAmJanpvKvWCiskrRDf0mpSfFHImQqdGwOFOyvaWQaDrTP1sXVm9GPG2ygRvP9XEy6MaBd/nkGKQLgTiqAoK4W6uChmxDpByvXBJMf6pLEAiSSDcddZ0c4yGBpNiW0Hxx13XMXPDz30ELbbbjvsu+++AIA///nP8DwPhx12WFPnzyzYnXvuuTj99NNRLBahtcYf//hH/PjHP8aVV16JW2+9tamLsFg6FSI4WJFWCHUkrCbGaacdQHf7IL0CrLsEXnDBPQ6/P1fpchOVol2rxLokaQ47K9qNbpQimRKItaZRSyyAhu5GtTMGvR2JSkONrY2jEzMOgUhUinXF2KKkjkAXR5Z44K4rORCeA6+Qg5IEUgatslLQSKgLFmzl16FMRS47SjVyrkTJY6lOukoRLvvw8CRpop1l00LpbHUmEJaVrTMtxNaZ0UXUhRNzz2lJ4ZecSKzL+jcXiHUEJZ9CagIBoEenO+mMUNdowEQ1l50hKdYlU2QbwYp2lniQRKPHA8PrsEumoc+aNavi5ylTpgzq/JkFu8997nPo6urC1772NfT39+NTn/oUttlmG3zve9/DJz/5yUFdjMVSi6FOhzXtT0QoED9016XkSBCfQHcp0LEeaJHDKXHIYrAgSy7i6ol0aTPv4q65ms9NOvoafJ5l0yRLS2y7mTNnDpYsWYK5c+dGs4iWLVuGSy65BO+88w5uuOGGYb7C+tjaOHqolhBLfQ1SiDnr/MrFSNQWG3/fj7c0lYI2V+OgEB6HV3ThRYuzQKCTkkHGnmdgTIJSFbnsgMBZZ6jnsjNw3lwgUiOinXXZWeJkaYltN7bOWDqJt84dGHQSjUVoQKyTCeedDEU/qSiKJQ4hA3ddUQMloqqKchI6VVhzNIEfE/TS3HZZHHg+GkuEBRoT7SyWJMPpsFu4cGFbz59JsBNCYNGiRZg5cyZmz56N/v5+9Pb2YquttmrX9VksAIZerAMAVtRw1qlgASYA1ClMutsHKZbAShxuqR9aUhTWdw9I/qtGUqyLz7GLjokJcI2c14p2mwZKsYo7tPWPJ5kddu1k0aJFWLx4MY488sho3z777IMpU6bghBNO6PiFlK2No4daYl0FcbEufC8msX0V8+skBWQQOhHczAlaXoXHUSzk0N8zJnBGmOCJGnNIeeiyUypoS825BCWv8b/9wZIU7ZJYsW70omNhKI2gFMvssGsnts5YOoU0sc4gY6IdgNS/uaRYZ2bdSREETkgVzK4raKAIDR+66qw6oPocO0N8nl01l12rgyasaGcZTWzcuBF33nknFixYgCeffDLz8zMJdpxzfOELX8Czzz4LIJj9MNLmP1hGHsMh1vFeF3yjBO2hIOtjiS5MQTsphS1cuJBuH6TggRUdcK8EJ1yU1aOZNNlGxDsr1lmq0UkOu3YkKg0ltjaODpJinQmciKeEZyLmftOCQUsSBkywoCXWD4InSsUcCoUcRLgw4+GNGpZwwrlOcD2UqsBp5wgIwcCYjlpj67nsmnXXxUkOOrdYqtFJDjtbZyydQC2xzmDm1hmxLinQRcfF3oelZKE7m0buuuS7vU+qBExUEdviLrus8+yaaYe1WIAweIVmuDnUYaETcR555BHcdtttWLJkCSZMmIBjjz22qfNk/hT6nve8B3/6058GDDy1WEYLrOCCFRRoLwHp4VDr8oFDgikQRwFcArEFEXEUwAi0o4OWqLEeaImD9bvgbhBC4RWac0AYl10zTjkr1llGCu1IVBpqbG0cPcSTYeuSdNclbp5on0InFltaUnhFF8VCDsVCHp7P0V/IwfODj2ScBYIcD2/kUKrAmYSH8lgF47LjXAZttCx9cdTI/Lpk8ISBJ24kiQypbRZLp2HrjKXTUbFkWBWKb/XwSy58nwfJsqo8v04gcKgJErjrfKKjttik+BYX8mrNqBtq0lx21nlnqcZwtsTGee2113D77bdj4cKFWL9+PdatW4dFixbhE5/4BAhp7u8rs2D3pS99Ceeddx5effVVTJ8+Hd3d3RWP77PPPk1diMXSKbCiBu/RIAUGXeTQJQ65sQuEKRCuQLgMxLucAHEUtJQAoyBSBU47J3iMjfEqXHZZIqrrQcLFlY61ThlRL03cs+Ld6EZJAiUbLwK6A1pi252oNNTY2jg6SIp1JiEWaS1DVcQ67dPAXSdYeZ8IZ9SFad7Cd1AqBM66QijaeaEbW4QiG+cyEu8Up6CSgTMZOe8CIU9BUA3GdMU8uzSquevSxLp6xOexGpdHu4KULJ2BVshWZyQZ9pZYW2csIwUz2xSodM7VCquTksH3OXzPgefz0F1XudYwIl2zwRJxl92AY8M22cG0wxo7QzXvd9rcOxs4YelEfvazn2HBggX47W9/iyOPPBJXX301jjzySHR3d+Pd735302Id0IRgZ4aannnmmdE+Qgi01iCEQMrBt1tYLMMJEeFg8SKD6nWhSw5UKUx95QrM9cHGeAhKogB8WiHcAQDp8kHzPpjrRy47UcqeQJacYTfgWpmKCnz5OVass9RnuFti252oNNTY2jiy2di/W1WxLggfSl+w1BLrInedeSyaSRR89T0nEOs8DhG6KoSkgAxDHQSDECx00QVhEwKACIU7IBTtQpcdQCPRrpprrlGS7jqzL81lZ4U6SzWGuyXW1hlLp6Nk+UaOlOWgCZkSOpFsgRVe4KzzPQdeyYVUNPU9Oim4VQuRSB5fzW3nEw3octurIHrQM+wYqot2lk0LKSgkadzkYpyow90Se/zxx+MrX/kK7rrrLowbN66l584s2L300kstvQCLpVPRfQ50iUOVOKTnQPQHLRR8TOCU4JKCdZdAciIQzVgg3BlIToDmRODMYwqUq0wuu7hYV0t0M6JdMiU2+bw35vw3tr7uhw2/vsXSTtqdqDTU2No4skkT61iRgvoatKCrpoQDSHfWxfcLVnZOyPJiTMaGiStFICSNueQoOIL3byPmweOROKZ4ei2pNr+ule66Wty18w04/sUvtvScFkuz2Dpj6XSMWKfDdlglaoe7SMmiRHHfc+B5buCuUzSagyoUga/rh0nUo5qzruKYFPEvNXU25bnJ37JR0W4OuQvXWZedJcFwt8SecsopmD9/Ph599FH893//N44//nhsvvnmLTl3ZsHOzk2wbEqovhyE2ULhTkkKp8uLjmFA0AorKHQJIDFnQtBGGzohmIxi2tvlSEgT7SyjHxUOG24Uremwt8TWY7CJSkONrY2jB+Osi4t1tFgnJTzhrIuQgctOhwsxoDxInIU3ZUwLk5Qk1s6kgNDHzaHKol3onPO81rehNnKuai67OFa0G53Ek4wbQSo67C2x9bB1xjKUJAMnTC3QMXddWqBPlCAeCnUydOV5nhs480Qw706KYI5dxXNTRLusARLVkNAtD5ewop1lpHLTTTdh3rx5+MlPfoLbbrsNZ599NmbOnAmtNZQa3Ge1zILdL3/5y9T9hBDk83nssssumDp1alMXc9VVV+HCCy/EWWedhXnz5gEAisUizjvvPCxevBilUgkzZ87E9ddfj0mTJlU9T7Ue4blz5+KCCy4AAOy444545ZVXKh6/8sor8dWvfrWpa7e0h+FIiI2jJYU0M+jCglnq6YIStKIVlXWXwm9UecB4ituNcgU0MM+8UXdd+nOrH29ddhbDcLfEVqNViUpDTTtro6W9xOtMPBGWCB20w/oket8mggA+ASQN9ks6UKyT5aAJLcyCjFSIdgbT2lryWMXsoeB7BcZ00N4ainacy+g4zhpLa01z19Vy1qW1w1oszTDcLbHVsHXGMtRUS4c1N9lVSn2oOE6wcriEZPA9ByIU+eJCupAEskZ7aqvEOiBw0iVn2DXqrmsFVrQbnVQTrmsdDwx/SywAdHV14cQTT8SJJ56Iv//971i4cCGefPJJvPe978VRRx2Fj33sYwNmqzZCZsHuox/9aDQvIU58hsIhhxyCe+65J5MNcPny5bjpppsGDEw955xzcN999+Huu+/GhAkTcMYZZ+C4447DY489VvVcb7zxRsXP999/P0455ZQBsysuvfRSnHrqqdHPre43tgyO4RbrDGaRpUPbupIUfiFojzXhD4Qp0Lxf2QIlKFQ4t44yCVnlz43y5My59HtLNPZaFkscrWg0H6uh4/Xwh07EaUei0lDTrtpoaS8b+3cDeKVQR30KIgFiouiSYl2MCrGuGJwjPrcOKIcDJeeNGqqJZ3HRrtrjkfMuREoCxnQYVlGlPTbl9ewcOktdNMlWZzrMYWfrjGW4SBPr4m2vskZnjJlVZ0S6yF3n88BZJ1mYDhskxFbDAWk4eKJZhlKss1iSNNoSe8kll+Ab3/hGxb7dd98dzz33HIDmzGJp7Lrrrrjiiitw2WWX4b777sOCBQtwwgknoFQqZToPYPotMvDggw/iwAMPxIMPPogNGzZgw4YNePDBB3HQQQfhV7/6FX7729/inXfewfnnn9/wOXt7ezF79mzccsstFQVmw4YNWLBgAb773e/i0EMPxfTp07Fw4UL84Q9/wOOPP171fJMnT67YfvGLX+C//uu/sNNOlQLQuHHjKo5Lpi1Zho/hEuvii7YkKhLsGITnQHoO/IIL6XHIfheq6IRCXSDWaUkjV0V0jsTds6RYl8SIdLVcc9UWgRZLLaZOnYqVK1di5cqVw7aI+tnPfoYPfehD2H333fH000/j6quvxuuvvw5K6aATlYaadtRGS3vZ2L8bgPT3feoHCxsTKlEh1sXcdcmACV3iocOuXAuAwB1hbrakuSiMyJbEzLULnBSkQqAz35v5dyWvfrtiu8Q6zpV15lkGYBx2ts60BltnRh7VnHUAKkbYVGuFTYp1nscHinUimF8Xd2k7iX/WnSbWNd5cb9nUkOGYnywbEDjspk2bhvnz59d9jb322gtvvPFGtP3+97+PHjvnnHNw77334u6778bSpUvx+uuvN+WKM1BK8ZGPfAT33HMPVq9e3dQ5MjvszjrrLNx8883493//92jfYYcdhnw+j9NOOw1/+9vfMG/ePHz2s59t+Jynn346jjrqKBx++OG47LLLov0rVqyA7/s4/PDDo3177LEHtt9+eyxbtgwHH3xw3XO/+eabuO+++3DHHXcMeOyqq67CN7/5TWy//fb41Kc+hXPOOQecp/8nKZVKFYroxo0bG/79LCMbM4POiHUGr5ALnHWxBZAJmdDhgk16DmSsnbYWSWddNZFOSxot/KxYZxnJtDNRaahpRW20dWb4obH5c4FoR6qKddqnFW2whrhQZ2pBLUSG9/GkaGeQkqSKcfF22EbEusGKbo3MuLNYhhJbZwZia83wEl8TmLCJ+GMmIVaFopzvORUtsCYR1sysE+FzOomhcNbZdlhLnCyhE5xzTJ48ecB+YxZbtGgRDj30UABBeNGee+6Jxx9/vCHtqRZbbbVVU8/L/Nf94osvpv7HGD9+PP75z38CCCyA//rXvxo63+LFi/HUU0/hyiuvHPDYmjVr4LouNttss4r9kyZNwpo1axo6/x133IFx48YNUEbPPPNMLF68GI888gg+//nP44orrsCXv/zlque58sorMWHChGgbaVHwI4nhbIWNL9aAcvsp5SpVQPMLLkSJQ/Tn4PcH4RSqFDrtJAnTnirTAaPXii2Mqol1lKW/rpbUinWWCClJeeBwA5uS5ZbYRu9GtQOTqHTEEUfgxhtvxLp164blOlpBK2qjrTOdAZHB/DoiUTlzNPmeK8sinmmDTRPrtExvJUxztnGmBghrcZedcdqpsPXJuOvqMZRtsNZpNzpRKludCUQHZetMC2nVGszWms5AhWETAKJQOqAc8KIUiWbW1RPrpCIDbpYwkJaHQjSCbYO1DAcbN26s2Gq1nv7973/HNttsg5122gmzZ8/GqlWrANQ3iw0XmVf806dPxwUXXIC333472vf222/jy1/+Mg488EAAwX+ERt78V69ejbPOOgt33nkn8vl81ktpiNtuuw2zZ88ecP5zzz0X//mf/4l99tkHX/jCF3D11Vfjuuuuq/o/98ILL4zs5xs2bGja0mgZffiFXCTaif4cRMENxToWGzgeuPPSBmkmAyYaaYMlTNlZdpZB0QktsTfddBPeeOMNnHbaafjxj3+MrbfeGsccc0xLEpWGmlbURltnhhbNKxOAzOw6VlBg/UHYRHJuXYW7DqhMhI2fOybWqdgc1EYxwl1cwJOhQGgEOlVjXlE1KFXRNuA1rdBmaTGd0BJr68xAbK0ZfoTHo5oQXxdIEYp1otzupxoQ64olHs2xY6S97a9J4qJgK8Q62y67aaPCf/8Nb6FjdcqUKRU3ItLMYABw0EEH4fbbb8cDDzyAG264AS+99BL+4z/+Az09PS0xi7WDzILdggUL8NJLL2G77bbDLrvsgl122QXbbbcdXn75Zdx6660Agpl0X/va1+qea8WKFXjrrbew//77g3MOzjmWLl2Ka6+9FpxzTJo0CZ7nYf369RXPe/PNN1NtjEl+97vf4fnnn8fnPve5uscedNBBEELg5ZdfTn08l8th/PjxFZtl0yDphFOCRjOIVFhIpedAlHiQKNuXg7dxDER/LrS6l1121RwNSaEuTawzIl1cqMsq2tmE2NFJ1lkPWtGOcNgB5USlpUuX4plnnsFee+2FSZMm4b3vfS8+9alPYcmSJcN2bVloRW20dWZ4MWId8VF21yVbYeMIluquS4p1xl2XHKsAACwlwbUWcdHObPUwgl8tR91gxTrbBjv6Ma6fRjelOsNhB9g6k8TWmuFDhXPpdOiuMzd0lCrf2AcwwF0nBavurAvrgJAkNSXW0SQ1HdbRpGJrFyxla+Q5FksWVq9eXXEj4sILL0w97sgjj8THP/5x7LPPPpg5cyb+93//F+vXr8dPfvKTQV+DlBK//e1vB2hXgyXzDLvdd98dK1euxP/93//hhRdeiPZ94AMfAKXBG8ZHP/rRhs512GGH4ZlnnqnYd/LJJ2OPPfbAV77yFUyZMgWO4+Dhhx+OEl6ff/55rFq1CjNmzKh7/gULFmD69OnYd9996x779NNPg1LadG+xZXSgHAXNCRAOZyUpixjKVSTaGTFPeMGfkpIscsyZx4yoZ0S++KIp6a6rhnXTWVrN1KlTcc899wz3ZVTQykSloaaVtdHSfpKjF6hPQX0N4gOsGHPXAZWtsMZdJ0mFuy5NrIujhbm5k/lj1wCkbH5h1QoXnZ1TZ2kU47DrJGydsQwXZj1QIdapslhXy10nI4FuoFgnRDklVigCv8rbfFK084muEOrM934dh178ObyG0GdFN8tQ0uzNh8022wy77bYb/vGPf+ADH/hAZBaLu+waNYsxxvDBD34Qzz777ACX3mBo6hMXpRRHHHEETjvtNMyZMwczZ86MCkUWxo0bh7333rti6+7uxhZbbIG9994bEyZMwCmnnIJzzz0XjzzyCFasWIGTTz4ZM2bMqBj6t8cee+DnP/95xbk3btyIu+++O9Vdt2zZMsybNw9//vOf8c9//hN33nknzjnnHHz605+2MejDzHDOrwMSM+yYBuEShKY73gzGMWEWY34hB7+QQ6mnC6WerroLNJpwzKVttbBinqXdXHLJJSCEVGx77LFH9HixWMTpp5+OLbbYAmPHjsWsWbPw5ptvNv16rUhUGg5aVRst7cWkwyYhQoeiHamcXQeku+uAyF1niIt1xl2nRFAbRMmBkhTCD5qGWJ1AiCzUc9ol3XVV3d41WmbjpAl/VsSzDAZbZxrD1pmRQ82EWCPWCVoRNAHUcNeFYp3nMZR8hmKJR2Jd3F1X0kBRA7LJZNhG3XZJATDeDtsKsc4KfpsmZk5vlg3IlhIbp7e3Fy+++CK23nprTJ8+PTKLGbKYxQBg7733jmaKtorM7/BKKXzzm9/Etttui7Fjx+Kll14CAHz961/HggULWnpxAHDNNdfgwx/+MGbNmoX3ve99mDx58gDr+vPPP48NGzZU7Fu8eDG01jjhhIFvlrlcDosXL8b73/9+7LXXXrj88stxzjnn4Oabb2759VsaZ7jFOiJ4NGwcHCCOApgCDRdVaaJdsjU2GSwRPFb/z6yWIGix1EObO7SNbjp76MRQRqDHGSmu56GujZbmMGIdERxElG+mEJn4ambX1XLXhY9VuOuimXWBWCeLTjDb1IxMKDnwPQ6ZkhzOmE5tc2Ws/qKrEcHPiGxGjIuLc2kiXSOiXZpwN+v54ZlVZmkvWpNsdaaJllhbZ2pj68zI4K1zTxgg1lXc3JcmBTZ0z5mvddx1IvpadtWVfIaST1HyKYQiKErA14APQAxill29NtlaYRZWaLMMB8uXL29oXur555+PpUuX4uWXX8Yf/vAHHHvssWCM4YQTTmjYLFaLyy67DOeffz5+9atf4Y033hgQhtEMmXszLrvsMtxxxx2YO3cuTj311Gj/3nvvjXnz5uGUU05p6kIMjz76aMXP+Xwe8+fPr1notR74hnTaaafhtNNOSz1+//33x+OPPz6o67S0lk4Q60xLFOvXAAjAFAhXIEyDZgh5SBftbPmydBZZW2KHKwJ9pNDu2mgZPNWcdcZZTfxwR5q7LibWJd11RqxTJV7hqtOChSIdD+aceg6E70B4HF7Rhe+V/QiUaqDGODsj2g2mHbYZKFUDgpJqYV12ljhZW2JtnamNrTOdT5qrrtoaID63zoh1vs/hew58z4Hnc3glF57HUfIdSBEIdHFXnVQERZ9C6qAVViAoJc2665I4mkQtsu2cc1cNhpql0WLJzKuvvooTTjgB77zzDrbccksccsghePzxx7HlllsCCMxilFLMmjULpVIJM2fOxPXXX9/w+T/0oQ8BAI4++mgQUv6b0VqDEAIps/+LzizY/eAHP8DNN9+Mww47DF/4whei/fvuuy+ee+65zBdg2bQZbqEuDitq0IIGLRKQYiwVkEsQpkCZBGUKolSZgWRm2cXn11U8bu6qxRY91ZwLWlLb4moZEpRSA+705HI55HK51ONNBHo+n8eMGTNw5ZVXYvvtt68bgb6pLKRsbexsqol1SSpaX9Pc0YKV3XWSQhWdVLFOlRx4/S6kF7bBeoFQ53scvu/AKwWCnRQMnCkI0dhNnVYId0rRuu65LNiZdpZa2DrTOmyd6WxqtcACiObXRUETibl1vs+rtsIasa7k8UioE5KgJCl8BUgNFML7PsZd50NDQkeCmw+dGj5Rj1pCnSAa0JWOO4nWuuzSRLvr9PEtfAVLJ6EkgySN/wsy6+wDDzwQjDGcfvrpNV12ixcvrnm+RsxitXjkkUeael4tMgt2r732GnbZZZcB+5VS8H0/5RkWSzqdItaxghu2wqpg2HiRAkUGXeRAKJ4RLkF5MMvOhE7EiYt2AAYId0rQmk4FJemQtMW+Mee/bVLsKMREoDeKVgQvvPACJkyYULH/4osvxiWXXDLgeBOBvvvuu+ONN97AN77xDfzHf/wH/vrXvw46Al1Kicceewz77LNPSwe0DjW2NnYm9YQ66lMQCVA/mF8HkHI7LFLcdUDZVSfYALFOFh1Ij8PrzwXuulCoU5LB9zhKhVwg3Pmhe8J3ICSNZrCkISUZ0BbLmM4s2glBoxbWWqIdD+uXyOAMj4t2d+18A45/8YuZrs3S+RihoVEC55CydaaF2DrTuVQT6+Luuop22FgrbDDPLphb53mBoy5w2DmBu67kVIh15fZXAqkDV10xbIOt5qzzq+1PBE80inmdagJgXLRzwkP8QZj+kqLdHHKXFe0sFSxfvrwjEq/f//73t/ycmQW7adOm4Xe/+x122GGHiv0//elPsd9++7XswiyWdmPaYI2zjhU1SCFYqJEigy5x6BKPHG+mLZYyOUCwA1CRGttoC2xZ4FORaGdddpahYLfddsMf//jHin3VXA9HHnlk9P0+++yDgw46CDvssAN+8pOfoKura1DX0a5EpaHG1saRiRHriAhXEol22GQrrPZDsa7kRKKdCltfVSl00fUHopwoBS2wvscjkU54HMVCHp7HUQi/Ri+VUYDLItoZgc6IapyrtjjtDD/bfb6dZWcBpRTr1q2r2GfrTPPYOtOZNCrWJd11USusIoHjOjG3LinWlXxa4aor6UAEKyIQ0OIz65LuOgekqmjXKoxkbPqQjGg3GKEuTnxlJWFFu9GKkhSKNO7aN6OoGnXYDRX9/f1YtWoVPM+r2L/PPvtkPldmwe6iiy7CiSeeiNdeew1KKSxZsgTPP/88fvCDH+BXv/pV5guwWIYDIjhYkYIVFFi/aYOlZbEudNjpUtDuZNx1ACKXnfD4gMWOEfJoYhC3ceUZl51SFBCocOVFx2YU7XQDoRZxrMtu9KEUajp0kmhN8Morr0RtRFmLWysj0IFyotLUqVMbvoZOw9bGkQcr0kisY/06CpwAAEg6IBk23gqrw7ZXWeJBG2ysBdYLXXQidNQZoc4ruYGzLhTrRDQ8vPnmoVqinVKk4tzmA58R7oxoZ/YZsjjrLJsOSpNsdUYRKKVsnWkhts6MTEwoXdJdB6AiZMLMrTOtsEKyoPVVlFNgS5KiKMvtr8WYUGcEOeN+8xOhE1naYU2LayOz8ExbrHleXLhrdXts+foCziZ3YZ4V7SzoHIfd22+/jZNPPhn3339/6uPNzLDLPHTkmGOOwb333ouHHnoI3d3duOiii/Dss8/i3nvvxQc+8IHMF2CxDDVEcDg9BHyjgrMWYOspyHoO0sNB+pxAqCs4gcNOmHancFETzrGjTNZuca0xz8csjKKEqPBOG1Au6uZOXHxrye+eITzDMrqZOnUqVq5c2VCiUpJWR6C3I1FpqLG1cWQRBU0IDeIHTjtSqGyHBTAgaCLaHybCRkmwXhAsUezpglfIoVTIob93DEqFPPp7xqCvtxt9fV3o6+1CX9+Y8o0bhMLaIN7j01JkzfmMaCcEi+0LXlsIGjnu4smereJnuzc3/8UyejChE7bOtAZbZ0Ye8c/0cXcdgIq08OT3UgSiXrHEy0mwMbGuiECsKxAFH+V5dcZVlxTralHrWAZSMxHWiIRGNDTXEDwW/j4NX4nFMvI5++yzsX79ejzxxBPo6urCAw88gDvuuAO77rorfvnLXzZ1zswOOwD4j//4Dzz44INNvaDFAgzf/DpWcKMWWN4DkB4ainQsmEcULuICsY5BlZyKmS2UK0iv/P2A9qnYcWn7Bsy+C1uSagp8ocBmRLtagpsV4yzt4Pzzz8dHPvIR7LDDDnj99ddx8cUXp0agT5w4EePHj8ecOXMyRaC3I1FpOLC1cWRB/UCsC76mLEiSIlpsdl20K5xZFyzCKITvwCu6KIYuu2Ihj0IhF7W/xgUx464TkkYuOc5UsF9S8Azv52lOOyPQcQTnSTr5zAdAISrbWVsl2tmWWEsWbJ1pDFtnRibGXWeI37BRkoIxGbjw4kEToaNVKgKhyimwRQClUKgDqjvqmiVNoGMgNefjOSCRaMd1/NjKc1n/tqUeUtJMoROyw1pif/Ob3+AXv/gFDjjgAFBKscMOO+ADH/gAxo8fjyuvvBJHHXVU5nM2JdhZLCMVIgG+UQUtsEas63UDV50wrU4sWJSFDooB52CqIiCilthWjag1Nt56VEX8A1DxeknhjrS5hdbS+Zh2ikZRiuKll17CtGnTANRvVWp3BHo7EpUslkYxrbCRuy6lHTYi5f1TSQrpORCeAyVJkOZXyMH3nEisKxbdAe2vWdoLs5AMqTDiX7JF1sARiHYV+1JuOiWpNv+ulS49S+egw7lajRIIFMrWGYslRtr7I2Uq+ttiXAK+M+AYqYOAiaIuC3RJoc6vEwTRCLXcdEnRTkJHx8dfWxANrknFMT7KLbIDz2uxDJ5OaYnt6+vDVlttBQDYfPPN8fbbb2O33XbDu9/9bjz11FNNnbMhwW7zzTevuBtVi7Vr1zZ1IRbLUED9YE4RKVbOqlN9OajQVQcAWhKQcMGj6yw+4s65NGddPZRkQZCFeZ3YwsmkzaalyMZFuqRYF0+pjYuCVqyzGKZOnYp77rmnoWPbHYHejkSlocDWxs6mWkKsaYcNvq8SNhFCHAUNWn6cKUCw8nsvl2V3XRggEbQzsVSxzoh0yRbYuEPOuOyyEj9HLdEu2icYOJfBY+a/BzVuvHI4RRqtDKuwjF5MS2wj2DqTjq0zI59kwrKM3einVAOQUIxCMgb4AAvfXxnV4FyBhXWqoMttpgaf6LaHScSp5bSLk3TbJWfbNYqT+KffqgALi6Vd7L777nj++eex4447Yt9998VNN92EHXfcETfeeCO23nrrps7ZkGA3b9686Pt33nkHl112GWbOnBnNjVi2bBl+/etf4+tf/3pTF2HZtBiudlgD8QlgQiUKJumPQvTlKsQ5UmVBQpmEZjQU0WQguCVDJiKhrfZ9I/P8clps5b2ntBTZOGnOuuQ5zGsAZTeeZXRhBIJG0YpkctgNFa1MVBoKbG3sXKqJdQYiw/l1EmU3XT13HVOAoCBcQgsa3dSJhxIZlKLBAHGPV4h18fZXoHL+XJpol6UtNnmOJPHzBYvEhGjHsifH8rDexIMqas13tYxclCKZ6kyQftm4w26osHXG0iqqpcPGqQibaGD8DaUKjEm4rgjeV/3QeUc0ukjgsmtn83bcNVeNuGhX63jjsktSLU02SVKsM/viop0NnBh9SMkytsQGx3ZKS+xZZ52FN954AwBw8cUX44gjjsCdd94J13Vx++23N3XOhgS7E088Mfp+1qxZuPTSS3HGGWdE+84880x8//vfx0MPPYRzzjmnqQuxjH6GXagTwT937ehA5JLB4kyXHKiSEw0PBwLXBEArhLh4K6ySKiaiNVY6s7bOKlF+fSPWpYl2jRAX7SwWIJvDrt20I1FpKLC1sTPZ4O0Ufbox7/tJzPw6AIAI22FTMHNNwWWwJJEKGgDhCkRKMFeAuwKi5IC7AqVCLhK9HNcH9x144eskxTpgoBMuKdpVO64WjR5nqCXamTTZenAmB4h2FksWh127sXXG0kpqiXXxILmKsInkKJwErlsWkaVk4EyCM4acIyEVgdQMQgZtsXEckCF12QHprbO1WnHThD3TJhs83nhrbFK0s1iAzmmJ/fSnPx19P336dLzyyit47rnnsP322+Nd73pXU+fMfAv017/+NY444ogB+4844gg89NBDTV2ExTIUaC6gHALNAN2lQPIyctdpSUCoCpwT0Rw7FqW4AoGIF90Bi9Jiy1uSrAJZNeEvXvjjX6PfK/ZzLfHQPGaDKSydRjsSlYYaWxs7g+SNIc0re12pTyN3nQmbiM+uq4oRwWKtsIQpsJwAzfngOR/c9cHCBHE358F1BVzHH9CKWo+sglujZHXrZT+/jBx3FkunYeuMZSiIf2avcNeFYl28HZYxCcolKNXB90zBdcPaEdaQMV0e8jmBnKOQYwo5AuRD4YuBwAldbIOZWwdUBlY00vLabtLcdVket1iGG8/z8Pzzz8N1Xey///5Ni3VAE4LdFltsgV/84hcD9v/iF7/AFlts0fSFWCxDgcwryDEkcF8wBZITIFyB5gQoD9wSNOdXbRs1LjvK1YDwiYogioRYl5YOW36ebNil1wyUq8ipR5nE5Hl3tu21LMODVrEPhA1sWpdDJ6ZNm9b0TKBW8Zvf/Abf/e53KxKVPv3pT2Pu3Lm48sorh/XaGsXWxs7FiHZGrGMFBdYftMNWDftJqwFcgjgKhKugfnAFmvPhjCmBuwLMFXDzHnJdwc+O48NxfVCqohZUxnTTgly1Vtd6cKYqxDpzLdHjfGD9qUizbSJYyTL60DpjnVHl0AlbZ1qDrTOdQb1W2CCIiEN4vOyui4l11UYGMCbhuH4g2jnBDR8j2nV3eejOS+SZxlgC5HT5HK0S7eJkEe0ckAGvnWyHTXfkJV+zNhyVrYFWtBudBOJ2hk2VU2I7odb09/fjlFNOwZgxY7DXXnth1apVAIA5c+bgqquuauqcmVNiv/GNb+Bzn/scHn30URx00EEAgCeeeAIPPPAAbrnllqYuwmIZKjQXEGNdsH4JMk6AymLwwMau8jGKYuBY1wDCJWi4kKNMQiFQveOut7hYl9YGW2u2T7yNKN6OGwVT1GmJNcdZLPXopJbYdiQqDTW2NnY+RqwjfjjDrjDQXVd1fp2BS4DRsD02eK+lOQHeXYJr2p9UEEDh5jz4voNx4/rDJ+fgmXlEdUS7WrPoslDLVZcm1LWCZHusZdOlk1pibZ2xtJtgJnUg1smwFVZ4HMJ36op1lOrIiU1ZMFIhLXxISAKpOcZJQGqKAlGQ0HA0gU/0oEQ7n+hI/APqz7Or9lqNiHV1r0VXF+Q4qt9rs2y6dEpL7IUXXog///nPePTRRysc0YcffjguueQSfPWrX818zsyC3UknnYQ999wT1157LZYsWQIA2HPPPfH73/8+Kh4WSyejHAUxnoJIBQqvbDMNRTvCNLQkUIINSIhVIgx5kLH5cgAQKf0sOq7ieRkHcNdLm80yyy4+C89i6UTakag01Nja2LnE59gRH2DF9FbYSKyrE8xDnDC8hykQAERQOOMKUVqs6/HATSGNOBdL7Vb56PtmkmCzzLJLE+uS7rrBIsI5SxZLp2PrjGUoUYIFN29Csc6sA2SDNzMc1684VgoK15Xo7vIhFYFfZOjWBCIUw+Ki3WBoVLQbjFhXKyW21iw7I9JlFi8sIwohKUSGJlDzWapTQifuuece3HXXXTj44IMrEr732msvvPjii02ds6l/8wcddBDuvNO21VlGJpoL+OM4AAoHCtT3o7cFUuLQpSA5loaz7GSp/GdCeSDQGZedEnRA+6yqcxctK61wzGUNvLCMLKSkDX8IBIK0v05KiW1HotJwYGvjyID4BKSYItbVEtCSbrdQrDMBFADgjitGs08NBSYrRDLOFTb2BDeHOMqCWpp4V81lZ/a1ctZdq+fbHfvsnJaezzL8KJWtzgSzuzonJdbWGUs7Me66+Nw6r5CLxDopGVQiZZmGLmfKZfRY/G/Mdb3gnDxIjTUIQSEkAXwKqSh6Sez9W2PQol09GhXrBj4vG9VcdtZdZ0mjUxx2b7/9duTmjtPX11ch4GWhIcFu48aNmf4D9PT0YNy4cU1dkGV0MtwJsUk0F/A2BwAHDiSoo0HyEqTHAUocusiDMIqSA8IlVCmYQ0GoAuWAFqpicWfcdYMR60w7bNwNFxfrGnbU2bZYSwN0UktsOxKVhgJbG0cW1A+CJhBz1sWFuigRthbhooo4QVosBA1TxQEKH+74/orDKZNgXIWDxRVcR4BShWLRDU5nRiwIBqVIU667JNXEt2ruusGKdab91TrtLEk6qSXW1hlLO0gLmDOtsMVCLnTahS64UNSjTIExCeXxivfluFhnRu04bnlEj1QUOfjo7gqEOgCQHgV0INr5xg0XnrJZ4S7usmumnTWNLGJd3GVXqzXWYulEDjjgANx3332YMye4cWlEultvvRUzZsxo6pwNCXabb7453njjjVS1MI1tt90WTz/9NHbaqbNEGoslibe5D+W44HkF3iNC0Y6DjKXQRQZdECD9LghTICUnbJUNnhuJdDGxLk2oqyagxefVpWGe04hQpyWtSH+1ot2mhbmr2yi6wxx2Bs/z8NJLL2HnnXfG/vvvP9yXUxdbGzufinZYoUGLBKRAQYoMkDQQ6YyLTaS4h8yct7THELjrNBAtadgYD1ywyHlNmQKlCtzxo/RYyhQ464LnO3BRDnUQgkWuu2aFu1Y65ZSiUZ0SgoLXGa1ghbvRjVYkU51RqrMcdgZbZyytQqUIbMLj8Iou+nvHoFTIwfecSKgDyu/tPAyuY0xGCbFpf1+MSUjGQKWC6/jw4CCX89GtCAAXQpHgJpROOO2AtrjtmnXXDYZqot231fFte03L8KEVhSJZ1jSd0RLb19eH7u5uXHHFFTjyyCOxcuVKCCHwve99DytXrsQf/vAHLF26tKlzNyTYaa1x6623YuzYsQ2d1PfTB/ZbLJ2IGOsBcKEdBd6jQLgfLOiYAsKWIxomAkZOu3ARpxsQSpLCWS2hLjlrzoh1jbrr4pgPEla4s6TRSQ67/v5+zJkzB3fccQcA4IUXXsBOO+2EOXPmYNttt21qQOtQYGvjyMHpIcH7ezEm1hUZIFgo2qW8jzMF+BTECd5/Ux14sdZYI+rx7lLF/FOTLE6ZhpfzQakGYxKFcDFHqRG6FIolJ3Jj1CKtHbaWWFctGdY8p94NpCTm5lTa82zghMXQSQ47W2csrSSaWR2rHV7BRbG3C/29Y1Do7YLnuSgWXXglNzpGxmoDMzdxqIoEPHPOuJjHmARnQWsskyx4nishpEC3JAAYIAiYpigC4NAQRqhrQrRzqohvWcW6wbjzkrPsrNPOUo9mWmKvuuoqXHjhhTjrrLMwb948AECxWMR5552HxYsXo1QqYebMmbj++usxadKkmufaZ599cMcdd+CQQw7B008/jauuugrvfve78X//93/Yf//9sWzZMrz73e9u6ndrSLDbfvvtM6UPTZ48GY6TtVPdYhk+xFgPmrkAFBjTgXDmaMDRoI6C7nUD30N8nl1UWFndGXGNLIYG2wprHB0kcbx121k6nXYkKg0FtjZ2FtVGLzg9BHyjClJhe4KRB2b0gerLQae8fxOughszTAEy9p5a4/2YxFJXeZcXfS+4AmEK3PXBnVw4WkHCcX30946B53NwJuH5wb+NYskBZHlWXdxtlybK1QuXaEQAbIQ0l13chWexdDK2zlhajRHrlKTw+nORs653w1gUCjkUC3l4HkcplhILADJ8T3ZdCV504bqiQrwDBi7QTWosYxKMU3AlMaYrfgQDkwSOBnxNIDVBERoFopp22sUFtzSxLourzkf2GXZWtLO0k+XLl+Omm27CPvvsU7H/nHPOwX333Ye7774bEyZMwBlnnIHjjjsOjz32WM3zzZo1C4ceeijOOussXH755S1N7m5IsHv55Zdb9oIWS6ciu0KnHdegjgZzFAjX5SRAn4LmBGiJQ3px4U6mtsOmLWKiZNnEArGVKa5pwp0R7ba6enHLXsfSOUhFM7XQSU07qiW2HYlKQ4GtjZ0NETwS62gvAenl0H0OdMGB7nch+3JQJQ7pOdF7cvy92AhrhGmw7hJIzg9n1tUX8AiXYOHsIRI6J0TY9mSI5paGizqDkBSeP9Cl1ohYlzanrpXJsFa023SRGWcsdlrohK0zllaRbIX1+nMo9nWhWMih0NuFvr4u9PaMQcl34HkMxRJHyWeRUAcALHxf5kwj5wrkHIlczkVX3gvm23EJKhU4K7vtOFMVLjshGXKOjMYqcJ/CkRRFGQYz6KBVthDOt2uWZsW6agmzWbCi3aaHFAxSZwk4Co7N0hLb29uL2bNn45ZbbsFll10W7d+wYQMWLFiARYsW4dBDDwUALFy4EHvuuScef/xxHHzwwVXPOXfuXBx33HH47Gc/i/vvvx8//OEPsd9++zX8e9TCJiNbLDGMaGegxm0nJUiXD1LiICwXPW4WYpQrKC/WAlVDrEt+X41m2mAtlkbppJbYdiQqWTZtiOBgRQq+UUZiHXoc6N4cxNpuiL4cpMehSg6Ex6veROGuAM35cCQBGxOKdTJ00jEVCXhmll181h1hOhLt4uTDefAsdD4zJsEK+ehxISiEYCjI9I9oae2wgxHlsoptVrSzNEontcTaOmNpNWliXW/vGPT2jEFPXx59BRclP0h0LUkKXwFSAyzxzy3POLpzEt1dDKWSgzFdJTCuwGgg1DmuD85irbShyw5hecnnkrmpwWsBiEQ7ACgmZ9w1QTWhzrTgtmOWXZpoZ7EkydISe/rpp+Ooo47C4YcfXiHYrVixAr7v4/DDD4/27bHHHth+++2xbNmymoIdABx88MH405/+hK997Wv493//d3zgAx8A55Wf5ZYsWZLhtwqwgp2l7XRaQmw9lKMAxBdvGnSsAPUJqGBgpRJYKVjoUSYhwz8jSlXVRUsrHXSNkhZE8dZ5n7QuO0vH0Y5EJcumRbLOUJ8GqbDFmFi3IQ+xthuldd3werogSg6kxyG8QLSLnhtL7Ha7SmCuD1VywL0SeJcHmgudc2HbbHztQLgMEsZNmizTIDQ4jkoKHi6s8okQRylZMJhcUbiuCAf2E5S8+neZW+mgG2DEcQoAAQAASURBVAzx+vfzPa/Dsc/OGeYrsljK2DpjaQVrzgrShpWkwRxrReF7PBLr+voCsW5Dbw4bixy+AkoaKMbfphNv2SVNIDVHyafozktIRYJ2WSbhugJSsqhdllIFCVbRYsuoThXt0IBo50NXnU2XpJ5YZ76PHxd32TXTFluLs8ldmKdt8IQlYOPGjRU/53I55HK5AcctXrwYTz31FJYvXz7gsTVr1sB1XWy22WYV+ydNmoQ1a9Y0dB2lUglvvfUWCCGYMGHCAMGuGaxgZ2krrRDrWMEFkQD1NZRDoJlxwrUHzQUUOEi89WOsBhECRHqgRQ5e4hD9wZsAZao8Iy5ZL1uAGT7bKqxoN/oIFvdZU2L/OeytSu1MVLJsOiTrDBE8qBkFDQiAFBnUhjz8t8bD68mjuK4bpd4uFHvHQEkCEc4X8v3yUsJxfHBXwCvkwB0ffqEEp99FblwR7vh+0JwIbopEDrswiCiWEGugXEIrCi0UKBCJdu6Y4O/WzflQikKKcpKgCtvcg6216a+NEHfQiYT7sF5arGV0ojXJVGeUIh3REmvrjKUVGKEOCD6XS4/DDx3aUrIgYKKQR09PVyTWrRNAnwmAqKKJcU1Q1EBREIyVFEIRlHyKMTmB7i4/bHv1IRWF6/hgXEYtgEnRjnMFqSik0gAUGCFRCrqvCRwEs+3i8+wckEyiXRKRMhuvHW67pMvOYEW70YeUDDL1/3b14wFgypQpFfsvvvhiXHLJJRX7Vq9ejbPOOgsPPvgg8vk8Ws2DDz6Iz372s9h6662xYsUK7Lnnni05b+OV12LJyGDEOlZw4a5zkHubI/eWRO4tCfctjdxbEs46hdzbHLzXDcQ80XrdWXMBzRAIhE7wVY1T0N0+yNgSWHcJfEwJNBomLoKB4rnG0rkokxVbGvXSZ+uRDJ+I89Z5nxzUuS0jn6lTp2LlypVYuXJl5kXUVVddBUIIzj777GhfsVjE6aefji222AJjx47FrFmz8Oabb9Y8zz777IPf//73UaKSECJKVNpqq62wbNkyTJ8+vZlfz7KJkFZnInedr0EKFLrXjZx1/f8aj8KGsehZOx69G7qxce0E9G7oxoa1E7Bh7QSse3vz6PuedeOwcd049Kwbh96141DYMBaF9d0orRsLEc6/UyUn+Fp0oIrOgAALHS6WjMvOvC9TrsBcAberBDfvwXF85LtKyOVLcB0B1/HDr7KqWDcYEU/EWnfriTCcq4qtUX6+53VNX59ldGBaYm2dsYxkkmKdkjSc0UihJIPwOIpFFz19XVi7MR+JdRuIQi+RKEJFWw+R0VZE8HiBKKwjEus0sEEQ9HkM/SWOvoKD/oKD/kIOnsdRKORQKORDUa7yfVsqAs7CNlqqwcPRCQ4N3Dl5ADlNkQcdkAJbTawz/nEz/84IcYLoaKuFeTzuQx9MhrGNz7PUYvXq1diwYUO0XXjhhQOOWbFiBd566y3sv//+4JyDc46lS5fi2muvBecckyZNgud5WL9+fcXz3nzzTUyePLnm63/+85/HRz7yEZx66qlYtmxZy8Q6oMMEu1YV55NOOgmEkIotnggFAGvXrsXs2bMxfvx4bLbZZjjllFPQ29vbjl/LkhHe68JZp4LtLYCtp6BvO9HmvE3gvIVIvHN6CFjBrX/ijAStsYDmgWinGaDHKZBuH2SMB95dgjuuAJ7zwVwfzBWBcJfzQbmq2OLQcPB4fOOuXyHcxQfapkGYqtjSHrdsOkgRuHMa3QKHXRA6MW3aNMyfP7/h16qVqnTvvffi7rvvxtKlS/H666/juOOOq3kuk6h0wQUXYMqUKbjlllvwxz/+EStXrsSPfvSjpuPPLZsuxl0HIGiH7XOgel34PV3of2cc+taOQ++6cSj0daHQ14WejWPRs2Ecenq6sWHDWPT0jkFfbxd6errRs2EcejeMxYa1E7Bx7QT0behGYX2wldZ3Q3oOZIkHwRX9LlTJgZbldlgj1qnwZ1MLKFPRzDzuCnDHR66rBO76cHMeHNeH4/pwXYF8zkfOrb1MqZcCGzidKjcgEO2yhAhYNm2UzFZnghZvZeuMZdSiRNAO29fbjUIhj409gbPuXwL4VyjI+URHW38Y/hBt4f4iUZDQ2EAEejTQK4H1RYaN/Q5KXiDcbezNo6cvcPD1F3IQxmWnSBRmYd7PeWLOKSNBUIODwPWWFgQRD6WolShbS6Sr+N0SIp8cROiFZdMi+DdNM2zBv+fDDjsMBx98MH74wx9i/Pjxqe2whx12GJ555hk8/fTT0XbAAQdg9uzZ0feO4+Dhhx+OnvP8889j1apVdUcnPPbYY/jDH/6Aiy66CIw17hBshKasSb/73e9w00034cUXX8RPf/pTbLvttvjhD3+IqVOn4pBDDmnqQlodrXvEEUdg4cKF0c/J/2mzZ8/GG2+8gQcffBC+7+Pkk0/GaaedhkWLFjV1/ZbWwHtd8I0KvAcgPQykz4H2KSBJtAgCl6B5CZ2XIGMFlK9ABAGRLmReQfPW9aVqBigQMBG04wIa2NwHlUXwcEYccwX8nq7orhthKhhmLmmF8EZjCYG1WlzNc8zX+LHVhDgr0Fmy0kzoRKtTldqZqDQctKM2WpqDCA3SE7jrVF8Ooi8Hr5BD34ZulAp5+D5HsZBHseiikEhpBYI5dq4rkMt5cJ3yTDmtjKsi2JwuL5hPF4pxJllWxRxsOuGESLqnedgSa1pjHemHYocXc7+5A+bZCUkjl50R4cw8u7iIlxTlOFNQikTHCknBkT18wmKpRzOhE7bO1MbWmeHDvO9rSaFEMHO0VMgF7rqeLvSXOHol8C8SrEOM8GXEK0k0WMzdZvY7CNpUHU3QSySgWRCs4ActsnlHgfmBa47R0O0cvvc3etMlR4KwBgaCrpR5dsnWWHM9ZgZd/FqTRL9f+HWwybAWSzM0Ejoxbtw47L333hX7uru7scUWW0T7TznlFJx77rmYOHEixo8fjzlz5mDGjBl1AyeeeuopuG7rDURAE4Ldz372M/z3f/83Zs+ejT/96U8olUoAgkJ6xRVX4H//938zX0Q7onVzuVxV6+Kzzz6LBx54AMuXL8cBBxwAALjuuuvwoQ99CN/5znewzTbbZP4dLJU00w7Le12471Qm+qleFwiLkRY0+p7kBMjYEqhPQAoSdJwCy0vIMQRirNuyGXfKUWCSQvNArFMg4Tw7L7KnEh4s0Pz+HLRgoExBMAnpOVBSRTPoaMINl3TUxUtumsPOinKWVqKUanhAq6EdqUrtSlQaatpRGy3ZMe2wrF+DFBlkrwvZ78Lrd+EVcvB9B8VCDqViDoVCDv3hlobn83C2nA8hacXcICWDeXNKBEES5sYKYSoS8JLJswYj2JlZd5QFg8S548MRFMLjcBw/PK6IoJkJqCbaGYwQl/aYDN1+jOlI6FOKQAgGzm2jkaV92DrTOmyd6RxMO6zvOejtGYO+goP1RYYeDYDUF+viGKHMJ2VnWugYgNQEUjMwosGpBme0QrxLtsbWoiv0HRSRbZ5dXIjzqzjl4g66uMjngERBFOX9zYVPtNazZLGkc80114BSilmzZqFUKmHmzJm4/vrr6z6vXWId0ERL7GWXXYYbb7wRt9xyCxyn/Of23ve+F0899VRTFxEvznHqFedaPProo9hqq62w++6744tf/CLeeeed6LFly5Zhs802i8Q6ADj88MNBKcUTTzyRer5SqYSNGzdWbJbWwgqqLNatd4ESBySFLnGovhxUXw7SbBu6oNZ3QW/IA+tzIOs5aC8B79HgvQq8d3B/NPG5eDqlQsjNFPRmHui7+kHHF8DHF5HbvA98TAnu+AJy44pwxpTAXAGny4PTVYIzpgSe8+GOKcEdUwLPiWChx9WAtloj5kWLwvCO3mAxQqANnRhdCMUgZOObVBQvvPACJkyYULFdeeWVVV/DpCqlHTPYVKVkolJyGwm0ojbaOtNiJIUuOfD7c/ALOfgeh/B4hVjn+cF7fbxd1PNZsHkc/QUX/eHcoGLRDdpkN45F74Zu9G/oRmHDWJR6uuD1uxAeh19wIfoDR58WLJxxV95k6OQrO6lD0Y4Hs1BZ+NXNedE8u66uIlxXBI6/lPZYKQmkJLGQChqJdeax+LHAQFeGkOXgmmZDJaxDb3SjNM1WZ8KWWFtnWker1mC21mRHVdwACW7WSBHcyCn5TtC+qpPz2uqLdWkUw9l3fdDoDVtk+wRBSVL0eQx9JYa+YjDnruTT1E3IUJBLvC3HW2OrzbMz1x1v5wWC381s0X+L2M/JY+P/DerNu7NYDFKGf1uNbuHf5oEHHph5/AIQaEbz5s2Lfs7n85g/fz7Wrl2Lvr4+LFmypO78unaT2WH3/PPP433ve9+A/RMmTBgwoK8R2hGte8QRR+C4447D1KlT8eKLL+J//ud/cOSRR2LZsmVgjGHNmjXYaqutKp7DOcfEiROrnvfKK6/EN77xjcy/n6UxWMEF65cgBRaIdYJBFzm0COYByb5cNA/IoGXguCM5AQITwCTAoUF8BaC5Ftm4WEf9MLHPIaG6HbjsqK+hxilQ3wcFQBwFWuSgOQFV4oH4lvchi8ECLem+ABC1+NJQkKNMQUkFUWpl6PlA6s3Hs2wa7LbbbvjjH/9Ysa+a66GdqUrtSlQaalpRG22dGRxmfh0RGrRIoIusHAwhKbQKFldC0kisi4cvpIlYSLTKysTcNylZ6LLjFU5qymTF/NK4207JIEk2SoQ1ol3MZTeQIoDARddfcBtqg5IyfYEoJQFj5dbZpMtOCJpZtLNinSUNSinWrVtXsc/WmeZp1RrM1prmiN6zY3XD94KU8ZKkQRsrUOGUa1SsSzrbonZVTQFNwIAgMRwAQMAIgSMBVkcIcygGiHYMYduqBvKEAlrVddoNmGsX+zH5WLKdNk6zLju7crHUo5GW2JFKZsvO5MmT8Y9//GPA/t///vfYaadsbZCmON95550tLc6f/OQncfTRR+Pd7343PvrRj+JXv/oVli9fjkcffbTpc1544YUVySOrV69u2fVaECyyJMrtr0asCxP4tCRQggVDvj0HSjCIvhzEhjFQfbnAadfjgPRy0B4KVtTgG4NACt4bJMmaLfX1Ux43Yh1J6RZSDgnc6hNlkBw7oQgyoQi2RR/4xD7w8UU44wrIbd6H3GZ94N0lOOMK4KHrjrki+D7vg+b8aD+AcCGXXpqaddk1kkprGblEc7Ua3LQmeOWVV3DwwQfXHdAKtC9VqZ2JSkNNK2qjrTPZiI9eiL93Ex+AQHDjRwQuZVFy4JUc+F6wxYm3kRpHWtyJ5nkcns/Dr8HzN24Yi94NY4PginXj0L+hG8XeLhR7ulDqDbee8uYXctEmYyLggHl2ocvacfyqTrsxXV6UHsuZAmM6EuDimP1pj5nfLYlx2QlBIaq09CapJtYd++ychp5vGRkEDtQMtUYTKKVsnWkhrVqD2VqTjfj7tPkcbv6dC1keeg8MdJJlcdYlKRKFAlHoJQp90EEghQYKGuhVZeed2YqycquGQ4JBCwykwmkXd9tVS441JN13aY9bLJsaQghceumlePXVV1t63swOu1NPPRVnnXUWbrvtNhBC8Prrr2PZsmU4//zz8fWvfz3TueLF2SClxG9/+1t8//vfx69//euoOMdddo1E68bZaaed8K53vQv/+Mc/cNhhh2Hy5Ml46623Ko4RQmDt2rVVz1tv5oZlcLCCAvHD4sA0wBRQCkS7OGWXQijsxWY3UKYCp51PQH0J0qWgiwoyT0B9EoZGAJo13i6bJtZpJ1wQopwcC4QOP0cFoRgACJfBTLucXy7wpeSfnARzg7t1cadHksiBwVSFaFdvrp0V5yzVyBI6YVKV4px88snYY4898JWvfAVTpkyJUpVmzZoFoLFUJZOoFK8BI5VW1EZbZwYPDS0ORBBon0JLAlEKZtEJz4nm0CUx4hVjOhLryg61uNNOwPgC+hAs2hzXh+87kcjGwvddynRwk4SqSAijVIXtr3702ip06Rkct5YrvAhKXVCq0F/IQSkSDR9P/i6GQNhDOIev/LslhbzybDsaiXDNuO0sFkOW0AlbZ+rTqjWYrTWDw8yvC9q+KYolDl8BEqg6460R0ubHFYmC1KGjDgQllJNei7rsUmNA5HpzSLjAV4HDLg5HWMUIIDWAmNOuCBWJdq0U3NJ+r2B/Yy47667btJCKQmbwlJk5jgceeCAYYzj99NNx+umnt+vyasI5x7e//W185jOfae15sz7hq1/9KpRSOOyww9Df34/3ve99yOVyOP/88zFnTra7qe0qzkleffVVvPPOO9h6660BADNmzMD69euxYsUKTJ8+HQDwm9/8BkopHHTQQZl+B8vgIYJHAljVY5gOKgzKraSmtVQUXBAuQfrDgApZApEUkBJESBBfQ/kqcMXFKkMQJBFgxLzoMVYp1plFoOYERFQWMe1ooCsU7YoAGAXJC2hBK8qTlhSE6ai1lzAdiXraM6lTdIDjQkkWm2lHK1Jjs4h3FkuztCtVqZ2JSkNNK2ujpTkCV7QGK2qgGMxAVYKFs0BZhZMuK5XtsYFoJ8KB411dJUjB4OY80IIG5YFIZ4Q7xhVI+DNlGlQZQa9cZExIhRHKGCPQnEJKBUo13FxlkJJSFJzLAem2SZJiXtXfr0b4RJrTLk3E47HfR9jRC5aM2DpTH1tnhp7Xz0hfeEsRJMR6JTcQGBL6llMjoME83ig+0YCOtZOScmKrEfAMptUVYcAEYm/VLBTpjGiXN8ckRDsAcBKBFO2i2QAKiyVJp7TEHnrooVi6dCl23HHHlp0zs2BHCMH/+3//DxdccAH+8Y9/oLe3F9OmTcPYsWMzv3irivMee+yBK6+8Esceeyx6e3vxjW98A7NmzcLkyZPx4osv4stf/jJ22WUXzJw5EwCw55574ogjjsCpp56KG2+8Eb7v44wzzsAnP/lJmxA73KQsLghTgVONy2AGUcyJpgQF5QqiL7hTSEWQtqoLCmSsF7j2ihosL0F5UKW0YwqQhswHIh4TulLAixVSI9YBGCDWGXRXWBUlBZEKWkqQnIAu8aBm1mktUmEyYJAsSyO3BU1ZFCVFu+gaJLWi3SaKEBQiw90opQheeuklTJs2DQBacjeqmVSl0bKIAlpbGy3NQwsapEBAwvl1WoU3QyQZ4K7jTEXOZs5Uqssu7kITkoKGxysVvNdSqsIh+xSlYg6UhcIcVWBcht9rcNeHm/NBpAJjJHwuBc/5oEwGN2ZiybKUaXCU72QJz4Gb86IWLEoVXEdAiMEJkUnSXHZJGnHccevuHnUE8w4zuB4EhVLK1pkWYuvM8GFuppvAicBlRyFVEPAABOKTrOOwyyLUxYnEs5TT+yTmYAtTYOOiXT5e+szbd+iDKD9UFu38RLBEq0mbbVeNtFs/xmPha2CePr51F2axtIAjjzwSX/3qV/HMM89g+vTp6O7urnj86KOPznzOzIKdwXXdqAi3k0aK8/PPP48NGzYAABhj+Mtf/oI77rgD69evxzbbbIMPfvCD+OY3v1lh/77zzjtxxhln4LDDDovOf+2117b997EMhPoU1JdAjTdvwiW05OXvB4h2gOjLRf+gSS5c6JQ4wCXANGheQDs6ErV0lwL3AZXXUA4BMS46pzzcMem8i6McUiHmgSMQHBkJXHaOgi6VH9YpLa+iLxfMHys5kbsuPr/OCJJApVBXTbSLk9YOG18QWjZtsrTEppGcCWpSlbKmM402hqo2WgbCioG7jhRYEDhRdCCLwU0QKRlkOM+uGknRLg3PDwQyQTU4l+AAikUXnsfBuQqCI5gEC112bjiPzg3nfzmOD83DNFZXRA5q7vrlaxPGWa1DgU8CbiDaBecPXkNxinzOR7HkRKJd2ly6RgIqmsWGTVhqkaUlNg1bZ9KxdWZ40KFrW/gO/JILzwve+6UiaCTerlmxDii3lcZdbz70gLlzInLjlUU7P9YamwyhcCKXHWBEu6GeP9esy67GEs0ygpGSQejGXfpSBcd2QkssAHzpS18CAHz3u98d8BghBFJmv6HZkGB33HHHNXzCJUuWZL6IOM0UZ63LbyxdXV349a9/Xfd1Jk6ciEWLFjV9nZbWEbWeCkShE0C6wBVHCxa1xcZFOyYYqKAgJSd4nCkQrqB7FUiXDzAN4iiQooLOS7CiBs1rqHyYmodAvIsLd0B1d12EAOBoaKlNoF90naZ11bTDqrANVoUpuErSAe66dpHm2rNYLNkZytpoqQ4RHNSnICKchSopdMGBKgUp3SYhNkmzQpaQFByBOy9y6HEJzw9EP0pV5ELzPR+OG8zQc6QPJVjF/DoAcLtMi6wKW3fL12XEOniAgBMIgSxw79GEY7CaWFctLbYZ7Dw7i2VosXWm8wjcowSex1EqOSiGIXXtdKUB6bPgks47I9oVNZA3oh2QOs/OEBftZHiOwf4uWQIsgGyina+tWGcZSKe0xJoOjFbS0KfVCRMmRNv48ePx8MMP48knn4weX7FiBR5++GFMmDCh5RdoGVls8HaqSO7LAhFBGxMaWFwYoS7ptNOKQpY4ZL8LsTEPsWEMxNqxEGu7oTZ2Qb3TDfXOGKh/BRveyYOsd0HWc7D1FLRIAgHP1yB+WaSLi3X15u0BAHFic+ZCUc4k3SrBAlddtJ8GQ9FrpMO2knd96ydtfw3L0JIpuU9RaE2jlthp06YNq1uhXYlKQ4GtjcNHWp0x6bCkyKBLHKLgQpQ4RMmBDG+ECEnh+YOfmCMkDVMzSbRw8zyOYsmB53P0FwLnRX8hh0Ihj76+LvT3jkGxkINXdFEs5FAq5CC84PoagVaZMVeNeNpt/LobRaWInJZNF60zpsSqckusrTPNY+vM8JE2v8782zaJ4yWfBams4TLBCGiDCZ+oRbXz+iRoY/XDTRCNIjSKCASu5Iy9JEYAM8mxDJWpsa0krR02rQraSaibJlLQzBsQOOyGu9YkKRaL9Q9qgIYcdgsXLoy+/8pXvoJPfOITuPHGG8FY8KckpcSXvvSljlA1LcNHs0Id9TVokYD0cOgigy44FU47VXIiZ1oa8RCKwJ1GIWODuI2jjPQpUC6jsAfCFGjeB8kJkLwAGesB3T7QpUA4QKSG9gHlVCbDxq/bECXcVrvGUKwzLo8KsS42u67d2HZYi2GwLbGtol2JSkOBrY1DT7U6Q2TwnkwEhfYpVF8OsujADwUy33eihZYI5w8lSYpZSbErLVU1eSxjGp7PwJmCB4BSDc8PxLwxXSUISeH7DvJdRShh/p0ErjpeJR2WMRkFUDAmoxl5tRiMUFeNpLvOtsNa6jHYlthWYeuMJStrzvo0KEPNz+ae76DkcUgdOMQKZPjfE+POOJ9odGkazbQTEsjVWK4YgaxVLrt2Yl12liSd4rCTUuKKK67AjTfeiDfffBMvvPACdtppJ3z961/HjjvuiFNOOSXzOTN/grvttttw/vnnR4UCCObGnXvuubjtttsyX4BldNCsWMcKLvhGBdJDofsc6IIDXeJQfTloQSOxTgkG6ZXvvxiRTkta3gQLZsGVHIj+HER/Lvi+LwfRl4Pf0wVv4xh4PXn4PV3wN3bBX9sduO825KHW5YH1OZBeDlIkIAUSOe5Yf7loUV9Xzq5LQfvBorFiX+iqi4t1yRbYaJCtSl9Q1iIeOJE2v84yepGKQEja8CZjoROdcDfKJCqNZGxtHB5MO2wSVXLgF1x4/TkI30GpkAvcECU3Cm4wgQ3m78KQ5kyL70/bkseUPBa6+VjktDNuu2LRRX/vGPT3daHQ14VSIYdiXxe80HEX/Q7heU1YhgmwqEerxDoryFniSNV4jRHhMP5OcdgBts5YGmfNWZ8GUF2sM++NUlCUfIqCBkpERQJXu9x1QO32Up/oyOEnw+CIAlEoEYUigKIGelXgtovfHvJ1sBmMA67RYIhqDPb5FstI5PLLL8ftt9+OuXPnVoQe7b333rj11lubOmfm0AkhBJ577jnsvvvuFfufe+65tvTsWkYGE9x/NiXa8V4F2ktA+hyo3hxUXy5I9ou1kMbbRw06VkSjghp+NT8n3WSm3ZQyCcoVuCtAmALhEk6JgxUd0BIHlQWQbh/IS8AJqhrhQDyaiTSqh4ViYtAKSyuERhVuaZhB5HHqhUxYLFnoFIcd0J5EpaHG1sbhRzkElOtoiSBKDoTvoBiKdZ7PI7HO83iqQy4rtcUw8/+dBpYF5KKbMVL40XONw04KilxXCZTpilTb5Py9WkJaK+fVxcnqrvvIX89uy3VYRhad4rADbJ2xNM7k7/0oEu2qYT6TC0mCttMhaIFtNLTCJ+UwCiPcgQAybHXNcqmOJm0PoWi2HbaOd8IyQpGKBKEpGY4HOid04gc/+AFuvvlmHHbYYfjCF74Q7d93333x3HPPNXXOzILdySefjFNOOQUvvvgi3vOe9wAAnnjiCVx11VU4+eSTm7oIy+ggq2jHe104axXoOgdqXR5yQ+B6Swp0uoawBVTeATMinZLloAeVIu5xV0CUnEi8UyUH3CvBkRSQFFQUQaQXpL46GshLUAHorsrqkNYKS3xSUQt1rBU2/vsYd52SbMD8unoOuaR4Z911lpFMOxKVhhpbG4eGunUm9qmGMgnf4ygVciiE7rb+gov+QnDHsz0iXeVxnKnodTwkQyICoS4fJtjmu0qQksHN+SBURXXDzGeRsRphkmJbDR/EjSEhWVuuyWJpBbbOWLKQtnYwsNgNDM408gB6ETrcaqhhaaERtTDnaiZddoDIphXyhAIaKII0lcpqsXQyndIS+9prr2GXXXYZsF8pBd9vYBB+CpkFu+985zuYPHkyrr76arzxxhsAgK233hoXXHABzjvvvKYuwjJ6yCraRa2w/S5kvxsk+oUDuI2oZYQ1QzLltKKlNCHUJYWwIE1WwSsESX2aUSipIEoO3FBAcwULkmZ9CtLlg+QDk3sgiqnKv5rkyCEj4IULtApXYLwNNibW6QYXf9WwYp1FiGwR6EqVQycADPvdqNHgDLC1cehIqzPJMQU054PnBBxXoFjIBw67MBgiTagzItxgBKt6SEkqRLtyWxVDV1fw+q4KBvs7jh8+Jzh+qAMgajnobKvspolSJEpFboRgVqSydaaF2DozdGzz/R+khk4YGJNwXYExOYGxRYYNOnDatZpmxLo0fKIBrcAICebTmWAJ83hLXqU1NPouM08f39brsFiaYdq0afjd736HHXbYoWL/T3/6U+y3335NnTOzYEcpxZe//GV8+ctfxsaNGwGgI9RMy8hDMwQto705yL5c0P4qGITHB4hZMvxKmKpobQXKra5JR50R61RywLgwX0KHHVOgTMHrz0XPdSQBR3nIIwEAFhZNR0PzhNNOpBRUSYOEWEkCx2CVNthqrbHJRZGS1LbFWlpGJ7XExikWi8jn88N9GZmxtXFoqXVziOQFaN4HcwW448NxfPT2TUR/wU11x2WZ8TbY8Ia4aJfP+fA8DsVjN50UhSN9KMEqUmFNQIVS6Ys3IWnb2mEtlmbppJbYOLbOWBphm+//AK9+8aTUxyjVcHMe8jmBPHMx1qfoIRIydLaxFiSstkqsMwSiXfgDQfh9yrzWFrX3Sui6c+ys08+SREgKoTN8LlPllNhOaIm96KKLcOKJJ+K1116DUgpLlizB888/jx/84Af41a9+1dQ5B/XJc/z48bZQWAYwwf1nQ8cpRwFMQYcz66THA4edoPALOfgFF8Lj0aYkjY7RkkJ6DqTnRKETasBWFuuCttOyU0GpIJ1VlBwIzwmTWjn8/hy8ni74/YGIqPvdIAijyABJQYoM8AmIqNwAxNx1FBAM2rTnhkJkhbMuIUjGXYAWS1aUIpk2rdFRoRNSSnzzm9/Etttui7Fjx+Kf/wzeQ77+9a9jwYIFw3ptzWBr49BQq9aQnADL+6Bcwc15cKsksAKmvVS11F1X63xSEng+Q7HkRKm1QjJ4PofvOcHmh3VPsAqxzsy7A4IW1CzXEr+mZOIt59adbamNzlhnlCYdFTph64ylGba74XYAgSlAJoLiXEfAdSXyjkKeBPPeWiHUZcHRJNoawSfBTDsfGiL8vh3XU4tOTp+1jFyWL1+OlStXDqtYBwDHHHMM7r33Xjz00EPo7u7GRRddhGeffRb33nsvPvCBDzR1zswOu6lTp4KQ6n+IpgBaNm0aaY3VXABwAaYgQ9FOhUJcEiO4lV11QcE0jrPKWRPh4ibprMPAMAel6IC2Vr8/B+4KSFeA9OXAmAJxFHQRIHmAFAGNMJAielJMrIv/jpJCK1qRCBt3BMbddcmQDGBg+6/F0io6yWF3+eWX44477sDcuXNx6qmnRvv33ntvzJs3r6kI9KHG1sbhIS0hFgDAFHiXB+4GjT6u48N1BUTBTT++CZoV+IxYZhJqOYJaVColrs0FgMpkWBmKd8bpN1jHX6uxc+wscTrJYWfrjKVZtrvhdqw+7bMAws/lsf7RnOMj5yigELw3OyBtE6TqCWHxx7MERTTihKtHrWurNbvPR3aXnWPN5JYORWuNf/zjH5g4cSLuv/9+cJ5Zaksl81nOPvvsip9938ef/vQnPPDAA7jgggtaclGW0UFD8+wcDcIVKJeQoeMtTtJ5NkBwS3GoRcETVWb+DGipjYl2lCkQpuD1u6A5H5RLkL4cCFcgYyW0T4OZcZKinAAYImkQOOFTaJ+GLbEsuk5RcuoGaBjqzQiq1h6bli4bZ6urF9d9bYtlqGlHotJQY2vj8DB2wnPoW7tH6mOEhyMPqAbjEq4jwhl2w+dojjvbhKSgYY2ggoLzUJBzgoIko3lhwXt6O69bCFbhslOKVq1DtR4DGnf+WSxDia0zlsEgY5/d45+zWcqN9Xa47Bp10CWPTwp38fRYAAABuCZNi3ZZrksQDT7I/zZWrBv9CEmjNteGjted0RL70ksv4eijj45uUm233Xb42c9+hgMOOGDQ584s2J111lmp++fPn48nn3xy0BdkGV00JNoxBcI0mCtASxwKlb3a9UW7sqMOGCjUVWs3rSduqZIDyRVoTkALCiIYwEQgzEFBm6uMBLyQxByhuHMuaO2tFAyTZB3orSWtCJ6wbHpIRSEyTDhQinRU6EQ7EpWGGlsbh4/uic+h+NpuAADtDHQVUC5BqQLnKmqNbcSZljYTjjMVpb/WItlymgZnKppJF9QtFV0bNXWrSievGe3AmcoUBFDv2hr53SybJlKTTI5OKTsrdMLWGctg2HHBrfjniZ+v2FdtrrQDUjMtNitZxbq05/pEVz2PEdKqOQMdTVIde4O5rrhA2KjLzop1llo0mhJ7ww034IYbbsDLL78MANhrr71w0UUX4cgjjwQQzDg977zzsHjxYpRKJcycORPXX389Jk2aVPO8F1xwAYQQ+NGPfoR8Po/vfOc7OO200/DUU08N+ndrWS/FkUceiZ/97GetOp1lFFFvph1xVJDoN6YEd4xX8RhlCtz1w3CI8mYYIObF5tXF59Y1ggmgANJbUQPXXLn1lfgExCcJsS7ZEhsuxsI5exXXGl6fKDmp7bD1aMSpl+St8z6Z+TmW0cnUqVOxcuXKjpj3YBKVkgwmUalTsLVxaMhv+0L0vXY0iBO8hxOmArEu/BrMHBINiVKM6WiLU+u5acenYc5h2l0pVak3a5Si8D0HUrDIcWecdtWeU4u0a6sWZBFHJGrUUKfWWkYmpiXW1pn2YuvM0LDTHTdBicBhTLkEYxKMKrDY+7ARsRyQ1G0wMJCaWy2S4lq947kOrtccl3x+I2Jd/DWMgCkaaNVN2hkcYsW6TQmpaOYNCBx2jcxL3W677XDVVVdhxYoVePLJJ3HooYfimGOOwd/+9jcAwDnnnIN7770Xd999N5YuXYrXX38dxx13XN3r/v3vf49bbrkFJ5xwAo499lj89Kc/xZ///Gf09fUN+r9JaxprERS8iRMntup0lk0EndcguTDRr+RAej542BrLmIpELspUaroqZbLpwAYj/AWFV1XsN62xJGUxpH0aLAYlDdyBfqKKxBZEhOmoLbbi2mOLnzRxMCng1XMDxl129Y5967xP2tZYS0fRjkSlTsHWxqGD+uF7b14CORG9J7LwPZ0zCcUD14+gGjw51iBB3E0UF7rizrtGxDlDFueaqXcsProhRiBCSnjg4FzC8wfWmXrXlryeem2xQlDwKnNVk3Pr7t17Hj7y17Nrvr7FMpTYOmNpJY7jw3F9MCbBuQIHkAdFsU5daSesifl5tebLDea8tV4r7ujL6rKLcwG9C99Wxw/6uiyjg0Yddh/5yEcqfr788stxww034PHHH8d2222HBQsWYNGiRTj00EMBAAsXLsSee+6Jxx9/HAcffHDV87711lvYddddo5+33nprdHV14a233sLUqVOb/K0CMgt2++23X8XAU6011qxZg7fffhvXX3/9oC7GsunhTwTc9T7IBgGa88FKHO64ArgrBgQykFhYQxIlWSB8ifLPhlriVRwj0lGugu+5DGbYmZbXMP01MHJXinZJiKOgw/0kXAARpkLhMXZtVRY/Zr8StOKYtLCNapZ8y6aDlAQyQ2tCp7XEmkSlSy+9NEpU2n///QeVqDTU2No4/CiHgMlgUWEcdjzno3tCH0qFPDyPQ0gGShXyOb9CkEu2lSpFKgStauJdNRoR54y7Lu1YISncGufgTEHxYKGolILrSHhgkJJkEhENShFQqqP/DuaajFBYy8lXLWTCinajC61Iapt4NaQkHdUSa+uMpdUwJuG4PnKOxBZdEoV+hiIZ+F7ZbBjEcGKccPH23mqtsWlUc/BZ0c7SLjZu3Fjxcy6XQy6Xq/kcKSXuvvtu9PX1YcaMGVixYgV838fhhx8eHbPHHntg++23x7Jly2oKdoQQ9Pb2oqurK9pHKUVPT0/FtTWT7p1ZsDvmmGMqigWlFFtuuSX+8z//E3vskT742WKpRmlLAd6jQdeXQPpy4N0lsFyQ0KpVkKqqBYtEOiPiKUHBEAp1kkJ4A0W6ak6ztIWHcdXF3XXMFSCJhY/2aVBWWGywUEJAJI6ClqF7LxQhuSuiQI1aAmKy3bdSrCvPvlMJ52Et0S4pCipBrcvOkikltl3zHoD2JSoNNbY2dhhMgU0ooGuLHoiSg1Ihh2Ihh0IhH7rEFGjMyVxNnIsELKia87uyuOfiqa/G0RZ3rhlnW9JlB1QGT5iW2GA+nwxbW5tvVU2KdkClcJe1/dZiyZISa+tMfWyd6QzKNzI0urpKGDeugJLP8C4/D+lz/Kva8FHUF++SjrdqradOJHJVnqMVbri0tlVz3qytsGnERTsAMJfLQJoS7SyjCykBUSMNe8Dx4b+fKVOmVOy/+OKLcckll6Q+55lnnsGMGTNQLBYxduxY/PznP8e0adPw9NNPw3VdbLbZZhXHT5o0CWvWrKl5HVpr7LbbbgP2mZELWmsQQiBlY0aiOJkrVrVf3GJpFn9zitwWRdCCA1Li0CUHNOcHiaolDiUYtCo77YyAF4h3CqLkVApWIlxcJISxaouNyFGX5q4LF1NasKD9FUFdIZyUXXYxtG+CKIJWX1JyIpceZRISPBQGB7b4JkW3uIuuIgU3IdxFoh3QUFss5aqpmXmWzkVIGqUkNYLS2Rx2Zt7DrrvuCq017rjjDhxzzDH405/+hL322gvnnHMO7rvvPtx9992YMGECzjjjDBx33HF47LHHal5HOxOVhhpbGzsPMqEIt68PXf05jCu4KBVyKBVzKBSCO66uU040NQswIShcqqKfTciEECxVtGtWqEtDKVpe5vHAMM5RFuzSUmLT2nwbCQaodt1x0Y5zOSCEolZbrGV0I1XG0AmVzWFn60x9bJ3pPBzXx4TxvZCCouRxFHscFBVDL4klycYErLiYVs2xFhft4mERzSa5ppE8l3G8pYl1xmXXjBiYdjwLz2d+xzS3ncWSldWrV1e412q563bffXc8/fTT2LBhA37605/ixBNPxNKlSwf1+o888signl+LzIIdYwxvvPEGttpqq4r977zzDrbaaqumVEPL6KZeSqy3uQ++JQOTfSD/6oLmClrQyLlGmIaWBEowMFdBh4sFWXRA4x8ewwTWitbYBgdjJ911xpUWnz9HOA3EOqYGtMZGQp1BEhBHgeR80BwHy/twI5dgKLhlDI2olpYbd9iZWXb12oCtu86SxWHXrnkP7UxUGmpsbewstKNBpAKbtBH5EodfcDG2dwyE5wAYDyGDlFXGJaRgEAjqhRGjRMwgYT4oVRPtalFPpDOYUAfOAU8FKbG5nBeUMq/yppSSNLoGxiVo2ObLuYyucTCYEIqkaJfmsku2w7LwJpfMmFxrGZ1kcdjZOlMfW2eGH8oVlBc67LgEUwT5riLGjuPY3HfQV2ToLVFAMxRS2mOTxFNcgcYTZo3gNZhEWiOQDXC81aCeaJcU3NKOj78ugIoWWSBojQ2ea7E0zvjx4xtuN3VdN0oNnz59OpYvX47/z955hzlRrm38npK2lb6AdBQWRECKCCIgomtHRJGiIHBsB5Bir6CgIBawICgieAAFVLAcFQ4HCwcFRFA+zwFBFAWEBVTYhS1JZub9/khmdiaZJJNssslmn991zUUy5Z13lmzunXue8sILL+DGG2+Ex+PByZMnDVF2R48eRcOGDcOO2bdv35jnHomoDTvGzH9J3W437HZ7pSdE1EykHB78aQm8txwQbeAkAcppO3iH5L/1EMD7+wYp/hspMcMN2SMaPsSB0Wi8LlLC7GbDrIacGlWnSOo5/bXzJJ8MMbcIyAog8OBcXrDAFCRdjRdOVMA7JIiSr/stJyiQPaKW1htqDr7z+w1Lf9qvIium0XaqOUf17IhoUBQl6fUeNm3ahHfffRe9e/cGAJx//vlo0qQJSkpKkJmZWYmrq3pIG5MPswHMCzCR+R6sCBwg8BDrlMB2PAd2lxsZ2aVQFA6nirIBQYHHW6Egqj6oxp1qovG8EpNpZ2bWqRFseiSZ143v+1cUAbfb7jPE7F6ICI7C1j+Q8hmNkiEiUDXeIkf2GW+wKgw6zjTSLlSUnaBrWKEaoQRBOhM/SGdSA7OMHafTA4fNC1FgsHGAk3EA4yFxTDOmwhld+mg7W0AEmj7KTk+sRp2eQNPOCqopp15PLFFxxtp1FeemKDtCVjjIUaXE+vbt3r07BEGIqV6qoihwu93o2rUrbDYbNmzYgMGDBwMA9uzZgwMHDqBnz55RjRlPLBt2L774IgBfQb3XX38dWVlZ2jZZlrFx40aqn0DEjOzkoGQp4CTJZ4oV6SLbtJsUASyg4LFg993diPAZXKJdgqdMMKR96k07M7Rusf50WEBt9uAzB1XTTmsiAX9aLABWZvOlxTr8d1nq/CRBi7rjHF7wku+GjON96baK2wbY9ddmhMk84P/bS63bJ7ltWhRhIIFRdtBdE5H+yDIXVUqsrHDYu3cvcnNzDeurut5DIjsqVRWkjSlI4F82ggIxww3R7oXd6YHdbUOGXIrS0xkAKtJi9VREvFVE3KnDejwVJwhMGbWC3rQLbHYB/3gej/8Bkx2AB1BEGXaTukiqKeaLsDPOU6u/J4aPutHPQW22YTToKrrHRlPLjhpOpBdKlCmxkuJL1SadqTykM6mHICpQFBkKL4AXFK10gcAzXykD+IwsKQpTLZxpp5IIQysW0w6wbtSFisoLlZYLRI6yq55VKIlEYrVL7IMPPojLL78czZo1w6lTp/DWW2/hiy++wLp165Cbm4uxY8diypQpqFOnDnJycjBhwgT07Nkz7IOhRGP58z5nzhwAvqc7CxYsgCBU/ArZ7Xa0aNECCxYsiP8MiRqBYlMgZ3DgyxlQzgBRBicqvsqTfpgkQNHdWPCicRsvKlBkBaLdC8ljM3Za9Zt2oW429MaZmgarAOBFQPbwEOxeKG4bOFkGJ/EVcxMUQPbVtkOYmyLe6TW+DzDSON2cmM5cVCRfbTom+f4g8PjX+9K6/PvoUmMBtRGFf11Al1mCUGnTpg2++eYbw7qqrveQyI5KVQVpY2pQ/rux0C9sDEz2pcUyAIJDgmCXINq8sDu88JRHF40iij4TDfBFrOmj7KIx7XieQVG4oKg2j0cMioQT/esVkVd9O58xF3AuUZC17rd60y5aIxGAFpmnmXZQTLvHBiJEMAWJmgnP8zhx4oRhHelM9JDOVA8EUYEoMAjqk30dVmu/BZp2KoG17IDYItv0NfP0UXuRTDvDXGKI7AuMygt33kimJJl16Y0kc1E1nZCijLA7duwYRo4ciSNHjiA3NxcdO3bEunXrtG7hc+bMAc/zGDx4sKHBUTKx/Jnfv38/AOCiiy7C6tWrUbt27YRNiqh5MFECE21gNsWfysR8Zpi6XeaDousUSdBMO06UIdolX1MKgYdo92rpo5phFRCcoK4PrA2kNm9gkqCrBGQDxyvg/XPgBAbe4TWaikJwEwrf3Hw3jKppx4myv3FGxVhBPw91G+9Px/XPVdQ93faUOipMyTBNJgjCDJ7no7pBSUS9h0R2VKoqSBtTEy0tVvd8XnR4DQ8weEGpeIzvJzAaW5/+GS41Vv1Xb2iZpb+GQ187TkP2RSkBMNa186Omnqq15CT40mlhoY6dGkVo16UCq+c3M+0CjxXVxhikPUQYSGcqD+lMasLzilbHzmvS21SGLx1Wb1KFSm/VozftAlNj1e3q+NGadoaxQ4wXKdLOahSembFnZtzpr8Msyg4MsHFk1BHhsRpht2jRorDbnU4n5s2bh3nz5sVrapUm6s9+IjtgEDUbxaar/WZTtK9y5u8Mq+8WqyJ7eJ+RJipgkgLRn5qqr/sGQIs+C1U3julqygEwmHZARdqRr3usAo5XwGQOvEMC54+C8xmMkqlpB/923un1jemQfI01AuagXb//N7Oi4QYgBETpif40Wb1pp5+/ApCJV0OQma+solUYQ1RdYs2IR72HdNKTdLqWtMHme/AT+J2sRjQLgmyIuFYUXjOxVPOK5ytq2ZmhmlrRoEbZhcLUuPOI2lxFQQ5ZL04UZK0kgh6ztF+7vcKo01KAdWmw+jmq9ewA3x+OVtNiifRBiVJnFAVRdYk1H4N0Rk86XUu6IAi+tNhI6CPbQkW5mWFmjlU22s4QxRfi/NGmx5qeJ0xUnlmarKH5BbN2TTOVGys1R4JINMXFxfjss8/Qtm1btGvXLqYxLBl2U6ZMwfTp05GZmYkpU6aE3ff555+PaSIEAQBMgOEmS5EEKG4bZLcIpvhMNaYzz3z40j45UQYv8xAdkq9Bg8SD03VkVWQFihDQlELXZdW4XjEYaFq6qcyDSb5zqeahGiHHaSagLv5BUACZ17ZpXWZlviLSTm0uodbI0zrUqudXm18Yb8KYP4JQn/ZqatpRSixhQjRdYhNV7yGRHZWqAtLG1ITZGDjJ94c+c8pAUfA++ogxM6NO3xnVt795lJ0aiRYt4Uw7fdMH9TySJMDp8Prq5/mzeQNNu0joTTt9VJxHa7Lhb7ihr10HxTTl1/czgxZlZ3UORM0imi6xpDPmkM6kDvtuvtN0PW/y3ScDWtdVr67xRKAJFiriTm+qmRF4nNWUW/34ZkRMR41gMIbqNGvWzVZv2oWK7tNH2YGi7GoEkpLYlNhEM2TIEPTp0wfjx49HWVkZunXrhl9//RWMMaxYsUJ76BQNlj733333Hbxe36/Mjh07wEXxQyQIqzDBF2XHiwyczEM5bYfiFiG7Rcge0Z9GWmGiMZnXTC71VoITFEPPVrX5hAIY1gc2blBTYdXIBMlj/quhmnuqcQdAi7YTMjzgRB7MLYJzBJt2AAzGHQBNkjTTTmeucQajzTdfwe7Vrl2ReIgyD4/kCFurjurYpT/R1nuQFQ4Hooiwq471HqoC0sYURkRQGYRAAruIa+mtJp1RzRoXiaIcFIlWWczq4qnjl7ttcDq8vrki2CALFfWmzl2tdReI/uegvjY17aBErGVHpC++phPR3URFE2FHOmMO6UxqomawKJ4KbZAlPuh3xCyKDAhdny4aYj0uFsyMOr2xF61hqB8j0LQDEDLKTgKZdoQ5VlNiE83GjRvx8MMPAwDWrFkDxhhOnjyJN998EzNmzEicYacPwf7iiy+iPglBRAMncWDlApjb5jPpFB6K26ZFuakERsFpx+tMO0XWRZ/BaNqZociCZuaFSidVDTD1/LzD94cUL/NQym2+tFcz004/R+hNuvBRCaopqUYWCnYvFImHvmS6p7TCtDOLsgMqIu3qPb0q7PmImkE0EXbVsd5DVUDamDowkQMnMSg2DoLM/OsYOC8H5g2ugQoAXq8NbrcdkuQz69Tur2qTBdW0A4INKn0qrBqtZ2bcRVvHTo9ZZJsk876u46KvVIMgyrpU2WATTZ1jUNosgtNkfVF2fMA6ObRpBwRF2QWei6jZRBNhRzpjDulM9SWa5gxm5lukKLtQx1WWcLXs4t2hVh1Tb9oBMKTGauEN/lp2ZNoRqUxRURHq1KkDAFi7di0GDx6MjIwMXHnllbj33ntjGjO6wisAxowZg1OnTgWtLykpwZgxY2KahMqsWbPAcRwmTZqkrSsvL8e4ceNQt25dZGVlYfDgwTh69GjIMbxeL+6//36cc845yMzMROPGjTFy5EgcPnzYsF+LFi3AcZxhmTVrVqXmT1Qe3suAcgGszKZF10klDkge0d9Egq+oR6d7bWbc8YJSsYgKRIfXZ+b5F/1+6niyR/Q3uOAhe2whF8ltg+T2zUk1FdUIQBZwo8LZFG3REBRfBJ2WBhuQ7irxQTXu9Ngy3OAdXohq50O/aajW4fMZj76fjz51VgkzJkEQsZNIbSTiC1N4eL2irwOrwsPjf62aW2Y15CSZN61VJ+q+u0MZc9FE4JkZdMHjqVF4+hRXxaB5evSRd2qUnVmkXai5iKLs64zrNynVena+1750Yl+EorHmK0EQ8YV0JvVQv19l9fsv4Ptenw6bKCKZepVFja6zYtaFS5kNVxMvMFov8GfmhS/N2JvYSyVSAJlxUS+ALyW2ffv2SX+o07RpU2zevBklJSVYu3YtLr30UgDAiRMn4HQ6Yxoz6rv3N998E2VlZUHry8rK8I9//COmSQC+MMZXX30VHTt2NKyfPHkyPvroI7zzzjv48ssvcfjwYVx33XUhxyktLcWOHTvw6KOPYseOHVi9ejX27NmDa665JmjfJ554AkeOHNGWCRMmxDx/wpwiTyvL+3Kyf/FyYG4RiluEVOrwGU+Sr8GCajqpZhkAU9NOjUrjg/6VDaZd4I2NOl6kRfaImimmpqfKbjHIrAu6xsDi5zrTDghv1AVGiAj2CrNOtEsVkYRhTDsiPVGiFDZ904lUELd0IFHaSEQPC/yq09cjlXjIEg9ZEnxmnUfUzCcAYU07dXugeRdo2qmLHjPTLpTBZzXdVPQ3zVDNOkEXFa5qmzoWzyuGFFpF4Q1L6HOYR+1FMu2I9ENBdDdSii4llnQmPpDOJJdIzXZkhYfAq91do08TDTTeEmXECeDCmm/qNr3BVrGuYrGC12++harhZ3Ze/bESxwI6yvpMOwkRq14QNYxt27Zh165dSa1fBwCTJk3CiBEj0KRJEzRu3Bj9+vUD4EuVPeecc2Ia03JEaXFxMRhjYIzh1KlTBodQlmV88sknaNCgQUyTOH36NEaMGIGFCxdixowZ2vqioiIsWrQIb731Fvr37w8AWLx4Mdq1a4ctW7aYFprNzc3F+vXrDetefvllnHfeeThw4ACaNWumrc/Ozo7YDp6oOnivP32pXARz2+AtdfiNJl19CI9NM+NMx4CueYOasiooFU0jAtJjA9NsQ2GWjivr6tzZBAUssE6czFeYceqNkiT4OuB6eUNtOz362nxmkYPaHEQZYobbsM5T5tBMTbP0WDnKboZE+hJNSmxVE4+OSlVFIrWRiB+cwHxaoPDwem1QFJ9pJ8k8PN7wJpOaHhsONXVUT7hU2XDrAWPKrVlqbChU006WBU37jGNFb6iZ1bOLlB47aDc9ACWiS4mtakhniHgj8AocNhkyC769ljkGoYrqzVlFNcjCGYvhI+YqmkKE6voa+N4GzrQBhX5OMpghLddXRwgAOM0opEg7IlX5+9//jvPOOw8HDx7EJZdcAp73/f3VqlUrg88VDZYNu1q1ammpo23atAnaznEcHn/88ZgmMW7cOFx55ZUYMGCA4UK2b98Or9eLAQMGaOvy8/PRrFkzbN68OWJnKJWioiJwHIdatWoZ1s+aNQvTp09Hs2bNMHz4cEyePBmiaP4jcbvdcLsrzJHi4uIorpCwAicxcOUcIHNgfsNJKvWZT4osaOadasppteZERdfpldfCRjlB0Uy7UOjTYfVYNfK06DqPCMHpjXyAKBtNuzDjAogYsceLMgSnN6gJBQBKf61BeBUO3miaTjAOB6NoOpFoEtFRqaqIpzaSzsQPrVOszINzSuAd3orve0mA12ODx+v7019vaAEhasdZMO1CEWsNu0gmXbiurIIgG0w7oCJVVUuptagRgZ1xI5l2MSRvENUAWeHgjaJ+lZdxUGC96USiIZ2pgLQmvgiCDEGQYbdLcDokZIp2/OG/JVCNKStmXay16BLdeCJcNJ7etAN0nXEjRBdGY9qpyGqrWKit+Ih0RFIAbxQfacn/MUqVLrGff/45LrroInTr1s2w/sorr4x5TMuG3eeffw7GGPr374/33ntPK6YHAHa7Hc2bN0fjxo2jnsCKFSuwY8cObNu2LWhbYWEh7HZ7kNGWl5eHwsJCS+OXl5fj/vvvx7BhwwydQ+666y506dIFderUwddff40HH3wQR44cCdkSfebMmTEbkjWZXPsvltNiZRfv+9YPMKlkj01L7dSbcnrTLhB9QwpD3Tv/DQovyFBQYYzpb2rM3vvGqWhGoZ8LJwsAJCiSr7A5k3hwjjAX6jftwqG4rZdTDdWEQhuLjDvChFSKsEtER6WqIp7aSDoTO5wU4ubAKYPL8oAv8vrqftp9STSSzGvppBLPfHk2AQQ2jNCMqRiNu0B4nkXdYVaShIhGnj5tSw6hkWoKq1VCmXYAtPRYgAqBE0ZSKcKOdKYC0prYCPed6XSVw+lyIKPcDqfNCadXwAmOhTWm9MRiuoU6JlzknJn5ZhYdlyjMGlmo6H9WetMOAMD5ov30pt1L7MaqmDJRTUiVLrGXXXYZmjRpgtGjR2PUqFFo2rRppce0/LdV3759AfjqHjVt2lQL76sMBw8exMSJE7F+/fqYi/CFw+v1YsiQIWCMYf78+YZtU6ZM0V537NgRdrsdt99+O2bOnAmHI9htefDBBw3HFBcXx+U/oCZg1bSTsjxQ6gjgA3qKcIICxZ9+ahZJp96aKLJQ0Wbdv58eM+NKH4EXGG2nN+30xqB6HrNzhL44oSItNgJMEqBIgiHlltel23K6mzHeP6Ytww0vAFFnMpJpR1QXEtFRqaqIpzaSzsSO84y9KP89IPJEBCABzCmDy/BAsEuwu9zIyC7FqeIs6EtBiYICt0eAIES+adGnvoYz79RtgamyemIx7eKBr94cb3otoQzBUKadKCgG044gUhHSmQpIa2LjzKXzse/mOwEYH4woHh6iXUJWVinKy5zIcEjILRdQrog4wQdXWgs0rBJh1ulfWzHjEtH9NfDc6jnCGZg23T5WTDsi/ZDNn6GG3R9InQi733//HUuXLsWbb76Jxx9/HP3798fYsWNx7bXXwm63Rx7AhKgfhjZv3hyAr7nDgQMH4PF4DNsDm0aEY/v27Th27Bi6dOmirZNlGRs3bsTLL7+MdevWwePx4OTJk4You6NHj0asPaeadb/99hs+++yziI5rjx49IEkSfv31V7Rt2zZou8PhMDXyiPgiZXNw5JYDR7MB+BorqCaUt8z3ITeYdgEprwp0hpo/rTaQSM0XDDXvtPPIpqadGUzmfbXpTM4dDibxUNw2X/MKXddZ3zZ9N9mKenyK5DPtOIHBpqtnp8gCRNnXqENbR6Zd2hKtuCmoaDoBJD9VSe2oVKdOHaxduxYrVqwAULmOSlVNPLSRdKZyOM/YC/evweliAMDXLoftrzI4ssuQkV2C7JzTKDntAlBRf85hl027sobDikmlN/XM9o/VtJMlAbzdGE0nCHLEyDk1FVaSBMN5zeYm+uv+8bwCUVQAKID/YZIkCYAMMu1qCL6mE1Hsz6A1nQBIZ+JBvO7BSGti58yl87F3mPFzzPMKZFmAze5FVlYp6tSyw+0V4C4RUcZ4eLnoo7IDG05YMfVCmW6VNeMCU1MjoRpygUZhNOOo0Xb66D+1nl2FaUcQFaRKhF29evUwefJkTJ48GTt27MDixYvx97//HX//+98xfPhwjB07Fp06dYpqzKgNu+PHj2P06NH49NNPTbfLsvUvpYsvvhg//PCDYd3o0aORn5+P+++/H02bNoXNZsOGDRu0UPU9e/bgwIED6NmzZ8hxVbPup59+wueff466detGnMv3338PnuepaGuCsBpl56kN2Gp7wWe6Ibpt4HgFnChrxpxkEmkHVETHqQaX7G/cEGismZl1ZnXu9NF2gaZdSKPOHxnHCcxXe85fQ08jwo0Mk/0ptQoPudxmuF6ty63/ujiTqA5OYBDsEpjkM+vUzrBk1BFmpFJKrNpRKSsrC82bN49LR6WqJp7aSMSOo8VeeH86y3SbUKcEjmIXXKUOZJa4kFPigsdrAy9VNGbgJcHQhCIwLVZdpxKuPp1Z3bvA96rBFalBReB5JIkHzwsQFBkSALtfI1TTLhRqV1it06tf+1TDjedZxNRfu13STDt1jGgaYxA1h1RKiSWdIeJFm7fn4ccbzRvrOF3lyMosQ26ZHW4vD69bgAwWtuOrl2OaIReLUZdIJI4ZGk94UdEh1kLl7iDCmXbh0mUDm1CEa4ZBEKlCly5d0LBhQ9StWxezZs3CG2+8gVdeeQU9e/bEggULcPbZZ1saJ+o7+UmTJuHkyZPYunUrXC4X1q5dizfffBNnnXUWPvzww6jGys7ORocOHQxLZmYm6tatiw4dOiA3Nxdjx47FlClT8Pnnn2P79u0YPXo0evbsaWg4kZ+fjzVr1gDwmXXXX389vv32WyxfvhyyLKOwsBCFhYXak6jNmzdj7ty52LlzJ3755RcsX74ckydPxk033YTatWtH+yMhLJJr/yXiPkyUINUG+NYnYGt0EvY6JbBnl8OeUwZXrRKt/hBgbAyh1qnTL8y/+GrgCQazLlwjCk5ntPFB/wb/MaRfxxQ1Is4fZSfzYF7zxQxFEvy18HxmnSL5ouQ8pQ5IbhGSR/RdVwjzT3BIEJy+Ok2C3QvR7pNT9Sat5ZLXQl43QSSLv//979i8eTPeeOMNbNq0KS4dlaqaeGojUTlCBTNwNgX2OiVwZpchM7cEteudQIMGfyI39zSyMsuQlVmODJcbGS6PIdJOUTjDoifUeqsEmmJWGlRU1I7jKxpJ6Lupy4K2hDre4xGDGm34xuSC9tVe6x7+qOlgPM8qusj6TcBkpPgSRCRIZ4h4kr/ypaB1PO/rRp6ZVYbszHLkZHhRWwTymIhsJiCbCXAyXlv0eLnwpl4kEpHSqkbKSRwzRLR5EZtZp2I1Os6mNZgITu2VKvGzIlIbKYYF8KXEtm/fHvPmzavyOQfi9Xrx7rvv4oorrkDz5s2xbt06vPzyyzh69Cj27duH5s2b44YbbrA8XtQRdp999hk++OADdOvWDTzPo3nz5rjkkkuQk5ODmTNnVqoDhhlz5swBz/MYPHgw3G43CgoK8Morrxj22bNnD4qKigD48oZV0ercubNhv88//xz9+vWDw+HAihUrMG3aNLjdbrRs2RKTJ0821HMgEoOVSLvyxhKYTYTdVgzhuAd8kRNCiQMc74t0U05m6mrLWfScTSLomD9aDYAxEs6PGnmnT78NbEzhm4MARVbAq+mrMldhqgmKT2YiRB4wSfAtii8tVvKIkNw2rTOuIvN+s9ILReAhCIo/5Re+tFhdQqTahEL0R9iJMg9PGaU+pDMyqxAsKygstVJiE9FRqaqpam0kQiPm/wTpR1+UHROZ7/td4ABRBp/phqP2aWT6I8QEUYHzlBulJS7IsoDycjtOncoIShe1glnknRplF9R1Vqclakqu/lh1rFAGni/tVYEkV0TZiUDIuqq+qDqhIh3WRDv1qa1qpJ1qKKppsXrTTr0mj4daTdQEotUZKcVSYklniEQiCDIU3fd4VnYp3F4bZIWHUCrCJfE4wQAHfMaTxDGA8SjnUjsyWR/1Fk1KayRTTtY1lwg8HxBc5y/wuFDbiZpLqqTETpgwAW+//TYYY7j55psxe/ZsdOjQQduemZmJZ599NqpGQVH/lVVSUqKljdauXRvHjx9HmzZtcM4552DHjh3RDhfEF198YXjvdDoxb968sG4pYxVfCi1atDC8N6NLly7YsmVLpeZJxI4V085d33f7YRPLwDsl4A8FNomH7BHhkHmUncwMm54KhDbzDKm0/nVmhp263sy0C4y8084p8eB4f1qsoIAT+YrSqPp99XML6kbri6qTPaLfrPP9ASAZS5UYIjPUPxLUJhSCXYLir1/HCwp4XomqIyCR/qRSSmwiOipVNYnWRiI6xPyfIP/QxvdXjo0B5bptOeVwekq0cgEcr0C0e1FW4oIi88hw+eqBlpY5Yo4WC+owG5Ayqqbgau9NTLtISBIPu13RatlJMm/6R12gOReuzlw0qa08rwTplyTzuP34KEvHE+lPKqXEks4Q8SZ/5UvYdf2koPWCIIPnFWS43PB4BEgyB0AAJA5lDJDBodx/cyAzrlKRdVVFYGpsvIhk3IXCSuddgkgGu3btwksvvYTrrrsuZK3QevXq4fPPP7c8ZtR38G3btsWePXsAAJ06dcKrr76K33//HQsWLECjRo2iHY4gQuKuL8HbAGDZErgsD/hMN+zZ5bC5PLC5fO6VmuoaadGjRqypryMRKj3WMKbkT8MNcSOkT5HV3+AwqSKFlskc5HKbls4reWyQ3Dbf2JK/Hp2s+1fXUKJiHoJm3nGCAl7w1f8za75BpA/eKBcZFRF2qRA+/vvvv2P8+PF499130apVKxQUFGDVqlVBBbVTGdLG1EM4Z6+vS6zIwGwMEJgv6lmUYcsugyO7HI4sX3qsw+X2LU43XC63PzXWbck4C4Vq9oUyyERBMZp4ohy266z5OXjDv4GoZp2WOqvbT5Y5yLL5DZihGYUc/hyBP6NX679pZepENUNGdDojoSLCjnQmPpDOpB7t352rveZ139+ioMBu8yLD5UGmy4tMh4xMkcHFAQJ8BpXIODjBh6xTF2v9Ohu4uEag6c2xUJFzlW0CEc3xhs6xRNrhBeBlUSz+41IlJXbDhg0YNmxY2MY+oihq3b+tELVhN3HiRBw5cgQAMHXqVHz66ado1qwZXnzxRTz11FPRDkfUUKzUswMAKYuHXEsBarnB1yqDmFsKe04pnNllsLvckQfwY2bg6c26cDXtAKNpF864Ayrq2GlYTNtV/F1hVfNPkfigGyTJ44+88+8jl9uCz6ebg2rUqZGIP4+8w9JciPSnZcuW2LVrF3bt2pXUNCWgoqPS999/j61bt6JNmzb4+9//jsaNG+Ouu+7Czp07kzo/K5A2pibCuXv9LxRwNt+iNgTSa4nD5YbTvzicbtjsXtjtEpyOylTqsUZgRFu0pp2keyilNoBQXwORTb3g8Srq2YUyG9W6qAQRDjXCjnQmPpDOpCbnvP+89loQZF+EnaBAEGXY7RIyXF5kurxw2hQ4BcDGAU5UmHYCuIQ0l4i3cReJcHXlBHCmi55oTDgBHG7i34p5rkT6sW3btohaM3PmTHTv3h3Z2dlo0KABrr32Wu0hiEp5eTnGjRuHunXrIisrC4MHD8bRo0ejns+uXbuwdu1afPjhh4YlFqJOib3pppu01127dsVvv/2GH3/8Ec2aNUO9evVimgRRM7GSGiu7PPDUtcNZroAv94Ir80D0R5GpkXKeUkfUEWRqOm1gl1mzFFvV4FNNOzVFVr8uEH0dO7OOrqbHKH4TzmOD5PGns/IV4/tq1inaPuq8FYmHYFe0MdR9VQLn+PPIO9D6HwsszYkgqpp4dVSqakgbUxdO8t8UCAogyuBExbcIDGKmGzaPCKc/elmW1MYJxoYMpWX2mLqg6lNj9emmgWUKIqXI6tG6uPrOAABaWqx+zuHmFEi4a9POJyhBPxczREHBooaLMbZwdMgxCSKZkM4Q8abTh89ix5X3a40nAN/3vMArcNi8kCUeGQ5/FUiPgNOyry4kwMHFeJRxCsCCu8TGQmC3VfV1ZdNII6XFioyLuhmEAK5S0XI38W9hmTI85uOJmsWXX36JcePGoXv37pAkCQ899BAuvfRS7Nq1C5mZmQCAyZMn4+OPP8Y777yD3NxcjB8/Htdddx2++uorS+f45ZdfMGjQIPzwww/gOE4r1cZx/lqQMXTzrvTj0YyMDHTp0oWEgogJK5F2UpYH3jqAUtsLvnY5+Ew3bDllsOeUwZFdBtHh1dJGoyEwVVaP3gD0pZVW/HKpXWQ5nckXyjC0atZVzImPmKarT42VPKLWrCLQrIsUNUikDwp8f/hZXRSkVkosEP+OSsmGtDF14Lvv8XXstjFwThmcUwLn8IJ3eCHYvf4yC244s0rhcLlhd3q0CDuHw4MMlyeoc2zIc/EsaAHM02J5XtEWwDxF1gqiIEOIUmvUVFhBCH2jpHZ9DYfanMPM8FvUcHFUcyJSG4bodEZGaqXEAqQzRGLp8vHTACqi7ERB8b0WFTgcXjgdEhw2BQ5BgYMDnLpIOxfjg+u4hTC/IkWqhaKy0XbhzLpo5hA4j0jHBhp6gokZSaQPCnz6YXVR//qwkhK7du1a3HLLLTj77LPRqVMnLFmyBAcOHMD27dsBAEVFRVi0aBGef/559O/fH127dsXixYvx9ddfW+5/MHHiRLRs2RLHjh1DRkYG/ve//2Hjxo3o1q1bUK8Gq1iKsIume+rzzz8feSeC0GEl0k7K4sF5FdgkDzgvD17iYVPTRtX6bv7XsdZrYzIPBRVRdvpxFIk3mHaq2aftq0uVVdEi2yxEZaipsOHQz0eLwBNkyP4OfZxYYRwyNQJR4sm4I0yJpunEzJkzsXr1avz4449wuVzo1asXnn76abRt21bbp7y8HHfffTdWrFhh6Oidl5cXcfxEdFSqCkgbqw/8hbvAPvNHzeii7HiHBDHD7e/ILcApm9R6k3zdVSVJgMcrGMwpfUdXK+ij7ET9d7o/8EJReEO0nVmkXTgjTz+3cAajILCg+nWB16KOFZgaGxgNSBChiKbpBOmMOaQz1Qv1O1QQZMiCAEGUIcgCBF6B3S5DkiV/EwoAXv/3KAPKVSMrwZ1jY4m2i5cpFhj1ZzaHaDrRkllH6NmwYYPWJba4uBgOhyNsHTnAZ9ABQJ06dQAA27dvh9frxYABA7R98vPz0axZM2zevBnnn39+xHls3rwZn332GerVqwee58HzPHr37o2ZM2firrvuwnfffRf1tVky7KwOrIb6EUS0RDLtZJcHnGwH75UheN3gZQ6Qedjcot+cElB20hfKGq1pF5gWqxKquYRvW7CpB/hMM44PPo4Tdead7kaHExVTybQSLVjRgEIBPCIEVDxlIJOuZqEW+baKvukEAIwbNy5szYdEh5AnoqNSVUDaWL3g+v8PbP05Fc0nHF5wEg9elCFmumH3N/xRJB4uuQyAz6xzuSq+TxXFaTDdYu0iq6KmxqrmnSRZNO0EY3SevnyC2s1cnaME+P7a8xobFanRdYEGpNl1hatnp58Tkb6oTSesom86AZDOxArpTPXivPVPYnP/x3w17AQZdpsEWRIgiDxERYbDxkPy/40vKRzgN+9kBoDxAKcYOsd6ORZTfTt9WqwaGadPVw1lmEVCNdNUNQn8ThBNut6GMuD0c9CnxgaadlYNPCI98AJR/Y+rn8HAzt9Tp07FtGnTQh6nKAomTZqECy64QHt4U1hYCLvdjlq1ahn2zcvLQ2FhoaX5yLKM7OxsAD5NOXz4MNq2bYvmzZsH1cuziiXDLtXEi6iZSFke8F4b+HIFnOwBLwlaPTu7RwSTeZSfcgGI3rQLRajGEmb7cP5urLwogzNLMzKLuDMx1tSurpI/co43MQBV9JF5vP9mU43sM3SUlYWgmklEzSaaCLu1a9ca3i9ZsgQNGjTA9u3b0adPHy2E/K233kL//v0BAIsXL0a7du2wZcuWiE+kNmzYEHEO0XZUqgpIG6sf3CU/gH3SCZzLC8i8FmUHAGIGr5UlUPxlBhRJ39BBQIbiQfFpo2lnhXBRcep3vGrc6U0733l50+NVk09NhxX9kd5qOpYsV9S68yh8WC0JnlPoyEFJEsLW2FOhGnZENBF2pDPmkM5UP3p+9gS+6jvNNMrO4fBC9n+3+v7lNdMODJAZB4EzmmmBpl00UWh6Ao27aEy7UOmweuMulnp0oUw7goiWgwcPahF2ACJG140bNw7//e9/sWnTprjOo0OHDti5cydatmyJHj16YPbs2bDb7XjttdfQqlX4jMJQRN10giAShZXUWE9tLzhJhE2SwHvLwXt52KSK+m2SR4SnzOEzp1Qjy6Jxp8i8r6hjQOMJTlC0Yo9q5IIeXq1p54+u4wTme21yXs5mXMcAIMQ8rRhsisyDkwWDwafOyQwy7QgVRVFQXFxsWGclfBxITAg54IuAOHDgADwej2H9NddcY+l4grCE/+aIc0q+CDv4yggIdgk2l0frwh2IvvtqaZm9IgJO932rbzIBhDbqFIWHJAGiCNht/ufDggxJFiCKAKBA8ncK1xt36vkq6t6Zj69G32nf9wFhEGoEX0UTjMg3SYYGGhHMOoJQIZ0haiIXfDkNG3tPBwDYbZK23uMR/amxPByKqh16086XFgvAkBpbGdMusFmEvjlEJNPOLO1UNelUFZD96xLfU50gQpOTk2Mw7MIxfvx4/POf/8TGjRvRpEkTbX3Dhg3h8Xhw8uRJQ5Td0aNH0bBhQ0tjP/LIIygpKQEAPPHEE7jqqqtw4YUXom7duli5cqX1C9JBhh2RUuibUIQy7zy1Ac4Ln2knlwMyD7t/m74Zgxa1EJBeatXAMxhxogzmv0FRR1P83WLVfbToOlEO2T0WAKDewEW44eFFxZ/LFHnOgRGFZvXreNFnYkYTZUFUD9TCq1ZhAPbu3Yvc3FzD+kjh40BiQsgT0VGJIELBX/299lpZ2hOAvwaozIETZdgzKm7kzZoT+cw2AWXlomnThkDTLpDA6DxJFgyNI9RbO1GsqG0HVPzBxvMKRNGYDquPrlPXC6IMSIDsj7AOJFKEYLT1+QKPJdILtRC4VdSmE6QzRE2lz6ZHAQDrus4O2uawqZ837ZsdssTBycHUtIslLTYc+mg7q5F2oQxCAcbvBluUkXKBUXZWjyHSExksqs+Pum/37t0hCELY8guMMUyYMAFr1qzBF198gZYtWxq2d+3aFTabDRs2bMDgwYMBAHv27MGBAwfQs2dPS/MpKCjQXp955pn48ccf8ddff6F27doxly4gw45IWQI7yKoGHhMleGv769nBoxlo+iYUZUVZIevAmaXLquabilmEWmDHVx4wmHVqdF1Ewhh1VlJwA2EyHxQVCBhvNHlBDtsVl6h5tGnTBt98841hnZWoh0SEkKsdlTZs2ICWLVvim2++wZ9//om7774bzz77bNzOQxCBiDdvNrz3zrgavMMLO9SO3Obfm77ot9B/eKmmnZo+ahV9t1cJ8Efb+c5nZrqF6w4r+7XG162Qh+g38ypjxAWakZWt4UekNzzP48SJE4Z1pDNETaNg+31B65Y1X4iK34QK067cJNIulHkRKcpOX8dORd1fHVONtquuBthiZViyp0CkCNu2bYsYYTdu3Di89dZb+OCDD5Cdna097MnNzYXL5UJubi7Gjh2LKVOmoE6dOsjJycGECRPQs2dPy1HcgM8Y/PPPP8FxHOrWratFiscKGXZEtUGfMiu7PPDUtcOOCtNOkztRgeiQ4Cl1aBFmal0ifbosLyq+2m6CbKhDFwo1Qk2Ntgs069ToOpVAg88MzqYAsj9Kj1e0iD3VYAsXWeebu1Lx2qSrrQIY6uTFo64fkR7wPG85dFwlUSHkieioRBCxkPPIRyhWTbsQzXvUNFO7XUJ5uT2oY6rHX54glGkXKbIt0LQDKoy7eCCKsjZHoCIaTp2j1XRXMusIK5DOEIQ5gqiYmnb69FiBCXBzSlAEnL5BA2A9Mk01+fT14vQpstEQ7i6H6tERqcj8+fMBAP369TOsX7x4MW655RYAwJw5c8DzPAYPHmzoRm6FwsJC3Hffffjwww9x6tQpAD4NHDRoEGbOnGmpo7kZZNgR1Qq9aSdleQDoTDvBlxYkZLphyy6Do8QBr78JheQRIbl9VRcCTTsV1fwKZ2pp2wLrzekaTfiMPJMGEwCYlw+qYwf//rw/lZYPVZPIv14f8aF1uIUxclA1F9Xr8kUQUrpHuiKDQYoyfDyaLrGJDiFPREclgogV1bQTM9wAjF231YczNrsXdrsEl0uE12ODJAtQFB4ejwiJZ/B4BYiCEjE91gy19pyZcacnVP060zEFBYIoQxQVKIoCSRdlF6runiQJYaPxQnWUJdKTWHQmmi6xpDNETeKm327FsuYLg0w7gWcQvTxsMg9/VQM4GY9y/x4ymGaulXOKliobKdpOX8fOzLSLBzZYS5vX19OLpQEGYB49SKQHEhj4KAxkiUWXEhsJp9OJefPmYd68eZbnAPjqtfbq1QunT5/G6NGjkZ+fD8YYdu3ahbfffhubNm3Cjh07kJWVFdW4ABl2RDVHb9rxohecrQTcKQ+4Igm8Q4KY6YZU4gBX6jBEo+lr3EULV8kacHrTjnl9N4Kc3/BTI/ZEuwRPmTGlVf9aNe3Uf32mXOiIPNEuaebeGa+8Wan5E+lBNF1iEx1CnoiOSgRRGfSmnb4BBe/vwGqzeWGzSfB6fYZdWZkDXo9N209RuIomEVCCouzM9EeWBM2k05t2suSrcSfpHtZEY9bpz6kugVF2erSGFP5OsJFSaM2MOzLxCCC6LrGkM0RN46bfbsWKVgsM3+eCrpyPwHGQGYdy2d/UgQHl4ABW0YTCqxpejIvKtNPO4Tftoo2y08dhy4b1nGbsq2PH2tU2EtEYfET6YyUlNpG88MILEAQB//vf/1C/fn3DtkceeQQXXHABXnzxRTz00ENRj03tIolqR2BtOynLA09dAVJtQKnvBeqXg69XCqFuCYScMthyymDPKYWY4YY9ww27y63dDJnVuQtV+84MXpTBR1GfSIV5ec2sU9GbdYLdC9Eeut9SYLScekOpnzsv+qL1BLsXnKBAsEsx1cgjUh8vGLyc9UUGtAi79u3bR3yKNH/+fBQVFaFfv35o1KiRtui7Hc2ZMwdXXXUVBg8ejD59+qBhw4ZYvXq1pfk/8sgjUPzd0p544gns378fF154IT755BO8+OKLMf9cCCIe2Fwe2PzaYXe5YXd64MoqR0Z2KVyZZXC6yuFyuWGzezVDzG6XIqa+qigKbzDj1JRbQ/MI+Ew6ddGvN0MQ5bDbfeMbb3bUVFh9iq+oGYiRb4yo0UR6IwNR6YxXF2FHOkMQ5gz95Q4AgMPhhcMmw+mQ4LDJyHTKcNoUOAQFmSKDgwNsXEWHVps/Qk5FNe4CI+ZCmVpmkXWBZl64Y4wmnTUiGWwUMUdUZz7++GM89NBDQWYdADRo0AAPPvggPvroo5jGpgg7olqiT40FANmpAODBbAoEQQEvusE5ZfAOCZzoSznlBGaIQCv3p8vqa8GpBDahCCTQpAvZbELmK9Ji/a8Do+sqxlAg2L1QJB6iv9utov0rBJl0ge/V5hNqaqzZ/MmuI1SiibBLZAg5kJiOSgRRWdQoO06UIdqNSanqAxF9xLUsC1AUj2E/qcxX484sys4MfZQdoIvEE833iRQprnaKVQcRBRmKyGvGRWC9vcpCabKEnmgi7EhniJrK0F/uwDtnvgKHwwu32wanQ0K5W4TDBgg8B1HhoK9t52UVUWz6KDsrBKbGBhJrPTt9p1gxwpz0c4j1fET6IoGBiyJ6Uv1dsJISm0j27t2LXr16hdzeq1cv3HPPPTGNTYYdUW3Rm3ZMlCA7RfiruUGxAaIo+WrC2RRwTgmcwwuu2GVo7lB2MhOAuUGnyDyECBESeqOO89egswpnU3ymnaymxSrgHRIESQCTBIgyDybzhtTYSAQaexRRR1QX4t1RiSDigb4JBS/zEPyRz4ocut6oHkkS4PFa+w6XZEGLnlMUY1dYNSocCB9ZFw5RUBAYtx2Y7hrOVFT3tWLujS0cHdMcCSKRkM4QqY4gKoDEw+mQIMk8BImH2yvAafMFJsgSBycAmXHQAtJYRfSrWWpsYL03s9RYPZFMNC8qIv1CEViTTp2TOhcy6Yh4k+yU2OLiYkNDpEBq1aqF4uLimMamlFiiWqNPj/WZdgqkLB5SDg9PAw5KHRmslgdcbjn4WmUQ65TAllMGR+3TcNUqgatWiXa8YtIRkMk8mIWOecbusOFNMi2yTpR90XaCokXhcYICwSFBcHotpcaGQpH4gLpLkRtqENUXxV8jxOrCdE0nrKQqJZLCwkKMHDkStWvXRl5eHho0aIDatWtjzJgxOHr0aNLmRRAqOY98BCYJEOwSRIfve1mwSxDtEpxZZXBlliEjqxR2pwdOVzmcTg8cDg/sdgkZLjfsNhmSzPvq2kmC/7VRb9T3kixA9mtO4D5quq3+PWDedVbW6ZYgyob02sBxKubgu4Gz2iVWjyjKEaMHieoNi1JnlChTYhMJ6QyR6tyw7+8AfKUPBFHxNZ8QFIiiAodNhsOmwGlT4BQAFwc4wUFknJYaawsw4CI1k4hkmIUy9MzSYm3+dF0B4Y089ViqPUeEIxqdURfAF2GXTK1hjIHnQ1trHMdZiiQ3gyLsiLSCiRJk/6daKLNDaSDALvgbUjhl8E4ZnFMCX+QEAHjL7JA9omZuMZmHAn+X1QhpsVpXWP9NSrjoOibx5kaeKANe/zZZ9u/nqzsnl9vAi77acwAgeWymER0VzTR4f3xhcLqsup+ZKUnUTKJJiU0UieyoRBDxJHfa+yiadq2vG7lDgiL7OnFzsgCnWfkB3XetxyuGTBGVJB6iiTYEpsbqiZQGGxidJwi+xhW8oICXFUNabKA5p0bP6SPt1OYTvnOHb0BBph2hJ5qU2ERBOkNUF4b+coexCYXEQ+BlrRGQdtvuESDJxtRYFTXKzgpWIu2s4GU+w05F33gi5DFx6PK6TBleqeOJ9CLZEXaMMbRp0yZkiYVYzTqADDsiDQhsQqGmySo2BbABnroCRJvvKRVsHl+aLACuKMN3A+M3svQGnVoHTjXBAGhGnhU4Kzct/hsgzqb468/5au0xLUXWVzdJkXgwgbeUfgVUmI4q6jUSRKqRyI5KBBFvcqe9r73+4/4h2mtOUODMLgs+4HQG3G679lZfyw7w/QHG84pm2qlGm1lqbGAUnRTi4YssCxACtEL2N7QQBBmiwAelxQaiN+3MUE07ajJBVAdIZ4jqhNqEQuXNMxZprx12CYAISebgVXh4mS81NrBenFlqbCiDLJJpF4rAtFhvgByEqmMXOCeAmk0Q6cHixYsTNjYZdkTaoRp4xVIbABUNKQAFgk0B75WBEtkQGWcw7RDcnIFHeBNOja6LlA6rEthwAoKvZgUALQWXFxUwSY3m8DWeAMyj51RC1eLTUmLJuEtLvGAQo0gx0HeJBZC0Aq1WOiotXLiQbqSIlKPe06u014WTRkCwe+HMrtiuKDy8nuDkINW0ixZJ5g2mnZUOtKFSawVRBry2kKaf75joDDkrDTWI6o2E6FLZ9F1iAdIZgoiFUb+PBQC80egNAIDo8gDwPwjy8pAVDiaPi6qMeHzrk3FH6PFyAKKocegFAJb8phOjRo1K2Nhk2BE1Ar1px7kUX2qs36jjBRlM8N24qGlMgaYdLyg+I62yNeAipKQyv2mnqNF3ogx4RC3qzjdHY2MJM5NOH2VnJb2XqHmkQkpsIjsqEURVoX4f6007p9cNySPC4XDhdImvBENFWhMiRtkB4dNiI6GadGp0nR5ZEiBJkcsjhDPtKMqOsEIqpMSSzhDphsMuQZI5SAoHN+PgYjxO8JLpvoENKMyoTJRdLISaE9W2IypDslNiEwkVtCLSHiZKYKIEKcsDxcWBuRggKOCdPqkRHRJsGW6tiLgZir9jqyLxYIpa7y60uJk1qmASH3aBHDwukwTTunNKwE2YYjD0+Ir5yoLWgEKRedSavjrknInqi8wBMscsLwqXGk0nEtlRiSCqgmN3DwXgezDCiwpsGW44s8vgzCyD3elBZmYZ7LYKXVEj48I1oAB8zSdUzLaHQ5YFbVG/+9UlXFRdLAR2mCXSFxaFxqg6kwpNJ0hniHRA4Jm2OGwyMl1eZDpkZAlAJjg4mfG73SwdNZwhFkvX1sCi//r1BEHED4qwI2oU3mwGvozzRdg5JLganoT3lMvX4MGfFiu5bZA9omlqLOePslMkAbzVyAf9DZJZlJu/Ey2TeGMkne69lWYRWnSgSRMKgggkFSLsEtlRiSCSAS8ocGSX+bTEI8JTbkdu7mkAPuMtMMIOUNNkAUlCUJSdnsC02EDMdEJv0CkK74uuM4m6M72WCNFzapSdPtKOTDtCTypE2JHOEOmIw+a7B5EVDl5FQLni++41M8us1LKLhlDnAGIz/lT086Jou5qL7/8+uvILQPJTYhMJGXZE2sNJIpjoi3BgogRvbTuEchm8fAq2LDeEky7IxS4obhGyxwZPsQseIMi04wVFi7LjRUBfuYHJPDhBMXSDZZIQXPcu4IYq0KhjkgBFEsAUXouuk9zhmqQHozfuyLQjUplEdlQiiGTiyC6D5LEhWz4Fnmew2yUUFWVp0XIejwhJ5oNqv+m7xqrGWixpseGMOo9HDIra0+9vpT5eIGqkHc8zjD48JurjCSJRkM4Q6YBgUpInQ/SVU5BkDvDysCkCTnOKwexSjbRENKAIZc5Fa7aZzcUGjkw7IirSOSU2pQy7WbNm4cEHH8TEiRMxd+5cAEB5eTnuvvturFixAm63GwUFBXjllVeQl5cXchzGGKZOnYqFCxfi5MmTuOCCCzB//nycddZZ2j5//fUXJkyYgI8++gg8z2Pw4MF44YUXqK17DUB2eVDeyA6H4IXglCE4ZfBZHiin7eCLXdp+7gBzTTXCOEmAAoDjBQiCpBlzoUy7UAQadUzmNLNOLvdF+Ulu0Z/aGn0ak/4YHoAc51QoInWQwKL6w0YGS4mmE4nsqEQQyUS0S8isa0yzEwQZZWWOimYUHtGXFhtQzw5AUJRdRQfZ8FF2QGizTlF4SBKvRfpJkmBIaQ15LaIcsWNsxfmoaHi6IkepM14gJZpOkM4Q6UxOttv3otQGeHkICo8SMIOZFsq0A4LNMqumnZlZZ2YUVgYy7QjCR8oYdtu2bcOrr76Kjh07GtZPnjwZH3/8Md555x3k5uZi/PjxuO666/DVV1+FHGv27Nl48cUX8eabb6Jly5Z49NFHUVBQgF27dsHp9BV/HjFiBI4cOYL169fD6/Vi9OjRuO222/DWW28l9DqJqiMnYy+KS9uYbpNdHpSdIcJhYxCdbvDHFV9BR396qlr3zX3KFdyAQjXZRAWyW4TgMDftwhHKqFMkf3qsVnNIqPhX4sFbaHph1pSCIAJJhZTYRHZUIoiqoMFzK3Ds7qGm3896004QZK2MgqLwao8/AEC522Yw7dTUWMA8yi6caaeadapRp44RaNaZHROOcKYdQYQiFVJiSWeIdOCm327FsuYLTbdluvztH/ymHRQOJcxnqqmGnJlpB8QnRVYdRyWeNexsAQYjkf7IHAMfRWq1DJYSXWJVpkyZYrqe4zg4nU6ceeaZGDhwIOrUqWN5zJQw7E6fPo0RI0Zg4cKFmDFjhra+qKgIixYtwltvvYX+/fsD8D0pa9euHbZs2YLzzz8/aCzGGObOnYtHHnkEAwcOBAD84x//QF5eHt5//30MHToUu3fvxtq1a7Ft2zZ069YNAPDSSy/hiiuuwLPPPovGjRtXwVUTVYFq2unTYlWYKMHdQASzATbJC17mwXl58OU22GROM+68ZQ6DaacI/jRTf2oskzlwgrUvFjOjDkCQWSd5RK2GnaGhhN8I9HV+NRpzetSmFOp2Mu3SG7XIt1UUhpSIsCOIdEA17QLhBAU2lwcufw07WfaVPJBlAeXldvCyALu/0VG5v/SBsZ6dv4OsWnNOhGltOzP0KbBmZp0aXRdo1gUagfp03VCmXWCUHZGeKEB0OgOWEhF2BJHuCKISZNrJCofyEKZdIIGmXTSpsYFGWrRmnVWzMDDabpkyPKrzEOlPqqTEfvfdd9ixYwdkWUbbtm0B+LqVC4KA/Px8vPLKK7j77ruxadMmTR8jkRJ38ePGjcOVV16JAQMGGNZv374dXq/XsD4/Px/NmjXD5s2bTcfav38/CgsLDcfk5uaiR48e2jGbN29GrVq1NLMOAAYMGACe57F161bTcd1uN4qLiw0LUT3IydgbchsTJXhqA1JtgNXygMvygM90Q8jwQMxww57hgWCXtK6rgK4LqxqJ5zff1PRXFsIcC5n+6hEhl9ug+JtdqGZdYCfYWPBF6Alo+upiNH2V0kKIClq2bIldu3Zh165ddBOVIpDOVF8aPLcCgPHhiPqd78guhyv3NDJzS5CZexrZOafhdHrgcHjA8wp4Xgkyw/QmmxnhouL0x4Qz62JBDFFPT206MbZwdEzjEumJGmFHOpNakNZUT2767daQ2wRRgdMhIdMpI8umIIsDnOA0480GTousMzPV4hHBZtWsE2KM6FPNPTLr0hsJCrxRLJI/pKZ79+5J7UiuMnDgQAwYMACHDx/G9u3bsX37dhw6dAiXXHIJhg0bht9//x19+vTB5MmTLY+ZdMNuxYoV2LFjB2bOnBm0rbCwEHa7Pagde15eHgoLC03HU9cH1rjTH1NYWIgGDRoYtouiiDp16oQcd+bMmcjNzdWWpk2bWro+IvXxNaLgIddSgFpurf4cL8rgHV6IDq+u82pFuixTu7sqvBYpp40Z2FzCxKyT1Sg6Qwosr5l1+ug6JUKKbTiav/56zMcSBFF1kM5Ub+o9vcp0PSfKcGSXw5FVBldmGTKyS2GzeX1GnSBDFBUtsk2SBF99OZkPMNsEyJIQ0sADgk28UGadnlCpteE6xJqZdqMPjyGzjiCqCaQ11ZdQpp0oyMhweZDp8sBhU5ApMjj9vpg+Wk5v2gUabJUx7WJJgw2MrhMZF7QEQmYdEYpt27alxMOhZ555BtOnTzdE++Xm5mLatGmYPXs2MjIy8Nhjj2H79u2Wx0xqSuzBgwcxceJErF+/Xqstl6o8+OCDhpzk4uJiErhqRE7GXhR5WoXcLrs88NS1w3WSQRi2FeotjStgvyMTboYCNSU1ODUWqOgMG2Ta6cy6zPs+DTmXIxNuBlARyaePtLNSw04PmXU1A/VplFWUFGk6QRghnUkfFJnXHvQAPtPOVasEAOApt+PC/zwW9vilzRYGpcdqRR20VFlfLbvAJhOAr27d4D3hf6cXNfRFXatjqOZdOLNOm4IuIpC6wtYMfE0nrOuMFwqlxKYopDXVm3D17DJcHkiyLyX2EU94c2sYvzxkTbto0mLDmWjD+OURjw93HpH55rJYGWZpLgQRiY0bN+KZZ57B9u3bceTIEaxZswbXXnuttt1K89JwFBUV4dixY0HprsePH9eimWvVqgWPx2N5zkk17LZv345jx46hS5cu2jpZlrFx40a8/PLLWLduHTweD06ePGmIsjt69CgaNmxoOqa6/ujRo2jUqJHhmM6dO2v7HDt2zHCcJEn466+/Qo7rcDjgcDhiuUwiRci1/xLWtMus8yMQof5jo5eWRjyPe15/7bVZl9hwZp3Vc/z2t78BgNHM09WzI6OOiEQ0TScSLW6ED9KZ6k+9p1fhj/uHGMw6wP+wxf/Ape2KlyOOc/OB0KlPKmu7PGNq1smSgEG7J0Q8PlJE3OLGb0Qcg8w6IhzRNp0grakaSGuqP+FMO1FQcE9J5Ei0t5UREfe5lVsRcpsVE62y57B6HiJ9kMHARRGxqUZ3Wm06UVJSgk6dOmHMmDG47rrrgrZbaV4ajoEDB2LMmDF47rnn0L17dwC+6L977rlH07NvvvkGbdqYN8Y0I6mG3cUXX4wffvjBsG706NHIz8/H/fffj6ZNm8Jms2HDhg0YPHgwAGDPnj04cOAAevbsaTpmy5Yt0bBhQ2zYsEEz6IqLi7F161bceeedAICePXvi5MmT2L59O7p27QoA+Oyzz6AoCnr06JGgqyVSgVz7Lwk/h2PcZwk/BxlyRFWSaHFLREclgkgWoVJjASArjue5bMe9cRwtGDLjiKomkVpDOkOkG+Fq2sWLhSy4oVJ1PAeR/lhtOnH55Zfj8ssvN91mpXlpJF599VVMnjwZQ4cOhST5ciNEUcSoUaMwZ84cAL6eDK9HcS+fVMMuOzsbHTp0MKzLzMxE3bp1tfVjx47FlClTUKdOHeTk5GDChAno2bOnoUNsfn4+Zs6ciUGDBoHjOEyaNAkzZszAWWedpQl648aNNVezXbt2uOyyy3DrrbdiwYIF8Hq9GD9+PIYOHUodYgmCqHZ4OQU8Zz1VSeaiS4lNtLgloqMSQRAEET9kToE3Cp2RYkiJTaTWkM4QBEGkL4HNc2KJJI7UvNTKPU1WVhYWLlyIOXPm4JdffIFCrVq1QlZWxSNbNajMKklvOhGJOXPm4KqrrsLgwYPRp08fNGzYEKtXrzbss2fPHhQVFWnv77vvPkyYMAG33XYbunfvjtOnT2Pt2rWGp2/Lly9Hfn4+Lr74YlxxxRXo3bs3XnvttSq7LoIgiGTSvHlzbNmyBVu2bMHNN9+M4uJiuN3uqMex0pk7EonoqEQQBEEkF57n46IzQOW1hnSGIAgi9fH6Hw5FswBA06ZNDc10zBqaRsJK89JILFu2DKWlpcjKykLHjh3RsWNHg1kXC0mNsDPjiy++MLx3Op2YN29e2Ba9jBnznDmOwxNPPIEnnngi5DF16tTBW2+9Vam5EgRBpAJm3b7CoYBh7969yM3NNayfOnUqpk2bFtW54yFuzzzzDNavX2/aUenSSy/FxIkT8dhjj+HSSy+Nam4EQRBEfFAQXSdIGQyKosRFZ4DKaw3pDEEQRPpy8OBBw/d7sup0Tp48GXfccQeuueYa3HTTTSgoKIAgBNe0j4aUM+wIgiCIxNOmTRt88803hnXJErdEdFQiCIIgkgvP8zhx4oRhHekMQRAEEW9ycnIs1bALh5XmpZE4cuQI1q5di7fffhtDhgxBRkYGbrjhBowYMQK9evWKaV4pnxJLEARBxB+e5zVxU5dYbqT04qYnXDfvQNSOSmvWrMGhQ4dw6NAhrFmzBmPHjo25oxJBEASRfOKhM0DltYZ0hiAIIvXxQIl6AXxdYtu3bx82KzMS+ualKmrz0lANTwMRRRFXXXUVli9fjmPHjmHOnDn49ddfcdFFF6F169YxzYsi7AiCIKo5XijgEEXTCUTXdCIcVjpzRyIRHZUIgiCI+CGBaTdGVvD6U2LjoTNA5bWGdIYgCCJ9sdol9vTp09i3b5/2fv/+/fj+++9Rp04dNGvWLGLz0mjIyMhAQUEBTpw4gd9++w27d++OegyADDuCIIgaScuWLfH+++9b2jfR4paIjkoEQRBEcuF5Hrt27bK8fyK1hnSGIAgi9VGgQI7i4ZCii7ATBCHiw6Fvv/0WF110kfZ+ypQpAIBRo0ZhyZIluO+++1BSUoLbbrsNJ0+eRO/evYOal0aitLQUa9aswfLly7FhwwY0bdoUw4YNw7vvvmt5DD1k2BEEQVRzfE0nohG36CLsEi1uy5Ytw3XXXad1VCIIgiBSCxbDTVS0EXaJ1BrSGYIgiPTFaoRdv379ghqW6rHSvDQcQ4cOxT//+U9kZGRgyJAhePTRRy2n04acEws3YyIkxcXFyM3NRVFRUaULHBIEUfOIx3eILMsQRRGdhKdh53IjH+DnqPI5zr263HKEXaKpX78+ysrK4tpRKR0gnSEIorLE43vksssuw5719VCfv9DyMW72B3Zx0+D1emM6Z7whnQkNaQ1BEJUhHt8h6hhdhDkQOJfl42RWhh3y5JT5/hoxYgRGjBhhqjH//e9/0aFDh6jHpKYTBEEQRFI5cuQIVqxYAY7jMGTIEDRq1Ajjxo3D119/neypEQRBEGkA6QxBEETq4+GUqBcgPk0n4sHy5ctxxRVXaGbdqVOn8Nprr+G8885Dp06dYhqTDDuCIIhqjjdKYZOgaCmxqSBuieioRBAEQcQPiWNR30SpKbGkMwRBEEQi2bZtG3bt2lWp5kbxZOPGjRg1ahQaNWqEZ599Fv3798eWLVtiGotq2BEEQdRAomk6UZXEq6MSQRAEkVyibTpRVZDOEARBEPGmsLAQS5YswaJFi1BcXIwhQ4bA7Xbj/fff1+q5xgJF2BEEQRBJp7S0VAsjP+OMMzB37lwMGjQI//vf/5I9NYIgCCINIJ0hCIJIbWR/g6NoFiD5KbFXX3012rZti//7v//D3LlzcfjwYbz00ktxGZsi7AiCIKo5XsgAZMv7R9slNtEkoqMSQRAEET9kKH6tsYYEOeousYmEdIYgCCJ9sdolNlF8+umnuOuuu3DnnXfirLPOiuvYZNgRBEHUQFIpJVYQBKxatSquHZUIgiCI5JJKKbGkMwRBEESi2LRpExYtWoSuXbuiXbt2uPnmmzF06NC4jE0psQRBENUcGb5GElYXWRdhlwrFwBPRUYkgCIKIHzJY1DqTSk0nSGcIgiBSHw8UeCBHsaRGSuz555+PhQsX4siRI7j99tuxYsUKNG7cGIqiYP369Th16lTMY1OEHUEQRA0klSLsVDZu3IhFixbhvffeQ+PGjXHdddcl/SaPIAiCiI1UirBTIZ0hCIJIP5KdEquSmZmJMWPGYMyYMdizZw8WLVqEWbNm4YEHHsAll1yCDz/8MOoxKcKOIAiCSBqFhYWYNWsWzjrrLNxwww3IycnROirNmjUL3bt3T/YUCYIgiGoM6QxBEET1wMsp8ESxeLnUiLAzo23btpg9ezYOHTqEt99+O+ZxyLAjCIKo5kQjbB5OgcwpKZESm8iOSgRBEET8kMGi1ppUSIklnSEIgkh/tm3bhl27diW1uVEoBEHAtddeG1N0HUApsQRBEDWSVEiJTWRHJYIgCCK5pEJKLOkMQRAEUZ2hCDuCIAgiKWzatAmnTp1C165d0aNHD7z88sv4448/kj0tgiAIIk0gnSEIgqg+eKFEvQCpmRIbL8iwIwiCqOZIYFEJW6p0iU1kRyWCIAgifshR6oyE1EiJJZ0hCIJIf1I5JbaykGFHEARRA2nZsiV27dqVEuKmdlTatGkTfvjhB9x9992YNWsWGjRogGuuuSapcyMIgiBiQ02JJZ0hCIIgiNggw44gCKKa4+YklEexeFOk6YQZ8eqoRBAEQcQPLydHpTNuTk6JCDszSGcIgiBSk2h0Rl0ASoklCIIg0oxYIuzmzZuHFi1awOl0okePHvjmm28SNr/KdlQiCIIgkkssEXakMwRBEES0RJMSW5U6Ew/IsCMIgiAisnLlSkyZMgVTp07Fjh070KlTJxQUFODYsWPJnhpBEASRBpDOEARBEImkOuoMGXYEQRDVHA+nRLXE0nTi+eefx6233orRo0ejffv2WLBgATIyMvDGG29UwRUSBEEQyUQCi0pnvDE0nSCdIQiCqNl4o7yn8XLRdYmtjjojJnsC1RXGGACguLg4yTMhCKI6on53qN8llUFRDoNxRdb3ZyfQvHlzLFu2zDAfh8MBh8MRtL/H48H27dvx4IMPaut4nseAAQOwefPmyk2eCAnpDEEQlSVeWsPYCcjKQcv7K6wYPM9jy5YthrmQzqQepDUEQVSGeN7TMLiBKIZhcAMANmzYgJycHG0+ZlpTXXWGDLsYUdvAN23aNMkzIQiiOnPq1Cnk5ubGdCzHcbjwwgvx00/roj62tPTsoPNOnToV06ZNC9r3jz/+gCzLyMvLM6zPy8vDjz/+GPW5CWuQzhAEES8qozU9e/bEzp0LAET3fe9yNSWdqQaQ1hAEEQ8qozN2ux0NGzZEYeGsqI/NysoK+v4y05rqqjNk2MVI48aNcfDgQWRnZ4PjOEvHFBcXo2nTpjh48KDmAKcLdG3Vj3S9LqB6XBtjDKdOnULjxo1jHoPneWzcuDGmY91uN9xut2GdWdQDkTxIZ4zQtVVP6NqSSzy0ZurUqZg6dWrUx5HOVA+i1Zrq8LmPFbq26gldW3KJh844nU7s378fHo8npvMHfnelk9aQYRcjPM+jSZMmMR2bk5OTsr9wlYWurfqRrtcFpP61xfoUKh6ESksyo169ehAEAUePHjWsP3r0KBo2bJiI6REgnQkFXVv1hK4teSRLa0hnqgexak2qf+4rA11b9YSuLXnEQ2ecTiecTmccZmNOddUZajpBEARBhMVut6Nr167YsGGDtk5RFGzYsAE9e/ZM4swIgiCIdIB0hiAIgkgk1VVnKMKOIAiCiMiUKVMwatQodOvWDeeddx7mzp2LkpISjB49OtlTIwiCINIA0hmCIAgikVRHnSHDrgpxOByYOnVqWuVUq9C1VT/S9bqA9L62ZHHjjTfi+PHjeOyxx1BYWIjOnTtj7dq1QYVbieSSzp99urbqCV0bYRXSmepBOn/u6dqqJ3RthFWqo85wLB79dwmCIAiCIAiCIAiCIAiCiAtUw44gCIIgCIIgCIIgCIIgUggy7AiCIAiCIAiCIAiCIAgihSDDjiAIgiAIgiAIgiAIgiBSCDLsCIIgCIIgCIIgCIIgCCKFIMOOCKJfv36YNGlSWp33lltuwbXXXlupMVq0aAGO48BxHE6ePBlyvyVLlqBWrVqVOldNp1+/ftrP+vvvv0/2dAiCSACkNeaQ1lQdpDUEkd6QzphDOlN1kM4QlYUMOyJlWL16NaZPn669b9GiBebOnZu8CZnwxBNP4MiRI8jNzU32VNKCUH8IrF69Gt98803VT4ggiLSHtKbmQVpDEERVQjpT8yCdIRKFmOwJEIRKnTp1kj2FiGRnZ6Nhw4bJngYAwOv1wmazJXsaCaFOnTooLi5O9jQIgkhDSGuig7SGIAgiOkhnooN0hiBCQxF2REROnDiBkSNHonbt2sjIyMDll1+On376SduuPlFYt24d2rVrh6ysLFx22WU4cuSIto8kSbjrrrtQq1Yt1K1bF/fffz9GjRplCOnWh4/369cPv/32GyZPnqyFEQPAtGnT0LlzZ8P85s6dixYtWmjvZVnGlClTtHPdd999YIwZjlEUBTNnzkTLli3hcrnQqVMnvPvuuzH9fJYsWYJmzZohIyMDgwYNwp9//hm0zwcffIAuXbrA6XSiVatWePzxxyFJkrb9xx9/RO/eveF0OtG+fXv8+9//BsdxeP/99wEAv/76KziOw8qVK9G3b184nU4sX74cAPD666+jXbt2cDqdyM/PxyuvvGI498GDBzFkyBDUqlULderUwcCBA/Hrr79avr5I499///1o06YNMjIy0KpVKzz66KPwer3a9p07d+Kiiy5CdnY2cnJy0LVrV3z77bf44osvMHr0aBQVFWn/x9OmTbM8L4Ig0gvSmvCQ1pDWEARROUhnwkM6QzpDpCCMIALo27cvmzhxovb+mmuuYe3atWMbN25k33//PSsoKGBnnnkm83g8jDHGFi9ezGw2GxswYADbtm0b2759O2vXrh0bPny4NsaMGTNYnTp12OrVq9nu3bvZHXfcwXJyctjAgQNNz/vnn3+yJk2asCeeeIIdOXKEHTlyhDHG2NSpU1mnTp0M850zZw5r3ry59v7pp59mtWvXZu+99x7btWsXGzt2LMvOzjaca8aMGSw/P5+tXbuW/fzzz2zx4sXM4XCwL774IuTPpXnz5mzOnDmGdVu2bGE8z7Onn36a7dmzh73wwgusVq1aLDc3V9tn48aNLCcnhy1ZsoT9/PPP7F//+hdr0aIFmzZtGmOMMUmSWNu2bdkll1zCvv/+e/af//yHnXfeeQwAW7NmDWOMsf379zMArEWLFuy9995jv/zyCzt8+DBbtmwZa9SokbbuvffeY3Xq1GFLlixhjDHm8XhYu3bt2JgxY9j//d//sV27drHhw4eztm3bMrfbHfJaVSKNzxhj06dPZ1999RXbv38/+/DDD1leXh57+umnte1nn302u+mmm9ju3bvZ3r172apVq9j333/P3G43mzt3LsvJydH+j0+dOqUdp17zd999F3GeBEFUP0hrzCGtIa0hCCI+kM6YQzpDOkNUH8iwI4LQi8zevXsZAPbVV19p2//44w/mcrnYqlWrGGM+cQPA9u3bp+0zb948lpeXp73Py8tjzzzzjPZekiTWrFmzkOLGmLmYWBG3Ro0asdmzZ2vvvV4va9KkiXau8vJylpGRwb7++mvDOGPHjmXDhg0L+XMxm8+wYcPYFVdcYVh34403GsTt4osvZk899ZRhn6VLl7JGjRoxxhj79NNPmSiKmoAzxtj69etNxW3u3LmGcVq3bs3eeustw7rp06eznj17audp27YtUxRF2+52u5nL5WLr1q0Lea1WxzfjmWeeYV27dtXeZ2dnG8RQz+LFiw0/Kz0kbgSR3pDWmENaEzy+GaQ1BEFEgnTGHNKZ4PHNIJ0hUgGqYUeEZffu3RBFET169NDW1a1bF23btsXu3bu1dRkZGWjdurX2vlGjRjh27BgAoKioCEePHsV5552nbRcEAV27doWiKHGdb1FREY4cOWKYryiK6NatmxZCvm/fPpSWluKSSy4xHOvxeHDuuedGdb7du3dj0KBBhnU9e/bE2rVrtfc7d+7EV199hSeffFJbJ8syysvLUVpaij179qBp06aGOhL6n5Webt26aa9LSkrw888/Y+zYsbj11lu19ZIkaQVkd+7ciX379iE7O9swTnl5OX7++eew12ZlfABYuXIlXnzxRfz88884ffo0JElCTk6Otn3KlCn429/+hqVLl2LAgAG44YYbDJ8VgiAI0prwkNaQ1hAEUTlIZ8JDOkM6Q6QmZNgRcSGwUCjHcUE1FuIBz/NB4+prC1jh9OnTAICPP/4YZ5xxhmGbw+Go3ARDnO/xxx/HddddF7TN6XRGNVZmZqZhXABYuHChQcwB3x8P6j5du3bVakPoqV+/fsR5Rxp/8+bNGDFiBB5//HEUFBQgNzcXK1aswHPPPaftO23aNAwfPhwff/wxPv30U0ydOhUrVqwI+qOAIAgiEqQ14c9HWkNaQxBE5SCdCX8+0hnSGaJqIcOOCEu7du0gSRK2bt2KXr16AQD+/PNP7NmzB+3bt7c0Rm5uLvLy8rBt2zb06dMHgO9pzI4dO4KKreqx2+2QZdmwrn79+igsLARjTCva+v333xvO1ahRI2zdulU7lyRJ2L59O7p06QIAaN++PRwOBw4cOIC+fftauoZQtGvXDlu3bjWs27Jli+F9ly5dsGfPHpx55pmmY7Rt2xYHDx7E0aNHkZeXBwDYtm1bxHPn5eWhcePG+OWXXzBixAjTfbp06YKVK1eiQYMGhidEVrAy/tdff43mzZvj4Ycf1tb99ttvQfu1adMGbdq0weTJkzFs2DAsXrwYgwYNMv0/Jgii5kFaEx7SGtIagiAqB+lMeEhnSGeI1IQMOyIsZ511FgYOHIhbb70Vr776KrKzs/HAAw/gjDPOwMCBAy2PM2HCBMycORNnnnkm8vPz8dJLL+HEiROaQJnRokULbNy4EUOHDoXD4UC9evXQr18/HD9+HLNnz8b111+PtWvX4tNPPzV8cU+cOBGzZs3CWWedhfz8fDz//PM4efKktj07Oxv33HMPJk+eDEVR0Lt3bxQVFeGrr75CTk4ORo0aZfm67rrrLlxwwQV49tlnMXDgQKxbt84QOg4Ajz32GK666io0a9YM119/PXiex86dO/Hf//4XM2bMwCWXXILWrVtj1KhRmD17Nk6dOoVHHnkEAML+fADg8ccfx1133YXc3FxcdtllcLvd+Pbbb3HixAlMmTIFI0aMwDPPPIOBAwfiiSeeQJMmTfDbb79h9erVuO+++9CkSZNKjX/WWWfhwIEDWLFiBbp3746PP/4Ya9as0Y4vKyvDvffei+uvvx4tW7bEoUOHsG3bNgwePBiA7//49OnT2LBhAzp16oSMjAxkZGRY/vkTBJEekNaEh7SGtIYgiMpBOhMe0hnSGSJFSU7pPCKVCSyU+tdff7Gbb76Z5ebmMpfLxQoKCtjevXu17WZFNtesWcP0Hy+v18vGjx/PcnJyWO3atdn999/PbrjhBjZ06NCQ5928eTPr2LEjczgchrHmz5/PmjZtyjIzM9nIkSPZk08+aSjQ6vV62cSJE1lOTg6rVasWmzJlChs5cqShGKyiKGzu3Lmsbdu2zGazsfr167OCggL25Zdfhvy5mBVoZYyxRYsWsSZNmjCXy8Wuvvpq9uyzzwb9PNauXct69erFXC4Xy8nJYeeddx577bXXtO27d+9mF1xwAbPb7Sw/P5999NFHDABbu3YtYyx8sdLly5ezzp07M7vdzmrXrs369OnDVq9erW0/cuQIGzlyJKtXrx5zOBysVatW7NZbb2VFRUUhrzWa8e+9915Wt25dlpWVxW688UY2Z84c7frdbjcbOnQoa9q0KbPb7axx48Zs/PjxrKysTDv+jjvuYHXr1mUA2NSpU7X1VKCVINIb0hpzSGtIawiCiA+kM+aQzpDOENUHjrEEJOUTRAQURUG7du0wZMgQTJ8+PdnTsUSLFi0wadIkTJo0KeHn+uqrr9C7d2/s27evxhYz/fXXX9GyZUt89913YdMMCIIgQkFaEx7SGtIagiAqB+lMeEhnSGeIysEnewJEzeC3337DwoULsXfvXvzwww+48847sX//fgwfPjzZU4uK+++/H1lZWSgqKorruGvWrMH69evx66+/4t///jduu+02XHDBBTVW2C6//HKcffbZyZ4GQRDVDNKa8JDWGCGtIQgiWkhnwkM6Y4R0hqgsVMOOqBJ4nseSJUtwzz33gDGGDh064N///jfatWuX7KlZ5ssvv9S6NwW2FK8sp06dwv33348DBw6gXr16GDBggKErUaLIysoKue3TTz/FhRdemPA5mPH666+jrKwMANCsWbOkzIEgiOoHaU14SGuMkNYQBBEtpDPhIZ0xQjpDVBZKiSWIGsy+fftCbjvjjDPgcrmqcDYEQRBEOkJaQxAEQSQS0hkiXSHDjiAIgiAIgiAIgiAIgiBSCKphRxAEQRAEQRAEQRAEQRApBBl2BEEQBEEQBEEQBEEQBJFCkGFHEARBEARBEARBEARBECkEGXYEQRAEQRAEQRAEQRAEkUKQYUcQBEEQBEEQBEEQBEEQKQQZdgRBEARBEARBEARBEASRQpBhRxBx5JZbbkFWVlaypxGRW265BS1atEj2NNIOxhg8Hk+yp0EQRJpDWlOzIa0hCCLRkM7UbEhnUoeUMexeeeUVcByHHj16JG0OS5YsAcdx2uJ0OtG4cWMUFBTgxRdfxKlTp5I2N6ucOHECoihi1apVQdtOnjyJRo0a4YILLgBjLGj7li1bwPM87r333qqYapVx+vRpTJo0CU2aNIHD4UC7du0wf/58031PnjyJ2267DfXr10dmZiYuuugi7Nixw7BPaWkppk2bhi+++KIKZh87hw8fxrRp0/D9999HfWzg70KoRRXIDRs2YMyYMWjTpg0yMjLQqlUr/O1vf8ORI0fiek2ffPIJpk2bZmlfRVGwZMkSXHPNNWjatCkyMzPRoUMHzJgxA+Xl5ZbP+fXXX6N3797IyMhAw4YNcdddd+H06dNB+y1btgz16tVDdnY2Ro8ebSpy5eXlmDNnDnr06IHc3Fw4nU60adMG48ePx969ey3N59ixY3jggQdwzjnnICsrC06nE2eeeSZGjx6NTZs2afutWrUKHMdhzZo1QWN06tQJHMfh888/D9rWrFkz9OrVS3vfokULXHXVVZbmVh0grYkPpDXBWNWacN+vhYWF2n6kNaQ1pDXVE9KZ+EA6E0w09zTbt2/HVVddhYYNGyIrKwsdO3bEiy++CFmWtX1IZ0hnSGeqASxF6NWrF2vRogUDwH766aekzGHx4sUMAHviiSfY0qVL2RtvvMGeeuopdumllzKO41jz5s3Zzp07kzI3q7z99ttMFEV24sQJ0+0rVqxgANirr75qWO/1elmnTp1YixYtWElJSRXMtGqQJIn16tWL2e12NnnyZPbKK6+wgQMHMgDsySefNOwryzLr1asXy8zMZNOmTWMvv/wya9++PcvOzmZ79+7V9jt+/DgDwKZOnRp0vlGjRrHMzMxEX5Yltm3bxgCwxYsXB20bNWoUa968echjf/75Z7Z06VLD4nA42IUXXmhYt2bNGsYYY127dmUtW7Zk9913H1u4cCF78MEHWXZ2NsvLy2NHjhyJ2zWNGzeOWf3aOnXqFAPAzj//fDZjxgz22muvsdGjRzOe51m/fv2YoigRx/juu++Y0+lk5557Lps/fz57+OGHmcPhYJdddplhv/3797OsrCz27LPPsnfeeYd16NCBPf3004Z9jh8/zrp27coAsKuuuorNnTuXvf766+zee+9lTZs2ZTabLeJ8tm7dyurVq8ccDgcbNWoUe/nll9nChQvZQw89xNq3b88AsC+//JIxxtjvv//OALApU6YYxigqKmI8zzNRFNn06dMN2w4cOMAAsHvvvVdb17x5c3bllVdGnFt1gbQmPpDWGIlGawL///VLWVmZth9pDWkNaU31hHQmPpDOGIlGZ7799ltmt9vZ2WefzZ5//nm2YMECbd+77rpL2490hnSGdCb1SQnD7pdffmEA2OrVq1n9+vXZtGnTkjIPVdy2bdsWtG3Dhg3M5XKx5s2bs9LS0iTMzho333wz69u3b9h9Lr/8cla7dm1WWFiorXv22WcZAPbJJ58keIY+Tp8+HXbbzz//HJfzrFq1igFgixYtMqwfPHgwczqd7OjRo9q6lStXMgDsnXfe0dYdO3aM1apViw0bNkxbVxPEzYzMzEw2atQo021ffvklk2U5aB0A9vDDD0d1nnBEI25ut5t99dVXQesff/xxBoCtX78+4hiXX345a9SoESsqKtLWLVy4kAFg69at09a988477Nprr9Xev//+++yqq64yjHXllVcynufZu+++G3Se8vJydvfdd4edy19//cUaNWrEGjZsyHbv3h20XVEU9tZbb7FvvvlGW9eyZUt23nnnGfZbu3Yt4ziODRs2jBUUFBi2vfXWWwwA++CDD7R11VXczCCtiR+kNUai0Zpw//96SGuCIa0hrUl1SGfiB+mMkWh05tZbb2V2u539+eefhn379OnDcnJytPekM8GQzpDOpBopYdhNnz6d1a5dm7ndbnbnnXeys846S9vm8XhY7dq12S233BJ0XFFREXM4HIYPxa+//squvvpqlpGRwerXr88mTZrE1q5dywCwzz//POw8Iv0R/dRTTzEA7LXXXtPW7dy5k40aNYq1bNmSORwOlpeXx0aPHs3++OMPbZ/PPvtME+9Ali9fzgCwr7/+mjHG2JEjR9gtt9zCzjjjDGa321nDhg3ZNddcw/bv3x927oz5IsTq16/PZs+eHXa//fv3s4yMDDZ8+HDGmM+BzsrKYjfeeKO2zyeffMJ69+7NMjIyWFZWFrviiivYf//7X8M4Vq6dMcamTp3KALD//e9/bNiwYaxWrVqsc+fOYefHcRy76KKL2PLlyw0RB9EyYcIEBiDoCds777wT9H95ww03sLy8vKAv6dtuu41lZGSw8vJytn//fgYgaFGFThW3Q4cOsYEDB7LMzExWr149dvfddzNJkiLOV/0iWbduHevUqRNzOBysXbt27L333gva98SJE2zSpEmsefPmzG63szPOOIPdfPPN7Pjx4+zzzz83nacqdPEWt1DUqVOHXXfddRH327hxI7v++utZ06ZNmd1uZ02aNGGTJk0y/CE5atQo02uKlv/7v/9jANiLL74Ydr+ioiImiqLhyQxjPtHMyspiY8eO1dZt376d1alTh/3rX/9iP/74I7viiivY5MmTte1btmxhANitt94a9XxV1O+fFStWWD7m5ptvZjabzfBzfPTRR1mHDh3YP/7xD5abm2v4vI8bN45xHGf4Ha6u4mYGaQ1pTeD8kqE1+v//4uJiU20grRkV1TGkNT5Ia5IP6QzpTOD8kqEzN954I8vJyQm6p7nxxhtZXl6eNjfSGeuQzvggnal6UqKG3fLly3HdddfBbrdj2LBh+Omnn7Bt2zYAgM1mw6BBg/D+++8H5U+///77cLvdGDp0KACgpKQE/fv3x7///W/cddddePjhh/H111/j/vvvj8s8b775ZgDAv/71L23d+vXr8csvv2D06NF46aWXMHToUKxYsQJXXHGFVlOhX79+aNq0KZYvX2567a1bt0bPnj0BAIMHD8aaNWswevRovPLKK7jrrrtw6tQpHDhwIOL8tm3bhuPHj+OKK64Iu1+LFi3w+OOP46233sL69etx1113QRRFzJ07FwCwdOlSXHnllcjKysLTTz+NRx99FLt27ULv3r3x66+/RnXtem644QaUlpbiqaeewq233hpyfo0aNcKzzz6L48ePY8SIEWjUqBHGjx+P7777LuLPIBC32w1BEGC32w3rMzIyAPjqO6h899136NKlC3je+Gtx3nnnobS0FHv37kX9+vW1WhGDBg3C0qVLsXTpUlx33XXa/rIso6CgAHXr1sWzzz6Lvn374rnnnsNrr71mac4//fQTbrzxRlx++eWYOXMmRFHEDTfcgPXr12v7nD59GhdeeCFeeuklXHrppXjhhRdwxx134Mcff8ShQ4fQrl07PPHEEwCA2267TZtnnz59ovjpVY7Tp0/j9OnTqFevXsR933nnHZSWluLOO+/ESy+9hIKCArz00ksYOXKkts/tt9+OSy65BAC061m6dGnU81JrREWa1w8//ABJktCtWzfDervdjs6dOxs+j126dMGIESNw6aWXIj8/H4cOHcKDDz6obf/www8BVHyHxMJHH30El8tl+KxFonfv3vB6vdi6dau27quvvkKvXr3Qq1cvFBUV4b///a9hW35+PurWrRvzPFMZ0hrSGj3J0hqViy66CDk5OcjIyMA111yDn376SdtGWmMd0hrSmlSCdIZ0Rk+ydKZfv34oLi7G7bffjt27d+O3337DggULsHr1au07g3TGOqQzpDNJJZluIWO+HHvoQjkVRWFNmjRhEydO1PZZt24dA8A++ugjw7FXXHEFa9Wqlfb+ueeeYwDY+++/r60rKytj+fn5cXkaxRhjubm57Nxzz9Xem4WSv/322wwA27hxo7buwQcfZA6Hg508eVJbd+zYMSaKovYk48SJEwwAe+aZZ8LOMxSPPvqo5ScMXq+Xde7cmdWpU4cBFfUfTp06xWrVqhXkmhcWFrLc3FzDeqvXrj6N0qeVWuWbb75hd9xxB6tVqxYDwM4991w2b968kPUsAlE/E//5z38M6x944AEGwBDim5mZycaMGRM0xscff8wAsLVr1zLGIoePA76aIXrOPfdc1rVr14jzbd68OQNgePpUVFTEGjVqZPjcPfbYYyGfcKp1DKoyfNyM6dOnMwBsw4YNEfc1+yzNnDmTcRzHfvvtN21dNOHjoRgwYADLycmJ+BlSn1jqP8sqN9xwA2vYsGHQ+p9//plt376deb1ew/pBgwYxAJY/t2bUrl3b9ClucXExO378uLboUzP+97//MQBaXQev18syMzPZm2++yRhjLC8vj82bN08bRxCEoN/96vo0KhDSGtKacFSl1qxcuZLdcsst7M0332Rr1qxhjzzyCMvIyGD16tVjBw4c0PYjrbEGaU0FpDXJhXSGdCYcVakzkiSx8ePHM5vNpkVvCYLA5s+fbziWdMYapDMVkM5UPUmPsFu+fDny8vJw0UUXAQA4jsONN96IFStWaF1s+vfvj3r16mHlypXacSdOnMD69etx4403auvWrl2LM844A9dcc422zul0hn3yES1ZWVmGzkoul0t7XV5ejj/++APnn38+ABi6i44cORJutxvvvvuutm7lypWQJAk33XSTNpbdbscXX3yBEydORD23Tz75BFdeeaWlfUVRxGuvvYa//voL559/vvYzWr9+PU6ePIlhw4bhjz/+0BZBENCjRw9DBxar165yxx13RH1N3bt3x/z583HkyBEsX74cderUwfjx49GoUSPcdNNNEZ/SDR8+HLm5uRgzZgzWr1+PX3/9Fa+99hpeeeUVAEBZWZm2b1lZGRwOR9AYTqczaN9IBF7rhRdeiF9++cXSsY0bN8agQYO09zk5ORg5ciS+++477UnKe++9h06dOhn2U+E4zvI8E8XGjRvx+OOPY8iQIejfv3/E/fWfpZKSEvzxxx/o1asXGGMxPYUMxVNPPYV///vfmDVrFmrVqhV2X/X/O9Rnwuzz0KpVK3Tp0gWiKBrWFxcXAwCys7NjnLlvjKysrKD1N998M+rXr68t+qfv7dq1Q926dbVOSzt37kRJSYnWMalXr1746quvAACbN2+GLMvo3bt3zHNMZUhrSGvCUZVaM2TIECxevBgjR47Etddei+nTp2PdunX4888/8eSTT0Y1b9Ia0ho9pDXJhXSGdCYcVakzgiCgdevWKCgowJtvvomVK1fi6quvxoQJE/D+++9HNW/SGdIZPaQzVU9SDTtZlrFixQpcdNFF2L9/P/bt24d9+/ahR48eOHr0KDZs2ADA90U8ePBgfPDBB3C73QCA1atXw+v1GsTtt99+Q+vWrYN+uc8888y4zfn06dOGD+hff/2FiRMnIi8vDy6XC/Xr10fLli0BAEVFRdp++fn56N69uyGEfPny5Tj//PO1+TkcDjz99NP49NNPkZeXhz59+mD27NnaF1o4CgsLsWPHDsviBviEAwC6du2q/czUlJz+/fsbfmHq16+Pf/3rXzh27FjU166ibosFp9OJ4cOHY+3atXjhhRegKAqWL19uKqJ6GjZsiA8//BButxuXXnopWrZsiXvvvRcvvfQSABi+LFwul/b50qO2y9Z/AUeaa/369Q3rateubfkPljPPPDPoM9ymTRsA0ML3f/75Z3To0MHSeFXNjz/+iEGDBqFDhw54/fXXLR1z4MAB3HLLLahTpw6ysrJQv3599O3bF4D5ZykWVq5ciUceeQRjx47FnXfeGXF/9f871GfC6ucB8P2BAsDwh3G0ZGdnm7Zef+KJJ7B+/XpDeoEKx3Ho1asXtmzZAkVR8NVXX6FBgwbad45e3NR/00Xc9JDWkNZYpSq0xozevXujR48e+Pe//x3VXElrSGv0kNYkD9IZ0hmrVIXOzJo1C08//TTefvttjBw5EkOGDMGaNWvQu3dvjBs3DpIkWZ4r6QzpjB7SmapHjLxL4vjss89w5MgRrFixAitWrAjavnz5clx66aUAgKFDh+LVV1/Fp59+imuvvRarVq1Cfn4+OnXqVGXzPXToEIqKigxiOWTIEHz99de499570blzZ2RlZUFRFFx22WVQFMVw/MiRIzFx4kQcOnQIbrcbW7Zswcsvv2zYZ9KkSbj66qvx/vvvY926dXj00Ucxc+ZMfPbZZzj33HNDzu3TTz+F0+nUnurFijrnpUuXomHDhkHb9S57NNcOWDe8zNi9ezcWL16MpUuXorCwEGeffTbGjh1r6Xr79OmDX375BT/88ANKSkrQqVMnHD58GECFaAC+OhNHjhwJOl5d17hxY0tzFQTB0n7pyMGDB3HppZciNzcXn3zyiaWnL7Is45JLLsFff/2F+++/H/n5+cjMzMTvv/+OW265xfSzFC3r16/HyJEjceWVV2LBggWWjmnUqBEAhPxMWP08AL4/bgFfDYkLL7zQ8nGBY+zcuRNerxc2m01b37Fjx7DH9e7dGx999BF++OEHrdaDSq9evXDvvffi999/x6ZNm9C4cWO0atUqpvmlMqQ1pDVWqQqtCUXTpk2xZ88ey3MlrSGtCYS0JnmQzpDOWKUqdOaVV15B//79gx4WXXPNNZgyZQp+/fVXS+Yv6QzpTCCkM1VPUg275cuXo0GDBpg3b17QttWrV2PNmjVYsGABXC4X+vTpg0aNGmHlypXo3bs3PvvsMzz88MOGY5o3b45du3aBMWZw8/ft2xeX+arFIAsKCgD4Qtg3bNiAxx9/HI899pi2n75wtJ6hQ4diypQpePvtt1FWVgabzWZ4mqbSunVr3H333bj77rvx008/oXPnznjuueewbNmykHP7+OOPcdFFF1VKQNRzA0CDBg0wYMCAkPtFe+2xUFRUhJUrV+KNN97A1q1bkZWVhRtvvBF/+9vftDB1qwiCgM6dO2vv1SgG/TV27twZ//nPf6AoiqHxxNatW5GRkaEJYaLDs/ft2xf0Gd67dy8AX3FdwPf/pC+saUZVh5H/+eefuPTSS+F2u7FhwwZNHCLxww8/YO/evXjzzTcNBVlDPV2Jlq1bt2LQoEHo1q0bVq1aFRTaHYoOHTpAFEV8++23GDJkiLbe4/Hg+++/N6yLxNVXX42ZM2di2bJlMYvbVVddhS1btmDNmjVRnVt9urRp0yZ89dVXmDRpkrata9eucDgc+OKLL7B169aIxZ2rK6Q1pDXhqGqtCcUvv/xiiGQgrTGHtCY0pDXJg3SGdCYcVa0zR48e1dKw9Xi9XgDQIuxIZ8whnQkN6UzVk7SU2LKyMqxevRpXXXUVrr/++qBl/PjxOHXqlNaJhOd5XH/99fjoo4+wdOlSSJIUJAwFBQX4/ffftWMAX5jnwoULKz3fzz77DNOnT0fLli0xYsQIABVPHVhA9yC1M1Eg9erVw+WXX45ly5Zh+fLluOyyywxdXUpLS7X0S5XWrVsjOzvbNIRVxev1Yv369VGFjoeioKAAOTk5eOqpp7QvdT3Hjx8HEP21R8OpU6dw0003oVGjRrj99tvBcRxef/11HDlyBK+//nrUwhbI8ePH8fTTT6Njx44Gcbv++utx9OhRrF69Wlv3xx9/4J133sHVV1+t5f2r3ZhOnjxZqXmE4vDhw1izZo32vri4GP/4xz/QuXNn7Qnh4MGDsXPnTsN+Kur/SWZmZkLnqaekpARXXHEFfv/9d3zyySc466yzLB9r9llijOGFF14I2jfaa9q9ezeuvPJKtGjRAv/85z/D/vH3448/GuqH5ObmYsCAAVi2bJkh7Hvp0qU4ffo0brjhBktzAICePXvisssuw+uvv25aO8Tj8eCee+4JO8add96JvLw8TJ48WftjR0/g76JKt27d4HQ6sXz5cvz++++Gp1EOhwNdunTBvHnzUFJSkjah43pIa0hrQpEsrVGvTc8nn3yC7du347LLLtPWkdYEQ1oTHtKa5EA6QzoTimTpTJs2bbB+/Xr8+eef2jpZlrFq1SpkZ2drZibpTDCkM+Ehnal6khZh9+GHH+LUqVOGYqp6zj//fNSvXx/Lly/XROzGG2/ESy+9hKlTp+Kcc85Bu3btDMfcfvvtePnllzFs2DBMnDgRjRo1wvLly7WmAVad7E8//RQ//vgjJEnC0aNH8dlnn2H9+vVo3rw5PvzwQ228nJwcrSaD1+vFGWecgX/961/Yv39/yLFHjhyJ66+/HgAwffp0w7a9e/fi4osvxpAhQ9C+fXuIoog1a9bg6NGjWpt3MzZt2oTi4uK4iFtOTg7mz5+Pm2++GV26dMHQoUNRv359HDhwAB9//DEuuOACvPzyyzFdu1X+/PNPrFu3DnfccQfGjh2Ls88+u1Lj9e3bFz179sSZZ56JwsJCvPbaazh9+jT++c9/GiLprr/+epx//vkYPXo0du3ahXr16uGVV16BLMt4/PHHtf1cLhfat2+PlStXok2bNqhTpw46dOgQt/oLbdq0wdixY7Ft2zbk5eXhjTfewNGjR7F48WJtn3vvvRfvvvsubrjhBowZMwZdu3bFX3/9hQ8//BALFixAp06d0Lp1a9SqVQsLFixAdnY2MjMz0aNHj0rV3QjFiBEj8M0332DMmDHYvXs3du/erW3LysrCtddeG/LY/Px8tG7dGvfccw9+//135OTk4L333jOtj9G1a1cAwF133YWCggIIghDyd+PUqVMoKCjAiRMncO+99+Ljjz82bG/dujV69uypvW/Xrh369u2LL774Qlv35JNPolevXujbty9uu+02HDp0CM899xwuvfRSw421Ff7xj3/g0ksvxXXXXYerr74aF198MTIzM/HTTz9hxYoVOHLkCJ599tmQx9epUwdr1qzB1VdfjU6dOmHo0KHo3r07bDYbDh48iHfeeQcA0KxZM8Nxdrsd3bt3x3/+8x84HA7tZ6jSq1cvPPfccwBC13rYt28fZsyYEbT+3HPPjcv3TiIhrSGtCUWytKZXr14499xz0a1bN+Tm5mLHjh1444030LRpUzz00EPafqQ1wZDWRIa0puohnSGdCUWydOaBBx7ATTfdhB49euC2226Dy+XC22+/je3bt2PGjBlaGiLpTDCkM5EhnaliqqgbbRBXX301czqdrKSkJOQ+t9xyC7PZbOyPP/5gjPlaOzdt2pQBYDNmzDA95pdffmFXXnklc7lcrH79+uzuu+9m7733HgPAtmzZEnZOagt0dbHb7axhw4bskksuYS+88AIrLi4OOubQoUNs0KBBrFatWiw3N5fdcMMN7PDhwyFbZLvdbla7dm2Wm5vLysrKDNv++OMPNm7cOJafn88yMzNZbm4u69GjB1u1alXYed9zzz2sffv2YfcJBQA2bty4oPWff/45KygoYLm5uczpdLLWrVuzW265hX377bdRX7vaAv348eOW5uTxeJjb7Y7pesyYPHkya9WqFXM4HKx+/fps+PDh7Oeffzbd96+//mJjx45ldevWZRkZGaxv375s27ZtQft9/fXXrGvXrsxutxuud9SoUSwzMzNof/VnEAm13fS6detYx44dmcPhYPn5+eydd94J2vfPP/9k48ePZ2eccQaz2+2sSZMmbNSoUdrvC2OMffDBB6x9+/ZMFEVDO/R4t0BXW7ebLVbOs2vXLjZgwACWlZXF6tWrx2699Va2c+fOoBbukiSxCRMmsPr16zOO48L+TPfv3x9yTgCCrgUA69u3b9A4//nPf1ivXr2Y0+lk9evXZ+PGjTP9LrBCaWkpe/bZZ1n37t1ZVlYWs9vt7KyzzmITJkxg+/btszTGkSNH2L333svat2/PXC4XczgcrFWrVmzkyJGm7doZY+zBBx9kAFivXr2Ctq1evZoBYNnZ2UySpKDt4f5vx44dG90PIAmQ1pDWhCJZWvPwww+zzp07s9zcXGaz2VizZs3YnXfeyQoLC4P2Ja0JnjdpTWRIa6oW0hnSmVAk855m7dq1rG/fvqxevXrMbrezc845hy1YsCBoP9KZ4HmTzkSGdKbq4BgLEXOYRsydOxeTJ0/GoUOHcMYZZyR1LpIkoXHjxrj66quxaNGiuIzZvn17XHXVVZg9e3ZcxiOSR4sWLdChQwf885//TPZUCIKIEtIaorpAWkMQ1RPSGaK6QDpDEPEhqU0nEkFZWZkhp7u8vByvvvoqzjrrrKQLGwC8//77OH78uKEQZWXweDy48cYboyrYSBAEQVQO0hqCIAgikZDOEARBEGln2F133XVo1qwZOnfujKKiIixbtgw//vgjli9fntR5bd26Ff/3f/+H6dOn49xzz0Xfvn3jMq7dbsfUqVPjMhZBEARhDdIagiAIIpGQzhAEQRBpZ9gVFBTg9ddfx/LlyyHLMtq3b48VK1aYthqvSubPn49ly5ahc+fOWLJkSVLnQhAEQVQO0hqCIAgikZDOEARBEDWihh1BEARBEARBEARBEARBVBf4yLsQBEEQBEEQBEEQBEEQBFFVkGFHEARBEARBEARBEARBEClE2tWwqyoURcHhw4eRnZ0NjuOSPR2CIKoZjDGcOnUKjRs3Bs9X7tlJeXk5PB5PVMfY7XY4nc5KnZdILKQzBEFUlnhpDelM+kJaQxBEZUimzgDprzVk2MXI4cOH0bRp02RPgyCIas7BgwfRpEmTmI8vLy9Hi5ZZOFooR3VcTk4OGjVqBJ7nMW7cOIwbNy7mORCJgXSGIIh4URmtIZ1Jb0hrCIKIB8nQGSD9tYYMuxjJzs4G4Ptg5uTkJHk2BEFUN4qLi9G0aVPtuyRWPB4PjhbK+N/PTZGdY+2p1qliBWe3PkjfXykO6QxBEJUlHlpDOpPekNYQBFEZkqUzQM3QGjLsYkQNGc/JyUnbDwdBEIknXuknORk25GQI1s4p+Z5ede/eHYIgpOXTqHSAdIYgiHgRD60hnUlPSGsIgogHVa0zQM3QGjLsCIIgaijbtm2jP84JgiCIhEE6QxAEQSSadNYa6hJLEARBEARBEARBEARBECkEGXYEQRBpAO/lo1oAX/h4+/btMW/evCTPniAIgkh1SGcIgiCIRBKtztQEraGUWIIgiBpKOoePEwRBEMmHdIYgCIJINOmsNRRhRxAEQRAEQRAEQRAEQRApBBl2BEEQaQAnR7cA6R0+ThAEQcQX0hmCIAgikUSrMzVBayglliAIooaSzuHjBEEQRPIhnSEIgiASTTprDUXYEQRBEARBEARBEARBEEQKQYYdQRBEGsBLDLzX4iIxAOkdPk4QBEHEF9IZgiAIIpFEpTM1RGsoJZYgCKKGks7h4wRBEETyIZ0hCIIgEk06aw1F2BEEQaQBnIdFtQDp/TSKIAiCiC+kMwRBEEQiiVZnaoLWUIQdQRBEDSWVnkb95z//wauvvoqff/4Z7777Ls444wwsXboULVu2RO/evZM9PYIgCCIGSGcIgiCIRJPOWkMRdgRBEERSee+991BQUACXy4XvvvsObrcbAFBUVISnnnoqybMjCIIgqjukMwRBEESiSYTWkGFHEASRBnBydAuQOuHjM2bMwIIFC7Bw4ULYbDZt/QUXXIAdO3YkcWYEQRCECukMQRAEkUii1ZmaoDWUEksQBFFDSZXw8T179qBPnz5B63Nzc3Hy5MmqnxBBEAQRF0hnCIIgiESTzlpDEXYEQRBEUmnYsCH27dsXtH7Tpk1o1apVEmZEEARBpBOkMwRBEESiSYTWkGFHEASRBvASwHuZtUXyHZMq4eO33norJk6ciK1bt4LjOBw+fBjLly/HPffcgzvvvDOpcyMIgiB8kM4QBEEQiSQqnakhWkOGHUEQRA1l27Zt2LVrF8aNGxdx31OnTmHSpElo3rw5XC4XevXqhW3btmnbGWN47LHH0KhRI7hcLgwYMAA//fSTpXk88MADGD58OC6++GKcPn0affr0wd/+9jfcfvvtmDBhQszXRxAEQSQX0hmCIAgi0VjVmkTqDJAYrSHDjiAIgojI3/72N6xfvx5Lly7FDz/8gEsvvRQDBgzA77//DgCYPXs2XnzxRSxYsABbt25FZmYmCgoKUF5eHnFsjuPw8MMP46+//sJ///tfbNmyBcePH8f06dMTfVkEQRBEikA6QxAEQSSSROoMkBitSRnDbtasWeA4DpMmTdLWvfbaa+jXrx9ycnLAcZylQn3Tpk0Dx3GGJT8/37BPeXk5xo0bh7p16yIrKwuDBw/G0aNH43xFBEEQVUciOyqVlZXhvffew+zZs9GnTx+ceeaZmDZtGs4880zMnz8fjDHMnTsXjzzyCAYOHIiOHTviH//4Bw4fPoz3338/4tyLiorw119/wW63o3379jjvvPOQlZWFv/76C8XFxXH46RAEQRCVhXSGIAiCSCSJ7BKbaJ0BEqM1KWHYbdu2Da+++io6duxoWF9aWorLLrsMDz30UFTjnX322Thy5Ii2bNq0ybB98uTJ+Oijj/DOO+/gyy+/xOHDh3HddddV+joIgiCqExs2bMCWLVtw8803o7i4GG6323Q/SZIgyzKcTqdhvcvlwqZNm7B//34UFhZiwIAB2rbc3Fz06NEDmzdvjjiPoUOHYsWKFUHrV61ahaFDh0Z5VQRBEESqQDpDEARBJBorWpNonQESozVJN+xOnz6NESNGYOHChahdu7Zh26RJk/DAAw/g/PPPj2pMURTRsGFDbalXr562raioCIsWLcLzzz+P/v37o2vXrli8eDG+/vprbNmyJS7XRBAEUdVwXoDzchYX3zFNmzZFbm6utsycOdN07OzsbPTs2RPTp0/H4cOHIcsyli1bhs2bN+PIkSMoLCwEAOTl5RmOy8vL07aFY+vWrbjooouC1vfr1w9bt26N8idBEARBJALSGYIgCCKRRKcz0WlNonUGSIzWiDEdFUfGjRuHK6+8EgMGDMCMGTPiMuZPP/2Exo0bw+l0omfPnpg5cyaaNWsGANi+fTu8Xq/BOc3Pz0ezZs2wefPmkOag2+02OLUUPk8QRHXn4MGDyMnJ0d47HI6Q+y5duhRjxozBGWecAUEQ0KVLFwwbNgzbt2+v9DzcbjckSQpa7/V6UVZWVunxqwukMwRBpBukM6kHaQ1BEOmGVa1JpM4AidGapEbYrVixAjt27Aj5tC0WevTogSVLlmDt2rWYP38+9u/fjwsvvBCnTp0CABQWFsJut6NWrVqG4yI5pzNnzjS4tk2bNo3bnAmCIJJBTk6OYQl3I9W6dWt8+eWXOH36NA4ePIhvvvkGXq8XrVq1QsOGDQEgqBbo0aNHtW3hOO+88/Daa68FrV+wYAG6du0a5VVVX0hnCIJIN0hnUg/SGoIg0g2rWpNInQESozVJi7A7ePAgJk6ciPXr1wflEVeGyy+/XHvdsWNH9OjRA82bN8eqVaswduzYmMd98MEHMWXKFO19cXExCRxBEKmD179Y3Re+Aq2CIGDcuHER26CrZGZmIjMzEydOnMC6deswe/ZstGzZEg0bNsSGDRvQuXNnAL7vyK1bt+LOO++MOOaMGTMwYMAA7Ny5ExdffDEAXy2Kbdu24V//+pfFi6r+kM4QBJHSkM6kBaQ1BEGkLNHojLo/oteaROgMkBitSZpht337dhw7dgxdunTR1smyjI0bN+Lll1+G2+2GIAiVPk+tWrXQpk0b7Nu3DwDQsGFDeDwenDx50hBlF8k5dTgcYZ8KEjWLHVfeD0XyfT67rXsqybMhiNjYtm2bIXw8HOvWrQNjDG3btsW+fftw7733Ij8/H6NHj9Y6fM+YMQNnnXUWWrZsiUcffRSNGzfGtddeG3HsCy64AJs3b8YzzzyDVatWweVyoWPHjli0aBHOOuusSl5l9YF0htDzuG2l9nqq98YkzoQgYod0JvUgrSH09LUtAgB86Y09sIUgko1VrUmkzgCJ0ZqkGXYXX3wxfvjhB8O60aNHIz8/H/fff39czDrA19Ti559/xs033wwA6Nq1K2w2GzZs2IDBgwcDAPbs2YMDBw6gZ8+ecTknkXqUPlegvWZ+o43JPLIe+mfUY+285h4AAnhRhiIJ+LbgISgKB1n2javIPHhBMRzT87MnYp88QaQARUVFePDBB3Ho0CHUqVMHgwcPxpNPPgmbzQYAuO+++1BSUoLbbrsNJ0+eRO/evbF27VrLEdSdO3fG8uXLE3kJBJFQPj33We21LAmaJgzcdVfUY82wr4DAATLjABjNu1CQqUdUd0hnCCI8o/m3De+9YACAZcrwqMdSjTqVAeJi0/0EcIb366Rboj4XQaQKidYZIP5awzHGWNxGqyT9+vVD586dMXfuXAC+enOFhYX49ttvceutt2Ljxo3Izs5Gs2bNUKdOHQA+42/QoEEYP348AOCee+7B1VdfjebNm+Pw4cOYOnUqvv/+e+zatQv169cHANx555345JNPsGTJEuTk5GDChAkAgK+//tryXIuLi5Gbm4uioiLLTw6J5KE37ACjaecttUP22OApdcBTZofksUHyiFD8N1vt350LAPjh2ilQFF/ZR/VGDAAUSYCicEHrQ6HIxtKRF3w5LaZrIqo38foOUcc58Vlr5GRZe9BRfFpG7f4/o02bNlGnKiUKRVGwb98+HDt2DIpiNLz79OmTpFklF9KZ6sW6rrMBoEIn/Drj8drg8Yhwu21wewW4PSLcXh7l3goteMQzVHs9w74iaGzVuIsVMvNqLvH4HiGdSW9Ia6oPgYYdUGHayWDwcgxeMMgcgxcKZBhv8/8tjQYQbNYBgI2Zl7YPNOxCQUZezSVZOgPUDK1JepfYcCxYsACPP/649l69wMWLF+OWW24BAPz888/4448/tH0OHTqEYcOG4c8//0T9+vXRu3dvbNmyRTPrAGDOnDngeR6DBw+G2+1GQUEBXnnllaq5KKLKKX2uALxo/GVRUGHaAYAi8aavAWDX9ZMg+9fxvAJF4SEIsmbO8aIM+E07/XozAs06gkgm0aQqJZItW7Zg+PDh+O233xD4DInjOMiynKSZEUT08Lxfb8QK0w4AZIWDJJlrgJlJp0fgWKVMu8dtK8m0I5IC6QxBxA9RpwMSZ/wcRzLrgIooOpuu76SXU4L20yODWTLtCsQlZNoRSSOdtSalDLsvvvjC8H7atGmYNm1a2GN+/fVXw/sVK8L/0QsATqcT8+bNw7x586KcIVEdCTTrQhFopskhbqzMTDvfehbWtAtl1n3VdxpF2RE1mjvuuAPdunXDxx9/jEaNGoHjKhdNRBCphOA38CSZh6zwcHt5SHJiP+NeBbDpJEfgGGbYVxgi+QiiJkE6Q6QbIuMgcQw2cBVRdiZmXaAhFxhJp773ckrIKDt1vEjG3VXiP/BPaWR0F0IQaUQitCalDDuCqEo4UQaLEPGmpjapaJETEQg07UKZdWqtu839H6M6d0TlkDjAa1EUJN9+sXTvSwQ//fQT3n33XZx55plJmwNBxBueV3xRdjotqIxRZxZl5w0hSYFmHUHEBdIZgqjWhDLkIm1TCRdtp0btkWlHVIpodEbdH+mtNWTYETUSXlSCUl+tEGjgBY3rj7KzNIeAxhRk2hFVTaqEj/fo0QP79u2jGymi2qLWrwuHKCgQBQbZokbEgi1AogLNulnOt/FA+bCEnZ8gAiGdIYiqwcY4lAfIS6R017icF1Tuh0g+6aw1ZNgRBHwGXqCBpiLLAgQhunzzSNF1gedSx//mkodx3vonozoXQQAAZN63WNrXdxOfKk+jJkyYgLvvvhuFhYU455xztE5NKh07dkzSzAii8uj1Q+AVCDyXkJTYSGadyPvek2lHxAzpDEGkFPq02PII5pyVCLp4IDAOA4Wl+EC+uUrOR6QZ0egMUCO0hgw7ouYgKKZfALyoQPYY3yuegHp2OgPOzLzjRRmKZL2jjWFauvF4nlKXiKojVZ5GDR48GAAwZswYbR3HcWCMUTFwotog6h7ESAFa4zPqwn+/q+mu8Uhh1Y8hmpyXTDuiqiCdIYj0ITAdNjC6TtCVbSDTjqhK0llryLAj0h99NFsI0w7wRb0JggKFD45+0xt2pg0l/GadlXRYfXQdmXUEAezfvz/ZUyCISiEGRE2LggJJ5sHzCmRUaIYoKpAVHrLCIIXQi1iMO6pZRxDhIZ0hqju3cpEbKwZiY3zYZhKVIZxZRxA1lURoDRl2RM3DxLTj/CYd57/pstpcIlrIrCMSBeflwFks0qrulyrh482bN0/auQkiUaimXcjtfIVpF9hMQl2nN9/M9gHCm3Vm0XWiQHpDxAbpDEGkHmpabCiqIhWWzDoiXkSjM+r+QHprDVWJJNKG8hcuqdTxvCCDFxTwogxeVCCIwaadIgmG1NfA91Yhs45IBbZt24Zdu3YlVdhUli5digsuuACNGzfGb7/9BgCYO3cuPvjggyTPjCAq2HDerJiPNdOUSMiM0xYzAuvWRYLMOqKqIZ0hiOi4g1uZ7ClERB9dR2YdkQqks9ZQhB2RFpS/cAkgKCh/+WJrBwgKeABMCr6BEgQFEvwGHs+bdoY1M+msdoetDN8WPBS0rtu6pxJ+XoJIJPPnz8djjz2GSZMm4cknn9TqO9SqVQtz587FwIEDkzxDgqgw66Ix7URBgRd+s86boImFOnfAwyC9WXdPyfCwxz6b+ZbhfaT9CSLVIZ0hqgOqWac37WRYe9BiYxy84ODlfLXmrB6XLK4XlhnevyvflKSZEET8SITWkGFHVGs0gy62fg8AfOmwvCiDKTx4WYEiKOB53yKYNKCIRKQOseE6zvJi6G1mZl249Spk6NUQvLxvsbRvanVUeumll7Bw4UJce+21mDWrwgzp1q0b7rnnnqTNiyBUrJh0Zp3GFZmHIMqA1wZRkCEKAtyJmGCcCDTqIq1XkWSOmljUBEhnCCJhhIqsC2z0AIQ38Wzg4UViSvsEEiq6zgYO3jBzDDTrQq0LhEy9GkA0OgPUCK0hw44gTOBFGbzCg1d8UXZmjSYsjSMommlndjMXeE4A2HHl/Yb1XT5+2r8utjl8W/BQUPSfLAsG4/C89U/GNDZRvUmVjkr79+/HueeeG7Te4XCgpKQkCTMiiApiNev0CLq6qL6OsRwkmdPq2AkcC5n2Gg2Bde9CEcmAi4VZzrfDbidDr2ZCOkMQkYk2DVY18aoyis7MOAyHDZwlE84LBpvFsQcKS4PWyRwzGIfUmbZmks5aQ4YdUW2xnP5qEY5X/HXsAroeiQoURY7ZtAsk0CxTUSTBNMIu0MCLBrM0XbPr+OaSh7XXZN4RVU3Lli3x/fffBxVqXbt2Ldq1a5ekWRGENSKZdXoEnhk7xcqJK6UgKZxp04m4niOK+esNPTLviKqGdIZIR8KlvlZFWmygWRYN+gi8cNF40Z5fNfXIuCOSQSK0hgw7ggDAi4qhLp0gKFD8abGKzuDiRTnqJhP6KLugbQm6mQpVTy+c6UjNL6o3TOHBwnSkNO6bWuHjU6ZMwbhx41BeXg7GGL755hu8/fbbmDlzJl5//fWkzYsgwhGNUQf4I+tEBaLCQdJF2VUF8Wo2UVXzJVIT0hmCIADAC8XQeCJ4e/iouVgNOhXZQiQ5UT2JRmd8+6e/1pBhR6Q1asSaFZMtsI5dIIJgLcrObD/VtFNv8NQoO0XhDEZZqCg7qyZhLEadHqp3V7NIlfDxv/3tb3C5XHjkkUdQWlqK4cOHo3HjxnjhhRcwdOjQZE+PqMGYpcNGa9Sp+Mw6GTLPG6PsTL63vf5TRNsFFjBPi5VkztS0E3gG2ULDJDLqiFghnSGIqsesjp2XU2BjMYhKBEJF2YUy7Spj1pFRR4QinbWGDDsiLQk0vcIZd7yoQPY3luB0dYZ4UQY8vl8RnldMu8UaxuGZZpiFMvf0pl0oQpl2kaiMWUfRdWmAxPsWS/umztMoSZLw1ltvoaCgACNGjEBpaSlOnz6NBg0aJGU+BBGKWI26SKh17BKBPi3WzLQjs46ICtIZgkg5BHCQ/CaYDRzAQhtb8TTt9FF20Zp2sUBmXQ0hGp0BaoTWkGFHpBWRjK5Qxp3PqFObQ1TUseNFBfCGP6c+Sk79V1G4iKZdqCg7dX7RmHZWa9URhJ5UeBoliiLuuOMO7N69GwCQkZGBjIyMpM6JIPTEw6gTBBmy/6GPICqAN7Hfz6GaT4SKtAsFmXVEZSGdIYj4YQt4H+EWxVDHzsZ4eLnY9UwGC2o8ESk11rdP5U07MuuISKSz1sQ/LpYg0gTBf5PG+xtEyLJgMPoUhQsZ1aYacGbNJcwwG6eyabAEES9kWcajjz6Kli1bwuVyoXXr1pg+fToYq/gDijGGxx57DI0aNYLL5cKAAQPw008/WRr/vPPOw3fffZeo6RNEzFg16wRBNiyGMQI6xMpRPDn2mpxe4JilTrChSKYJJwqMGk4QppDOEDUVqx1iA826QKx0cbUaXSeA0xY94ZpYhDPV1DTYWNJhyawj4kWidQZIjNZQhB2RNsSSRhpyLEGBOpoiC0HpsIEmmVmUnBX0HWNDRdrFQqzRdd8WPER17KorXsF61I7/kazV8PGnn34a8+fPx5tvvomzzz4b3377LUaPHo3c3FzcddddAIDZs2fjxRdfxJtvvomWLVvi0UcfRUFBAXbt2gWn0xl2On//+99x991349ChQ+jatSsyMzMN2zt27GjtuggiCVh9MANAi7KLy3k5BtlCdz6zbrGRIu0Saeo9m/kW7ikZnrDxiQRCOkMQSSGSWRdv9NF0gd1mzSLtrFDZRhNEDSEanQGi0ppE6wyQGK3hmN5SJCxTXFyM3NxcFBUVJT38sqZS/vLFhvdRpZBKAiDzkMttYDIP2SNCkXgokuB7LQvwltnhLnOg7JQLisJrEXaRouq0c/j305tnardYfdSG/mYvWtOvsk0m1HMHnpdMu8QTr+8QdZwT/+iMnAxr/+/FpTJqj/ze8rmvuuoq5OXlYdGiRdq6wYMHw+VyYdmyZWCMoXHjxrj77rtxzz33AACKioqQl5eHJUuWRCyyyvPBJgbHcWCMgeM4yHL8zPjqBOlM8vm8Z+jvwkhGnSwLUGQekszD67HB47XB4xHhdtvg9gpwe0S4vTwkmYOkcAbzLTCyLrD5hBphF86wC4zCCzTtAPPusYk06/TnI9OuaojH9wjpTHpDWpNcQkXYWTXpvADcnAIvGMr9/8oc05pOhIuKC4felAscQ78tMCXWrJZdZYgUYWd2PjUF9135prjOhTAnWToDRKc1idYZIDFaQxF2RFoQr+g65o9+UGQessxD8acuWTHAVPNMX88uXC07lUhRduHOlQjiGalIpDbFxcWG9w6HAw6HI2i/Xr164bXXXsPevXvRpk0b7Ny5E5s2bcLzzz8PANi/fz8KCwsxYMAA7Zjc3Fz06NEDmzdvjihw+/fvj8PVEETVEE1EHQDtgQ8ASLIAWeEgSXxIY8wsDdarGE07tUZduCi7UHXs9CQzPZYi7WoGpDMEYZ3KRNKZGXOxmnWBhIuyC6xjF6oBRTK4XlhGpl0NwYrWJFpn1DHiDRl2RLXEs6Af+Ep+ehWJB9OZctp6f1SEL5qO19JhQ0XX6Q03INi408MLihZlF2qMVKhJt+PK+9Hl46eTPQ0iCpjMgVm8+Vb3a9q0qWH91KlTMW3atKD9H3jgARQXFyM/Px+CIECWZTz55JMYMWIEAKCwsBAAkJeXZzguLy9P2xaO5s2bW5o3QSSTaI06AJB03/dmtevMouusYsWQM5zLJDWWIKKBdIYgEkNljbrAdFM1ui5WzFJeo0mDjZdpF0v9unh1pSWSQzQ6o+4PWNOaROsMkBitIcOOqJmYmGaKJPjSYmW/kafwUCJExulfB97MhUydDVHE3GyMWIhHd1gy62oGBw8eNISPm0U9AMCqVauwfPlyvPXWWzj77LPx/fffY9KkSf/P3pmHO1Ge7/+e953kgAgHRQGpgLgiKlbAIurXBbCgVq3S1gWtInVpD8jiilVBRaHWBRcURYq4UKyWulb8ISJWBYsgaqtSd1BZrBaOgCSZeef3x+SdzExmkplkcrI9n+uai5PJZPLmcE6eM3fu57nRpUsXnHvuuZGs5ZFHHsGMGTPw2WefYenSpejevTumTZuGHj164JRTTonkOQiiEIp9X9Y1Dl0wc5OubcGge9QIL3cdkN0Sa5075AVRS4h2Wvp1kThIAFRnCCIfUc2nSymG1Q7rfb9ZYIIGT4R6bo+0WLmOSnHbEbVNkFrTEnUGiL7WUEosUZXEL36l4MfKIAdD/itMl52RFujs7bC65j27zksUi0Io03UeyXkIIgjt2rVzbH4XUpdffjmuuuoqnHHGGTjooINwzjnnYNy4cZgyZQoAoHPnzgCADRs2OB63YcMG675c3HfffRg/fjxOOOEEbNq0yZrv0L59e0ybNq2IV0gQxVGoWCfn18l2WLu7TvP4wMiPGPMX6wpFE4q1EUSpoTpDENHB05skBUBTjLytr0HEOr9k2KD4ufp0xV9EJIioCFJrSl1ngNLUGhLsiKqlENFOinWyDTbzr8tdp2Unw0ryzaPz2sLSksKdvXWX5tdVMRoDNB5wM3+2Dz30UPTq1QvTp0/Peept27ZlDVHlnEMI84+zHj16oHPnzli0aJF1f3NzM958800MGDAg79LvvvtuzJw5E7///e/Beebnvl+/fnjvvfcCfwsIImqOeu3ayM7lNb8uVztsFEJdPgdeKUQ7lRmB3HU0v64KoTpDEJFzt3F63mPcQp0Xbndd0Pl1xYh0BBE5oepMuFpT6joDlKbWUEssUX+k3Q2GnhHtssImpNvOJdq1tPstbJssufOIMCxfvjxQmtNJJ52Em266Cd26dcMBBxyAt99+G7fffjvOP/98AGb60dixYzF58mTss88+Vgx6ly5d8POf/zzv+T/77DMccsghWfsbGhqwdevW0K+LIKLkqNeuxatH3hj6cZrOHO2wySQ396XbYd2BD37tsGEJM9cOoNl2RGmhOkMQ+bnbOB2jfdJigwh1Kdv7vtvpJt115SbsXDty5RFhCFJrSl1ngNLUmkCC3WmnnRb6xDNmzEDHjh1DP44gwhC/+BUkZxxT8OPNdti0607nGXedrX1JtsOWSwyLarYdUdsYIsQw8LSr5tBDDwXnHE1NTWhqavI9/u6778a1116L3/3ud9i4cSO6dOmCiy66CNddd511zBVXXIGtW7fiwgsvxKZNm3DkkUdiwYIFaNWqVd719OjRA6tWrcoa1LpgwQLsv//+gV5TOaDaSHjhrhW6xqDppliXSKpm0EQesa5Qd11YsY4gwkB1puWhOlM/2EW7oFccjuTWPO46r3bYWnTWpWBQ8EQVE6bOyOOBYLWm1HUGKE2tUQzDyPvXHWMMv/rVr9C6detAJ507dy4++OAD7LnnnoEXMnXqVEyYMAFjxoyx+nsfeOABzJ07FytXrsT333+P//3vf2jfvn3O80yZMgXz58/Hhx9+iNatW+Pwww/HH/7wB+y3337WMccccwyWLFnieNxFF12EGTNmBF5vc3MzGhsbsXnz5kCfHBKlJTnzaOcOj190KcxBZ9C3x2DoDHpShbY9Zv6bNP9N/NAALakilYghlYqVXbCTBBHtwqxRns+vJZaCJ0pLVO8h8jzf3t8P7VoHM003/6Chw0VvVcz714MPPohJkybhtttuw8iRI/Hggw/ik08+wZQpU/Dggw8GilEvB6WujVRnKougLjv5Prx9exzJRBw/bI8jkYhh6w8xJFLcctfJdlTdUCIR7IoR61rKYafyzPNwZmDc98Nb5HnrmSjeR6jOlI+WuAajWlNZjPVx2tmRf61vh4Etio5tioCuGJa7zi7Y+c2vi1KscwdO+FEKh53XOd2C3ZP62YGflwhPueoMUB+1JvB346677gr8ac2TTz4ZahHLly/H/fffj969ezv2b9u2DUOHDsXQoUMxYcKEQOdasmQJmpqacOihh0LTNFx99dX46U9/ivfffx9t2rSxjrvgggtwww03WLd32GGHUGsmKov4BUucoh03PEU7pMMkALMV1i9swqsdttzkc9pFLSiuPPFKEu2IFuE3v/kNWrdujWuuuQbbtm3DWWedhS5duuDOO++s2IsoSSlrI1FZhGmNFXomDVbOrvPCS6zzw0uQkzPqinXWlast9o62j5FoR7QIVGeIWkMGTmRu+4t1pSKoUNfSkMuOKBelqDWBfssWL16MnXfeOfBJX3jhBfzoRz8KdOyWLVswfPhwzJw5EzvttJPjvrFjx+Kqq67CYYcdFvi5FyxYgPPOOw8HHHAADj74YDz00ENYs2YNVqxY4Thuhx12QOfOna2tEhRZImK498WHoXFrfp1jv55pj5W402EjWxrXC2pz9Qu1KLf7jyg/hsZDbUDwYeCl4JlnnkEqlbJuDx8+HB999BG2bNmC9evX48svv8TIkSNbfF1hKGVtJCqTo1671nr/dm8SobPM/Lr0B0Tu2XW5wh683HV+ghxXjKptg5Ui5h1tHyvzSoigUJ1peajO1B/T8oRQ6DDFOh2GNb8uynlvMTDHluu4chPUsfcL/miJV0JERdg6Uw+1JpDD7uijj85/kI0jjzwy8LFNTU048cQTMXjwYEyePDnU8wRh8+bNAJBV7B577DE8+uij6Ny5M0466SRce+21OV12iUQCiUTCut3c3Bz5WonSITTucNcBmfl1Mh3WCpsQzLrIKjWc62UR2/zEQqFxSoqtI4IOAy8Fp556KtavX49dd90VnHOsW7cOHTt2xA477FA1jueoayPVmepHvp8LwcwPU2z1xD23zi/BNSWiSYgliEqA6kxxlOIajGpN9aPDgKYYGdHOw11nx2t+XVDswpx8npYQ67ih5BQiw7TXErVPLdeaglJihRD4+OOPsXHjRisGV3LUUUcFPs+8efOwcuVKLF++vJBl5EUIgbFjx+KII47AgQceaO0/66yz0L17d3Tp0gXvvvsurrzySqxevRrz58/3PdeUKVNw/fXXl2SdRMsh3XVSuLO3wwKw2mHtMGaUxGUniVK0k0Kc/XwUWEFUIrvuuiuWLVuGk046CYZhQFGq/w+vYmsj1ZnK5/WjJ+W83+Gus7XDajbRLpe7zotqddAFhVpiiVJBdcYbqjXVQcz145pKlwLZCpvPXZevHVaHEXqOXUu76qQoFzZhliBaklLXmtCC3bJly3DWWWfhiy++gDuvQlEU6HowcWDt2rUYM2YMFi5cGDh1IyxNTU3417/+hddee82x/8ILL7S+Puigg7Dbbrth0KBB+OSTT7DXXnt5nmvChAkYP368dbu5uRldu3YtybqJ0pDtrkuLdDrzbIdtSaJ22pFIV3/YbeH5jzXfu4Om95WCiy++GKeccgoURYGiKOjcubPvsUHrSjmJojZSnakN5PxTXWPphNhMOyxBVDNUZ8pLVNdgVGsqH7dY54XbTee+XYyrrtIgsa5+CFNnzONrv9aEFuwuvvhi9OvXD88//zx22223ghXEFStWYOPGjejTp4+1T9d1vPrqq7jnnnuQSCTAeeECxqhRo/Dcc8/h1Vdfxe67757z2P79+wMAPv74Y1/BrqGhAQ0NDQWvh6gc7MKd/NreDmuHqbol5EUpqnkFSBR7fhLpiLCU0z4+adIknHHGGfj4449x8sknY/bs2XlTwCuZKGoj1ZnaQbbDAoCmMdNZ5xWE5IO9LVY3lBZx2UnnXznCJ4jahepMdER1DUa1prK5nHmnxMaUjMtOkvJpgS2GFETJnHTklCNKRS3XmtCC3UcffYQnn3wSe++9d1FPPGjQILz33nuOfSNGjEDPnj1x5ZVXFizWGYaB0aNH429/+xteeeUV9OjRI+9jVq1aBQDYbbfdCnpOogrwCJmQQpxw3adrzJo/5IfQGRiPPnmppWfaMbowI8pIz5490bNnT0ycOBG//OUvq2amkBdR1UaiNrC3w+oeieN+8+t8z9dCoh0AR8suiXdEtUN1hqh1UhC+s+vyUUhbbLG4W3hbQsB7Uj+75M9B1DelrDWh5fP+/fvj448/LvqJ27ZtiwMPPNCxtWnTBh06dLDmza1fvx6rVq2ynu+9997DqlWr8N1331nnGTRoEO655x7rdlNTEx599FHMnTsXbdu2xfr167F+/Xr88MMPAIBPPvkEN954I1asWIHPP/8czzzzDH7961/jqKOOQu/evYt+XUQFYXczcAGmZgS2TDsst9phRY6gCSlsSRcb4yJL6Au9PB9hrhCnXCGPIbGuxtCVcBvKm6hkZ+LEiYjH43jppZdw//334/vvvwcAfP3119iyZUtZ1xaUqGojUVto6Toh22HDzK9LuT4TCivyVQuUEltFUJ0pK1RniFokyoRbScwlQlJKbBURts7UQa0J5LB79913ra9Hjx6NSy+9FOvXr8dBBx2EWCzmODZK0WvGjBmOoahymOrs2bNx3nnnATAFuP/+97/WMffddx8A4JhjjnGcSz5GfgOnTZuGrVu3omvXrhg2bBiuueaayNZNVAjccIp2aZgqoCdl4EQmHRZAVuBELqRoVyqnHeAv6hVDGKFu5YlXos/zf4h8DURlUE77uJ0vvvgCQ4cOxZo1a5BIJHDcccehbdu2+MMf/oBEIoEZM2aUe4melKs2EtWBrjGrHTYXMZYtzvmes0innXTMhQ2+IIhCoTpTHFRnCIm7HTYqvFx2pWyLLRe/4I+S066GqeVaE0iw+/GPfwxFURwDTs8//3zra3lfmIGnXrzyyiuO25MmTcKkSZNyPubzzz933HYPYXXTtWtXLFmypIDVEZVMcmaw2HtF1WHYnHGOdFhhDgj3Q6bF2ttWczntggh5XrPs7EQh3BXrpHvn5Mtw8DO3FnUOovSYLtFgPyeiAga02hkzZgz69euHd955Bx06dLD2n3rqqbjgggvKtq58tFRtJKofezpsGKecfZZdsdjbW1VmVJRod0fbxygttgqgOtPyUJ0h7FTK/zA3lJI444rF7awjqo8wdcY8vvZrTSDB7rPPPivo5ARRiVjtsNa/cpZd+VJi81HIbLtChTqhcTDV+ScBiXa1SaV8GvWPf/wDb7zxBuLxuGP/Hnvsga+++qpMq8oP1UbCjv09Wn74455fV0hbq1u0i2qeXRDRThNKZHPsVJ77PCTa1SZUZ4qD6gwBBHfXpRRRUDpsGJednDkn/y2XcFeIOEcuu9qllmtNIMGue/fu1tevvvoqDj/8cKiq86GapuGNN95wHEsQZcfVFusVE+1Oh3UnxXqeNqDLzv2YlqIU8+lItCNKhRDC0xnw5Zdfom3btmVYUTCoNtYXrx89KdBxerrOyLl1egHOumLwEtg0oXiKby3ltMsn1klItCNKBdUZolbIFTBRiFhXLPmEu2LceKVwzJFoR5SSUtSa0L/Vxx57rCP0QbJ582Yce+yxBS2CIIohaDusF/b2WJkOCwRrQbULcKWYY5fr+VqKcjwnUSCCmWnIQbb0z3mlDGj96U9/imnTplm3FUXBli1bMHHiRJxwwgnlW1gIqDbWNkHFukzIhPPPq3yiWNi210LFPync2SlFEqzKDccWBgqhqGCozpQVqjO1z+Xs8ax9Xu66FAykIna2eQmBKQS/vvFKe7W78fzSYFsiJdYNhVBUMGHqTJ3UmkAOOztyToKbb7/9Fm3atCloEQRRKGHEOncKrGyBFRpztMOGaT0txGlXDIW0xkbJez8fj4Oeur1sz09ES6XYx2+77TYMGTIEvXr1wvbt23HWWWfho48+wi677II///nP5V5eIKg21i52sc7rwxkzvIhDpAOM5Hu0FmE9iHKWHZDd6hqV0y6sOEfUPlRnooPqTO3iJdS50QGkAGiKYYlrlThHLhdh3XYpGDSXjghELdeawILdaaedBsBUCc877zw0NDRY9+m6jnfffReHH354QYsgiLCkZh8JAFB8foKt1lddMWfUpS+cDJ2Z6bCyZckeQGG70AIQauBlOQnrgpPz6fxen3t+nR0S64hSsPvuu+Odd97B448/jnfeeQdbtmzByJEjMXz4cLRu3brcy8sJ1cbaJp9YJ3HMrxMMWo4PVuyJsHYRTn4dNDG2WKKcTweEE+u4x/Oq3ICmK9QSS5QEqjNEpRJErANMsU6HgVRarEvBCOWAiwpdMXK65aQgV0mOOoJoKUpRawILdo2NjQDMT3fatm3reMJ4PI7DDjusolOWiNpBinV50XMXBD83XDFCnd1lJ3RWklbZqFx2dmEuyGum+XWVjdBYlos017FA5SQqAYCqqhg+fDiGD89crK9btw6XX3457rnnnjKuLDdUG2sTdwus33u5dNcBpqNO1zh0zUwc1zQGLU8dkuJcuYQ7u2jXUvPs/MQ6ACTWVThUZ8oD1ZnaJZ9YJ9th5V/sWloMs7fD5pppFxav8ImwVLogR/PrKpswdUYeD9R2rQks2M2ePduKFL/77rux4447hn4ygiiWwGJdGumuExqDoXHrl9oIECxRKC3ZGhvVjLlcrjqAxLpaJah9fI899sAXX3yRtf93v/sdpk+fju3bt+PSSy/FvHnzkEgkMGTIENx7773o1KlT3nP/+9//xuLFixGPx/GrX/0K7du3x3//+1/cdNNNmDFjBvbcc8+CXltLQbWx9ggr1tnbYb3IJ9r50dKOOz+COPH83HVe4pzfY0dvoouoWoTqTPFQnak9vIQ6eVGuufbLVlirDdZHoEspZrHIFzxhF+SiFPtKRZRtsSTW1S61XGtCqQqGYeCxxx7DunXrCnoygigFiqpD8RCc3K4xGTAhRTuhs5zOMiGUrK2SKFSsC+oglOcnsY5Yvnw51q1bZ20LFy4EAPzyl78EAIwbNw7PPvssnnjiCSxZsgRff/211cKTi2eeeQaHHHIILrnkElx88cXo168fFi9ejP333x8ffPAB/va3v+Hf//53SV9bFFBtrE0YF4Fd0pa7TudZgRP5yDebLorZdX6CWykCJyRBxDqCkFCdyQ3VmdpFhdNB4+Wm0WFAUwwrbCIFI2sWXMxgoVNivdx0lSjipSpwTUR1Uo21JtRvNWMM++yzD7799tuCnowgosZLqHNgc9cBGWedFO3yOcsKJYpWWMYMsAIveCpNXCRKj5H+OQ+2hUtU2nXXXdG5c2dre+6557DXXnvh6KOPxubNmzFr1izcfvvtGDhwIPr27YvZs2fjjTfewLJly3Ked/LkyWhqakJzczNuv/12fPrpp7jkkkvw97//HQsWLMDQoUMj+/6UEqqNtUfQ93B32IScX6dH/B4cZeBEIbREqyxR+VCdKR9UZ2oTL3HO7bBL2b4uhZhWbAtsS0GiXX0Qrs7UR60J/Sfg1KlTcfnll+Nf//pXwU9KEKXAT7wzNA5Dz/TDt1SYRDGinV2ok8JdoeJd1rld3yfO9ayNqA8WLVqEZcuW4ZxzzkFzczMSiUTexySTSTz66KM4//zzoSgKVqxYgVQqhcGDB1vH9OzZE926dcPSpUtznmv16tVoamrCjjvuiNGjR4MxhjvuuAOHHnpo0a+tpaHaWH9kza4TLDO/rsRjEaLAy12nMiMS111Ydx21w9YuVGeig+pMbRF4LhXg6a6LMnCCQ7E2gqhGarnWhHmvAAD8+te/xrZt23DwwQcjHo9npV189913RS+KILxwz6/L6a5LzwyyD60002Ezt92CGmMikjCHasJPnCPRrvowdGa1fQc5FgC6du3q2D9x4kRMmjQp52OfeuopbNq0Ceeddx4AYP369YjH42jfvr3juE6dOmH9+vU5z/X9999b8yY452jdunXFzxLyg2pjfeKeV6oLZrnrwrbGlpowoRLuY4sV8cIkyBKVC9WZ8kJ1pv7QkX92Xfhz5g6WINGOKCdh6ow8HqjtWhNasJs2bVokT0wQxZBLrJPtr0j/ArvddYZgEOk2JgBgqgATOnSNgXO9ZA68KEIiGDOKane1u+tIlCPWrl3rGNDa0NCQ9zGzZs3C8ccfjy5dukSyhhdffNFKwBNCYNGiRVnugZNPPjmS5yolVBtrB/eHOV7J3PZ0WKsdVjPbYQFA80g444oB3VAQY84giZRouZZXKcQFEeCimm9HYl19Q3UmOqjO1BcpI9MO6+eua4l5cykIxMI35bU4MVc6bco1448CJ2qbWq41oQW7c889N/STEESU5J1bh0zbq2ET3zKiXe6iw9TSiXZRUKxoRxCSdu3aBUpUknzxxRd46aWXMH/+fGtf586dkUwmsWnTJscnUhs2bEDnzp3zntNdUy666CLHbUVRoOuVLy5Tbaxt5AccbuHO0Q4rnO2wmq5EPsvOjm4o4Er4i7VSBk1kPReJdXUP1ZnooDpTO4Rth7VcdgW859c6bqHOvd8t3BG1SS3XmtCCHQDouo6nnnoKH3zwAQDggAMOwMknnwzOK1fkIKqf2IjXoD0yIPDxdoFOhk1YoRMedluuCohkbjGvXC2z0hkXlZDo5a5jzOkqERXWzkXkRmgsrxhtPxYwB7RyztHU1ISmpqa8j5s9ezY6duyIE0880drXt29fxGIxLFq0CMOGDQNgznFYs2YNBgzI/fsqRHTzVyoBqo21Ry4nsiNsQstujc2F22UXJUEddAQRFqoz5YfqTH2hwzm7DkCLuusqjRQMxKhlt6YJU2fk8UBt15rQgt3HH3+ME044AV999RX2228/AMCUKVPQtWtXPP/889hrr70iXyRBSNRzluYX7fTMG7ldlJNil8ghujlEK41nOdm8WqP8EDrLaq0K2hYrhOIImXALdYW47OztsPZ1uIW6zHMIEu1qnOXLlwf+NEoIgdmzZ+Pcc8+FqmZKR2NjI0aOHInx48dj5513Rrt27TB69GgMGDAAhx12WKmWXnFQbawdBrx8A5YOvC5rv/2936sOyHRYr3bYfLRkW2ypCRs4QdQ2VGeig+pM7TBFnI4J7PGs/TIhNmWY8+sqhUppi/US61KK4euyI+qHWq41oX/zLrnkEuy1115Yu3YtVq5ciZUrV2LNmjXo0aMHLrnkklKskSACYeRwn+Vrh2VMgHm6zoq78HAPJK82Dn7m1nIvgagQXnrpJaxZswbnn39+1n133HEHfvazn2HYsGE46qij0LlzZ4fFvB6g2lhbDHj5ButrXecOga6Q93XpeLO3sLoFulI57giiWqA6kxuqM7XFFHF64GPdglSx7jq/xwc9L7XmEtVMtdUaxTCMUL9xbdq0wbJly3DQQQc59r/zzjs44ogjsGXLlkgXWKk0NzejsbERmzdvDtUvTUSDl8vOsM2tEwkV0Bn07TFoP8QhNAY9GTNttunACT2pQtcZNLk/HUaha5k2J+HhspMEcdpZwRYeQ8zzEUQszOWycz+euWb/5XPYAcBBT92edw1EYUT1HiLPs+aqE9CuIRbsMYkUuk39O/bdd99Q9nHCn1LURqoz5cXLZQdkAid0nSOZVJFKxvDDDw3Y9kMDdKFge0KFLpg1w05Lu75l6qpuu/DyEunCOu385tiVc1ad3WUXZI7d6E00DLxURPE+QnWmMijVNRjVmvIinXZ2dx1gOuxSABKKwHYIbFOEFTgRRTusVxqsPK9fUqzdZcfL4GrL1Q7r5bKzz6+j0InSUa46A9RHrQndEtvQ0IDvv/8+a/+WLVsQj8cjWRRB5CNfayxTdUssU5gAwKx/s45lwvxNSFdKOYvIEtV8RDu36FaO+Xa5WmO92mrdol0+3vv5eBLtapgw9nEiN1Qbaw+/1lg/uCqgJ/3rgExolWmxgPcsu6jaY1tylp2mKxQwQXhCdSY6qM7UJlPE6bjcoz3WDoeCGJRI59bpMHyFuVz3WccoRouKdjS7jshFLdea0H8S/uxnP8OFF16IN998E4ZhwDAMLFu2DBdffHFVRKITtYN6ztK8xyiqDqYKMDXbxqBwAc4FmKqbLbGqAGMCPP0vYwKc6+n78xdIzyCHtLPO3UIVpbgXpm3XK7Qi35y6d06+LPSaCKLeoNpYm9hbY4Hs93KVC3BVB1cFVI8awJnhELK8WmOLRa/A2T32dFxNz7++u9s/WsrlEERNQHWmPlHlBzyGAm4oiIHlFdMAIKXkn7OQSwD0uk8GXpSDVB6xMqUYORNhf8GpzhDVSWjB7q677sJee+2FAQMGoFWrVmjVqhWOOOII7L333rjzzjtLsUaCCITido9xU4RTuIDC0sKcas6qY1yAceEp2qmxFGINKUu4s4t2cvMjl2gXljChEn5rEkIprqU3LeaRaFf5yFSloBtgJir16tUL06dPL/PqgU2bNuHBBx/EhAkT8N133wEAVq5cia+++qrMKwsG1cb6hPF0nbA2A2r6AyIp1EnRTt52u9683HRh59lVgmiXS5gj0a42oDpTXqjO1C5/zDPPLgbFctmFcbWlFJFXuAsr2jnub+FZdvlEOyDTBusl3pFoV/mErTP1UGtCt8S2b98eTz/9ND766CN8+OGHAID9998fe++9d0ELIIhSo6g6mM5gCAGhCTDXT72A6ZIwuEAqEXO0xwLOr6VDTQpkQUU1xoVnamyUBG2PtbfGyrRYIVjOWXZEbVIp9vF3330XgwcPRmNjIz7//HNccMEF2HnnnTF//nysWbMGDz/8cLmXmBeqjfWLys0PdrjKTKedUIC4lk6LzahxujDbRjU93aoqEGlrrG4oWc69lmyLBZytsbpQQifG3t3+UZpnV2NQnYkOqjP1i2ooVhpqSjHSs+SCz7JLKQIxw7+gyBZYHqDtttyJsVK0y9Uim89pR/Psao9arjUF/7bts88+OOmkk3DSSSdRoSAqD26YohTPbodVZPurzWkn3XaxhhTUeApqXLOSY91Ou6gopC3WdAn6ryGX+88u5vm1xtrbY92tsuSyq2wMLR2SEmCTAS2V8mnU+PHjcd555+Gjjz5Cq1atrP0nnHACXn311TKuLDxUG+sHu6M6Fk+BM7Mt1nTUCaiq6boDTLcdZ4ajRVZlRs7U2GrF7qZzt8YGcdoRlQvVmcqA6kx9Idtf7S47931BCOq0c5+z0lx2kiBuOz/IaVe5hKkz9VJrQjvsdF3HQw89hEWLFmHjxo0QwvnL//LLLxe0EIKICkXVrV9ea1/a2cZUHULjadEu+7ECgHwkEyyn0y5My6r1uJAuO3dwRBByuf+8nHbSZZc5xv+q8Z2TL8PBz9waaj1E5VIpn0YtX74c999/f9b+H/3oR1i/fn0ZVhQeqo0E57rZFqsKQDPdDwBstcPnvTWP064ayRVCkS+gglx2tQXVmeigOlO75AudcMMNBVAYUhCBXHFBsTvtclFul10UkNOutqjlWhNasBszZgweeughnHjiiTjwwAOhKPRpKVGd5BPthE96rJc7rZR4iWxSeMyFn3DnJ9oB3jP4CKLUNDQ0oLm5OWv/f/7zH+y6665lWFF4qDbWF5zrni5psyVWJozbRDsA0ABdMEfLqPU4W3JsrSCFOa/WWHLaES0N1RmiGokBSAU4zku0ixksy1WXrzU2jGOPIIhsSlFrQgt28+bNw1/+8heccMIJBT2hH1OnTsWECRMwZswYTJs2DQDwwAMPYO7cuVi5ciW+//57/O9//0P79u3znmv69On44x//iPXr1+Pggw/G3XffjZ/85CfW/du3b8ell16KefPmIZFIYMiQIbj33nvRqVOnSF8TUX6YqlupfgoTAJhD7JKindAYmN1lln4sNAAqIJLFfYrkThaUuN1tgc5lm0FX8Hp8nHv51kPuusrFEAxGnsRf+7GAaR/nnKOpqQlNTU2lXF5OTj75ZNxwww34y1/+AgBQFAVr1qzBlVdeiWHDhpVtXWEoVW0kqgeu6uA2Ec/utNN0ZoZRpEU7iRTvtAIc2154zbGrBKQ4GWSm3bjvh5d6OUSBUJ0pL1Rn6hcOBTAAKOb7vJzRJl1u0mkHOFtYCxHtCkFXjFBhGFGRgpFzll0uyF1XmYSpM/J4oLZrTejf1ng8Hvm8BGkd7N27t2P/tm3bMHToUFx99dWBz/X4449j/PjxmDhxIlauXImDDz4YQ4YMwcaNG61jxo0bh2effRZPPPEElixZgq+//hqnnXZaZK+HqBBky41HC6pd8JIz7bIenk6PrQSyXHIRuvzc5/KbrUdiXe2xfPlyvP/++2UtbABw2223YcuWLejYsSN++OEHHH300dh7773Rtm1b3HTTTWVdW1BKURuJymDAyzf43sfTs1BVW52Rs+zU9AxUa6adba6dnGnXEkQlBhb03C4nnZ5nLSTW1R5UZ6KD6kztkislNua5z5kWa29PLdQlVy/uuif1s0msq0FqudaEFuwuvfRS3HnnnTCMaP7Q3LJlC4YPH46ZM2dip512ctw3duxYXHXVVTjssMMCn+/222/HBRdcgBEjRqBXr16YMWMGdthhB/zpT38CAGzevBmzZs3C7bffjoEDB6Jv376YPXs23njjDSxbtiyS10S0HOo5S3MfkBbtlLTwxuOaJc55BTjYgygUD6HPT8zSdW5tpaKgmXk5HpMrhML9WkisI0pJY2MjFi5ciGeffRZ33XUXRo0ahb///e9YsmQJ2rRpU+7lBSLq2khUFvlEO/vX3BE2kRHtCqEW5tkFhcQ6opRQnSEqHbdoZ//LPIaMoGYX1vxEOztebrp8ARRBSKEyClQx4RMEETWlqDWhW2Jfe+01LF68GC+88AIOOOAAxGJO3X/+/PmhztfU1IQTTzwRgwcPxuTJk8Mux0EymcSKFSswYcIEax9jDIMHD8bSpaaws2LFCqRSKQwePNg6pmfPnujWrRuWLl3qKw4mEgkkEgnrtldvMlEe1HOWQntkgGOfV/CEdZ9tdp3QMkWMqcJxOxd2sSuISMe48G2L9TzednHnENZcraxScIzCcefValtKAZKIFqExCB7w51erLPv42rVr0bVrVxx55JE48sgjy7aOYoiiNlKdqWzc7+H2OXaMCzBdQAc3xTrb/FMgMwPVemx6REM+x1kt4A6Z8JppR1QHVGfKS1TXYFRrKpc/itMxVskEUHAA8i/zGDLtsDFDARRTrOKGkpXUGmUQRS1BzrrKJ0ydkccDtV1rQgt27du3x6mnnhrJk8+bNw8rV67E8uXLIznff//7X+i6njWLrlOnTvjwww8BAOvXr0c8Hs+ahdepU6ecyR1TpkzB9ddfH8k6icpDYcK3X15oDCKHsy4oYdJhHY/LlfoaIoTCi0JSaInaoVISlfbYYw8ceeSROPvss/GLX/wiy21dDURRG6nO1A5StJNCnTs5Vgv44RBBVDtUZ6IjqmswqjWVTUwBUjn+NLdmtqVn2tkdZjEwT+eb1yy7qCnXHDuCAGq71oQW7GbPnh3ouNdffx39+vVDQ0OD5/1r167FmDFjsHDhQrRq1SrsMlqcCRMmYPz48dbt5uZmdO3atYwrIorBLtAVKnaVyn2WT0Bzi2zFuOvczxVFoAVBhOWtt97C3LlzccMNN2D06NEYOnQozj77bJx00km+NaTSiKI2Up2pbPI5pVUuINLvn1bytiqgFxlalBJArEh9TxMK1Ar6cKYenIVEZUF1JgPVmurC7rLjUKB5OOe8XHYEQbQ8pag1JfuI9/jjj8dXX33le/+KFSuwceNG9OnTB6qqQlVVLFmyBHfddRdUVYWuhxcNdtllF3DOsWHDBsf+DRs2oHPnzgCAzp07I5lMYtOmTb7HeNHQ0IB27do5NqL6cYdNMFU40mK9jqkWCpl550Wf5/8QyXmI0iJ0HmoDTPt4r169MH369LKu/ZBDDsEf//hHrFmzBi+88AJ23XVXXHjhhejUqRPOP//8sq4tanLVRqozlY/bKe2Xqs25bs2xkyEU9gAK67iIRbRKTIglageqM9VBvmswqjWVy+XMbIeNBfgTnkNBzFB8U1LdIRL5kmGphZaoBMLWmXqoNSUT7PINRB00aBDee+89rFq1ytr69euH4cOHY9WqVeA8vGsoHo+jb9++WLRokbVPCIFFixZhwABzxlnfvn0Ri8Ucx6xevRpr1qyxjiGqE/fMOsXlFOMNmkN8U1jma6bqztuuizK3iAf4X6iFpRCnXlRiHFHfVEqikkRRFBx77LGYOXMmXnrpJfTo0QNz5swp97IihYaF1zaMCXBb7eFMmO2wLtQq/SCIIMJCdabloTpTG9hFO/uVguoh0tlTY/3CJ0rdEksQ5aSWa03Zhqi0bdsWBx54oGNr06YNOnTogAMPPBCAOW9u1apV+PjjjwHAEvi+++476zyDBg3CPffcY90eP348Zs6ciTlz5uCDDz7Ab3/7W2zduhUjRowAYCZ3jBw5EuPHj8fixYuxYsUKjBgxAgMGDAiVRktUHm6BzvMYLjxddfUIza6rLYRQIHQWbEsLvpXyaZTkyy+/xC233IIf//jH+MlPfoIdd9yxYtZGEJJ8Ljsp2jnSY20uOz+icMfpND+IKCFUZwiiZcnltItBsVx2oc+bx21XTfg5DInqJFSdqZNaE3qGXUsyY8YMx1DUo446CoA5w+G8884DAHzyySf473//ax1z+umn45tvvsF1112H9evX48c//jEWLFjgCKK44447wBjDsGHDkEgkMGTIENx7770t86KIsiGTYxUuwGCGSWTPsgOkjs24AOcCgon0BZiAEOa8O8YMCKE4UgJrjSiSZ4nKplIGtN5///2YO3cuXn/9dfTs2RPDhw/H008/je7du5d7aQQRCCnOqQA0nYHJpFhJOjVW0zk4M6Dppoin6RwqN6DZXNMxZs6tKxQp2rkFwEqbY2fnsq1nlXsJRImgOkMQxSFDKDiAFNKtrgagpd/j5W0oAAwz/EGGT/ilxaYUUVOiHUHUcq2pqN/UV155BdOmTbNuT5o0CYZhZG1SrAOAzz//HJMmTXKcZ9SoUfjiiy+QSCTw5ptvon///o77W7VqhenTp+O7777D1q1bMX/+/Jzz64jKRnskdyuzV4iC22nn/lq2wJrH6dY+xoR1PulQi6I11i76eabBhmyBLbZllsQ6ws1XX32Fs88+Gx06dEDr1q1x0EEH4a233rLuNwwD1113HXbbbTe0bt0agwcPxkcffRTo3JMnT0b//v2xYsUK/Otf/8KECRPoIoqoaIKkfns57aTLTuUCqiqgcm8BrdiQCYKoRqjOEERuYlm3/VtjCyHsHDt3Ii0FXxCVTinrDFCaWlMyh52ikD2VaHmki84NUwWEln0FZO6Hp8vOngjIbC47IIUUkOW0A6JLjnUnwbYkJNZVJ9IaHvRYwLSPc87R1NSUc+bD//73PxxxxBE49thjrQGqH330kSOq/JZbbsFdd92FOXPmoEePHrj22msxZMgQvP/++3mTwNesWVM3NaNeXmc9o3IBzfW7yKXjzsNlx5npfNOEAq4Y1NZKVCxUZ6qDenmd9Y5qKFkuu5RNMHO77GIGc8yxqzeX3ZP62eVeAhGAMHVGHg8EqzWlrjNAaWpNyQQ7GnhKVApBBCiFCTDVbJNlqp4R8DgDTzspmGBgwhTmhPw3LdqZ+6JtkS1GtJNCYq77CSKoffwPf/gDunbtitmzZ1v7evToYX1tGAamTZuGa665BqeccgoA4OGHH0anTp3w1FNP4Ywzzsh5fkVRsGnTJsyaNQsffPABAKBXr14YOXIkGhsbC3lpFQvVxupk6cDrsvbZP9Txw90aq4NBle2z6fdoXTDowvy5sIt29tbYlIjGdVeOtlg/F6FE00lcqGWozrQ8VGdqD9kWK7G3usagICW/NhRHa2zWeVyinRc6jKyEWYKodILUmlLXGaA0taagP/80TcNLL72E+++/H99//z0A4Ouvv8aWLVusY77//nvsueeeBS2KIEqFPZhCkUKcR2usKeAJMC6gpGfZMSagxjUwriMW1xCLpdLtsunN1iJr34pBCKXg9tYoRLl+L95c9DmIyqW5udmxJRIJz+OeeeYZ9OvXD7/85S/RsWNHHHLIIZg5c6Z1/2effYb169dj8ODB1r7Gxkb0798fS5cuzbuOt956C3vttRfuuOMOfPfdd/juu+9wxx13YK+99sLKlSuLf6EtBNXG+iM7Udy8rbqDKdKtsVwVmQAKZkDlIu2yM6BywxLT5Py5Wm6N1XSFxLo6gOpMtFCdqW2KcdK4RTZuKFZirP0+6aqrJ3cdUfsEqTWlrjNAaWpN6N/UL774AgcddBBOOeUUNDU14ZtvvgFgKpaXXXZZQYsgiEpAYXbhLj2njuuWaGeKchnRjqsCsVjKIcp5iWRBRbuwzrwgQp7XeoIKeSTWVReGzkJtANC1a1c0NjZa25QpUzzP/emnn+K+++7DPvvsgxdffBG//e1vcckll1jx5OvXrwcAR7iPvC3vy8W4ceNw8skn4/PPP8f8+fMxf/58fPbZZ/jZz36GsWPHFvFdaTmoNhJ+MFtt4dZoBaTFO8OaZRe1aFcprbVSmLMLdPLfq7afiau2n1nO5REhoDpTXqjO1Af5RDv7HDs1/T4vZ9nJ1Nh8yan5xLqws+wcj63AOXbUDls9hK0zYWpNqesMUJpaE1rIHzNmDPr164d33nkHHTp0sPafeuqpuOCCCwpaBEGUG/uMO/m1bJNletoxEdegAYBmfi00ASAGaE6xLV87apQEaZv9ycKbPPe/NeRq38eQWFcfrF271mEfb2ho8DxOCIF+/frh5pvNn4tDDjkE//rXvzBjxgyce+65Ra/jrbfewsyZM6GqmZKkqiquuOIK9OvXr+jztwRUGwk5EkG2yrpn2fH0B0G6YFZCLJCeeccE3J+herXHVqrjzk90m9rqz1n77GIdUftQnYkOqjO1zQT2uPW1/CnVXMdwAGF7d/IlxvpRaa2xT+vn+N73C/6o534S6uqHILWm1HUGKE2tCf2n3z/+8Q9cc801iMfjjv177LEHvvrqq4IWQRClwCt8ohCky45x010Xa0il02IzabKc655ptOWk34s35xTe/O4jsa5+aNeunWPzu5Dabbfd0KtXL8e+/fffH2vWrAEAK2V7w4YNjmM2bNgQKIG7Xbt21rnsrF27Fm3btg30WsoN1cbaJt+ogzDjDziTbbO6w2VntspmO+0k9pl2lUQu4c3vPhLr6geqM9FBdYawI8U0t8sOgOWycyfGVpIAF4ZcYh1gCnMplxhJYl19EaTWlLrOyHVEXWtCC3ZCCOh69h+mX375ZdUUPKL2scS6PPNxFNecIXtbrLwthTkp2tnbY5mtvakasYtz+QQ+orLRdRZqA8xEpV69emH69Ok5z33EEUdg9erVjn3/+c9/rJjyHj16oHPnzli0aJF1f3NzM958800MGDAg79pPP/10jBw5Eo8//jjWrl2LtWvXYt68efjNb36DM8+sjgt7qo31iQyd8BtpIGfZMeaeaScs4U4e5xbtrGMjbi/SInSABxHe3MeQWFe9UJ0pL1RnCD/UPCMQYrZL/qhFuxRKex2UT6yzk4KBFAwS66qYsHUmTK0pdZ0BSlNrQrfE/vSnP8W0adPwwAMPADCTMLZs2YKJEyfihBNOKGgRBBElhsYtoU5oHAgRDW2HqTqExq3WWDucCyt9lnEdjLHM81UZJNLVL0HT+8aNG4fDDz8cN998M371q1/hn//8Jx544AFHHRg7diwmT56MffbZx4pB79KlC37+85/nPf+tt94KRVHw61//GppmNoDEYjH89re/xdSpU4t6jS0F1cb6I19CbBBkYiw0BsiLHg0AnMmxkkpvjfWDRLr6hepMdFCdISQxACkgUJsrNxToimG1xiLg44DKa4vNRRhhj6g9gtSaUtcZoDS1RjFCZn9/+eWXGDJkCAzDwEcffYR+/frho48+wi677IJXX30VHTt2LGgh1UZzczMaGxuxefPmQH+IEKVBe8SpdhsatwQ7t1gnZ9S5W2XlsErrfuG8LUU4QzAIjUFLxiB0Bi2pQkvGoCVVCJ0jlVSRSsQ959eFCZQImyzrN8OOhLjKJqr3EHmeVWech7auNhk/vk8m8eN5D2HfffcF5xxNTU1oamrK+ZjnnnsOEyZMwEcffYQePXpg/Pjxjpk5hmFg4sSJeOCBB7Bp0yYceeSRuPfee7HvvvsGfi3btm3DJ598AgDYa6+9sMMOOwR+bLkpRW2kOlM5/PO43ztu6zr3dNfZ90vkHDshGHSNQ9c59HSdSSaXBLrEAAEAAElEQVRVa5adrjHoQoGmMySSKjRdgS4UbE+ln6eAEAk/d5673bZQSIirfKJ4H6E6UxmU6hqMak1lYJ9hJ5Ez7FK2t2x5lZCybqc/3FFMd5m8nUq//6dgWEEQbjdcENEun2AX82jYc7fiFgMJcZVPueoMEL7WtESdAaKtNaEFO8CMFJ83bx7effddbNmyBX369MHw4cPRunXrghdSbVBxKz9usQ5IC3YJ1RLrpOjmPsZxO4RgBwBawhTpDJ0hlf5aS8WQSqrmxZrGLdEubPJrWLEO8BbsSKyrfCpBsKvE96+1a9cCMNOeqo2oayPVmcrALdYBGWHOLdYB2c67XIKdKdIxaDq3BDsA2J5QoQuGRIqZ6apCCS3Y5WqlJcGufii3YFeJ719UZ5xQrSk/ucQ6ILdgZ+4zPAU7OdfNntwaVrTLJdh5iXUACXb1RiUIdpX4/hVVrQndEguYSRdnn0294UT58BLrHETQqiTTYmVrrLWf62A80zPvu4SQYl3e9bRg+ixBtCSapuH666/HXXfdhS1btgAAdtxxR4wePRoTJ05ELBYr8wqDQbWxtvAS6grBnRYr0T0+ULIeowpAAzgz3/ODzp2Let4dQdQKVGeISsVLrLOTKvHbetj0WImfWEcQ9Uwpak1Bv2mPPPIIjjzySHTp0gVffPEFAOCOO+7A008/XcjpCCIUecW6PCgh01y95tI5wid8QicKccv5IV10jBnWZq3P40LurSFXR/bcRHUgBAu1AcGHgZea0aNH44EHHsAtt9yCt99+G2+//TZuueUWzJo1C5dccklZ1xYGqo0EACugyA/prsuFDKFQuflerzKDxDii7FCdKT9UZ2qLfGJdLmLpDTBFN3vwRCrCeuEl5hUr1sVCzMU7hT9S1HMR1UXYOlMPtSb0b9t9992H8ePH4/jjj8f//vc/K61op512wrRp0wpaBEGUCj8xLYhoZ2+nNVwXWDI1lnMzKZZFKM658ZtR57dfQqIdkY/ly5fj/fffzztXqNTMnTsXDz30EC666CL07t0bvXv3xkUXXYRZs2Zh7ty5ZV1bUKg21hZB3HX5XNRewp1um1cH+It3dkcej6h9tRRMbfXnci+BqHCozkQH1ZnagyuZLfBjXLf9/Dpe7bD+54yug8erHTYGxbHJfQQRFbVca0ILdnfffTdmzpyJ3//+91DVTEdtv3798N577xW0CIKInBzuBokU7ZQcx+ZKfZUuOwBgTCAW17KOKdZll0+Uk/f7tcqSaEdUAw0NDdhjjz2y9vfo0QPxEHMsygnVxvrBK1iioPPIuag5hD/OzBqjcqNiXXYk2hHVANUZotIpRrQDzNCJaiOoaEcuO6JaKEWtCf0X52effYZDDjnEc3Fbt24taBEEUSwyHdYdKAH4u+xyITSWFTYh9wOASF9gMVtbLON6UQJdoY/NJ9oR9YGRDlkJssmglUqxj48aNQo33ngjEomEtS+RSOCmm27CqFGjyriy4FBtrA+KmU2qa/Y0WDNowgqbsAVOaDqDppkbAKstNh/lFPNItKsPqM6UF6oztcU13L8d1isd1o1fNbK3wwZx10VJIWETJNoRdsLUmXqpNaFDJ3r06IFVq1ahe/fujv0LFizA/vvvX9AiCKIYHEKdruR0xeU9l5BvAE6xzi3U2eFcQLB0aywzBTzYkmI51yMPoHAjAynkc9qdeW8NuZpSYwlPli9fXrZEpdNOO81x+6WXXsLuu++Ogw8+GADwzjvvIJlMYtCgQeVYXmioNtY+fimwEq/3eqEzaHpmxorEnQpr7ks77jzaZR1tsQLQDQUpAcQqaOb31FZ/ptRYIguqM9FBdYbIh5UQCyNnMqwXucIndBiRts26iUGxWnhzcQp/hFJjCU9qudaEFuzGjx+PpqYmbN++HYZh4J///Cf+/Oc/Y8qUKXjwwQcLWgRBFIOhcUA3i4jQeOCEWC83nnVfDqHOfbFmhU8IBq4KCKGHFg393HVCKHnbYv0eB2SEOxLtah/74NUgxwLmp1GcczQ1NbX4zIfGxkbH7WHDhjluFxuB3tJQbawt/FK55ft/oR/C6MJ01+kaQyLFLXHOfQwAaHr+iyO7aKcbStEuO+nmC/LcRP1Bdaa8UJ2pD4K469z4CW1BhLqoxLhC3HV2pNMun3BHol1tE6bOyOOB2q41oQW73/zmN2jdujWuueYabNu2DWeddRa6dOmCO++8E2eccUZRiyGIfPglxIYR6vxwu+u0hDnG1S7Q+TsrTJedGktZ+1KJeCCXXVRpsl4XmNQmS+SinJ9GzZ49uyzPWyqoNtYOXrM/7XPrChbr0q46xz7BfMUxXShZgRNWe6wAwEyXXVjUAB8Cudtwgwp45LIj3FCdiQ6qM7VDrnbYUuAn6AW9Xx5TSpedJKjbjiDs1HKtCSXYaZqGuXPnYsiQIRg+fDi2bduGLVu2oGPHjqVaH0EA8BfqHHDhK9oxVThSX9247xMag9DNzQgoBEqXHRM6GGNgqg6RDK2JZ6+tQJedY20VnDJIEF40Nzfjsccew6xZs/DWW2+Vezk5odpYO+QL6gkj1vl9wKPpHLpQzDl1uuJoi816vhz3ccWwRLugLrsgYp3n4wI670isI6oJqjNEOfAT63Tb23MYd531GFc7rHTXBRHjiiWfuy4Fg1Jhibql2FoTypKkqiouvvhibN++HQCwww47UKEgSk4QsY6p0bjUhMZNp13aTaElVaQSMSR+aEAqEUMqEYOuM88NgDXHjqsCnOsVIZRVwhqI0mMKzDzgVlkDWu0sXrwY55xzDnbbbTfceOON6N+/f7mXlBeqjbWBXawrxJ3sJ+YJwaBr3NEOq+nM0fqq6Qq2pxi2p5h1277ZkYmxkhhDIKddoWKd+7mJ+oXqTPmgOlMbFOqs87rK8dqXquCk2DCuORL36pdwdaY+ak1o+89PfvITvP3221kDTwmiIijAZed20El3nZlSw6ElVQjBwJh5RcRsffXWvrRg6OWyswdQZC03onZYgiiEctrH7Xz11Vd46KGHMHv2bGzatAn/+9//MHfuXPzqV7+ColTHH21UG+uXXK47zc9lZwVLpJNhhWK54zRXvVCZYYl2dsFMZYYVQOFYTwSz7AgiKqjORAfVmdpHQ8ZdF/QKIQVA83nPj9pdJ9tiUxCIhfP9EERJqeVaE1qw+93vfodLL70UX375Jfr27Ys2bdo47u/du3dBCyGIQsgVHBH2cUJjMASDloxBTzvrpFiXSsTAVQHGdYhkRryzfoO0zHnsLjshzHIbRWtsIZC7jqhk/vrXv2LWrFl49dVXcfzxx+O2227D8ccfjzZt2uCggw6qmosogGpjLRHGXefrqgswSsFy0KXFOsApvknRTROK5Y7TdMV02HHD4byTLruWSI11PzdBVDJUZ4hqQA/w57oOwF1xUsiIci3R+tqS+M2yo8AJohIpZa0JrSLIoaaXXHKJtU9RFBiGAUVRoOvkGCJaBrfoxtTc6ay5Zti50XVmpdTIhNhUUgVj5jmkeGcJda7fJKYKy2Wn6xyMGSjmVyPoHDsZPEFCXf2hawy6EuxnXA6+L2eiEgCcfvrpuPLKK/H444+jbdu2Lf78UUK1sbqxt8O6A3z8RLl8Yp2WriO6xq3ACTm/DjDddW43nXseHQCH687utnOTS6yLoh2WIKjOlBeqM7VNEHedfb8U6zTFqNmQBgqgqD/C1Bl5PFDbtSa0YPfZZ59FugCCKAQp1gVx2LmFOvtjZDus4REfLR+na+mLLt286OJ2oU7ius1U04EnXXZC4zmTYgvBPrfPLlT6iXVvDbka/V68ObLnJ6qfctvHR44cienTp+OVV17BOeecg9NPPx077bRT2dZTDFQbaxP5ni10Fuj9O4izLus5DH/RTt5vd9sBTgGOK0bWOcoFpcQSbqjORAfVmdrE7a4LIrum3OeoAlEr31w6uzBnP9Yt2p3CHyGXHZFFLdea0IIdzU0gKgVLeMvRmhPEVWc/JistVueWWJfl3kuLdIzrEG7Bzy3oeUDz64h65/7778e0adPwl7/8BX/6058wduxYDBkyBIZhQIgAU/QrCKqNtYN01xUq1vnNrbOje3xIZA+OyCXaAchy5uUiqLuumEAJ+2NvbTMXl209q+BzEUSUUJ0hKoVCAyckdpFOCnR2d10q/bU9IbaUlGqOnZewZ9+XgkGiHVFxlLLWhBbsnnnmGc/9iqKgVatW2HvvvdGjR4+CFjN16lRMmDABY8aMwbRp0wAA27dvx6WXXop58+YhkUhgyJAhuPfee9GpUyff8/j1CN9yyy24/PLLAQB77LEHvvjiC8f9U6ZMwVVXXVXQ2onS4JUQa2jcFOtcQl2udlj7Y62vs8Imsh+va06xTggFsB9nE+0c5/K4GCsGt2uu0FRcctnVLrKFO+ixQPnt4wDQunVrnHvuuTj33HPx0UcfYfbs2XjrrbdwxBFH4MQTT8QvfvELnHbaaWVZWxhKWRuJ0mJvh3XjJ9a59wmdOYS6sDUgSMprKcMkChXrKDW2vqA6U16oztQe0l1n/5yfI9tlJ8U6u1BXLmTwRBiiTH2V5/oFfxRP6mdHdl6iMghTZ+TxQG3XmtCC3c9//nNrXoId+wyFI488Ek899VQoG+Dy5ctx//33Zw1MHTduHJ5//nk88cQTaGxsxKhRo3Daaafh9ddf9z3XunXrHLdfeOEFjBw5EsOGDXPsv+GGG3DBBRdYt6t9tkWt4SXWeRFEqAPyiXUBnHhpR4MQihkiEZcLNf/JEu3SM4uKJdc8uqCvnSC8CGofnzRpEq6//nrHvv322w8ffvghgMI+WPFin332wc0334zJkyfj+eefx6xZs3DmmWcikUiEOk85KFVtJEpLLrEuKHaxzv1Hpp5+j9YF83TV5cIrRMIt2pWzFZbEOiIIVGeig+pMdRKVs84t1uWa7RYDaxGXHQDwPHUoqFgXpahH1B+1XGtCqwkLFy7EoYceioULF2Lz5s3YvHkzFi5ciP79++O5557Dq6++im+//RaXXXZZ4HNu2bIFw4cPx8yZMx0FZvPmzZg1axZuv/12DBw4EH379sXs2bPxxhtvYNmyZb7n69y5s2N7+umnceyxx2LPPfd0HNe2bVvHce60JaJ8RCHW2UU4P7FOJsMWgkgPEjeHimfCKYTOra8B/8HkQfAT64Tm0aJL1DVyxmLQDTA/jerVqxemT5+e9/wHHHAA1q1bZ22vvfaadd+4cePw7LPP4oknnsCSJUvw9ddfF+VWYIzhpJNOwlNPPYW1a9cWfJ6WpBS1kSgtXmKdvR02SCusn1inazwj1qXPwVn4iycv551uKNaW79gg7bBhhTeZVEvUH1RnygvVmeojSrFOUzIBE3axrpLn15EIR4QlbJ2ph1oT2mE3ZswYPPDAAzj88MOtfYMGDUKrVq1w4YUX4t///jemTZuG888/P/A5m5qacOKJJ2Lw4MGYPHmytX/FihVIpVIYPHiwta9nz57o1q0bli5disMOOyzvuTds2IDnn38ec+bMybpv6tSpuPHGG9GtWzecddZZGDduHFTV+1uSSCQcimhzc3Pg10eUAF3JFqwCzA1yi3XF4Nceay1Hy1h6RYh5QxJKeiVKTZgBraqqonPnzln75Qcrc+fOxcCBAwEAs2fPxv77749ly5YFep/ORceOHYt6fEsRRW2kOlMd2GfbSdxiXVhiLFhbbNQUIryRWEeEgepMdER1DUa1prLwG3vtFuvMfd5CXcrmvOaGAr2MLbMtBbXDEnZqudaEViw++eQTz29Gu3bt8OmnnwIwLYD//e9/A51v3rx5WLlyJaZMmZJ13/r16xGPx9G+fXvH/k6dOmH9+vWBzj9nzhy0bds2Sxm95JJLMG/ePCxevBgXXXQRbr75ZlxxxRW+55kyZQoaGxutrWvXroGenwhPLnedNb/OTY4W1yBJskHJmlsklCynndwyawn//CTWES1Bc3OzY8tl0/7oo4/QpUsX7Lnnnhg+fDjWrFkDIP8HK/VCFLWR6kzl464B7oAJt1hXiMM6xpxbUCFPts8GmXOncoOEN6JFoDoTHVFdg1GtqQzc6bCp9G37gJ2wYl0lQe46oiWp5VoTWrDr27cvLr/8cnzzzTfWvm+++QZXXHEFDj30UADmNyHIm//atWsxZswYPPbYY2jVqlXYpQTiT3/6E4YPH551/vHjx+OYY45B7969cfHFF+O2227D3Xff7fufO2HCBMt+vnnz5qqxz9cSduHNEsF0FshZ50YKemHbYTnX84p2DqeFT2usV0IsY4a1EURYRDocJdCW/lns2rWr4492rw9OAKB///546KGHsGDBAtx333347LPP8H//93/4/vvvI/lgpRaIojZSnSkv7nTYnMd6uOv8xLpcs+vsLatSoHPjta8YSKgjCoXqTHmJ6hqMak35cYdNpNziHdy3K0esCxI4QWIdUSih6kyd1JrQLbGzZs3CKaecgt13390qCGvXrsWee+6Jp59+GoA5k+6aa67Je64VK1Zg48aN6NOnj7VP13W8+uqruOeee/Diiy8imUxi06ZNjm/chg0bPG2Mbv7xj39g9erVePzx/PMD+vfvD03T8Pnnn2O//fbLur+hoQENDQ15z0OUBinWOdJhfYQ6v1ZX2Q4bpBWW89yWBl3nWaJbMTPlSKQjysHatWsdn9b7vccdf/zx1te9e/dG//790b17d/zlL39B69ati1qDrut4/fXX0bt376wCWU1EURupzlQOwqO+5HPX5TpW18KHTpQCEuuIlobqTHREdQ1GtablkB+45HJK28U6t7vOTS6xLlcIhR928S3sLLyYj++HxDqiHNRyrQkt2O233354//338f/+3//Df/7zH2vfcccdB8bMX9yf//zngc41aNAgvPfee459I0aMQM+ePXHllVeia9euiMViWLRokZXwunr1aqxZswYDBuQPJZg1axb69u2Lgw8+OO+xq1atAmOsauZY1CNSrBMaDyzW5WqHle46oTEIjVu3GRdWweSqQMr9MVcau2gnhALGDEu0q9RAiH4v3lzuJRAVRLt27QLPe7DTvn177Lvvvvj4449x3HHHFfXBCuccP/3pT/HBBx9U9YVUlLWRKD1+6bB+rmg/152Xu87xuKzU2JYT7dyBEyTWEeWA6kx0UJ2pDdzuOmt/+l/7ZYcMmTDvD+asK3R+HYdS0QEWBJGLWq41oQU7wEy7GDp0KI455hg0NDRAUQpT0tu2bYsDDzzQsa9Nmzbo0KGDtX/kyJEYP348dt55Z7Rr1w6jR4/GgAEDHEP/evbsiSlTpuDUU0+19jU3N+OJJ57AbbfdlvW8S5cuxZtvvoljjz0Wbdu2xdKlSzFu3DicffbZFINeZvzm1znEuiIIEzTBVAGkzPZVv+d1i3Z+5GqxIncdEQVCKIHDTeRxhx56KDjnaGpqQlNTU+Dn2rJlCz755BOcc8456Nu3b1EfrADAgQceiE8//RQ9evQIvIZKJKraSLQ89t8dL3ed49iAzjq7WKcLBk3n0IXi68xLiejbXyUk1hFRQHWm/FCdqR6uj+Xv8HLPrbMHTdiRt/2EOkvUK7JFVjruSLgjykWYOiOPB2q71oQW7IQQuOmmmzBjxgxs2LAB//nPf7Dnnnvi2muvxR577IGRI0dGtjgAuOOOO8AYw7Bhw5BIJDBkyBDce++9jmNWr16NzZs3O/bNmzcPhmHgzDPPzDpnQ0MD5s2bh0mTJiGRSKBHjx4YN24cxo8fH+naiXDkFOvccJEzaCLrHK5j/dx1fjBVh0gWpG9n4TW/zv48XlSqY4+oboImKl122WU46aST0L17d3z99deYOHEiOOc488wz0djYGOiDlVxMnjwZl112GW688Ub07dsXbdq0cdxfyCdmLU1L10aiMPycdW783HVSrLOLbtJd5zWzTjrqNJ1nvtYYND37j1EZMFEq0a6luGzrWeVeAlFBUJ2JDqoz1YNdrPNqh7W76/zEOru7zg+/+1MoLnbcS7gLMruOIMpFLdea0ArE5MmTMWfOHNxyyy244IILrP0HHnggpk2bVnSxeOWVVxy3W7VqhenTp2P69Om+jzGM7DerCy+8EBdeeKHn8X369MGyZcuKWicRLbmSYSVhRStr7p1rdp1brMucn5mDLm0XYozrYIzldMh5zbPLRdjjAX8hDyAxjyg9X375Jc4880x8++232HXXXXHkkUdi2bJl2HXXXQEE+2AlFyeccAIA4OSTT3a4BQzDgKIo0PVwvy/loNS1kSiefGJdvrAJt1hnJoN7i3VSnJPOOvNr012Xa5ZdKcQ6ctcR1QDVmfxQnal88rnq7Mmw9tl1ucQ6L3edW6jTHfcFF+t0GDmFuEJEuhQMmmNHVCzVWGtCC3YPP/wwHnjgAQwaNAgXX3yxtf/ggw/Ghx9+GHoBRH0TRKgLQr7ZdbnEOkMwh1inJ1XoOgNjzoLHmOFr0S1EhCOIKBE6h64EE29lolJQ+/i8efNyni/IByu5WLx4cUGPqySoNlY2QZ117nZXv6AJrzRwu1jnEOo0Bj1dOzRLyFMslx1XDOhG/ouboMdZaxVK1hw7x/nyjGPQQ7SkEPUB1ZnyQnWmsgnSAgt4u+vMr/OLdbmEOvP+zLVLLbe1PqmfXe4lECUiTJ2RxwO1XWtCC3ZfffUV9t5776z9Qgik/KbzE4QHocQ6j/YhP+xinb0V1q8N1i7WCZ1B1837RchEvyhEO6HxnG46goiSoPbxUnP00UeXewlFQ7Wxcskn1uX6IMY6xlZL7EETus59XXVSqJMin2YJeZnn04QSSoQLK9oVA2dGQaLdrW3mUlssYUF1JjqozlQufmKdXzpsyvAPmZDkEutyCXWVQKlddr/gj5JoRzio5VoTuvmiV69e+Mc//pG1/8knn8QhhxwSyaIIAjCFN7mFeYz1ta0V1nLR5RHrDJtY5zcTL5cop+s8y5Hh1WbluBBsQRdDUJcJUX3In+GgG2B+GtWrV6+CP0WKmm3btuHDDz/Eu+++69iqAaqNlUlQsS5XIqzf3Dq3WKcLhkQqhkQihmSSI5Hi2J5QoWkMiaRqiXXSXacV+N7PQw4V95qXB5TWQXdrm7klOzdRPqjOlBeqM5VJUGedbpjuOr9WWM0mzPmJdbpiZLW/eol1Lemu8wu6yDd/r1h+wR8t6fmJ8hC2ztRDrQntsLvuuutw7rnn4quvvoIQAvPnz8fq1avx8MMP47nnnitoEQThxpo/5yfW5Unxc8+ty/zLs1pgpVCn21qdhMbMf/PMNPIj3ywkeYw9YVamxYZ12THVP8XWi5UnXok+z/8h8PFE7VIpn0Z98803GDFiBF544QXP+6ththDVxurD/WFJvnRY8zFpgc4u8NlaYO2uOrejzmqLzSHWBQ2dkKJdULedXIN7np1cU772WIIoFKoz0UF1pnrRXW+xXq2wgFPk8hLrMseVV6TLem7FAPeoR+S0I1qKWq41oR12p5xyCp599lm89NJLaNOmDa677jp88MEHePbZZ3HccceFXgBB+GGJdbqSU5DKml/nGzKRLdbpSRVaUkUqEYPQOLRkzDw2LdbpaeFOIkW1qObV+TnthMZLEiZB7bZEJTJ27Fhs2rQJb775Jlq3bo0FCxZgzpw52GefffDMM8+Ue3mBoNpYvQT5gMVxvJaZWecW66SrLpFUkUhxy1FnuepcYp1dcJOtU34tVIHXZzhbbu3PJ9eQ9RiRWWdUkMuOqCSozhCVjJbHpZZLrNNtjryoiIFZW1Dc7j9JqZ12BFFJlKLWhHbYAcD//d//YeHChQU9IUEA+efX5RTrArggrPNYrbBOsU5LqjB0Zgp1tvZX99w6IXInxEaB22kHoGC3HVG/6DoLPKRVt9nHgwxoLTUvv/wynn76afTr1w+MMXTv3h3HHXcc2rVrhylTpuDEE08s29rCQLWx+pDv79JdF1q8czvrPFx18utCW2Bz4TfTTjcUR+usO4DCz3EHRNcqS3Psag+qM+WH6kz1Id11fu2wWccHFLjKHSqRgvAU9LzcdpQcSwQlTJ2RxwO1XWsKEuwIopTYxbqCHueDW6zTddM9pyXNXwPhanGScK57XsRJka0YQU/oDIwL6xyFtMi6BU2/Y0vh2COqm0qxj2/duhUdO3YEAOy000745ptvsO++++Kggw7CypUry7w6ggiGpjPHnDpzX8bhlq99VbbChnXY5RLt7MfYBUMp3uUS7ggiCqjOEER+VEPxddnZKUe4RAzM93n9RDvvYzOvj8Q7ImpqudYEEux22mknKEqwX6zvvvuuoIUQhBf53HVewRAKF462WHvIBABLrNOSsYyzLu2QALJddvkI0h7rJ+oxLizRzn4c53qWaJf1WA9hLpewR049olLZb7/9sHr1auyxxx44+OCDcf/992OPPfbAjBkzsNtuu5V7eb5QbaxsgobsBJld5wdnwnTYpd11dsKIdXb8Zti5XXPFINdlF+5ItCNqGaozREsT9MMXDsXhlrPfjkGxhK5SiXU6DHC4HXHBn8tLtPObaZd5TOb1EUQtUYpaE0iwmzZtmvX1t99+i8mTJ2PIkCEYMMBsa1y6dClefPFFXHvttQUtgqgv8rXDFoqi6lkuO6amhbCkFOYy8+zswRJyVp3b6eY8lxnuwJgRebKrXbQDMm2ydtEu6zG210piHKHrPESrknlcpdjHx4wZg3Xr1gEAJk6ciKFDh+Kxxx5DPB7HQw89VLZ15YNqY+USNB3WTr4PVgCAMeH4y0nP4SwoRKwrFD+XncRP7LO3ypLbjsgH1ZmWh+pM5RI0HRYw22El8i/2GJxtsaqhAIr/zLdcTrdyEmbOXdRQ4ETtEabOyOOB2q41imEYof4yGzZsGI499liMGjXKsf+ee+7BSy+9hKeeeqqghVQbzc3NaGxsxObNmyvCflktBJ1dZ2g8e36dhwsiK3BCPl4359LZZ9iZARMx6OmQCS2pOgS7VCqWdX6vtleh8dCCXa62Wbu7Q4p2bsHQT7Sz7g8p2FFKbPmJ6j1EnufZfteijdoq0GO2attx0ls3Vuz7l4xC79atG3bZZZdyLycQpaiNVGcKI4izTggFejp8SOJ+n3a876eP0/RMEFEyEYcuGJJJFYlEDIkURyKpWi2x0mHnFSwRJAnWiyAOO7dwl+8xqkd9iUK0oxl2lUEU7yNUZyqDUl2DUa0JTy6xzu6s042MWGefX2f/qz1l7Ut/eKIYSKWDJOxJsbridNpFOcPO7bALg5dgl8tdl/34wp+bBLvKoFx1BqiPWhP6T8YXX3wRQ4cOzdo/dOhQvPTSSwUtgqgPSi3W2VG4AFMFFJapmkwVYFyk79PBVOFw13m56vwcd/kENK9z+MG4sDY/onb0EUQlkkwmsXr1asTjcfTp06dqLqIAqo2VQlCxzk2Q92kAULkwXXYw6wJnwtxUAZWbX/tRbPorEMytxxXDseWjmDAMzoysjSAqGaozRKlIiWyxzrrP9dboVXGkaKYaCmJQwKEgln7Pj0EJJYKVmkJSZAminoiy1oT+LevQoQOefvrprP1PP/00OnToUPBCiNpH4QaUPJ/aS7HOQRHzhYC0UJd2oDGug3EBnr7oYmkhzn4BZsd+W35tnSvPhYmu80gTZqMS7chdV5uYbtGAm8gkKvXq1QvTp08v69q3bduGkSNHYocddsABBxyANWvWAABGjx6NqVOnlnVtQaHaWB3I91G7u87vfTrrgxqXaMdVHZzriMc1NMRS4MyAqgqo3BSuVG5AZd6iWRTiXRSozPB02GkBQp9InKs/qM6UF6oz1YNfMqwdWXlijn2Z914/0U6KZMW44rLWm3b05SOISBdWWPRrASbqk1B1pk5qTeiU2Ouvvx6/+c1v8Morr6B///4AgDfffBMLFizAzJkzC1oEUV8o3IDhuhgwNO6YPxdFoqnCBRgAoQFMBYQmwFRARcqcGafqUOOmUV3oHNBgOe1yXcDpOs87z64YoS5IiIWdfO2wUaTZErVJpSQqTZgwAe+88w5eeeUVh3tg8ODBmDRpEq666qoyri4YVBsrH9kGCyCvWOeH3zw7rjI0NNimEaXTxzU9PSNOAGAKUqLwdlhJVOETXkJdUHKJdZwZVkouQVCdiQ6qM5VBvtl1drHO2mf72qvq2OfZcSiAASs1Vt6W7bHcUAClNPPsvAIowlCoCzAFgwIoiKKo5VoTWrA777zzsP/+++Ouu+7C/PnzAQD7778/XnvtNat4EETBBGiFzYVf8ISeZGmRzRTvpFAn0ZLyi0IW7aTShLFKWw9RGjSdQQtomtb0zKdRlTCg9amnnsLjjz+Oww47zJGGd8ABB+CTTz4p27rCQLWxeihUrJMwLqDC/D2Sol3cdYymMWg6R0NMZMSrtGgXBVEmxvpBybGEG6oz5YXqTOXidk3b59a5P1b3+5jdS7STIRTu27AdF+Usu1xE7awjCC/C1Bl5PFDbtSa0YAcA/fv3x2OPPVbQExKELyHFOqaKnHPsANNlZ+gMCjPddYDpuFPtTgjI/QKMmyEUHAK669zScut22TleQonFsVypsQQRlkr5NOqbb75Bx44ds/Zv3brVUewqHaqN5cfrfdmLXAET7lEIfu/rKhfWH4pc1S3RTtM5WjWYl2u6cNUo0TKpsaVGF0pOlx0FThASqjPRQnWm/Lid0rnm1slq4r7qyI65y8ZLpIulk2TN/abLLmrRrliXnR+5HHQpGOSyI4qilmtNIPmyubk51Em///77ghZD1C763MMct92z7NyuuCixB1DIeXYKE1AbUoi3TiDeOomG1gmocc3cYimosRRiDebGVbMSsxzDxIFoxLqw7bBANO3DBFFO+vXrh+eff966LQvagw8+iAEDcofVlBOqjdWJfK+Wc0a9xDu/+ySeIRSqGULREEshHtfRqkFDQ1wLNNPOCznA3G/eXbmFP2p7JaoJqjNElOR7f5ZiXQrOFFgprNn3W4/xeS57CIWklPPsCiGfuy6fECfvp3l2RLVTiloTyGG30047Yd26dZ5qoRc/+tGPsGrVKuy5554FLYqoLzzFOpe7Lp+Tzn4uJT3TzX5e9zw7pL/mceE4RkuqEIxDCAYGp4NP18z2J+F2TJQYctQRQTAEg1AC/p6IyrCPb926FW3atMHNN9+M448/Hu+//z40TcOdd96J999/H2+88QaWLFnS4usKCtXG6sIdNCHyubhzJHfL++3z7IRgiDckHcdwLT1zSGPQBQNnClShYHvK+dzlFt8KxS3aURBFbUN1puWhOlN9/JAW67bDsObQ2dHSopRqKNCRW2yzu+cs0ctnnl0p22NztcOWqxX2Sf3ssjwvUVrC1Bl5PFDbtSaQYGcYBh588EHsuOOOgU6aSvl9RkAQ/lhOMdtFVFChzgsv4c5so82Ido7n1zkYF9CTKnSdmetRkT3XTktf7NnOWyp3XVCxTmg8b/gEQbgpt328d+/emDNnDo488kisWrUKU6dOxUEHHYT/9//+H/r06YOlS5fioIMOKtv68kG1sXoQQoHQmeWak0KblkO08/sDyS3kydZYL9EugRgabMdwjSGR4mgVE9B0BVpa8OKKkVO0iyKsIh/2eXUyJTbsHLt8rbJE/UF1pjiozlQHshXWLdalfNJXORSkFLMFVIMB1eP9Xwp5fiEU1nw7l2gHoCjhzi0gtoRYR846olhqudYEEuy6desWKn2oc+fOiMWCdOcT9Y6VDitTY9MXT0Jjvm2yio8wFaatVs60ExqzhC4mBLREDIhrUHQGgwtTuEs77rSkKerZHXZeCbFhKaQN1gsS7YiWYurUqZgwYQLGjBmDadOmAQC2b9+OSy+9FPPmzUMikcCQIUNw7733olOnTr7nGTZsGAYOHIgxY8bgpptuqrqUO6qNlcXKE6/03C+EglQy5hDrNJ1BCAbdp25wVUdSMKvdVbWJdF7OPPs8O8B8X+cqQwNS0LkZQME1wzqPOdsuc7wmFIdo59dmFRXuhFi3KKdywxLtCKIcUJ0xoTpT2dgTYVMGsB2mWKYpBranU1xTHi67FAzEDCUzL871dqva77Pt85pn5xbtAEQi3AHRiXU0n46oRKKqM0Bpa00gwe7zzz+P7AkJAgAMXXGIdULjWWKd4eN8cO9XXG4He1usFzKswh5EYT6vGUYhNDN4QujMEu5SiZjpnijCSRdWmCukFVa6/ki4qz90jUM3gv18SkdoIfbx5cuX4/7770fv3r0d+8eNG4fnn38eTzzxBBobGzFq1CicdtppeP31133Pdcstt+C0007D+eefjxdeeAGPPPIIDjnkkEDrqASoNlY+brEumVQtoU7XOXTBsgKGAICrAlzOpuM6hKp7indupMtOhlAkEXO4tOVvqCnuqeBMQSLFoDIjS7QrN1LEK8RlR9QmVGdaHqozlYufWPeDIqDDsIQ6P/eYdNilYL7vS5EtBiXTSuvz1ivbX71EO/M5heO4oASdhVeIs85PtHN/f+y3SeSrP8LUGaDwWhNlnQFKW2sKSokliNCoOmBzMniJdXahTrbCGjnmxSnpiyeGYKKdTIwFnKKdIRhYOljCq11WwBTAmGBgQgdjzHpzYMwI5LKLykUXNAHRz23X5/k/RLIOojYIax/fsmULhg8fjpkzZ2Ly5MnW/s2bN2PWrFmYO3cuBg4cCACYPXs29t9/fyxbtgyHHXaY3ylx2GGH4e2338Y111yDww8/HMcddxxU1flLOH/+/JCvjCBMsc4eHiHSrrpkIm4Jdbow3W9StJMhQ3L6t8p1xOMauM4Rb0iCMaeTLpd4BwDxWMoS7TgTprPOVPKAuAYkVQAiS7QDU/K67IKGVxRLGLGOINxQnSFqGQ3AdsMsGXaxbpuSeQPXc7xX65bQlnHdOVCQ1S4rRT6vfdxQoCsGYmAFi3b5KKYN1nIHerwGv+NJtCOCEKbWlKLOAKWrNS07PZ+oS/S/HOq4bVjtr06xTiRi0BMq9KQKQzDoSRXa9hhS2xqQ2tYAbXvM2oTGrOOExjzdeJYo6IMU6Szhz5Ygy1QBxnUwLsC4SCfIpqDGNdN5wXWHIJZLkItKrMusWw/koPMS9vxaxoj6pLm52bElEomcxzc1NeHEE0/E4MGDHftXrFiBVCrl2N+zZ09069YNS5cuzbuORCKBjRs3QlEUNDY2Zm0EUShybp3QGZIpFclEHMmkim0/NKS3OLb9EEMixZFIcWz7IWZtySRHIhHDth8akEyaj00lYxCCWaMRgoh3nAlLCORMpEVAHSoXVoqsFMXsraqlnllHEC0B1RmilriGPw4g465Lpf/1ctbpigFdMZCCyLnpioHtikAq/djt6fPoMGfgaUomvMJr1p0U+aSwxa3btjENFSZ6hZlZ53fsL/ijUS2HqAHC1JpS1RmgNLWGHHZESXGLddkHZMQ6KcIJjVvOOnt7KpAZ9s24nnbJmSKbIYQltAHZjjsgu5XWD9PFlu2041xAMAE1ljKdGQHaY6MW6+wEcdt5Oe1WnnglOe1qEF3n0BGuValr166O/RMnTsSkSZM8HzNv3jysXLkSy5cvz7pv/fr1iMfjaN++vWN/p06dsH79+pxrWbhwIc4//3zstttuWLFiBfbff/9Ar4EgJPYPIuR7ot1dB8AS637YHkciYQp0WtphByBrXpvKDWjpD3NaNWiWGy8e1xC3DjL/0azH2BwVtvdmqw6oZtq4bLWVTjuNCTTEAICZ7afMAETxybFS/NMCuMDDtr3m49Y2c3HZ1rMiOx9RGVCdIeoZKdZJ5Ht/CmYghBTrUsgIdUDuOXIcinUcFJZpgVVgzbgDXG2yHsQMxTErz8tpFwT33LwURNYcO10xfF12uj29tkBijufPtBV7Oe1+wR+ltNgaI0ydkccDwWtNqeoMULpaQ4IdUTIssc7eCpu+KDI0M2XVctYl1bRgF3OIdFKoM3QGXWfgXEDhAowzMN10wZkuO5EWpjQwVeSdc+eHbJVlqg49mTkH4wKIa9B1BiYYYg1mv1RC41ZbrLwoiyIxNihBW2TdkGhHAMDatWsd9vGGhgbf48aMGYOFCxeiVatWkT3/RRddhDlz5uDqq6/G73//e3Decr87RG3g5Rp2jymQ7ji7WJdIqtB0xTFvzS7a6cIAZ+mh3YKhIe6MC7dENyBLuMsFV4XVfqtyHTpjUFWRbpnNJLMCCNwaW6mQaEcAVGeI6sct1OlG5v1eBywnnFusC9KG6jxGWDPoZAqs9XVAvNtlMwmyUbXGuoU5d9tvocKdW5Szvx5qjyVyEaTWlKrOAKWtNRXVcDF16lQoioKxY8da+7Zv346mpiZ06NABO+64I4YNG4YNGzbkPM95550HRVEc29ChQx3HfPfddxg+fDjatWuH9u3bY+TIkdiyZUspXlZ94yEmWamwOksLdwza9pgl1mnJGLSk2RqrJVUktzUg8UMDtGQMqYR5X/KHOFI/xJH8oQGaJfhx6OnHCS0zBw/Idte57/fC3horcbfGxhqSAMxZdjIkwu6qszs8wuK+6PRrg5Utsl73U/hE/aALxZzLFWgzf7YGDRqEww47DI888gjatWvneyG1YsUKbNy4EX369IGqqlBVFUuWLMFdd90FVVXRqVMnJJNJbNq0yfG4DRs2oHPnzr5rfv311/HGG2/guuuuo4soomjcH15Y7bCCIZlUkUjEsPUHc0ukzN8DTTe37SkGTSiZTVesYxIpZj3ObKE1W2St+Xiaucl2Wd/kWZatvHFVQOUCqiqgcgOcRdcaK1+L330EERaqM0S9o9t0KdkOK911AHKKdSlFOLasc8PmykuLf+bjnG2ycsuc1/BMovUiSGtsGFFPtv3muj8ofmJcDIq1EbVPuDoTrtaUqs4Apa01BTns/vGPf+D+++/HJ598gieffBI/+tGP8Mgjj6BHjx448sgjC1pI1EkdQ4cOxezZs63b7v+04cOHY926dVi4cCFSqRRGjBiBCy+8EHPnzi1o/YQHrosWQ89+ozV0BkOYm5aIWa46LS3ACZER1uTMIJYW0gQTliDFuD2kIgWmmoJbPlHOjft4c74dyxLthMahxlLWPl03HYOZZFfdIdTpOi+oPVYIJVRaLAl0RBiCDmgdNGgQ3nvvPce+ESNGoGfPnrjyyivRtWtXxGIxLFq0CMOGDQMArF69GmvWrMGAAQN8z7ty5UrE43Hf+6uNUtRGojDsqbAyZELOqZN/3Gl6RtDKaj/1cLUlUtl/gHHVDJKQYpz9vT7fhzVa+n5TpBPpFlwGXRgOl51uKIgxICUKE+/cr02GVWhCcQiDBFEKqM5EC9WZyiIFONx1XmKdlzgn98cMV7spzHoQAzPFrvSpYlACi3JeFBNC4dUW63ec/fkkuVpoM8eTGEcUR5BaU6o6A5S21oQW7P7617/inHPOwfDhw/H2229bA/02b96Mm2++GX//+99DL6IUSR0NDQ2+SugHH3yABQsWYPny5ejXrx8A4O6778YJJ5yAW2+9FV26dAn9Gggn+lzv/xsZBGG1w9o33Zxhp+sMQuOmaCfM9lg7qUQMsYYUGBNQYdrSuZxtp8u0VwG1VQpuFJfDwS+F1u7UkG2y1m0urJZYi6TzsabIRuIZURu0bdsWBx54oGNfmzZt0KFDB2v/yJEjMX78eOy8885o164dRo8ejQEDBuR8n66li6hS1EaiOKS7ThcMiUQsPbNOscQwX7EuvY8rhiVqycfYRTtdY2hoSJnpr2CZhFm/9eRIPQcAVRXQk8xy2ck1StHOa40Sr8RYvxl48rXJ81uz7iKeY0cQYaA6kx+qM+VFuus0mO462Q4rW2J1xQgs1tnvzyfaccPZ5hpU3JJz7DKPCz7PzmuWnTxH1mvwOGcQkY9EOqKlKVWdAUpba0J/Vjt58mTMmDEDM2fORCwWs/YfccQRWLlyZUGLKEVSxyuvvIKOHTtiv/32w29/+1t8++231n1Lly5F+/btLbEOAAYPHgzGGN58803P8yUSiaz0EaII0u2wgG1IeHpOnWyJtYt1usagawyp9P5UIgYtZR4nW2VlC61skZVpsvb2V+nmk5sdy8mXK1k2LQzyuIZYg9kaq8ZSiMU1xGIpKz2WqXqWM062zfptWd8iPTM8PWpofl3toevM+j3Ju6VbxA899FD06tUL06dPL/r577jjDvzsZz/DsGHDcNRRR6Fz584FRZdXK1HURqoz4RHWB0DO90vp1papsLLFQqIJBbqh5Ax2kPfb22TNFlmO7QnVdOwlYkikYtDTbbe6lv7X1bIh75PhFZrrgyjODKhcpFNkjfRtwxLTuGJYLrtca7VvkpTIbPbjvXCHbxCEHaoz5SWqazCqNdFjD5mQuMW6mME8N8/zudpj7cJbSs7M89lyIUW0oK2xWQKkR8qt/XjfcxXhDiTqi1B1pgS1phLrTGiH3erVq3HUUUdl7W9sbMzq9w1CKZI6hg4ditNOOw09evTAJ598gquvvhrHH388li5dCs451q9fj44dOzoeo6oqdt55Z9/zTpkyBddff33o10eYONphZfCErR1WCnOW8852YaW7bgPp9lgNAGJQkQLUjPNOS6pQ0+l7hmBZrrpcuMU6u6jHuA6hczAuIHRmhl+ouvVLxLj5OpgmMu27tnP6tata96cvzOQFp7uF1ivxlSCKIWirkhevvPKK43arVq0wffr0SC7KqpEoaiPVmWiQH3ZoukyAzf4QJkwCq91tJ7E77VShQGemw07TuRkmkWSOmXV+7joZQGGdKx1A4fl5qgDAstedq1XWLfB5HUutsUQpoToTHVFdg1GtiQ5Nke2wua81/IS5XEinHYBMi6yNQpNYw4ZQyGNyiXzWDD+bMy9oK20QKHCCyEehtaYa6kzo36LOnTvj448/ztr/2muvYc899wx1LpnU8dhjj0Wa1HHGGWfg5JNPxkEHHYSf//zneO6557B8+fKs/5AwTJgwAZs3b7a2tWvXRrbeesGwiWEZN1v6X51lB0O4LrKsAd/pLZWKQdcYtFTMEvlkKIV03km3nSFkAi3PuTnWm6d9iXEBNa6Zol1cy7jtGjKOu1gshVhDEkzVwXn2BuQOjXBTSCIsQVQymqbhhhtuwJdfflnupRRFFLWR6kw43hpyte99UrSTQpmmMytgwi9wwe5Ec7vSpMDndtolkqrltksmzS2RiFnOO7lpOndsfsgACrfTDjCDKLxaX4OKdfn2S6Jw2XFmUEIsUTFQnXFCtSY8uuutV0dmfp0fuRx0YXG72YCM885vA/xFvTBOO+v5XKEXXgEY8rhgrym/IzCzXhLriMqnVLUm9LvIBRdcgDFjxuDNN9+Eoij4+uuv8dhjj+Gyyy7Db3/721DnKmVSh50999wTu+yyi1XkOnfujI0bNzqO0TQN3333ne95Gxoa0K5dO8dGhMfwEMZyBUPY3QdeoQ1eop2cfydbZIWemZcnxbsgmxcyfEK2xtpFOxmGwZiZLMtVAZ6+HYuZM/fcuMU7P+xtsSTaEV6ES1SKvlWpUFRVxR//+Edomla2NURBFLWR6kx0yPl1YcgX6OAW7RIplt4ywp3VKmsT8NybvZUjH0FFO7/X47eVAs4MayNqE6oz5SWqazCqNYUj59fZyduGWoRol5UI69GS6ufuCyPahRHuCsWvLTaMcEfUPmHrTD3UmtAtsVdddRWEEBg0aBC2bduGo446Cg0NDbjsssswevToUOcqZVKHnS+//BLffvstdtttNwDAgAEDsGnTJqxYsQJ9+/YFALz88ssQQqB///6hXgORH690WABpES0zv07CVAGRdBY3xsw2U871rOQ9d3usAAMTDIIJq0WWcWaJbNK5x1wiGcszMDwXMvQCGmAlYaSfSw4id1+c2QU8KTTahTn5Or3EPGcoBrXJEoVRTKtSlAwcOBBLlizBHnvsUe6lFEyUtZEoHF3njnoihTEZOJEP95w4P3HLy6VntsMa0NP1KwE42mJVq8YIT1GLMwO6UKDa6okumCMIQtPT7asiXFuvF/bwiezXEi6AgkQ6wg+qM9FBdabluYY/7ntfmKTVYsnXluqX0CpxB1DYjy00PTYI9rbYfC281PZKFEMt15rQgp2iKPj973+Pyy+/HB9//DG2bNmCXr16Yccddwz95FEldfTs2RNTpkzBqaeeii1btuD666/HsGHD0LlzZ3zyySe44oorsPfee2PIkCEAgP333x9Dhw7FBRdcgBkzZiCVSmHUqFE444wzKCG2hBgaN+fX2QIngOzWVyAt0KXFrnwuBEvAS4t2jOuWcJZKxNLtqAI6AMXmjJMXdYybjjhHEmxI8U7hAvJV2MVGOffO8bo8XB92EdItSOYjiHjnJXQSRKVw/PHH46qrrsJ7772Hvn37ok2bNo77Tz755DKtLDhR1kaiMOQHHlK003O4kf2SV4HcDjT5GHvKKpARuKQoyK3kVZ4RvjRTtDNn6xUn2uV7DQRBOKE6Q0RJKv1vS4p2uZ7PnegqRTKZMgtkBDO3cFdq0a6UPKmfXe4lEISDUtSa0IKdJB6Po1evXoU+PDB33HEHGGMYNmwYEokEhgwZgnvvvddxzOrVq7F582YAAOcc7777LubMmYNNmzahS5cu+OlPf4obb7wRDQ0N1mMee+wxjBo1CoMGDbLOf9ddd5X89dQr9kRYe2JrGPzELsAt2pmIJDPbUwWDln68XdDiXFgCnuN50uJdPtFOin528U+uU7rs5Hql6OgZnmF9nX3hla9V1o1fMAWJdbWPrnNoRrD/Z12Yxx166KHgnKOpqQlNTU2lXF5Ofve73wEAbr/99qz7FEWBrlePi7SlaiPhjbCCJpg571SwrITYKLALd5ZoZwtvkKKa06XGHDVKinZ+LmwgE0LhXr/KDN9ZfFER1mVH1D5UZyoDqjMti9f8OsAMnAAyAlg5hS572APgL9oB3m67ShHtyGVHhKkzQH3UmkCC3WmnnRb4hMXG3haS1GEYmTeV1q1b48UXX8z7PDvvvDPmzp1b8DqJYMh2WMtdZ+3PFu103S5eFZCm5CXa6RxMpOfOpcU7+bX5IAbOBQQXaaedSCfBBhPtwuAl1sk1S5ccCWtES1Ip9nEhovs9a0lasjYS2TBVdziMraCJ9Pw6XbCcAQ9RYHe4uZNkvTHTYzVLnMu4s3M5ys3W2sx9UbvsKCmWKBVUZ4qD6kxloqfnrqUCzhRtCXKJdkGwi3ZREVVKLEHko5ZrTaDfosbGRmtr164dFi1ahLfeesu6f8WKFVi0aBEaGxsjXyBRfehzD/PcLzRutcPKEAjAmRBrznIrYjhr+kItlVSt2UWpRMxMj01lEmST2+NIbmuAloxZybJCZ7aNO8IqrNeQ5+KPc5HlcPO6AJNJt/L7IoTSYmJdn+f/0CLPQ7Qs9kH2QTagMga0utm+fXu5lxAYqo3lY+WJVzpue7XDWvPrLNedbUZoCVpJdUOxNokMqJDJsua/aTExvbZ8Yx/UCD44cqfeEkQhUJ1peajOlI9c8+vsRC1yRYlcm1/ggx8tEUJBEF6ErTP1UGsCOexmz55tfX3llVfiV7/6FWbMmAHO5afZOn73u99VhKpJlA8/oc4xr04zxTlDhizYxbA86adBXXd2l4WEcz3dImtLnVUFmNAdzjuvNlkZTpFPrAuy9qzADEp8JcpIpXwapes6br75ZsyYMQMbNmzAf/7zH+y555649tprsccee2DkyJHlXqInVBvLw1tDrgaQ+73T3g5rCmOZttWWwCEIinQLa3oNulDS8+syLbKqtS9acgl0QdNmCaIYqM4UB9WZ8uAn1tkTYvUKTDe1t7JK0U067dytsV6UwmUXBmqHJQqllmtNaCvTn/70J1x22WVWoQDMuXHjx4/Hn/70p9ALIGoDL7EudzpsWrSTbgidWe46kd4P+M9+y4cpBnJHq6muc6RSMaRSMcuFl/ihwea8M912hsNpx6AlY55inX1uXRD8xDr37Lqw5yWIauemm27CQw89hFtuuQXxeNzaf+CBB+LBBx8s48qCQ7WxZTDFOm8c6bDpdlhNZ5ZwZ92XL6ku5LWK3b3m9VjdUEynXdphZ64v47aznHZC8U2yVVUBzgRUbhQl7OUK1CgUSoglqgGqM0RQgjrr7FRiUIMOw1qX3WkX1G3X0i47EuuIWqAUtSb0n26apuHDDz/M2v/hhx9W7XwIonj4WctCP0ZoUhAzxTItqUJLmaKZtLgKwRzuNL+2UbtA53atue8TGkcqEc+IeEnVaovVbF+7hTv35v+6cifgeq0xczxz/BsWv5RYovaRF/tBN6By7OMPP/wwHnjgAQwfPtxxIXLwwQd71ptKhGpj+WEud7SuMYe7LghScPMT7dzinNdxfqId4N8iK0W7TPuuswZomvO2yo28M+fK2f467vvh5XtyomRQnSkvVGdaHnfgRKwKNSW7aBfEPVfs3DlqpyWKIWydqYdaEzoldsSIERg5ciQ++eQT/OQnPwEAvPnmm5g6dSpGjBhR0CKI2oCftcy3LRZIDwhPZH7khM6hJ1WrNVakxTM7Qea6eYlfXomrWbgel0rEEGtIQUuqZotsPGWFUug6g5IOpch6Lrurw0doc4dLuNcp7/c6f1BIrCPCUin28a+++gp777131n4hBFKpVBlWFB6qjS1Dvxdvzumy80PTTYebl7uuFMJWSmS72azn9myRNYU7zpyLcQt1BFFtUJ2JDqozLcNk/fRALrtqEqbsgRRhwyj8kOerRHchUX/Ucq0JLdjdeuut6Ny5M2677TasW7cOALDbbrvh8ssvx6WXXlrQIoj6gakCTBXQk+Zt2QqrpWKWWGcX6bzEOLsw5SeA2fES/TjXM8emHW/yvFwVUJGChhgYE9Z9HAg11UE6A61/7c67iOcokVhHVDO9evXCP/7xD3Tv3t2x/8knn8QhhxxSplWFg2pjy5FPtNN0U/ji6bAGrzbTfCJdsa2j+R7vP0uPOZLO7eghR0PEWHFipMqDXYRlZvIRROVCdYYIw2T9dExg2aJdyu24gwIdDDoq/+9wt2gHeLvp7C48DqViBbkn9bPLvQSCyKIUtSa0YMcYwxVXXIErrrgCzc3NAFARaiZRGeRz2QFwhDoIjUNLJ7oC/k40O77tpAHFOvt+KdwxZpitsgCAFICYFUihxjXrOeXZ7K/BCNm+6l5nLhehrnNwXvl/BBDlR9MZNCPYz6ImMolKnHM0NTWhqamplMvLyXXXXYdzzz0XX331FYQQmD9/PlavXo2HH34Yzz33XNnWFQaqjS1LLtFO5QKc61C5bs5+SxnYnmKBkmGjmPGW7xy6oViBD5pQHG47k8IXIQU6uQYp2pVidp0dEu3qA6oz5YXqTMsyRThFO/tnKRwKYlCwHQA3FHClcoUtO3bRDigu4dZ9LqC6XIdEZRKmzgD1UWtCC3Z2qEgQXvCzlkF7ZIDPnWZhYKpNtAsYxgAAzOeCIJ9YJ9tWs2YcpQUxu9suBUAIHbG4PDeznHaAmSTrJdLZ22Fl+q0VmpHjNQVFCosEERWVYh8/5ZRT8Oyzz+KGG25AmzZtcN1116FPnz549tlncdxxx5V7eaGphO8pgXRIQ7CLkaCCVlTCl120c+y31Qgvh1uueXxusU5SarFOQqId4QXVmdJQCd/TesAt2rmJGQpSimGmsLaQyy6XKBZENPQS2vzSYfOdT95PQh1Rbmq51oQW7Hr06AFF8f+l/PTTTwtaCFFbqOcs9RbtdAZF1aEwAcZ1MFUH4zpSSRW6zvMKW0EFL99wCp35inbW+TXzdkZsE4AKh2gHmMKd9xqdV0eWa7AAsS6Iu47aYYlqxjAMfPzxx9h5553xwgsvQFWL+hypbFBtbHlyuey4qoOrApwZmYAGkZkjJ0WslnCf+SFFO7vLTuWGJXzJ20Ep1+sgiEqH6gxRDHlFuxZqGw0iigWdK+cn2gHhW2JJrCMIk1LVmtBnGTt2rON2KpXC22+/jQULFuDyyy+PZFFEbaCesxSp2UcGOpYx4RDZGDMKdqN5iXWMC0cCay7Rzn4ODmEKcBocoh1TdejpQArzeI85dQECM6LAPn8vH32e/0OJV0OUC01nli0877FGZdjHP/vsM5x88sl4//33AQC77747/vrXv6Jfv34tvpZiodpYHvq9eDP+edzvC358pYhcbtHO2m+7HSbtNgh+Lr+izkkuu5qG6kx5oTpTPnxGi4JDQarEYl0hgljY2XOFBlHkW5v9vLpigAcYTUHUN2HqDFAftSa0YDdmzBjP/dOnT8dbb71V9IKI2iI24rW8oh1j3k61QkS7XPPg7KJd4POlXXZ+a/RLhXUHTgRZH0G0NEHt4/fddx/uu+8+fP755wCAAw44ANdddx2OP/54AMD27dtx6aWXYt68eUgkEhgyZAjuvfdedOrUKed5L7/8cmiahkcffRStWrXCrbfeigsvvBArV64s+rW1NFQby8dPFt6EpQOvA2C6klOIZR1jiWA2l105CSOUuYU6rxCNKJCiof15wzj8JKVaH1GdUJ2JDqoz5eOP4nRcnnbZcQClzBWOyrFW6sAIctYRlUQt15rIPls+/vjj8de//jWq0xE1hGEPifBpI+WqOSTc7RRjzLC2KGBcZLnr3M8n0XWeJbjZE19zBWP4CXylCpDItRY7K0+8siTPT5QfXbBQG2B+GtWrVy9Mnz4957l33313TJ06FStWrMBbb72FgQMH4pRTTsG///1vAMC4cePw7LPP4oknnsCSJUvw9ddf47TTTsu75tdeew0zZ87EmWeeiVNPPRVPPvkk3nnnHWzdurX4b0iFQLWxZRjw8g3W1/I9nvu9DytG5M4yP+RzuTc3UigLKpBF7bYjiCBQnalMqM60DH8UpztuqyX68Kdc4RUpCM85dlEKc3oL1V6ieglbZ+qh1kQ2xOHJJ5/EzjvvHNXpiBoifsESJGce7dhnpH+5OBdgqkgnstpDGzzaWj1Eu2JCHOx4CWmc6w7hTWjMEZZh7svl6NMBqIHFNC8oJZYoJUE/jTrppJMct2+66Sbcd999WLZsGXbffXfMmjULc+fOxcCBAwEAs2fPxv77749ly5bhsMP8U6M3btyIffbZx7q92267oXXr1ti4cSN69OhR4KuqLKg2thx2B7V87zbn2AnIzydVZs6MA7xdblG570otCKo8kyzLFSOydUfhsuPMwB1tH8O474dHsiaiuqE6U3qozrQ8MSArYqLUjjbzef29NsUkvubD/dqKEfH8WmNTMBALcd5f8EfxpH52wesgaotarjWhBbtDDjnEMfDUMAysX78e33zzDe69996iFkPUF0wV4HENLGG2L3FV+A6J8BO9pIgnhTvO9aLbTvPNg5OpsYHOxQSYqkcm2kWRFLvyxCtplh0BAGhubnbcbmhoQENDQ87H6LqOJ554Alu3bsWAAQOwYsUKpFIpDB482DqmZ8+e6NatG5YuXZqzuCmKgi1btqB169bWPsYYvv/+e8faKiH1KR9UGyuXVg1a+hNYZgpQNtHOjV1oK0QEi0qoCzoLLtdrCfQ8hr94KQkj2tnXTKIdAVCdiRKqM5UFh2IGThgKUlAApeWSYoMSpYhY6LmkkGgXG0m0I6KmlmtNaMHulFNOcRQLxhh23XVXHHPMMejZs2foBRD1CY9rEBqDqjHEGlRoSRVC55Zox5jIakf1ohghzFpLWgyzC2HSXcdVM81WinTSZZdLtGPMTJUVSWady5ku6y8qegVfuJHiZDHCHYl2tYeuA1qO9DjHsekfna5duzr2T5w4EZMmTfJ8zHvvvYcBAwZg+/bt2HHHHfG3v/0NvXr1wqpVqxCPx9G+fXvH8Z06dcL69etzrsMwDOy7775Z+w455BDra0VRoOuV9QewF1Qby499TqnKzTELDTFz0pCma9D0mOVKky6yXGKXFLHKNfMun2gXpcvOHkAhvydhnHYUNFEfUJ0pL1RnKhMp3MEAuBKNQOaV4loJFCMAuoU72R5LQRSEnTB1BqiPWhNasPN74QQRFLOtNAWuMQiNg8c1xHdIQGxxCXR+kUxpdJ1b7jV7QEUxLrugaau5kEKjFPygpc+rccca8+EW79yPcbvtwqTFEgQArF271vFJT65Povbbbz+sWrUKmzdvxpNPPolzzz0XS5YsKer5Fy9eXNTjKwmqjZVHLJ6yaoGuMWgagy4Ux/w31SU0eQl4QYW7KNx1lqCYFseCOO2kyy5K0Q7IFu78RDsS64hcUJ2JDqozlYdqKICSrg8KoIMBEGWZQxcDK2lbrKRYIdGdSOsW7sK67AgCqO1aE1qw45xj3bp16Nixo2P/t99+i44dO1bFJ1REy+KeXwek22EbNBiCQU+qEDpDvFUSWlK13HWM6xA6h6453WxCZJxrxYp2fu46O0LngV12blcg4zp0jWXOaRPtwmB/Pfb1BW2Rdb+mg5+5NfQaiNqjXbt2ga3Z8Xgce++9NwCgb9++WL58Oe68806cfvrpSCaT2LRpk+MTqQ0bNqBz5845z3n00dnvDdUK1cbKJN6QhC4YGhpSSKQ4GoQ5z84vtCFfu2xLue3s4lgu0c7uspNrBAp3BbpFOyB7rp0dEuuIfFCdiQ6qM5WJaijgiumwk62x8BDOwop4Ubjscj1nOV18btEu+34S7Yhw1HKtCZ0Saxjev/iJRALxeLzoBRF1gEzwa5UCj2vmHDsuEGtIQY1rZgiFTQjjrqAHxoS1WYKbh7Msn4vNfb/XOXQtnQqbFssyKbHMum3t82nh5WpmrUzVHcm3QTb3eoud0UfUJrpQQm1A8EQlL4QQSCQS6Nu3L2KxGBYtWmTdt3r1aqxZswYDBgyI7PVVOlQbKwfOdTAuoHLzvTceS4Ez8zZnApwZULnh2+KpMsNfoPJx0RXrrtOE4tgAU7STYpzuIyK6W3zt6wmz2dENxVfwc4iDJNbVHVRnygvVmcoln7hUqOOuXImxLUE+N2Cqhl874U/YOlMPtSaww+6uu+4CYA7Ue/DBB7Hjjjta9+m6jldffZXmJxCeKKoOQ+MAN8DgnOemcDOUgXEBkf4aGiBcWrIU7aSAJpHCnnTauR1sYdtjne4100GnawxcFQ6nHQCrZVe67fxglrsu8DKcOOb0ma/HL4jCqy2WUmYJP4ImKk2YMAHHH388unXrhu+//x5z587FK6+8ghdffBGNjY0YOXIkxo8fj5133hnt2rXD6NGjMWDAgJzDWWsFqo2VD0+/J3JmQFVFOoDCXwQDPNpB8zijvZxpxWB/fum2yzvTrogAinxhG14uu3zrocAJAqA6EwVUZyoXv5lupWpPzedMC0u5Z+XZX49fEAVBBKGWa01gwe6OO+4AYH66M2PGDHCeERHi8Tj22GMPzJgxI/oVErUJF0B6ODhLBzswzsC5SItOwgptyIddtPN8Kg/RLqiI5RbnPNHkXD7b49zCYhGinVy/bPvNJ9q5H0sQxbJx40b8+te/xrp169DY2IjevXvjxRdfxHHHHQfArA+MMQwbNgyJRAJDhgypm8Q6qo2VwdKB1+W83+7U5kxAC+lUtothfq2xQRJXwxwHZIQyu2iXb53ycYUiX5/XTDv7WggiSqjO+EN1pjKJAUjZblupsS4Br1iXnFtUi1q0Kzck2hEtSTXWmsCC3WeffQYAOPbYYzF//nzstNNOJVsUUX8wVQBJj/3pOXZAtrvO+zzZLjvAfwacG79j7G47ANmOO5vbzrEeV9qtDKEIgxCZGXjuVFwv0Y7CJ+oTTVcCpyppRsY+zjlHU1MTmpqafI+fNWtWzvO1atUK06dPL8iGXu1QbaxM5PuiCiApMu/bqlCg5akldrEr1/w2P/K57bzu8xIA7amtUigDEEgsC7Nmv6ANL9HOTZBQDKJ2oDpTHqjOEF6iHYC8wp18TKXOsssHzbKrP8LUGaA+ak3o0IlaSlsiyoMMicjab2uLFUnmELt0LXtOnH3OndtlZ7WIutpjc2EXurxTWW1F0U90yyHGsQLcbu7ADfM5OKTLzo1btPN6HQQhCWofb2mam5vx8ssvY7/99sP+++9f7uUEgmpjdZPLkeYl2rVEAIXdiWdfg93h5g6dKAQ/V14u0Y5cdkRQqM5EB9WZyoQjHTjh9WFMSHddLoHNS1gL2nZrf1zQc/uROyyisDbgQl12T+pnF/R8RO1Ry7UmkGA3fvx43HjjjWjTpg3Gjx+f89jbb7+9oIUQtY01xy4kMiUWyA51kLcdARX2GXRWC6k8PtxFTa4WW69WWXuSrB23y84u3Amf55DOQq5mXH2O89pEOxLlCCA9tL5EzodS86tf/QpHHXUURo0ahR9++AH9+vXD559/DsMwMG/ePAwbNqxsa8sF1cbaIEj7aC4XmhdhZ9oFOZfXXDsgI9rlEtCCiHpBZuAVMsuOqB2ozrQ8VGcqh7HK44GPdQtXKUUgZgRvY/WbjReFG84uCgY5V5j222Jm9/mJduSyqy/C1BmgPmpNIMHu7bffRipldumvXLkSSohvIkE44AagK6aLLs+hdpHLctp5tKz6BT64nXyMGb6iXZh2UvsasgIofIQzt4hniY22ll/rWNc5wop25LIjglIpn0a9+uqr+P3vfw8A+Nvf/gbDMLBp0ybMmTMHkydPrtgLKaqNlQ/jwjlkqIIJKwC6RbtcBHXiuUU7L5ed1yw7Eu0IP6jOFAfVmcrGPcfOnmxaqoRXed6ohLtKotbm8xEtRy3XmkCCnd2C/corr4R+EoIIi9CY5a5zi3X2r72EKPu8Oim+SeHOq1U2+7nzOwGlECafTwgGDmGJb1ltrO7H21x3btHOy3UXpWh38DO35n19BNGSbN68GTvvvDMAYMGCBRg2bBh22GEHnHjiibj88svLvDp/qDZWJn7p4LrGoOneFwJuoSyoOy7q1tigTruwc/WiWFM+0ZBEO6KSoTpDRIn517eTfAJdEJddGJEvKuGu0pCiHbnsiGqkFLUmtIR9/vnn4/vvv8/av3XrVpx//vkFLUIydepUKIqCsWPHWvu2b9+OpqYmdOjQATvuuCOGDRuGDRs2+J4jlUrhyiuvxEEHHYQ2bdqgS5cu+PWvf42vv/7acdwee+wBRVEc29SpU4taP5Ebxe1cS184uRNVhcYtMUsIZolRXuRqW7XDVN3hnPNKVA3TMis0bgl7cg32UAw/159jTfYZfCEdcIwJcG6+JsYM6/Xavx/y9bjXSdQm8oI66AaY9vFevXqVfbBq165dsXTpUmzduhULFizAT3/6UwDA//73P7Rq1aqsawtKKWsjUTz292c9/f6s6UpRaaoSP3HP/rsWBTmDLJiRtbkpZuacfG75ehzBHEXO0COqB6oz5YXqTOWihRiBEDWlcvL5wQ3F2ryI0iGnl/H7SpSHsHWmHmpN6N+oOXPm4Icffsja/8MPP+Dhhx8uaBGAaWO8//770bt3b8f+cePG4dlnn8UTTzyBJUuW4Ouvv8Zpp53me55t27Zh5cqVuPbaa7Fy5UrMnz8fq1evxsknn5x17A033IB169ZZ2+jRowteP+FNavaROe83bG4HoTMYOnMETTgEKB/nWy5BLxeMGVnCnRCKtbmx3+cWwxzHuVtcVWFt2WsINuchSEKudSyJdkRAli9fjvfff7+ssx4AYOzYsRg+fDh23313dOnSBccccwwA01Z+0EEHlXVtQSlVbSQKx/ogw+N9Wre9x0chquVMUg15fq9zhZmHZz2mRG63XKKdHoEAStQWVGeig+pMdZJSCpvpFoZSiXZu8c0t0gUNhwiL1xy8VAsLk0R1Ucu1JnBKbHNzMwzDgGEY+P777x0Koa7r+Pvf/46OHTsWtIgtW7Zg+PDhmDlzJiZPnmzt37x5M2bNmoW5c+di4MCBAIDZs2dj//33x7Jly3DYYYdlnauxsRELFy507Lvnnnvwk5/8BGvWrEG3bt2s/W3btkXnzp0LWjMRHUwV0JPm17p03ekcqVTM/FrzFu3cs+bcbbJebVFeCbV+bbL5HHey7dT+3NDM9lU/pGjndhUWgl9rrH09Zrtu8Pl8BFEOfve73+EnP/kJ1q5di+OOOw6Mmb8fe+65p6MmVCKlrI1E9Ggas8QlTUTvgIsyhCKSNTEjMhHN/vpytefqQsFlW8+K5DkJIiqozhBEcQQV5woNnfA6D7XGEtVGKWpNYMGuffv2Vuvovvvum3W/oii4/vrrC1pEU1MTTjzxRAwePNjxQlasWIFUKoXBgwdb+3r27Ilu3bph6dKlnoKdF5s3b4aiKGjfvr1j/9SpU3HjjTeiW7duOOusszBu3Dioqve3JJFIIJFIWLebm5tDvEICgCMlVopmCs+8qQtbi6yVDJtjnpyfAFVoyEKuUArfNQgF8FhH3hl2qvAV7cK46XKJdkDme2EX7YjaRBNAKuCPr5a+vq6URKXFixfj2GOPRb9+/Rz7TzzxxDKtKDhR1kaqM9EjdAau6uCqcHyYUoo2TrtYZ28hjUp8cyNbXEs9N84eROElStpDKOzrImoPqjPlIeprMKo1pYFDQcwn3bUlnjsMxaS5Zj23oZSsdZVEu/ojTJ0B6qPWBBbsFi9eDMMwMHDgQPz1r3+1hukBQDweR/fu3dGlS5fQC5g3bx5WrlyJ5cuXZ923fv16xOPxLKGtU6dOWL9+faDzb9++HVdeeSXOPPNMR3LIJZdcgj59+mDnnXfGG2+8gQkTJmDdunW+kehTpkwpWJCsZ2IjXvNvi+UCiqpDYQIKE2BcOAQlzp1uOLujzdqXQ7QrhCChFC2JbwKuSxC0BMoAoh1BSColUWno0KHYfffdMWLECJx77rno2rVruZcUmChrI9WZ6GFcgOkCnAmoXIeqCmxLBP7TpyDsAl2pxLooCDO/z0u080qOBWimHeGE6kzxRH0NRrUmelRDARTzQ5oUlFCCQ7EUEjoRlVjXEviJdk/r55R5ZUQlUcu1JvBfrUcffTQA4LPPPkPXrl0te18xrF27FmPGjMHChQtLMvA1lUrhV7/6FQzDwH333ee4b/z48dbXvXv3Rjwex0UXXYQpU6agoaEh61wTJkxwPKa5ubmqin2lIVtTM/PddPPCStWBpJp1nDWLzUe0sx+fi6AOs2KFOyFY4Pl0fo8Per89cdYPu2hHEJXGV199hUceeQRz5szB9ddfj4EDB2LkyJH4+c9/jng8Xu7l5STK2kh1pnAGvHwDXj96kud9KheINySRTKpQuUBDTECTYwMiTnmtFgoJ2wgj2hFEpUF1JgPVmsKYZpyOscrjnvdxKIABcEW67BgA4XDb+SXE2gW3sO68WkuIJYhqpxS1JvQ7fvfu3cEYw7Zt2/Dhhx/i3XffdWxhWLFiBTZu3Ig+ffpAVVWoqoolS5bgrrvugqqq6NSpE5LJJDZt2uR43IYNG/LOnpNi3RdffIGFCxfmVVz79+8PTdPw+eefe97f0NCAdu3aOTYiGLERrznaYd0o3BTt1IYU1LjmE86QKWC5RCeZ3hpV62dLO9J0zRa6kQ7TsG9++KXGOs5dYDgHUR3oRrgNqJxEpV122QXjxo3DqlWr8Oabb2LffffF7373O3Tp0gWXXHIJ3nnnnbKuLwhR1EaqM8VxxJJJvvcxJhCPa4jHdTTENTTEcn+gEkbEizoNNihBgx7ytczmW7td3PMS5OyP10Q0ybtEZUJ1prxEdQ1GtaZwphmn57w/BgWxdIKqPbDBT6xzw6Hk3NzH1BoxMM+UWekIpNTY2idsnamHWhO6L+Sbb77BiBEj8MILL3jer+vBZ4cNGjQI7733nmPfiBEj0LNnT1x55ZXo2rUrYrEYFi1ahGHDhgEAVq9ejTVr1mDAgAG+55Vi3UcffYTFixejQ4cOedeyatUqMMZoaGuJiF+wBMmZR3vep6g6eFyDtj0GNa5BjacQa1CR+KHBKTzlcdq5cTvvChXxCpltBxTnspPCmvs12AU3+b2Rz5Nvnh1BuKkU+7idPn36oHPnzujQoQOmTp2KP/3pT7j33nsxYMAAzJgxAwcccEC5l+hJlLWRKB6v0CHOdTTEUtA1Bk1j0IWC7SnmmDcXhqgCJloau6DmDpEIgnTaeYVQEIQbqjPRQXWmMsjltFMNxXLZSccd8oh1OozA4lstinR2ZPur14w9r9ZYgpDUcq0J7bAbO3YsNm3ahDfffBOtW7fGggULMGfOHOyzzz545plnQp2rbdu2OPDAAx1bmzZt0KFDBxx44IFobGzEyJEjMX78eCxevBgrVqzAiBEjMGDAAEfgRM+ePfG3v/0NgCnW/eIXv8Bbb72Fxx57DLquY/369Vi/fj2SSTOKdOnSpZg2bRreeecdfPrpp3jssccwbtw4nH322dhpp53CfkuIgMQvWGJ+4RpIzVQBhQuorVKWyy7eKonWbX5ALK4hFks53GOSMCJaqcIWstpzhRmYIXyEMqExa3Mj3XVusc4SKQM4B/M57Qa8fEOwF0ZUHVrIDaicT6MA8737ySefxAknnIDu3bvjxRdfxD333IMNGzbg448/Rvfu3fHLX/6y3Mv0JcraSBROLpcdV3XE4xoaGlJo1WC67No06FCZAZUZaOACXDFqVniyz5bzcgUW4hK0f6/qsbW43qA6U16ozlQOdqddzHWfdNnFYLrscrnlJOUIqgiCXTTzc7flcr1F/brIaVf7hK0z9VBrQjvsXn75ZTz99NPo168fGGPo3r07jjvuOLRr1w5TpkyJPG3pjjvuAGMMw4YNQyKRwJAhQ3Dvvfc6jlm9ejU2b94MwOwblkXrxz/+seO4xYsX45hjjkFDQwPmzZuHSZMmIZFIoEePHhg3bpxjngNRGrKcdlwA6XRYHtcgNAY1bpY+pupgSTNNVQiGVCKGFABhm3HXUkEKQVx2Xum0QrDMO0kO3AJflliX/lcmvbpn9dndfIyJLGeJ0BkYr54Bs0TLUCmfRo0ePRp//vOfYRgGzjnnHNxyyy048MADrfvbtGmDW2+9taBgo5aipWsj4c8RSybh1SNvBJBxIqsAkoKBcx2cCcTjOoAktqcDKOxtpXb3WC7xLuz8O3s7abFto/ZkVpUb0IXi2/qar2XWcazPa3bPp7PPsyOIXFCdiQ6qM5UNtyXEypl2SAt2doFJik7246PC3U4adbhEWKGs1gMxiMqhlmtNaMFu69atVtvoTjvthG+++Qb77rsvDjroIKxcuTLs6bJ45ZVXHLdbtWqF6dOn51RLDSPz5rHHHns4bnvRp08fLFu2rKh1EoUTv2AJkjOOcbSpKqoOQ2fgcQ2x1pmoec4FUokYtKQKrgoIoQNxQHOJdmEpRORzi3a5zqGnHXTMY5acHa+wCHvIBpBpj3UnvboFQrtoZ7XLpr9PjAsIvfigGIIoBe+//z7uvvtunHbaaZ6hP4A5E2Lx4sUtvLLglLo2EuE46rVrLdHOTTyuQRcMOmNo1WB+oqIL5nCgSTGqVK2epRC8vES7MGJdPuR6pXDn9Rp0Q8HEVO4ZTwRRDqjOEFFzt3E6Rvu0xsZsYlzMEu9Mwcur3RMI1xobFL/nCoNsRQ16bKkhQY+oZEpRa0Jfwe+3335YvXo1AODggw/G/fffj6+++gozZszAbrvtFvZ0BGEhwyeYqlutsUo6OZapAozriMU1s+WzSFedEEqkQp+9VdUtwuVLcLVjd8a5QyLsX/u1xgrB4EyQ9Q6gIGqPFICUEXBLP6ZS7OOLFi3CmWee6VvYAEBVVSsprxKh2lh5HPXatY7b9jmfsjWWMwOtGjRwJqByA5wZULnZIhtktp2fmFcJ7aFeYl0QkTBICIU9XML9Pbg+5n0BS1Q/VGfKC9WZyuNu43TrZ90NT7fGAmnRDrBmr0kBrNZn0hFEWELVmTqpNaEddmPGjMG6desAABMnTsTQoUPx2GOPIR6P46GHHgp7OqJOiV/8CrbfM8jzPh7P9JAKnYFzAWELVWCMmS2hLidaIRTSUhtVC26hwl4ul10uGBdYOvA6mmNHWAS1j0+ZMgXz58/Hhx9+iNatW+Pwww/HH/7wB+y3337WMdu3b8ell16KefPmOcYXdOrUKfB63n//faxZs8aaNyo5+eSTg7+oMkG1sTKRH3Sw9PgFnh4noAsGzgS4KgCNQVVFenyB631YOOe9eQl0QdpnvYjKZSfbYgF/R53dPRgECpEgooLqTHRQnalMZhin42Kb0041FGiKszU2pRiIQUHKowXW3hpbCy67lmKI+hBe1M4r9zKICiFIrWmpOgNEW2sUI1//aB5ktHi3bt2wyy67FHOqqqK5uRmNjY3YvHlzRfRLVyuWaKenwxgSMQiNwRAM2vYY9KQKLWn+K1tjtVQMqaQKXeeO1thCiUqAk3PlTAegeSEoW2K90mLtAp3QueM15Up35dY5DTBV9xXsdJ1b7bXu85FoV36ieg+R57kYf0IDdgj0mAS2YQbOD/zcQ4cOxRlnnIFDDz0Umqbh6quvxr/+9S+8//77aNOmDQDgt7/9LZ5//nk89NBDaGxsxKhRo8AYw+uvv573/J9++ilOPfVUvPfee1AUxRproCjptsQqTL6LojZSnYmGxQNuBmB+AJRMqdA1072sC4ZkUoWmcySTHJpupscmUtwSvjTddJLZXWfFCFmqT70pRLhzzJTjudckBTv3a5G4X1MugdILr+/PNckzAj2WKC1RvI9QnalMoroGo1oTDRcrj1vCm6YYljgn96Xs+9Lvk45QB5uYV6hol09UK0a4K7YtNur5ejEwPKf9uqhzENFQrjoDhKs1pa4zQGlqTdFS+Q477IA+ffrUlVhHREerUYsctxVbMEKmFVaUrDXWC/N5srd8j4mCIGm27tbYXOIeUT8IAHrATf6WBbWPL1iwAOeddx4OOOAAHHzwwXjooYewZs0arFixAgCwefNmzJo1C7fffjsGDhyIvn37Yvbs2XjjjTcCzQsdM2YMevTogY0bN2KHHXbAv//9b7z66qvo169f1lzTaoFqY+Vw7NKrrRme5ocpmfdrsxVWT7fCCqhqpjXWj2LaXf2EOZlUG8W5wuLnGiz2XJPj8wpeE1GZUJ2pLKjOVBbuAImYh+jm1xoLOEW6QsMo8olgUbrkuKFYmxf5XkMUa/mZ+nDR5yAqizB1JmytKXWdAUpTawLZk8Kkp95+++0FLYSoX1qNWoTtdx4HpgroGgdTBUS6NUlhpkDHeO7W2FTK/FEu5bw2Kcq5RTW7WBfm+RkToebb2TFFOt23NZZEPCIIhSYqyVTunXfeGQCwYsUKpFIpDB482DqmZ8+e6NatG5YuXYrDDjss5/mWLl2Kl19+Gbvssov5e80YjjzySEyZMgWXXHIJ3n777dBrbAmoNlYPg/55FRb9ZKp123q/VAFosFpjAQGNCfi1xkoqKYwin7tOHhO2LTZMqy+1zxJ+UJ0pDqoz1cNM4wxcoGR/UCFbXmOG4miN5YbS4iEUQGEtsm5xzU+kcxxjW3vU7bSV1p5LlJ9Cak3UdQYoTa0JJNgFPbG0+hFEoShS/NJYWrgzBTymC4i0y04FrNRYIIUUAJ5u+7QnqpaKvG47V/urVzus/b5iRTtoHEzVs1NjA7j1iPqmubnZcbuhoSHnkFQAEEJg7NixOOKII6yY8vXr1yMej6N9+/aOYzt16oT169fnXYeu62jbti0AMznp66+/xn777Yfu3btbA7YrEaqN1cWgf16FF/veAsYEdDjfH1WeqT355tlJChXtNKHkdNMFFe28zmF3BvrNs5NCXBAKnc9HEBKqM8VBdaa6kKKdnGNniXMBRTv7PDugMNEuyLy5YubaeYl18nWEJYr5egQBhK81pagzQGlqTSDBrpIjzonaoNWYhdh22xDrNlMF9KTpoBMawLgONW7epwFggoGJtONN6EAcQDLjLAsTxuAOnhBpASws9uezz6+zw9RMURJapkVLBHDE5XLQCZto59gfUcsUUfmkgMB/0slEpa5duzr2T5w4EZMmTcr52KamJvzrX//Ca6+9FnaJvhx44IF455130KNHD/Tv3x+33HIL4vE4HnjgAey5556RPU/UUG2sPoasuAIv9r3F0RYrXXbWTfm+6SHa6bqClABi6d1RzLaTDjm7A64Qp527jVfe9hPuwlCIaEcz7GoPqjMtD9WZ6sPPaecmCtEu6rlw5YBEO8JOmDojjwfC15pS1BmgNLWm+In9BBERO1z6IrbdNsRy2SlMgNl/QpNwtMaqcQ1a0myNBQDBOOxzHMO47fzSYt2PzddqmstNl3WsKizRLrNPh/AI0sj1GuTa7Y66qGbqEbXN2rVrHfbxfK6HUaNG4bnnnsOrr76K3Xff3drfuXNnJJNJbNq0yfGp1IYNG9C5c+e867jmmmuwdetWAMANN9yAn/3sZ/i///s/dOjQAY8//nieRxNEOLxczdxy1QE6M1tjAWSJdpowAOYU7STFOtHcwp3dQecW7+R9fu2w9hZYzoyiRLswjjyCcEN1hqhHZhpnYAT7MwD4uuwc90XcHhuly47aT4lqIEytKVWdAUpTa+g3kKgodrj0RRgah6Fn/2jKAAoe1xBrSIGl59lx1fyXqTpiMfOKS9geL1tl84ltQdxonOvW5t5n3VaL+5QoiiANaoetP3QYoTYAGDRoEA477DA88sgjaNeunW9xMwwDo0aNwt/+9je8/PLL6NGjh+P+vn37IhaLYdGiTIjM6tWrsWbNGgwYMCDv2ocMGYLTTjsNALD33nvjww8/xH//+19s3LgRAwcOLPRbQhCeHP/2ZTj+7cus29z1QQtXhSOEwgymMIMoVGZYolxKmJudKIQtLxEuXyiFV0iG33kKgVpiCYDqDEGEYbY40/e+mE9bKWAKZG5xrhSz7ORzRUWQuXYtsQ6iuglbZ8LUmlLXGaA0tYYcdkTF0ebKv3vu/+8Vp0NtSEFLxKx5dkwwqEgBiME+z054CH5AONddPuznyJpbl77Pvp95CHleLjt57mIdgtQOS+Qj6IDWpqYmzJ07F08//TTatm1rzXFobGxE69at0djYiJEjR2L8+PHYeeed0a5dO4wePRoDBgwINKAVMIvot99+C0VR0KFDB2sALEGUip+9Ny5r37w9Zzjm2bmddrow32dbAdiuK1kuOyAj2kXptrP2hxTc7E47r+AJEuKIUkN1hqhnvES7M9ljAJA1zw6Aw2kHiLxJq3YnndstF8RlB4RrSS1GlItqHfbX9Jz265Kth6gugtSalqgzQPS1huRsomrY5RbTRmqmxpotsWo8Zf4bS5muCK5DjWtoaJV0uOHcLjgvx50UvPzaSZktoda9X1KMuy5MOy1BtCT33XcfNm/ejGOOOQa77babtdmt3XfccQd+9rOfYdiwYTjqqKPQuXNnzJ8/P++5169fj1//+tfYaaed0KlTJ3Ts2BE77bQTzj//fGzYsKGUL4sgsjjj04sBmCEUXk67hpjptlOZgVZ50llzue385tO5nXJBEmA9z68r1uZ1XoKoNKjOEPXCn8Vw6+uY5apTEEu76OyiGIeS112Xgih6Bhw53Ih6oJR1BihdrSGHHVF1MFVARQpaMgbOzQIlf5AZ15FKxCC4M4BBaNzTcZYrnMLPQecnrBUl1nEdusbAuR6onVU68HKtnzEj6zVznh1MQdQGGgywgG4ZzTCPO/TQQ8E5R1NTE5qamnyPN4z8523VqhWmT5+O6dOnB1swzESnww8/HFu2bMGIESPQs2dPGIaB999/H3/+85/x2muvYeXKldhxxx0Dn5MgiuWMTy/O6bRriAGcKVBFWhRzvc8GSZJ1zKfTFYcw5w6L8HLGFYvfXLpCwi6I+oHqDEFEw5/F8CynHeCcaQclI6IVOtcuqMvOfO6WCX+QgqRXqiwFUBBh6gwQrtaUqs4Apa01JNgRVcUutzyOjeNNeznjAohrQDqkQYU5TJwxASEYhM6haywzYFzjAEzBSujMfHxE2MU6r3TYoOcQMhnXI3giH37BGYBTfBzw8g0FrY+oPYK2KpWKO++8E5xz/Pvf/8auu+7quO+aa67BEUccgbvuugtXX311mVZI1Cv5RDuApefaKVaqrF28k7PtYixcGIUuFN95dFntsTkCJ+zkEvu81ibFRBLuiCigOkMQ3rhFOwCOFllH+6mPeCdbZnMJd2FEuygIKrrJ9t+wtPTrIaqDWq419NNOVB0dbzdTl2RrLI9r4HKmHRNmOAUTYOmWJsbMVlnzfsMSr/zm3IUlCrHOenzavWdfp+/zpu/P5ZiLIsCCIErF888/j6uvvjqrsAFAx44dMWHCBDz77LNlWBlBZGNvj22Ia44wCsdximHNtnOHUgD559D5JbrahbhCW2Xtz59PRCw0oIIgKgmqM0QlY2+PBbxbZIH8s+Psw/e9COpacwth7tuFCGxhnt/rOf3OR048opIoZa0hwY6oSuyiHQAoXFiinTXXLj3bLtaQQiyuIRZLWaJdLJ4qeg1cFTnbYAudSeclLgbFq+2XMYOEuzpAg4FUwE1Dxj7eq1ev0LbvqPjPf/6Dww8/3Pf+ww8/HKtXr27BFRFEBvc8O8Am2nklyHJnkqs9kCJsemwu0S6sWJfvePva3K46Eu0IO1RnCKI02B1y9gRZv9l2fkQl2smtFOiKkVf4Iwdd/RKmztRLraGWWKJq6Xj7n7Fx/JlWa6sA4PCaaTB/wjX7zhQE15FKxBGLp3LOgZP7vYQ3t1BXrLPOcY70eoPOsnPj1xpLoh3hptz28ebmZrRv3973/vbt26O5ubnlFkQQLtytsbrGwJkBXShQubNF1oHICGEpkb811j3HrlS4W2v9ZtkVyzXJMyI/J1GdUJ0hiNz8WQzH2WwuOBRLcLPPtXMjW07tx9vJNdsuinZSXTEiSYsNItqRi44ISi3XGpKviaom47QTYFxASW+cC8QaUp5uO8YEYg1JqHHNW/ByCWXWDDwfvMS6MO462cKbvV8nkY2oaQzDAGP+v1+KogQaEEsQpcTPaedOkHW3x/q1xvrNhvObNRdVwmuu8+Ry2RFENUN1hqgGHhVnAch22tlbZIFsl12+0AkvSimC5RLySHwjaplS1hpy2BE1hd1tB9gcdza3XawhBaFzIAkIZgZRALlDG+zkm1lXaCus4/xaZjadV9prGJiafn0BHHtE9aLDAMvRCuE+Fgie3lcqDMPAvvvuC0Xx/vmmiyiiUsgKoUDGbecOo9BFJrSBKwbAFEuwyxdA4ee0cyfH5sLrHG5nXb40WE0o1A5LZEF1hiBKx6PiLJzN5mbtl247d4JsLgEsX4JsqYMb/NxxUT7vi9p5kZyHqCzC1Bl5PFDbtYYEO6Lq6Xzno9bXX4/6teM+hYss0Y5BQEvCoYK722KFxsFU3dov02fzkesYoTGwADPvRFqos8IxChDa7OKjfC1M1Um0IxyU2z4+e/bssj03QYRFOu0e7T7Tsd8t2jXEANnAYBfFZGus3O8niLkFN3tybBjhzg+vxFkgW0wkpx0RBVRnCCI40mkHwEqQBWxz7RQgJdtmi2wZLVfaKqW8EqWglmsNCXZETdHlnoetr7/87XkAskU7L/HNEscCuuy8yCfo5RLrSoHXa5FuO6L2SCkAAqZ3pQDAKP+nUeeee26LPydBFMvZX1zguD3nR7PSLbKZOaScmRdXXqKXFMbCiHZu5Bw9+bXX4/M9zot8DkCivqE6QxAthz1BVop3MUMBFABG/hlw+Vx2xRDVHDuCcBOmzgD1UWtIsCPqgizRLscnO1LoKtRlFxShea9B99uvZ9xxodNj06+FIOyU+9Mogqg1GmLpcAqf2ad2l12xRDXbjiBKCdUZgoieGBTAAKAwACJnOmwuinG72QXDXOKdl3BILjsiamq51tBvClE3yDAKwOl2y+Woi7KFVGjMsWXdL5jVDpsv6MIu3uV8TpeTos/zfwi4WoIgCCIfc340C4A5e1RuKhdoiGtQuYFWsUytibGMWCdDHnLOkbO55IppgbUjz0PtrgRBENWJPYgiCpdbFGEQumJYm+f9BQqKJOoRBAl2RJ2hcGE5zbhqpsUyVYca18CYYW1uoUu3CWl+DrhiyCfQFXdu87XQ/LraJgUj1AaY9vFevXph+vTpZV49QVQv3DXuIB7XoXIzOZYzAw1c5HTVlVI804Xi2AD/dlnrMdTmRPhAdYYgKoOY7X06Bubb+hpUKIs6wdV+Pg6lqNZcEu3qi7B1ph5qDbXEEnUH5wKCCcRbJ8C4DqFzU4SLpUxBTueATdxyt8YC6XRA10VaIS2zUqiTzjq7GKjrvGiRLdPOW/hsPqJ2qWX7OEG0BO46IInHs0cQuIW5sPPi7OETQY71wi3WeYlzxc6wuyZ5RlGPJ2oLqjMEURrs8+zsrbF2cUyHUdJZdpKoBT83xQZsELVPLdcaEuyIuoJxAaTbYnXdFNi0pAo1ZopnqUTMOlZLqp4il12YEzoHs6fL5hHt3E46YWttlWJd1jFCCdwCa53Lfl6baEcQBEGUHpXrQNy1TyiWYKYJU6wLEkARFi+xzi7UUTssQRBEbeAW7WLIdrcFFe0KnSvXUkKaXBsJd0S9UVGC3dSpUzFhwgSMGTMG06ZNAwBs374dl156KebNm4dEIoEhQ4bg3nvvRadOnXzPYxgGJk6ciJkzZ2LTpk044ogjcN9992Gfffaxjvnuu+8wevRoPPvss2CMYdiwYbjzzjux4447lvplEi3E7vc9ZCXFuuFxDYrOoMH8JRCCmcl+qgA08xjBuG8ABeDtsgOCt7e6XXX2x0XhrrNjXzdRm+iKARbQGaPDqIhEJcn48eM99yuKglatWmHvvffGKaecgp133rmFV0YQuTn7iwvwaPeZnve5RTsziIKZCbDMKKloZ4fEOiIqqM4QROXhTo51u9FKKdqVQzwjt11tE6bOAPVRaypGsFu+fDnuv/9+9O7d27F/3LhxeP755/HEE0+gsbERo0aNwmmnnYbXX3/d91y33HIL7rrrLsyZMwc9evTAtddeiyFDhuD9999Hq1atAADDhw/HunXrsHDhQqRSKYwYMQIXXngh5s6dW9LXSbQsbtFO6Mx02cF026lxDQYXprNOBZhIi1oaEGtIIpWIe4p2AMCYcLSwsoCCWC5XnTUrT5Mz86K7uArr0iNqn0qxj7/99ttYuXIldF3HfvvtBwD4z3/+A845evbsiXvvvReXXnopXnvtNfTq1avMqyUIJ0FFu+0JFQ0xoBjRLl9brNtdl0uso1l1REtAdYYgiufPYjjOZI/53p9PtJMEFe2A/LPjyimavaidV7bnJiqTWq41FTHFccuWLRg+fDhmzpyJnXbaydq/efNmzJo1C7fffjsGDhyIvn37Yvbs2XjjjTewbNkyz3MZhoFp06bhmmuuwSmnnILevXvj4Ycfxtdff42nnnoKAPDBBx9gwYIFePDBB9G/f38ceeSRuPvuuzFv3jx8/fXXLfGSiQqAcQHGBRQuEGtIgTFTwGNct5xzTNUdIRR215slsoUIobC76kop1rnddANevgEDXr6h4PMRRCk55ZRTMHjwYHz99ddYsWIFVqxYgS+//BLHHXcczjzzTHz11Vc46qijMG7cuHIvlSA8OfuLCxy3NdsHJCrXwZmBVg2aFUShclN0s4tzXsmx+QIiclGIs47m1xG1CtUZotr5sxie8/6YoTiSY+2Cm3uuXbkpJkjiOe3XEa6EIKKlFLWmIgS7pqYmnHjiiRg8eLBj/4oVK5BKpRz7e/bsiW7dumHp0qWe5/rss8+wfv16x2MaGxvRv39/6zFLly5F+/bt0a9fP+uYwYMHgzGGN9980/O8iUQCzc3Njo2oDna/7yHf+7xEOzmDjjHhEL7sApo9Nda6P4+DzX2/EMxXrMtHmPZWEurqAw0CqYCblv5UtFISlf74xz/ixhtvdHwy1tjYiEmTJuGWW27BDjvsgOuuuw4rVqwo4ypLD9WZ6sYt2tlpaEhZol1DTM9yyUmhzE+0K0q48xDronbXXZM8g8S6OoDqTG1AtaZ6ySfaAXCIdn7IFtlcVGLbKYl1tU+YOlMvtabsLbH/n737Dm+q7N8AfifppLRltyBljzJklS0/hqBlioDKUlmC8JaNiLwOQFAQB1VEmS8oFgu+Ak5AQAHZtQjiCyIgGwoo0AHSNsnz+6PmkKQZ56RJTpLen+vKJT05OXlS297tN9/neVJTU3Ho0CGkpaUVui8jIwMhISEoVaqUxfGYmBhkZGTYvJ7puPUad+aPycjIQIUKFSzuDwoKQpkyZexed+7cuZg1a5as10S+x9F6dqYpsqZYqvOp7W/0gw+9WFC0c7CenfUmFCbW02Dv3/iOzef4KfHfBefbKA4Cygp1Op0BLbe+Jvt8Kn7kto/v2rULb775JtLT03HlyhVs2LABjz76qHS/nHVDHcnMzMS1a9cKtYZfv35d+kOiVKlSyMvLk//i/BBzJrCFhuYjNzcYQUFG/OuG7T86ZgWvtZgeC6DQFFl702JtbTbxwt2Bdp9HCVvTdE3jY6GOHFEyTcmTWcOcuYdZ498cTY8NFhrk//MGkKPiVpeglQDuddsVZRdZe9NTE4NW2R6jjX4h8wKjwU6nN4t15Egg/02jasHuwoULmDBhArZu3SqtLeerpk+fbrGIYFZWFuLi4lQcESllr2in1Rmg1RlQ4Z1PHT5eTvHr2GMTpX/b67izV6wDgOZbXnf6HAcfetHpOYC88VLgMEBAI3Oag+kXNLkLtN6+fRuNGzfG8OHD0bdv30L3y1k31JHevXtj+PDhePvtt9GiRQsABcH73HPPSSF68OBB1KlTR9br81fMGf/naD073T/d26OuDbX7+Bn5/Z0+x1sRhdfatS7WPXd7kMNrOHueOSGp0r8dranHYl3x4smcATybNcyZe5g1ge8Lw1MO79+mH+b0GolBq5yuZ+doLTk568z1DPq4UDegdfGOhbriRUnOmM4HAvtvGlULdunp6bh27RqaNWsmHTMYDNi1axfef/99bNmyBXl5ebh165ZFl93Vq1cRGxtr85qm41evXkXFihUtHtOkSRPpnGvXrlk8Tq/X48aNG3avGxoaitDQUFdeJvkQR9Nj3aH+f5M9en2AhThyH7nvRnXr1g3dunWzeZ/1uqEA8PHHHyMmJgYbN27EgAHO/6BfsmQJJk2ahAEDBkCvL9imOSgoCEOGDMGCBQsAFCyHsHz5crkvzS8xZwKDo6mx7uCsGOcOLMSRuyjpsPNk1jBn7mHW+D85U2OLyhsbO7AYR+4SyH/TqFqw69y5M44ePWpxbNiwYYiPj8e0adMQFxeH4OBgbN++Hf369QMAnDhxAufPn0ebNm1sXrN69eqIjY3F9u3bpQJdVlYWDhw4gDFjxgAA2rRpg1u3biE9PR0JCQkAgO+//x5GoxGtWrXy0KslIvIt1uvWuPJLvLN1Q+WEW8mSJbFs2TIsWLAAf/zxBwCgRo0aKFmypHSO6ec5ERH5D3fkDFD0rGHOEBEFrkD+m0bVTSciIyPRsGFDi1tERATKli2Lhg0bIjo6GiNGjMDkyZPxww8/ID09HcOGDUObNm3QunVr6Trx8fHYsGEDAECj0WDixImYM2cOvvzySxw9ehRPP/00KlWqJLUh1qtXD127dsXIkSNx8OBB7NmzB2PHjsWAAQNQqVIlNT4VRERFkq8xKroBQFxcHKKjo6Xb3LlzFT+vnHVDnfnkk09w584dlCxZEo0aNUKjRo0sgo2IiNSnVs4ARc8a5gwRke9TmjPF4W8a1TedcGbBggXQarXo168fcnNzkZiYiA8++MDinBMnTiAzM1P6+Pnnn8ft27cxatQo3Lp1C+3atcPmzZst5h2npKRg7Nix6Ny5s3T99957z2uvi4hIbRcuXLBoH1drisykSZMwevRoPPLII3jyySeRmJgInU7ejslEROS7mDNERORpgZw1qnbY2bJjxw4kJydLH4eFhWHRokW4ceMGbt++jfXr1xdaZ04IgaFDh0ofazQavPrqq8jIyMDdu3exbdu2Qgv7lSlTBmvWrEF2djYyMzPxn//8h++0EVGxEhUVZXFzJdzM1w0152itUWtXrlxBamoqNBoNnnjiCVSsWBFJSUnYu3ev4vEQEZHvcEfOAEXPGuYMEVHgCuS/aXyuYEdERMrlwajoBhTsqFS/fn0sWrTI5ec1XzfUxLRuqL21Rq0FBQWhZ8+eSElJwbVr17BgwQKcPXsWnTp1Qs2aNV0eGxERuY9aOQMUPWuYM0REvk9pzhSHv2l8fkosERF5htwdlXJycnDq1Cnp4zNnzuDw4cMoU6YMqlSpIq0bWrt2bWkLdPN1Q5UoUaIEEhMTcfPmTZw7dw7Hjx9XfA0iIvINSnaJ9VbWMGeIiAJLIP9Nw4IdERE59NNPP6FTp07Sx5MnTwYADBkyBKtWrZK1bqgzd+7cwYYNG5CSkoLt27cjLi4OAwcOxH//+1+3vx4iIvI9ns4a5gwRUfHmj3/TaIQQwqVHFnNZWVmIjo5GZmam7HcOiYhM3PUzxHSdZroF0GnCZT3GIP7GIcMk1KlTBzqdDklJSUhKSnJ5DEU1YMAAfP311yhRogSeeOIJDB48WHbreSBjzhBRUbnj5whzJrAxa4ioKNTKGaB4ZA077IiIiiklU5U8SafTYd26dTZ3Uvr111/RsGFDlUZGRERFwZwhIiJPC+Ss4aYTREQBIE9jVHQD3LcYeFGlpKSge/fuUrBlZ2dj6dKlaNmyJRo3bqzq2IiIqABzhoiIPElpzhSHrGGHHRFRMeUr70aZ7Nq1CytWrMDnn3+OSpUqoW/fvqoHLxERuY45Q0REnhbIWcOCHRERqSYjIwOrVq3CihUrkJWVhSeeeAK5ubnYuHEj6tevr/bwiIjIzzFniIjI0zyVNZwSS0QUAAwwKroB6reP9+rVC3Xr1sUvv/yC5ORkXL58GQsXLlRlLERE5BhzhoiIPElpzhSHrGGHHRFRMaV2+/imTZswfvx4jBkzBrVr11ZtHERE5BnMGSIi8rRAzhp22BERkSp2796N7OxsJCQkoFWrVnj//ffx559/qj0sIiIKEMwZIiLyNE9mDQt2REQBIA9G5MEg8+Yb7eOtW7fGsmXLcOXKFTz77LNITU1FpUqVYDQasXXrVmRnZ6syLiIiKow5Q0REnqQsZ4pH1miEEMKNYy02srKyEB0djczMTJ/akYSI/IO7foaYrlMz6A3oNGGyHmMQd3FaP80nf36dOHECK1aswOrVq3Hr1i089NBD+PLLL9UeliqYM0RUVO74OcKcCWzMGiIqCrVyBigeWcMOOyKiAJCvMSJP5i1f4xvvRtlSt25dzJ8/HxcvXsSnn36q9nCIiOgfzBkiIvIkJTlTXLKGm04QERVTai/Q6ohOp8Ojjz6KRx99VO2hEBGRi5gzRETkaYGcNeywIyIiIiIiIiIi8iEs2BERBYB8GBXdAN9sHyciIt/EnCEiIk9SmjPFIWs4JZaIqJjy5fZxIiLyf8wZIiLytEDOGnbYERERERERERER+RAW7IiIAsBdjV7RDQjs9nEiInIv5gwREXmS0pwpDlnDgh0RUTGVlpaGY8eOISkpSdb5ixYtQrVq1RAWFoZWrVrh4MGDHh4hERH5M+YMERF5mpKs8becYcGOiIicWrt2LSZPnowZM2bg0KFDaNy4MRITE3Ht2jW1h0ZERAGAOUNERJ7kjznDgh0RUQDI1xiRJ/OWr1G+o9I777yDkSNHYtiwYahfvz4WL16MEiVK4D//+Y+nXxoREfkA5gwREXmSkpxxJWv8MWe4S6yLhBAAgKysLJVHQkT+yPSzw/SzpKgEcgGZlxLIBQBs375d2lEpKysLoaGhCA0NLXR+Xl4e0tPTMX36dOmYVqtFly5dsG/fvqIPnmxizhBRUbkza5gzgYlZQ0RFoVbOSOdDXtb4a86wYOei7OxsAEBcXJzKIyEif5adnY3o6GiXHx8SEoLY2FhkZMxT9LiSJUsW+vk1Y8YMzJw5s9C5f/75JwwGA2JiYiyOx8TE4LffflM8ZpKHOUNE7lKUrGHOBDZmDRG5gxo5A8jPGn/NGRbsXFSpUiVcuHABkZGR0Gg0sh6TlZWFuLg4XLhwQaoABwq+Nv8TqK8L8I/XJoRAdnY2KlWqVKTrhIWF4cyZM8jLy1P8/NY/u2x1PZB6mDOW+Nr8E1+butyRNcyZwKY0a/zh695VfG3+ia9NXWrmjOn5AzlrWLBzkVarReXKlV16bFRUlM9+wxUVX5v/CdTXBfj+aytKZ525sLAwhIWFueVatpQrVw46nQ5Xr161OH716lXExsZ67HmLO+aMbXxt/omvTT3uyBrmTOByNWt8/eu+KPja/BNfm3qYM57DTSeIiMihkJAQJCQkYPv27dIxo9GI7du3o02bNiqOjIiIAgFzhoiIPMlfc4YddkRE5NTkyZMxZMgQNG/eHC1btkRycjJu376NYcOGqT00IiIKAMwZIiLyJH/MGRbsvCg0NBQzZswIqDnVJnxt/idQXxcQ2K9NLf3798f169fxyiuvICMjA02aNMHmzZsLLdxK6grkr32+Nv/E10ZyMWf8QyB/3fO1+Se+NpLLH3NGI9yx/y4RERERERERERG5BdewIyIiIiIiIiIi8iEs2BEREREREREREfkQFuyIiIiIiIiIiIh8CAt2REREREREREREPoQFOyqkY8eOmDhxYkA979ChQ/Hoo48W6RrVqlWDRqOBRqPBrVu37J63atUqlCpVqkjPVdx17NhR+lwfPnxY7eEQkQcwa2xj1ngPs4YosDFnbGPOeA9zhoqKBTvyGevXr8fs2bOlj6tVq4bk5GT1BmTDq6++iitXriA6OlrtoQQEe78IrF+/HgcPHvT+gIgo4DFrih9mDRF5E3Om+GHOkKcEqT0AIpMyZcqoPQSnIiMjERsbq/YwAAD5+fkIDg5WexgeUaZMGWRlZak9DCIKQMwaZZg1RETKMGeUYc4Q2ccOO3Lq5s2bePrpp1G6dGmUKFEC3bp1w8mTJ6X7Te8obNmyBfXq1UPJkiXRtWtXXLlyRTpHr9dj/PjxKFWqFMqWLYtp06ZhyJAhFi3d5u3jHTt2xLlz5zBp0iSpjRgAZs6ciSZNmliMLzk5GdWqVZM+NhgMmDx5svRczz//PIQQFo8xGo2YO3cuqlevjvDwcDRu3Bj//e9/Xfr8rFq1ClWqVEGJEiXQp08f/PXXX4XO+eKLL9CsWTOEhYWhRo0amDVrFvR6vXT/b7/9hnbt2iEsLAz169fHtm3boNFosHHjRgDA2bNnodFosHbtWnTo0AFhYWFISUkBACxfvhz16tVDWFgY4uPj8cEHH1g894ULF/DEE0+gVKlSKFOmDHr37o2zZ8/Kfn3Orj9t2jTUqVMHJUqUQI0aNfDyyy8jPz9fuv/IkSPo1KkTIiMjERUVhYSEBPz000/YsWMHhg0bhszMTOn/8cyZM2WPi4gCC7PGMWYNs4aIioY54xhzhjlDPkgQWenQoYOYMGGC9PEjjzwi6tWrJ3bt2iUOHz4sEhMTRa1atUReXp4QQoiVK1eK4OBg0aVLF5GWlibS09NFvXr1xKBBg6RrzJkzR5QpU0asX79eHD9+XIwePVpERUWJ3r1723zev/76S1SuXFm8+uqr4sqVK+LKlStCCCFmzJghGjdubDHeBQsWiKpVq0ofv/HGG6J06dLi888/F8eOHRMjRowQkZGRFs81Z84cER8fLzZv3ixOnz4tVq5cKUJDQ8WOHTvsfl6qVq0qFixYYHFs//79QqvVijfeeEOcOHFCvPvuu6JUqVIiOjpaOmfXrl0iKipKrFq1Spw+fVp89913olq1amLmzJlCCCH0er2oW7eueOihh8Thw4fFjz/+KFq2bCkAiA0bNgghhDhz5owAIKpVqyY+//xz8ccff4jLly+LTz75RFSsWFE69vnnn4syZcqIVatWCSGEyMvLE/Xq1RPDhw8Xv/zyizh27JgYNGiQqFu3rsjNzbX7Wk2cXV8IIWbPni327Nkjzpw5I7788ksRExMj3njjDen+Bg0aiCeffFIcP35c/P7772LdunXi8OHDIjc3VyQnJ4uoqCjp/3F2drb0ONNr/vnnn52Ok4j8D7PGNmYNs4aI3IM5YxtzhjlD/oMFOyrEPGR+//13AUDs2bNHuv/PP/8U4eHhYt26dUKIgnADIE6dOiWds2jRIhETEyN9HBMTI958803pY71eL6pUqWI33ISwHSZywq1ixYpi/vz50sf5+fmicuXK0nPdvXtXlChRQuzdu9fiOiNGjBADBw60+3mxNZ6BAweK7t27Wxzr37+/Rbh17txZvP766xbnrF69WlSsWFEIIcSmTZtEUFCQFOBCCLF161ab4ZacnGxxnZo1a4o1a9ZYHJs9e7Zo06aN9Dx169YVRqNRuj83N1eEh4eLLVu22H2tcq9vy5tvvikSEhKkjyMjIy3C0NzKlSstPlfmGG5EgY1ZYxuzpvD1bWHWEJEzzBnbmDOFr28Lc4Z8AdewI4eOHz+OoKAgtGrVSjpWtmxZ1K1bF8ePH5eOlShRAjVr1pQ+rlixIq5duwYAyMzMxNWrV9GyZUvpfp1Oh4SEBBiNRreONzMzE1euXLEYb1BQEJo3by61kJ86dQp37tzBQw89ZPHYvLw8NG3aVNHzHT9+HH369LE41qZNG2zevFn6+MiRI9izZw9ee+016ZjBYMDdu3dx584dnDhxAnFxcRbrSJh/rsw1b95c+vft27dx+vRpjBgxAiNHjpSO6/V6aQHZI0eO4NSpU4iMjLS4zt27d3H69GmHr03O9QFg7dq1eO+993D69Gnk5ORAr9cjKipKun/y5Ml45plnsHr1anTp0gWPP/64xdcKERGzxjFmDbOGiIqGOeMYc4Y5Q76JBTtyC+uFQjUaTaE1FtxBq9UWuq752gJy5OTkAAC++eYb3HfffRb3hYaGFm2Adp5v1qxZ6Nu3b6H7wsLCFF0rIiLC4roAsGzZMoswBwp+eTCdk5CQIK0NYa58+fJOx+3s+vv27cPgwYMxa9YsJCYmIjo6GqmpqXj77belc2fOnIlBgwbhm2++waZNmzBjxgykpqYW+qWAiMgZZo3j52PWMGuIqGiYM46fjznDnCHvYsGOHKpXrx70ej0OHDiAtm3bAgD++usvnDhxAvXr15d1jejoaMTExCAtLQ3t27cHUPBuzKFDhwottmouJCQEBoPB4lj58uWRkZEBIYS0aOvhw4ctnqtixYo4cOCA9Fx6vR7p6elo1qwZAKB+/foIDQ3F+fPn0aFDB1mvwZ569erhwIEDFsf2799v8XGzZs1w4sQJ1KpVy+Y16tatiwsXLuDq1auIiYkBAKSlpTl97piYGFSqVAl//PEHBg8ebPOcZs2aYe3atahQoYLFO0RyyLn+3r17UbVqVbz44ovSsXPnzhU6r06dOqhTpw4mTZqEgQMHYuXKlejTp4/N/8dEVPwwaxxj1jBriKhomDOOMWeYM+SbWLAjh2rXro3evXtj5MiRWLJkCSIjI/HCCy/gvvvuQ+/evWVfZ9y4cZg7dy5q1aqF+Ph4LFy4EDdv3pQCypZq1aph165dGDBgAEJDQ1GuXDl07NgR169fx/z58/HYY49h8+bN2LRpk8UP7gkTJmDevHmoXbs24uPj8c477+DWrVvS/ZGRkXjuuecwadIkGI1GtGvXDpmZmdizZw+ioqIwZMgQ2a9r/PjxeOCBB/DWW2+hd+/e2LJli0XrOAC88sor6NmzJ6pUqYLHHnsMWq0WR44cwa+//oo5c+bgoYceQs2aNTFkyBDMnz8f2dnZeOmllwDA4ecHAGbNmoXx48cjOjoaXbt2RW5uLn766SfcvHkTkydPxuDBg/Hmm2+id+/eePXVV1G5cmWcO3cO69evx/PPP4/KlSsX6fq1a9fG+fPnkZqaihYtWuCbb77Bhg0bpMf//fffmDp1Kh577DFUr14dFy9eRFpaGvr16weg4P9xTk4Otm/fjsaNG6NEiRIoUaKE7M8/EQUGZo1jzBpmDREVDXPGMeYMc4Z8lDpL55Evs14o9caNG+Kpp54S0dHRIjw8XCQmJorff/9dut/WIpsbNmwQ5l9e+fn5YuzYsSIqKkqULl1aTJs2TTz++ONiwIABdp933759olGjRiI0NNTiWh9++KGIi4sTERER4umnnxavvfaaxQKt+fn5YsKECSIqKkqUKlVKTJ48WTz99NMWi8EajUaRnJws6tatK4KDg0X58uVFYmKi2Llzp93Pi60FWoUQYsWKFaJy5coiPDxc9OrVS7z11luFPh+bN28Wbdu2FeHh4SIqKkq0bNlSLF26VLr/+PHj4oEHHhAhISEiPj5efPXVVwKA2Lx5sxDC8WKlKSkpokmTJiIkJESULl1atG/fXqxfv166/8qVK+Lpp58W5cqVE6GhoaJGjRpi5MiRIjMz0+5rVXL9qVOnirJly4qSJUuK/v37iwULFkivPzc3VwwYMEDExcWJkJAQUalSJTF27Fjx999/S48fPXq0KFu2rAAgZsyYIR3nAq1EgY1ZYxuzhllDRO7BnLGNOcOcIf+hEcIDk/KJnDAajahXrx6eeOIJzJ49W+3hyFKtWjVMnDgREydO9Phz7dmzB+3atcOpU6eK7WKmZ8+eRfXq1fHzzz87nGZARGQPs8YxZg2zhoiKhjnjGHOGOUNFo1V7AFQ8nDt3DsuWLcPvv/+Oo0ePYsyYMThz5gwGDRqk9tAUmTZtGkqWLInMzEy3XnfDhg3YunUrzp49i23btmHUqFF44IEHim2wdevWDQ0aNFB7GETkZ5g1jjFrLDFriEgp5oxjzBlLzBkqKq5hR16h1WqxatUqPPfccxBCoGHDhti2bRvq1aun9tBk27lzp7R7k/WW4kWVnZ2NadOm4fz58yhXrhy6dOlisSuRp5QsWdLufZs2bcL//d//eXwMtixfvhx///03AKBKlSqqjIGI/A+zxjFmjSVmDREpxZxxjDljiTlDRcUpsUTF2KlTp+zed9999yE8PNyLoyEiokDErCEiIk9izlCgYsGOiIiIiIiIiIjIh3ANOyIiIiIiIiIiIh/Cgh0REREREREREZEPYcGOiIiIiIiIiIjIh7BgR0RERERERERE5ENYsCMiIiIiIiIiIvIhLNgRERERERERERH5EBbsiNxo6NChKFmypNrDcGro0KGoVq2a2sMIOEII5OXlqT0MIgpwzJrijVlDRJ7GnCnemDO+w2cKdh988AE0Gg1atWql2hhWrVoFjUYj3cLCwlCpUiUkJibivffeQ3Z2tmpjk+vmzZsICgrCunXrCt1369YtVKxYEQ888ACEEIXu379/P7RaLaZOneqNoXpNTk4OJk6ciMqVKyM0NBT16tXDhx9+aPPcW7duYdSoUShfvjwiIiLQqVMnHDp0yOKcO3fuYObMmdixY4cXRu+6y5cvY+bMmTh8+LDix1p/L9i7mQJy+/btGD58OOrUqYMSJUqgRo0aeOaZZ3DlyhW3vqZvv/0WM2fOlH3+wYMH8a9//QsJCQkIDg6GRqNR9HwdO3a0+bq7du1a6NxPPvkE5cqVQ2RkJIYNG2Yz5O7evYsFCxagVatWiI6ORlhYGOrUqYOxY8fi999/lzWma9eu4YUXXsD999+PkiVLIiwsDLVq1cKwYcOwe/du6bx169ZBo9Fgw4YNha7RuHFjaDQa/PDDD4Xuq1KlCtq2bSt9XK1aNfTs2VPW2PwBs8Y9mDWFyc0aRz9fMzIypPOYNcwaZo1/Ys64B3OmMCV/06Snp6Nnz56IjY1FyZIl0ahRI7z33nswGAzSOcwZ5gxzxvcFqT0Ak5SUFFSrVg0HDx7EqVOnUKtWLdXG8uqrr6J69erIz89HRkYGduzYgYkTJ+Kdd97Bl19+iUaNGqk2Nme2bNkCjUaDhx9+uNB9pUqVQnJyMgYMGIBly5Zh1KhR0n16vR6jR49G1apVMWvWLG8O2aMMBgMSExPx008/ISkpCbVr18aWLVvwr3/9Czdv3sS///1v6Vyj0YgePXrgyJEjmDp1KsqVK4cPPvgAHTt2RHp6OmrXrg2gINxMn6OOHTuq8bJkuXz5MmbNmoVq1aqhSZMmih7bvn17rF692uLYM888g5YtW1p83ZjeeZs2bRpu3LiBxx9/HLVr18Yff/yB999/H19//TUOHz6M2NjYIr8eoCDcFi1aJDvgvv32WyxfvhyNGjVCjRo1ZAeIucqVK2Pu3LkWxypVqmTx8dmzZzFmzBjMnDlT+h5KTk7G888/L53z559/omvXrtIvUIMGDULJkiVx4sQJpKamYunSpU7fyTp48CB69OiB7OxsDBgwAKNHj0ZoaCjOnDmDjRs3YtWqVdi5cyfat2+Pdu3aAQB2796NPn36SNfIysrCr7/+iqCgIOzZswedOnWS7rtw4QIuXLiAAQMGKP48+QtmjXswaywpyRoT0/9/c6VKlZL+zaxh1jBr/BNzxj2YM5aU5Ex6ejratm2L2rVrY9q0aShRogQ2bdqECRMm4PTp03j33XcBMGeYM8wZvyB8wB9//CEAiPXr14vy5cuLmTNnqjKOlStXCgAiLS2t0H3bt28X4eHhomrVquLOnTsqjE6ep556SnTo0MHhOd26dROlS5cWGRkZ0rG33npLABDffvuth0dYICcnx+F9p0+fdsvzrFu3TgAQK1assDjer18/ERYWJq5evSodW7t2rQAgPvvsM+nYtWvXRKlSpcTAgQOlY9evXxcAxIwZMwo935AhQ0RERIRbxl5UaWlpAoBYuXJlofuGDBkiqlatquh6ERERYsiQITbv27lzpzAYDIWOARAvvviioudxJCkpSSj5sZWRkSF9vyp9rBBCdOjQQTRo0MDpeZ999pl49NFHpY83btwoevbsaXFOjx49hFarFf/9738LPf7u3btiypQpDp/jxo0bomLFiiI2NlYcP3680P1Go1GsWbNGHDx4UDpWvXp10bJlS4vzNm/eLDQajRg4cKBITEy0uG/NmjUCgPjiiy+kY1WrVhU9evRwODZ/waxxH2aNJSVZ4+j/vzlmTWHMGmaNr2POuA9zxpKSnBk5cqQICQkRf/31l8W57du3F1FRUdLHzJnCmDPMGV/jEwW72bNni9KlS4vc3FwxZswYUbt2bem+vLw8Ubp0aTF06NBCj8vMzBShoaEWXxRnz54VvXr1EiVKlBDly5cXEydOFJs3bxYAxA8//OBwHM5+iX799dcFALF06VLp2JEjR8SQIUNE9erVRWhoqIiJiRHDhg0Tf/75p3TO999/L4W3tZSUFAFA7N27VwghxJUrV8TQoUPFfffdJ0JCQkRsbKx45JFHxJkzZxyOXQghDAaDKF++vJg/f77D886cOSNKlCghBg0aJIQQ4vz586JkyZKif//+0jnffvutaNeunShRooQoWbKk6N69u/j1118triPntQshxIwZMwQA8b///U8MHDhQlCpVSjRp0sTh+DQajejUqZNISUkRf//9t9PXbs+4ceMEAHH79m2L45999lmh/5ePP/64iImJKfRDetSoUaJEiRLi7t274syZMwJAoZsp6EzhdvHiRdG7d28REREhypUrJ6ZMmSL0er3T8Zp+kGzZskU0btxYhIaGinr16onPP/+80Lk3b94UEydOFFWrVhUhISHivvvuE0899ZS4fv26+OGHH2yO0xR07g43e8qUKSP69u3r9Lxdu3aJxx57TMTFxYmQkBBRuXJlMXHiRItfJIcMGWLzNclVlHDLz88X2dnZds9LT08XZcqUEd9995347bffRPfu3cWkSZOk+/fv3y8AiJEjRyp6fnOmnz+pqamyH/PUU0+J4OBgi8/jyy+/LBo2bCg+/vhjER0dbfH1npSUJDQajcX3sL+Gmy3MGmaN9fjUyBrz//9ZWVk2s4FZM0TRY5g1BZg16mPOMGesx6dGzvTv319ERUUV+pumf//+IiYmRhobc0Y+5kwB5oz3+cQadikpKejbty9CQkIwcOBAnDx5EmlpaQCA4OBg9OnTBxs3bizUWrlx40bk5uZKrY63b9/Ggw8+iG3btmH8+PF48cUXsXfvXkybNs0t43zqqacAAN999510bOvWrfjjjz8wbNgwLFy4EAMGDEBqaiq6d+8uranQsWNHxMXFISUlxeZrr1mzJtq0aQMA6NevHzZs2IBhw4bhgw8+wPjx45GdnY3z5887HV9aWhquX7+O7t27OzyvWrVqmDVrFtasWYOtW7di/PjxCAoKQnJyMgBg9erV6NGjB0qWLIk33ngDL7/8Mo4dO4Z27drh7Nmzil67uccffxx37tzB66+/jpEjR9odX8WKFfHWW2/h+vXrGDx4MCpWrIixY8fi559/dvo5sJabmwudToeQkBCL4yVKlABQ0DJu8vPPP6NZs2bQai2/LVq2bIk7d+7g999/R/ny5aW1Ivr06YPVq1dj9erV6Nu3r3S+qWW9bNmyeOutt9ChQwe8/fbbWLp0qawxnzx5Ev3790e3bt0wd+5cBAUF4fHHH8fWrVulc3JycvB///d/WLhwIR5++GG8++67GD16NH777TdcvHgR9erVw6uvvgoAGDVqlDTO9u3bK/jsFU1OTg5ycnJQrlw5p+d+9tlnuHPnDsaMGYOFCxciMTERCxcuxNNPPy2d8+yzz+Khhx4CAOn1WLe4e8Lvv/+OiIgIREZGIjY2Fi+//DLy8/MtzmnWrBkGDx6Mhx9+GPHx8bh48SKmT58u3f/ll18CuPczxBVfffUVwsPDLb7WnGnXrh3y8/Nx4MAB6diePXvQtm1btG3bFpmZmfj1118t7ouPj0fZsmVdHqcvY9Ywa8yplTUmnTp1QlRUFEqUKIFHHnkEJ0+elO5j1sjHrGHW+BLmDHPGnFo507FjR2RlZeHZZ5/F8ePHce7cOSxevBjr16+XfmYwZ+RjzjBnVKVmtVAIIX766ScBQGzdulUIUdACWblyZTFhwgTpnC1btggA4quvvrJ4bPfu3UWNGjWkj99++20BQGzcuFE69vfff4v4+Hi3vBslhBDR0dGiadOm0se2Wsk//fRTAUDs2rVLOjZ9+nQRGhoqbt26JR27du2aCAoKkt7JuHnzpgAg3nzzTYfjtOfll1+W/Q5Dfn6+aNKkiShTpowAIJYsWSKEECI7O1uUKlWqUNU8IyNDREdHWxyX+9pN70aZTyuV6+DBg2L06NGiVKlSAoBo2rSpWLRokbh586asx5u+Jn788UeL4y+88IIAYNHiGxERIYYPH17oGt98840AIDZv3iyEcN4+DkC8+uqrFsebNm0qEhISnI63atWqAoDFu0+ZmZmiYsWKFl93r7zyit13OI1GoxDCu+3jtsyePVsAENu3b3d6rq2vpblz5wqNRiPOnTsnHXPlHaWiPHb48OFi5syZ4vPPPxcff/yxeOSRRwQA8cQTT9g8//Tp0yI9PV3k5+dbHO/Tp48AIPvr1pbSpUvbfBc3KytLXL9+XbqZT8343//+JwCI2bNnCyEKvu8jIiLERx99JIQQIiYmRixatEi6jk6nK/S976/vRllj1jBrHPFm1qxdu1YMHTpUfPTRR2LDhg3ipZdeEiVKlBDlypUT58+fl85j1sjDrLmHWaMu5gxzxhFv5oxerxdjx44VwcHBUveWTqcTH374ocVjmTPyMGfuYc54n+oFu0mTJomYmBiL1topU6ZYHMvPzxflypUTTz75pHTOjRs3RHBwsJg+fbp07KGHHhL33Xef9M1tYvoB545wu++++0StWrVs3vf333+L69evSy3GycnJ0n3Hjx8XAMTy5culYwsXLhQAxMmTJ4UQBXO+Q0JCRI8ePcSNGzccjtWWhIQE8a9//Uv2+QcPHhQAROvWraXP2fr16wUA8f3331t8w1y/fl08/PDDLr12U7jt3LlT8Wsyv35KSoro3Lmz0Gg0IiwsTAwePNjiB58tV65cEdHR0aJ27driu+++E2fOnBFLliwRUVFRAoDo3LmzdK5WqxVjxowpdI3t27cLAGLDhg1CCHnhdu3aNYvj48ePF6VLl3b6OqtWrSoqVapU6Gt42rRpAoC4cuWKEEKIBg0aiMaNGzu8lprhtnPnThEUFGQ3BBzJyckR169fl9aLMP9l1dvhZsvIkSMFALFv3z7Zj+ncubMAIGsKgT06nU60a9eu0PHevXtbtNMnJSVJ9xmNRlG2bFlpXQfTHxOmnzl9+vSRppGY/ogwBZ+Jv4abNWYNs0YOb2SNLT/++KPQaDTi2WeflY4xa5xj1lhi1qiLOcOckcNbObNgwQLRs2dP8dFHH4m1a9eKRx99VAQFBUl/zwjBnJGDOWOJOeN9qk6JNRgMSE1NRadOnXDmzBmcOnUKp06dQqtWrXD16lVs374dABAUFIR+/frhiy++QG5uLgBg/fr1yM/PR//+/aXrnTt3DjVr1iy0zbE7d2fKyclBZGSk9PGNGzcwYcIExMTEIDw8HOXLl5d2fcvMzJTOi4+PR4sWLSxayFNSUtC6dWtpfKGhoXjjjTewadMmxMTEoH379pg/fz4yMjKcjisjIwOHDh1Cjx49ZL+WFi1aAAASEhKkz5lpSs6DDz6I8uXLW9y+++47XLt2TfFrN7HeDU+JsLAwDBo0CJs3b8a7774Lo9GIlJQUHDp0yOHjYmNj8eWXXyI3NxcPP/wwqlevjqlTp2LhwoUA7u0IBADh4eHS15e5u3fvSvfLHWv58uUtjpUuXRo3b96U9fhatWoV+hquU6cOAEjt+6dPn0bDhg1lXc/bfvvtN/Tp0wcNGzbE8uXLZT3m/PnzGDp0KMqUKYOSJUuifPny6NChAwDbX0tqmjJlCgBg27Ztsh8TFRUFAMjOznb5eSMjI5GTk1Po+KuvvoqtW7daTC8w0Wg0aNu2Lfbv3w+j0Yg9e/agQoUK0s+ctm3bYs+ePQAg/de0E1MgYdYwa+TyRtbY0q5dO7Rq1UrRzxVmDbPGGrNGPcwZ5oxc3siZefPm4Y033sCnn36Kp59+Gk888QQ2bNiAdu3aISkpCXq9XvZYmTPMGXPMGe8LUvPJv//+e1y5cgWpqalITU0tdH9KSoq0lfeAAQOwZMkSbNq0CY8++ijWrVuH+Ph4NG7c2GvjvXjxIjIzMy3C8oknnsDevXsxdepUNGnSBCVLloTRaETXrl1hNBotHv/0009jwoQJuHjxInJzc7F//368//77FudMnDgRvXr1wsaNG7Flyxa8/PLLmDt3Lr7//ns0bdrU7tg2bdqEsLAwi+2MXWEa8+rVq21uWx0UdO9LRslrB+QXvGw5fvw4Vq5cidWrVyMjIwMNGjTAiBEjZL3e9u3b448//sDRo0dx+/ZtNG7cGJcvXwZwLzSAgnUmrly5UujxpmPW217bo9PpZJ0XiC5cuICHH34Y0dHR+Pbbby1+EbTHYDDgoYcewo0bNzBt2jTEx8cjIiICly5dwtChQ21+LakpLi4OQMEvd3LFx8cDAI4ePYr/+7//c+l54+PjceTIEeTn5yM4OFg63qhRI4ePa9euHb766iscPXpUWuvBpG3btpg6dSouXbqE3bt3o1KlSqhRo4ZL4/NlzBpmjVzeyBp74uLicOLECdljZdYwa6wxa9TDnGHOyOWNnPnggw/w4IMPFnqz6JFHHsHkyZNx9uxZWcVf5gxzxhpzxvtULdilpKSgQoUKWLRoUaH71q9fjw0bNmDx4sUIDw9H+/btUbFiRaxduxbt2rXD999/jxdffNHiMVWrVsWxY8cghLCo5p86dcot4zUtBpmYmAgAuHnzJrZv345Zs2bhlVdekc4zXzja3IABAzB58mR8+umn+PvvvxEcHGzxbppJzZo1MWXKFEyZMgUnT55EkyZN8Pbbb+OTTz6xO7ZvvvkGnTp1KlKAmJ4bACpUqIAuXbrYPU/pa3dFZmYm1q5di//85z84cOAASpYsif79++OZZ55B69atFV1Lp9OhSZMm0semdxLMX2OTJk3w448/wmg0Wmw8ceDAAZQoUUIKQut3itzt1KlThb6Gf//9dwAFi+sCBf+fzBfWtMXT47T2119/4eGHH0Zubi62b9+OihUrynrc0aNH8fvvv+Ojjz6yWJDV3rsravvjjz8AoNA7jo706tULc+fOxSeffOJyuPXs2RP79+/Hhg0b8MQTT8h+nOndpd27d2PPnj2YOHGidF9CQgJCQ0OxY8cOHDhwwOnizv6KWcOsccTbWWPPH3/8YfFzhVljG7PGPmaNepgzzBlHvJ0zV69ehcFgKPRY0wYDpg475oxtzBn7mDPep9qU2L///hvr169Hz5498dhjjxW6jR07FtnZ2dJOJFqtFo899hi++uorrF69Gnq9vlAwJCYm4tKlS9JjgILpjMuWLSvyeL///nvMnj0b1atXx+DBgwHce9dBWO0eZNqZyFq5cuXQrVs3fPLJJ0hJSUHXrl0tdpu5c+eONP3SpGbNmoiMjLQ5VdMkPz8fW7duVdQ6bk9iYiKioqLw+uuvF9o1BgCuX78OQPlrVyI7OxtPPvkkKlasiGeffRYajQbLly/HlStXsHz5csXBZu369et444030KhRI4twe+yxx3D16lWsX79eOvbnn3/is88+Q69evRAaGgrg3m5Mt27dKtI47Ll8+TI2bNggfZyVlYWPP/4YTZo0kd4h7NevH44cOWJxnonp/0lERIRHx2nu9u3b6N69Oy5duoRvv/0WtWvXlv1YW19LQgi8++67hc715Gv67bffLHYuy8rKKvR9J4TAnDlzANz7JVeONm3aoGvXrli+fDk2btxY6P68vDw899xzDq8xZswYxMTEYNKkSdIvO9Zjs6V58+YICwtDSkoKLl26ZPFuVGhoKJo1a4ZFixbh9u3bAdM6bo5Zw6yxR62sMb02c99++y3S09PRtWtX6RizpjBmjWPMGnUwZ5gz9qiVM3Xq1MHWrVvx119/SccMBgPWrVuHyMhIqZjJnCmMOeMYc8b7VOuw+/LLL5GdnY1HHnnE5v2tW7dG+fLlkZKSIoVY//79sXDhQsyYMQP3338/6tWrZ/GYZ599Fu+//z4GDhyICRMmoGLFikhJSUFYWBgA+ZXsTZs24bfffoNer8fVq1fx/fffY+vWrahatSq+/PJL6XpRUVHSmgz5+fm477778N133+HMmTN2r/3000/jscceAwDMnj3b4r7ff/8dnTt3xhNPPIH69esjKCgIGzZswNWrV6Vt3m3ZvXs3srKy3BJuUVFR+PDDD/HUU0+hWbNmGDBgAMqXL4/z58/jm2++wQMPPID333/fpdcu119//YUtW7Zg9OjRGDFiBBo0aFCk63Xo0AFt2rRBrVq1kJGRgaVLlyInJwdff/21RSfdY489htatW2PYsGE4duwYypUrhw8++AAGgwGzZs2SzgsPD0f9+vWxdu1a1KlTB2XKlEHDhg3dtv5CnTp1MGLECKSlpSEmJgb/+c9/cPXqVaxcuVI6Z+rUqfjvf/+Lxx9/HMOHD0dCQgJu3LiBL7/8EosXL0bjxo1Rs2ZNlCpVCosXL0ZkZCQiIiLQqlWrIq27Yc/gwYNx8OBBDB8+HMePH8fx48el+0qWLIlHH33U7mPj4+NRs2ZNPPfcc7h06RKioqLw+eef21wfIyEhAQAwfvx4JCYmQqfTOfzeOHfunPQu8k8//QQAUjhVrVrVYkvyevXqoUOHDtixYwcA4NChQxg4cCAGDhyIWrVq4e+//8aGDRuwZ88ejBo1Cs2aNZP3yfnHxx9/jIcffhh9+/ZFr1690LlzZ0RERODkyZNITU3FlStX8NZbb9l9fJkyZbBhwwb06tULjRs3xoABA9CiRQsEBwfjwoUL+OyzzwAAVapUsXhcSEgIWrRogR9//BGhoaHS59Ckbdu2ePvttwHYX+vh1KlT0ufNXNOmTd3yc8eTmDXMGnvUypq2bduiadOmaN68OaKjo3Ho0CH85z//QVxcHP79739L5zFrCmPWOMes8T7mDHPGHrVy5oUXXsCTTz6JVq1aYdSoUQgPD8enn36K9PR0zJkzR5qGyJwpjDnjHHPGy7y0uUUhvXr1EmFhYeL27dt2zxk6dKgIDg4Wf/75pxCiYHeQuLg4AUDMmTPH5mP++OMP0aNHDxEeHi7Kly8vpkyZIj7//HMBQOzfv9/hmEw7KpluISEhIjY2Vjz00EPi3XffFVlZWYUec/HiRdGnTx9RqlQpER0dLR5//HFx+fJluzvu5ObmitKlS4vo6Gjx999/W9z3559/iqSkJBEfHy8iIiJEdHS0aNWqlVi3bp3DcT/33HOifv36Ds+xB1Y7sJj88MMPIjExUURHR4uwsDBRs2ZNMXToUPHTTz8pfu2mHZWuX78ua0x5eXkiNzfXpddjy6RJk0SNGjVEaGioKF++vBg0aJA4ffq0zXNv3LghRowYIcqWLStKlCghOnToYHOHrb1794qEhAQREhJi8XqHDBkiIiIiCp1v+hw4Y9q9ZsuWLaJRo0YiNDRUxMfHi88++6zQuX/99ZcYO3asuO+++0RISIioXLmyGDJkiPT9IoQQX3zxhahfv74ICgqy2F3J3TsqmbZut3WT8zzHjh0TXbp0ESVLlhTlypUTI0eOFEeOHCm0I5Rerxfjxo0T5cuXFxqNxunn9IcffrA7rg4dOlica33sjz/+EI8//rioVq2aCAsLEyVKlBAJCQli8eLFhXa8kuvOnTvirbfeEi1atBAlS5YUISEhonbt2mLcuHHi1KlTsq5x5coVMXXqVFG/fn0RHh4uQkNDRY0aNcTTTz8tdu3aZfMx06dPFwBE27ZtC91n2kUtMjLS5o5Pjv7fjhgxQtknQAXMGmaNPWplzYsvviiaNGkioqOjRXBwsKhSpYoYM2aMyMjIKHQus6bwuJk1zjFrvIs5w5yxR82/aTZv3iw6dOggypUrJ0JCQsT9998vFi9eXOg85kzhcTNnnGPOeI9GCDs9hwEkOTkZkyZNwsWLF3HfffepOha9Xo9KlSqhV69eWLFihVuuWb9+ffTs2RPz5893y/VIPdWqVUPDhg3x9ddfqz0UIlKIWUP+gllD5J+YM+QvmDNE7qHqphOe8Pfff1ssUnr37l0sWbIEtWvXVj3YAGDjxo24fv26xUKURZGXl4f+/fsrWrCRiIiKhllDRESexJwhIqKAK9j17dsXVapUQZMmTZCZmYlPPvkEv/32G1JSUlQd14EDB/DLL79g9uzZaNq0KTp06OCW64aEhGDGjBluuRYREcnDrCEiIk9izhARUcAV7BITE7F8+XKkpKTAYDCgfv36SE1NtbnVuDd9+OGH+OSTT9CkSROsWrVK1bEQEVHRMGuIiMiTmDNERFQs1rAjIiIiIiIiIiLyF1rnpxAREREREREREZG3sGBHRERERERERETkQwJuDTtvMRqNuHz5MiIjI6HRaNQeDhH5GSEEsrOzUalSJWi1RXvv5O7du8jLy1P0mJCQEISFhRXpecmzmDNEVFTuyhrmTOBi1hBRUaiZM0DgZw0Ldi66fPky4uLi1B4GEfm5CxcuoHLlyi4//u7du6hWvSSuZhgUPS4qKgoVK1aEVqtFUlISkpKSXB4DeQZzhojcpShZw5wJbMwaInIHNXIGCPysYcHORZGRkQAKvjCjoqJUHg0R+ZusrCzExcVJP0tclZeXh6sZBvzvdBwio+S9q5WdZUSDmhf488vHMWeIqKjckTXMmcDGrCGiolArZ4DikTUs2LnI1DIeFRUVsF8cROR57pp+EhmlRZSCgCPfx5whIndxR9YwZwITs4aI3IE54xn8bBARBQCNXgeNPkjmTQcAaNGiBerXr49FixapPHoiIvJ1zBkiIvIkZTlTPLKGHXZERMVUWloa300nIiKPYc4QEZGnBXLWsMOOiCgAaPO1im5AYL8bRURE7sWcISIiT1KaM8Uha9hhR0RUTAXyu1FERKQ+5gwREXlaIGcNO+yIiIiIiIiIiIh8CAt2REQBQGNQdgMCu32ciIjcizlDRESepDRnikPWcEosEVExFcjt40REpD7mDBEReVogZw077IiIiIiIiIiIiHwIC3ZERAFAqxfQ5su86QWAwG4fJyIi92LOEBGRJynKmWKSNZwSS0RUTAVy+zgREamPOUNERJ4WyFnDDjsiIiIiIiIiIiIfwoIdEVEA0OQJRTfAt9rHf/zxRzz55JNo06YNLl26BABYvXo1du/erfLIiIgIYM4QEZFnKc2Z4pA1LNgRERVTaWlpOHbsGJKSklQdx+eff47ExESEh4fj559/Rm5uLgAgMzMTr7/+uqpjIyIi1zFniIjI0wI5a1iwIyIKABqDshvgO+9GzZkzB4sXL8ayZcsQHBwsHX/ggQdw6NAhFUdGREQmzBkiIvIkpTlTHLKGm04QERVTvrJA64kTJ9C+fftCx6Ojo3Hr1i3vD4iIiNyCOUNERJ4WyFnDDjsiIlJVbGwsTp06Vej47t27UaNGDRVGREREgYQ5Q0REnuaJrGHBjogoAGj1gDZfyLvpCx7jK+3jI0eOxIQJE3DgwAFoNBpcvnwZKSkpeO655zBmzBhVx0ZERAWYM0RE5EmKcqaYZA0LdkRExZSSBVqzs7MxceJEVK1aFeHh4Wjbti3S0tKk+4UQeOWVV1CxYkWEh4ejS5cuOHnypKxxvPDCCxg0aBA6d+6MnJwctG/fHs888wyeffZZjBs3zuXXR0RE6mLOEBGRp8nNGk/mDOCZrGHBjoiInHrmmWewdetWrF69GkePHsXDDz+MLl26SNuVz58/H++99x4WL16MAwcOICIiAomJibh7967Ta2s0Grz44ou4ceMGfv31V+zfvx/Xr1/H7NmzPf2yiIjIRzBniIjIkzyZM4BnssZnCnbz5s2DRqPBxIkTpWNLly5Fx44dERUVBY1GI2uhvpkzZ0Kj0Vjc4uPjLc65e/cukpKSULZsWZQsWRL9+vXD1atX3fyKiIi8x5M7Kv3999/4/PPPMX/+fLRv3x61atXCzJkzUatWLXz44YcQQiA5ORkvvfQSevfujUaNGuHjjz/G5cuXsXHjRqdjz8zMxI0bNxASEoL69eujZcuWKFmyJG7cuIGsrCw3fHaIiKiomDNERORJntwl1tM5A3gma3yiYJeWloYlS5agUaNGFsfv3LmDrl274t///rei6zVo0ABXrlyRbrt377a4f9KkSfjqq6/w2WefYefOnbh8+TL69u1b5NdBRORP5LaP6/V6GAwGhIWFWRwPDw/H7t27cebMGWRkZKBLly7SfdHR0WjVqhX27dvndBwDBgxAampqoePr1q3DgAEDZL4aIiLyNcwZIiLyNDlZ4+mcATyTNaoX7HJycjB48GAsW7YMpUuXtrhv4sSJeOGFF9C6dWtF1wwKCkJsbKx0K1eunHRfZmYmVqxYgXfeeQcPPvggEhISsHLlSuzduxf79+93y2siIvIHWVlZFrfc3Fyb50VGRqJNmzaYPXs2Ll++DIPBgE8++QT79u3DlStXkJGRAQCIiYmxeFxMTIx0nyMHDhxAp06dCh3v2LEjDhw44MIrIyIiX8CcISIiT5OTNZ7OGcAzWaN6wS4pKQk9evSwqGQW1cmTJ1GpUiXUqFEDgwcPxvnz56X70tPTkZ+fb/F88fHxqFKlisPKaW5ubqEvBCIiX6HJBzT5Gpm3gsfExcUhOjpaus2dO9fu9VevXg0hBO677z6Ehobivffew8CBA6HVFj1GcnNzodfrCx3Pz8/H33//XeTr+wvmDBH5MuZMYGDWEJGvUpYzyrPGkzkDeCZrVC3Ypaam4tChQw7DW6lWrVph1apV2Lx5Mz788EOcOXMG//d//4fs7GwAQEZGBkJCQlCqVCmLxzmrnM6dO9fiiyAuLs5tYyYiUsOFCxeQmZkp3aZPn2733Jo1a2Lnzp3IycnBhQsXcPDgQeTn56NGjRqIjY0FgEJrgV69elW6z5GWLVti6dKlhY4vXrwYCQkJCl+V/2LOEFGgYc74HmYNEQUauVnjyZwBPJM1QS49yg0uXLiACRMmYOvWrYXmERdFt27dpH83atQIrVq1QtWqVbFu3TqMGDHC5etOnz4dkydPlj7OyspiwBGR78j/5yb3XACdO3eGTqdDUlKS0/WFTCIiIhAREYGbN29iy5YtmD9/PqpXr47Y2Fhs374dTZo0AVDwM/LAgQMYM2aM02vOmTMHXbp0wZEjR9C5c2cAwPbt25GWlobvvvtO5ovyf8wZIvJpzJmAwKwhIp+lJGdM50N51ngiZwDPZI1qBbv09HRcu3YNzZo1k44ZDAbs2rUL77//PnJzc6HT6Yr8PKVKlUKdOnVw6tQpAEBsbCzy8vJw69Ytiy47Z5XT0NBQhIaGFnk8FBi2JMyH0ahFt5+fU3soRC5LS0tDVFSUrHO3bNkCIQTq1q2LU6dOYerUqYiPj8ewYcOkHb7nzJmD2rVro3r16nj55ZdRqVIlPProo06v/cADD2Dfvn148803sW7dOoSHh6NRo0ZYsWIFateuXcRX6T+YM2RuomYtACBZ9Fd5JESuY874HmYNmWsTvAz78keqPQyiIpGbNZ7MGcAzWaNawa5z5844evSoxbFhw4YhPj4e06ZNc0uxDijY1OL06dN46qmnAAAJCQkIDg7G9u3b0a9fPwDAiRMncP78ebRp08Ytz0m+51CPadK/jfqCr63mW1536VpbEuYDALRaIzY1favQ/Qa95dduz6OTXHoeIl9iai+/ePEiypQpg379+uG1115DcHAwAOD555/H7du3MWrUKNy6dQvt2rXD5s2bZXdQN2nSBCkpKZ58CUQeNS/sUwCA3qiRjr2U59qOYKZinfW/7WFRjwIBc4bIscSgVQAAA4R0bJt+mEvXahO8zOa/zYWYrZ61M9/1mWpEvsLTOQO4P2s0Qgjh/DTv6NixI5o0aYLk5GQABevNZWRk4KeffsLIkSOxa9cuREZGokqVKihTpgyAgsJfnz59MHbsWADAc889h169eqFq1aq4fPkyZsyYgcOHD+PYsWMoX748AGDMmDH49ttvsWrVKkRFRWHcuHEAgL1798oea1ZWFqKjo5GZmSn7nUNSj3nBzsRUuDMaNTAYdDAatDAYdNK/AaDzwRcsHmMq1llcx+h4KUjrAp45FvOKL3f9DDFd5+b3NRFVUt4bHVk5BpR+8DTq1KmjeKqSpxiNRpw6dQrXrl2D0Wi0uK99+/YqjUpdzBn/YirYAZZFOwAwCA3yjaZ/A+bLEb9ptCy2ySnQKcWCXvHljp8jzJnAxqzxH6aCHWBZtDPJ11h+Xefh3sfmXXT2CnTWQhQsd8+CXvGlVs4AxSNrVOuwk2Px4sWYNWuW9LHpBa5cuRJDhw4FAJw+fRp//vmndM7FixcxcOBA/PXXXyhfvjzatWuH/fv3S8U6AFiwYAG0Wi369euH3NxcJCYm4oMPPvDOiyK/sr3lPACA3mA/sLRao9OinT1f37+ARTtSjZKpSp60f/9+DBo0COfOnYP1e0gajQYGg0GlkRG5JkgrLIp25sU6a54o0Nl6DhbtSA3MGSLvcFSsA+QX6WxdQ07hrkPwChbtSDWBnDU+VbDbsWOHxcczZ87EzJkzHT7m7NmzFh+npqY6fZ6wsDAsWrQIixYtUjhC8ke2uusAQBtkkLrsHHFUrHMHFu2ouBs9ejSaN2+Ob775BhUrVoRGo3H+ICIfZ160C9YCd//5Hc28uy7fZ+Y4EAU25gwFGh00NrvsgMLFuqLKg5FFOyIZPJE1PlWwI/I1ni7WmbBoR0Wm1wD5MkNBX3BeixYtfKJ9/OTJk/jvf/+LWrVqqTYGIm/QOz/FY9hlR0XGnCHyee4u1llf11nhjkU7KhIlOWM6H4GdNSzYEblBUabFAoAuyIBNTd/irrPkVb7SPt6qVSucOnWKf0iR3zJfv86c9dRYc2p017FoR97GnCHyHPMuu2ChLTQtlqi4COSsYcGOyMN0QQa7G0/ogiznsbNoR8XRuHHjMGXKFGRkZOD++++XdmoyadSokUojI3IfnQaAUK/LLvifuuFU7dpCG10QBTrmDFHRyd2Egl12VFx5ImtYsCNSiXWxjqhIDNqCm6xzC96N9ZX28X79+gEAhg8fLh3TaDQQQnAxcCI3CLZq8mPRjlzCnCHyWb7WXceiHblESc4AxSJrWLCjgKfTWX5jGAzyt4p2B1vddSzWkS/wlfbxM2fOqD0EooBlXawj8ibmDJF7JAatUvX55XbXEakhkLOGBTsiK6YCn1FBdV/J+nUs1pEnaPI10MhcpNV0nq+8G1W1alXVnpsokLFYR+7EnCHyPY52i1VTsGCBj5RTkjOm84HAzhp+J1GxY91xZ6LVGe3eZ1KUjSUA58U6rda32tkpsKWlpeHYsWOqBpvJ6tWr8cADD6BSpUo4d+4cACA5ORlffPGFyiMj8gwW06g4YM4QFR/BQivdiLwpkLOGHXYUEA71mGbxsbPCmzWdziB7qmxRi3a2KCnUbUmYL/07Mf15t4+FyNs+/PBDvPLKK5g4cSJee+01aX2HUqVKITk5Gb1791Z5hET2d4L1VY4Kgs7Wr5uqXSv7XCJ/wJwhf6D2tFdv6hK0Uvr3Nv0wFUdC5D6eyBoW7MivWRfq3C1IZ4ReycKXVuztDusK80Kdo2MmRqOWO84WJ/nagpusc31rgdaFCxdi2bJlePTRRzFv3jzpePPmzfHcc/waJvW5s1gXBPV2ipXDvFhn62NbWNQrJpgzRB7jrmJdCLTIg3tn7Lh7/TrzYp2tj21hUa+YUJIzQLHIGhbsKODI6a4z76jTagWMRk2h+wFl69i5i60OOkeFOVtMXYCbmr7l9FwW9YovX1qgtWnTpoWOh4aG4vbt2yqMiOgeucW6IN29NYT0BqtM0QhAqwGM0oZmqrIuwr1p7C+rMCf3ekDB79CmLj8W9Iov5gyRc77cWaekWJevMVpMh7XVRSenOGeL6XGOdsPlrrTFVyBnDQt25LdsddcpnQpri/X0WFOXndKpsO7orlNSqHN1qq51UY8FPPK26tWr4/Dhw4UWat28eTPq1aun0qiI5DMv1pk+ti7a+bKiFOvsMZ+Sa6tASORNzBkKdMFC67CY5Qp3dta5Wqgz5+z1dQheIf2bxTtSgyeyhgU7CgjuKNQBBRtP2Ouq02qNineDdeeUWFs8sZ4e+Sdh1ELI7AgVRt9qH588eTKSkpJw9+5dCCFw8OBBfPrpp5g7dy6WL1+u2riIzNkrwlkX62RdCwXTYoM10mwOtzPvcCNyB+YMkX/wxLRYNbm7EEm+S0nOFJwf+FnDgh35PXcV65RsPOEqg15nc6dYo1GreIdYTxTr2F1XvPhK+/gzzzyD8PBwvPTSS7hz5w4GDRqESpUq4d1338WAAQPUHh4Vc+YFOSXFOVsFvmAtfGZaLJE3MGeI/FcejIq77KynxRYFC3UkVyBnDQt25NfcVaxzdO2irGNnq8uuqEU7dtWRTXptwU3Wub7zbpRer8eaNWuQmJiIwYMH486dO8jJyUGFChVUGQ+Rubci1hT5GkFaAb3RRmcePL/5hDe77NjRVwwwZ4j8hru67EzXKMpadkoeR8WckpwBikXWsGBHxZo2yACjVUFNqysIC09227latGOxjtzJF96NCgoKwujRo3H8+HEAQIkSJVCiRAlVx0TkDb68U6wS5lN6zf/N4h0BzBmiQKG0205p0Y7FOiqKQM4a/vVPfssbU1jdwVZhzhEW5cjXGAwGvPzyy6hevTrCw8NRs2ZNzJ49G0Lc++tcCIFXXnkFFStWRHh4OLp06YKTJ0/Kun7Lli3x888/e2r4RD5Bp7E9D9bThS1PrZHn7Nr27uOGE2QLc4aKK1/eIdaa0o49uUU4FuvIGzydM4BnsoYdduTXPDklVk2mop15t503Cnmbmr7Fdez8Vb6u4Cbr3IL/yG0ff+ONN/Dhhx/io48+QoMGDfDTTz9h2LBhiI6Oxvjx4wEA8+fPx3vvvYePPvoI1atXx8svv4zExEQcO3YMYWFhDofzr3/9C1OmTMHFixeRkJCAiIgIi/sbNWok73URkU2emK7qaiFwqnYti3b+ijlDVGyYuumsi3Tu6rTzZJHOXWvokQqU5AygKGs8nTOAZ7JGI8xLiiRbVlYWoqOjkZmZqXr7ZXF15JGiFZZM3XlGvQ7Gf9YXko4ZtDAYdDAYdDAatND/s45dUYpm1mvZKe288wZTgTAx/XmVRxL43PUzxHSdmx83QVQJeQGXdceA0k8flv3cPXv2RExMDFasWCEd69evH8LDw/HJJ59ACIFKlSphypQpeO65gu/LzMxMxMTEYNWqVU4XWdVqC39faTQaCCGg0WhgMPje94o3MGfUV9Q17EybTuiNGhhEwb/z/9l0wnxKrCe74My5q2gnd7yOno9FO+9wx88R5kxgY9aoyx0ddgYU/FC2LoIVdQ07e+vgKd2IwlusC3Xb9MNUGknxolbOAMqyxtM5A3gma9hhR8Wa9fp1tuiLsOkEkS/Lysqy+Dg0NBShoaGFzmvbti2WLl2K33//HXXq1MGRI0ewe/duvPPOOwCAM2fOICMjA126dJEeEx0djVatWmHfvn1OA+7MmTNueDVE/slbxTp3cUexDmCnXXHBnCFShzs3nPAHtrrqugStZNGumJCTNZ7OGdM13I0FO/JLRe2uc0VRp6Ra7xhr+rcvdtptSZjPLjs/IwwaCIO89hnTeXFxcRbHZ8yYgZkzZxY6/4UXXkBWVhbi4+Oh0+lgMBjw2muvYfDgwQCAjIwMAEBMTIzF42JiYqT7HKlataqscRP5I/MdYvP9528fm/ytuEjuxZwh8rxgq+61fB8omvlqRx0FHiU5YzofkJc1ns4ZwDNZw4IdFUuONquwng7r8bHY2TFWTSzWFQ8XLlywaB+31fUAAOvWrUNKSgrWrFmDBg0a4PDhw5g4cSIqVaqEIUOGuGUsq1evxuLFi3HmzBns27cPVatWRXJyMqpXr47evXu75TmIijtX17JjoY5cxZwhks+6WGfvGFC4kOepNeH8pVindFdaCixyssYbOQO4P2v4VU1+qfGXb7n9mt7YcdZeYc56fTsib4iKirK42ftDaurUqXjhhRcwYMAA3H///XjqqacwadIkzJ07FwAQGxsLALh69arF465evSrd58iHH36IyZMno3v37rh165a0vkOpUqWQnJxchFdI5BtM69cV/FvFgaCg+GZ+k3M+kauYM0TOBUNrtzDnjGn9OncLgdZvinVEcrLG0zkDeCZr+F1IxZZp/Tqj2XQlT2w2YU0XZLBZuDPodSzckev0WkCvk3kr+Hpu0aIF6tevj0WLFjm89J07dwotoqrT6WA0FrybW716dcTGxmL79u3S/VlZWThw4ADatGnjdOgLFy7EsmXL8OKLL0Knu/c90Lx5cxw9elT2p4DI3Z67Pcjlx+qtpnT46nRY6wKe0oKeq7h+nR9izhC53Rb9UMWPcTZNVsnac6bCnPXNH3ly51nyEkU5oyxrPJ0zgGeyhlNiyW81/vItl9ays9VJ543uOmvWa9pJY/HBKbIUmNLS0mTt5tSrVy+89tprqFKlCho0aICff/4Z77zzDoYPHw6gYPejiRMnYs6cOahdu7a0DXqlSpXw6KOPOr3+mTNn0LRp00LHQ0NDcfv2bcWvi8idnrs9qMi7xZqYuutMO8QGaveau3ajJf/HnCFy7mv90+gZ9LHLj2ehioo7OVnj6ZwBPJM1sgp2ffv2VXzhxYsXo0KFCoofR6SEq0U7E1vddYXP8f67TGoU7bRahr0/E0YFi4H/83XfokUL6HQ6JCUlISkpye75CxcuxMsvv4x//etfuHbtGipVqoRnn30Wr7zyinTO888/j9u3b2PUqFG4desW2rVrh82bNyMsLMzpeKpXr47Dhw8XWqh18+bNqFevnqzXpAZmIzmjN2pgEBqL7jq9/dOJfBpzxvuYM8VHUYt2rvDXTjoKXEpyxnQ+IC9rPJ0zgGeyRiOEcPr+rlarxRNPPIHw8HBZF12zZg2OHz+OGjVqyB7IvHnzMH36dEyYMEGa37t06VKsWbMGhw4dQnZ2Nm7evIlSpUo5vM7cuXOxfv16/PbbbwgPD0fbtm3xxhtvoG7dutI5HTt2xM6dOy0e9+yzz2Lx4sWyx5uVlYXo6GhkZmbKeueQPEtJ0c5UlDPqdVLBzjQF1nqzCb1B6/GCnb1psGoX7LjxhGe562eI6Tp/LWmOqHB5TdNZf+tR9tmffObn1/LlyzFz5ky8/fbbGDFiBJYvX47Tp09j7ty5WL58uaxt1NXg6WxkzvgWpV12eoPGomBnEJbFOnbXcVqsN7jj5whzRj3e+BuMWeNb5BTtzKfEGiAsOuyUTocNROabT2zTD1NxJMWDWjkDFI+skf3ZeO+992S/W/Pf//5X0SDS0tKwZMkSNGrUyOL4nTt30LVrV3Tt2hXTp0+Xda2dO3ciKSkJLVq0gF6vx7///W88/PDDOHbsGCIiIqTzRo4ciVdffVX6uESJEorGTL5FbqedebHO+pg1vcwdYm0V3NxRbFN7auyWhPks2pFXPPPMMwgPD8dLL72EO3fuYNCgQahUqRLeffddn/0jysST2Ui+RcnUWOv16/yJdcHNVmHRXVNep2rXsmhHXsGcoUDkqQ0nAkmXoJUs2pHXeCJrZFUkfvjhB5QpU0b2RTdt2oT77rtP1rk5OTkYPHgwli1bhtKlS1vcN3HiRLzwwgto3bq17OfevHkzhg4digYNGqBx48ZYtWoVzp8/j/T0dIvzSpQogdjYWOnmCxVZUo+96bD2mDaIsNcdZ36/s40kuF4duYPQ6xTdAPmLgXvCl19+ifz8fOnjwYMH4+TJk8jJyUFGRgYuXryIESNGeH1cSngyG8k3KdmEwrq7zpy/dtcFa9y/Pt1U7Vr3XpA8hjnjfcyZ4udr/dOKzjd11+XBqKi7rjjpErRS7SGQTEpzpjhkjawOuw4dOii6aLt27WSfm5SUhB49eqBLly6YM2eOoueRIzMzEwAKhV1KSgo++eQTxMbGolevXnj55Zcddtnl5uYiNzdX+jgrK8vtYyXPsu6uM1+/zvwco5POOld2cnXWLccNKEgNchcD94Q+ffogIyMD5cuXh06nw5UrV1ChQgWUKFHCbzqe3Z2NzJnAYK+7zt/XruNGEuQK5kzReOJvMGaNfzNNhzXvrmOhjoq7QM4al3aJNRqNOHXqFK5duyZtg2vSvn172ddJTU3FoUOHkJaW5sownDIajZg4cSIeeOABNGzYUDo+aNAgVK1aFZUqVcIvv/yCadOm4cSJE1i/fr3da82dOxezZs3yyDhJPc4KdGpsOKEWo1HLjSfIa8qXL4/9+/ejV69eEEJAo/H/akBRs5E54/tkT4m1sdmEia931+ULFucoMDBnbGPWEBG5j6ezRnHBbv/+/Rg0aBDOnTsH6/0qNBoNDAZ53UAXLlzAhAkTsHXrVtm7biiVlJSEX3/9Fbt377Y4PmrUKOnf999/PypWrIjOnTvj9OnTqFmzps1rTZ8+HZMnT5Y+zsrKQlxcnEfGTa7Rao1OC2xGO91xps0mnHGlu878sa502RHJYd4W7vzcgp/dcnfv84TRo0ejd+/e0Gg00Gg0iI2NtXuu3FxRkzuykTnjH3Ray/+/Bhvd2hb3+3iBzhZvFhW5hp3/YM6oy11/gzFr/J+pu858swmiQKAkZwrOD/ysUVywGz16NJo3b45vvvkGFStWdLmCmJ6ejmvXrqFZs2bSMYPBgF27duH9999Hbm4udDrXixdjx47F119/jV27dqFy5coOz23VqhUA4NSpU3YLdqGhoQgNDXV5POQdpg4xp4U7O39gme8Oa0tRi2quFO04LZY8Rc328ZkzZ2LAgAE4deoUHnnkEaxcudLpLuC+zB3ZyJzxfdbFOtMxZ0U7Toel4oo54z7u+huMWePb5OwSS0SWAjlrFBfsTp48if/+97+oVatWkZ64c+fOOHr0qMWxYcOGIT4+HtOmTXO5WCeEwLhx47Bhwwbs2LED1atXd/qYw4cPAwAqVqzo0nNS4JLTtedu9op2pvuIAkV8fDzi4+MxY8YMPP74436zppAt7spG8k+mop35+nW2psMSkXcxZ4iIu8SSp3kyaxRXIlq1aoVTp04V+YkjIyPRsGFDi1tERATKli0rrTeXkZGBw4cPS8939OhRHD58GDdu3JCu07lzZ7z//vvSx0lJSfjkk0+wZs0aREZGIiMjAxkZGfj7778BAKdPn8bs2bORnp6Os2fP4ssvv8TTTz+N9u3bo1GjRkV+XeQb7K3FprUqeJl2hzWfDmuvu87EG0UzXZDB5vPI2XWWiimDRtkN6u6oZG7GjBkICQnBtm3bsGTJEmRnZwMALl++jJycHFXHJpe7spECS6DsDksEgDmjMuYMuVtx2ayCu8T6EaU5UwyyRlaH3S+//CL9e9y4cZgyZQoyMjJw//33Izg42OJcdxa9Fi9ebLEoqmkx1ZUrV2Lo0KEACgpwf/75p3TOhx9+CADo2LGjxbVMjzF9ApOTk3H79m3ExcWhX79+eOmll9w2bvINzrrjrNesc7Y7rKuKUuBTe/fYLQnzkZj+vMefh9ShZvu4uXPnzqFr1644f/48cnNz8dBDDyEyMhJvvPEGcnNzsXjxYrWHaJNa2Ui+K0gnCu0S6+/TYT1tqnYt17ELYMyZomHOkKflwYgQ5T08PilY2H8dXYJWstMugAVy1sgq2DVp0gQajcZigdPhw4dL/zbdp2TBU1t27Nhh8fHMmTMxc+ZMh485e/asxcfWi7Bai4uLw86dO10YHfmyo49Odn6SG3lzgwhTYU6tte1YtPMPRr3O7qYqhc9Vf4FWcxMmTEDz5s1x5MgRlC1bVjrep08fjBw5UrVxOeOtbKTAwO46+6Zr12Iui3Y+jznjfcwZciYQu+QcFd4osCnJmYLzAz9rZBXszpw549LFifyBwaBzutmELd7e1VXNXWRZtAtMvvJu1I8//oi9e/ciJCTE4ni1atVw6dIllUblHLORrJm66wyCuzQoxaJdYGLOFA1zhszle6g4F0hddo6wyy5wBXLWyCrYVa1aVfr3rl270LZtWwQFWT5Ur9dj7969FucSqUnOZhHW02KVCqSinb11/wAgSBd4796R7zAajTY7Ay5evIjIyEgVRiQPs5Fs0dvYMZbddfKwaEeewpwhcoxFO6Ki80TWKP6u7NSpk8WmDyaZmZno1KmTS4MgKgol02Edtdgq6a4z52xaqrsLbN7eKdZUrNvecp5Xn5cUMmoBg8zbP8VsX1mg9eGHH0ZycrL0sUajQU5ODmbMmIHu3burNzAFmI2BbUFkChZEpih6jEFw/TpXTNeuVXsIZA9zRlXMmcDXM+hj2efma/hmuqu4CYUPU5IzxSRrZHXYmTOtk2Dtr7/+QkREhEuDIHKVvWKddXddUTvpnFFzuqo3bW85D50PvqD2MMhNfKV9/O2330ZiYiLq16+Pu3fvYtCgQTh58iTKlSuHTz/9VO3hycJsDFxvRayBzsn7OQYbXXUkj+JfRMmvMGfchzkTuJQU6gzwXMt2cemyo8ATyFkj+/ekvn37AiioEg4dOhShoaHSfQaDAb/88gvatm3r0iCIXOHqRhPGf/6wcrYrrJwptWpypdPO3rRXua+VxTryhMqVK+PIkSNYu3Ytjhw5gpycHIwYMQKDBw9GeHi42sNziNkY2AqKdfL+ODLfHTY/wBof8gUQ7KWaJKfEkicwZ8hXyS3WeWr9Ol+UrzFy4wnyS57IGtkFu+joaAAF7+5ERkZaPGFISAhat27t07ssUeBwx46wjjruXC3UOeqyc/eOrkqv5Wh9Okf3m69dx2KdbzPqtTDq5X3tms7zlR2VACAoKAiDBw/G4MGDpWNXrlzB1KlT8f7776s4MseYjYFrQWSK0846wLK7Tm/UcMOJImCxzrcxZ9TBnAlcSot1pu46Tod1Hdev821KcsZ0PhDYWSO7YLdy5UppS/GFCxeiZMmSip+MqKhcKdaZinP21q9z1mmnhC9OjXVWrJODxbrAJLd9vFq1ajh37lyh4//617+waNEi3L17F1OmTEFqaipyc3ORmJiIDz74ADExMU6v/b///Q8//PADQkJC8MQTT6BUqVL4888/8dprr2Hx4sWoUaOGS6/NW5iNgUfJWnWmYp15dx3JZ/5LKIt1gYk5U3TMmcBkXazT/fNmj0Fj2dVdnDrrzHmiy47FusAVyFmj6LtACIGUlBRcuXLFpScj8jWeXtsuELBYR2lpabhy5Yp027p1KwDg8ccfBwBMmjQJX331FT777DPs3LkTly9flqbwOPLll1+iadOmGD9+PEaPHo3mzZvjhx9+QL169XD8+HFs2LAB//vf/zz62tyB2UiA7d1hyT6uXUfmmDOOMWcCi71infW/zXly7TpzeT5UIGQnIbmbP2aNooKdVqtF7dq18ddff7n0ZETeZt1dZ/TCH1Te3sWVCACEXguh18m8KdtRqXz58oiNjZVuX3/9NWrWrIkOHTogMzMTK1aswDvvvIMHH3wQCQkJWLlyJfbu3Yv9+/c7vO6cOXOQlJSErKwsvPPOO/jjjz8wfvx4fPvtt9i8eTO6du3qts+PJzEbiydb3XUGoQm49evcIcjGjfwPc0Y9zJnAZa9AZ4+7iljcXIJ8kbKcKR5Zo/g7dd68eZg6dSp+/fVXl5+UyJdode7/6yoQinZBHvi8kG/Zvn079u/fj6eeegpZWVnIzc11+pi8vDx88sknGD58ODQaDdLT05Gfn48uXbpI58THx6NKlSrYt2+fw2udOHECSUlJKFmyJMaNGwetVosFCxagRYsWRX5t3sZsLN6su+sM3mmE8Kp8F1+TnOIcp8MGLuaM+zBnAo+tYp31lFjAfnddUbvh/KFoxy47kiOQs0bxm5xPP/007ty5g8aNGyMkJKTQbhc3btwo8qCIbHF5V1ir7jo1psH6cgGPhbnAIAxaCJnrMZrOi4uLszg+Y8YMzJw50+FjN27ciFu3bmHo0KEAgIyMDISEhKBUqVIW58XExCAjI8PhtbKzs6X1JnQ6HcLDw31+LSF7mI3E7rrC2EkXWJgz6mLOkDuLV3kwIgRahEDrU9NgqXhTkjOm84HAzhrFv0slJye75YmJPM3hTrAGLdevA4t1xd2FCxcsFmgNDQ11+pgVK1agW7duqFSpklvGsGXLFmkHPKPRiO3btxfqHnjkkUfc8lyexGwsnhxtNqH34ji8JV8AwTJnb7FYRwBzxp2YM4FPyYYTLLIR3RPIWaP496khQ4YofhIibzIa71XlrXeGVatIZ9DrfK7LjsU6ioqKkrWjksm5c+ewbds2rF+/XjoWGxuLvLw83Lp1y+IdqatXryI2NtbpNa0z5dlnn7X4WKPRwGDwre8dW5iNxZfeqIFBmK9jp+JgisC8EOfq9Fcia8wZ92HOBB5Tgc7ROnam6bDm3XXuKtb5S9HPHTvGcofYwBbIWePSG6AGgwEbN27E8ePHAQANGjTAI488Ap2OHUvkOfdvfEf2tFjrwpz5ZhNGJ222Wq3RoujnLr5YtKPAYdRrYdTL+7o1mi3QqtPpkJSUhKSkJKePW7lyJSpUqIAePXpIxxISEhAcHIzt27ejX79+AArWcTh//jzatGnjeBxG//hFUS5mY/FhsLGBUSBNhzUV71i4I3PMGfUxZwKTrXXrzHEdNyoulOSM6XwgsLNGccHu1KlT6N69Oy5duoS6desCAObOnYu4uDh88803qFmzptsHSeQO5kU8W512QToj9ArmzDt8Lr3v/OJkNGqh1TLoqbC0tDTZ70YZjUasXLkSQ4YMQVDQveiIjo7GiBEjMHnyZJQpUwZRUVEYN24c2rRpg9atW3tq6D6H2Rg4JmUPxoLIFKfnFYfpsMEay6Kd3OmwRCbMGfdhzgSOr/VPo2fQxxaddXKnw7qrK87da9eZd8CxwEjeFshZo7g6MX78eNSsWRMXLlzAoUOHcOjQIZw/fx7Vq1fH+PHjPTFGIsn9G99R/BijjU4IZzxV4PKlQp4cnQ++oPYQyEds27YN58+fx/Dhwwvdt2DBAvTs2RP9+vVD+/btERsba9FiXhwwGwPLpOzBag/BZ7BIR97CnHGMORNYvtY/Lf3bWYedp5g2nbBW1EJeUaevEnmSv2WNRgih6CdEREQE9u/fj/vvv9/i+JEjR/DAAw8gJyfHrQP0VVlZWYiOjkZmZqai+dLkHo6mxpqmsxoMOhj1ukK7w5pvOGF+DIBFh11RpsU6K8x5e2qsrQKknDXsWLDzHHf9DDFd5/wL3REVGizvMbn5qDLvW9SpU0dR+zjZ54lsZM6oy1GXnWlKrN6gkdawyzfeW7/OusPOX6aWurs4J3cax1xjf/c+MUnc8XOEOeMbPPU3GLNGXT2DPrZ53NRhZ72GnTs77MxZX9dWIc8R6yKdu7vsuIad71IrZ4DikTWKp8SGhoYiOzu70PGcnByEhIS4ZVBEzihZz06rFU677LQ6I4wGrcW0WFORyxPr2fmL7S3nsWgXwJS0j5NjzMbAI3dqrBzWU0uJigvmjPswZwKTaXqst+XBaFGUK+oUWXdsDEHkqkDOGsXfVT179sSoUaNw4MABCCEghMD+/fsxevRov9gSnQKHnOmxWiedbDpd4futO89cmR7rrIPO21NjbRUd5a7Xt73lPHcPhyjgMBsDk62psbY2nNBpBIK1gM7Be0PBGt+fXqpWUXG6dq06T0zkR5gzxZMOBcFhKoYp7XxzxFGBzpXiXb7GKN18TZeglWoPgcglir/j33vvPdSsWRNt2rRBWFgYwsLC8MADD6BWrVp49913PTFGIpeYF+O0WmHxsU5nkD42/VerM0L7T7HOHUU7X1OUoh35PtOuSnJvQMGOSvXr18eiRYtUHj1w69YtLF++HNOnT8eNGzcAAIcOHcKlS5dUHpk8zMbA5Wg9uyCdQJBWWZXL20U7U6HQ/OYtSjbfYNHO9zFn1MWcCVzm69nJ4aminbNpsmpyRxGQRTvfpzRnikPWKJ4SW6pUKXzxxRc4efIkfvvtNwBAvXr1UKtWLZcGQORp2iADjGYdbaainNGghU5ngMGgk/5rut80PRaAxRRZf58e6+qOsZwaG5h8pX38l19+QZcuXRAdHY2zZ89i5MiRKFOmDNavX4/z58/j44+9P1VEKWZj4FI6LVanAWBnLTsTtafIevP59ZD/y+Z07VquZxdgmDPuw5yhYKH1SPea+fRY66mx1lNn1eSOabddglZyPbsAFMhZ4/JXfO3atdGrVy/06tWLQUF+w6Lr7p+CnHmnnXm3nYl5t10gdNpZ49TYwCD0/2yyIuMm/ilg+8q7UZMnT8bQoUNx8uRJhIWFSce7d++OXbt2qTgy5ZiNxVOQVkjTYi2Ow36xytemx3qy+05Jpx35LuaMb2DOkKf5SoHOU9hp57uU5ExxyRrFHXYGgwGrVq3C9u3bce3aNRiNlgWM77//3qWBELmLs044W910pkKdebed6T4AhTaj8OdOO1tddnqz12mP3qDFloT5SEx/3qPjI+/xlXej0tLSsGTJkkLH77vvPmRkZKgwIuWYjWROp7m3Yyxw75ctNQpX+cJ+Ic7b02Pl/NLJLrvAwpxxH+ZM4HK26YQOGmm3WE+xtQmFvfvUxM0tyJZAzhrFBbsJEyZg1apV6NGjBxo2bAiNxsfeIiYyY1GcM9st1lbRzvy4o6KdPxfrTOxNjdWbTQUm8pbQ0FBkZWUVOv7777+jfPnyKoxIOWZj8WBrwwlrwVog31i4aAcU/NJlXrRTc2qsGh1+pteu+JdPoiJizhC5zh3FOlORzR1Tejk1lnyVJ7JG8e9MqampWLduHbp37+7SE9ozb948TJ8+HRMmTEBycjIAYOnSpVizZg0OHTqE7Oxs3Lx5E6VKlXJ6rUWLFuHNN99ERkYGGjdujIULF6Jly5bS/Xfv3sWUKVOQmpqK3NxcJCYm4oMPPkBMTIxbXxP5Dut17ABYdNU5WtfOvGgXSBwV7QDLbjvTMXbX+S5h1ELILCabzmvRogV0Oh2SkpKQlJTkyeE59Mgjj+DVV1/FunXrAAAajQbnz5/HtGnT0K9fP9XGpYSnspH8i04jYBAas499u2inFhbu/BNzRl3MGfJX5sU1T63DpxSLdb5JSc6YzgcCO2sUVyFCQkLcvl6CqXWwUaNGFsfv3LmDrl274t///rfsa61duxaTJ0/GjBkzcOjQITRu3BiJiYm4du2adM6kSZPw1Vdf4bPPPsPOnTtx+fJl9O3b122vh/yL+bp2jijdUVUXJO+6anLULag3aKUbwGJdIEpLS8OxY8dUDTYAePvtt5GTk4MKFSrg77//RocOHVCrVi1ERkbitddeU3VscnkiG8k3ONolFijYKdaa+Vp2Os0/G1GoyFcLgnqzGwDMNfbndNgAw5xxH+ZM4JKzS6wOlkESAq3PTFNVQ1GKfizWBZ5AzhrFb25OmTIF7777Lt5//323tGLn5ORg8ODBWLZsGebMmWNx38SJEwEAO3bskH29d955ByNHjsSwYQXfiIsXL8Y333yD//znP3jhhReQmZmJFStWYM2aNXjwwQcBACtXrkS9evWwf/9+tG7dusivibzn/o3v4Oijkx2eY29arNzHeII/FPPMsVhHnhQdHY2tW7di9+7d+OWXX5CTk4NmzZqhS5cuag9NNndnI/mfIK2A3ixfTFNjbZ4LbsRgjYU68iTmDPm6r/VPW6xlFwwt8lE4RKw71KzXmnNVcSn+sVhHnuSJrFFcsNu9ezd++OEHbNq0CQ0aNEBwcLDF/evXr1d0vaSkJPTo0QNdunQpVLBTKi8vD+np6Zg+fbp0TKvVokuXLti3bx8AID09Hfn5+RaftPj4eFSpUgX79u2zW7DLzc1Fbm6u9LGtucmkDltFO0cbQ9gq2nlq+qsuyACD3nOFP3ewNzWW/ItRr4VRJ+9r16j3rfbxCxcuIC4uDu3atUO7du1UG0dRuCMbmTO+a1L2YLwVsUbWuebTYs2LdramxxL5E+aMutz1NxizxndZF+3IvVis831KcsZ0PhDYWaO4YFeqVCn06dPHLU+empqKQ4cOIS0tzS3X+/PPP2EwGAqtRRcTE4PffvsNAJCRkYGQkJBCa+HFxMQ43Llj7ty5mDVrllvGSeqT22nnDp4u2pmKbYGwGQZ5l6/sqFStWjW0a9cOTz75JB577DGULl1a7SEp5o5sZM74ryCdgN5gvnad7aIdUXHDnHEfd/0NxqzxH9ZddqbdYu2tAxcCbZG67Ij8VSBnjeKC3cqVK2Wdt2fPHjRv3hyhoaE2779w4QImTJiArVu3IiwsTOkwvG769OmYPPleF1dWVhbi4uJUHBH5E9MUWE8U7tghR/7up59+wpo1a/Dqq69i3Lhx6Nq1K5588kn06tXLbob4GndkI3PG/5lPi7XegKLgGLvsiNTAnLmHWeNf5E6N9VVF3c2VyJ94Ims89h3UrVs3XLp0ye796enpuHbtGpo1a4agoCAEBQVh586deO+99xAUFASDQfkaX+XKlYNOp8PVq1ctjl+9ehWxsbEAgNjYWOTl5eHWrVt2z7ElNDQUUVFRFjfyb1qt7b+atDrPhZ+nCndF7a5z9HiuX+cfjAadohtQ0D5ev359LFq0SNWxN23aFG+++SbOnz+PTZs2oXz58hg1ahRiYmIwfPhwVcfmbo6ykTkTuIJ94O8VX914gvwHc8Y/OPsbjFnju9wxHdaX1qJjsY6UUpozxSFrPPZdJITj3ww7d+6Mo0eP4vDhw9KtefPmGDx4MA4fPgydTnlBIyQkBAkJCdi+fbt0zGg0Yvv27WjTpg0AICEhAcHBwRbnnDhxAufPn5fOocCgtOvM1m6x1sc47ZQCia/sqGSi0WjQqVMnLFu2DNu2bUP16tXx0UcfqT0st3KWjeS7dHbe5LEWZHaeTlP4MWrvGEvkTcwZ72POBJ5gO3+yu7Mg5u6ptI7GxkIeuVsgZ41q3y2RkZFo2LChxS0iIgJly5ZFw4YNARSsN3f48GGcOnUKAKQC340bN6TrdO7cGe+//7708eTJk7Fs2TJ89NFHOH78OMaMGYPbt29Lu8ZGR0djxIgRmDx5Mn744Qekp6dj2LBhaNOmDXeIDXBaGzuz2uuy8wZf34yC/IvRqIHRoJV3+2fKnq+8G2Vy8eJFzJ8/H02aNEHLli1RsmRJnxkbEVC0op11l53iNUmIVMacIVKfDpbv+tgqfvlSlx2REopypphkjU//vrh48WKLRVHbt28PoGANh6FDhwIATp8+jT///FM6p3///rh+/TpeeeUVZGRkoEmTJti8ebPFRhQLFiyAVqtFv379kJubi8TERHzwwQfeeVGkKlPRziijWGbaOdbi8Q52n/VnXAOvePKVBVqXLFmCNWvWYM+ePYiPj8fgwYPxxRdfoGrVqmoPjagQnVbAYGPTIuuNJ8zXsyPH3jT2V3sI5CHMGSL/kQcji33klwI5a3zqO3LHjh1ITk6WPp45cyaEEIVupmIdAJw9exYzZ860uM7YsWNx7tw55Obm4sCBA2jVqpXF/WFhYVi0aBFu3LiB27dvY/369Q7XryPfdvTRyc5PcsC8y840BdbWOnZB/xxzZ3HL17vsArE4Sa65dOkSnnzySZQtWxbh4eG4//778dNPP0n3CyHwyiuvoGLFiggPD0eXLl1w8uRJWdeeM2cOWrVqhfT0dPz666+YPn06/4gin6a00866y840LdYX3zXlWnekFuYMkX3m02LZZcdpteQaT+YM4Jms8djvihoN31WmwKHVGWE0aBGkM0Jv0LrcaWerQGfQ66TNKHwJi3X+xdQaLvdcoKB9XKfTISkpyeGaDzdv3sQDDzyATp06SQuonjx50mKr8vnz5+O9997DRx99hOrVq+Pll19GYmIijh075nQn8PPnzxebzCgur7M4sNdp54pgjXcKZfmi4LmcjYXIFuaMfygur5Ms+dqusaax2CqsqTHObfphXn9OUk5JzpjOB+RljadzBvBM1nisYMcFT8mfaLVCmgNvPhXW1HFnMOiKXLSz103nS8U6FumKF7nt42+88Qbi4uKwcuVK6Vj16tWlfwshkJycjJdeegm9e/cGAHz88ceIiYnBxo0bMWDAAIfX12g0uHXrFlasWIHjx48DAOrXr48RI0YgOjralZfms5iNgcW6aGc9LRa4NzVWpxEwCA2CtUC+saDLziAKfhHTe3nczsgp7BHJwZzxPuZM8aCDBgY4/n8dAq3izSTcPS3WvHAnp1BnXuCzdT4768gWOVnj6ZwBPJM1Ln3F6/V6bNu2DUuWLEF2djYA4PLly8jJyZHOyc7ORo0aNVwaFJGn2dqAotA5ZtNirXeLNZ8eW5Qpsr5SrDMatSzWFUNZWVkWt9zcXJvnffnll2jevDkef/xxVKhQAU2bNsWyZcuk+8+cOYOMjAx06dJFOhYdHY1WrVph3759Tsfx008/oWbNmliwYAFu3LiBGzduYMGCBahZsyYOHTpU9BfqJczGwLUgMkX2uUG6wn9A2Zsaa86XCmS+NBbyb8wZ92LOkCO+XswKFlqXx1iUx1Lgk5M1ns4ZwDNZo/ir/ty5c7j//vvRu3dvJCUl4fr16wAKKpbPPfecS4Mg8gW21rKz/rdWZ5QKeUFmBT05RTtdkKHQzZd1+/k5dPuZ39P+Qhi0im4AEBcXh+joaOk2d+5cm9f+448/8OGHH6J27drYsmULxowZg/Hjx0vbk2dkZACAxeY+po9N9zkyadIkPPLIIzh79izWr1+P9evX48yZM+jZsycmTpxYhM+K9zAbiy+569nZfKzVWnbFsVDGDSf8B3NGXcwZksO6qOVKt5zSrjw5zLvlHBXePDFdltNh/YfSnFGSNZ7OGcAzWaN4SuyECRPQvHlzHDlyBGXLlpWO9+nTByNHjnRpEES+wnxqLHBv7Trzop2t6bG+zF7RbVPTtxQ/hgLLhQsXLNrHQ0NDbZ5nNBrRvHlzvP766wCApk2b4tdff8XixYsxZMiQIo/jp59+wrJlyxAUdC+SgoKC8Pzzz6N58+ZFvr43MBvJGblTYz29np0nprvaK7pN1a5V/BgKLMwZ92HOBLaeQR87PScYWuSbFdPkTIt1lZo7xtoq2jkqunUJWmn3Phbrigc5WePpnAE8kzWKvwt//PFHvPTSSwgJCbE4Xq1aNVy6dMmlQRCpwdm0WOsdY00fO9pJ1tc4KrzZu4/FuuIjKirK4mbvD6mKFSuifv36Fsfq1auH8+fPA4C0y/bVq1ctzrl69aqsHbijoqKka5m7cOECIiMjZb0WtTEbyR38sdPOUeHN3n0s1hUfzBn3Yc6QLdY7xgK+OzVWbpedtZ35IxQ/1zb9MBbrihE5WePpnDGNw91Zo/i72Wg0wmAoXOi4ePGi3wQeBT53r8lmr2gHWK5n50vkFN7Mz+EUWP9mMGgV3YCCHZXq16+PRYsWObz2Aw88gBMnTlgc+/3336VtyqtXr47Y2Fhs375duj8rKwsHDhxAmzZtnI69f//+GDFiBNauXYsLFy7gwoULSE1NxTPPPIOBAwcq/VSogtlYfLljp1id2SX8qWgnp/BmfQ6Ldf6LOaMu5gy5ytVOOU9NjTXd5JBTrLMuzLFQ57+U5oySrPF0zgCeyRrFU2IffvhhJCcnY+nSpQAKdsLIycnBjBkz0L17d5cGQeRO3DxBGRbpii+5u/dNmjQJbdu2xeuvv44nnngCBw8exNKlSy1yYOLEiZgzZw5q164tbYNeqVIlPProo06v/9Zbb0Gj0eDpp5+GXl+wX2ZwcDDGjBmDefPmFek1eguzkZSwnhZbcKxgaqw5T0+P9RYW6Yov5oz7MGcCm07ce5fGoLH/g996WizguR1j/QmLdMWbnKzxdM4AnskajVC49/fFixeRmJgIIQROnjyJ5s2b4+TJkyhXrhx27dqFChUquDQQf5OVlYXo6GhkZmbK+kWEPOPoo5MLHTMv2BkMOoePN+pt32++jp35NYz/VPENBp103HRMb/CtnVZZiPNt7voZYrrO4QFDEWk1Tcae7Lw8NEldhTp16kCn0yEpKQlJSUkOH/P1119j+vTpOHnyJKpXr47JkydbrJkjhMCMGTOwdOlS3Lp1C+3atcMHH3yAOnXqyH4td+7cwenTpwEANWvWRIkSJWQ/Vm2eyEbmjO+wt0usre46vcF2a5ze6lzDP3+c5Vv9/WQq2un/+dgTBTt3de+xEOf73PFzhDnjGzz1Nxizxjf01q2W/u2oYAegUMEOQKGCnb0ONqVFO3etY2dvCqyzTjtXpsOSd6mVM4DyrPFGzgDuzRrFBTugYEvx1NRU/PLLL8jJyUGzZs0wePBghIeHuzwQf8NwU5+tYh1wr2DnrFgnnW+jaGe0/sPKqmhn+thg0LFgRy7xhYKdL/78unDhAoCC3Z78jbuzkTnjG+wV6wD3FOwAy6KdeZedp4p2LNgVH2oX7Hzx5xdzxhKzRn3mxTrAswU7E7mFO3cU7IqyKywLdr7PFwp2vvjzy11Zo3hKLFCw08WTTz5ZpCcmKgp7xTp3sC7W2aLTGWwWBLVao08U7VisI3+i1+sxa9YsvPfee8jJyQEAlCxZEuPGjcOMGTMQHBys8gjlYTYGFkeFuqIyL9ZZszU1loiKhjlDvkppsc5dvDVF1tnmEsFCK3s9OyJf54mscamysHr1arRr1w6VKlXCuXPnAAALFizAF1984crliBTxZLFODvPdYXU6g0/uFrup6VtqD4G8zLTRitwbIH8xcE8bN24cli5divnz5+Pnn3/Gzz//jPnz52PFihUYP368qmNTgtlInuIPG1BQ4GPOqI85E1isi3VFYWu3WGfcNd21qHx1V1vyPqU5UxyyRvF3x4cffojJkyejW7duuHnzprRbUenSpZGcnOzSIIj8kflOsb6IRTtyJi0tDceOHXO6rpCnrVmzBqtWrcKzzz6LRo0aoVGjRnj22WexYsUKrFmzRtWxycVsDCxyuuvs7Q4bpBPSzRXm3XUuTYPwoqnatWoPgXwcc8Z9mDPFg85BB7ZJMLQIdvBnvK90rCkpxNk7t0PwCncNhwJYIGeN4oLdwoULsWzZMrz44osICrr3q2Tz5s1x9OhRlwZB5MvkroUX5IOddkT+IDQ0FNWqVSt0vHr16ghRsI6FmpiNgc3V4putx1lPh7XedMLfsGhH/oA5Q4HIUdFOLmdddr6wsyyLduQvPJE1ir/Lz5w5g6ZNm9oc3O3bt10aBJG7uHv9OFvFOtMmEya+Oi0WYJddcSIMWhj18m7C4Fvt42PHjsXs2bORm5srHcvNzcVrr72GsWPHqjgy+ZiNgaeonXJyKCnW+fK0WBbtigfmjLqYM4HF3nRYV9ews950wh856shj0a54UJIzxSVrFM+2qF69Og4fPoyqVataHN+8eTPq1avn0iCI3MEdxTrThhNyCnWA7c0nfGXjCZNNTd/iJhRkU1pammo7KvXt29fi423btqFy5cpo3LgxAODIkSPIy8tD586d1RieYszGwGJdpLPe+dXedFhr9naMBQoX62xtNKG3+jhY4/4dY4k8iTnjPswZcje53XN5MHptvTtHm1B0CF7BXWPJpkDOGsUFu8mTJyMpKQl3796FEAIHDx7Ep59+irlz52L58uUuDYKoqIpSINMGGWDU2572aqtIZ1KoUKczIgiA3sFj1MKiHfma6Ohoi4/79etn8XFRt0D3NmZj8SG3WOeIqVjnaDdY62Kdr5uqXYs3jf3VHgaRhDlD/sYdO8TaKnb5wrRWJbhzLPkTT2eN4oLdM888g/DwcLz00ku4c+cOBg0ahEqVKuHdd9/FgAEDijQYImc8sUOsqVhn3V1nXaxztJaddaedr3XZUeAz3ylJzrlAQfu4TqdDUlKS1xdpXblypVefz9OYjYFjYalPPHZt8/XrTMU6pYW5onbZeXJqLYt2gY05oy7mTOBw5+6wzrirWOfNLjugcNHONF22S9BKbNMP89o4yLuU5IzpfCCws0ZRwU6v12PNmjVITEzE4MGDcefOHeTk5KBChQqeGh+RRGmxztZ0VblMxTqljzfvsmPRjnydmu3jzmRlZSElJQUrVqzATz/9pPZwHGI2Bg5nxTpXu+v0Zo8znwrr7S46T6+Dx2IdWWPOuAdzJnD4Y7FOLUp2maXiLZCzRtF3QVBQEEaPHo27d+8CAEqUKMGgII87+uhklzvrdDqDovPNC3QGg07RzRx3jCVvMxq0MBp0Mm++tUCruR9++AFPPfUUKlasiNmzZ6NVq1ZqD8kpZmNg8GRnnTVHU2Hl8OUNKChwMWfUw5wJDN4s1hH5I2U5UzyyRvGU2JYtW+Lnn38utOApka8yFe3kdstZT4U1/1jObrBanVF6jCe67LRayzGwi49c5SvvRl26dAmrVq3CypUrcevWLdy8eRNr1qzBE088AY3GPyoTzEayVmijCuFgAwobBTxnRTmlU2NZ5CM1MGfchzkT2FxZvy7fQQedJ7rrvD0tlkiuQM4axQW7f/3rX5gyZQouXryIhIQEREREWNzfqFEjlwZC5GlKpsja6poDChfzHBXwgnRGn9yAgshXfP7551ixYgV27dqFbt264e2330a3bt0QERGB+++/32/+iAKYjYHM0U6vRXmMaTqsvaKb+XEW24hcw5whCixcv458kSezRnHBzrSo6fjx46VjGo0GQghoNBoYDMqmIBJ5iyvr2ZkKdNaFN9OUV6mTzqpw5+kuO4vn4lp5BMCg18Kgkfd1YNCrv0ArAPTv3x/Tpk3D2rVrERkZ6fXndydmo3+zNx3WvPAmd/0662Kd3h27ygoW7Uh9zBl1MWfInHl3nQGF3/kJgVb1NezyNUauQ0eKKMkZ0/lAYGeN4oLdmTNn3DoAIm9wVqwzWv1BZX6+rS45vUFrsU6dvWmz3uqyY9GOXKF2+/iIESOwaNEi7NixA0899RT69++P0qVLqzaeomA2Bh5PddaZUzKl1XSuK4U7bxX7uEssWWPOuA9zhoDC02DNi3Xmu6oGKu4SS7YEctYo/gu/atWqDm9E3uRs62d7U1ulx+sd3Ge9lp3Vc9krxBUslumZ4pn1+nVKbGr6lhtHQlR0S5YswZUrVzBq1Ch8+umnqFixInr37g0hBIxG//qlk9kYWAqtP+ekS05v0Ngs1rmju85avrh380VTtWvVHgKRhDlDgSAfRunmL4KF1mPddV2CVnrkukSu8mTWKP4u+vLLL23evvrqK2zdurVI7/7MmzcPGo0GEydOlI7dvXsXSUlJKFu2LEqWLIl+/frh6tWrDq+j0Whs3t58803pnGrVqhW6f968eS6PnTzD0e6wnuwo0+kMFp1y5s8lp2jnq1i0C1ymgrLcG+AbOyqFh4djyJAh2LlzJ44ePYoGDRogJiYGDzzwAAYNGoT169erNjYlPJmN5FnOdoeVU6yTK99//tYiKoQ5oy7mjH9ztEOsow0nHBXpbE2F9TQ502y9MQ2WRbvApDRnikPWKJ4S++ijj0rrJZgzX0OhXbt22Lhxo6I2wLS0NCxZsqTQgqmTJk3CN998g88++wzR0dEYO3Ys+vbtiz179ti91pUrVyw+3rRpE0aMGIF+/fpZHH/11VcxcuRI6WN/X9si0Dgq1snhypp1chmNWqnbzXp6rDl/K+hR8SK3fXzmzJmYNWuWxbG6devit99+A1DwxsqUKVOQmpqK3NxcJCYm4oMPPkBMTIyi8dSuXRuvv/465syZg2+++QYrVqzAwIEDkZubq+g6avBUNpJnFbVY54gnuutcwbXvSE3MGfdhzvgnR4U6Z+wV69Qo1MnFNetIDYGcNYq/o7Zu3YoWLVpg69atyMzMRGZmJrZu3YpWrVrh66+/xq5du/DXX3/hueeek33NnJwcDB48GMuWLbMImMzMTKxYsQLvvPMOHnzwQSQkJGDlypXYu3cv9u/fb/d6sbGxFrcvvvgCnTp1Qo0aNSzOi4yMtDjPerclUo+zYp07uuscTYeV9XgnnXbuLtYVZTosUVE1aNAAV65ckW67d++W7ps0aRK++uorfPbZZ9i5cycuX76Mvn37uvxcWq0WvXr1wsaNG3HhwgV3DN/jPJGN5FnOinVy2NpgwnQjImWYM44xZ/xPUYp19vhysY7IH/hb1ijusJswYQKWLl2Ktm3bSsc6d+6MsLAwjBo1Cv/73/+QnJyM4cOHy75mUlISevTogS5dumDOnDnS8fT0dOTn56NLly7Ssfj4eFSpUgX79u1D69atnV776tWr+Oabb/DRRx8Vum/evHmYPXs2qlSpgkGDBmHSpEkICrL9KcnNzbWoiGZlZcl+feR9nuyuMye3084b46DizWDQwaCV93Vv+v5QsqNSUFAQYmNjCx03vbGyZs0aPPjggwCAlStXol69eti/f7+sn9OOVKhQoUiP9xZ3ZCNzxr+4sikFkT9jzqjLXX+DMWt8i73psLa661isu4cbTwQmJTljOh8I7KxRXLA7ffq0zXbDqKgo/PHHHwAKWgD//PNPWddLTU3FoUOHkJaWVui+jIwMhISEoFSpUhbHY2JikJGRIev6H330ESIjIwtVRsePH49mzZqhtLTd0gAAbTFJREFUTJky2Lt3L6ZPn44rV67gnXfesXmduXPnFmqfJM8o6lRYOYraXWdxLauina37i4rddeQJ27dvl36eZ2VlITQ0FKGhoTbPPXnyJCpVqoSwsDC0adMGc+fORZUqVdzyxkogcEc2Mmd8g6uFOEdddQZRcF+w1rvr2HE6LKmNOeM+7vobjFnjn/yhWMfpsKSWQM4axd9VCQkJmDp1Kq5fvy4du379Op5//nm0aNECQMEnIS4uzum1Lly4gAkTJiAlJQVhYWFKhyLLf/7zHwwePLjQ9SdPnoyOHTuiUaNGGD16NN5++20sXLjQ7rzi6dOnS+3nmZmZftM+H4iKWgBTUqyTO63VEx1uWq1RuhE5YzToYNTLvP3zblRcXByio6Ol29y5c21eu1WrVli1ahU2b96MDz/8EGfOnMH//d//ITs72y1vrAQCd2Qjc0Z97Jojso85oy53/Q3GrPEdcrvr/KFYR+QOinKmmGSN4g67FStWoHfv3qhcubIUCBcuXECNGjXwxRdfAChYk+6ll15yeq309HRcu3YNzZo1k44ZDAbs2rUL77//PrZs2YK8vDzcunXL4hN39epVm22M1n788UecOHECa9eudXpuq1atoNfrcfbsWdStW7fQ/Y6qtOQ9cgpjSqfDGh10RQTpjIqKdu4qrim9jlZr5LRYUuzChQsW79bb+xnXrVs36d+NGjVCq1atULVqVaxbtw7h4eFFGoPBYMCePXvQqFGjQgHpT9yRjcwZ/+XqmnXBGiCff4dRAGPOuI+7/gZj1gSmfI3yv0FC/undkbPzK5EvC+SsUVywq1u3Lo4dO4bvvvsOv//+u3TsoYceglZb8E3/6KOPyrpW586dcfToUYtjw4YNQ3x8PKZNm4a4uDgEBwdj+/bt0g6vJ06cwPnz59GmTRun11+xYgUSEhLQuHFjp+cePnwYWq3Wb9axKI48vdGEO9a9sx6jK4U3V8kp2nX7mQsR0z1RUVGydlSyVqpUKdSpUwenTp3CQw89VKQ3VnQ6HR5++GEcP37cr/+Qcmc2kufZ2nDCurvO0Q6xSjrxTNNhAe9OhyXyBcwZ92HOBBYla9d5Wgi0sot2Icon6BF5XCBnjeKCHVCw20XXrl3RsWNHhIaGQqNx7Z3lyMhINGzY0OJYREQEypYtKx0fMWIEJk+ejDJlyiAqKgrjxo1DmzZtLOYQx8fHY+7cuejTp490LCsrC5999hnefvvtQs+7b98+HDhwAJ06dUJkZCT27duHSZMm4cknn+Q26Cor6vp1ntpswtUONiWPcUd3Hjvtii+jUeOwW9T6XEDZAq3mcnJycPr0aTz11FNISEgo0hsrANCwYUP88ccfqF69uuwx+CJ3ZSP5Fzlr1wH3inUGL3XUcf06cjfmjPqYM/7DXTvEypkO60p3nTUlRTsiT1GSM6bzgcDOGsUFO6PRiNdeew2LFy/G1atX8fvvv6NGjRp4+eWXUa1aNYwYMcJtgwOABQsWQKvVol+/fsjNzUViYiI++OADi3NOnDiBzMxMi2OpqakQQmDgwIGFrhkaGorU1FTMnDkTubm5qF69OiZNmoTJkz2/2QHZV5RinZxCna3uOtM3uenxRpnTX30Zi3YkV1pamqx3o5577jn06tULVatWxeXLlzFjxgzodDoMHDgQ0dHRst5YcWTOnDl47rnnMHv2bCQkJCAiIsLiflfeMfM2b2cjucZWZ50t7uiuMy/WFSdvGvurPQTyIcwZ92HO+A9nxTp3ddcVtVCXB6NFxxynyJK/CuSsUVywmzNnDj766CPMnz8fI0eOlI43bNgQycnJRQ6LHTt2WHwcFhaGRYsWYdGiRXYfI0ThH3qjRo3CqFGjbJ7frFkz7N+/v0jjJPdyVqxzVIAqarEuELFoR+508eJFDBw4EH/99RfKly+Pdu3aYf/+/ShfvjwAeW+sONK9e3cAwCOPPGLRLSCEgEajgcFgcO8L8gBPZyMVndxinSPWxTpb3XW2CnXW3XX6Io/EuXzBLjvyH8wZ55gzvq8oXXW2inX2uuvkFOqKUnTjtFcKVP6YNYoLdh9//DGWLl2Kzp07Y/To0dLxxo0b47ffflM8AKKiToN1xrpYZ16oMy/2mbrrPDWtlsiTjAYdDBp5X7umHZXkto+npqY6vJ6cN1Yc+eGHH1x6nC9hNvo2JcU6e911rhTruGYdBRLmjLqYM75NbrHOVnedu4t1Slh32RGpSUnOmM4HAjtrFBfsLl26hFq1ahU6bjQakZ+f75ZBUfHg6UId4HqxLhCKduyyI2fkto97WocOHdQeQpExG32XnGKdkk0kgMLFOmeFOvN168y76wJxh9ip2rWcFksS5oz7MGd8lzc665QU6tSa0pqvMSJY8G8P8r5AzhrFBbv69evjxx9/RNWqVS2O//e//0XTpk3dNjAiORwV1uQU68zXrLMu1nl7PTujUeuWjScc2dT0Le4USz7rzp07OH/+PPLy8iyON2rUSKURycds9E3uKtY5OsdRsc7RBhOBWKwj8nXMGXI3JcU66+46dxfrfGHtOW8V7boErcQ2/TCPPw+RK9yZNYoLdq+88gqGDBmCS5cuwWg0Yv369Thx4gQ+/vhjfP3114oHQOQJ9op1nALLol2gMhq0MGrk/YJk+tp3dUcld7t+/TqGDRuGTZs22bzfH9YWYjb6J6WddYBld52tXWALjls9xuoa3irWqbWOHbvsAhNzRl3MmcDnarHOFwp15li0I1cpyRnT+UBgZ43i76TevXvjq6++wrZt2xAREYFXXnkFx48fx1dffYWHHnpI8QCInCnqtE45xTqDQSfdzO835+nuN6DgtbpzGqtWayx0IzJJS0vDsWPHVA02AJg4cSJu3bqFAwcOIDw8HJs3b8ZHH32E2rVr48svv1R1bHIxGwODo91hZV/DavqrWsU6tZ7PZKp2rTpPTD6FOeM+zBn/Jqe7zpqzYl0ejD5XrDNx91p7RI4EctYo7rADgP/7v//D1q1bXXpCIkD++nXuXoPN0Vp1top0ei9PizXx5PRYFu0Ck8Gglb1Iq8HH3o36/vvv8cUXX6B58+bQarWoWrUqHnroIURFRWHu3Lno0aOHamNTgtnoX1zprjNnq7vO0S6wak6BNT23N7vt2GEXeJgz6mPOEOB7HXX2cE07UkpJzpjOBwI7a1wq2BF5gzuKdba66+SuVeeNYp1Br4MuyHZrrOn1s8BGnuIrC7Tevn0bFSpUAACULl0a169fR506dXD//ffj0KFDKo+OyDFnxTpfWqtOjcIdFW/MGaLijUU78oZAzhpZBbvSpUtDo5H3292NGzdcGgiRN1kX6+QU5zyx46ouyOCwaGfreVnAo0BTt25dnDhxAtWqVUPjxo2xZMkSVKtWDYsXL0bFihXVHp5dzMbAonQ6rE4jCm04Yc2XinXmWLij4oY5Q2qyng5b3JhPj2XxjgKZJ7JGVsEuOTlZ+vdff/2FOXPmIDExEW3atAEA7Nu3D1u2bMHLL7/s0iCoeJE7Hba4cFSss8Ubu8mS/zEYdAqmKhWc5yvt4xMmTMCVK1cAADNmzEDXrl2RkpKCkJAQrFq1SrVxOcNs9F1ydof1NF8t1plTa1MK8k/MGe9jzvg2JTvEWguGttA6djpoLDaeCBZar6wFlwcjQpQvbe8S0+txR+GOG04EHiU5YzofCOys0QghFP1K2a9fP3Tq1Aljx461OP7+++9j27Zt2Lhxo0sD8TdZWVmIjo5GZmamT7Rf+gslxTq5HW22dnk13yXWaNRYdNTZ2lzC1GHnrBjmiS47VxWlaJeY/rwbR0KucNfPENN1vmr+MiKCwmQ95rb+Lnr9NNtnf37duXMHv/32G6pUqYJy5cqpPRxZPJGNzBnXyC3Wma9hJ6e7zvx8006xpg67fGPhKbH+ULADPFew4xp2vsEdP0eYM77BU3+DMWuUU1Koc9ZdZ2vzCevdYm0V7dy9jp03Cnbu7q5jwc43qJUzQPHIGsXfNVu2bEHXrl0LHe/atSu2bdvm0iCoePBEsc4erZOuNZ3O9v3OimC+1NnmS8VDInfIy8vDiRMnEBISgmbNmvnNH1EAs9FXuFKskytId+8PqCBtwb91Zn+I6awuyc41It/DnCFfE2zjz3EdvB8gnt7IglNhqThxZ9Yo/s4pW7Ysvvjii0LHv/jiC5QtW9blgRCZuKsQZV60My/Q2SvWyb6uDxXtXMHuusBkNGphNMi8Ge/tqFS/fn0sWrRI1bHfuXMHI0aMQIkSJdCgQQOcP38eADBu3DjMmzdP1bHJxWz0H9bFOiVr19kr2gWbxZY/7ebFoiIpwZxRF3PG/8hdu85W0c7ifi8VuzxVtGOxjuRSlDPFJGsU/145a9YsPPPMM9ixYwdatWoFADhw4AA2b96MZcuWuTQIIhNXinU6ncHmtFjgn6KdXgejUSMV6kzTYM0/DtIZvbIrLJEv8ZUdlaZPn44jR45gx44dFt0DXbp0wcyZM/HCCy+oODp5mI3+oSjFOmeCtQDMpsYCBQUxX5way0IdeQtzxn2YM75B7nRYpRtNWK9p52w9uxBoPVJg8+Z6dkTuEshZo7hgN3ToUNSrVw/vvfce1q9fDwCoV68edu/eLYUHkSs8OcVTqxUw/vOHmVZXEG4Gg87lop1Wa/SJKancgIICwcaNG7F27Vq0bt3aYje8Bg0a4PTp0yqOTD5mo/9xtVgXpBNS4S9IK6A3aix2jNVpAIh7a9n5atGOqDhhzpA3uWtXWLWKdu7E7joqTjyRNS7N3GjVqhVSUlJcekIib9MGGaRNKEzdeI6KdibOine+UrQjAgq+XvUy3xE1fW37yo5K169fR4UKFQodv337tkXY+Tpmo/8oamedvaIdtBrkG20X7QDfKdx5endYbjgRmJgz6mPO+K98GJ1OfbVFrZ1jidSgJGdM5wOBnTWyPhtZWVmKLpqdne3SYIhssTfdVe79QEHRTmtab+ifAp1WZ4ROZ5BuWp1RKuQBQJDOaFHAIwo0aWlpOHbsmKrBBgDNmzfHN998I31sCrTly5ejTZs2ag3LKWajf3Flowm5bK1np9MUvCtq/s4op6JSccOcKRrmjP8x767Lh1G62frYnK1jJtabUJh3rXH6KlFgZ42sDrvSpUvjypUrNquFttx33304fPgwatSo4dKgKPA42yHWXqeaqRhnXZRzdeMI09RYuY8377rjGnfky4RRC6NG3teoMPrGu1G3b99GREQEXn/9dXTr1g3Hjh2DXq/Hu+++i2PHjmHv3r3YuXOn18clF7PRP9nqrrMu5plvLmGPeZcdYL/TziDu/bKlB6fIkv9izngfc8Z/OSrAyblfCV+dGuvJ6bDb9MM8dm1Sj5KcMZ0PBHbWyCrYCSGwfPlylCxZUtZF8/PzXRoMkYk7uuqsmabGmq9nZz1F1vr6Wp1R2qSChTsKNGov0NqoUSN89NFHaNeuHQ4fPox58+bh/vvvx3fffYdmzZph3759uP/++1UbnzPMRv9hKqyZF+scddxZFOIcFO/kFu2Ae4U7U9EOYOGOAh9zpmiYM/7FoBEWhTjzqay2WHfOOTvX39ezI/KUQM4aWQW7KlWqKNp9KDY2FsHBwS4NiAKTLsgIg952ocu6u86VYlyh5/ung87u7rFWRTtpLAatxcfmxTyjioU608YSXDOPfMG8efMwffp0TJgwAcnJyQCAu3fvYsqUKUhNTUVubi4SExPxwQcfICYmxu51+vXrhwcffBATJkzAa6+95ne73DEbfcvCUp/YPG5drDMvsOntrGVnmuJqfT4gr/vOxDQ9lt12RMowZwowZ/yDaRqsqVjnrFAnPe6f85QU7szZKtoBKFLhzp1TbPM1Rm46QT7LXTkDeDZrZBXszp4967YnpOLLVtHOvABlXVwzbRRhjzbI/rRW880krB9juq5pTTtbhTtb1zPvtlOL+Y6wps8dd4olADDodTAIecVu0/eaK+3jaWlpWLJkCRo1amRxfNKkSfjmm2/w2WefITo6GmPHjkXfvn2xZ88eu9eaP38++vbti+HDh2PTpk1YvXo1mjZtKmscvoDZ6PvsFetMhTrTzq6FWP1ItVfAs1W8s7VzbLAWDrvtWLQjf8Cc8T7mjO9zVKxztjmEqZglt3Bn3WVnuob187ijcOcuLNqREkpyBnA9a9yZM4Bns8alXWKJ3MFesc5eoc5o1gmh1Yp7hTc7hTu73XVmRTvpWjamyJq67UzFP3d0/rmTededdeedvQJeYvrzHh8X+Q+l7eM5OTkYPHgwli1bhjlz5kjHMzMzsWLFCqxZswYPPvggAGDlypWoV68e9u/fj9atW9u9ZuvWrfHzzz/jpZdeQtu2bfHQQw8hKMgymtavX6/wlREV7owzL9aZF+rybfy4zIdG6o4DABgti3b2nsOaddHO9Hzm3XYs2lEgY85QILNXrJO7i6vpPOvCnYmrnXcmSqfJemoDC+vXqeRxLPaRHEqyxhM5A3gua/gdQB537LGJDu83FcKMel3BzagpdMvPt/xiNx03f5wS1kU+rdkfYua7yJp/bDrmDzvH2ps6uyVhvpdHQr4sKyvL4pabm+vw/KSkJPTo0QNdunSxOJ6eno78/HyL4/Hx8ahSpQr27dvndBy5ubm4du0aNBoNoqOjC92IisJi3bp/inX5Rkg3g7B9M90PFHTi6Y0au1NoHdFprLohzHaRBe69c8odZCkQMWcokPTWrZb+XdRinbl8jdHm4+ROrXVU1FJShPN0R57Sz42t7kGTLkEr3TEkChBKssZTOQN4JmvYYUceZV6sM58OayoomRfrCo5rCk+N/Wcaqum/lhtE/FNc+6fjztE0WWtKO+3MxxukM3p18wlH0161WqOite22JMxnp10AMhh0MEDZVKW4uDiL4zNmzMDMmTNtPiY1NRWHDh1CWlpaofsyMjIQEhKCUqVKWRyPiYlBRkaGw7Fs3boVw4cPR8WKFZGeno569erJeg1EJvbWrjOnN2gsinUGs7+B9Fbnmn4xks4x/ejVaiw+ttVxB9heF8+80w6wnCJrPpZA6bSbql2LN4391R4GuRlzhoozW8U6Z+QWwExFNVudaHKnyzoqbvnShhRKu+bYYVe8KMkZ0/mA/KzxVM4AnssaFuzIY5x11pnYKtaZrxUnbyrqP4U6hUU7a+ZFO1tMhTu117IrKhbtCAAuXLhg0T4eGhpq97wJEyZg69atCAsLc9vzP/vss/joo4/w73//Gy+++CJ0Ot+adk6+T85GE/aKddaFOumx1gfMCnfB2oJuO51GWBTm7BXvHDEV7YB7U2O9KV94tquPRTsCmDMUGMyLdeYcddcpKZDlwWjRCWerqCW3266orMfiCa5OkbXWJWgltumHuWNI5OfkZI2ncgbwbNb4VNVh3rx50Gg0mDhxonTs7t27SEpKQtmyZVGyZEn069cPV69edXidoUOHQqPRWNy6du1qcc6NGzcwePBgREVFoVSpUhgxYgRycnI88bIIsLlDrMW6dVbFOoNBJ+tmNGil803XkabWOpkm6+x+OVNjfWWzB63WWOhG5ExUVJTFzd4fUunp6bh27RqaNWuGoKAgBAUFYefOnXjvvfcQFBSEmJgY5OXl4datWxaPu3r1KmJjY+0+/549e7B371688sor/COKPMq8WKeHZXEsX9i+mZjON02TNV3PnGm6rKMps9ZTY+8dL/gvp8ZSIGLOkL+zLtbZ665ztVhn7zGuTK11xNNFOFfYmwpMpJScrPFUzgCezRqXvnN//PFHPPnkk2jTpg0uXboEAFi9ejV2797t8kAc7dTx1Vdf4bPPPsPOnTtx+fJl9O3b1+n1unbtiitXrki3Tz/91OL+wYMH43//+x+2bt2Kr7/+Grt27cKoUaNcHj/ZZ6tYZ2Jas85W8c1UlAMg3WermGe6Xyrimf3BZKt458qadyY6ncGicGcq2rFARmozGDUwGLUybwXfIy1atED9+vWxaNEih9fu3Lkzjh49isOHD0u35s2bY/DgwdK/g4ODsX37dukxJ06cwPnz59GmTRu71z106BCaNWvmnk+AD/BENpLrzLvrgHvFOhPrwpw1W4U787XtDEJjf5dZPxEI02/Je5gz6mPO+B7rzreiTD21VbRTo6DlyemzwUIr3UxYtCMTZTmjLGs8lTOAZ7NG8ZTYzz//HE899RQGDx6Mn3/+WVrQLzMzE6+//jq+/fZbxYPwxE4doaGhdiuhx48fx+bNm5GWlobmzZsDABYuXIju3bvjrbfeQqVKlRS/BrLkaDqso/XWzIt1gO2pp+Zr2VlPl713zGCxkQTgvKOuYGyWf3xZr2VnOmY+FtN6dua7thL5A7k7KkVGRqJhw4YWxyIiIlC2bFnp+IgRIzB58mSUKVMGUVFRGDduHNq0aePw53RISEjRXoAP8UQ2knuYuuvMKSlUmU8d1QOAKOiKyzebImtir4vO5nXNxsRdYylQMWfchzmjPvPuunyzopap4OSOQpetKanmBS1b00jlFrx8aS07gOvTkfvIyRpP5Qzg2axR/F0yZ84cLF68GMuWLUNwcLB0/IEHHsChQ4dcGoQndurYsWMHKlSogLp162LMmDH466+/pPv27duHUqVKScU6AOjSpQu0Wi0OHDjg0msg2+x11xkM9rvc7BXr9Aatxc10v3mRz7wLz3qnWXcyddqZpsua7xzrDx13XL8u8BgMWhj0Mm//fO/I7XyQY8GCBejZsyf69euH9u3bIzY21qWty/2VJ7KRXGNavw6wvQGEK4UwW5121jvJAve67uzdpOs5iAhvT41lYZDkYs6oiznj29xZCHN0LVPX3f+3d+fhTVX5/8DfSbpRSgtlKSBlU2iLCAoIVhxEFiujAwrKIgpWvjKjgCyi6Liw6YC4gCiIAoILDI4sjstPGHYGBISyiAJFdpQWGLULKG2TnN8f9YYkzXJvcm/uTfJ+PU8ebZabc5vSd/PJ55zjfFFC7tTYctgVr8MnB7vpyBdFOaNB1hgxZxR32OXn56NLly5Vrk9JSaky31cOLXbquOOOO9C3b180a9YMx44dw9///nf06tUL27dvh8ViQWFhIerVq+fymJiYGKSmpno9bllZmcv2wCUlJQrOkrxxng7rPsXVmacdWa02s8cfYIvF5vJ4x0YRTm/cpO47f4U8fxteOG9C4b5zrKeOO/dCnpJuPF87xRIFQm7ngyebNm1y+TohIQFz5sxR5U1ZOFIjG5kz6nIu3AHBb+ogFbZiTZXHikFl0U7qtpPEBtgw4L5jrPRczs9NFG6YM+pR6z0YsyYwntaus0FoVoDScvMHJZ120v28jcX5OKHYsILIk0CzJhxyRvG/qPr16+Po0aNVrt+6dSuaN2+u6FjSTh1LlixRdaeOgQMHonfv3rjuuutw991344svvsCuXbuqvCBKTJs2DSkpKY6L+9bBJE8g00U9Fevcb3PutnNf9859vbvKcfjvuvM03daZVKyTOu2kjSiki+NxPjaCULpRBKfbUqSxWq2YMmUKfvzxR72HEhQ1spE5o4y3HWLdOe8MqwapeCYVAKVuO+k5pK47Xxdv3DegkITjRhTcIZaMgjnjilkTvIoQTSvVcvqq0sKa1HHnfvF0P6JopFXWKH73/8gjj2D06NHYuXMnTCYTzp49iyVLlmD8+PF49NFHFR1Ly506nDVv3hx16tRxhFz9+vVx/vx5l/tYrVb88ssvXo/7zDPPoLi42HE5c+aMonMl7Xgr2rkX7txv83RRynk9O8C1qOdeuFOL3W52XIgkyhZoVX+qUqBiYmLwyiuvwGoNtv9JX2pkI3NGPTaVl0Fw51y0c9511rl4F2yRMBKKdhRZmDP6Uus9GLMmvCidmqoEu+HIaJTmTDRkjeIpsU8//TTsdju6d++O3377DV26dEF8fDzGjx+PUaNGKTqWtFOHs9zcXGRmZmLChAlIT0937NTRr18/APJ36nD2448/4ueff0aDBg0AANnZ2SgqKkJeXh7at28PANiwYQPsdjs6derk8Rjx8fFet6Kn4DjvDKvGsTxtRuE+TRao2jEH+J8C67z5hPvxpce638d9qqyapKIdp8pSIIKZqqSmbt26YfPmzWjatKneQwmYGtnInAme+zRYLXdxdd6MAnCdciv9ceWpaGfxMyTnqbHSJhQSbkZB4YY5ox613oMxa9QXiq4y5+dQs9BmtI0oiAIRyVmjuGBnMpnw7LPP4sknn8TRo0dx8eJFtGrVCklJSYqfXK2dOjIzMzFt2jTcc889uHjxIiZPnox+/fqhfv36OHbsGJ566ilcc801yMnJAQBkZWXhjjvuwCOPPIJ58+ahoqICI0eOxMCBA7lDrIrcN5yQiktqFeecWf9YQw6ouousczENcF13TuKpeKeU+/M4k1O0C6ZjjuvbUTjr1asXnn76aRw4cADt27dH9erVXW7v3bu3TiOTT81spPDhXrSTuH+26vzHlrTunVzuRTsteDsPokjBnKFA9LF8CMDz+nV68lRgC6aIF+qiXYXJrsoOseusuSqMhkg9WmSN4oKdJC4uDq1atQr04bLNnDkTZrMZ/fr1Q1lZGXJycjB37lyX++Tn56O4uBgAYLFY8O233+L9999HUVERGjZsiNtvvx1Tp051+TRpyZIlGDlyJLp37+44/uzZszU/H6ok7RArp3gnt0NNuo9z4c69285TUc29+05p8c7TMd277LTqsHPGol10s9kssAp5xXCbvfJ+N954IywWC0aMGIERI0ZoOTyfHnvsMQDA66+/XuU2k8kEm81W5XqjClU2knc2uwlWm8njDrFacO9481bAU1K0c9+Awrloxy470gtzxhiYM8Zgg7F+EQe74YP0WHbbkZ6U5AwQHVkjq2DXt29f2QcMdtvbQHbqEOLKL8xq1aphzZo1fp8nNTUVS5cuDXicpIyn7jF/mz4EylO3HQCvU2WlLjzHuP4o9HniaTqsp+MpxfXoSA9GaR+328Pzj8NQZiP55z4dVi/OxTT3KbPBdNoRhSPmTHCYM8bjvOGEVjvERjo1uuuInEVy1sj61+K8k1BycjLWr1+P3bt3O27Py8vD+vXrkZKSovoAKfwcvHeM19u0mA7ridVmdrkAVzaf8LUJhcRfYU7pWHwJdbEuJ++pkD4fkRKXL1/WewiyMRv1I2eHWJsw+dyRNRQqhGsBz31qq5KNKQKeEkFELpgzJIc0HTacqNEdx40oiNShVtbI+vtv0aJFjv+fMGEC+vfvj3nz5sFikaY12vDYY48ZoqpJ+nEu1LmvX+dMmg7rznlXV2dqTCd1P0aM05RVT2vdOU+TVWN9O2/YWUdqsVnNsJnl/Tw576hkhPZxm82Gf/zjH5g3bx7OnTuHI0eOoHnz5nj++efRtGlTDBs2TLex+cJs1IevYp20O6zzdFibuFIo02sqqfMacdJYginAaTEtluvYkT/MmdBjzugjHIt1Eqlox8IbhSMlOQNER9Yo/pf83nvvYfz48Y6gACrXjRs3bhzee+89xQOgyOCrq84Tu90Em83i2CFW7WKd3W72WQxz7r6TOu6cx+HebefclacGf+MjCoVdu3bh4MGDugYbALz00ktYvHgxZsyYgbi4OMf1rVu3xoIFC3QcmXzMxtCQ01lnVO4FNqlwZxPKOu2IwglzRj3MmdBQWqwz6ppv5bAbdmxEaovkrFFcMbBarTh8+HCV6w8fPhy260NQ8FotnxX0Mdw3aZBbrJOKX84XX7e5F8qcp8wCcCnaqT2FV8tCHTeciG42u0nRBaj8NKpVq1Y+1wgNhQ8++ADvvvsuBg8e7PJGpG3bth7zxoiYjcZgMV+pfFXYXbvrjMBb0Q5wLdp5K+CFy7TYV+wD9B4CaYA5oy/mDAUikKIdu/NIL0pzJhqyRvHffrm5uRg2bBiOHTuGjh07AgB27tyJ6dOnIzeXWytHs1bLZ+HgvWN8Tod15t7VBijvqAu0+OX8OLPZ7tiown1nWfdpssFgRx0ZjVEWaP3pp59wzTXXVLnebrejoqJChxEpx2wMjVFFDwTcZWeUnVWlcThPkZX+GFO7005KrdCsHktUFXNGPcyZ0Pi37UFFXXZxMBu+ky2U02RZ7CM9RHLWKC7Yvfrqq6hfvz5ee+01FBQUAAAaNGiAJ598Ek888URAg6DIIbdY587uo6POU6HLbLarVgCTjuO+rpC3wp0RsbuOwlmrVq3w3//+F02aNHG5fvny5bjhhht0GpUyzMbQkVu0izUDtuA+Z/HKhuCLYGqva+dMo9MmClvMGVLCV9EuFmaXnWLDSTnsEVNQW2dlkZqMR4usUfy3odlsxlNPPYWnnnoKJSUlAGCIaiYZw3Wfvo4Dd4/zervUpWYvr/zRk9ax88RXQU6LbjW73eyx204aZzAdduyuI61ZbWZYhbyfM6vBFmh94YUXMHToUPz000+w2+1YuXIl8vPz8cEHH+CLL77QbVxKMBtDS27RzmICrEL97jq1Pr5x3+jBX+FOzvReFutIK8wZfTFnQksq2tlMrgHiXKyLFWZUmOy6dtlJBTi5z2+Eol2FyY5Ymb9LKLooyRkgOrImqA9zGRLkib+iHQCYzQJmi92xG6t7d53aBS7bHzvTWmJ8v5UJtmjn3oWnxg63/rC7jgJllPbxPn364PPPP8eUKVNQvXp1vPDCC2jXrh0+//xz9OzZU+/hKWaE72m0izEL2GyVlbAYANBgV1W1eNqd1XmarPN1vnhLJ7nFRe4QS1pgzmjDCN/TaPBv24O4K+YDj7dZYIIN+gWLe9FNSeFOi6Kd3kVAim6RnDWKC3bNmjWDyeT9r7rjx48HNBCKLHKKdt5o0Y3mr1DnSTCddmruKEsUyYQQOHr0KFJTU/HVV18hJiZcltV3xWwMPecuO6st8qpNSqfJWlC1aGfchRyIQoc5Q2ryNiVW6y47uQUxuePwV7STe5xAC3XssqNIo1XWKD7KmDFjXL6uqKjA3r17sXr1ajz55JOqDIoiQyBFOyNMHZW67FyuU3F6rJ5y8p7SewikEavN7GgL93tfYYz28RMnTqB37944ePAgAKBRo0ZYsWIFOnToEPKxBIvZqA9fU2MtJoEKXHlzGxtmXXYSJbvcSkU7FupIC8wZfTFn9PGFdYjXLjvgyrRYLQRaDAvFFF01OupYtCN3SnIGiI6sUVywGz16tMfr58yZg927dwc9IIo+ZosdMaj8B+pcKDNC8U7qsnPnqWhn5E0piDyR2z7+9ttv4+2338bJkycBANdeey1eeOEF9OrVCwBw+fJlPPHEE1i2bBnKysqQk5ODuXPnIi0tzedxn3zySVitVnz00UdISEjAq6++iuHDh2PPnj1Bn1uoMRv1M6roAcyssUTvYRhGoEnkq2hIFCjmjHqYM/rxV7STqFUoC9X00kCnxnL6KxlNJGeNav/aevXqhRUrVqh1OIoQ1336OoDA1lkzm+2OixHZbBbHRU9KCptr2s/QcCSkJ5vdrOgCVH4a1apVK8yZM8fnsRs1aoTp06cjLy8Pu3fvRrdu3dCnTx98//33AICxY8fi888/xyeffILNmzfj7Nmz6Nu3r98xb926FfPnz8egQYNwzz33YPny5di/fz8uXboU/DfEIJiNoTG2dLCs+8WaIqMoZdROQYpszBljYs6ExhfWIXoPQZFwKapp1Z1I4UlpzkRD1qi2iMPy5cuRmpqq1uEoglz36evY33u83/vFWOxeN2moMkXVAN13znwV7Yw2ViKJ3E+j/vKXv7h8/dJLL+Htt9/Gjh070KhRIyxcuBBLly5Ft27dAACLFi1CVlYWduzYgZtuusnrcc+fP48WLVo4vm7QoAGqVauG8+fPo1mzZgGelbEwG43JaNNjlRYRtRq/Wl12T5o/xiv2AcEfiMIec0Z7zJnQkdtpFylCtfttIFNje8QswjprrkYjonATyVmjuGB3ww03uCx4KoRAYWEhLly4gLlz5wY1GIoednvlz5DFYoPNZoH5j80dfBXtnGk5dVbtjj6z2W6oot2a9jO4lh0BAEpKSly+jo+PR3x8vM/H2Gw2fPLJJ7h06RKys7ORl5eHiooK9OjRw3GfzMxMNG7cGNu3b/cZbiaTCRcvXkS1atUc15nNZpSWlrqMzQi7PvnDbDSmWDMAOwBRdS04qTBlpMKdEkYrOhJ5wpxRD3NGf0qKdf463EJRCIt0LNqRJJKzRnHBrk+fPi5hYTabUbduXXTt2hWZmZmKB0DRyWwWkPazk4p2EmnNODmFu8pjaVcQ87R+nRxSAdKoWLSLPDYbYPWxe5zLff94k5+enu5y/cSJEzFp0iSPjzlw4ACys7Nx+fJlJCUlYdWqVWjVqhX27duHuLg41KxZ0+X+aWlpKCws9DkOIQRatmxZ5bobbrjB8f8mkwk2m/E3eWE2GkuM5Y8fcjsAswm+foTcO8qivQjmfP7BdNuxyy7yMGf0xZwxHgtMsEG4bDyhxm6uga4tpwUjd9lR5FGSM0B0ZI3igp23EydSwhxjA6wW2Gxw2bzBucjlXCyTW7zTkvNOsXJJHYNad9l52tmWyJ8zZ864fNLj65OojIwM7Nu3D8XFxVi+fDmGDh2KzZs3B/X8GzduDOrxRsJsNDaLCR677DwJp841rccqHTsS1v0jfTBn1MOcMTYtd4vVm1Q81Lpwx6IdBSqSs0Zxwc5isaCgoAD16tVzuf7nn39GvXr1wuITKgot9/XrHNNgY2yw2D2/C3DvTvM3VVatgpi/olcgRTvnYxtlaiy76wiobMuW25odFxeHa665BgDQvn177Nq1C2+88QYGDBiA8vJyFBUVuXwide7cOdSvX9/nMW+99daAx240zEbjce+y8zY11pNQF+0ipTAm/VEp53tM0YE5ox7mjDFJXXaBCJcuO4lz4U6rsbFoR4GI5KxR/K9BCM+/kMrKyhAXFxf0gCg6SF11ZrNw6bCTeCqKBTo9NRieioRKp7o6j5tdcKQVm92k6ALI31HJE7vdjrKyMrRv3x6xsbFYv36947b8/HycPn0a2dnZqp2f0TEbjSvGHHjlLdQFtApx5SJXKMaotHip2o5mZCjMGX0xZyKTVoUvLbvhjFZIpMihNGeiIWtk/001e/ZsAJUL6i1YsABJSUmO22w2G7Zs2cL1E0gxc4wN9vIYlwKdrx1XnbmvdRdsB5u3Ypr1j80wnElFO7ndds4dgtI4lRTv1OrM06PoScYld0elZ555Br169ULjxo1RWlqKpUuXYtOmTVizZg1SUlIwbNgwjBs3DqmpqUhOTsaoUaOQnZ3tc3HWSMFsNB6LWTj+gHNcZxKoQOV1MVDWAabXFFmjdd0p3UGW69cRwJxRA3PG+OR22bkX0aTCl7cpp6HosjNiJx+77EipSM4a2QW7mTNnAqj8dGfevHmwWK4UVeLi4tC0aVPMmzdP/RFS2PNWSHNMjTULnwuC+yN3Z9lgSMcPpnDnXrRTQrq/r8KdvyIgi3UUqPPnz2PIkCEoKChASkoK2rRpgzVr1qBnz54AKvPBbDajX79+KCsrQ05OTtTsWMdsNIaZNZZ4vD7GImC1XakyWUxXFij2xb1Ap+e6dnIKZUbZ8dYKdtdRYJgz3jFnIoOcjrdQbfDgzohFOyIthGPWyP676sSJEwCA2267DStXrkStWrU0GxRFN/ddYz3xVnzSep04X4U7pUU7LXDziehltZlk76pkFVfaxy0WC0aMGIERI0Z4vf/ChQt9Hi8hIQFz5swJqA093DEbI49z0UtpV5lW5HbbhWITCiN8P0gfzBl9MGfCh7eNJ5QU4dyLdoEW05QW/4xWtGOXXXRSkjNAdGSN4g9CI2m3JTIO53Xs5E6JdeZpyqlSSopdngp37kU7s8Xucb07991v/XW+ORf4Aj03dteRJ3Lbx0OtpKQEGzZsQEZGBrKysvQejizMRuOLNQMVfn4Veip26d215kxJt52/42iFG06QM+aMepgz4SmQjjlPRTvpeqXHCXQMRuCvaLfOmhvC0ZCRRXLWyCrYjRs3DlOnTkX16tUxbtw4n/d9/fXXAxoIRTd/RTr3rjT3r90LUqEo2jmPQ3p+uUU7iZxCmntXnr9zY5dddLLatet80Fr//v3RpUsXjBw5Er///js6dOiAkydPQgiBZcuWoV+/frqNzRdmY3gJpFhnRGp0uTk/PlzOm/THnAk95oxx3BXzgd/7uHfXqV0o07rbzmhddhR9lOQMEB1ZI6tgt3fvXlRUVAAA9uzZA5OCbyIRENxUVTlTSD3dR866b3K4P95TMcy5U07u9FgllG6wwaIdyWGUT6O2bNmCZ599FgCwatUqCCFQVFSE999/Hy+++KJh30gxGyNHuBWt1JyaqnQaLafFkhLMmeAwZ4wpFmZU+CmABVusU3s9u3DttuPUWJIjkrNGVsHOuQV706ZNip+EyBf7H7v5eepEU2O9N6WFO3/3k1MMcy7a+euyC5SSop1zQbH7N0+rPhaiYBQXFyM1NRUAsHr1avTr1w+JiYm488478eSTT+o8Ou+YjeHH04YT4Vas04Kem2oQhQJzhrQkZ4dYTwLpaAu2C87TY9VYM4+ItMkaxf8aH374YZSWlla5/tKlS3j44YcDGoRk+vTpMJlMGDNmjOO6y5cvY8SIEahduzaSkpLQr18/nDt3zusxKioqMGHCBFx33XWoXr06GjZsiCFDhuDs2bMu92vatClMJpPLZfr06UGNn4Jns1lgs1k0K3Cp1XVmt5urFMvci4tqn4OvLsJAH0+RwyZMii5AZft4q1atdF9YNT09Hdu3b8elS5ewevVq3H777QCAX3/9FQkJCbqOTS4ts5GCJ/3MRxI9C2ws7kUn5oy+mDORKZCON7W75OJgdrkYjaeNPCgyKc2ZaMgaxf8i33//ffz+++9Vrv/999/xwQf+5/Z7s2vXLrzzzjto06aNy/Vjx47F559/jk8++QSbN2/G2bNn0bdvX6/H+e2337Bnzx48//zz2LNnD1auXIn8/Hz07t27yn2nTJmCgoICx2XUqFEBj588O3C39/U2bDYL7Fbva9eFQ4HJbzeeNIVVp00f/BUVKbrt2rULBw8e1HWtBwAYM2YMBg8ejEaNGqFhw4bo2rUrgMq28uuuu07XscmlVTZSYCxmeRWlcC486T0tNZy/dxQ6zBn1MGciVznsHi/+HkNElSI5a2TvEltSUgIhBIQQKC0tdakQ2mw2/L//9/9Qr169gAZx8eJFDB48GPPnz8eLL77ouL64uBgLFy7E0qVL0a1bNwDAokWLkJWVhR07duCmm26qcqyUlBSsXbvW5bq33noLHTt2xOnTp9G4cWPH9TVq1ED9+vUDGjOFr2DW0/PH066v0vRYs9Mad2rienYU7h577DF07NgRZ86cQc+ePWE2V/48N2/e3CUTjEjLbCRtSLuYRnPBSa1z97ae3Sv2Aeo8AZFKmDMUiaJp+irXsqNwoEXWyC7Y1axZ0zF1tGXLllVuN5lMmDx5ckCDGDFiBO6880706NHD5UTy8vJQUVGBHj16OK7LzMxE48aNsX37do8FO0+Ki4thMplQs2ZNl+unT5+OqVOnonHjxrj//vsxduxYxMR4/paUlZWhrKzM8XVJSYmCMyRfzGYBm83//aSilNGKT/4KYu7ddf4Kd4F0wbFoR1Y7UCGz48b6x5t1o+yotHHjRtx2223o0KGDy/V33nmnTiOST81sZM5ox2ISgNkkK2sindqFSm5CET2YM/pQ+z0Ys0Y7scIc8umb0VS0o8inJGeA6Mga2QW7jRs3QgiBbt26YcWKFY7F9AAgLi4OTZo0QcOGDRUPYNmyZdizZw927dpV5bbCwkLExcVVKbSlpaWhsLBQ1vEvX76MCRMmYNCgQS47hzz++ONo164dUlNT8fXXX+OZZ55BQUGB1y3Rp02bFnBBMppd9+nrXqfFWiyV75zsVgssFhvsNrPX9euci1FqFJ+07LLTi5yiHZEzo+yodMcdd6BRo0bIzc3F0KFDkZ6erveQZFMzG5kz2rOYKv+4i+buOqJQYs4ET+33YMwa9VlgCmjjCW+FNiXTXaOpWCcVQ2OFGeusuTqPhowkkrNGdsHu1ltvBQCcOHEC6enpjva+YJw5cwajR4/G2rVrNVnwtaKiAv3794cQAm+//bbLbePGXSkitWnTBnFxcfjrX/+KadOmIT4+vsqxnnnmGZfHlJSUhFXY60lJoUgq3Mk5ZjBFO7WLV+xgIwrcTz/9hA8//BDvv/8+Jk+ejG7dumHYsGG4++67ERcXp/fwfFIzG5kzgRtbOhgzayzxeFuMpfJNlM1W+ZFtDACE+a6ogXa0aXXO7LIjo2POXMGsCcwX1iG4K8b7WoFKi3a+Cm1xMHONOh+4CQUZlRZZo/g3fpMmTWA2m/Hbb7/h8OHD+Pbbb10uSuTl5eH8+fNo164dYmJiEBMTg82bN2P27NmIiYlBWloaysvLUVRU5PK4c+fO+V17TirWnTp1CmvXrvVbce3UqROsVitOnjzp8fb4+HgkJye7XEietp+96vN2c0xlp53ZYofFYvO4QYO0w6vzRdqp1fnij9z7qcXXZhOebgt2UwgWDaOXTSi7AMbZUalOnToYO3Ys9u3bh507d6Jly5Z47LHH0LBhQzz++OPYv3+/ruOTQ41sZM5oy2ISiI2eRoSQC+cCKMnDnNGXWu/BmDWB+8I6xO99pHXW/BXk/FHrPkThRGnOREPWyO6wk1y4cAG5ubn46quvPN5uU7BATPfu3XHgwAGX63Jzc5GZmYkJEyYgPT0dsbGxWL9+Pfr16wcAyM/Px+nTp5Gdne31uFKx7ocffsDGjRtRu3Ztv2PZt28fzGYzF23VSNvPXsX+3uO93m42CwCV3XVyu+w80Wvap3OhzNPGE4FyPh+5xbhInO5L2jBK+7izdu3aoX79+qhduzamT5+O9957D3PnzkV2djbmzZuHa6+9Vu8heqRmNlJgfHXZAUCMWcBmM8FiAiAQ1l12Ru1oC9fvJ2mHOaMe5owx+Oq0k7rspLXs3DvllBbYvHXasVBH5CqSs0bxv/YxY8agqKgIO3fuRLVq1bB69Wq8//77aNGiBT777DNFx6pRowZat27tcqlevTpq166N1q1bIyUlBcOGDcO4ceOwceNG5OXlITc3F9nZ2S4bTmRmZmLVqlUAKot19957L3bv3o0lS5bAZrOhsLAQhYWFKC8vBwBs374ds2bNwv79+3H8+HEsWbIEY8eOxQMPPIBatWop/ZaQTL467cwxNpjNwqXLLsbp4i5cClLB7Ajrfo5KugOdi3s2q8Xx/732ei+aUnizKrwAxvk0Cqj83b18+XL8+c9/RpMmTbBmzRq89dZbOHfuHI4ePYomTZrgvvvu03uYXqmZjRS4saWDHf9vMVetHklddhZT5SeWsSbXCxF5x5zRF3PGOCoUrjEnXQLh/LhgjhNq5bBzWi8ppjRnoiFrFHfYbdiwAf/+97/RoUMHmM1mNGnSBD179kRycjKmTZum+m5LM2fOhNlsRr9+/VBWVoacnBzMnTvX5T75+fkoLi4GUDlvWAqt66+/3uV+GzduRNeuXREfH49ly5Zh0qRJKCsrQ7NmzTB27FiX9RxIG+6ddhaLDTZbZUHJHGODxV75jikurhw2m8Vxm/SDGuyUUa34WsPObjNXmf7qr5Cn5o6vlhh+4kqeGeXTqFGjRuGf//wnhBB48MEHMWPGDLRu3dpxe/Xq1fHqq68GtLFRqIQ6G8k7T512MRYBq821Iid12lmdrpOKdmp3irkXA9U4vlG77IicMWfUw5wxjjXWh5ATs9jnfdTcMTZcinREeonkrFFcsLt06ZJj2mitWrVw4cIFtGzZEtdddx327Nmj9HBVbNq0yeXrhIQEzJkzx2e1VIgrf/k2bdrU5WtP2rVrhx07dgQ1TtKGNDXWE/sfU02NWrRz5j4tVira+SrUqX1enBpL4eLgwYN488030bdvX4+b/gCVa0Js3LgxxCOTT+tsJGV8TY+1mARgNqHCfqVoB7gW7rQWG8bTcf2ZJQboPQSiKpgzpLZQF+3CEQuNFG20yBrF/4oyMjKQn58PAGjbti3eeecd/PTTT5g3bx4aNGig9HAUhfxtQgHAMS3WYrlSvJO61GIs9rDcXEHu9Fi5m2cQOatAZQFA1uWPxxilfXz9+vUYNGiQ12ADgJiYGMdOeUbEbDQe5+mxkhinabLSBhSWP7rUnD/BDEXnmhrPEalFPzIm5oy+mDPGs8b6kN/7SJtQeBOJ00Yj8ZwoNBTlTJRkjeIOu9GjR6OgoAAAMHHiRNxxxx1YsmQJ4uLisHjxYqWHoyjlbRMKc4wNsFpgt195JyMV7Ww2i0uXmtE6yJROVdXj+b664VWuY0cORmkflxw8eBCnT592rDcq6d27t04jko/ZGF4sJgGbMCHWDEennU1U/lEkddqp2QVnhOmroSrujTF9zC47cmDOqIc5Y0zBdtpJG0uwG02ZW2MXYnPFML2HQQYRyVmjuGD3wAMPOP6/ffv2OHXqFA4fPozGjRujTp06igdA0Usq2jmvYydxnhorFeic7ydNjTVa0S4csGhHSk2bNg0rV67E4cOHUa1aNdx88814+eWXkZGR4bjP5cuX8cQTT2DZsmUu642mpaX5Pf7x48dxzz334MCBAzCZTI5lDUymygpHOOx8x2w0pvGX7ser1Zcqekwoi3aROjWWRTtSijnjH3PGuOQU7XxhsY5Ie1rnDKBN1gT92yExMRHt2rVjUFBA5EyP9TU11gjMZrvjEmosVpLEjsoSt5yL9JMqt3188+bNGDFiBHbs2IG1a9eioqICt99+Oy5duuS4z9ixY/H555/jk08+webNm3H27Fn07dtX1thHjx6NZs2a4fz580hMTMT333+PLVu2oEOHDlXWNQ0XzEbjGH/pfgCVG08AV6bFWkyV/3WfGgsE8GmmTJ6Kc5G6Q+0Y08d6D4FUxpwxFuaMMVng+Re6v6mxkSRUBchbYxeG5HkodJTkjNKs0TpnAG2yxiT87dAAKNo99fXXXw9oIOGmpKQEKSkpKC4uNlT7Zbjac+cEx//brZVddNK0WJvN4uiyc9451m4zOzZq0KNw5a9Ap7SgGMy5eN2h1sux2GGnP7V+h0jHGY73EIdEWY8px294Fw8H/NwXLlxAvXr1sHnzZnTp0gXFxcWoW7culi5dinvvvRcAcPjwYWRlZWH79u246aabfB6vTp062LBhA9q0aYOUlBR88803yMjIwIYNG/DEE09g7969iscYClpnI3NGXa9WX+rYKdYq5Yuo/G+F069Qm4dNKNTsgvNVnAvkeeQW+/To5GOXnf7U+D3CnNFPKN6DMWvUI3XZ2eD5F240b0ChFU6L1Z9eOQMElzVq5wygTdbI+hBZ7oGlVj+iYJhjbLBbLTCbBex2k6O7zkhTYwPppjO77RqrNXbfkT8lJSUuX8fHx/tcJFVSXFwMAEhNTQUA5OXloaKiAj169HDcJzMzE40bN5YVcDabDTVq1ABQGXRnz55FRkYGmjRp4lhg24iYjeFl/KX7MT3hnwAqu+ysdlOVtewAfdezU/o8SjrzInX6LRkbcyY4zJnwssb6EHrELPJ6u5xdY7meHZFygWSN2jkDaJM1sgp2Rt7inCJDuy9fdumy80QqeEnFOmkDCq2Kdu5FOVWPrXLxLtQbXpDxVABeJmF4vi8ApKenu1w/ceJETJo0yedj7XY7xowZg86dO6N169YAgMLCQsTFxaFmzZou901LS0NhYaHf8bRu3Rr79+9Hs2bN0KlTJ8yYMQNxcXF499130bx5c5lnFXrMxsjiXLSTaFW08zmOCCmssbsu8jBnQo85E37WWXN9Fu38kTahkP6fKJooyRnp/oDyrNEiZwBtskarZVqIFHMu2nnqspMKdZ52jZWKdlpSUhDTY309qaDIwh3JdebMGZf2cTldDyNGjMB3332HrVu3qjaO5557zrF+xJQpU3DXXXfhT3/6E2rXro2PP+Y6WKSepy8PcnTZSaQuO9frrkyN1aJo52/XWDnPE4nr3lHkYc5QNFKraMfCHZE8SrNGi5wBtMkaFuzIUHx12jlPjfVUtANg2F1jzSEs4Bnx/El7Ngiva6Z4ui8AdO/eHRaLBSNGjMCIESP8Pm7kyJH44osvsGXLFjRq1Mhxff369VFeXo6ioiKXT6XOnTuH+vXr+z1uTk6O4/+vueYaHD58GL/88gtq1arFaT6kOk9FO4mnqbGAa9EuVKSCXCAFQumPu1CPmSIbc4aIiLSkJGek+wPKskarnAG0yRoW7Mhw2n35ssfrv+n5rKz17NQQTJeaUXavJfJn165dshZoFUJg1KhRWLVqFTZt2oRmzZq53N6+fXvExsZi/fr16NevHwAgPz8fp0+fRnZ2tqyxCCHw888/w2QyoXbt2o71JIi08PTlQR6vnxz7sd+iXainrAZbuGPRjvTEnKFotc6a6/F6uTubOk+NJc+44QRJ5GRNKHJGeh41s4atOBQ2Oq59yfH/ZovdUbyzWGyODrYYix1ms123aaEs1lEkGjFiBD766CMsXboUNWrUQGFhIQoLC/H7778DAFJSUjBs2DCMGzcOGzduRF5eHnJzc5Gdne13gdbCwkIMGTIEtWrVQlpaGurVq4datWrh4Ycfxrlz50JxekQOEysq112LdfrryOL0gaian3IqLcDFmlwv3vCTWApHzBmKFkqKTHEwczoskUq0zBlAu6zh33UUdqSuOudNKKTiHeB7KpC/6aLsrKNwZYWA2SSvAmAVlfe78cYbZbWPv/322wCArl27uly/aNEiPPTQQwCAmTNnwmw2o1+/figrK0NOTg7mzp3rcxwlJSW4+eabcfHiReTm5iIzMxNCCBw8eBD//Oc/sXXrVuzZswdJSUmyzotIDRMrBmByrOs6I+6ddt5WRDb6ZhG+ugOdi4BGPw/SB3OGSB2bK4bJ7rQjiiZKcgZQljVa5QygbdawYEdhpePal7C92wsu10kFPPdCnvMPt5ypsnoX67Ta7dZdr73jNTs2hRclU5X8SUhIwJw5czBnzhzZz//GG2/AYrHg+++/R926dV1ue+6559C5c2fMnj0bf//732Ufk0gt7rvGVinaIbKmmzpvhBEpO9WS/pgzRJ5trhjm2JiiwhTY+4hy2NmBRwT5U2L9CSRnAG2zhv/CKexkb5ji+H+pOOc8PVb6f7PF7jJVFtBmB9Vgi3Wh3JCCyEi+/PJL/P3vf68SbABQr149PPPMM/j88891GBlFu4kVA1yKdRKLW2ddDFw/HIqknVsj6VwoejFnyMikde5ihRmxIrC35Vznjkh/WmYNC3YUlrwV7bwV7gBtinZaTYPVaw0+Cl9WCFTIvFhxpX28VatWij9FUsuRI0dw8803e7395ptvRn5+fghHRHTFi7YBHq93L9r5YvN/F80pmUrh3lXHoh05Y84QaSuYoh0LdxQJlORMtGQNp8RS2MreMMUxPdZssXvcOdZ5mqzdZlZ12qkWxTo1d7ol8kfuVCWtlJSUuGyb7q5mzZooKSkJ3YCI3LxoG4DnLB9XuV4q2rnvIAu4Tie1aD5CY5olPBc7KfowZ4h8W2fNdUyNBSqLdnKnyLrvJMspshStIjlr+C+aIpJ7tx2gbqedkmJdoFNe2WVHkU4IAbPZewyZTCZZ600QaelF2wDEml13jpV420FWz840f+vq+Rsb166jSMKcoXAgTY2VBDNFlohCT8usYYcdhTX3LjsAjk47wP+GFFZc2TnWbjfLKpLJLdYpKdQ5dwiyy44CYYOAGfKCwAZlu/dpRQiBli1bwmTyXEHgmygyCmnnWPeNKICqm1F4KpjZ4LnbznmjBy15G5c3oRoXhRfmDJF23DvtAGXddtFmc8UwvYdAGlCSM9L9gcjOGhbsKOxJ69m57x4rcS/aAa4FMmf+inZaFOt8HkelHWPZrUee6N0+vmjRIv93IjKIiRWep8cCnot2ek6NtSL4P/DYaUdqYM4QyRdI0c7I02KNNBaKbJGcNSzYUcRw3ogCALbdOsnx/85FOwCOwh1RpKgwATDJe4ddAQBC/0+jhg4dGvLnJAqGp40opCKec9HOE29ddlpR0lFHJAdzhkh77tNjAaBHzCKvRTsjbzYhFRNZtCO5lOQMEB1Zw4IdRQ3nDjtffHXZWf/YuIIoEuj9aRRRpJGKdp6moHpLH04/pUjGnCFSh7dOO/cOO8B4nW1GGw9FnkjOGv7Loagiddj5o8Y01EBpsX6ddD45eU+pfmwiomjlawfZGLAQR0RE6uFGFETRh//qKeqZzXaXixo8rY/n675K7h8oPYuQpL0KCEUXoLJ9vFWrVpgzZ47OoyeKLM67x8op2nG9OAoHzBki/XnaQdZT95qRp8oSeaM0Z6IhazgllqKOc5ddnNPmE1Jnm7+inRrTYkNRoCPyJ5Lbx4n0ZjEBEFU3oNCb1uOYJaqu80fRizlDpA33KbKcckrRLJKzhgU7ikrOm09IO7pK/xjUmpIaSFHO/bnZFUdEFF5i//i1XWG/UrQDALh12rkXzkKxlp1RioZERBQ8fzvIElH4M1TBbvr06XjmmWcwevRozJo1CwBw+fJlPPHEE1i2bBnKysqQk5ODuXPnIi0tzetxhBCYOHEi5s+fj6KiInTu3Blvv/02WrRo4bjPL7/8glGjRuHzzz+H2WxGv3798MYbbyApKUnr06QQ6bx5kstOse6cO+2kwp39j+45f0U7LTaf0GLtOooeNpOAWeauSjYIQ+yoJBk3bpzH600mExISEnDNNdegT58+SE1NDfHIiHx70TbA4zp2QGXhTiraeduIwp2WRTsW6yhYzBki4zFy0Y7TckkpJTkDREfWGKZgt2vXLrzzzjto06aNy/Vjx47Fl19+iU8++QQpKSkYOXIk+vbti23btnk91owZMzB79my8//77aNasGZ5//nnk5OTg4MGDSEhIAAAMHjwYBQUFWLt2LSoqKpCbm4vhw4dj6dKlmp4nhZacop3NZnH8V0nRTi3enofddaQ1o7SP7927F3v27IHNZkNGRgYA4MiRI7BYLMjMzMTcuXPxxBNPYOvWrWjVqpXOoyVy5V60q7Bf6bLzVbTzNk1Wi6Idi3WkF+YMUfDWWXPRI2aR19uNXLQD1J+uu7limKrHo/AXyVljiIrAxYsXMXjwYMyfPx+1atVyXF9cXIyFCxfi9ddfR7du3dC+fXssWrQIX3/9NXbs2OHxWEIIzJo1C8899xz69OmDNm3a4IMPPsDZs2fx6aefAgAOHTqE1atXY8GCBejUqRNuueUWvPnmm1i2bBnOnj0bilMmA5Cmwkqddu47yPrroAu2oGe1mUNarOu1d7zqxyRSQ58+fdCjRw+cPXsWeXl5yMvLw48//oiePXti0KBB+Omnn9ClSxeMHTtW76ESefSizfu6bVLxznn3WMdtXgpz4Vpg4/p1ZFTMGQp366y5eg8hICzWUTTRImsMUbAbMWIE7rzzTvTo0cPl+ry8PFRUVLhcn5mZicaNG2P79u0ej3XixAkUFha6PCYlJQWdOnVyPGb79u2oWbMmOnTo4LhPjx49YDabsXPnTo/HLSsrQ0lJicuFwkPnzZO83uapaGf2UqhTs4jmq9indrGu197xLNZFASvsqJB5sf4xRcEoOyq98sormDp1qssnYykpKZg0aRJmzJiBxMREvPDCC8jLy9NxlNpjzkQuPYt2ahwn1uR68YTFusjHnIkMzJrw5ato575zrBGwWEdKKcmZaMka3afELlu2DHv27MGuXbuq3FZYWIi4uDjUrFnT5fq0tDQUFhZ6PJ50vfsad86PKSwsRL169Vxuj4mJQWpqqtfjTps2DZMnT5Z1TmQ87lNjnYtyzv9vs1lw2/a/V3n8Vze86vG4ga5ll5P3lOznCAYLdeSL3PbxLVu24JVXXkFeXh4KCgqwatUq3H333Y7b5awb6ktxcTHOnz9fpTX8woULjjcSNWvWRHl5ufyTC0PMmfDmaz074Mr0WACYZq9a3BpjqvpYqdgW6BTZV2Q+jy+enlu6rkKwUEe+KZmmpGXWMGeuYNaEN3/TYyXeilvZsfMBqFdM8/Q8t8YuVOXY/p6HSBLJ72l0LdidOXMGo0ePxtq1ax1ryxnVM88847KIYElJCdLT03UcESnlbz07AOiy9XmP18spfK3vOF3WOLp/83TAz6GkqMdiXXSxQcAEBYuBQ/4CrZcuXULbtm3x8MMPo2/fvlVul7NuqC99+vTBww8/jNdeew033ngjgMrgHT9+vCNEv/nmG7Rs2VLW+YUr5kz481e0AzwX6wB5ha8nzfKKbZ4KdXKfx7mg569QyGJddNEyZwBts4Y5cwWzJvL5Km5tr3jE7+PlFty8PY+c4lqwz0GRSUnOSPcHIvs9ja4Fu7y8PJw/fx7t2rVzXGez2bBlyxa89dZbWLNmDcrLy1FUVOTSZXfu3DnUr1/f4zGl68+dO4cGDRq4POb666933Of8+fMuj7Narfjll1+8Hjc+Ph7x8fGBnCYZiK/pscHyVohTE4twpCa5n0b16tULvXr18nib+7qhAPDBBx8gLS0Nn376KQYOHOj3+O+88w7Gjh2LgQMHwmqtXJI/JiYGQ4cOxcyZMwFULoewYMECuacWlpgzkcHXenbB8lWIUwuLcKQmJR12WmYNc+YKZk3403o9u1AUyViIIzVF8nsaXQt23bt3x4EDB1yuy83NRWZmJiZMmID09HTExsZi/fr16NevHwAgPz8fp0+fRnZ2tsdjNmvWDPXr18f69esdBbqSkhLs3LkTjz76KAAgOzsbRUVFyMvLQ/v27QEAGzZsgN1uR6dOnTQ6WyIiY3FftyaQP+L9rRsqJ9ySkpIwf/58zJw5E8ePHwcANG/eHElJSY77SL/PiYgofKiRM0DwWcOcISKKXJH8nkbX1Slr1KiB1q1bu1yqV6+O2rVro3Xr1khJScGwYcMwbtw4bNy4EXl5ecjNzUV2djZuuukmx3EyMzOxatUqAIDJZMKYMWPw4osv4rPPPsOBAwcwZMgQNGzY0NGGmJWVhTvuuAOPPPIIvvnmG2zbtg0jR47EwIED0bBhQz2+FUREQakw2RVdACA9PR0pKSmOy7Rp0xQ/r5x1Q/356KOP8NtvvyEpKQlt2rRBmzZtXIKNiIj0p1fOAMFnDXOGiMj4lOZMNLyn0X3TCX9mzpwJs9mMfv36oaysDDk5OZg7d67LffLz81FcXOz4+qmnnsKlS5cwfPhwFBUV4ZZbbsHq1atd5h0vWbIEI0eORPfu3R3Hnz17dsjOi4hIb2fOnHFpH9driszYsWPxt7/9Db1798YDDzyAnJwcWCwWXcZCRETqYc4QEZHWIjlrDLf/86ZNmzBr1izH1wkJCZgzZw5++eUXXLp0CStXrqyyzpwQAg899JDja5PJhClTpqCwsBCXL1/GunXrqizsl5qaiqVLl6K0tBTFxcV47733+EkbEUWV5ORkl0sg4ea8bqgzX2uNuisoKMCyZctgMpnQv39/NGjQACNGjMDXX3+teDxERGQcauQMEHzWMGeIiCJXJL+nMVzBjoiIlCuHXdEFqNxRqVWrVpgzZ07Az+u8bqhEWjfU21qj7mJiYnDXXXdhyZIlOH/+PGbOnImTJ0/itttuw9VXXx3w2IiISD165QwQfNYwZ4iIjE9pzkTDexrDT4klIiJtyN1R6eLFizh69Kjj6xMnTmDfvn1ITU1F48aNHeuGtmjRwrEFuvO6oUokJiYiJycHv/76K06dOoVDhw4pPgYRERmDkl1iQ5U1zBkiosgSye9pWLAjIiKfdu/ejdtuu83x9bhx4wAAQ4cOxeLFi2WtG+rPb7/9hlWrVmHJkiVYv3490tPTMWjQICxfvlz18yEiIuPROmuYM0RE0S0c39OYhBAioEdGuZKSEqSkpKC4uFj2J4dERBK1fodIx2lnmQmLqZqsx9jE79hjG4uWLVvCYrFgxIgRGDFiRMBjCNbAgQPxxRdfIDExEf3798fgwYNlt55HMuYMEQVLjd8jzJnIxqwhomDolTNAdGQNO+yIiKKUkqlKWrJYLPjXv/7lcSel7777Dq1bt9ZpZEREFAzmDBERaS2Ss4abThARRYByk13RBVBvMfBgLVmyBH/+858dwVZaWop3330XHTt2RNu2bXUdGxERVWLOEBGRlpTmTDRkDTvsiIiilFE+jZJs2bIFCxcuxIoVK9CwYUP07dtX9+AlIqLAMWeIiEhrkZw1LNgREZFuCgsLsXjxYixcuBAlJSXo378/ysrK8Omnn6JVq1Z6D4+IiMIcc4aIiLSmVdZwSiwRUQSwwa7oAujfPv6Xv/wFGRkZ+PbbbzFr1iycPXsWb775pi5jISIi35gzRESkJaU5Ew1Zww47IqIopXf7+FdffYXHH38cjz76KFq0aKHbOIiISBvMGSIi0lokZw077IiISBdbt25FaWkp2rdvj06dOuGtt97C//73P72HRUREEYI5Q0REWtMya1iwIyKKAOWwoxw2mRdjtI/fdNNNmD9/PgoKCvDXv/4Vy5YtQ8OGDWG327F27VqUlpbqMi4iIqqKOUNERFpSljPRkTUmIYRQcaxRo6SkBCkpKSguLjbUjiREFB7U+h0iHefqmJdhMSXIeoxNXMYx6wRD/v7Kz8/HwoUL8eGHH6KoqAg9e/bEZ599pvewdMGcIaJgqfF7hDkT2Zg1RBQMvXIGiI6sYYcdEREZRkZGBmbMmIEff/wR//znP/UeDhERRRjmDBERaU2trGHBjogoAlSY7CiXeakwGaN93BeLxYK77747qrseiIiMhDlDRERaUpIz0ZI13CWWiChK6b2jEhERRTbmDBERaS2Ss4YddkREEaACdkUXwNifRhERkbEwZ4iISEtKcyYasoYddkREUSqSP40iIiL9MWeIiEhrkZw17LAjIiIiIiIiIiIyEBbsiIgiwGWTVdEFiOz2cSIiUhdzhoiItKQ0Z6IhazgllogoSkVy+zgREemPOUNERFqL5Kxhhx0REckyZ84cNG3aFAkJCejUqRO++eYbvYdEREQRhDlDRERaCrecYcGOiCgCVJjsKJd5qTAp31Hp448/xrhx4zBx4kTs2bMHbdu2RU5ODs6fP6/1qRERkQEwZ4iISEtKciaQrAnHnDEJIYTegwhHJSUlSElJQXFxccS2XxKRdtT6HSIdJyl+IkymBFmPEeIyLpZNVvTcnTp1wo033oi33noLAGC325Geno5Ro0bh6aefDnj85B1zhoiCpcbvEeZMZGPWEFEw9MoZQHnWhGPOcA27AEl1zpKSEp1HQkThSPrdodZnJgJlgMxDCZS5jEESHx+P+Pj4KvcvLy9HXl4ennnmGcd1ZrMZPXr0wPbt2wMfNPnEnCGiYKmZNcyZyMSsIaJg6JUzjvtDXtaEa86wYBeg0tJSAEB6errOIyGicFZaWoqUlJSAHx8XF4f69eujsHC6osclJSVV+f01ceJETJo0qcp9//e//8FmsyEtLc3l+rS0NBw+fFjxmEke5gwRqSWYrGHORDZmDRGpQY+cAeRnTbjmDAt2AWrYsCHOnDmDGjVqwGQyyXpMSUkJ0tPTcebMmYhrOee5hZ9IPS8gPM5NCIHS0lI0bNgwqOMkJCTgxIkTKC8vV/z87r+7PHU9kH6YM654buGJ56YvNbKGORPZlGZNOPzcB4rnFp54bvrSM2ek54/krGHBLkBmsxmNGjUK6LHJycmG/QcXLJ5b+InU8wKMf27BdNY5S0hIQEKC/PUelKpTpw4sFgvOnTvncv25c+dQv359zZ432jFnPOO5hSeem37UyBrmTOQKNGuM/nMfDJ5beOK56Yc5ox3uEktERD7FxcWhffv2WL9+veM6u92O9evXIzs7W8eRERFRJGDOEBGRlsI1Z9hhR0REfo0bNw5Dhw5Fhw4d0LFjR8yaNQuXLl1Cbm6u3kMjIqIIwJwhIiIthWPOsGAXQvHx8Zg4cWJEzamW8NzCT6SeFxDZ56aXAQMG4MKFC3jhhRdQWFiI66+/HqtXr66ycCvpK5J/9nlu4YnnRnIxZ8JDJP/c89zCE8+N5ArHnDEJNfbfJSIiIiIiIiIiIlVwDTsiIiIiIiIiIiIDYcGOiIiIiIiIiIjIQFiwIyIiIiIiIiIiMhAW7IiIiIiIiIiIiAyEBbsgvPTSS7j55puRmJiImjVrVrl9//79GDRoENLT01GtWjVkZWXhjTfecLnPpk2bYDKZqlwKCwt9Pve3336LP/3pT0hISEB6ejpmzJih5qmpcm7Otm3bhpiYGFx//fU+n/fkyZMevx87duwI8owq6XVeQHi8Zlu3bkXnzp1Ru3ZtVKtWDZmZmZg5c6bP59X6NdPz3ADtXzciX5gz4ZczALOGWcOsofDBnGHOAMwZIHxeN+ZMdInRewDhrLy8HPfddx+ys7OxcOHCKrfn5eWhXr16+Oijj5Ceno6vv/4aw4cPh8ViwciRI13um5+fj+TkZMfX9erV8/q8JSUluP3229GjRw/MmzcPBw4cwMMPP4yaNWti+PDhhju3oqIiDBkyBN27d8e5c+dkPf+6detw7bXXOr6uXbt2cCf0B73OK1xes+rVq2PkyJFo06YNqlevjq1bt+Kvf/0rqlev7necWr1mep5bKF43Il+YM+GXMwCzhlnDrKHwwZxhzjBnXBn9dWPORBlBQVu0aJFISUmRdd/HHntM3HbbbY6vN27cKACIX3/9VfbzzZ07V9SqVUuUlZU5rpswYYLIyMiQfQy5gjk3yYABA8Rzzz0nJk6cKNq2bevzGCdOnBAAxN69e5UPVoFQn1e4vWbO7rnnHvHAAw94vT1Ur5kQoT+3UL5uRL4wZyqFU84IwayRMGuYNWR8zJlKzJm2Po8Rbq+bM+YMcyYccUpsiBUXFyM1NbXK9ddffz0aNGiAnj17Ytu2bT6PsX37dnTp0gVxcXGO63JycpCfn49ff/1V9THL5encFi1ahOPHj2PixImKjtW7d2/Uq1cPt9xyCz777DM1h6mYGucVTq+Zs7179+Lrr7/Grbfe6vdYRnrNAHXOzaivG5EvzBl5wuF3FrOmqnB43ZwxaygSMWfkCYffV8yZqsLhdXPGnIlsnBIbQl9//TU+/vhjfPnll47rGjRogHnz5qFDhw4oKyvDggUL0LVrV+zcuRPt2rXzeJzCwkI0a9bM5bq0tDTHbbVq1dLuJLzwdG4//PADnn76afz3v/9FTIy8H7WkpCS89tpr6Ny5M8xmM1asWIG7774bn376KXr37q3V8L1S67zC5TWTNGrUCBcuXIDVasWkSZPwf//3f16PY7TXDFDv3Iz4uhH5wpzxL1x+ZzFrXIXL6yZh1lCkYs74Fy6/r5gzrsLldZMwZ6KE3i1+RjNhwgQBwOfl0KFDLo+R09J64MABUadOHTF16lS/Y+jSpYvPltaePXuK4cOHu1z3/fffCwDi4MGDhjg3q9UqOnToIN5++23HdXLarD158MEHxS233BLW5xUOr5mz48ePi2+//Va8++67IjU1VSxdutTn8dz5e83C5dwCfd2IfGHOhF/OhMu5hcPr5oxZU4lZQ2pjzjBntDq3cHjdnDFnKjFnwhc77Nw88cQTeOihh3zep3nz5oqOefDgQXTv3h3Dhw/Hc8895/f+HTt2xNatW73eXr9+/SoLgkpf169f3+vjQnlupaWl2L17N/bu3etYINNut0MIgZiYGPznP/9Bt27dZD1Hp06dsHbtWq+3h8N5hcNr5kz6BOa6667DuXPnMGnSJAwaNEj2c/h7zYDwOLdAXzciX5gz4ZczQHicWzi8bs6YNZWYNaQ25gxzRsKcYc4AzJlwxoKdm7p166Ju3bqqHe/7779Ht27dMHToULz00kuyHrNv3z40aNDA6+3Z2dl49tlnUVFRgdjYWADA2rVrkZGR4bOdNZTnlpycjAMHDrhcN3fuXGzYsAHLly+v0pLri7/vRzicVzi8Zt7Y7XaUlZUpeh5/rxkQHucW6OtG5IsRfvaZM66M9juLWSOP0V43b5g1FGpG+Llnzrgy2u8r5ow8RnvdvGHORDA92/vC3alTp8TevXvF5MmTRVJSkti7d6/Yu3evKC0tFUJUtrHWrVtXPPDAA6KgoMBxOX/+vOMYM2fOFJ9++qn44YcfxIEDB8To0aOF2WwW69atc9znzTffFN26dXN8XVRUJNLS0sSDDz4ovvvuO7Fs2TKRmJgo3nnnHUOdmztPbdbu57Z48WKxdOlScejQIXHo0CHx0ksvCbPZLN57772wPq9wec3eeust8dlnn4kjR46II0eOiAULFogaNWqIZ5991uu5af2a6XluoXjdiHxhzoRfzuh5buHyujFrmDVkHMwZ5oyScwuX1405w5yJFCzYBWHo0KEe56hv3LhRCFH5S8/T7U2aNHEc4+WXXxZXX321SEhIEKmpqaJr165iw4YNLs8zceJEl8cIIcT+/fvFLbfcIuLj48VVV10lpk+fbrhzc+cpBNzPbfHixSIrK0skJiaK5ORk0bFjR/HJJ5+E/XkJER6v2ezZs8W1117r+P7fcMMNYu7cucJms3k9N61fMz3PTQjtXzciX5gz4Zczep6bEOHxujFrmDVkHMwZ5oyScxMiPF435gxzJlKYhBACREREREREREREZAhmvQdAREREREREREREV7BgR0REREREREREZCAs2BERERERERERERkIC3ZEREREREREREQGwoIdERERERERERGRgbBgR0REREREREREZCAs2BERERERERERERkIC3ZURdeuXTFmzJiIet6HHnoId999d1DHaNq0KUwmE0wmE4qKirzeb/HixahZs2ZQzxXtunbt6vhe79u3T+/hEJEGmDWeMWtCh1lDFNmYM54xZ0KHOUPBYsGODGPlypWYOnWq4+umTZti1qxZ+g3IgylTpqCgoAApKSl6DyUiePtDYOXKlfjmm29CPyAiinjMmujDrCGiUGLORB/mDGklRu8BEElSU1P1HoJfNWrUQP369fUeBgCgoqICsbGxeg9DE6mpqSgpKdF7GEQUgZg1yjBriIiUYc4ow5wh8o4dduTXr7/+iiFDhqBWrVpITExEr1698MMPPzhulz5RWLNmDbKyspCUlIQ77rgDBQUFjvtYrVY8/vjjqFmzJmrXro0JEyZg6NChLi3dzu3jXbt2xalTpzB27FhHGzEATJo0Cddff73L+GbNmoWmTZs6vrbZbBg3bpzjuZ566ikIIVweY7fbMW3aNDRr1gzVqlVD27ZtsXz58oC+P4sXL0bjxo2RmJiIe+65Bz///HOV+/z73/9Gu3btkJCQgObNm2Py5MmwWq2O2w8fPoxbbrkFCQkJaNWqFdatWweTyYRPP/0UAHDy5EmYTCZ8/PHHuPXWW5GQkIAlS5YAABYsWICsrCwkJCQgMzMTc+fOdXnuM2fOoH///qhZsyZSU1PRp08fnDx5Uvb5+Tv+hAkT0LJlSyQmJqJ58+Z4/vnnUVFR4bh9//79uO2221CjRg0kJyejffv22L17NzZt2oTc3FwUFxc7XuNJkybJHhcRRRZmjW/MGmYNEQWHOeMbc4Y5QwYkiNzceuutYvTo0Y6ve/fuLbKyssSWLVvEvn37RE5OjrjmmmtEeXm5EEKIRYsWidjYWNGjRw+xa9cukZeXJ7KyssT999/vOMaLL74oUlNTxcqVK8WhQ4fE3/72N5GcnCz69Onj8Xl//vln0ahRIzFlyhRRUFAgCgoKhBBCTJw4UbRt29ZlvDNnzhRNmjRxfP3yyy+LWrVqiRUrVoiDBw+KYcOGiRo1arg814svvigyMzPF6tWrxbFjx8SiRYtEfHy82LRpk9fvS5MmTcTMmTNdrtuxY4cwm83i5ZdfFvn5+eKNN94QNWvWFCkpKY77bNmyRSQnJ4vFixeLY8eOif/85z+iadOmYtKkSUIIIaxWq8jIyBA9e/YU+/btE//9739Fx44dBQCxatUqIYQQJ06cEABE06ZNxYoVK8Tx48fF2bNnxUcffSQaNGjguG7FihUiNTVVLF68WAghRHl5ucjKyhIPP/yw+Pbbb8XBgwfF/fffLzIyMkRZWZnXc5X4O74QQkydOlVs27ZNnDhxQnz22WciLS1NvPzyy47br732WvHAAw+IQ4cOiSNHjoh//etfYt++faKsrEzMmjVLJCcnO17j0tJSx+Okc967d6/fcRJR+GHWeMasYdYQkTqYM54xZ5gzFD5YsKMqnEPmyJEjAoDYtm2b4/b//e9/olq1auJf//qXEKIy3ACIo0ePOu4zZ84ckZaW5vg6LS1NvPLKK46vrVaraNy4sddwE8JzmMgJtwYNGogZM2Y4vq6oqBCNGjVyPNfly5dFYmKi+Prrr12OM2zYMDFo0CCv3xdP4xk0aJD485//7HLdgAEDXMKte/fu4h//+IfLfT788EPRoEEDIYQQX331lYiJiXEEuBBCrF271mO4zZo1y+U4V199tVi6dKnLdVOnThXZ2dmO58nIyBB2u91xe1lZmahWrZpYs2aN13OVe3xPXnnlFdG+fXvH1zVq1HAJQ2eLFi1y+V45Y7gRRTZmjWfMmqrH94RZQ0T+MGc8Y85UPb4nzBkyAq5hRz4dOnQIMTEx6NSpk+O62rVrIyMjA4cOHXJcl5iYiKuvvtrxdYMGDXD+/HkAQHFxMc6dO4eOHTs6brdYLGjfvj3sdruq4y0uLkZBQYHLeGNiYtChQwdHC/nRo0fx22+/oWfPni6PLS8vxw033KDo+Q4dOoR77rnH5brs7GysXr3a8fX+/fuxbds2vPTSS47rbDYbLl++jN9++w35+flIT093WUfC+XvlrEOHDo7/v3TpEo4dO4Zhw4bhkUcecVxvtVodC8ju378fR48eRY0aNVyOc/nyZRw7dsznuck5PgB8/PHHmD17No4dO4aLFy/CarUiOTnZcfu4cePwf//3f/jwww/Ro0cP3HfffS4/K0REzBrfmDXMGiIKDnPGN+YMc4aMiQU7UoX7QqEmk6nKGgtqMJvNVY7rvLaAHBcvXgQAfPnll7jqqqtcbouPjw9ugF6eb/Lkyejbt2+V2xISEhQdq3r16i7HBYD58+e7hDlQ+ceDdJ/27ds71oZwVrduXb/j9nf87du3Y/DgwZg8eTJycnKQkpKCZcuW4bXXXnPcd9KkSbj//vvx5Zdf4quvvsLEiROxbNmyKn8UEBH5w6zx/XzMGmYNEQWHOeP7+ZgzzBkKLRbsyKesrCxYrVbs3LkTN998MwDg559/Rn5+Plq1aiXrGCkpKUhLS8OuXbvQpUsXAJWfxuzZs6fKYqvO4uLiYLPZXK6rW7cuCgsLIYRwLNq6b98+l+dq0KABdu7c6Xguq9WKvLw8tGvXDgDQqlUrxMfH4/Tp07j11ltlnYM3WVlZ2Llzp8t1O3bscPm6Xbt2yM/PxzXXXOPxGBkZGThz5gzOnTuHtLQ0AMCuXbv8PndaWhoaNmyI48ePY/DgwR7v065dO3z88ceoV6+eyydEcsg5/tdff40mTZrg2WefdVx36tSpKvdr2bIlWrZsibFjx2LQoEFYtGgR7rnnHo+vMRFFH2aNb8waZg0RBYc54xtzhjlDxsSCHfnUokUL9OnTB4888gjeeecd1KhRA08//TSuuuoq9OnTR/ZxRo0ahWnTpuGaa65BZmYm3nzzTfz666+OgPKkadOm2LJlCwYOHIj4+HjUqVMHXbt2xYULFzBjxgzce++9WL16Nb766iuXX9yjR4/G9OnT0aJFC2RmZuL1119HUVGR4/YaNWpg/PjxGDt2LOx2O2655RYUFxdj27ZtSE5OxtChQ2Wf1+OPP47OnTvj1VdfRZ8+fbBmzRqX1nEAeOGFF3DXXXehcePGuPfee2E2m7F//3589913ePHFF9GzZ09cffXVGDp0KGbMmIHS0lI899xzAODz+wMAkydPxuOPP46UlBTccccdKCsrw+7du/Hrr79i3LhxGDx4MF555RX06dMHU6ZMQaNGjXDq1CmsXLkSTz31FBo1ahTU8Vu0aIHTp09j2bJluPHGG/Hll19i1apVjsf//vvvePLJJ3HvvfeiWbNm+PHHH7Fr1y7069cPQOVrfPHiRaxfvx5t27ZFYmIiEhMTZX//iSgyMGt8Y9Ywa4goOMwZ35gzzBkyKH2WziMjc18o9ZdffhEPPvigSElJEdWqVRM5OTniyJEjjts9LbK5atUq4fzjVVFRIUaOHCmSk5NFrVq1xIQJE8R9990nBg4c6PV5t2/fLtq0aSPi4+NdjvX222+L9PR0Ub16dTFkyBDx0ksvuSzQWlFRIUaPHi2Sk5NFzZo1xbhx48SQIUNcFoO12+1i1qxZIiMjQ8TGxoq6deuKnJwcsXnzZq/fF08LtAohxMKFC0WjRo1EtWrVxF/+8hfx6quvVvl+rF69Wtx8882iWrVqIjk5WXTs2FG8++67jtsPHTokOnfuLOLi4kRmZqb4/PPPBQCxevVqIYTvxUqXLFkirr/+ehEXFydq1aolunTpIlauXOm4vaCgQAwZMkTUqVNHxMfHi+bNm4tHHnlEFBcXez1XJcd/8sknRe3atUVSUpIYMGCAmDlzpuP8y8rKxMCBA0V6erqIi4sTDRs2FCNHjhS///674/F/+9vfRO3atQUAMXHiRMf1XKCVKLIxazxj1jBriEgdzBnPmDPMGQofJiE0mJRP5IfdbkdWVhb69++PqVOn6j0cWZo2bYoxY8ZgzJgxmj/Xtm3bcMstt+Do0aNRu5jpyZMn0axZM+zdu9fnNAMiIm+YNb4xa5g1RBQc5oxvzBnmDAXHrPcAKDqcOnUK8+fPx5EjR3DgwAE8+uijOHHiBO6//369h6bIhAkTkJSUhOLiYlWPu2rVKqxduxYnT57EunXrMHz4cHTu3Dlqg61Xr1649tpr9R4GEYUZZo1vzBpXzBoiUoo54xtzxhVzhoLFNewoJMxmMxYvXozx48dDCIHWrVtj3bp1yMrK0ntosm3evNmxe5P7luLBKi0txYQJE3D69GnUqVMHPXr0cNmVSCtJSUleb/vqq6/wpz/9SfMxeLJgwQL8/vvvAIDGjRvrMgYiCj/MGt+YNa6YNUSkFHPGN+aMK+YMBYtTYomi2NGjR73edtVVV6FatWohHA0REUUiZg0REWmJOUORigU7IiIiIiIiIiIiA+EadkRERERERERERAbCgh0REREREREREZGBsGBHRERERERERERkICzYERERERERERERGQgLdkRERERERERERAbCgh0REREREREREZGBsGBHRERERERERERkICzYERERERERERERGcj/B/Sdjaojr7TkAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "### Now, let's plot a comparison of the results\n", "\n", @@ -1149,10 +15991,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "b8fc1eec", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:19:04.996002Z", + "iopub.status.busy": "2026-01-02T23:19:04.995669Z", + "iopub.status.idle": "2026-01-02T23:21:20.154533Z", + "shell.execute_reply": "2026-01-02T23:21:20.153592Z", + "shell.execute_reply.started": "2026-01-02T23:19:04.995982Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:19:08 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e235dc59ffa44a2e9ce56549162e068c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Processing batches: 0%| | 0/1 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "return_periods = [10, 100]\n", "one_in_x_data = (cd\n", @@ -1207,10 +16105,629 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "536c54b2", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:21:20.155312Z", + "iopub.status.busy": "2026-01-02T23:21:20.155108Z", + "iopub.status.idle": "2026-01-02T23:22:37.778092Z", + "shell.execute_reply": "2026-01-02T23:22:37.777184Z", + "shell.execute_reply.started": "2026-01-02T23:21:20.155294Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:21:22 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 50MB\n",
+       "Dimensions:                              (sim: 5, time: 1051200)\n",
+       "Coordinates:\n",
+       "  * sim                                  (sim) object 40B 'wrf_ucla_ec-earth3...\n",
+       "  * time                                 (time) datetime64[ns] 8MB 1980-09-01...\n",
+       "Data variables:\n",
+       "    Sacramento Executive Airport (KSAC)  (sim, time) float64 42MB dask.array<chunksize=(1, 1051200), meta=np.ndarray>\n",
+       "Attributes: (12/121)\n",
+       "    description:                      temp at 2 m\n",
+       "    grid_mapping:                     Lambert_Conformal\n",
+       "    units:                            K\n",
+       "    AERCU_FCT:                        1.0\n",
+       "    AERCU_OPT:                        0\n",
+       "    AUTO_LEVELS_OPT:                  2\n",
+       "    ...                               ...\n",
+       "    history:                          [2026-01-02 23:22:31] : Bias-adjusted w...\n",
+       "    bias_adjustment:                  QuantileDeltaMapping(group=Grouper(name...\n",
+       "    bias_adjust_model_to_station:     {'stations': ['KSAC'], 'historical_slic...\n",
+       "    update_attributes:                Process 'update_attributes' applied to ...\n",
+       "    filter_unadjusted_models:         yes\n",
+       "    concat:                           Process 'concat' applied to the data. T...
" + ], + "text/plain": [ + " Size: 50MB\n", + "Dimensions: (sim: 5, time: 1051200)\n", + "Coordinates:\n", + " * sim (sim) object 40B 'wrf_ucla_ec-earth3...\n", + " * time (time) datetime64[ns] 8MB 1980-09-01...\n", + "Data variables:\n", + " Sacramento Executive Airport (KSAC) (sim, time) float64 42MB dask.array\n", + "Attributes: (12/121)\n", + " description: temp at 2 m\n", + " grid_mapping: Lambert_Conformal\n", + " units: K\n", + " AERCU_FCT: 1.0\n", + " AERCU_OPT: 0\n", + " AUTO_LEVELS_OPT: 2\n", + " ... ...\n", + " history: [2026-01-02 23:22:31] : Bias-adjusted w...\n", + " bias_adjustment: QuantileDeltaMapping(group=Grouper(name...\n", + " bias_adjust_model_to_station: {'stations': ['KSAC'], 'historical_slic...\n", + " update_attributes: Process 'update_attributes' applied to ...\n", + " filter_unadjusted_models: yes\n", + " concat: Process 'concat' applied to the data. T..." + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Let's manually bias adjust our climate data to KSAC (the SAC airport weather station).\n", "manual_bias_adjustment = (cd\n", @@ -1299,10 +16816,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "3157320f-7c85-40a9-9259-f25f4f391133", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:22:37.778869Z", + "iopub.status.busy": "2026-01-02T23:22:37.778674Z", + "iopub.status.idle": "2026-01-02T23:22:44.865028Z", + "shell.execute_reply": "2026-01-02T23:22:44.863981Z", + "shell.execute_reply.started": "2026-01-02T23:22:37.778852Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:22:40 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n", + "2026-01-02 23:22:41 - climakitae.new_core.processors.warming_level - WARNING - \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.mon.d03.r1i1p1f1 at 1.2C. \n", + "Skipping this warming level.\n", + "2026-01-02 23:22:41 - climakitae.new_core.processors.warming_level - WARNING - No valid slices found for WRF.UCLA.EC-Earth3-Veg.ssp370.mon.d03.r1i1p1f1. Ensure the warming level times table is correctly configured.\n", + "Exporting specified data to NetCDF...\n", + "Saving file locally as NetCDF4...\n", + "Saved! You can find your file in the panel to the left and download to your local machine from there.\n" + ] + } + ], "source": [ "# Let's export all of Humboldt County's data into one file\n", "location = \"Humboldt County\"\n", @@ -1330,9 +16877,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "1cc9720a-fd39-4830-ad10-78e6fe48e4fe", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:22:44.865904Z", + "iopub.status.busy": "2026-01-02T23:22:44.865665Z", + "iopub.status.idle": "2026-01-02T23:22:44.869214Z", + "shell.execute_reply": "2026-01-02T23:22:44.868242Z", + "shell.execute_reply.started": "2026-01-02T23:22:44.865883Z" + } + }, "outputs": [], "source": [ "lat_lons = [\n", @@ -1343,10 +16898,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "b7e81b62-1904-4995-997d-0e9238ccb8ea", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-02T23:22:44.869917Z", + "iopub.status.busy": "2026-01-02T23:22:44.869729Z", + "iopub.status.idle": "2026-01-02T23:22:55.693764Z", + "shell.execute_reply": "2026-01-02T23:22:55.692734Z", + "shell.execute_reply.started": "2026-01-02T23:22:44.869900Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-01-02 23:22:47 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", + "\n", + "Your query selected models that do not have a-priori bias adjustment. \n", + "These models have been removed from the returned query. \n", + "To include them, please add the following processor to your query: \n", + "ClimateData().processes('filter_unadjusted_models': 'no')\n", + "\n", + "2026-01-02 23:22:48 - climakitae.new_core.processors.warming_level - WARNING - \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.mon.d03.r1i1p1f1 at 1.2C. \n", + "Skipping this warming level.\n", + "2026-01-02 23:22:48 - climakitae.new_core.processors.warming_level - WARNING - No valid slices found for WRF.UCLA.EC-Earth3-Veg.ssp370.mon.d03.r1i1p1f1. Ensure the warming level times table is correctly configured.\n", + "Exporting specified data to NetCDF...\n", + "Saving file locally as NetCDF4...\n", + "Saved! You can find your file in the panel to the left and download to your local machine from there.\n", + "Exporting specified data to NetCDF...\n", + "Saving file locally as NetCDF4...\n", + "Saved! You can find your file in the panel to the left and download to your local machine from there.\n" + ] + } + ], "source": [ "# Now, let's export this list of `lat_lons` from before into separate files.\n", "export_data = (\n", @@ -1407,7 +16995,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "cae", "language": "python", "name": "python3" }, @@ -1421,7 +17009,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.12" } }, "nbformat": 4, From c47e11e7da8e09e72a1a93a0d267a74b33e971e9 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Wed, 11 Feb 2026 18:09:18 +0000 Subject: [PATCH 19/53] fixing over commit on basic data access --- data-access/basic_data_access.ipynb | 15790 +------------------------- 1 file changed, 101 insertions(+), 15689 deletions(-) diff --git a/data-access/basic_data_access.ipynb b/data-access/basic_data_access.ipynb index ba93fb4c..18925990 100644 --- a/data-access/basic_data_access.ipynb +++ b/data-access/basic_data_access.ipynb @@ -34,17 +34,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "9d4ff9a3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:11:20.412931Z", - "iopub.status.busy": "2026-01-02T23:11:20.412754Z", - "iopub.status.idle": "2026-01-02T23:11:25.229645Z", - "shell.execute_reply": "2026-01-02T23:11:25.228729Z", - "shell.execute_reply.started": "2026-01-02T23:11:20.412912Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -87,16 +79,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "8a4da135-f64c-415e-a03b-0863cd67bd99", "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:11:25.230500Z", - "iopub.status.busy": "2026-01-02T23:11:25.230256Z", - "iopub.status.idle": "2026-01-02T23:11:25.236454Z", - "shell.execute_reply": "2026-01-02T23:11:25.235598Z", - "shell.execute_reply.started": "2026-01-02T23:11:25.230479Z" - }, "tags": [] }, "outputs": [], @@ -107,388 +92,10 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "d61e8e1b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-05 19:18:05 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n" - ] - } - ], - "source": [ - "pressure_data = (cd\n", - " .catalog(\"cadcat\")\n", - " .variable(\"psfc\")\n", - " .activity_id(\"WRF\")\n", - " .institution_id(\"UCLA\")\n", - " .grid_label(\"d03\")\n", - " .table_id('1hr')\n", - " .processes({\n", - " \"warming_level\": {\n", - " \"warming_levels\": [2.0]\n", - " },\n", - " \"clip\": \"Southern California Edison\",\n", - " \"convert_units\": \"mb\"\n", - " }).get()\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e8f83638", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "pressure_data.isel(warming_level=0, sim=0, time_delta=10).psfc.plot.contourf(x='lon', y='lat', levels=100)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d8e10216", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - Variables:\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - ----------\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - cape: Convective Available Potential Energy\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - cf: Capacity factor\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - cin: Convective Inhibition\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - dew_point: No description available\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - effective_temp_index: No description available\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - etrans_sfc: Evapotranspiration\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - evap_sfc: Evaporation\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - ffwi: Fosberg fire weather index\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - gen: Power generation\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - gh_sfc: Ground heat flux\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - hursmax: Maximum relative humidity\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - hursmin: Minimum relative humidity\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - huss: Specific humidity at 2m\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - iwp: Ice water path\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lcl: Lifting Condensation Level\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lfc: Level of Free Convection\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lh_sfc: Latent heat flux at the surface\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lw_dwn: Instantaneous downwelling longwave flux at bottom\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lw_sfc: Longwave flux at the surface\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lwdnb: Instantaneous downwelling longwave flux at bottom\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lwdnbc: Instantaneous downwelling clear sky longwave flux at bottom\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lwp: Liquid water path\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lwupb: Instantaneous upwelling longwave flux at bottom\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - lwupbc: Instantaneous upwelling clear sky longwave flux at bottom\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - noaa_heat_index: No description available\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - pblh: Planetary boundary layer height\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - pr: Precipitation (total)\n", - "2026-01-05 19:23:39 - climakitae.new_core.user_interface - INFO - prec: Precipitation (total)\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - prec_c: Precipitation (convective only)\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - prec_max: Maximum precipitation\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - prec_snow: Snowfall\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - psfc: Surface Pressure\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - q2: Water Vapor Mixing Ratio at 2m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rainc: Precipitation (cumulus portion only)\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rainnc: Precipitation (grid-scale portion only)\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rh: Relative humidity\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rh_max: Maximum relative humidity\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rh_min: Minimum relative humidity\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - rsds: Shortwave flux at the surface\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - runsb: Subsurface runoff\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - runsf: Surface runoff\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - sfc_runoff: Surface runoff\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - sh_sfc: Sensible heat flux at the surface\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - snow: Snow water equivalent\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - snownc: Snowfall (snow and ice)\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - subsfc_runoff: Subsurface runoff\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - sw_dwn: Instantaneous downwelling shortwave flux at bottom\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - sw_sfc: Shortwave flux at the surface\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swddif: Shortwave surface downward diffuse irradiance\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swddir: Shortwave surface downward direct irradiance\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swddni: Shortwave surface downward direct normal irradiance\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swdnb: Instantaneous downwelling shortwave flux at bottom\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swdnbc: Instantaneous downwelling clear sky shortwave flux at bottom\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swupb: Instantaneous upwelling shortwave flux at bottom\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - swupbc: Instantaneous upwelling clear sky shortwave flux at bottom\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - t2: Air Temperature at 2m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - t2max: Maximum air temperature at 2m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - t2min: Minimum air temperature at 2m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - tasmax: Maximum air temperature at 2m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - tasmin: Minimum air temperature at 2m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - tsk: Surface skin temperature\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - tskin: Surface skin temperature\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - u10: West-East component of Wind at 10m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - uas: West-East component of Wind at 10m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - v10: North-South component of Wind at 10m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - vas: North-South component of Wind at 10m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - wspd10max: Maximum wind speed at 10m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - wspd10mean: Mean wind speed at 10m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - wspeed: Wind speed at 10m\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - znt: Surface roughness length\n", - "2026-01-05 19:23:40 - climakitae.new_core.user_interface - INFO - \n", - "\n" - ] - } - ], - "source": [ - "cd.show_variable_options()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "0a0cd8f7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:11:25.237308Z", - "iopub.status.busy": "2026-01-02T23:11:25.237103Z", - "iopub.status.idle": "2026-01-02T23:11:25.421141Z", - "shell.execute_reply": "2026-01-02T23:11:25.420530Z", - "shell.execute_reply.started": "2026-01-02T23:11:25.237290Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ========================================================\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CAL ADAPT DATA -- ALL AVAILABLE OPTIONS USING CLIMAKITAE\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ========================================================\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - catalog options (Cloud data collections):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -----------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cadcat\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - hdp\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - renewable energy generation\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - activity_id options (Downscaling methods):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - LOCA2\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - WRF\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - institution_id options (Data producers):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ----------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CAE\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ERA\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - UCLA\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - UCSD\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_institution_id_options() to see all institution ids.\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - source_id options (Climate model simulations):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ----------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 10 of 17 total options\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ACCESS-CM2\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CESM2\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CESM2-LENS\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CNRM-ESM2-1\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - EC-Earth3\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - EC-Earth3-Veg\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ERA5\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - FGOALS-g3\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - GFDL-ESM4\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - HadGEM3-GC31-LL\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_source_id_options() to see all source ids.\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - experiment_id options (Simulation runs):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ----------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - historical\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - reanalysis\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ssp245\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ssp370\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ssp585\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - table_id options (Temporal resolutions):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ----------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - 1hr\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - day\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - mon\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - yrmax\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - grid_label options (Spatial resolutions):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -----------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - d01 (45 km)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - d02 ( 9 km)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - d03 ( 3 km)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Variables:\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ----------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 15 of 70 total options\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cape: Convective Available Potential Energy\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cf: Capacity factor\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cin: Convective Inhibition\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - dew_point: No description available\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - effective_temp_index: No description available\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - etrans_sfc: Evapotranspiration\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - evap_sfc: Evaporation\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ffwi: Fosberg fire weather index\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - gen: Power generation\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - gh_sfc: Ground heat flux\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - hursmax: Maximum relative humidity\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - hursmin: Minimum relative humidity\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - huss: Specific humidity at 2m\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - iwp: Ice water path\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lcl: Lifting Condensation Level\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_variable_options() to see all variables.\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - installation options (Renewable energy generation types):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ---------------------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - pv_distributed\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - pv_utility\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - windpower_offshore\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - windpower_onshore\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - station_id options (Weather station names):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 15 of 14982 total options\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_69007093217\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72012200114\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72019300117\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72020200118\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72025400119\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72026723224\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72027294282\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72032204129\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72033353175\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72033900121\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72034594086\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72036524267\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72036904135\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72038500419\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS_72038800469\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_station_id_options() to see all station ids.\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - network_id options (Weather network names):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ASOSAWOS\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CAHYDRO\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CDEC\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CIMIS\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CNRFC\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CRN\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CW3E\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - CWOP\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - HADS\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - HNXWFO\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - HOLFUY\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - HPWREN\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - LOXWFO\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - MAP\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - MARITIME\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - MTRWFO\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - NCAWOS\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - NDBC\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - NOS-NWLON\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - NOS-PORTS\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - OtherISD\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - RAWS\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - SCAN\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - SGXWFO\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - SHASAVAL\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - SNOTEL\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - VALLEYWATER\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - VCAPCD\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Processors (Methods for transforming raw catalog data):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -------------------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing all processors\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - bias_adjust_model_to_station\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - clip\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - concat\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - convert_units\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - export\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - filter_unadjusted_models\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - metric_calc\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - time_slice\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - update_attributes\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - warming_level\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_processors() to see all processors.\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Stations (Available weather stations for localization):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -------------------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 15 of 33 total stations\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Arcata Eureka Airport (KACV)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Bakersfield Meadows Field (KBFL)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Blythe Asos (KBLH)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Burbank-Glendale-Pasadena Airport (KBUR)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Desert Resorts Regional Airport (KTRM)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Downtown Los Angeles USC Campus (KCQT)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Fresno Yosemite International Airport (KFAT)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Gillespie Field Airport (KSEE)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Imperial County Airport (KIPL)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Lancaster William J Fox Field (KWJF)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Long Beach Daugherty Field (KLGB)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Los Angeles International Airport (KLAX)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - McCarran International Airport (KLAS)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Merced Municipal Airport MacReady Field (KMCE)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Modesto City-County Airport (KMOD)\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Use show_station_options() to see all stations.\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "============================================================\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Current Query Status:\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ============================================================\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Current Query:\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - --------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - catalog: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - installation: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - activity_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - institution_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - source_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - experiment_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - table_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - grid_label: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - variable_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - station_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - network_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - processes: UNSET\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Get a comprehensive overview of all available options\n", "cd.show_all_options()\n", @@ -521,52 +128,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "0c193f9f-c154-413f-92dc-084d2ea05ae7", "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:11:25.422419Z", - "iopub.status.busy": "2026-01-02T23:11:25.422152Z", - "iopub.status.idle": "2026-01-02T23:11:25.466224Z", - "shell.execute_reply": "2026-01-02T23:11:25.465347Z", - "shell.execute_reply.started": "2026-01-02T23:11:25.422398Z" - }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== Available Catalogs ===\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - catalog options (Cloud data collections):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - -----------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cadcat\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - hdp\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - renewable energy generation\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "\n", - "=== Choose 'renewable energy generation' catalog and explore installations ===\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - installation options (Renewable energy generation types):\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ---------------------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - pv_distributed\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - pv_utility\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - windpower_offshore\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - windpower_onshore\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n", - "\n", - "=== Choose 'pv_utility' installation and explore variables ===\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Variables (constrained by current query)::\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cf: Capacity factor\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - gen: Power generation\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Explore options step by step\n", "print(\"=== Available Catalogs ===\")\n", @@ -592,49 +159,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "6bf820b0-9940-4ec9-b8d4-ad9e8cd4cb9c", "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:11:25.467098Z", - "iopub.status.busy": "2026-01-02T23:11:25.466907Z", - "iopub.status.idle": "2026-01-02T23:11:25.504914Z", - "shell.execute_reply": "2026-01-02T23:11:25.503793Z", - "shell.execute_reply.started": "2026-01-02T23:11:25.467080Z" - }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== Climate Data Catalog ===\n", - "\n", - "=== WRF (Dynamical Downscaling) Variables ===\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Variables (constrained by current query)::\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 15 of 58 total options\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cape: Convective Available Potential Energy\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cin: Convective Inhibition\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - dew_point: No description available\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - effective_temp_index: No description available\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - etrans_sfc: Evapotranspiration\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - evap_sfc: Evaporation\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ffwi: Fosberg fire weather index\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - gh_sfc: Ground heat flux\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - iwp: Ice water path\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lcl: Lifting Condensation Level\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lfc: Level of Free Convection\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lh_sfc: Latent heat flux at the surface\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lw_dwn: Instantaneous downwelling longwave flux at bottom\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lw_sfc: Longwave flux at the surface\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - lwdnb: Instantaneous downwelling longwave flux at bottom\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(\"=== Climate Data Catalog ===\")\n", "cd.reset()\n", @@ -655,52 +185,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "7fae3993-ead4-49d8-87ad-066ec5c3181f", "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:11:25.505894Z", - "iopub.status.busy": "2026-01-02T23:11:25.505610Z", - "iopub.status.idle": "2026-01-02T23:11:25.548127Z", - "shell.execute_reply": "2026-01-02T23:11:25.547290Z", - "shell.execute_reply.started": "2026-01-02T23:11:25.505866Z" - }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Current Query:\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - --------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - catalog: cadcat\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - installation: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - activity_id: WRF\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - institution_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - source_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - experiment_id: ['historical']\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - table_id: mon\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - grid_label: d01\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - variable_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - station_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - network_id: UNSET\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - processes: UNSET\n", - "\n", - "Available variables for this query:\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Variables (constrained by current query)::\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - ------------------------------------------\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - Showing 5 of 35 total options\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cape: Convective Available Potential Energy\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - cin: Convective Inhibition\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - dew_point: No description available\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - effective_temp_index: No description available\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - etrans_sfc: Evapotranspiration\n", - "2026-01-02 23:11:25 - climakitae.new_core.user_interface - INFO - \n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Build a partial query and check its state\n", "partial_query = (cd\n", @@ -729,947 +219,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "52e1efe9-553f-4ac8-8bfe-749923318492", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:11:25.548850Z", - "iopub.status.busy": "2026-01-02T23:11:25.548662Z", - "iopub.status.idle": "2026-01-02T23:11:26.964493Z", - "shell.execute_reply": "2026-01-02T23:11:26.963543Z", - "shell.execute_reply.started": "2026-01-02T23:11:25.548832Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:11:26 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n", - "2026-01-02 23:11:26 - climakitae.new_core.processors.processor_utils - WARNING - \n", - "\n", - "No historical data found for WRF.UCLA.MIROC6.ssp370.mon.d02 with key WRF.UCLA.MIROC6.historical.mon.d02. \n", - "Historical data is required for time domain extension. \n", - "Keeping original SSP data without historical extension.\n", - "\n", - "2026-01-02 23:11:26 - climakitae.new_core.processors.processor_utils - WARNING - \n", - "\n", - "No historical data found for WRF.UCLA.TaiESM1.ssp370.mon.d02 with key WRF.UCLA.TaiESM1.historical.mon.d02. \n", - "Historical data is required for time domain extension. \n", - "Keeping original SSP data without historical extension.\n", - "\n", - "2026-01-02 23:11:26 - climakitae.new_core.processors.processor_utils - WARNING - \n", - "\n", - "No historical data found for WRF.UCLA.EC-Earth3.ssp370.mon.d02 with key WRF.UCLA.EC-Earth3.historical.mon.d02. \n", - "Historical data is required for time domain extension. \n", - "Keeping original SSP data without historical extension.\n", - "\n", - "2026-01-02 23:11:26 - climakitae.new_core.processors.processor_utils - WARNING - \n", - "\n", - "No historical data found for WRF.UCLA.EC-Earth3-Veg.ssp370.mon.d02 with key WRF.UCLA.EC-Earth3-Veg.historical.mon.d02. \n", - "Historical data is required for time domain extension. \n", - "Keeping original SSP data without historical extension.\n", - "\n", - "2026-01-02 23:11:26 - climakitae.new_core.processors.processor_utils - WARNING - \n", - "\n", - "No historical data found for WRF.UCLA.MPI-ESM1-2-HR.ssp370.mon.d02 with key WRF.UCLA.MPI-ESM1-2-HR.historical.mon.d02. \n", - "Historical data is required for time domain extension. \n", - "Keeping original SSP data without historical extension.\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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<xarray.Dataset> Size: 2GB\n",
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<xarray.Dataset> Size: 2GB\n",
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-       "    ...                               ...\n",
-       "    intake_esm_attrs:_data_format_:   zarr\n",
-       "    intake_esm_dataset_key:           WRF.UCLA.EC-Earth3.ssp370.mon.d02\n",
-       "    resolution:                       9 km\n",
-       "    update_attributes:                Process 'update_attributes' applied to ...\n",
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<xarray.Dataset> Size: 327MB\n",
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-       "    historical_prepended:             True\n",
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<xarray.Dataset> Size: 653MB\n",
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-       "    ...                               ...\n",
-       "    intake_esm_attrs:_data_format_:   zarr\n",
-       "    intake_esm_dataset_key:           WRF.UCLA.EC-Earth3.ssp370.mon.d01\n",
-       "    resolution:                       45 km\n",
-       "    concat:                           Process 'concat' applied to the data. M...\n",
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<xarray.Dataset> Size: 168GB\n",
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-       "    ...                               ...\n",
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-       "    historical_prepended:             True\n",
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<xarray.Dataset> Size: 1TB\n",
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T...\n", - " warming_level_simple: Process 'warming_level_simple' applied ..." - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "wrf_wl_data = (cd\n", " .catalog(\"cadcat\")\n", @@ -8617,62 +626,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "60be13cc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:12:00.390942Z", - "iopub.status.busy": "2026-01-02T23:12:00.390730Z", - "iopub.status.idle": "2026-01-02T23:12:00.416242Z", - "shell.execute_reply": "2026-01-02T23:12:00.415482Z", - "shell.execute_reply.started": "2026-01-02T23:12:00.390924Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Stations (Available weather stations for localization):\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - -------------------------------------------------------\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Arcata Eureka Airport (KACV)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Bakersfield Meadows Field (KBFL)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Blythe Asos (KBLH)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Burbank-Glendale-Pasadena Airport (KBUR)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Desert Resorts Regional Airport (KTRM)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Downtown Los Angeles USC Campus (KCQT)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Fresno Yosemite International Airport (KFAT)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Gillespie Field Airport (KSEE)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Imperial County Airport (KIPL)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Lancaster William J Fox Field (KWJF)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Long Beach Daugherty Field (KLGB)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Los Angeles International Airport (KLAX)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - McCarran International Airport (KLAS)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Merced Municipal Airport MacReady Field (KMCE)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Modesto City-County Airport (KMOD)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Needles Airport (KEED)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Oakland Metro International Airport (KOAK)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Ontario International Airport (KONT)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Oxnard Ventura County Airport (KOXR)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Palm Springs Regional Airport (KPSP)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Red Bluff Municipal Airport (KRBL)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Redding Airport (KRDD)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Riverside Municipal Airport (KRAL)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Sacramento Executive Airport (KSAC)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - San Diego Lindbergh Field (KSAN)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - San Diego Miramar Wscmo (KNKX)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - San Francisco International Airport (KSFO)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - San Jose International Airport (KSJC)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - San Luis Obispo Airport (KSBP)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Santa Ana John Wayne Airport (KSNA)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Santa Barbara Municipal Airport (KSBA)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Stockton Airport (KSCK)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Ukiah Municipal Airport (KUKI)\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - \n", - "\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Let's take a look at all HADISD station options\n", "cd.show_station_options()" @@ -8680,34 +637,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "97841200", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:12:00.417193Z", - "iopub.status.busy": "2026-01-02T23:12:00.417001Z", - "iopub.status.idle": "2026-01-02T23:12:00.436395Z", - "shell.execute_reply": "2026-01-02T23:12:00.435638Z", - "shell.execute_reply.started": "2026-01-02T23:12:00.417176Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Boundary Types (call again with boundary_type='...' to see options):\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - --------------------------------------------------------------------\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - No boundaries available with current parameters\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Available Ca Counties Boundaries:\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - ---------------------------------\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - No boundaries available with current parameters\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - Available Ious Pous Boundaries:\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - -------------------------------\n", - "2026-01-02 23:12:00 - climakitae.new_core.user_interface - INFO - No boundaries available with current parameters\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Now, let's look at other boundaries that `climakitae` supports\n", "cd.show_boundary_options()\n", @@ -8725,847 +658,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "57df3126", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:12:00.437372Z", - "iopub.status.busy": "2026-01-02T23:12:00.437171Z", - "iopub.status.idle": "2026-01-02T23:12:08.672462Z", - "shell.execute_reply": "2026-01-02T23:12:08.671586Z", - "shell.execute_reply.started": "2026-01-02T23:12:00.437354Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:12:03 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "    ...                               ...\n",
-       "    warming_level:                    {'warming_levels': [1.5, 2.0, 3.0]}\n",
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Cli...\n", - " update_attributes: Process 'update_attributes' applied to ...\n", - " filter_unadjusted_models: yes\n", - " concat: Process 'concat' applied to the data. T...\n", - " warming_level_simple: Process 'warming_level_simple' applied ..." - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "box_lat_lons = (\n", " (34.05, 37.77),\n", @@ -11419,52 +772,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "62e37ada", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:12:39.027715Z", - "iopub.status.busy": "2026-01-02T23:12:39.027488Z", - "iopub.status.idle": "2026-01-02T23:12:53.305141Z", - "shell.execute_reply": "2026-01-02T23:12:53.304158Z", - "shell.execute_reply.started": "2026-01-02T23:12:39.027696Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:12:45 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "clip_counties = (cd\n", " .catalog(\"cadcat\")\n", @@ -11495,887 +806,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "6156519a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:12:53.306032Z", - "iopub.status.busy": "2026-01-02T23:12:53.305817Z", - "iopub.status.idle": "2026-01-02T23:13:00.178678Z", - "shell.execute_reply": "2026-01-02T23:13:00.177730Z", - "shell.execute_reply.started": "2026-01-02T23:12:53.306014Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:12:56 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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<xarray.Dataset> Size: 8GB\n",
-       "Dimensions:            (county: 2, sim: 5, warming_level: 3, time_delta: 10950,\n",
-       "                        y: 100, x: 64)\n",
-       "Coordinates:\n",
-       "  * county             (county) <U18 144B 'Alameda County' 'Los Angeles County'\n",
-       "  * sim                (sim) object 40B 'WRF_UCLA_EC-Earth3_ssp370_day_d03_r1...\n",
-       "  * warming_level      (warming_level) float64 24B 1.5 2.0 3.0\n",
-       "  * time_delta         (time_delta) int64 88kB -5475 -5474 -5473 ... 5473 5474\n",
-       "  * y                  (y) float64 800B 7.369e+05 7.399e+05 ... 1.4e+06\n",
-       "  * x                  (x) float64 512B -4.215e+06 -4.212e+06 ... -4.026e+06\n",
-       "    lakemask           (county, y, x) float32 51kB nan nan nan ... nan nan nan\n",
-       "    landmask           (county, y, x) float32 51kB nan nan nan ... nan nan nan\n",
-       "    lat                (county, y, x) float32 51kB nan nan nan ... nan nan nan\n",
-       "    lon                (county, y, x) float32 51kB nan nan nan ... nan nan nan\n",
-       "    centered_year      (sim, warming_level) float64 120B 2.018e+03 ... 2.083e+03\n",
-       "    Lambert_Conformal  int64 8B 0\n",
-       "Data variables:\n",
-       "    t2                 (county, sim, warming_level, time_delta, y, x) float32 8GB dask.array<chunksize=(1, 1, 1, 3652, 28, 2), meta=np.ndarray>\n",
-       "Attributes: (12/120)\n",
-       "    AERCU_FCT:                        1.0\n",
-       "    AERCU_OPT:                        0\n",
-       "    AUTO_LEVELS_OPT:                  2\n",
-       "    BL_PBL_PHYSICS:                   1\n",
-       "    BOTTOM-TOP_GRID_DIMENSION:        40\n",
-       "    BOTTOM-TOP_PATCH_END_STAG:        40\n",
-       "    ...                               ...\n",
-       "    warming_level:                    {'warming_levels': [1.5, 2.0, 3.0]}\n",
-       "    clip:                             Process 'clip' applied to the data. Sep...\n",
-       "    update_attributes:                Process 'update_attributes' applied to ...\n",
-       "    filter_unadjusted_models:         yes\n",
-       "    concat:                           Process 'concat' applied to the data. T...\n",
-       "    warming_level_simple:             Process 'warming_level_simple' applied ...
" - ], - "text/plain": [ - " Size: 8GB\n", - "Dimensions: (county: 2, sim: 5, warming_level: 3, time_delta: 10950,\n", - " y: 100, x: 64)\n", - "Coordinates:\n", - " * county (county) \n", - "Attributes: (12/120)\n", - " AERCU_FCT: 1.0\n", - " AERCU_OPT: 0\n", - " AUTO_LEVELS_OPT: 2\n", - " BL_PBL_PHYSICS: 1\n", - " BOTTOM-TOP_GRID_DIMENSION: 40\n", - " BOTTOM-TOP_PATCH_END_STAG: 40\n", - " ... ...\n", - " warming_level: {'warming_levels': [1.5, 2.0, 3.0]}\n", - " clip: Process 'clip' applied to the data. Sep...\n", - " update_attributes: Process 'update_attributes' applied to ...\n", - " filter_unadjusted_models: yes\n", - " concat: Process 'concat' applied to the data. T...\n", - " warming_level_simple: Process 'warming_level_simple' applied ..." - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "clip_counties_separated = (cd\n", " .catalog(\"cadcat\")\n", @@ -12411,973 +845,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "a6246068", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:13:00.179519Z", - "iopub.status.busy": "2026-01-02T23:13:00.179293Z", - "iopub.status.idle": "2026-01-02T23:13:08.605372Z", - "shell.execute_reply": "2026-01-02T23:13:08.604472Z", - "shell.execute_reply.started": "2026-01-02T23:13:00.179499Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:13:03 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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<xarray.Dataset> Size: 3GB\n",
-       "Dimensions:            (sim: 5, warming_level: 3, y: 72, x: 64,\n",
-       "                        time_delta: 10950)\n",
-       "Coordinates:\n",
-       "  * sim                (sim) object 40B 'WRF_UCLA_EC-Earth3_ssp370_day_d03_r1...\n",
-       "  * warming_level      (warming_level) float64 24B 1.5 2.0 3.0\n",
-       "  * y                  (y) float64 576B 7.369e+05 7.399e+05 ... 9.499e+05\n",
-       "  * x                  (x) float64 512B -4.215e+06 -4.212e+06 ... -4.026e+06\n",
-       "  * time_delta         (time_delta) int64 88kB -5475 -5474 -5473 ... 5473 5474\n",
-       "    lakemask           (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
-       "    landmask           (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
-       "    lat                (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
-       "    lon                (y, x) float32 18kB dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
-       "    centered_year      (sim, warming_level) float64 120B 2.018e+03 ... 2.083e+03\n",
-       "    Lambert_Conformal  int64 8B 0\n",
-       "Data variables:\n",
-       "    t2                 (sim, warming_level, time_delta, y, x) float32 3GB dask.array<chunksize=(1, 1, 3652, 44, 2), meta=np.ndarray>\n",
-       "Attributes: (12/121)\n",
-       "    AERCU_FCT:                        1.0\n",
-       "    AERCU_OPT:                        0\n",
-       "    AUTO_LEVELS_OPT:                  2\n",
-       "    BL_PBL_PHYSICS:                   1\n",
-       "    BOTTOM-TOP_GRID_DIMENSION:        40\n",
-       "    BOTTOM-TOP_PATCH_END_STAG:        40\n",
-       "    ...                               ...\n",
-       "    clip:                             Process 'clip' applied to the data. Cli...\n",
-       "    convert_units:                    Process 'convert_units' applied to the ...\n",
-       "    update_attributes:                Process 'update_attributes' applied to ...\n",
-       "    filter_unadjusted_models:         yes\n",
-       "    concat:                           Process 'concat' applied to the data. T...\n",
-       "    warming_level_simple:             Process 'warming_level_simple' applied ...
" - ], - "text/plain": [ - " Size: 3GB\n", - "Dimensions: (sim: 5, warming_level: 3, y: 72, x: 64,\n", - " time_delta: 10950)\n", - "Coordinates:\n", - " * sim (sim) object 40B 'WRF_UCLA_EC-Earth3_ssp370_day_d03_r1...\n", - " * warming_level (warming_level) float64 24B 1.5 2.0 3.0\n", - " * y (y) float64 576B 7.369e+05 7.399e+05 ... 9.499e+05\n", - " * x (x) float64 512B -4.215e+06 -4.212e+06 ... -4.026e+06\n", - " * time_delta (time_delta) int64 88kB -5475 -5474 -5473 ... 5473 5474\n", - " lakemask (y, x) float32 18kB dask.array\n", - " landmask (y, x) float32 18kB dask.array\n", - " lat (y, x) float32 18kB dask.array\n", - " lon (y, x) float32 18kB dask.array\n", - " centered_year (sim, warming_level) float64 120B 2.018e+03 ... 2.083e+03\n", - " Lambert_Conformal int64 8B 0\n", - "Data variables:\n", - " t2 (sim, warming_level, time_delta, y, x) float32 3GB dask.array\n", - "Attributes: (12/121)\n", - " AERCU_FCT: 1.0\n", - " AERCU_OPT: 0\n", - " AUTO_LEVELS_OPT: 2\n", - " BL_PBL_PHYSICS: 1\n", - " BOTTOM-TOP_GRID_DIMENSION: 40\n", - " BOTTOM-TOP_PATCH_END_STAG: 40\n", - " ... ...\n", - " clip: Process 'clip' applied to the data. Cli...\n", - " convert_units: Process 'convert_units' applied to the ...\n", - " update_attributes: Process 'update_attributes' applied to ...\n", - " filter_unadjusted_models: yes\n", - " concat: Process 'concat' applied to the data. T...\n", - " warming_level_simple: Process 'warming_level_simple' applied ..." - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Here, we'll take the temperature data that's natively in Kelvin to be returned in Fahrenheit instead\n", "degF_data = (cd\n", @@ -13401,39 +872,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "cbf697e4", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:13:08.606124Z", - "iopub.status.busy": "2026-01-02T23:13:08.605925Z", - "iopub.status.idle": "2026-01-02T23:13:12.306141Z", - "shell.execute_reply": "2026-01-02T23:13:12.305330Z", - "shell.execute_reply.started": "2026-01-02T23:13:08.606107Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Here we'll see that the values are within the reasonable ranges of we'd see for Fahrenheit for Los Angeles County\n", "degF_data.mean(dim='time_delta').isel(sim=0, warming_level=0).t2.plot(x=\"lon\", y=\"lat\")" @@ -13465,46 +907,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "3c8ed48e-0666-43c3-adc3-35369beab38e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:13:12.307185Z", - "iopub.status.busy": "2026-01-02T23:13:12.306903Z", - "iopub.status.idle": "2026-01-02T23:13:29.622530Z", - "shell.execute_reply": "2026-01-02T23:13:29.621641Z", - "shell.execute_reply.started": "2026-01-02T23:13:12.307156Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:13:15 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n", - "2026-01-02 23:13:21 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n", - "2026-01-02 23:13:26 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "metrics = ['min', 'mean', 'max']\n", "data = []\n", @@ -13536,29 +942,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "cb78c222-caf1-46af-a44b-959a3d4cf0c6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:13:29.623540Z", - "iopub.status.busy": "2026-01-02T23:13:29.623339Z", - "iopub.status.idle": "2026-01-02T23:15:57.780003Z", - "shell.execute_reply": "2026-01-02T23:15:57.779125Z", - "shell.execute_reply.started": "2026-01-02T23:13:29.623524Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Taking a look at the 3 warming levels\n", "fig, axes = plt.subplots(3, 3, figsize=(15, 12), sharex=True, sharey=True)\n", @@ -13598,37 +985,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "f8363e54-c20c-4bb3-8af6-60726ddb09eb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:15:57.780815Z", - "iopub.status.busy": "2026-01-02T23:15:57.780572Z", - "iopub.status.idle": "2026-01-02T23:16:02.164796Z", - "shell.execute_reply": "2026-01-02T23:16:02.163951Z", - "shell.execute_reply.started": "2026-01-02T23:15:57.780796Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:16:00 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n", - "2026-01-02 23:16:01 - climakitae.new_core.processors.warming_level - WARNING - \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 1.2C. \n", - "Skipping this warming level.\n", - "2026-01-02 23:16:01 - climakitae.new_core.processors.warming_level - WARNING - No valid slices found for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1. Ensure the warming level times table is correctly configured.\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Let's first find the 90th, 95th, and 98th percentiles over Humboldt County for a 1.2°C reference warming level period.\n", "percentiles = [90, 95, 98]\n", @@ -13657,32 +1017,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "f482fc76-2e5b-423d-b4fe-023616d62934", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:16:02.166088Z", - "iopub.status.busy": "2026-01-02T23:16:02.165583Z", - "iopub.status.idle": "2026-01-02T23:16:08.007029Z", - "shell.execute_reply": "2026-01-02T23:16:08.006069Z", - "shell.execute_reply.started": "2026-01-02T23:16:02.166057Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:16:05 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Now, we'll get all the raw data from the future WLs and count the number of days each simulation is above the different 1.2°C WL percentiles.\n", "wrf_wl_data = (\n", @@ -13705,2163 +1043,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "56c0d85b-4d4e-4887-8eba-35ae6ed1db5f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:16:08.007940Z", - "iopub.status.busy": "2026-01-02T23:16:08.007664Z", - "iopub.status.idle": "2026-01-02T23:16:08.043239Z", - "shell.execute_reply": "2026-01-02T23:16:08.042487Z", - "shell.execute_reply.started": "2026-01-02T23:16:08.007914Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Size: 2GB\n", - "Dimensions: (sim: 5, warming_level: 3, y: 52, x: 47,\n", - " time_delta: 10950)\n", - "Coordinates:\n", - " * sim (sim) object 40B 'WRF_UCLA_EC-Earth3_ssp370_day_d03_r1...\n", - " * warming_level (warming_level) float64 24B 1.5 2.0 3.0\n", - " * y (y) float64 416B 1.643e+06 1.646e+06 ... 1.796e+06\n", - " * x (x) float64 376B -4.143e+06 -4.14e+06 ... -4.005e+06\n", - " * time_delta (time_delta) int64 88kB -5475 -5474 -5473 ... 5473 5474\n", - " lakemask (y, x) float32 10kB dask.array\n", - " landmask (y, x) float32 10kB dask.array\n", - " lat (y, x) float32 10kB dask.array\n", - " lon (y, x) float32 10kB dask.array\n", - " centered_year (sim, warming_level) float64 120B 2.018e+03 ... 2.083e+03\n", - " Lambert_Conformal int64 8B 0\n", - "Data variables:\n", - " t2 (sim, warming_level, time_delta, y, x) float32 2GB dask.array\n", - "Attributes: (12/121)\n", - " AERCU_FCT: 1.0\n", - " AERCU_OPT: 0\n", - " AUTO_LEVELS_OPT: 2\n", - " BL_PBL_PHYSICS: 1\n", - " BOTTOM-TOP_GRID_DIMENSION: 40\n", - " BOTTOM-TOP_PATCH_END_STAG: 40\n", - " ... ...\n", - " clip: Process 'clip' applied to the data. Cli...\n", - " convert_units: Process 'convert_units' applied to the ...\n", - " update_attributes: Process 'update_attributes' applied to ...\n", - " filter_unadjusted_models: yes\n", - " concat: Process 'concat' applied to the data. T...\n", - " warming_level_simple: Process 'warming_level_simple' applied ..." - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "display(ref_percentiles)\n", "display(wrf_wl_data)" @@ -15877,17 +1062,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "5d21461c-246b-4068-9ec2-5bb2b0ff5f64", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:16:08.044203Z", - "iopub.status.busy": "2026-01-02T23:16:08.043990Z", - "iopub.status.idle": "2026-01-02T23:16:08.075643Z", - "shell.execute_reply": "2026-01-02T23:16:08.074692Z", - "shell.execute_reply.started": "2026-01-02T23:16:08.044185Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Dropping `EC-Earth3-Veg` by only keeping the other sims\n", @@ -15908,29 +1085,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "11bfc1c2-1ccc-45e4-9e9e-ec674b19cbc2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:16:08.076551Z", - "iopub.status.busy": "2026-01-02T23:16:08.076306Z", - "iopub.status.idle": "2026-01-02T23:19:04.995037Z", - "shell.execute_reply": "2026-01-02T23:19:04.993933Z", - "shell.execute_reply.started": "2026-01-02T23:16:08.076525Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "### Now, let's plot a comparison of the results\n", "\n", @@ -15991,66 +1149,10 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "b8fc1eec", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:19:04.996002Z", - "iopub.status.busy": "2026-01-02T23:19:04.995669Z", - "iopub.status.idle": "2026-01-02T23:21:20.154533Z", - "shell.execute_reply": "2026-01-02T23:21:20.153592Z", - "shell.execute_reply.started": "2026-01-02T23:19:04.995982Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:19:08 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e235dc59ffa44a2e9ce56549162e068c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing batches: 0%| | 0/1 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "return_periods = [10, 100]\n", "one_in_x_data = (cd\n", @@ -16105,629 +1207,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "536c54b2", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:21:20.155312Z", - "iopub.status.busy": "2026-01-02T23:21:20.155108Z", - "iopub.status.idle": "2026-01-02T23:22:37.778092Z", - "shell.execute_reply": "2026-01-02T23:22:37.777184Z", - "shell.execute_reply.started": "2026-01-02T23:21:20.155294Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:21:22 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset> Size: 50MB\n",
-       "Dimensions:                              (sim: 5, time: 1051200)\n",
-       "Coordinates:\n",
-       "  * sim                                  (sim) object 40B 'wrf_ucla_ec-earth3...\n",
-       "  * time                                 (time) datetime64[ns] 8MB 1980-09-01...\n",
-       "Data variables:\n",
-       "    Sacramento Executive Airport (KSAC)  (sim, time) float64 42MB dask.array<chunksize=(1, 1051200), meta=np.ndarray>\n",
-       "Attributes: (12/121)\n",
-       "    description:                      temp at 2 m\n",
-       "    grid_mapping:                     Lambert_Conformal\n",
-       "    units:                            K\n",
-       "    AERCU_FCT:                        1.0\n",
-       "    AERCU_OPT:                        0\n",
-       "    AUTO_LEVELS_OPT:                  2\n",
-       "    ...                               ...\n",
-       "    history:                          [2026-01-02 23:22:31] : Bias-adjusted w...\n",
-       "    bias_adjustment:                  QuantileDeltaMapping(group=Grouper(name...\n",
-       "    bias_adjust_model_to_station:     {'stations': ['KSAC'], 'historical_slic...\n",
-       "    update_attributes:                Process 'update_attributes' applied to ...\n",
-       "    filter_unadjusted_models:         yes\n",
-       "    concat:                           Process 'concat' applied to the data. T...
" - ], - "text/plain": [ - " Size: 50MB\n", - "Dimensions: (sim: 5, time: 1051200)\n", - "Coordinates:\n", - " * sim (sim) object 40B 'wrf_ucla_ec-earth3...\n", - " * time (time) datetime64[ns] 8MB 1980-09-01...\n", - "Data variables:\n", - " Sacramento Executive Airport (KSAC) (sim, time) float64 42MB dask.array\n", - "Attributes: (12/121)\n", - " description: temp at 2 m\n", - " grid_mapping: Lambert_Conformal\n", - " units: K\n", - " AERCU_FCT: 1.0\n", - " AERCU_OPT: 0\n", - " AUTO_LEVELS_OPT: 2\n", - " ... ...\n", - " history: [2026-01-02 23:22:31] : Bias-adjusted w...\n", - " bias_adjustment: QuantileDeltaMapping(group=Grouper(name...\n", - " bias_adjust_model_to_station: {'stations': ['KSAC'], 'historical_slic...\n", - " update_attributes: Process 'update_attributes' applied to ...\n", - " filter_unadjusted_models: yes\n", - " concat: Process 'concat' applied to the data. T..." - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Let's manually bias adjust our climate data to KSAC (the SAC airport weather station).\n", "manual_bias_adjustment = (cd\n", @@ -16816,40 +1299,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "3157320f-7c85-40a9-9259-f25f4f391133", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:22:37.778869Z", - "iopub.status.busy": "2026-01-02T23:22:37.778674Z", - "iopub.status.idle": "2026-01-02T23:22:44.865028Z", - "shell.execute_reply": "2026-01-02T23:22:44.863981Z", - "shell.execute_reply.started": "2026-01-02T23:22:37.778852Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:22:40 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n", - "2026-01-02 23:22:41 - climakitae.new_core.processors.warming_level - WARNING - \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.mon.d03.r1i1p1f1 at 1.2C. \n", - "Skipping this warming level.\n", - "2026-01-02 23:22:41 - climakitae.new_core.processors.warming_level - WARNING - No valid slices found for WRF.UCLA.EC-Earth3-Veg.ssp370.mon.d03.r1i1p1f1. Ensure the warming level times table is correctly configured.\n", - "Exporting specified data to NetCDF...\n", - "Saving file locally as NetCDF4...\n", - "Saved! You can find your file in the panel to the left and download to your local machine from there.\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Let's export all of Humboldt County's data into one file\n", "location = \"Humboldt County\"\n", @@ -16877,17 +1330,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "1cc9720a-fd39-4830-ad10-78e6fe48e4fe", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:22:44.865904Z", - "iopub.status.busy": "2026-01-02T23:22:44.865665Z", - "iopub.status.idle": "2026-01-02T23:22:44.869214Z", - "shell.execute_reply": "2026-01-02T23:22:44.868242Z", - "shell.execute_reply.started": "2026-01-02T23:22:44.865883Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "lat_lons = [\n", @@ -16898,43 +1343,10 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "b7e81b62-1904-4995-997d-0e9238ccb8ea", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-02T23:22:44.869917Z", - "iopub.status.busy": "2026-01-02T23:22:44.869729Z", - "iopub.status.idle": "2026-01-02T23:22:55.693764Z", - "shell.execute_reply": "2026-01-02T23:22:55.692734Z", - "shell.execute_reply.started": "2026-01-02T23:22:44.869900Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2026-01-02 23:22:47 - climakitae.new_core.processors.filter_unadjusted_models - WARNING - \n", - "\n", - "Your query selected models that do not have a-priori bias adjustment. \n", - "These models have been removed from the returned query. \n", - "To include them, please add the following processor to your query: \n", - "ClimateData().processes('filter_unadjusted_models': 'no')\n", - "\n", - "2026-01-02 23:22:48 - climakitae.new_core.processors.warming_level - WARNING - \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.mon.d03.r1i1p1f1 at 1.2C. \n", - "Skipping this warming level.\n", - "2026-01-02 23:22:48 - climakitae.new_core.processors.warming_level - WARNING - No valid slices found for WRF.UCLA.EC-Earth3-Veg.ssp370.mon.d03.r1i1p1f1. Ensure the warming level times table is correctly configured.\n", - "Exporting specified data to NetCDF...\n", - "Saving file locally as NetCDF4...\n", - "Saved! You can find your file in the panel to the left and download to your local machine from there.\n", - "Exporting specified data to NetCDF...\n", - "Saving file locally as NetCDF4...\n", - "Saved! You can find your file in the panel to the left and download to your local machine from there.\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Now, let's export this list of `lat_lons` from before into separate files.\n", "export_data = (\n", @@ -16995,7 +1407,7 @@ ], "metadata": { "kernelspec": { - "display_name": "cae", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -17009,7 +1421,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.12.10" } }, "nbformat": 4, From b303dfb3b7ef4609bf3d454588a344bd1ad149bc Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Wed, 11 Feb 2026 18:12:57 +0000 Subject: [PATCH 20/53] removing outputs --- analysis/internal_variability.ipynb | 1748 +-------------------------- 1 file changed, 31 insertions(+), 1717 deletions(-) diff --git a/analysis/internal_variability.ipynb b/analysis/internal_variability.ipynb index 92500172..d045d3b7 100644 --- a/analysis/internal_variability.ipynb +++ b/analysis/internal_variability.ipynb @@ -1,24 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "e0f320a0-fcc6-48e7-bc56-2d8895209127", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T18:17:17.151218Z", - "iopub.status.busy": "2026-02-09T18:17:17.150972Z", - "iopub.status.idle": "2026-02-09T18:17:17.191691Z", - "shell.execute_reply": "2026-02-09T18:17:17.190897Z", - "shell.execute_reply.started": "2026-02-09T18:17:17.151185Z" - } - }, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, { "cell_type": "markdown", "id": "39c7ab89-d6db-4fa2-a4ec-08dfae9b20d9", @@ -88,1195 +69,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "25f3706b-fb81-4ae5-b3fe-04f71ac4fc34", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T18:17:20.521181Z", - "iopub.status.busy": "2026-02-09T18:17:20.520952Z", - "iopub.status.idle": "2026-02-09T18:17:29.319394Z", - "shell.execute_reply": "2026-02-09T18:17:29.318474Z", - "shell.execute_reply.started": "2026-02-09T18:17:20.521157Z" - }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - " const version = '3.8.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = false;\n", - " const Bokeh = root.Bokeh;\n", - " const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n", - " const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n", - "\n", - " // Set a timeout for this load but only if we are not already initializing\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || 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recent versions we use the root._bokeh_is_initializing flag\n", - " // to determine whether there is an ongoing attempt to initialize\n", - " // bokeh, however for backward compatibility we also try to ensure\n", - " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", - " // before older versions are fully initialized.\n", - " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", - " // If the timeout and bokeh was not successfully loaded we reset\n", - " // everything and try loading again\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " root._bokeh_is_loading = 0;\n", - " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", - " load_or_wait();\n", - " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== 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root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n const missingOrBroken = Bokeh == null || Bokeh.Panel == null || (Bokeh.version != version && !Bokeh.versions?.has(version)) || Bokeh.versions?.get(version)?.Panel == null;\n if (shouldSkip && !(isBokehOrPanel && missingOrBroken)) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.8.7/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false;\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true;\n root._bokeh_onload_callbacks = [];\n const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || 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comm_promise.then((comm) => {\n", - " window.PyViz.comms[comm_id] = comm;\n", - " if (msg_handler) {\n", - " var messages = comm.messages[Symbol.asyncIterator]();\n", - " function processIteratorResult(result) {\n", - " var message = result.value;\n", - " var content = {data: message.data};\n", - " var metadata = message.metadata || {comm_id};\n", - " var msg = {content, metadata}\n", - " msg_handler(msg);\n", - " return messages.next().then(processIteratorResult);\n", - " }\n", - " return messages.next().then(processIteratorResult);\n", - " }\n", - " })\n", - " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", - " return comm_promise.then((comm) => {\n", - " comm.send(data, metadata, buffers, disposeOnDone);\n", - " });\n", - " };\n", - " var comm = {\n", - " send: sendClosure\n", - " };\n", - " }\n", - " window.PyViz.comms[comm_id] = comm;\n", - " return comm;\n", - " }\n", - " window.PyViz.comm_manager = new JupyterCommManager();\n", - " \n", - "\n", - "\n", - "var JS_MIME_TYPE = 'application/javascript';\n", - "var HTML_MIME_TYPE = 'text/html';\n", - "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", - "var CLASS_NAME = 'output';\n", - "\n", - "/**\n", - " * Render data to the DOM node\n", - " */\n", - "function render(props, node) {\n", - " var div = document.createElement(\"div\");\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(div);\n", - " node.appendChild(script);\n", - "}\n", - "\n", - "/**\n", - " * Handle when a new output is added\n", - " */\n", - "function handle_add_output(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - " if (id !== undefined) {\n", - " var nchildren = toinsert.length;\n", - " var html_node = toinsert[nchildren-1].children[0];\n", - " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var scripts = [];\n", - " var nodelist = html_node.querySelectorAll(\"script\");\n", - " for (var i in nodelist) {\n", - " if (nodelist.hasOwnProperty(i)) {\n", - " scripts.push(nodelist[i])\n", - " }\n", - " }\n", - "\n", - " scripts.forEach( function (oldScript) {\n", - " var newScript = document.createElement(\"script\");\n", - " var attrs = [];\n", - " var nodemap = oldScript.attributes;\n", - " for (var j in nodemap) {\n", - " if (nodemap.hasOwnProperty(j)) {\n", - " attrs.push(nodemap[j])\n", - " }\n", - " }\n", - " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", - " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", - " oldScript.parentNode.replaceChild(newScript, oldScript);\n", - " });\n", - " if (JS_MIME_TYPE in output.data) {\n", - " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", - " }\n", - " output_area._hv_plot_id = id;\n", - " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", - " window.PyViz.plot_index[id] = Bokeh.index[id];\n", - " } else {\n", - " window.PyViz.plot_index[id] = null;\n", - " }\n", - " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - "}\n", - "\n", - "/**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - "function handle_clear_output(event, handle) {\n", - " var id = handle.cell.output_area._hv_plot_id;\n", - " var server_id = handle.cell.output_area._bokeh_server_id;\n", - " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", - " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", - " if (server_id !== null) {\n", - " comm.send({event_type: 'server_delete', 'id': server_id});\n", - " return;\n", - " } else if (comm !== null) {\n", - " comm.send({event_type: 'delete', 'id': id});\n", - " }\n", - " delete PyViz.plot_index[id];\n", - " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", - " var doc = window.Bokeh.index[id].model.document\n", - " doc.clear();\n", - " const i = window.Bokeh.documents.indexOf(doc);\n", - " if (i > -1) {\n", - " window.Bokeh.documents.splice(i, 1);\n", - " }\n", - " }\n", - "}\n", - "\n", - "/**\n", - " * Handle kernel restart event\n", - " */\n", - "function handle_kernel_cleanup(event, handle) {\n", - " delete PyViz.comms[\"hv-extension-comm\"];\n", - " window.PyViz.plot_index = {}\n", - "}\n", - "\n", - "/**\n", - " * Handle update_display_data messages\n", - " */\n", - "function handle_update_output(event, handle) {\n", - " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", - " handle_add_output(event, handle)\n", - "}\n", - "\n", - "function register_renderer(events, OutputArea) {\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[0]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " events.on('output_added.OutputArea', handle_add_output);\n", - " events.on('output_updated.OutputArea', handle_update_output);\n", - " events.on('clear_output.CodeCell', handle_clear_output);\n", - " events.on('delete.Cell', handle_clear_output);\n", - " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", - "\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " safe: true,\n", - " index: 0\n", - " });\n", - "}\n", - "\n", - "if (window.Jupyter !== undefined) {\n", - " try {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " } catch(err) {\n", - " }\n", - "}\n" - ], - "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n let retries = 0;\n const open = () => {\n if (comm.active) {\n comm.open();\n } else if (retries > 3) {\n console.warn('Comm target never activated')\n } else {\n retries += 1\n setTimeout(open, 500)\n }\n }\n if (comm.active) {\n comm.open();\n } else {\n setTimeout(open, 500)\n }\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n })\n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import climakitae as ck\n", "from climakitae.core.data_interface import DataParameters\n", @@ -1312,16 +110,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "cca191af-ce6e-4f77-b1e6-c8fc189c9014", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T18:17:45.372218Z", - "iopub.status.busy": "2026-02-09T18:17:45.371974Z", - "iopub.status.idle": "2026-02-09T18:18:02.484893Z", - "shell.execute_reply": "2026-02-09T18:18:02.483979Z", - "shell.execute_reply.started": "2026-02-09T18:17:45.372192Z" - }, "tags": [] }, "outputs": [], @@ -1373,16 +164,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "eb4a30bf-e17b-4a94-822c-6703aa0dfec7", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T18:18:02.486053Z", - "iopub.status.busy": "2026-02-09T18:18:02.485785Z", - "iopub.status.idle": "2026-02-09T18:18:02.527361Z", - "shell.execute_reply": "2026-02-09T18:18:02.526616Z", - "shell.execute_reply.started": "2026-02-09T18:18:02.486022Z" - }, "tags": [] }, "outputs": [], @@ -1400,29 +184,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "fde659c8-575f-4206-a182-9f989c0edd84", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T18:23:19.064955Z", - "iopub.status.busy": "2026-02-09T18:23:19.064709Z", - "iopub.status.idle": "2026-02-09T18:23:37.085725Z", - "shell.execute_reply": "2026-02-09T18:23:37.084847Z", - "shell.execute_reply.started": "2026-02-09T18:23:19.064931Z" - }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model FGOALS-g3\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MIROC6\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-HR\n" - ] - } - ], + "outputs": [], "source": [ "hist_wrf = wrf_ds.sel(\n", " time=slice('1981','2010'))\n", @@ -1464,16 +231,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "c6a86c4b-199f-4e27-9467-b36097e02e11", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T19:05:00.175476Z", - "iopub.status.busy": "2026-02-09T19:05:00.175252Z", - "iopub.status.idle": "2026-02-09T19:05:00.181194Z", - "shell.execute_reply": "2026-02-09T19:05:00.180141Z", - "shell.execute_reply.started": "2026-02-09T19:05:00.175453Z" - }, "tags": [] }, "outputs": [], @@ -1538,31 +298,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "c8e8171f-897a-4d12-9a7f-079f312408ef", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T19:05:00.215554Z", - "iopub.status.busy": "2026-02-09T19:05:00.215344Z", - "iopub.status.idle": "2026-02-09T19:05:00.264886Z", - "shell.execute_reply": "2026-02-09T19:05:00.263951Z", - "shell.execute_reply.started": "2026-02-09T19:05:00.215533Z" - }, "tags": [] }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'cads_delta_percentile' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m delta_vmax = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 5\u001b[39m ncols = \u001b[32m3\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m num_simulations = \u001b[38;5;28mlen\u001b[39m(\u001b[43mcads_delta_percentile\u001b[49m.simulation.values)\n\u001b[32m 8\u001b[39m cads_hist_plots = make_precip_unc_map(\n\u001b[32m 9\u001b[39m data=cads_hist_percentile.isel(simulation=\u001b[32m0\u001b[39m),\n\u001b[32m 10\u001b[39m title=(cads_hist_percentile.isel(simulation=\u001b[32m0\u001b[39m\n\u001b[32m 11\u001b[39m ).simulation.item()), vmin=vmin, \n\u001b[32m 12\u001b[39m vmax=vmax, sopt=\u001b[38;5;28;01mFalse\u001b[39;00m, width=\u001b[32m300\u001b[39m, height=\u001b[32m300\u001b[39m)\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sim_i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[32m1\u001b[39m, num_simulations): \u001b[38;5;66;03m# plot remaining simulations\u001b[39;00m\n", - "\u001b[31mNameError\u001b[39m: name 'cads_delta_percentile' is not defined" - ] - } - ], + "outputs": [], "source": [ "vmin = 0\n", "vmax = 2000\n", @@ -1685,16 +426,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "3efbf741-92bc-4634-900b-c6671e2a6cec", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T16:36:56.213760Z", - "iopub.status.busy": "2026-02-09T16:36:56.213465Z", - "iopub.status.idle": "2026-02-09T16:36:56.223141Z", - "shell.execute_reply": "2026-02-09T16:36:56.222245Z", - "shell.execute_reply.started": "2026-02-09T16:36:56.213728Z" - }, "tags": [] }, "outputs": [], @@ -1718,44 +452,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "761cf95b-b710-47e3-8d14-9822654ed521", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T16:36:56.224795Z", - "iopub.status.busy": "2026-02-09T16:36:56.224377Z", - "iopub.status.idle": "2026-02-09T16:37:14.637288Z", - "shell.execute_reply": "2026-02-09T16:37:14.636278Z", - "shell.execute_reply.started": "2026-02-09T16:36:56.224762Z" - }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model FGOALS-g3\n", - "3.0°C warming level not reached for ensemble member r2i1p1f1 of model FGOALS-g3\n", - "3.0°C warming level not reached for ensemble member r3i1p1f1 of model FGOALS-g3\n", - "3.0°C warming level not reached for ensemble member r4i1p1f1 of model FGOALS-g3\n", - "3.0°C warming level not reached for ensemble member r5i1p1f1 of model FGOALS-g3\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MIROC6\n", - "3.0°C warming level not reached for ensemble member r2i1p1f1 of model MIROC6\n", - "3.0°C warming level not reached for ensemble member r3i1p1f1 of model MIROC6\n", - "3.0°C warming level not reached for ensemble member r10i1p1f1 of model MPI-ESM1-2-HR\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-HR\n", - "3.0°C warming level not reached for ensemble member r2i1p1f1 of model MPI-ESM1-2-HR\n", - "3.0°C warming level not reached for ensemble member r3i1p1f1 of model MPI-ESM1-2-HR\n", - "3.0°C warming level not reached for ensemble member r4i1p1f1 of model MPI-ESM1-2-HR\n", - "3.0°C warming level not reached for ensemble member r5i1p1f1 of model MPI-ESM1-2-HR\n", - "3.0°C warming level not reached for ensemble member r6i1p1f1 of model MPI-ESM1-2-HR\n", - "3.0°C warming level not reached for ensemble member r7i1p1f1 of model MPI-ESM1-2-HR\n", - "3.0°C warming level not reached for ensemble member r8i1p1f1 of model MPI-ESM1-2-HR\n", - "3.0°C warming level not reached for ensemble member r9i1p1f1 of model MPI-ESM1-2-HR\n" - ] - } - ], + "outputs": [], "source": [ "hist_cae_ds, warm_cae_ds = get_ensemble_data(\n", " variable=copt.variable, selections=selections, \n", @@ -1773,16 +475,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "16e10ef0-1bba-41d6-a617-9eb4af510d44", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T16:37:14.638336Z", - "iopub.status.busy": "2026-02-09T16:37:14.638004Z", - "iopub.status.idle": "2026-02-09T16:37:14.663798Z", - "shell.execute_reply": "2026-02-09T16:37:14.662348Z", - "shell.execute_reply.started": "2026-02-09T16:37:14.638306Z" - }, "tags": [] }, "outputs": [], @@ -1808,16 +503,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "36d2ac31-ae62-47fd-8fe2-0de2f535ac12", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T16:37:14.664523Z", - "iopub.status.busy": "2026-02-09T16:37:14.664339Z", - "iopub.status.idle": "2026-02-09T16:37:14.674754Z", - "shell.execute_reply": "2026-02-09T16:37:14.673807Z", - "shell.execute_reply.started": "2026-02-09T16:37:14.664503Z" - }, "tags": [] }, "outputs": [], @@ -1865,48 +553,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "db16eeb1-0866-48fe-9788-9982cac07f5b", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T16:37:14.675729Z", - "iopub.status.busy": "2026-02-09T16:37:14.675493Z", - "iopub.status.idle": "2026-02-09T16:56:00.131345Z", - "shell.execute_reply": "2026-02-09T16:56:00.129842Z", - "shell.execute_reply.started": "2026-02-09T16:37:14.675701Z" - }, "tags": [] }, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m 1\u001b[39m all_hist_ens = hvplot_percentile_column(\n\u001b[32m 2\u001b[39m data=hist_cae_percentile.pr, \n\u001b[32m 3\u001b[39m col_title=\u001b[33m\"\u001b[39m\u001b[33m### 99th percentile monthly precipitation accumulation across model ensemble members, 1981-2010\u001b[39m\u001b[33m\"\u001b[39m, \n\u001b[32m 4\u001b[39m vmin=\u001b[32m0\u001b[39m, vmax=\u001b[32m900\u001b[39m, \n\u001b[32m 5\u001b[39m sopt=\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 6\u001b[39m ) \n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m all_ssp_ens = \u001b[43mhvplot_percentile_column\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 9\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mssp_cae_percentile\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 10\u001b[39m \u001b[43m \u001b[49m\u001b[43mcol_title\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m### 99th percentile monthly precipitation accumulation across model ensemble members, 2071-2100\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 11\u001b[39m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m900\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 12\u001b[39m \u001b[43m \u001b[49m\u001b[43msopt\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[32m 13\u001b[39m \u001b[43m)\u001b[49m \n\u001b[32m 15\u001b[39m all_diff_ens = hvplot_percentile_column(\n\u001b[32m 16\u001b[39m data=cae_delta_percentile.pr, \n\u001b[32m 17\u001b[39m col_title=\u001b[33m\"\u001b[39m\u001b[33m### Change in 99th percentile monthly precipitation accumulation across ensemble members by end-of-century\u001b[39m\u001b[33m\"\u001b[39m, \n\u001b[32m 18\u001b[39m vmin=-\u001b[32m150\u001b[39m, vmax=\u001b[32m150\u001b[39m, \n\u001b[32m 19\u001b[39m sopt=\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 20\u001b[39m ) \n\u001b[32m 22\u001b[39m pn.Tabs((\u001b[33m'\u001b[39m\u001b[33mPresent-day\u001b[39m\u001b[33m'\u001b[39m,all_hist_ens),\n\u001b[32m 23\u001b[39m (\u001b[33m'\u001b[39m\u001b[33m+ \u001b[39m\u001b[33m'\u001b[39m+ \u001b[38;5;28mstr\u001b[39m(warm_level)+\u001b[33m'\u001b[39m\u001b[33m C\u001b[39m\u001b[33m'\u001b[39m,all_ssp_ens),\n\u001b[32m 24\u001b[39m (\u001b[33m'\u001b[39m\u001b[33mDifference\u001b[39m\u001b[33m'\u001b[39m,all_diff_ens))\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 35\u001b[39m, in \u001b[36mhvplot_percentile_column\u001b[39m\u001b[34m(data, col_title, vmin, vmax, sopt, num_cols)\u001b[39m\n\u001b[32m 33\u001b[39m col = pn.Column(col_title)\n\u001b[32m 34\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m sim \u001b[38;5;129;01min\u001b[39;00m np.unique(data.simulation.values):\n\u001b[32m---> \u001b[39m\u001b[32m35\u001b[39m pl_by_sim = \u001b[43m_hvplot_one_sim\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 36\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msim_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43msim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msopt\u001b[49m\u001b[43m=\u001b[49m\u001b[43msopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_cols\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m6\u001b[39;49m\n\u001b[32m 37\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 38\u001b[39m col += pn.Row(pl_by_sim)\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m col\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 15\u001b[39m, in \u001b[36m_hvplot_one_sim\u001b[39m\u001b[34m(data, sim_name, vmin, vmax, sopt, num_cols)\u001b[39m\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# Make a plot for each individual member_id\u001b[39;00m\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m member_id_i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(to_plot_by_sim.member_id.values)):\n\u001b[32m---> \u001b[39m\u001b[32m15\u001b[39m plot_i = \u001b[43mmake_precip_unc_map\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 16\u001b[39m \u001b[43m \u001b[49m\u001b[43mto_plot_by_sim\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msimulation\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43misel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmember_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmember_id_i\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 17\u001b[39m \u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{0}\u001b[39;49;00m\u001b[33;43m member \u001b[39;49m\u001b[38;5;132;43;01m{1}\u001b[39;49;00m\u001b[33;43m\"\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43msim_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmember_id_i\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 18\u001b[39m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 19\u001b[39m \u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 20\u001b[39m \u001b[43m \u001b[49m\u001b[43msopt\u001b[49m\u001b[43m=\u001b[49m\u001b[43msopt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 21\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 22\u001b[39m plots_by_sim = plot_i \u001b[38;5;28;01mif\u001b[39;00m plots_by_sim \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m plots_by_sim + plot_i\n\u001b[32m 23\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (plots_by_sim + hv.Layout()).cols(\u001b[32m6\u001b[39m)\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 41\u001b[39m, in \u001b[36mmake_precip_unc_map\u001b[39m\u001b[34m(data, title, vmin, vmax, sopt, width, height, xlim, ylim, xticks, yticks, xaxis, yaxis, absolute)\u001b[39m\n\u001b[32m 38\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 39\u001b[39m cmap = read_ae_colormap(cmap=\u001b[33m\"\u001b[39m\u001b[33mae_blue\u001b[39m\u001b[33m\"\u001b[39m, cmap_hex=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m _plot = \u001b[43mhv\u001b[49m\u001b[43m.\u001b[49m\u001b[43mQuadMesh\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlon\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlat\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m.opts(\n\u001b[32m 42\u001b[39m tools=[hover],\n\u001b[32m 43\u001b[39m colorbar=\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[32m 44\u001b[39m cmap=cmap,\n\u001b[32m 45\u001b[39m symmetric=sopt,\n\u001b[32m 46\u001b[39m clim=(vmin, vmax),\n\u001b[32m 47\u001b[39m xaxis=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 48\u001b[39m yaxis=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 49\u001b[39m clabel=\u001b[33m\"\u001b[39m\u001b[33mPrecipitation (mm/month)\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 50\u001b[39m title=title,\n\u001b[32m 51\u001b[39m width=width,\n\u001b[32m 52\u001b[39m height=height,\n\u001b[32m 53\u001b[39m xlim=xlim,\n\u001b[32m 54\u001b[39m ylim=ylim,\n\u001b[32m 55\u001b[39m )\n\u001b[32m 56\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _plot\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/holoviews/element/raster.py:830\u001b[39m, in \u001b[36mQuadMesh.__init__\u001b[39m\u001b[34m(self, data, kdims, vdims, **params)\u001b[39m\n\u001b[32m 828\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mlist\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m data == []:\n\u001b[32m 829\u001b[39m data = ([], [], np.zeros((\u001b[32m0\u001b[39m, \u001b[32m0\u001b[39m)))\n\u001b[32m--> \u001b[39m\u001b[32m830\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 831\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.interface.gridded:\n\u001b[32m 832\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m DataError(\n\u001b[32m 833\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m).\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m type expects gridded data, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 834\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.interface.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m is columnar. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 835\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mTo display columnar data as gridded use the HeatMap \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 836\u001b[39m \u001b[33m\"\u001b[39m\u001b[33melement or aggregate the data (e.g. using np.histogram2d).\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 837\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/holoviews/element/selection.py:25\u001b[39m, in \u001b[36mSelectionIndexExpr.__init__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m25\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 26\u001b[39m \u001b[38;5;28mself\u001b[39m._index_skip = \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/holoviews/core/data/__init__.py:337\u001b[39m, in \u001b[36mDataset.__init__\u001b[39m\u001b[34m(self, data, kdims, vdims, **kwargs)\u001b[39m\n\u001b[32m 334\u001b[39m kdims, vdims = kwargs.get(\u001b[33m'\u001b[39m\u001b[33mkdims\u001b[39m\u001b[33m'\u001b[39m), kwargs.get(\u001b[33m'\u001b[39m\u001b[33mvdims\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 336\u001b[39m validate_vdims = kwargs.pop(\u001b[33m'\u001b[39m\u001b[33m_validate_vdims\u001b[39m\u001b[33m'\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m337\u001b[39m initialized = \u001b[43mInterface\u001b[49m\u001b[43m.\u001b[49m\u001b[43minitialize\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mdatatype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mdatatype\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 339\u001b[39m (data, \u001b[38;5;28mself\u001b[39m.interface, dims, extra_kws) = initialized\n\u001b[32m 340\u001b[39m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m(data, **\u001b[38;5;28mdict\u001b[39m(kwargs, **\u001b[38;5;28mdict\u001b[39m(dims, **extra_kws)))\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/holoviews/core/data/interface.py:257\u001b[39m, in \u001b[36mInterface.initialize\u001b[39m\u001b[34m(cls, eltype, data, kdims, vdims, datatype)\u001b[39m\n\u001b[32m 255\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m257\u001b[39m (data, dims, extra_kws) = \u001b[43minterface\u001b[49m\u001b[43m.\u001b[49m\u001b[43minit\u001b[49m\u001b[43m(\u001b[49m\u001b[43meltype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvdims\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 258\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m DataError:\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/holoviews/core/data/grid.py:107\u001b[39m, in \u001b[36mGridInterface.init\u001b[39m\u001b[34m(cls, eltype, data, kdims, vdims)\u001b[39m\n\u001b[32m 105\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mValues for dimension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdim\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not found\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 106\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data[name], get_array_types()):\n\u001b[32m--> \u001b[39m\u001b[32m107\u001b[39m data[name] = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 109\u001b[39m kdim_names = [dimension_name(d) \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m kdims]\n\u001b[32m 110\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m vdim_tuple \u001b[38;5;129;01min\u001b[39;00m data:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/xarray/core/common.py:181\u001b[39m, in \u001b[36mAbstractArray.__array__\u001b[39m\u001b[34m(self, dtype, copy)\u001b[39m\n\u001b[32m 179\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m 180\u001b[39m copy = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m181\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m np.array(\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m, dtype=dtype, copy=copy)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/xarray/core/dataarray.py:798\u001b[39m, in \u001b[36mDataArray.values\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 785\u001b[39m \u001b[38;5;129m@property\u001b[39m\n\u001b[32m 786\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mvalues\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> np.ndarray:\n\u001b[32m 787\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 788\u001b[39m \u001b[33;03m The array's data converted to numpy.ndarray.\u001b[39;00m\n\u001b[32m 789\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 796\u001b[39m \u001b[33;03m to this array may be reflected in the DataArray as well.\u001b[39;00m\n\u001b[32m 797\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m798\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/xarray/core/variable.py:556\u001b[39m, in \u001b[36mVariable.values\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 553\u001b[39m \u001b[38;5;129m@property\u001b[39m\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mvalues\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> np.ndarray:\n\u001b[32m 555\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"The variable's data as a numpy.ndarray\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m556\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_as_array_or_item\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_data\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/xarray/core/variable.py:336\u001b[39m, in \u001b[36m_as_array_or_item\u001b[39m\u001b[34m(data)\u001b[39m\n\u001b[32m 322\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_as_array_or_item\u001b[39m(data):\n\u001b[32m 323\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Return the given values as a numpy array, or as an individual item if\u001b[39;00m\n\u001b[32m 324\u001b[39m \u001b[33;03m it's a 0d datetime64 or timedelta64 array.\u001b[39;00m\n\u001b[32m 325\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 334\u001b[39m \u001b[33;03m TODO: remove this (replace with np.asarray) once these issues are fixed\u001b[39;00m\n\u001b[32m 335\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m336\u001b[39m data = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data.ndim == \u001b[32m0\u001b[39m:\n\u001b[32m 338\u001b[39m kind = data.dtype.kind\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/dask/array/core.py:1718\u001b[39m, in \u001b[36mArray.__array__\u001b[39m\u001b[34m(self, dtype, copy, **kwargs)\u001b[39m\n\u001b[32m 1711\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[32m 1712\u001b[39m warnings.warn(\n\u001b[32m 1713\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mCan\u001b[39m\u001b[33m'\u001b[39m\u001b[33mt acquire a memory view of a Dask array. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1714\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mThis will raise in the future.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 1715\u001b[39m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[32m 1716\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1718\u001b[39m x = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1720\u001b[39m \u001b[38;5;66;03m# Apply requested dtype and convert non-numpy backends to numpy.\u001b[39;00m\n\u001b[32m 1721\u001b[39m \u001b[38;5;66;03m# If copy is True, numpy is going to perform its own deep copy\u001b[39;00m\n\u001b[32m 1722\u001b[39m \u001b[38;5;66;03m# after this method returns.\u001b[39;00m\n\u001b[32m 1723\u001b[39m \u001b[38;5;66;03m# If copy is None, finalize() ensures that the returned object\u001b[39;00m\n\u001b[32m 1724\u001b[39m \u001b[38;5;66;03m# does not share memory with an object stored in the graph or on a\u001b[39;00m\n\u001b[32m 1725\u001b[39m \u001b[38;5;66;03m# process-local Worker.\u001b[39;00m\n\u001b[32m 1726\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m np.asarray(x, dtype=dtype)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/dask/base.py:378\u001b[39m, in \u001b[36mDaskMethodsMixin.compute\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 354\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute\u001b[39m(\u001b[38;5;28mself\u001b[39m, **kwargs):\n\u001b[32m 355\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[32m 356\u001b[39m \n\u001b[32m 357\u001b[39m \u001b[33;03m This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 376\u001b[39m \u001b[33;03m dask.compute\u001b[39;00m\n\u001b[32m 377\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m378\u001b[39m (result,) = \u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraverse\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 379\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.12/site-packages/dask/base.py:686\u001b[39m, in \u001b[36mcompute\u001b[39m\u001b[34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[39m\n\u001b[32m 683\u001b[39m expr = expr.optimize()\n\u001b[32m 684\u001b[39m keys = \u001b[38;5;28mlist\u001b[39m(flatten(expr.__dask_keys__()))\n\u001b[32m--> \u001b[39m\u001b[32m686\u001b[39m results = \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 688\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m repack(results)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/queue.py:171\u001b[39m, in \u001b[36mQueue.get\u001b[39m\u001b[34m(self, block, timeout)\u001b[39m\n\u001b[32m 169\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 170\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._qsize():\n\u001b[32m--> \u001b[39m\u001b[32m171\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mnot_empty\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 172\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m timeout < \u001b[32m0\u001b[39m:\n\u001b[32m 173\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m\u001b[33mtimeout\u001b[39m\u001b[33m'\u001b[39m\u001b[33m must be a non-negative number\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/threading.py:355\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[32m 354\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m355\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 356\u001b[39m gotit = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 357\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: " - ] - } - ], + "outputs": [], "source": [ "all_hist_ens = hvplot_percentile_column(\n", " data=hist_cae_percentile.pr, \n", @@ -1964,129 +616,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "96160b74-abd5-47e2-97fe-da1c1b2a7a20", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T16:56:12.703405Z", - "iopub.status.busy": "2026-02-09T16:56:12.703168Z", - "iopub.status.idle": "2026-02-09T16:56:25.776020Z", - "shell.execute_reply": "2026-02-09T16:56:25.775256Z", - "shell.execute_reply.started": "2026-02-09T16:56:12.703379Z" - }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--> The keys in the returned dictionary of datasets are constructed as follows:\n", - "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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\n", - "\n", - "```" - ], - "text/plain": [ - "div((progress((),{'max': 6, 'value': 6}), ' ', '100.00% [6/6 00:00<00:00]'),{})" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model BCC-ESM1\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model CAMS-CSM1-0\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model FGOALS-g3\n", - "End year for SSP time slice occurs after 2100; skipping ensemble member r1i1p1f1 of model GFDL-ESM4\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model IITM-ESM\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model INM-CM4-8\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model INM-CM5-0\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MIROC6\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM-1-2-HAM\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-HR\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-LR\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model NorESM2-LM\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model NorESM2-MM\n" - ] - } - ], + "outputs": [], "source": [ "mdls_ds = grab_multimodel_data(copt,alpha_sort=True)\n", "\n", @@ -2112,16 +647,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "c1735845-4ef3-419c-80d3-b7def064e15d", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T16:56:25.776986Z", - "iopub.status.busy": "2026-02-09T16:56:25.776799Z", - "iopub.status.idle": "2026-02-09T16:56:35.209133Z", - "shell.execute_reply": "2026-02-09T16:56:35.207858Z", - "shell.execute_reply.started": "2026-02-09T16:56:25.776967Z" - }, "tags": [] }, "outputs": [], @@ -2155,12 +683,6 @@ "execution_count": null, "id": "177a3cd3-a380-4ee9-8bf1-3630119a5950", "metadata": { - "execution": { - "iopub.status.busy": "2026-02-09T16:56:00.135017Z", - "iopub.status.idle": "2026-02-09T16:56:00.135755Z", - "shell.execute_reply": "2026-02-09T16:56:00.135160Z", - "shell.execute_reply.started": "2026-02-09T16:56:00.135143Z" - }, "tags": [] }, "outputs": [], @@ -2290,16 +812,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "2d359a58-0665-4598-8266-0c499810f1ed", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T16:56:35.210193Z", - "iopub.status.busy": "2026-02-09T16:56:35.209914Z", - "iopub.status.idle": "2026-02-09T16:56:43.303954Z", - "shell.execute_reply": "2026-02-09T16:56:43.303022Z", - "shell.execute_reply.started": "2026-02-09T16:56:35.210161Z" - }, "tags": [] }, "outputs": [], @@ -2327,127 +842,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "4737e327-7f07-40e7-8225-a0aaebad5389", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T16:56:43.304799Z", - "iopub.status.busy": "2026-02-09T16:56:43.304542Z", - "iopub.status.idle": "2026-02-09T16:56:47.187202Z", - "shell.execute_reply": "2026-02-09T16:56:47.185217Z", - "shell.execute_reply.started": "2026-02-09T16:56:43.304778Z" - }, "tags": [] }, - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
\n", - "
\n", - "
\n", - "" - ], - "text/plain": [ - ":Layout\n", - " .Overlay.I :Overlay\n", - " .QuadMesh.I :QuadMesh [x,y] (z)\n", - " .Bounds.I :Bounds [x,y]\n", - " .Overlay.II :Overlay\n", - " .QuadMesh.I :QuadMesh [x,y] (z)\n", - " .Bounds.I :Bounds [x,y]\n", - " .Overlay.III :Overlay\n", - " .QuadMesh.I :QuadMesh [x,y] (z)\n", - " .Bounds.I :Bounds [x,y]\n", - " .Overlay.IV :Overlay\n", - " .QuadMesh.I :QuadMesh [x,y] (z)\n", - " .Bounds.I :Bounds [x,y]\n", - " .Overlay.V :Overlay\n", - " .QuadMesh.I :QuadMesh [x,y] (z)\n", - " .Bounds.I :Bounds [x,y]" - ] - }, - "execution_count": 15, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "27ebd242-15c1-4bd5-9970-8a8b44afa3d4" - } - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lat0 = selections.latitude[0]\n", "lat1 = selections.latitude[1]\n", @@ -2505,29 +905,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "6bedd466-e867-46d0-9a17-20ffbe7b010e", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T16:56:47.188005Z", - "iopub.status.busy": "2026-02-09T16:56:47.187745Z", - "iopub.status.idle": "2026-02-09T16:57:29.078575Z", - "shell.execute_reply": "2026-02-09T16:57:29.077592Z", - "shell.execute_reply.started": "2026-02-09T16:56:47.187970Z" - }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model FGOALS-g3\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MIROC6\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-HR\n" - ] - } - ], + "outputs": [], "source": [ "# first for the downscaled results\n", "reg_hist_wrf = reg_wrf_ds.sel(\n", @@ -2577,12 +960,6 @@ "execution_count": null, "id": "55622c8b-dcde-4031-922a-aa0cf6052360", "metadata": { - "execution": { - "iopub.status.busy": "2026-02-09T16:56:00.139241Z", - "iopub.status.idle": "2026-02-09T16:56:00.139516Z", - "shell.execute_reply": "2026-02-09T16:56:00.139368Z", - "shell.execute_reply.started": "2026-02-09T16:56:00.139352Z" - }, "tags": [] }, "outputs": [], @@ -2652,16 +1029,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "16e3b462-3eae-4b42-a592-beb6b24eb8f0", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T18:18:20.129245Z", - "iopub.status.busy": "2026-02-09T18:18:20.129002Z", - "iopub.status.idle": "2026-02-09T18:18:30.504247Z", - "shell.execute_reply": "2026-02-09T18:18:30.503326Z", - "shell.execute_reply.started": "2026-02-09T18:18:20.129222Z" - }, "tags": [] }, "outputs": [], @@ -2679,29 +1049,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "dbed3ea6-b9d2-40b2-ad21-68f3f7587772", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T18:24:33.027293Z", - "iopub.status.busy": "2026-02-09T18:24:33.027049Z", - "iopub.status.idle": "2026-02-09T18:24:52.337546Z", - "shell.execute_reply": "2026-02-09T18:24:52.336627Z", - "shell.execute_reply.started": "2026-02-09T18:24:33.027270Z" - }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model FGOALS-g3\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MIROC6\n", - "3.0°C warming level not reached for ensemble member r1i1p1f1 of model MPI-ESM1-2-HR\n" - ] - } - ], + "outputs": [], "source": [ "box_hist_wrf = box_wrf_ds.sel(\n", " time=slice('1981','2010'))\n", @@ -2733,16 +1086,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "ce7a9586-7590-4c22-947e-3d7d160ab50f", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T18:24:52.338267Z", - "iopub.status.busy": "2026-02-09T18:24:52.338036Z", - "iopub.status.idle": "2026-02-09T18:24:57.895414Z", - "shell.execute_reply": "2026-02-09T18:24:57.894599Z", - "shell.execute_reply.started": "2026-02-09T18:24:52.338246Z" - }, "tags": [] }, "outputs": [], @@ -2772,16 +1118,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "f6f6ecb9-98f5-4cc8-be8b-673e3d27556e", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T18:24:57.896377Z", - "iopub.status.busy": "2026-02-09T18:24:57.896180Z", - "iopub.status.idle": "2026-02-09T18:25:41.598172Z", - "shell.execute_reply": "2026-02-09T18:25:41.597292Z", - "shell.execute_reply.started": "2026-02-09T18:24:57.896357Z" - }, "tags": [] }, "outputs": [], @@ -2817,31 +1156,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "28dfeb20-d40a-4895-9777-6e742489f58b", "metadata": { - "execution": { - "iopub.execute_input": "2026-02-09T19:04:41.216787Z", - "iopub.status.busy": "2026-02-09T19:04:41.216531Z", - "iopub.status.idle": "2026-02-09T19:04:41.404137Z", - "shell.execute_reply": "2026-02-09T19:04:41.402796Z", - "shell.execute_reply.started": "2026-02-09T19:04:41.216764Z" - }, "tags": [] }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'make_precip_unc_map' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m delta_vmin = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 4\u001b[39m delta_vmax = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m mmm_hist_plot = \u001b[43mmake_precip_unc_map\u001b[49m(\n\u001b[32m 7\u001b[39m data=hist_percentile_mmm, title=(\u001b[33m\"\u001b[39m\u001b[33mMulti-model mean\u001b[39m\u001b[33m\"\u001b[39m),\n\u001b[32m 8\u001b[39m vmin=vmin, vmax=vmax, sopt=\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m 9\u001b[39m height=\u001b[32m300\u001b[39m, width=\u001b[32m300\u001b[39m)\n\u001b[32m 11\u001b[39m mmm_ssp_plot = make_precip_unc_map(\n\u001b[32m 12\u001b[39m data=ssp_percentile_mmm, title=(\u001b[33m\"\u001b[39m\u001b[33mMulti-model mean\u001b[39m\u001b[33m\"\u001b[39m),\n\u001b[32m 13\u001b[39m vmin=vmin, vmax=vmax, sopt=\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m 14\u001b[39m height=\u001b[32m300\u001b[39m, width=\u001b[32m300\u001b[39m)\n\u001b[32m 16\u001b[39m mmm_diff_plot = make_precip_unc_map(\n\u001b[32m 17\u001b[39m data=delta_percentile_mmm, title=(\u001b[33m\"\u001b[39m\u001b[33mMulti-model mean\u001b[39m\u001b[33m\"\u001b[39m),\n\u001b[32m 18\u001b[39m vmin=delta_vmin, vmax=delta_vmax, sopt=\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[32m 19\u001b[39m height=\u001b[32m300\u001b[39m, width=\u001b[32m300\u001b[39m)\n", - "\u001b[31mNameError\u001b[39m: name 'make_precip_unc_map' is not defined" - ] - } - ], + "outputs": [], "source": [ "vmin = 0\n", "vmax = 2000\n", @@ -2917,12 +1237,6 @@ "execution_count": null, "id": "1969c1c5-ead1-4fae-ab7c-e8f6e4815c6b", "metadata": { - "execution": { - "iopub.status.busy": "2026-02-09T16:56:00.144288Z", - "iopub.status.idle": "2026-02-09T16:56:00.144526Z", - "shell.execute_reply": "2026-02-09T16:56:00.144413Z", - "shell.execute_reply.started": "2026-02-09T16:56:00.144398Z" - }, "tags": [] }, "outputs": [], From 157f5bc77eb4386809510c1327414a5167ad0350 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Wed, 18 Feb 2026 11:18:17 -0600 Subject: [PATCH 21/53] updating after fixing nicole's comments --- analysis/derived_variables_demo.ipynb | 107 +++++++++++++++++--------- 1 file changed, 72 insertions(+), 35 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 9ff81d74..80f5968c 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -57,10 +57,21 @@ "from dask.diagnostics import ProgressBar\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import pandas as pd\n", "import xarray as xr\n", "\n", "import climakitae as ck\n", "\n", + "# Set default plotting parameters for readability\n", + "plt.rcParams.update({\n", + " 'font.size': 15, # default font size\n", + " 'axes.titlesize': 18, # title\n", + " 'axes.labelsize': 15, # axis labels\n", + " 'xtick.labelsize': 15, # x tick labels\n", + " 'ytick.labelsize': 15, # y tick labels\n", + " 'legend.fontsize': 15, # legend\n", + "})\n", + "\n", "# Initialize ClimateData\n", "cd = ck.ClimateData(verbosity=-1)" ] @@ -77,9 +88,6 @@ " \"\"\"\n", " Convert time_delta dimension to a dummy time dimension for easier handling.\n", " \"\"\"\n", - " import xarray as xr\n", - " import pandas as pd\n", - "\n", " if 'time_delta' in data.dims:\n", " wl_values = data['time_delta'].values\n", " time_values = pd.to_datetime(wl_values, unit='D', origin='2050-01-01')\n", @@ -303,8 +311,16 @@ "cooling_days_per_year = xr.where(cooling_days_per_year > 0, cooling_days_per_year, np.nan)\n", "\n", "# Average years for spatial plot\n", - "spt = cooling_days_per_year.mean(dim=\"time\") \n", - "\n", + "spt = cooling_days_per_year.mean(dim=\"time\") " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c8af8ad", + "metadata": {}, + "outputs": [], + "source": [ "# Compute with progress bar\n", "with ProgressBar():\n", " spt = spt.median(dim='sim').compute()" @@ -317,10 +333,8 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "\n", "# Create figure with 3 subplots (one per warming level)\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5), gridspec_kw={'wspace': 0.3})\n", "\n", "warming_levels = spt.warming_level.values\n", "\n", @@ -337,15 +351,15 @@ " vmax=300\n", " )\n", " \n", - " ax.set_title(f'{wl}°C Warming Level', fontsize=13, fontweight='bold')\n", - " ax.set_xlabel('Longitude', fontsize=11)\n", - " ax.set_ylabel('Latitude', fontsize=11)\n", + " ax.set_title(f'{wl}°C Warming Level', fontweight='bold')\n", + " ax.set_xlabel('Longitude')\n", + " ax.set_ylabel('Latitude')\n", "\n", "# Add shared colorbar\n", "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", "\n", "fig.suptitle('Cooling Days (65 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", - " fontsize=15, fontweight='bold', y=1.02)" + " fontweight='bold', y=1.07)" ] }, { @@ -374,10 +388,17 @@ "outputs": [], "source": [ "# Average over spatial dimensions for county-wide average\n", - "from dask.diagnostics import ProgressBar\n", "with ProgressBar():\n", - " cooling_days_per_year = cooling_days_per_year.mean(dim=[\"y\", \"x\"]).compute()\n", - "\n", + " cooling_days_per_year = cooling_days_per_year.mean(dim=[\"y\", \"x\"]).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bbdd2828", + "metadata": {}, + "outputs": [], + "source": [ "# Plot time series for each warming level\n", "fig, ax = plt.subplots(figsize=(14, 7))\n", "\n", @@ -401,16 +422,16 @@ " color=colors[idx], linewidth=3, label=f'{wl}°C Warming', \n", " marker='o', markersize=4, markevery=5)\n", "\n", - "ax.set_xlabel('Year', fontsize=13, fontweight='bold')\n", - "ax.set_ylabel('Annual Cooling Days', fontsize=13, fontweight='bold')\n", + "ax.set_xlabel('Year', fontweight='bold')\n", + "ax.set_ylabel('Annual Cooling Days', fontweight='bold')\n", "ax.set_title('Annual Cooling Days by Global Warming Level\\nLos Angeles County (UCLA WRF)', \n", - " fontsize=15, fontweight='bold', pad=15)\n", - "ax.legend(loc='upper left', fontsize=11, framealpha=0.95)\n", + " fontweight='bold', pad=15)\n", + "ax.legend(loc='upper left', framealpha=0.95)\n", "ax.grid(True, alpha=0.3, linestyle='--')\n", "\n", "# Add annotation\n", "ax.text(0.98, 0.02, 'Higher warming → More cooling days', \n", - " transform=ax.transAxes, fontsize=10, \n", + " transform=ax.transAxes,\n", " verticalalignment='bottom', horizontalalignment='right',\n", " bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.3))\n", "\n", @@ -529,7 +550,16 @@ "cooling_days_per_year = xr.where(cooling_days_per_year > 0, cooling_days_per_year, np.nan)\n", "\n", "# Average years for spatial plot\n", - "spt = cooling_days_per_year.mean(dim=\"time\") \n", + "spt = cooling_days_per_year.mean(dim=\"time\") " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dadfd819", + "metadata": {}, + "outputs": [], + "source": [ "\n", "# Compute with progress bar\n", "with ProgressBar():\n", @@ -544,7 +574,7 @@ "outputs": [], "source": [ "# Create figure with 3 subplots (one per warming level)\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5), gridspec_kw={'wspace': 0.3})\n", "\n", "warming_levels = spt.warming_level.values\n", "\n", @@ -561,15 +591,15 @@ " vmax=340\n", " )\n", " \n", - " ax.set_title(f'{wl}°C Warming Level', fontsize=13, fontweight='bold')\n", - " ax.set_xlabel('Longitude', fontsize=11)\n", - " ax.set_ylabel('Latitude', fontsize=11)\n", + " ax.set_title(f'{wl}°C Warming Level', fontweight='bold')\n", + " ax.set_xlabel('Longitude')\n", + " ax.set_ylabel('Latitude')\n", "\n", "# Add shared colorbar\n", "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", "\n", "fig.suptitle('Cooling Days (75 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", - " fontsize=15, fontweight='bold', y=1.02)" + " fontweight='bold', y=1.07)" ] }, { @@ -597,7 +627,6 @@ "source": [ "from climakitae.new_core.derived_variables.registry import register_user_function\n", "from climakitae.util.utils import f_to_k\n", - "import xarray as xr\n", "\n", "THRESHOLD_F = 85.0\n", "threshold_k = f_to_k(THRESHOLD_F)\n", @@ -629,8 +658,7 @@ " func=calc_custom_cdd,\n", " description=\"Cooling Degree Days from WRF with 75°F base using daily minimum temperature\",\n", " units=\"K\",\n", - ")\n", - " " + ")" ] }, { @@ -675,7 +703,16 @@ "cooling_days_per_year = xr.where(cooling_days_per_year > 0, cooling_days_per_year, np.nan)\n", "\n", "# Average years for spatial plot\n", - "spt = cooling_days_per_year.mean(dim=\"time\") \n", + "spt = cooling_days_per_year.mean(dim=\"time\") " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39878030", + "metadata": {}, + "outputs": [], + "source": [ "\n", "# Compute with progress bar\n", "with ProgressBar():\n", @@ -690,7 +727,7 @@ "outputs": [], "source": [ "# Create figure with 3 subplots (one per warming level)\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5), gridspec_kw={'wspace': 0.3})\n", "\n", "warming_levels = spt.warming_level.values\n", "\n", @@ -707,15 +744,15 @@ " vmax=30\n", " )\n", " \n", - " ax.set_title(f'{wl}°C Warming Level', fontsize=13, fontweight='bold')\n", - " ax.set_xlabel('Longitude', fontsize=11)\n", - " ax.set_ylabel('Latitude', fontsize=11)\n", + " ax.set_title(f'{wl}°C Warming Level', fontweight='bold')\n", + " ax.set_xlabel('Longitude')\n", + " ax.set_ylabel('Latitude')\n", "\n", "# Add shared colorbar\n", "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", "\n", "fig.suptitle('Cooling Days (Min Temp > 85 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", - " fontsize=15, fontweight='bold', y=1.02)" + " fontweight='bold', y=1.07)" ] }, { From e2ee1498cd64bccfa365c6bc05a9d79ef87cdd97 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Wed, 18 Feb 2026 12:41:34 -0600 Subject: [PATCH 22/53] updating communication around GWL --- analysis/derived_variables_demo.ipynb | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 80f5968c..3e2868c2 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -410,19 +410,28 @@ "for idx, wl in enumerate(warming_levels):\n", " wl_data = cooling_days_per_year.sel(warming_level=wl)\n", " \n", + " # Get centered years for this warming level to show in legend\n", + " cy = cdd_data.centered_year.sel(warming_level=wl).dropna(\"sim\")\n", + " min_year = int(cy.min().values)\n", + " max_year = int(cy.max().values)\n", + " \n", + " # Use relative years (offset from dummy origin) for x-axis\n", + " relative_years = wl_data.time.dt.year - 2050\n", + " \n", " # Plot individual simulations with low alpha\n", " for sim in range(wl_data.sizes['sim']):\n", " sim_data = wl_data.isel(sim=sim)\n", - " ax.plot(sim_data.time.dt.year, sim_data.values, \n", + " ax.plot(relative_years, sim_data.values, \n", " alpha=0.15, color=colors[idx], linewidth=0.8)\n", " \n", " # Plot ensemble mean with solid line\n", " ensemble_mean = wl_data.mean(dim='sim')\n", - " ax.plot(ensemble_mean.time.dt.year, ensemble_mean.values,\n", - " color=colors[idx], linewidth=3, label=f'{wl}°C Warming', \n", + " ax.plot(relative_years, ensemble_mean.values,\n", + " color=colors[idx], linewidth=3, \n", + " label=f'{wl}°C Warming ({min_year}–{max_year})', \n", " marker='o', markersize=4, markevery=5)\n", "\n", - "ax.set_xlabel('Year', fontweight='bold')\n", + "ax.set_xlabel('Years from Centered Year', fontweight='bold')\n", "ax.set_ylabel('Annual Cooling Days', fontweight='bold')\n", "ax.set_title('Annual Cooling Days by Global Warming Level\\nLos Angeles County (UCLA WRF)', \n", " fontweight='bold', pad=15)\n", @@ -443,7 +452,7 @@ "id": "086ad63a-7070-4e12-a423-fe6a2d1757de", "metadata": {}, "source": [ - "The plot above shows the median number of cooling degree days (65 °F) as a function of time averaged over Los Angeles County. As the Global warming level increases so too does the number of cooling days. Please note that the time axis is a \"dummy\" sequence, and the true timescale for the data can be found in the dataset's metadata." + "The plot above shows the annual cooling degree days (65 °F) averaged over Los Angeles County, plotted relative to each model's centered year for a given warming level. The x-axis shows years before and after the centered year (the year when a given GWL is reached), and the legend shows the range of calendar years across simulations when each GWL is reached. As the global warming level increases, so too does the number of cooling days." ] }, { From ef818faae8344dfec51d7327124c31596178577d Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Wed, 18 Feb 2026 18:54:49 +0000 Subject: [PATCH 23/53] qol updates --- analysis/derived_variables_demo.ipynb | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 3e2868c2..4a4b55c5 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -22,7 +22,7 @@ "\n", "> [NOTE]\n", "> Custom metrics defined by users DO NOT show up in the cadcat catalog and ARE NOT available to other users.\n", - "> Custom metrics are defined locally and exist in the users environment only at run time and do not persist between sessions.\n", + "> Custom metrics are defined locally, exist in the users environment only at run time, and do not persist between sessions.\n", "\n", "### Outcomes\n", "\n", @@ -46,12 +46,7 @@ "cell_type": "code", "execution_count": null, "id": "2f59f965", - "metadata": { - "execution": { - "iopub.execute_input": "2026-02-06T22:45:35.572679Z", - "iopub.status.busy": "2026-02-06T22:45:35.572414Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from dask.diagnostics import ProgressBar\n", @@ -597,7 +592,7 @@ " cmap='plasma', levels=100,\n", " add_colorbar=False,\n", " vmin=0,\n", - " vmax=340\n", + " vmax=175\n", " )\n", " \n", " ax.set_title(f'{wl}°C Warming Level', fontweight='bold')\n", From 74326c0c2ec1fb6e65b373ee3dc09eacaf0fb19f Mon Sep 17 00:00:00 2001 From: Neil Schroeder Date: Thu, 19 Feb 2026 13:06:46 -0700 Subject: [PATCH 24/53] small clean up --- analysis/derived_variables_demo.ipynb | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 4a4b55c5..c7be1318 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -354,7 +354,7 @@ "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", "\n", "fig.suptitle('Cooling Days (65 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", - " fontweight='bold', y=1.07)" + " fontweight='bold', y=1.07);" ] }, { @@ -564,7 +564,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Compute with progress bar\n", "with ProgressBar():\n", " spt = spt.median(dim='sim').compute()" @@ -603,7 +602,7 @@ "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", "\n", "fig.suptitle('Cooling Days (75 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", - " fontweight='bold', y=1.07)" + " fontweight='bold', y=1.07);" ] }, { @@ -756,7 +755,7 @@ "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", "\n", "fig.suptitle('Cooling Days (Min Temp > 85 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", - " fontweight='bold', y=1.07)" + " fontweight='bold', y=1.07);" ] }, { From ad452d8e27d3ea2629896562912d7517d52b8101 Mon Sep 17 00:00:00 2001 From: Neil Schroeder Date: Mon, 2 Mar 2026 09:40:46 -0600 Subject: [PATCH 25/53] Update analysis/derived_variables_demo.ipynb Co-authored-by: Nicole Keeney --- analysis/derived_variables_demo.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index c7be1318..f021eeaa 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -744,7 +744,7 @@ " cmap='plasma', levels=100,\n", " add_colorbar=False,\n", " vmin=0,\n", - " vmax=30\n", + " vmax=10\n", " )\n", " \n", " ax.set_title(f'{wl}°C Warming Level', fontweight='bold')\n", From 8b0609b132f786389c316fb61993e87cafad9289 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Mon, 2 Mar 2026 14:18:45 -0600 Subject: [PATCH 26/53] reworking with goldilocks days --- analysis/derived_variables_demo.ipynb | 772 ++++++++------------------ 1 file changed, 217 insertions(+), 555 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index f021eeaa..b7ed1a70 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -5,41 +5,27 @@ "id": "a97eee28", "metadata": {}, "source": [ - "# Derived Variables Demo: Heating & Cooling Degree Days\n", + "# Goldilocks Days Demo: Temperature + Humidity Comfort Window\n", "\n", - "This notebook demonstrates how to use derived variables in ClimakitAE, focusing on **cooling degree days (CDD)** for energy demand analysis in Los Angeles County.\n", + "This notebook demonstrates a custom **Goldilocks day** metric in ClimakitAE for Los Angeles County.\n", "\n", - "### Purpose\n", + "A Goldilocks day is defined here as a day when:\n", + "- Temperature at 2m is between **65°F and 80°F**\n", + "- Relative humidity (`rh`) is between **40% and 50%**\n", "\n", - "This notebook uses (and serves as a living example) for the following tools in ClimakitAE:\n", - "- ClimateData(): user interface\n", - "- Climakitae custom metrics over the Cal-Adapt Data Catalog (cadcat)\n", - " - Built in functions like Cooling Degree Days\n", - " - Modifying the built in functions\n", - " - Defining **your own** metric for notebook specific custom metric analysis\n", - "- Spatial subsetting using climakitae\n", - "- Warming level approaches using climakitae\n", + "### Purpose\n", "\n", - "> [NOTE]\n", - "> Custom metrics defined by users DO NOT show up in the cadcat catalog and ARE NOT available to other users.\n", - "> Custom metrics are defined locally, exist in the users environment only at run time, and do not persist between sessions.\n", + "This notebook demonstrates how to:\n", + "- Build a custom derived metric with `ClimateData` and `register_user_function`\n", + "- Query WRF data from `cadcat` with a single `.get()` call\n", + "- Apply spatial clipping and warming-level processing in the same query\n", + "- Visualize spatial and temporal changes in Goldilocks days across warming levels\n", "\n", "### Outcomes\n", "\n", - "Users can expect to take away visualizations of cooling degree days for various thresholds across 3 global warming levels.\n", - "\n", - "\n", - "**RUNTIME**: 7 minutes\n", + "You will produce spatial maps and time-series summaries of annual Goldilocks days for warming levels of 1.2°C, 2.0°C, and 3.0°C.\n", "\n", - "## What are Degree Days?\n", - "\n", - "- **Cooling Degree Days (CDD)**: Measure of energy demand for cooling when outdoor temperature rises above 65°F\n", - "\n", - "These metrics are essential for:\n", - "- Building energy modeling\n", - "- Utility demand forecasting\n", - "- Climate adaptation planning\n", - "- Energy efficiency assessments" + "**RUNTIME**: ~7 minutes" ] }, { @@ -51,24 +37,28 @@ "source": [ "from dask.diagnostics import ProgressBar\n", "import matplotlib.pyplot as plt\n", - "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "\n", - "import climakitae as ck\n", + "from climakitae.new_core.user_interface import ClimateData\n", + "from climakitae.new_core.derived_variables.registry import (\n", + " list_derived_variables,\n", + " register_user_function,\n", + " )\n", + "from climakitae.util.utils import f_to_k\n", "\n", "# Set default plotting parameters for readability\n", "plt.rcParams.update({\n", - " 'font.size': 15, # default font size\n", - " 'axes.titlesize': 18, # title\n", - " 'axes.labelsize': 15, # axis labels\n", - " 'xtick.labelsize': 15, # x tick labels\n", - " 'ytick.labelsize': 15, # y tick labels\n", - " 'legend.fontsize': 15, # legend\n", + " \"font.size\": 15,\n", + " \"axes.titlesize\": 18,\n", + " \"axes.labelsize\": 15,\n", + " \"xtick.labelsize\": 15,\n", + " \"ytick.labelsize\": 15,\n", + " \"legend.fontsize\": 15,\n", "})\n", "\n", - "# Initialize ClimateData\n", - "cd = ck.ClimateData(verbosity=-1)" + "# Initialize ClimateData (quiet mode)\n", + "cd = ClimateData(verbosity=-1)" ] }, { @@ -78,17 +68,15 @@ "metadata": {}, "outputs": [], "source": [ - "# Add dummy time dimension to warming level data for easier handling\n", + "# Add a dummy time dimension to warming-level data for easier plotting\n", "def add_dummy_time_to_wl(data):\n", - " \"\"\"\n", - " Convert time_delta dimension to a dummy time dimension for easier handling.\n", - " \"\"\"\n", - " if 'time_delta' in data.dims:\n", - " wl_values = data['time_delta'].values\n", - " time_values = pd.to_datetime(wl_values, unit='D', origin='2050-01-01')\n", - " data = data.rename({'time_delta': 'time'})\n", + " \"\"\"Convert time_delta to a dummy datetime coordinate for plotting.\"\"\"\n", + " if \"time_delta\" in data.dims:\n", + " wl_values = data[\"time_delta\"].values\n", + " time_values = pd.to_datetime(wl_values, unit=\"D\", origin=\"2050-01-01\")\n", + " data = data.rename({\"time_delta\": \"time\"})\n", " data = data.assign_coords(time=time_values)\n", - " return data\n" + " return data" ] }, { @@ -96,97 +84,25 @@ "id": "e42bba08", "metadata": {}, "source": [ - "## Why Derived Variables? Old vs New Approach\n", + "## 1. Why a Custom Goldilocks Metric?\n", "\n", - "### ❌ Previous Approach (Legacy Interface)\n", + "For this public notebook, we focus on a temperature-humidity comfort-window metric.\n", "\n", "```mermaid\n", "flowchart TD\n", - " A[Step 1: Fetch first variable
tasmax_data = get_data] --> B[Step 2: Fetch second variable
tasmin_data = get_data]\n", - " B --> C[Step 3: Define calculation function
def calc_cdd]\n", - " C --> D[Step 4: Manually compute
cdd = calc_cdd]\n", - " D --> E[Step 5: Manual post-processing
cdd_clipped = cdd.sel
cdd_annual = cdd.resample]\n", - " E --> F[⏱️ RESULT]\n", - " \n", - " style A fill:#ffcccc,stroke:#cc0000,stroke-width:2px\n", - " style B fill:#ffcccc,stroke:#cc0000,stroke-width:2px\n", - " style C fill:#ffcccc,stroke:#cc0000,stroke-width:2px\n", - " style D fill:#ffcccc,stroke:#cc0000,stroke-width:2px\n", - " style E fill:#ffcccc,stroke:#cc0000,stroke-width:2px\n", - " style F fill:#ffeeee,stroke:#cc0000,stroke-width:2px\n", - "```\n", - "\n", - "**Pain Points:**\n", - "- 💥 Multiple `get_data()` calls might load full datasets into memory (lazy dataset handling was inconsistent)\n", - "- 🔧 Manual coordination of multiple variables\n", - "- 📦 Post-processing happens after data is loaded\n", - "- 🔁 No reusability - repeat for each analysis\n", - "- ⚠️ Error-prone parameter matching\n", - "\n", - "---\n", - "\n", - "### ✅ New Approach (Derived Variables System)\n", + " A[Step 1: Register custom derived variable\\nGoldilocks from t2 and rh] --> B[Step 2: Single query\\nClimateData.variable.processes.get]\n", + " B --> C[Step 3: Analyze outputs\\nSpatial + temporal views by warming level]\n", "\n", - "```mermaid\n", - "flowchart TD\n", - " A[Step 1: Define function ONCE and register
@register_derived] --> B[Step 2: Single call with processing
ClimateData.variable.processes.get]\n", - " B --> C[⚡ LAZY RESULT
computed on demand]\n", - " \n", - " style A fill:#ccffcc,stroke:#00cc00,stroke-width:2px\n", - " style B fill:#ccffcc,stroke:#00cc00,stroke-width:2px\n", - " style C fill:#eeffee,stroke:#00cc00,stroke-width:3px\n", + " style A fill:#ccffcc,stroke:#00aa00,stroke-width:2px\n", + " style B fill:#ccffcc,stroke:#00aa00,stroke-width:2px\n", + " style C fill:#eeffee,stroke:#00aa00,stroke-width:3px\n", "```\n", "\n", "**Benefits:**\n", - "- ✨ **Single `.get()` call** - All processing in one query\n", - "- 🔄 **Automatic variable fetching** - System knows CDD needs t2max & t2min\n", - "- 💾 **Lazy evaluation** - Data not loaded until needed\n", - "- 🎯 **Processing pipeline** - Clipping, time slicing happen efficiently\n", - "- ♻️ **Reusable** - Define once, use everywhere\n", - "- 🛡️ **Type-safe** - Registry validates dependencies\n", - "- 📦 **Composable** - Chain with other processors seamlessly\n", - "\n", - "---\n", - "\n", - "### Code Comparison\n", - "\n", - "**Old Approach:**\n", - "```python\n", - "# Step 1 & 2: Multiple data fetches\n", - "tasmax_data = get_data({'variable': 'tasmax', 'area': 'Los Angeles', ...})\n", - "tasmin_data = get_data({'variable': 'tasmin', 'area': 'Los Angeles', ...})\n", - "\n", - "# Step 3 & 4: Manual calculation\n", - "def calc_cdd(tasmax, tasmin):\n", - " t_avg = (tasmax + tasmin) / 2\n", - " return np.maximum(0, t_avg - 291.48) # 65°F threshold\n", - "\n", - "cdd = calc_cdd(tasmax_data, tasmin_data)\n", - "\n", - "# Step 5: Manual post-processing\n", - "cdd_clipped = cdd.sel(lat=..., lon=...)\n", - "cdd_annual = cdd_clipped.resample(time='Y').sum()\n", - "```\n", - "\n", - "**New Approach:**\n", - "```python\n", - "# Step 1: Register once (already done in builtin!)\n", - "@register_derived(variable='CDD_wrf', query={'variable_id': ['t2max', 't2min']})\n", - "def calc_cdd_wrf(ds): ...\n", - "\n", - "# Step 2: Single call - that's it!\n", - "cdd_data = (\n", - " ClimateData()\n", - " .variable('CDD_wrf') # Derived variable!\n", - " .activity_id('WRF')\n", - " .processes({\n", - " 'clip': 'Los Angeles County',\n", - " 'time_slice': (2020, 2050)\n", - " })\n", - " .get()\n", - ")\n", - "# Returns lazy dataset with all processing applied\n", - "```" + "- One reusable metric definition\n", + "- Single `.get()` query with clipping + warming-level processing\n", + "- Lazy xarray/dask workflow (compute only when needed)\n", + "- Clear translation to business-facing “comfortable day” framing" ] }, { @@ -196,9 +112,14 @@ "metadata": {}, "outputs": [], "source": [ - "# Show all available derived variables\n", - "# Notice the HDD and CDD variables for both WRF and LOCA2 data\n", - "cd.show_derived_variables()" + "# Show available WRF daily variables (includes t2 and rh)\n", + "(\n", + " cd\n", + " .catalog(\"cadcat\")\n", + " .activity_id(\"WRF\")\n", + " .table_id(\"day\")\n", + " .show_variable_options()\n", + " )" ] }, { @@ -206,9 +127,11 @@ "id": "7db2357a", "metadata": {}, "source": [ - "## 2. Understanding the Degree Days Variables\n", + "## 2. Define the Goldilocks Day Derived Variable\n", "\n", - "The degree days metrics are built-in derived variables that automatically fetch and compute from temperature data:" + "We define a Goldilocks day as:\n", + "- 65°F ≤ `t2` ≤ 80°F\n", + "- 40 ≤ `rh` ≤ 50" ] }, { @@ -218,19 +141,41 @@ "metadata": {}, "outputs": [], "source": [ - "from climakitae.new_core.derived_variables import list_derived_variables\n", - "\n", - "# Get info about the degree days variables\n", - "derived_vars = list_derived_variables()\n", + "TEMP_MIN_F = 65.0\n", + "TEMP_MAX_F = 80.0\n", + "RH_MIN = 40.0\n", + "RH_MAX = 50.0\n", + "\n", + "TEMP_MIN_K = f_to_k(TEMP_MIN_F)\n", + "TEMP_MAX_K = f_to_k(TEMP_MAX_F)\n", + "\n", + "def calc_goldilocks_day_wrf(ds):\n", + " \"\"\"Add a binary Goldilocks day variable from daily t2 and rh.\"\"\"\n", + " temp_ok = (ds.t2 >= TEMP_MIN_K) & (ds.t2 <= TEMP_MAX_K)\n", + " rh_ok = (ds.rh >= RH_MIN) & (ds.rh <= RH_MAX)\n", + "\n", + " ds[\"goldilocks_day_wrf\"] = xr.where(temp_ok & rh_ok, 1, 0)\n", + " ds[\"goldilocks_day_wrf\"].attrs = {\n", + " \"units\": \"count\",\n", + " \"long_name\": \"Goldilocks Day (WRF)\",\n", + " \"comment\": (\n", + " \"Binary day indicator where 65F <= t2 <= 80F and 40 <= rh <= 50\"\n", + " ),\n", + " \"derived_from\": \"t2, rh\",\n", + " \"derived_by\": \"climakitae\",\n", + " }\n", + " return ds\n", "\n", - "degree_days_vars = ['HDD_wrf', 'CDD_wrf', 'HDD_loca', 'CDD_loca']\n", + "if \"goldilocks_day_wrf\" not in list_derived_variables():\n", + " register_user_function(\n", + " name=\"goldilocks_day_wrf\",\n", + " depends_on=[\"t2\", \"rh\"],\n", + " func=calc_goldilocks_day_wrf,\n", + " description=\"Goldilocks day from temperature and relative humidity thresholds\",\n", + " units=\"count\",\n", + " )\n", "\n", - "for var_name in degree_days_vars:\n", - " if var_name in derived_vars:\n", - " info = derived_vars[var_name]\n", - " print(f\"\\n{var_name}:\")\n", - " print(f\" Depends on: {info.depends_on}\")\n", - " print(f\" Description: {info.description}\")" + "print(\"Registered variable: goldilocks_day_wrf\")" ] }, { @@ -238,9 +183,9 @@ "id": "fff155f9", "metadata": {}, "source": [ - "## 3. Fetching Cooling Degree Days for Los Angeles\n", + "## 3. Fetch Goldilocks Days for Los Angeles County\n", "\n", - "Let's fetch CDD data for Los Angeles County to analyze cooling energy demand under future warming:" + "Now we query our custom variable with warming-level processing and spatial clipping." ] }, { @@ -250,26 +195,22 @@ "metadata": {}, "outputs": [], "source": [ - "cd = ck.ClimateData(verbosity=-2)\n", + "cd = ClimateData(verbosity=-2)\n", "\n", - "# Fetch cooling degree days for Los Angeles County\n", - "cdd_data = (\n", + "goldilocks_data = (\n", " cd\n", " .catalog(\"cadcat\")\n", - " .variable(\"CDD_wrf\") # Cooling degree days from WRF\n", + " .variable(\"goldilocks_day_wrf\")\n", " .activity_id(\"WRF\")\n", " .institution_id(\"UCLA\")\n", " .table_id(\"day\")\n", - " .grid_label(\"d03\") # 3km resolution\n", - " # .experiment_id(\"ssp370\") # High emissions scenario\n", + " .grid_label(\"d03\")\n", " .processes({\n", - " \"warming_level\": {\n", - " \"warming_levels\": [1.2, 2.0, 3.0]\n", - " },\n", + " \"warming_level\": {\"warming_levels\": [1.2, 2.0, 3.0]},\n", " \"clip\": \"Los Angeles County\",\n", " })\n", " .get()\n", - ")" + " )" ] }, { @@ -279,9 +220,14 @@ "metadata": {}, "outputs": [], "source": [ - "cdd_data = add_dummy_time_to_wl(cdd_data).drop_sel(sim=\"WRF_UCLA_EC-Earth3-Veg_ssp370_day_d03_r1i1p1f1\") \n", + "goldilocks_data = add_dummy_time_to_wl(goldilocks_data)\n", + "\n", + "# Optional: drop known problematic simulation if present\n", + "bad_sim = \"WRF_UCLA_EC-Earth3-Veg_ssp370_day_d03_r1i1p1f1\"\n", + "if \"sim\" in goldilocks_data.dims and bad_sim in goldilocks_data.sim.values:\n", + " goldilocks_data = goldilocks_data.drop_sel(sim=bad_sim)\n", "\n", - "cdd_data" + "goldilocks_data" ] }, { @@ -289,9 +235,9 @@ "id": "d245123a", "metadata": {}, "source": [ - "## 4. Visualizing Cooling Degree Days: Spatial Based\n", + "## 4. Spatial Pattern of Goldilocks Days\n", "\n", - "Let's calculate and visualize annual cooling degree days:" + "We aggregate annual Goldilocks-day counts and map the median across simulations." ] }, { @@ -301,12 +247,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Count days with cooling degree days > 0 per year\n", - "cooling_days_per_year = cdd_data[\"CDD_wrf\"].resample(time=\"Y\").sum()\n", - "cooling_days_per_year = xr.where(cooling_days_per_year > 0, cooling_days_per_year, np.nan)\n", + "# Count Goldilocks days per year\n", + "goldilocks_days_per_year = goldilocks_data[\"goldilocks_day_wrf\"].resample(time=\"Y\").sum()\n", "\n", - "# Average years for spatial plot\n", - "spt = cooling_days_per_year.mean(dim=\"time\") " + "# Average across years for spatial plotting\n", + "spt = goldilocks_days_per_year.mean(dim=\"time\")" ] }, { @@ -316,9 +261,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Compute with progress bar\n", + "# Compute median across simulations for each warming level\n", "with ProgressBar():\n", - " spt = spt.median(dim='sim').compute()" + " spt = spt.median(dim=\"sim\").compute()" ] }, { @@ -329,32 +274,34 @@ "outputs": [], "source": [ "# Create figure with 3 subplots (one per warming level)\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5), gridspec_kw={'wspace': 0.3})\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5), gridspec_kw={\"wspace\": 0.3})\n", "\n", "warming_levels = spt.warming_level.values\n", "\n", - "for idx, (ax, wl) in enumerate(zip(axes, warming_levels)):\n", - " # Select data for this warming level\n", + "for ax, wl in zip(axes, warming_levels):\n", " wl_data = spt.sel(warming_level=wl)\n", - " \n", - " # Create contourf plot\n", + "\n", " im = wl_data.plot.contourf(\n", - " ax=ax, x='lon', y='lat',\n", - " cmap='plasma', levels=100,\n", + " ax=ax,\n", + " x=\"lon\",\n", + " y=\"lat\",\n", + " cmap=\"viridis\",\n", + " levels=100,\n", " add_colorbar=False,\n", - " vmin=1,\n", - " vmax=300\n", + " vmin=0,\n", + " vmax=180,\n", " )\n", - " \n", - " ax.set_title(f'{wl}°C Warming Level', fontweight='bold')\n", - " ax.set_xlabel('Longitude')\n", - " ax.set_ylabel('Latitude')\n", "\n", - "# Add shared colorbar\n", - "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", + " ax.set_title(f\"{wl}°C Warming Level\", fontweight=\"bold\")\n", + " ax.set_xlabel(\"Longitude\")\n", + " ax.set_ylabel(\"Latitude\")\n", "\n", - "fig.suptitle('Cooling Days (65 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", - " fontweight='bold', y=1.07);" + "fig.colorbar(im, ax=axes, label=\"Annual Goldilocks Days\", pad=0.02, shrink=0.8)\n", + "fig.suptitle(\n", + " \"Goldilocks Days Distribution Across Warming Levels\\nLos Angeles County\",\n", + " fontweight=\"bold\",\n", + " y=1.07,\n", + ");" ] }, { @@ -362,7 +309,7 @@ "id": "cf08ad93-8e95-430f-9d3c-5d29255cc269", "metadata": {}, "source": [ - "The plot above shows the median number of cooling days (65 °F) per year across Los Angeles County. Note the increase in the number of cooling days as the targeted Global Warming Level increases." + "The maps show median annual Goldilocks days across Los Angeles County for each warming level. Spatial differences highlight where the temperature-humidity comfort window is most often met." ] }, { @@ -372,7 +319,7 @@ "source": [ "## 5. Temporal Trends by Warming Level\n", "\n", - "Let's visualize how cooling days evolve over time for each warming level:" + "Now we compute county-mean annual Goldilocks days and compare trajectories across warming levels." ] }, { @@ -382,9 +329,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Average over spatial dimensions for county-wide average\n", + "# Average over spatial dimensions for county-wide annual series\n", + "spatial_dims = [dim for dim in [\"y\", \"x\", \"lat\", \"lon\"] if dim in goldilocks_days_per_year.dims]\n", + "\n", "with ProgressBar():\n", - " cooling_days_per_year = cooling_days_per_year.mean(dim=[\"y\", \"x\"]).compute()" + " goldilocks_days_per_year_series = goldilocks_days_per_year.mean(dim=spatial_dims).compute()" ] }, { @@ -397,47 +346,56 @@ "# Plot time series for each warming level\n", "fig, ax = plt.subplots(figsize=(14, 7))\n", "\n", - "# Define colors for each warming level\n", - "colors = ['#2E86AB', '#A23B72', '#F18F01'] # Blue, Purple, Orange\n", - "warming_levels = cooling_days_per_year.warming_level.values\n", + "colors = [\"#2E86AB\", \"#A23B72\", \"#F18F01\"]\n", + "warming_levels = goldilocks_days_per_year_series.warming_level.values\n", "\n", - "# Plot ensemble mean for each warming level\n", "for idx, wl in enumerate(warming_levels):\n", - " wl_data = cooling_days_per_year.sel(warming_level=wl)\n", - " \n", - " # Get centered years for this warming level to show in legend\n", - " cy = cdd_data.centered_year.sel(warming_level=wl).dropna(\"sim\")\n", - " min_year = int(cy.min().values)\n", - " max_year = int(cy.max().values)\n", - " \n", - " # Use relative years (offset from dummy origin) for x-axis\n", + " wl_data = goldilocks_days_per_year_series.sel(warming_level=wl)\n", + "\n", + " # Calendar-year range where models reach this warming level\n", + " centered_year = goldilocks_data.centered_year.sel(warming_level=wl).dropna(\"sim\")\n", + " min_year = int(centered_year.min().values)\n", + " max_year = int(centered_year.max().values)\n", + "\n", " relative_years = wl_data.time.dt.year - 2050\n", - " \n", - " # Plot individual simulations with low alpha\n", - " for sim in range(wl_data.sizes['sim']):\n", + "\n", + " # Individual simulations\n", + " for sim in range(wl_data.sizes[\"sim\"]):\n", " sim_data = wl_data.isel(sim=sim)\n", - " ax.plot(relative_years, sim_data.values, \n", - " alpha=0.15, color=colors[idx], linewidth=0.8)\n", - " \n", - " # Plot ensemble mean with solid line\n", - " ensemble_mean = wl_data.mean(dim='sim')\n", - " ax.plot(relative_years, ensemble_mean.values,\n", - " color=colors[idx], linewidth=3, \n", - " label=f'{wl}°C Warming ({min_year}–{max_year})', \n", - " marker='o', markersize=4, markevery=5)\n", - "\n", - "ax.set_xlabel('Years from Centered Year', fontweight='bold')\n", - "ax.set_ylabel('Annual Cooling Days', fontweight='bold')\n", - "ax.set_title('Annual Cooling Days by Global Warming Level\\nLos Angeles County (UCLA WRF)', \n", - " fontweight='bold', pad=15)\n", - "ax.legend(loc='upper left', framealpha=0.95)\n", - "ax.grid(True, alpha=0.3, linestyle='--')\n", - "\n", - "# Add annotation\n", - "ax.text(0.98, 0.02, 'Higher warming → More cooling days', \n", - " transform=ax.transAxes,\n", - " verticalalignment='bottom', horizontalalignment='right',\n", - " bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.3))\n", + " ax.plot(relative_years, sim_data.values, alpha=0.15, color=colors[idx], linewidth=0.8)\n", + "\n", + " # Ensemble mean\n", + " ensemble_mean = wl_data.mean(dim=\"sim\")\n", + " ax.plot(\n", + " relative_years,\n", + " ensemble_mean.values,\n", + " color=colors[idx],\n", + " linewidth=3,\n", + " label=f\"{wl}°C Warming ({min_year}–{max_year})\",\n", + " marker=\"o\",\n", + " markersize=4,\n", + " markevery=5,\n", + " )\n", + "\n", + "ax.set_xlabel(\"Years from Centered Year\", fontweight=\"bold\")\n", + "ax.set_ylabel(\"Annual Goldilocks Days\", fontweight=\"bold\")\n", + "ax.set_title(\n", + " \"Annual Goldilocks Days by Global Warming Level\\nLos Angeles County (UCLA WRF)\",\n", + " fontweight=\"bold\",\n", + " pad=15,\n", + " )\n", + "ax.legend(loc=\"upper left\", framealpha=0.95)\n", + "ax.grid(True, alpha=0.3, linestyle=\"--\")\n", + "\n", + "ax.text(\n", + " 0.98,\n", + " 0.02,\n", + " \"Tracks changes in days with 65–80°F and RH 40–50%\",\n", + " transform=ax.transAxes,\n", + " verticalalignment=\"bottom\",\n", + " horizontalalignment=\"right\",\n", + " bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.3),\n", + ")\n", "\n", "plt.tight_layout()" ] @@ -447,7 +405,7 @@ "id": "086ad63a-7070-4e12-a423-fe6a2d1757de", "metadata": {}, "source": [ - "The plot above shows the annual cooling degree days (65 °F) averaged over Los Angeles County, plotted relative to each model's centered year for a given warming level. The x-axis shows years before and after the centered year (the year when a given GWL is reached), and the legend shows the range of calendar years across simulations when each GWL is reached. As the global warming level increases, so too does the number of cooling days." + "The plot shows county-mean annual Goldilocks days relative to each model’s centered year for each warming level. The legend reports the range of calendar years in which simulations reach each warming level." ] }, { @@ -457,13 +415,16 @@ "metadata": {}, "outputs": [], "source": [ - "# Summary statistics for cooling days by warming level\n", + "# Summary statistics for annual Goldilocks days by warming level\n", "for wl in warming_levels:\n", - " wl_data = cooling_days_per_year.sel(warming_level=wl).mean(dim='sim')\n", - " mean_days = wl_data.mean().values\n", + " wl_data = goldilocks_days_per_year_series.sel(warming_level=wl).mean(dim=\"sim\")\n", + " mean_days = float(wl_data.mean().values)\n", + " min_days = float(wl_data.min().values)\n", + " max_days = float(wl_data.max().values)\n", + "\n", " print(f\"\\n{wl}°C Warming Level:\")\n", - " print(f\" Average cooling days per year: {mean_days:.0f} days\")\n", - " print(f\" Range: {wl_data.min().values:.0f} - {wl_data.max().values:.0f} days\")" + " print(f\" Average Goldilocks days per year: {mean_days:.0f} days\")\n", + " print(f\" Range: {min_days:.0f} - {max_days:.0f} days\")" ] }, { @@ -471,11 +432,12 @@ "id": "7337c751", "metadata": {}, "source": [ - "## 6. Customizing CDD\n", + "## 6. Summary: Key Takeaways\n", "\n", - "There are two ways to customize the calculation of Cooling Degree Days:\n", - "1. Use the built-in methods to pass your own threshold and register it\n", - "2. Define your own Cooling Degree Days method and register it" + "1. A custom comfort-window metric can be registered once and queried like any other variable.\n", + "2. Goldilocks days are computed from `t2` and `rh` in one query pipeline.\n", + "3. Warming-level analysis provides a climate-relevant framing for temporal and spatial shifts.\n", + "4. The workflow stays lazy until explicit `.compute()` steps for plotting and summary statistics." ] }, { @@ -483,340 +445,40 @@ "id": "99060031", "metadata": {}, "source": [ - "### 6.1 Customizing CDD: Built-in Methods" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e6398c0", - "metadata": {}, - "outputs": [], - "source": [ - "from climakitae.new_core.derived_variables.registry import register_user_function\n", - "from climakitae.new_core.derived_variables.builtin.temperature import calc_cdd_wrf\n", - "\n", - "# The builtin calc_cdd_wrf accepts threshold_f as an argument\n", - "# We can wrap it with a custom threshold and register as a new variable\n", - "\n", - "def calc_cdd_wrf_75f(ds):\n", - " \"\"\"CDD with 75°F threshold - wraps builtin with custom threshold.\"\"\"\n", - " return calc_cdd_wrf(ds, threshold_f=75.0)\n", - "\n", - "register_user_function(\n", - " name=\"CDD_wrf_75f\",\n", - " depends_on=[\"t2\"], # Same dependency as CDD_wrf\n", - " func=calc_cdd_wrf_75f,\n", - " description=\"Cooling Degree Days from WRF with 75°F base\",\n", - " units=\"K\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51ef713c", - "metadata": {}, - "outputs": [], - "source": [ - "cd = ck.ClimateData(verbosity=-2)\n", + "## 7. Metric Definition\n", "\n", - "# Fetch cooling degree days with 75°F threshold\n", - "cdd_data_75f = (\n", - " cd\n", - " .catalog(\"cadcat\")\n", - " .variable(\"CDD_wrf_75f\") # Now using our custom 75°F variable\n", - " .activity_id(\"WRF\")\n", - " .institution_id(\"UCLA\")\n", - " .table_id(\"day\")\n", - " .grid_label(\"d03\")\n", - " .processes({\n", - " \"warming_level\": {\n", - " \"warming_levels\": [1.2, 2.0, 3.0]\n", - " },\n", - " \"clip\": \"Los Angeles County\",\n", - " })\n", - " .get()\n", - ")\n", + "| Condition | Threshold |\n", + "|----------|-----------|\n", + "| Temperature (`t2`) | 65°F to 80°F |\n", + "| Relative humidity (`rh`) | 40% to 50% |\n", "\n", - "cdd_data_75f = add_dummy_time_to_wl(cdd_data_75f).drop_sel(sim=\"WRF_UCLA_EC-Earth3-Veg_ssp370_day_d03_r1i1p1f1\") " + "A day is counted as a Goldilocks day when **both** conditions are true." ] }, { "cell_type": "code", "execution_count": null, - "id": "3fee2320", - "metadata": {}, - "outputs": [], - "source": [ - "# Count days with cooling degree days > 0 per year\n", - "cooling_days_per_year = cdd_data_75f[\"CDD_wrf\"].resample(time=\"Y\").sum()\n", - "cooling_days_per_year = xr.where(cooling_days_per_year > 0, cooling_days_per_year, np.nan)\n", - "\n", - "# Average years for spatial plot\n", - "spt = cooling_days_per_year.mean(dim=\"time\") " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dadfd819", - "metadata": {}, - "outputs": [], - "source": [ - "# Compute with progress bar\n", - "with ProgressBar():\n", - " spt = spt.median(dim='sim').compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aca87b14", - "metadata": {}, - "outputs": [], - "source": [ - "# Create figure with 3 subplots (one per warming level)\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5), gridspec_kw={'wspace': 0.3})\n", - "\n", - "warming_levels = spt.warming_level.values\n", - "\n", - "for idx, (ax, wl) in enumerate(zip(axes, warming_levels)):\n", - " # Select data for this warming level\n", - " wl_data = spt.sel(warming_level=wl)\n", - " \n", - " # Create contourf plot\n", - " im = wl_data.plot.contourf(\n", - " ax=ax, x='lon', y='lat',\n", - " cmap='plasma', levels=100,\n", - " add_colorbar=False,\n", - " vmin=0,\n", - " vmax=175\n", - " )\n", - " \n", - " ax.set_title(f'{wl}°C Warming Level', fontweight='bold')\n", - " ax.set_xlabel('Longitude')\n", - " ax.set_ylabel('Latitude')\n", - "\n", - "# Add shared colorbar\n", - "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", - "\n", - "fig.suptitle('Cooling Days (75 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", - " fontweight='bold', y=1.07);" - ] - }, - { - "cell_type": "markdown", - "id": "c367d24b-38f2-4ac8-b436-57fff6d331a9", - "metadata": {}, - "source": [ - "The plot above shows the median number of cooling days (75 °F) per year across Los Angeles County. Note the increase in the number of cooling days as the targeted Global Warming Level increases. " - ] - }, - { - "cell_type": "markdown", - "id": "935475dc", - "metadata": {}, - "source": [ - "### 6.2: Define your own cooling degree days function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9fe3aa8", - "metadata": {}, - "outputs": [], - "source": [ - "from climakitae.new_core.derived_variables.registry import register_user_function\n", - "from climakitae.util.utils import f_to_k\n", - "\n", - "THRESHOLD_F = 85.0\n", - "threshold_k = f_to_k(THRESHOLD_F)\n", - "\n", - "def calc_custom_cdd(ds):\n", - " \"\"\"\n", - " A custom CDD function that runs on a different variable. \n", - "\n", - " In this example we look at days where the daily minimum temperature exceeds 75°F.\n", - " \"\"\"\n", - " cdd_values = np.maximum(0, ds.t2min - threshold_k)\n", - " \n", - " # Replace zeros with NaN (days below threshold become NaN)\n", - " cdd_values = xr.where(cdd_values > 0, cdd_values, np.nan)\n", - " ds[\"CDD_min_wrf_75f\"] = cdd_values\n", - " ds[\"CDD_min_wrf_75f\"].attrs = {\n", - " \"units\": \"K\",\n", - " \"long_name\": \"Cooling Degree Days (WRF)\",\n", - " \"comment\": f\"Cooling degree days calculated from daily minimum temperature with base {threshold_k} K\",\n", - " \"derived_from\": \"t2min\",\n", - " \"derived_by\": \"climakitae\",\n", - " \"threshold\": f\"{threshold_k} K\",\n", - " }\n", - " return ds\n", - "\n", - "register_user_function(\n", - " name=\"CDD_min_wrf_75f\",\n", - " depends_on=[\"t2min\"],\n", - " func=calc_custom_cdd,\n", - " description=\"Cooling Degree Days from WRF with 75°F base using daily minimum temperature\",\n", - " units=\"K\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2983f13b", - "metadata": {}, - "outputs": [], - "source": [ - "cd = ck.ClimateData(verbosity=-2)\n", - "\n", - "# Fetch cooling degree days for Los Angeles County\n", - "cdd_data_min_75f = (\n", - " cd\n", - " .catalog(\"cadcat\")\n", - " .variable(\"CDD_min_wrf_75f\") # Cooling degree days from WRF with 75°F base\n", - " .activity_id(\"WRF\")\n", - " .institution_id(\"UCLA\")\n", - " .table_id(\"day\")\n", - " .grid_label(\"d03\") # 3km resolution\n", - " .processes({\n", - " \"warming_level\": {\n", - " \"warming_levels\": [1.2, 2.0, 3.0]\n", - " },\n", - " \"clip\": \"Los Angeles County\",\n", - " })\n", - " .get()\n", - ")\n", - "\n", - "cdd_data_min_75f = add_dummy_time_to_wl(cdd_data_min_75f).drop_sel(sim=\"WRF_UCLA_EC-Earth3-Veg_ssp370_day_d03_r1i1p1f1\") " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78a28dd0", - "metadata": {}, - "outputs": [], - "source": [ - "# Count days with cooling degree days > 0 per year\n", - "cooling_days_per_year = cdd_data_min_75f[\"CDD_min_wrf_75f\"].resample(time=\"Y\").sum()\n", - "cooling_days_per_year = xr.where(cooling_days_per_year > 0, cooling_days_per_year, np.nan)\n", - "\n", - "# Average years for spatial plot\n", - "spt = cooling_days_per_year.mean(dim=\"time\") " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39878030", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Compute with progress bar\n", - "with ProgressBar():\n", - " spt = spt.median(dim='sim').compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1c1ddb25", + "id": "1e6398c0", "metadata": {}, "outputs": [], "source": [ - "# Create figure with 3 subplots (one per warming level)\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5), gridspec_kw={'wspace': 0.3})\n", - "\n", - "warming_levels = spt.warming_level.values\n", - "\n", - "for idx, (ax, wl) in enumerate(zip(axes, warming_levels)):\n", - " # Select data for this warming level\n", - " wl_data = spt.sel(warming_level=wl)\n", - " \n", - " # Create contourf plot\n", - " im = wl_data.plot.contourf(\n", - " ax=ax, x='lon', y='lat',\n", - " cmap='plasma', levels=100,\n", - " add_colorbar=False,\n", - " vmin=0,\n", - " vmax=10\n", - " )\n", - " \n", - " ax.set_title(f'{wl}°C Warming Level', fontweight='bold')\n", - " ax.set_xlabel('Longitude')\n", - " ax.set_ylabel('Latitude')\n", - "\n", - "# Add shared colorbar\n", - "fig.colorbar(im, ax=axes, label='Annual Cooling Days', pad=0.02, shrink=0.8)\n", - "\n", - "fig.suptitle('Cooling Days (Min Temp > 85 °F) Distribution Across Warming Levels\\nLos Angeles County', \n", - " fontweight='bold', y=1.07);" - ] - }, - { - "cell_type": "markdown", - "id": "9350277e", - "metadata": {}, - "source": [ - "## 7. Summary: Key Takeaways" - ] - }, - { - "cell_type": "markdown", - "id": "f8c1cc86", - "metadata": {}, - "source": [ - "**What We've Learned:**\n", - "\n", - "1. **Derived variables simplify analysis** - Single `.get()` call automatically fetches and computes HDD/CDD from temperature data\n", - "\n", - "2. **Warming levels provide climate-relevant framing** - Analyzing by 1.2°C, 2.0°C, and 3.0°C warming shows progressive impacts\n", - "\n", - "3. **Los Angeles shows clear cooling demand increase** - CDD rises significantly with warming while HDD decreases\n", - "\n", - "4. **Spatial patterns matter** - Inland areas experience more extreme cooling demands than coastal regions\n", - "\n", - "5. **Energy costs will increase** - Higher warming levels translate directly to higher HVAC energy costs\n", + "# Confirm the registered custom variable metadata\n", + "derived_vars = list_derived_variables()\n", + "info = derived_vars.get(\"goldilocks_day_wrf\")\n", "\n", - "6. **Customizing Functions** - Metrics can be customized and added to the catalog (locally, this does not change the catalog other users see). " - ] - }, - { - "cell_type": "markdown", - "id": "8e93d8ff", - "metadata": {}, - "source": [ - "## 8. Available Degree Days Variables" - ] - }, - { - "cell_type": "markdown", - "id": "9f9e064a", - "metadata": {}, - "source": [ - "| Variable | Data Source | Input Variables | Description |\n", - "|----------|-------------|-----------------|-------------|\n", - "| `HDD_wrf` | WRF | t2 | Heating degree days (base 65°F) |\n", - "| `CDD_wrf` | WRF | t2 | Cooling degree days (base 65°F) |\n", - "| `HDD_loca` | LOCA2 | tasmax, tasmin | Heating degree days (base 65°F) |\n", - "| `CDD_loca` | LOCA2 | tasmax, tasmin | Cooling degree days (base 65°F) |\n", - "\n", - "All variables automatically:\n", - "- Fetch required temperature data\n", - "- Calculate daily average: (max + min) / 2\n", - "- Apply 65°F (291.48K) threshold\n", - "- Return lazy-evaluated xarray datasets" + "if info is not None:\n", + " print(\"goldilocks_day_wrf\")\n", + " print(f\" Depends on: {info.depends_on}\")\n", + " print(f\" Description: {info.description}\")\n", + " print(f\" Units: {info.units}\")\n", + "else:\n", + " print(\"goldilocks_day_wrf is not registered in this session.\")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (climakitae)", + "display_name": "climakitae", "language": "python", "name": "python3" }, @@ -830,7 +492,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.12.11" } }, "nbformat": 4, From a024505ede7af573b914a6456210155b755bfafc Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Mon, 2 Mar 2026 14:39:43 -0600 Subject: [PATCH 27/53] reworking with correct masking --- analysis/derived_variables_demo.ipynb | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index b7ed1a70..a6bee1d8 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -154,12 +154,16 @@ " temp_ok = (ds.t2 >= TEMP_MIN_K) & (ds.t2 <= TEMP_MAX_K)\n", " rh_ok = (ds.rh >= RH_MIN) & (ds.rh <= RH_MAX)\n", "\n", - " ds[\"goldilocks_day_wrf\"] = xr.where(temp_ok & rh_ok, 1, 0)\n", + " # Preserve missing-data regions as NaN instead of forcing 0\n", + " valid = xr.ufuncs.isfinite(ds.t2) & xr.ufuncs.isfinite(ds.rh)\n", + " goldilocks = xr.where(temp_ok & rh_ok, 1.0, 0.0)\n", + " ds[\"goldilocks_day_wrf\"] = xr.where(valid, goldilocks, float(\"nan\"))\n", + "\n", " ds[\"goldilocks_day_wrf\"].attrs = {\n", " \"units\": \"count\",\n", " \"long_name\": \"Goldilocks Day (WRF)\",\n", " \"comment\": (\n", - " \"Binary day indicator where 65F <= t2 <= 80F and 40 <= rh <= 50\"\n", + " \"Binary day indicator where 65F <= t2 <= 80F and 40 <= rh <= 50; NaN where source data are missing\"\n", " ),\n", " \"derived_from\": \"t2, rh\",\n", " \"derived_by\": \"climakitae\",\n", @@ -250,6 +254,13 @@ "# Count Goldilocks days per year\n", "goldilocks_days_per_year = goldilocks_data[\"goldilocks_day_wrf\"].resample(time=\"Y\").sum()\n", "\n", + "# Restore map-style NaN handling: show only cells with at least one Goldilocks day\n", + "goldilocks_days_per_year = xr.where(\n", + " goldilocks_days_per_year > 0,\n", + " goldilocks_days_per_year,\n", + " float(\"nan\"),\n", + " )\n", + "\n", "# Average across years for spatial plotting\n", "spt = goldilocks_days_per_year.mean(dim=\"time\")" ] @@ -492,7 +503,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.11" + "version": "3.12.7" } }, "nbformat": 4, From 623ed3d706330bb6bce53573b9c1bee3dc47dc36 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Mon, 2 Mar 2026 14:40:17 -0600 Subject: [PATCH 28/53] reworking with correct masking --- analysis/derived_variables_demo.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index a6bee1d8..ec63893a 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -272,9 +272,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Compute median across simulations for each warming level\n", + "# Compute median across simulations for each warming level (ignore NaNs at boundary)\n", "with ProgressBar():\n", - " spt = spt.median(dim=\"sim\").compute()" + " spt = spt.median(dim=\"sim\", skipna=True).compute()" ] }, { @@ -300,7 +300,7 @@ " levels=100,\n", " add_colorbar=False,\n", " vmin=0,\n", - " vmax=180,\n", + " vmax=35,\n", " )\n", "\n", " ax.set_title(f\"{wl}°C Warming Level\", fontweight=\"bold\")\n", From 8cad38b8572125866f5ebdbc8aac8d1e2b171d99 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Mon, 2 Mar 2026 15:24:42 -0600 Subject: [PATCH 29/53] reworking with violin plot --- analysis/derived_variables_demo.ipynb | 67 ++++++++++++++++----------- 1 file changed, 39 insertions(+), 28 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index ec63893a..1020b4fa 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -300,7 +300,7 @@ " levels=100,\n", " add_colorbar=False,\n", " vmin=0,\n", - " vmax=35,\n", + " vmax=40,\n", " )\n", "\n", " ax.set_title(f\"{wl}°C Warming Level\", fontweight=\"bold\")\n", @@ -354,13 +354,16 @@ "metadata": {}, "outputs": [], "source": [ - "# Plot time series for each warming level\n", + "# Violin plot of annual Goldilocks-day distributions by warming level\n", "fig, ax = plt.subplots(figsize=(14, 7))\n", "\n", "colors = [\"#2E86AB\", \"#A23B72\", \"#F18F01\"]\n", "warming_levels = goldilocks_days_per_year_series.warming_level.values\n", "\n", - "for idx, wl in enumerate(warming_levels):\n", + "violin_data = []\n", + "x_labels = []\n", + "\n", + "for wl in warming_levels:\n", " wl_data = goldilocks_days_per_year_series.sel(warming_level=wl)\n", "\n", " # Calendar-year range where models reach this warming level\n", @@ -368,40 +371,48 @@ " min_year = int(centered_year.min().values)\n", " max_year = int(centered_year.max().values)\n", "\n", - " relative_years = wl_data.time.dt.year - 2050\n", - "\n", - " # Individual simulations\n", - " for sim in range(wl_data.sizes[\"sim\"]):\n", - " sim_data = wl_data.isel(sim=sim)\n", - " ax.plot(relative_years, sim_data.values, alpha=0.15, color=colors[idx], linewidth=0.8)\n", - "\n", - " # Ensemble mean\n", - " ensemble_mean = wl_data.mean(dim=\"sim\")\n", - " ax.plot(\n", - " relative_years,\n", - " ensemble_mean.values,\n", - " color=colors[idx],\n", - " linewidth=3,\n", - " label=f\"{wl}°C Warming ({min_year}–{max_year})\",\n", - " marker=\"o\",\n", - " markersize=4,\n", - " markevery=5,\n", - " )\n", + " # Flatten across time and simulations to show distribution\n", + " values = pd.Series(wl_data.values.ravel()).dropna().values\n", + " if values.size == 0:\n", + " continue\n", + "\n", + " violin_data.append(values)\n", + " x_labels.append(f\"{wl}°C ({min_year}–{max_year})\")\n", + "\n", + "positions = list(range(1, len(violin_data) + 1))\n", + "parts = ax.violinplot(\n", + " violin_data,\n", + " positions=positions,\n", + " widths=0.8,\n", + " showmeans=False,\n", + " showmedians=True,\n", + " showextrema=False,\n", + " )\n", + "\n", + "for idx, body in enumerate(parts[\"bodies\"]):\n", + " body.set_facecolor(colors[idx % len(colors)])\n", + " body.set_edgecolor(colors[idx % len(colors)])\n", + " body.set_alpha(0.65)\n", + "\n", + "if \"cmedians\" in parts:\n", + " parts[\"cmedians\"].set_color(\"black\")\n", + " parts[\"cmedians\"].set_linewidth(2)\n", "\n", - "ax.set_xlabel(\"Years from Centered Year\", fontweight=\"bold\")\n", + "ax.set_xticks(positions)\n", + "ax.set_xticklabels(x_labels, rotation=10, ha=\"right\")\n", + "ax.set_xlabel(\"Global Warming Level (Years Spanned)\", fontweight=\"bold\")\n", "ax.set_ylabel(\"Annual Goldilocks Days\", fontweight=\"bold\")\n", "ax.set_title(\n", - " \"Annual Goldilocks Days by Global Warming Level\\nLos Angeles County (UCLA WRF)\",\n", + " \"Distribution of Annual Goldilocks Days by Global Warming Level\\nLos Angeles County (UCLA WRF)\",\n", " fontweight=\"bold\",\n", " pad=15,\n", " )\n", - "ax.legend(loc=\"upper left\", framealpha=0.95)\n", - "ax.grid(True, alpha=0.3, linestyle=\"--\")\n", + "ax.grid(True, axis=\"y\", alpha=0.3, linestyle=\"--\")\n", "\n", "ax.text(\n", " 0.98,\n", " 0.02,\n", - " \"Tracks changes in days with 65–80°F and RH 40–50%\",\n", + " \"Violin width shows distribution density\",\n", " transform=ax.transAxes,\n", " verticalalignment=\"bottom\",\n", " horizontalalignment=\"right\",\n", @@ -416,7 +427,7 @@ "id": "086ad63a-7070-4e12-a423-fe6a2d1757de", "metadata": {}, "source": [ - "The plot shows county-mean annual Goldilocks days relative to each model’s centered year for each warming level. The legend reports the range of calendar years in which simulations reach each warming level." + "The violin plot compares the full distribution of county-mean annual Goldilocks days for each warming level. Each x-axis label includes the warming level and the spanned calendar years across simulations, while the violin width indicates where values are most concentrated." ] }, { From ecd158d81494c9e061157e594964ebee8b6e1948 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Mon, 2 Mar 2026 16:30:38 -0600 Subject: [PATCH 30/53] reworking with violin plot --- analysis/derived_variables_demo.ipynb | 117 ++++++++++++++++---------- 1 file changed, 74 insertions(+), 43 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 1020b4fa..51fe597b 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -328,9 +328,9 @@ "id": "78da135d", "metadata": {}, "source": [ - "## 5. Temporal Trends by Warming Level\n", + "## 5. GWL Comparison (No Time-Series View)\n", "\n", - "Now we compute county-mean annual Goldilocks days and compare trajectories across warming levels." + "Instead of a time-series plot, we compare distributions of **annual county-mean Goldilocks days** directly across warming levels." ] }, { @@ -338,13 +338,24 @@ "execution_count": null, "id": "df7c9f7b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[######################## ] | 60% Completed | 574.25 s" + ] + } + ], "source": [ - "# Average over spatial dimensions for county-wide annual series\n", + "# Build non-time-series comparison data by warming level\n", "spatial_dims = [dim for dim in [\"y\", \"x\", \"lat\", \"lon\"] if dim in goldilocks_days_per_year.dims]\n", "\n", "with ProgressBar():\n", - " goldilocks_days_per_year_series = goldilocks_days_per_year.mean(dim=spatial_dims).compute()" + " county_annual_goldilocks = goldilocks_days_per_year.mean(dim=spatial_dims).compute()\n", + "\n", + "# One value per simulation and warming level: mean annual Goldilocks days\n", + "goldilocks_by_sim = county_annual_goldilocks.mean(dim=\"time\", skipna=True)" ] }, { @@ -354,33 +365,37 @@ "metadata": {}, "outputs": [], "source": [ - "# Violin plot of annual Goldilocks-day distributions by warming level\n", - "fig, ax = plt.subplots(figsize=(14, 7))\n", + "# Compare Goldilocks days across GWLs without using a time axis\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 7), gridspec_kw={\"wspace\": 0.28})\n", "\n", "colors = [\"#2E86AB\", \"#A23B72\", \"#F18F01\"]\n", - "warming_levels = goldilocks_days_per_year_series.warming_level.values\n", + "warming_levels = goldilocks_by_sim.warming_level.values\n", "\n", "violin_data = []\n", "x_labels = []\n", + "mean_days = []\n", + "q10_days = []\n", + "q90_days = []\n", "\n", "for wl in warming_levels:\n", - " wl_data = goldilocks_days_per_year_series.sel(warming_level=wl)\n", + " sim_values = pd.Series(goldilocks_by_sim.sel(warming_level=wl).values).dropna().values\n", + " if sim_values.size == 0:\n", + " continue\n", "\n", - " # Calendar-year range where models reach this warming level\n", " centered_year = goldilocks_data.centered_year.sel(warming_level=wl).dropna(\"sim\")\n", " min_year = int(centered_year.min().values)\n", " max_year = int(centered_year.max().values)\n", "\n", - " # Flatten across time and simulations to show distribution\n", - " values = pd.Series(wl_data.values.ravel()).dropna().values\n", - " if values.size == 0:\n", - " continue\n", - "\n", - " violin_data.append(values)\n", + " violin_data.append(sim_values)\n", " x_labels.append(f\"{wl}°C ({min_year}–{max_year})\")\n", + " mean_days.append(float(pd.Series(sim_values).mean()))\n", + " q10_days.append(float(pd.Series(sim_values).quantile(0.10)))\n", + " q90_days.append(float(pd.Series(sim_values).quantile(0.90)))\n", "\n", "positions = list(range(1, len(violin_data) + 1))\n", - "parts = ax.violinplot(\n", + "\n", + "# Left: distribution across simulations\n", + "parts = axes[0].violinplot(\n", " violin_data,\n", " positions=positions,\n", " widths=0.8,\n", @@ -398,27 +413,40 @@ " parts[\"cmedians\"].set_color(\"black\")\n", " parts[\"cmedians\"].set_linewidth(2)\n", "\n", - "ax.set_xticks(positions)\n", - "ax.set_xticklabels(x_labels, rotation=10, ha=\"right\")\n", - "ax.set_xlabel(\"Global Warming Level (Years Spanned)\", fontweight=\"bold\")\n", - "ax.set_ylabel(\"Annual Goldilocks Days\", fontweight=\"bold\")\n", - "ax.set_title(\n", - " \"Distribution of Annual Goldilocks Days by Global Warming Level\\nLos Angeles County (UCLA WRF)\",\n", - " fontweight=\"bold\",\n", - " pad=15,\n", - " )\n", - "ax.grid(True, axis=\"y\", alpha=0.3, linestyle=\"--\")\n", - "\n", - "ax.text(\n", - " 0.98,\n", - " 0.02,\n", - " \"Violin width shows distribution density\",\n", - " transform=ax.transAxes,\n", - " verticalalignment=\"bottom\",\n", - " horizontalalignment=\"right\",\n", - " bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.3),\n", + "axes[0].set_xticks(positions)\n", + "axes[0].set_xticklabels(x_labels, rotation=10, ha=\"right\")\n", + "axes[0].set_xlabel(\"Global Warming Level (Years Spanned)\", fontweight=\"bold\")\n", + "axes[0].set_ylabel(\"Annual Goldilocks Days\", fontweight=\"bold\")\n", + "axes[0].set_title(\"Distribution Across Simulations\", fontweight=\"bold\")\n", + "axes[0].grid(True, axis=\"y\", alpha=0.3, linestyle=\"--\")\n", + "\n", + "# Right: central tendency with uncertainty band\n", + "axes[1].plot(positions, mean_days, color=\"black\", marker=\"o\", linewidth=2.5, label=\"Simulation mean\")\n", + "axes[1].fill_between(\n", + " positions,\n", + " q10_days,\n", + " q90_days,\n", + " color=\"#888888\",\n", + " alpha=0.25,\n", + " label=\"10th–90th percentile\",\n", ")\n", "\n", + "for idx, x in enumerate(positions):\n", + " axes[1].scatter([x], [mean_days[idx]], color=colors[idx % len(colors)], s=70, zorder=3)\n", + "\n", + "axes[1].set_xticks(positions)\n", + "axes[1].set_xticklabels(x_labels, rotation=10, ha=\"right\")\n", + "axes[1].set_xlabel(\"Global Warming Level (Years Spanned)\", fontweight=\"bold\")\n", + "axes[1].set_ylabel(\"Annual Goldilocks Days\", fontweight=\"bold\")\n", + "axes[1].set_title(\"Mean Change Across GWLs\", fontweight=\"bold\")\n", + "axes[1].grid(True, axis=\"y\", alpha=0.3, linestyle=\"--\")\n", + "axes[1].legend(loc=\"upper right\", framealpha=0.95)\n", + "\n", + "fig.suptitle(\n", + " \"Goldilocks Days by Global Warming Level (No Time-Series Axis)\\nLos Angeles County (UCLA WRF)\",\n", + " fontweight=\"bold\",\n", + " y=1.02,\n", + ")\n", "plt.tight_layout()" ] }, @@ -427,7 +455,7 @@ "id": "086ad63a-7070-4e12-a423-fe6a2d1757de", "metadata": {}, "source": [ - "The violin plot compares the full distribution of county-mean annual Goldilocks days for each warming level. Each x-axis label includes the warming level and the spanned calendar years across simulations, while the violin width indicates where values are most concentrated." + "The left panel shows the distribution of simulation-level mean annual Goldilocks days at each warming level, while the right panel emphasizes the average change with a 10th–90th percentile band. This avoids a time axis and keeps the interpretation centered on differences between GWL states." ] }, { @@ -439,14 +467,17 @@ "source": [ "# Summary statistics for annual Goldilocks days by warming level\n", "for wl in warming_levels:\n", - " wl_data = goldilocks_days_per_year_series.sel(warming_level=wl).mean(dim=\"sim\")\n", - " mean_days = float(wl_data.mean().values)\n", - " min_days = float(wl_data.min().values)\n", - " max_days = float(wl_data.max().values)\n", + " sim_values = pd.Series(goldilocks_by_sim.sel(warming_level=wl).values).dropna()\n", + " mean_days = float(sim_values.mean())\n", + " min_days = float(sim_values.min())\n", + " max_days = float(sim_values.max())\n", + " p10_days = float(sim_values.quantile(0.10))\n", + " p90_days = float(sim_values.quantile(0.90))\n", "\n", " print(f\"\\n{wl}°C Warming Level:\")\n", - " print(f\" Average Goldilocks days per year: {mean_days:.0f} days\")\n", - " print(f\" Range: {min_days:.0f} - {max_days:.0f} days\")" + " print(f\" Mean across simulations: {mean_days:.1f} days/year\")\n", + " print(f\" Range: {min_days:.1f} - {max_days:.1f} days/year\")\n", + " print(f\" 10th-90th percentile: {p10_days:.1f} - {p90_days:.1f} days/year\")" ] }, { From 90de7833aa93c8e8e97ffff6551c24483363f154 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Tue, 3 Mar 2026 08:42:57 -0600 Subject: [PATCH 31/53] adding some different plots --- analysis/derived_variables_demo.ipynb | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 51fe597b..9e620725 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -338,15 +338,7 @@ "execution_count": null, "id": "df7c9f7b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[######################## ] | 60% Completed | 574.25 s" - ] - } - ], + "outputs": [], "source": [ "# Build non-time-series comparison data by warming level\n", "spatial_dims = [dim for dim in [\"y\", \"x\", \"lat\", \"lon\"] if dim in goldilocks_days_per_year.dims]\n", From 0df41f12fcec85d442c4d807070554cdd35cfb59 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Tue, 3 Mar 2026 08:58:06 -0600 Subject: [PATCH 32/53] adding some different plots --- analysis/derived_variables_demo.ipynb | 227 +++++++++++++++++--------- 1 file changed, 148 insertions(+), 79 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 9e620725..1507d1bf 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -328,9 +328,12 @@ "id": "78da135d", "metadata": {}, "source": [ - "## 5. GWL Comparison (No Time-Series View)\n", + "## 5. GWL Comparison\n", "\n", - "Instead of a time-series plot, we compare distributions of **annual county-mean Goldilocks days** directly across warming levels." + "This section compares warming levels directly using three diagnostics:\n", + "1. Violin plot of county-mean daily `t2` by GWL\n", + "2. Violin plot of county-mean daily `rh` by GWL\n", + "3. 2D histogram (`x = rh`, `y = t2`) for each GWL with Goldilocks thresholds overlaid" ] }, { @@ -340,14 +343,59 @@ "metadata": {}, "outputs": [], "source": [ - "# Build non-time-series comparison data by warming level\n", - "spatial_dims = [dim for dim in [\"y\", \"x\", \"lat\", \"lon\"] if dim in goldilocks_days_per_year.dims]\n", + "# Build non-time-series driver datasets by warming level (county mean daily)\n", + "spatial_dims = [dim for dim in [\"y\", \"x\", \"lat\", \"lon\"] if dim in goldilocks_data.dims]\n", + "\n", + "cd_driver = ClimateData(verbosity=-2)\n", + "\n", + "def fetch_driver(variable_name):\n", + " return (\n", + " cd_driver\n", + " .catalog(\"cadcat\")\n", + " .variable(variable_name)\n", + " .activity_id(\"WRF\")\n", + " .institution_id(\"UCLA\")\n", + " .table_id(\"day\")\n", + " .grid_label(\"d03\")\n", + " .processes({\n", + " \"warming_level\": {\"warming_levels\": [1.2, 2.0, 3.0]},\n", + " \"clip\": \"Los Angeles County\",\n", + " })\n", + " .get()\n", + " )\n", + "\n", + "t2_data = add_dummy_time_to_wl(fetch_driver(\"t2\"))\n", + "rh_data = add_dummy_time_to_wl(fetch_driver(\"rh\"))\n", + "\n", + "# Drop incomplete simulation if present (dot or underscore naming)\n", + "incomplete_sim_dot = \"WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1\"\n", + "target_norm = incomplete_sim_dot.replace(\".\", \"_\")\n", + "\n", + "for dataset_name, ds in [(\"t2\", t2_data), (\"rh\", rh_data)]:\n", + " if \"sim\" in ds.dims:\n", + " sims_to_drop = [\n", + " str(sim)\n", + " for sim in ds.sim.values\n", + " if str(sim) == incomplete_sim_dot or str(sim).replace(\".\", \"_\") == target_norm\n", + " ]\n", + " if sims_to_drop:\n", + " if dataset_name == \"t2\":\n", + " t2_data = ds.drop_sel(sim=sims_to_drop)\n", + " else:\n", + " rh_data = ds.drop_sel(sim=sims_to_drop)\n", "\n", "with ProgressBar():\n", - " county_annual_goldilocks = goldilocks_days_per_year.mean(dim=spatial_dims).compute()\n", + " t2_county_daily_f = ((t2_data[\"t2\"].mean(dim=spatial_dims) - 273.15) * 9 / 5 + 32).compute()\n", + " rh_county_daily = rh_data[\"rh\"].mean(dim=spatial_dims).compute()\n", + "\n", + "warming_levels = t2_county_daily_f.warming_level.values\n", + "gwl_labels = []\n", "\n", - "# One value per simulation and warming level: mean annual Goldilocks days\n", - "goldilocks_by_sim = county_annual_goldilocks.mean(dim=\"time\", skipna=True)" + "for wl in warming_levels:\n", + " centered_year = goldilocks_data.centered_year.sel(warming_level=wl).dropna(\"sim\")\n", + " min_year = int(centered_year.min().values)\n", + " max_year = int(centered_year.max().values)\n", + " gwl_labels.append(f\"{wl}°C ({min_year}–{max_year})\")" ] }, { @@ -357,85 +405,73 @@ "metadata": {}, "outputs": [], "source": [ - "# Compare Goldilocks days across GWLs without using a time axis\n", - "fig, axes = plt.subplots(1, 2, figsize=(16, 7), gridspec_kw={\"wspace\": 0.28})\n", + "# Violin plots: t2 and rh by GWL\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 7), gridspec_kw={\"wspace\": 0.25})\n", "\n", "colors = [\"#2E86AB\", \"#A23B72\", \"#F18F01\"]\n", - "warming_levels = goldilocks_by_sim.warming_level.values\n", + "positions = list(range(1, len(warming_levels) + 1))\n", "\n", - "violin_data = []\n", - "x_labels = []\n", - "mean_days = []\n", - "q10_days = []\n", - "q90_days = []\n", + "temp_violin = []\n", + "rh_violin = []\n", "\n", "for wl in warming_levels:\n", - " sim_values = pd.Series(goldilocks_by_sim.sel(warming_level=wl).values).dropna().values\n", - " if sim_values.size == 0:\n", - " continue\n", - "\n", - " centered_year = goldilocks_data.centered_year.sel(warming_level=wl).dropna(\"sim\")\n", - " min_year = int(centered_year.min().values)\n", - " max_year = int(centered_year.max().values)\n", - "\n", - " violin_data.append(sim_values)\n", - " x_labels.append(f\"{wl}°C ({min_year}–{max_year})\")\n", - " mean_days.append(float(pd.Series(sim_values).mean()))\n", - " q10_days.append(float(pd.Series(sim_values).quantile(0.10)))\n", - " q90_days.append(float(pd.Series(sim_values).quantile(0.90)))\n", - "\n", - "positions = list(range(1, len(violin_data) + 1))\n", - "\n", - "# Left: distribution across simulations\n", - "parts = axes[0].violinplot(\n", - " violin_data,\n", + " t_vals = pd.Series(t2_county_daily_f.sel(warming_level=wl).values.ravel()).dropna().values\n", + " r_vals = pd.Series(rh_county_daily.sel(warming_level=wl).values.ravel()).dropna().values\n", + " temp_violin.append(t_vals)\n", + " rh_violin.append(r_vals)\n", + "\n", + "# t2 violin (y=t2, x=GWL)\n", + "parts_t = axes[0].violinplot(\n", + " temp_violin,\n", " positions=positions,\n", " widths=0.8,\n", " showmeans=False,\n", " showmedians=True,\n", " showextrema=False,\n", " )\n", - "\n", - "for idx, body in enumerate(parts[\"bodies\"]):\n", + "for idx, body in enumerate(parts_t[\"bodies\"]):\n", " body.set_facecolor(colors[idx % len(colors)])\n", " body.set_edgecolor(colors[idx % len(colors)])\n", " body.set_alpha(0.65)\n", - "\n", - "if \"cmedians\" in parts:\n", - " parts[\"cmedians\"].set_color(\"black\")\n", - " parts[\"cmedians\"].set_linewidth(2)\n", - "\n", + "if \"cmedians\" in parts_t:\n", + " parts_t[\"cmedians\"].set_color(\"black\")\n", + " parts_t[\"cmedians\"].set_linewidth(2)\n", + "axes[0].axhline(65, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", + "axes[0].axhline(80, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", "axes[0].set_xticks(positions)\n", - "axes[0].set_xticklabels(x_labels, rotation=10, ha=\"right\")\n", - "axes[0].set_xlabel(\"Global Warming Level (Years Spanned)\", fontweight=\"bold\")\n", - "axes[0].set_ylabel(\"Annual Goldilocks Days\", fontweight=\"bold\")\n", - "axes[0].set_title(\"Distribution Across Simulations\", fontweight=\"bold\")\n", + "axes[0].set_xticklabels(gwl_labels, rotation=10, ha=\"right\")\n", + "axes[0].set_xlabel(\"GWL (Years Spanned)\", fontweight=\"bold\")\n", + "axes[0].set_ylabel(\"t2 (°F)\", fontweight=\"bold\")\n", + "axes[0].set_title(\"Violin Plot: t2 by GWL\", fontweight=\"bold\")\n", "axes[0].grid(True, axis=\"y\", alpha=0.3, linestyle=\"--\")\n", "\n", - "# Right: central tendency with uncertainty band\n", - "axes[1].plot(positions, mean_days, color=\"black\", marker=\"o\", linewidth=2.5, label=\"Simulation mean\")\n", - "axes[1].fill_between(\n", - " positions,\n", - " q10_days,\n", - " q90_days,\n", - " color=\"#888888\",\n", - " alpha=0.25,\n", - " label=\"10th–90th percentile\",\n", - ")\n", - "\n", - "for idx, x in enumerate(positions):\n", - " axes[1].scatter([x], [mean_days[idx]], color=colors[idx % len(colors)], s=70, zorder=3)\n", - "\n", + "# rh violin (y=rh, x=GWL)\n", + "parts_r = axes[1].violinplot(\n", + " rh_violin,\n", + " positions=positions,\n", + " widths=0.8,\n", + " showmeans=False,\n", + " showmedians=True,\n", + " showextrema=False,\n", + " )\n", + "for idx, body in enumerate(parts_r[\"bodies\"]):\n", + " body.set_facecolor(colors[idx % len(colors)])\n", + " body.set_edgecolor(colors[idx % len(colors)])\n", + " body.set_alpha(0.65)\n", + "if \"cmedians\" in parts_r:\n", + " parts_r[\"cmedians\"].set_color(\"black\")\n", + " parts_r[\"cmedians\"].set_linewidth(2)\n", + "axes[1].axhline(40, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", + "axes[1].axhline(50, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", "axes[1].set_xticks(positions)\n", - "axes[1].set_xticklabels(x_labels, rotation=10, ha=\"right\")\n", - "axes[1].set_xlabel(\"Global Warming Level (Years Spanned)\", fontweight=\"bold\")\n", - "axes[1].set_ylabel(\"Annual Goldilocks Days\", fontweight=\"bold\")\n", - "axes[1].set_title(\"Mean Change Across GWLs\", fontweight=\"bold\")\n", + "axes[1].set_xticklabels(gwl_labels, rotation=10, ha=\"right\")\n", + "axes[1].set_xlabel(\"GWL (Years Spanned)\", fontweight=\"bold\")\n", + "axes[1].set_ylabel(\"rh (%)\", fontweight=\"bold\")\n", + "axes[1].set_title(\"Violin Plot: rh by GWL\", fontweight=\"bold\")\n", "axes[1].grid(True, axis=\"y\", alpha=0.3, linestyle=\"--\")\n", - "axes[1].legend(loc=\"upper right\", framealpha=0.95)\n", "\n", "fig.suptitle(\n", - " \"Goldilocks Days by Global Warming Level (No Time-Series Axis)\\nLos Angeles County (UCLA WRF)\",\n", + " \"Driver Distributions by Global Warming Level\\nLos Angeles County (UCLA WRF)\",\n", " fontweight=\"bold\",\n", " y=1.02,\n", ")\n", @@ -447,7 +483,7 @@ "id": "086ad63a-7070-4e12-a423-fe6a2d1757de", "metadata": {}, "source": [ - "The left panel shows the distribution of simulation-level mean annual Goldilocks days at each warming level, while the right panel emphasizes the average change with a 10th–90th percentile band. This avoids a time axis and keeps the interpretation centered on differences between GWL states." + "The violin plots show how `t2` and `rh` distributions shift across warming levels, and the 2D histograms show their joint density relative to the Goldilocks thresholds (red dashed lines). The in-zone percentage in each panel quantifies how much density remains inside the target window." ] }, { @@ -457,19 +493,52 @@ "metadata": {}, "outputs": [], "source": [ - "# Summary statistics for annual Goldilocks days by warming level\n", - "for wl in warming_levels:\n", - " sim_values = pd.Series(goldilocks_by_sim.sel(warming_level=wl).values).dropna()\n", - " mean_days = float(sim_values.mean())\n", - " min_days = float(sim_values.min())\n", - " max_days = float(sim_values.max())\n", - " p10_days = float(sim_values.quantile(0.10))\n", - " p90_days = float(sim_values.quantile(0.90))\n", - "\n", - " print(f\"\\n{wl}°C Warming Level:\")\n", - " print(f\" Mean across simulations: {mean_days:.1f} days/year\")\n", - " print(f\" Range: {min_days:.1f} - {max_days:.1f} days/year\")\n", - " print(f\" 10th-90th percentile: {p10_days:.1f} - {p90_days:.1f} days/year\")" + "# 2D histogram panels (3 columns, 1 row): x=rh, y=t2\n", + "fig, axes = plt.subplots(1, len(warming_levels), figsize=(18, 5), sharex=True, sharey=True)\n", + "\n", + "if len(warming_levels) == 1:\n", + " axes = [axes]\n", + "\n", + "mesh = None\n", + "\n", + "for ax, wl, label in zip(axes, warming_levels, gwl_labels):\n", + " t_da = t2_county_daily_f.sel(warming_level=wl)\n", + " rh_da = rh_county_daily.sel(warming_level=wl)\n", + " valid = xr.ufuncs.isfinite(t_da) & xr.ufuncs.isfinite(rh_da)\n", + "\n", + " t_vals = t_da.where(valid).values.ravel()\n", + " rh_vals = rh_da.where(valid).values.ravel()\n", + " paired_valid = pd.notna(t_vals) & pd.notna(rh_vals)\n", + " t_vals = t_vals[paired_valid]\n", + " rh_vals = rh_vals[paired_valid]\n", + "\n", + " hist = ax.hist2d(rh_vals, t_vals, bins=50, cmap=\"magma\")\n", + " mesh = hist[3]\n", + "\n", + " # Red threshold lines\n", + " ax.axvline(40, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", + " ax.axvline(50, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", + " ax.axhline(65, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", + " ax.axhline(80, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", + "\n", + " in_zone = ((rh_vals >= 40) & (rh_vals <= 50) & (t_vals >= 65) & (t_vals <= 80))\n", + " zone_pct = float(in_zone.mean() * 100) if len(t_vals) > 0 else float(\"nan\")\n", + " ax.set_title(f\"{label}\\nIn-zone: {zone_pct:.1f}%\", fontweight=\"bold\")\n", + " ax.set_xlabel(\"rh (%)\", fontweight=\"bold\")\n", + " ax.grid(True, alpha=0.2, linestyle=\"--\")\n", + "\n", + "axes[0].set_ylabel(\"t2 (°F)\", fontweight=\"bold\")\n", + "\n", + "if mesh is not None:\n", + " cbar = fig.colorbar(mesh, ax=axes, shrink=0.88, pad=0.02)\n", + " cbar.set_label(\"Count per bin\")\n", + "\n", + "fig.suptitle(\n", + " \"2D Histogram by GWL: rh vs t2 with Goldilocks Thresholds\",\n", + " fontweight=\"bold\",\n", + " y=1.05,\n", + ")\n", + "plt.tight_layout()" ] }, { From edfa8941b267f7de8bc7879d61e6a0b5ac3c5994 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Tue, 3 Mar 2026 09:08:07 -0600 Subject: [PATCH 33/53] removing extra cells --- analysis/derived_variables_demo.ipynb | 35 --------------------------- 1 file changed, 35 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 1507d1bf..0479d7cc 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -553,41 +553,6 @@ "3. Warming-level analysis provides a climate-relevant framing for temporal and spatial shifts.\n", "4. The workflow stays lazy until explicit `.compute()` steps for plotting and summary statistics." ] - }, - { - "cell_type": "markdown", - "id": "99060031", - "metadata": {}, - "source": [ - "## 7. Metric Definition\n", - "\n", - "| Condition | Threshold |\n", - "|----------|-----------|\n", - "| Temperature (`t2`) | 65°F to 80°F |\n", - "| Relative humidity (`rh`) | 40% to 50% |\n", - "\n", - "A day is counted as a Goldilocks day when **both** conditions are true." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e6398c0", - "metadata": {}, - "outputs": [], - "source": [ - "# Confirm the registered custom variable metadata\n", - "derived_vars = list_derived_variables()\n", - "info = derived_vars.get(\"goldilocks_day_wrf\")\n", - "\n", - "if info is not None:\n", - " print(\"goldilocks_day_wrf\")\n", - " print(f\" Depends on: {info.depends_on}\")\n", - " print(f\" Description: {info.description}\")\n", - " print(f\" Units: {info.units}\")\n", - "else:\n", - " print(\"goldilocks_day_wrf is not registered in this session.\")" - ] } ], "metadata": { From 4d192265726ab026a65fd53d05c0bddf89cdd238 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Tue, 3 Mar 2026 13:03:59 -0600 Subject: [PATCH 34/53] fixing plotting --- analysis/derived_variables_demo.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 0479d7cc..f7ed0a4c 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -537,8 +537,7 @@ " \"2D Histogram by GWL: rh vs t2 with Goldilocks Thresholds\",\n", " fontweight=\"bold\",\n", " y=1.05,\n", - ")\n", - "plt.tight_layout()" + ");" ] }, { From 3ee85991e7ad91a09687fc653a695ecaf689352b Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Tue, 3 Mar 2026 13:17:22 -0600 Subject: [PATCH 35/53] updating with formal add dummy time --- analysis/derived_variables_demo.ipynb | 20 +------------------- 1 file changed, 1 insertion(+), 19 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index f7ed0a4c..9ff52e24 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -45,7 +45,7 @@ " list_derived_variables,\n", " register_user_function,\n", " )\n", - "from climakitae.util.utils import f_to_k\n", + "from climakitae.util.utils import f_to_k, add_dummy_time_to_wl\n", "\n", "# Set default plotting parameters for readability\n", "plt.rcParams.update({\n", @@ -61,24 +61,6 @@ "cd = ClimateData(verbosity=-1)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "a70213f9", - "metadata": {}, - "outputs": [], - "source": [ - "# Add a dummy time dimension to warming-level data for easier plotting\n", - "def add_dummy_time_to_wl(data):\n", - " \"\"\"Convert time_delta to a dummy datetime coordinate for plotting.\"\"\"\n", - " if \"time_delta\" in data.dims:\n", - " wl_values = data[\"time_delta\"].values\n", - " time_values = pd.to_datetime(wl_values, unit=\"D\", origin=\"2050-01-01\")\n", - " data = data.rename({\"time_delta\": \"time\"})\n", - " data = data.assign_coords(time=time_values)\n", - " return data" - ] - }, { "cell_type": "markdown", "id": "e42bba08", From 332e2747cec7737e212d7559e82fa5206db616fb Mon Sep 17 00:00:00 2001 From: Neil Schroeder Date: Mon, 9 Mar 2026 09:18:01 -0500 Subject: [PATCH 36/53] Update analysis/derived_variables_demo.ipynb Co-authored-by: Will Krantz --- analysis/derived_variables_demo.ipynb | 61 ++++++++++++++++++++++++--- 1 file changed, 54 insertions(+), 7 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 9ff52e24..58526345 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -66,9 +66,44 @@ "id": "e42bba08", "metadata": {}, "source": [ - "## 1. Why a Custom Goldilocks Metric?\n", + "## Workflow comparison\n", + "\n", + "#### Without register_user_function\n", + "Without the ```register_user_function``` tool, the workflow for calculating a derived metric that uses temperature and relative humidity would look like this:\n", + "\n", + "```mermaid\n", + "flowchart TD\n", + " A[Step 1: Define function for custom metric] --> B[Step 2: Individually load each of the required input metrics e.g. t2, rh]\n", + " B --> C[Step 3: Compute metric by sending input metrics through custom function]\n", + " C --> D[Step 4: Perform spatial and temporal aggregation]\n", + " D --> E[Step 5: Load resulting data into local memory for plotting or analysis]\n", + "\n", + " style A fill:#FFD580,stroke:#FF8C00,stroke-width:2px\n", + " style B fill:#FFD580,stroke:#FF8C00,stroke-width:2px\n", + " style C fill:#FFD580,stroke:#FF8C00,stroke-width:2px\n", + " style D fill:#FFD580,stroke:#FF8C00,stroke-width:2px\n", + " style E fill:#FFD580,stroke:#FF8C00,stroke-width:2px\n", + "```\n", + "\n", + "\n", + " \n", + "### With register_user_function\n", + "With the ```register_user_function``` tool, only one call to fetch data from the catalog is necessary, which returns the custom metric directly:\n", + "\n", + "```mermaid\n", + "flowchart TD\n", + " A[Step 1: Define function for custom metric] --> B[Step 2: Register function with climakitae]\n", + " B --> C[Step 3: Use one call to ClimateData to load custom derived metric]\n", + " C --> D[Step 4: Perform spatial and temporal aggregation]\n", + " D --> E[Step 5: Load resulting data into local memory for plotting or analysis]\n", + "\n", + " style A fill:#ccffcc,stroke:#00aa00,stroke-width:2px\n", + " style B fill:#ccffcc,stroke:#000000,stroke-width:5px\n", + " style D fill:#ccffcc,stroke:#00aa00,stroke-width:2px\n", + " style C fill:#ccffcc,stroke:#000000,stroke-width:5px\n", + " style E fill:#ccffcc,stroke:#00aa00,stroke-width:3px\n", + "```\n", "\n", - "For this public notebook, we focus on a temperature-humidity comfort-window metric.\n", "\n", "```mermaid\n", "flowchart TD\n", @@ -80,11 +115,23 @@ " style C fill:#eeffee,stroke:#00aa00,stroke-width:3px\n", "```\n", "\n", - "**Benefits:**\n", - "- One reusable metric definition\n", - "- Single `.get()` query with clipping + warming-level processing\n", - "- Lazy xarray/dask workflow (compute only when needed)\n", - "- Clear translation to business-facing “comfortable day” framing" + "## Benefits to registering custom metric\n", + "\n", + "This second workflow enables a **simpler** workflow after the initial metric has been defined, with only one `.get()` query that can be combined clipping + warming-level processing that would otherwise need to be repeated for each input variable.\n", + "\n", + "Registering the metric function should produce a significantly **faster** workflow overall, because it enables full utilization of a lazy xarray/dask workflow (compute only when needed) and backened efficiencies in the ClimateData framework.\n", + "\n", + "Once defined and registered, a custom metric can also **easily be re-used** in other contexts and analysis workflows.\n" + + "## Example: Custom *Goldilocks Day* metric\n", + "\n", + "To demonstrate the workflow for defining a custom derived metric, this notebook introduces a **Goldilocks Day** metric that uses temperature and relative humidity as inputs. We will use this metric to examine shifts to the climate in Los Angeles County.\n", + "\n", + "A Goldilocks day is defined here as a day when:\n", + "- Temperature at 2m (`t2`) is between **65°F and 80°F**\n", + "- Relative humidity (`rh`) is between **40% and 50%**\n", + "\n", + "\n" ] }, { From 252b6dc8705907c8f7b0fac497381837736ad367 Mon Sep 17 00:00:00 2001 From: Neil Schroeder Date: Mon, 9 Mar 2026 09:18:14 -0500 Subject: [PATCH 37/53] Update analysis/derived_variables_demo.ipynb Co-authored-by: Will Krantz --- analysis/derived_variables_demo.ipynb | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 58526345..b73cafa2 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -5,27 +5,23 @@ "id": "a97eee28", "metadata": {}, "source": [ - "# Goldilocks Days Demo: Temperature + Humidity Comfort Window\n", + "# Custom Derived Metrics in ClimakitAE\n", "\n", - "This notebook demonstrates a custom **Goldilocks day** metric in ClimakitAE for Los Angeles County.\n", + "This notebook demonstrates how to define and use custom derived metrics within ClimakitAE using the ```register_user_function``` tool. \n", "\n", - "A Goldilocks day is defined here as a day when:\n", - "- Temperature at 2m is between **65°F and 80°F**\n", - "- Relative humidity (`rh`) is between **40% and 50%**\n", + "**Derived metrics** use one or more existing variables from the climate data catalog (e.g. temperature, relative humidity), and perform additional calculations to produce a new value at each time step and grid cell. Some examples of derived metrics are Heat Index, drought metrics like SPEI, or Cooling Degree Days. The [Cal-Adapt data catalog](https://github.com/cal-adapt/climakitae/blob/main/climakitae/data/variable_descriptions.csv) contains some derived metrics that have already been computed for the entire data catalog.\n", "\n", - "### Purpose\n", + "For users who want to work with a derived metric that is not already in the data catalog, the ```register_user_function``` tool provides a convenient and efficient workflow that avoids individually loading and processing all of the input variables. \n", "\n", "This notebook demonstrates how to:\n", "- Build a custom derived metric with `ClimateData` and `register_user_function`\n", "- Query WRF data from `cadcat` with a single `.get()` call\n", "- Apply spatial clipping and warming-level processing in the same query\n", - "- Visualize spatial and temporal changes in Goldilocks days across warming levels\n", - "\n", - "### Outcomes\n", + "- Visualize spatial and temporal changes across warming levels\n", "\n", - "You will produce spatial maps and time-series summaries of annual Goldilocks days for warming levels of 1.2°C, 2.0°C, and 3.0°C.\n", + "**Intended Application**: As a user, I want to know **how to register a custom derived metric** within ClimakitAE, and understand the advantage that this approach has over manually loading input variables to compute the metric.\n", "\n", - "**RUNTIME**: ~7 minutes" + "**RUNTIME**: With the default settings, this notebook takes approximately **7 minutes** to run from start to finish. Modifications to selections may increase the runtime. \n" ] }, { From ff9fc44deb473d9fc6e3349a4a5ae746bcd7d6b4 Mon Sep 17 00:00:00 2001 From: neilSchroeder Date: Mon, 9 Mar 2026 15:24:39 -0500 Subject: [PATCH 38/53] final updates --- analysis/derived_variables_demo.ipynb | 368 ++++++++++++-------------- 1 file changed, 175 insertions(+), 193 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index b73cafa2..9cf7f4ab 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -33,6 +33,7 @@ "source": [ "from dask.diagnostics import ProgressBar\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "\n", @@ -62,7 +63,7 @@ "id": "e42bba08", "metadata": {}, "source": [ - "## Workflow comparison\n", + "## Workflow comparison\n", "\n", "#### Without register_user_function\n", "Without the ```register_user_function``` tool, the workflow for calculating a derived metric that uses temperature and relative humidity would look like this:\n", @@ -88,46 +89,31 @@ "\n", "```mermaid\n", "flowchart TD\n", - " A[Step 1: Define function for custom metric] --> B[Step 2: Register function with climakitae]\n", - " B --> C[Step 3: Use one call to ClimateData to load custom derived metric]\n", - " C --> D[Step 4: Perform spatial and temporal aggregation]\n", - " D --> E[Step 5: Load resulting data into local memory for plotting or analysis]\n", + " A[Step 1: Define and register function for custom metric] --> B[Step 2: Use one call to ClimateData to load custom derived metric]\n", + " B --> C[Step 3: Perform spatial and temporal aggregation]\n", + " C --> D[Step 4: Load resulting data into local memory for plotting or analysis]\n", "\n", " style A fill:#ccffcc,stroke:#00aa00,stroke-width:2px\n", " style B fill:#ccffcc,stroke:#000000,stroke-width:5px\n", - " style D fill:#ccffcc,stroke:#00aa00,stroke-width:2px\n", " style C fill:#ccffcc,stroke:#000000,stroke-width:5px\n", - " style E fill:#ccffcc,stroke:#00aa00,stroke-width:3px\n", + " style D fill:#ccffcc,stroke:#00aa00,stroke-width:2px\n", "```\n", "\n", + "## Benefits of registering a custom metric\n", "\n", - "```mermaid\n", - "flowchart TD\n", - " A[Step 1: Register custom derived variable\\nGoldilocks from t2 and rh] --> B[Step 2: Single query\\nClimateData.variable.processes.get]\n", - " B --> C[Step 3: Analyze outputs\\nSpatial + temporal views by warming level]\n", - "\n", - " style A fill:#ccffcc,stroke:#00aa00,stroke-width:2px\n", - " style B fill:#ccffcc,stroke:#00aa00,stroke-width:2px\n", - " style C fill:#eeffee,stroke:#00aa00,stroke-width:3px\n", - "```\n", - "\n", - "## Benefits to registering custom metric\n", + "**Simpler queries**: Rather than making a separate `.get()` call for each input variable, merging the results, and computing the metric manually, a registered metric lets you fetch the derived result in a single `.get()` call.\n", "\n", - "This second workflow enables a **simpler** workflow after the initial metric has been defined, with only one `.get()` query that can be combined clipping + warming-level processing that would otherwise need to be repeated for each input variable.\n", + "**Access to the full processor pipeline**: A registered metric is treated as a first-class variable in the ClimateData framework, so it can be combined with any processor — `warming_level`, `clip`, `metric_calc`, `export`, and others — in the same query. Without registration, you would need to apply these transformations manually after computing the metric.\n", "\n", - "Registering the metric function should produce a significantly **faster** workflow overall, because it enables full utilization of a lazy xarray/dask workflow (compute only when needed) and backened efficiencies in the ClimateData framework.\n", + "**Reusability**: Once defined and registered, a custom metric can be queried in entirely different contexts (different regions, time ranges, warming levels) without redefining anything.\n", "\n", - "Once defined and registered, a custom metric can also **easily be re-used** in other contexts and analysis workflows.\n" - - "## Example: Custom *Goldilocks Day* metric\n", + "## Example: Custom *Goldilocks Day* metric\n", "\n", "To demonstrate the workflow for defining a custom derived metric, this notebook introduces a **Goldilocks Day** metric that uses temperature and relative humidity as inputs. We will use this metric to examine shifts to the climate in Los Angeles County.\n", "\n", "A Goldilocks day is defined here as a day when:\n", "- Temperature at 2m (`t2`) is between **65°F and 80°F**\n", - "- Relative humidity (`rh`) is between **40% and 50%**\n", - "\n", - "\n" + "- Relative humidity (`rh`) is between **40% and 50%**" ] }, { @@ -251,7 +237,7 @@ "source": [ "goldilocks_data = add_dummy_time_to_wl(goldilocks_data)\n", "\n", - "# Optional: drop known problematic simulation if present\n", + "# Drop EC-Earth3-Veg simulation as it does not have a complete warming level for 1.2C\n", "bad_sim = \"WRF_UCLA_EC-Earth3-Veg_ssp370_day_d03_r1i1p1f1\"\n", "if \"sim\" in goldilocks_data.dims and bad_sim in goldilocks_data.sim.values:\n", " goldilocks_data = goldilocks_data.drop_sel(sim=bad_sim)\n", @@ -350,219 +336,214 @@ }, { "cell_type": "markdown", - "id": "78da135d", + "id": "50a7fce7", "metadata": {}, "source": [ - "## 5. GWL Comparison\n", + "## 5. Goldilocks Days Distribution by Warming Level\n", "\n", - "This section compares warming levels directly using three diagnostics:\n", - "1. Violin plot of county-mean daily `t2` by GWL\n", - "2. Violin plot of county-mean daily `rh` by GWL\n", - "3. 2D histogram (`x = rh`, `y = t2`) for each GWL with Goldilocks thresholds overlaid" + "A violin plot of annual Goldilocks day counts summarizes how the distribution shifts across warming levels — directly using the custom metric without needing to load individual driver variables." ] }, { "cell_type": "code", "execution_count": null, - "id": "df7c9f7b", + "id": "821fe0a4", "metadata": {}, "outputs": [], "source": [ - "# Build non-time-series driver datasets by warming level (county mean daily)\n", - "spatial_dims = [dim for dim in [\"y\", \"x\", \"lat\", \"lon\"] if dim in goldilocks_data.dims]\n", - "\n", - "cd_driver = ClimateData(verbosity=-2)\n", - "\n", - "def fetch_driver(variable_name):\n", - " return (\n", - " cd_driver\n", - " .catalog(\"cadcat\")\n", - " .variable(variable_name)\n", - " .activity_id(\"WRF\")\n", - " .institution_id(\"UCLA\")\n", - " .table_id(\"day\")\n", - " .grid_label(\"d03\")\n", - " .processes({\n", - " \"warming_level\": {\"warming_levels\": [1.2, 2.0, 3.0]},\n", - " \"clip\": \"Los Angeles County\",\n", - " })\n", - " .get()\n", - " )\n", - "\n", - "t2_data = add_dummy_time_to_wl(fetch_driver(\"t2\"))\n", - "rh_data = add_dummy_time_to_wl(fetch_driver(\"rh\"))\n", - "\n", - "# Drop incomplete simulation if present (dot or underscore naming)\n", - "incomplete_sim_dot = \"WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1\"\n", - "target_norm = incomplete_sim_dot.replace(\".\", \"_\")\n", - "\n", - "for dataset_name, ds in [(\"t2\", t2_data), (\"rh\", rh_data)]:\n", - " if \"sim\" in ds.dims:\n", - " sims_to_drop = [\n", - " str(sim)\n", - " for sim in ds.sim.values\n", - " if str(sim) == incomplete_sim_dot or str(sim).replace(\".\", \"_\") == target_norm\n", - " ]\n", - " if sims_to_drop:\n", - " if dataset_name == \"t2\":\n", - " t2_data = ds.drop_sel(sim=sims_to_drop)\n", - " else:\n", - " rh_data = ds.drop_sel(sim=sims_to_drop)\n", + "# County-mean annual Goldilocks day counts per simulation and warming level\n", + "spatial_dims = [dim for dim in [\"y\", \"x\", \"lat\", \"lon\"] if dim in goldilocks_days_per_year.dims]\n", + "annual_county_mean = goldilocks_days_per_year.mean(dim=spatial_dims)\n", "\n", - "with ProgressBar():\n", - " t2_county_daily_f = ((t2_data[\"t2\"].mean(dim=spatial_dims) - 273.15) * 9 / 5 + 32).compute()\n", - " rh_county_daily = rh_data[\"rh\"].mean(dim=spatial_dims).compute()\n", - "\n", - "warming_levels = t2_county_daily_f.warming_level.values\n", + "warming_levels = annual_county_mean.warming_level.values\n", "gwl_labels = []\n", - "\n", "for wl in warming_levels:\n", " centered_year = goldilocks_data.centered_year.sel(warming_level=wl).dropna(\"sim\")\n", " min_year = int(centered_year.min().values)\n", " max_year = int(centered_year.max().values)\n", - " gwl_labels.append(f\"{wl}°C ({min_year}–{max_year})\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bbdd2828", - "metadata": {}, - "outputs": [], - "source": [ - "# Violin plots: t2 and rh by GWL\n", - "fig, axes = plt.subplots(1, 2, figsize=(16, 7), gridspec_kw={\"wspace\": 0.25})\n", + " gwl_labels.append(f\"{wl}°C ({min_year}–{max_year})\")\n", + "\n", + "with ProgressBar():\n", + " annual_county_mean = annual_county_mean.compute()\n", "\n", + "# Build violin data\n", "colors = [\"#2E86AB\", \"#A23B72\", \"#F18F01\"]\n", "positions = list(range(1, len(warming_levels) + 1))\n", - "\n", - "temp_violin = []\n", - "rh_violin = []\n", - "\n", + "violin_data = []\n", "for wl in warming_levels:\n", - " t_vals = pd.Series(t2_county_daily_f.sel(warming_level=wl).values.ravel()).dropna().values\n", - " r_vals = pd.Series(rh_county_daily.sel(warming_level=wl).values.ravel()).dropna().values\n", - " temp_violin.append(t_vals)\n", - " rh_violin.append(r_vals)\n", - "\n", - "# t2 violin (y=t2, x=GWL)\n", - "parts_t = axes[0].violinplot(\n", - " temp_violin,\n", - " positions=positions,\n", - " widths=0.8,\n", - " showmeans=False,\n", - " showmedians=True,\n", - " showextrema=False,\n", - " )\n", - "for idx, body in enumerate(parts_t[\"bodies\"]):\n", - " body.set_facecolor(colors[idx % len(colors)])\n", - " body.set_edgecolor(colors[idx % len(colors)])\n", - " body.set_alpha(0.65)\n", - "if \"cmedians\" in parts_t:\n", - " parts_t[\"cmedians\"].set_color(\"black\")\n", - " parts_t[\"cmedians\"].set_linewidth(2)\n", - "axes[0].axhline(65, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", - "axes[0].axhline(80, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", - "axes[0].set_xticks(positions)\n", - "axes[0].set_xticklabels(gwl_labels, rotation=10, ha=\"right\")\n", - "axes[0].set_xlabel(\"GWL (Years Spanned)\", fontweight=\"bold\")\n", - "axes[0].set_ylabel(\"t2 (°F)\", fontweight=\"bold\")\n", - "axes[0].set_title(\"Violin Plot: t2 by GWL\", fontweight=\"bold\")\n", - "axes[0].grid(True, axis=\"y\", alpha=0.3, linestyle=\"--\")\n", - "\n", - "# rh violin (y=rh, x=GWL)\n", - "parts_r = axes[1].violinplot(\n", - " rh_violin,\n", + " vals = pd.Series(annual_county_mean.sel(warming_level=wl).values.ravel()).dropna().values\n", + " violin_data.append(vals)\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "parts = ax.violinplot(\n", + " violin_data,\n", " positions=positions,\n", " widths=0.8,\n", " showmeans=False,\n", " showmedians=True,\n", " showextrema=False,\n", - " )\n", - "for idx, body in enumerate(parts_r[\"bodies\"]):\n", + ")\n", + "for idx, body in enumerate(parts[\"bodies\"]):\n", " body.set_facecolor(colors[idx % len(colors)])\n", " body.set_edgecolor(colors[idx % len(colors)])\n", " body.set_alpha(0.65)\n", - "if \"cmedians\" in parts_r:\n", - " parts_r[\"cmedians\"].set_color(\"black\")\n", - " parts_r[\"cmedians\"].set_linewidth(2)\n", - "axes[1].axhline(40, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", - "axes[1].axhline(50, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", - "axes[1].set_xticks(positions)\n", - "axes[1].set_xticklabels(gwl_labels, rotation=10, ha=\"right\")\n", - "axes[1].set_xlabel(\"GWL (Years Spanned)\", fontweight=\"bold\")\n", - "axes[1].set_ylabel(\"rh (%)\", fontweight=\"bold\")\n", - "axes[1].set_title(\"Violin Plot: rh by GWL\", fontweight=\"bold\")\n", - "axes[1].grid(True, axis=\"y\", alpha=0.3, linestyle=\"--\")\n", - "\n", - "fig.suptitle(\n", - " \"Driver Distributions by Global Warming Level\\nLos Angeles County (UCLA WRF)\",\n", - " fontweight=\"bold\",\n", - " y=1.02,\n", - ")\n", + "if \"cmedians\" in parts:\n", + " parts[\"cmedians\"].set_color(\"black\")\n", + " parts[\"cmedians\"].set_linewidth(2)\n", + "\n", + "ax.set_xticks(positions)\n", + "ax.set_xticklabels(gwl_labels, rotation=10, ha=\"right\")\n", + "ax.set_xlabel(\"Global Warming Level (Years Spanned)\", fontweight=\"bold\")\n", + "ax.set_ylabel(\"Annual Goldilocks Days\", fontweight=\"bold\")\n", + "ax.set_title(\"Goldilocks Days by Global Warming Level\\nLos Angeles County\", fontweight=\"bold\")\n", + "ax.grid(True, axis=\"y\", alpha=0.3, linestyle=\"--\")\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", - "id": "086ad63a-7070-4e12-a423-fe6a2d1757de", + "id": "ab07fc57", "metadata": {}, "source": [ - "The violin plots show how `t2` and `rh` distributions shift across warming levels, and the 2D histograms show their joint density relative to the Goldilocks thresholds (red dashed lines). The in-zone percentage in each panel quantifies how much density remains inside the target window." + "## 6. Reusing the Custom Metric: Time-Based Analysis for Sacramento\n", + "\n", + "One key advantage of registering a custom metric is **reusability**. Because `goldilocks_day_wrf` is now a registered variable, we can query it again with completely different processors — a different region and a time-slice instead of warming levels — without redefining anything.\n", + "\n", + "This demonstrates how a single `register_user_function` call enables the metric to be used across many different analyses." ] }, { "cell_type": "code", "execution_count": null, - "id": "e73ae1a4", + "id": "243026b9", "metadata": {}, "outputs": [], "source": [ - "# 2D histogram panels (3 columns, 1 row): x=rh, y=t2\n", - "fig, axes = plt.subplots(1, len(warming_levels), figsize=(18, 5), sharex=True, sharey=True)\n", + "cd_sac = ClimateData(verbosity=-2)\n", "\n", - "if len(warming_levels) == 1:\n", - " axes = [axes]\n", + "goldilocks_sacramento = (\n", + " cd_sac\n", + " .catalog(\"cadcat\")\n", + " .variable(\"goldilocks_day_wrf\")\n", + " .activity_id(\"WRF\")\n", + " .institution_id(\"UCLA\")\n", + " .table_id(\"day\")\n", + " .grid_label(\"d03\")\n", + " .processes({\n", + " \"time_slice\": (\"2015-01-01\", \"2060-12-31\"),\n", + " \"clip\": \"Sacramento County\",\n", + " })\n", + " .get()\n", + ")\n", + "\n", + "goldilocks_sacramento" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28d85adb", + "metadata": {}, + "outputs": [], + "source": [ + "# Annual Goldilocks day time series for Sacramento County\n", + "spatial_dims_sac = [dim for dim in [\"y\", \"x\", \"lat\", \"lon\"] if dim in goldilocks_sacramento.dims]\n", + "\n", + "with ProgressBar():\n", + " sac_annual = (\n", + " goldilocks_sacramento[\"goldilocks_day_wrf\"]\n", + " .resample(time=\"YE\")\n", + " .sum()\n", + " .mean(dim=spatial_dims_sac)\n", + " .compute()\n", + " )\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "\n", + "# Plot individual simulations in grey\n", + "for sim in sac_annual.sim.values:\n", + " sim_data = sac_annual.sel(sim=sim)\n", + " ax.plot(sim_data.time.values, sim_data.values, color=\"grey\", alpha=0.2, linewidth=0.8)\n", + "\n", + "# Plot ensemble median\n", + "ensemble_median = sac_annual.median(dim=\"sim\")\n", + "ax.plot(ensemble_median.time.values, ensemble_median.values, color=\"#2E86AB\", linewidth=2.5, label=\"Ensemble median\")\n", + "\n", + "ax.set_xlabel(\"Year\", fontweight=\"bold\")\n", + "ax.set_ylabel(\"Annual Goldilocks Days\", fontweight=\"bold\")\n", + "ax.set_title(\"Goldilocks Days Time Series (2015–2060)\\nSacramento County\", fontweight=\"bold\")\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3, linestyle=\"--\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "7f0fc42e", + "metadata": {}, + "source": [ + "## 7. Comparison: Traditional Workflow (Without `register_user_function`)\n", "\n", - "mesh = None\n", + "For comparison, the cell below shows how the same Goldilocks Day analysis would be done *without* registering the custom metric. This approach requires:\n", "\n", - "for ax, wl, label in zip(axes, warming_levels, gwl_labels):\n", - " t_da = t2_county_daily_f.sel(warming_level=wl)\n", - " rh_da = rh_county_daily.sel(warming_level=wl)\n", - " valid = xr.ufuncs.isfinite(t_da) & xr.ufuncs.isfinite(rh_da)\n", + "- **Two separate `.get()` calls** — one for `t2`, one for `rh`\n", + "- **Manual merging** of the returned datasets\n", + "- **Manual metric computation** after the data is loaded\n", + "- **No direct access to processors** like `metric_calc` or `export` on the derived result during the query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2209d879", + "metadata": {}, + "outputs": [], + "source": [ + "cd_trad = ClimateData(verbosity=-2)\n", "\n", - " t_vals = t_da.where(valid).values.ravel()\n", - " rh_vals = rh_da.where(valid).values.ravel()\n", - " paired_valid = pd.notna(t_vals) & pd.notna(rh_vals)\n", - " t_vals = t_vals[paired_valid]\n", - " rh_vals = rh_vals[paired_valid]\n", + "def fetch_driver(variable_name):\n", + " \"\"\"Fetch a single input variable — must be repeated for each dependency.\"\"\"\n", + " return (\n", + " cd_trad\n", + " .catalog(\"cadcat\")\n", + " .variable(variable_name)\n", + " .activity_id(\"WRF\")\n", + " .institution_id(\"UCLA\")\n", + " .table_id(\"day\")\n", + " .grid_label(\"d03\")\n", + " .processes({\n", + " \"warming_level\": {\"warming_levels\": [1.2, 2.0, 3.0]},\n", + " \"clip\": \"Los Angeles County\",\n", + " })\n", + " .get()\n", + " )\n", "\n", - " hist = ax.hist2d(rh_vals, t_vals, bins=50, cmap=\"magma\")\n", - " mesh = hist[3]\n", + "# Two separate data fetches — each takes several minutes\n", + "t2_data = fetch_driver(\"t2\")\n", + "rh_data = fetch_driver(\"rh\")\n", "\n", - " # Red threshold lines\n", - " ax.axvline(40, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", - " ax.axvline(50, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", - " ax.axhline(65, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", - " ax.axhline(80, color=\"red\", linestyle=\"--\", linewidth=1.8)\n", + "# Manually merge and compute the metric\n", + "ds_trad = xr.merge([t2_data, rh_data])\n", + "ds_trad = calc_goldilocks_day_wrf(ds_trad)\n", + "ds_trad = add_dummy_time_to_wl(ds_trad)\n", "\n", - " in_zone = ((rh_vals >= 40) & (rh_vals <= 50) & (t_vals >= 65) & (t_vals <= 80))\n", - " zone_pct = float(in_zone.mean() * 100) if len(t_vals) > 0 else float(\"nan\")\n", - " ax.set_title(f\"{label}\\nIn-zone: {zone_pct:.1f}%\", fontweight=\"bold\")\n", - " ax.set_xlabel(\"rh (%)\", fontweight=\"bold\")\n", - " ax.grid(True, alpha=0.2, linestyle=\"--\")\n", + "bad_sim = \"WRF_UCLA_EC-Earth3-Veg_ssp370_day_d03_r1i1p1f1\"\n", + "if \"sim\" in ds_trad.dims and bad_sim in ds_trad.sim.values:\n", + " ds_trad = ds_trad.drop_sel(sim=bad_sim)\n", "\n", - "axes[0].set_ylabel(\"t2 (°F)\", fontweight=\"bold\")\n", + "# Count Goldilocks days per year\n", + "goldilocks_days_per_year_trad = ds_trad[\"goldilocks_day_wrf\"].resample(time=\"Y\").sum()\n", "\n", - "if mesh is not None:\n", - " cbar = fig.colorbar(mesh, ax=axes, shrink=0.88, pad=0.02)\n", - " cbar.set_label(\"Count per bin\")\n", + "goldilocks_days_per_year_trad = xr.where(\n", + " goldilocks_days_per_year_trad > 0,\n", + " goldilocks_days_per_year_trad,\n", + " float(\"nan\"),\n", + ")\n", "\n", - "fig.suptitle(\n", - " \"2D Histogram by GWL: rh vs t2 with Goldilocks Thresholds\",\n", - " fontweight=\"bold\",\n", - " y=1.05,\n", - ");" + "spt_trad = goldilocks_days_per_year_trad.mean(dim=\"time\")\n", + "\n", + "with ProgressBar():\n", + " spt_trad = spt_trad.median(dim=\"sim\", skipna=True).compute()" ] }, { @@ -570,18 +551,19 @@ "id": "7337c751", "metadata": {}, "source": [ - "## 6. Summary: Key Takeaways\n", + "## 8. Summary: Key Takeaways\n", "\n", - "1. A custom comfort-window metric can be registered once and queried like any other variable.\n", - "2. Goldilocks days are computed from `t2` and `rh` in one query pipeline.\n", - "3. Warming-level analysis provides a climate-relevant framing for temporal and spatial shifts.\n", - "4. The workflow stays lazy until explicit `.compute()` steps for plotting and summary statistics." + "1. **Register once, query anywhere**: A custom metric registered with `register_user_function` can be queried like any built-in variable, with any combination of processors.\n", + "2. **Simpler code, fewer steps**: The registered workflow replaces multiple `.get()` calls, manual merging, and post-hoc metric computation with a single query — resulting in significantly less code to write and maintain.\n", + "3. **Full processor access**: Registered metrics integrate with the ClimateData processor pipeline (`warming_level`, `clip`, `metric_calc`, `export`, etc.), which is not available when computing metrics manually after loading raw variables.\n", + "4. **Reusable across contexts**: The same `goldilocks_day_wrf` metric was used for warming-level analysis in LA County *and* a time-based analysis in Sacramento County — with no redefinition needed.\n", + "5. **Lazy evaluation**: The workflow stays lazy until explicit `.compute()` steps, keeping memory usage efficient for large datasets." ] } ], "metadata": { "kernelspec": { - "display_name": "climakitae", + "display_name": "Python 3 (climakitae)", "language": "python", "name": "python3" }, @@ -595,7 +577,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.12.12" } }, "nbformat": 4, From 96fdc82b4e73313bde02bc1082f7364ad0c465a8 Mon Sep 17 00:00:00 2001 From: Neil Schroeder Date: Wed, 11 Mar 2026 12:42:07 -0500 Subject: [PATCH 39/53] Update analysis/derived_variables_demo.ipynb Co-authored-by: Will Krantz --- analysis/derived_variables_demo.ipynb | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/analysis/derived_variables_demo.ipynb b/analysis/derived_variables_demo.ipynb index 9cf7f4ab..550767d9 100644 --- a/analysis/derived_variables_demo.ipynb +++ b/analysis/derived_variables_demo.ipynb @@ -358,10 +358,7 @@ "warming_levels = annual_county_mean.warming_level.values\n", "gwl_labels = []\n", "for wl in warming_levels:\n", - " centered_year = goldilocks_data.centered_year.sel(warming_level=wl).dropna(\"sim\")\n", - " min_year = int(centered_year.min().values)\n", - " max_year = int(centered_year.max().values)\n", - " gwl_labels.append(f\"{wl}°C ({min_year}–{max_year})\")\n", + " gwl_labels.append(f\"{wl}°C\")\n", "\n", "with ProgressBar():\n", " annual_county_mean = annual_county_mean.compute()\n", From 49d1a7966f6687c355514b749f156d4ee52a13fd Mon Sep 17 00:00:00 2001 From: claalmve Date: Thu, 12 Mar 2026 11:48:52 -0700 Subject: [PATCH 40/53] Working on drought metrics notebook some more --- work-in-progress/drought_metrics.ipynb | 105 +++++++++---------------- 1 file changed, 37 insertions(+), 68 deletions(-) diff --git a/work-in-progress/drought_metrics.ipynb b/work-in-progress/drought_metrics.ipynb index d20b41eb..20b8628c 100644 --- a/work-in-progress/drought_metrics.ipynb +++ b/work-in-progress/drought_metrics.ipynb @@ -55,18 +55,7 @@ "cell_type": "markdown", "id": "5c04c18f", "metadata": {}, - "source": [ - "**Variables needed:**\n", - "- `tasmin`\n", - "- `tasmax`\n", - "- `relative humidity`\n", - "- `radiation flux`\n", - " - rsds\n", - " - rsus\n", - " - rlds\n", - " - rlus\n", - "- `wind speed (10m wind will be converted to 2m)`" - ] + "source": "**Variables needed:**\n- `t2` (hourly) → daily `tasmin` and `tasmax` derived via resample\n- `relative humidity`\n- `radiation flux`\n - rsds (daily)\n - rsus (hourly → daily mean)\n - rlds (daily)\n - rlus (hourly → daily mean)\n- `wind speed (10m wind will be converted to 2m)`" }, { "cell_type": "markdown", @@ -78,11 +67,14 @@ }, { "cell_type": "code", + "execution_count": null, "id": "xypsmbxjeoj", - "source": "# Install climate_indices at a specific commit compatible with the AE hub environment\n!pip install -qq git+https://github.com/monocongo/climate_indices.git@43c5451", "metadata": {}, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "# Install climate_indices at a specific commit compatible with the AE hub environment\n", + "!pip install -qq git+https://github.com/monocongo/climate_indices.git@43c5451" + ] }, { "cell_type": "code", @@ -90,7 +82,7 @@ "id": "a7e19917", "metadata": {}, "outputs": [], - "source": "import climakitae as ck\nimport xclim\nimport os\nimport xarray as xr\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pyproj import CRS\nfrom climate_indices.palmer import pdsi as palmer_pdsi\nfrom xclim.indices.stats import standardized_index\n\nfrom climakitae.core.data_export import export\nfrom climakitae.util.utils import reproject_data\nfrom climakitae.new_core.derived_variables import register_user_function, list_derived_variables" + "source": "import climakitae as ck\nimport xclim\nimport os\nimport xarray as xr\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pyproj import CRS\nfrom climate_indices.palmer import pdsi as palmer_pdsi\nfrom xclim.indices.stats import standardized_index\n\nfrom climakitae.core.data_export import export\nfrom climakitae.util.utils import reproject_data, add_dummy_time_to_wl\nfrom climakitae.new_core.derived_variables import register_user_function, list_derived_variables" }, { "cell_type": "markdown", @@ -106,22 +98,21 @@ "id": "8b31d378", "metadata": {}, "outputs": [], - "source": [ - "lat = 37.787964\n", - "lon = -122.065063\n", - "\n", - "variables_dict = {\n", - " \"tasmax\": \"Maximum air temperature at 2m\",\n", - " \"tasmin\": \"Minimum air temperature at 2m\",\n", - " \"hurs\": \"Relative humidity\",\n", - " \"rsds\": \"Instantaneous downwelling shortwave flux at bottom\",\n", - " \"rsus\": \"Instantaneous upwelling shortwave flux at bottom\",\n", - " \"rlds\": \"Instantaneous downwelling longwave flux at bottom\",\n", - " \"rlus\": \"Instantaneous upwelling longwave flux at bottom\",\n", - " \"wspd10mean\": \"Mean wind speed at 10m\",\n", - " \"precip\": \"Precipitation (total)\",\n", - "}" - ] + "source": "lat = 37.787964\nlon = -122.065063\n\nvariables_dict = {\n \"t2\": \"Air temperature at 2m (hourly; daily min/max derived)\",\n \"rh\": \"Relative humidity\",\n \"sw_dwn\": \"Instantaneous downwelling shortwave flux at bottom\",\n \"swupb\": \"Instantaneous upwelling shortwave flux at bottom (hourly)\",\n \"lw_dwn\": \"Instantaneous downwelling longwave flux at bottom\",\n \"lwupb\": \"Instantaneous upwelling longwave flux at bottom (hourly)\",\n \"wspd10mean\": \"Mean wind speed at 10m\",\n \"prec\": \"Precipitation (total)\",\n}" + }, + { + "cell_type": "code", + "id": "9ze8p42ly5w", + "source": "# Making an `input_data` folder to hold intermediate data variables needed for PET\nif not os.path.exists(\"input_data\"):\n os.mkdir(\"input_data\")", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "id": "485befa5f23", + "source": "**IMPORTANT NOTE**: The following cell saves the intermediate data required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files.", + "metadata": {} }, { "cell_type": "code", @@ -129,7 +120,7 @@ "id": "a0132b29-804a-4eef-893f-2f60adfefc62", "metadata": {}, "outputs": [], - "source": "cd = ck.ClimateData(verbosity=-1)\nwarming_levels = [0.8, 1.5, 2.0, 3.0]\nprocesses = {\n \"clip\": [(lat - 0.04, lon - 0.04), (lat + 0.04, lon + 0.04)],\n \"warming_level\": {\"warming_levels\": warming_levels},\n}\n\ndef fetch(variable, table_id=\"1day\"):\n return (\n cd.catalog(\"cadcat\")\n .activity_id(\"WRF\")\n .table_id(table_id)\n .grid_label(\"d03\")\n .variable(variable)\n .processes(processes)\n .get()\n )\n\nprint(\"Fetching variables...\")\ntasmin = fetch(\"tasmin\")\ntasmax = fetch(\"tasmax\")\nhurs = fetch(\"hurs\")\nrsds = fetch(\"rsds\")\nrsus = fetch(\"rsus\", table_id=\"1hr\")\nrlds = fetch(\"rlds\")\nrlus = fetch(\"rlus\", table_id=\"1hr\")\nwspd10mean = fetch(\"wspd10mean\")\nprecip = fetch(\"precip\")\nprint(\"Done.\")" + "source": "cd = ck.ClimateData(verbosity=-1)\nwarming_levels = [0.8, 1.5, 2.0, 3.0]\nprocesses = {\n \"clip\": [(lat - 0.04, lon - 0.04), (lat + 0.04, lon + 0.04)],\n \"warming_level\": {\"warming_levels\": warming_levels},\n}\n\ndef fetch(variable, table_id=\"day\", hourly=False):\n da = (\n cd.catalog(\"cadcat\")\n .activity_id(\"WRF\")\n .institution_id(\"UCLA\")\n .table_id(table_id)\n .grid_label(\"d03\")\n .variable(variable)\n .processes(processes)\n .get()\n )\n freq_name = \"hourly\" if hourly else \"daily\"\n return add_dummy_time_to_wl(da, freq_name=freq_name)\n\ndef fetch_or_load(variable, file_path, table_id=\"day\", hourly=False):\n if os.path.exists(file_path):\n print(f\"Reading {variable} from file.\")\n return xr.open_dataarray(file_path)\n print(f\"Fetching {variable}...\")\n da = fetch(variable, table_id=table_id, hourly=hourly)\n export(da, file_path, format=\"NetCDF\", mode=\"local\")\n return xr.open_dataarray(file_path)\n\n# Daily variables — fetch once and cache\nrh = fetch_or_load(\"rh\", \"input_data/rh_daily.nc\")\nsw_dwn = fetch_or_load(\"sw_dwn\", \"input_data/sw_dwn_daily.nc\")\nlw_dwn = fetch_or_load(\"lw_dwn\", \"input_data/lw_dwn_daily.nc\")\nwspd10mean = fetch_or_load(\"wspd10mean\", \"input_data/wspd10mean_daily.nc\")\nprec = fetch_or_load(\"prec\", \"input_data/prec_daily.nc\")\n\n# t2 is hourly; derive and cache the daily min/max directly to avoid storing large hourly data\ntasmin_path = \"input_data/tasmin_daily.nc\"\ntasmax_path = \"input_data/tasmax_daily.nc\"\nif os.path.exists(tasmin_path) and os.path.exists(tasmax_path):\n print(\"Reading tasmin, tasmax from file.\")\n tasmin = xr.open_dataarray(tasmin_path)\n tasmax = xr.open_dataarray(tasmax_path)\nelse:\n print(\"Fetching t2 (hourly)...\")\n t2 = fetch(\"t2\", table_id=\"1hr\", hourly=True)\n tasmin = t2.resample(time=\"1D\").min()\n tasmax = t2.resample(time=\"1D\").max()\n export(tasmin, tasmin_path, format=\"NetCDF\", mode=\"local\")\n export(tasmax, tasmax_path, format=\"NetCDF\", mode=\"local\")\n tasmin = xr.open_dataarray(tasmin_path)\n tasmax = xr.open_dataarray(tasmax_path)\n\nprint(\"Done.\")" }, { "cell_type": "code", @@ -137,7 +128,7 @@ "id": "df54b41e", "metadata": {}, "outputs": [], - "source": "# Convert relative humidity from % to fraction as required by xclim\nhurs_frac = (hurs / 100).assign_attrs(units='1')\n\n# Resample hourly radiation fluxes to daily means\nrsus_daily = rsus.resample(time=\"1D\").mean()\nrlus_daily = rlus.resample(time=\"1D\").mean()" + "source": "# Convert relative humidity from % to fraction as required by xclim\nhurs_frac = (rh / 100).assign_attrs(units='1')\n\n# swupb and lwupb are hourly; derive and cache daily means to avoid storing large hourly data\nrsus_path = \"input_data/swupb_daily.nc\"\nrlus_path = \"input_data/lwupb_daily.nc\"\n\nif os.path.exists(rsus_path):\n print(\"Reading swupb (daily) from file.\")\n rsus_daily = xr.open_dataarray(rsus_path)\nelse:\n print(\"Fetching swupb (hourly)...\")\n swupb_hrly = fetch(\"swupb\", table_id=\"1hr\", hourly=True)\n rsus_daily = swupb_hrly.resample(time=\"1D\").mean()\n export(rsus_daily, rsus_path, format=\"NetCDF\", mode=\"local\")\n rsus_daily = xr.open_dataarray(rsus_path)\n\nif os.path.exists(rlus_path):\n print(\"Reading lwupb (daily) from file.\")\n rlus_daily = xr.open_dataarray(rlus_path)\nelse:\n print(\"Fetching lwupb (hourly)...\")\n lwupb_hrly = fetch(\"lwupb\", table_id=\"1hr\", hourly=True)\n rlus_daily = lwupb_hrly.resample(time=\"1D\").mean()\n export(rlus_daily, rlus_path, format=\"NetCDF\", mode=\"local\")\n rlus_daily = xr.open_dataarray(rlus_path)" }, { "cell_type": "code", @@ -145,7 +136,7 @@ "id": "32f5bcba", "metadata": {}, "outputs": [], - "source": "# Calculating PET from xclim using the Penman-Monteith method\npet_calc = xclim.indices.potential_evapotranspiration(\n tasmin=tasmin,\n tasmax=tasmax,\n hurs=hurs_frac,\n rsds=rsds,\n rsus=rsus_daily,\n rlds=rlds,\n rlus=rlus_daily,\n sfcWind=wspd10mean,\n method=\"FAO_PM98\"\n)" + "source": "# Calculating PET from xclim using the Penman-Monteith method\npet_calc = xclim.indices.potential_evapotranspiration(\n tasmin=tasmin,\n tasmax=tasmax,\n hurs=hurs_frac,\n rsds=sw_dwn,\n rsus=rsus_daily,\n rlds=lw_dwn,\n rlus=rlus_daily,\n sfcWind=wspd10mean,\n method=\"FAO_PM98\"\n)" }, { "cell_type": "code", @@ -153,11 +144,7 @@ "id": "58cbcd75", "metadata": {}, "outputs": [], - "source": [ - "# Preserving CRS and a spatial mask for later\n", - "crs = CRS.from_cf(pet_calc['Lambert_Conformal'].attrs)\n", - "spatial_mask = ~pet_calc.isel(warming_level=0, time=0, simulation=0).isnull()" - ] + "source": "# Preserving CRS and a spatial mask for later\ncrs = CRS.from_cf(pet_calc['Lambert_Conformal'].attrs)\nspatial_mask = ~pet_calc.isel(warming_level=0, time=0, sim=0).isnull()" }, { "cell_type": "markdown", @@ -171,7 +158,7 @@ "cell_type": "markdown", "id": "415dcfc7", "metadata": {}, - "source": "PDSI is implemented as a derived variable using the new core `register_user_function` pattern. The underlying computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by drought.gov. The function is vectorized over spatial dimensions using `xr.apply_ufunc`." + "source": "PDSI is registered as a derived variable using `register_user_function`. The underlying computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by drought.gov. The function is applied across all spatial and ensemble dimensions via `groupby().apply()`." }, { "cell_type": "code", @@ -179,11 +166,7 @@ "id": "deb75906", "metadata": {}, "outputs": [], - "source": [ - "# Resampling PET and precip to monthly since the function only takes monthly variables\n", - "mon_pet = (pet_calc * 86400 / 25.4).resample(time='1ME').sum()\n", - "mon_precip = (precip / 25.4).resample(time='1ME').sum()" - ] + "source": "# Resampling PET and precip to monthly since the function only takes monthly variables\nmon_pet = (pet_calc * 86400 / 25.4).resample(time='1ME').sum()\nmon_precip = (prec / 25.4).resample(time='1ME').sum()" }, { "cell_type": "code", @@ -268,7 +251,7 @@ "id": "c6b5de48", "metadata": {}, "outputs": [], - "source": "def pdsi_func(ds):\n \"\"\"Compute Palmer Drought Severity Index (PDSI) from a Dataset.\n\n Derived variable function: receives an xr.Dataset with 'precip' and 'pet'\n variables (both monthly, in inches) and adds 'pdsi' to it.\n\n Parameters\n ----------\n ds : xr.Dataset\n Dataset with 'precip' and 'pet' variables along a 'time' dimension.\n\n Returns\n -------\n xr.Dataset\n Input dataset with 'pdsi' variable added.\n \"\"\"\n def _pdsi_1d(precip_arr, pet_arr):\n result = palmer_pdsi(\n precips=precip_arr,\n pet=pet_arr,\n awc=5,\n data_start_year=2000,\n calibration_year_initial=2000,\n calibration_year_final=2030,\n )\n return result[0].clip(-10, 10)\n\n pdsi_values = xr.apply_ufunc(\n _pdsi_1d, ds[\"precip\"], ds[\"pet\"],\n input_core_dims=[[\"time\"], [\"time\"]],\n output_core_dims=[[\"time\"]],\n vectorize=True,\n output_dtypes=[float],\n )\n ds[\"pdsi\"] = pdsi_values.assign_coords(time=ds.time).assign_attrs({\n \"long_name\": \"Palmer Drought Severity Index\",\n \"units\": \"from -10 (dry) to +10 (wet)\",\n })\n return ds\n\nregister_user_function(\n name=\"pdsi\",\n depends_on=[\"precip\", \"pet\"],\n func=pdsi_func,\n description=\"Palmer Drought Severity Index computed from monthly precipitation and PET.\",\n units=\"unitless\",\n drop_dependencies=False,\n)" + "source": "def pdsi_func(ds: xr.Dataset) -> xr.Dataset:\n \"\"\"Compute Palmer Drought Severity Index (PDSI) from a Dataset.\n\n Registered derived variable function: receives an xr.Dataset with 'precip' and\n 'pet' variables (both monthly, in inches) and adds 'pdsi' to it.\n\n Parameters\n ----------\n ds : xr.Dataset\n Dataset with 'precip' and 'pet' variables along a 'time' dimension,\n plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n\n Returns\n -------\n xr.Dataset\n Input dataset with 'pdsi' variable added.\n \"\"\"\n def _calc_pdsi_1d(timeseries: xr.Dataset) -> xr.DataArray:\n precip = timeseries['precip'].squeeze()\n pet = timeseries['pet'].squeeze()\n result = palmer_pdsi(\n precips=precip.values,\n pet=pet.values,\n awc=5,\n data_start_year=2000,\n calibration_year_initial=2000,\n calibration_year_final=2030,\n )\n return xr.DataArray(\n result[0], coords={\"time\": precip.time.values}, dims=['time']\n ).clip(min=-10, max=10)\n\n pdsi_da = ds.groupby(['combined_wl', 'x', 'y', 'sim']).apply(_calc_pdsi_1d)\n pdsi_da.attrs.update({\n \"long_name\": \"Palmer Drought Severity Index\",\n \"units\": \"from -10 (dry) to +10 (wet)\",\n })\n ds[\"pdsi\"] = pdsi_da\n return ds\n\nregister_user_function(\n name=\"pdsi\",\n depends_on=[\"precip\", \"pet\"],\n func=pdsi_func,\n description=\"Palmer Drought Severity Index computed from monthly precipitation and PET.\",\n units=\"unitless\",\n drop_dependencies=False,\n)" }, { "cell_type": "code", @@ -276,7 +259,7 @@ "id": "c625f3af", "metadata": {}, "outputs": [], - "source": "# Apply PDSI derived variable to the combined dataset\npdsi_ds = list_derived_variables()[\"pdsi\"].func(combined_ds)\npdsi_da = pdsi_ds[\"pdsi\"]\n\n# Writing crs and reprojecting PDSI to lat/lon\npdsi_da = pdsi_da.rio.write_crs(crs.to_wkt())\npdsi_da = pdsi_da.transpose('time', 'combined_wl', 'simulation', 'y', 'x')\npdsi_latlon = reproject_data(pdsi_da, 'EPSG:4326')\ndel pdsi_latlon.attrs[\"_FillValue\"]" + "source": "# Apply PDSI derived variable to the combined dataset\npdsi_ds = list_derived_variables()[\"pdsi\"].func(combined_ds)\npdsi_da = pdsi_ds[\"pdsi\"]\n\n# Writing crs and reprojecting PDSI to lat/lon\npdsi_da = pdsi_da.rio.write_crs(crs.to_wkt())\npdsi_da = pdsi_da.transpose('time', 'combined_wl', 'sim', 'y', 'x')\npdsi_latlon = reproject_data(pdsi_da, 'EPSG:4326')\ndel pdsi_latlon.attrs[\"_FillValue\"]" }, { "cell_type": "markdown", @@ -290,14 +273,7 @@ "cell_type": "markdown", "id": "2d745c44", "metadata": {}, - "source": [ - "The results will have the following dimensions:\n", - "- time\n", - "- wl (showing which WL PDSI was calibrated on, and then which WL PDSI was calculated on)\n", - "- lat\n", - "- lon\n", - "- simulation" - ] + "source": "The results will have the following dimensions:\n- time\n- wl (showing which WL PDSI was calibrated on, and then which WL PDSI was calculated on)\n- lat\n- lon\n- sim" }, { "cell_type": "code", @@ -358,7 +334,7 @@ "id": "3228b9fb", "metadata": {}, "outputs": [], - "source": "def eddi_func(ds):\n \"\"\"Compute Evaporative Demand Drought Index (EDDI) from a Dataset.\n\n Derived variable function: receives an xr.Dataset with a 'pet' variable\n (monthly) and adds 'eddi' to it.\n\n Parameters\n ----------\n ds : xr.Dataset\n Dataset with 'pet' variable along a 'time' dimension.\n\n Returns\n -------\n xr.Dataset\n Input dataset with 'eddi' variable added.\n \"\"\"\n times = ds.time.values\n\n def _eddi_1d(pet_arr):\n pet_da = xr.DataArray(pet_arr, dims=[\"time\"], coords={\"time\": times})\n eddi = standardized_index(\n da=pet_da,\n freq=\"MS\",\n window=1,\n dist=\"gamma\",\n method=\"ML\",\n zero_inflated=True,\n fitkwargs={},\n cal_start=\"2000-01-31\",\n cal_end=\"2029-12-31\",\n )\n return eddi.clip(-2.5, 2.5).values\n\n eddi_values = xr.apply_ufunc(\n _eddi_1d, ds[\"pet\"],\n input_core_dims=[[\"time\"]],\n output_core_dims=[[\"time\"]],\n vectorize=True,\n output_dtypes=[float],\n )\n ds[\"eddi\"] = eddi_values.assign_coords(time=ds.time).assign_attrs({\n \"long_name\": \"Evaporative Demand Drought Index\",\n \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n })\n return ds\n\nregister_user_function(\n name=\"eddi\",\n depends_on=[\"pet\"],\n func=eddi_func,\n description=\"Evaporative Demand Drought Index computed from monthly PET.\",\n units=\"unitless\",\n drop_dependencies=False,\n)" + "source": "def eddi_func(ds: xr.Dataset) -> xr.Dataset:\n \"\"\"Compute Evaporative Demand Drought Index (EDDI) from a Dataset.\n\n Registered derived variable function: receives an xr.Dataset with a 'pet'\n variable (monthly) and adds 'eddi' to it.\n\n Parameters\n ----------\n ds : xr.Dataset\n Dataset with 'pet' variable along a 'time' dimension,\n plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n\n Returns\n -------\n xr.Dataset\n Input dataset with 'eddi' variable added.\n \"\"\"\n def _calc_eddi_1d(timeseries: xr.DataArray) -> xr.DataArray:\n eddi = standardized_index(\n da=timeseries,\n freq='MS',\n window=1,\n dist=\"gamma\",\n method=\"ML\",\n zero_inflated=True,\n fitkwargs={},\n cal_start=\"2000-01-31\",\n cal_end=\"2029-12-31\",\n )\n return eddi.clip(min=-2.5, max=2.5)\n\n eddi_da = ds['pet'].groupby(['combined_wl', 'x', 'y', 'sim']).apply(_calc_eddi_1d)\n eddi_da.attrs.update({\n \"long_name\": \"Evaporative Demand Drought Index\",\n \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n })\n ds[\"eddi\"] = eddi_da\n return ds\n\nregister_user_function(\n name=\"eddi\",\n depends_on=[\"pet\"],\n func=eddi_func,\n description=\"Evaporative Demand Drought Index computed from monthly PET.\",\n units=\"unitless\",\n drop_dependencies=False,\n)" }, { "cell_type": "code", @@ -366,7 +342,7 @@ "id": "1240839c", "metadata": {}, "outputs": [], - "source": "# Apply EDDI derived variable to the combined dataset\neddi_ds = list_derived_variables()[\"eddi\"].func(combined_ds)\neddi_da = eddi_ds[\"eddi\"]\n\n# Writing crs and reprojecting EDDI to lat/lon\neddi_da = eddi_da.rio.write_crs(crs.to_wkt())\neddi_da = eddi_da.transpose('time', 'combined_wl', 'simulation', 'y', 'x')\neddi_da_latlon = reproject_data(eddi_da, 'EPSG:4326')\ndel eddi_da_latlon.attrs[\"_FillValue\"]" + "source": "# Apply EDDI derived variable to the combined dataset\neddi_ds = list_derived_variables()[\"eddi\"].func(combined_ds)\neddi_da = eddi_ds[\"eddi\"]\n\n# Writing crs and reprojecting EDDI to lat/lon\neddi_da = eddi_da.rio.write_crs(crs.to_wkt())\neddi_da = eddi_da.transpose('time', 'combined_wl', 'sim', 'y', 'x')\neddi_da_latlon = reproject_data(eddi_da, 'EPSG:4326')\ndel eddi_da_latlon.attrs[\"_FillValue\"]" }, { "cell_type": "markdown", @@ -380,14 +356,7 @@ "cell_type": "markdown", "id": "d59ee541", "metadata": {}, - "source": [ - "The results will have the following dimensions:\n", - "- time\n", - "- wl (showing which WL EDDI was calibrated on, and then which WL EDDI was calculated on)\n", - "- lat\n", - "- lon\n", - "- simulation" - ] + "source": "The results will have the following dimensions:\n- time\n- wl (showing which WL EDDI was calibrated on, and then which WL EDDI was calculated on)\n- lat\n- lon\n- sim" }, { "cell_type": "code", @@ -429,7 +398,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (climakitae)", + "display_name": "climakitae", "language": "python", "name": "python3" }, @@ -443,7 +412,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.12.7" } }, "nbformat": 4, From 4fb66944de4c8ae5a4263d9c979be6969c67daea Mon Sep 17 00:00:00 2001 From: claalmve Date: Mon, 20 Apr 2026 10:14:19 -0700 Subject: [PATCH 41/53] Checking out nav guide from main --- AE_navigation_guide.ipynb | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/AE_navigation_guide.ipynb b/AE_navigation_guide.ipynb index 0b2bdf41..ad5480e1 100644 --- a/AE_navigation_guide.ipynb +++ b/AE_navigation_guide.ipynb @@ -49,23 +49,12 @@ "\n", "1. [basic_data_access.ipynb](data-access/basic_data_access.ipynb): Overview to accessing, spatially and temporally subsetting, and exporting climate data from the AE data catalog using helper functions in `climakitae`. Notebook type: Tool\n", "\n", -<<<<<<< HEAD - "2. [interactive_data_access_and_viz.ipynb](data-access/interactive_data_access_and_viz.ipynb): Retrieve, subset, and visualize data options using a simple and intuitive interactive graphical user interface (GUI). This notebook leverages functionality from both `climakitae` and our visualizations library `climakitaegui`. Notebook type: Tool\n", - "\n", - "3. [outside_AE_data_access.ipynb](data-access/intake_direct_data_download.ipynb): Access and export AE catalog data without using the helper functions from AE's python libraries. This notebook instead leverages the python library `intake` for interfacing with the data catalog. This method may be useful for users accessing the data outside of the Analytics Engine and who don't want to set up `climakitae` in their computational environment. Notebook type: Tool\n", - "\n", - "4. [renewables_data_access](data-access/renewables_data_access.ipynb\n", - "): Access and plot our derived renewables data products. This notebook leverages the Python library `intake` for interfacing with the renewables data catalog, which is separate from the larger AE catalog. Eventually, data access will be fully integrated into `climakitae`, but for now, this is the best way to access the renewables data. Notebook type: Tool\n", - "\n", - "5. [weather_station_data_access](data-access/weather_station_data_access\n", -======= "2. [outside_AE_data_access.ipynb](data-access/intake_direct_data_download.ipynb): Access and export AE catalog data without using the helper functions from AE's python libraries. This notebook instead leverages the python library `intake` for interfacing with the data catalog. This method may be useful for users accessing the data outside of the Analytics Engine and who don't want to set up `climakitae` in their computational environment. Notebook type: Tool\n", "\n", "3. [renewables_data_access](data-access/renewables_data_access.ipynb\n", "): Access and plot our derived renewables data products. This notebook leverages the Python library `intake` for interfacing with the renewables data catalog, which is separate from the larger AE catalog. Eventually, data access will be fully integrated into `climakitae`, but for now, this is the best way to access the renewables data. Notebook type: Tool\n", "\n", "4. [weather_station_data_access](data-access/weather_station_data_access\n", ->>>>>>> main "): Access and plot quality controlled, standardized historical weather station data. This notebook leverages the Python library `intake` for interfacing with the historical weather station data catalog, which is separate from the larger AE catalog. Eventually, data access will be fully integrated into `climakitae`, but for now, this is the best way to access the data. Notebook type: Tool\n", "\n", "------------------------------------------\n", From 04c7f5bce8d93ff4f70ff9246a54f42342339e5a Mon Sep 17 00:00:00 2001 From: claalmve Date: Mon, 20 Apr 2026 13:06:06 -0700 Subject: [PATCH 42/53] Reformatting notebook --- work-in-progress/drought_metrics.ipynb | 338 ++++++++++++++++++++++--- 1 file changed, 304 insertions(+), 34 deletions(-) diff --git a/work-in-progress/drought_metrics.ipynb b/work-in-progress/drought_metrics.ipynb index 20b8628c..8adb0070 100644 --- a/work-in-progress/drought_metrics.ipynb +++ b/work-in-progress/drought_metrics.ipynb @@ -55,7 +55,17 @@ "cell_type": "markdown", "id": "5c04c18f", "metadata": {}, - "source": "**Variables needed:**\n- `t2` (hourly) → daily `tasmin` and `tasmax` derived via resample\n- `relative humidity`\n- `radiation flux`\n - rsds (daily)\n - rsus (hourly → daily mean)\n - rlds (daily)\n - rlus (hourly → daily mean)\n- `wind speed (10m wind will be converted to 2m)`" + "source": [ + "**Variables needed:**\n", + "- `t2` (hourly) → daily `tasmin` and `tasmax` derived via resample\n", + "- `relative humidity`\n", + "- `radiation flux`\n", + " - rsds (daily)\n", + " - rsus (hourly → daily mean)\n", + " - rlds (daily)\n", + " - rlus (hourly → daily mean)\n", + "- `wind speed (10m wind will be converted to 2m)`" + ] }, { "cell_type": "markdown", @@ -82,7 +92,22 @@ "id": "a7e19917", "metadata": {}, "outputs": [], - "source": "import climakitae as ck\nimport xclim\nimport os\nimport xarray as xr\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pyproj import CRS\nfrom climate_indices.palmer import pdsi as palmer_pdsi\nfrom xclim.indices.stats import standardized_index\n\nfrom climakitae.core.data_export import export\nfrom climakitae.util.utils import reproject_data, add_dummy_time_to_wl\nfrom climakitae.new_core.derived_variables import register_user_function, list_derived_variables" + "source": [ + "import climakitae as ck\n", + "import xclim\n", + "import os\n", + "import xarray as xr\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pyproj import CRS\n", + "from climate_indices.palmer import pdsi as palmer_pdsi\n", + "from xclim.indices.stats import standardized_index\n", + "\n", + "from climakitae.core.data_export import export\n", + "from climakitae.util.utils import reproject_data, add_dummy_time_to_wl\n", + "from climakitae.new_core.derived_variables import register_user_function, list_derived_variables" + ] }, { "cell_type": "markdown", @@ -98,21 +123,41 @@ "id": "8b31d378", "metadata": {}, "outputs": [], - "source": "lat = 37.787964\nlon = -122.065063\n\nvariables_dict = {\n \"t2\": \"Air temperature at 2m (hourly; daily min/max derived)\",\n \"rh\": \"Relative humidity\",\n \"sw_dwn\": \"Instantaneous downwelling shortwave flux at bottom\",\n \"swupb\": \"Instantaneous upwelling shortwave flux at bottom (hourly)\",\n \"lw_dwn\": \"Instantaneous downwelling longwave flux at bottom\",\n \"lwupb\": \"Instantaneous upwelling longwave flux at bottom (hourly)\",\n \"wspd10mean\": \"Mean wind speed at 10m\",\n \"prec\": \"Precipitation (total)\",\n}" + "source": [ + "lat = 37.787964\n", + "lon = -122.065063\n", + "\n", + "variables_dict = {\n", + " \"t2\": \"Air temperature at 2m (hourly; daily min/max derived)\",\n", + " \"rh\": \"Relative humidity\",\n", + " \"sw_dwn\": \"Instantaneous downwelling shortwave flux at bottom\",\n", + " \"swupb\": \"Instantaneous upwelling shortwave flux at bottom (hourly)\",\n", + " \"lw_dwn\": \"Instantaneous downwelling longwave flux at bottom\",\n", + " \"lwupb\": \"Instantaneous upwelling longwave flux at bottom (hourly)\",\n", + " \"wspd10mean\": \"Mean wind speed at 10m\",\n", + " \"prec\": \"Precipitation (total)\",\n", + "}" + ] }, { "cell_type": "code", + "execution_count": null, "id": "9ze8p42ly5w", - "source": "# Making an `input_data` folder to hold intermediate data variables needed for PET\nif not os.path.exists(\"input_data\"):\n os.mkdir(\"input_data\")", "metadata": {}, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "# Making an `input_data` folder to hold intermediate data variables needed for PET\n", + "if not os.path.exists(\"input_data\"):\n", + " os.mkdir(\"input_data\")" + ] }, { "cell_type": "markdown", "id": "485befa5f23", - "source": "**IMPORTANT NOTE**: The following cell saves the intermediate data required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files.", - "metadata": {} + "metadata": {}, + "source": [ + "**IMPORTANT NOTE**: The following cell saves the intermediate data required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files." + ] }, { "cell_type": "code", @@ -120,7 +165,63 @@ "id": "a0132b29-804a-4eef-893f-2f60adfefc62", "metadata": {}, "outputs": [], - "source": "cd = ck.ClimateData(verbosity=-1)\nwarming_levels = [0.8, 1.5, 2.0, 3.0]\nprocesses = {\n \"clip\": [(lat - 0.04, lon - 0.04), (lat + 0.04, lon + 0.04)],\n \"warming_level\": {\"warming_levels\": warming_levels},\n}\n\ndef fetch(variable, table_id=\"day\", hourly=False):\n da = (\n cd.catalog(\"cadcat\")\n .activity_id(\"WRF\")\n .institution_id(\"UCLA\")\n .table_id(table_id)\n .grid_label(\"d03\")\n .variable(variable)\n .processes(processes)\n .get()\n )\n freq_name = \"hourly\" if hourly else \"daily\"\n return add_dummy_time_to_wl(da, freq_name=freq_name)\n\ndef fetch_or_load(variable, file_path, table_id=\"day\", hourly=False):\n if os.path.exists(file_path):\n print(f\"Reading {variable} from file.\")\n return xr.open_dataarray(file_path)\n print(f\"Fetching {variable}...\")\n da = fetch(variable, table_id=table_id, hourly=hourly)\n export(da, file_path, format=\"NetCDF\", mode=\"local\")\n return xr.open_dataarray(file_path)\n\n# Daily variables — fetch once and cache\nrh = fetch_or_load(\"rh\", \"input_data/rh_daily.nc\")\nsw_dwn = fetch_or_load(\"sw_dwn\", \"input_data/sw_dwn_daily.nc\")\nlw_dwn = fetch_or_load(\"lw_dwn\", \"input_data/lw_dwn_daily.nc\")\nwspd10mean = fetch_or_load(\"wspd10mean\", \"input_data/wspd10mean_daily.nc\")\nprec = fetch_or_load(\"prec\", \"input_data/prec_daily.nc\")\n\n# t2 is hourly; derive and cache the daily min/max directly to avoid storing large hourly data\ntasmin_path = \"input_data/tasmin_daily.nc\"\ntasmax_path = \"input_data/tasmax_daily.nc\"\nif os.path.exists(tasmin_path) and os.path.exists(tasmax_path):\n print(\"Reading tasmin, tasmax from file.\")\n tasmin = xr.open_dataarray(tasmin_path)\n tasmax = xr.open_dataarray(tasmax_path)\nelse:\n print(\"Fetching t2 (hourly)...\")\n t2 = fetch(\"t2\", table_id=\"1hr\", hourly=True)\n tasmin = t2.resample(time=\"1D\").min()\n tasmax = t2.resample(time=\"1D\").max()\n export(tasmin, tasmin_path, format=\"NetCDF\", mode=\"local\")\n export(tasmax, tasmax_path, format=\"NetCDF\", mode=\"local\")\n tasmin = xr.open_dataarray(tasmin_path)\n tasmax = xr.open_dataarray(tasmax_path)\n\nprint(\"Done.\")" + "source": [ + "cd = ck.ClimateData(verbosity=-1)\n", + "warming_levels = [0.8, 1.5, 2.0, 3.0]\n", + "processes = {\n", + " \"clip\": [(lat - 0.04, lon - 0.04), (lat + 0.04, lon + 0.04)],\n", + " \"warming_level\": {\"warming_levels\": warming_levels},\n", + "}\n", + "\n", + "def fetch(variable, table_id=\"day\", hourly=False):\n", + " da = (\n", + " cd.catalog(\"cadcat\")\n", + " .activity_id(\"WRF\")\n", + " .institution_id(\"UCLA\")\n", + " .table_id(table_id)\n", + " .grid_label(\"d03\")\n", + " .variable(variable)\n", + " .processes(processes)\n", + " .get()\n", + " )\n", + " freq_name = \"hourly\" if hourly else \"daily\"\n", + " return add_dummy_time_to_wl(da, freq_name=freq_name)\n", + "\n", + "def fetch_or_load(variable, file_path, table_id=\"day\", hourly=False):\n", + " if os.path.exists(file_path):\n", + " print(f\"Reading {variable} from file.\")\n", + " return xr.open_dataarray(file_path)\n", + " print(f\"Fetching {variable}...\")\n", + " da = fetch(variable, table_id=table_id, hourly=hourly)\n", + " export(da, file_path, format=\"NetCDF\", mode=\"local\")\n", + " return xr.open_dataarray(file_path)\n", + "\n", + "# Daily variables — fetch once and cache\n", + "rh = fetch_or_load(\"rh\", \"input_data/rh_daily.nc\")\n", + "sw_dwn = fetch_or_load(\"sw_dwn\", \"input_data/sw_dwn_daily.nc\")\n", + "lw_dwn = fetch_or_load(\"lw_dwn\", \"input_data/lw_dwn_daily.nc\")\n", + "wspd10mean = fetch_or_load(\"wspd10mean\", \"input_data/wspd10mean_daily.nc\")\n", + "prec = fetch_or_load(\"prec\", \"input_data/prec_daily.nc\")\n", + "\n", + "# t2 is hourly; derive and cache the daily min/max directly to avoid storing large hourly data\n", + "tasmin_path = \"input_data/tasmin_daily.nc\"\n", + "tasmax_path = \"input_data/tasmax_daily.nc\"\n", + "if os.path.exists(tasmin_path) and os.path.exists(tasmax_path):\n", + " print(\"Reading tasmin, tasmax from file.\")\n", + " tasmin = xr.open_dataarray(tasmin_path)\n", + " tasmax = xr.open_dataarray(tasmax_path)\n", + "else:\n", + " print(\"Fetching t2 (hourly)...\")\n", + " t2 = fetch(\"t2\", table_id=\"1hr\", hourly=True)\n", + " tasmin = t2.resample(time=\"1D\").min()\n", + " tasmax = t2.resample(time=\"1D\").max()\n", + " export(tasmin, tasmin_path, format=\"NetCDF\", mode=\"local\")\n", + " export(tasmax, tasmax_path, format=\"NetCDF\", mode=\"local\")\n", + " tasmin = xr.open_dataarray(tasmin_path)\n", + " tasmax = xr.open_dataarray(tasmax_path)\n", + "\n", + "print(\"Done.\")" + ] }, { "cell_type": "code", @@ -128,7 +229,34 @@ "id": "df54b41e", "metadata": {}, "outputs": [], - "source": "# Convert relative humidity from % to fraction as required by xclim\nhurs_frac = (rh / 100).assign_attrs(units='1')\n\n# swupb and lwupb are hourly; derive and cache daily means to avoid storing large hourly data\nrsus_path = \"input_data/swupb_daily.nc\"\nrlus_path = \"input_data/lwupb_daily.nc\"\n\nif os.path.exists(rsus_path):\n print(\"Reading swupb (daily) from file.\")\n rsus_daily = xr.open_dataarray(rsus_path)\nelse:\n print(\"Fetching swupb (hourly)...\")\n swupb_hrly = fetch(\"swupb\", table_id=\"1hr\", hourly=True)\n rsus_daily = swupb_hrly.resample(time=\"1D\").mean()\n export(rsus_daily, rsus_path, format=\"NetCDF\", mode=\"local\")\n rsus_daily = xr.open_dataarray(rsus_path)\n\nif os.path.exists(rlus_path):\n print(\"Reading lwupb (daily) from file.\")\n rlus_daily = xr.open_dataarray(rlus_path)\nelse:\n print(\"Fetching lwupb (hourly)...\")\n lwupb_hrly = fetch(\"lwupb\", table_id=\"1hr\", hourly=True)\n rlus_daily = lwupb_hrly.resample(time=\"1D\").mean()\n export(rlus_daily, rlus_path, format=\"NetCDF\", mode=\"local\")\n rlus_daily = xr.open_dataarray(rlus_path)" + "source": [ + "# Convert relative humidity from % to fraction as required by xclim\n", + "hurs_frac = (rh / 100).assign_attrs(units='1')\n", + "\n", + "# swupb and lwupb are hourly; derive and cache daily means to avoid storing large hourly data\n", + "rsus_path = \"input_data/swupb_daily.nc\"\n", + "rlus_path = \"input_data/lwupb_daily.nc\"\n", + "\n", + "if os.path.exists(rsus_path):\n", + " print(\"Reading swupb (daily) from file.\")\n", + " rsus_daily = xr.open_dataarray(rsus_path)\n", + "else:\n", + " print(\"Fetching swupb (hourly)...\")\n", + " swupb_hrly = fetch(\"swupb\", table_id=\"1hr\", hourly=True)\n", + " rsus_daily = swupb_hrly.resample(time=\"1D\").mean()\n", + " export(rsus_daily, rsus_path, format=\"NetCDF\", mode=\"local\")\n", + " rsus_daily = xr.open_dataarray(rsus_path)\n", + "\n", + "if os.path.exists(rlus_path):\n", + " print(\"Reading lwupb (daily) from file.\")\n", + " rlus_daily = xr.open_dataarray(rlus_path)\n", + "else:\n", + " print(\"Fetching lwupb (hourly)...\")\n", + " lwupb_hrly = fetch(\"lwupb\", table_id=\"1hr\", hourly=True)\n", + " rlus_daily = lwupb_hrly.resample(time=\"1D\").mean()\n", + " export(rlus_daily, rlus_path, format=\"NetCDF\", mode=\"local\")\n", + " rlus_daily = xr.open_dataarray(rlus_path)" + ] }, { "cell_type": "code", @@ -136,7 +264,20 @@ "id": "32f5bcba", "metadata": {}, "outputs": [], - "source": "# Calculating PET from xclim using the Penman-Monteith method\npet_calc = xclim.indices.potential_evapotranspiration(\n tasmin=tasmin,\n tasmax=tasmax,\n hurs=hurs_frac,\n rsds=sw_dwn,\n rsus=rsus_daily,\n rlds=lw_dwn,\n rlus=rlus_daily,\n sfcWind=wspd10mean,\n method=\"FAO_PM98\"\n)" + "source": [ + "# Calculating PET from xclim using the Penman-Monteith method\n", + "pet_calc = xclim.indices.potential_evapotranspiration(\n", + " tasmin=tasmin,\n", + " tasmax=tasmax,\n", + " hurs=hurs_frac,\n", + " rsds=sw_dwn,\n", + " rsus=rsus_daily,\n", + " rlds=lw_dwn,\n", + " rlus=rlus_daily,\n", + " sfcWind=wspd10mean,\n", + " method=\"FAO_PM98\"\n", + ")" + ] }, { "cell_type": "code", @@ -144,7 +285,11 @@ "id": "58cbcd75", "metadata": {}, "outputs": [], - "source": "# Preserving CRS and a spatial mask for later\ncrs = CRS.from_cf(pet_calc['Lambert_Conformal'].attrs)\nspatial_mask = ~pet_calc.isel(warming_level=0, time=0, sim=0).isnull()" + "source": [ + "# Preserving CRS and a spatial mask for later\n", + "crs = CRS.from_cf(pet_calc['Lambert_Conformal'].attrs)\n", + "spatial_mask = ~pet_calc.isel(warming_level=0, time=0, sim=0).isnull()" + ] }, { "cell_type": "markdown", @@ -158,7 +303,9 @@ "cell_type": "markdown", "id": "415dcfc7", "metadata": {}, - "source": "PDSI is registered as a derived variable using `register_user_function`. The underlying computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by drought.gov. The function is applied across all spatial and ensemble dimensions via `groupby().apply()`." + "source": [ + "PDSI is registered as a derived variable using `register_user_function`. The underlying computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by drought.gov. The function is applied across all spatial and ensemble dimensions via `groupby().apply()`." + ] }, { "cell_type": "code", @@ -166,7 +313,11 @@ "id": "deb75906", "metadata": {}, "outputs": [], - "source": "# Resampling PET and precip to monthly since the function only takes monthly variables\nmon_pet = (pet_calc * 86400 / 25.4).resample(time='1ME').sum()\nmon_precip = (prec / 25.4).resample(time='1ME').sum()" + "source": [ + "# Resampling PET and precip to monthly since the function only takes monthly variables\n", + "mon_pet = (pet_calc * 86400 / 25.4).resample(time='1ME').sum()\n", + "mon_precip = (prec / 25.4).resample(time='1ME').sum()" + ] }, { "cell_type": "code", @@ -251,7 +402,56 @@ "id": "c6b5de48", "metadata": {}, "outputs": [], - "source": "def pdsi_func(ds: xr.Dataset) -> xr.Dataset:\n \"\"\"Compute Palmer Drought Severity Index (PDSI) from a Dataset.\n\n Registered derived variable function: receives an xr.Dataset with 'precip' and\n 'pet' variables (both monthly, in inches) and adds 'pdsi' to it.\n\n Parameters\n ----------\n ds : xr.Dataset\n Dataset with 'precip' and 'pet' variables along a 'time' dimension,\n plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n\n Returns\n -------\n xr.Dataset\n Input dataset with 'pdsi' variable added.\n \"\"\"\n def _calc_pdsi_1d(timeseries: xr.Dataset) -> xr.DataArray:\n precip = timeseries['precip'].squeeze()\n pet = timeseries['pet'].squeeze()\n result = palmer_pdsi(\n precips=precip.values,\n pet=pet.values,\n awc=5,\n data_start_year=2000,\n calibration_year_initial=2000,\n calibration_year_final=2030,\n )\n return xr.DataArray(\n result[0], coords={\"time\": precip.time.values}, dims=['time']\n ).clip(min=-10, max=10)\n\n pdsi_da = ds.groupby(['combined_wl', 'x', 'y', 'sim']).apply(_calc_pdsi_1d)\n pdsi_da.attrs.update({\n \"long_name\": \"Palmer Drought Severity Index\",\n \"units\": \"from -10 (dry) to +10 (wet)\",\n })\n ds[\"pdsi\"] = pdsi_da\n return ds\n\nregister_user_function(\n name=\"pdsi\",\n depends_on=[\"precip\", \"pet\"],\n func=pdsi_func,\n description=\"Palmer Drought Severity Index computed from monthly precipitation and PET.\",\n units=\"unitless\",\n drop_dependencies=False,\n)" + "source": [ + "def pdsi_func(ds: xr.Dataset) -> xr.Dataset:\n", + " \"\"\"Compute Palmer Drought Severity Index (PDSI) from a Dataset.\n", + "\n", + " Registered derived variable function: receives an xr.Dataset with 'precip' and\n", + " 'pet' variables (both monthly, in inches) and adds 'pdsi' to it.\n", + "\n", + " Parameters\n", + " ----------\n", + " ds : xr.Dataset\n", + " Dataset with 'precip' and 'pet' variables along a 'time' dimension,\n", + " plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n", + "\n", + " Returns\n", + " -------\n", + " xr.Dataset\n", + " Input dataset with 'pdsi' variable added.\n", + " \"\"\"\n", + " def _calc_pdsi_1d(timeseries: xr.Dataset) -> xr.DataArray:\n", + " precip = timeseries['precip'].squeeze()\n", + " pet = timeseries['pet'].squeeze()\n", + " result = palmer_pdsi(\n", + " precips=precip.values,\n", + " pet=pet.values,\n", + " awc=5,\n", + " data_start_year=2000,\n", + " calibration_year_initial=2000,\n", + " calibration_year_final=2030,\n", + " )\n", + " return xr.DataArray(\n", + " result[0], coords={\"time\": precip.time.values}, dims=['time']\n", + " ).clip(min=-10, max=10)\n", + "\n", + " pdsi_da = ds.groupby(['combined_wl', 'x', 'y', 'sim']).apply(_calc_pdsi_1d)\n", + " pdsi_da.attrs.update({\n", + " \"long_name\": \"Palmer Drought Severity Index\",\n", + " \"units\": \"from -10 (dry) to +10 (wet)\",\n", + " })\n", + " ds[\"pdsi\"] = pdsi_da\n", + " return ds\n", + "\n", + "register_user_function(\n", + " name=\"pdsi\",\n", + " depends_on=[\"precip\", \"pet\"],\n", + " func=pdsi_func,\n", + " description=\"Palmer Drought Severity Index computed from monthly precipitation and PET.\",\n", + " units=\"unitless\",\n", + " drop_dependencies=False,\n", + ")" + ] }, { "cell_type": "code", @@ -259,7 +459,17 @@ "id": "c625f3af", "metadata": {}, "outputs": [], - "source": "# Apply PDSI derived variable to the combined dataset\npdsi_ds = list_derived_variables()[\"pdsi\"].func(combined_ds)\npdsi_da = pdsi_ds[\"pdsi\"]\n\n# Writing crs and reprojecting PDSI to lat/lon\npdsi_da = pdsi_da.rio.write_crs(crs.to_wkt())\npdsi_da = pdsi_da.transpose('time', 'combined_wl', 'sim', 'y', 'x')\npdsi_latlon = reproject_data(pdsi_da, 'EPSG:4326')\ndel pdsi_latlon.attrs[\"_FillValue\"]" + "source": [ + "# Apply PDSI derived variable to the combined dataset\n", + "pdsi_ds = list_derived_variables()[\"pdsi\"].func(combined_ds)\n", + "pdsi_da = pdsi_ds[\"pdsi\"]\n", + "\n", + "# Writing crs and reprojecting PDSI to lat/lon\n", + "pdsi_da = pdsi_da.rio.write_crs(crs.to_wkt())\n", + "pdsi_da = pdsi_da.transpose('time', 'combined_wl', 'sim', 'y', 'x')\n", + "pdsi_latlon = reproject_data(pdsi_da, 'EPSG:4326')\n", + "del pdsi_latlon.attrs[\"_FillValue\"]" + ] }, { "cell_type": "markdown", @@ -273,7 +483,14 @@ "cell_type": "markdown", "id": "2d745c44", "metadata": {}, - "source": "The results will have the following dimensions:\n- time\n- wl (showing which WL PDSI was calibrated on, and then which WL PDSI was calculated on)\n- lat\n- lon\n- sim" + "source": [ + "The results will have the following dimensions:\n", + "- time\n", + "- wl (showing which WL PDSI was calibrated on, and then which WL PDSI was calculated on)\n", + "- lat\n", + "- lon\n", + "- sim" + ] }, { "cell_type": "code", @@ -334,7 +551,55 @@ "id": "3228b9fb", "metadata": {}, "outputs": [], - "source": "def eddi_func(ds: xr.Dataset) -> xr.Dataset:\n \"\"\"Compute Evaporative Demand Drought Index (EDDI) from a Dataset.\n\n Registered derived variable function: receives an xr.Dataset with a 'pet'\n variable (monthly) and adds 'eddi' to it.\n\n Parameters\n ----------\n ds : xr.Dataset\n Dataset with 'pet' variable along a 'time' dimension,\n plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n\n Returns\n -------\n xr.Dataset\n Input dataset with 'eddi' variable added.\n \"\"\"\n def _calc_eddi_1d(timeseries: xr.DataArray) -> xr.DataArray:\n eddi = standardized_index(\n da=timeseries,\n freq='MS',\n window=1,\n dist=\"gamma\",\n method=\"ML\",\n zero_inflated=True,\n fitkwargs={},\n cal_start=\"2000-01-31\",\n cal_end=\"2029-12-31\",\n )\n return eddi.clip(min=-2.5, max=2.5)\n\n eddi_da = ds['pet'].groupby(['combined_wl', 'x', 'y', 'sim']).apply(_calc_eddi_1d)\n eddi_da.attrs.update({\n \"long_name\": \"Evaporative Demand Drought Index\",\n \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n })\n ds[\"eddi\"] = eddi_da\n return ds\n\nregister_user_function(\n name=\"eddi\",\n depends_on=[\"pet\"],\n func=eddi_func,\n description=\"Evaporative Demand Drought Index computed from monthly PET.\",\n units=\"unitless\",\n drop_dependencies=False,\n)" + "source": [ + "def eddi_func(ds: xr.Dataset) -> xr.Dataset:\n", + " \"\"\"Compute Evaporative Demand Drought Index (EDDI) from a Dataset.\n", + "\n", + " Registered derived variable function: receives an xr.Dataset with a 'pet'\n", + " variable (monthly) and adds 'eddi' to it.\n", + "\n", + " Parameters\n", + " ----------\n", + " ds : xr.Dataset\n", + " Dataset with 'pet' variable along a 'time' dimension,\n", + " plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n", + "\n", + " Returns\n", + " -------\n", + " xr.Dataset\n", + " Input dataset with 'eddi' variable added.\n", + " \"\"\"\n", + " def _calc_eddi_1d(timeseries: xr.DataArray) -> xr.DataArray:\n", + " eddi = standardized_index(\n", + " da=timeseries,\n", + " freq='MS',\n", + " window=1,\n", + " dist=\"gamma\",\n", + " method=\"ML\",\n", + " zero_inflated=True,\n", + " fitkwargs={},\n", + " cal_start=\"2000-01-31\",\n", + " cal_end=\"2029-12-31\",\n", + " )\n", + " return eddi.clip(min=-2.5, max=2.5)\n", + "\n", + " eddi_da = ds['pet'].groupby(['combined_wl', 'x', 'y', 'sim']).apply(_calc_eddi_1d)\n", + " eddi_da.attrs.update({\n", + " \"long_name\": \"Evaporative Demand Drought Index\",\n", + " \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n", + " })\n", + " ds[\"eddi\"] = eddi_da\n", + " return ds\n", + "\n", + "register_user_function(\n", + " name=\"eddi\",\n", + " depends_on=[\"pet\"],\n", + " func=eddi_func,\n", + " description=\"Evaporative Demand Drought Index computed from monthly PET.\",\n", + " units=\"unitless\",\n", + " drop_dependencies=False,\n", + ")" + ] }, { "cell_type": "code", @@ -342,7 +607,17 @@ "id": "1240839c", "metadata": {}, "outputs": [], - "source": "# Apply EDDI derived variable to the combined dataset\neddi_ds = list_derived_variables()[\"eddi\"].func(combined_ds)\neddi_da = eddi_ds[\"eddi\"]\n\n# Writing crs and reprojecting EDDI to lat/lon\neddi_da = eddi_da.rio.write_crs(crs.to_wkt())\neddi_da = eddi_da.transpose('time', 'combined_wl', 'sim', 'y', 'x')\neddi_da_latlon = reproject_data(eddi_da, 'EPSG:4326')\ndel eddi_da_latlon.attrs[\"_FillValue\"]" + "source": [ + "# Apply EDDI derived variable to the combined dataset\n", + "eddi_ds = list_derived_variables()[\"eddi\"].func(combined_ds)\n", + "eddi_da = eddi_ds[\"eddi\"]\n", + "\n", + "# Writing crs and reprojecting EDDI to lat/lon\n", + "eddi_da = eddi_da.rio.write_crs(crs.to_wkt())\n", + "eddi_da = eddi_da.transpose('time', 'combined_wl', 'sim', 'y', 'x')\n", + "eddi_da_latlon = reproject_data(eddi_da, 'EPSG:4326')\n", + "del eddi_da_latlon.attrs[\"_FillValue\"]" + ] }, { "cell_type": "markdown", @@ -356,7 +631,14 @@ "cell_type": "markdown", "id": "d59ee541", "metadata": {}, - "source": "The results will have the following dimensions:\n- time\n- wl (showing which WL EDDI was calibrated on, and then which WL EDDI was calculated on)\n- lat\n- lon\n- sim" + "source": [ + "The results will have the following dimensions:\n", + "- time\n", + "- wl (showing which WL EDDI was calibrated on, and then which WL EDDI was calculated on)\n", + "- lat\n", + "- lon\n", + "- sim" + ] }, { "cell_type": "code", @@ -398,23 +680,11 @@ ], "metadata": { "kernelspec": { - "display_name": "climakitae", + "display_name": "Python 3 (climakitae)", "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": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From a1ba22bbfcca30fb29641d5103fdce95b2bcda7f Mon Sep 17 00:00:00 2001 From: claalmve Date: Tue, 21 Apr 2026 14:03:15 -0700 Subject: [PATCH 43/53] Working on drought metrics more --- work-in-progress/drought_metrics.ipynb | 290 +++++++++++-------------- 1 file changed, 126 insertions(+), 164 deletions(-) diff --git a/work-in-progress/drought_metrics.ipynb b/work-in-progress/drought_metrics.ipynb index 8adb0070..2345cf1c 100644 --- a/work-in-progress/drought_metrics.ipynb +++ b/work-in-progress/drought_metrics.ipynb @@ -13,7 +13,7 @@ "id": "64641b82", "metadata": {}, "source": [ - "This notebook calculates two drought metrics, the Palmer Drought Severity Index (PDSI) and the Evaporative Demand Drought Index (EDDI), using WRF data in the AE catalog. Both PDSI and EDDI require Potentinal Evaportranspiration (PET). PET is computed first using the Penman-Monteith method. At the end of the notebook, the user will be able to export monthly PDSI and EDDI for under different global warming levels for a specific lat/lon as netcdf files to be used for further analyses. " + "This notebook calculates two drought metrics, the Palmer Drought Severity Index (PDSI) and the Evaporative Demand Drought Index (EDDI), using WRF data in the AE catalog. Both PDSI and EDDI require Potential Evapotranspiration (PET). PET is computed first using the Penman-Monteith method. At the end of the notebook, the user will be able to export monthly PDSI and EDDI for under different global warming levels for a specific lat/lon as netcdf files to be used for further analyses. " ] }, { @@ -21,7 +21,7 @@ "id": "98506237", "metadata": {}, "source": [ - "**Intended Application:** As a user, I want to export future drought indicies for different global warming levels by:\n", + "**Intended Application:** As a user, I want to export future drought indices for different global warming levels by:\n", "1. Calculating PET using the Penman-Montieth Method\n", "2. Calculating the PDSI using PET and exporting the monthly timeseries to a netcdf\n", "3. Calculating the EDDI using PET and exporting the monthly timeseries to a netcdf" @@ -43,36 +43,12 @@ "**Troubleshooting:** Getting an `IndexError: index 40 is out of bounds for axis 0 with size 40` when trying to run the PDSI calculation? Try changing your lat/lon or point of interest further away from the boundaries of our 3 km WRF domain (as seen in [this graphic](https://analytics.cal-adapt.org/faq/#what-data-is-available))." ] }, - { - "cell_type": "markdown", - "id": "a5de287f", - "metadata": {}, - "source": [ - "### Using the Penman-Monteith method (most physically accurate) to calculate Potential Evapotranspiration" - ] - }, - { - "cell_type": "markdown", - "id": "5c04c18f", - "metadata": {}, - "source": [ - "**Variables needed:**\n", - "- `t2` (hourly) → daily `tasmin` and `tasmax` derived via resample\n", - "- `relative humidity`\n", - "- `radiation flux`\n", - " - rsds (daily)\n", - " - rsus (hourly → daily mean)\n", - " - rlds (daily)\n", - " - rlus (hourly → daily mean)\n", - "- `wind speed (10m wind will be converted to 2m)`" - ] - }, { "cell_type": "markdown", "id": "3babc840", "metadata": {}, "source": [ - "### Imports" + "## Step 0: Setup" ] }, { @@ -106,57 +82,59 @@ "\n", "from climakitae.core.data_export import export\n", "from climakitae.util.utils import reproject_data, add_dummy_time_to_wl\n", - "from climakitae.new_core.derived_variables import register_user_function, list_derived_variables" + "from climakitae.new_core.derived_variables import register_user_function, list_derived_variables\n", + "\n", + "# Making an `input_data` folder to hold intermediate data variables needed for PET\n", + "if not os.path.exists(\"input_data\"):\n", + " os.mkdir(\"input_data\")" ] }, { - "cell_type": "markdown", - "id": "7c5626f6", + "cell_type": "code", + "execution_count": null, + "id": "8b31d378", "metadata": {}, + "outputs": [], "source": [ - "### Initial Setup" + "# Define the lat/lon for the location of interest and the variables needed for the Penman-Monteith PET method\n", + "LAT = 37.787964\n", + "LON = -122.065063\n", + "WARMING_LEVELS = [0.8, 2.0]" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "8b31d378", + "cell_type": "markdown", + "id": "485befa5f23", "metadata": {}, - "outputs": [], "source": [ - "lat = 37.787964\n", - "lon = -122.065063\n", + "## Step 1: Fetch Data and Calculate PET\n", "\n", - "variables_dict = {\n", - " \"t2\": \"Air temperature at 2m (hourly; daily min/max derived)\",\n", - " \"rh\": \"Relative humidity\",\n", - " \"sw_dwn\": \"Instantaneous downwelling shortwave flux at bottom\",\n", - " \"swupb\": \"Instantaneous upwelling shortwave flux at bottom (hourly)\",\n", - " \"lw_dwn\": \"Instantaneous downwelling longwave flux at bottom\",\n", - " \"lwupb\": \"Instantaneous upwelling longwave flux at bottom (hourly)\",\n", - " \"wspd10mean\": \"Mean wind speed at 10m\",\n", - " \"prec\": \"Precipitation (total)\",\n", - "}" + "**IMPORTANT NOTE**: The following cell saves the intermediate data required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files." ] }, { - "cell_type": "code", - "execution_count": null, - "id": "9ze8p42ly5w", + "cell_type": "markdown", + "id": "522c9425", "metadata": {}, - "outputs": [], "source": [ - "# Making an `input_data` folder to hold intermediate data variables needed for PET\n", - "if not os.path.exists(\"input_data\"):\n", - " os.mkdir(\"input_data\")" + "### Penman-Monteith PET Method (most physically consistent)\n", + "We will be using the PET method for calculating PDSI, since it is the most physically consistent method. Below, we list out the variables we will need." ] }, { "cell_type": "markdown", - "id": "485befa5f23", + "id": "cc88bd71", "metadata": {}, "source": [ - "**IMPORTANT NOTE**: The following cell saves the intermediate data required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files." + "**Variables needed:**\n", + "- `t2` (hourly) → daily `tasmin` and `tasmax` derived via resample\n", + "- `relative humidity`\n", + "- `radiation flux`\n", + " - rsds (daily)\n", + " - rsus (hourly → daily mean)\n", + " - rlds (daily)\n", + " - rlus (hourly → daily mean)\n", + "- `wind speed (10m wind will be converted to 2m)`" ] }, { @@ -166,15 +144,19 @@ "metadata": {}, "outputs": [], "source": [ + "# Initialize the ClimateData object and define the processors needed.\n", "cd = ck.ClimateData(verbosity=-1)\n", - "warming_levels = [0.8, 1.5, 2.0, 3.0]\n", "processes = {\n", - " \"clip\": [(lat - 0.04, lon - 0.04), (lat + 0.04, lon + 0.04)],\n", - " \"warming_level\": {\"warming_levels\": warming_levels},\n", + " \"clip\": [(LAT - 0.04, LON - 0.04), (LAT + 0.04, LON + 0.04)],\n", + " \"warming_level\": {\n", + " \"warming_levels\": WARMING_LEVELS,\n", + " \"add_dummy_time\": True\n", + " },\n", "}\n", "\n", - "def fetch(variable, table_id=\"day\", hourly=False):\n", - " da = (\n", + "def fetch(variable, table_id=\"day\"):\n", + " \"\"\"Fetch a WRF variable from the AE catalog with the configured processes.\"\"\"\n", + " return (\n", " cd.catalog(\"cadcat\")\n", " .activity_id(\"WRF\")\n", " .institution_id(\"UCLA\")\n", @@ -184,78 +166,59 @@ " .processes(processes)\n", " .get()\n", " )\n", - " freq_name = \"hourly\" if hourly else \"daily\"\n", - " return add_dummy_time_to_wl(da, freq_name=freq_name)\n", "\n", - "def fetch_or_load(variable, file_path, table_id=\"day\", hourly=False):\n", + "def fetch_or_load(variable, file_path, table_id=\"day\", transform=None):\n", + " \"\"\"Load from cache if available, otherwise fetch, optionally transform, and cache.\"\"\"\n", " if os.path.exists(file_path):\n", " print(f\"Reading {variable} from file.\")\n", " return xr.open_dataarray(file_path)\n", " print(f\"Fetching {variable}...\")\n", - " da = fetch(variable, table_id=table_id, hourly=hourly)\n", + " da = fetch(variable, table_id=table_id)\n", + " if transform:\n", + " da = transform(da)\n", " export(da, file_path, format=\"NetCDF\", mode=\"local\")\n", " return xr.open_dataarray(file_path)\n", "\n", - "# Daily variables — fetch once and cache\n", + "# Daily variables\n", "rh = fetch_or_load(\"rh\", \"input_data/rh_daily.nc\")\n", "sw_dwn = fetch_or_load(\"sw_dwn\", \"input_data/sw_dwn_daily.nc\")\n", "lw_dwn = fetch_or_load(\"lw_dwn\", \"input_data/lw_dwn_daily.nc\")\n", "wspd10mean = fetch_or_load(\"wspd10mean\", \"input_data/wspd10mean_daily.nc\")\n", "prec = fetch_or_load(\"prec\", \"input_data/prec_daily.nc\")\n", "\n", - "# t2 is hourly; derive and cache the daily min/max directly to avoid storing large hourly data\n", - "tasmin_path = \"input_data/tasmin_daily.nc\"\n", - "tasmax_path = \"input_data/tasmax_daily.nc\"\n", - "if os.path.exists(tasmin_path) and os.path.exists(tasmax_path):\n", - " print(\"Reading tasmin, tasmax from file.\")\n", - " tasmin = xr.open_dataarray(tasmin_path)\n", - " tasmax = xr.open_dataarray(tasmax_path)\n", - "else:\n", - " print(\"Fetching t2 (hourly)...\")\n", - " t2 = fetch(\"t2\", table_id=\"1hr\", hourly=True)\n", - " tasmin = t2.resample(time=\"1D\").min()\n", - " tasmax = t2.resample(time=\"1D\").max()\n", - " export(tasmin, tasmin_path, format=\"NetCDF\", mode=\"local\")\n", - " export(tasmax, tasmax_path, format=\"NetCDF\", mode=\"local\")\n", - " tasmin = xr.open_dataarray(tasmin_path)\n", - " tasmax = xr.open_dataarray(tasmax_path)\n", + "# Hourly variables resampled to daily — t2 is fetched twice (min/max) but cached after first run\n", + "tasmin = fetch_or_load(\"t2\", \"input_data/tasmin_daily.nc\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").min())\n", + "tasmax = fetch_or_load(\"t2\", \"input_data/tasmax_daily.nc\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").max())\n", + "rsus_daily = fetch_or_load(\"swupb\", \"input_data/swupb_daily.nc\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", + "rlus_daily = fetch_or_load(\"lwupb\", \"input_data/lwupb_daily.nc\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", + "\n", + "# xclim requires relative humidity as a fraction (0–1), not a percentage\n", + "hurs_frac = (rh / 100).assign_attrs(units='1')\n", "\n", - "print(\"Done.\")" + "print(\"Finished.\")" + ] + }, + { + "cell_type": "markdown", + "id": "c74e6120", + "metadata": {}, + "source": [ + "#### Calculate PET from `xclim` using the Penman-Monteith Method" ] }, { "cell_type": "code", "execution_count": null, - "id": "df54b41e", + "id": "1e29e94d", "metadata": {}, "outputs": [], "source": [ - "# Convert relative humidity from % to fraction as required by xclim\n", - "hurs_frac = (rh / 100).assign_attrs(units='1')\n", - "\n", - "# swupb and lwupb are hourly; derive and cache daily means to avoid storing large hourly data\n", - "rsus_path = \"input_data/swupb_daily.nc\"\n", - "rlus_path = \"input_data/lwupb_daily.nc\"\n", - "\n", - "if os.path.exists(rsus_path):\n", - " print(\"Reading swupb (daily) from file.\")\n", - " rsus_daily = xr.open_dataarray(rsus_path)\n", - "else:\n", - " print(\"Fetching swupb (hourly)...\")\n", - " swupb_hrly = fetch(\"swupb\", table_id=\"1hr\", hourly=True)\n", - " rsus_daily = swupb_hrly.resample(time=\"1D\").mean()\n", - " export(rsus_daily, rsus_path, format=\"NetCDF\", mode=\"local\")\n", - " rsus_daily = xr.open_dataarray(rsus_path)\n", - "\n", - "if os.path.exists(rlus_path):\n", - " print(\"Reading lwupb (daily) from file.\")\n", - " rlus_daily = xr.open_dataarray(rlus_path)\n", - "else:\n", - " print(\"Fetching lwupb (hourly)...\")\n", - " lwupb_hrly = fetch(\"lwupb\", table_id=\"1hr\", hourly=True)\n", - " rlus_daily = lwupb_hrly.resample(time=\"1D\").mean()\n", - " export(rlus_daily, rlus_path, format=\"NetCDF\", mode=\"local\")\n", - " rlus_daily = xr.open_dataarray(rlus_path)" + "for name, da in [\n", + " (\"tasmin\", tasmin), (\"tasmax\", tasmax), (\"hurs_frac\", hurs_frac),\n", + " (\"sw_dwn\", sw_dwn), (\"rsus_daily\", rsus_daily),\n", + " (\"lw_dwn\", lw_dwn), (\"rlus_daily\", rlus_daily), (\"wspd10mean\", wspd10mean)\n", + " ]:\n", + " print(name, da.sizes)" ] }, { @@ -265,18 +228,23 @@ "metadata": {}, "outputs": [], "source": [ - "# Calculating PET from xclim using the Penman-Monteith method\n", - "pet_calc = xclim.indices.potential_evapotranspiration(\n", - " tasmin=tasmin,\n", - " tasmax=tasmax,\n", - " hurs=hurs_frac,\n", - " rsds=sw_dwn,\n", - " rsus=rsus_daily,\n", - " rlds=lw_dwn,\n", - " rlus=rlus_daily,\n", - " sfcWind=wspd10mean,\n", - " method=\"FAO_PM98\"\n", - ")" + "pet_calc = xr.concat(\n", + " [\n", + " xclim.indices.potential_evapotranspiration(\n", + " tasmin=tasmin.sel(warming_level=wl),\n", + " tasmax=tasmax.sel(warming_level=wl),\n", + " hurs=hurs_frac.sel(warming_level=wl),\n", + " rsds=sw_dwn.sel(warming_level=wl),\n", + " rsus=rsus_daily.sel(warming_level=wl),\n", + " rlds=lw_dwn.sel(warming_level=wl),\n", + " rlus=rlus_daily.sel(warming_level=wl),\n", + " sfcWind=wspd10mean.sel(warming_level=wl),\n", + " method=\"FAO_PM98\",\n", + " )\n", + " for wl in WARMING_LEVELS\n", + " ],\n", + " dim=\"warming_level\",\n", + ").assign_coords(warming_level=WARMING_LEVELS)" ] }, { @@ -296,7 +264,7 @@ "id": "f8d01841", "metadata": {}, "source": [ - "# PDSI" + "## Step 2: Calculate PDSI" ] }, { @@ -387,8 +355,8 @@ "outputs": [], "source": [ "# Creating one Dataset of PET and precip with WLs combined\n", - "mon_pet_transform = combine_wl_to_dummy_time(mon_pet, baseline_wl=0.8, future_wls=[1.5,2.0,3.0])\n", - "mon_precip_transform = combine_wl_to_dummy_time(mon_precip, baseline_wl=0.8, future_wls=[1.5,2.0,3.0])\n", + "mon_pet_transform = combine_wl_to_dummy_time(mon_pet, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:]\n", + "mon_precip_transform = combine_wl_to_dummy_time(mon_precip, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", "\n", "combined_ds = xr.Dataset({'precip': mon_precip_transform, 'pet': mon_pet_transform})\n", "\n", @@ -476,7 +444,7 @@ "id": "6cc3157f", "metadata": {}, "source": [ - "### Exporting the results" + "### Export PDSI" ] }, { @@ -484,12 +452,13 @@ "id": "2d745c44", "metadata": {}, "source": [ - "The results will have the following dimensions:\n", - "- time\n", - "- wl (showing which WL PDSI was calibrated on, and then which WL PDSI was calculated on)\n", - "- lat\n", - "- lon\n", - "- sim" + "The combined time axis has 720 months total: months 0–359 are the baseline warming level (0.8°C, used for calibration), and months 360–719 are the future warming level period. The export below slices to `time=slice(360, 720)` to retain only the future period.\n", + "\n", + "The exported file will have the following dimensions:\n", + "- `time`: 360 months (30-year future warming level period)\n", + "- `wl`: warming level pair (e.g., `\"08_to_20\"` = calibrated on 0.8°C, evaluated on 2.0°C)\n", + "- `lat`, `lon`: spatial coordinates (EPSG:4326)\n", + "- `sim`: ensemble member" ] }, { @@ -506,7 +475,7 @@ " \"long_name\": \"Palmer Drought Severity Index\",\n", " \"units\": \"from -10 (dry) to +10 (wet)\",\n", "})\n", - "pdsi_filename = f\"pdsi_wl_lat{str(lat).replace('.', '_')}_lon{str(lon).replace('.', '_')}.nc\"" + "pdsi_filename = f\"pdsi_wl_lat{str(LAT).replace('.', '_')}_lon{str(LON).replace('.', '_')}.nc\"" ] }, { @@ -516,17 +485,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Exporting the DataArray\n", - "if os.path.exists(pdsi_filename):\n", - " raise Exception(\n", - " (\n", - " f\"File {pdsi_filename} exists. \"\n", - " \"Please either delete that file from the work space \"\n", - " \"or specify a new file name here.\"\n", - " )\n", - " )\n", - "else:\n", - " final_pdsi.to_netcdf(pdsi_filename, encoding={\"pdsi\": {\"_FillValue\": -9999.0}})" + "export(final_pdsi, pdsi_filename, format=\"NetCDF\", mode=\"local\")" ] }, { @@ -534,7 +493,7 @@ "id": "3dc2e324", "metadata": {}, "source": [ - "## EDDI" + "## Step 3: Calculate EDDI" ] }, { @@ -624,7 +583,7 @@ "id": "45176f23", "metadata": {}, "source": [ - "### Exporting the results" + "### Export EDDI" ] }, { @@ -632,12 +591,13 @@ "id": "d59ee541", "metadata": {}, "source": [ - "The results will have the following dimensions:\n", - "- time\n", - "- wl (showing which WL EDDI was calibrated on, and then which WL EDDI was calculated on)\n", - "- lat\n", - "- lon\n", - "- sim" + "The combined time axis has 720 months total: months 0–359 are the baseline warming level (0.8°C, used for calibration), and months 360–719 are the future warming level period. The export below slices to `time=slice(360, 720)` to retain only the future period.\n", + "\n", + "The exported file will have the following dimensions:\n", + "- `time`: 360 months (30-year future warming level period)\n", + "- `wl`: warming level pair (e.g., `\"08_to_20\"` = calibrated on 0.8°C, evaluated on 2.0°C)\n", + "- `lat`, `lon`: spatial coordinates (EPSG:4326)\n", + "- `sim`: ensemble member" ] }, { @@ -654,7 +614,7 @@ " \"long_name\": \"Evaporative Demand Drought Index\",\n", " \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n", "})\n", - "eddi_filename = f\"eddi_wl_lat{str(lat).replace('.', '_')}_lon{str(lon).replace('.', '_')}.nc\"" + "eddi_filename = f\"eddi_wl_lat{str(LAT).replace('.', '_')}_lon{str(LON).replace('.', '_')}.nc\"" ] }, { @@ -664,17 +624,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Exporting the DataArray\n", - "if os.path.exists(eddi_filename):\n", - " raise Exception(\n", - " (\n", - " f\"File {eddi_filename} exists. \"\n", - " \"Please either delete that file from the work space \"\n", - " \"or specify a new file name here.\"\n", - " )\n", - " )\n", - "else:\n", - " final_eddi.to_netcdf(eddi_filename, encoding={\"eddi\": {\"_FillValue\": -9999.0}})" + "export(final_eddi, eddi_filename, format=\"NetCDF\", mode=\"local\")" ] } ], @@ -683,6 +633,18 @@ "display_name": "Python 3 (climakitae)", "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": "3.12.12" } }, "nbformat": 4, From 7734720e20d881653583b86c3e08dc7817901799 Mon Sep 17 00:00:00 2001 From: claalmve Date: Fri, 24 Apr 2026 15:12:27 -0600 Subject: [PATCH 44/53] Adding the latest progress notebook of drought metrics and debugging --- work-in-progress/drought_metrics_latest.ipynb | 936 ++++++++++++++++++ 1 file changed, 936 insertions(+) create mode 100644 work-in-progress/drought_metrics_latest.ipynb diff --git a/work-in-progress/drought_metrics_latest.ipynb b/work-in-progress/drought_metrics_latest.ipynb new file mode 100644 index 00000000..01458b14 --- /dev/null +++ b/work-in-progress/drought_metrics_latest.ipynb @@ -0,0 +1,936 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5e0361f0", + "metadata": {}, + "source": [ + "# Drought Metrics" + ] + }, + { + "cell_type": "markdown", + "id": "64641b82", + "metadata": {}, + "source": [ + "This notebook calculates two drought metrics, the Palmer Drought Severity Index (PDSI) and the Evaporative Demand Drought Index (EDDI), using WRF data in the AE catalog. Both PDSI and EDDI require Potential Evapotranspiration (PET). PET is computed first using the Penman-Monteith method. At the end of the notebook, the user will be able to export monthly PDSI and EDDI for under different global warming levels for a specific lat/lon as netcdf files to be used for further analyses. " + ] + }, + { + "cell_type": "markdown", + "id": "98506237", + "metadata": {}, + "source": [ + "**Intended Application:** As a user, I want to export future drought indices for different global warming levels by:\n", + "1. Calculating PET using the Penman-Montieth Method\n", + "2. Calculating the PDSI using PET and exporting the monthly timeseries to a netcdf\n", + "3. Calculating the EDDI using PET and exporting the monthly timeseries to a netcdf" + ] + }, + { + "cell_type": "markdown", + "id": "cc35f46e", + "metadata": {}, + "source": [ + "**Runtime:** With the default settings, this notebook takes approximately 25 minutes to run from start to finish. Modifications to selections may increase the runtime." + ] + }, + { + "cell_type": "markdown", + "id": "89ab2f9c", + "metadata": {}, + "source": [ + "**Troubleshooting:** Getting an `IndexError: index 40 is out of bounds for axis 0 with size 40` when trying to run the PDSI calculation? Try changing your lat/lon or point of interest further away from the boundaries of our 3 km WRF domain (as seen in [this graphic](https://analytics.cal-adapt.org/faq/#what-data-is-available))." + ] + }, + { + "cell_type": "markdown", + "id": "3babc840", + "metadata": {}, + "source": [ + "## Step 0: Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "xypsmbxjeoj", + "metadata": {}, + "outputs": [], + "source": [ + "# Install climate_indices at a specific commit compatible with the AE hub environment\n", + "!pip install -qq git+https://github.com/monocongo/climate_indices.git@43c5451" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a7e19917", + "metadata": {}, + "outputs": [], + "source": [ + "import climakitae as ck\n", + "import xclim\n", + "import xarray as xr\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import flox\n", + "from pyproj import CRS\n", + "from dask.diagnostics import ProgressBar\n", + "from climate_indices.palmer import pdsi as palmer_pdsi\n", + "from xclim.indices.stats import standardized_index" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "id": "8b31d378", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the lat/lon for the location of interest and the variables needed for the Penman-Monteith PET method\n", + "LAT = 37.787964\n", + "# LAT = 37.830358\n", + "LON = -122.065063\n", + "# LON = -122.261082\n", + "WARMING_LEVELS = [0.8, 2.0]" + ] + }, + { + "cell_type": "markdown", + "id": "485befa5f23", + "metadata": {}, + "source": [ + "## Step 1: Fetch Data and Calculate PET\n", + "\n", + "**IMPORTANT NOTE**: The following cell saves the intermediate data required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files." + ] + }, + { + "cell_type": "markdown", + "id": "522c9425", + "metadata": {}, + "source": [ + "### Penman-Monteith PET Method (most physically consistent)\n", + "We will be using the PET method for calculating PDSI, since it is the most physically consistent method. Below, we list out the variables we will need." + ] + }, + { + "cell_type": "markdown", + "id": "cc88bd71", + "metadata": {}, + "source": [ + "**Variables needed:**\n", + "- `t2` (hourly) → daily `tasmin` and `tasmax` derived via resample\n", + "- `relative humidity`\n", + "- `radiation flux`\n", + " - rsds (daily)\n", + " - rsus (hourly → daily mean)\n", + " - rlds (daily)\n", + " - rlus (hourly → daily mean)\n", + "- `wind speed (10m wind will be converted to 2m)`" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "a0132b29-804a-4eef-893f-2f60adfefc62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching rh...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching sw_dwn...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[161]\u001b[39m\u001b[32m, line 49\u001b[39m\n\u001b[32m 47\u001b[39m \u001b[38;5;66;03m# Daily variables\u001b[39;00m\n\u001b[32m 48\u001b[39m rh = get_and_transform(\u001b[33m\"\u001b[39m\u001b[33mrh\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m49\u001b[39m sw_dwn = \u001b[43mget_and_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msw_dwn\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 50\u001b[39m lw_dwn = get_and_transform(\u001b[33m\"\u001b[39m\u001b[33mlw_dwn\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 51\u001b[39m wspd10mean = get_and_transform(\u001b[33m\"\u001b[39m\u001b[33mwspd10mean\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[161]\u001b[39m\u001b[32m, line 37\u001b[39m, in \u001b[36mget_and_transform\u001b[39m\u001b[34m(variable, table_id, units, transform)\u001b[39m\n\u001b[32m 35\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Fetching variables and applying a transformation if it's passed in.\"\"\"\u001b[39;00m\n\u001b[32m 36\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mFetching \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvariable\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m...\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m37\u001b[39m da = \u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtable_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtable_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m=\u001b[49m\u001b[43munits\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 38\u001b[39m da = da.unify_chunks() \u001b[38;5;66;03m# Ensure the data is chunked for Dask processing\u001b[39;00m\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m transform:\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[161]\u001b[39m\u001b[32m, line 31\u001b[39m, in \u001b[36mfetch\u001b[39m\u001b[34m(variable, table_id, units)\u001b[39m\n\u001b[32m 21\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m units \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 22\u001b[39m proc[\u001b[33m\"\u001b[39m\u001b[33mconvert_units\u001b[39m\u001b[33m\"\u001b[39m] = units\n\u001b[32m 23\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[32m 24\u001b[39m \u001b[43mcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcatalog\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcadcat\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 25\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mactivity_id\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mWRF\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 26\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43minstitution_id\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mUCLA\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 27\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mtable_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtable_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 28\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mgrid_label\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43md03\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 29\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 30\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mprocesses\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m---> \u001b[39m\u001b[32m31\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 32\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/new_core/user_interface.py:872\u001b[39m, in \u001b[36mClimateData.get\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 869\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 870\u001b[39m \u001b[38;5;66;03m# Execute the query with the snapshot\u001b[39;00m\n\u001b[32m 871\u001b[39m logger.debug(\u001b[33m\"\u001b[39m\u001b[33mExecuting query\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m872\u001b[39m data = \u001b[43mdataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_snapshot\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 873\u001b[39m \u001b[38;5;66;03m# check if empty dataset\u001b[39;00m\n\u001b[32m 874\u001b[39m \u001b[38;5;66;03m# Check if data is empty/null\u001b[39;00m\n\u001b[32m 875\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 876\u001b[39m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 877\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28mhasattr\u001b[39m(data, \u001b[33m\"\u001b[39m\u001b[33mnbytes\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m data.nbytes == \u001b[32m0\u001b[39m)\n\u001b[32m 878\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data)\n\u001b[32m 879\u001b[39m ):\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/new_core/dataset.py:246\u001b[39m, in \u001b[36mDataset.execute\u001b[39m\u001b[34m(self, parameters)\u001b[39m\n\u001b[32m 243\u001b[39m \u001b[38;5;66;03m# Execute the current step\u001b[39;00m\n\u001b[32m 244\u001b[39m \u001b[38;5;66;03m# context is updated in place by the step\u001b[39;00m\n\u001b[32m 245\u001b[39m logger.debug(\u001b[33m\"\u001b[39m\u001b[33mBEFORE:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m, current_result)\n\u001b[32m--> \u001b[39m\u001b[32m246\u001b[39m current_result = \u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_result\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 247\u001b[39m logger.debug(\u001b[33m\"\u001b[39m\u001b[33mAFTER:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m, current_result)\n\u001b[32m 249\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m current_result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/new_core/processors/clip.py:416\u001b[39m, in \u001b[36mClip.execute\u001b[39m\u001b[34m(self, result, context)\u001b[39m\n\u001b[32m 413\u001b[39m \u001b[38;5;28;01mcase\u001b[39;00m xr.Dataset() | xr.DataArray():\n\u001b[32m 414\u001b[39m \u001b[38;5;66;03m# Clip the single dataset\u001b[39;00m\n\u001b[32m 415\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.is_single_point:\n\u001b[32m--> \u001b[39m\u001b[32m416\u001b[39m ret = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_clip_data_to_point\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlon\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 417\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.is_multi_point:\n\u001b[32m 418\u001b[39m ret = \u001b[38;5;28mself\u001b[39m._clip_data_to_points_as_mask(\n\u001b[32m 419\u001b[39m result, \u001b[38;5;28mself\u001b[39m.point_list, \u001b[38;5;28mself\u001b[39m.extract_points\n\u001b[32m 420\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/new_core/processors/clip.py:690\u001b[39m, in \u001b[36mClip._clip_data_to_point\u001b[39m\u001b[34m(dataset, lat, lon)\u001b[39m\n\u001b[32m 667\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 668\u001b[39m \u001b[33;03mClip data to the closest gridcell with valid data for a single point.\u001b[39;00m\n\u001b[32m 669\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 686\u001b[39m \u001b[33;03m or None if no valid gridcell found within search radius\u001b[39;00m\n\u001b[32m 687\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 689\u001b[39m \u001b[38;5;66;03m# First try the original method\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m690\u001b[39m closest_gridcell = \u001b[43mget_closest_gridcell\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlon\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_coords\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 692\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m closest_gridcell \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 693\u001b[39m \u001b[38;5;66;03m# Check if this gridcell has valid data (check first variable, first time step)\u001b[39;00m\n\u001b[32m 694\u001b[39m first_var = \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(closest_gridcell.data_vars))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/util/utils.py:578\u001b[39m, in \u001b[36mget_closest_gridcell\u001b[39m\u001b[34m(data, lat, lon, print_coords)\u001b[39m\n\u001b[32m 575\u001b[39m gridcell = data.isel({lat_dim: lat_idx, lon_dim: lon_idx})\n\u001b[32m 576\u001b[39m test_data = _reduce_to_single_point(gridcell, lat_dim, lon_dim)\n\u001b[32m--> \u001b[39m\u001b[32m578\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43m_check_has_valid_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_data\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m 579\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m print_coords:\n\u001b[32m 580\u001b[39m _print_closest_coords(gridcell, lat, lon, lat_dim, lon_dim)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/util/utils.py:201\u001b[39m, in \u001b[36m_check_has_valid_data\u001b[39m\u001b[34m(test_data)\u001b[39m\n\u001b[32m 199\u001b[39m \u001b[38;5;66;03m# Handle dask arrays - compute only the boolean result\u001b[39;00m\n\u001b[32m 200\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(is_all_null.data, \u001b[33m\"\u001b[39m\u001b[33mcompute\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m201\u001b[39m is_all_null = \u001b[43mis_all_null\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 202\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_all_null:\n\u001b[32m 203\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:1242\u001b[39m, in \u001b[36mDataArray.compute\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 1212\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Trigger loading data into memory and return a new dataarray.\u001b[39;00m\n\u001b[32m 1213\u001b[39m \n\u001b[32m 1214\u001b[39m \u001b[33;03mData will be computed and/or loaded from disk or a remote source.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1239\u001b[39m \u001b[33;03mVariable.compute\u001b[39;00m\n\u001b[32m 1240\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1241\u001b[39m new = \u001b[38;5;28mself\u001b[39m.copy(deep=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m-> \u001b[39m\u001b[32m1242\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnew\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:1168\u001b[39m, in \u001b[36mDataArray.load\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 1138\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mload\u001b[39m(\u001b[38;5;28mself\u001b[39m, **kwargs) -> Self:\n\u001b[32m 1139\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Trigger loading data into memory and return this dataarray.\u001b[39;00m\n\u001b[32m 1140\u001b[39m \n\u001b[32m 1141\u001b[39m \u001b[33;03m Data will be computed and/or loaded from disk or a remote source.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1166\u001b[39m \u001b[33;03m Variable.load\u001b[39;00m\n\u001b[32m 1167\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1168\u001b[39m ds = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_to_temp_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1169\u001b[39m new = \u001b[38;5;28mself\u001b[39m._from_temp_dataset(ds)\n\u001b[32m 1170\u001b[39m \u001b[38;5;28mself\u001b[39m._variable = new._variable\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataset.py:558\u001b[39m, in \u001b[36mDataset.load\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 555\u001b[39m chunkmanager = get_chunked_array_type(*chunked_data.values())\n\u001b[32m 557\u001b[39m \u001b[38;5;66;03m# evaluate all the chunked arrays simultaneously\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m558\u001b[39m evaluated_data: \u001b[38;5;28mtuple\u001b[39m[np.ndarray[Any, Any], ...] = \u001b[43mchunkmanager\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 559\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mchunked_data\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m 560\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 562\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k, data \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(chunked_data, evaluated_data, strict=\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[32m 563\u001b[39m \u001b[38;5;28mself\u001b[39m.variables[k].data = data\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/namedarray/daskmanager.py:85\u001b[39m, in \u001b[36mDaskManager.compute\u001b[39m\u001b[34m(self, *data, **kwargs)\u001b[39m\n\u001b[32m 80\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute\u001b[39m(\n\u001b[32m 81\u001b[39m \u001b[38;5;28mself\u001b[39m, *data: Any, **kwargs: Any\n\u001b[32m 82\u001b[39m ) -> \u001b[38;5;28mtuple\u001b[39m[np.ndarray[Any, _DType_co], ...]:\n\u001b[32m 83\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mdask\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01marray\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m compute\n\u001b[32m---> \u001b[39m\u001b[32m85\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/dask/base.py:681\u001b[39m, in \u001b[36mcompute\u001b[39m\u001b[34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[39m\n\u001b[32m 678\u001b[39m expr = expr.optimize()\n\u001b[32m 679\u001b[39m keys = \u001b[38;5;28mlist\u001b[39m(flatten(expr.__dask_keys__()))\n\u001b[32m--> \u001b[39m\u001b[32m681\u001b[39m results = \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 683\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m repack(results)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/queue.py:171\u001b[39m, in \u001b[36mQueue.get\u001b[39m\u001b[34m(self, block, timeout)\u001b[39m\n\u001b[32m 169\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 170\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._qsize():\n\u001b[32m--> \u001b[39m\u001b[32m171\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mnot_empty\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 172\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m timeout < \u001b[32m0\u001b[39m:\n\u001b[32m 173\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m\u001b[33mtimeout\u001b[39m\u001b[33m'\u001b[39m\u001b[33m must be a non-negative number\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/threading.py:355\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[32m 354\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m355\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 356\u001b[39m gotit = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 357\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[31mKeyboardInterrupt\u001b[39m: " + ] + } + ], + "source": [ + "# Initialize the ClimateData object and define the processors needed.\n", + "cd = ck.ClimateData(verbosity=-2)\n", + "processes = {\n", + " \"clip\": (LAT, LON),\n", + " \"warming_level\": {\n", + " \"warming_levels\": WARMING_LEVELS,\n", + " \"add_dummy_time\": True\n", + " },\n", + "}\n", + "\n", + "def rename_sims_to_gcm(ds):\n", + " \"\"\"\"Renames the 'sim' coordinate in the dataset to only contain the GCM name for easier grouping later.\"\"\"\n", + " ds = ds.copy()\n", + " new_sims = [s.split('_')[2] for s in ds['sim'].values]\n", + " ds['sim'] = ('sim', new_sims)\n", + " return ds\n", + "\n", + "def fetch(variable, table_id=\"day\", units=None):\n", + " \"\"\"Fetch a WRF variable from the AE catalog with the configured processes.\"\"\"\n", + " proc = {**processes}\n", + " if units is not None:\n", + " proc[\"convert_units\"] = units\n", + " return (\n", + " cd.catalog(\"cadcat\")\n", + " .activity_id(\"WRF\")\n", + " .institution_id(\"UCLA\")\n", + " .table_id(table_id)\n", + " .grid_label(\"d03\")\n", + " .variable(variable)\n", + " .processes(proc)\n", + " .get()\n", + " )\n", + "\n", + "def get_and_transform(variable, table_id=\"day\", units=None, transform=None):\n", + " \"\"\"Fetching variables and applying a transformation if it's passed in.\"\"\"\n", + " print(f\"\\nFetching {variable}...\")\n", + " da = fetch(variable, table_id=table_id, units=units)\n", + " da = da.unify_chunks() # Ensure the data is chunked for Dask processing\n", + " if transform:\n", + " da = transform(da)\n", + " # Drop leap days\n", + " da = da.sel(time=~((da['time.month'] == 2) & (da['time.day'] == 29)))\n", + " # Rename sims to only contain the GCM name for easier grouping later\n", + " da = rename_sims_to_gcm(da)\n", + " return da\n", + "\n", + "# Daily variables\n", + "rh = get_and_transform(\"rh\")\n", + "sw_dwn = get_and_transform(\"sw_dwn\")\n", + "lw_dwn = get_and_transform(\"lw_dwn\")\n", + "wspd10mean = get_and_transform(\"wspd10mean\")\n", + "prec = get_and_transform(\"prec\")\n", + "tasmin = get_and_transform(\"t2min\")\n", + "tasmax = get_and_transform(\"t2max\")\n", + "rsus_daily = get_and_transform(\"swupb\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", + "rlus_daily = get_and_transform(\"lwupb\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", + "\n", + "# xclim requires relative humidity as a fraction (0–1), not a percentage\n", + "hurs_frac = (rh / 100)\n", + "hurs_frac.rh.attrs['units'] = '1' # Update the units to reflect the transformation to a fraction\n", + "\n", + "# Ensure all radiation flux variables carry the same units\n", + "sw_dwn.sw_dwn.attrs['units'] = 'W m-2'\n", + "lw_dwn.lw_dwn.attrs['units'] = 'W m-2'\n", + "rsus_daily.swupb.attrs['units'] = 'W m-2'\n", + "rlus_daily.lwupb.attrs['units'] = 'W m-2'\n", + "\n", + "print(\"Finished.\")" + ] + }, + { + "cell_type": "markdown", + "id": "c74e6120", + "metadata": {}, + "source": [ + "#### Calculate PET from `xclim` using the Penman-Monteith Method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "384dff53-3011-4ecc-958a-ed3655d9b87a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tasmin Frozen({'sim': 5, 'warming_level': 2, 'time': 10950})\n", + "tasmax Frozen({'sim': 5, 'warming_level': 2, 'time': 10950})\n", + "hurs_frac Frozen({'warming_level': 2, 'time': 10950, 'sim': 5})\n", + "sw_dwn Frozen({'sim': 5, 'warming_level': 2, 'time': 10950})\n", + "rsus_daily Frozen({'time': 10950, 'sim': 5, 'warming_level': 2})\n", + "lw_dwn Frozen({'sim': 5, 'warming_level': 2, 'time': 10950})\n", + "rlus_daily Frozen({'time': 10950, 'sim': 5, 'warming_level': 2})\n", + "wspd10mean Frozen({'sim': 5, 'warming_level': 2, 'time': 10950})\n" + ] + } + ], + "source": [ + "for name, da in [\n", + " (\"tasmin\", tasmin), (\"tasmax\", tasmax), (\"hurs_frac\", hurs_frac),\n", + " (\"sw_dwn\", sw_dwn), (\"rsus_daily\", rsus_daily),\n", + " (\"lw_dwn\", lw_dwn), (\"rlus_daily\", rlus_daily), (\"wspd10mean\", wspd10mean)\n", + " ]:\n", + " print(name, da.sizes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c96f440", + "metadata": {}, + "outputs": [], + "source": [ + "# Call xclim.indicies.potential_evapotranspiration on the entire dataset as it is\n", + "pet_pm = xclim.indices.potential_evapotranspiration(\n", + " tasmin=tasmin.t2min,\n", + " tasmax=tasmax.t2max,\n", + " hurs=hurs_frac.rh,\n", + " rsds=sw_dwn.sw_dwn,\n", + " rsus=rsus_daily.swupb,\n", + " rlds=lw_dwn.lw_dwn,\n", + " rlus=rlus_daily.lwupb,\n", + " sfcWind=wspd10mean.wspd10mean,\n", + " method=\"FAO_PM98\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "5aedbf79", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "inputs = {\n", + " 'tasmin': tasmin.t2min,\n", + " 'tasmax': tasmax.t2max,\n", + " 'hurs': hurs_frac.rh,\n", + " 'rsds': sw_dwn.sw_dwn,\n", + " 'rsus': rsus_daily.swupb,\n", + " 'rlds': lw_dwn.lw_dwn,\n", + " 'rlus': rlus_daily.lwupb,\n", + " 'sfcWind': wspd10mean.wspd10mean,\n", + "}\n", + "inputs_sub = {name: da.isel(warming_level=1, sim=3) for name, da in inputs.items()}\n", + "\n", + "fig, axes = plt.subplots(2, 4, figsize=(18, 8))\n", + "axes = axes.flatten()\n", + "\n", + "for ax, (name, da) in zip(axes, inputs_sub.items()):\n", + " vals = da.values.flatten()\n", + " vals = vals[~np.isnan(vals)]\n", + " ax.hist(vals, bins=50, color='steelblue', edgecolor='none')\n", + " ax.set_title(f\"{name}\\nunits: {da.attrs.get('units', 'NOT SET')}\")\n", + " ax.set_xlabel('value')\n", + " ax.set_ylabel('count')\n", + " ax.axvline(np.median(vals), color='red', linestyle='--', linewidth=1, label=f'median: {np.median(vals):.2f}')\n", + " ax.legend(fontsize=8)\n", + "\n", + "plt.suptitle('PET Input Diagnostics', fontsize=14, y=1.02)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "id": "44713a20", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching swupb...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.1hr.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# Grab rsus as an example to test out the distribution of values and ensure they look reasonable\n", + "rsus_testing = get_and_transform(\"swupb\", table_id=\"1hr\")" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "bf08d862", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "n_wl = rsus_testing.sizes['warming_level']\n", + "n_sim = rsus_testing.sizes['sim']\n", + "\n", + "fig, axes = plt.subplots(n_wl, n_sim, figsize=(n_sim * 4, n_wl * 3), sharey=True)\n", + "\n", + "for i, wl in enumerate(rsus_testing.warming_level.values):\n", + " for j, sim in enumerate(rsus_testing.sim.values):\n", + " ax = axes[i, j]\n", + " vals = rsus_testing.sel(warming_level=wl, sim=sim).swupb.values[180*24: 210*24] # first year hourly\n", + " ax.plot(vals, linewidth=0.5, color='steelblue')\n", + " ax.axhline(np.nanmedian(vals), color='red', linestyle='--', linewidth=1)\n", + " # ax.set_title(f\"WL={wl} | {sim}\\nmedian={np.nanmedian(vals):.1f}\", fontsize=8)\n", + " ax.set_xlabel('hours', fontsize=7)\n", + " ax.set_ylabel('W/m2', fontsize=7)\n", + "\n", + "plt.suptitle('swupb timeseries by WL and sim', fontsize=12, y=1.01)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "1de94e8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([4.6790e+03, 1.9767e+04, 1.7113e+04, 1.6921e+04, 2.5409e+04,\n", + " 1.0806e+04, 3.0500e+03, 6.7000e+02, 1.2000e+02, 1.5000e+01]),\n", + " array([-0.00891502, 0.00670978, 0.02233457, 0.03795937, 0.05358417,\n", + " 0.06920896, 0.08483376, 0.10045855, 0.11608335, 0.13170815,\n", + " 0.14733294]),\n", + " )" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(loaded_pm).plot.hist()" + ] + }, + { + "cell_type": "markdown", + "id": "f8d01841", + "metadata": {}, + "source": [ + "## Step 2: Calculate PDSI" + ] + }, + { + "cell_type": "markdown", + "id": "415dcfc7", + "metadata": {}, + "source": [ + "PDSI is registered as a derived variable using `register_user_function`. The underlying computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by drought.gov. The function is applied across all spatial and ensemble dimensions via `groupby().apply()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "deb75906", + "metadata": {}, + "outputs": [], + "source": [ + "# Resampling PET and precip to monthly since the function only takes monthly variables\n", + "one_prec = prec.sel(sim=prec.sim.str.contains(one_sim)).squeeze()\n", + "mon_pet = (pet_calc * 86400 / 25.4).resample(time='1ME').sum()\n", + "mon_precip = (one_prec / 25.4).resample(time='1ME').sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffd18cf8", + "metadata": {}, + "outputs": [], + "source": [ + "### Combining WL objects together, historical WL as 2000-2030, future WL as 2030-2060\n", + "def combine_wl_to_dummy_time(\n", + " da: xr.DataArray,\n", + " baseline_wl: float,\n", + " future_wls: list[float],\n", + " start_date: str = \"2000-01-31\",\n", + ") -> xr.DataArray:\n", + " \"\"\"\n", + " Combine baseline warming level with multiple future warming levels into one\n", + " DataArray along a new 'combined_wl' dimension.\n", + "\n", + " Parameters\n", + " ----------\n", + " da : xr.DataArray\n", + " Original data with dims including 'warming_level' and 'time'.\n", + " baseline_wl : float\n", + " The warming level used for the first time segment.\n", + " future_wls : list of float\n", + " Warming levels to concatenate after baseline.\n", + " start_date : str\n", + " Start date for the combined time series (monthly freq).\n", + " \n", + " Returns\n", + " -------\n", + " xr.DataArray\n", + " Combined DataArray with new dimension 'combined_wl' and coordinate labels like \"0.8 to 1.5\".\n", + " \"\"\"\n", + " months_per_wl = da.sizes['time']\n", + " total_months = 2 * months_per_wl\n", + " new_time = pd.date_range(start_date, periods=total_months, freq='ME')\n", + "\n", + " combined_list = []\n", + " combined_labels = []\n", + "\n", + " for fw in future_wls:\n", + " da_base = da.sel(warming_level=baseline_wl)\n", + " da_future = da.sel(warming_level=fw)\n", + "\n", + " combined = xr.concat([da_base, da_future], dim='time')\n", + " combined = combined.assign_coords(time=new_time)\n", + "\n", + " wl_flag = np.array([baseline_wl] * months_per_wl + [fw] * months_per_wl)\n", + " combined = combined.assign_coords(warming_level_flag=('time', wl_flag))\n", + "\n", + " combined_list.append(combined)\n", + " combined_labels.append(f\"{int(baseline_wl * 10):02d}_to_{int(fw * 10):02d}\")\n", + "\n", + " combined_da = xr.concat(combined_list, dim='combined_wl')\n", + " combined_da = combined_da.assign_coords(combined_wl=combined_labels)\n", + "\n", + " return combined_da" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "119fa7d5", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating one Dataset of PET and precip with WLs combined\n", + "mon_pet_transform = combine_wl_to_dummy_time(mon_pet, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", + "mon_precip_transform = combine_wl_to_dummy_time(mon_precip, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", + "mon_pet_transform = mon_pet_transform.assign_coords(mon_precip_transform.coords)\n", + "combined_ds = xr.Dataset({'precip': mon_precip_transform, 'pet': mon_pet_transform})\n", + "\n", + "# Applying spatial mask\n", + "combined_ds = combined_ds.where(spatial_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6b5de48", + "metadata": {}, + "outputs": [], + "source": [ + "def pdsi_func(ds: xr.Dataset) -> xr.Dataset:\n", + " \"\"\"Compute Palmer Drought Severity Index (PDSI) from a Dataset.\n", + "\n", + " Registered derived variable function: receives an xr.Dataset with 'precip' and\n", + " 'pet' variables (both monthly, in inches) and adds 'pdsi' to it.\n", + "\n", + " Parameters\n", + " ----------\n", + " ds : xr.Dataset\n", + " Dataset with 'precip' and 'pet' variables along a 'time' dimension,\n", + " plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n", + "\n", + " Returns\n", + " -------\n", + " xr.Dataset\n", + " Input dataset with 'pdsi' variable added.\n", + " \"\"\"\n", + " def _calc_pdsi_1d(timeseries: xr.Dataset) -> xr.DataArray:\n", + " precip = timeseries['precip'].squeeze()\n", + " pet = timeseries['pet'].squeeze()\n", + " result = palmer_pdsi(\n", + " precips=precip.values,\n", + " pet=pet.values,\n", + " awc=5,\n", + " data_start_year=2000,\n", + " calibration_year_initial=2000,\n", + " calibration_year_final=2030,\n", + " )\n", + " return xr.DataArray(\n", + " result[0], coords={\"time\": precip.time.values}, dims=['time']\n", + " ).clip(min=-10, max=10)\n", + "\n", + " pdsi_da = ds.groupby(['combined_wl', 'x', 'y']).apply(_calc_pdsi_1d)\n", + " pdsi_da.attrs.update({\n", + " \"long_name\": \"Palmer Drought Severity Index\",\n", + " \"units\": \"from -10 (dry) to +10 (wet)\",\n", + " })\n", + " ds[\"pdsi\"] = pdsi_da\n", + " return ds\n", + "\n", + "register_user_function(\n", + " name=\"pdsi\",\n", + " depends_on=[\"precip\", \"pet\"],\n", + " func=pdsi_func,\n", + " description=\"Palmer Drought Severity Index computed from monthly precipitation and PET.\",\n", + " units=\"unitless\",\n", + " drop_dependencies=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c625f3af", + "metadata": {}, + "outputs": [], + "source": [ + "# Apply PDSI derived variable to the combined dataset\n", + "pdsi_ds = list_derived_variables()[\"pdsi\"].func(combined_ds)\n", + "pdsi_da = pdsi_ds[\"pdsi\"]\n", + "\n", + "# Writing crs and reprojecting PDSI to lat/lon\n", + "pdsi_da = pdsi_da.rio.write_crs(crs.to_wkt())\n", + "pdsi_da = pdsi_da.transpose('time', 'combined_wl', 'y', 'x')\n", + "# pdsi_latlon = reproject_data(pdsi_da, 'EPSG:4326')\n", + "# del pdsi_latlon.attrs[\"_FillValue\"]" + ] + }, + { + "cell_type": "markdown", + "id": "6cc3157f", + "metadata": {}, + "source": [ + "### Export PDSI" + ] + }, + { + "cell_type": "markdown", + "id": "2d745c44", + "metadata": {}, + "source": [ + "The combined time axis has 720 months total: months 0–359 are the baseline warming level (0.8°C, used for calibration), and months 360–719 are the future warming level period. The export below slices to `time=slice(360, 720)` to retain only the future period.\n", + "\n", + "The exported file will have the following dimensions:\n", + "- `time`: 360 months (30-year future warming level period)\n", + "- `wl`: warming level pair (e.g., `\"08_to_20\"` = calibrated on 0.8°C, evaluated on 2.0°C)\n", + "- `lat`, `lon`: spatial coordinates (EPSG:4326)\n", + "- `sim`: ensemble member" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1dc40f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Cleaning and labeling the data before exporting it in the next cell\n", + "final_pdsi = pdsi_da.isel(time=slice(360, 720))\n", + "final_pdsi = final_pdsi.rename({'combined_wl': 'wl'}).rename(\"pdsi\")\n", + "final_pdsi = final_pdsi.assign_attrs({\n", + " \"long_name\": \"Palmer Drought Severity Index\",\n", + " \"units\": \"from -10 (dry) to +10 (wet)\",\n", + "})\n", + "pdsi_filename = f\"pdsi_wl_lat{str(LAT).replace('.', '_')}_lon{str(LON).replace('.', '_')}.nc\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c6f3455", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exporting specified data to NetCDF...\n", + "Saving file locally as NetCDF4...\n" + ] + }, + { + "ename": "Exception", + "evalue": "File pdsi_wl_lat37_787964_lon-122_065063.nc exists. Please either delete that file from the work space or specify a new file name here.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mException\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[16]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinal_pdsi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpdsi_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mNetCDF\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlocal\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/core/data_export.py:948\u001b[39m, in \u001b[36mexport\u001b[39m\u001b[34m(data, filename, format, mode)\u001b[39m\n\u001b[32m 946\u001b[39m _export_to_zarr(data, save_name, mode)\n\u001b[32m 947\u001b[39m \u001b[38;5;28;01mcase\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mnetcdf\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m948\u001b[39m \u001b[43m_export_to_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 949\u001b[39m \u001b[38;5;28;01mcase\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mcsv\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 950\u001b[39m _export_to_csv(data, save_name)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/core/data_export.py:299\u001b[39m, in \u001b[36m_export_to_netcdf\u001b[39m\u001b[34m(data, save_name)\u001b[39m\n\u001b[32m 296\u001b[39m path = os.path.join(os.getcwd(), save_name)\n\u001b[32m 298\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m os.path.exists(path):\n\u001b[32m--> \u001b[39m\u001b[32m299\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\n\u001b[32m 300\u001b[39m (\n\u001b[32m 301\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFile \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msave_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m exists. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 302\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mPlease either delete that file from the work space \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 303\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mor specify a new file name here.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 304\u001b[39m )\n\u001b[32m 305\u001b[39m )\n\u001b[32m 306\u001b[39m encoding = _fillvalue_encoding(_data) | _compression_encoding(_data)\n\u001b[32m 308\u001b[39m \u001b[38;5;66;03m# Recursively validate attribute types\u001b[39;00m\n", + "\u001b[31mException\u001b[39m: File pdsi_wl_lat37_787964_lon-122_065063.nc exists. Please either delete that file from the work space or specify a new file name here." + ] + } + ], + "source": [ + "export(final_pdsi, pdsi_filename, format=\"NetCDF\", mode=\"local\")" + ] + }, + { + "cell_type": "markdown", + "id": "3dc2e324", + "metadata": {}, + "source": [ + "## Step 3: Calculate EDDI" + ] + }, + { + "cell_type": "markdown", + "id": "f0645679", + "metadata": {}, + "source": [ + "Now, we will calculate EDDI using PET." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3228b9fb", + "metadata": {}, + "outputs": [], + "source": [ + "def eddi_func(ds: xr.Dataset) -> xr.Dataset:\n", + " \"\"\"Compute Evaporative Demand Drought Index (EDDI) from a Dataset.\n", + "\n", + " Registered derived variable function: receives an xr.Dataset with a 'pet'\n", + " variable (monthly) and adds 'eddi' to it.\n", + "\n", + " Parameters\n", + " ----------\n", + " ds : xr.Dataset\n", + " Dataset with 'pet' variable along a 'time' dimension,\n", + " plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n", + "\n", + " Returns\n", + " -------\n", + " xr.Dataset\n", + " Input dataset with 'eddi' variable added.\n", + " \"\"\"\n", + " def _calc_eddi_1d(timeseries: xr.DataArray) -> xr.DataArray:\n", + " eddi = standardized_index(\n", + " da=timeseries,\n", + " freq='MS',\n", + " window=1,\n", + " dist=\"gamma\",\n", + " method=\"ML\",\n", + " zero_inflated=True,\n", + " fitkwargs={},\n", + " cal_start=\"2000-01-31\",\n", + " cal_end=\"2029-12-31\",\n", + " )\n", + " return eddi.clip(min=-2.5, max=2.5)\n", + "\n", + " eddi_da = ds['pet'].groupby(['combined_wl', 'x', 'y']).apply(_calc_eddi_1d)\n", + " eddi_da.attrs.update({\n", + " \"long_name\": \"Evaporative Demand Drought Index\",\n", + " \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n", + " })\n", + " ds[\"eddi\"] = eddi_da\n", + " return ds\n", + "\n", + "register_user_function(\n", + " name=\"eddi\",\n", + " depends_on=[\"pet\"],\n", + " func=eddi_func,\n", + " description=\"Evaporative Demand Drought Index computed from monthly PET.\",\n", + " units=\"unitless\",\n", + " drop_dependencies=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1240839c", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'x'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:876\u001b[39m, in \u001b[36mDataArray._getitem_coord\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 875\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m876\u001b[39m var = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_coords\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 877\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", + "\u001b[31mKeyError\u001b[39m: 'x'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[72]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Apply EDDI derived variable to the combined dataset\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m eddi_ds = \u001b[43mlist_derived_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43meddi\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcombined_ds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m eddi_da = eddi_ds[\u001b[33m\"\u001b[39m\u001b[33meddi\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 5\u001b[39m \u001b[38;5;66;03m# Writing crs and reprojecting EDDI to lat/lon\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/new_core/derived_variables/registry.py:116\u001b[39m, in \u001b[36m_wrap_with_metadata_preservation..wrapped\u001b[39m\u001b[34m(ds)\u001b[39m\n\u001b[32m 113\u001b[39m before_vars = \u001b[38;5;28mset\u001b[39m(ds.data_vars) \u001b[38;5;28;01mif\u001b[39;00m ds \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mset\u001b[39m()\n\u001b[32m 115\u001b[39m \u001b[38;5;66;03m# Call the original function\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m116\u001b[39m result = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 118\u001b[39m \u001b[38;5;66;03m# Determine newly-added variables (robust to functions that name the\u001b[39;00m\n\u001b[32m 119\u001b[39m \u001b[38;5;66;03m# derived variable differently than the registered name)\u001b[39;00m\n\u001b[32m 120\u001b[39m after_vars = \u001b[38;5;28mset\u001b[39m(result.data_vars) \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mset\u001b[39m()\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[65]\u001b[39m\u001b[32m, line 32\u001b[39m, in \u001b[36meddi_func\u001b[39m\u001b[34m(ds)\u001b[39m\n\u001b[32m 19\u001b[39m eddi = standardized_index(\n\u001b[32m 20\u001b[39m da=timeseries,\n\u001b[32m 21\u001b[39m freq=\u001b[33m'\u001b[39m\u001b[33mMS\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 28\u001b[39m cal_end=\u001b[33m\"\u001b[39m\u001b[33m2029-12-31\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 29\u001b[39m )\n\u001b[32m 30\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m eddi.clip(\u001b[38;5;28mmin\u001b[39m=-\u001b[32m2.5\u001b[39m, \u001b[38;5;28mmax\u001b[39m=\u001b[32m2.5\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m32\u001b[39m eddi_da = \u001b[43mds\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mpet\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcombined_wl\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mx\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43my\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m.apply(_calc_eddi_1d)\n\u001b[32m 33\u001b[39m eddi_da.attrs.update({\n\u001b[32m 34\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mlong_name\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mEvaporative Demand Drought Index\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 35\u001b[39m \u001b[33m\"\u001b[39m\u001b[33munits\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mfrom -2.5 (wet) to +2.5 (dry)\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 36\u001b[39m })\n\u001b[32m 37\u001b[39m ds[\u001b[33m\"\u001b[39m\u001b[33meddi\u001b[39m\u001b[33m\"\u001b[39m] = eddi_da\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/util/deprecation_helpers.py:119\u001b[39m, in \u001b[36m_deprecate_positional_args.._decorator..inner\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 115\u001b[39m kwargs.update(zip_args)\n\u001b[32m 117\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m func(*args[:-n_extra_args], **kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m119\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:6992\u001b[39m, in \u001b[36mDataArray.groupby\u001b[39m\u001b[34m(self, group, squeeze, restore_coord_dims, eagerly_compute_group, **groupers)\u001b[39m\n\u001b[32m 6985\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mxarray\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mgroupby\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 6986\u001b[39m DataArrayGroupBy,\n\u001b[32m 6987\u001b[39m _parse_group_and_groupers,\n\u001b[32m 6988\u001b[39m _validate_groupby_squeeze,\n\u001b[32m 6989\u001b[39m )\n\u001b[32m 6991\u001b[39m _validate_groupby_squeeze(squeeze)\n\u001b[32m-> \u001b[39m\u001b[32m6992\u001b[39m rgroupers = \u001b[43m_parse_group_and_groupers\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 6993\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroupers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meagerly_compute_group\u001b[49m\u001b[43m=\u001b[49m\u001b[43meagerly_compute_group\u001b[49m\n\u001b[32m 6994\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6995\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m DataArrayGroupBy(\u001b[38;5;28mself\u001b[39m, rgroupers, restore_coord_dims=restore_coord_dims)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/groupby.py:437\u001b[39m, in \u001b[36m_parse_group_and_groupers\u001b[39m\u001b[34m(obj, group, groupers, eagerly_compute_group)\u001b[39m\n\u001b[32m 434\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m groupers:\n\u001b[32m 435\u001b[39m grouper_mapping = cast(\u001b[33m\"\u001b[39m\u001b[33mMapping[Hashable, Grouper]\u001b[39m\u001b[33m\"\u001b[39m, groupers)\n\u001b[32m--> \u001b[39m\u001b[32m437\u001b[39m rgroupers = \u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 438\u001b[39m \u001b[43m \u001b[49m\u001b[43mResolvedGrouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[43mgrouper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meagerly_compute_group\u001b[49m\u001b[43m=\u001b[49m\u001b[43meagerly_compute_group\u001b[49m\n\u001b[32m 440\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 441\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrouper\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgrouper_mapping\u001b[49m\u001b[43m.\u001b[49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 442\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 443\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m rgroupers\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/groupby.py:438\u001b[39m, in \u001b[36m\u001b[39m\u001b[34m(.0)\u001b[39m\n\u001b[32m 434\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m groupers:\n\u001b[32m 435\u001b[39m grouper_mapping = cast(\u001b[33m\"\u001b[39m\u001b[33mMapping[Hashable, Grouper]\u001b[39m\u001b[33m\"\u001b[39m, groupers)\n\u001b[32m 437\u001b[39m rgroupers = \u001b[38;5;28mtuple\u001b[39m(\n\u001b[32m--> \u001b[39m\u001b[32m438\u001b[39m \u001b[43mResolvedGrouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[43mgrouper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meagerly_compute_group\u001b[49m\u001b[43m=\u001b[49m\u001b[43meagerly_compute_group\u001b[49m\n\u001b[32m 440\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 441\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m group, grouper \u001b[38;5;129;01min\u001b[39;00m grouper_mapping.items()\n\u001b[32m 442\u001b[39m )\n\u001b[32m 443\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m rgroupers\n", + "\u001b[36mFile \u001b[39m\u001b[32m:7\u001b[39m, in \u001b[36m__init__\u001b[39m\u001b[34m(self, grouper, group, obj, eagerly_compute_group)\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/groupby.py:331\u001b[39m, in \u001b[36mResolvedGrouper.__post_init__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 327\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mxarray\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mgroupers\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BinGrouper, UniqueGrouper\n\u001b[32m 329\u001b[39m \u001b[38;5;28mself\u001b[39m.grouper = copy.deepcopy(\u001b[38;5;28mself\u001b[39m.grouper)\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m \u001b[38;5;28mself\u001b[39m.group = \u001b[43m_resolve_group\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.eagerly_compute_group:\n\u001b[32m 334\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 335\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\"\"\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mEagerly computing the DataArray you\u001b[39m\u001b[33m'\u001b[39m\u001b[33mre grouping by (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.group.name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m) \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 336\u001b[39m \u001b[33m has been removed.\u001b[39m\n\u001b[32m (...)\u001b[39m\u001b[32m 341\u001b[39m \u001b[33m `.groupby(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.group.name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m=BinGrouper(bins=...))`; as appropriate.\u001b[39m\u001b[33m\"\"\"\u001b[39m\n\u001b[32m 342\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/groupby.py:497\u001b[39m, in \u001b[36m_resolve_group\u001b[39m\u001b[34m(obj, group)\u001b[39m\n\u001b[32m 491\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m hashable(group):\n\u001b[32m 492\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 493\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m`group` must be an xarray.DataArray or the \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 494\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mname of an xarray variable or dimension. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 495\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mReceived \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgroup\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m instead.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 496\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m497\u001b[39m group_da: DataArray = \u001b[43mobj\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 498\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m group_da.name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m obj._indexes \u001b[38;5;129;01mand\u001b[39;00m group_da.name \u001b[38;5;129;01min\u001b[39;00m obj.dims:\n\u001b[32m 499\u001b[39m \u001b[38;5;66;03m# DummyGroups should not appear on groupby results\u001b[39;00m\n\u001b[32m 500\u001b[39m newgroup = _DummyGroup(obj, group_da.name, group_da.coords)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:885\u001b[39m, in \u001b[36mDataArray.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 883\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: Any) -> Self:\n\u001b[32m 884\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m885\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem_coord\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 886\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 887\u001b[39m \u001b[38;5;66;03m# xarray-style array indexing\u001b[39;00m\n\u001b[32m 888\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.isel(indexers=\u001b[38;5;28mself\u001b[39m._item_key_to_dict(key))\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:879\u001b[39m, in \u001b[36mDataArray._getitem_coord\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 877\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[32m 878\u001b[39m dim_sizes = \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m.dims, \u001b[38;5;28mself\u001b[39m.shape, strict=\u001b[38;5;28;01mTrue\u001b[39;00m))\n\u001b[32m--> \u001b[39m\u001b[32m879\u001b[39m _, key, var = \u001b[43m_get_virtual_variable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_coords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim_sizes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 881\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._replace_maybe_drop_dims(var, name=key)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataset_utils.py:79\u001b[39m, in \u001b[36m_get_virtual_variable\u001b[39m\u001b[34m(variables, key, dim_sizes)\u001b[39m\n\u001b[32m 77\u001b[39m split_key = key.split(\u001b[33m\"\u001b[39m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m1\u001b[39m)\n\u001b[32m 78\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(split_key) != \u001b[32m2\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m79\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[32m 81\u001b[39m ref_name, var_name = split_key\n\u001b[32m 82\u001b[39m ref_var = variables[ref_name]\n", + "\u001b[31mKeyError\u001b[39m: 'x'" + ] + } + ], + "source": [ + "# Apply EDDI derived variable to the combined dataset\n", + "eddi_ds = list_derived_variables()[\"eddi\"].func(combined_ds)\n", + "eddi_da = eddi_ds[\"eddi\"]\n", + "\n", + "# Writing crs and reprojecting EDDI to lat/lon\n", + "eddi_da = eddi_da.rio.write_crs(crs.to_wkt())\n", + "eddi_da = eddi_da.transpose('time', 'combined_wl', 'y', 'x')\n", + "# eddi_da_latlon = reproject_data(eddi_da, 'EPSG:4326')\n", + "# del eddi_da_latlon.attrs[\"_FillValue\"]" + ] + }, + { + "cell_type": "markdown", + "id": "45176f23", + "metadata": {}, + "source": [ + "### Export EDDI" + ] + }, + { + "cell_type": "markdown", + "id": "d59ee541", + "metadata": {}, + "source": [ + "The combined time axis has 720 months total: months 0–359 are the baseline warming level (0.8°C, used for calibration), and months 360–719 are the future warming level period. The export below slices to `time=slice(360, 720)` to retain only the future period.\n", + "\n", + "The exported file will have the following dimensions:\n", + "- `time`: 360 months (30-year future warming level period)\n", + "- `wl`: warming level pair (e.g., `\"08_to_20\"` = calibrated on 0.8°C, evaluated on 2.0°C)\n", + "- `lat`, `lon`: spatial coordinates (EPSG:4326)\n", + "- `sim`: ensemble member" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d86782f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Saving these results and cleaning the data\n", + "final_eddi = eddi_da_latlon.isel(time=slice(360, 720))\n", + "final_eddi = final_eddi.rename({'combined_wl': 'wl'}).rename(\"eddi\")\n", + "final_eddi = final_eddi.assign_attrs({\n", + " \"long_name\": \"Evaporative Demand Drought Index\",\n", + " \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n", + "})\n", + "eddi_filename = f\"eddi_wl_lat{str(LAT).replace('.', '_')}_lon{str(LON).replace('.', '_')}.nc\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "707286f3", + "metadata": {}, + "outputs": [], + "source": [ + "export(final_eddi, eddi_filename, format=\"NetCDF\", mode=\"local\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (climakitae)", + "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": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1699fcaf1222e0d07faf3a2c5a422e7a5da7dfe4 Mon Sep 17 00:00:00 2001 From: claalmve Date: Wed, 6 May 2026 14:51:51 -0700 Subject: [PATCH 45/53] Adding latest. I am so confused on why PET is such a wrong caclulation --- .../drought_metrics_latest_final.ipynb | 572 ++++++++++++++++++ 1 file changed, 572 insertions(+) create mode 100644 work-in-progress/drought_metrics_latest_final.ipynb diff --git a/work-in-progress/drought_metrics_latest_final.ipynb b/work-in-progress/drought_metrics_latest_final.ipynb new file mode 100644 index 00000000..eae05c17 --- /dev/null +++ b/work-in-progress/drought_metrics_latest_final.ipynb @@ -0,0 +1,572 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5e0361f0", + "metadata": {}, + "source": [ + "# Drought Metrics" + ] + }, + { + "cell_type": "markdown", + "id": "64641b82", + "metadata": {}, + "source": [ + "This notebook calculates two drought metrics, the Palmer Drought Severity Index (PDSI) and the Evaporative Demand Drought Index (EDDI), using WRF data in the AE catalog. Both PDSI and EDDI require Potential Evapotranspiration (PET). PET is computed first using the Penman-Monteith method. At the end of the notebook, the user will be able to export monthly PDSI and EDDI for under different global warming levels for a specific lat/lon as netcdf files to be used for further analyses. " + ] + }, + { + "cell_type": "markdown", + "id": "98506237", + "metadata": {}, + "source": [ + "**Intended Application:** As a user, I want to export future drought indices for different global warming levels by:\n", + "1. Calculating PET using the Penman-Montieth Method\n", + "2. Calculating the PDSI using PET and exporting the monthly timeseries to a netcdf\n", + "3. Calculating the EDDI using PET and exporting the monthly timeseries to a netcdf" + ] + }, + { + "cell_type": "markdown", + "id": "cc35f46e", + "metadata": {}, + "source": [ + "**Runtime:** With the default settings, this notebook takes approximately 25 minutes to run from start to finish. Modifications to selections may increase the runtime." + ] + }, + { + "cell_type": "markdown", + "id": "89ab2f9c", + "metadata": {}, + "source": [ + "**Troubleshooting:** Getting an `IndexError: index 40 is out of bounds for axis 0 with size 40` when trying to run the PDSI calculation? Try changing your lat/lon or point of interest further away from the boundaries of our 3 km WRF domain (as seen in [this graphic](https://analytics.cal-adapt.org/faq/#what-data-is-available))." + ] + }, + { + "cell_type": "markdown", + "id": "3babc840", + "metadata": {}, + "source": [ + "## Step 0: Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "xypsmbxjeoj", + "metadata": {}, + "outputs": [], + "source": [ + "# Install climate_indices at a specific commit compatible with the AE hub environment\n", + "!pip install -qq git+https://github.com/monocongo/climate_indices.git" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7e19917", + "metadata": {}, + "outputs": [], + "source": [ + "import climakitae as ck\n", + "import xclim\n", + "import xarray as xr\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import flox\n", + "from pyproj import CRS\n", + "from dask.diagnostics import ProgressBar\n", + "from climate_indices import palmer\n", + "from xclim.indices.stats import standardized_index\n", + "from climakitae.core.data_export import export" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b31d378", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the lat/lon for the location of interest and the variables needed for the Penman-Monteith PET method\n", + "LAT = 37.787964\n", + "LON = -122.065063\n", + "WARMING_LEVELS = [0.8, 2.0]" + ] + }, + { + "cell_type": "markdown", + "id": "485befa5f23", + "metadata": {}, + "source": [ + "## Step 1: Fetch Data and Calculate PET\n", + "\n", + "**IMPORTANT NOTE**: The following cell saves the intermediate data required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files." + ] + }, + { + "cell_type": "markdown", + "id": "522c9425", + "metadata": {}, + "source": [ + "### Penman-Monteith PET Method (most physically consistent)\n", + "We will be using the PET method for calculating PDSI, since it is the most physically consistent method. Below, we list out the variables we will need." + ] + }, + { + "cell_type": "markdown", + "id": "cc88bd71", + "metadata": {}, + "source": [ + "**Variables needed:**\n", + "- `t2` (hourly) → daily `tasmin` and `tasmax` derived via resample\n", + "- `relative humidity`\n", + "- `radiation flux`\n", + " - rsds (daily)\n", + " - rsus (hourly → daily mean)\n", + " - rlds (daily)\n", + " - rlus (hourly → daily mean)\n", + "- `wind speed (10m wind will be converted to 2m)`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0132b29-804a-4eef-893f-2f60adfefc62", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the ClimateData object and define the processors needed.\n", + "cd = ck.ClimateData(verbosity=-2)\n", + "processes = {\n", + " \"clip\": (LAT, LON),\n", + " \"warming_level\": {\n", + " \"warming_levels\": WARMING_LEVELS,\n", + " \"add_dummy_time\": True\n", + " },\n", + "}\n", + "\n", + "def rename_sims_to_gcm(ds):\n", + " \"\"\"\"Renames the 'sim' coordinate in the dataset to only contain the GCM name for easier grouping later.\"\"\"\n", + " ds = ds.copy()\n", + " new_sims = [s.split('_')[2] for s in ds['sim'].values]\n", + " ds['sim'] = ('sim', new_sims)\n", + " return ds\n", + "\n", + "def fetch(variable, table_id=\"day\", units=None):\n", + " \"\"\"Fetch a WRF variable from the AE catalog with the configured processes.\"\"\"\n", + " proc = {**processes}\n", + " if units is not None:\n", + " proc[\"convert_units\"] = units\n", + " return (\n", + " cd.catalog(\"cadcat\")\n", + " .activity_id(\"WRF\")\n", + " .institution_id(\"UCLA\")\n", + " .table_id(table_id)\n", + " .grid_label(\"d03\")\n", + " .variable(variable)\n", + " .processes(proc)\n", + " .get()\n", + " )\n", + "\n", + "def get_and_transform(variable, table_id=\"day\", units=None, transform=None):\n", + " \"\"\"Fetching variables and applying a transformation if it's passed in.\"\"\"\n", + " print(f\"\\nFetching {variable}...\")\n", + " da = fetch(variable, table_id=table_id, units=units)\n", + " da = da.unify_chunks() # Ensure the data is chunked for Dask processing\n", + " if transform:\n", + " da = transform(da)\n", + " # Drop leap days\n", + " da = da.sel(time=~((da['time.month'] == 2) & (da['time.day'] == 29)))\n", + " # Rename sims to only contain the GCM name for easier grouping later\n", + " da = rename_sims_to_gcm(da)\n", + " return da\n", + "\n", + "# Daily variables\n", + "rh = get_and_transform(\"rh\")\n", + "sw_dwn = get_and_transform(\"sw_dwn\")\n", + "lw_dwn = get_and_transform(\"lw_dwn\")\n", + "wspd10mean = get_and_transform(\"wspd10mean\")\n", + "prec = get_and_transform(\"prec\")\n", + "tasmin = get_and_transform(\"t2min\")\n", + "tasmax = get_and_transform(\"t2max\")\n", + "rsus_daily = get_and_transform(\"swupb\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", + "rlus_daily = get_and_transform(\"lwupb\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", + "\n", + "# xclim requires relative humidity as a fraction (0–1), not a percentage\n", + "hurs_frac = (rh / 100)\n", + "hurs_frac.rh.attrs['units'] = '1' # Update the units to reflect the transformation to a fraction\n", + "\n", + "# Ensure all radiation flux variables carry the same units\n", + "sw_dwn.sw_dwn.attrs['units'] = 'W m-2'\n", + "lw_dwn.lw_dwn.attrs['units'] = 'W m-2'\n", + "rsus_daily.swupb.attrs['units'] = 'W m-2'\n", + "rlus_daily.lwupb.attrs['units'] = 'W m-2'\n", + "\n", + "print(\"Finished.\")" + ] + }, + { + "cell_type": "markdown", + "id": "c74e6120", + "metadata": {}, + "source": [ + "#### Calculate PET from `xclim` using the Penman-Monteith Method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c96f440", + "metadata": {}, + "outputs": [], + "source": [ + "# Call xclim.indicies.potential_evapotranspiration on the entire dataset as it is\n", + "pet_pm = xclim.indices.potential_evapotranspiration(\n", + " tasmin=tasmin.t2min,\n", + " tasmax=tasmax.t2max,\n", + " hurs=hurs_frac.rh,\n", + " rsds=sw_dwn.sw_dwn,\n", + " rsus=rsus_daily.swupb,\n", + " rlds=lw_dwn.lw_dwn,\n", + " rlus=rlus_daily.lwupb,\n", + " sfcWind=wspd10mean.wspd10mean,\n", + " method=\"FAO_PM98\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f8d01841", + "metadata": {}, + "source": [ + "## Step 2: Calculate PDSI" + ] + }, + { + "cell_type": "markdown", + "id": "415dcfc7", + "metadata": {}, + "source": [ + "PDSI is registered as a derived variable using `register_user_function`. The underlying computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by drought.gov. The function is applied across all spatial and ensemble dimensions via `groupby().apply()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "deb75906", + "metadata": {}, + "outputs": [], + "source": [ + "# Resampling PET and precip to monthly since the function only takes monthly variables\n", + "mon_pet = (pet_pm * 86400 / 25.4).resample(time='1ME').sum()\n", + "mon_precip = (prec / 25.4).resample(time='1ME').sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffd18cf8", + "metadata": {}, + "outputs": [], + "source": [ + "### Combining WL objects together, historical WL as 2000-2030, future WL as 2030-2060\n", + "def combine_wl_to_dummy_time(\n", + " da: xr.DataArray,\n", + " baseline_wl: float,\n", + " future_wls: list[float],\n", + " start_date: str = \"2000-01-31\",\n", + ") -> xr.DataArray:\n", + " \"\"\"\n", + " Combine baseline warming level with multiple future warming levels into one\n", + " DataArray along a new 'combined_wl' dimension.\n", + "\n", + " Parameters\n", + " ----------\n", + " da : xr.DataArray\n", + " Original data with dims including 'warming_level' and 'time'.\n", + " baseline_wl : float\n", + " The warming level used for the first time segment.\n", + " future_wls : list of float\n", + " Warming levels to concatenate after baseline.\n", + " start_date : str\n", + " Start date for the combined time series (monthly freq).\n", + " \n", + " Returns\n", + " -------\n", + " xr.DataArray\n", + " Combined DataArray with new dimension 'combined_wl' and coordinate labels like \"0.8 to 1.5\".\n", + " \"\"\"\n", + " months_per_wl = da.sizes['time']\n", + " total_months = 2 * months_per_wl\n", + " new_time = pd.date_range(start_date, periods=total_months, freq='ME')\n", + "\n", + " combined_list = []\n", + " combined_labels = []\n", + "\n", + " for fw in future_wls:\n", + " da_base = da.sel(warming_level=baseline_wl)\n", + " da_future = da.sel(warming_level=fw)\n", + "\n", + " combined = xr.concat([da_base, da_future], dim='time')\n", + " combined = combined.assign_coords(time=new_time)\n", + "\n", + " wl_flag = np.array([baseline_wl] * months_per_wl + [fw] * months_per_wl)\n", + " combined = combined.assign_coords(warming_level_flag=('time', wl_flag))\n", + "\n", + " combined_list.append(combined)\n", + " combined_labels.append(f\"{int(baseline_wl * 10):02d}_to_{int(fw * 10):02d}\")\n", + "\n", + " combined_da = xr.concat(combined_list, dim='combined_wl')\n", + " combined_da = combined_da.assign_coords(combined_wl=combined_labels)\n", + "\n", + " return combined_da" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "119fa7d5", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating one Dataset of PET and precip with WLs combined\n", + "mon_pet_transform = combine_wl_to_dummy_time(mon_pet, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", + "mon_precip_transform = combine_wl_to_dummy_time(mon_precip.prec, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", + "mon_pet_transform = mon_pet_transform.assign_coords(mon_precip_transform.coords)\n", + "combined_ds = xr.Dataset({'precip': mon_precip_transform, 'pet': mon_pet_transform})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "999a3437-e29f-402f-9e4e-492f726fdf7f", + "metadata": {}, + "outputs": [], + "source": [ + "# Load `combined_ds` into memory\n", + "with ProgressBar():\n", + " combined_ds = combined_ds.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7d02265-022d-4f50-8d31-d56687e88cc5", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_pdsi(precip_1d, pet_1d, awc, data_start_year, calibration_year_initial, calibration_year_final):\n", + " pdsi, _, _, _, _ = palmer.pdsi(\n", + " precips=precip_1d,\n", + " pet=pet_1d,\n", + " awc=awc,\n", + " data_start_year=data_start_year,\n", + " calibration_year_initial=calibration_year_initial,\n", + " calibration_year_final=calibration_year_final,\n", + " )\n", + " return pdsi\n", + "\n", + "pdsi_result = xr.apply_ufunc(\n", + " compute_pdsi,\n", + " combined_ds[\"precip\"], # shape (time, sim)\n", + " combined_ds[\"pet\"], # shape (time, sim)\n", + " input_core_dims=[[\"time\"], [\"time\"]], # operate along time\n", + " output_core_dims=[[\"time\"]], # output also has time dim\n", + " vectorize=True, # loop over non-core dims (sim)\n", + " kwargs={\n", + " \"awc\": 6,\n", + " \"data_start_year\": 2000,\n", + " \"calibration_year_initial\": 2000,\n", + " \"calibration_year_final\": 2030,\n", + " }\n", + ")\n", + "\n", + "pdsi_result.plot.hist();" + ] + }, + { + "cell_type": "markdown", + "id": "6cc3157f", + "metadata": {}, + "source": [ + "### Export PDSI" + ] + }, + { + "cell_type": "markdown", + "id": "2d745c44", + "metadata": {}, + "source": [ + "The combined time axis has 720 months total: months 0–359 are the baseline warming level (0.8°C, used for calibration), and months 360–719 are the future warming level period. The export below slices to `time=slice(360, 720)` to retain only the future period.\n", + "\n", + "The exported file will have the following dimensions:\n", + "- `time`: 360 months (30-year future warming level period)\n", + "- `wl`: warming level pair (e.g., `\"08_to_20\"` = calibrated on 0.8°C, evaluated on 2.0°C)\n", + "- `lat`, `lon`: spatial coordinates (EPSG:4326)\n", + "- `sim`: ensemble member" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1dc40f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Cleaning and labeling the data before exporting it in the next cell\n", + "final_pdsi = pdsi_result.isel(time=slice(360, 720))\n", + "final_pdsi = final_pdsi.rename({'combined_wl': 'wl'}).rename(\"pdsi\")\n", + "final_pdsi = final_pdsi.assign_attrs({\n", + " \"long_name\": \"Palmer Drought Severity Index\",\n", + " \"units\": \"from -10 (dry) to +10 (wet)\",\n", + "})\n", + "pdsi_filename = f\"pdsi_wl_lat{str(LAT).replace('.', '_')}_lon{str(LON).replace('.', '_')}.nc\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c6f3455", + "metadata": {}, + "outputs": [], + "source": [ + "export(final_pdsi, pdsi_filename, format=\"NetCDF\", mode=\"local\")" + ] + }, + { + "cell_type": "markdown", + "id": "3dc2e324", + "metadata": {}, + "source": [ + "## Step 3: Calculate EDDI" + ] + }, + { + "cell_type": "markdown", + "id": "f0645679", + "metadata": {}, + "source": [ + "Now, we will calculate EDDI using PET." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb36cbf7-aa3b-4aed-895d-d69c8fe5544b", + "metadata": {}, + "outputs": [], + "source": [ + "# Import `standardized_index` from xclim, which we will apply to our PET data object to generate EDDI\n", + "from xclim.indices.stats import standardized_index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9941a79-198c-494b-be56-fc6b344238d5", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_eddi_numpy(timeseries_np, time_coords):\n", + " \"\"\"Wrapper that reconstructs DataArray from numpy for apply_ufunc.\"\"\"\n", + " ts = xr.DataArray(timeseries_np, coords={\"time\": time_coords}, dims=[\"time\"])\n", + " eddi = standardized_index(\n", + " da=ts,\n", + " freq='MS',\n", + " window=1,\n", + " dist=\"gamma\",\n", + " method=\"ML\",\n", + " zero_inflated=True,\n", + " fitkwargs={},\n", + " cal_start=\"2000-01-31\",\n", + " cal_end=\"2029-12-31\"\n", + " )\n", + " return eddi.clip(min=-2.5, max=2.5) # return numpy\n", + "\n", + "time_coords = combined_ds.time.values\n", + "\n", + "eddi_result = xr.apply_ufunc(\n", + " compute_eddi_numpy,\n", + " combined_ds['precip'],\n", + " input_core_dims=[['time']],\n", + " output_core_dims=[['time']],\n", + " vectorize=True,\n", + " kwargs={\"time_coords\": time_coords},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "45176f23", + "metadata": {}, + "source": [ + "### Export EDDI" + ] + }, + { + "cell_type": "markdown", + "id": "d59ee541", + "metadata": {}, + "source": [ + "The combined time axis has 720 months total: months 0–359 are the baseline warming level (0.8°C, used for calibration), and months 360–719 are the future warming level period. The export below slices to `time=slice(360, 720)` to retain only the future period.\n", + "\n", + "The exported file will have the following dimensions:\n", + "- `time`: 360 months (30-year future warming level period)\n", + "- `wl`: warming level pair (e.g., `\"08_to_20\"` = calibrated on 0.8°C, evaluated on 2.0°C)\n", + "- `lat`, `lon`: spatial coordinates (EPSG:4326)\n", + "- `sim`: ensemble member" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d86782f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Saving these results and cleaning the data\n", + "final_eddi = eddi_result.isel(time=slice(360, 720))\n", + "final_eddi = final_eddi.rename({'combined_wl': 'wl'}).rename(\"eddi\")\n", + "final_eddi = final_eddi.assign_attrs({\n", + " \"long_name\": \"Evaporative Demand Drought Index\",\n", + " \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n", + "})\n", + "eddi_filename = f\"eddi_wl_lat{str(LAT).replace('.', '_')}_lon{str(LON).replace('.', '_')}.nc\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "707286f3", + "metadata": {}, + "outputs": [], + "source": [ + "export(final_eddi, eddi_filename, format=\"NetCDF\", mode=\"local\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (climakitae)", + "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": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 58bef7dcbc005779b896e25ceaea42a12cdf110d Mon Sep 17 00:00:00 2001 From: claalmve Date: Tue, 12 May 2026 18:00:16 +0000 Subject: [PATCH 46/53] Making small changes to the notebook --- .../drought_metrics_latest_final.ipynb | 16 +++------------- 1 file changed, 3 insertions(+), 13 deletions(-) diff --git a/work-in-progress/drought_metrics_latest_final.ipynb b/work-in-progress/drought_metrics_latest_final.ipynb index eae05c17..089cead0 100644 --- a/work-in-progress/drought_metrics_latest_final.ipynb +++ b/work-in-progress/drought_metrics_latest_final.ipynb @@ -32,15 +32,7 @@ "id": "cc35f46e", "metadata": {}, "source": [ - "**Runtime:** With the default settings, this notebook takes approximately 25 minutes to run from start to finish. Modifications to selections may increase the runtime." - ] - }, - { - "cell_type": "markdown", - "id": "89ab2f9c", - "metadata": {}, - "source": [ - "**Troubleshooting:** Getting an `IndexError: index 40 is out of bounds for axis 0 with size 40` when trying to run the PDSI calculation? Try changing your lat/lon or point of interest further away from the boundaries of our 3 km WRF domain (as seen in [this graphic](https://analytics.cal-adapt.org/faq/#what-data-is-available))." + "**Runtime:** With the default settings, this notebook takes approximately **15 minutes** to run from start to finish. Modifications to selections may increase the runtime." ] }, { @@ -382,9 +374,7 @@ " \"calibration_year_initial\": 2000,\n", " \"calibration_year_final\": 2030,\n", " }\n", - ")\n", - "\n", - "pdsi_result.plot.hist();" + ")" ] }, { @@ -484,7 +474,7 @@ " cal_start=\"2000-01-31\",\n", " cal_end=\"2029-12-31\"\n", " )\n", - " return eddi.clip(min=-2.5, max=2.5) # return numpy\n", + " return eddi.clip(min=-2.5, max=2.5) # Clipping extraneous values to be within the realistic range of EDDI\n", "\n", "time_coords = combined_ds.time.values\n", "\n", From 73494567d65906cdbcd5f1a840dee78ce301818a Mon Sep 17 00:00:00 2001 From: claalmve Date: Tue, 12 May 2026 18:01:00 +0000 Subject: [PATCH 47/53] Changing filenames --- work-in-progress/drought_metrics.ipynb | 342 +++---- work-in-progress/drought_metrics_latest.ipynb | 936 ------------------ .../drought_metrics_latest_final.ipynb | 562 ----------- 3 files changed, 126 insertions(+), 1714 deletions(-) delete mode 100644 work-in-progress/drought_metrics_latest.ipynb delete mode 100644 work-in-progress/drought_metrics_latest_final.ipynb diff --git a/work-in-progress/drought_metrics.ipynb b/work-in-progress/drought_metrics.ipynb index 2345cf1c..089cead0 100644 --- a/work-in-progress/drought_metrics.ipynb +++ b/work-in-progress/drought_metrics.ipynb @@ -32,15 +32,7 @@ "id": "cc35f46e", "metadata": {}, "source": [ - "**Runtime:** With the default settings, this notebook takes approximately 25 minutes to run from start to finish. Modifications to selections may increase the runtime." - ] - }, - { - "cell_type": "markdown", - "id": "89ab2f9c", - "metadata": {}, - "source": [ - "**Troubleshooting:** Getting an `IndexError: index 40 is out of bounds for axis 0 with size 40` when trying to run the PDSI calculation? Try changing your lat/lon or point of interest further away from the boundaries of our 3 km WRF domain (as seen in [this graphic](https://analytics.cal-adapt.org/faq/#what-data-is-available))." + "**Runtime:** With the default settings, this notebook takes approximately **15 minutes** to run from start to finish. Modifications to selections may increase the runtime." ] }, { @@ -59,7 +51,7 @@ "outputs": [], "source": [ "# Install climate_indices at a specific commit compatible with the AE hub environment\n", - "!pip install -qq git+https://github.com/monocongo/climate_indices.git@43c5451" + "!pip install -qq git+https://github.com/monocongo/climate_indices.git" ] }, { @@ -71,22 +63,16 @@ "source": [ "import climakitae as ck\n", "import xclim\n", - "import os\n", "import xarray as xr\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import flox\n", "from pyproj import CRS\n", - "from climate_indices.palmer import pdsi as palmer_pdsi\n", + "from dask.diagnostics import ProgressBar\n", + "from climate_indices import palmer\n", "from xclim.indices.stats import standardized_index\n", - "\n", - "from climakitae.core.data_export import export\n", - "from climakitae.util.utils import reproject_data, add_dummy_time_to_wl\n", - "from climakitae.new_core.derived_variables import register_user_function, list_derived_variables\n", - "\n", - "# Making an `input_data` folder to hold intermediate data variables needed for PET\n", - "if not os.path.exists(\"input_data\"):\n", - " os.mkdir(\"input_data\")" + "from climakitae.core.data_export import export" ] }, { @@ -145,17 +131,27 @@ "outputs": [], "source": [ "# Initialize the ClimateData object and define the processors needed.\n", - "cd = ck.ClimateData(verbosity=-1)\n", + "cd = ck.ClimateData(verbosity=-2)\n", "processes = {\n", - " \"clip\": [(LAT - 0.04, LON - 0.04), (LAT + 0.04, LON + 0.04)],\n", + " \"clip\": (LAT, LON),\n", " \"warming_level\": {\n", " \"warming_levels\": WARMING_LEVELS,\n", " \"add_dummy_time\": True\n", " },\n", "}\n", "\n", - "def fetch(variable, table_id=\"day\"):\n", + "def rename_sims_to_gcm(ds):\n", + " \"\"\"\"Renames the 'sim' coordinate in the dataset to only contain the GCM name for easier grouping later.\"\"\"\n", + " ds = ds.copy()\n", + " new_sims = [s.split('_')[2] for s in ds['sim'].values]\n", + " ds['sim'] = ('sim', new_sims)\n", + " return ds\n", + "\n", + "def fetch(variable, table_id=\"day\", units=None):\n", " \"\"\"Fetch a WRF variable from the AE catalog with the configured processes.\"\"\"\n", + " proc = {**processes}\n", + " if units is not None:\n", + " proc[\"convert_units\"] = units\n", " return (\n", " cd.catalog(\"cadcat\")\n", " .activity_id(\"WRF\")\n", @@ -163,37 +159,43 @@ " .table_id(table_id)\n", " .grid_label(\"d03\")\n", " .variable(variable)\n", - " .processes(processes)\n", + " .processes(proc)\n", " .get()\n", " )\n", "\n", - "def fetch_or_load(variable, file_path, table_id=\"day\", transform=None):\n", - " \"\"\"Load from cache if available, otherwise fetch, optionally transform, and cache.\"\"\"\n", - " if os.path.exists(file_path):\n", - " print(f\"Reading {variable} from file.\")\n", - " return xr.open_dataarray(file_path)\n", - " print(f\"Fetching {variable}...\")\n", - " da = fetch(variable, table_id=table_id)\n", + "def get_and_transform(variable, table_id=\"day\", units=None, transform=None):\n", + " \"\"\"Fetching variables and applying a transformation if it's passed in.\"\"\"\n", + " print(f\"\\nFetching {variable}...\")\n", + " da = fetch(variable, table_id=table_id, units=units)\n", + " da = da.unify_chunks() # Ensure the data is chunked for Dask processing\n", " if transform:\n", " da = transform(da)\n", - " export(da, file_path, format=\"NetCDF\", mode=\"local\")\n", - " return xr.open_dataarray(file_path)\n", + " # Drop leap days\n", + " da = da.sel(time=~((da['time.month'] == 2) & (da['time.day'] == 29)))\n", + " # Rename sims to only contain the GCM name for easier grouping later\n", + " da = rename_sims_to_gcm(da)\n", + " return da\n", "\n", "# Daily variables\n", - "rh = fetch_or_load(\"rh\", \"input_data/rh_daily.nc\")\n", - "sw_dwn = fetch_or_load(\"sw_dwn\", \"input_data/sw_dwn_daily.nc\")\n", - "lw_dwn = fetch_or_load(\"lw_dwn\", \"input_data/lw_dwn_daily.nc\")\n", - "wspd10mean = fetch_or_load(\"wspd10mean\", \"input_data/wspd10mean_daily.nc\")\n", - "prec = fetch_or_load(\"prec\", \"input_data/prec_daily.nc\")\n", - "\n", - "# Hourly variables resampled to daily — t2 is fetched twice (min/max) but cached after first run\n", - "tasmin = fetch_or_load(\"t2\", \"input_data/tasmin_daily.nc\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").min())\n", - "tasmax = fetch_or_load(\"t2\", \"input_data/tasmax_daily.nc\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").max())\n", - "rsus_daily = fetch_or_load(\"swupb\", \"input_data/swupb_daily.nc\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", - "rlus_daily = fetch_or_load(\"lwupb\", \"input_data/lwupb_daily.nc\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", + "rh = get_and_transform(\"rh\")\n", + "sw_dwn = get_and_transform(\"sw_dwn\")\n", + "lw_dwn = get_and_transform(\"lw_dwn\")\n", + "wspd10mean = get_and_transform(\"wspd10mean\")\n", + "prec = get_and_transform(\"prec\")\n", + "tasmin = get_and_transform(\"t2min\")\n", + "tasmax = get_and_transform(\"t2max\")\n", + "rsus_daily = get_and_transform(\"swupb\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", + "rlus_daily = get_and_transform(\"lwupb\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", "\n", "# xclim requires relative humidity as a fraction (0–1), not a percentage\n", - "hurs_frac = (rh / 100).assign_attrs(units='1')\n", + "hurs_frac = (rh / 100)\n", + "hurs_frac.rh.attrs['units'] = '1' # Update the units to reflect the transformation to a fraction\n", + "\n", + "# Ensure all radiation flux variables carry the same units\n", + "sw_dwn.sw_dwn.attrs['units'] = 'W m-2'\n", + "lw_dwn.lw_dwn.attrs['units'] = 'W m-2'\n", + "rsus_daily.swupb.attrs['units'] = 'W m-2'\n", + "rlus_daily.lwupb.attrs['units'] = 'W m-2'\n", "\n", "print(\"Finished.\")" ] @@ -209,54 +211,22 @@ { "cell_type": "code", "execution_count": null, - "id": "1e29e94d", + "id": "6c96f440", "metadata": {}, "outputs": [], "source": [ - "for name, da in [\n", - " (\"tasmin\", tasmin), (\"tasmax\", tasmax), (\"hurs_frac\", hurs_frac),\n", - " (\"sw_dwn\", sw_dwn), (\"rsus_daily\", rsus_daily),\n", - " (\"lw_dwn\", lw_dwn), (\"rlus_daily\", rlus_daily), (\"wspd10mean\", wspd10mean)\n", - " ]:\n", - " print(name, da.sizes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32f5bcba", - "metadata": {}, - "outputs": [], - "source": [ - "pet_calc = xr.concat(\n", - " [\n", - " xclim.indices.potential_evapotranspiration(\n", - " tasmin=tasmin.sel(warming_level=wl),\n", - " tasmax=tasmax.sel(warming_level=wl),\n", - " hurs=hurs_frac.sel(warming_level=wl),\n", - " rsds=sw_dwn.sel(warming_level=wl),\n", - " rsus=rsus_daily.sel(warming_level=wl),\n", - " rlds=lw_dwn.sel(warming_level=wl),\n", - " rlus=rlus_daily.sel(warming_level=wl),\n", - " sfcWind=wspd10mean.sel(warming_level=wl),\n", - " method=\"FAO_PM98\",\n", - " )\n", - " for wl in WARMING_LEVELS\n", - " ],\n", - " dim=\"warming_level\",\n", - ").assign_coords(warming_level=WARMING_LEVELS)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58cbcd75", - "metadata": {}, - "outputs": [], - "source": [ - "# Preserving CRS and a spatial mask for later\n", - "crs = CRS.from_cf(pet_calc['Lambert_Conformal'].attrs)\n", - "spatial_mask = ~pet_calc.isel(warming_level=0, time=0, sim=0).isnull()" + "# Call xclim.indicies.potential_evapotranspiration on the entire dataset as it is\n", + "pet_pm = xclim.indices.potential_evapotranspiration(\n", + " tasmin=tasmin.t2min,\n", + " tasmax=tasmax.t2max,\n", + " hurs=hurs_frac.rh,\n", + " rsds=sw_dwn.sw_dwn,\n", + " rsus=rsus_daily.swupb,\n", + " rlds=lw_dwn.lw_dwn,\n", + " rlus=rlus_daily.lwupb,\n", + " sfcWind=wspd10mean.wspd10mean,\n", + " method=\"FAO_PM98\",\n", + ")" ] }, { @@ -283,7 +253,7 @@ "outputs": [], "source": [ "# Resampling PET and precip to monthly since the function only takes monthly variables\n", - "mon_pet = (pet_calc * 86400 / 25.4).resample(time='1ME').sum()\n", + "mon_pet = (pet_pm * 86400 / 25.4).resample(time='1ME').sum()\n", "mon_precip = (prec / 25.4).resample(time='1ME').sum()" ] }, @@ -355,88 +325,56 @@ "outputs": [], "source": [ "# Creating one Dataset of PET and precip with WLs combined\n", - "mon_pet_transform = combine_wl_to_dummy_time(mon_pet, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:]\n", - "mon_precip_transform = combine_wl_to_dummy_time(mon_precip, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", - "\n", - "combined_ds = xr.Dataset({'precip': mon_precip_transform, 'pet': mon_pet_transform})\n", - "\n", - "# Applying spatial mask\n", - "combined_ds = combined_ds.where(spatial_mask)" + "mon_pet_transform = combine_wl_to_dummy_time(mon_pet, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", + "mon_precip_transform = combine_wl_to_dummy_time(mon_precip.prec, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", + "mon_pet_transform = mon_pet_transform.assign_coords(mon_precip_transform.coords)\n", + "combined_ds = xr.Dataset({'precip': mon_precip_transform, 'pet': mon_pet_transform})" ] }, { "cell_type": "code", "execution_count": null, - "id": "c6b5de48", + "id": "999a3437-e29f-402f-9e4e-492f726fdf7f", "metadata": {}, "outputs": [], "source": [ - "def pdsi_func(ds: xr.Dataset) -> xr.Dataset:\n", - " \"\"\"Compute Palmer Drought Severity Index (PDSI) from a Dataset.\n", - "\n", - " Registered derived variable function: receives an xr.Dataset with 'precip' and\n", - " 'pet' variables (both monthly, in inches) and adds 'pdsi' to it.\n", - "\n", - " Parameters\n", - " ----------\n", - " ds : xr.Dataset\n", - " Dataset with 'precip' and 'pet' variables along a 'time' dimension,\n", - " plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n", - "\n", - " Returns\n", - " -------\n", - " xr.Dataset\n", - " Input dataset with 'pdsi' variable added.\n", - " \"\"\"\n", - " def _calc_pdsi_1d(timeseries: xr.Dataset) -> xr.DataArray:\n", - " precip = timeseries['precip'].squeeze()\n", - " pet = timeseries['pet'].squeeze()\n", - " result = palmer_pdsi(\n", - " precips=precip.values,\n", - " pet=pet.values,\n", - " awc=5,\n", - " data_start_year=2000,\n", - " calibration_year_initial=2000,\n", - " calibration_year_final=2030,\n", - " )\n", - " return xr.DataArray(\n", - " result[0], coords={\"time\": precip.time.values}, dims=['time']\n", - " ).clip(min=-10, max=10)\n", - "\n", - " pdsi_da = ds.groupby(['combined_wl', 'x', 'y', 'sim']).apply(_calc_pdsi_1d)\n", - " pdsi_da.attrs.update({\n", - " \"long_name\": \"Palmer Drought Severity Index\",\n", - " \"units\": \"from -10 (dry) to +10 (wet)\",\n", - " })\n", - " ds[\"pdsi\"] = pdsi_da\n", - " return ds\n", - "\n", - "register_user_function(\n", - " name=\"pdsi\",\n", - " depends_on=[\"precip\", \"pet\"],\n", - " func=pdsi_func,\n", - " description=\"Palmer Drought Severity Index computed from monthly precipitation and PET.\",\n", - " units=\"unitless\",\n", - " drop_dependencies=False,\n", - ")" + "# Load `combined_ds` into memory\n", + "with ProgressBar():\n", + " combined_ds = combined_ds.compute()" ] }, { "cell_type": "code", "execution_count": null, - "id": "c625f3af", + "id": "c7d02265-022d-4f50-8d31-d56687e88cc5", "metadata": {}, "outputs": [], "source": [ - "# Apply PDSI derived variable to the combined dataset\n", - "pdsi_ds = list_derived_variables()[\"pdsi\"].func(combined_ds)\n", - "pdsi_da = pdsi_ds[\"pdsi\"]\n", - "\n", - "# Writing crs and reprojecting PDSI to lat/lon\n", - "pdsi_da = pdsi_da.rio.write_crs(crs.to_wkt())\n", - "pdsi_da = pdsi_da.transpose('time', 'combined_wl', 'sim', 'y', 'x')\n", - "pdsi_latlon = reproject_data(pdsi_da, 'EPSG:4326')\n", - "del pdsi_latlon.attrs[\"_FillValue\"]" + "def compute_pdsi(precip_1d, pet_1d, awc, data_start_year, calibration_year_initial, calibration_year_final):\n", + " pdsi, _, _, _, _ = palmer.pdsi(\n", + " precips=precip_1d,\n", + " pet=pet_1d,\n", + " awc=awc,\n", + " data_start_year=data_start_year,\n", + " calibration_year_initial=calibration_year_initial,\n", + " calibration_year_final=calibration_year_final,\n", + " )\n", + " return pdsi\n", + "\n", + "pdsi_result = xr.apply_ufunc(\n", + " compute_pdsi,\n", + " combined_ds[\"precip\"], # shape (time, sim)\n", + " combined_ds[\"pet\"], # shape (time, sim)\n", + " input_core_dims=[[\"time\"], [\"time\"]], # operate along time\n", + " output_core_dims=[[\"time\"]], # output also has time dim\n", + " vectorize=True, # loop over non-core dims (sim)\n", + " kwargs={\n", + " \"awc\": 6,\n", + " \"data_start_year\": 2000,\n", + " \"calibration_year_initial\": 2000,\n", + " \"calibration_year_final\": 2030,\n", + " }\n", + ")" ] }, { @@ -469,7 +407,7 @@ "outputs": [], "source": [ "# Cleaning and labeling the data before exporting it in the next cell\n", - "final_pdsi = pdsi_latlon.isel(time=slice(360, 720))\n", + "final_pdsi = pdsi_result.isel(time=slice(360, 720))\n", "final_pdsi = final_pdsi.rename({'combined_wl': 'wl'}).rename(\"pdsi\")\n", "final_pdsi = final_pdsi.assign_attrs({\n", " \"long_name\": \"Palmer Drought Severity Index\",\n", @@ -507,75 +445,47 @@ { "cell_type": "code", "execution_count": null, - "id": "3228b9fb", + "id": "bb36cbf7-aa3b-4aed-895d-d69c8fe5544b", "metadata": {}, "outputs": [], "source": [ - "def eddi_func(ds: xr.Dataset) -> xr.Dataset:\n", - " \"\"\"Compute Evaporative Demand Drought Index (EDDI) from a Dataset.\n", - "\n", - " Registered derived variable function: receives an xr.Dataset with a 'pet'\n", - " variable (monthly) and adds 'eddi' to it.\n", - "\n", - " Parameters\n", - " ----------\n", - " ds : xr.Dataset\n", - " Dataset with 'pet' variable along a 'time' dimension,\n", - " plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n", - "\n", - " Returns\n", - " -------\n", - " xr.Dataset\n", - " Input dataset with 'eddi' variable added.\n", - " \"\"\"\n", - " def _calc_eddi_1d(timeseries: xr.DataArray) -> xr.DataArray:\n", - " eddi = standardized_index(\n", - " da=timeseries,\n", - " freq='MS',\n", - " window=1,\n", - " dist=\"gamma\",\n", - " method=\"ML\",\n", - " zero_inflated=True,\n", - " fitkwargs={},\n", - " cal_start=\"2000-01-31\",\n", - " cal_end=\"2029-12-31\",\n", - " )\n", - " return eddi.clip(min=-2.5, max=2.5)\n", - "\n", - " eddi_da = ds['pet'].groupby(['combined_wl', 'x', 'y', 'sim']).apply(_calc_eddi_1d)\n", - " eddi_da.attrs.update({\n", - " \"long_name\": \"Evaporative Demand Drought Index\",\n", - " \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n", - " })\n", - " ds[\"eddi\"] = eddi_da\n", - " return ds\n", - "\n", - "register_user_function(\n", - " name=\"eddi\",\n", - " depends_on=[\"pet\"],\n", - " func=eddi_func,\n", - " description=\"Evaporative Demand Drought Index computed from monthly PET.\",\n", - " units=\"unitless\",\n", - " drop_dependencies=False,\n", - ")" + "# Import `standardized_index` from xclim, which we will apply to our PET data object to generate EDDI\n", + "from xclim.indices.stats import standardized_index" ] }, { "cell_type": "code", "execution_count": null, - "id": "1240839c", + "id": "c9941a79-198c-494b-be56-fc6b344238d5", "metadata": {}, "outputs": [], "source": [ - "# Apply EDDI derived variable to the combined dataset\n", - "eddi_ds = list_derived_variables()[\"eddi\"].func(combined_ds)\n", - "eddi_da = eddi_ds[\"eddi\"]\n", + "def compute_eddi_numpy(timeseries_np, time_coords):\n", + " \"\"\"Wrapper that reconstructs DataArray from numpy for apply_ufunc.\"\"\"\n", + " ts = xr.DataArray(timeseries_np, coords={\"time\": time_coords}, dims=[\"time\"])\n", + " eddi = standardized_index(\n", + " da=ts,\n", + " freq='MS',\n", + " window=1,\n", + " dist=\"gamma\",\n", + " method=\"ML\",\n", + " zero_inflated=True,\n", + " fitkwargs={},\n", + " cal_start=\"2000-01-31\",\n", + " cal_end=\"2029-12-31\"\n", + " )\n", + " return eddi.clip(min=-2.5, max=2.5) # Clipping extraneous values to be within the realistic range of EDDI\n", + "\n", + "time_coords = combined_ds.time.values\n", "\n", - "# Writing crs and reprojecting EDDI to lat/lon\n", - "eddi_da = eddi_da.rio.write_crs(crs.to_wkt())\n", - "eddi_da = eddi_da.transpose('time', 'combined_wl', 'sim', 'y', 'x')\n", - "eddi_da_latlon = reproject_data(eddi_da, 'EPSG:4326')\n", - "del eddi_da_latlon.attrs[\"_FillValue\"]" + "eddi_result = xr.apply_ufunc(\n", + " compute_eddi_numpy,\n", + " combined_ds['precip'],\n", + " input_core_dims=[['time']],\n", + " output_core_dims=[['time']],\n", + " vectorize=True,\n", + " kwargs={\"time_coords\": time_coords},\n", + ")" ] }, { @@ -608,7 +518,7 @@ "outputs": [], "source": [ "# Saving these results and cleaning the data\n", - "final_eddi = eddi_da_latlon.isel(time=slice(360, 720))\n", + "final_eddi = eddi_result.isel(time=slice(360, 720))\n", "final_eddi = final_eddi.rename({'combined_wl': 'wl'}).rename(\"eddi\")\n", "final_eddi = final_eddi.assign_attrs({\n", " \"long_name\": \"Evaporative Demand Drought Index\",\n", diff --git a/work-in-progress/drought_metrics_latest.ipynb b/work-in-progress/drought_metrics_latest.ipynb deleted file mode 100644 index 01458b14..00000000 --- a/work-in-progress/drought_metrics_latest.ipynb +++ /dev/null @@ -1,936 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5e0361f0", - "metadata": {}, - "source": [ - "# Drought Metrics" - ] - }, - { - "cell_type": "markdown", - "id": "64641b82", - "metadata": {}, - "source": [ - "This notebook calculates two drought metrics, the Palmer Drought Severity Index (PDSI) and the Evaporative Demand Drought Index (EDDI), using WRF data in the AE catalog. Both PDSI and EDDI require Potential Evapotranspiration (PET). PET is computed first using the Penman-Monteith method. At the end of the notebook, the user will be able to export monthly PDSI and EDDI for under different global warming levels for a specific lat/lon as netcdf files to be used for further analyses. " - ] - }, - { - "cell_type": "markdown", - "id": "98506237", - "metadata": {}, - "source": [ - "**Intended Application:** As a user, I want to export future drought indices for different global warming levels by:\n", - "1. Calculating PET using the Penman-Montieth Method\n", - "2. Calculating the PDSI using PET and exporting the monthly timeseries to a netcdf\n", - "3. Calculating the EDDI using PET and exporting the monthly timeseries to a netcdf" - ] - }, - { - "cell_type": "markdown", - "id": "cc35f46e", - "metadata": {}, - "source": [ - "**Runtime:** With the default settings, this notebook takes approximately 25 minutes to run from start to finish. Modifications to selections may increase the runtime." - ] - }, - { - "cell_type": "markdown", - "id": "89ab2f9c", - "metadata": {}, - "source": [ - "**Troubleshooting:** Getting an `IndexError: index 40 is out of bounds for axis 0 with size 40` when trying to run the PDSI calculation? Try changing your lat/lon or point of interest further away from the boundaries of our 3 km WRF domain (as seen in [this graphic](https://analytics.cal-adapt.org/faq/#what-data-is-available))." - ] - }, - { - "cell_type": "markdown", - "id": "3babc840", - "metadata": {}, - "source": [ - "## Step 0: Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "xypsmbxjeoj", - "metadata": {}, - "outputs": [], - "source": [ - "# Install climate_indices at a specific commit compatible with the AE hub environment\n", - "!pip install -qq git+https://github.com/monocongo/climate_indices.git@43c5451" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a7e19917", - "metadata": {}, - "outputs": [], - "source": [ - "import climakitae as ck\n", - "import xclim\n", - "import xarray as xr\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import flox\n", - "from pyproj import CRS\n", - "from dask.diagnostics import ProgressBar\n", - "from climate_indices.palmer import pdsi as palmer_pdsi\n", - "from xclim.indices.stats import standardized_index" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "id": "8b31d378", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the lat/lon for the location of interest and the variables needed for the Penman-Monteith PET method\n", - "LAT = 37.787964\n", - "# LAT = 37.830358\n", - "LON = -122.065063\n", - "# LON = -122.261082\n", - "WARMING_LEVELS = [0.8, 2.0]" - ] - }, - { - "cell_type": "markdown", - "id": "485befa5f23", - "metadata": {}, - "source": [ - "## Step 1: Fetch Data and Calculate PET\n", - "\n", - "**IMPORTANT NOTE**: The following cell saves the intermediate data required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files." - ] - }, - { - "cell_type": "markdown", - "id": "522c9425", - "metadata": {}, - "source": [ - "### Penman-Monteith PET Method (most physically consistent)\n", - "We will be using the PET method for calculating PDSI, since it is the most physically consistent method. Below, we list out the variables we will need." - ] - }, - { - "cell_type": "markdown", - "id": "cc88bd71", - "metadata": {}, - "source": [ - "**Variables needed:**\n", - "- `t2` (hourly) → daily `tasmin` and `tasmax` derived via resample\n", - "- `relative humidity`\n", - "- `radiation flux`\n", - " - rsds (daily)\n", - " - rsus (hourly → daily mean)\n", - " - rlds (daily)\n", - " - rlus (hourly → daily mean)\n", - "- `wind speed (10m wind will be converted to 2m)`" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "id": "a0132b29-804a-4eef-893f-2f60adfefc62", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching rh...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching sw_dwn...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[161]\u001b[39m\u001b[32m, line 49\u001b[39m\n\u001b[32m 47\u001b[39m \u001b[38;5;66;03m# Daily variables\u001b[39;00m\n\u001b[32m 48\u001b[39m rh = get_and_transform(\u001b[33m\"\u001b[39m\u001b[33mrh\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m49\u001b[39m sw_dwn = \u001b[43mget_and_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msw_dwn\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 50\u001b[39m lw_dwn = get_and_transform(\u001b[33m\"\u001b[39m\u001b[33mlw_dwn\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 51\u001b[39m wspd10mean = get_and_transform(\u001b[33m\"\u001b[39m\u001b[33mwspd10mean\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[161]\u001b[39m\u001b[32m, line 37\u001b[39m, in \u001b[36mget_and_transform\u001b[39m\u001b[34m(variable, table_id, units, transform)\u001b[39m\n\u001b[32m 35\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Fetching variables and applying a transformation if it's passed in.\"\"\"\u001b[39;00m\n\u001b[32m 36\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mFetching \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvariable\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m...\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m37\u001b[39m da = \u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtable_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtable_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m=\u001b[49m\u001b[43munits\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 38\u001b[39m da = da.unify_chunks() \u001b[38;5;66;03m# Ensure the data is chunked for Dask processing\u001b[39;00m\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m transform:\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[161]\u001b[39m\u001b[32m, line 31\u001b[39m, in \u001b[36mfetch\u001b[39m\u001b[34m(variable, table_id, units)\u001b[39m\n\u001b[32m 21\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m units \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 22\u001b[39m proc[\u001b[33m\"\u001b[39m\u001b[33mconvert_units\u001b[39m\u001b[33m\"\u001b[39m] = units\n\u001b[32m 23\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[32m 24\u001b[39m \u001b[43mcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcatalog\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcadcat\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 25\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mactivity_id\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mWRF\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 26\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43minstitution_id\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mUCLA\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 27\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mtable_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtable_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 28\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mgrid_label\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43md03\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 29\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 30\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mprocesses\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m---> \u001b[39m\u001b[32m31\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 32\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/new_core/user_interface.py:872\u001b[39m, in \u001b[36mClimateData.get\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 869\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 870\u001b[39m \u001b[38;5;66;03m# Execute the query with the snapshot\u001b[39;00m\n\u001b[32m 871\u001b[39m logger.debug(\u001b[33m\"\u001b[39m\u001b[33mExecuting query\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m872\u001b[39m data = \u001b[43mdataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_snapshot\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 873\u001b[39m \u001b[38;5;66;03m# check if empty dataset\u001b[39;00m\n\u001b[32m 874\u001b[39m \u001b[38;5;66;03m# Check if data is empty/null\u001b[39;00m\n\u001b[32m 875\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 876\u001b[39m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 877\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28mhasattr\u001b[39m(data, \u001b[33m\"\u001b[39m\u001b[33mnbytes\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m data.nbytes == \u001b[32m0\u001b[39m)\n\u001b[32m 878\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data)\n\u001b[32m 879\u001b[39m ):\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/new_core/dataset.py:246\u001b[39m, in \u001b[36mDataset.execute\u001b[39m\u001b[34m(self, parameters)\u001b[39m\n\u001b[32m 243\u001b[39m \u001b[38;5;66;03m# Execute the current step\u001b[39;00m\n\u001b[32m 244\u001b[39m \u001b[38;5;66;03m# context is updated in place by the step\u001b[39;00m\n\u001b[32m 245\u001b[39m logger.debug(\u001b[33m\"\u001b[39m\u001b[33mBEFORE:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m, current_result)\n\u001b[32m--> \u001b[39m\u001b[32m246\u001b[39m current_result = \u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_result\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 247\u001b[39m logger.debug(\u001b[33m\"\u001b[39m\u001b[33mAFTER:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m, current_result)\n\u001b[32m 249\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m current_result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/new_core/processors/clip.py:416\u001b[39m, in \u001b[36mClip.execute\u001b[39m\u001b[34m(self, result, context)\u001b[39m\n\u001b[32m 413\u001b[39m \u001b[38;5;28;01mcase\u001b[39;00m xr.Dataset() | xr.DataArray():\n\u001b[32m 414\u001b[39m \u001b[38;5;66;03m# Clip the single dataset\u001b[39;00m\n\u001b[32m 415\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.is_single_point:\n\u001b[32m--> \u001b[39m\u001b[32m416\u001b[39m ret = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_clip_data_to_point\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlon\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 417\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.is_multi_point:\n\u001b[32m 418\u001b[39m ret = \u001b[38;5;28mself\u001b[39m._clip_data_to_points_as_mask(\n\u001b[32m 419\u001b[39m result, \u001b[38;5;28mself\u001b[39m.point_list, \u001b[38;5;28mself\u001b[39m.extract_points\n\u001b[32m 420\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/new_core/processors/clip.py:690\u001b[39m, in \u001b[36mClip._clip_data_to_point\u001b[39m\u001b[34m(dataset, lat, lon)\u001b[39m\n\u001b[32m 667\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 668\u001b[39m \u001b[33;03mClip data to the closest gridcell with valid data for a single point.\u001b[39;00m\n\u001b[32m 669\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 686\u001b[39m \u001b[33;03m or None if no valid gridcell found within search radius\u001b[39;00m\n\u001b[32m 687\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 689\u001b[39m \u001b[38;5;66;03m# First try the original method\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m690\u001b[39m closest_gridcell = \u001b[43mget_closest_gridcell\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlon\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_coords\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 692\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m closest_gridcell \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 693\u001b[39m \u001b[38;5;66;03m# Check if this gridcell has valid data (check first variable, first time step)\u001b[39;00m\n\u001b[32m 694\u001b[39m first_var = \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(closest_gridcell.data_vars))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/util/utils.py:578\u001b[39m, in \u001b[36mget_closest_gridcell\u001b[39m\u001b[34m(data, lat, lon, print_coords)\u001b[39m\n\u001b[32m 575\u001b[39m gridcell = data.isel({lat_dim: lat_idx, lon_dim: lon_idx})\n\u001b[32m 576\u001b[39m test_data = _reduce_to_single_point(gridcell, lat_dim, lon_dim)\n\u001b[32m--> \u001b[39m\u001b[32m578\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43m_check_has_valid_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_data\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m 579\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m print_coords:\n\u001b[32m 580\u001b[39m _print_closest_coords(gridcell, lat, lon, lat_dim, lon_dim)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/util/utils.py:201\u001b[39m, in \u001b[36m_check_has_valid_data\u001b[39m\u001b[34m(test_data)\u001b[39m\n\u001b[32m 199\u001b[39m \u001b[38;5;66;03m# Handle dask arrays - compute only the boolean result\u001b[39;00m\n\u001b[32m 200\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(is_all_null.data, \u001b[33m\"\u001b[39m\u001b[33mcompute\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m201\u001b[39m is_all_null = \u001b[43mis_all_null\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 202\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_all_null:\n\u001b[32m 203\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:1242\u001b[39m, in \u001b[36mDataArray.compute\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 1212\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Trigger loading data into memory and return a new dataarray.\u001b[39;00m\n\u001b[32m 1213\u001b[39m \n\u001b[32m 1214\u001b[39m \u001b[33;03mData will be computed and/or loaded from disk or a remote source.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1239\u001b[39m \u001b[33;03mVariable.compute\u001b[39;00m\n\u001b[32m 1240\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1241\u001b[39m new = \u001b[38;5;28mself\u001b[39m.copy(deep=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m-> \u001b[39m\u001b[32m1242\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnew\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:1168\u001b[39m, in \u001b[36mDataArray.load\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 1138\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mload\u001b[39m(\u001b[38;5;28mself\u001b[39m, **kwargs) -> Self:\n\u001b[32m 1139\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Trigger loading data into memory and return this dataarray.\u001b[39;00m\n\u001b[32m 1140\u001b[39m \n\u001b[32m 1141\u001b[39m \u001b[33;03m Data will be computed and/or loaded from disk or a remote source.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1166\u001b[39m \u001b[33;03m Variable.load\u001b[39;00m\n\u001b[32m 1167\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1168\u001b[39m ds = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_to_temp_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1169\u001b[39m new = \u001b[38;5;28mself\u001b[39m._from_temp_dataset(ds)\n\u001b[32m 1170\u001b[39m \u001b[38;5;28mself\u001b[39m._variable = new._variable\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataset.py:558\u001b[39m, in \u001b[36mDataset.load\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m 555\u001b[39m chunkmanager = get_chunked_array_type(*chunked_data.values())\n\u001b[32m 557\u001b[39m \u001b[38;5;66;03m# evaluate all the chunked arrays simultaneously\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m558\u001b[39m evaluated_data: \u001b[38;5;28mtuple\u001b[39m[np.ndarray[Any, Any], ...] = \u001b[43mchunkmanager\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 559\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mchunked_data\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m 560\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 562\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k, data \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(chunked_data, evaluated_data, strict=\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[32m 563\u001b[39m \u001b[38;5;28mself\u001b[39m.variables[k].data = data\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/namedarray/daskmanager.py:85\u001b[39m, in \u001b[36mDaskManager.compute\u001b[39m\u001b[34m(self, *data, **kwargs)\u001b[39m\n\u001b[32m 80\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute\u001b[39m(\n\u001b[32m 81\u001b[39m \u001b[38;5;28mself\u001b[39m, *data: Any, **kwargs: Any\n\u001b[32m 82\u001b[39m ) -> \u001b[38;5;28mtuple\u001b[39m[np.ndarray[Any, _DType_co], ...]:\n\u001b[32m 83\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mdask\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01marray\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m compute\n\u001b[32m---> \u001b[39m\u001b[32m85\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/dask/base.py:681\u001b[39m, in \u001b[36mcompute\u001b[39m\u001b[34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[39m\n\u001b[32m 678\u001b[39m expr = expr.optimize()\n\u001b[32m 679\u001b[39m keys = \u001b[38;5;28mlist\u001b[39m(flatten(expr.__dask_keys__()))\n\u001b[32m--> \u001b[39m\u001b[32m681\u001b[39m results = \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 683\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m repack(results)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/queue.py:171\u001b[39m, in \u001b[36mQueue.get\u001b[39m\u001b[34m(self, block, timeout)\u001b[39m\n\u001b[32m 169\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 170\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._qsize():\n\u001b[32m--> \u001b[39m\u001b[32m171\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mnot_empty\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 172\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m timeout < \u001b[32m0\u001b[39m:\n\u001b[32m 173\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m\u001b[33mtimeout\u001b[39m\u001b[33m'\u001b[39m\u001b[33m must be a non-negative number\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/threading.py:355\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[32m 354\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m355\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 356\u001b[39m gotit = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 357\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: " - ] - } - ], - "source": [ - "# Initialize the ClimateData object and define the processors needed.\n", - "cd = ck.ClimateData(verbosity=-2)\n", - "processes = {\n", - " \"clip\": (LAT, LON),\n", - " \"warming_level\": {\n", - " \"warming_levels\": WARMING_LEVELS,\n", - " \"add_dummy_time\": True\n", - " },\n", - "}\n", - "\n", - "def rename_sims_to_gcm(ds):\n", - " \"\"\"\"Renames the 'sim' coordinate in the dataset to only contain the GCM name for easier grouping later.\"\"\"\n", - " ds = ds.copy()\n", - " new_sims = [s.split('_')[2] for s in ds['sim'].values]\n", - " ds['sim'] = ('sim', new_sims)\n", - " return ds\n", - "\n", - "def fetch(variable, table_id=\"day\", units=None):\n", - " \"\"\"Fetch a WRF variable from the AE catalog with the configured processes.\"\"\"\n", - " proc = {**processes}\n", - " if units is not None:\n", - " proc[\"convert_units\"] = units\n", - " return (\n", - " cd.catalog(\"cadcat\")\n", - " .activity_id(\"WRF\")\n", - " .institution_id(\"UCLA\")\n", - " .table_id(table_id)\n", - " .grid_label(\"d03\")\n", - " .variable(variable)\n", - " .processes(proc)\n", - " .get()\n", - " )\n", - "\n", - "def get_and_transform(variable, table_id=\"day\", units=None, transform=None):\n", - " \"\"\"Fetching variables and applying a transformation if it's passed in.\"\"\"\n", - " print(f\"\\nFetching {variable}...\")\n", - " da = fetch(variable, table_id=table_id, units=units)\n", - " da = da.unify_chunks() # Ensure the data is chunked for Dask processing\n", - " if transform:\n", - " da = transform(da)\n", - " # Drop leap days\n", - " da = da.sel(time=~((da['time.month'] == 2) & (da['time.day'] == 29)))\n", - " # Rename sims to only contain the GCM name for easier grouping later\n", - " da = rename_sims_to_gcm(da)\n", - " return da\n", - "\n", - "# Daily variables\n", - "rh = get_and_transform(\"rh\")\n", - "sw_dwn = get_and_transform(\"sw_dwn\")\n", - "lw_dwn = get_and_transform(\"lw_dwn\")\n", - "wspd10mean = get_and_transform(\"wspd10mean\")\n", - "prec = get_and_transform(\"prec\")\n", - "tasmin = get_and_transform(\"t2min\")\n", - "tasmax = get_and_transform(\"t2max\")\n", - "rsus_daily = get_and_transform(\"swupb\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", - "rlus_daily = get_and_transform(\"lwupb\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", - "\n", - "# xclim requires relative humidity as a fraction (0–1), not a percentage\n", - "hurs_frac = (rh / 100)\n", - "hurs_frac.rh.attrs['units'] = '1' # Update the units to reflect the transformation to a fraction\n", - "\n", - "# Ensure all radiation flux variables carry the same units\n", - "sw_dwn.sw_dwn.attrs['units'] = 'W m-2'\n", - "lw_dwn.lw_dwn.attrs['units'] = 'W m-2'\n", - "rsus_daily.swupb.attrs['units'] = 'W m-2'\n", - "rlus_daily.lwupb.attrs['units'] = 'W m-2'\n", - "\n", - "print(\"Finished.\")" - ] - }, - { - "cell_type": "markdown", - "id": "c74e6120", - "metadata": {}, - "source": [ - "#### Calculate PET from `xclim` using the Penman-Monteith Method" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "384dff53-3011-4ecc-958a-ed3655d9b87a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tasmin Frozen({'sim': 5, 'warming_level': 2, 'time': 10950})\n", - "tasmax Frozen({'sim': 5, 'warming_level': 2, 'time': 10950})\n", - "hurs_frac Frozen({'warming_level': 2, 'time': 10950, 'sim': 5})\n", - "sw_dwn Frozen({'sim': 5, 'warming_level': 2, 'time': 10950})\n", - "rsus_daily Frozen({'time': 10950, 'sim': 5, 'warming_level': 2})\n", - "lw_dwn Frozen({'sim': 5, 'warming_level': 2, 'time': 10950})\n", - "rlus_daily Frozen({'time': 10950, 'sim': 5, 'warming_level': 2})\n", - "wspd10mean Frozen({'sim': 5, 'warming_level': 2, 'time': 10950})\n" - ] - } - ], - "source": [ - "for name, da in [\n", - " (\"tasmin\", tasmin), (\"tasmax\", tasmax), (\"hurs_frac\", hurs_frac),\n", - " (\"sw_dwn\", sw_dwn), (\"rsus_daily\", rsus_daily),\n", - " (\"lw_dwn\", lw_dwn), (\"rlus_daily\", rlus_daily), (\"wspd10mean\", wspd10mean)\n", - " ]:\n", - " print(name, da.sizes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c96f440", - "metadata": {}, - "outputs": [], - "source": [ - "# Call xclim.indicies.potential_evapotranspiration on the entire dataset as it is\n", - "pet_pm = xclim.indices.potential_evapotranspiration(\n", - " tasmin=tasmin.t2min,\n", - " tasmax=tasmax.t2max,\n", - " hurs=hurs_frac.rh,\n", - " rsds=sw_dwn.sw_dwn,\n", - " rsus=rsus_daily.swupb,\n", - " rlds=lw_dwn.lw_dwn,\n", - " rlus=rlus_daily.lwupb,\n", - " sfcWind=wspd10mean.wspd10mean,\n", - " method=\"FAO_PM98\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "id": "5aedbf79", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "inputs = {\n", - " 'tasmin': tasmin.t2min,\n", - " 'tasmax': tasmax.t2max,\n", - " 'hurs': hurs_frac.rh,\n", - " 'rsds': sw_dwn.sw_dwn,\n", - " 'rsus': rsus_daily.swupb,\n", - " 'rlds': lw_dwn.lw_dwn,\n", - " 'rlus': rlus_daily.lwupb,\n", - " 'sfcWind': wspd10mean.wspd10mean,\n", - "}\n", - "inputs_sub = {name: da.isel(warming_level=1, sim=3) for name, da in inputs.items()}\n", - "\n", - "fig, axes = plt.subplots(2, 4, figsize=(18, 8))\n", - "axes = axes.flatten()\n", - "\n", - "for ax, (name, da) in zip(axes, inputs_sub.items()):\n", - " vals = da.values.flatten()\n", - " vals = vals[~np.isnan(vals)]\n", - " ax.hist(vals, bins=50, color='steelblue', edgecolor='none')\n", - " ax.set_title(f\"{name}\\nunits: {da.attrs.get('units', 'NOT SET')}\")\n", - " ax.set_xlabel('value')\n", - " ax.set_ylabel('count')\n", - " ax.axvline(np.median(vals), color='red', linestyle='--', linewidth=1, label=f'median: {np.median(vals):.2f}')\n", - " ax.legend(fontsize=8)\n", - "\n", - "plt.suptitle('PET Input Diagnostics', fontsize=14, y=1.02)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "id": "44713a20", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching swupb...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.1hr.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# Grab rsus as an example to test out the distribution of values and ensure they look reasonable\n", - "rsus_testing = get_and_transform(\"swupb\", table_id=\"1hr\")" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "id": "bf08d862", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "n_wl = rsus_testing.sizes['warming_level']\n", - "n_sim = rsus_testing.sizes['sim']\n", - "\n", - "fig, axes = plt.subplots(n_wl, n_sim, figsize=(n_sim * 4, n_wl * 3), sharey=True)\n", - "\n", - "for i, wl in enumerate(rsus_testing.warming_level.values):\n", - " for j, sim in enumerate(rsus_testing.sim.values):\n", - " ax = axes[i, j]\n", - " vals = rsus_testing.sel(warming_level=wl, sim=sim).swupb.values[180*24: 210*24] # first year hourly\n", - " ax.plot(vals, linewidth=0.5, color='steelblue')\n", - " ax.axhline(np.nanmedian(vals), color='red', linestyle='--', linewidth=1)\n", - " # ax.set_title(f\"WL={wl} | {sim}\\nmedian={np.nanmedian(vals):.1f}\", fontsize=8)\n", - " ax.set_xlabel('hours', fontsize=7)\n", - " ax.set_ylabel('W/m2', fontsize=7)\n", - "\n", - "plt.suptitle('swupb timeseries by WL and sim', fontsize=12, y=1.01)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "1de94e8d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([4.6790e+03, 1.9767e+04, 1.7113e+04, 1.6921e+04, 2.5409e+04,\n", - " 1.0806e+04, 3.0500e+03, 6.7000e+02, 1.2000e+02, 1.5000e+01]),\n", - " array([-0.00891502, 0.00670978, 0.02233457, 0.03795937, 0.05358417,\n", - " 0.06920896, 0.08483376, 0.10045855, 0.11608335, 0.13170815,\n", - " 0.14733294]),\n", - " )" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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qs7oWQng12rTukxOPmqLUGS7eh1EfwMSPKVDohK+gFn77CNdRVGUvulKsrikRfZXdxE1FEtbaKPRFv5Yuva9vzWpGUkjRcE2FB12NVkEipA+XXlvLpeYuzUPbWMun8KZruJzuTySoiUzD3jW8Xq+tmi+d3NV8Fl07oUCqywTovdVyalm+/vprV7ujvhpax4wofB+Tcx0NBQVV5T///PPuBJG4D8qZ6pOj19cBPKwpef/99yNXFtbJITw4x9tfFdRUG6gOnzqpqIZR+5bmpSCdmE54mq5LFiR1MFeN1h133OG+tKjzsjpia5i09hstQ9iUyf56ZvdXnZzDY1M0NTOr9ljNrzpWaX9Rja6+2Km2+HS/GCVFTU06DqoDv5p6R40a5V5LX/JC6rOoMjq+qFOxand0DSYtm/ZxHVNOpvb/NqnqeKTPqz63uhp3cq5kr+OmjmvhlzVRMKtSpYqr9Yr+CZUmTZq4vxnyGjwnHX+HVLFs2TI3hFjDSaNpyN9ll10WlCxZMti5c2eG29pJDb/U9HhX0jzVIcFJDSFfv3590KxZs6BIkSJBrly5gsqVKwfDhw93w8jj0ZBdDaXUMuum8j179gzWrVt3wtdP7vDj1atXu+XREOGCBQu6IeLbtm07rpyGej733HNu+PtZZ50VlC5dOhg0aFBw6NChk77GyYaQJ75aalLbLhxaquHVyRmSq21cv379ILmWLl3q5q/tkV60Hkntb9H7Zrz99fnnn3f7itZbn00Nzb/++uvd5R3imTlzppvHs88+e8Jl0ns8dOhQt2x67ytUqBA888wzccuyv6b9/hq+9/FuF154oSujY2/nzp3dPHU1dQ0RX7t2rXtdPf9kyzhkyBA3/eeffz7h8TP6M/zUU0+544KOa40aNQq+/vrr45b9+++/D26//fagePHibl86//zzg+uuuy5y6YyTbTd59NFH3fN06ZKUXP04nG/0cTFc/uhtItpOumVECfonvYMWgP+jamp983z33XddR1hdXC61qNZDNQwaQaSaRI2KEl3wTH1qdL9ly5bJmpe+Saovki5slniILbKOzLK/ImviZx2ADEjDzNXmnlTfj9M5IWm+iX/qQp3V1RcqJScMNb9p1Jr6lyBrywz7K7ImanKADEYXDwuHGusAf6oXd4xHnRvDC4ud6rzV70XfpNVRXD/tEP5ECbKmjL6/4vgOxdG/n5hRLt+QVgg5AFLc4VMdIDVSSp011cEbQObQqVOnyIVVk+JTLxZCDgAAWcQ333wTGY2YlLQcyXumEXIAAICX6HgMAAC8lKUvBqjfB1G1nfoUcPlrAAAyh/CnV3SBzfCq8vFk6ZCjgJP4V2MBAEDmoMsL6MrlScnSISccFaKNlJoXsAIAAGlnz549rpLiZKM7s3TICZuoFHAIOQAAZC4n62pCx2MAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFyAACAl3Kk9wIAyBzKDZhhmc3GES3TexEAZJaanOHDh9tll11m55xzjhUtWtRatWpl69atiylz9dVXW0JCQsyte/fuMWU2bdpkLVu2tLx587r59O/f344cORJTZv78+XbppZdarly5rEKFCjZhwoTjlmfMmDFWrlw5y507t9WrV8+WLl2asrUHAADeSlHIWbBggfXs2dOWLFlis2fPtsOHD1uzZs1s3759MeW6du1qW7dujdyeeOKJyGNHjx51AefQoUO2aNEie+2111yAGTx4cKTMhg0bXJnGjRvbihUrrE+fPnbnnXfaRx99FCkzadIk69evnw0ZMsSWL19uNWvWtObNm9uOHTtOb4sAAAAvJARBEJzqk3/++WdXE6Pwc+WVV0ZqcmrVqmWjRo2K+5wPP/zQrrvuOtuyZYsVK1bMTRs3bpw98MADbn45c+Z0/58xY4atXr068ry2bdvarl27bObMme6+am5Uq/T888+7+8eOHbPSpUvb3XffbQMGDEjW8u/Zs8cKFChgu3fvtvz585/qZgCyBJqrAGQUyT1/n1bHY81cChUqFDP9jTfesCJFili1atVs4MCB9scff0QeW7x4sVWvXj0ScEQ1MFrgNWvWRMo0bdo0Zp4qo+miWqBly5bFlMmWLZu7H5aJ5+DBg+51om8AAMBPp9zxWDUnaka6/PLLXZgJ3XbbbVa2bFkrWbKkrVy50tXKqN/OO++84x7ftm1bTMCR8L4eO1EZhZL9+/fbzp07XbNXvDJr1649YZ+iRx555FRXGQAAZIWQo745ak769NNPY6Z369Yt8n/V2JQoUcKaNGli33//vV144YWWnlSrpH48IYUmNXEBAAD/nFLI6dWrl02fPt0WLlxopUqVOmFZ9Z2R9evXu5BTvHjx40ZBbd++3f3VY+HfcFp0GbW75cmTx7Jnz+5u8cqE84hHI7V0AwAA/ktRnxz1UVbAmTp1qs2dO9fKly9/0udodJSoRkcaNGhgq1atihkFpZFaCjBVq1aNlJkzZ07MfFRG00Wdk2vXrh1TRs1nuh+WAQAAWVuOlDZRTZw40d599113rZywD416OKuGRU1SerxFixZWuHBh1yenb9++buRVjRo1XFkNOVeY6dChgxtarnkMGjTIzTusZdF1dTRq6v7777c77rjDBarJkye7EVchNTt17NjR6tSpY3Xr1nWjuTSUvXPnzqm7hQAAgP8hZ+zYsZFh4tHGjx9vnTp1cjUsH3/8cSRwqL9L69atXYgJqZlJTV09evRwtS758uVzYWXYsGGRMqohUqBRQBo9erRrEnv55ZfdCKtQmzZt3JBzXV9HQUnD1jW8PHFnZAAAkDWd1nVyMjuukwMkH9fJAZClrpMDAACQURFyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXUhRyhg8fbpdddpmdc845VrRoUWvVqpWtW7cupsyBAwesZ8+eVrhwYTv77LOtdevWtn379pgymzZtspYtW1revHndfPr3729HjhyJKTN//ny79NJLLVeuXFahQgWbMGHCccszZswYK1eunOXOndvq1atnS5cuTdnaAwAAb6Uo5CxYsMAFmCVLltjs2bPt8OHD1qxZM9u3b1+kTN++fe3999+3KVOmuPJbtmyxm2++OfL40aNHXcA5dOiQLVq0yF577TUXYAYPHhwps2HDBlemcePGtmLFCuvTp4/deeed9tFHH0XKTJo0yfr162dDhgyx5cuXW82aNa158+a2Y8eO098qAAAg00sIgiA41Sf//PPPriZGYebKK6+03bt323nnnWcTJ060W265xZVZu3atValSxRYvXmz169e3Dz/80K677joXfooVK+bKjBs3zh544AE3v5w5c7r/z5gxw1avXh15rbZt29quXbts5syZ7r5qblSr9Pzzz7v7x44ds9KlS9vdd99tAwYMSNby79mzxwoUKOCWO3/+/Ke6GYAsodyAGZbZbBzRMr0XAUAaSO75+7T65GjmUqhQIfd32bJlrnanadOmkTKVK1e2MmXKuJAj+lu9evVIwBHVwGiB16xZEykTPY+wTDgP1QLptaLLZMuWzd0PywAAgKwtx6k+UTUnaka6/PLLrVq1am7atm3bXE1MwYIFY8oq0OixsEx0wAkfDx87URkFof3799vOnTtds1e8Mqo5SsrBgwfdLaT5AQAAP51yTY765qg56a233rLMQh2nVb0V3tS8BQAA/HRKIadXr142ffp0mzdvnpUqVSoyvXjx4q4pSX1noml0lR4LyyQebRXeP1kZtbvlyZPHihQpYtmzZ49bJpxHPAMHDnRNbOHtxx9/PJXVBwAAvoUc9VFWwJk6darNnTvXypcvH/N47dq17ayzzrI5c+ZEpmmIuYaMN2jQwN3X31WrVsWMgtJILQWYqlWrRspEzyMsE85DTWJ6regyaj7T/bBMPBqOrteJvgEAAD/lSGkTlUZOvfvuu+5aOWEfGjX9qIZFf7t06eKGdqszskKERjspeGhklWjIucJMhw4d7IknnnDzGDRokJu3Qoh0797djZq6//777Y477nCBavLkyW7EVUiv0bFjR6tTp47VrVvXRo0a5Yayd+7cOXW3UBbC6BkAQJYNOWPHjnV/r7766pjp48ePt06dOrn/P/PMM26kky4CqE6+GhX1wgsvRMqqmUlNXT169HDhJ1++fC6sDBs2LFJGNUQKNLrmzujRo12T2Msvv+zmFWrTpo0bcq7r6ygo1apVyw0vT9wZGQAAZE2ndZ2czI7r5MSiJgcnwv4BIEtdJwcAACCjIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC/lSO8FAE5HuQEzMt0G3DiiZXovAgBkCdTkAAAAL1GTA5xhmbH2CQAyI2pyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvJTikLNw4UK7/vrrrWTJkpaQkGDTpk2LebxTp05uevTtmmuuiSnz22+/Wfv27S1//vxWsGBB69Kli+3duzemzMqVK61Ro0aWO3duK126tD3xxBPHLcuUKVOscuXKrkz16tXtgw8+SOnqAAAAT6U45Ozbt89q1qxpY8aMSbKMQs3WrVsjtzfffDPmcQWcNWvW2OzZs2369OkuOHXr1i3y+J49e6xZs2ZWtmxZW7ZsmY0cOdKGDh1qL730UqTMokWLrF27di4gffXVV9aqVSt3W716dUpXCQAAeCghCILglJ+ckGBTp0514SK6JmfXrl3H1fCEvv32W6tatap98cUXVqdOHTdt5syZ1qJFC9u8ebOrIRo7dqw99NBDtm3bNsuZM6crM2DAADfPtWvXuvtt2rRxgUshKVS/fn2rVauWjRs3LlnLrzBVoEAB2717t6tVyurKDZiR3osApKqNI1qyRQEPJff8nSZ9cubPn29Fixa1SpUqWY8ePezXX3+NPLZ48WLXRBUGHGnatKlly5bNPv/880iZK6+8MhJwpHnz5rZu3TrbuXNnpIyeF01lND0pBw8edBsm+gYAAPyU6iFHTVWvv/66zZkzxx5//HFbsGCBXXvttXb06FH3uGpnFICi5ciRwwoVKuQeC8sUK1Yspkx4/2RlwsfjGT58uEt+4U19fQAAgJ9ypPYM27ZtG/m/OgPXqFHDLrzwQle706RJE0tPAwcOtH79+kXuqyaHoAMAgJ/SfAj5BRdcYEWKFLH169e7+8WLF7cdO3bElDly5IgbcaXHwjLbt2+PKRPeP1mZ8PF4cuXK5druom8AAMBPaR5y1JlYfXJKlCjh7jdo0MB1TNaoqdDcuXPt2LFjVq9evUgZjbg6fPhwpIxGYqmPz7nnnhspoyaxaCqj6QAAACkOObqezYoVK9xNNmzY4P6/adMm91j//v1tyZIltnHjRhdCbrzxRqtQoYLrFCxVqlRx/Xa6du1qS5cutc8++8x69erlmrk0skpuu+021+lYw8M11HzSpEk2evTomKam3r17u1FZTz31lBtxpSHmX375pZsXAABAikOOgsQll1zibqLgof8PHjzYsmfP7i7id8MNN1jFihVdSKldu7Z98sknrqko9MYbb7iL+KmPjoaOX3HFFTHXwFGn4FmzZrkApeffe++9bv7R19Jp2LChTZw40T1P1+15++233RDzatWq8a4CAIDTu05OZsd1cmJxnRz4huvkAH5K1+vkAAAApDdCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeClHei8AAKSVcgNmZLqNu3FEy/ReBMAb1OQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHgpxSFn4cKFdv3111vJkiUtISHBpk2bFvN4EAQ2ePBgK1GihOXJk8eaNm1q3333XUyZ3377zdq3b2/58+e3ggULWpcuXWzv3r0xZVauXGmNGjWy3LlzW+nSpe2JJ544blmmTJlilStXdmWqV69uH3zwQUpXBwAAeCrFIWffvn1Ws2ZNGzNmTNzHFUaeffZZGzdunH3++eeWL18+a968uR04cCBSRgFnzZo1Nnv2bJs+fboLTt26dYs8vmfPHmvWrJmVLVvWli1bZiNHjrShQ4faSy+9FCmzaNEia9eunQtIX331lbVq1crdVq9enfKtAAAAvJMQqOrlVJ+ckGBTp0514UI0K9Xw3HvvvXbfffe5abt377ZixYrZhAkTrG3btvbtt99a1apV7YsvvrA6deq4MjNnzrQWLVrY5s2b3fPHjh1rDz30kG3bts1y5szpygwYMMDVGq1du9bdb9OmjQtcCkmh+vXrW61atVzASg6FqQIFCrhlVK1SVpcZL5wG+IaLAQKpd/5O1T45GzZscMFETVQhLUS9evVs8eLF7r7+qokqDDii8tmyZXM1P2GZK6+8MhJwRLVB69ats507d0bKRL9OWCZ8nXgOHjzoNkz0DQAA+ClVQ44CjqjmJpruh4/pb9GiRWMez5EjhxUqVCimTLx5RL9GUmXCx+MZPny4C13hTX19AACAn7LU6KqBAwe6qq3w9uOPP6b3IgEAgMwQcooXL+7+bt++PWa67oeP6e+OHTtiHj9y5IgbcRVdJt48ol8jqTLh4/HkypXLtd1F3wAAgJ9SNeSUL1/ehYw5c+ZEpqnfi/raNGjQwN3X3127drlRU6G5c+fasWPHXN+dsIxGXB0+fDhSRiOxKlWqZOeee26kTPTrhGXC1wEAAFlbikOOrmezYsUKdws7G+v/mzZtcqOt+vTpY3/729/svffes1WrVtntt9/uRkyFI7CqVKli11xzjXXt2tWWLl1qn332mfXq1cuNvFI5ue2221ynYw0P11DzSZMm2ejRo61fv36R5ejdu7cblfXUU0+5EVcaYv7ll1+6eQEAAORI6SZQkGjcuHHkfhg8Onbs6IaJ33///W5ot657oxqbK664woURXbAv9MYbb7gw0qRJEzeqqnXr1u7aOiF1Cp41a5b17NnTateubUWKFHEXGIy+lk7Dhg1t4sSJNmjQIHvwwQftoosuckPMq1WrxrsKAABO7zo5mR3XyYnFdXKA9Md1coAMep0cAACAjIKQAwAAvETIAQAAXiLkAAAALxFyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8lOohZ+jQoZaQkBBzq1y5cuTxAwcOWM+ePa1w4cJ29tlnW+vWrW379u0x89i0aZO1bNnS8ubNa0WLFrX+/fvbkSNHYsrMnz/fLr30UsuVK5dVqFDBJkyYkNqrAgAAMrE0qcm5+OKLbevWrZHbp59+Gnmsb9++9v7779uUKVNswYIFtmXLFrv55psjjx89etQFnEOHDtmiRYvstddecwFm8ODBkTIbNmxwZRo3bmwrVqywPn362J133mkfffRRWqwOAADIhHKkyUxz5LDixYsfN3337t32yiuv2MSJE+1Pf/qTmzZ+/HirUqWKLVmyxOrXr2+zZs2yb775xj7++GMrVqyY1apVyx599FF74IEHXC1Rzpw5bdy4cVa+fHl76qmn3Dz0fAWpZ555xpo3b54WqwQAADKZNKnJ+e6776xkyZJ2wQUXWPv27V3zkyxbtswOHz5sTZs2jZRVU1aZMmVs8eLF7r7+Vq9e3QWckILLnj17bM2aNZEy0fMIy4TzSMrBgwfdfKJvAADAT6kecurVq+eal2bOnGljx451TUuNGjWy33//3bZt2+ZqYgoWLBjzHAUaPSb6Gx1wwsfDx05URqFl//79SS7b8OHDrUCBApFb6dKlU229AQCA581V1157beT/NWrUcKGnbNmyNnnyZMuTJ4+lp4EDB1q/fv0i9xWKCDoAAPgpzYeQq9amYsWKtn79etdPRx2Kd+3aFVNGo6vCPjz6m3i0VXj/ZGXy589/wiClkVgqE30DAAB+SvOQs3fvXvv++++tRIkSVrt2bTvrrLNszpw5kcfXrVvn+uw0aNDA3dffVatW2Y4dOyJlZs+e7QJJ1apVI2Wi5xGWCecBAACQ6iHnvvvuc0PDN27c6IaA33TTTZY9e3Zr166d6wfTpUsX12Q0b9481xG5c+fOLpxoZJU0a9bMhZkOHTrY119/7YaFDxo0yF1bRzUx0r17d/vvf/9r999/v61du9ZeeOEF1xym4ekAAABp0idn8+bNLtD8+uuvdt5559kVV1zhhofr/6Jh3tmyZXMXAdRoJ42KUkgJKRBNnz7devTo4cJPvnz5rGPHjjZs2LBIGQ0fnzFjhgs1o0ePtlKlStnLL7+coYaPlxswI70XAQCALC0hCILAsih1PFbtkq7fk9r9cwg5AE7FxhEt2XBAKp2/+e0qAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvJQjvRcAAPB/yg2Ykek2x8YRLdN7EYC4qMkBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALxEyAEAAF4i5AAAAC8RcgAAgJcIOQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFyAACAlwg5AADAS4QcAADgJUIOAADwEiEHAAB4iZADAAC8RMgBAABeIuQAAAAvEXIAAICXCDkAAMBLhBwAAOAlQg4AAPASIQcAAHiJkAMAALyUI70XAACQuZUbMMMym40jWqb3IuAMoCYHAAB4iZADAAC8RMgBAABeIuQAAAAvZfqQM2bMGCtXrpzlzp3b6tWrZ0uXLk3vRQIAABlApg45kyZNsn79+tmQIUNs+fLlVrNmTWvevLnt2LEjvRcNAACks0wdcp5++mnr2rWrde7c2apWrWrjxo2zvHnz2quvvpreiwYAANJZpr1OzqFDh2zZsmU2cODAyLRs2bJZ06ZNbfHixem6bACAjI1r+2QNmTbk/PLLL3b06FErVqxYzHTdX7t2bdznHDx40N1Cu3fvdn/37NmT6st37OAfqT5PAEDWlRbnqsy+LYIg8DPknIrhw4fbI488ctz00qVLp8vyAACQXAVGsa0S+/33361AgQLmXcgpUqSIZc+e3bZv3x4zXfeLFy8e9zlq2lJH5dCxY8fst99+s8KFC1tCQoJlxiSrgPbjjz9a/vz5zVdZYT2zwjoK6+kP3ku/7MlkxyDV4CjglCxZ8oTlMm3IyZkzp9WuXdvmzJljrVq1ioQW3e/Vq1fc5+TKlcvdohUsWNAyO+2QmWGnPF1ZYT2zwjoK6+kP3ku/5M9Ex6AT1eBk+pAjqpXp2LGj1alTx+rWrWujRo2yffv2udFWAAAga8vUIadNmzb2888/2+DBg23btm1Wq1Ytmzlz5nGdkQEAQNaTqUOOqGkqqeYp36npTRdCTNwE55ussJ5ZYR2F9fQH76Vfcnl6DEoITjb+CgAAIBPK1Fc8BgAASAohBwAAeImQAwAAvETIAQAAXiLkZCBjxoyxcuXKWe7cua1evXq2dOnSE5afMmWKVa5c2ZWvXr26ffDBBzGPq0+5hteXKFHC8uTJ43689LvvvjOf1vPw4cP2wAMPuOn58uVzV7+8/fbbbcuWLebb+xmte/fu7irdujaUj+v57bff2g033OAu9qX39bLLLrNNmzaZL+u4d+9eNyq0VKlS7rNZtWpVGzdunKW3lKznmjVrrHXr1q78ifbFlG67zLie+skg7aPnnHOOFS1a1F2gdt26debbexkaMWKEK9enTx/L8DS6CunvrbfeCnLmzBm8+uqrwZo1a4KuXbsGBQsWDLZv3x63/GeffRZkz549eOKJJ4JvvvkmGDRoUHDWWWcFq1atipQZMWJEUKBAgWDatGnB119/Hdxwww1B+fLlg/379we+rOeuXbuCpk2bBpMmTQrWrl0bLF68OKhbt25Qu3btwLf3M/TOO+8ENWvWDEqWLBk888wzgW/ruX79+qBQoUJB//79g+XLl7v77777bpLzzIzrqHlceOGFwbx584INGzYEL774onuO1jO9pHQ9ly5dGtx3333Bm2++GRQvXjzuvpjSeWbW9WzevHkwfvz4YPXq1cGKFSuCFi1aBGXKlAn27t0b+LKO0WXLlSsX1KhRI+jdu3eQ0RFyMgidmHv27Bm5f/ToUXcSGz58eNzyt956a9CyZcuYafXq1Qvuuusu9/9jx465nXXkyJGRxxUIcuXK5XZkX9YzqQ+h8vsPP/wQ+LaemzdvDs4//3x3MC1btmy6h5y0WM82bdoEf/3rX4OMIi3W8eKLLw6GDRsWU+bSSy8NHnrooSCzrGe0pPbF05lnZlrPxHbs2OGOQQsWLAh8Wsfff/89uOiii4LZs2cHV111VaYIOTRXZQCHDh2yZcuWueakULZs2dz9xYsXx32OpkeXl+bNm0fKb9iwwV0FOrqMqv5VbZnUPDPjesaze/duV5WaXr9Lllbrqd9m69Chg/Xv398uvvhiS29psZ5axxkzZljFihXddFX9a5+dNm2a+fReNmzY0N577z376aefXLPyvHnz7D//+Y81a9bMMst6psc8T9eZWiYdg6RQoULm0zr27NnTWrZsedz+nZERcjKAX375xY4ePXrcz1HovoJKPJp+ovLh35TMMzOuZ2IHDhxwfXTatWuXbj8yl1br+fjjj1uOHDnsnnvusYwgLdZzx44drr+K2vyvueYamzVrlt100012880324IFC8yX9/K5555z/XDUJ0c/Nqx1VR+KK6+80tLDqaxneszzdJ2JZVJQV1+Vyy+/3KpVq2a+rONbb71ly5cvd/2PMpNM/7MOQHQn5FtvvdV9Mx47dqxXG0bfzEaPHu0OMqql8pVOEHLjjTda37593f/1m3SLFi1yHXOvuuoq84FCzpIlS1xtTtmyZW3hwoXuW7I6zmemb8k4nt7H1atX26effurN5vnxxx+td+/eNnv2bNeROTOhJicDKFKkiGXPnt22b98eM133ixcvHvc5mn6i8uHflMwzM65n4oDzww8/uA9ietXipNV6fvLJJ66Wo0yZMq42Rzet67333utGRPiynpqn1k21HNGqVKmSLqOr0mId9+/fbw8++KA9/fTTdv3111uNGjXcSCv94PCTTz5p6eFU1jM95nm60nqZ9D5Onz7dNT+qls6XdVy2bJk7/lx66aWR449qVp999ln3f9UcZVSEnAxA1dW1a9e2OXPmxHyj1f0GDRrEfY6mR5cXndzD8uXLl3c7dHSZPXv22Oeff57kPDPjekYHHA2P//jjj61w4cKWntJiPdUXZ+XKlbZixYrITd/61T/no48+Ml/WU/PUUNzEw2/VX0U1Hj6so/ZX3dRPIppOTGFNVmZYz/SY5+lKq2VS7bECztSpU23u3Lnu+Jte0mIdmzRpYqtWrYo5/tSpU8fat2/v/q99N8NK757P+L8hfxr5NGHCBDfstFu3bm7I37Zt29zjHTp0CAYMGBAzTDVHjhzBk08+GXz77bfBkCFD4g4h1zw0LHXlypXBjTfemCGGkKfmeh46dMgNjS9VqpQburl169bI7eDBg96sZzwZYXRVWqynhshr2ksvvRR89913wXPPPeeGV3/yySferKNGpmiElYaQ//e//3XDj3Pnzh288MILQXpJ6Xrq8/XVV1+5W4kSJdwQZP1f71ly5+nLevbo0cNdrmP+/Pkxx6A//vjDm3VMLLOMriLkZCA6mOvaCrq+gYYALlmyJGaH6tixY0z5yZMnBxUrVnTldcCcMWNGzOMaRv7www8HxYoVczt8kyZNgnXr1gU+raeuMaKsHu+mE4hP72dGDDlptZ6vvPJKUKFCBXfi1zWBdK0nn9ZRJ8BOnTq5Yb1ax0qVKgVPPfWU+8xmlvVM6rOncsmdpy/rmdQxSOHVp/cyM4acBP2T3rVJAAAAqY0+OQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFygCxu/vz57kc/d+3adcZfW6+rW8GCBSPThg4d6n6UEyfWqVOnyPabNm0amwuIg5ADZCFXX3219enTJ2Zaw4YNbevWrVagQIF0Wabx48e736bKLIYPH+5+X+ucc86xokWLWqtWrY77ra3TtWbNGmvdurX78VWFmFGjRh1XRr9Kr/cNQNIIOUAWpx/004+56mSaHlSLo7CQWejXl3v27GlLlixxP7ypH9ts1qyZ7du3L9Ve448//rALLrjARowYkeQvRyuUpteveQOZBSEHyELNGzpBqwYgbObYuHHjcc1VEyZMcMFj+vTpVqlSJcubN6/dcsst7sT72muvudqFc8891+655x47evRoZP4HDx60++67z84//3zLly+f1atXz837dH3//ffuhK9feQ5/heYf//iHlS5d2i3bTTfdZE8//XRMk1diWk+t4+TJk61Ro0aWJ08eVxujGqQvvvjC/aLy2Wefbddee639/PPPJ1yemTNnum158cUXW82aNd322rRpky1btizJ52g71K1b120XLefll19uP/zwQ5LltWwjR460tm3bWq5cuZK1nQAcj5ADZBEKNw0aNLCuXbu6Zg7dFBTiUaB59tln7a233nIndZ2kFSY++OADd/vnP/9pL774or399tuR5yiELF682D1n5cqV9pe//MWuueYa++677055mTWfK664wm677TZ7/vnnXVD57LPPrHv37ta7d29bsWKF/fnPf7bHHnssWfMbMmSIDRo0yJYvX245cuRw873//vvdtvnkk09s/fr1Nnjw4BQt4+7du93fQoUKxX38yJEjrknrqquucuujbdStW7d0qzkDspIc6b0AAM4MNW+oaUq1Hydr5lATzNixY+3CCy9091WTo2Czfft2V+NRtWpVa9y4sc2bN8/atGnjajLUt0Z/S5Ys6Z6jWh0FJE3/+9//nuLlXbRokV133XX20EMP2b333huZ/txzz7kaF81fKlas6Mqq5ulk9JzmzZu7/ysktWvXzubMmeNqVqRLly6uZia5jh075vo46fnVqlWLW2bPnj0uCGldwu1ZpUqVZL8GgFNHTQ6A4ygIhSdkKVasmGumUsCJnrZjxw73/1WrVrmmKwUOlQlvah5Tc1NKKSyphka1KtEBR9TJV00/0RLfT0qNGjVill+qV68ed51UsxO9Lm+88cZx81PfnNWrV7vaq6SohkfNWwpX119/fUyHYa1n9GucShgEkDRqcgAc56yzzoq5r6aVeNNUkyF79+617Nmzu34p+hstOhgl13nnnedqhN5880274447LH/+/KnyLkWvQ9hclHhauE7qp6PmsMShKLp5TrVHCxcutFKlSp3wdVWbpT5MqtmaNGmSazJTp+XEr5FUkxeAU0PIAbIQNVdFdxZOLZdccombr2pB1LH3dKljsAJEixYtXA3IrFmz3JBtUWdodRaOlvh+atAyVKhQ4bjp6vx8991329SpU11fpfLlyyd7G+k2cOBA1zdq4sSJVr9+/bivASB10FwFZCFqcvr888/daKNffvklUmtxutRM1b59e7v99tvtnXfesQ0bNtjSpUvdNWVmzJhxSvPUSCQ9Vx2E1QdHtUWigKHOzxpRpU7N6gD94YcfnrGOvGqi+te//uVCioLXtm3b3G3//v1xy2tbKNiow7FGVCmwablP1C/n0KFDroZHN/3/p59+cv9Xx2gAyUfIAbIQdbxVc5I6DqtJSH1CUouaZBRy1IdGtS0aUaQaljJlypzyPNXUpQCj2pOWLVu6a9Gok++4ceNcyNEQbjUB9e3b13Lnzm1ngjpkqyOxLqxYokSJyE3NUEn1b1q7dq27uJ/CoEZWKSjdddddSb7Gli1bIjU/6r/z5JNPuv/feeedabhmgH8SgvDCEwBwpg9ACQmu2UeB6HRoWLyChDoLZzWptQ0BH1GTAyBdaRj3yTruJqaaja+//to132hIuS5S2LFjR8tKdK2gU+nUDWQl1OQASDdhHxM1oSW3A6/ceuutrtPv77//7q6GrH46OulnJerkrWvwiJrL1IcJQCxCDgAA8BLNVQAAwEuEHAAA4CVCDgAA8BIhBwAAeImQAwAAvETIAQAAXiLkAAAALxFyAACAlwg5AADAfPT/AMTSXoDy7emEAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(loaded_pm).plot.hist()" - ] - }, - { - "cell_type": "markdown", - "id": "f8d01841", - "metadata": {}, - "source": [ - "## Step 2: Calculate PDSI" - ] - }, - { - "cell_type": "markdown", - "id": "415dcfc7", - "metadata": {}, - "source": [ - "PDSI is registered as a derived variable using `register_user_function`. The underlying computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by drought.gov. The function is applied across all spatial and ensemble dimensions via `groupby().apply()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "deb75906", - "metadata": {}, - "outputs": [], - "source": [ - "# Resampling PET and precip to monthly since the function only takes monthly variables\n", - "one_prec = prec.sel(sim=prec.sim.str.contains(one_sim)).squeeze()\n", - "mon_pet = (pet_calc * 86400 / 25.4).resample(time='1ME').sum()\n", - "mon_precip = (one_prec / 25.4).resample(time='1ME').sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ffd18cf8", - "metadata": {}, - "outputs": [], - "source": [ - "### Combining WL objects together, historical WL as 2000-2030, future WL as 2030-2060\n", - "def combine_wl_to_dummy_time(\n", - " da: xr.DataArray,\n", - " baseline_wl: float,\n", - " future_wls: list[float],\n", - " start_date: str = \"2000-01-31\",\n", - ") -> xr.DataArray:\n", - " \"\"\"\n", - " Combine baseline warming level with multiple future warming levels into one\n", - " DataArray along a new 'combined_wl' dimension.\n", - "\n", - " Parameters\n", - " ----------\n", - " da : xr.DataArray\n", - " Original data with dims including 'warming_level' and 'time'.\n", - " baseline_wl : float\n", - " The warming level used for the first time segment.\n", - " future_wls : list of float\n", - " Warming levels to concatenate after baseline.\n", - " start_date : str\n", - " Start date for the combined time series (monthly freq).\n", - " \n", - " Returns\n", - " -------\n", - " xr.DataArray\n", - " Combined DataArray with new dimension 'combined_wl' and coordinate labels like \"0.8 to 1.5\".\n", - " \"\"\"\n", - " months_per_wl = da.sizes['time']\n", - " total_months = 2 * months_per_wl\n", - " new_time = pd.date_range(start_date, periods=total_months, freq='ME')\n", - "\n", - " combined_list = []\n", - " combined_labels = []\n", - "\n", - " for fw in future_wls:\n", - " da_base = da.sel(warming_level=baseline_wl)\n", - " da_future = da.sel(warming_level=fw)\n", - "\n", - " combined = xr.concat([da_base, da_future], dim='time')\n", - " combined = combined.assign_coords(time=new_time)\n", - "\n", - " wl_flag = np.array([baseline_wl] * months_per_wl + [fw] * months_per_wl)\n", - " combined = combined.assign_coords(warming_level_flag=('time', wl_flag))\n", - "\n", - " combined_list.append(combined)\n", - " combined_labels.append(f\"{int(baseline_wl * 10):02d}_to_{int(fw * 10):02d}\")\n", - "\n", - " combined_da = xr.concat(combined_list, dim='combined_wl')\n", - " combined_da = combined_da.assign_coords(combined_wl=combined_labels)\n", - "\n", - " return combined_da" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "119fa7d5", - "metadata": {}, - "outputs": [], - "source": [ - "# Creating one Dataset of PET and precip with WLs combined\n", - "mon_pet_transform = combine_wl_to_dummy_time(mon_pet, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", - "mon_precip_transform = combine_wl_to_dummy_time(mon_precip, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", - "mon_pet_transform = mon_pet_transform.assign_coords(mon_precip_transform.coords)\n", - "combined_ds = xr.Dataset({'precip': mon_precip_transform, 'pet': mon_pet_transform})\n", - "\n", - "# Applying spatial mask\n", - "combined_ds = combined_ds.where(spatial_mask)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6b5de48", - "metadata": {}, - "outputs": [], - "source": [ - "def pdsi_func(ds: xr.Dataset) -> xr.Dataset:\n", - " \"\"\"Compute Palmer Drought Severity Index (PDSI) from a Dataset.\n", - "\n", - " Registered derived variable function: receives an xr.Dataset with 'precip' and\n", - " 'pet' variables (both monthly, in inches) and adds 'pdsi' to it.\n", - "\n", - " Parameters\n", - " ----------\n", - " ds : xr.Dataset\n", - " Dataset with 'precip' and 'pet' variables along a 'time' dimension,\n", - " plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n", - "\n", - " Returns\n", - " -------\n", - " xr.Dataset\n", - " Input dataset with 'pdsi' variable added.\n", - " \"\"\"\n", - " def _calc_pdsi_1d(timeseries: xr.Dataset) -> xr.DataArray:\n", - " precip = timeseries['precip'].squeeze()\n", - " pet = timeseries['pet'].squeeze()\n", - " result = palmer_pdsi(\n", - " precips=precip.values,\n", - " pet=pet.values,\n", - " awc=5,\n", - " data_start_year=2000,\n", - " calibration_year_initial=2000,\n", - " calibration_year_final=2030,\n", - " )\n", - " return xr.DataArray(\n", - " result[0], coords={\"time\": precip.time.values}, dims=['time']\n", - " ).clip(min=-10, max=10)\n", - "\n", - " pdsi_da = ds.groupby(['combined_wl', 'x', 'y']).apply(_calc_pdsi_1d)\n", - " pdsi_da.attrs.update({\n", - " \"long_name\": \"Palmer Drought Severity Index\",\n", - " \"units\": \"from -10 (dry) to +10 (wet)\",\n", - " })\n", - " ds[\"pdsi\"] = pdsi_da\n", - " return ds\n", - "\n", - "register_user_function(\n", - " name=\"pdsi\",\n", - " depends_on=[\"precip\", \"pet\"],\n", - " func=pdsi_func,\n", - " description=\"Palmer Drought Severity Index computed from monthly precipitation and PET.\",\n", - " units=\"unitless\",\n", - " drop_dependencies=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c625f3af", - "metadata": {}, - "outputs": [], - "source": [ - "# Apply PDSI derived variable to the combined dataset\n", - "pdsi_ds = list_derived_variables()[\"pdsi\"].func(combined_ds)\n", - "pdsi_da = pdsi_ds[\"pdsi\"]\n", - "\n", - "# Writing crs and reprojecting PDSI to lat/lon\n", - "pdsi_da = pdsi_da.rio.write_crs(crs.to_wkt())\n", - "pdsi_da = pdsi_da.transpose('time', 'combined_wl', 'y', 'x')\n", - "# pdsi_latlon = reproject_data(pdsi_da, 'EPSG:4326')\n", - "# del pdsi_latlon.attrs[\"_FillValue\"]" - ] - }, - { - "cell_type": "markdown", - "id": "6cc3157f", - "metadata": {}, - "source": [ - "### Export PDSI" - ] - }, - { - "cell_type": "markdown", - "id": "2d745c44", - "metadata": {}, - "source": [ - "The combined time axis has 720 months total: months 0–359 are the baseline warming level (0.8°C, used for calibration), and months 360–719 are the future warming level period. The export below slices to `time=slice(360, 720)` to retain only the future period.\n", - "\n", - "The exported file will have the following dimensions:\n", - "- `time`: 360 months (30-year future warming level period)\n", - "- `wl`: warming level pair (e.g., `\"08_to_20\"` = calibrated on 0.8°C, evaluated on 2.0°C)\n", - "- `lat`, `lon`: spatial coordinates (EPSG:4326)\n", - "- `sim`: ensemble member" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1dc40f2", - "metadata": {}, - "outputs": [], - "source": [ - "# Cleaning and labeling the data before exporting it in the next cell\n", - "final_pdsi = pdsi_da.isel(time=slice(360, 720))\n", - "final_pdsi = final_pdsi.rename({'combined_wl': 'wl'}).rename(\"pdsi\")\n", - "final_pdsi = final_pdsi.assign_attrs({\n", - " \"long_name\": \"Palmer Drought Severity Index\",\n", - " \"units\": \"from -10 (dry) to +10 (wet)\",\n", - "})\n", - "pdsi_filename = f\"pdsi_wl_lat{str(LAT).replace('.', '_')}_lon{str(LON).replace('.', '_')}.nc\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c6f3455", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exporting specified data to NetCDF...\n", - "Saving file locally as NetCDF4...\n" - ] - }, - { - "ename": "Exception", - "evalue": "File pdsi_wl_lat37_787964_lon-122_065063.nc exists. Please either delete that file from the work space or specify a new file name here.", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mException\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[16]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinal_pdsi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpdsi_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mNetCDF\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlocal\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/core/data_export.py:948\u001b[39m, in \u001b[36mexport\u001b[39m\u001b[34m(data, filename, format, mode)\u001b[39m\n\u001b[32m 946\u001b[39m _export_to_zarr(data, save_name, mode)\n\u001b[32m 947\u001b[39m \u001b[38;5;28;01mcase\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mnetcdf\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m948\u001b[39m \u001b[43m_export_to_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 949\u001b[39m \u001b[38;5;28;01mcase\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mcsv\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 950\u001b[39m _export_to_csv(data, save_name)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/core/data_export.py:299\u001b[39m, in \u001b[36m_export_to_netcdf\u001b[39m\u001b[34m(data, save_name)\u001b[39m\n\u001b[32m 296\u001b[39m path = os.path.join(os.getcwd(), save_name)\n\u001b[32m 298\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m os.path.exists(path):\n\u001b[32m--> \u001b[39m\u001b[32m299\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\n\u001b[32m 300\u001b[39m (\n\u001b[32m 301\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFile \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msave_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m exists. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 302\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mPlease either delete that file from the work space \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 303\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mor specify a new file name here.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 304\u001b[39m )\n\u001b[32m 305\u001b[39m )\n\u001b[32m 306\u001b[39m encoding = _fillvalue_encoding(_data) | _compression_encoding(_data)\n\u001b[32m 308\u001b[39m \u001b[38;5;66;03m# Recursively validate attribute types\u001b[39;00m\n", - "\u001b[31mException\u001b[39m: File pdsi_wl_lat37_787964_lon-122_065063.nc exists. Please either delete that file from the work space or specify a new file name here." - ] - } - ], - "source": [ - "export(final_pdsi, pdsi_filename, format=\"NetCDF\", mode=\"local\")" - ] - }, - { - "cell_type": "markdown", - "id": "3dc2e324", - "metadata": {}, - "source": [ - "## Step 3: Calculate EDDI" - ] - }, - { - "cell_type": "markdown", - "id": "f0645679", - "metadata": {}, - "source": [ - "Now, we will calculate EDDI using PET." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3228b9fb", - "metadata": {}, - "outputs": [], - "source": [ - "def eddi_func(ds: xr.Dataset) -> xr.Dataset:\n", - " \"\"\"Compute Evaporative Demand Drought Index (EDDI) from a Dataset.\n", - "\n", - " Registered derived variable function: receives an xr.Dataset with a 'pet'\n", - " variable (monthly) and adds 'eddi' to it.\n", - "\n", - " Parameters\n", - " ----------\n", - " ds : xr.Dataset\n", - " Dataset with 'pet' variable along a 'time' dimension,\n", - " plus 'combined_wl', 'sim', 'x', and 'y' dimensions.\n", - "\n", - " Returns\n", - " -------\n", - " xr.Dataset\n", - " Input dataset with 'eddi' variable added.\n", - " \"\"\"\n", - " def _calc_eddi_1d(timeseries: xr.DataArray) -> xr.DataArray:\n", - " eddi = standardized_index(\n", - " da=timeseries,\n", - " freq='MS',\n", - " window=1,\n", - " dist=\"gamma\",\n", - " method=\"ML\",\n", - " zero_inflated=True,\n", - " fitkwargs={},\n", - " cal_start=\"2000-01-31\",\n", - " cal_end=\"2029-12-31\",\n", - " )\n", - " return eddi.clip(min=-2.5, max=2.5)\n", - "\n", - " eddi_da = ds['pet'].groupby(['combined_wl', 'x', 'y']).apply(_calc_eddi_1d)\n", - " eddi_da.attrs.update({\n", - " \"long_name\": \"Evaporative Demand Drought Index\",\n", - " \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n", - " })\n", - " ds[\"eddi\"] = eddi_da\n", - " return ds\n", - "\n", - "register_user_function(\n", - " name=\"eddi\",\n", - " depends_on=[\"pet\"],\n", - " func=eddi_func,\n", - " description=\"Evaporative Demand Drought Index computed from monthly PET.\",\n", - " units=\"unitless\",\n", - " drop_dependencies=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1240839c", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'x'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:876\u001b[39m, in \u001b[36mDataArray._getitem_coord\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 875\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m876\u001b[39m var = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_coords\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 877\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", - "\u001b[31mKeyError\u001b[39m: 'x'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[72]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Apply EDDI derived variable to the combined dataset\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m eddi_ds = \u001b[43mlist_derived_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43meddi\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcombined_ds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m eddi_da = eddi_ds[\u001b[33m\"\u001b[39m\u001b[33meddi\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 5\u001b[39m \u001b[38;5;66;03m# Writing crs and reprojecting EDDI to lat/lon\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/src/climakitae/climakitae/new_core/derived_variables/registry.py:116\u001b[39m, in \u001b[36m_wrap_with_metadata_preservation..wrapped\u001b[39m\u001b[34m(ds)\u001b[39m\n\u001b[32m 113\u001b[39m before_vars = \u001b[38;5;28mset\u001b[39m(ds.data_vars) \u001b[38;5;28;01mif\u001b[39;00m ds \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mset\u001b[39m()\n\u001b[32m 115\u001b[39m \u001b[38;5;66;03m# Call the original function\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m116\u001b[39m result = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 118\u001b[39m \u001b[38;5;66;03m# Determine newly-added variables (robust to functions that name the\u001b[39;00m\n\u001b[32m 119\u001b[39m \u001b[38;5;66;03m# derived variable differently than the registered name)\u001b[39;00m\n\u001b[32m 120\u001b[39m after_vars = \u001b[38;5;28mset\u001b[39m(result.data_vars) \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mset\u001b[39m()\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[65]\u001b[39m\u001b[32m, line 32\u001b[39m, in \u001b[36meddi_func\u001b[39m\u001b[34m(ds)\u001b[39m\n\u001b[32m 19\u001b[39m eddi = standardized_index(\n\u001b[32m 20\u001b[39m da=timeseries,\n\u001b[32m 21\u001b[39m freq=\u001b[33m'\u001b[39m\u001b[33mMS\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 28\u001b[39m cal_end=\u001b[33m\"\u001b[39m\u001b[33m2029-12-31\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 29\u001b[39m )\n\u001b[32m 30\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m eddi.clip(\u001b[38;5;28mmin\u001b[39m=-\u001b[32m2.5\u001b[39m, \u001b[38;5;28mmax\u001b[39m=\u001b[32m2.5\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m32\u001b[39m eddi_da = \u001b[43mds\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mpet\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcombined_wl\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mx\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43my\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m.apply(_calc_eddi_1d)\n\u001b[32m 33\u001b[39m eddi_da.attrs.update({\n\u001b[32m 34\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mlong_name\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mEvaporative Demand Drought Index\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 35\u001b[39m \u001b[33m\"\u001b[39m\u001b[33munits\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mfrom -2.5 (wet) to +2.5 (dry)\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 36\u001b[39m })\n\u001b[32m 37\u001b[39m ds[\u001b[33m\"\u001b[39m\u001b[33meddi\u001b[39m\u001b[33m\"\u001b[39m] = eddi_da\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/util/deprecation_helpers.py:119\u001b[39m, in \u001b[36m_deprecate_positional_args.._decorator..inner\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 115\u001b[39m kwargs.update(zip_args)\n\u001b[32m 117\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m func(*args[:-n_extra_args], **kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m119\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:6992\u001b[39m, in \u001b[36mDataArray.groupby\u001b[39m\u001b[34m(self, group, squeeze, restore_coord_dims, eagerly_compute_group, **groupers)\u001b[39m\n\u001b[32m 6985\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mxarray\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mgroupby\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 6986\u001b[39m DataArrayGroupBy,\n\u001b[32m 6987\u001b[39m _parse_group_and_groupers,\n\u001b[32m 6988\u001b[39m _validate_groupby_squeeze,\n\u001b[32m 6989\u001b[39m )\n\u001b[32m 6991\u001b[39m _validate_groupby_squeeze(squeeze)\n\u001b[32m-> \u001b[39m\u001b[32m6992\u001b[39m rgroupers = \u001b[43m_parse_group_and_groupers\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 6993\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroupers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meagerly_compute_group\u001b[49m\u001b[43m=\u001b[49m\u001b[43meagerly_compute_group\u001b[49m\n\u001b[32m 6994\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6995\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m DataArrayGroupBy(\u001b[38;5;28mself\u001b[39m, rgroupers, restore_coord_dims=restore_coord_dims)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/groupby.py:437\u001b[39m, in \u001b[36m_parse_group_and_groupers\u001b[39m\u001b[34m(obj, group, groupers, eagerly_compute_group)\u001b[39m\n\u001b[32m 434\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m groupers:\n\u001b[32m 435\u001b[39m grouper_mapping = cast(\u001b[33m\"\u001b[39m\u001b[33mMapping[Hashable, Grouper]\u001b[39m\u001b[33m\"\u001b[39m, groupers)\n\u001b[32m--> \u001b[39m\u001b[32m437\u001b[39m rgroupers = \u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 438\u001b[39m \u001b[43m \u001b[49m\u001b[43mResolvedGrouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[43mgrouper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meagerly_compute_group\u001b[49m\u001b[43m=\u001b[49m\u001b[43meagerly_compute_group\u001b[49m\n\u001b[32m 440\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 441\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrouper\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgrouper_mapping\u001b[49m\u001b[43m.\u001b[49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 442\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 443\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m rgroupers\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/groupby.py:438\u001b[39m, in \u001b[36m\u001b[39m\u001b[34m(.0)\u001b[39m\n\u001b[32m 434\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m groupers:\n\u001b[32m 435\u001b[39m grouper_mapping = cast(\u001b[33m\"\u001b[39m\u001b[33mMapping[Hashable, Grouper]\u001b[39m\u001b[33m\"\u001b[39m, groupers)\n\u001b[32m 437\u001b[39m rgroupers = \u001b[38;5;28mtuple\u001b[39m(\n\u001b[32m--> \u001b[39m\u001b[32m438\u001b[39m \u001b[43mResolvedGrouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[43mgrouper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meagerly_compute_group\u001b[49m\u001b[43m=\u001b[49m\u001b[43meagerly_compute_group\u001b[49m\n\u001b[32m 440\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 441\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m group, grouper \u001b[38;5;129;01min\u001b[39;00m grouper_mapping.items()\n\u001b[32m 442\u001b[39m )\n\u001b[32m 443\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m rgroupers\n", - "\u001b[36mFile \u001b[39m\u001b[32m:7\u001b[39m, in \u001b[36m__init__\u001b[39m\u001b[34m(self, grouper, group, obj, eagerly_compute_group)\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/groupby.py:331\u001b[39m, in \u001b[36mResolvedGrouper.__post_init__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 327\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mxarray\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mgroupers\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BinGrouper, UniqueGrouper\n\u001b[32m 329\u001b[39m \u001b[38;5;28mself\u001b[39m.grouper = copy.deepcopy(\u001b[38;5;28mself\u001b[39m.grouper)\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m \u001b[38;5;28mself\u001b[39m.group = \u001b[43m_resolve_group\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.eagerly_compute_group:\n\u001b[32m 334\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 335\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\"\"\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mEagerly computing the DataArray you\u001b[39m\u001b[33m'\u001b[39m\u001b[33mre grouping by (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.group.name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m) \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 336\u001b[39m \u001b[33m has been removed.\u001b[39m\n\u001b[32m (...)\u001b[39m\u001b[32m 341\u001b[39m \u001b[33m `.groupby(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.group.name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m=BinGrouper(bins=...))`; as appropriate.\u001b[39m\u001b[33m\"\"\"\u001b[39m\n\u001b[32m 342\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/groupby.py:497\u001b[39m, in \u001b[36m_resolve_group\u001b[39m\u001b[34m(obj, group)\u001b[39m\n\u001b[32m 491\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m hashable(group):\n\u001b[32m 492\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 493\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m`group` must be an xarray.DataArray or the \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 494\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mname of an xarray variable or dimension. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 495\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mReceived \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgroup\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m instead.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 496\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m497\u001b[39m group_da: DataArray = \u001b[43mobj\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 498\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m group_da.name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m obj._indexes \u001b[38;5;129;01mand\u001b[39;00m group_da.name \u001b[38;5;129;01min\u001b[39;00m obj.dims:\n\u001b[32m 499\u001b[39m \u001b[38;5;66;03m# DummyGroups should not appear on groupby results\u001b[39;00m\n\u001b[32m 500\u001b[39m newgroup = _DummyGroup(obj, group_da.name, group_da.coords)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:885\u001b[39m, in \u001b[36mDataArray.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 883\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: Any) -> Self:\n\u001b[32m 884\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m885\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem_coord\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 886\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 887\u001b[39m \u001b[38;5;66;03m# xarray-style array indexing\u001b[39;00m\n\u001b[32m 888\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.isel(indexers=\u001b[38;5;28mself\u001b[39m._item_key_to_dict(key))\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataarray.py:879\u001b[39m, in \u001b[36mDataArray._getitem_coord\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 877\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[32m 878\u001b[39m dim_sizes = \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m.dims, \u001b[38;5;28mself\u001b[39m.shape, strict=\u001b[38;5;28;01mTrue\u001b[39;00m))\n\u001b[32m--> \u001b[39m\u001b[32m879\u001b[39m _, key, var = \u001b[43m_get_virtual_variable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_coords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim_sizes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 881\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._replace_maybe_drop_dims(var, name=key)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataset_utils.py:79\u001b[39m, in \u001b[36m_get_virtual_variable\u001b[39m\u001b[34m(variables, key, dim_sizes)\u001b[39m\n\u001b[32m 77\u001b[39m split_key = key.split(\u001b[33m\"\u001b[39m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m1\u001b[39m)\n\u001b[32m 78\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(split_key) != \u001b[32m2\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m79\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[32m 81\u001b[39m ref_name, var_name = split_key\n\u001b[32m 82\u001b[39m ref_var = variables[ref_name]\n", - "\u001b[31mKeyError\u001b[39m: 'x'" - ] - } - ], - "source": [ - "# Apply EDDI derived variable to the combined dataset\n", - "eddi_ds = list_derived_variables()[\"eddi\"].func(combined_ds)\n", - "eddi_da = eddi_ds[\"eddi\"]\n", - "\n", - "# Writing crs and reprojecting EDDI to lat/lon\n", - "eddi_da = eddi_da.rio.write_crs(crs.to_wkt())\n", - "eddi_da = eddi_da.transpose('time', 'combined_wl', 'y', 'x')\n", - "# eddi_da_latlon = reproject_data(eddi_da, 'EPSG:4326')\n", - "# del eddi_da_latlon.attrs[\"_FillValue\"]" - ] - }, - { - "cell_type": "markdown", - "id": "45176f23", - "metadata": {}, - "source": [ - "### Export EDDI" - ] - }, - { - "cell_type": "markdown", - "id": "d59ee541", - "metadata": {}, - "source": [ - "The combined time axis has 720 months total: months 0–359 are the baseline warming level (0.8°C, used for calibration), and months 360–719 are the future warming level period. The export below slices to `time=slice(360, 720)` to retain only the future period.\n", - "\n", - "The exported file will have the following dimensions:\n", - "- `time`: 360 months (30-year future warming level period)\n", - "- `wl`: warming level pair (e.g., `\"08_to_20\"` = calibrated on 0.8°C, evaluated on 2.0°C)\n", - "- `lat`, `lon`: spatial coordinates (EPSG:4326)\n", - "- `sim`: ensemble member" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d86782f2", - "metadata": {}, - "outputs": [], - "source": [ - "# Saving these results and cleaning the data\n", - "final_eddi = eddi_da_latlon.isel(time=slice(360, 720))\n", - "final_eddi = final_eddi.rename({'combined_wl': 'wl'}).rename(\"eddi\")\n", - "final_eddi = final_eddi.assign_attrs({\n", - " \"long_name\": \"Evaporative Demand Drought Index\",\n", - " \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n", - "})\n", - "eddi_filename = f\"eddi_wl_lat{str(LAT).replace('.', '_')}_lon{str(LON).replace('.', '_')}.nc\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "707286f3", - "metadata": {}, - "outputs": [], - "source": [ - "export(final_eddi, eddi_filename, format=\"NetCDF\", mode=\"local\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (climakitae)", - "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": "3.12.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/work-in-progress/drought_metrics_latest_final.ipynb b/work-in-progress/drought_metrics_latest_final.ipynb deleted file mode 100644 index 089cead0..00000000 --- a/work-in-progress/drought_metrics_latest_final.ipynb +++ /dev/null @@ -1,562 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5e0361f0", - "metadata": {}, - "source": [ - "# Drought Metrics" - ] - }, - { - "cell_type": "markdown", - "id": "64641b82", - "metadata": {}, - "source": [ - "This notebook calculates two drought metrics, the Palmer Drought Severity Index (PDSI) and the Evaporative Demand Drought Index (EDDI), using WRF data in the AE catalog. Both PDSI and EDDI require Potential Evapotranspiration (PET). PET is computed first using the Penman-Monteith method. At the end of the notebook, the user will be able to export monthly PDSI and EDDI for under different global warming levels for a specific lat/lon as netcdf files to be used for further analyses. " - ] - }, - { - "cell_type": "markdown", - "id": "98506237", - "metadata": {}, - "source": [ - "**Intended Application:** As a user, I want to export future drought indices for different global warming levels by:\n", - "1. Calculating PET using the Penman-Montieth Method\n", - "2. Calculating the PDSI using PET and exporting the monthly timeseries to a netcdf\n", - "3. Calculating the EDDI using PET and exporting the monthly timeseries to a netcdf" - ] - }, - { - "cell_type": "markdown", - "id": "cc35f46e", - "metadata": {}, - "source": [ - "**Runtime:** With the default settings, this notebook takes approximately **15 minutes** to run from start to finish. Modifications to selections may increase the runtime." - ] - }, - { - "cell_type": "markdown", - "id": "3babc840", - "metadata": {}, - "source": [ - "## Step 0: Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "xypsmbxjeoj", - "metadata": {}, - "outputs": [], - "source": [ - "# Install climate_indices at a specific commit compatible with the AE hub environment\n", - "!pip install -qq git+https://github.com/monocongo/climate_indices.git" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7e19917", - "metadata": {}, - "outputs": [], - "source": [ - "import climakitae as ck\n", - "import xclim\n", - "import xarray as xr\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import flox\n", - "from pyproj import CRS\n", - "from dask.diagnostics import ProgressBar\n", - "from climate_indices import palmer\n", - "from xclim.indices.stats import standardized_index\n", - "from climakitae.core.data_export import export" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b31d378", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the lat/lon for the location of interest and the variables needed for the Penman-Monteith PET method\n", - "LAT = 37.787964\n", - "LON = -122.065063\n", - "WARMING_LEVELS = [0.8, 2.0]" - ] - }, - { - "cell_type": "markdown", - "id": "485befa5f23", - "metadata": {}, - "source": [ - "## Step 1: Fetch Data and Calculate PET\n", - "\n", - "**IMPORTANT NOTE**: The following cell saves the intermediate data required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files." - ] - }, - { - "cell_type": "markdown", - "id": "522c9425", - "metadata": {}, - "source": [ - "### Penman-Monteith PET Method (most physically consistent)\n", - "We will be using the PET method for calculating PDSI, since it is the most physically consistent method. Below, we list out the variables we will need." - ] - }, - { - "cell_type": "markdown", - "id": "cc88bd71", - "metadata": {}, - "source": [ - "**Variables needed:**\n", - "- `t2` (hourly) → daily `tasmin` and `tasmax` derived via resample\n", - "- `relative humidity`\n", - "- `radiation flux`\n", - " - rsds (daily)\n", - " - rsus (hourly → daily mean)\n", - " - rlds (daily)\n", - " - rlus (hourly → daily mean)\n", - "- `wind speed (10m wind will be converted to 2m)`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0132b29-804a-4eef-893f-2f60adfefc62", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the ClimateData object and define the processors needed.\n", - "cd = ck.ClimateData(verbosity=-2)\n", - "processes = {\n", - " \"clip\": (LAT, LON),\n", - " \"warming_level\": {\n", - " \"warming_levels\": WARMING_LEVELS,\n", - " \"add_dummy_time\": True\n", - " },\n", - "}\n", - "\n", - "def rename_sims_to_gcm(ds):\n", - " \"\"\"\"Renames the 'sim' coordinate in the dataset to only contain the GCM name for easier grouping later.\"\"\"\n", - " ds = ds.copy()\n", - " new_sims = [s.split('_')[2] for s in ds['sim'].values]\n", - " ds['sim'] = ('sim', new_sims)\n", - " return ds\n", - "\n", - "def fetch(variable, table_id=\"day\", units=None):\n", - " \"\"\"Fetch a WRF variable from the AE catalog with the configured processes.\"\"\"\n", - " proc = {**processes}\n", - " if units is not None:\n", - " proc[\"convert_units\"] = units\n", - " return (\n", - " cd.catalog(\"cadcat\")\n", - " .activity_id(\"WRF\")\n", - " .institution_id(\"UCLA\")\n", - " .table_id(table_id)\n", - " .grid_label(\"d03\")\n", - " .variable(variable)\n", - " .processes(proc)\n", - " .get()\n", - " )\n", - "\n", - "def get_and_transform(variable, table_id=\"day\", units=None, transform=None):\n", - " \"\"\"Fetching variables and applying a transformation if it's passed in.\"\"\"\n", - " print(f\"\\nFetching {variable}...\")\n", - " da = fetch(variable, table_id=table_id, units=units)\n", - " da = da.unify_chunks() # Ensure the data is chunked for Dask processing\n", - " if transform:\n", - " da = transform(da)\n", - " # Drop leap days\n", - " da = da.sel(time=~((da['time.month'] == 2) & (da['time.day'] == 29)))\n", - " # Rename sims to only contain the GCM name for easier grouping later\n", - " da = rename_sims_to_gcm(da)\n", - " return da\n", - "\n", - "# Daily variables\n", - "rh = get_and_transform(\"rh\")\n", - "sw_dwn = get_and_transform(\"sw_dwn\")\n", - "lw_dwn = get_and_transform(\"lw_dwn\")\n", - "wspd10mean = get_and_transform(\"wspd10mean\")\n", - "prec = get_and_transform(\"prec\")\n", - "tasmin = get_and_transform(\"t2min\")\n", - "tasmax = get_and_transform(\"t2max\")\n", - "rsus_daily = get_and_transform(\"swupb\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", - "rlus_daily = get_and_transform(\"lwupb\", table_id=\"1hr\", transform=lambda da: da.resample(time=\"1D\").mean())\n", - "\n", - "# xclim requires relative humidity as a fraction (0–1), not a percentage\n", - "hurs_frac = (rh / 100)\n", - "hurs_frac.rh.attrs['units'] = '1' # Update the units to reflect the transformation to a fraction\n", - "\n", - "# Ensure all radiation flux variables carry the same units\n", - "sw_dwn.sw_dwn.attrs['units'] = 'W m-2'\n", - "lw_dwn.lw_dwn.attrs['units'] = 'W m-2'\n", - "rsus_daily.swupb.attrs['units'] = 'W m-2'\n", - "rlus_daily.lwupb.attrs['units'] = 'W m-2'\n", - "\n", - "print(\"Finished.\")" - ] - }, - { - "cell_type": "markdown", - "id": "c74e6120", - "metadata": {}, - "source": [ - "#### Calculate PET from `xclim` using the Penman-Monteith Method" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c96f440", - "metadata": {}, - "outputs": [], - "source": [ - "# Call xclim.indicies.potential_evapotranspiration on the entire dataset as it is\n", - "pet_pm = xclim.indices.potential_evapotranspiration(\n", - " tasmin=tasmin.t2min,\n", - " tasmax=tasmax.t2max,\n", - " hurs=hurs_frac.rh,\n", - " rsds=sw_dwn.sw_dwn,\n", - " rsus=rsus_daily.swupb,\n", - " rlds=lw_dwn.lw_dwn,\n", - " rlus=rlus_daily.lwupb,\n", - " sfcWind=wspd10mean.wspd10mean,\n", - " method=\"FAO_PM98\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "f8d01841", - "metadata": {}, - "source": [ - "## Step 2: Calculate PDSI" - ] - }, - { - "cell_type": "markdown", - "id": "415dcfc7", - "metadata": {}, - "source": [ - "PDSI is registered as a derived variable using `register_user_function`. The underlying computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by drought.gov. The function is applied across all spatial and ensemble dimensions via `groupby().apply()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "deb75906", - "metadata": {}, - "outputs": [], - "source": [ - "# Resampling PET and precip to monthly since the function only takes monthly variables\n", - "mon_pet = (pet_pm * 86400 / 25.4).resample(time='1ME').sum()\n", - "mon_precip = (prec / 25.4).resample(time='1ME').sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ffd18cf8", - "metadata": {}, - "outputs": [], - "source": [ - "### Combining WL objects together, historical WL as 2000-2030, future WL as 2030-2060\n", - "def combine_wl_to_dummy_time(\n", - " da: xr.DataArray,\n", - " baseline_wl: float,\n", - " future_wls: list[float],\n", - " start_date: str = \"2000-01-31\",\n", - ") -> xr.DataArray:\n", - " \"\"\"\n", - " Combine baseline warming level with multiple future warming levels into one\n", - " DataArray along a new 'combined_wl' dimension.\n", - "\n", - " Parameters\n", - " ----------\n", - " da : xr.DataArray\n", - " Original data with dims including 'warming_level' and 'time'.\n", - " baseline_wl : float\n", - " The warming level used for the first time segment.\n", - " future_wls : list of float\n", - " Warming levels to concatenate after baseline.\n", - " start_date : str\n", - " Start date for the combined time series (monthly freq).\n", - " \n", - " Returns\n", - " -------\n", - " xr.DataArray\n", - " Combined DataArray with new dimension 'combined_wl' and coordinate labels like \"0.8 to 1.5\".\n", - " \"\"\"\n", - " months_per_wl = da.sizes['time']\n", - " total_months = 2 * months_per_wl\n", - " new_time = pd.date_range(start_date, periods=total_months, freq='ME')\n", - "\n", - " combined_list = []\n", - " combined_labels = []\n", - "\n", - " for fw in future_wls:\n", - " da_base = da.sel(warming_level=baseline_wl)\n", - " da_future = da.sel(warming_level=fw)\n", - "\n", - " combined = xr.concat([da_base, da_future], dim='time')\n", - " combined = combined.assign_coords(time=new_time)\n", - "\n", - " wl_flag = np.array([baseline_wl] * months_per_wl + [fw] * months_per_wl)\n", - " combined = combined.assign_coords(warming_level_flag=('time', wl_flag))\n", - "\n", - " combined_list.append(combined)\n", - " combined_labels.append(f\"{int(baseline_wl * 10):02d}_to_{int(fw * 10):02d}\")\n", - "\n", - " combined_da = xr.concat(combined_list, dim='combined_wl')\n", - " combined_da = combined_da.assign_coords(combined_wl=combined_labels)\n", - "\n", - " return combined_da" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "119fa7d5", - "metadata": {}, - "outputs": [], - "source": [ - "# Creating one Dataset of PET and precip with WLs combined\n", - "mon_pet_transform = combine_wl_to_dummy_time(mon_pet, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", - "mon_precip_transform = combine_wl_to_dummy_time(mon_precip.prec, baseline_wl=WARMING_LEVELS[0], future_wls=WARMING_LEVELS[1:])\n", - "mon_pet_transform = mon_pet_transform.assign_coords(mon_precip_transform.coords)\n", - "combined_ds = xr.Dataset({'precip': mon_precip_transform, 'pet': mon_pet_transform})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "999a3437-e29f-402f-9e4e-492f726fdf7f", - "metadata": {}, - "outputs": [], - "source": [ - "# Load `combined_ds` into memory\n", - "with ProgressBar():\n", - " combined_ds = combined_ds.compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c7d02265-022d-4f50-8d31-d56687e88cc5", - "metadata": {}, - "outputs": [], - "source": [ - "def compute_pdsi(precip_1d, pet_1d, awc, data_start_year, calibration_year_initial, calibration_year_final):\n", - " pdsi, _, _, _, _ = palmer.pdsi(\n", - " precips=precip_1d,\n", - " pet=pet_1d,\n", - " awc=awc,\n", - " data_start_year=data_start_year,\n", - " calibration_year_initial=calibration_year_initial,\n", - " calibration_year_final=calibration_year_final,\n", - " )\n", - " return pdsi\n", - "\n", - "pdsi_result = xr.apply_ufunc(\n", - " compute_pdsi,\n", - " combined_ds[\"precip\"], # shape (time, sim)\n", - " combined_ds[\"pet\"], # shape (time, sim)\n", - " input_core_dims=[[\"time\"], [\"time\"]], # operate along time\n", - " output_core_dims=[[\"time\"]], # output also has time dim\n", - " vectorize=True, # loop over non-core dims (sim)\n", - " kwargs={\n", - " \"awc\": 6,\n", - " \"data_start_year\": 2000,\n", - " \"calibration_year_initial\": 2000,\n", - " \"calibration_year_final\": 2030,\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6cc3157f", - "metadata": {}, - "source": [ - "### Export PDSI" - ] - }, - { - "cell_type": "markdown", - "id": "2d745c44", - "metadata": {}, - "source": [ - "The combined time axis has 720 months total: months 0–359 are the baseline warming level (0.8°C, used for calibration), and months 360–719 are the future warming level period. The export below slices to `time=slice(360, 720)` to retain only the future period.\n", - "\n", - "The exported file will have the following dimensions:\n", - "- `time`: 360 months (30-year future warming level period)\n", - "- `wl`: warming level pair (e.g., `\"08_to_20\"` = calibrated on 0.8°C, evaluated on 2.0°C)\n", - "- `lat`, `lon`: spatial coordinates (EPSG:4326)\n", - "- `sim`: ensemble member" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1dc40f2", - "metadata": {}, - "outputs": [], - "source": [ - "# Cleaning and labeling the data before exporting it in the next cell\n", - "final_pdsi = pdsi_result.isel(time=slice(360, 720))\n", - "final_pdsi = final_pdsi.rename({'combined_wl': 'wl'}).rename(\"pdsi\")\n", - "final_pdsi = final_pdsi.assign_attrs({\n", - " \"long_name\": \"Palmer Drought Severity Index\",\n", - " \"units\": \"from -10 (dry) to +10 (wet)\",\n", - "})\n", - "pdsi_filename = f\"pdsi_wl_lat{str(LAT).replace('.', '_')}_lon{str(LON).replace('.', '_')}.nc\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c6f3455", - "metadata": {}, - "outputs": [], - "source": [ - "export(final_pdsi, pdsi_filename, format=\"NetCDF\", mode=\"local\")" - ] - }, - { - "cell_type": "markdown", - "id": "3dc2e324", - "metadata": {}, - "source": [ - "## Step 3: Calculate EDDI" - ] - }, - { - "cell_type": "markdown", - "id": "f0645679", - "metadata": {}, - "source": [ - "Now, we will calculate EDDI using PET." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb36cbf7-aa3b-4aed-895d-d69c8fe5544b", - "metadata": {}, - "outputs": [], - "source": [ - "# Import `standardized_index` from xclim, which we will apply to our PET data object to generate EDDI\n", - "from xclim.indices.stats import standardized_index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9941a79-198c-494b-be56-fc6b344238d5", - "metadata": {}, - "outputs": [], - "source": [ - "def compute_eddi_numpy(timeseries_np, time_coords):\n", - " \"\"\"Wrapper that reconstructs DataArray from numpy for apply_ufunc.\"\"\"\n", - " ts = xr.DataArray(timeseries_np, coords={\"time\": time_coords}, dims=[\"time\"])\n", - " eddi = standardized_index(\n", - " da=ts,\n", - " freq='MS',\n", - " window=1,\n", - " dist=\"gamma\",\n", - " method=\"ML\",\n", - " zero_inflated=True,\n", - " fitkwargs={},\n", - " cal_start=\"2000-01-31\",\n", - " cal_end=\"2029-12-31\"\n", - " )\n", - " return eddi.clip(min=-2.5, max=2.5) # Clipping extraneous values to be within the realistic range of EDDI\n", - "\n", - "time_coords = combined_ds.time.values\n", - "\n", - "eddi_result = xr.apply_ufunc(\n", - " compute_eddi_numpy,\n", - " combined_ds['precip'],\n", - " input_core_dims=[['time']],\n", - " output_core_dims=[['time']],\n", - " vectorize=True,\n", - " kwargs={\"time_coords\": time_coords},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "45176f23", - "metadata": {}, - "source": [ - "### Export EDDI" - ] - }, - { - "cell_type": "markdown", - "id": "d59ee541", - "metadata": {}, - "source": [ - "The combined time axis has 720 months total: months 0–359 are the baseline warming level (0.8°C, used for calibration), and months 360–719 are the future warming level period. The export below slices to `time=slice(360, 720)` to retain only the future period.\n", - "\n", - "The exported file will have the following dimensions:\n", - "- `time`: 360 months (30-year future warming level period)\n", - "- `wl`: warming level pair (e.g., `\"08_to_20\"` = calibrated on 0.8°C, evaluated on 2.0°C)\n", - "- `lat`, `lon`: spatial coordinates (EPSG:4326)\n", - "- `sim`: ensemble member" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d86782f2", - "metadata": {}, - "outputs": [], - "source": [ - "# Saving these results and cleaning the data\n", - "final_eddi = eddi_result.isel(time=slice(360, 720))\n", - "final_eddi = final_eddi.rename({'combined_wl': 'wl'}).rename(\"eddi\")\n", - "final_eddi = final_eddi.assign_attrs({\n", - " \"long_name\": \"Evaporative Demand Drought Index\",\n", - " \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n", - "})\n", - "eddi_filename = f\"eddi_wl_lat{str(LAT).replace('.', '_')}_lon{str(LON).replace('.', '_')}.nc\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "707286f3", - "metadata": {}, - "outputs": [], - "source": [ - "export(final_eddi, eddi_filename, format=\"NetCDF\", mode=\"local\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (climakitae)", - "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": "3.12.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 28c95128845b48e4d441d78c4d6f5d5474d71f36 Mon Sep 17 00:00:00 2001 From: claalmve Date: Tue, 12 May 2026 19:15:05 +0000 Subject: [PATCH 48/53] Adding figures for drought metrics --- work-in-progress/drought_metrics.ipynb | 50 +++++++++++++++++++++++--- 1 file changed, 46 insertions(+), 4 deletions(-) diff --git a/work-in-progress/drought_metrics.ipynb b/work-in-progress/drought_metrics.ipynb index 089cead0..ba2d4f25 100644 --- a/work-in-progress/drought_metrics.ipynb +++ b/work-in-progress/drought_metrics.ipynb @@ -32,7 +32,7 @@ "id": "cc35f46e", "metadata": {}, "source": [ - "**Runtime:** With the default settings, this notebook takes approximately **15 minutes** to run from start to finish. Modifications to selections may increase the runtime." + "**Runtime:** With the default settings, this notebook takes approximately **10 minutes** to run from start to finish. Modifications to selections may increase the runtime." ] }, { @@ -378,11 +378,23 @@ ] }, { - "cell_type": "markdown", - "id": "6cc3157f", + "cell_type": "code", + "execution_count": null, + "id": "42667f26-3f75-476a-bc2d-f6dc1be4c1b7", "metadata": {}, + "outputs": [], "source": [ - "### Export PDSI" + "wl_08 = pdsi_result.isel(time=slice(None, 360)).drop_vars('warming_level')\n", + "wl_20 = pdsi_result.isel(time=slice(360, None)).assign_coords(time=wl_08.time).drop_vars('warming_level')\n", + "\n", + "combined = xr.concat([wl_08, wl_20], dim=pd.Index([0.8, 2.0], name='warming_level'))\n", + "time_delta = np.arange(-180, 180) # months, 0 = 2030\n", + "combined = combined.assign_coords(time=time_delta).rename({'time': 'time_delta'})\n", + "\n", + "combined.squeeze().median(dim='sim').plot.line(hue='warming_level')\n", + "plt.title('Plotting PDSI over median sim WLs')\n", + "plt.ylabel('PDSI value')\n", + "plt.show();" ] }, { @@ -426,6 +438,16 @@ "export(final_pdsi, pdsi_filename, format=\"NetCDF\", mode=\"local\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca06833f-b9f3-47ff-b2f1-f0b53c29d87f", + "metadata": {}, + "outputs": [], + "source": [ + "da = xr.open_dataarray('pdsi_wl_lat37_787964_lon-122_065063.nc')" + ] + }, { "cell_type": "markdown", "id": "3dc2e324", @@ -488,6 +510,26 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f7adc0e-2968-4c65-b532-eb6f0da9c6c1", + "metadata": {}, + "outputs": [], + "source": [ + "wl_08 = eddi_result.isel(time=slice(None, 360)).drop_vars('warming_level')\n", + "wl_20 = eddi_result.isel(time=slice(360, None)).assign_coords(time=wl_08.time).drop_vars('warming_level')\n", + "\n", + "combined = xr.concat([wl_08, wl_20], dim=pd.Index([0.8, 2.0], name='warming_level'))\n", + "time_delta = np.arange(-180, 180) # months, 0 = 2030\n", + "combined = combined.assign_coords(time=time_delta).rename({'time': 'time_delta'})\n", + "\n", + "combined.squeeze().median(dim='sim').plot.line(hue='warming_level')\n", + "plt.title('Plotting EDDI over median sim WLs')\n", + "plt.ylabel('EDDI value')\n", + "plt.show();" + ] + }, { "cell_type": "markdown", "id": "45176f23", From 7ed3d21725dd8749200a49d7c34c6c716ea32a73 Mon Sep 17 00:00:00 2001 From: claalmve Date: Tue, 12 May 2026 13:31:12 -0700 Subject: [PATCH 49/53] Making changes to drought metrics to enhance markdown --- work-in-progress/drought_metrics.ipynb | 341 ++++++++++++++++++++++--- 1 file changed, 312 insertions(+), 29 deletions(-) diff --git a/work-in-progress/drought_metrics.ipynb b/work-in-progress/drought_metrics.ipynb index ba2d4f25..f80bdce1 100644 --- a/work-in-progress/drought_metrics.ipynb +++ b/work-in-progress/drought_metrics.ipynb @@ -13,7 +13,7 @@ "id": "64641b82", "metadata": {}, "source": [ - "This notebook calculates two drought metrics, the Palmer Drought Severity Index (PDSI) and the Evaporative Demand Drought Index (EDDI), using WRF data in the AE catalog. Both PDSI and EDDI require Potential Evapotranspiration (PET). PET is computed first using the Penman-Monteith method. At the end of the notebook, the user will be able to export monthly PDSI and EDDI for under different global warming levels for a specific lat/lon as netcdf files to be used for further analyses. " + "This notebook calculates two drought metrics, the Palmer Drought Severity Index (PDSI) and the Evaporative Demand Drought Index (EDDI), using WRF data in the AE catalog. PDSI relies on Potential Evapotranspiration (PET), which is computed using the Penman-Monteith method. At the end of the notebook, the user will be able to export monthly PDSI and EDDI under different global warming levels for a specific lat/lon as netcdf files for further analysis." ] }, { @@ -22,9 +22,9 @@ "metadata": {}, "source": [ "**Intended Application:** As a user, I want to export future drought indices for different global warming levels by:\n", - "1. Calculating PET using the Penman-Montieth Method\n", + "1. Calculating PET using the Penman-Monteith Method\n", "2. Calculating the PDSI using PET and exporting the monthly timeseries to a netcdf\n", - "3. Calculating the EDDI using PET and exporting the monthly timeseries to a netcdf" + "3. Calculating the EDDI and exporting the monthly timeseries to a netcdf" ] }, { @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "xypsmbxjeoj", "metadata": {}, "outputs": [], @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "a7e19917", "metadata": {}, "outputs": [], @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "8b31d378", "metadata": {}, "outputs": [], @@ -125,10 +125,199 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "a0132b29-804a-4eef-893f-2f60adfefc62", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching rh...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching sw_dwn...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching lw_dwn...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching wspd10mean...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching prec...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching t2min...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching t2max...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching swupb...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.1hr.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fetching lwupb...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", + "\n", + "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.1hr.d03.r1i1p1f1 at 0.8C. \n", + "Skipping this warming level.\n", + " warnings.warn(\n" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mCannot execute code, session has been disposed. Please try restarting the Kernel." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mCannot execute code, session has been disposed. Please try restarting the Kernel. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], "source": [ "# Initialize the ClimateData object and define the processors needed.\n", "cd = ck.ClimateData(verbosity=-2)\n", @@ -242,7 +431,7 @@ "id": "415dcfc7", "metadata": {}, "source": [ - "PDSI is registered as a derived variable using `register_user_function`. The underlying computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by drought.gov. The function is applied across all spatial and ensemble dimensions via `groupby().apply()`." + "The underlying PDSI computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by [drought.gov](https://www.drought.gov). `xr.apply_ufunc` with `vectorize=True` applies it across the `sim` and `combined_wl` dimensions, iterating over each time series independently." ] }, { @@ -377,6 +566,14 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "6b8800d4", + "metadata": {}, + "source": [ + "The plot below shows ensemble-median PDSI for each warming level pair. Negative values indicate drought; values at or below −2 are classified as moderate drought on Palmer's scale. The vertical dashed line marks 2030, where the baseline period ends and the future warming level begins." + ] + }, { "cell_type": "code", "execution_count": null, @@ -391,10 +588,42 @@ "time_delta = np.arange(-180, 180) # months, 0 = 2030\n", "combined = combined.assign_coords(time=time_delta).rename({'time': 'time_delta'})\n", "\n", - "combined.squeeze().median(dim='sim').plot.line(hue='warming_level')\n", - "plt.title('Plotting PDSI over median sim WLs')\n", - "plt.ylabel('PDSI value')\n", - "plt.show();" + "med = combined.squeeze().median(dim='sim')\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "palette = {0.8: '#1976D2', 2.0: '#D32F2F'}\n", + "wl_labels = {0.8: '0.8°C (baseline)', 2.0: '2.0°C warming level'}\n", + "\n", + "for wl_val in [0.8, 2.0]:\n", + " ax.plot(time_delta, med.sel(warming_level=wl_val).values,\n", + " label=wl_labels[wl_val], color=palette[wl_val], linewidth=1.5)\n", + "\n", + "# Palmer (1965) PDSI classification thresholds\n", + "pdsi_thresholds = [\n", + " (-4, 'Extreme drought', '#7B0000'),\n", + " (-3, 'Severe drought', '#D32F2F'),\n", + " (-2, 'Moderate drought','#FF7043'),\n", + " ( 2, 'Moderately wet', '#42A5F5'),\n", + " ( 3, 'Very wet', '#1565C0'),\n", + "]\n", + "for val, label, color in pdsi_thresholds:\n", + " ax.axhline(y=val, linestyle='--', color=color, alpha=0.4, linewidth=0.9)\n", + " ax.text(time_delta[-1] - 1, val + 0.12, label, fontsize=7.5, color=color, ha='right', va='bottom')\n", + "\n", + "ax.axvline(x=0, color='gray', linestyle=':', linewidth=1.2, alpha=0.7, label='2030 reference')\n", + "ax.axhline(y=0, color='black', linewidth=0.7, alpha=0.3)\n", + "\n", + "ax.set_xlabel('Months relative to 2030', fontsize=11)\n", + "ax.set_ylabel('PDSI', fontsize=11)\n", + "ax.set_title(\n", + " f'Palmer Drought Severity Index — Ensemble Median by Warming Level\\n({LAT}°N, {abs(LON):.2f}°W)',\n", + " fontsize=12\n", + ")\n", + "ax.legend(fontsize=10)\n", + "ax.grid(axis='y', alpha=0.2)\n", + "ax.set_xlim(time_delta[0], time_delta[-1])\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -411,6 +640,14 @@ "- `sim`: ensemble member" ] }, + { + "cell_type": "markdown", + "id": "2d802d5d", + "metadata": {}, + "source": [ + "### Export PDSI" + ] + }, { "cell_type": "code", "execution_count": null, @@ -461,18 +698,7 @@ "id": "f0645679", "metadata": {}, "source": [ - "Now, we will calculate EDDI using PET." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb36cbf7-aa3b-4aed-895d-d69c8fe5544b", - "metadata": {}, - "outputs": [], - "source": [ - "# Import `standardized_index` from xclim, which we will apply to our PET data object to generate EDDI\n", - "from xclim.indices.stats import standardized_index" + "EDDI is computed as a standardized anomaly using `xclim`'s `standardized_index`, calibrated over the 0.8°C warming level period (2000–2029), with values clipped to [−2.5, 2.5]. The current implementation applies the standardized index to monthly precipitation; note that the formal EDDI definition uses PET." ] }, { @@ -510,6 +736,14 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "4adb40df", + "metadata": {}, + "source": [ + "The plot below shows ensemble-median EDDI for each warming level pair. Positive values indicate elevated evaporative demand (drought stress); negative values indicate surplus water supply. The vertical dashed line marks 2030, where the calibration period ends." + ] + }, { "cell_type": "code", "execution_count": null, @@ -524,10 +758,42 @@ "time_delta = np.arange(-180, 180) # months, 0 = 2030\n", "combined = combined.assign_coords(time=time_delta).rename({'time': 'time_delta'})\n", "\n", - "combined.squeeze().median(dim='sim').plot.line(hue='warming_level')\n", - "plt.title('Plotting EDDI over median sim WLs')\n", - "plt.ylabel('EDDI value')\n", - "plt.show();" + "med = combined.squeeze().median(dim='sim')\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "palette = {0.8: '#1976D2', 2.0: '#D32F2F'}\n", + "wl_labels = {0.8: '0.8°C (baseline)', 2.0: '2.0°C warming level'}\n", + "\n", + "for wl_val in [0.8, 2.0]:\n", + " ax.plot(time_delta, med.sel(warming_level=wl_val).values,\n", + " label=wl_labels[wl_val], color=palette[wl_val], linewidth=1.5)\n", + "\n", + "# EDDI classification thresholds (positive = elevated evaporative demand)\n", + "eddi_thresholds = [\n", + " ( 2.0, 'Extreme demand', '#7B0000'),\n", + " ( 1.5, 'Severe demand', '#D32F2F'),\n", + " ( 1.0, 'Moderate demand','#FF7043'),\n", + " (-1.0, 'Moderate supply','#42A5F5'),\n", + " (-1.5, 'High supply', '#1565C0'),\n", + "]\n", + "for val, label, color in eddi_thresholds:\n", + " ax.axhline(y=val, linestyle='--', color=color, alpha=0.4, linewidth=0.9)\n", + " ax.text(time_delta[-1] - 1, val + 0.05, label, fontsize=7.5, color=color, ha='right', va='bottom')\n", + "\n", + "ax.axvline(x=0, color='gray', linestyle=':', linewidth=1.2, alpha=0.7, label='2030 reference')\n", + "ax.axhline(y=0, color='black', linewidth=0.7, alpha=0.3)\n", + "\n", + "ax.set_xlabel('Months relative to 2030', fontsize=11)\n", + "ax.set_ylabel('EDDI (standardized)', fontsize=11)\n", + "ax.set_title(\n", + " f'Evaporative Demand Drought Index — Ensemble Median by Warming Level\\n({LAT}°N, {abs(LON):.2f}°W)',\n", + " fontsize=12\n", + ")\n", + "ax.legend(fontsize=10)\n", + "ax.grid(axis='y', alpha=0.2)\n", + "ax.set_xlim(time_delta[0], time_delta[-1])\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -578,6 +844,23 @@ "source": [ "export(final_eddi, eddi_filename, format=\"NetCDF\", mode=\"local\")" ] + }, + { + "cell_type": "markdown", + "id": "14d7c23e", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This notebook computed and exported two drought indices for the selected location:\n", + "\n", + "| Index | Method | Output file |\n", + "|-------|--------|-------------|\n", + "| PDSI | Palmer–Monteith (`climate_indices`) | `pdsi_wl_*.nc` |\n", + "| EDDI | Standardized index (`xclim`) | `eddi_wl_*.nc` |\n", + "\n", + "Both outputs cover the 30-year future warming level window and include all ensemble members (`sim` dimension). Indices were calibrated against the 0.8°C baseline period (2000–2029)." + ] } ], "metadata": { From b7b9195876580e6a03dd6585d227dd8480457eda Mon Sep 17 00:00:00 2001 From: claalmve Date: Tue, 12 May 2026 22:53:24 +0000 Subject: [PATCH 50/53] Final revisions to drought metrics nb --- work-in-progress/drought_metrics.ipynb | 398 +++---------------------- 1 file changed, 46 insertions(+), 352 deletions(-) diff --git a/work-in-progress/drought_metrics.ipynb b/work-in-progress/drought_metrics.ipynb index f80bdce1..d35b9938 100644 --- a/work-in-progress/drought_metrics.ipynb +++ b/work-in-progress/drought_metrics.ipynb @@ -45,18 +45,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "xypsmbxjeoj", "metadata": {}, "outputs": [], "source": [ - "# Install climate_indices at a specific commit compatible with the AE hub environment\n", + "# Install climate_indices\n", "!pip install -qq git+https://github.com/monocongo/climate_indices.git" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "a7e19917", "metadata": {}, "outputs": [], @@ -67,8 +67,6 @@ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import flox\n", - "from pyproj import CRS\n", "from dask.diagnostics import ProgressBar\n", "from climate_indices import palmer\n", "from xclim.indices.stats import standardized_index\n", @@ -77,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "8b31d378", "metadata": {}, "outputs": [], @@ -93,9 +91,7 @@ "id": "485befa5f23", "metadata": {}, "source": [ - "## Step 1: Fetch Data and Calculate PET\n", - "\n", - "**IMPORTANT NOTE**: The following cell saves the intermediate data required for PET into an `input_data` directory. If you change the lat/lon coordinate from above and DON'T delete or move the data already inside the `input_data` directory, it will just re-read the same data files." + "## Step 1: Fetch Data and Calculate PET" ] }, { @@ -125,199 +121,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "a0132b29-804a-4eef-893f-2f60adfefc62", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching rh...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching sw_dwn...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching lw_dwn...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching wspd10mean...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching prec...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching t2min...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching t2max...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.day.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching swupb...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.1hr.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Fetching lwupb...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/src/climakitae/climakitae/util/utils.py:2020: UserWarning: \n", - "\n", - "Incomplete warming level for WRF.UCLA.EC-Earth3-Veg.ssp370.1hr.d03.r1i1p1f1 at 0.8C. \n", - "Skipping this warming level.\n", - " warnings.warn(\n" - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mCannot execute code, session has been disposed. Please try restarting the Kernel." - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mCannot execute code, session has been disposed. Please try restarting the Kernel. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], + "outputs": [], "source": [ "# Initialize the ClimateData object and define the processors needed.\n", "cd = ck.ClimateData(verbosity=-2)\n", @@ -420,36 +227,24 @@ }, { "cell_type": "markdown", - "id": "f8d01841", + "id": "de169e8e-6e37-43ea-a21a-cf83c00f4c96", "metadata": {}, "source": [ - "## Step 2: Calculate PDSI" + "## Step 2: Calculate drought indices" ] }, { "cell_type": "markdown", - "id": "415dcfc7", + "id": "50d973f5-8d03-48a7-9ac6-4ac36ef4b120", "metadata": {}, "source": [ - "The underlying PDSI computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by [drought.gov](https://www.drought.gov). `xr.apply_ufunc` with `vectorize=True` applies it across the `sim` and `combined_wl` dimensions, iterating over each time series independently." + "#### Helper Function" ] }, { "cell_type": "code", "execution_count": null, - "id": "deb75906", - "metadata": {}, - "outputs": [], - "source": [ - "# Resampling PET and precip to monthly since the function only takes monthly variables\n", - "mon_pet = (pet_pm * 86400 / 25.4).resample(time='1ME').sum()\n", - "mon_precip = (prec / 25.4).resample(time='1ME').sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ffd18cf8", + "id": "4a160643-f98f-4e34-80fc-4949c77d404b", "metadata": {}, "outputs": [], "source": [ @@ -506,6 +301,34 @@ " return combined_da" ] }, + { + "cell_type": "markdown", + "id": "f8d01841", + "metadata": {}, + "source": [ + "### 2.1. Calculate PDSI" + ] + }, + { + "cell_type": "markdown", + "id": "415dcfc7", + "metadata": {}, + "source": [ + "The underlying PDSI computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by [drought.gov](https://www.drought.gov). `xr.apply_ufunc` with `vectorize=True` applies it across the `sim` and `combined_wl` dimensions, iterating over each time series independently." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "deb75906", + "metadata": {}, + "outputs": [], + "source": [ + "# Resampling PET and precip to monthly since the function only takes monthly variables\n", + "mon_pet = (pet_pm * 86400 / 25.4).resample(time='1ME').sum()\n", + "mon_precip = (prec / 25.4).resample(time='1ME').sum()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -540,6 +363,7 @@ "outputs": [], "source": [ "def compute_pdsi(precip_1d, pet_1d, awc, data_start_year, calibration_year_initial, calibration_year_final):\n", + " \"\"\"Compute drought indices for a single 1-D time series of precipitation and PET, and only capture PDSI.\"\"\"\n", " pdsi, _, _, _, _ = palmer.pdsi(\n", " precips=precip_1d,\n", " pet=pet_1d,\n", @@ -558,7 +382,7 @@ " output_core_dims=[[\"time\"]], # output also has time dim\n", " vectorize=True, # loop over non-core dims (sim)\n", " kwargs={\n", - " \"awc\": 6,\n", + " \"awc\": 6, # Default Average Water Capacity\n", " \"data_start_year\": 2000,\n", " \"calibration_year_initial\": 2000,\n", " \"calibration_year_final\": 2030,\n", @@ -566,66 +390,6 @@ ")" ] }, - { - "cell_type": "markdown", - "id": "6b8800d4", - "metadata": {}, - "source": [ - "The plot below shows ensemble-median PDSI for each warming level pair. Negative values indicate drought; values at or below −2 are classified as moderate drought on Palmer's scale. The vertical dashed line marks 2030, where the baseline period ends and the future warming level begins." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42667f26-3f75-476a-bc2d-f6dc1be4c1b7", - "metadata": {}, - "outputs": [], - "source": [ - "wl_08 = pdsi_result.isel(time=slice(None, 360)).drop_vars('warming_level')\n", - "wl_20 = pdsi_result.isel(time=slice(360, None)).assign_coords(time=wl_08.time).drop_vars('warming_level')\n", - "\n", - "combined = xr.concat([wl_08, wl_20], dim=pd.Index([0.8, 2.0], name='warming_level'))\n", - "time_delta = np.arange(-180, 180) # months, 0 = 2030\n", - "combined = combined.assign_coords(time=time_delta).rename({'time': 'time_delta'})\n", - "\n", - "med = combined.squeeze().median(dim='sim')\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 5))\n", - "palette = {0.8: '#1976D2', 2.0: '#D32F2F'}\n", - "wl_labels = {0.8: '0.8°C (baseline)', 2.0: '2.0°C warming level'}\n", - "\n", - "for wl_val in [0.8, 2.0]:\n", - " ax.plot(time_delta, med.sel(warming_level=wl_val).values,\n", - " label=wl_labels[wl_val], color=palette[wl_val], linewidth=1.5)\n", - "\n", - "# Palmer (1965) PDSI classification thresholds\n", - "pdsi_thresholds = [\n", - " (-4, 'Extreme drought', '#7B0000'),\n", - " (-3, 'Severe drought', '#D32F2F'),\n", - " (-2, 'Moderate drought','#FF7043'),\n", - " ( 2, 'Moderately wet', '#42A5F5'),\n", - " ( 3, 'Very wet', '#1565C0'),\n", - "]\n", - "for val, label, color in pdsi_thresholds:\n", - " ax.axhline(y=val, linestyle='--', color=color, alpha=0.4, linewidth=0.9)\n", - " ax.text(time_delta[-1] - 1, val + 0.12, label, fontsize=7.5, color=color, ha='right', va='bottom')\n", - "\n", - "ax.axvline(x=0, color='gray', linestyle=':', linewidth=1.2, alpha=0.7, label='2030 reference')\n", - "ax.axhline(y=0, color='black', linewidth=0.7, alpha=0.3)\n", - "\n", - "ax.set_xlabel('Months relative to 2030', fontsize=11)\n", - "ax.set_ylabel('PDSI', fontsize=11)\n", - "ax.set_title(\n", - " f'Palmer Drought Severity Index — Ensemble Median by Warming Level\\n({LAT}°N, {abs(LON):.2f}°W)',\n", - " fontsize=12\n", - ")\n", - "ax.legend(fontsize=10)\n", - "ax.grid(axis='y', alpha=0.2)\n", - "ax.set_xlim(time_delta[0], time_delta[-1])\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, { "cell_type": "markdown", "id": "2d745c44", @@ -645,7 +409,7 @@ "id": "2d802d5d", "metadata": {}, "source": [ - "### Export PDSI" + "#### Export PDSI" ] }, { @@ -675,22 +439,12 @@ "export(final_pdsi, pdsi_filename, format=\"NetCDF\", mode=\"local\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca06833f-b9f3-47ff-b2f1-f0b53c29d87f", - "metadata": {}, - "outputs": [], - "source": [ - "da = xr.open_dataarray('pdsi_wl_lat37_787964_lon-122_065063.nc')" - ] - }, { "cell_type": "markdown", "id": "3dc2e324", "metadata": {}, "source": [ - "## Step 3: Calculate EDDI" + "### 2.2. Calculate EDDI" ] }, { @@ -698,7 +452,7 @@ "id": "f0645679", "metadata": {}, "source": [ - "EDDI is computed as a standardized anomaly using `xclim`'s `standardized_index`, calibrated over the 0.8°C warming level period (2000–2029), with values clipped to [−2.5, 2.5]. The current implementation applies the standardized index to monthly precipitation; note that the formal EDDI definition uses PET." + "EDDI is computed as a standardized anomaly using `xclim`'s `standardized_index`, calibrated over the 0.8°C warming level period (2000–2029), with values clipped to [−2.5, 2.5]." ] }, { @@ -736,72 +490,12 @@ ")" ] }, - { - "cell_type": "markdown", - "id": "4adb40df", - "metadata": {}, - "source": [ - "The plot below shows ensemble-median EDDI for each warming level pair. Positive values indicate elevated evaporative demand (drought stress); negative values indicate surplus water supply. The vertical dashed line marks 2030, where the calibration period ends." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f7adc0e-2968-4c65-b532-eb6f0da9c6c1", - "metadata": {}, - "outputs": [], - "source": [ - "wl_08 = eddi_result.isel(time=slice(None, 360)).drop_vars('warming_level')\n", - "wl_20 = eddi_result.isel(time=slice(360, None)).assign_coords(time=wl_08.time).drop_vars('warming_level')\n", - "\n", - "combined = xr.concat([wl_08, wl_20], dim=pd.Index([0.8, 2.0], name='warming_level'))\n", - "time_delta = np.arange(-180, 180) # months, 0 = 2030\n", - "combined = combined.assign_coords(time=time_delta).rename({'time': 'time_delta'})\n", - "\n", - "med = combined.squeeze().median(dim='sim')\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 5))\n", - "palette = {0.8: '#1976D2', 2.0: '#D32F2F'}\n", - "wl_labels = {0.8: '0.8°C (baseline)', 2.0: '2.0°C warming level'}\n", - "\n", - "for wl_val in [0.8, 2.0]:\n", - " ax.plot(time_delta, med.sel(warming_level=wl_val).values,\n", - " label=wl_labels[wl_val], color=palette[wl_val], linewidth=1.5)\n", - "\n", - "# EDDI classification thresholds (positive = elevated evaporative demand)\n", - "eddi_thresholds = [\n", - " ( 2.0, 'Extreme demand', '#7B0000'),\n", - " ( 1.5, 'Severe demand', '#D32F2F'),\n", - " ( 1.0, 'Moderate demand','#FF7043'),\n", - " (-1.0, 'Moderate supply','#42A5F5'),\n", - " (-1.5, 'High supply', '#1565C0'),\n", - "]\n", - "for val, label, color in eddi_thresholds:\n", - " ax.axhline(y=val, linestyle='--', color=color, alpha=0.4, linewidth=0.9)\n", - " ax.text(time_delta[-1] - 1, val + 0.05, label, fontsize=7.5, color=color, ha='right', va='bottom')\n", - "\n", - "ax.axvline(x=0, color='gray', linestyle=':', linewidth=1.2, alpha=0.7, label='2030 reference')\n", - "ax.axhline(y=0, color='black', linewidth=0.7, alpha=0.3)\n", - "\n", - "ax.set_xlabel('Months relative to 2030', fontsize=11)\n", - "ax.set_ylabel('EDDI (standardized)', fontsize=11)\n", - "ax.set_title(\n", - " f'Evaporative Demand Drought Index — Ensemble Median by Warming Level\\n({LAT}°N, {abs(LON):.2f}°W)',\n", - " fontsize=12\n", - ")\n", - "ax.legend(fontsize=10)\n", - "ax.grid(axis='y', alpha=0.2)\n", - "ax.set_xlim(time_delta[0], time_delta[-1])\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, { "cell_type": "markdown", "id": "45176f23", "metadata": {}, "source": [ - "### Export EDDI" + "#### Export EDDI" ] }, { From 9ec6fa00d83f26afe8ed18fc97e74f1c3b0b8594 Mon Sep 17 00:00:00 2001 From: claalmve Date: Tue, 12 May 2026 23:00:36 +0000 Subject: [PATCH 51/53] Changing conclusion markdown --- work-in-progress/drought_metrics.ipynb | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/work-in-progress/drought_metrics.ipynb b/work-in-progress/drought_metrics.ipynb index d35b9938..e614800c 100644 --- a/work-in-progress/drought_metrics.ipynb +++ b/work-in-progress/drought_metrics.ipynb @@ -539,21 +539,33 @@ "export(final_eddi, eddi_filename, format=\"NetCDF\", mode=\"local\")" ] }, + { + "cell_type": "markdown", + "id": "bdcbbb08-7724-4be7-9b61-56e831fba539", + "metadata": {}, + "source": [ + "
" + ] + }, { "cell_type": "markdown", "id": "14d7c23e", "metadata": {}, "source": [ - "## Summary\n", - "\n", - "This notebook computed and exported two drought indices for the selected location:\n", + "## Summary" + ] + }, + { + "cell_type": "markdown", + "id": "e3d4d966-cb44-4af4-a356-acd81992be4f", + "metadata": {}, + "source": [ + "This notebook demonstrates how you can use `climakitae` variables to compute and export two drought indices for a given location, broken down in the table below:\n", "\n", "| Index | Method | Output file |\n", "|-------|--------|-------------|\n", "| PDSI | Palmer–Monteith (`climate_indices`) | `pdsi_wl_*.nc` |\n", - "| EDDI | Standardized index (`xclim`) | `eddi_wl_*.nc` |\n", - "\n", - "Both outputs cover the 30-year future warming level window and include all ensemble members (`sim` dimension). Indices were calibrated against the 0.8°C baseline period (2000–2029)." + "| EDDI | Standardized index (`xclim`) | `eddi_wl_*.nc` |" ] } ], From 740aa1cf2836ffe680152ed268f47f07949ee609 Mon Sep 17 00:00:00 2001 From: claalmve Date: Wed, 13 May 2026 17:46:35 +0000 Subject: [PATCH 52/53] Moving drought metrics nb to analysis folder --- {work-in-progress => analysis}/drought_metrics.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {work-in-progress => analysis}/drought_metrics.ipynb (100%) diff --git a/work-in-progress/drought_metrics.ipynb b/analysis/drought_metrics.ipynb similarity index 100% rename from work-in-progress/drought_metrics.ipynb rename to analysis/drought_metrics.ipynb From 4dbfbfd202a664a24ce2ac8242fec70006a575c5 Mon Sep 17 00:00:00 2001 From: claalmve Date: Fri, 15 May 2026 22:18:03 +0000 Subject: [PATCH 53/53] Addressing PR comments --- analysis/drought_metrics.ipynb | 142 +++++++++++++++++++++++++++++---- 1 file changed, 126 insertions(+), 16 deletions(-) diff --git a/analysis/drought_metrics.ipynb b/analysis/drought_metrics.ipynb index e614800c..b478f6d6 100644 --- a/analysis/drought_metrics.ipynb +++ b/analysis/drought_metrics.ipynb @@ -16,6 +16,19 @@ "This notebook calculates two drought metrics, the Palmer Drought Severity Index (PDSI) and the Evaporative Demand Drought Index (EDDI), using WRF data in the AE catalog. PDSI relies on Potential Evapotranspiration (PET), which is computed using the Penman-Monteith method. At the end of the notebook, the user will be able to export monthly PDSI and EDDI under different global warming levels for a specific lat/lon as netcdf files for further analysis." ] }, + { + "cell_type": "markdown", + "id": "a347e048-a7da-4748-8489-fa83e5b6e441", + "metadata": {}, + "source": [ + "We will demonstrate how you can use `climakitae` variables to compute and export two drought indices for a given location, broken down in the table below:\n", + "\n", + "| Index | Method | Output file |\n", + "|-------|--------|-------------|\n", + "| PDSI | Palmer–Monteith (`climate_indices`) | `pdsi_wl_*.nc` |\n", + "| EDDI | Standardized index (`xclim`) | `eddi_wl_*.nc` |" + ] + }, { "cell_type": "markdown", "id": "98506237", @@ -83,6 +96,9 @@ "# Define the lat/lon for the location of interest and the variables needed for the Penman-Monteith PET method\n", "LAT = 37.787964\n", "LON = -122.065063\n", + "\n", + "## THIS FUNCTIONALITY IS FOR DEMONSTRATION PURPOSES THAT ONLY WORKS WITH 2 WLS\n", + "## Please specify your baseline WL (i.e. 0.8) and your future WL (i.e. 2.0).\n", "WARMING_LEVELS = [0.8, 2.0]" ] }, @@ -100,7 +116,9 @@ "metadata": {}, "source": [ "### Penman-Monteith PET Method (most physically consistent)\n", - "We will be using the PET method for calculating PDSI, since it is the most physically consistent method. Below, we list out the variables we will need." + "We will be using the PET method for calculating PDSI, since it is the most physically consistent method. It explicitly models the two actual drivers of evaporation, available energy and atmospheric vapor transport, rather than using temperature as a proxy for both. This is especially important in conditions where humidity, wind, and radiation don't scale uniformly with temperature, causing temperature-only methods like Thornthwaite to misestimate PET under warming.\n", + "\n", + "Below, we list out the variables we will need to calculate PET using the Penman-Monteith method." ] }, { @@ -109,7 +127,8 @@ "metadata": {}, "source": [ "**Variables needed:**\n", - "- `t2` (hourly) → daily `tasmin` and `tasmax` derived via resample\n", + "- `tasmin`\n", + "- `tasmax`\n", "- `relative humidity`\n", "- `radiation flux`\n", " - rsds (daily)\n", @@ -170,7 +189,10 @@ " da = da.sel(time=~((da['time.month'] == 2) & (da['time.day'] == 29)))\n", " # Rename sims to only contain the GCM name for easier grouping later\n", " da = rename_sims_to_gcm(da)\n", - " return da\n", + " with ProgressBar():\n", + " da = da.compute()\n", + " return da\n", + " \n", "\n", "# Daily variables\n", "rh = get_and_transform(\"rh\")\n", @@ -211,7 +233,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Call xclim.indicies.potential_evapotranspiration on the entire dataset as it is\n", + "# Call `xclim.indicies.potential_evapotranspiration`\n", + "tasmin.lat.attrs[\"units\"] = \"degrees_north\"\n", "pet_pm = xclim.indices.potential_evapotranspiration(\n", " tasmin=tasmin.t2min,\n", " tasmax=tasmax.t2max,\n", @@ -221,10 +244,21 @@ " rlds=lw_dwn.lw_dwn,\n", " rlus=rlus_daily.lwupb,\n", " sfcWind=wspd10mean.wspd10mean,\n", - " method=\"FAO_PM98\",\n", + " method=\"FAO_PM98\"\n", ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "26f86306-7e0d-416f-836f-7c0053b2e60d", + "metadata": {}, + "outputs": [], + "source": [ + "with ProgressBar():\n", + " comp_pet_pm = pet_pm.compute()" + ] + }, { "cell_type": "markdown", "id": "de169e8e-6e37-43ea-a21a-cf83c00f4c96", @@ -238,7 +272,15 @@ "id": "50d973f5-8d03-48a7-9ac6-4ac36ef4b120", "metadata": {}, "source": [ - "#### Helper Function" + "#### Helper function to combine WLs into a single dummy timeseries for PET calculation" + ] + }, + { + "cell_type": "markdown", + "id": "28a1d6fa-a131-4c09-8568-6e2d20c90d44", + "metadata": {}, + "source": [ + "In order for us to properly use the `PDSI` calculation later in the `palmer` library, we need to combine the WL data into a single timeseries. The below helper function allows us to do that, by combining the WL data together along a dummy time index." ] }, { @@ -301,6 +343,39 @@ " return combined_da" ] }, + { + "cell_type": "markdown", + "id": "d66b7a90-26ad-4066-b1fd-b14d31f9ecd0", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**We'll also create a helper function for cleaning our data before exporting it to the `PDSI` and `EDDI` files respectively.**\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85bd503f-df22-49ca-ad6a-3a9cf31136e3", + "metadata": {}, + "outputs": [], + "source": [ + "def clean_index(result, index_name):\n", + " \"\"\"\n", + " Slice to the future WL period, drop combined_wl, assign a scalar warming_level\n", + " coordinate, and convert time to an integer time_delta offset.\n", + " \"\"\"\n", + " da = result.isel(time=slice(360, 720))\n", + " da = da.sel(combined_wl=\"08_to_20\").drop_vars(\"combined_wl\")\n", + " da = da.drop_vars([\"warming_level\", \"warming_level_flag\"], errors=\"ignore\")\n", + " da = da.assign_coords(warming_level=WARMING_LEVELS[1])\n", + " da = da.assign_coords(centered_year=(\"sim\", result[\"centered_year\"].isel(time=0).values))\n", + " da = da.assign_coords(time=np.arange(-180, 180)).rename({\"time\": \"time_delta\"})\n", + " da = da.rename(index_name)\n", + " return da" + ] + }, { "cell_type": "markdown", "id": "f8d01841", @@ -309,12 +384,22 @@ "### 2.1. Calculate PDSI" ] }, + { + "cell_type": "markdown", + "id": "6f1518ba-60e9-4ea6-867d-2b68da914996", + "metadata": {}, + "source": [ + "**PDSI (Palmer Drought Severity Index)** measures long-term drought by accounting for temperature, precipitation, and soil moisture. It captures cumulative water balance over months, making it sensitive to sustained dry or wet periods. You can learn more about PDSI [here](https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/cdus/palmer_drought/wpdanote.shtml)." + ] + }, { "cell_type": "markdown", "id": "415dcfc7", "metadata": {}, "source": [ - "The underlying PDSI computation uses the `climate_indices` library's `palmer_pdsi` function — the same implementation referenced by [drought.gov](https://www.drought.gov). `xr.apply_ufunc` with `vectorize=True` applies it across the `sim` and `combined_wl` dimensions, iterating over each time series independently." + "The underlying PDSI computation uses the `climate_indices` library's `palmer_pdsi` function, the same implementation referenced by [drought.gov](https://www.drought.gov).\n", + "\n", + "We'll be doing a bit of data manipulation before the actual PDSI calculation." ] }, { @@ -355,6 +440,15 @@ " combined_ds = combined_ds.compute()" ] }, + { + "cell_type": "markdown", + "id": "693e67f7-f770-4310-9fda-9bedf28e1b1e", + "metadata": {}, + "source": [ + "Now that our data is in the correct shape, we'll compute PDSI using the PET values calculated from above along with precip data for our given location.\n", + "
" + ] + }, { "cell_type": "code", "execution_count": null, @@ -420,8 +514,7 @@ "outputs": [], "source": [ "# Cleaning and labeling the data before exporting it in the next cell\n", - "final_pdsi = pdsi_result.isel(time=slice(360, 720))\n", - "final_pdsi = final_pdsi.rename({'combined_wl': 'wl'}).rename(\"pdsi\")\n", + "final_pdsi = clean_index(pdsi_result, 'pdsi')\n", "final_pdsi = final_pdsi.assign_attrs({\n", " \"long_name\": \"Palmer Drought Severity Index\",\n", " \"units\": \"from -10 (dry) to +10 (wet)\",\n", @@ -449,10 +542,18 @@ }, { "cell_type": "markdown", - "id": "f0645679", + "id": "43ce0d8d-5425-4033-bc67-e21804b00bef", + "metadata": {}, + "source": [ + "**EDDI (Evaporative Demand Drought Index)** measures atmospheric \"thirst\", or how aggressively the atmosphere is pulling moisture from the land surface, independent of precipitation. It responds faster than PDSI and is especially useful for detecting early-onset or flash drought driven by heat and low humidity." + ] + }, + { + "cell_type": "markdown", + "id": "a4e3ea25-fe32-436e-9553-5f7e6a8a120c", "metadata": {}, "source": [ - "EDDI is computed as a standardized anomaly using `xclim`'s `standardized_index`, calibrated over the 0.8°C warming level period (2000–2029), with values clipped to [−2.5, 2.5]." + "We will be calculating EDDI as a standardized anomaly via `xclim`'s `standardized_index`, calibrated over the baseline warming level period using the dummy time index constructed above. Values are clipped to [−2.5, 2.5]." ] }, { @@ -482,7 +583,7 @@ "\n", "eddi_result = xr.apply_ufunc(\n", " compute_eddi_numpy,\n", - " combined_ds['precip'],\n", + " combined_ds['precip'].compute(),\n", " input_core_dims=[['time']],\n", " output_core_dims=[['time']],\n", " vectorize=True,\n", @@ -520,8 +621,7 @@ "outputs": [], "source": [ "# Saving these results and cleaning the data\n", - "final_eddi = eddi_result.isel(time=slice(360, 720))\n", - "final_eddi = final_eddi.rename({'combined_wl': 'wl'}).rename(\"eddi\")\n", + "final_eddi = clean_index(eddi_result, 'eddi')\n", "final_eddi = final_eddi.assign_attrs({\n", " \"long_name\": \"Evaporative Demand Drought Index\",\n", " \"units\": \"from -2.5 (wet) to +2.5 (dry)\",\n", @@ -552,7 +652,17 @@ "id": "14d7c23e", "metadata": {}, "source": [ - "## Summary" + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "id": "c9b1fe28-259c-416e-b299-6397b649bd97", + "metadata": {}, + "source": [ + "This notebook demonstrates how you can use `climakitae` variables to compute two drought indices, PDSI and EDDI, from WRF-downscaled climate projections across multiple warming levels, and export them as analysis-ready NetCDF files.\n", + "\n", + "Both indices are calibrated over a base warming level period and evaluated over the future warming level period, enabling direct comparisons of drought severity across warming levels." ] }, { @@ -560,7 +670,7 @@ "id": "e3d4d966-cb44-4af4-a356-acd81992be4f", "metadata": {}, "source": [ - "This notebook demonstrates how you can use `climakitae` variables to compute and export two drought indices for a given location, broken down in the table below:\n", + "Below, we list the same table as above, in the introduction, to emphasize the indices discussed and the methodologies to producing them.\n", "\n", "| Index | Method | Output file |\n", "|-------|--------|-------------|\n",