diff --git a/.github/workflows/integration.yml b/.github/workflows/integration.yml
index 1974cdc5..2b30487d 100644
--- a/.github/workflows/integration.yml
+++ b/.github/workflows/integration.yml
@@ -6,26 +6,38 @@ name: Integration Tests
 # events but only for the main branch
 on:
   push:
-    branches: [ main ]
+    branches: 
+      - main
+      - release_*
   pull_request:
-    branches: [ main ]
+    branches: 
+      - main
+      - release_*
     types: [opened, synchronize, reopened, ready_for_review]
 
 jobs:
   run:
-    # Only execute integration test if PR is *not* a draft
+    # Only execute integration tests if PR is *not* a draft
     if: github.event.pull_request.draft == false
 
-    runs-on: ubuntu-latest
     strategy:
-      # Add a list of python versions we want to use for testing.
       matrix:
+        # Test all supported Python versions under Ubuntu
+        os: [ubuntu-latest]
         python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+        # Additionally, test one Python version under MacOS and Windows, to detect OS-specific issues
+        include:
+          - os: macos-latest
+            python-version: '3.12'
+          - os: windows-latest
+            python-version: '3.12'
+
+    runs-on: ${{ matrix.os }}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies
@@ -38,3 +50,27 @@ jobs:
         run: |
           python -m unittest python/snewpy/test/simplerate_integrationtest.py
           pytest -m 'snowglobes'
+      - name: Run Jupyter notebooks
+        if: ${{ ! startsWith(matrix.os, 'windows') }}
+        run: |
+          pip install nbclient ipykernel
+          jupyter kernelspec list
+          cd doc/nb/
+          jupyter execute ccsn/*.ipynb
+          jupyter execute presn/*.ipynb
+          jupyter execute AnalyticFluence.ipynb FlavorTransformation.ipynb
+          # Download necessary progenitor file, before executing the notebook
+          python -c 'from snewpy.models.ccsn import Zha_2021; from astropy import units as u; _ = Zha_2021(progenitor_mass=17*u.solMass)'
+          jupyter execute SNOwGLoBES_usage.ipynb
+      - name: Run Jupyter notebooks on Windows
+        if: ${{ startsWith(matrix.os, 'windows') }}
+        run: |
+          pip install nbclient ipykernel
+          jupyter kernelspec list
+          cd doc\nb\
+          jupyter execute (get-item ccsn\*.ipynb)
+          jupyter execute (get-item presn\*.ipynb)
+          jupyter execute AnalyticFluence.ipynb FlavorTransformation.ipynb
+          # Download necessary progenitor file, before executing the notebook
+          python -c 'from snewpy.models.ccsn import Zha_2021; from astropy import units as u; _ = Zha_2021(progenitor_mass=17*u.solMass)'
+          jupyter execute SNOwGLoBES_usage.ipynb
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index 450d44a4..1a870171 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -13,31 +13,35 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: '3.11'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         pip install setuptools wheel twine
+
+    - name: Check and get Version Number
+      id: get_version
+      run: |
+        pip install .
+        PYTHON_VERSION=`python -c 'import snewpy; print(snewpy.__version__)'`
+        echo "PYTHON_VERSION=${PYTHON_VERSION}"
+        GIT_VERSION=${GITHUB_REF/refs\/tags\/v/}
+        echo "GIT_VERSION=${GIT_VERSION}"
+        if [ $PYTHON_VERSION != $GIT_VERSION ]; then exit 1; fi
+        echo "VERSION=${PYTHON_VERSION}" >> $GITHUB_OUTPUT
+
     - name: Build and publish
       env:
         TWINE_USERNAME: __token__
         TWINE_PASSWORD: ${{ secrets.PYPI_JM }}
       run: |
-        python -c 'import python.snewpy; python.snewpy._get_model_urls()'
-        cat python/snewpy/_model_urls.py
-        pip install .
-        python -c 'import snewpy; snewpy.get_models(models="Bollig_2016")'
-        rm -r SNEWPY_models/
         python setup.py sdist bdist_wheel
         twine upload dist/*
 
-    - name: Get Version Number
-      id: get_version
-      run: echo ::set-output name=VERSION::${GITHUB_REF/refs\/tags\//}
     - name: Create Draft Release
       # Go to https://github.com/SNEWS2/snewpy/releases to edit this draft release and publish it
       # Once it is published, the release automatically is pushed to Zenodo: https://doi.org/10.5281/zenodo.4498940
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 64c8e3fa..dfbe0ad7 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -3,32 +3,44 @@
 name: tests
 
 # Controls when the action will run. Triggers the workflow on push or pull request
-# events but only for the master branch
+# events but only for the main branch
 on:
   push:
-    branches: [ main ]
+    branches: 
+      - main
+      - release_*
   pull_request:
-    branches: [ main ]
+    branches:
+      - main
+      - release_*
 
 # A workflow run is made up of one or more jobs that can run sequentially or in parallel
 jobs:
   # This workflow contains a single job called "build"
   build:
-    # The type of runner that the job will run on.
-    runs-on: ubuntu-latest
     strategy:
-      # Add a list of python versions we want to use for testing.
       matrix:
+        # Test all supported Python versions under Ubuntu
+        os: [ubuntu-latest]
         python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+        # Additionally, test one Python version under MacOS and Windows, to detect OS-specific issues
+        include:
+          - os: macos-latest
+            python-version: '3.12'
+          - os: windows-latest
+            python-version: '3.12'
+
+    # The type of runner that the job will run on.
+    runs-on: ${{ matrix.os }}
 
     # Steps represent a sequence of tasks that will be executed as part of the job
     steps:
       # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       # Set up the Python environment and dependencies
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies
@@ -36,9 +48,13 @@ jobs:
           python -m pip install --upgrade pip
       - name: Install SNEWPY
         run: |
-          pip install ".[dev]"
+          pip install ".[dev,docs]"
       
-      # Run the unit tests
-      - name: Test with pytest
+      - name: Run unit tests with pytest
         run: |
           pytest -m 'not snowglobes'
+
+      - name: Build HTML docs
+        run: |
+          cd doc/
+          make html
diff --git a/.gitignore b/.gitignore
index cb98acfe..74af6083 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,13 +9,15 @@ dist/
 # default directory for downloaded model files
 SNEWPY_models/
 
-# snewpy.snowglobes output files
+## SNEWPY output files
+# snewpy.snowglobes intermediate results
 *_SNOprocessed.tar.gz
 *.npy
 *.npz
-
 # used for integration tests in test_snowglobes.py
 fluence_Bollig_2016_s*
+# plots generated by FlavorTransformation.ipynb
+doc/nb/*_adiabaticmsw*.pdf
 
 # editor-specific
 .ipynb_checkpoints/
diff --git a/README.md b/README.md
index 2aad5c2a..e5df6af7 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # SNEWPY: Supernova Neutrino Early Warning Models for Python
 
-<img src="/doc/source/snewpy-logo.png" alt="snewpy logo: The word 'snewpy' in a monospace font, with an explosion emoji inside the letter 'p'." style="width: 300px; max-width: 100%;" />
+<img src="https://github.com/SNEWS2/snewpy/blob/v1.4/doc/source/snewpy-logo.png?raw=true" alt="snewpy logo: The word 'snewpy' in a monospace font, with an explosion emoji inside the letter 'p'." style="width: 300px; max-width: 100%;" />
 
 [![DOI](https://zenodo.org/badge/221705586.svg)](https://zenodo.org/badge/latestdoi/221705586)
 [![PyPI](https://img.shields.io/pypi/v/snewpy)](https://pypi.org/project/snewpy/)
@@ -53,9 +53,9 @@ flux = bollig.get_flux(distance=10*u.kpc, transformation=msw_nmo)
 
 You can also calculate the observed event rate in all neutrino detectors supported by SNOwGLoBES, use the included SN models and flavor transformations in event generators like sntools, and much more.
 
-Example scripts which show how SNEWPY can be used are available in the
-`python/snewpy/scripts/` subfolder as well as notebooks in `doc/nb/`.
-Most downloadable models also include a Jupyter notebook with simple usage examples.
+Jupyter notebooks showcasing the downloadable supernova models available through SNEWPY and much of its functionality are available in the `doc/nb/` subfolder.
+Additional example scripts are in the
+`python/snewpy/scripts/` subfolder.
 
 Papers describing SNEWPY and the underlying physics are published in the Astrophysical Journal ([DOI:10.3847/1538-4357/ac350f](https://dx.doi.org/10.3847/1538-4357/ac350f), [arXiv:2109.08188](https://arxiv.org/abs/2109.08188)) and the Journal of Open Source Software ([DOI:10.21105/joss.03772](https://dx.doi.org/10.21105/joss.03772)).
 
diff --git a/doc/Makefile b/doc/Makefile
index d7361384..557f89af 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -15,7 +15,7 @@ endif
 # Internal variables.
 PAPEROPT_a4     = -D latex_paper_size=a4
 PAPEROPT_letter = -D latex_paper_size=letter
-ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
+ALLSPHINXOPTS   = -W --keep-going -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
 # the i18n builder cannot share the environment and doctrees with the others
 I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
 
diff --git a/doc/nb/AnalyticFluence.ipynb b/doc/nb/AnalyticFluence.ipynb
index c21eec59..474b216f 100644
--- a/doc/nb/AnalyticFluence.ipynb
+++ b/doc/nb/AnalyticFluence.ipynb
@@ -25,23 +25,17 @@
     "import matplotlib as mpl\n",
     "import numpy as np\n",
     "\n",
-    "from snewpy import snowglobes\n",
+    "from snewpy import snowglobes, model_path\n",
     "from snewpy.neutrino import Flavor\n",
-    "from snewpy.models import Analytic3Species\n",
+    "from snewpy.models.ccsn import Analytic3Species\n",
     "\n",
     "mpl.rc('font', size=14)\n",
     "\n",
     "SNOwGLoBES_path = None  # change to SNOwGLoBES directory if using a custom detector configuration\n",
-    "SNEWPY_models_base = \"/path/to/snewpy/models/\"  # change to local directory where models are located\n",
-    "\n",
-    "model_folder = f\"{SNEWPY_models_base}/AnalyticFluence/\"\n",
-    "try:\n",
-    "    os.mkdir(model_folder)\n",
-    "except OSError as e:\n",
-    "    print(model_folder)\n",
-    "    print(f\"Folder {model_folder} already exists or path is wrong\")\n",
-    "else:\n",
-    "    print(f\"Created folder {model_folder}\")\n"
+    "\n",
+    "model_folder = f\"{model_path}/AnalyticFluence/\"\n",
+    "os.makedirs(model_folder, exist_ok=True)\n",
+    "print(f\"Using folder `{model_folder}`.\")"
    ]
   },
   {
@@ -97,7 +91,7 @@
     "    print(\"incorrect second moment method: rms or pinch\")\n",
     "\n",
     "filename = \"AnalyticFluence_demo.dat\"\n",
-    "table.write(model_folder+\"/\"+filename,format='ascii',overwrite=True)"
+    "table.write(model_folder + filename, format='ascii', overwrite=True)"
    ]
   },
   {
@@ -115,7 +109,7 @@
    "source": [
     "%matplotlib inline\n",
     "filename = \"AnalyticFluence_demo.dat\"\n",
-    "model = Analytic3Species(model_folder+\"/\"+filename)\n",
+    "model = Analytic3Species(model_folder + filename)\n",
     "flavors = [Flavor.NU_E,Flavor.NU_E_BAR,Flavor.NU_X]\n",
     "\n",
     "fig,axes = plt.subplots(1,3,figsize=(16,3))\n",
diff --git a/doc/nb/FlavorTransformation.ipynb b/doc/nb/FlavorTransformation.ipynb
index 78ff6118..a87dbe91 100644
--- a/doc/nb/FlavorTransformation.ipynb
+++ b/doc/nb/FlavorTransformation.ipynb
@@ -244,9 +244,11 @@
    "outputs": [],
    "source": [
     "fig = plot_total_flux(model, AdiabaticMSW(), AdiabaticMSW(mh=MassHierarchy.INVERTED) )\n",
-    "fig.savefig('flux_adiabaticmsw.pdf')\n",
+    "fig.show()\n",
+    "# fig.savefig('flux_adiabaticmsw.pdf')\n",
     "fig = plot_spectra(model, AdiabaticMSW(), AdiabaticMSW(mh=MassHierarchy.INVERTED), 100*u.ms)\n",
-    "fig.savefig('spectra_adiabaticmsw.pdf')"
+    "fig.show()\n",
+    "# fig.savefig('spectra_adiabaticmsw.pdf')"
    ]
   },
   {
@@ -264,9 +266,11 @@
     "xf_imo = AdiabaticMSWes(angles_imo)\n",
     "\n",
     "fig = plot_total_flux(model, xf_nmo, xf_imo)\n",
-    "fig.savefig('flux_adiabaticmswes.pdf')\n",
+    "fig.show()\n",
+    "# fig.savefig('flux_adiabaticmswes.pdf')\n",
     "fig = plot_spectra(model, xf_nmo, xf_imo, 100*u.ms)\n",
-    "fig.savefig('spectra_adiabaticmswes.pdf')"
+    "fig.show()\n",
+    "# fig.savefig('spectra_adiabaticmswes.pdf')"
    ]
   }
  ],
@@ -286,7 +290,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.3"
   },
   "vscode": {
    "interpreter": {
diff --git a/doc/nb/Fornax_2022.ipynb b/doc/nb/Fornax_2022.ipynb
deleted file mode 100644
index be905277..00000000
--- a/doc/nb/Fornax_2022.ipynb
+++ /dev/null
@@ -1,602 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1e8dbfcc",
-   "metadata": {},
-   "source": [
-    "# FORNAX 2D Neutrino Spectra\n",
-    "\n",
-    "Neutrino spectra from the 2D long-duraction models produced for 100 progenitors (1/3 form black holes).\n",
-    "\n",
-    "Data take from the [Princeton group webpage](https://www.astro.princeton.edu/~burrows/nu-emissions.2d.large/) and converted to HDF5 format for use in SNEWPY."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f3dcba05",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from snewpy.neutrino import Flavor\n",
-    "from snewpy.models.ccsn import Fornax_2022\n",
-    "\n",
-    "from astropy import units as u\n",
-    "from glob import glob\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "69e2e682",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mpl.rc('font', size=16)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2c2ac5d",
-   "metadata": {},
-   "source": [
-    "## Initialize Models\n",
-    "\n",
-    "To start, see what progenitors are avaialble for the `Fornax_2022` model. Use the `param` property to view all physics parameters and their possible values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7e311770",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'progenitor': ['9.0',\n",
-       "  '9.25',\n",
-       "  '9.5',\n",
-       "  '9.75',\n",
-       "  '10.0',\n",
-       "  '10.25',\n",
-       "  '10.5',\n",
-       "  '10.75',\n",
-       "  '11.0',\n",
-       "  '11.25',\n",
-       "  '11.5',\n",
-       "  '11.75',\n",
-       "  '12.00.bh',\n",
-       "  '12.03.bh',\n",
-       "  '12.07.bh',\n",
-       "  '12.1.bh',\n",
-       "  '12.13',\n",
-       "  '12.15',\n",
-       "  '12.18.bh',\n",
-       "  '12.20.bh',\n",
-       "  '12.25',\n",
-       "  '12.33.bh',\n",
-       "  '12.40.bh',\n",
-       "  '12.45.bh',\n",
-       "  '12.50.bh',\n",
-       "  '12.54.bh',\n",
-       "  '12.60.bh',\n",
-       "  '12.63',\n",
-       "  '12.70',\n",
-       "  '12.72.bh',\n",
-       "  '12.75',\n",
-       "  '12.80.bh',\n",
-       "  '12.85.bh',\n",
-       "  '12.90.bh',\n",
-       "  '12.93',\n",
-       "  '12.97.bh',\n",
-       "  '13.00.bh',\n",
-       "  '13.05.bh',\n",
-       "  '13.11',\n",
-       "  '13.25.bh',\n",
-       "  '13.27.bh',\n",
-       "  '13.32.bh',\n",
-       "  '13.40.bh',\n",
-       "  '13.45',\n",
-       "  '13.50.bh',\n",
-       "  '13.60.bh',\n",
-       "  '13.75',\n",
-       "  '13.82.bh',\n",
-       "  '13.90.bh',\n",
-       "  '13.96',\n",
-       "  '14.01',\n",
-       "  '14.13.bh',\n",
-       "  '14.25.bh',\n",
-       "  '14.40.bh',\n",
-       "  '14.41.bh',\n",
-       "  '14.43',\n",
-       "  '14.44.bh',\n",
-       "  '14.70.bh',\n",
-       "  '14.87.bh',\n",
-       "  '15.00.bh',\n",
-       "  '15.01',\n",
-       "  '15.04.bh',\n",
-       "  '15.05',\n",
-       "  '15.38.bh',\n",
-       "  '16.43',\n",
-       "  '16.65',\n",
-       "  '16.99',\n",
-       "  '17.00',\n",
-       "  '17.07',\n",
-       "  '17.10',\n",
-       "  '17.40',\n",
-       "  '17.48',\n",
-       "  '17.50',\n",
-       "  '17.51',\n",
-       "  '17.83',\n",
-       "  '18.04',\n",
-       "  '18.05',\n",
-       "  '18.09',\n",
-       "  '18.10',\n",
-       "  '18.50',\n",
-       "  '19.02',\n",
-       "  '19.56',\n",
-       "  '19.83',\n",
-       "  '19.99',\n",
-       "  '20.08',\n",
-       "  '20.09',\n",
-       "  '20.18',\n",
-       "  '20.37',\n",
-       "  '21.00',\n",
-       "  '21.68',\n",
-       "  '22.00',\n",
-       "  '22.30',\n",
-       "  '22.82',\n",
-       "  '23.00',\n",
-       "  '23.04',\n",
-       "  '23.43',\n",
-       "  '24.00',\n",
-       "  '25.00',\n",
-       "  '26.00',\n",
-       "  '26.99'],\n",
-       " 'progenitor_mass': <Quantity [ 9.  ,  9.25,  9.5 ,  9.75, 10.  , 10.25, 10.5 , 10.75, 11.  ,\n",
-       "            11.25, 11.5 , 11.75, 12.  , 12.03, 12.07, 12.1 , 12.13, 12.15,\n",
-       "            12.18, 12.2 , 12.25, 12.33, 12.4 , 12.45, 12.5 , 12.54, 12.6 ,\n",
-       "            12.63, 12.7 , 12.72, 12.75, 12.8 , 12.85, 12.9 , 12.93, 12.97,\n",
-       "            13.  , 13.05, 13.11, 13.25, 13.27, 13.32, 13.4 , 13.45, 13.5 ,\n",
-       "            13.6 , 13.75, 13.82, 13.9 , 13.96, 14.01, 14.13, 14.25, 14.4 ,\n",
-       "            14.41, 14.43, 14.44, 14.7 , 14.87, 15.  , 15.01, 15.04, 15.05,\n",
-       "            15.38, 16.43, 16.65, 16.99, 17.  , 17.07, 17.1 , 17.4 , 17.48,\n",
-       "            17.5 , 17.51, 17.83, 18.04, 18.05, 18.09, 18.1 , 18.5 , 19.02,\n",
-       "            19.56, 19.83, 19.99, 20.08, 20.09, 20.18, 20.37, 21.  , 21.68,\n",
-       "            22.  , 22.3 , 22.82, 23.  , 23.04, 23.43, 24.  , 25.  , 26.  ,\n",
-       "            26.99] solMass>}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Fornax_2022.param"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e5f0052",
-   "metadata": {},
-   "source": [
-    "Initialize some progenitors and plot the luminosity of different neutrino flavors for two of them. Note that the `Fornax_2022` set of models do not distinguish between $\\nu_x$ and $\\bar{\\nu}_x$ so both have the same luminosity. If this is the first time you are using a progenitor model, `snewpy` will download the data files for you."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d0750b09",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "9.0\n",
-      "12.20.bh\n",
-      "13.11\n",
-      "14.70.bh\n",
-      "18.05\n",
-      "23.43\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'9.0': Fornax_2022 Model\n",
-       " Progenitor       : 9.0\n",
-       " Progenitor mass  : 9.0 solMass\n",
-       " PNS mass         : 1.35 solMass,\n",
-       " '12.20.bh': Fornax_2022 Model\n",
-       " Progenitor       : 12.20.bh\n",
-       " Progenitor mass  : 12.2 solMass\n",
-       " PNS mass         : 1.69 solMass,\n",
-       " '13.11': Fornax_2022 Model\n",
-       " Progenitor       : 13.11\n",
-       " Progenitor mass  : 13.11 solMass\n",
-       " PNS mass         : 1.78 solMass,\n",
-       " '14.70.bh': Fornax_2022 Model\n",
-       " Progenitor       : 14.70.bh\n",
-       " Progenitor mass  : 14.7 solMass\n",
-       " PNS mass         : 1.89 solMass,\n",
-       " '18.05': Fornax_2022 Model\n",
-       " Progenitor       : 18.05\n",
-       " Progenitor mass  : 18.05 solMass\n",
-       " PNS mass         : 1.84 solMass,\n",
-       " '23.43': Fornax_2022 Model\n",
-       " Progenitor       : 23.43\n",
-       " Progenitor mass  : 23.43 solMass\n",
-       " PNS mass         : 1.98 solMass}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models = {}\n",
-    "for m in Fornax_2022.param['progenitor'][::19]:\n",
-    "    # Initialize every 20th progenitor.\n",
-    "    print(m)\n",
-    "    models[m] = Fornax_2022(progenitor=m)\n",
-    "    \n",
-    "models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5d7c2a2c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Progenitor': '23.43',\n",
-       " 'Progenitor mass': <Quantity 23.43 solMass>,\n",
-       " 'PNS mass': <Quantity 1.98 solMass>}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models['23.43'].metadata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "7ded8b91",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1500 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(3, 2, figsize=(12, 15), sharex=True, sharey=True, tight_layout=True)\n",
-    "axes = axes.flatten()\n",
-    "\n",
-    "for i, (key, model) in enumerate(models.items()):\n",
-    "    ax = axes[i]\n",
-    "    for flavor in Flavor:\n",
-    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
-    "                label=flavor.to_tex(),\n",
-    "                color='C0' if flavor.is_electron else 'C1',\n",
-    "                ls='-' if flavor.is_neutrino else ':',\n",
-    "                lw=2)\n",
-    "        \n",
-    "    modtitle = '{} $M_\\odot$'.format(model.metadata['Progenitor mass'].value)\n",
-    "    if 'bh' in model.metadata['Progenitor']:\n",
-    "        modtitle += ' (BH)'\n",
-    "        \n",
-    "    ax.set(xlim=(-0.05, 1.5),\n",
-    "           xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
-    "           title=modtitle)\n",
-    "    ax.grid()\n",
-    "    ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
-    "\n",
-    "axes[0].set(ylabel=r'luminosity [foe s$^{-1}$]');\n",
-    "# axes[5].set_axis_off();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78d70089",
-   "metadata": {},
-   "source": [
-    "## Spectra of All Flavors vs. Time for the 19.02 $M_\\odot$ Model\n",
-    "\n",
-    "### Use Default Linear Interpolation in Flux Retrieval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4a301327",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x1200 with 20 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model = models['23.43']\n",
-    "\n",
-    "times = np.arange(-0.2, 3.8, 0.2) * u.s\n",
-    "E = np.arange(0, 101, 1) * u.MeV\n",
-    "\n",
-    "fig, axes = plt.subplots(5,4, figsize=(15,12), sharex=True, sharey=True, tight_layout=True)\n",
-    "\n",
-    "linestyles = ['-', '--', '-.', ':']\n",
-    "\n",
-    "for t, ax in zip(times, axes.flatten()):\n",
-    "    spectra = model.get_initial_spectra(t, E)\n",
-    "    for line, flavor in zip(linestyles, Flavor):\n",
-    "        ax.plot(E, spectra[flavor], lw=3, ls=line, label=flavor.to_tex())\n",
-    "    ax.set(xlim=(0,100))\n",
-    "    ax.set_title('$t$ = {:g}'.format(t), fontsize=16)\n",
-    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
-    "\n",
-    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
-    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "27ef0892",
-   "metadata": {},
-   "source": [
-    "### Use Nearest-Bin \"Interpolation\" in Flux Retrieval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "3cea3d5b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x1200 with 20 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "times = np.arange(-0.2, 3.8, 0.2) * u.s\n",
-    "E = np.arange(0, 101, 1) * u.MeV\n",
-    "\n",
-    "fig, axes = plt.subplots(5,4, figsize=(15,12), sharex=True, sharey=True, tight_layout=True)\n",
-    "\n",
-    "linestyles = ['-', '--', '-.', ':']\n",
-    "\n",
-    "for t, ax in zip(times, axes.flatten()):\n",
-    "    spectra = model.get_initial_spectra(t, E, interpolation='nearest')\n",
-    "    for line, flavor in zip(linestyles, Flavor):\n",
-    "        ax.plot(E, spectra[flavor], lw=3, ls=line, label=flavor.to_tex())\n",
-    "    ax.set(xlim=(0,100))\n",
-    "    ax.set_title('$t$ = {:g}'.format(t), fontsize=16)\n",
-    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
-    "\n",
-    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
-    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c9dc1934",
-   "metadata": {},
-   "source": [
-    "## Progenitor Mass Dependence\n",
-    "\n",
-    "### Luminosity vs. Time for a Selected List of Progenitor Masses\n",
-    "\n",
-    "Plot $L_{\\nu_e}(t)$ for a selection of progenitor masses to observe the dependence of the emission on mass."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ab022bc4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1300 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(3,1, figsize=(10,13), sharex=True, sharey=True,\n",
-    "                         gridspec_kw = {'hspace':0.02})\n",
-    "\n",
-    "colors0 = mpl.cm.viridis(np.linspace(0.1,0.9, len(models)))\n",
-    "colors1 = mpl.cm.inferno(np.linspace(0.1,0.9, len(models)))\n",
-    "colors2 = mpl.cm.cividis(np.linspace(0.1,0.9, len(models)))\n",
-    "\n",
-    "linestyles = ['-', '--', '-.', ':']\n",
-    "\n",
-    "for i, model in enumerate(models.values()):\n",
-    "    ax = axes[0]\n",
-    "    flavor = Flavor.NU_E\n",
-    "    ax.plot(model.time, model.luminosity[flavor], lw=2, color=colors0[i], ls=linestyles[i%4],\n",
-    "            label='${0.value:g}$ {0.unit:latex}{1}'.format(model.progenitor_mass, ' (BH)' if 'bh' in model.progenitor else ''))\n",
-    "    ax.set(xscale='log',\n",
-    "           xlim=(1e-3, 4),\n",
-    "           yscale='log',\n",
-    "           ylim=(0.4e52, 9e53),\n",
-    "           ylabel=r'$L_{\\nu_e}(t)$ [erg s$^{-1}$]')\n",
-    "    ax.grid(ls=':', which='both')\n",
-    "    ax.legend(ncol=3, fontsize=12, title=r'$\\nu_e$');\n",
-    "    \n",
-    "    ax = axes[1]\n",
-    "    flavor = Flavor.NU_E_BAR\n",
-    "    ax.plot(model.time, model.luminosity[flavor], lw=2, color=colors1[i], ls=linestyles[i%4],\n",
-    "        label='${0.value:g}$ {0.unit:latex}{1}'.format(model.progenitor_mass, ' (BH)' if 'bh' in model.progenitor else ''))\n",
-    "    ax.set(ylabel=r'$L_{\\bar{\\nu}_e}(t)$ [erg s$^{-1}$]')\n",
-    "    ax.grid(ls=':', which='both')\n",
-    "    ax.legend(ncol=3, fontsize=12, title=r'$\\bar{\\nu}_e$');\n",
-    "    \n",
-    "    ax = axes[2]\n",
-    "    flavor = Flavor.NU_X\n",
-    "    ax.plot(model.time, model.luminosity[flavor], lw=2, color=colors2[i], ls=linestyles[i%4],\n",
-    "        label='${0.value:g}$ {0.unit:latex}{1}'.format(model.progenitor_mass, ' (BH)' if 'bh' in model.progenitor else ''))\n",
-    "    ax.set(xlabel='time [s]',\n",
-    "           ylabel=r'$L_{\\nu_X}(t)$ [erg s$^{-1}$]')\n",
-    "    ax.grid(ls=':', which='both')\n",
-    "    ax.legend(ncol=3, fontsize=12, title=r'$\\nu_X$');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89a17c23",
-   "metadata": {},
-   "source": [
-    "## Progenitor Dependence of Spectra at 70 ms\n",
-    "\n",
-    "### Use Default Linear Interpolation in Flux Retrieval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0f0eba54",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "t = 70*u.ms\n",
-    "E = np.arange(0, 101, 1) * u.MeV\n",
-    "\n",
-    "fig, axes = plt.subplots(2,3, figsize=(12,6), sharex=True, sharey=True, tight_layout=True)\n",
-    "\n",
-    "linestyles = ['-', '--', '-.', ':']\n",
-    "\n",
-    "for model, ax in zip(models.values(), axes.flatten()):\n",
-    "    spectra = model.get_initial_spectra(t, E)\n",
-    "    for line, flavor in zip(linestyles, Flavor):\n",
-    "        ax.plot(E, spectra[flavor], lw=3, ls=line, label=flavor.to_tex())\n",
-    "    ax.set(xlim=(0,100))\n",
-    "    ax.set_title('${0.value:g}$ {0.unit:latex}{1}'.format(model.progenitor_mass, ' (BH)' if 'bh' in model.progenitor else ''))\n",
-    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
-    "    ax.grid(ls=':')\n",
-    "\n",
-    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
-    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "456450c5",
-   "metadata": {},
-   "source": [
-    "### Use Nearest-Bin \"Interpolation\" in Flux Retrieval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "52dca852",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "t = 70*u.ms\n",
-    "E = np.arange(0, 101, 1) * u.MeV\n",
-    "\n",
-    "fig, axes = plt.subplots(2,3, figsize=(12,6), sharex=True, sharey=True, tight_layout=True)\n",
-    "\n",
-    "linestyles = ['-', '--', '-.', ':']\n",
-    "\n",
-    "for model, ax in zip(models.values(), axes.flatten()):\n",
-    "    spectra = model.get_initial_spectra(t, E, interpolation='nearest')\n",
-    "    for line, flavor in zip(linestyles, Flavor):\n",
-    "        ax.plot(E, spectra[flavor], lw=3, ls=line, label=flavor.to_tex())\n",
-    "    ax.set(xlim=(0,100))\n",
-    "    ax.set_title('${0.value:g}$ {0.unit:latex}{1}'.format(model.progenitor_mass, ' (BH)' if 'bh' in model.progenitor else ''))\n",
-    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
-    "    ax.grid(ls=':')\n",
-    "\n",
-    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
-    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/doc/nb/Mori_2023.ipynb b/doc/nb/Mori_2023.ipynb
deleted file mode 100644
index 1a9ef13d..00000000
--- a/doc/nb/Mori_2023.ipynb
+++ /dev/null
@@ -1,338 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "04bf2ca6",
-   "metadata": {},
-   "source": [
-    "# Mori 2023 2D Models with Axionlike Production\n",
-    "\n",
-    "Neutrino spectra from a set of 2D simulations with axionlike particle production. The models are described in the paper [Multi-messenger signals of heavy axionlike particles in core-collapse supernovae: two-dimensional simulations](https://arxiv.org/abs/2304.11360) by K. Mori, T. Takiwaki, K. Kotake and S. Horiuchi, Phys. Rev. D 108:063027, 2023."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "29ceae1a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from snewpy.neutrino import Flavor\n",
-    "from snewpy.models.ccsn import Mori_2023\n",
-    "\n",
-    "from astropy import units as u\n",
-    "from glob import glob\n",
-    "\n",
-    "from scipy.interpolate import PchipInterpolator\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "1b7566ed",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mpl.rc('font', size=16)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66d13225",
-   "metadata": {},
-   "source": [
-    "## Initialize the 2D models\n",
-    "\n",
-    "Use the `param` property of the model class to see the available parameters. Models are initialized using the `axion_mass` and `axion_coupling` parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f62e690f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'axion_mass': [0, 100, 200],\n",
-       " 'axion_coupling': [0, 2, 4, 6, 8, 10, 12, 14, 16, 20]}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mori_2023.param"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55479166",
-   "metadata": {},
-   "source": [
-    "The model with `axion_mass=0` and `axion_coupling=0` is a standard simulation with no ALP production."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "649f5ae4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Axion mass': <Quantity 0. MeV>,\n",
-       " 'Axion coupling': <Quantity 0. 1 / GeV>,\n",
-       " 'Progenitor mass': <Quantity 20. solMass>,\n",
-       " 'PNS mass': <Quantity 1.78 solMass>}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_std = Mori_2023(axion_mass=0, axion_coupling=0)\n",
-    "model_std.metadata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "da03c721",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Catalog of axion models available.\n",
-    "axion_mass_coupling = [(100,2), (100,4), (100,10), (100,12), (100,14), (100,16), (100,20),\n",
-    "                       (200,2), (200,4), (200,6), (200,8), (200,10), (200,20)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6f15de7a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{(100,\n",
-       "  2): Mori_2023 Model: t-prof_100_2.dat\n",
-       " Axion mass       : 100.0 MeV\n",
-       " Axion coupling   : 2e-10 1 / GeV\n",
-       " Progenitor mass  : 20.0 solMass\n",
-       " PNS mass         : 1.77 solMass,\n",
-       " (200,\n",
-       "  2): Mori_2023 Model: t-prof_200_2.dat\n",
-       " Axion mass       : 200.0 MeV\n",
-       " Axion coupling   : 2e-10 1 / GeV\n",
-       " Progenitor mass  : 20.0 solMass\n",
-       " PNS mass         : 1.77 solMass,\n",
-       " (100,\n",
-       "  10): Mori_2023 Model: t-prof_100_10.dat\n",
-       " Axion mass       : 100.0 MeV\n",
-       " Axion coupling   : 1e-09 1 / GeV\n",
-       " Progenitor mass  : 20.0 solMass\n",
-       " PNS mass         : 1.77 solMass,\n",
-       " (100,\n",
-       "  20): Mori_2023 Model: t-prof_100_20.dat\n",
-       " Axion mass       : 100.0 MeV\n",
-       " Axion coupling   : 2e-09 1 / GeV\n",
-       " Progenitor mass  : 20.0 solMass\n",
-       " PNS mass         : 1.74 solMass,\n",
-       " (200,\n",
-       "  10): Mori_2023 Model: t-prof_200_10.dat\n",
-       " Axion mass       : 200.0 MeV\n",
-       " Axion coupling   : 1e-09 1 / GeV\n",
-       " Progenitor mass  : 20.0 solMass\n",
-       " PNS mass         : 1.73 solMass,\n",
-       " (200,\n",
-       "  20): Mori_2023 Model: t-prof_200_20.dat\n",
-       " Axion mass       : 200.0 MeV\n",
-       " Axion coupling   : 2e-09 1 / GeV\n",
-       " Progenitor mass  : 20.0 solMass\n",
-       " PNS mass         : 1.62 solMass}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Initialize a handful of axion models.\n",
-    "models = {}\n",
-    "for (am, ac) in ((100,2), (200,2), (100,10), (100,20), (200,10), (200,20)):\n",
-    "    models[(am,ac)] = Mori_2023(axion_mass=am, axion_coupling=ac)\n",
-    "\n",
-    "models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3cab5a9c",
-   "metadata": {},
-   "source": [
-    "## Plot Model Luminosities\n",
-    "\n",
-    "Compare axion model luminosity to the standard 2D simulation.\n",
-    "\n",
-    "Higher mass models with stronger coupling constants should produce a decrease in neutrino luminosity at all flavors relative to the reference simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8a398333",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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l77//Xr1799aoUaOsddu3b6+wsLAMWdRz585VcnKyTfa0s64BAAA8DONhGA/DAADegPEaxmsYrwEAeBPGaxivYbzGQQZeJTY21kgysbGxru4KXOzBfy801YYttr5+PxLj6i4VuISEBLNnzx6TkJDg6q54vO7du5vKlSubpKQkm+3Tp083kszixYuderyYmBizYsUKh16Ovr979+41wcHB5pZbbjEpKSkOtYmNjTXNmjUzpUqVMiVKlDC33367iY+Pz9G5bNmyxUgy06ZNs1tes2ZN06lTpwzbDx48aCSZDz/8MEfHc7Zjx44ZSaZv374Zyho3bmwkWV/jxo0zxhiTlpZmQkNDzeDBg010dLTNa9q0aUaSWbdunXU/L774opFkDh06ZN12++23m/Llyzv8XuVVdp8XfL+6BtcduIa4xnl+//134+vra4YPH26zfdCgQUaSGTNmjFOPt3r1apvvy6xe138XZmfmzJlGkpk6dWq2dRMSEky7du1MiRIlzMaNG83KlSuNv7+/uf/++01ycrLDx8wuromIiDDt27fPsH337t1Gkvnss88cPpYxxowcOTLDNbr+2GfOnDGdOnUygYGBpk6dOmbFihVZ7i/9e+XBBx/MUHb+/HmbmOX6mG/QoEEmLCzMnD171qZN7969TUhIiLVuSkqKCQsLM7fccotNvc8++8xIMsuWLcvR+RuT/TWwh7jGPXHdgWuIa5yH8RrGaxiv4fvVFbjuwDXENc7DeA3jNYzXOP796pfb5CAA7s3cMHFPUorjWabA9RITE/Xjjz9qyJAhKlKkiE3ZpUuXJMnpGdQlS5bUXXfd5bT9nT59Wp07d1ZISIh1WkJHBAcH6/3331f79u0lSR999FGOs3mzExgYqMTExAzbr1y5Yi13paCgIEnX3uvrff7557p48aL+/vtv9e3b17o9OjpaFy5c0OTJkzV58mS7+71+WsTIyEh9+OGHmjVrll577TUdP35ca9eu1XPPPefwewUAgKPeffddhYWF6V//+pfN9ttuu01Tp051elzTuHFjrVixwqG6jk6zvG/fPg0dOlS33HKL+vfvn239gIAALVy4UHfeeafuu+8+JSUlqVWrVvrf//4nPz/nDQskJCTI39/f7vHTy3Ni1KhRGjVqVKblZcuW1ZIlSxzeX/qa8PbWgb/jjju0Y8cO6+/jxo3Tyy+/LGOM5s+fr549e8oYo7Nnz1rr3HvvvZozZ462bt2qW2+9Vb6+vurdu7c+/PBDHT58WNWrV5d0dern8uXLq0OHDg73NV121wAAUHgxXsN4jcR4DQDAezBew3gN4zWOI8kH8FJGtlk+iST5IJcOHjyo+Ph4u+ud/vXXXwoODlbVqlWdesykpCTFxMQ4VLds2bJZDizExsaqU6dOunDhgtauXauKFSs63I+//vpLkZGRqlevnhISEtS9e3f9+uuvdqdrzq2wsDCbtUnTnTp1SpJy1N/8EBISorCwMO3atStDWfq0iYcPH7bZnj51Zd++fTMNZBs1amT9uXnz5qpXr55mz56t1157TbNnz5YxJldTPwMAkBUehvEwTOJhGADAOzBew3gN4zUAAG/BeA3jNRLjNTlBkg/grW6YyScxuQDXAYRXSc/g9fHxsdmemJio2bNn26z9ffToUQ0ePFi///67kpOT9cgjj2jSpEmyWCy6ePGiypcvr48++kiDBw+WdDU7Nzw8XGvXrlWdOnWs+1m/fr3uvPNOh/p36NAha9btja5cuaIHHnhAUVFRWrlyperXr+/weZ86dUp33323ihQpohUrVighIUFt27bVPffco19++SXLddFzokmTJlq9erXi4uJs1kPdtGmTtdzVOnfurClTpmjz5s3WNVqzUrZsWQUFBSk1NdXhIDkyMlIjRozQzp07NWvWLNWpU0ctWrTIa9cBALDBwzAehvEwDADgLRivYbyG8RoAgLdgvIbxGsZrcsYn+yoAPNENOT5KTCHJB7lTrVo1SdK6detsto8dO1anTp2yGTS6ePGiXn/9dZ08eVJ//PGHFi1apJ9//lnS1UzcmTNnatasWdb677//vrp27WozYCRdmybRkVdm0ySmpqaqV69e2rBhg+bOnatbbrnF4XM+f/687r33Xl26dEkrV65U5cqVVadOHS1btkzHjh1T586ddfnyZYf3l5WHH35YqampNpnGiYmJmjZtmlq1aqUqVao45Th58corr6hYsWJ67LHH9Pfff2coNzesD+jr66vu3btr/vz5doOy6OjoDNvSA6k333xT27dv99jACgDg3nL6MKxjx44qW7asQkNDNWTIEOt33sWLF1WsWDGb7++4uDhVqFBB+/fvt9n3+vXrFRYW5tDr2LFjmfb9+odhixcvztPDsBUrVujy5cu65557HB7QckSTJk0UFRVlnWY5nbs9DDtw4IA2b97sUP0bH4bZe5UrV86mTWRkpHbt2sXDMABAvmK8hvEaxmsAAN6C8RrGaxivySEDY4wxSUlJZuXKlebll182N998swkJCTF+fn6mfPny5oEHHjCLFy+2227kyJFGV/MpMn3t3bs30+Pu37/f9O/f31SqVMkULVrUVKpUyfTv398cPHgwV+cRGxtrJJnY2NhctYf3uH/M96basMXW1+yNh1zdpQKXkJBg9uzZYxISElzdFY/XsWNH4+PjY5555hkzefJk07NnT1OrVi0jyfznP//JtF23bt3M/Pnzrb8fP37cVKxY0RhjzJkzZ0yZMmXM8ePH86XPzz//vJFkHnjgATNz5swMr6yMHj3ahIaGmu3bt2coW7t2rQkMDDSffPJJlvv4z3/+Y95++20zZMgQI8k89NBD5u233zZvv/22uXDhgk3dHj16GD8/P/PPf/7TfP7556ZNmzbGz8/P/PLLLzk/8Xzy3XffmcDAQBMSEmKefvpp8/nnn5vPPvvMDBs2zFSpUsX4+PiY2bNnW+ufPn3aVKtWzRQrVsw8//zz5vPPPzdjxowxPXr0MCVLlrR7jDZt2li/O/fv319Qp2aMyf7zwtO+X4lrAO9DXOMc0dHRRpJ56qmnbLaPGjXKSDKDBw+2btu1a5dZs2aNSUpKMkePHjWVKlUyP/30k7V83rx5pl27dtbfR4wYYdM+XUxMjFmxYoVDr8ze35SUFNOlSxfj5+dnfvjhhxydc0xMjGnYsKEpV66ciYqKsm7ftm2bCQkJMa1btzaXLl1yaF9btmwxksy0adPslm/cuNFIMuPGjbNuu3Lliqldu7Zp1apVjvqdX6KiokyxYsVMRESEOX36dIbyv/76K8M5DBgwwBQtWtT88ccfGeqfOXMm03107drVSDKjRo1y7klkw9viGmO8I7bxxOsO5BfiGudhvOYaxmsYr/EUxDWAdyGucQ7GaxivYbwmZ9+vJPn8vxUrVlgDoQoVKpjOnTubnj17mgYNGli3Dx482KSlpdm0Sw+sGjdubPr372/3dfLkSbvHXLdunSlWrJiRZCIiIkyvXr1MRESEkWSKFy9uNmzYkOPzILhCuhuTfKas3ufqLhU4givn+fvvv82DDz5ogoODTVhYmHn++efNt99+aySZjRs3Wut9+eWXpkWLFqZUqVImJCTE+Pj42Hy5pqWlmcDAQHPp0iXz/PPPm3/84x/51ud27dpl+R+9WUlOTja7du3KtHzHjh0Zvg9uVK1atUyPfejQIZu6CQkJ5uWXXzYVKlQw/v7+pkWLFmbp0qUOn2tBOXDggBkyZIipXbu2CQgIMIGBgaZevXrmqaeesjvA9vfff5uhQ4eaKlWqmCJFipgKFSqYDh06mMmTJ9vd/yeffGIkmZYtW+b3qWTgbYNGxDWA9yGucR4ehl3DwzAehnkKb4htPPG6A/mFuMZ5GK+xxXgN4zWegLgG8C7ENc7DeM01jNcwXpMdknz+36pVq0z37t3NmjVrMpTNmTPH+Pr6GklmxowZNmXpgdXIkSNzdLzLly+bihUrGklm+PDhNmXDhw83kkyVKlVMfHx8jvZLcIV0ncd8Z5Pk8+GSHa7uUoEjuMpfQ4cONWXKlDHJycnGGGOWLl1q6tWrZ3bs2GFSUlLMqVOnTPHixa3l6W666Sbz/fffm7Jly5qzZ8+6outABt42aERcA3gf4hrn4WGYLR6G8TDME3hDbOOJ1x3IL8Q1+YvxGngT4ppriGsA90Rc4zyM19hivIbxmqyQ5OOgQYMGGUmmQ4cONttzG1il/wMKDw83qampNmWpqakmPDzcSDKfffZZjvZLcIV0971rm+TzzoItru5SgSO4co4rV65kCCR++eUX4+fnZ9544w3rtn//+9+mY8eOJjk52Rw5csR06NDBNG3aNMP+OnfubCpXrlzg0+ABWfHGQaOsENcAnoe4Jn/xMAzepLDFNcZ4RmzjjdcdyC3iGudgvAaFAXHNNcQ1gHsirslfjNfAmzgzrvERHNK0aVNJ0rFjx5yyv2+//VaS1Lt3b/n42L4NPj4+6tWrlyRpwYIFTjkeCh9zw+/xiUku6Qc837Jly9SiRQuNHTtWn332mQYPHqwOHTqoadOmeu2116z1IiMjde7cOZUsWVIDBw5URESEGjdunGF/tWrV0pUrV/TSSy8V5GkAuA5xDYDCKjExUcbYRspr1qzR559/rqeeekp+fn6SpJ07d6p69eqqX7++Tpw4ob59+yo8PNxanq5mzZoaOnSohg4dqtKlSxfYeQCwRWwDoDBivAbwTsQ1AAojxmuAnPHLvgokaf/+/ZKksLAwu+Vbt27Vq6++qpiYGIWEhKhp06Z64IEHFBQUZLf+tm3bJEk333yz3fL07en1gJy68cswPjHFRT2BpytVqpSKFi2qsWPHKj4+XtWrV9ewYcM0fPhwBQYGWutVrFhRmzdvznZ/xYsX14ABAzL9fASQ/4hrABRWy5Yt01tvvaWHH35YoaGh2rp1q6ZNm2b3YdjcuXNVsmRJtWzZUg0aNFBcXFyG/dWqVUubNm3iYRjgYsQ2AAojxmsA70RcA6A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1L0lXvyjbka16/5ztmjhxopYuXaqJEydq7df/0pdXR8gx+J+K7H+HZG7YpckB4BjW9qkjXbt21euvv67k5GRlZ2fr0KFDmj17tvr27asDBw7o0ksv1YIFC1zH5+TkuLZDQipPfIaGhkqqfsL0wgsvKCIiwvV1bEKG00dOQdnyA2GB/KLEVx0L+aTNfFU5a2fpyIxJ2rN9s/q2Luvcs+5goevNrdoyGAzV7k/el6VZjq6Kv/lVWePbauPGjXI6nRo7dqzee++9k3pOAL6LeQ382aFDh7RqVWm4hFbkNRdoMbkCPpLUqVmE2sSFKTas4i+0joV8HAW5rrEdew/q3NaNZDEZZLQXy2krXXL2yJEjta5l/fr1JzymfMhn//79tX4OAP6DeQ1OR1m/vKi8Sa0UueKV0oCPJIdTOpx4sSzjdij2jmkynGrA53gGg6xtBqr5A7Olu3/XrpDuKrKX7ooy5Crsl/uV+nSiUn/5l2Qrqv5aAIBKMa/B6Sh38XvKeS5JEXMe0sr//Vu5ubnq2rWr5s2bp2mzFin+7m9lqIOAjxuDQea4trrwwgu1ePFiTZ8+XYP79tTbF1oUbixU5MJ/6pl7rtamTZvq9nkBoIYI+dSRsWPH6r777lPHjh0VFhamuLg4nX/++Vq8eLEuu+wylZSU1KoVfU09/vjjysrKcn3t2bOnzp8D3i2v2O7aDgusftkCeK9jIR9b1iGl//KW8rcs1e7duxUbFqBWsaX/ALPGtZLD4ZDT6azuUpU6UcjnmR82uLYj+9+gjRs3atasWXrttdd055131vr5APg25jXwZ7d/tETRF4xRx+HXKy6usafL8UvHQj7Zy7/R4enP6eCXj+nw3l1qFBqgT/9+ltpv+lRZv30hqfSX1LVlNp842F4+PLRjxw4NHTpUBoNBF1100UnNpQD4LuY1OK2UFCr1zfMV8fsLCjGWLqnpcEqHG/WR4Z7fFff3KVJwdL2XYWpyppIeWSDr2LU6EtbRNR5jylPM7xOV+XRzHZ4+TirKreYqAIDjMa/B6WblihWa9Z/xCjMWSpJeuChG3333ndasWaPBgwc3SA0Gg0FXXHGF5s2epaBW50iSFu6y6en3v9eZZ56pkSNHavXq1Q1SCwAcQ8innhkMBj3zzDOSpHXr1rkmP2FhZetQ5+XlVXrusXZz4eFVr4kdEBCg8PBwty+cXvJtpW9SGJ02Wc28pH1VZGSka3vQoEGSpN27d0uSEqJKPzVvMFtkDAxT48aNa/QPKZvNdsJjjjmUXe5TdAaTSkpK9O2337qGSkpKanwtAP6LeQ18XW6RTX9mmhXWdbjU7XKdIAOLk3Qs5FO0d4MKti1T0d4N2vxXaaC4b+sY2YryXcceOHBAhYWFtbr+iUI+RTa7dheHKqTTUAW1Pktr167VvHnzJJUu1UZreQAS8xr4oYIMpb3cWzFpy11Dh5oMlvEfKxQ35hcZGp/Z4CUZolsq9sElct70ndKiyj5hH2kuUtyfk3Xk2TZKXfV9g9cFAP6GeQ380Y8//qjBQ4bo6i9T9dZqgw5G9lKvJ2bp0ksvPeGHmutFSIxix8yWbp2jmFGTddlll8npdGratGnq2bOnPr69uw4s/KTh6wJwWiIR0AA6dOjg2t67d68kqUWLFq6xlJSUSs87NhFLSkqqv+Lg8wrtpS9ji+wnOBLezGg0asaMGfr000914YUXSpImTZqkjIwMNS63DIYpNFpHjhzRb7/9dsJrFhQUuLZPNOnNLSoLBDmLSv/BV36ZrszMzBrdBwD/x7wGvmzjvkw57KVzpnNahHvml0KngWMhH0myWEo7Tc6ePds1dvwvlzds2KD84pqHk6sK+RzOKdTf3lysdv+cpSNnjlTMxWMV0feaiseVW8oLwOmNeQ38hTNthzL+1VONikr/zNocUtagSWp817dSbFvPFmcwyNBqkBrdv0C6bZ7Sm/SX42hTvVhLgaK+u1GbJg1QwY5lEt32AOCkMa+B33A69fnnn+vKK69Ubm6uOnfpootfX6Mm98+RoWk3T1cnNe+tjsNu0rfffqv169fr+uuvV/d4s26M3674+fdr5XNDeT8FQL0j5NMA0tLSXNvHktPh4eFq06aNJGnlypWVnndsvEePHvVcIXxZ8dGXcaCRX4T4uhEjRuimm25y+8dXdHS0/vfp+67HprDSN81q8uZUfn7Zp+SrW5aiyGZXdkFZp56A4OAKx2RkZJzw+QCcHpjXwJc5Dm/TntevU+5P/6cH/tbL0+X4rcDAQNf2NdeUhmymT5+u/v37KyEhQUuXLnU7/v/m7NCZT/6i52dulCSV2CsuT1q+q+Crr76qG264wfXY4XDotdde0+Qfl+nPfVlu5xkDQyvUN378+JO8MwD+hnkN/IHz0EZlv36uohylf57zHRbp5h8UMeheD1dWiYSeir7rRxnHrFROo9IlO01GqX3xOgV9NkxHnk5S0dZFHi4SAHwT8xr4hYzdOvBse73y6GgVFxfrkksu0R9//KGWLVvKG9sxd+7cWV988YVmv3KHzMbS+j798Tc1b95cDzzwgLKysk5wBQA4OYR8GsDUqVMllU6o2rVr5xq//PLLJUlTpkypcE5ubq5++OEHSdIVV1zRAFXCF9kdTtkNpZ+ODqp+1QL4kOPXkk3ft8O1bY5orNCuw/Xlwca67K3FmrfpkL5ds0+vzdmiZTvS9P26/SosKe1QUL6Tz7vvvqu1a9e6Hm/cuFFJ7Tqq7yXXqd0/Z8nmKHsjLTgyrkJN6enpdXV7AHwc8xr4slmzZslpK9LgdnHqmBDl6XL8WlRU6fd3woQJuvLKKyVJixcv1uEis8LPvkqRg26RNb6tDJZALcsoDRh//Ns2vT5nqzo++Yuufvd3peaWLSealum+xNaXX34ph8Ph2h47dqxefvH5CnU0PfR7hbGvv/5aO3bsqDAO4PTDvAa+zr5npfLeHqgIY+mHfFINMQq8e77MrQd4uLITiDlDYffMlW3AOBUYyj5oFGvIVPEnl6no8HYPFgcAvol5DXydM32nMl/rq3jnQc2+IVgvP3a7vvnmGwUFBXm6tBOKufpNOYZNUrq1qZYVnaHc3Fy9/vrrGjBgAEuGA6gXhHzqwNq1a/X999/LZnNvMe9wOPThhx/qiSeekCTdd999rnb1kvTAAw8oODhYc+bM0fvvl3XqsNvtuueee5SZmanevXtr2LBhDXMj8Dnll1gKsfJy9hdxcXFuS2XZc8sCNtFDb1ejC8boiMK1bm+Wbv9slR74aq1em7NV17z3h+777xq9+usWSe6dfDZt2qTu3btLknJycnTWtffLedkL2t+p7FPwxxiDK66pfNttt9XZ/QHwbsxr4M9+/vlnSXItjYn6s2nTJv35559q27atnnvuOde4tXFrRQ0arYizr1T8Ta+oyY0vu/bZZNKKbQcUEmDSil0Z6jVxjt5ftEP/W7FH/V5dppi/PeL2HBMnTtSePXt00003SZLshbmufbl/zlXoio/VKjBflSHkA5wemNfAn9m2L1TRe8MVaiyWJB2xNlfMIytkjO/s4cpqyGSRecjjCnp8mxwX/ks51saSpDCLQ5v+7zzl5+Z4uEAA8C7Ma+DPHA6HHn7qJW3YkylJMobG6qHHn3L7s+zVjEYZ+96r6Mc3atmaZP30009q0qSJ1q9fr9uvuVgZRw56ukIAfsbgrG4NF9TIt99+q8svv1xRUVHq0aOHGjdurMzMTCUnJ7vWOb3uuuv02WefyWx2b7fy9ddf67rrrpPdbtfZZ5+tpKQkrVixQjt27FDjxo21ePFiVzvFmsjOzlZERISysrIUHl7xjXr4lz3p+er/f/MlSV2j7fru0Us9XBHqytatW9W2bVtJUtO2nWW5/IVanb/rxYu1cuVK9e7d22186dKluuqqq2S58b0qzpRMsmvHy1dKdvd/MGZkZCgyMrJWdQD+4nT6+cq8Bv7qyJEjaty4sZxOp/bt26emTZt6uqTTym+//aaSkhJtyDTq38vz3PY5iwu0560b5Cwp0uszFuuz5Hyl5hZXep2UV66Us6So0n0Gs1UGc4AcRXmS06Fbb71VZrNZ7777bqXH5+XlKbiSZUoBf3c6/XxlXgN/Vbxhppxf3aAAY2lXu8PBbRV331wp0If/bBVkqvDV7gosLv2g0+KDgYo870F17H+JDI07erg4AN7qdPr5yrwG/spms+m2227Tp59+qshAg35/aqDa/+N/Umisp0s7JStXrNCHYwbrxUFGbc22KP+8/1P/y/8ugxcuOwbAO9Tm5yutP+pA165d9cADD6hjx47atGmTvvnmG82dO1eSdNVVV2nmzJmaMmVKhYmVJI0cOVLLli3TFVdcoR07dmjGjBmy2+269957tW7dulpNrHD6ySoocW1HBQd4sBLUtTPOOEOrVq1SSkqK9m5ap5hQa63OLyi26/dd2TIGhLiN9+3bV/v376/2XLtMCkyo+Au0jRs31qoGAL6JeQ381WOfL1J4v+t15uDL1bhJvKfLOe30799fQ4YM0bXnn6P/3NBT/7mhhxoFm+S021Swe70ruNMioEADospaWVtNRpkMpZ9LsWUdqjLgI0lOW7EchTmSs/QNzw4dOrgFlI8tIXbM0qVL6+r2AHgp5jXwR449K2X46vqygE9EN8WNXeTbAR9JCopU4DUfymEwSZL6NSlUp+RJMkzuK8fqzz1cHAB4HvMa+COn0+kK+JhMJr35/mdqP26+zwd8JKlXUoTevjhYEYEG9Yqz6dH77tTll1+ukpKSE58MACdAJx8/Q4L69LJoyxHd9NFySdI1nSL00g39PFwR6sv9U9fou7XVh3MqU7Rvkw5+8bCMwZFylhSUvYH22I/Vnpf52xfKWjpVI0aM0Lfffusa5xPvOF3x89Uz+L6jrjidTrV9bIZKjAGyOEu06YXLZDLyySlPy8nJUUR0rIxBoQrteoFsGfs16ZG71chcrNufeUsGc4CmfTJZV910u8yR8ZLTrpx1s9Xogn/Inpuh7OUzFNJxkKIG3ux23aKD23Tov0/o26+natWGzZr4zNNyFOXpzTff1OHDh13Lh02ePFl33XWXJ24d8Ch+vnoG33fUiYJMZf5fV0U6MyVJh2PPVdyd30rm2n0wyKttny/H/0bLWJTpGio2BMr6xC7JEuSxsgB4J36+egbfd9SJkkItnXyv+j/wkQxGk6ZNm6YRI0Z4uqq6tXeVHP+9TjuzDDrzX7tUXFysF154QePGjfN0ZQC8EJ18gNNEWm6hazu+EZNpf/bAeW2r3R/uzFXh3g0VxgOatVfkwJvVfMwXanrrZJnC42r0fKawGCU0T9Sox19VYMservG//vqrdoUDAOAFdqTmqcRY2vWwXbSJgI+XCAsLU0xUuJx2uyLPvU4xlzyk91aka8OGDSrY+oeCWnbTnVM3KvysK5T310JlL5+hyL7XKqR9f4X3ulQJ93xcIeAjSQFN2ih66G166A+TPs3qoNCuwyVJbdq00YQnn1K/K26RJG3ZsqVB7xcAgFPidCr141FlAR9rC8Xd9Z1/BXwkqfVgGcf+KV38imvI6iyUc8svKiws1G+//abU1FQPFggAAOpC2n/vVN/0afrlhmC98uxj/hfwkaSEnjI+sE6tH5qt9957T5L06quvyJaXoZKSEi1ZskQHDhzwcJEAfBEhH8CH7UvNcm0nxER4sBLUt5Yx7stu5f451+1xfm6O7HmZlZ4b0WekJMkcEaeEuz/S5df//YTP13/YJXr+y9l65JsNanz1s4roe60kafv27SdRPQAAnhVlKtbed25W6g8v664hHTxdDsoJCwuTtUlZa/gjhw7qhRdekCRZG7eR0Rooa5M2cuSXznuNIZE1um5ol2EqcZQ2rTWFxUiS0oMSdNEbi3W43eWSCPkAAHxLxux/KebwEklSrt2i2Lt/kEwWD1dVTwLDpd63Ku/iya4hw9c3675BjTVgwAB17txZxcXFHiwQAACcisxtKxS57VtJUv8WFv3jlms9W1B9sgRJUS00atQoxcbGamSLTJn/laSnhsepX79+ateunTIyMjxdJQAfQ8gH8GH707Jd2zHhtCz2d/+6qoskyZp3SOm/viNHUZ5r36Gl38hRRcjneKsTrqhyn+loY4NiU7C2p5etDRvZ/wY1f+B/+iy5oPaFAwDgYUuXLpU9J00J9gO6pHebE5+ABtO+fXvZMvbL6bBLkrKXf+Pal7vuF9ly0lSSmqKw3iPU4rEfFdpxsNJ+fl0HPh2r1B9eVkTBAQXtX6WRkbu0793b3a7dtnGoWgUVqiRtjyRpxl/Z2no4V0UOowzmAO3fv18ldode+XWLPvhth1jJGgDgrRz71ylkySTXY8tV78kQ1cKDFTWMkC6XKs8Z6Hr83gVS2qNhuqBJmtauXeu5wgAAwElzOp26c/zLGvJZng7lG2U7f5KM8Z09XVa9s1gsmvzodRrXr7TTdJfoIpkMpUuZL1myxMPVAfA1hHwAH3Yosyzk0SgkwIOVoCGM7NVcK/95nt66tLmuuPQSdd76mTIXfa6s5d8o9885Kti1RpLkdNhVuHvdST1HbFjpL88O5xTqYHah2z5jQLCSi2O05VDOqd0IAAANbPHixZKkfv36ebgSHO/ZZ5/VlcP6q+Oe73Tg07EqTFnv2pez+kftf+92pf7wsgKatneNm6MTVHxwq85vG6m1r92qvz57Ujde1F+2zANK/eFlFR3YqqK5b2n22IH6+h+DFJGarMv/8ZQKSxwyGw2KDzXJGBSqgwcPasqyFL0xd6smzvxLv29P88S3AACA6pUUKv2Dq2Q1loZRM9pfp4CuVX94x68EhCro+s/dhqKDDPr4siBlfz1Gi+bP8VBhAADgZH355Zf63//+p6X7DNo34huF9L/b0yU1mCuuHqWE8NK35ve1vVnDLrhQkjRlyhT98ssvfPgIQI0R8gF8WGpOWQgjKsRPWzTDTUxogIadf76mTZum6dOm6R9D2ihz/keSvUQFW//Qvg/u0r7Jt+jQ1PFy2ktOfMFyinatUZOI0pBPam6x9mZU3rXnvUU7dPk7S/Tg/9bK4WDSCQDwfoR8vFfPnj01depU/eelp9QtMcptX9u2beW0FaskdbdyVnwre16m8rf8rqzFU/T2229rxowZMhhK2xB26dJFCxcuVN7GBTr42Vjlb1kqSWrUqJHe+G6pVof01p/7stS2cZh6JkYouP0AZQbF68eFy13PN+7dGfxCDQDgddKnP6QYx2FJUqo5XlFXveHhihqWse0w6eFtUqtBbuPnhWxS4+kjtPijf0oOh2eKAwAAtXLgwAHde++9kqQnn3xSPfoOlo7+u/50YEg8Wxq3RxoyQQ9Nmqzrr79ekrRuzlf66KFL9M5bb3q4QgC+gpAP4MNS88rWH48JpZPP6SYwMFC33nqr21iLyADZc9MlSXkbFkiSHMWFOvz5WN3aO0a9k6KOv4xL/oL31SjE6npcVceeaav2ak1Kpr5ZvU+Lth45xbsAAKB+Ldt2SBuKomSJa6lz+p7r6XJQhcTERP3+++8ymUyusX//+996/PHHdcMNN6gwZb32vnWDjsx4Xk5bkXr16lXhGgMGDHBtFxaWheH/OlC2xG3nZhH6cWO6oofcqriRz2rZYYNK0vYqb/MSrfr+E23durWe7hAAgNpzpm5VxF9fSJJKHAY1um2aZLae4Cw/FBor3fSddOdvbsPtYkzql/Km9GyUtPQtqb7CuinLpOXvSyWFJz4WAABULi9N/x5/j7Kzs9W7d289/vjjnq7IMwLDpQEPS5KuveZqzb4zURvuCdVXVwXrmzfHq+C7h6QFLxFiBlAtQj6AD8ssKv2vxVmsQIup+oPhl+Lj490ef//99+rZs6ckKWP+R0qbPVn3ty9Q6ta1mnDl2fr81rN1Xoc4nZUU7XZeYcqfstrzFRFc1hGqxH7iX459+u2vdXAXAADUn4/nrlfE0LvU9JY3tc8e7ulyUA2j0ajo6LI5ytlnn61JkyapR48ebsdFRESod+/ebmNTlqUoadxMtXjsR5kjGqu4uCwMf2HneD31tzN135A2MpncPyHoKC7Q/g/uUuq3Lyh/y1ItW7asHu4MAICTc+B/j+jYj66c7nfK0KSTZwvytPgu0uN7K983e7z0TKTm3x6rhU8OVc5v/5Ec9lN/zrw06bPLpJ8eluZPPPXrAQBwmjr87Xi9lDBXX1wRpPdfflJms9nTJXmcyWzReS1Kwzyb0qS8/AKlLv1CWjBJuROi1bJlS/37kdHKmPuaZCs69Sd02KWi3FO/DgCPI+QD+CiHw6l8Z2kgI9xUB7+0gE8KDAx0e9y+fXutXLlSH3/8sRyFOcpdM1Odk+IUHBxcerzFpA9u7q3/3XWO23mOkkJZrVZFBtXuE4Hf/jLP7ZPyAAB4mzUpma7t7olVd7SDdwgJCXFtN2rUSJLcQj5vvvmmduzY4Vqm65gXf/7LtR3Ysod69uypXzce0l2fr9Iz329Ql4QIPTisnZ67rJN+HFO2bJs5rJEizh3lerxy5co6vycAAE5K1j41PrRAkpTnsCr6wvGercdbBIRJ/R+ucvfgZsUaaFypsLmPST8/6tbd59lnn9Wll16q7OzsKs+v4MBayXZ0OfOlb9LNBwCAk1GYrfDNX8tkNGhkxwB17dbT0xV5DcPg0jleixYtlOuw6vcdpSssvL28UI6M3brL8o2ifntK+vYet/PeeustDRs2TIcOHar5kx3+S3qhmfRyO2nRy3V2DwAaHiEfwEel5RXLaSjt3tMomMTz6ax80MdoLP1rfciQIa6xyMjIE17Dnn1EI0eOVGS5Tj41YWnUXJs2barVOQAANKSI7b8ofe576hqUoaiQ03B5Cx/Tvn171/axec3AgQN1++23q3379rrpppvcuv0c8/p13SVJUYEmXdY9QV999ZX2ZuRr1oaDWrc3S7tS8yVJJqNBnZpF6KF+ca5z7fmZru033nhDP/30U33cGgAAtZL7x8cyGUoDKrkdri1d2gGlBj8h3btcuuojpUVX8ybhig+kt8+WtszWoUOH9NRTT+mHH35QRESEnn32WbdD8/Pz5axsua/0He6Pl02ugxsAAOD0kp+XrcmrbNqT5dDh+CFSePyJTzpdnH2nNGa1gu77XZ98v0hBQUGSpHUH7dqf49TGI6Wdfhz56ZLdJql03jJmzBj9+uuvatKkie6//363SxYUFMhR2XJfadtK/5t7UFI9LXMKoEEQ8gF81KHssk8ONY0M8mAl8LSkpKQKY4mJibrsssvUqVMndenS5YTXuHhQHz3//PO1DvkEt+6t0dP36G9vLtbBLD7NBgDwPsmLflLOyu/1yNAkT5eCGnjyySclSSNGjHAbf++99/TXX38pPLzyNzgHt4vTH48P1eLHz9MXH72n1q1bKz6idI5sMEgZ+cVux/do31IjujVV96YhsqXvkyQZQyIVftbluvhvl9bxXQEAUEtOpwqWfeJ62Hj4WM/V4o2MJim2ndTpSjW6b5723bpeUxuP0/7ulXT4Sd0s53+v0fJJF2lU57IPyT311FN69tlnlZ6ert9//11hYWEV3iDT7qWly3SVt+WXerghAAD827SZ8/TgT9ka/H2Mmo7+0NPleBeDQWrUWrKGqFevXjrn5Y36X5PHNfbDxTp0JE3vrS79fcZzH/+s6264UXa7XZ8+fatu6WbRsR7Hb7zxhh599FEdOHBAmzZtUlRUlK677rqKAWZzoJR4jhQcI0W3btj7BFCnDM5KP6IAX5Wdna2IiAhlZWVV+Qtw+IeZ6/fr3ilrJEm39IrVU1ed5eGK4CkLFy7UoEGDdM899+jtt9+u8XlJ42a6tv91VReN7NVc363dp/unrnU7bnTfJH2ydJfbWPGhHbI2buU2dkX3Znrlmm61LR/wCfx89Qy+7zhV+/btU0JCgoxGo7Kzs92WgoL32r59u2JjY0/5dZ9bZFNmfrFCrGZFBlsqLPF1zJQpU/RL8gEtdJR2Ecrf+of+/Hi84sICqjwH8GX8fPUMvu+oDeeB9TK821+SdMjaQo2fWO/hinxEcZ702WVK3bFe323I0dCWZiVFun/G1SGDLp2Sp5lbbQqxSL37DtD+Awe1ZcsWSdLSJYvVrXW8UldMV/OVEys+R3CM9Oj2hrgbADXAz1fP4PuO2ho0aJAWLlyoiRMnavx4liCtjTvvvEN5v3+iI/kOzd5uV9fGRq28I0Rmo0G/Gc7RgKfLAsjdunVTVFSU5s+fL0n6/vvvNfScbkr9Y6oSBo2WMTS27MK2YilztxTVUjKxWgjgDWrz85VOPoCP2rC7bJ3NLi1pbXg6GzhwoPbv368333yzVuc9eH5bSVKw1aTzOjSWJEUGuy9jEhZo1tOXdtSGZ4a7jafPfU/FqSluY9+s2Vfb0gEAqFcrVqyQJHXs2JGAjw9p3bp1nfyiODTArMhgq7o/96va/XOW7vx8ZaXHjRo1SlddMlxOe4kkKSChoz774F3FxMRo3bp1p1wHAAC1lbagbEmo8P53eLASH2MNkW6bI+tjW3Xb94Vq91auUrLcl6owyqkfRwXL+VS4cp8I1/xBa3Vh1C5J0jUdzTrz+wsVNLl75QEfScpPlX4eJ1W2BAYAAKjg0KFDWrRokSTpxhtv9HA1vufNN9/S1I2lAR9J6tbEJLOx9ANJ/c/uru/v66G7e5Wu0LB27VpXwEeSLr30Uv32QHslrpyoGf8cUTZ/mfWENCleequXlE54GfBFhHwAH/VXyhHXdsu4MA9WAm8QHx8vo7F2f6Xf3r+VXryis6bc3kdRIaXhnkYh7iGfmNAASVJIgFnBVpNrfOe63zXu7Ipvlp7Tt69SU1NrWz4AAPXiu+VbZQyJVO/evT1dCjxk/d5MSVKx3eGa11RmcJckHZwyTplLp+rwtKc1bvwEZeYX69FHH3Ud8+nSXRr71Vq3ZXMBAKhzTqdMm0s775Y4DArqOcrDBfme8IgILV26VOPGP6k9Q95RgcNU7fGvXRCoXfeHaupVwYoIrNjF78NdTVXY+YaygWWTpT//V9dlAwDgl5ZMe0cmg1O9evVSYmKip8vxOVarVWvXrtW4ceO0cOFCxfa4uGznwEc1uP85eufiIG3+4A6ZzWZd3dGscf2sigiQYoMNGt6mtEvP8OA/ZTCZdO655yqrWJLDVnqNnYsa/qYAnDL6bwE+andanqTSQEZidLBni4FPCvp/9u47Oqpy6+P4dya9VwgQQujSm4giKk0F7AW7cpWrolw711fsoqJeC4oFe0PFjigKCiIgTXqvoYdAem+Tae8fQyYzaSSQMEn4fdaa5cypz4zK2Zxnn719vbh+gHtQ3ToiwO1zpEvSz/SxA3hm9lZGdGtBy4hgeneOJ3/WTwT3vMC5zbq9qbwyZSr/e+G5+h28iIjIMaTnm1ho60bcPV+SFFjo6eGIhwT5enNJr5asP5hN3zYRVW4XHBxMyeGdGH38aXnLawBkLf6c4mJH9cyElDye/mUrAPv37uaLe0cSHBxc/19AREROPRl7iCAHgNSAjsQGRnp4QI3TwIEDGThwoOPDoEGwYhqs/rDK7ePDK39watYOMw/N2gExPfi36y2TeU9A5xEQUHV8ISIicsorzOSK1DdIfziE7YFhnh5No9WjRw9efPFFAM7r2xlWfwSdRwIGgn2A8Hg6J37Dri8ewi9xCa0KtmLGhzeXF3DzzCK+vCqAYF8DgT6wfPlyXp8dyTNtfOH0W6HrpZ78aiJynFTJR6SRyihylOYz2i1EBPp4eDTSVIQFuP+35Jrk079tJL/eey73Du8EQFxcHBm/v01x4hbnNi1vncqv5h7kFTvaXSRlF7HhYBZ5eXknYfQiIiJl1h/Mcr4/La5ZNVtKU9Y7Lpy3b+zHsonDuKpvbLXbzp07lyCXx2DCz70ZU4uefLXyAPv273cuX58TwDnnnsv+/ft58MEH2b17dz2NXkRETkW5639yvg/rd6UHR9KERLaHi1+Fp7Nh4D012sXiG8p1q3pz7UwLuSa4/eUf2O19WtkGBWmw8IX6Ga+IiEgTUbJzHkYDhPkb6Nixo6eH0zSEtIBhT0Dr/hAQDsExkH0AvPxoN3wsra57HYCXRncm75eJfPlPMntuWcfIfwYSFO64Pzbpo195Nv8GuOgVx/EAcg976AuJyPFQJR+RRirf5gMGCDaaMRgqlhIWOR7l/1vq0qLqVnCxsbFgs1KwbTH+cT2cywu8gpm1Pok5m5NZsTcDgIyfX2LKQ2O4/fbbSUpKonXr1vXzBURERErlZ5Dzzw/4t+7KqBtv8vRopAEwGquPmUeOHEnKrhF0vucjrKGtMBi9SG43isd/KktoTvvlFYr3reVAcT7t2rUD4I033iA3N5eQELXQFRGRE5e/YRahR98H97nCk0NpegwGGDHZ8UrZBoFRYDXB3Imw5y/w8oVul8LgiXj7BvHtY5GYTCb8/f0BOO2p1Sz+6jXO2fsKmAth1QdYO17IS9+von///owYMaJeh2+xWDhy5AhxcXH1eh4REZG6suVAJjs2mxnZ0YeoM6/z9HCapuFPQu8bwNsPwuMgPQFa9cM7TISp+AABAABJREFUYw/e+YngF0qHDpH8/vvv2O12jEZH/Y+nX36L1qf14barR2BY/BKs/xL7le/zxoIk2rRpw9VXX12vw7bb7Rw8eJD4+Ph6PY9IU6VKPiKNUF6xGavBkaMX4af/jaVuXdyrpfP9qB4tq9zOz8+PTZs28fZLT1dY9+TPW50JPgARo+7n3nvv5YEHHiAuLo5PP/20bgctIiJSTvqeTWQv/oz43TMZ2av6Ci4ipbyMBub/34UcefdfFB/aVmF98b51vDJ5UoXlY8aMASAhIYEJEyaQlpZW72MVEZEmyGomMm8HADm2AGjezcMDasJiukFIDIS3gRtmwBPJ8OhBuPwdxwTZ0TZpfn5+PPHEEwDY7HDujROYW9zHeZisT29g6gtPMnLkSF588UVycnLqbcj//ve/adOmDXfeeSfp6en1dh4REZG6MmtdMjfNLOLexIswtDvP08NpuqI7OuIXgOhOcOdCR1xz3ZfgVVbvw2Aw8MYbbzg///vf/+bTp2+DddPBbiP/+//w/OMTGD16NE8//TQpKSn1NuRHHnmEtm3bct1113H4sKoIidSWsgNEGqGU3GLn+5hQPw+ORJqi+4d34rzOzXh0VBe6tQqtdtuePXty7dD+tIkMqHY7o28AphIzb775JgBjx46ts/GKiIhUZvXq1QCcccYZHh6JNDbt28aTn36Egs1/kv33F/ibcwEo2rOG33/5kQcffLBsY4OR6Msnsip6BNEdetG/f3+mTJnC6NGjsdvtHvoGIiLSaB3ZiL/RAkBeVB9H5RnxuOeee45NmzY5P1/0/O/8tc/x7ynaz8y6cUEMbevFmy88zgevP1/5Qex2WPcFLHkNrOZaj+Gvv/5i+vTpAHz44Yf0798fq9Va+y8jIiJyEi1cuBCAIUOHgdHLw6MRgPvvv58DBw44P/976ny+3WIm12Rn4h85ZBc77mVMeelZpjz7SL2MYePGjbzyyisAfPfdd/Ts2ZP8/Px6OZdIU6UkH5FGKDnH5HwfF622AFK3OseEMH3sAMYN7lCj7QN8vZj34GBWP34+3oVVP0nm366f2+ely5ad0DhFRESqoyQfORE+Pj78+MoEclZ8S+YX95P03r9J/fFZ+vXrh8FgICwsDIDO51xCUJdz8ImKw9Z+ELm5joSgv//+m48//hibzebJryEiIo1M7qY5zvcRfS/x4EikvJ49e5KUlMRDDz0EwF2/FpNrckyCtQ418te/gjgyIYSH+QD7JxfBoTVQmAmbvoN/3oNXOsIv98CCZ+Gzi8FcBEBxcTEmk4mioiKSk5MdJzMXgcUEu+bBzrns27evQsuMAwcOkJiYePJ+ABERkVoqLCxk5cqVAAwdOtTDoxFXbdq0ISUlhaeeegpfX1/unVtM13fy+Xp3MLajzyt1iDDyv2Y/YXvvPMjcW7Zzxh748mpH4nI5ZrOZwsJCSkpKSEpKqvTcaWlpjBo1ym1ZZmYm27ZVrKYsIlVTko9II7QvNdv5vkOrKM8NROQofx8vmoX44RMcUeU2YWdfR9So+4kc8R9a3f4ut/50mC1JOezcuZNXX31VmdoiIlJnsguKWbdhI6AkHzl+AwYMwGAwkJaWhiUnhbjWsURFRfH3rjRue2MW3e+cwjXjy55qKzmyy23/Rx99lPDwcEaMGEFJScnJHr6IiDRCeVt+d74P6nqhB0cilWnVqhWvvfYa27dvp/+I6zhwwccQ0bbCdoaDy+Cj4fByO5h5B/z+CLg+FJW4El5uj3n153Tp0oW2bdvSsWNH4uPjWTnzHXipDTzfHGZcA19fz1/vPkx2djZdunQhKyuLLl26ANC5c2fFGCIi0mCtXDwPs9lM69at6dChZg8Uy8nTvHlzJk2axO7duxl59c18PnMemZmZFBQU8Oabb7IxxcbOdCvWw5uY9cV79OrVi9k/z4QlUyB5M/iHlx1s0/fYV3/CGWf0JzY2lq5du9KmTRu++eabCuf95JNPOHLkCK1btyYpKYnzznO0cTvzzDOdD06JyLEpyUekEdqyP9n5/rTYaA+ORMTdaVHebp9Tvn2S5oGO8uL+sV0J7nUBIX1G4RMVh8U7kNHvLGb8+PE8/PDDDBs2jIKCAk8MW0REmpgXf1pN9LjPib31DSwhLTw9HGmkwsPDOeuss5yf+/btC8Drf+7ipx0F5Ed05qL+nXnqkm6c2yaAksPb8fLy4tVXXwUgPT2dvLw85s2bR5s2bXjhhRdIT6+66qGIiJzibDYi8h0Jo3m2AIju5OEBSVW6dOnCjBkz6DnsGrh7OVzxHvbTLmZTVvWtzN2YC/H57T5OD0wiOTmZw4cPU1JSwr6Zz4PVPXHn34HzWfSvQN557r+Eh4c7H5Iym83MnDmzLr+aiIhInen5z33suz+Yz6+JxKAWpA1WXFwc06dP5/zzzwcgMDCQe++9l+uvuZqUAjs+RjuzPn2DzZs3c83VV7M9uRCKssBeVrnYsn85ht8e5Gyf7WRnZ7N3715sNhv33XcfS5cu5fLLL2fFihWkpqay7OtXWTY2kDcfu4NWrVrh7+/vPM6HH3540r+/SGOlJB+RRmhvco7zfZvIQA+ORMTdfUPaYS3IpiR1Hwdfv4bi/eu5fVDbKrcvtnmxeJWj0sLq1auZOnUqGzZscOsJKyIiUltr96Zh8PbFO6Yj4YF+nh6ONGLXXHON8/11110HwNkdHJU0u7QIIS4ykLHntOOL8cNYueA3tm3bxr333ouPj4/bcVJSUnj88cfp1KkTVqu1Xsdst9vr9fgiIlJPMhIINJoByAvvCpoMaxx8g6DPDRhumEGvqcl86n0Lf+y1sy3N/XqfXeLF9sEfwiWvQ0gr5/Ifrw3kyysDeGHMIB48y5er2lX+8NPgtt4MLZkHwJAhQ5zLx40bx969eyvdR0RExGMKM4k25NA23Ei3KP0dtTH6+rsfOHL6/zF3r4EOkY50gkfP9aNr8kwKTWbWpno5NiwpZOfaxQC8MdKfuFADV155JeBozXXuuefinTCHn/47hNFntMLfnM3Zcd5cblwIdjunn36685yPPfYY69evP7lfVKSR8j72JiLS0CTlFAOO7NbWEUrykYZjaL8ueF1zCYf2OW4wtWjRgpvO6cyLf+6jqvkm31adKdq1AoDHH3+cxx9/HD8/P/bs2UNsbOzJGrqIiDQhJVmHMRf4EhTRnNYRtXiiWqSce++9l71795Kfn8+1114LwL/Obsvfu9IJ9vPGz6fsuZm+ffuy9kAWRVYDAQEBmM3mCsfLzs5m3bp19dZGbuLEiXzwwQf8+uuvnH322fVyDhERqR+mvcspTU0OOm2wR8cix++2J96moOB/LFu2DJ8IL1576Ab+2pbBoVwbXm/9m3vvvZeWLe6i5/7HGNLWcWv+pl4+wGZo51/tsQ27/oCMPbz2ystEBvsy44df8C/JYOszZ9ByyDkEnPVvaNUHwtvU/xcVERGphr0gnX+SoHdzO8b2aqPeWF13z9OYTBPxXbqUXe+34eKLL+Lj1/eQVWynhLu5777ttGjRguZbdtG9pxf3zi3m2amfcOutt/Lpp5/iP+debuju5XbMQ3mOfxqPrINDq3nssccoKiri66+/JiUlhfPPP58lS5bQrUUA2KwQ3dED31yk4TPY9Zhfk5Kbm0tYWBg5OTmEhoZ6ejhST7o8MpNigx9+dhM7/3eVp4cj4ubee+/l7bffBuCCCy5g3rx5DHxxAUdyiivdPnf1LK7paOSDDz5wWx4XF8eNN97INddc45bNLeIJur56hn53OV79+/dn7dq1fPXNd9x43TXH3kGkDszflsId09dwSa+WrHx9HGvWrAFg1KhRREVF8eWXXwKOJOiHH36YpKQkRo8ezcCBA2t1HpvNxsSJE+ncuTP//ve/OXToEK1bt2bdunX0798fgEsuuYTZs2fX7ReUJkPXV8/Q7y7HcvijG2l16DfHhzG/QHsl+jQFubm5rFy5kmeeeYbly5c7lxsNMGmIH0+cV33VyX2+XWkx4AoClr7ovsLgRX78+WxZ8SdntSyrGmTDiPHSN6DHVeAXUpdfRaRB0vXVM/S7y7EkJCTQuXNnggL8yUpNwic40tNDkjpQVFTEihUreOWVV/j999/d1p3f3ovzbn2aJ554wtGezVwEk1u4bZNkaEXI0PsI3f4NDPwPdLsCvH0ByFnyIVfc9z8WbdjDfWf6MnXk0eTni16FAXecjK8n4nG1ub6qXZdII1NstlJscNwAiPRVjp40PA899JDzfelE091DOgDgbTTw838GsWDCYIxHK4+HnnEFbS65B2OA+wUrMTGR//3vfwwaNIiSEvd+9CIiIlUxm81s3rwZgLPOUJKonBxFJVb+7wdHC9LNSTl89Mln9OrVi59++ok5c+bwxRdfsHz5ckJCQkhOTmbChAlMmTKFYcOGsXPnzhqfx2azcdlll/HKK69wxx13MHToUNq0acMTTzzBhRde6Nzu119/Zfr06dhsNrf9rVYrCxYsYNKkSQwbNoxFixa5rd+/fz8ff/wxqampx/9jiIjIcTEecbQmsNmBVn09OxipM6GhoVxwwQX89ttvTJw4kU6dOgGOf89fJMXDA1vgzLuh66Vu+5U060nO+a/R7qE/CBg0DvzD3Q9stxK8/w+3BB8AIzaYfR+82BoOrqzPryYiIlKlVatWAdCzdx8l+DQhAQEBDBs2jB9//JFJkybRq1cv57p12WE8/vjjjgQfgN8mONdZwtuTNeRFYv+7lNBzxsGdi6DXtc4EHzb/QNiC/7LgmmKaBRrw94Y/dlvINdkZcfsTbonSIuKgSj5NjDKom749afkMf83R3/L0ZnZ+nHCJh0ckUtH27dtZtmwZ11xzDWFhYdhsdn7dfITY8ABOj48AYPS7y1lzIMu5T3HiVlJmPFLp8V566SUeeaTydbVltVrZsWMHXbt2xWg0UlhYyIEDB+jatWudHF+aJl1fPUO/uxyPjRs30qdPH8LDw8nMzCy7uSBSz75aeYDHf9qCwQDfjRvIGW0r3sjctWsXd955J4sXL3YuKy1jvXjxYq6//noiIyOZPn26WyXDgoICdu7cyTPPPFPrCj3PPPMMTz/9NAD/93//xyuvvOJc5+Pjw7PPPkt8fDxjxozBYrEAcMsttzB9+vRanUcaD11fPUO/u1TLYsLyXAzeBjtpRNHsmb2eHpHUo82bN/PTTz9x6aWX0revS0KXzQpJ6yDnIJx2Efi4tJ1NXAXfjYG8I7U7WY/RMPQxiOpQN4MXaWB0ffUM/e5yLA8++CBvvPEG9913H1OnTvX0cKQe7d69m2+++YahQ4cyaNCgshU2G6RshrRd0HkE+FfxZ4W5GKb2gvwU6Hszc1OaEbZuGme3MDF9Ywn/mlWM0Wjkm2++4Zo2WRA/EFr0PDlfTuQkq831VUk+TYyCq6Zv0c5Ubv10NQDX9gjj5ZvP8fCIRI7PH1uTGffFWrdl1sIcnupTQu8e3di7dy833HCDc91LL73E6aefTkhICGeeeeZxndNsNjN06FCWLVvGa6+9xr/+9S/OPPNM9uzZw48//shVV6n9nVRO11fP0O8ux+PTTz9l7NixDBkyhIULF3p6OHKKWZqQTnigDz1iwzicXcRP65MY0C7SLeHHZrM7EoG++47rr7++ymP17dsXk8lEcHAwO3fuJCcnp0Zj2Lt3L+eccw6HDx92LnvooYdITk5mxowZNTpG586d2blzJ2lpaTzxxBO0adOGxx9/HLvdjs1mw8vLq0bHkYZJ11fP0O8u1UreAu85JkWSIgcRe98cDw9IGqSSAtj0LQQ1g5CW8PllYC6A+HOgwxAsoW349p3nuCk2seK+va6Di6eAXzApKSmMGzeOPn368NRTT2E0qti/NF66vnqGfnc5luHDh/PXX3/xySefcNttt3l6ONLQpSfAR+dDST7ctRT2/Y3NJ5Axr83lqxlfAxASEkLqE63wLzwCp10Ml78NgZHk5uZyxx130Lp1a1588UV8fX09/GVEjl9trq/eJ2lMIlJHth5Mc77v3jbGgyMROTEjurdgZPcW/L412bnMKzAMv67dGTCgLQMGDOC6667j9ttv55NPPmHixInO7f744w+3lhQ1MWvWLDZu3MiyZcsAWLBgAStXrmTPnj0AjB8/nosvvhgAPz8/5365ubnMnTuXzz//nISEBD766CMGDx7sXG+z2SgpKcHf37/2P4KIiNS5N7f50uyKxwjpEO3pocgp6JxOZf/d5RabmbHyIK/8sZObzmzDs5f34P2/9/DavF3cdnZbHr/2WubPn8/HH39c6bHWr19f6fKRI0fSvn17pk2b5ra8RYsWJCQkEBwczDfffMN5553nXDdlypQajT8+Pp4DBw6wa9curr76ajZs2MDevY5qEtHR0axYsYKff/6ZdevW0a5duxodU0REjs2SvNV5kzawrdqNShV8g6D/2LLPdy2B/FSIGwBGL7yBmz68jpIdf3DkwxuID3Fp5bXpW8x7l7I9fgz3vTOXxYv/5ueff6Z58+aMHz/+pH8VERFpwhJX8XS7dVw+0o8zW6rOhNRAdCdHck/WfojqCM27YgS+/OoWPp/+BYMGDSJp+ypHgg9gK8wg8XAKBw5v4bXXXuOXX34BIDg4mEmTJnnue4icRKrk08Qog7rpu2nqXJYdsQEw6z+D6BMX7tkBiZyAzIISXpiznR/WHnJbvurx4TQPcSTNFBQUMGrUKJYsWeK2zVNPPcXNN9/MunXrKCwsrPBEwJw5czCbzQwfPpwHHnigygm0ykybNo27774bgMsuu8ytLcaVV17JzJkzAcjPz+fMM89k586dTJo0iZEjR7q11pCmQ9dXz9DvLrWVmlfMgMkLAGgXZGbhk1d4dkByykrKLmLoq4sosdjw8zby3OU9uKJvLGdM/pOcIjMAa544nwCDhb59+5KQkABAcnIyv/zyC3feeWeFY5bGPh06dMBut/Poo48SGRlJp06dWLZsGffddx9t27Z1bj9//nwOHTrE008/TWKi44n+kJAQrrrqKiIjI9m7dy9//PEH06ZNIyEhgbvuuou4uDiioqLIysqqcH5XKrneuOn66hn63aU66d8/SPTWTwCwXfcVxq5qzS4n5vDhw1x8/rlM6n2Ey07zcVtnttrZn21j9WEb764pwdZ6AA89NIEzzjiDNm3aVHnMrKwswsLCVPlHGhRdXz1Dv7tUp2Dh6wQtfgaA4gtewn/Q3Z4dkDR6mZmZXDJiOHe13sEVXXxo83oeOaay9c8M8aNHMyPvry0hO7IP9953P4MGDaJdu3YYDIZKj5mTk0NQUBDe3qqHIg2H2nWdwhRcNX2nP/4DGdYAsNvY8fxF+PuoVL40foUlFro99Yfzs5fRwJrHzyciyJeMfBOLd6XRt4UvOamHufnmm9m6dWuFYyxbtozY2FhuuukmNm/eTG5ubo3OPXLkSC699FL+85//uC1PTEwkIiKC4ODgCvv07duX66+/nkceecRtucFgYMuWLXTr1q1G55bGQ9dXz9DvLrW1Zn8GV7+5EINvAFd1DWXKv8719JDkFDZrfRJPzNrCN3eeRY/YMAD+9/sO3l3kqCL4+wPn0qVFKHa7nXFv/UxUoA8v/PsibDYb77zzDpGRkfTu3ZtFixbh4+PDXXfddVzj2LJlC8uXL6djx460aNHCGacUFxeTk5NDTIx7ddAvv/ySV199lY0bNwIwYMAA8vLy2L59u9t27777rrPlqdFoxGg0YrPZnFUS//zzT4YPH35cY5b6peurZ+h3l+okvT6c2Jw1jg/3roOoDp4dkDQJhw8f5sknn2TF7M95e5Qfw9pVPom1KcXKt1vNzE2wsD/bxjXX3cA5w0fRvXt3unXrxoYNGxg7dizbt2/nnHPOYf78+apmLA2Grq+eod9dqpP0ya3EHvzJ8WHsH9DmLM8OSJqEzMxMJk2axLvvvovZ7Hh4qlWrVnTo0IHPb2xNu+TfABj9XSE/brc497vmmtGMHDmKnj170r17d/bu3cvtt9/OypUr6dmzJ0uWLCEsLMwj30mkPCX5nMIUXDVtxWYrXZ+Yg91gJNKrmHWTr/b0kETqzHdrEvm/Hza5LevWMpRtRxzJOud2iuaLf59JSkoKLVq0OKFzPfXUU0yePJnAwEC2bt1KXFwcycnJtGvXjuLiYud2oaGhNU4WKvXKK6/w3//+94TGJw2Prq+eod9daishIYHOnU8jqEU7tm7aQHyzEE8PScRNQkoeO5LzGNWjBd5eRux2Oy/N3cH7f++lS4sQ7h/eiVE9W3p6mACkpaUREBBAQEAAVquVtWvXMn78eDZs2FDjY7zyyivcc889lSYTiefo+uoZ+t2lOmnPtKcZGZTYjPg+kw5GPdAldSczM5P8vDxaZKzAd9U7kLEHzAXV7rMi0cL8vVbm7bWy8pAFi61s3ZgxY5gyZQpRUVH1PHKRY9P11TP0u0t1PvjgAx5/8C5uu/gsXv5yvqPdpEgdycnJIScnh6ioKIKCjv63tfAFWPw/7EZvHsu6lqnvf05RUREAl3b25pkhfvyxx8KMzRa2pFrdjnfppZfywQcfnPCck0hdqM31VbU1RRqRXSl52A2O/21Pax7o4dGI1K3R/Vrzr4HxbstKE3wAliSk8+3qg3y0JoOPFmylZdtOdOvWrdoeq3fffTcXX3wxjzzyCGPHjqV58+ZcdNFFTJo0ieTkZPbt20dcXBwALVq04MCBA3z77bf07NkTwJngEx0dzaefflrpOaKiovj7778ZNGgQAA8//DCtWrXi2muvdUsYEhGR+rdu3TrATvc20UrwkQapU0wIl/ZuhbeXI6bfl17A90fblsZHBToTfH7ekMSd09ew7mD1bbPqQ0a+iXyThWbNmhEcHIyXlxe+vr4MHDiQ9evXV7nf5MmTmTJlituyhx9+mICAAOLi4njzzTd56aWXyM/PP+6xWa1W5xN7IiJNhqWECHsmAFle0UrwkToXGRlJm/h4fPtdD3ctgccPwxOpcOUHENu/0n0Gxnnz1GA/lt4WSMFjIez7b3MW3h3HM0P8MK+bwaV9W3LFhefx9ltvkZCQgNVqrfQ4IiJy6tm2bRvphXascQOV4CN1LiwsjDZt2pQl+ACc939w7XQMg+7nxdffpbCwkISEBB599FEeu6Ib/Vp68eg5frQJc7TuGjhwIBdffDEtgw2EH/id0WfGMWrIQF577TW2b9+OxWKp4uwiDYcq+TQxyqBu2j5atIPnf3eUv//vsHjuubCHh0ckUvf2puUz5pNVHMoqqna7sABvPr/tDPq0ieSiiy5i7ty5ABw8eJD//Oc/tGzZkvfff99tH4vFgpeXV5V9WEvZ7Xbuvfde3nnnHQB27dpFp06d+OCDD9i0aRMTJkxg1qxZtGjRgnPPPZfWrVtz5MgRunXrRnZ2ttuxXnzxRcxmM5s3b+b888/nlltuISkpiQ4dOhxzHNJw6PrqGfrdpbYmTpzI//73P8aNG8d7773n6eGI1IjFauOpX7byzaqD/PHAeXSKCWF/egHjvljLgcwCZt49iG6tyv4MzCs28+f2FJoF+3NOp+g6Hcs7C3fzyh878fUy8s5N/bigWwx2u90Zs3y96iBzlm+ieM0PXHnRhcTFxXHw4EESExP5v//7P4xGI5s2beLKK69k7969lZ4jMDCQ119/nTvuuMN53Pz8fObNm8f//vc/Vq1axciRIykuLuaqq67innvuITs7m4ULFzJt2jQ2bNjAunXraNOmTYVj2+12CgoKCAoKYseOHZhMJrp06cKXX36Jl5cX/fr1Izo6mtjY2Dr93RobXV89Q7+7VCl1O0xztLFIihxI7H2/e3hAcspJ3QGbvoWcQ5B3BPJTIH1XjXbNLrazM93GxnQvcoPaEhHXBb/oeOwWMzne0dj9QomLi6Nnz560bt0aLy8v0tLSKCwsJCoqirCwMN0bkROi66tn6HeX6lx44YXMnz+fjz/+mLFjx3p6OHKqm3457F0EAREkXvcXZhu0b98egMVv38fg9M8B+O+8Yl5bUQJAUFAQA8/ox3X9m+Ef0wGL1UaGd0t8fHxo1aoVPXv2JD4+Hi8vL7KyssjNzSUyMpKIiAjFNXJC1K7rFKbgqmm7dspvrEp1vP/x7oGcHh/p2QGJ1JP0fBPvLNzNp8v2H3PbYV2ac1o4fPb2qzxwzXDGjbuzTsaQmZnJ6NGjGTp0KE8++WSN9vnggw8YN25cjbYtfTq+sLCQBx98kM6dOzNgwIBTftKpodL11TP0u0ttjRgxgnnz5vHee+/V+M9jkYbCYrXh7WVkZ3IeI974G4CIQB/+ePA8mof4A44KP0/O2kJusYX/XtiZe4Z1cu6fW2zGZLZhs9uJCfV3O3ax2YrNbifQ17vaMXzxzwGenLUFgH8NjOeWgfGcP8Uxll/uGcTEHzeTU2Tm9PgI3ryhLwALd6QSHxVI+2bBzuMkJCTw1FNP4e3tzZw5c8jMzKxwrq5duzJixAj279/PrFmzqhzTq6++yjvvvMO+ffucy3x8fOjQoQPp6el4e3vTq1cv7r77bn799Vc+++yzyqsJGL1pcfMr+ETGMiLei5v6RBISEsLpp5/uttlDDz3EwYMHmTFjBr6+vhUOk52dTWhoKEZj4y2MrOurZ+h3l6pYN/2I10zHBFhmn/FEXvGih0ckAmTugwPLYfefkLYTMhLAWlLrwyTn29iRbmNnhuOfG5Ot7Mu2kVNsJ68EAv196dKhLQGRLTCbLYSEhODv709OTg5BQUG0b9+eK6+8koEDB+Lv73/sE8opR9dXz9DvLtWJjY3l8OHDrFixgrPOOsvTwxE5msicAq3d//7PX5Ph75cB+C3kJqbM2cmKFSsoKiqidaiBxAcdVcJn7zRz2TfuD6Vf2tkbk9WR7Hwgx5Fq4ePjQ9u2bWnZsiUWi4WgoCCCgoLIysoiODiY2NhYrrjiCgYPHkxgoLq1SEVK8jmFKbhquux2O90m/kSRwQ8vu4UdL1yKj1fjvbEsUhOJmYUs253OvG0p/LXDkeEW5OtFVLAfBzMLK91n+tgBnB4fgZfRgL/PyS1zbrPZePfdd+nRowf33XcfmzZtqvUxlixZwjnnnFMPo5MToeurZ+h3l9pYdyCLS++ZRM7+zXz/1vOMGjLQ00MSOW5r9mdy39freerS7ozsUdYXftLsrXy6bD+X9m7Fm9f3cXtCbNBLf5GU7bjp9M6N/bi4V0sKTBYe+HYDf25PwW6HT27tz7AuMQCk5hbz/G/bmb8thXkPnkdcpOMG07uL9vC/33e4jefS3q2Yel0f2j82B4ALusXw4lU9mTR7G7M3Hgbgqr6xvHZtbzIKSli+J4MhpzUj1N8HAJPJREJCAo888ghz5sypp1/NnXdEK7BZseSkOJeFD7mNsDOvBiDlu6co3reO7t27c88993DnnXcyb948Ro0a5fjOl17Kzz//7PyNd+3axemnn+5sN/bZZ58xZswYdu/eTceOHWv9tJ7dbmfx4sX079+f4ODgY+9Qh3R99Qz97lKVjB8fJmrzBwBYr5mOV/fLPTwikUpYLZC1H9J2QOZeyNrnSATK3APZB0/48AUldvZl29iXZWNvto3EHDu5Jjv7s23sznRMnoVHRNKtWze8vLzIz8+nQ4cO9OnTBx8fHxITE4mOjsZsNtOqVSuys7OJiIigU6dODB06lIKCAjZu3Mj27dsZPXo04eHhJzxmaRh0ffUM/e5SlfzNc3nyzivYlmblu+X7CGse5+khiVQtaR0cXOGIaQb+ByLbYbPZ2Lp1K3tWzOaKw/8DYG5OR2akd6OkpIS9e/eybds2lt9ioHcLL5LzbXR8z1FVuNQ9A3wY39+Xfdk2nl5kYs1hm3OdAfAyQnBoOKeddhpBQUHk5OQQFxdH//79CQgI4ODBg4SHh2Oz2WjZsqXzz9z4+HhGjBiByWRi8+bNbNy4kcsuu4yYmJiT/ctJPanN9bX6x/hEpMHYl15AkcEPgPYhdiX4yCkhLjKQ6we04erTW7NybyaBfl70axNBer6JIa8sIt9UsTfqmE9WAeDrbWToac24f3hntxYXdW3RzlS+WnmQ+dtS6Ng8mHdvGkOnmBA2btzIe++9x9133+3cdvDgwfz999+U5tdefvnl/Pzzz27HO/fccxk7dize3t60atWKr776ik8//ZSzzz67RpNHri01AH7++WeOHDnCuHHj6qRUZHFxMSaTibCwsBM+lsVioaSkhIKCApo1a3bCxxORU9sfG/bh0/0CortfQI6//nIrjVv/tpEsfHgIft7uCctjB7Xj8+X7OZBRQGJmEW2iyp78ah0RQFJ2ET1jwxjR3fH/QICPF81D/Ch9tMe1kk/zUH9CA7zxNhpYdzCL1hEBGAwG7jyvPe2bBXFaTAjP/7aNP7enYrHa+HN7WbLMtsO55BaZ2ZiYDUB0sC+X943FYDBgsdq57+v1tAzz543r+nBm+yh8fX3ZYQon/rqneWrktcT6mZzVtjp378UtN1zHuDvvoFmzZmRnZzNjxgx+/W0umzdt4NChQxiNRvr06UP3Hj2wWa189dVXzrG88cYbbNq5hxm/L6N43zoA7nr4KeYaBwDgZSlm8rAoDHkpjLn/ccLOvBpLbhpGf8cTeVu3buXuu+9m8uTJHDp0yHnc2bNn8+eff3LBBRdgt9sZO3asM8EH4NZbb+Wnn35yxnKffPIJN998Mz4+Ps5tpk+fTl5eHuPHj3eLwywWC/fccw/vv/8+11xzDd99912N/rsQkaapKHGD871XTDfPDUSkOl7eEN3R8SqvOAcOb3C0+Mo+AAUZYDQ6JszSE6Ag9ZiHD/I10KO5Fz2aV/6wlsliJ6u4hDzTevJKIM9kJ7t4G5vm/8yBXBsmK+wvspNRaMfXC3Zn2kgpsNMqxIB/aDQ7DqY5j/Xiiy9yzjnn4O3tzcCBA7n22mux2+34+PjoqXoRkROUt3oGr484Wnmt8CCgJB9pwGL7OV4ujEYjPXv2pGfb5rCyBPJSGNXnRka1O9e5jd1mhcktwWoiOrY9+fkbKS4uJjU1lV27dhG/5U06ZS+mazMvkjrexAhrDG3atGHDhg1sWPAji68tIrPIwtSV63nxL0elxLVr1zJr1ixu7uWDjxH2FtqZvct9/is2xEBIeCQ7D2U655gmTZrE8OHD8fLyom/fvtx00034+PhgMBhO+gNFcnKpkk8TowzqpuujRTt4/vc9ANx9dkseuazfMfYQadpW7Mngl41JbDuS55xgqoyvt5H/DOlIh+ZBnNU+iuhgvxM+t91u54e1h/jinwNsOpRTYf2NZ7Zh06FsLFY7t/X0Z+eyP/hz1Wa+/fQ9mkeGceONN1JSUsL3339Peno6F110EevXr6/2nJ07d+bll1/m4osvZvny5UyePJnw8HBmzpyJl5cXffr0YceOHYSGhnLzzTezceNGVq5cSUZGBgDff/89o0ePBhyTUF999RWnn346zz77rLPk9ZIlS5g7dy4DBw7kwQcf5LzzzmPatGmYbF78vvUwM16eyC8/fANAfHw8a9asITo6mq1bt7Jv3z5mz57Nbbfd5laGNT09nejoaDIyMggPD8fLy4vdu3dzww03sGbNGgBCQkJYv349HTp0IC8vD4vFQkRERKW/Q3FxMR999BGXXHIJbdu2rfY3KykpYfPmzfTr1++EE5x0ffUM/e5SG1e9+ivr0h3/r8978Dw6x4R4eEQi9eOfvRkMaBuJ0eh+bXtn4W5+XHuIMQPjuXVQO+dyu93Of2asY+GONGaOP5uuLR1/nu5IzmXkG0uc2338r/4M7+qeIGe12VmSkEagrzcD2jlaBW9JyiEm1J9mIX7kFpt5ae4ObhzQhh6xZQnAF7+5hK2Hc7lnaEcmXNgZAJsdhr+2CF9vI3PvPw+L2cwLc7bx+cokANY/eQERQY72WDNWHuSZ2VsZe0Zz/Pb+zdChQ+nWoxeD/vcXrSMCuP2c9uRtX0Jc61gGDRrE7Z+v4e+dyQwx/cNLjz9EREQEby7YzVcrD5CaZ2LjUxcSFuhDQZGJ31Zs5urB/diyZTN9+vSp8PuWXnfAkcgzduxYFixYwKRJk4757+bee+/lzTffpKCggAkTJvD+++8D8PDDD/Pyy44S4Dk5OYwZM4ZffvnFud+iRYsYPHjwMY9fV3R99Qz97lKVtEkdaGZPx2I34P1UuiOZQqQpKcqGjN2Otl9JaxyfTXmOl5cPGL0gJ8lREchqqpch5JfYScwzsC/TjMUGsSFGCsyOpKD8EjtZxXYyiyCVKApCO+IVGU98uw4EBAQQEBDA6aefjq+vL61atSI8PJyAgIA6eZBKTpyur56h312qkvTi6cSadjs+PLwXgqI8OyCR+mA1w5aZjrjGLwSGP+W+fvYDsPEbsBTBhF0QUnavxbZjDsZvbgBgY/goNkSMIjIyknXr1nHgwAGmtvqFEG8z6SX+XLqwPT4+PrRo0YIjR44wueM6zov3ptBsZ9R38PfuPOdxe8cYeWaIH/kldr7bamH2LgtxcXF07tyZVq1a0bZtW0JDQ/H29qZfv34EBgYSExNDVFSU4poGRO26TmEKrpquS176hS3ZjqdZZv1nEH3iwj07IJEGxGazU2S2Mn3FAd5ckECR2VrltrHhAXRoHkxUkC/92oQTGeRHRJAP/dpE4G004F2DKln/+30H7y7ac9zjPb9rDM1D/diSlMOtZ7flqn6tWbt2LXfddRdbtmyhuLj4uI9dHX9/fyIiIjhy5Ijb8i5dutCjRw9++OEH/Nv2BaMRU9IOIoaOJaT3CLdtsxZ/Tu4/35d9l/PP588//3R+btasGVu3bqVZs2a8+eab3H///c5148eP58orr+SCCy6oMLannnqKBx98kK5du5Kamsp9993HlClTnMFlQUEBTz75JK+//rrzPM899xwLFizgggsuoGfPnhV6PE+aNIlnnnmG7t27M2zYMMaNG0f37t2P67fT9dUz9LtLbTw2aTKvf/o9Z426hvlvP1qjP89FTmW7U/P5af0hWoT6c0XfWEL8fY69Uw0UlVi59+t1/Lk91dk2DOCJWZv58h9HO48r+rTi9ev68MofO5l2NKb644HzOK2FIznvhTnb+eDvvbQM8+evCUMI8HX8Pejl33cwbdEezmgbwYw7zsLHy4jZauOXDYeZPGc7HZsF891djlZ9H/69lxfnbuf6AW144cqeVY534cKFDBs2zDHGJ57gueee4++//6406Wby5MncfvvtzJ8/nylTppCQkEBeXp7bNq5JQq42b95Mjx49mDBhAlOmTHFb17FjR7Zv346398mZ2Nf11TP0u0ulrGYszzbD22An1dCM5k/v9vSIRDzHZoP8ZMg6AHlHHBWCsvY7WoJl7nN8NuVBST7YKlZ2rktmq6NdmNkGJVZIK7CTlOdoHXY4z47NL5zwlm0xBjeDkBgi47sTFhVD8+bNad26NSaTieLiYjp27Eh0dHS9jvVUp+urZ+h3l6q89t8xbFnwDTeMOIsLX/rb08MR8Ry7HfJTIbg5uCbQ7JwLCydDUQ4Mug8G3OG+37PRYDND7Olwx19uq3Km30TY3l8BsNy7iQLvcL7++mv2799PP/9ErsWx7uH5xby6vMS5n7cRkicEY7HB1jQbw6cXuh334l7RnNm9HQRFkxvUnti2jvglOjqaNm3aYDabKSwspF27drRo0QKpP0ryOYUpuGqais1Wuj3+KzajN372Era/eEWFJ3dFxKHYbMVstWEHJny3kfnbUo65Tyk/byPdW4WSnFPM2R2j6dg8mLPaR9G7dRgGg4EtSTlc/e5yTBbbsQ9WCw+e35nwQB+u6BuLj93Mjz/+iK+vLzabDV9fXyZPnlxppZ+IiAiysrLclrVt25ZBgwZx8OBBlixZUmGfUl26dGHHjh1lC4zeNL9mEgFte1c71rSfX6Jwx9Jqtxk1ahTvvPMO7du3r3a7W265hRYtWvDKK6/QokUL7r//fh599FHn+oEDB/LWW2/Rs2dPLr/8cn7//fcqj2UwGJg2bRr9+/dn9OjRXHLJJbzzzjtu24SGhrJz506CgoJYv349oaGhzif4ExMTmTBhAvHx8UycOJGoqCi2bNnCaaedRkpKCnPnzuXOO+/U9fUkU1wjtXHllVcya9YspkyZwoMPPujp4Yic0kwWK2//tZuLe7WkSwvHn98/b0hizuYjWKx2xpzdlsGdm2Gx2uj0xFzsdvdKQr9vSeauL9cC8NYNfbm0dysA/tyWwu3T1+BlNLD2ifMJD/Tlk6X7eO63bdjt8NhFXbjzvA7OcRSVWJ0JQtX57LPPCAwM5JprrsFgMGCz2WjTpg1JSUnObS644ALmzp2Ll5f78Ww2G4cOHSI+Pr7CcceOHUtycjJz5sxh4sSJPP300wwbNowVK1YwduxYpk6dStu2bcnIyGDcuHGEh4fz0EMP0bx5c8CR5HznnXfSu3dvbrnlFiZPnsyDDz7oFmNt2LCBu+66i/j4eGbMmFGjRCFdXz1Dv7tUKm0XvHMGAIfCz6T1A/M8PCCRRsBuB4sJ8lMgeZMj+cdigsJMKM52bHNkoyMRKKiZIykoOxFyEsFy9KEqgxHsdXtvJ6PQkQCUWeSoDpRrghyTnWK7DzafIAIjW+AXEk2uyU6OCXKKbWRYgwht0Y7w8HDat29Phw4daNeuHSEhIc7WYa6tQKUiXV89Q7+7VGXkyJH88ccffPDBB9xxxx3H3kFEytjtsGGGo7JhQAR0v9J9/dafYP1XkJsEdy11VEMstfoj+G0CADmDn8fW52a2b9/O7t27STt8kAnFrwCwOT+Ci761YrPZSE1NxWKx8O3oAK7t7og3Or2Vz+7MshjpvHgvHhnkS64Jpm80s/iwL82bN6dz587Ex8fT1bifghIbaYVw0BRC69atCQ0NdcY17du3JyQkhJCQEOd8l1RNST6nMAVXTdOf25K5fbrjJve5sd58ce+IY+whIqUOZhTyx9Zkpv+zn0KTlYISC8Xm2t3IMRigdUQAiZlFFdZd2C2GMQPbkphVyO7UfLYfycVstbF6f1YlR6peh2ZBfHbbANYcyOTtv3bTPz6S285pi9liJ8AbLjy7L/sTD3P5A5O5/+YrGNi9LRZTMT///DM2m43k5GQeeughjEZH9YoPPviAoqIizjrrLLcqNy+++CITJkwgrt8QikPiMPoHEzrgyqqG5WbrpAsJ8vNh3759rFu3jjvuuMOZaNSrVy8SEhIoKqr4O5XXo0cPNm3ahNlspnPnzhw4cKDS7cLDwznvvPPcWlqciLvvvpt3330XgMDAQPbs2cPcuXMZO3as23a9e/dm48aNnHbaaWRkZJCeng6g6+tJprhGaqNt27YcOHCAhQsXMmTIEE8PR0RqyGqzk1FgonmIv3NZam4xC3akcm3/OLxcHm6w2+0sSUjnt01HuOmsNvRqHU5anokf1x0iItCH0ae7b38i5s2bx1tvvUWfPn0ICwvjjjvuICwsrMrt33//fR555BG6dOnCwIED6dOnD//617947733uPvuuwHw9fWlpMTxNN22bdvo2rUrL7zwAo8//rjzOJdddhk///wzdrudCRMmOKsYumrTpg25ublceeWVfPrpp87lX331FTfeeGOF7Tdt2sSwYcPo1q0bP//8M15eXrq+eoDiGqmMddMPeM38NwAZvcYRddXLHh6RSBNmt0NBmiP5JzgGrCVQlAUlhY7koLwjjuSgtB2QuR/MBWC1OBKDCjPAXnXV6BORU2zHbHNM0eSZYE+WjdQCOzlHk4FMxmBMdm/iwr1pGepNsakEq5c/AaGRhIdH4tX8NKJaxhHbazBEdyI8PJzc3FxSUlKwWq0EBQXRpk2behl7Q6Drq2fod5eqtGrViiNHjrBs2TLOPvtsTw9H5NRhKXHEK+ZCCIx0JAmVKimA9851VAjqdCFc/BoAZrOZoqIirJ9cRET2ZgCeLL6dbQn7yc3NJTk5mUHBibw3whGn3Du3mLdXlbif9skQvIwGVidZGfBRgdu6ief4cmF7b5Lz7TzwRzGpBXZiYmIICAjg/HZGLmpnw2w2sygtnKzwnrRv39756t69O97e3kRERFBQUMDhw4edSULt2rVrsu3FlORzClNw1TTd+s48FiWaAZh2Qx8u6h3r4RGJNF42m53cYjN70vLZkpTL2gNZ/LLx8HEda9Xjw90mpFwt2pnKJ8v28/euNACah/gx446zWHcgix3JeXy7+iAFJbW7QXRL/5Z8seZIheU3DIjjwu4t+GbVQbIKzfRuHca4wR1IzCxk/rYUgvy8iQ335+9fvqZlxx5sLIpkY2I2eaZjl7c+PcpKUmYeyfZwHji/Ew+c39ltfW5uLjabjfnz53PxxRezePFiLr/8csxmMzExMaxcuZKgoCBSUlL45ZdfyMnJ4V//+hcdOnRwZm2/+uqrPPzww4AjUWjhwoX897//dZu0ApzVOex2O59++imbNm3iueeeIzAwkPHjx/PZZ585J85KBQcH88svv7B69WoeeeSRCt/Pz88Pk8l0zN+hlK6vJ5fiGqmpjIwMZxn8rKwswsPDPTsgEZGjCgsLOffcc1m3bp1zWVhYGBkZGXh5eVFQUMDo0aPdKhY+9NBDbNy4kQULFtTqXP7+/sycORODwcDbb7/NrbfeSseOHRkxYgSpqakAPPzwwzzxxBO6vnqA4hqpTNo399Jsx3QAbNd9hbHrJR4ekYhUymZzJAFl7oXCdEfVoKJMKMhwtBjLPQJ5hyEv2ZE85AEvLTXx6AITMTExpKenY7WW3XMaPnw4gwcPZu/evZjNZnJyckhPT6dVq1b06tWLrl270qZNGyIiIujQoYOzMmBJSQlms5nAwMAGO5mm66tn6HeXyqSlpTmrkubm5hISEuLhEYlIjWz7GdITHNUPz3/Gfd2aT+BXR8V008VvcijyHJKTk9m0aRM5aYeZaHsTgCTfjvwUehtJSUnk5OSwZ88e/tt6HRfEOeKiFq/mkVJQlpIy/gwf3rkoAIB75xbx9ipzpUOLjo4mNzfXbd5n4MCBjBo1igMHDlBcXExeXh6pqak0b96cnj170q1bN9q1c1RKdJ2HMpvNmEwmAgMDnQ/KNzRK8jmFKbhqeorNVno8PhuL0QejzcyW5y8h0PfYJdhFpObsdju5RRaMRli4M401+zMJ8vMmPMCHXzcdYX96ATa7nZgwf6w2O1f0iWX80A74eR+7/UNhiYX521LoExdOfFSQ27qk7CI+W7aPxMwi/tmXQXZh5YHMyXZmu0i6tQrloQs6E+Lvg8VqI7vITFSQb41u6syfP5/vv/+e//u//6Njx47H3D4vL4+77rqL1q1b89hjjzmfkj9y5AitWrVybnesa1tBQQFFRUUEBQXx8MMP06xZM/7zn/8QHR1NSkoKbdq0cQaDjz76KO+//z6ZmZnO/b/77jueeuop9zZmOKoOzZs3j1atWun6epIprpGaeuPbeTz6yP/RMsDK3u2bPT0cERE3xcXFZGRksHv3bv7880/OP/98Bg8e7LaNzWZjzJgxfPXVV85lRqOR//73v7z22mtuE3XnnHMOS5c62qf26NGDlStXcu211/Lbb7/VaDxRUVFkZGTo+nqSKa6RyiS9MojYgi2OD/dvgoiKrf9EpBEpbSVWUgCm3KOvPMeruNznoixH0lDOIcDu2Lco09F+7Dg8u9jE04vKHmIKDQ3Fy8uL7OxsajMF5OvrS+fOnQkKCmLdunWYzWYMBgPR0dF06NCBIUOGcNFFFxEQEIDZbGbTpk0UFBRgt9sZOnQo/fr1O67xHy9dXz1Dv7tUZtWs97nl9vHYI9qyK2GPp4cjInXBaj4au+Q6qgP5u1Q4LimEVe871oe3gdNvdd/34xGQ+A8AmeO3U2IzkpKSQnFxMb6JS+i75TkA1ra+jYXZrdm3bx979+5l9+7d7N692+1QwcHB+Pj4kJub63Z/5Fi8vb3p1KkTERERrFu3juJiR+vWyMhI2rdvz7nnnsull15KSEgIVquVLVu2kJOTg81mY+DAgQwaNKj2v9kJUJLPKUzBVdPz4+r9TPhxKwCDWnnx1X0jPTwiEakPmQUlvPLHDpbvyeBARiEAseEBRAb5sjnp+G7w1NR/hnbgkl6t6NIipME9mfXjjz9y8803M3ToUObMmXNCx3rrrbf4/vvvee655xg8eDDbtm3jiSeeoHv37kycOJGgoCA2b97MDTfcQG5uLp9//jk2m43TTz8do9Go66sHKK6Rmjr98R/JsPqD3caO5y/C3+fYSZgiIg1NWloa06ZNIyMjA5PJxI033sjgwYM5dOgQAQEBREVFUVBQQGBgIH/88QffffcdTz/9NPHx8ZjNZi655BLmzZtX6bGHDRtG+/bt+eijj5zLdH09uRTXSGWyn2pFuLGAQpsPgZPSHL2iReTUZbc7kn+Ksx1JQcXZjqpBxTkQ0gJCWgJ2x6SapQgKsxwVhKxmCqJ6Yml9Fhs3bqR169a0b98ecLQInTFjBocOHaJ9+/b4+/sTERFBVFQUBw4cYNOmTezatYtDhw6Rnp5OYWHhCX2F2NhYIiIiiI6O5owzziArK4vU1FS8vb2x2WwYjUb8/f1p3749BoOB9PR0kpOTsVqtBAQEEBAQQFBQEC1btuSMM84gNDSUyMhImjdvjpeXF2lpaezZs4edO3dy5ZVXYrFYaNu2ra6vJ5niGqlM8rNdaGE7QqHVi8Cnj4C3n6eHJCKeVpzjiGUi2rr/XceU56geZDBCWGsIinbbLT8/H7vdzsaNG2nWrBmdO3fGYDCwb98+Pv/8c/bv3098fDxBQUGEhYXRrFkzkpKS2LRpEzt37uTgwYNkZmaSl5d3QsNv2bIlkZGRREZGcsYZZ1BYWMjhw4edcY3BYMDf35+2bdvi6+tLRkYGhw8fxmw2O+OawMBAWrZsSd++fYmOjiY8PJzmzZvj5+dHWloa+/fvZ9OmTVx88cX4+vrSpk0bJfmcihRcNT3Dn5/FnnwfAL789wDO6dTMwyMSkfpkt9spMlvx9/bCaHQEPT+sPcSny/YRE+pP26ggLurZgv5tI1mSkMYtH68C4LSYEHrHhfHdmkPOY0UH+3HvsI40C/Fj+Z50Zq0/TEGJhdIrv7+PkR/vPpvurcIqjKMhycvLIzAwEC8vz03a6/rqGfrdpSZMFitdHp+D3WAkwljM+heu9vSQREQ8Yvv27Zx99tmMHDmSzz77jNGjR/Prr7/i5eXFwYMHadWqFbt37+bAgQOcf/75ur6eZIprpILCTHi5HQCHfdrR6vENnh2PiJzybDYbBw8eZNu2beTk5DBgwABiYmLIz88nOTmZLVu2MHPmTNauXQs47mHFx8c7k2x+++03bDabR8au6+vJpbhGKrBaKJnUDF+jjXQiiH5mv6dHJCKnOLvdTlJSEtu2bSMtLY3+/fvTunVrCgoKSElJYfv27fz8888sW7bMWfWwRYsWdOrUiaKiImbPno3Z7JnuG0ryOQUpuGpadibnMuKNJQCEGExsnHylc9JfRKRUvslCsJ+jjV+x2cpHS/bSMiyAS3u3wte7rLdoicWGr7eREouNhNQ8YsMDCA/09dSwGxVdXz1Dv7vURLHZSs+LbyXDFsjN11zOm+Mu8vSQREQ8pvRJMoPBQGFhIV9//TVXXXUVERERzm10ffUM/e5Swb6/4fNLATjS5jJajv3CwwMSETkxqampHDx4kOzsbA4ePMjKlSuJiYkhNjYWm82Gl5cXhYWF5OXlcfDgQXx9fYmMjKRly5b4+vpSWFhIUVEROTk5bNu2jVWrVuHt7U1OTg65ubkABAQEEBwcTFpamtu5dX09uRTXSAUlBUy7pRsx9jT6DL6YDv/53tMjEhE5IRkZGRw4cICsrCySkpJYsWIFERERxMfHO6sTmkwmcnNzOXToEAaDwRnX+Pv7U1RURFFREXl5eWzfvp2VK1diMBjIy8sjOzsbAH9/f8LDw0lOTnY7t5J8TkEKrpqWW6fNZ9HBEgAeGhLHfSN7eXhEIiKnJl1fPUO/u9REfn4+oaGh2O12kpOTiYmJ8fSQREQaNF1fPUO/u5SX/eszhK95HQDTyNfwO+t2D49IRKThKi4uxmq1EhQUBDiezi8oKCA3N5fY2FhdX08yxTVSXkFBAWFhYVitVg4ePEhcXJynhyQi0mCVlJRQUlJCUFAQBoMBu91OYWEhWVlZxMXF1ej66n2SxioitXQgo4BFB4rA4IWXzczYod08PSQRERGRBmf9+vXY7XZatWqlBB8RERFpNAp2LiT86Hu/Dud6cigiIg2ev7+/22eDwUBwcLDH2oOJiLs1a9ZgtVqJjY1Vgo+IyDH4+vri61vWZcNgMBAUFITVaq3xMYzH3kREPOHhL5aAwQuAm/q3dLbiEREREZEyy5cvB2DgwIEeHomIiIhIDdnthOTsBKDQ5gtRnTw8IBEREZHjt2LFCkD3ZkREThYl+Yg0QL+s3c+qZEe2no+thIcv6+fhEYmIiIg0PEUlVhas3QEGI2effbanhyMiIiJSM9kHCDUWOd4GdwCjbtGKiIhII2UxsWX1EkBJPiIiJ4tKg4g0MOn5Jv777Tow+gHwxCXdCPH38fCoRERERBqeVfsy2NV+NHH3X0Rui2hPD0dERESkRkp2/01pcXZ/teoSERGRRsy292++7LmUJ1oG4dWmwNPDERE5JSjJR6QBsdvt3PH+X5QcTfDpHW1gzLmdPTwqERERkYbpzw17ADD6BdKzc3sPj0ZERETkGEoKKVgyDb+l/3Muiuh3mQcHJCIiInL8srOzyVvwAXFAl2gvzB27eHpIIiKnBNWCFWlA3l+wjfVpNgB8bSY+vms4BoPBw6MSERERaZhKUvdTsGMpRlMeZ3Zo5unhiIiIiFQrd+FUgpY8h7e9BIA8mz+GeLUcFRERkcYnLy+PTp06ceeUWaw8ZKXEZsSn6yhPD0tE5JSgJB+RBuLbFQm8NH+v8/Mr1/UjOtjPgyMSERERadiytywi/eeXuNpnHbHhAZ4ejoiIiEi1vNd86PbZ1G00GL08NBoRERGR43dg83JCLBksT7Rw1scFbB70DvgFe3pYIiKnBLXrEmkAvliykyd/3QUGR97d1d3Dufz0tp4dlIiIiEgDt2TJEgDOHjhQ1Q9FRESkwSvwDifQnOH8HH3JM54bjIiIiMgJiNz0AXvvDwHg4T0DOX3EjR4ekYjIqUNJPiIeZLPZmTxrLR+vSnEm+FzQIYhXb1apZhEREZHqHDp0iO3bt2MwGBg8eLCnhyMiIiJyTBaz2fne+u8/8QpWu1ERERFpnGwFZYnLz09514MjERE59SjJR8RDjuQUcft7f7E1q2zZxZ1DeOvWc/UkuoiIiMgxzPp9AQD9+/cnMjLSw6MREREROTYfSx4ARTYfAuLO8PBoRERERI7ffmJZsnkVXds2p094C08PR0TklGL09ABETjXFZiv/+2Ud50z+vSzBx27jjjNjePu2czEaleAjIiIiUp1is5XXEiJodeeHxAwf6+nhiIiIiNSIv60QgCJDgIdHIiIiInJiFhV15caZRbyTewH4KLYRETmZVMlH5CQpNlv5evluXpm7lUL8wOgLgJ/dxNu3nMkFPWI9PEIRERGRxmHprlTsBi98IloSEau/0oiIiEgjYLMSaDABUOIV7OHBiIiIiJyY9PR0AKKiojw8EhGRU48q+TQQ33//PUOGDCEiIoKgoCB69+7Nyy+/jNmlV7c0Pna7ne1Hcnn06+X0euIXJs3d7UjwAbDbuKB9IKuevlQJPiIi0qQorpH6tnvnNor2r8dus3LDeT08PRwREWnCFNdInSnOobR4s9UvzLNjERGRU5LiGqlLGRkZAERHR3t4JCIipx499toAPPDAA0ydOhVvb2+GDRtGcHAwf/31F4888gizZ89m3rx5BASo1F1jYbXZ2ZiYzVeLNzNvexp5dn/HCoOvc5vuEXam/Gswp7UI9dAoRURE6ofiGjkZti36mdRv3+Km2+5kWLeLPT0cERFpohTXSJ0qzHS+tQfqiXcRETm5FNdIXVMlHxERz1GSj4fNmjWLqVOnEhwczOLFi+nXrx/guDgOGzaMpUuX8uSTT/Lqq696eKRSFZPFyuZDOcxbv5dFWw+xNw8sBp+ja/3LNrTbOCPGm0evPpN+8ZEeGauIiEh9UlwjJ4PVamXmzJkAXHflJfh5e3l4RCIi0hQprpE6V5jhfOsV3MyDAxERkVON4hqpc9kHebvbGhJjAmlmXQ3c5ukRiYicUtSuy8NeeOEFACZOnOgMrMBR3m7atGkAvP322+Tk5HhkfFLGbreTlmdi2e503vxjM2Pe+p2BT/5I18d/ZfR7K/hgZQq78n1cEnwAu412gSX83/ltWfn4BXz/0Cgl+IiISJOluEZOhtmzZ5OUlERUVBQXXHCBp4cjIiJNlOIaqXNFZZV8fMNbeHAgIiJyqlFcI3UuP40OISUMaetNpCHX06MRETnlqJKPByUlJbF69WoAbrzxxgrrzznnHOLi4khMTGTOnDnccMMNJ3uIpwyz1UZ2oZmcohIyC8wkZRaw/WAKu49kciizkNRCC3lWH6yG8v/L+IPBfYmf3UTXKB+uGtiFS/rFExnki4iISFOnuEZOhuScYl796GsA7rjjDvz9/Y+xh4iISO0prpH6YM5JpvSxsIDIWI+ORURETh2Ka6RelORRaIZAH/ANU/KyiMjJpiQfD1q/fj0AkZGRtGvXrtJt+vfvT2JiIuvXr1dwdZTdbsdstWO22iix2DBbbZgsNorMVgpMFopKrBSWWCkssZBTUER2fhG5BcXkFprIKigmI6+Y7CIzeSYrhRYDxXYv9+o7Ffg4XobK1wZTTM8WgYw8vSPndW1F26hADIYqNhYREWmiFNdIfbPb7Yz78C8O9RhDTGhfRl13laeHJCIiTZTiGqkPhRmHCDv6PiCqtUfHIiIipw7FNVIfzHGDCHohlwBvSEy839PDERE55SjJx4P27dsHQJs2barcJi4uzm3bmnrz9434+gdhtdmx2+1YbTZsNjtWux2bzYbdDlabHZvd5vinzY4dxz8dy+3Y7WCzO5aV/tOxzdHlrtuUvmxgx3W5Y0LG8dmxzA7Yjy632cGOYzmA2eYYl8UOVpsBK2C1G7BhwIYRGwbsBq/j+bldGABf94/HYLRbCTaaiQ4w0rVVGGd2aUPPNlF0igkh2E//G4mIiNRnXPPHlmQCgwu4uFdL93OmF7DtsKMkcO+4MFpHBDrXlVhszN+WAkCzED8GtHNvl7l6fyapuSYAzu/WHD/vsvjicHYR6w9mA9C1ZQjtmwW77Ttn8xHsdggL8OGcTtFu6zYmZnMoqwiA8zpHE+Jflkicnm9i5V5Hm4YOzYPo0iLUbd8/t6Vgstjw9zEyvGuM27rtR3LZm1YAwFntI4kK9nOuyy02s2RXOgBxkQH0ah3utu/fu9LIK7ZgNMConu6/4Z60fHYcyQOgX3w4LcMCnOuKzVYWbE8FICbUj/5t3X/Df/ZmkJFfAsCI7jF4e5V1Ak7MLGTTIUeZ7x6xocRHBTnX2e125mxOBiAi0IezO7r/husOZnE42/EbDj2tOVZTIXv27GHdrkQ2JFsxePsSGteZ3p0rvzkpIiJyouozrmHbLxAceOztmiS7pwfgWQdXOt8aAtXKXURETo56jWu2z3bENR2GgX9Y2fL8NDiw1PE+ujPEdHffb+dcMBeBTwCcNsp9XfIWSN/leN/2XAhuVrauOAd2L3C8j4iH2NPd9929wLGNwQjdr3Bfl54AyZuPfuEBEOaScGsuhp1zHO9DWkL8QPd99y+FfMf9EbpeCl4uD41nHYCktY73LXtDVAf3fbfMdPwzMBLaD3Ffl7gacg463ncaAX4u95/ykuHAMsf7Zl0hppv7vtt/BasJfIOh8wj3dUc2QsZux/t2QyAoqmxdURbs+cvxPrI9tOrrvm/CfDDlgtEbul3uvi51B6RudXxt/44AmGxGwpu1QkRETi5lJ3hQXp5jQiUoKKjKbYKDHRf13NzKe1qaTCZMJpPzc1ZWFgDv/LEFL79T9aZRDdlt+GDBz2gnyAdCfI2EBfoSEeRP62ahdGwVTYswf1qEBRAZ6IvRWD4bqIDstAKyPTF2ERE5qUqvw1ar1cMjabjqM6657+MFGH39OfzeWPfj9TyfsHNudmz75/sUJqxwrjP4BdFq7DsAFB3cROZvU9z2jbzoQQLiewNw5NN7sRXnOdcFdBhA5IXjAchZPoP8jfPc9m017iMMRm9K0vaR9sMkt3XhQ/9NUJdzAUj55jEsWYed6/xadyP60v8DIH/tbHJW/ei2b8yY1/EOisCSn0HKFxPc1oWddS3BfS8CIP3nFzEd3ulc5xMZR/PrngOgYPtishd96rZvs2sn4RsVj81SwpEP73RbF9JnJKEDrwcg84+3KNq71rnOGBhOy3+9AUDhvnVk/f6m275Rlz6Mf2vHjbrDH92F3VzsXBd42iAiht0BQPaS6RRs+ctlTwOxdzvGaEpOIP2nyW7HjTh/HIGdHDfUkmc8jDUnzbnOv/3pRF34HyZd3o28zBTyMhERkVpSXHNs9RnXHJo+jlB/Y6X7SNPnjDhzzHDokCeHIiLSJCiuObZ6jWs+v5NQfyPDpxeyK8PmXD8wzovvRjseInpndQkvLS1xO96q2wNpGWLkcK6NMz8udFs38Rxf/nOG4yHta38oYkVi2b/bzlFGFoxxzHt9vdnM//1pctt3zo0B9IzxosRip8NbBW7r7ujnw1ODHQ9M3f1rEb8mlB03MhA2jnP8BvP3Whj7c7Hbvl9e6c/gto7p1G7v5JPn8nWu6uLF1FGO7/rEX8V8vtHitm/ig47jrj1s5Ypvi9zWTR3hx1XdHAlD53yaz4HssnWD2xr58krHd52ywsTr/5jd9t0wLpCoQCP7sm2c96n7b/jUYF/u6Of4DS//poB1R8oSrXs0MzD3Zsd/C59vKOGJhe7/bhbcEkDnaC/yTHa6TXP/Df/T34eJ5zp+w9tmOc4ZGhrKkSNHEBGRE1eruMYuHjN58mQ7YB80aFCV2zz22GN2wH7hhRdWuv7pp5+243gUSi+99NJLL730qufXqlWr6issaPQU1+ill1566aVX43oprqma4hq99NJLL730alwvxTVVU1yjl1566aWXXo3rVZO4RpV8PCgkJASAgoKCKrfJz88HHNmwlXn00Ud56KGHnJ+zsrJo27YtiYmJVe4jIiIitZObm0tcXBwdO3b09FAaLMU1IuKqx9N/APDiVT24tHesh0cjIq4U1xyb4hoREZHGQXHNsSmuERERaRxqE9coyceD2rZtC0BiYmKV25SuK922PD8/P/z8/CosDw0NVXAl0sisO5hFfrGF8zo3O/bGIuIRXl5enh5Cg6W4RkRcGY+2Dg4O0f+/Ig2V4pqqKa4RERFpXBTXVE1xjYiISONSk7hGTcA9qG/fvgBkZGSwb9++SrdZs2YNAP369Ttp4xIRz7hq2nLGfLKKlNziY28sItLAKK4RkcoYDAZPD0FEpNYU14iIiEhTobhGRESk6VGSjwe1bt2aM844A4AZM2ZUWL906VISExPx8/PjoosuOtnDE5GTyG63O9+n5Zk8OBIRkeOjuEZEKuOlJB8RaYQU14iIiEhTobhGRESk6VGSj4c99thjALz00kusW7fOuTwjI4Px48cDcM899xAWFuaR8YnIyWG12Y+9kYhIA6e4RkTKMyrHR0QaKcU1IiIi0lQorhEREWlalOTjYVdccQX33Xcf+fn5nHXWWYwaNYrRo0fTsWNHNm/ezKBBg3juuec8PUwRqWcWlyQfPfAuIo2V4hoRAbC5xTUKbESkcVJcIyIiIk2F4hoREZGmRUk+DcDUqVP59ttvGThwIMuXL2fOnDm0bt2al156ib/++ouAgABPD1FE6plbkg+aDBORxktxjYiYbTbne1XyEZHGTHGNiIiINBWKa0RERJoOb08PQByuvfZarr32Wk8PQ0Q8xGItmwzTA+8i0tgprhE5tbm2ITUqsBGRRk5xjYiIiDQVimtERESaBlXyERFpANSuS0RERJoK17jGS6V8REREREREREREROqMknxERBoAi7VsMsz16XcRERGRxsY1rlHysoiIiIiIiIiIiEjdUZKPiEgDYLGVtetyeSsiIiLS6FgUzIiIiIiIiIiIiIjUCyX5iIg0AK7VezQxJiIiIo2ZayUfm10VCkVERERERERERETqipJ8REQaALMmw0RERKSJcE1eVu6yiIiIiIiIiIiISN1Rko+ISAPgOhlm1WSYiIiINGIWm5KXRUREREREREREROqDknxERBoAs0tmj2vCj4iIiEhjY3GJaxTWiIiIiIiIiIiIiNQdJfmIiDQAVj3xLiIiIk2EayUfu+IaERERERERERERkTqjJB8RkQbAYrO5vNdkmIiIiDReFqtr8rIHByIiIiIiIiIiIiLSxCjJR0SkAXCbDNNsmIiIiDRirsnLVlXyEREREREREREREakzSvIREWkAXNt1WZXkIyIiIo2YVe26REREREREREREROqFknxERBoAs2uSjybDREREpBEzu7XrUlwjIiIiIiIiIiIiUleU5CMi0gBYXdpaqF2XiIiINGaulXxcQhwREREREREREREROUFK8hERaQBcn3i3KMlHREREGjGza/KyKvmIiIiIiIiIiIiI1Bkl+YiINABuT7xrMkxEREQaMatL8rLCGhEREREREREREZG6oyQfEZEGwGwte+Ldqko+IiIi0oi5ViW0KstHREREREREREREpM4oyUdEpAFwTexRko+IiIg0Zha16xIRERERERERERGpF0ryERFpACxq1yUiIiJNhHsbUg8ORERERERERERERKSJUZKPiEgDYLGWzYBZNBsmIiIijZjZJa6xK3lZREREREREREREpM4oyUdEpAGwura1UJKPiIiINGKKa0RERERERERERETqh5J8REQaANcn3q2aDBMREZFGzKJ2XSIiIiIiIiIiIiL1Qkk+IiINgGtij1WTYSIiItKIubYhtaldl4iIiIiIiIiIiEidUZKPiEgDYHZpa+Ha4kJERESksXGv5KMkHxEREREREREREZG6oiQfEZEGwOrWrsuDAxERERE5QWaXYEbtukRERERERERERETqjpJ8REQaAD3xLiIiIk2FxS3JR3GNiIiIiIiIiIiISF1Rko+ISANgcWvXpckwERERabxKXCoUKsdHREREREREREREpO4oyUdEpAGwuLXr0myYiIiINF5ulXwU14iIiIiIiIiIiIjUmVM2yef7779nyJAhREREEBQURO/evXn55Zcxm821Os769et58cUXGT58ODExMfj4+BAREcG5557LO++8U+XxFi1ahMFgqPb13nvv1cVXFZFGoMSqSj4icvwU14hIQ2J2a9flwYGISKOkuEZERESaEsU2IiIiUte8PT0AT3jggQeYOnUq3t7eDBs2jODgYP766y8eeeQRZs+ezbx58wgICDjmcSwWC/369QMgODiYM844g5iYGA4dOsSKFStYunQp06dP548//iA8PLzSY8TExDBy5MhK15122mnH/R1FpHFxq+SjvhYiUguKa0SkoTErrhGR46S4RkRERJoSxTYiIiJSH065JJ9Zs2YxdepUgoODWbx4sTMwSk9PZ9iwYSxdupQnn3ySV199tUbHO/3003nkkUe47LLL8PPzcy7fvHkzI0aMYNWqVTz00EN88sknle7fpUsXPvvssxP+XiLSuJnLtbU4nF3E6v2ZXNKrFV5GgwdHJiINmeIaEWmIXOMau5J8RKSGFNeIiIhIU6LYRkREROrLKdeu64UXXgBg4sSJzqAKIDo6mmnTpgHw9ttvk5OTc8xjeXt7s2bNGq655hq3oAqgZ8+evPzyywB88803tS69KCKnFrcn3m12Rk1dwv3fbOCLFfs9NygRafAU14hIQ+RaodCmJB8RqSHFNSIiItKUKLYRERGR+nJKJfkkJSWxevVqAG688cYK68855xzi4uIwmUzMmTPnhM/Xt29fAIqKikhPTz/h44lI0+X6xLvVbienyPGXsT+3p3pqSCLSwCmuEZGGyq1CoXJ8RKQGFNeIiIhIU6LYRkREROrTKdWua/369QBERkbSrl27Srfp378/iYmJrF+/nhtuuOGEzpeQkACAr68vkZGRlW6TkpLCs88+S1JSEv7+/nTp0oWLL76YNm3anNC5RaRxsdhcknxcZsOKzVZPDEdEGgHFNSLSUJlt7pV8VuzJ4KMle3nmsu7ERQZ6cGQi0lAprhEREZGmRLGNiIiI1Kd6S/LJzs7m0KFDtGjRgujo6Po6Ta3s27cPoNqgJS4uzm3b42W3250lEi+55JIKJRRL7dixg6efftptmbe3N/feey8vv/wy3t7V/ysymUyYTCbn59zc3BMat4h4RonFvV1XqWKLknxEpHKKa0SkoTJbypKX7Xa44cN/ACgosfDNnQM9NSwRacAU14iIiEhT0tRiG8U1IiIiDUu9tOsaNGgQ0dHR9O7dm5iYGC644AK2bNlSH6eqlby8PACCgoKq3CY4OBg48SBl0qRJrFixguDgYF566aUK68PCwnjggQdYvHgxR44coaCggE2bNvHggw9iMBh4/fXXGT9+/DHP8+KLLxIWFuZ8lQaGItK4uFbysdldK/nYKttcRERxjYg0WFVVKDyYUeiJ4YhII6C4RkRERJqSphbbKK4RERFpWOolyadLly789ttvrF27li+//JK8vDzOPPNMZs6cWR+na3CmT5/Os88+i9Fo5JNPPqFTp04Vtunbty+vv/465513Hi1atCAwMJCePXsyZcoUvvnmGwA+/PBDNmzYUO25Hn30UXJycpyvxMTE+vhKIlLPzNbKJ8NMquQjIh6muEZEaqvE6t6uq+y9J0YjIlJGcY2IiIg0JScrtlFcIyIi0rDUS5LPxx9/zIgRI+jTpw833HAD//zzD08++SQ33HADn3/+eX2cskZCQkIAKCgoqHKb/Px8AEJDQ4/rHN9//z1jx44FHIHRNddcU+tjXHXVVfTp0weA2bNnV7utn58foaGhbi8RaXzMbu26ypbXtJJPer4Jm2bORE4pimtEpKGyWN3bdZVyTfgREXGluEZERESakqYW2yiuERERaVjqJcmnMhMnTuTbb79l/PjxzJgx42Sd1k3btm0Bqs0yLl1Xum1tzJw5kxtvvBGbzcb777/vDLCOR9euXQE4dOjQcR9DRBoPs0tbC9cWFybzsSv5LN+TTv/n/+ThHzbVy9hEpGFSXCMiDZVrhcLaVvIxW21M/m0bf+9Kq4+hiUgDpbhGREREmhLFNiIiIlKf6i3Jp7CwkISEBBYtWsRXX33Fyy+/zMKFC2nVqhW33XZbfZ22Wn379gUgIyODffv2VbrNmjVrAOjXr1+tjj1r1iyuv/56rFYr7777LnfccccJjTUjIwMoy/gWkabNdTKsqKQssafEeuxKPm/MTwDgx3X6i5jIqURxjYg0VFW164JjZ/l8+c8BPlyyjzGfrKqHkYlIQ6W4RkRERJoSxTYiIiJSn+olySc8PJyQkBC6dOnC8OHDueWWW5g4cSJffPEF/v7+DB06tD5Oe0ytW7fmjDPOAKi0mtDSpUtJTEzEz8+Piy66qMbHnT17Ntdeey0Wi4V3332XcePGndA4k5KSWLJkCQADBgw4oWOJSONgcZkMyzdZnO9r8sR7odly7I1EpMlRXCMiDZXFrZIPlb6vyq6UvHoYkYg0dIprREREpClRbCMiIiL1qV6SfG677TZefvllvvrqKxYuXEhCQgKFhYVkZmayefNmfv/99/o4bY089thjALz00kusW7fOuTwjI4Px48cDcM899xAWFuZc99NPPzkTlsqbM2cOo0ePxmKx8N5779U4qJo6dSrp6ekVlm/atIlLL72UoqIiOnTowOWXX16r7ycijZNrxR7XJB+z1YbdXv2MWGHJsVt6iUjTpLhGROqK3W4/ZsxRU27tumxVVfWpXJHiGpFTluIaERERaUoU24iIiEh98a6Pg77++uv1cdg6ccUVV3Dffffx5ptvctZZZzF8+HCCgoJYsGAB2dnZDBo0iOeee85tn5ycHHbu3ElxcbHb8tTUVK666ipKSkpo3bo1y5cvZ/ny5ZWe99VXXyU6Otr5+emnn2bChAn06dOHdu3aYTQa2bNnD+vXr8dms9GmTRtmz56Nn59f3f8IItLguFXyKS5L8rHbwWSx4e/jVeW+hSZNhomcqhTXiEhduWfGerYfyWXO/edWG3fUhGtcY7KUJfzUJIdIycsipy7FNSIiItKUKLYRERGR+lIvST4N3dSpUxk0aBDvvPMOy5cvx2w206FDByZOnMiDDz6Ir69vjY5TWFiIyWQC4NChQ3z++edVbvvMM8+4BVaPP/44y5YtY+vWrcyfP5+CggJCQ0M5++yzufzyyxk3bpx6oIqcQlyfeM8zubffKiyxVp/kU6J2XSKnMsU1IlIXftt8BIDFu9IY0b3FCR2rqgqFNarkY1aSj8ipTHGNiIiINCWKbURERKQ+GOx1VZO9GiNGjCAzM5PVq1fX96lOebm5uYSFhZGTk0NoaKinhyPS5BWWWAj0PfF8yf7P/0l6vuMvagE+Xm4TXEsfGUrriMAq9+342BwsR1th7H/p4hMei4hUpOurZ+h3Fzl52k78DYCp1/fh8j6xJ3Sss15YQHKu48nTge2jWLE3AwBfLyM7nx+JwWCoct/R7y5nzYEsQHGNSH3R9dUz9LuLiIjUPV1fPUO/u4iISN2rzfXVeDIGVFJS4tZzVESkKZiz+QjdnvqDT5buO+FjuVbyKf8E+7HaVpQm+IiIiIicqOI6qKTjGtcUuFQcLLHaSM8vqXZftesSERERERERERERqdpJSfIREWmK7pnhSF589tdtJ3ws18mw8jTZJSIiIidLUR3EHW5JPuXakB7MLKx237pIMhIRERGpS7nFZrYfyfX0MEREREROWFGJlS1JOZyERj8iUo+U5CMicpzqsoCOxVr1wQpLLFWuExEREalLReaqE49ryuwS1xSXO17iMZJ8XCsa6oaTiIiINAQXv7mEUVOXsHp/pqeHIiIiInJCxnyykkveWsqvm454eigicgKU5CMi4mF2u52Sair51MUT9SIiIiJVsdlck3JOLO7ILixxS9Qpf7xjVfKxuozFXE0StIiIiMjJkphZBMDsjYc9PBIRERGRE7N6fxYAX/xzwMMjEZETUaskn+7duzNmzBhef/11Fi9eTG6uypSKyKkhLc/EzR+tZM7mus9uth6jJFBt2nVZqkkWOlnsdjv/98NGJs3e6umhiIiISA1YXSrmFFuOP8mnxGKjz7Pz3ZaVT/JJzi2u8fFK234Vm61MX7H/mFWAREREROqTHsISERGRpkLt0kUaN+/abLx9+3Z27NjBV1995VzWvn17+vXr5/aKioqq84GKiHjSi3O3s3R3Okt3p7P/pYvr9NjHekq9Nu26Sqw2vL08W6TtUFYR3605BMAjI7vg7+Pl0fGIiIhI9VwTjotPYPIqOadiAk+xxT0BOfU4knzeW7yHN/5MwN9nOzueG3Xc4xMRERE5EeXjGhEREZHGSsnLIo1brZJ8pk+fztq1a1m3bh0bNmwgLy+PPXv2sGfPHn744Qfndq1bt3ZL+ikoKKjzgYuInExpeaZ6O3Z1rboACkxVB1vlqwCVWGwE+tbJsI6b6/fJKTIryUdERKSBs7lW8jEf/+RVVmFJhWXlY5WU3OpjKtexlMYUaw9knfDYRERERE6UJsNERESkqShSJR+RRq1WST4333wzN998s/NzQkKCM+ln3bp1rF+/nqysLBITE0lMTOSXX36p8wGLiHiCvfpiOyfkcHZRtesrmzArZS6XIFTSAJ4qK3RJSsopMhMT6u/B0YiIiMixWGx1064rs6DqmCU80IfsQjMpx6jk4xrLlFY7jArycAYzjnakBoPB08MQERERD1JbCxEREWkq9CCVSONWqySf8jp16kSnTp24/vrrncv27dvnTPopfaWlpemGqIg0anaqzvLx9zmx9lh70wqcx6kssErPr/qJ9/JJPqYGkOSTbyprL5ZTZPbgSERERKQmbC5JPoUn8IR6RjVJPnERgWQX5pCeb8JSTXtR1zam5qNxTWSQn9tYjcaT+3fLf3+2mtQ8Ez+NP9vjbVFFRETEc/TEu4iIiDQVSl4Wadzq/A5lu3btuPrqq5k8eTJz584lJSWFgwcP8tNPP9X1qURETprqKvn4eZ9YO6q9afkAdI4JcVvufXQC6+tViSzamVrpvq4TYXDs1l8nQ4Frkk+hknxEREQaOteWWpYTiCUyC8oSk+8b1tFtXcswf7yMBmz26pOBXBOYS99HBPqUnaOaCofgSAKy12EJxhKLjQU7UtmclMOulPw6O66IiIg0Dq5xxdoDWcesSigiIiLSGOSbLOxOzfP0METkONUqyeeHH36gsLCw1idp3bo1l112Wa33ExFpKMrPFbne5PHzPrF8ycQsx5+rHZoFuy1vGV7W5mrij5sr3bf8RFxDaNelSj4iIiINn2ssY3V579q6q7ZKk3duG9SWttFBbuv8fbxoFuyoyFPV5JjNZnc7f2nysmtR2Oom1ixWGyOn/s1tn60+rvFXxjWWUXFaERGRU0/5h6t+35LsoZGIiIiI1K3v1hzy9BBE5DjVamb62muvpVmzZlx99dV89dVX5Obm1te4REQalPLtulwr5nifYMuI0smjZiF+bstbhQU43/tWkUhUvnKPa5LPfV+v56KpS0564o+SfERERBq21NxiBrywgMm/bQPcK/mUbwVaG5n5jiSf6GA/vMrFR77eRmJCHbFOck7liTrl45rSSTVLDduJ7UzJY1dKPot2pp1QRSJXOUVllYNOpJWZiIiINE7lY6MTiZVEREREGhI9yyTSeNUqyefJJ5+kffv2/PTTT4wZM4bmzZtz8cUX88knn5Cenl5fYxQR8TjXSj4Wq81tMuxEG0LkFjmSYiKDfN2Wuyb9xEUGUBlLFe26UnKL+WXjYbYdyWVfesEJjrB2XJN8spXkIyIi0uC8vXA3aXkmPlyyDyif5HP8kU3m0Uo+kUG+FdqZ+ngZaR7qqFKYkmeqsK/j3JVPotU0CSnI19v5Pq/YUuV2tZHt0nrUtSWpiIiInBrKPziVq/scIiIi0oi5PpOVp/scIo1WrZJ8Jk2axObNm9mxYwfPPfccPXr0YO7cudxxxx20bNmS4cOHM23aNI4cOVJf4xUR8bhii83tifLyrbxqq7TaTfkkn+6two65b/mJrge+2UBusZkVezKcy052awnXCbBU9aoXERFpcI6Uq6RjcwknTqQCToZLko+/j/tfNf1cKvlUFR+UTzAyH51Uc427rNW0E3ONeeqqmqCSfERERE5t5SsNnkiMsTs1j5V7M469oYiIiEg9sFhtuN5WOZG4JjGzkL93pdXBqETkeNQqyadU586deeyxx1izZg379u3jlVdeYcCAASxatIh77rmHuLg4Bg0axJQpU9i/f38dD1lE5OSzuWTyFJVYsbpMQtlOIMvHbreTmFkIQFS5JB8/byOf3NofqPpp9PKTYUnZRbwxP4H9GWXVe8pX+ykvNbeYojpsP+FayWdPWn6dHVdERETqRlq5SjpWl1impA4q+UQF+eLv417Jx9fbSMujrUiTsosq3b/8k/IllVTysVST5OO6XV0l+bgep0DtukRERE45FSr5nEC1wPOn/M11H/zDwYzCEx2WiIiISK2VT14+kQqF5768kDGfrGLtgawTHZaIHIfjSvJxFR8fz0MPPcSyZctISkri7bffZvDgwaxatYr//ve/dOjQgf79+/PCCy+wY8eOuhiziMhJV+gyqVNstrpNMJ1Iks+r83Y6SyJGlEvy8fU2EurvA1QdbFXWsuJgZqFbYo/FVvUT+ck5xQx4YQGDX1lYq3EfzCgkt7jyMbk+5Z6Qmo/9REsdiYiISJ2qkOTjEitU1w7rWDLdKvmUS/LxMtIuOgiAPWmVtxKt2K7LEUO4xjXbj+SSXVhS6f6uMVltknxW789k+5HcSte5th5VJR8REZFTj6lcks/iXWnsTM6r9XFyXKoDJqTWfn8RERGRE1U+eXnlvkw2Hcqu9XFcH7JatS/zRIclIsfhhJN8XLVo0YLx48ezYMECUlJS+Oijjxg5ciRbtmzhiSeeoHv37rz66qt1eUoRkZPCtdJNkdnqFsSUr6ZTG+8s3ON8X1kln5DSJJ8qnhSrKoHH7DZZV/X4Vu13BGCpeaYaJ+McyCjgvFcWMvCFBZWuLzaXnTu70OzW5kJEREQ8L88lUddut2Otg3ZdJovVWc0vKsivQrsuX28jHZsHA7CniiTg8k+UWZyVfMqWv/z7Tga++FelY3DdvaZJPqm5xVzz3gpGTV1S6focl4SifCX5iIiInHLKT4ZlFpQw4o2/sVVTXbAyBzLLkpyzdJ9EREREPKBCBWWLjcveXuaWjFwTyS5t2O3oIW8RT6jTJB9XkZGRjB07lt9++43U1FS++OILrrzySgwGQ32dUkSk3hSUlE3qFJVY3ZJrTuSJd1eRlVXyCfAGHJV8Kp0Ms1Rc9uf2FH7deMT52VrNjafIwLJz1rQFxfI9GdVub7K4Ly80q7WFiIhIQ+KaAGyy2Ookebm0io+30UBogDf+3hXbdbWNCsLLaCDfZCEl1+Tc79Nl+1izP7PKdl3lW3QVVRFbHE+7rkMurcMqm6xzjXdUyUdEROTUUz4JuVRGQeWVBatywKVF1+EqWpeKiIiI1KfyFQpLJWbVrpXogYyy5OXknGJ1cxDxgHpL8snOzmbLli2kp6cTGhrKTTfdxA8//MCECRPq65QiIvXCbreTVVA2UVRY4l7Jx3IClXxcBfl6u332c2nXZbHZnU+P22x2Jv64iUmzt1Y5gZXkcsOouifyXZ+yf+jbDdWOL6fITGJmIcZyuZrp+Sb2pZcFdeUDxfITdiIiIuJZrgnKxWarW5urqiayjqU0ASbIzxuDwVChXZePlxFfbyMtQv2Bsljlrb8SmDR7G6PfW0FeucqFpQlH1SUsuzqedl1Gl4dQSqw2is1WjuSUxVHFLglFhTVMiBYREZGmo6p7GrVN1HG9bzJl/i76PTefD//ee0JjExEREamNqu75pOYVV7q8Kq5xzfQVB+jz7Hxe/n3HCY1NRGqnXpJ8Bg0aRHR0NL179yYmJoYLLriALVu21MepRETqXZ7J4hb8FFvKteuy2Y47Uzk62FFJp32zIIxGA67Fzny9jQT6ejknw7YezgVgR3Ie36xO5NNl+/lq5YFjnsNczcSY66p521KqrUrU//n5nPvyQg5nlwV86w5m0f/5Pxn66iJSj5ZoNJmV5CMiItJQFZutbpVxisp9tlhtLN+dTmJm7Z7iKk3I8fFyBDMBPhUr+QDEhPoBkHI0bkjMLJsg234kt9wxK6/kA1Qae7km+aTnm2o0btfk5WKzlUvfWsrAF/9iT1o+f+1IYe2BLOf6dQezKjmCiIiINGWl8Uiov/uDWbVN8tmclOP2ObOghC/+OfY9HREREZG6UhrXhPi5xzVJ2bVL8tlSLq7JKTLz+fL9JzQ2Eamdekny6dKlC7/99htr167lyy+/JC8vjzPPPJOZM2fWx+lEROpVRr57CeaScm0t7PaaP2FeXumk1Qe3nA6Aj7Hsj2VfLy8MBgOnx0cAOCeZXEsnZtagPLTVVnWSTflxp+ZVPSFWOnm3PjHbueyqacud70tvWBWXa9elJB8REZGGo3y1nKJyFQqzCs3c+NFKzn15Ya2OW1rZ0PtoLOPn4/5XTT8vx+cWYY7k5dIkn8yCstjj6V+2uu3z0twdfLRkLz+sPVThfJVV1XH9Hq7JQ5Wx2uxkF5a4JTybLDYSUvMBePuv3Yz9bA07kvOc6zcdymFPWn61xxUREZGmpfSeRqvwALflB2uZEL3pUHaFZVW1IBURERGpD6VxTXC55OXaPui1MTGnwrIis1Vtu0ROonpJ8vn4448ZMWIEffr04YYbbuCff/7hySef5IYbbuDzzz+vj1OKiNSbjHJPgpsstgpPlFf2hHlNlLa2CDzaqsvL5XHy0smx01qEAGV9Tg9llU1a1aRthLmadmK2ckFXcs6xM7Z9vSq/dJS26apQyceqm1YiIiINRW6xexurwhJrhXjgeJiPJhV7H63k4+dtrFChECDmaIXC5KNJPhnVJCznFJl5/rftla4rbWPqyvV7HOsG1U0f/UOfZ+ezw6V6kGtrLtfkHle5NWwDJiIiIk1D6b2OQF/3KoXfrkmsthqyq8yCElJyTRgM8PG/+nPjmW0A99hDREREpL6VJvmU3qMp9dP6pErvs1TGYrWRkOq4Z/LOjf2487z2gKNrRHVzUSJSt+olyacyEydO5Ntvv2X8+PHMmDHjZJ1WROSElW/3YDJbK1TAqaqXaXVMFqsz6Ak6Wh7R2yXJpzSZpvRGUumNpUMulXwKS44deFVXZaj8uqvfXY6tku0tLt/P19tQYT2UBYimcpV8TKrkIyIi0mCUT1IpriSuOR4WZ7suR/xiMBjwc7lpVHoDqbQNacrRxOLM/GNXJaxMZTefXMOxg5mF1T5B9s/eTAC+WZ3oXOYas1RVCVFxjYiIyKml9B5H+cmwvWkF/LbpSI2OsftopcDY8ACGd43hnqEdHcc2K64QERGRk6f0nkb5B7nT8kx8s+pgjY5xILMQs9VOgI8Xo3q0YMKFnZ3rynd5EJH6U29JPoWFhSQkJLBo0SK++uorXn75ZRYuXEirVq247bbb6uu0IiJ1Li2vBpV8jiNDucBUFvAEHU3kKX36HcpuIJVOkJXe/HGr5GOqSSWfatp1VTL5lVfJpFmxy4SW0VB5kk/pja/yk19q1yUiItJw5JZv11VnST5HK/m4JCz7+5Q98V56Ayk80AdwtA0zWayVxh01UVBpkk/Z9ygyWzlcgwqFrtV/XJ+mr6p9hp64FxERObXsS3dUVW4W4l9h3d8JaTU6RunT7h2bBwNlMVKJ1VZtHJaebyI199jxjIiIiEhN7M8ojWv8KqxbkpBeo2MkpDiSlzs0D8JoNODrVVbJubp7JjmFZpKyq2+tLiI1533sTWovPDycvLyy8ualT1CGh4cTGxvL0KFD6+O0IiL1Yv3BbLfPJoutwtPdluOo5FM6OeXvY8T76MSXl7HiE+9+3qWVfBwBkluSTw0mmqpLQDpW1Z5SRS5twapq6eFs13X0nwE+XhSZrUryERERaUCyC90r5xSVWN2ScY6X+WhM4e3yNJi/txfgqBzkczSuKa30U2K1kVlNq65jOVa7LoBBL/3FtmdHONuiVsZ1Ys01UTm7oPK2XKrkIyIicupYtS+TN/5MAKB9dBA3DGjD16sOMrB9FCv2ZpBRw4qEe9McE2odmpUm+ZTFSyaLtdJYxWqz0//5PwHY/uxIAnxPPF4TERGRU9eulDwe/2kLAO2bBdG/bSRvLkhgQLtIVu3LJKPAdIwjOOxNdyT5dDwa1xgMBvy9HXNB1VUpPPPFPyk221j52HBnK3cROX71kuRz22230bp1a2JjY2nVqhWxsbHExsbi76//aUWk8Vm625HBHBcZQGJmESaLtULizJoDWVzUs2WtjltwtNVWsF/ZH8WuN3pKK/j4HV1WWbuumjx5b7LYSM0rpnklT52V7t61ZSjbj+RWeUy3J9tLKk8sKq1MZDq6bYi/tyPJ5zgSoKTh+GtHCjNWHuSlq3sRHVwxw19ERBqHvGIzIf4+7Dk6yVSqyGx1JuCUZ7fbMVRRwa+80iRhHy/XSj4uycte7kk+ZquN/OLjq+ID7hURS1WWiLwzOY++bSKqPI5r3LNmf5bzfVUVhj5eso8LusZgNNbsdxEREZHG64O/9zrfx0cFct/wTtx5Xnv2puU7knyOMRlmt9v5aMk+Z/uL1hEBQGkitEOx2Uagb8V9XasKHs4pciYIiYiIiByPmeuSnO9bhPozfkhHrujTipwiM1dOW16j5OWvVh7gk6X7AGgdEehc7u9jpMhsrbaST/HRBKC1xzGXJiIV1Uu7rtdff50JEyZw/fXXc95559GhQwcl+IhIo1RisZF6tF1XnzjHBJHJXLGc8viv1rH5UE6tjl06OeX6xJZbW4vy7bosNnKKzOTVckLssZ82M2DyAmcSj6vS7xHo6+U8X/lWZOCe5FNYRZLPkZwiRk1d4mwDEuLv+F7qMd+4jf1sDX9uT+XZ2ds8PRQRETlOH/69l57PzOPnDUnsSs5zW1dstlZa2Q+oVaKu+WgCtI9rJZ9K4pqyJB97lVVxbj277THPl2+qWGmnskTl6qr4lN/nf7/vqHK70jZjq/Zn8uvmI8ccn4iIiHhOicXGlqQc5m4+UmWcUxMxoWUPunSOCcHLaKBddBBRRx+AyTzGZNjGQzlMnrOdgqP3UVqGOe6Pl7a2gKrbWrjdh6lBq/bK/LUjhYk/buK71Yms3p95XMcQERERz7La7Gw/ksvsjYcxn8AD1cF+ZfdourUKxWg00L5ZsPPB3oyCEmdnnsqk5hbz+E9bSD8a/7QIK5v3L73/U1zFXJBrPFbVQ+THsnp/Jg99t4Hv1yTy966atUwVacrqJcmnMfj+++8ZMmQIERERBAUF0bt3b15++WXM5srLsh/L2rVrueaaa4iJicHf35927dpx7733kpqaWu1+KSkp3HPPPbRr1w4/Pz9iYmK45pprWLdu3XGNQ0TqVm5x2Z8JUUGOR6tMFluliTDzt6fU6tilbax8XZ6e93Z5KtzPyxEY+fmUtetyreJTWzPXHaqwrPSJd6Oh7NyVtfdyfYJs5b7KbwxNX3HALZEoxN8xGaZKPk1Dab9eaZgU14hIdSbP2Q7AQ99tZFeKI8kn4mjSSlGJtdK4BmrXmspytJWpWyzjkuTjVy552Wy1VXr8/wztQGiAzzHPV1nScWWVfCq7AeZ6c8lazQ0sV2EuY6oscVpE6o7iGhE5UXd/uZZL3lrK3V+tY9625OM+Tml70N6tw+gRG+ZcXnp/6FiTYblF7n9utQgLcL4vrdpcPsmnsMTC+VMW89B3G53LsouOr8Xp2M/W8M3qRP7vx01c896KascqIvVDcY2InKjHZm5m1NQl3Pv1emasPHjcx8k82pY8KsiXoac1dy6PCnbENSUWmzMxuTLZ5eKalpUl+Vjc97fa7FzxzjJu/Wy1c1lRNdV+qnPrJ6uYuS6Jh3/YxJhPVlFYcvzVoUWagpOS5DNixAjOOOOMk3GqGnnggQe49tprWbZsGQMGDGDkyJEcPHiQRx55hGHDhlFUVFSr4/3www+cddZZ/PDDD8THx3P55ZdjNBp5++236dWrF7t37650v127dtGrVy/eeecdjEYjV1xxBfHx8fzwww+ceeaZ/PTTT3XxdUXkBJTekAnx9ybQtyzZprInxQuraOtQlcomw4wuLTEqVPIx28gtOv7AxfVmUqnS72E0GMqSfGwVJ8OOJ7u6tJJPSS0mCKXhUkWmhktxjYjUlNVmJy3fUaGw/dGWD0VmW9WVfGqT5FNZJR+XRObylXxKLLZKj+9lNOLrdexWWOZK9q0sr7iyBCbX5KKaPt0f7pLko05dIvVHcY2I1IUFO8omu1fsyTiuY2QWlPDzhsMAjBnY1m1d6WSY6RiTYeWTkiudDCv3d+2FO9LYnZrv9oR6VmH1yQDrDmYx+bdtFLjcl6oszjqcU1ztcUSkbimuEZG68O2aROf7uVuOr7JwvsnCJ8scbbbGDW7v1po90NebgKNxSUZ+1a1IKyYvl8U1pXNY5ZOX96TlsyEx2y2uqa6lFzjarj/36zYyC9yTnMvHXLtT86s9jkhTd1KSfEpKShpMRvCsWbOYOnUqwcHBrFy5kj/++IMff/yRhIQEevbsydKlS3nyySdrfLzDhw/zr3/9C4vFwvvvv8+qVav49ttv2bVrFzfffDMpKSnceOONFZ6UsNvtXH/99aSmpnLLLbewa9cuvv32W1atWsX777+PxWJhzJgxJCcf/9MmIlJzliqqzZS2ngr198HPuzTJp/JKPqVtvWp+zoqTYUaXP5XLknzKzltVOcbSBKTqlCbduCp94t3LaMDbq+p2XceTXV36FL6SfJoGVWRqmBTXiIgru93O4z9t5qFvN1SZvFL6RHpp+4kis7XKaja1qeRTGqN4uyToBLjEJ6UtKXyOri+x2jBZKsYXPkaDW2xU9fkqjrk0ebltVKDLsorfwfW8Na3k41pdyICyfBqrjYnZ3Pv1epKyazehIieH4hoRqQ/+NbhfUpnnfytrWR15tHJPKdfJsNRc98SZXzYe5uOl+5i2aDdrD5RVQvYyGpztMAD8Syv5lIuHXKs9l8oqqL6Sz1XTlvPhkn188Pde57K96RUnvhJS8iosE5H6obhGROpDlEssURvvLdrjfB8R6FthfWkCc/k5rgXbU5i2aDcfLdnL3wnpbutiw8seKq8qebmyh6TKJwuVN3Lq33y8dB+Tf9vuXJZWydxbQoqSfOTUdsq163rhhRcAmDhxIv369XMuj46OZtq0aQC8/fbb5OTk1Oh4b7zxBoWFhZx//vnceeedzuVeXl68++67hIWFsXr1aubNm+e239y5c1m/fj3h4eFMmzYNL6+yv3DeeeedDB8+nPz8fKZOnXrc31VEauaLfw7Q/ek/mL+trN1WUYmVncl55BwNOEIDfJyllE1mW6UTRjuSa9e6obLJMNdKPl5HIyBnJR+Ltcokn/I3nKo7n6vSyTAvo8F5vsradR0ru7oyQUdvpFWWHJKRbyIx8/hbj8nJdzz/DUj9U1wjIq4yC0r4auVBZq5PYmcVkzil93ybhzieuCo2V16hEGpZyefoMbyNrpV8yv4s8Cmt5OPSrquy43t7GWuU5FNZfFGavNw8xJ/2zYKOnqeyuMZW6fvqBPmWJUsblOPTaF05bRmzNx7mvq/Xe3ooUgnFNSJyPIrNVm788B8mH03KKT/B/f7ivdz95dpaH/cvl2pAEZXcc+kU46iKuPVw2b0gk8XKfV+v57lft/Hy7zv5cMk+57r3bj7ded8FyuKk8n/X9q6komFWYc3adbm22V57IAuAAW0jubhnSwBWVdF+XUTqnuIaETkeVpudO6av4b/fb6x0/W+bjnDd+ytqXJW41Kr9ZTFAZUk+nZofjWuSyv5Mstvt/PvzNbz8+06e/207by5IcK6ben0fwl2O419FG9LK7rkcq0JhaSi39XDZWErjmvbNghgzMN7xnRTXyCnulErySUpKYvVqR9+/G2+8scL6c845h7i4OEwmE3PmzKnRMUtLGVZ2vODgYC677DIAZs6cWel+l112GcHBwRX2LT1e+f1EpG7lFZt5ctYWTBYbM9cdci5/5Y+djHjjb95d5ChzGhbg7ZZsU1m1m10p+WTX8MYLlE2G+bhMhhkqmTUqDZAclXwqD96iapDkU9lEWunXMBoM+By92VTZRF9NJ8Bc+XqXjbu8y95exrkvL9RT1I1Ibao5yMmhuEZEytuXXjax4zrhVJ6/j5Gwo5VpikqqTvKprNJOVUqrIvq4TEyVJkhDWSWf0n+aLfZKE3V8vAzORKDqVBbXONuQGsviq8q+m+v3quxpsMpUNuEmjU/pfw4bE7M9Og6pSHGNiByvxbvSWL4ngw+X7OPb1Qcr/bvr3C3JNb7ml4qPLKsM6Nq2s1SfuHAANrhcUx6dubnSY910Zhsu6Bbjtqz0iffyrbFNlTxgU75dhSvXB7qKzVbe/iuBO6ev4fGftgAwpEszRvVsAcDny/er2rLISaC4RkSO1/YjuczflsIPaw8xZd5OAIL93Ds0rNyXSUItW1XFhFZsGeqqT1wE4B7XvP1X5S0AR3SP4fI+sW7Lyir5lE/yqV1c48pqs/PRkr3c/vka7jqasD30tOaM6uFIXv5h3aFjVjsUacpOqSSf9esdT+tFRkbSrl27Srfp37+/27bVycvLc/Y5Ld2vpscr/Xys/RISEigoKKh0GxE5cdtcJsCCXIKl0v6k/+x1ZAOXb9dVOmEUHeznVpZwQy0mDCqr5FPZ/JHzvOaq23VVFpiVV2mSj2sln6MnN1dSpeh42nX5Hn0ypLLzlib3/HK0v700TK5PQFZ2o1E8S3GNSNNTbLbWKrGmvL0uST5bkqp+IjTU38fZSquomko+5SedqlOaiOztUoXn/9u77/go6vQP4J/Zmk3vjYSEJBB6B+lFUBAVUSyoKGC7A7ue/izneZ71PM+znR1FRfQsoChWBKSJdKTXAKGF9F62zO+P3ZnM7s4mu2ST7MLn/Xr5MpmZnf3uDsk+me/zfR7lanUp+Veq0mO22lTPr9MIMHiRUKMWE0mVfDSCIMdXasf5mrjaOy0KOsVrYQvL4KeWsE/ti3ENEXlSUFHnMVY5VlqDj38/Kn//f19ulysyu9p8tNSn521wxDbpsSZkKFqBSnp1iALQuMq8vMaMhZuPq54rIsQ9SUjjiC3++cMep+1q91+aGvup8sZ2YT/uLMALP+3DT4pK1aO7JODiXimIMOpQ3WBVbeNFRP7FuIaIPCmqqveYcHu6sg5v/NrYVuuVZQdQVFWP6gaL27FSZRtvKRenD8mKddvfO80e1+xwzJeJooh//7xP9VyRKnGNtED+0a92OG33FNe4Vl6U1Che6/7TVXhqyW4s3e0c1wzNjkNGXCisNhG7TvrWXYPobKJr/pBGPXr0wIABA9CvXz/0798f/fr1Q2RkZGuNze/y8uyT9h07dvR4THp6utOxTTl8+LD8tadzejpfc2ORHieKIg4fPowePXo0Ox5Xoii6VQURRRGuvztdf5Uqf7m673N9rGuP1+bG5Nvjmxqb6363527F51J/vPOGyBA9rDYRWo2A/JIaRJr0sNhExIUZoNdqYLWJsNpEhOg1qtVbmuPpQxBQrwYjKatpwK4TFeiRGoWoUD1OlddBhIhwow4Wq6hagtjT8zdWgXF+TlG0vzarKEKv0cg3LpT7RRFO29X+vUoaLDYcLalGSpRJTsQRRRE/7yrAZxvzYROBcd0SMW1QR2g1AkRRxMLNx/Hd9pOY3DfVLatYSdnKorKu8UZQQoTRaZVXpEmvqOTTmOTTPTUSH940GFe/9RvW55Wgqt494PLEojIZpvYeKCsIeQoAL+/XAb83U55QdcW7YjKsqRXvtQ2+TTjqNII8mffmrwdx7wWd5WQl5fm5ijqw7T7Z+PPBSj6Bh3FN4/b2dCaf4eSb2gYras1WhBq00GoE6DSC/L6X15ixOb8U/TvGoKS6AXqtgNQok1vsYbOJqpP6apfPdZMgCE7JKt6y2UTUWawwORJxpTGvzyvBvLV52HykDOmxJkSZ7DHjkeIaHCqqRohegzljcvDn0dkw6DQorzXj1V/244/j5fjz6Cyc3zXJ43MeViT5KFdGaYTGCiaAPa6RxlVrtsrJMa58SWaxOJKE9Rpl8rKiqo+c5CPI55bOrxyf1+26VCsUNiYv6/zUhnRCjyT897r+uOd/W+VtvsZFRNS8czGuAdzjCLV7NoD7/QjlOZo/Tu18Ko/1MqRqyfm8fR2ej/XuQG/HY7bZoBUExIYZfIrpVN97L16v2WrDN9tOoFtKJHp2iJLjW+n+inQPSRl32GwiasxWlFY3IDJEjxCDBgdPVyMlKgRGvQZHS2oQZdIjWbE6WxSB/NIaRIboEWXSy3GRKIrIL6lFfIQBBq0GpyvrERGig16rgdlqQ5hBh8p6i30cgoDqBguiQ/Xyfa3aBhtMBg0SIkJQb7ZiS34ZIkP0yIgLRUWdGacr6tEpPgwajYDiqnqcKq+DTQRyEsORnRB2RnHz3xfvxLy1h3HdeR3xzOW95O0vL92PUxV1WH2gEPklzhWC9xfYk1iMOg1mDe+Ezzbmo6S6AZuOlGJCj+Qmn6+wsh4HC6swJCsOhZX25Jk3pw9QHXtucgQA4MBpe/xVUee59UREiPsteOl+yJ5TlbBYbfK9IbVKyjuOV+B0ZZ3cblWpuQrJOYnhEAQBXVMisOFwKXafrEDX5OC5n08UjM6luMYeuzCucT72zF9LS+Iai02ECBEJ4cY2iWtsoojvtp9Eh2gTBmbGOt23k+7BaATnuQ9RFFFrtqK4qgERITqEGXU4cLoKiRFGmAxanCirhVGnRVqMyem5jju2x4YZ5DhJFEUcL6tFpEmPcIMOBZV1CDXoEKLXoN5iQ4RRh4paCyw2+2dsTYMF4UYdjDotjpXWoLreihC9BvHhRogANh8phcmgRcfYUNRbrDhRVoeMuFBoNQLKa804Vmr/vO0YG4quyRFnFNe88st+vPjzPlzYPQlv39iYePfB2sPYdKQU+aU12HK0zOkxe05WytdjzphsLNpyHCfL67DxSAmuO8/z7xjAfs/sj+NlGJETL891fXjTYKdrIuniiGsOF1XDbPW82BxQT17edMQ+7gaLDSXVDYh1zDOq3T85WV6HfQVVciyldKKZuEZ6TLfkSBwprsHukxUYnhPf5GOIzlY+Jfns3r0be/bswccffyxvy8rKQv/+/Z3+i4uL8/tA/aGy0j5ZGRYW5vEYqWRhRUXz2X/S+Zo6p6fzNTcWZenEpsZSX1+P+vp6t2MHPvUzGjT2P/wEwT5ZwcWKgcubeMDXOUyNYE/c0AgCBME+2aERBKcklCiT3m2FU6hBizqzFYIg2JN3YP8HJH0t/Xuqdvlw1jnOb3Uk+Ljuk16jSa9FndkGmygiNswAq02EQadBQUUd4sON6JYSiT5pUTDoNMgrqsG+gkrsPVWJBqsNWo2A87smontKJNYdKnZKalm25zS+3nICl/VLxc+7CrBibyEA4Jc9p2Gxipg6IA37Cyrx9dYTuLRPqhwM7DnV+HNcUdv43sSGGpySfKJMernlRL25sV2XNIEkJ+L4sOJdbTJMbQ5RSo6xiY2ZzwadRp7cevHqPpjStwNW7C3EDztPeXw+tYk6ua2F0Lja3h8r3p+a0hMHCxtXh206XIphjmBLWaGgxIf2ZtT2Jr2ySv7a6usvIWp151JcM/jpn1EnMK4JBO2R06T260eaAFNL9Ag1aJEYYYTZKsJstaHObEVlvcXnWEpJpxFgMmhh0mthMmgRGaJHmNGedBRm0KFzUjgsNhGl1Q2oM9twsLAKBwurUGe2OWIwoEtSBCxW0SnB+FRFndtz1ZltePHnffhu+0lc2D0JC9bno6jK/nOx/Vg5frp3FNJjQ7F6fxHmrzuCG4dlYFi2/TNW2a5LmbwcoteiRhG7RYbo5CSfugYrpI/+TvFh6JYSge+22+OJ9XklCNFp0cuxoqspjZV8lHGNopKPVq2Sj31MYUYdKussjv2C0+M8P59aXNP4vNJNK7XkLl/jGp1W45QsVMMkn6DxxoqDOFZagycv64nfDhW393CoCedSXDPo6Z9Rz7gmoCj/Dm9yMZkfSR91Oo3g1JbboNOgU1wYzDYbCsrr3O69nOlzKV9LqEELmyieUVvuMxUXZkBiZAg0jrEUVtUjyqTHsOw4RJv0KKs145fdp2HQafD3yT0wuksC8ktqMG/tYQDAgt+P4nRFPV67rh+sNhH/Waq+shxorKwTEaLHQxd1RefEcNz/+TZsPNz0wigAuH3BZqzPK8EV/TqgqMp+v0ItsQYAshPsvwuKqupRVtPglOTz4z2jMOGllfL3epUqhV2SwrHPkZB0srwO6Y72YJ6SibceLcOFKklKzbWNl+4pdUuJxIbDpfjjWDku75fW5GOIqGXOrbhmKczaEIhiY1wjonU/Q6lp0nUA2j6u0QqC09/gOo2AnMRwNFhtKKysl//ubwnXRUxGnQY6jeCXmMlb0aH2BGtpPqW4qgEheg2G5cQjPsyAynoLVuwtRIPFhocndcUlvVNRXmvGi47KOD/tKsCU/67BBzcNRoRRh8cX7/T4XFJcIwjAAxNyMbhTLGa+v8GrSj5//XoHvtl2AuO6JsrzYAkRRtVjU6NCEGqw3zs6UlzjlKD85exhmPrGWvl7tfsxvTpEYrljTu5oSY2c5FPn4f7L5qOlqkk+pTWek6YBINEx/m4pkfhh5yn8ccxzFWuis51PST4ffvghNm3ahM2bN2Pr1q2orKzEwYMHcfDgQXzxxRfycWlpaU5JPyzz13qeffZZPPHEE27b68w2aBy/q0XRc1Yx+ZcUzPgaMLVGgGUTpdXM7ifXa+03kdRKGMuTFqII+1feDc4evKkfqwzszNbGQO60Sz/005X1OF1ZiF/3FbqdI0SvQZ3Zhp93FeBnR9lhg1aDWcMzEaLX4o0VB7H+cAnWO27aGHQapEaF4HBxDR5fvBOxYQbcvmAzahqs+PC3w/j1gbGICTNgj6Kcn/KGTIjBuf2Vp3ZdWpckH29WvJfXmvHiT3vlG2qeJsMkUnIRAFQ7krTCDFp5YjMm1ACNRsAb0/vjjV8PorzGjLdWHnI7T3Mr3qXXolbJx5ee7d1TIjFtcEdc/+46eVu94n1RJkJZ2O4i4HiulmJP0JJ+Dohag6e4pqaBcU2gCJQbdVJFRCUpqbimwYrDxTV+fT6LTURlncXjDSllSwTVsQLY6Sh5bNBqMHVAGi7tk4JDhdWoM1th1GuREG7A4E5xWLW/EE98swt7TlXKN2Gy4sNQVFWPijoLHlm0HXee3xnT5/4OAFh/uAQrHhiDyBC9U5KPMrHbNcknIkQvxzo1DVY5kbNrcgRev34AJr+2Gn8cK8e/ftyLf/24F3ufmujx9//pyjqcKKtTrVCoUSwO07jETGarKMdNYYbGJB+dRqO6UtGVWsylbEMqV/JRaUPqSyUf6WmU8WyNS6nsZXsKsPVoGe4Z38WtihS1L6kFygXdkzDz/Q1O+2w2kdeLWpWnuKaWcU3AkJJf2iPJSoqpzC4V5xosNqeEYIlBqznjdpGu8ZsUE2g1gsc2WK5C9BpEmwyoNVtRXmuGIAC5SRGoM1txrLQWJoMWcWEGHC6ugUGrgcmgRWq0CfVmKw4VVaO4ugHF1c7JKIWV9Thw2r111Mz312NK3w7441iZ0/aluwvw1Zbj8kpzVyM7x2PV/iI8+739d3+Y0R67DMyMAQBsO1aOOrPVrd356ysOoLbBivsvzMV6x2KyhVvsbbdSo0IQ56HidZhRhw7RJhwvq8W2Y+UIccQ4WfFhbpNWuSqVc169tr+cCHS0pKYxycdDnPLHsXLVJJ+KJiZMlZUQhmXH4cPfjuCnnQX42yXdWZGUiHzS5DyU4+8+xjXtT4pt2vo6SLGGxSXosNhEp0XWEml+6ky4hi71Fhvq4VtcY9BqEBduj2vKHEklnRPDYXNUPdRrBSRFhiCvuBp6jQYGnQbpsaFosFiRV1SNshqz/Dilw8VH3bbdsWALvtt+Ekdc7lNtzS/D/HVHMLlPquoYJ/RIwo87CxrjGoMOgiCgf0YMBAE4UlyD0xV1SIx0Tkaev+4IDpyuwuOXdsc3204AsC+EB+yVBdVakAL2SlydE8Ox7Vg5/jhWht5p0QDsi8QGZMQ4HduvY7Tb45+Y3BPL/7UcAHCkuBp90+3H1HlIvvrjWBmuHexeiaiyicqIWkVl7+E5cfjPUmD5ntOcs6Bzlk9JPtOnT8f06dPl7/fv3y8n/WzevBlbtmxBaWkp8vPzkZ+fj8WLF/t9wC0REWH/A6uppKOqKvsfl960IZPOJ50zKsp9haun80VERKCkpMTjWKTHNTeWhx9+GPfdd5/8fUVFBdLT0/Hd3SOQHG/vqyjd7JYrswiCShsCl+9dj3D9tundbn8oNvd8as/Z3N+azY3Z32P0tcThwcIqRIToUdtghU4roLTajKRIIxqsNlisInRaAVEmvU8rp5p6TdL4RNE+LWJzlMO02kT5a5soQiMISIkKwYbDpTDqNeiSFAEB9lYGGo0969jkmPSRHiNK5xWlING+4kuvFRATaoDF1tieS+dIFpH+b7aKqLdYIUCACPsEWahBC0EQUFrdAJ1WQG2DFclRIcgvqcW+gkpsPloKrSAgPTYUOYnh6JkahfRYEw6crsIn6/NRZ7EiNSoEl/dPQ4do+w2LKwekYf66I9h1sgJpMaG4aXgmshPCMfXNtdhytAyz5jXe1K+os+DHnadwzaB0edWUfbvnACLSZC/1CNhXVblX8rG/Z94kwzz21Q4sdgRYgMtkmMq/M4NivzQBFmrQyVnNUhKQIAiYMyYHW/PLVJN83lp5CKcq6vDytH7yNrmSj0aQV9arrXhvqjyjKynoG5GTgDUH7CumSxU38+oUlXxq23DlIDVPFEVMe3sdBAGYN2uw2/6qOguM4QyYA8W5FNd8c+dwpCbENd6scCSjSauT2usGdXu1Cmuvm2btleAjCIBeo4GgsY/BbLXJST5Wm4j4cKO8AijapMfh4hqU1jRAr9VArxVg1Gkdn+POv7+8LY1tE0XUW2yoNVsdrcMsKKsxo6bB3uaqtLoB+05XwajTIMqkh0GnQU5COHISw5EQYUR1vb3d5q6T9tVFgzJjERdun92VKvAoXda3A0bkxOOFn/bhYGEVLumdgmsGpeNEWR0mvrQSq/YXYdX+Ivn4kuoGrD1QjAu7J+FwsbKST+OEj5RYI3Ft12WV2ma5JOJITpbVITNefeXniH8uR4PFhtFdEgA4VyhU+9mU4g2rTURtgyPJx9h4bXRa727MfbI+H/kltbh1VBbyCqswc3gnpzakUhK1WrsuXyovSgnRymQh10o+N83bCADITgxvsk0stS3l7+ht+e6r+2rMVoQbfbolQq3oXIxrpFXIrnGNp2pm3txHcWz08rFqx7lvVT/Ou7F4G6J5Os7bc7bktTRYbfakFZWDpOcXnLb5dn7Xw0INOjRYbahpsKCi1gxRBKJDDfbqyBoBR4trcKqiDiF6DVKjTYg26RGi1yLUoEWt2YoTZbXIjAuT78PotRrUmq3IL6lBkqNSDmBPQBFFe2KqVRTlyszhjrYYJoMWneLCUG+xwWKzod5iQ1FVPdJiQuWqejqNgIOFVYgJNciLiwB76wcI9orHgP2+o+C432i22tzablbXW7C3oBJVdRY51NI77pOt2HsatWYrkiJD0DstCqv2F+GLTcewyJFko9cKeOGqPrj7060AgKW7T6vGwi9e3Qe7T1Y4xUhhBvvv+I6xoYgNM6CkugFdH/vBKXm5qt6C53/YCwC4YUiG23lvGtGpyYTQMbkJ+Pj3o1i89QQm9bIn4Egr368dnI5P1ufjsUu6Y1Rn95gvNzkCY3MTsHxvIY4U12B4jn17vYckn20uSU+SCpcFfPeO74Kfd59CcmQI/nZJY9udMbmJCDVocbysFlvzy9CvY4zrqVqszmyV/x1R61u5rxDvrcnDveO7oI9jMpUCw7kU13x71wgkx8dAKwiqcY3aXBRwdsc1LT1nS16LxSaitKahzeIak0ELq01EVb0FVXUWNFhtiAszynHNibJa5JfUIESvRUpUCGLCDAjRaxFm0KLeYsOx0hp0jA2DCFFuW2qxisgrqkZihFFelGwyaKERBNQ0WGGx2eS4Jsygw9ESe/JMTmI4zI624FariFMV9ip5eq0AAQL0WgGHi2sQZtAiLrzx3JV1ZlhtIqJD7Um9zcU1dWYr9hVUorzWLCcc6TWCXL2notaMhAgj+qRHYVt+OeatPSxXSxYE4J9Te+PBL/4AYE9g7pwYDld3np+DMKMOP+5sXFAW6piziwzRIzcpAntOVWLwM79g2+MXyjGZKIr461c7AAAX905xO+/0IRkINXj+G3h0biK2HSvH4m0nkOWoWCi15rpjbA5eW34Ad43rjEtVEpM6xoXiygFp+GLTMRxVJDR5Sl5W+xsdcO62AQC3j83GbweLYdRp8cRljXFN/44xSI4MwamKOqzaV4Tx3T23tj9TDRYbCirqkBZjYnJ0G9h0pBSvLtuPW0dmsQWbl1p0R6tz587o3Lkzpk2bJm/Ly8uTk36k/woLCwPiByAzMxMAkJ+f7/EYaZ90bFMyMhr/+Dt69Ch69erldoyn82VmZqKkpARHj7pndiofJwiC0/O4MhqNMBrdy6t1jA1DZLh62TVqfYIgICfRedVOmoe/nT1U/W11Q7PV2+qp9dP0pxTF3yBSgk7jPhMGd4rFdJUbKwDQOSkCf7u0u+q+9NhQPDypm9v2Zy7vhctfX4M6sw1Z8WEY3z0Jb688hKW7CzA8J95plbsygHCtMBNl0iPGEegVVzfIk2FSMGhwTIY9vngn9pyqwLNX9FYdJwCs2Hva6Xundl3u7VCh0Qhyey5pvKGKSkOuk5ahBs9JGF9vPYGXp/XD4aJqXP/u7/L4tUJjJR+1yTBfVgxK78Ws4Zny6ukSRZKPcmKtqr7p8ovUtsprzXIbvK35ZW77q+ot8sQ0tb9zKa7pFB+OSA/lZIkAOP3xl6Nyg6Q9SbFVRw+rpdTEhRvx7BXOP4Od4sPw2CXd5Rs2fdKjkR0fhoVbjmPD4RLkJIY7JY8rk3xcYwWndl1mK6SPfq3gHNdIjpXWekzykRKcpUqMzsnL7sfrFeeudlTECVMkWug0Glis3lXaWX2gCKsP2Cfz+mfEyInKWo39PIB68rIy4bg5UsLRoMxYuR2sp3ZdfxwrZ5JPAFG2ZVvuEn8D9uRlJvkEDsY11F5CNFq3z8nWZtBpEG7UqbaB6pUWhV5Qb5MZatDJ95qUi5UNOg2iOqg/xqRyf6Cn4lj7fi0iAMSr/K3nem8LAKJCne8bKZNgXCfCAPvnfH8PCSWuE1CT+6Ti/K6JWHuwCFnx4Ridm4BsR/L0xa+sxtLdBW7tqaJMelzRPw0ny2vxzqo8ebv0O14QBESb9PJ9iSPFNeiSZH9dJ8pq5ePVKkE2F1dO6dcBH/9+FN/vOCkn94Q7/v/M5b3w98k9mlxZnuq4L6Zs4eppMmz78XLVyruulSav6N8Bd4/v7Pb4EL0W47ol4ZttJ7Bw83GPST6io5Wb2r8dV+W1Zrz560FkxIZCr9XgH9/ukquGD+4Ui6en9ETnJPXKS4Ekv6QGRr3GY2s2f9l0pBRGncbpZ9AXxVX1ePHnfTi/ayIKKurxyKLtAIAVewvxwIRc3D42x5/DpRY4l+KazLgwRLbXBAe5MWjsFWjaWqhBB5WQAVEmPbqlqCePhei1qnGGUQePvyfVYjZl9TytIq6LUanE10nlvobrfFhzcU2IXitXuXE1waXi3uX90jA6NwG/7C5Ax9hQjMhJQPfUSIzIiceIfy7DlqNleFcRu0juPL8zahos+PdPe+WKR8r7JqnRJrlC0o7j5fI9MeUcTFW9BTqNc/u0nIRm4pq+qXjll/1Yua8QPVLt102Kb/4yIRd3jsvxOa7xVEl5b0GlaoVF10o+47sl4YEJXd0er9EImNgzGfPWHsbCLcc8JvmIoohas7XJ5CZJbYMVb6w4gIQII+LDjXjs6x1y+9Y+6dF4YnIPuUJRIDtVXgebKMrXo7XsOF6OeovNrdKTt6rqLXjhx73o1zEaWo2AOxZsAWCPa/40OgsPX+Q+30vOVKaTW6ZTp06YOnUqnn76aXz//fcoKCjA0aNHsWjRIn8/lc/69bNXryguLkZenvsvTgDYuNG+CrN///7Nni8yMhI5OTlOj/P2fNL3zT2uc+fOTn1Ricg33VIi8d1dI/HsFb3w1R3DcXEv+w2k9XklcjJDSpQ98K2sM8uVr1wTXSJD9HK/0pLqejm40qmseP9kfT5OldfBE9dSys1V8lGeX67kowjqQlwCK1MzNyjrzFY8/d1uHC+rlTPdtRpB7hFvVWlr4Uu7LmliMESvxYyh9j8OS2vUK/lU+aEPL/mPMplLaiuj5I++yeQ/jGuIzj3Th2Rg8R3D8cb1/fG/24ZglKN6zsbDJfhpl31lmJRErUxkNrjcmIo06WEyOCoUmq1Oba4AuN20OVbqffuz5tqQ6hX7GysUOlfyOZOiUZNfW4PHHAlQyrhGrTXomVTyuWVkJ5zfNdFp3K6KqupVt1PbsNlEubUt4Hyd1ZOXmWweSBjXEBFgn2Se1CsFT03phZtGdEK2YzKqR2qUvGp845FSp8dISSUpUSasfeh8ebsyJnlwYq78dZGjdXyd2YoL/7NS3r77pPvfwClRTU+ODOgYgw7RJtQ0WDFv7WEAzslFzbWOkFbeK6vxqFX8Nmg1KKsxy/dwlFyrUitbdLm6emAaAOCjdUewaMsx1WMeWbQD/Z/8Gav2FzY59toGK/7y+Ta8seIgHlq4Hfd/vk2+FoD9vtuseRuw6UgpRFHEsj0F+O1gcZPnbA8bD5dg3L9/xZTX1nisYr0tvwxPL9nl1F5uyR8nsWLvafy085TTNThVXod3Vx1ymqS0WG14bdl+TH1jLS55dTVe/GmvalXYeosVvx0sxsp9hbDaRKc49lR5Ha5/93d8/PtR3PzBRjnBR3LUz+2KqWUY1xCRZGxuIp6a0gu3jcpGd0fiTGq0CTcOzQRgb8HuyqDTIDrUgD8enyBvU96H+NOoLPlrqR2qzSZiyLO/yNuPFte4LXqS5sE8yUoIR++0KNhE4L/LDwJwThj3Oq5RjFUteTk+3ACrTcTOE+7VfFznzlxboCpdNTANggB8t/0U3l3l3tkCAP7z8z70eeInfPvHiSYrsjdYbPjb1zvwyrIDeOzrnZj98WY5wQewxwI3z9uA1fuLIIoi1h4owvK9p9utyrsne09VYvyLv+Kil1d57Fyyr6ASz3y326k17rI9Bfhp5yks3VWAYsW9rdLqBry76pDTNptNxEfrjmDya6sx9Y21+OtX2+X7ikpmqw0bD5dg6a4CWKw2pzirrKYBM95bj3lrD+PuT7fKCT6Sgyotfcldqy9bKy4uxueff465c+di8uTJrf10TUpLS8OgQYOwYcMGLFiwAI8++qjT/tWrVyM/Px9GoxGTJk3y6pyXX345/vWvf2HBggWYNWuW076qqip88803AIArrrjC7XHvvvsuFi9ejOrqaoSFOWeQLliwQPVxROS7rIRwubxgj9RIhBt1qKizYO5q+x9ZE3sm4/01h2ETgaoGCyJD9DC7JLpEmvSIdWR/28TGSRyNRn3F+5Bnf8GhZyY1WdZZopzs6tcxxqm8tMSo06ISFnniIsypko/zcze32qqizuy0shmwT8JJE3tq/XBdK/k8MCEX//pxr+r5lROJUsZ8qaJHrXLCpbre+5X01PqU12bHcfcgWzlhTO2PcQ3Rual3WrS8amxQJ3t73u3Hy1FQYY9NrhyQhpd/2Y+qeou82tviGteE6OVVYGU1ZkUFnMZS2EpqK9sB9ZZ1ek3TycvK/dLnirKail4rtPgmiaCMa1RuNLjGQW9c3x+zP96sei6pko9Rp8XDF3XFsj2nkV9SI7+3yrEyyad93fvZVvyw4xSW/WUMOkSbmq3YxOTlwMK4hoiac3GvFHyjaH0uUbYpSokKQVKkEQUV9ZjUq7FK0MSeKRiYEYONR0pR6Pi8/u2Qc8LJa8sPuJ07JbrpyTCNRkC3lAgcV1QE8qEQsmqSj+tk2IXdk3CoqBoHTlfheFktMuKcfycpP89enta3yWr6IzsnYHy3JCzdXYD3Vh+GXquBTbS3VCuvNWPniQr5Pb5h7nr0TovCv67sgy5J4U7nrWmwYPJra5ySXmLDDEiNDsGInAT8uq8Qu09W4FhpLaa+sdZpDE9O6anaGq21nCqvw84T5Ti/a6L8Gmw2EQ1WG4w6Df761Q40WG04UV6Hno//iIcv6opJvVNwrLQWT327C0adFuvyiiGKwDur7K2xas1WvPnrQafn0WsFXNwrBXtOVWLPqUos3V2AS3qn4r01eThWWuu0eO6VZQdQVW/FY5d0k8e06UgJ/vTRZqd40qTXYlKvFAzPicNbvx7C3oJKp+e8bVQWZo/Oxm+HipHczMQttS3GNUTUnEv7pMgJwkpJkY0Vs0wGLbqlRGL3yQpcOzhd3n5eVhwm9EjCjzsLUOhIXt51ssJpXuffP7nP3aR4Udmle0ok/jjWOC+grA7UHCmuKW8irumeEomIEB2KqkpwvKwOA1xCAmViytOX92yyAk+P1Chcf15HzF93FO+tzkNmXBjKas3QaQQcLanB6co6zF9nr2J2x4IteClxP/51ZW/0SYt2mrczW22Y9vZv2Hy0zOm1pESFYHRuAtbnlWDL0TIUVzdg+tzfncZwz/jOuGd8F+/eID8orW7AukPFmNAjWX4Noiii3mJDiF6Lp5bsku+19f77T3hwYi4u79cBlXUW/O1r+8K4TUdKYbaKeHvlIcwek40wgxYv/LTP6Xn0WgFjchNRUt2ATUdKsWjLccwYlol3Vx1Cfkmt03Wdv+4oauqteP7K3nJBg90nK3DbRxuRX9IYIxt0GkzokYxh2XFYtPk4Nrkk7l81IA2PXtwNv+eVIIJVl73i90o+gP0f1Pfff48rr7wSHTp0wF/+8hfs3r27NZ7KZ4888ggA4LnnnsPmzY03couLizFnzhwAwB133OHU13TRokXo2rUrxo0b53a+e+65B6GhoVi6dCneeecdebvVasWcOXNQVlaGQYMG4cILL3R63EUXXYR+/fqhrKwMc+bMgVVRkv7tt9/GL7/8gvDwcNx9993+eeFEBMBeNUcqHyet0hqbmyhPLhU7snNdK/lEmfTQazWIdpSlLnCUHJQq+biukAe8nzjQKSa75ozJxgMTcvHjPaOcjpEq+ai163KdiGuqXRfg3tcUcG5rYVXLunWZDMt2Ke34uKKNmjLhSUqMKq1uQEFFHT7bkI8yRZDXYLWh3oeWGdS6lJOe+0/bbx6N75Yk3zRl5aXAw7iG6NzWIdqEDtEm2ER7OeQIow7Xn9cRgP3zXFoJ7rp6K9KkQ2ZcGDSC/ebL1nz7H9ZSu65Il3LVrq1GJWorzZWr5rUqyc4ajSDHT9VyXOPcrss1efqTW4dgbG6C6hjUaAVBvrFgVZlpcy0XndBE2xxlXJQRFwatRkBVvUUuP628iVZc5f3NL/K/r7eeQL3FhvnrjgBQLwuelRCGSEe5cSYvBx7GNUTUlFFd4p2+nzE0AzcOzcATk3vI2wRBwHszB+H9mYPcWsFLLQsKK+tR02DBXJf2GNIkmXJyzZsJBtc44mR5rYcj3alNhv20016d8Y6xOXh0Ujc8N7W33CpD7W9yKUHoqSk9vWob+vAke8uL7cfLcceCLbjrky14eOF2PPf9Hrckqj+OlWPCSysx7sVf5c9Vm03Eo4t24MDpKkSG6PDezIHY/Y+J2PTX8fj2zpF46KKu+P7ukfjl/tHo3zHa7fmfWLwTf1+8U46xFm4+huHPLcP7a/Ic8avne0SnK+tw1ydb8Ml69bZDrurMVlzz9m+4+YON+Me3u+RV5jPeX4+ej/+I69/9XW53Atjvifz9m10Y/PQvuOL1tdh8tAy/HbIn+Ej+s3SfW4IPYI8Jv9p6Qj7fukMl+OtXO3CosFpO8BnfLRF/d9w/e29NHjo9/B3uWLAZU/67BlPf+M0tYbzWbMWXm4/hvs+2YW9BJYw6DV6/vj8MWg36pkfj/yZ2RUyYAZN6pXhsiUfth3ENETWlj0vLryv6d8CNQzPwn6v7Om1/9dp+eHN6f9x3gXMiiTKuqbdY8dZK50o2UkWcDorEnmQvWrm5tm9VtjZtjlpc86uj7fmMoRl4dFI3vDdzkNwiTT2usW+7Z3xnXH9e80nBD1xoj2tOlNfhlg834i+fb8M9/9uKF3/eJyf4SA6crsLlr6/FsOeWodSRvCSKIp77fg82Hy1DiF6Dl6f1xZ4nJ2LLYxfgh3tG4eGLumHRnOH47eHzMbJzvNvzv7R0P/7y+TZ5juuX3QUY8c9leHnpflistibjmvJaMx78YptqXKHGahNx43vrMfvjzbjr0y1yxb/7PtuGro/9gOnv/u5WQOD5H/Zi6LPLcOF/VmLdoRKsO1TidB/rjRUH3RJ8AHtc8/OuAjkRZ+eJCjz4xR/YV1AlJ/gMzIjBC1f1gVYjYOGW48h59Hvc8sFGXPfOOlz08iqnBB/AXi3pm20n8PDC7XIFqzeu748wgxZZ8WF4ckpPRIca7IlAOe7vNbnzaypUXl4e3nvvPcybNw8nTjSWvmpqBUFbmzJlCu666y688sorGDJkCMaNG4ewsDD88ssvKCsrw/Dhw/Hkk086Paa8vBx79+5FXZ17+53U1FTMmzcP1157LW677TbMnTsXmZmZ2LBhAw4dOoSkpCQsWLDA7T0QBAGffPIJRo4ciQ8//BCrV6/GoEGDkJeXh/Xr10On0+HDDz9EcnKy23MSUcuclxWLX/fZgwu9VsCgzFjEhxtQVW9BYWU9OsWHubV2iDTZf13GhxtRVmOWJ3a0jsQYo949yae81uzWq16NcjIsRK9V7aEtnb/SMRlhVLTkcm3XFWrQ4e+Xdse8tYdVV96X15rh+ltZIwjyOFwnAgH3Sj7KFfc/3zsKaTGheOKbXQCcJ8OiQ+1JPiXVDXhp6T58st69F/UT3+zCM5e795KmtqcMOqUVeZEhOnk7J8MCD+MaIrq4dwredtzMuWpgOhIijBAEQBTtrUhNBq1qG1Jl3/MfdxYAaEzUlZIgJHtOVeJ0ZR0SI5xvCKl9Lih71keb1OMgvVYDi82KZXvsyUPK3vI6rb2venZCGA4WVgMAhmbHobCqHsv3Nt02QqLVCNA4XrJaXONaySfJ5UZX/47R8goum2JWx6DTICMuFIcK7avpU6JMTquXXOMlah/SRJrrdQaA0V0SsON4OTYcLmXycgBiXENETQk16PDRzYNxw9z1AICbRnRyq2oD2Fd190h1f7yUjFNYWY87FmzB6gPuVZQBYFBmLC7onoTkyBCv7mm7ToY1NZnjKlJua2GfDFu87YQ8KZcUacQNjlYe0mSY2mIyaVtEiHe3+TNiQ6HXCqpVnCVX9O+A+y/MxV2fbMGmI6U4VFiNdYeKMbpLAu78dAuW/HESAPDStL44v2uS6jmyE8Lx5exhWLztBLYcLcOfR2fjrk+2YP3hEsxbexgaQUDX5Ag8+tV2mK0invhmF574ZhciQnT45NYhAICeHezJD3lF1Vi9vxCPfb1Tfp9Oltfh510FuHpgGm4cmqmaXP7L7tM44rgv9v6awxiYEYuBmY0VtNc62oc9OqkbQvQaPPPdHlgdVX4kYQYtkiJDMGt4Jt789RCqGywoqzEjMcKIW0dmYVhOHEqrzXh/TR6W7z2N9NhQzBmTjaeX7EZFnQW906IwuU8qxndLQma8/d9rmFGHhxduh8Um4lvHewkAk/uk4tkreqG0pgFbjpbhfxvysf5wCSxWG2wicPOITnJCT6RJp/qaKXAwriGipui0Gnx310hMemUVAOD68zpiQEas23E5ieHISXRvpaeMax7/eqdqtUMA6Bgbiicm94BWKzTbAQKwt9JSaipecCXdS6p0JPmsOVAkJ7+GGLS41dFmrHHhjXs7KandpeviM0+iQvVIiDDKydpqRnVJwH+u7oP7PtuGX/cV4lRFHX7ZcxpXDkjD3xfvxAe/2RcK/WOy54TplCgTPrr5PCzdVYBf9xXitlFZePLbXfhpVwG+2HQMNlHEyM7x+PviXSivNeM/S/fhP0v3QRCAL/48FFqNBn3SoiAIAvJLavB7Xgn+8vk2+fwVtWb8uq8QE3sk40+js90WvwHAhsMl2O7ovvDtHycxuFMsrhqQjkVbjgOAHNvePjYbGbFhePq73Wiw2JzuW5n0WkSH6nH72By8u+oQymrNqKg1I9Kkx60jszAiJx61Zis+/v0ovtt+EnFhBtx/YRe8+PM+FFTUIycxHFcPTMPoLolyK7Uokx53frIZdWYblu4ukJ9rbG4C/nNNXzRYbNh8tBSfbTyG3w8Vo7rBPp6rB6bhol4pGJAZA5Ne63SfkrzT4iSf+vp6fPHFF5g7dy5WrlwJURQhiiKio6Nx7bXXYtasWXjggQewcuXK5k/WRl5++WUMHz4c//3vf7F27VqYzWZkZ2fjoYcewr333guDwdD8SRSuuuoqZGVl4ZlnnsGqVauwZcsWpKSk4Pbbb8djjz2GpCT1P3Zyc3Pxxx9/4KmnnsK3336LRYsWISoqCldccQUeffRRr/qxEpHvLu6Vgud/sJcrHJ4TD5NBi/hwIw4X18irZlxbO0hBRXy4AQdOQ26J0VjJx/0DSJmxLHFNHgKcJ8M8kfqdVjmCHK3iDza1BKOZwzshRK/FQwu3u+2rqDPD9T6VclW9cow/7jyFZ7/b7RbMhRobX2+USe/UMkzZWzM2VGrX1YDle9RbfSz4/SiTfAKEcjJMqs4QHqJDjSPwqmSST0BiXEN0bps5LBM/7ypAbJgBs8dkQxAERDhak5bVmpEYGeLerssxoTSuWxJ+3tX4B7h0wydSJTmnsLLeLcmnpsH9c0GnmGyYNaITfs8rwcSezjeM9VoByjBJ2YZUr9XAqNPil/vH4GBhFUyOP/J1PkxiaAQBUrCjjGE2HravqI5yeX3RoXrcOrITftl9Ghf3TsGfRmej5+M/ArC3aVXqkhiBQ4XVmLs6D08v2Y27x3WW99U1nBvVCavqLThwukq+QRUIlBOqUpKP2iRrfLhRTlZnXBOYGNcQUVO6pUTKX7sm6TZHmgxzXenuKibU4FVFHIkyWVmrEfDklJ5eP9Z1xfvripZhaTGh8tdSRSG1BOsKHyfDdFoNsuLD5dZPPTtE4sYhmdh1sgKnyuvw8KSuSIkywaDT4MvZw3DHgs349o+TWLmvCMlRIXKCz30XdPGY4CMRBAGX9e0gv5+f3DYE172zDr/nleC9NXmqj6mss+CSV1cDAN6c3h9rDxbjQ8fkm9Irv+wHYF849vwPe3H3+M44UlyNT9bno1/HaLxwVR/sPVXh9JhV+wudqknHhxtx7eB0edLxhqGZEEURO09UoKiqHt1TIxEfZoQI+7WVkq6Ol9UiIkTn9J6P6ByP4qp6RDoqgY/ukohTFXWq8dJVA9MxJCsOX2w6hryialhFEed1isWNjvOHGXVIiwnFpX3s2Wo2m4i84mpkOZKE2JoreDCuIaKmdE5qTN5Jjmq+lZZSgiPJ+MvNx5o8LiZMj/Hdm/68djqvy32fN673/veDtOBdimuUFWpyFJ0hwqVkILVKPj4mLwNA58RwOcknMy4Us8dk43BxDfacrMCjF3dDWkwoQvRafHDTYPx98U7MW3sYK/cVYlSXeDnBZ+awTFw5IK3Z5xrfPUl+P9+6YQDmfLwZ3+84hYWbj2Ph5uNux4siMPWN3wDY248VVtbjpaX73Y57fYX9vdp5ogIv/7IffxqdBYtNxFu/HkJ2QhheubYf9p5ybtu5cl+RU8J7TKgel/ZJxV8uzIUgCLh6kL3F276CSpwoq0XnpAikRoXAahOh02rkqpcFFXUwaDWICWv8TBqSFYcnL+uBEEfyzfhuSThcXI1+6TFOrc4A4ILuSVjxl7FYtOU49pyqgMUmontKJOY47lEC9ta5E3umON4TEYeKqpERa491Xe81kvfOOMln06ZNmDt3Lj799FOUl5dDFEVoNBpccMEFmDVrFqZMmQKj0XPZ8/Z29dVX4+qrr/bq2JkzZ2LmzJlNHjNgwAB8+eWXPo8jOTkZr732Gl577TWfH0tEZyYjLgzPXdELq/YX4e+Oss7KzGegMdGlQ7QJ53WKlW/axDmCpwK5ko/9Q0ot0aas1r1lQ53KimJvJq2kdl3V9fabEemxJgiCfbtrJR+JpxU9FSqVfLSCILfrUq54/9NHm5odW6RJ73TDQrniKSbMHtiVVJvRMdYkV0ByZbWJXIEUANRap0Uokny44j1wMa4hOnelRpuw/C9jnLalRJlQUVeJk+V16JIUoVLJxx7XPH5pd6ckH2nVjLKSj04jwGITVfuwq0006RTJy+FGHebfcp7bMQadFkDjY0ONzs8nUbYH9SXJR6txJPoAsCoSnK59Z53qKjSjTotHL+6ORy9WtB/VatBgtWFIlvNKul5pUfhh5ymscFQVmv1xY+l9b1u1Brtpb/+GHccr8Pr1/TGpV0p7DwcAUFbTmDUmxS1qlXziwgwIb6I0OAUGxjVE5El8uBHv3jgQGg18Xu2bEK5+n/qvF3fDU0t2y9/HeFGRWUnZyunA0xf5lACrTPIprKyXV7v/47IeGN2lsVWpnKBa576Y7GS5/T6LL0lPOUmNST4jOyfIk0BqJvVKsSf57C/E4E72uKhPWhTuUiQ6e0urEfDPqb1x+4LNKK1uQHyEEWO6JGDO2Bx8teW420K1P8/f7HaOf13ZG4u2HJer8GgEe1ur577fIx+z5WgZrnlrHbo4Jk+HZsXht0PF+GHnKex3VC2+Y2wO/jIh1+38giDIFYQ8UbY+UYpT/BtLjgppMhknPTYU97q0XvFEoxGc4mIKLoxriMgTvVaDBbech5KaBo+fLZ54ajv+yKSueOa7xs9EqdOCt5SLu3c8McGpo0NzlHFNndmK3/PsLZnuOj9HTlwFlHGN+9/kJx3twXyJazonhstxweBOsbhmUEePx17UMxnz1h7G6gNFOFxkX5TeIdokzxP6QhAE/OOyniioqMOJsjrEhRswLDsOd47rjN8OFrvNrT26aIfbOZ68rAdW7S/CT4p7cxabiP8ub0yQOlhYjavf/A1Ds+MANMY1K/c1Ji9fOzgdz17RW3WcXZIi0CUpQv5e2VkE8PxeK//txIUbneIcV8lRIZg9JtvjfiVBYFzjLz4l+ZSWlmL+/PmYO3cutm+3B92iKCI7OxuzZs3CjTfeiLS05jPdiIja27TBHTFtcOOHvVReWarkI02Gzb/lPHSKb8yGlW4KSZMHjZV81Nt1uVJbUazzqpKP/RhpMi3UoMOOv0+wt6PwMOnlqUJQRa3Z7aaTViNAq3Wv5ONJWkxj0Ol6Y81sabzDFeMIBMpqGpAZFwpPTlXU+RzIUsu9s/IQdp+qwPNTe0On1ahOhoUb9Rg5IAGDO8W69QomIqLA1CHGhL0FlThear85Ynb5bJdaPqS6rBSTquYoK/l0jA3FoaJq1SSfGpXKNQZt8xNbrsc4JxWpxy96lVLFnmg1gpw8rEzq8VRmWq8y5qX3jcayPQVO8SIA9E7zPOlT1WCBzSZ6jM3OFjuO21fF/29Dfqsn+Ww8XIIPfzuCv17cDYlN3OQrrWn893m60j7ZqRZ3J0Q0VvJhG1IiouDky2p0pXgPk2EzhmXip10FWO+YiIrycTJM2ULD1wp3UiWYgop6jH1hBQB7tSKpootEWs2urEJXb7HCYm1MxE6L9f6eSmfFmJX3d9QMz46HRrC39N54uMTxGM/3d5qTGR+GJXeNdNs+bXBHDMuOR3WDBZNeWeWUPPXUlJ7oFB+G/h1jYDJocdXAdJitNui1GjRYbHht+QG5so+kqKpevsc3Y1gG9p+uRFFVAzYdKQUAp8lGIiKi9jIsJ/6MHucpyWdKvw7Yml+G77afAuC5jbonKYoEVV8SfIDGuMZiEzHwqaVosNiQGGHEvRd0cYqRpHtSVS5xjUGrwTHHfaz0WO9jjRxFAktzMUr/jBiEGbQoqW7ATztPOR5z5vNSCRFGLJwz3G37hB7JWP1/Y2G1ibjsv2ucFibdf0EXDMyMRffUSESZ9LhhaCbMVht0GgFWm4j5647g79/scjpfdYMVS3fbW95f0b8DTpbX4nBxjdyC9NLejGvORT79hKakpMBsNkMURYSFheGqq67CrFmzMHKke2BORBRMpCQfqZKP2bHq23XVuGtPUikxRtkjU1r5XV5rxos/7cXX205g4exhiAs3qiZR6L2p5OOYdJMCH71WcCoJrcZTZZwT5XVulXw0gqJdl2tfChWJESH46vbhqoFeg7VxQkVK8rHYRPnmimR8t0RszS9DUVUDjhRXM8mnHTz9nX214pBOcVh3qBiFVe69ayNCdBiSFYchWXFtPTwiIjpD0mfqCccKKNfPdilpWaMRYNBp5PZGoY62WUZFlcB0R5JPUZV7kk91M5V8PHFN2FFrD+ZKLaHaE41ThcLmk5fVJuQ6xoVi5vBObtuVbUJciaI90cfbdhkSq03EtW+vQ6hRi/dnDgqYFljNqVVJovG3K9+0l7W22Gx4/foBHo9TJvms2l+E/JIaufWoJC3GhKHZcfJqQib5EBGdW9Qq+XSINkGv1SA9JlRO8vG1ks/4bol4ZFJX9D6DRTGxintM0ufS8Gz3v71d21r8sOMk7liwBbeMtLeZijLpfYo/lIvZRjQzuRgVqkef9GhsOVqGd1fbW2ylRrdOW4WOjsVhh56ZhJoGKz5ZfxTJUSG4RGXiSlrYZtBpcN8FXXBRz2SU1jTAqNPglV8O4Nd99qqLo7skYExuIuaMycE/vrVPmEUYdXKVHyIiomDkKcknIdyIdEWiS4yPyctdkyPx5JSe6HAGn/WhBi1C9BrUmW1yXDMsO87tHkdjXGNPfFl3qBg3zP0dN4/Iku8z+BJrKFuBKSshqtFrNRiaHY+luwvkuKa15qWkhKMtj12ABqsNn208BqNWg6sGprm9J1Jco9MKmDm8E0Z0TkBRVT20GgEf/XYEi7edAAAMzIjBhd2TYdBpcPenWwHYu9X36xjTKq+BAptPST4NDQ0QBAFpaWn44IMPMHbs2NYaFxFRm4p1tJWSMmqlSj6u1XDiXW4KSYkxRsVkVUZcKPafrkJ5rRmvLLP3U393dR7+b2LXFlfykXiq0uN8jPoE0doDRW69VbWaxpXzVi+SfACgb3q06vZBmY1tLUwGLUx6LWrNVpwod27VFWbUISshHEVVJarVAajtPPjlHx73+dL/loiIAkOq4wbFcQ9JPlGKyasQRZKPVMlHmSec7lgVXlJdj9MVddh8tAwXdE+CViPIbUSVvGmr5dpq1JtJKW9iH4mvycu+iAszIDpU77QKS+mpb3fh+Sv7+HTO3ScrsN6xMr7ObIPJ4Fv7kfZSq1LJqbUccLTW8KS02vl6jP7Xcvmm5+BOsbhucEcMyIhBqEGHgRkxaBiWiYEZvAlGRHQuUZsMW3LXCABApKnx715fJ8MEQcBto7xrT+Aq3KjD81N7O/1NPixHJclHqkLnSPKR2li9+au9lYOvK9Av6J6EC7snYXRuAjLiwpo9fkROPLYcLZO/T23lRVqCYF/YJiUxeUOZiH3/hV1QUt2AS/uk4NaRWRAEAQMzGz/3k6NCgiapmoiISE1cmHtcs/7RcRAEwWkhVbSPycsAcMOQjDMakyAIeOmavpj98Wa5It+wbPdk4giX6roPL9wOs1WU45qkSKPT4rPmDMyMwZS+qeiSHIE+HuaslEZ3sSf5SNoirjHqtD69rzmJ4XK1yPhwI44UV2N0lwTcPb4LtBoBAxVzcAatJmjuI5F/eX+nFECvXr0giiKOHTuG8ePHo1evXvj3v/+NgoKC5h9MRBTApHLMZbUNEEVRnhBy7U/p2ncy2mR/nMElyQcAnv9hr7ytwtG6q96sUsnHi7YWZ5Lk49ruYmRne0C17Vi5UxAD2Ffyt3QybNWDY/Hadf3cSgPGhtnfowaXKkYhOq3cnqOqzoLS6ga8u+oQilWqyZD/uV4PT5jkQ0QUfKQWmQcL7YkRylacrpX+lG03Qxw3BZTVAqUE5/8uP4hJr6zCn+dvwucb8wEA1Q3ulVC8iVFC9K6VfJr/rFGLly7rm4oHJuS6bddqBDmJ2uKhRdeZEgTBqcWFq882HrPHklYb1h4sQo3Ke6RUb7HiyjfXyt+rvaeBqrrBgh3Hy71OEFcjit491lMbN4mykg8A2ER76xPAHodP6ddBLvd9YY9k/H1yD1zYI/kMRkxERMFKujchOa9TLKId94JMingoOap1qtR4cvWgdBx+7mI8dFFXXNG/A0bkuK9Aj1Rpa6Hka5JPqEGHt28ciOvP826yybV9aUYT7dgDQe+0aHxz5wjcNipbTuZRJgH152p3IiIKcq73dmJC9Uh0LOxWtkRPiWrb7gkTe6Yg79mL8ewVvXBRz2Rc3Nu9xXdEiHPysmvlZl/bguq1Grw0rR/mjMnx6njXCoEdAzyu6RQfhq/vGIH7LsyVr3tqVIg8Zzj8DFu+UfDzKcln27ZtWL9+PW677TZERERg586dePDBB5Geno7LLrsMixYtgsUSPDcliYgkUm/SshqzU5KLXuNaycf5ppB0k0i5AmhKvw5u55faBdRZzmzFu8Etyaf5x2gVx/xpdBY+uvk83Hm+eqCjFQT5+DOdDEuPDcUlvVOhcXk9KR5ukPVKi5JXo1XWWfDn+Zvw1JLdeGTR9jN6/kBms4moqFNf8d9empt0lIQbfc/2JyKi9iVNYuw9VQmz1QabvIIqDt/eOcLpWGWSjzTBNSQrDuO7JWHOmGynyn1Sy66fd9mThdXakLomSKtRPidw5pV8Xp7WD5P7uLdvUFbyaUkCiifNrXg/WV6HN389iOve+V0un+zJL7tPO7WVUmuBFqgOFVbjkldX48tNx87o8Q8v3I4R/1yO8lr1GEmZANTcv6uyGs9VIdVazRER0bnHdTLsH5f1lL+WWpwCQG5SRJuNSenPo7Px4tV93e7/AI1tLaQFZK4LwXydDPNVh2gT/jy6sVpRz9SoVn2+1qDXavDzvaNwzcB0/Gm09xWCiIiIgsFbNwyUv66oa7yvoKxk15auHdwRb0wf4LSITCLNCUnjdF0I5mvysq9iwgx4ckpjHNgj1XNb9kAlCAKW3jca1w5Ox30XdGnv4VA78SnJBwAGDhyIN998EydPnsT777+P4cOHw2Kx4JtvvsGVV16J1NRU3Hvvvdi2bVtrjJeIqFVI5ZjLasxOSS6uEwquZZulJJ86RbuCST1T3Pp41juSe860XZfrBJVX7boUCUrS1/dfmOuWqATYJ8P0ciWfpiu8PDG5R7PPrTTAQyB53eCOiHBM6lXWmfF7nr1NxdLdp306PwCUVjfgjgWbsdLRcz3QzJq3Ab3//hOOFFe391BkNV622Aj0FXpEROSuY2woQg1a1Fts2FdQKW9/Y/oAp1XMgPPKdelrrUbAuzMG4sGJXTEmNxG905wncqSEXrNakk8zFVcA9yQfp8pBEeotMlwnvK51rChXu/mj1TTGcGard5XrfJEc2fQK/z2nKvDmr4cANCZEKZ0qr5MTWFxjF08r9APZx78f8fkxoijik/VHcbysFn2e+An//GGP2zHK5B/XiVlXpR7apwFAEatEEhGRw8e3nIe/XtwN2x6/ELnJjck8M4d3AgD8aVSW28KlQCDde5Janbsn+bT+Kv1wY2P8lthMLBSoOidF4J9X9kZWgueqjERERMFi4ZxheGBCLjY/dgEGd2ps33RxrxTotQKm9k9zu/8SCOIc81NSRwejyxjbIq6JVczzdU5snwTvlkqPDcWzV/RGzw7Bl3xN/uFzko/EZDJhxowZWLlyJfbu3YsHH3wQSUlJKCoqwiuvvIL+/ftjwIABeOWVV1BZWdn8CYmI2pHUm7SstgFmRZKLa5KPsp8p0HijRdlaQaMR3Hq9Syu01Va8e1OVx7W1klftuhTnVd6kUusxqtEI0Dom5Zpq17VozjDMGJbZ7HMrDcqIVd2u0QjyarRKxYRWx1jnpBJRFHGkuFqeDCuvMeOCF39F5kNL8JajT+uTS3bh2z9O4sb31vs0trbyq2MC7/ONZ7bSXamyzoy7P92CZXta1irTm0o+fdKikBSkN++IiM5lGo0gT1xtP1Yub1eLOZQrpkwG9fhiaFac0/e6JhKDvYlrXOOYUIMWC249D29OH+CxlLTyMfdd0AXPXtELgH310oyhzq0mRLHx+OYqFP7ryt7NjtdVUqTRbZvyfTxeWusxWeezDfkY8uwveGnpfgDA9uPlTvur671Lwg0kneKbrmykZLbasPdUpTxJKXljxUG3YwsrG5Nzthwtw7I9BU5Vg37YcRJbjpYCcG/XRUREpGZ4TjxuGZmFKJd7O33To7HjiQn4v4ld22lkTZPuMUmfja4Tdq6tyFrDtYM7onNiOO4dz9XiREREgaB/xxjcPjbHLQ7IjA/DtscvxD+n9mqnkTUtIdw+31BRZ0Gd2eqWvKwVWj/hemzXBPTqEIWZwzJVqygSBQP3OllnoHPnznjuuefw9NNPY8mSJXj33Xfx/fffY8uWLdi6das/noKIqFVFOZJ86sw2pzYJru26IkKcf21KAZRrtqxrtRypgk+9WiUfL1a8uyf5NB/oKNuAKQMjtZs/WkGQz9lUWwtvkotcqZWE7O6oIiC9nyfL6uR9rpnaLy3dj5d/2Y+/XtwNt4zMwqcbjmL/6SoAwLPf78GfRmdjy9Eyn8fVHqxiy1uG/Ofn/fh66wl8vfUEDj938Rmfp7lJxBuGZMhVEoiIKPh0S4nElqNlTkkkajGHcsWUpxVe2S6rneVKPioJNN5UKHS9X2MyaDEsu+ke4k5xjcsK+ycu64kPfmusJlNvscnHNJW8/N7MgTi/a1Kz43UVF96Y5HPryE7o2SEKY7ok4unvduGzjcecSmO7emjhHwCAl3/Zj7vGdcYBR0wTbtShqt4SUO26rDbR7b0WVWKZpl6vq/dW5+HZ7/fg4t4pbvtEUXRqgXu60rkCz03zNgKwJ+d3TozAn+dvBgA8MqkrFm4+DgC4rG8qwow6LPj9qPy4awamez0+IiI6d4WrtJMIFFKST2W9fTLMNWZzrdTYGuLCjfj5vtGt/jxERETUcqGGwI1rIk06GLQaNFhtKKqqh1HnHNec57LQrDWEGnT4xqWdPVGw8Wt6mlarxeTJk7F48WLk5+fj6aefRnZ2tuqNQCKiQBJh1MmTGNvyywDYJ5BcyzTrtRqniSkpYWZQZizenzkIK/4yBgAQF+a8wltqN6BWyce1WpCaBpdWE9Ghza/SUk6yKZ9DNclH0zhh1lRbC9esam9EhxqcqvNcOSANc2fae8RK7bqOltTI+zUuM38v/2Jf6f7Ukt0AgBKXldqiKMqlHQOdrYmJRm+8s/IQ3luT55exVLtU8pk+pDGhJzpUjyen9ET3IOxHS0REdtJkzy+KNpg6lfYTISrtulxJydCSWkfLR9ckZABy+8+muB4S6kX5aOXKKtdYwVWd2dpktSGJ640kbynbiz0woSsu69sBUaF6RDrimoo6z62jlEks+SU1qLfYYNRp0NVReSlQ2nU9+91u9PvHT8hXxGiAetJUmQ9VdJ793t6Wa8kfJ932rTlQLH8tiiKOl9aqnuO9NXn4adcp+ftnvmts9TWpVwqeudx5teITl/nWapaIiCjQRBh1cixUWFnvdG/mjrE56JIUnG0miIiI6NwjCI2dMIqqGpzu24zqkoBh2a2f5EN0Nmi1GlTJycl4+OGHsW/fPixfvhzTp09vraciImoxQRDQy1GN5+2VhwCoT4QB9hYQklBD4+TQ2K6JyHS0K4hzqeQjJVTUqVTy8aY6jmtykDd9SZXjd5oMU8kz0WgE+Xh/V/IBgIy4xiSfZ6/oJbfiiHBMkp0ob5zEqVV5j5ROKKr+SMf7soK8rVhtoltST73FhgOnK1FntqLe4ls7DlEU8fR3u/02PmmCVhKjSByL8SKJjIiIAtuYLgkI0WtwqsL+uakR4Ja8DAAhOmW7LvWklzCXFWBSUodaAo1XlXzgPA5vHqOMQZqr3Fxrtrq161KLb5QttnyRk9hY2UiZfCS1da2o9S4ukZKcM+JC5cd6006zLby18hAq6iz41497nbarJaxvPlqGdxzxsycf/34EE19a2eQx0+f+Ll+nB774Aw9++YfqcWsOFMvJ366kGEaqFtk7LcpjhSoiIqJgIQgCEhyVBAur6mFT3JiaPSa7vYZFREREdEakThiFlfVO8xT/urK30+IoIvKsTRrNjR49Gh988EFbPBUR0Rmb47gxIrW18CahxVPA4VotRwpU6swqk2FerHh3XSmfHBnS7GOU1XuUz6FWBUgrCPIEW1OVfM60P2mc4v1Qvq/SBExZTeOKd7VEKIkoitiaX+q0zXXFe1NJSm3FbLVh4ksrcdVbvzlVs5u39jDGv7gSXR/7Aee/8CssTbzXpyvqsPFwifx9c8lPvqpWBM9ajYBrBqXjk1uHoF/HaLx6bT+/PhcREbW99NhQzBzWSf7eUyKN8mMzVK9ezjnU6JwkUeaoUKjersuLSj5nEE4oW5XamqkUW2e2yeOQ4prCSveqf2dayadDtAnzbz4Pi+8Y7rQ90hHXlNc2VrZpqgpikaMSYWJEiFwdqKqZdpptbfG2E06xobL17Pd3j5S/fvq73dh9ssLjeR5dtAN7TlU2+3zSOb7YdEze1ictytPhbqQY+bM/DcVlfVPxyjTGNEREdHaQVrwrJ8M+vuU8pwqDRERERMHAKa5x3Gd48eo+SPJi3ouI7NokyYeIKBj0dFTykSasvJmk8kRq1yCpcdyAUaveYvRidfFQlxKF3qx41ylm0JQr928fm42rB6ZhfLckeZtWI8gtOqREJNcqNIIARJmcX5e3Yl3al0niw9231zR4ntw6WFiF/BLn1g3PuKzkrmyiRUZb2VdQif2nq7DpSClG/2uF6jHHy2pxzEMbCgCY8NJKXPnmb9h81J7UpFYVwNt2mJV1Ztzz6RZkPrQET367CzabiBpHctT5XROx5W8XIC0mFEOz47BoznD5Z4GIiILbBd0T5a89tdFSJma4tuWSuFbyKa+RknzU2nX5XsnHG8okYatKcpFSvcUqJ/BIlWfmrzvidlxLbh6N6ByP3mnRTtukajzHFVUHXZN8lK/8ZLn9uPhwA8IdiVRPfrsLX289jsFPL8XTS3ad8fhawjW++HyjPeFm2Z4CvL7iIADAoNW4JbWfViRSWW0idhwv9zn5evme027b+nWM8eqxKVEh6OioHtktJRIvT+snV9kkIiIKdtL9k6KqxsmwxAj1ey1EREREgayxXVe9PB8kdX8gIu8wyYeIyCE5MkROdAGck2TUSKu11US47JNuwKhV8jF4kbBz1/mdMTTLnugT42ECzpVyxbsyXyku3Ijnr+yDUV3i5W2CIMgtOqQ2EQ0uE3dZ8WFnvELMtX2ZpHdaFEbkxDttc20jpfR7nr2yzfCcOCRF2gPBr7aecDqmrMaMBovNLUmpLSlfg9SKQ400uedKFEWUOiZQV+4rxPGyWpTXuicvKav7iKJ7ezDJo4t2yO/T3NV5+Hl3gVzJJ9yoc0tKIyKis0OH6MZ2mZEeEnVDPbToauqYslozRFFUTfLxJklaWQixZ4fIZo8HXJJ8mklyrTfb5FZcUoVA18/juDCDfFPJX6TP02OK51J+NNtsolMVon0F9so28eFGp0Squz/ditOV9XhnVV6TFRZby73/2+r0/cp9hQCAm+ZtxNzVeQDsyUvRLjFpcVVjks+/f9qLS15djRd+2ttk5cJtf7sQM4dlyt9//PtRtwqW/TpGe3x8mEELrUbAyM7x+ObOEU29LCIioqCmXPEuTYaxJSUREREFI7kNqaJCoacW8kSkjkk+REQOGo2A7qmNE016D5NUM4ZmAAAeu6S7x3NFuCRNNFhssNpE1Uo+3rTAMhm0WHDreXh/1iD8eM+oZo8H7NV55K9VEoniFNV1tALkBCe56pBLQlKvFlR3GZObAABwLSIgCAKuO6+j0zbXtlTKVmMbD9ur2vROi/aYcHSoqApjX1iBq9/67YzH21JqCTlqjpepV/JRthR5ael+DH9uGf63Id/tuFnvb5C/nvPxZox8fjkqFJWM/v3TXvzt6x1YvM05EWprfhlqHclc3kzuEhFRcIpXJNl6ahv1r6v6YFh2HL5tIkHC9bPCahNRVW+BRaWijqf4SUmjyPJZNGd4E0c2UsY1akmt4Yq4oM5iRYhOqlBojytc23vmJkd49by+kBKpiqsb23VV1VvwxDc7sWLvaZTUNDgl/ew56UjyiTBiQs9k1XPeMPd3v4+zOa4J1GrxilGvcWt3pkxelir+vLHioNP74SoqVI+/XdIdvz4wBlqNgFMVdXLyEwDMHJaJCT3U3xsAeOfGgTj4zCR8dPN5qhUiiYiIzhZSkk9BRZ1cqZB/zxMREVEwildp18W4hsg3TPIhIlK4SDHB4mkl+t8u7YFl94/GlQPSPJ7HtZIPYE9eUavk42nSzZUgCBibm4hEL1tLKFe8awX316KsrqPVCHIQJU2G1bkkJLWk3UGP1CgsmjMMq//vfLd9AzKcWzAoq+CIovOK9+V77S0cenWI8hj0/eXzP3C8rBYbj5S2y+p3AE1OZikdL61FTYMF17+7DnNX56HE8bgjKtV/3luT57bt97wSLNtTAAD4fscpHC+rxReOlhpV9Ra8uuwAPvzNvT3JvlOVciWfUMOZVWciIqLAp2zvqfXQrqtvejQW3DqkyVaNaom1ZTVmt6p/QPOVEAHnpF+9FxUNXalV8vnx3sYk6HqzTV4BJsVerkk+rdHGKdKk/pn6/prDmPn+BpxwSZbZ60hmSQg3YlBmLD6+5Ty3x647VOJzyyt/O1xU7VaNxzXBBwBOOZJ8XCvxTHxpJQAgKdKILY9dgJeu6QsAeHBiLgB7on1GXBjSY+ylubc4WpVGmfT4++QeCNFr8fBFXZ3OefOITvj+7pEY5lIRkoiI6GyV4LiHo6xOyL/niYiIKBjJlXyq6uXOEiZWKCTyCZN8iIgUeqQ2TnCFqExeAPZJsqyEcAgqiTMStSSfmgaLaiUfb5N8fKWsgKM2f6Zc3a/RKNt1qVfyaenq6H4dY5Aa7d5XNcklaanWbIXomLyrbrA6rXgvc7Sw6pse7VTtRqlEkWAjHd+WNh0pwa4TFV4d+5+l+zDr/Q1Yc6AYT367C/2f/BnfbT+JQ4VVXj/fQ19ud0pm2n/a/timqgmt2l+Ek46JxjAjg2cionOBN8k3nqjFKmU1ZphVKvl4166r+WOaYlFJeumgiDFyEsMb23U5Yq+qOuckn2Qvk6Z9kREb1mSFxhNl6m06k6PsY4kNU29v+vyPe/C+SrJvaxBVEqiqGiyorneOYaV/E3eP6yxvO1leh+p6C0Y+v8zpWKkFaVZ8OGLCDJjSrwPWPTwOs0dnOx3XyZF49djXOwE4t6i9dWSWUzXL2DADuqV41+qNiIjobCBV8ll7sBiA/XNSineIiIiIgokU12w5Woo6sw16reD3lupEZzum+xMRKSREGBRfn3lQ4dquCwDqGmyqlXy8add1JpqbzFO267KJjZnSUiUdZdsnoGXvR3NiwwxOyTkXvbwKRp0Gu09Wuh2blRCG1GgTCiqck3xGdUnAyn2FTtvKahraNDhcvuc0Zs3b0PyBCr/nlTh9/9hXOzDRQ8sOALi4VwqWbD8pf3+6sh57TzW+T19vPY4ok77JczRYbXIrDva6JSI6NwzIjGn+IA/UknLKahvcqrsA3lXmuf68jli05TiGZMWe0XjU2nUBwLd3jsD/NuTj7vGd5aoyUlxT3eCc5OMpoaYlTAYthmfHYfneQtX9O0+Uq27vnBgOwLnKotJbvx4CAFzerwOiQ/0/bqV6i/s1FUVgwfqjTtukRKs7zs9BrdmKt1ceQn5JDdbnlbjFaAAwe0w2bhiSIX8vJTYpdU6KcHrvlK9VoxGQmxwBvVaA2SpiVOcE318cERFREEuIsH92Svm4AzJiW5w4TURERNQeEh1xjXR7p2eHKISwkg+RT5juT0SkoEx8iQ51T9TxlmolH7NFboWlpNbuwB+UK+nV5sKiTI2vr6LWLJd5lir5HCl2bhnV0ko+Tfn69uG4S7ESfM+pSmw7Vq7aBuSC7kmq5xiRE+eWMFXiZdssf3l9xYEWn6O4ugEf/37U4/6YMPd/l+sOFctf1zRY8eavB/H2yoNePV8Yy3sTEZ3Vvr1zBG4e0Qn/N7Fr8wf7wF7JR61dV/OTTQMzY7H2ofPx0c3u7am84al7Vc8OUXhySk/EhxsV7brUK/nEtUKSDwCneMbVq8vU4wQpITnGJYFnbK5zIktRVevHNcq2qUBja7V//rDHabuUkKTXajC5TyoAe/ux5753Pg4AeqRG4v8mdlWt6Kg0c1im0/dqsefKB8fiy9lD0SvNc3s5IiKis1GWS6vR8zqdWbI0ERERUXtLjQ6BQbFIbHAm4xoiXzHJh4hIQZn4om1BWwu1rOMP1h5RbaHUWpV8tIpJNpXOC9Ao9pdWNyjaddknwY6UVDsdn9iKFXHSY0Nx3wVdkKqyqtvV9PPsq8DfnN7faYKuc1KEU6sOoLE9RFs5WFjd5P4zrVig1KtDFH68ZxSemtITV/TvAAB4aslu97Gcdh5L1+QILJwzDAvnDHPaHspKPkREZ7WeHaLw2CXdnWIcfyitaVBt16X3Mq5JjTZ5VfVHjdVTlo+CFIvVOSrTVNU7J/nEt1Jc06+jbxWTsuLD5FX4ru+Ha1JMcZV6q1J/cq14FGZ0TwbOTYrAq9f2k79PUcRvewvcqzDmJkd49dyp0SYcePoi9E2PRmSIDvde4J4wlRJlwoAM3vwjIqJzT4xLgvKFPdQXQBEREREFOp1W41TN+MIenrsSEJE6JvkQESkoE1+8WIjuk0/WH8Wq/UVu21sryUc5USSi6cmwkpoGOdlDail21KWSj6cWEv7UJz26yf2RITqkx4YCACb2TMHGv45HRlwoDDoNBmbEIDXaOUmotKZtK/k0qLS4UPrvdf29PtcXfx6quv28TnHITY7A9CEZuHlEJ4+P17j8A85NjkD/jjHo3zHGqUqV2uQdERGRK6m9VfeUSAD2in9SJR/lR46+BUnS3vIqyccRXzVYbKi3WOU2VMOy4zAgIwb9mok5WmLpfaMxoUcSkiPVk5dvGJIBQQBSo0Iwb9Zgj+dxbTla3AYVCmsUlXyW3jcK4Spxwo/3jkJaTKj8fXOtz3KTvEvyAew3+r6cPQzrHhmHHqms1kNERKQk/S1v1GmQERfWzNFEREREgUs539Sa92iIzlZM8iEi8sC1ZYKvnp/aGzcMyUBmXKjbPmUpQmMbVPLxNBcmPffgzFiYHCveG6w2WKw2HC+rBWC/iXT3uM5yO6/W5CnJ54LuSfjLhV2w9P7RTtsFQcC3d47A+kfGISJEr1LJp+2SfCxWm9vqd1dx4UZc1tfe0qKp9meX9knFQA8lKjMU/556pEbhsz+pJwOdLK91+l753ijbyZlYyYeIiLyw9L7RWDhnGG4Yaq+ot6+gEhZHJR+TooKhsl1oa7GqlSh0oayqWKxoc/XBTYPx5exh0J1hFSFv5CSG460bBuKS3imq+y/v3wFbHrsAax46Hx1d4sRrB6cDsCc2u8YKRW1RycdR8SgtxoScxAjVJB9XUiUiT3ydhNRqhDaJO4mIiILN01N6YUBGDJbcNaK9h0JERETUIg9N7Ia+6dH46vbhbguWiah5vHNGROTiick98NnGfNw+NqdF57l6UDquHpSOaW//hsMuVXGMeg0aHKvfdW0RwHiYDFvxwBhsOlKKi3qmyKvxAaDGbEWJY7X4f67ui7FdE1t/jAA6J4arbs9KCMMd57u3bACAiJDGqjQdop0nygrK6/w3uGYUVTV4epudvHRNX8wZk4POieG49LXV2Hmiwu2YC7t7LrvtOpE2uJN6MlCZS6syZcuPyBA9AHsSUBgn0YiIyAuxYQbEhhkgOj7sNh0plRNITQYdqh0VYNoirrH50K4LAL794wQAe3LzmbYIOxPdUyNVt6dGmRDtIZn875N7IDEiBBf2SMKxUueE3aKq1k9ernVcR6nCo7cV/5bdPxrn//tX+ftpg9Lx6YZ8APaWoURERNRyF/dOwcUekoiJiIiIgsmIzvEY0Tm+vYdBFLRYyYeIyMWMYZlYctdItxYJZ+rGoZlu25QTT82tfvYHT1NhKVEmXNI7FVqNAKNOI7fbqG2wotSR5NNcCwZ/ykpQT/JJaKLqjVKHGOdKPh/8dgQHTle2eFzNKaysx8YjJR73T+qVjA9usrfjEAQBuckR0GgEZMarr2yX/u29cm0/jO6SAL2jKkLHWPeqUAAwqkuC22NdKStTKSv5hLKSDxER+aBbSiSiTHrUNFix/3QVAMBkaPyzsi3imnQPn4dKWo0gV0585rs9AOwVdtrSxJ7qPeWbijGNOi3uvaALeqRGId6lVeryPadR46gaaLOJePzrHfj3T3ubHUd1vQV7TrknFaseKyf52GMFZczQlKyEcGQnNMY1qdEmvHF9f7xwVR+P8Q4RERERERERERH5jkk+REStbFKvFNx/QRenba3VossTbyrMCIKASJO9Kk5hZT2K2yHJJ90lSUcyJCvOq8enRoe4bZs9f3OLxuTJkj9OYpejCs+gp5fijgVbPB77+vUDMFqRiCOJ9DBxJk3+Te6TKrcVGd8tEe/NHKR6/EvX9MVto7Kw9L7Rbv/WJCmK90ZZ/YhJPkRE5ItQgw4zh2U6b9O3TVW4T24dgptHdMLNIzp5dbxR7xxvzRnTsiqNvgo16LD2ofNxw5AMedvL0/o6tVRtSlyYczLQ9uPleO57e8LSyv2F+OC3I3h12QHUW6xNnudPH23CxJdWYe3BomafU0oiCjM6Kvm4VPwbmBHj8bHKtqvRoXpc1CsFVw5Ia/Y5iYiIiIiIiIiIyHtM8iEiagMml0QKZSWftmDzJssHQK8OUQCA1QeKUG+xt+9qyyQfnVaDawd3dNrWOy0KPTy0u3DVv2MMRnaOx4Qeje2upCoD/rTlaCluX7AZk15ZhXKXtlhRJj2y4sNwWd9UAMBd49TbjAFARlzjyvZ5sxoTeFxX+PdOi8a7MwZ5rEAQG2bAI5O6IScxXE7UklzRvwP+NCoL/RQTb8pWKqFs10VERD7KdWm/5BrntJah2XF47JLuXsdRJsVxCRHGdmlvkRptwpNTeuLVa/vh69uH47K+Hbx+bGZ8GJ6Y3AN3K2KJD387AgBYsbdQ3lZZZ2nyPKsP2JN7rnvnd/x3+QHVY2oaLGiw2FBVbz+XyZG4FWlqjBNuGt4J79w40OPz3DS8E/RaAQadBn3SopscExEREREREREREZ0ZzuwREbUB10SKEH3gVfIBgAEZMVi1vwi/7C4AYK841NaVXp69oheevaIX8ktqoNUIiAk1eN36I0SvxUc3n4d6ixW5f/1B3n64qNqvrSIOFlY3fl3knET0872jkBgZAlEUce/4LsiI89xSZNbwTBRX1ePCHsnolx6NLknhiAzRI8Lon4/nyX1S8eLVfd22K5O+Ev3Ulo6IiM4d6THOn22mNk5e9lZEiA6nK+sBoN2TTi7tk3pGj5sxLBNmqw0v/7Lfafup8jr568o6C+K9bG36rx/34taRWTAoqkpW1Vsw5l/LkRwVgpGd7ZUHpeqIPTtE4bONxwAAN43IREwTyd89O0RhxQNjEaLTIM7L8RAREREREREREZFvWMmHiKgNuCbKhOjadjLMyxwfdIi2t8s64Kh+ExfmfYKNv6XHhiI12nRG1QGMOi3evmGA/P317/7uz6FBq/j0vOL1tU77pMkvQRCQGR/W5Ptn1Gnx6MXdMSgzFjqtBt/fPQqf/3loi97zNEXLs1eu7ad6zJwxORiUGYPP/zwUGi9bhhAREUnSXNprBmrrx8v7NVbN8dQiMxjotRq5HadGAD5adwQ/7Dwl799+vBzrDhWrPlatldcBlyqHq/cXoaiqATuOV2CnoxVpJ0dydP+Oje25vEkk6hBtYoIPERERERERERFRKwreO51EREHENVHF2MaVfOLDvWu5JY2z1NGCKtbLxwWi87Li5K+Pl9VCFEW/JSyVVps97tNrz/zaav2QcNM7LRqvX99fnpxT0yc9Gp//eViLn4uIiM5NUS6tIUMCNMlnZOcEvPDTPgBAeBAn+QDAbaOz8O+f98EmAo99tcNp312fbAEAPDG5B2YMy3TaV1Ld4Hau3Scr0D01Em+vPIgfdxbgvE6x8r61jtZeUhzRIzUSc8ZkI9SgbfN2s0REREREREREROTunKzkU1lZiUceeQS5ubkwmUyIj4/HxRdfjGXLlvl8rpqaGnz77be444470KdPH0RERMBgMCA9PR3Tpk3DmjVrPD525syZEAShyf/q6uo8Pp6Igkd7VfJ5/fr+mDU8E5f09q5FhOs4Y0KDN8nHdcX+gdNVuGneBjz/wx6vHm+22vD3xTvxw45TbvvKatwnzALJpF4p6JYS2d7DoDbCuIaI2ppGI0CnSEyNDdB4IV7RkjLcT60w24vRi9jxP0v3uW0rrmqMWQyOROSDhfZKPs98twebjpTi9RUH5WMsNnv9x+yEcAD2yoQPTuyKO87vfOaDJ/IB4xoiIiI6WzCuISIiotYS3Hc6z8Dp06cxcuRI7Nu3DykpKbj00ktRUFCA77//Ht9//z1efvll3HnnnV6fb8GCBbj11lsBABkZGRg3bhx0Oh22bduG//3vf/jss8/w5JNP4tFHH/V4juHDhyMnJ0d1n1bL1ZJEZwPX5JmpA9Lwy57Tbu0u/G1SrxRM6pXi9fEmvfPHQlxYYE7aeUMQBPzvtiG45u11AIAL/rMSALBsz2k8OLFrs49ftPk45q09jHlrD+Pwcxc77SsJ8CQfOncwriGi9mLQaWBpsLeCumFoBvacqsCY3MR2HpUzZRzjbevSQDZ9SEfMX3fU4/6yGjOq6i1OCU2FVfUAgG4pkZjavwOeWrIbR0pqmnye3mlRSI8N9c+giXzAuIaIiIjOFoxriIiIqDWdc0k+t912G/bt24dx48Zh8eLFCA2137z87rvvMHnyZNxzzz0YPXo0evfu7dX59Ho9brrpJtxxxx3o16+fvF0URfznP//B/fffj7/+9a8YMWIERo8erXqOW265BTNnzmzxayOiwBVqaPx1KwjART2T8dXtw5tsqdQeXJORYsOMHo4MDudlxWFK31R8tfWE03abTYSmmdZYh4ur5a9dW31J7cxcJUeGtGC0RL5jXENE7cWg06DGkeQTbdIHZBtIZXupOrO1HUfiH09N6YV6sw2fbzrm8Zjtx8oxNLuxZemOY+UAgIzYUHR0JO7kl9RAFD2nPd08opOfRkzkG8Y1REREdLZgXENERESt6Zxq17Vr1y58/fXX0Gq1mDt3rhxYAcCkSZMwc+ZM2Gw2PPvss16fc8aMGZg7d65TYAXYK0jcd999GDduHADgo48+8s+LIKKgpEyeMeo0EAQBfdOjEWXSt+Oo3JlcknziwoO3ko9ELZmnxouJPqutcfKrusH5+JNltW7H/+OyHvhi9tAzGCHRmWFcQ0TtSaNIfjXoAv/Pyjqzrb2H4BfPXNELT03p6XG/Mkl554ly/PtnewuvEZ3j0THO/jnxx7Fy/Haw2OlxHaJNGN8tEbeM6ITJfbxr80rkT4xriIiI6GzBuIaIiIhaW+DfjfWjRYsWAbCXJczIyHDbf9111wEAvvnmG5jN6lUafCUFXfn5+X45HxEFJ2XyjEEbuL96TXrnJJ+Y0OBP8lGbqKqqszT7uPLaxs+B0xWNfamLquqxNb/M7fgbh2YiLYatLajtMK4hovakrARj1AVuafc+6dEAgMv7dWjfgfiJXqvBRT2TPe5/eOF27CuoRFW9BRe/slre3q9jNDpEN7aJve7d350eNzAzBu/OGIS/XtLdqXohUVthXENERERnC8Y1RERE1NrOqXZdW7ZsAQAMHDhQdb+0vbq6Gvv370f37t1b/Jz79+8HAKSkpHg8Zvny5di+fTsqKysRFxeHwYMHY9KkSTAag7tNDhE1cm7XFbgTJ67tuuLPgko+Y3ITMSQrFusOlcjbDhZW4d8/7cW0wekYkBGr+riT5Y2JPYWV9chKCAcAbMsvg00EcpMicP2Qjvjb1zvx76v6tO6LIFLBuIaIAkUgV/L59NYhOFFei2zH5/jZIC7ciM2PXYBV+wtx96db3fZ/sekY8oqqnbYlRYYg3Oj5z/8Gy9lR6YiCF+MaIiIiOlswriEiIqLWdk4l+eTl5QEAOnbsqLo/MjISkZGRqKioQF5eXouDq+3bt2PJkiUAgKlTp3o87sMPP3TblpKSgvfeew8TJ05s0RiIKDAoK+TYFG2gAo1zMhIwJDuuHUfjP2NzE52SfJ5ashu7T1bg803HsO1vFyIq1LltWl5RNX7dVyh/f7qyHoA92WfTkVIAQHJUCG4cmolLe6ciJiz4k6Eo+DCuIaL2pIxmtCqtMQOFyaA9qxJ8JLFhBlzQPcnj/p93FTh9HxNqaDLRvK+j4hFRe2FcQ0RERGcLxjVERETU2gJ3yWUrqKysBACEhYV5PCY83H4DuKKiokXPVVVVheuuuw4WiwUTJkzApZde6nZMnz598PLLL2PHjh2oqKhAQUEBfvrpJwwbNgwnT57E5MmTsWLFiiafp76+HhUVFU7/EVHgUU5+2cTATfIxKlbi9+8Yg8gQfRNHBw+LS2LV7pONvyv3na50O/6K19c4fV9S3QAAuGneBry+4iAAINqRGMQEH2ovjGuIqD0FcDhzzgg16JAaFQLAnvQj2VfgHtt4SsQyaDW474IumDEss1XGSOQtxjVERER0tmBcQ0RERK0taCr5PPjgg1i8eLHPj3v33XcxYsSIVhiRZ2azGVdddRV27NiBrKwsfPTRR6rH3XvvvU7fR0RE4IILLsD48eNx+eWX4+uvv8Y999yDrVu3enyuZ599Fk888YQ/h09ErcwawLNiGsUEUFLk2VOqNTvB8x/VhY4qPZIDp6tQWuPcD7u0xp7ks/14ubwt2nR2JEBR+2Bco45xDRGRb76cMwz//H4PZg3vhKW7C/DqsgPYecL7G+4jOsfjrnGdW3GEdC5gXKOOcQ0REVHwYVyjjnENERFRYAmaJJ8TJ05g7969Pj+uqqpK/joiIgKAvddpc8dHRkb6/FwAYLFYMG3aNPzwww/IyMjAsmXLkJCQ4NM5BEHAE088ga+//hrbtm1Dfn4+0tPTVY99+OGHcd9998nfV1RUeDyWiAJDAHfrcpIYEdLeQ/CbCT2S8cikrnj1lwOorLc47TtdUSd//f6aPDzxzS75+yFZsVh3qARlNWaILslZ0aGs4ENnjnEN4xqiYOf6uUjtIyXKhJem9QMAbDhsb03qmsDclNzkiFYZF51bGNcwriEiIjpbMK5hXENERBQMgqZd1/z58yGKos//KXuJZmZmAgCOHj2q+hzKMoPSsb6wWq24/vrrsXDhQqSnp2P58uXIyMjw+TwA0K1bN/nrY8eOeTzOaDTKPVyl/4gosNmCJMunW8rZM+kjCAJuG5WNMV0T3faddkyE5ZfUOCX4AMC4rkkA7JV86sw2p31RrORDLcC4Rh3jGqLgERzRzLkl0iU2mdQrGT07ROK16/rJ2165th+kwo06jYBpg3hjnlqOcY06xjVERETBh3GNOsY1REREgSVoKvn4Q//+/bFw4UJs3LhRdb+0PSwsDF26dPHp3FarFdOnT8dnn30mB1adOnU647EWFxfLX0uZ30R0drAF+Mr3l67piw2HSzC1f1p7D8XvYkPdE3OkJJ+1B4uctt8wJAPRjuNLa8yobrC4PZaoPTGuIaJ2FdjhzDnJNQH50Yu7o0O0yWnb5D6puKBbEhqsNtQ0WJAS5byfqL0wriEiIqKzBeMaIiIiam1BU8nHH6ZMmQIAWLNmjWoW9YIFCwAAl156KfR67ys02Gw23Hjjjfj000/lwCo7O7tFY/30008B2Ms15ubmtuhcRBRYAr2Qz5R+HfD05b2g0559HxHJKhNZvx0sRkWdGa8uO+C0/aGLuiLG0ZJr5b5CvLnioNP+QE/WorMf4xoiIlJSJvlc0jvFLcFHYjJoEWXSM8GHAgrjGiIiIjpbMK4hIiKi1nb2zeA2oUePHrjssstgtVpx8803o7a2Vt73/fffY968edBoNHj44YfdHnvjjTeia9eueO2115y222w2zJo1CwsWLPApsNq6dSsWL14Mi8W5MoTNZsPcuXPxyCOPAADuuusunwI9IiLyLCUqRP66R2okokP1OF5Wi7d/PYRjpbVOx4YZdYgJM8jfv7s6T/46Kz7srKx0RMGFcQ0RtSemugaenMRw+eseqVHtOBIi3zGuISIiorMF4xoiIiJqbedUuy4AePvtt7Fr1y4sXboU2dnZGDlyJE6fPo1ff/0Voiji5ZdfRu/evd0ed/ToUezduxdFRc7tXF577TV8+OGHAIDs7Gw8+eSTqs/btWtXPPTQQ/L3hw8fxuWXX46YmBj0798fSUlJKCsrw44dO+Ts7muvvRaPP/64v146EdE5L1mR5JMSZUJcuBEr9xVi0ZbjqsfHKpJ8JOmxJiz7y5jWGiKRTxjXEBGRJD7ciE9uHYJv/jiBqwcyGZmCD+MaIiIiOlswriEiIqLWdM4l+SQmJmLjxo149tln8eWXX+Lrr79GWFgYJkyYgL/85S8YN26cT+crKSmRv16xYoXH40aPHu0UXPXp0wf33HMPNm7ciD179mDNmjUQRRFJSUm48sorMWvWLEyaNMnn10dERJ4pK/kYdRrEhNpXqBwvq1U9PjHC6LYtVH/OfXRSAGNcQ0TtRWTbyoA0NDsOQ7Pj2nsYRGeEcQ0RERGdLRjXEBERUWsSRN6dPatUVFQgKioK5eXliIyMbO/hEJFC/yd/Rkl1A3ISw7H0vtHtPZxzksVqQ86j3wMARnaOR5+0aLy2/IC8/+GLuuJfP+7F05f3xDWDOgIAMh9a4nSOfh2jsWjO8LYbNAUEfr62D77vRIGr+99+QE2DFQBw+LmL23k0ROQLfr62D77vRERE/sfP1/bB952IiMj/fPl81bTRmIiIznn/u20IpvZPw9wZA9t7KOcsnVaD92cOQkZcKP48OhtJiso+8eFG3DYqCzuemCAn+KgJNWjbYqhEREQBjUtFiIiIiIiIiIiIiNoee44QEbWRzkkR+PfVfdp7GOe8sV0TMbZrIgCgzmyVt0/okQRBEBCibzqJJ9TAj04iIiIiIiIiIiIiIiIianus5ENEROesUV0SMCInHhEhOkwfkqF6TJRJ7/Q9K/kQEREBD07MBQDc4OHzk4iIiIiIiIiIiIj8j+UIiIjonKXXajD/lvNgs4nQaATVY76cPRTP/7AXP+0qAAB0SYpoyyESEREFpJnDMjEmNxEZsaHtPRQiIiIiIiIiIiKicwaTfM4yoigCACoqKtp5JEREZ4fEEODpS7Lxw5Y8AEDPBB1/x56DpGsufc5S22BcQxTY4gxAVVVlew+DiHzEuKZ9MK4hIiLyP8Y17YNxDRERkf/5EtcwyecsU1lpv8menp7eziMhIjo7jXmpvUdA7amyshJRUVHtPYxzBuMaIiKi1sO4pm0xriEiImo9jGvaFuMaIiKi1uNNXCOITHE+q9hsNpw4cQIREREQBPXWM76oqKhAeno68vPzERkZ6YcRUmvjNQsuvF7Bhdcr+PjrmomiiMrKSqSmpkKj0fhxhNQUxjXEaxZceL2CC69X8GFcE9wY1xCvWXDh9QouvF7Bh3FNcPN3XAPw5zjY8HoFF16v4MNrFlzaI65hJZ+zjEajQVpamt/PGxkZyV8iQYbXLLjwegUXXq/g449rxhVhbY9xDUl4zYILr1dw4fUKPoxrghPjGpLwmgUXXq/gwusVfBjXBKfWimsA/hwHG16v4MLrFXx4zYJLW8Y1TG0mIiIiIiIiIiIiIiIiIiIiIgpwTPIhIiIiIiIiIiIiIiIiIiIiIgpwTPKhJhmNRjz++OMwGo3tPRTyEq9ZcOH1Ci68XsGH14yU+O8h+PCaBRder+DC6xV8eM1Iif8egg+vWXDh9QouvF7Bh9eMXPHfRHDh9QouvF7Bh9csuLTH9RJEURTb7NmIiIiIiIiIiIiIiIiIiIiIiMhnrORDRERERERERERERERERERERBTgmORDRERERERERERERERERERERBTgmORDRERERERERERERERERERERBTgmORDTiorK/HII48gNzcXJpMJ8fHxuPjii7Fs2TKfz1VTU4Nvv/0Wd9xxB/r06YOIiAgYDAakp6dj2rRpWLNmTSu8grPL559/jjFjxiAmJgZhYWHo06cPnn/+eZjN5jM636ZNm3DVVVchKSkJISEh6NSpE+68806cPn3azyM/d/nrmm3ZsgXPPvssxo0bh6SkJOj1esTExGDkyJH473//e8b/BsiZv3/GlL777jsIggBBEDB+/Hg/jJZa43p9/fXXmDx5MpKTk2EwGJCYmIhhw4bhH//4hx9HTu2FcU1gYVwTfBjXBBfGNcGFcQ35inFN4GFsE1wY1wQXxjXBh7EN+YJxTeBhXBNcGNcEF8Y1wSco4hqRyKGgoEDs0qWLCEBMSUkRr7rqKnHUqFGiIAiiIAjiK6+84tP53nnnHRGACEDMyMgQL7vsMnHq1KliTk6OCEAUBEF86qmnWunVBL+7775bBCDqdDrxwgsvFK+44goxOjpaBCCOGDFCrKmp8el8n3/+uajT6UQA4qBBg8Srr75azMrKEgGISUlJ4v79+1vplZw7/HXNzGaz/LMTHh4ujh07Vpw2bZo4YsQIUavVigDEwYMHi6Wlpa37gs5y/v4ZUyopKRFTU1NFQRBEAOK4ceP8OPJzk7+vV319vXjVVVeJAESTySSef/754rXXXiuOHTtWTExMFOPi4lrplVBbYVwTWBjXBB/GNcGFcU1wYVxDvmJcE3gY2wQXxjXBhXFN8GFsQ75gXBN4GNcEF8Y1wYVxTfAJlriGST4ku+yyy+RfAtXV1fL2JUuWiFqtVtRoNOK2bdu8Pt+8efPEm266Sdy8ebPTdpvNJv773/+WPzxWrFjht9dwtli0aJH8wbpp0yZ5e2FhodirVy8RgHj//fd7fb7jx4+LoaGhIgDxrbfekrdbLBZx+vTpcrBls9n8+jrOJf68ZmazWRwwYID42WefiXV1dU77/vjjDzElJUUEIM6aNcuvr+Fc4u+fMVfXX3+9qNVqxdmzZzO48oPWuF433nijCECcMmWKWFhY6LTParWKv/32m1/GTu2HcU3gYFwTfBjXBBfGNcGFcQ2dCcY1gYWxTXBhXBNcGNcEH8Y25CvGNYGFcU1wYVwTXBjXBJ9gimuY5EOiKIrizp07RQCiVqsVDx8+7Lb/5ptvFgGI06ZN89tzjhs3TgQg3nzzzX4759li0KBBIgDVDPNVq1aJAESj0SiWlZV5db4HHnhABCCOHz/ebV9lZaUYFRUlAhB/+OGHFo/9XOXva9aUjz76SM74bGhoaPH5zkWteb0WLlwoAhAfeOAB8f3332dw5Qf+vl5Lly4VAYg9e/bkz9BZinFNYGFcE3wY1wQXxjXBhXEN+YpxTeBhbBNcGNcEF8Y1wYexDfmCcU3gYVwTXBjXBBfGNcEnmOIaDYgALFq0CAAwfPhwZGRkuO2/7rrrAADffPON33ow9uvXDwCQn5/vl/OdLY4fP44NGzYAaHzflUaMGIH09HTU19fju+++8+qc0vVVO194eDgmT54MAFi4cOGZDvuc1hrXrCnSz05tbS2KiopafL5zTWter6KiIvz5z39Gbm4u+4P7SWtcr1dffRUAcM8990Cv1/tvsBQwGNcEDsY1wYdxTXBhXBNcGNfQmWBcE1gY2wQXxjXBhXFN8GFsQ75iXBNYGNcEF8Y1wYVxTfAJtriGST4EANiyZQsAYODAgar7pe3V1dXYv3+/X55TOk9KSopfzne2kK5FbGwsOnXqpHqMdD2kY5tSWVmJAwcOOD2uJecjd/6+Zs2RfnYMBgNiY2NbfL5zTWter9mzZ6OoqAhz585FSEhIywZKAPx/vaxWK3755RcAwKhRo3Dq1Cm89NJLmD17Nu655x588MEHqKqq8tPoqb0wrgkcjGuCD+Oa4MK4JrgwrqEzwbgmsDC2CS6Ma4IL45rgw9iGfMW4JrAwrgkujGuCC+Oa4BNscY3ujB9JZ5W8vDwAQMeOHVX3R0ZGIjIyEhUVFcjLy0P37t1b9Hzbt2/HkiVLAABTp05t0bnONs1dCwBIT093OrYphw8flr/2dE5fzkfu/H3NmiKKIp5//nkAwCWXXAKj0dii852LWut6ffrpp/jiiy9w9913Y/jw4S0bJMn8fb0OHTokB07r1q3DnDlz3AKpBx54AJ9++inOP//8Mx02tTPGNYGDcU3wYVwTXBjXBBfGNXQmGNcEFsY2wYVxTXBhXBN8GNuQrxjXBBbGNcGFcU1wYVwTfIItrmElHwJgz7AFgLCwMI/HhIeHAwAqKipa9FxVVVW47rrrYLFYMGHCBFx66aUtOt/Zxt/XQjpfU+f017U9V7Xlz88TTzyB3377DeHh4XjuuedadK5zVWtcr1OnTuH2229HdnY2nnnmmZYPkmT+vl7FxcXy1zfffDMGDBiADRs2oLKyElu3bsWkSZNQWFiIyy67zG8rhqjtMa4JHIxrgg/jmuDCuCa4MK6hM8G4JrAwtgkujGuCC+Oa4MPYhnzFuCawMK4JLoxrggvjmuATbHENK/mcBR588EEsXrzY58e9++67GDFiRCuMyDOz2YyrrroKO3bsQFZWFj766KM2fX6iYPbhhx/iH//4BzQaDd577z107ty5vYdEDrfddhtKS0vx5ZdfIjQ0tL2HQ00QRVH+ukOHDvjxxx/llQh9+vTB4sWL0bdvX+zYsQPPPfcc5s6d215DPWcxriE6NzCuCVyMa4IH45rAx7iG6NzAuCZwMa4JLoxtAhvjGqJzA+OawMW4Jri0dlzDJJ+zwIkTJ7B3716fH6csCRUREQHA3uu0ueMjIyN9fi4AsFgsmDZtGn744QdkZGRg2bJlSEhIOKNznc38fS2k80nnjIqKatH5yF1b/Px8/vnnuOmmmwAA77zzDq666qozOg/5/3p98MEH+OabbzB79myMGTPGL2OkRq35O3HmzJlupUa1Wi3+9Kc/4c4778TSpUvPZMjUQoxrzi6Ma4IP45rgwrgmuDCuOfcwrjn7MLYJLoxrggvjmuDD2Obcwrjm7MO4JrgwrgkujGuCT7DFNUzyOQvMnz8f8+fPb9E5MjMzsXnzZhw9elR1f0VFhVx6KjMz0+fzW61WXH/99Vi4cCHS09OxfPlyZGRktGTIZy3p/c3Pz/d4jLTPm2uhfJ+PHj2KXr16teh85M7f18zVwoULcd1118Fms+Gtt96Sgyw6M/6+XosWLQIAbNiwwS24OnXqFABg06ZN8r5PP/0UycnJvg36HObv65WZmQlBECCKIrKyslSPkbafPHnSt8GSXzCuObswrgk+jGuCC+Oa4MK45tzDuObsw9gmuDCuCS6Ma4IPY5tzC+Oasw/jmuDCuCa4MK4JPsEW1zDJhwAA/fv3x8KFC7Fx40bV/dL2sLAwdOnSxadzW61WTJ8+HZ999pkcWHXq1KnFYz5b9evXD4C9V19eXp7qeyVdj/79+zd7vsjISOTk5ODAgQPYuHGjamDly/nInb+vmdJXX32FadOmwWq14o033sCtt97a8gGf41rrenn6/QkAZWVl+PXXXwEAdXV1vgz3nOfv6xUeHo7c3Fzs2bMHRUVFqsdI26X+qhR8GNcEDsY1wYdxTXBhXBNcGNfQmWBcE1gY2wQXxjXBhXFN8GFsQ75iXBNYGNcEF8Y1wYVxTfAJurhGJBJFcceOHSIAUavVikeOHHHbf/cMCq4AAAyuSURBVPPNN4sAxGnTpvl0XqvVKl533XUiADE9PV08cOCAv4Z8Vhs0aJAIQHzqqafc9q1atUoEIBqNRrGsrMyr8z3wwAMiAHH8+PFu+yorK8Xo6GgRgPjDDz+0eOznKn9fM1EUxcWLF4t6vV4UBEF88803/Tncc15rXC8177//vghAHDduXIvOc67z9/V67LHHRADipEmTVPdPnz5dBCBOmDChReOm9sO4JrAwrgk+jGuCC+Oa4MK4hnzFuCbwMLYJLoxrggvjmuDD2IZ8wbgm8DCuCS6Ma4IL45rgE0xxDZN8SHbZZZfJH741NTXy9u+++07UarWiRqMRt23b5va4G264QczNzRVfffVVp+1Wq1W88cYbGVidgUWLFokAxPDwcHHTpk3y9qKiIrFXr14iAPH+++93eszChQvF3Nxc8fzzz3c73/Hjx8XQ0FARgPj222/L2y0Wi3jDDTeIAMRBgwaJNput9V7UWc7f12zJkiWiwWAQBUEQ33rrrVYf/7nG39fLEwZX/uHv61VYWCjGxMSIANz+cPnkk09EQRBEAOKSJUta5wVRm2BcEzgY1wQfxjXBhXFNcGFcQ2eCcU1gYWwTXBjXBBfGNcGHsQ35inFNYGFcE1wY1wQXxjXBJ5jiGib5kKygoEDs3LmzCEBMSUkRr776anHMmDHyP7CXX35Z9XGjR48WAYiPP/640/aXX35ZBCACEMeMGSPOmDFD9b9nn322DV5d8LnrrrtEAKJerxcnTpwoTp06Vc5yHj58uFMALIqNv8QzMjJUz/fZZ5+JWq1WBCCed9554jXXXCNmZWWJAMSkpCRx//79bfCqzm7+umYFBQWi0WgUAYhpaWkef3ZmzJghFhYWtuErPLv4+2dMDYMr//H39frpp5/EkJAQEYDYo0cP8corrxT79esnf2499thjbfCqqDUxrgksjGuCD+Oa4MK4JrgwriFfMa4JPIxtggvjmuDCuCb4MLYhXzCuCTyMa4IL45rgwrgm+ARLXMMkH3JSXl4uPvTQQ2Lnzp1Fo9EoxsbGihMnThSXLl3q8TGegqvHH39c/gfa1H+jR49u3RcVxP73v/+Jo0aNEiMjI0WTyST27NlTfO6558T6+nq3Y735xb9x40bxiiuuEBMSEkSDwSBmZGSIt99+u3jq1KlWfBXnFn9cs7y8PK9+dgCIeXl5bfPCzlL+/hnz9BgGV/7h7+u1d+9eccaMGWKHDh1EvV4vxsXFiZMmTRJ//PHHVnwV1JYY1wQWxjXBh3FNcGFcE1wY15CvGNcEHsY2wYVxTXBhXBN8GNuQLxjXBB7GNcGFcU1wYVwTfIIhrhFEURRBREREREREREREREREREREREQBS9PeAyAiIiIiIiIiIiIiIiIiIiIioqYxyYeIiIiIiIiIiIiIiIiIiIiIKMAxyYeIiIiIiIiIiIiIiIiIiIiIKMAxyYeIiIiIiIiIiIiIiIiIiIiIKMAxyYeIiIiIiIiIiIiIiIiIiIiIKMAxyYeIiIiIiIiIiIiIiIiIiIiIKMAxyYeIiIiIiIiIiIiIiIiIiIiIKMAxyYeIiIiIiIiIiIiIiIiIiIiIKMAxyYeIzlmHDx+GIAjIzMxs76GcsTFjxkAQBPm/nJycFp1v3bp1TucTBAHz5s3zz2CJiIiIiIiIiIiIiIiIiOiM6dp7AERErSUzMxNHjhxBXl5eUCfyeGPChAlITk5GUlJSi86TkJCAGTNmAABWr16NgwcP+mN4RERERERERERERERERETUQkzyIaJzVocOHbB7927o9fr2HkqLPfTQQxgzZkyLz5OdnS1X7pk5cyaTfIiIiIiIiIiIiIiIiIiIAgSTfIjonKXX69G1a9f2HgYREREREREREREREREREVGzNO09ACIif5s3bx4EQcCRI0cAAJ06dYIgCPJ/K1asAAAcPnwYgiCotvKSjgWA+fPnY/DgwQgPD0dCQgKuvfZaHD16FAAgiiJee+019O3bF2FhYYiPj8fMmTNx+vRpj+Pbt28f/vSnPyE7OxshISGIiorCqFGjMH/+fP++EQ779+/HTTfdhE6dOsFoNCI8PBwZGRm4+OKL8f7777fKcxIRERERERERERERERERkX+xkg8RnXVycnIwY8YMfPHFF6iursbUqVMRHh4u709OTvb6XA8//DBeeOEFjBo1ChdddBHWr1+PTz/9FGvWrMG2bdvw5z//GYsXL8aYMWOQlZWFNWvW4IMPPsCWLVuwYcMGGAwGp/N9/vnnuPHGG1FXV4euXbti0qRJKC8vx++//44bbrgBy5Ytw3vvvee392LHjh0YPnw4KioqkJubi0suuQRarRbHjh3DypUrcfz4ccyaNctvz0dERERERERERERERERERK2DST5EdNYZMWIERowYgRUrVqC6uhovvPCCarUeb7zzzjvYuHEj+vTpAwCora3FhRdeiNWrV2P06NGoqanBnj17kJGRAQAoKirC0KFD8ccff+Dzzz/H9ddfL59r+/btuOGGGyAIAr788ktcccUV8r4jR47g0ksvxfvvv48xY8bgxhtvPPM3QOHFF19ERUUFnnrqKTz66KNO+2pra7Fhwwa/PA8REREREREREREREREREbUutusiImrCP/7xDznBBwBMJhPuu+8+APaknVdeeUVO8AGA+Ph4zJ49GwDwyy+/OJ3r6aefRn19PZ566imnBB8AyMjIwNy5cwEAr7zyit/GX1BQAACYNGmS2z6TyYRRo0b57bmIiIiIiIiIiIiIiIiIiKj1MMmHiKgJaskxnTt3BgDodDpceOGFHvefOHFC3maz2fD9998DAK655hrV5xo4cCDCw8OxZcsW1NXVtXjsADB48GAAwOzZs/Hjjz/67bxERERERERERERERERERNS2mORDRNSEjh07um0LDw8HAKSkpECnc+96GBERAQBOCTXFxcWoqKgAAKSnp0MQBLf/NBoNqqqqYLPZUFxc7JfxP/DAAxg/fjx+//13TJw4EZGRkRg0aBDuv/9+tuoiIiIiIiIiIiIiIiIiIgoi7rPTREQk02g850I2tc+VzWaTv54xY0azxxuNRq/P3ZTQ0FD8/PPP2LBhA3744QesXbsWa9euxcaNG/Hiiy9izpw5+O9//+uX5yIiIiIiIiIiIiIiIiIiotbDJB8iojYQHx8Pk8mE2tpavPDCC4iPj2/T5x80aBAGDRoEALBYLPjqq69w44034vXXX8eVV16JsWPHtul4iIiIiIiIiIiIiIiIiIjIN2zXRURnLYPBAMCe1NLetFotLrjgAgDAZ5991q5j0el0uPLKKzFhwgQAwNatW9t1PERERERERERERERERERE1Dwm+RDRWSstLQ0AsHPnznYeid3jjz8Og8GABx54AB988IFTCy/Jjh07sHDhQr895+uvv469e/e6bT916hQ2btwIAMjIyPDb8xERERERERERERERERERUetgkg8RnbWmTp0KAJg+fTqmTp2KW265Bbfccotq0ktb6N+/P+bPnw8AmDlzJjIyMjBhwgRMnz4dkyZNQnp6Onr16uXXSj9vv/02unbtiqysLEyePBnTp0/HhAkTkJWVhWPHjuH888/H5MmT/fZ8RERERERERERERERERETUOnTtPQAiotYye/ZsVFZWYv78+fjuu+9QV1cHwJ70k5ub2y5juuqqqzBo0CC88sor+Pnnn7FmzRpYrVYkJSUhJycHd9xxB6688kq/Pd/TTz+NJUuWYN26dVi3bh3Ky8uRmJiI8847D7NmzcK1114LnY4fBUREREREREREREREREREgU4QRVFs70EQEdGZGTNmDH799VcsX74cY8aM8eu5Z86ciQ8++ADvv/8+Zs6c6ddzExERERERERERERERERGRb1i+gYjoLPDcc89h3rx5SEpKwj//+c8zPs/Bgwfx5JNPAgBWr17tr+EREREREREREREREREREVELMcmHiOgs8OOPPwIAsrOzW5TkU1hYiA8++MBfwyIiIiIiIiIiIiIiIiIiIj9huy4iIiIiIiIiIiIiIiIiIiIiogCnae8BEBERERERERERERERERERERFR05jkQ0REREREREREREREREREREQU4JjkQ0REREREREREREREREREREQU4JjkQ0REREREREREREREREREREQU4JjkQ0REREREREREREREREREREQU4JjkQ0REREREREREREREREREREQU4JjkQ0REREREREREREREREREREQU4JjkQ0REREREREREREREREREREQU4JjkQ0REREREREREREREREREREQU4JjkQ0REREREREREREREREREREQU4P4fN1Wnb3my8OYAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 2800x800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2800x800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2800x800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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9+uijNjMOXa8gn8+82LZtm/Xnrl27qn79+lq6dKnOnDmj3bt3q3v37pKuzH7xxRdfqHPnztq0aZM++ugjHThwQKdOndL06dMVFBQkSRo9enS+9dUYo/j4+Dw9bjTjgrOcOHHCuszOTTfdlGXdGjVqSJKioqJkjHHqPnKje/fuKlasmI4dO2YzE5okrV69WkePHlWxYsWs742sOONz0KNHD+v7K2NGDEdmzpxpPff+/fvn5JRzZNOmTerUqZNiYmIUHh6uGTNm6NixYzp8+LCmTJmiwMBADR48WLt375YkNW7cON/6kh3u+LnJ+I4vWbKk6tata1NWEO/7119/XW+99ZakKzNTPfbYY3bvvW+//VZ9+/ZVWlqabrvtNi1ZskTFihXL9jHyylWf/7y4++67VblyZc2cOVN//fWXdfuePXs0Z84ctWjRQvXq1cvRPjds2GCdTSdj9hxnyOu1yd2uSwAAAAAKEVdlFwEAAAAovF599VXrTCxHjhyxbk9NTTVhYWFGkmnXrl2W+1i7dq31r+sHDx5sjLkyw8S9995rJJng4GBz8OBBu3Y3mhnlwQcftM7EsGXLFrvyhIQE6yxEAQEBdn/dnTFTjSTToUMHh39NPW7cOGudvXv35qiPuZnJ59ChQyY8PNxIMmFhYWb79u0O2z7//PPWOocPH3ZYZ9u2bdbZbebNm+ewTlaufX46duxoUlNT7epERkYaScbPz8/4+fmZdevW2dXZu3ev9S/jv/jiC7vyvL6ODzzwgLXM0SwTJ06cMOXLl7eei6OZfJzxfBbETD5VqlSxnsfAgQPtZrRITEw05cqVM5KMj4+PKVu2rMPP1uuvv27dz4ULF3Ldn6wcOnQoyxkXsvPIbNal7MjJLBzbt2+31v3vf/+bZd0XX3zRWjcuLs6p+8iO62fyMcaYvn37Gkmmf//+NnUzPsOPPvqoMcbccCYfZ11XMq4LRYsWNefOnXNYJ+M6Fx4ens0zz7kLFy5YZzJq3bq1uXjxol2dRYsW2bznNmzYkG/9yQ5Xf26u9+WXX1r3O2zYMLvygnrfG2PMiBEjrO0HDBhgvf59++231tljbr311lzt25GCvoY4w5gxY0z//v1N//79rd97/fr1s267/vtz0aJFxsfHx5QsWdIMHTrUPP3006ZEiRImMDDQ/P777zk+/uTJk63nNmfOHGedllOuTe5wXcrp6wMAAADA8zGTDwAAAIAClZ6erq+//lqS1LZtW1WtWtVa5uvrq8jISElXZ4vITJs2bTRixAhJV2b2mT9/vsaNG6fly5dLkiZNmnTDv3y/3pkzZ7Rw4UJJ0hNPPKFbbrnFrk5gYKAmTpwoSbp8+XKWf709YcIE+fn52W0fMGCA9ectW7bkqI859eeff6p169aKiopS7dq19dtvv6lRo0Z29S5duqQpU6ZIkt5++21Vq1bN4f4aN26sRx55RJL0zTff5KlvH3/8sXx9fe229+7dW5KUmpqqXr166bbbbrOrU7duXTVp0kTSlVk1rpXX1/HMmTP68ccfJUlDhgxRRESEXfuKFSvq9ddfz/TcCur5PHz4sIwxOnz4cI7bStK5c+d07NgxSVKDBg00adIk+fjYDhUEBATo5ptvlnTl8/vVV185/GxdO1vJ5cuXc9Ufb3Lp0iXrzwEBAVnWDQwMtP4cHx/v1H3kVsbMavPnz1dCQoIkKSEhwToDS0Z5Vpz5OciYASM5OVlz5syxK9+4caOioqKy3bfc+uijj3T06FEFBwdr9uzZKl68uF2dzp07q1KlSpIkHx8fNWjQIN/642l2796t5557TpJUrVo16+xt1yrI9/3bb7+t1157TZI0ffp0Pf744/r2228VGRmp1NRUtWrVSkuXLlVwcHCO951Xrvz8X2vp0qWaMWOGZsyYoX/++UfSldmPMrZdf7zOnTtrxYoVatCggaZPn66ZM2fq1ltv1fr169WsWbMcH//a2fpKlCiRab2UlJRsz0TlrGuTO1yXcvr6AAAAAPB8JPkAAAAAKFCrVq3S8ePHJdku1ZUh4yaIMSbLBBpJeuONN9SmTRtJ0qBBg6xJF3379lXfvn1z3Lf169dbl2Po2bNnpvVatmxpvSG0du1ah3VuuukmhYeHOywrVaqUypYtK+nK8mL5Zc2aNbr99tt16tQpNWvWTOvWrbMu6XG9DRs2WG8o3nnnnVku29KwYUNJeUtQyur5qVmzpvXn++67L9N91KpVS5J06tQpm+15fR2vbf/QQw9l2j6rpYoK+vnMre3bt1t/HjZsWKY3ks+fPy9JatGihTp06OCwzrlz5yRJfn5+KlWqlHM7+v9Vr15dxpg8PUaNGpUvfcuKxWLJU7mz9pET7du3V6VKlRQfH68FCxZIkhYsWKD4+HhVqlRJ7dq1u+E+nPk5yOiP5HhpnIxtFovF4XeLMyQkJGjChAmSriQAXpuker2MRLhatWoV6BJPjrjL5yY6Olpdu3ZVfHy8ihYtqlmzZik0NDTLNgXxvn/33Xc1bNgwSdK0adPUu3dvpaamqkWLFlq6dKlCQkLyfIy8KujP/7V++eWXLN8b1atXt2vTrl07/frrr4qPj1dcXJyWLFnilGXrsjrPTz/9VMHBwQ4f7733nk1dZ12b3OG6lJvXBwAAAIBnI8kHAAAAQIGaMWOGpCt/df7www/blTdo0MB6I+irr77Kcl++vr765ptvVKJECcXGxio5OVk33XSTJk2alKu+HTlyxPpzvXr1sqybMbtLZjOoVKxYMcv2QUFBkmSdIcPZ9u7dq/vuu0+xsbFq3769Vq9erXLlymVaf9++fdafa9eunemNsuDgYL300kuSrtywza2snp9rZyTITr3ExESb7Xl9Ha/9OWMGG0cqVqyY6Q3qgn4+c2vbtm2SJH9/f3Xp0sVhnZSUFOv5OPrMZti9e7ekK+dbpEgRJ/fU81yb2HH9e/R615ZfOzOMM/aRWz4+PurTp4+kq9fijH8jIyPtZnxyxJmfAx8fH+tN8o0bN+rAgQPWsuTkZH377beSpLvuuivL5Ju8WLlypWJjYyVJjz/+eJZ109LSJMkpiQ3eIC4uTh06dNDBgwfl4+Ojr7/+Wq1bt3ZY1xXv+/fff18PPvig9fdSpUpp6dKlN0xCyk+u/Py7k2uTRi9cuOCUfTrr2uQO1yUAAAAAhQ9JPgAAAAAKzMWLF/Xdd99Jkpo3b66oqCj9/vvvdo+MG39RUVHauHFjlvusUKGCzY2TBx98MNfLaly8eNH68432kVF+bZtrOVqGyhFjTDZ7lzMJCQnWJZNCQkJuuNRHxo3rnEhKSspV36TsPz/ZqXf9c5jX1/HapS1udLM0s/KCfj5zK2Mmn8aNG2f6XO3atcu61Mntt9+e6b4yEoYyllHLD8aYLGdcyM7j+mVb8kuZMmWsP585cybLuhlLrPj7+9u8p5yxj7zImFlt1apV2rJli1atWmWz/Uac/TnIWBpHsk0C/fHHH60zSV1bx9lWrlwpSapcubJq166dZd0TJ05Ico8kH1d/bhISEtSpUydt3bpVFotFU6ZMUY8ePTKt74r3/ffff29dplG6skTUq6++mm/f0dnh6s+/u7h2Jpprk3Ou98ILL9jMYHPo0KFM6zrz2uTq6xIAAACAwockHwAAAAAFZu7cudaZa9asWaPmzZs7fFw7E0/GzD+Zee2117Rz507r7xMnTrxhYlBmrk1yuDbRw5GM8twmFOW3Zs2a6ZNPPpEkfffdd+rZs6dSUlIyrX/tTcG4uLhsL+HijvL6Ol77XGS3/fU85fnMTmJORh1fX181atTIYR1jjHbs2HHDfeXVkSNHspxxITuP65dtyS+VKlWyvg8OHjyYZd2Mm9Hh4eE2y9E4Yx95Ub9+fTVp0kTp6el65JFHlJ6erqZNm1pnwLoRZ38O6tatqxYtWkiSvv76a2vdjBvrxYsXz3IZvbw6evSoJFmX+cvMqVOnrDOKZfaZKUiu/NwkJSXpwQcf1Lp16yRd+Y4eOHBglm0K+n2/cOFC63fkrbfeqpdfflmSNHnyZD399NMu+65z9effXdx6663y8/OTlPkSqTnlzGuTq69LAAAAAAofknwAAAAAFJgbJew48u2332b619PLly/XRx99JEl6+umnFR4ertTUVPXp00dxcXE5Pta1fy2esfRQZnbt2mXXxt0MHTpUkyZNksVi0ffff6+HH34409kYbrrpJuvPN7qZ6O7y+jpe+/PevXszbXvy5MlMZwPwhOczMTFRf/31lySpadOmmdbbunWrpCs3Mq9dSu1aBw4csM6G5Ghfly9f1vjx43XPPfeocuXKCggI0E033aQXXnjBacuvuKNmzZpJUpaJh8nJydbnOKO+s/eRFxmz9mS8j7M7i4+UP5+DjBkxDh06pN9++03nzp3TkiVLJEkPPfSQzRJHzpYxK0fRokWzrDdz5kzrz45m8snu52HChAkKDAxUZGSkdfmva23cuFEWi8WalOJuUlJS1KNHD61YsUKSNGbMGD3zzDPZaltQ7/vFixerR48eSklJUatWrbR06VKNHTtWw4cPlyR9/vnnGjp0qMsSfVz9+XcHxYsX1z333CNJ+vnnn/X333/neZ/Ovja58roEAAAAoPAhyQcAAABAgTh06JD1L7CfffbZG/619Lx58yRJ58+f18KFC+32Fx0drf79+8sYo6ZNm2r8+PGaNWuWihQpokOHDmnIkCE57mPr1q2ty0NlHN+RLVu2WGdpyGr5IncwZMgQff7557JYLFq4cKG6d+/uMNGnbdu28vf3lyTNmTOnoLvpVHl9HVu3bi0fnyv/XV6wYEGm7efPn59pmSc8n3/++ac1cSCrJJ+MmXyyunmcseyX5DipYcuWLXrjjTfk7++v7t2769lnn1VYWJgmTJigRx99NNt9rl69erZnXMjsMWrUqGwfL68eeOABSdLff/+tP/74w2GdhQsXWpfW69KlS77sIy/69OljnUXDz89Pffr0yXbb/Pgc9O7d25pk89V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N1uPo0aPZ3u8333yjEydOqFevXjbbT5065TAQyNh28uTJTPfp5+enb7/9Vq1atdKDDz6oTZs2adWqVerTp486duyoadOmyWKxZLuPWclLPx0ZPXq0AgMDNWXKFL377rsKDAzUzJkzJV0JGk+ePKnSpUvrpZde0rfffqtSpUplub+4uDidPHnS4fvhwoULNoN9iYmJ1rLXX39daWlp2rZtm0aMGKGnnnpKP/zwg3r37q1Ro0ZZ67Zr105hYWF2WdRz585VSkqKNXvaWc8BAMB7cTPMMW6G2eJmGADA3TFew3gN4zUAAG/CeI1jjNfYYrwmhwy8SmxsrJFkYmNjXd0VuNiD/1loqg1bbH38cSTG1V1ymsTERLNnzx6TmJjo6q54tO7du5vKlSub5ORkm+3Tp083kszixYuderyYmBizYsWKbD2y+9ru3bvXhISEmFtvvdWkpqbalN10002mY8eOdm0OHjxoJJnx48ffcP+xsbGmadOmplSpUqZ48eLmjjvuMAkJCdnqW4YtW7YYSWbatGkOy53Rz/x07NgxI8n07dvXrqxRo0ZGkvUxduxYY4wx6enppkSJEmbw4MEmOjra5jFt2jQjyaxbt866nxdffNFIMocOHbJuu+OOO0z58uXtXtf8cqPrCt+vrsHzjsKAuMY5/vjjD+Pr62uGDx9us33QoEFGkhkzZoxTj7d69Wqb78CsHtd+v93IzJkzjSQzdepUm+0RERGmXbt2dvV3795tJJnPP/88y/0mJiaatm3bmuLFi5uNGzealStXGn9/f9O5c2eTkpKS7f7dKK7Jaz+vN3LkSLvnM+PYZ86cMR07djSBgYGmdu3aZsWKFTfcX8b3yoMPPmhXdv78eZuY5dqYb9CgQSYsLMycPXvWpk3v3r1NaGiotW5qaqoJCwszt956q029zz//3Egyy5Yty9H5G5P1c5AZ4hr3xPOOwoC4xjkYrxl/w/0zXsN4TQa+X12D5x2FAXGNczBew3gN4zVXOTOu8ctNYhAA93f9xD3JqdnPSIX3S0pK0k8//aQhQ4aoSJEiNmXx8fGS5PQM6pIlS+ruu+922v5Onz6tTp06KTQ01DrV4LUCAwOVlJRk1+7y5cvW8hsJCQnRhx9+qHbt2kmSPv744xxn896IM/qZn4KDgyVdfV9c64svvtDFixf1zz//qG/fvtbt0dHRunDhgiZPnqzJkyc73O+10yJGRkZq/PjxmjVrll577TUdP35ca9eu1XPPPWf3ugIA4Mh7772nsLAwvfXWWzbbb7/9dk2dOtXpcU2jRo20YsWKbNXN7tTJ+/bt09ChQ3Xrrbeqf//+NmWJiYny9/e3axMQEGAtz0pAQIAWLlyou+66S/fff7+Sk5PVsmVL/e9//5Ofn/OGBfLaz+uNGjVKo0aNclhWtmxZLVmyJEf7y1gX3tFa8Hfeead27Nhh/X3s2LF6+eWXZYzR/Pnz1bNnTxljdPbsWWud++67T3PmzNHWrVt12223ydfXV71799b48eN1+PBhVa9eXdKVqZ/Lly+v9u3b56i/UtbPAQDA+zBew3hNdjFeAwDwBIzXMF6THYzX5BxJPoCXMrLN8kkiyQfXOHjwoBISEhyud/r3338rJCREVatWdeoxk5OTFRMTk626ZcuWzXKwIDY2Vh07dtSFCxe0du1aVaxY0a5OWFiYzbqfGU6dOiVJDttc7++//1ZkZKTq1q2rxMREde/eXb/99pvD6Zpzyxn9zE+hoaEKCwvTrl277Moypk08fPiwzfaMaS779u1rF/RmaNiwofXnZs2aqW7dupo9e7Zee+01zZ49W8aYXE39DAAofLgZxs2w7OJmGADA3TFew3hNdjFeAwBwd4zXMF6TXYzX5BxJPoC3um4mn6SUAlwHEG4vIyvXx8fHZntSUpJmz55ts5730aNHNXjwYP3xxx9KSUnRI488okmTJslisejixYsqX768Pv74Yw0ePFjSlYzb8PBwrV27VrVr17buZ/369brrrruy1b9Dhw5ZM2mvd/nyZT3wwAOKiorSypUrVa9ePYf1GjdurNWrVysuLs5mndFNmzZZy7Ny6tQp3XPPPSpSpIhWrFihxMREtWnTRvfee69+/fXXG66Nnl157WdB6NSpk6ZMmaLNmzdb12jNStmyZRUcHKy0tLRsB9SRkZEaMWKEdu7cqVmzZql27dpq3rx5XrsOACgEuBnGzbDs4mYYAMDdMV7DeE1OMF4DAHBnjNcwXpNdjNfknM+NqwDwRNfl+CgplSQfXFWtWjVJ0rp162y2v//++zp16pTNoNHFixf1+uuv6+TJk/rzzz+1aNEi/fLLL5KuZNfOnDlTs2bNstb/8MMP1bVrV5sBI+nqNInZeWQ2TWJaWpp69eqlDRs2aO7cubr11lszPceHH35YaWlpNhm8SUlJmjZtmlq2bKkqVapk2vb8+fO67777FB8fr5UrV6py5cqqXbu2li1bpmPHjqlTp066dOlSpu1zIi/9LCivvPKKgoKC9Nhjj+mff/6xKzfXrQ/o6+ur7t27a/78+Q6DsujoaLttGYHUm2++qe3bt3tsYAUAKHg5vRnWoUMHlS1bViVKlNCQIUOs32MXL15UUFCQzXdyXFycKlSooP3799vse/369QoLC8vW49ixY5n2/dqbYYsXL87yZlhUVJR1+uIMub0ZtmLFCl26dEn33ntvtge/siOv/SwInTp10oEDB7R58+Zs1b/+ZpijR7ly5WzaREZGateuXdwMAwDkGOM1jNfkBOM1AAB3xngN4zU5wXhNDhkYY4xJTk42K1euNC+//LK55ZZbTGhoqPHz8zPly5c3DzzwgFm8eLHDdiNHjjS6kk+R6WPv3r2ZHnf//v2mf//+plKlSqZo0aKmUqVKpn///ubgwYO5Oo/Y2FgjycTGxuaqPbxH5/d/MNWGLbY+Zm885OouOU1iYqLZs2ePSUxMdHVXPFqHDh2Mj4+PeeaZZ8zkyZNNz549Tc2aNY0k89///jfTdt26dTPz58+3/n78+HFTsWJFY4wxZ86cMWXKlDHHjx/Plz4///zzRpJ54IEHzMyZM+0e1+vRo4fx8/Mz//73v80XX3xhWrdubfz8/Myvv/6a5XFGjx5tSpQoYbZv325XtnbtWhMYGGg+/fTTLPfx3//+17zzzjtmyJAhRpJ56KGHzDvvvGPeeecdc+HCBaf0syB9//33JjAw0ISGhpqnn37afPHFF+bzzz83w4YNM1WqVDE+Pj5m9uzZ1vqnT5821apVM0FBQeb55583X3zxhRkzZozp0aOHKVmypMNjtG7d2vrduX///oI6NWPMja8rnvb9SlwDeA7imryLjo42ksxTTz1ls33UqFFGkhk8eLB1265du8yaNWtMcnKyOXr0qKlUqZL5+eefreXz5s0zbdu2tf4+YsQIm/YZYmJizIoVK7L1yOy1TU1NNV26dDF+fn7mxx9/zPIcN27caCSZsWPHWrddvnzZ1KpVy7Rs2TLLtjExMaZBgwamXLlyJioqyrp927ZtJjQ01LRq1crEx8dnuY8MW7ZsMZLMtGnTnN7PghIVFWWCgoJMRESEOX36tF3533//bXcOAwYMMEWLFjV//vmnXf0zZ85kuo+uXbsaSWbUqFHOPYkb8La4xhjviG088XkHcoq4xjkYr8kc4zX2GK/xvO9X4hrAMxDX5B3jNYzX5ATjNTn7fiXJ5/9bsWKFNRCqUKGC6dSpk+nZs6epX7++dfvgwYNNenq6TbuMwKpRo0amf//+Dh8nT550eMx169aZoKAgI8lERESYXr16mYiICCPJFCtWzGzYsCHH50FwhQydx9gm+fzf6n2u7pLTEFw5xz///GMefPBBExISYsLCwszzzz9vvvvuOyPJbNy40Vrvq6++Ms2bNzelSpUyoaGhxsfHx+YLMz093QQGBpr4+Hjz/PPPm3/961/51ue2bdtm+R/Z6yUmJpqXX37ZVKhQwfj7+5vmzZubpUuX3vA4KSkpZteuXZmW79ixw+774HrVqlXLtJ+HDh1ySj8L2oEDB8yQIUNMrVq1TEBAgAkMDDR169Y1Tz31lMMBtn/++ccMHTrUVKlSxRQpUsRUqFDBtG/f3kyePNnh/j/99FMjybRo0SK/T8WOtw0aEdcAnoO4xjm4GZY5bobZ42aY532/ekNs44nPO5BTxDXOwXhN5hivcYzxGs/6fiWuATwDcY1zMF6TOcZr7DFeQ5JPjq1atcp0797drFmzxq5szpw5xtfX10gyM2bMsCnLCKxGjhyZo+NdunTJVKxY0Ugyw4cPtykbPny4kWSqVKliEhIScrRfgitk6DTme5skn/FLdri6S05DcJV/hg4dasqUKWNSUlKMMcYsXbrU1K1b1+zYscOkpqaaU6dOmWLFilnLM9x8883mhx9+MGXLljVnz551RdeBPPG2QSPiGsBzENc4BzfDMsfNMMe4GeZZ36/eENt44vMO5BRxTf5hvAaFFXHNVcQ1QMEirnEOxmsyx3iNY4zXkOTjVIMGDTKSTPv27W225zawyngDhYeHm7S0NJuytLQ0Ex4ebiSZzz//PEf7JbhChvvfs03yeXfBFld3yWkIrvLu8uXLdsHBr7/+avz8/Mwbb7xh3faf//zHdOjQwaSkpJgjR46Y9u3bmyZNmtjtr1OnTqZy5coFPrUd4CzeOGiUFeIawH0Q1+QfboahsCpscY0xnhHbeOPzDlyPuCbvGK8BbBHXXEVcAxQs4pr8w3gNCitnxjU+QrY0adJEknTs2DGn7O+7776TJPXu3Vs+PrYvg4+Pj3r16iVJWrBggVOOh8LHXPd7QlKyS/oB97Rs2TI1b95c77//vj7//HMNHjxY7du3V5MmTfTaa69Z60VGRurcuXMqWbKkBg4cqIiICDVq1MhufzVr1tTly5f10ksvFeRpAMgl4hoA3iQpKUnG2Ea/a9as0RdffKGnnnpKfn5+kqSdO3eqevXqqlevnk6cOKG+ffsqPDzcWp7hppt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",
-      "text/plain": [
-       "<Figure size 2800x800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2800x800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2800x800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for (m,c), model in models.items():\n",
-    "    \n",
-    "    fig, axes = plt.subplots(2, 4, figsize=(28, 8), sharex=True,\n",
-    "                         gridspec_kw={'height_ratios':[3.2,1], 'hspace':0})\n",
-    "    \n",
-    "    Lmin,  Lmax  = 1e99, -1e99\n",
-    "    dLmin, dLmax = 1e99, -1e99\n",
-    "    \n",
-    "    for j, (flavor) in enumerate([Flavor.NU_E, Flavor.NU_E_BAR, Flavor.NU_X, Flavor.NU_X_BAR]):\n",
-    "        ax = axes[0][j]\n",
-    "        \n",
-    "        ax.plot(model_std.time, model_std.luminosity[flavor]/1e51, 'k', label='$20M_\\odot$ reference')  # Report luminosity in [foe/s]\n",
-    "        Lmin = np.minimum(Lmin, np.min(model_std.luminosity[flavor].to_value('1e51 erg/s')))\n",
-    "        Lmax = np.maximum(Lmax, np.max(model_std.luminosity[flavor].to_value('1e51 erg/s')))\n",
-    "        \n",
-    "        modlabel = '{}: $m_a={}$ MeV\\n     $g_{{a\\gamma}}={}\\\\times10^{{-10}}$ GeV$^{{-1}}$'.format(flavor.to_tex(), m, c)\n",
-    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
-    "                label=modlabel,\n",
-    "                color='C0' if flavor.is_electron else 'C1',\n",
-    "                ls='-' if flavor.is_neutrino else ':',\n",
-    "                lw=2)\n",
-    "        if j==0:\n",
-    "            ax.set(ylabel=r'luminosity [$10^{51}$ erg s$^{-1}$]')\n",
-    "        \n",
-    "        ax.legend(fontsize=12)\n",
-    "        ax.set(xlim=(model_std.time[0].to_value('s'), model_std.time[-1].to_value('s')))\n",
-    "        \n",
-    "        ax = axes[1][j]\n",
-    "        tmin = np.maximum(model.time[0], model_std.time[0]).to_value('s')\n",
-    "        tmax = np.minimum(model.time[-1], model_std.time[-1]).to_value('s')\n",
-    "        times = np.arange(tmin, tmax, 0.001)*u.s\n",
-    "\n",
-    "        Lstd = PchipInterpolator(model_std.time, model_std.luminosity[flavor].to_value('1e51 erg/s'))\n",
-    "        Lstd_t = Lstd(times)\n",
-    "        select = Lstd_t != 0\n",
-    "        \n",
-    "        Lmod = PchipInterpolator(model.time, model.luminosity[flavor].to_value('1e51 erg/s'))\n",
-    "        Lmod_t = Lmod(times)\n",
-    "        dL = (Lmod_t[select] - Lstd_t[select]) / Lstd_t[select]\n",
-    "        \n",
-    "        dLmin = np.minimum(dLmin, np.min(dL))\n",
-    "        dLmax = np.maximum(dLmax, np.max(dL))\n",
-    "\n",
-    "        ax.plot(times[select], dL)\n",
-    "        if j==0:\n",
-    "            ax.set(xlabel='time [s]',\n",
-    "                   ylabel=r'$\\Delta L_\\nu/L_\\nu$')\n",
-    "            \n",
-    "    for j in range(4):\n",
-    "        axes[0][j].set(ylim=(Lmin, 1.1*Lmax))\n",
-    "        axes[1][j].set(ylim=(dLmin, dLmax))\n",
-    "        \n",
-    "    fig.suptitle('Axionlike model: $m_a={}$ MeV, $g_{{a\\gamma}}={}\\\\times10^{{-10}}$ GeV$^{{-1}}$'.format(m, c))\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/doc/nb/README.md b/doc/nb/README.md
index 1af96627..768117f6 100644
--- a/doc/nb/README.md
+++ b/doc/nb/README.md
@@ -1,6 +1,10 @@
 # SNEWPY Usage Examples
 
-The Jupyter notebooks in this directory contain different examples for how to use SNEWPY. Please see also the example notebooks for each supernova model, which are included in the same directory as the model files when downloading them via `python -c 'import snewpy; snewpy.get_models()'`.
+The Jupyter notebooks in this directory contain different examples for how to use SNEWPY.
+
+## `ccsn` and `presn` Directories
+
+These directories contain notebooks demonstrating how to use the core-collapse and pre-supernova models available through SNEWPY.
 
 ## AnalyticFluence
 
diff --git a/doc/nb/snowglobes_models.ipynb b/doc/nb/SNOwGLoBES_models.ipynb
similarity index 100%
rename from doc/nb/snowglobes_models.ipynb
rename to doc/nb/SNOwGLoBES_models.ipynb
diff --git a/doc/nb/SNOwGLoBES_usage.ipynb b/doc/nb/SNOwGLoBES_usage.ipynb
index 38cd9578..3954c0e2 100644
--- a/doc/nb/SNOwGLoBES_usage.ipynb
+++ b/doc/nb/SNOwGLoBES_usage.ipynb
@@ -2,41 +2,43 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# `snewpy.snowglobes` Usage Example\n",
     "\n",
     "This notebook demonstrates how to use SNEWPY with SNOwGLoBES.\n",
     "\n",
     "To start, make sure you have SNOwGLoBES installed and have downloaded one of the models that are part of SNEWPY. Adjust the directory paths in the following cell."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from astropy import units as u\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "from snewpy import snowglobes\n",
+    "from snewpy import snowglobes, model_path\n",
     "\n",
     "SNOwGLoBES_path = None  # to use custom SNOwGLoBES detector/channel/smearing files, set SNOwGLoBES directory\n",
-    "SNEWPY_models_base = \"/path/to/snewpy/models/\"  # directory containing SNEWPY models"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "SNEWPY_models_base = model_path  # directory containing SNEWPY models"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Next, we will set up some basic parameters for the supernova we want to simulate."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# set distance in kpc\n",
     "distance = 10\n",
@@ -72,20 +74,20 @@
     "tstart = np.linspace(window_tstart, window_tend, window_bins, endpoint=False) * u.s\n",
     "tend = tstart + (window_tend - window_tstart) / window_bins * u.s\n",
     "tmid = (tstart + tend) * 0.5"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Now that everything’s set up, let’s start using SNOwGLoBES! Be patient—these three steps together may take a few minutes."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# snowglobes.generate_fluence integrates the model over the specified time window(s)\n",
     "# and generates input files for SNOwGLoBES. It returns the full file path of the output file.\n",
@@ -99,40 +101,20 @@
     "# Finally, we collate SNOwGLoBES’ results into a dictionary\n",
     "print(\"Collating results ...\")\n",
     "tables = snowglobes.collate(SNOwGLoBES_path, tarredfile, skip_plots=True)"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Preparing fluences ...\n",
-      "Running SNOwGLoBES ...\n",
-      "Calculations are 10.0% completed. ( 6  of  60 )\n",
-      "Calculations are 20.0% completed. ( 12  of  60 )\n",
-      "Calculations are 30.0% completed. ( 18  of  60 )\n",
-      "Calculations are 40.0% completed. ( 24  of  60 )\n",
-      "Calculations are 50.0% completed. ( 30  of  60 )\n",
-      "Calculations are 60.0% completed. ( 36  of  60 )\n",
-      "Calculations are 70.0% completed. ( 42  of  60 )\n",
-      "Calculations are 80.0% completed. ( 48  of  60 )\n",
-      "Calculations are 90.0% completed. ( 54  of  60 )\n",
-      "Calculations are 100.0% completed. ( 60  of  60 )\n",
-      "Collating results ...\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Finally, since we chose option 3 above, and calculated the fluence in 60 time bins, we can now plot the event counts over time."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "nevents = np.zeros(len(tmid))\n",
@@ -150,35 +132,16 @@
     "plt.show()\n",
     "# compare to Figure 5 of Zha et al. (2021)\n",
     "print(\"Total Events:\", sum(nevents))"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Total Events: 17459.043408289574\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "12a4e164d15418f42fe0584c567a58ace9669f8a11b564e22ebd17e6959ef919"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.5 64-bit ('snews': conda)"
+   "display_name": "Python 3.9.5 64-bit ('snews': conda)",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -191,11 +154,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.5"
-  },
-  "interpreter": {
-   "hash": "12a4e164d15418f42fe0584c567a58ace9669f8a11b564e22ebd17e6959ef919"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/doc/nb/ccsn/Bollig_2016.ipynb b/doc/nb/ccsn/Bollig_2016.ipynb
new file mode 100644
index 00000000..61901819
--- /dev/null
+++ b/doc/nb/ccsn/Bollig_2016.ipynb
@@ -0,0 +1,189 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bollig 2016 Models\n",
+    "\n",
+    "Data from Mirizzi et al., particular the models that go into Figure 17. Models (s11.2c and s27.0c) taken from the Garching Supernova archive (https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Bollig2016/) with permission for use in SNEWS2.0.\n",
+    "\n",
+    "Reference: Mirizzi et al. Rivista del Nuovo Cimento Vol 39 N. 1-2 (2016)\n",
+    "- doi:10.1393/ncr/i2016-10120-8\n",
+    "- arXiv:1508.00785"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import Bollig_2016\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Bollig_2016` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Bollig_2016.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll initialise both of these progenitors. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m11 = Bollig_2016(progenitor_mass=11.2*u.solMass)\n",
+    "m27 = Bollig_2016(progenitor_mass=27*u.solMass)\n",
+    "\n",
+    "m11"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `Bollig_2016` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, model in enumerate([m11, m27]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':',\n",
+    "                lw=2)\n",
+    "    ax.set(xlim=(0.0, 0.35),\n",
+    "           xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "           title=r'{}: {} $M_\\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
+    "\n",
+    "axes[0].set(ylabel=r'luminosity [foe s$^{-1}$]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 50*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [erg$^{-1}$ s$^{-1}$]')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.5 ('snews')",
+   "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": "3.9.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/ccsn/Fornax_2019.ipynb b/doc/nb/ccsn/Fornax_2019.ipynb
new file mode 100644
index 00000000..4e84464b
--- /dev/null
+++ b/doc/nb/ccsn/Fornax_2019.ipynb
@@ -0,0 +1,483 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c2dac99b",
+   "metadata": {},
+   "source": [
+    "# FORNAX 3D Neutrino Spectra\n",
+    "\n",
+    "Some I/O and plotting using the Fornax 3D models from [Vartanyan, Burrows, et al., MNRAS 482(1):351, 2019](https://arxiv.org/abs/1809.05106), which express the angular dependence of the neutrino luminosity from CCSNe in terms of a spherical harmonic expansion up to degree $\\ell=2$. Note: Caching and plotting the angular dependence requires the `healpy` package, which can be installed via `pip install healpy`.\n",
+    "\n",
+    "The data are available on [the Burrows group website](https://www.astro.princeton.edu/~burrows/nu-emissions.3d/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4df4d343",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from snewpy.neutrino import Flavor\n",
+    "from snewpy.models.ccsn import Fornax_2019\n",
+    "\n",
+    "from astropy import units as u\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "try:\n",
+    "    import healpy as hp\n",
+    "    healpy_available = True\n",
+    "except ModuleNotFoundError:\n",
+    "    healpy_available = False\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61a9c046",
+   "metadata": {},
+   "source": [
+    "## Initialization and Time Evolution of the Energy Spectrum\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Fornax_2019` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83a8ee2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Fornax_2019.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbe1497e",
+   "metadata": {},
+   "source": [
+    "We’ll initialise one of these progenitors and plot the evolution of the predicted neutrino spectrum across four time steps. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c50291f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Fornax_2019(progenitor_mass=10*u.solMass)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7ea4be2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "E = np.arange(0,111) * u.MeV\n",
+    "theta = 23.55*u.degree\n",
+    "phi = 22.5*u.degree\n",
+    "\n",
+    "fig, axes = plt.subplots(2,2, figsize=(11,8), sharex=True, sharey=True,\n",
+    "                         gridspec_kw = {'hspace':0.04, 'wspace':0.025})\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for ax, t in zip(axes, [10., 100., 300., 600.]):\n",
+    "    t *= u.ms\n",
+    "    spectra = model.get_initial_spectra(t, E, theta, phi)\n",
+    "    \n",
+    "    lines = ['--', '-.', '-', ':']\n",
+    "    for line, flavor in zip(lines, Flavor):\n",
+    "        ax.plot(E, spectra[flavor].to('1e52 erg/(s MeV)'), ls=line, label=flavor.to_tex())\n",
+    "    ax.legend(fontsize=14, ncol=2, loc='upper right', title='{:g}'.format(t))\n",
+    "    ax.set(xlim=(E[0].to_value('MeV'), E[-1].to_value('MeV')))\n",
+    "    ax.grid(ls=':')\n",
+    "\n",
+    "axes[0].set(ylabel=r'$dL/dE$ [$10^{52}$ erg s$^{-1}$ MeV$^{-1}$]')\n",
+    "axes[2].set(xlabel=r'energy [MeV]');\n",
+    "\n",
+    "fig.suptitle('Time Evolution of Spectra'.format(t))\n",
+    "fig.subplots_adjust(top=0.925, bottom=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89906e58",
+   "metadata": {},
+   "source": [
+    "## Test Caching of Full Model Angular Dependence\n",
+    "\n",
+    "Here, if the user specifies `cache_flux=True` in the model constructor, the entire angular dependence of the model will be computed using a [HEALPix grid](https://healpy.readthedocs.io/en/latest/) with 192 pixels, using a HEALPix grid with parameter `nside=4` and `ordering=RING`. The model constructor will calculate the CCSN fluxes on the unit sphere at the centers of the 192 healpixels for all energy and time bins in the model.\n",
+    "\n",
+    "The resulting tables are then saved to a FITS file in the same location as the input HDF5 file. If the user instantiates this model with `cache_flux=True` in the future, the model will be initialized using the cached FITS file.\n",
+    "\n",
+    "Comments:\n",
+    "1. The HDF5 files store the angular dependence of the models in a highly compressed format using the spherical harmonic expansion up to degree $\\ell=2$.\n",
+    "1. The time bins are equal across all flavors but the energy binning is a function of both time and flavor.\n",
+    "1. Computing the fluxes at 192 locations on the sky takes about 30 seconds per flavor due to the overhead of reading out the HDF5 files. So construction of the cached model is **slow** when called for the first time.\n",
+    "1. With `nside=4` and 192 bins, the angular resolution per bin is about $15^\\circ$, which is reasonable given the highest-resolution features of the models are the quadrupole moments ($\\ell=2$)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03fcdbb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cmodel = Fornax_2019(progenitor_mass=10*u.solMass, cache_flux=True)\n",
+    "cmodel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfc21f0c",
+   "metadata": {},
+   "source": [
+    "### Test Equivalence of the Cached and Uncached Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec5bc0c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = 100 * u.ms\n",
+    "\n",
+    "Es, dEs, spectras = model._get_binnedspectra(t, theta, phi)\n",
+    "Ec, dEc, spectrac = cmodel._get_binnedspectra(t, theta, phi)\n",
+    "\n",
+    "for flavor in Flavor:\n",
+    "    print('\\n{}'.format(str(flavor)))\n",
+    "    print(Es[flavor])\n",
+    "    print(Ec[flavor])\n",
+    "    print(spectras[flavor])\n",
+    "    print(spectrac[flavor])\n",
+    "    \n",
+    "    j = np.argmax(spectrac[flavor])\n",
+    "    print('Max value:')\n",
+    "    print(Ec[flavor][j], spectrac[flavor][j])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de0e525b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = 600 * u.ms\n",
+    "E = np.arange(0,111) * u.MeV\n",
+    "theta = 23.55*u.degree\n",
+    "phi = 22.5*u.degree\n",
+    "\n",
+    "spectra = model.get_initial_spectra(t, E, theta, phi)\n",
+    "cspectra = cmodel.get_initial_spectra(t, E, theta, phi)\n",
+    "    \n",
+    "fig, axes = plt.subplots(2,2, figsize=(11,8), sharex=True, sharey=True,\n",
+    "                         gridspec_kw = {'hspace':0.04, 'wspace':0.025})\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "spectra = model.get_initial_spectra(t, E, theta, phi)\n",
+    "\n",
+    "for ax, flavor in zip(axes, Flavor):\n",
+    "    ax.plot(E, spectra[flavor].to('1e52 erg/(s MeV)'), ls='-', lw=1, label='{}: model'.format(flavor.to_tex()))\n",
+    "    ax.plot(E, cspectra[flavor].to('1e52 erg/(s MeV)'), ls=':', lw=3, label='{}: cached model'.format(flavor.to_tex()))\n",
+    "    ax.legend(fontsize=14, ncol=1, loc='upper right')\n",
+    "    \n",
+    "    ax.set(xlim=(E[0].to_value('MeV'), E[-1].to_value('MeV')))\n",
+    "    ax.grid(ls=':')\n",
+    "\n",
+    "axes[0].set(ylabel=r'$dL/dE$ [$10^{52}$ erg s$^{-1}$ MeV$^{-1}$]')\n",
+    "axes[2].set(xlabel=r'energy [MeV]')\n",
+    "\n",
+    "fig.suptitle('Spectra at $t={{{:g}}}$'.format(t))\n",
+    "fig.subplots_adjust(top=0.925, bottom=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f9fed98",
+   "metadata": {},
+   "source": [
+    "### Plot $L_\\nu(t,\\theta,\\varphi)$ at Several Times\n",
+    "\n",
+    "Plot the angular dependence $L(t)$ for several values of $t$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d880473",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if healpy_available:\n",
+    "    times = np.asarray([10., 100., 300., 600.]) * u.ms\n",
+    "    nt = len(times)\n",
+    "\n",
+    "    fig, axes = plt.subplots(nt,3, figsize=(8*3, nt*4.5))\n",
+    "\n",
+    "    L_nue = cmodel.luminosity[Flavor.NU_E].to('1e52*erg/s')\n",
+    "    L_nue_bar = cmodel.luminosity[Flavor.NU_E_BAR].to('1e52*erg/s')\n",
+    "    L_nux = cmodel.luminosity[Flavor.NU_X].to('1e52*erg/s')\n",
+    "\n",
+    "    for i, t in enumerate(times):\n",
+    "        j = (np.abs(t - cmodel.time)).argmin()\n",
+    "        \n",
+    "        ax = axes[i,0]\n",
+    "        plt.axes(ax)\n",
+    "        hp.mollview(L_nue[j], title=r'$L_{{\\nu_e}}(t={:.3f})$'.format(cmodel.time[j]),\n",
+    "                    unit=r'$10^{52}$ erg s$^{-1}$', cmap='viridis',\n",
+    "                    hold=True)\n",
+    "        \n",
+    "        ax = axes[i,1]\n",
+    "        plt.axes(ax)\n",
+    "        hp.mollview(L_nue_bar[j], title=r'$L_{{\\bar{{\\nu}}_e}}(t={:.3f})$'.format(cmodel.time[j]),\n",
+    "                    unit=r'$10^{52}$ erg s$^{-1}$', cmap='magma',\n",
+    "                    hold=True)\n",
+    "        \n",
+    "        ax = axes[i,2]\n",
+    "        plt.axes(ax)\n",
+    "        hp.mollview(L_nux[j], title=r'$L_{{\\nu_X}}(t={:.3f})$'.format(cmodel.time[j]),\n",
+    "                    unit=r'$10^{52}$ erg s$^{-1}$', cmap='cividis',\n",
+    "                    hold=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "824a3550",
+   "metadata": {},
+   "source": [
+    "### Plot $\\Delta L_\\nu(t,\\theta,\\varphi) / \\langle L_\\nu(t,\\theta,\\varphi)\\rangle$ at Several Times\n",
+    "\n",
+    "Same plot as above, but here plot the deviation from the average over the sky.\n",
+    "\n",
+    "This demonstrates how the deviation from isotropy is very small initially and approaches 20% at later times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "128e8cac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if healpy_available:\n",
+    "    times = np.asarray([10., 100., 300., 600.]) * u.ms\n",
+    "    nt = len(times)\n",
+    "\n",
+    "    fig, axes = plt.subplots(nt,3, figsize=(8*3, nt*4.5))\n",
+    "\n",
+    "    L_nue = cmodel.luminosity[Flavor.NU_E].to('1e52*erg/s')\n",
+    "    L_nue_bar = cmodel.luminosity[Flavor.NU_E_BAR].to('1e52*erg/s')\n",
+    "    L_nux = cmodel.luminosity[Flavor.NU_X].to('1e52*erg/s')\n",
+    "\n",
+    "    vmin, vmax = -0.2, 0.2\n",
+    "\n",
+    "    for i, t in enumerate(times):\n",
+    "        j = (np.abs(t - cmodel.time)).argmin()\n",
+    "        \n",
+    "        ax = axes[i,0]\n",
+    "        plt.axes(ax)\n",
+    "        Lavg = np.average(L_nue[j])\n",
+    "        dL_on_L = (L_nue[j] - Lavg) / Lavg\n",
+    "        hp.mollview(dL_on_L, title=r'$\\nu_e$: $t=${:.3f}'.format(cmodel.time[j]),\n",
+    "                    unit=r'$(L_\\nu - \\langle L_\\nu\\rangle) / \\langle L_\\nu\\rangle$', cmap='viridis',\n",
+    "                    min=vmin, max=vmax,\n",
+    "                    hold=True)\n",
+    "        \n",
+    "        ax = axes[i,1]\n",
+    "        plt.axes(ax)\n",
+    "        Lavg = np.average(L_nue_bar[j])\n",
+    "        dL_on_L = (L_nue_bar[j] - Lavg) / Lavg\n",
+    "        hp.mollview(dL_on_L, title=r'$\\bar{{\\nu}}_e$: $t=${:.3f}'.format(cmodel.time[j]),\n",
+    "                    unit=r'$\\Delta L_{\\bar{\\nu}_e}/\\langle L_{\\bar{\\nu}_e}\\rangle$', cmap='magma',\n",
+    "                    min=vmin, max=vmax,\n",
+    "                    hold=True)\n",
+    "        \n",
+    "        ax = axes[i,2]\n",
+    "        plt.axes(ax)\n",
+    "        Lavg = np.average(L_nux[j])\n",
+    "        dL_on_L = (L_nux[j] - Lavg) / Lavg\n",
+    "        hp.mollview(dL_on_L, title=r'$\\nu_X$: $t=${:.3f}'.format(cmodel.time[j]),\n",
+    "                    unit=r'$(L_\\nu - \\langle L_\\nu\\rangle) / \\langle L_\\nu\\rangle$', cmap='cividis',\n",
+    "                    min=vmin, max=vmax,\n",
+    "                    hold=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6beba86",
+   "metadata": {},
+   "source": [
+    "### Superimpose $L_\\nu(t,\\theta,\\varphi)$ at All Locations on the Sphere\n",
+    "\n",
+    "Plot $L(t)$ at all locations as well as the average, and then the deviation from the average."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c342a3c8",
+   "metadata": {},
+   "source": [
+    "#### Electron Neutrinos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea2d9627",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if healpy_available:\n",
+    "    Lavg = np.average(L_nue, axis=1)\n",
+    "    dL_over_L = (L_nue - Lavg[:, np.newaxis]) / Lavg[:, np.newaxis]\n",
+    "\n",
+    "    fig, axes = plt.subplots(2, 1, figsize=(10, 8),\n",
+    "                             gridspec_kw={'height_ratios': [3, 1], 'hspace': 0},\n",
+    "                             sharex=True, tight_layout=True)\n",
+    "\n",
+    "    ax = axes[0]\n",
+    "    ax.plot(cmodel.time, L_nue, color='gray', alpha=0.05)\n",
+    "    ax.plot(cmodel.time, Lavg, color='r', ls='--')\n",
+    "    ax.set(xlim=cmodel.time[0::len(cmodel.time)-1].value,\n",
+    "           ylabel=r'$L_{\\nu_e}(t,\\theta,\\varphi)$ [$10^{52}$ erg s$^{-1}$]',\n",
+    "           ylim=(0, 1.1*np.max(L_nue).value),\n",
+    "           title=r'$L_{\\nu_e}(t,\\theta,\\varphi)$')\n",
+    "    ax.grid(ls=':')\n",
+    "\n",
+    "    ax = axes[1]\n",
+    "    ax.plot(cmodel.time, dL_over_L, color='gray', alpha=0.1)\n",
+    "    ax.plot(cmodel.time, np.zeros(cmodel.time.shape), ls='--', color='r')\n",
+    "    ax.set(xlabel='time [s]',\n",
+    "           ylabel=r'$(L_\\nu - \\bar{L}_\\nu)/\\bar{L}_\\nu$',\n",
+    "           ylim=(-0.3, 0.3))\n",
+    "    ax.grid(ls=':')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94bfc6a9",
+   "metadata": {},
+   "source": [
+    "#### Electron Antineutrinos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "643757c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if healpy_available:\n",
+    "    Lavg = np.average(L_nue_bar, axis=1)\n",
+    "    dL_over_L = (L_nue_bar - Lavg[:, np.newaxis]) / Lavg[:, np.newaxis]\n",
+    "\n",
+    "    fig, axes = plt.subplots(2, 1, figsize=(10, 8),\n",
+    "                             gridspec_kw={'height_ratios': [3, 1], 'hspace': 0},\n",
+    "                             sharex=True, tight_layout=True)\n",
+    "\n",
+    "    ax = axes[0]\n",
+    "    ax.plot(cmodel.time, L_nue_bar, color='gray', alpha=0.05)\n",
+    "    ax.plot(cmodel.time, Lavg, color='r', ls='--')\n",
+    "    ax.set(xlim=cmodel.time[0::len(cmodel.time)-1].value,\n",
+    "           ylabel=r'$L_{\\bar{\\nu}_e}(t,\\theta,\\varphi)$ [$10^{52}$ erg s$^{-1}$]',\n",
+    "           ylim=(0, 1.1*np.max(L_nue_bar).value),\n",
+    "           title=r'$L_{\\bar{\\nu}_e}(t,\\theta,\\varphi)$')\n",
+    "    ax.grid(ls=':')\n",
+    "\n",
+    "    ax = axes[1]\n",
+    "    ax.plot(cmodel.time, dL_over_L, color='gray', alpha=0.1)\n",
+    "    ax.plot(cmodel.time, np.zeros(cmodel.time.shape), ls='--', color='r')\n",
+    "    ax.set(xlabel='time [s]',\n",
+    "           ylabel=r'$(L_\\bar{\\nu} - \\bar{L}_\\bar{\\nu})/\\bar{L}_\\bar{\\nu}$',\n",
+    "           ylim=(-0.3, 0.3))\n",
+    "    ax.grid(ls=':')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de60253b",
+   "metadata": {},
+   "source": [
+    "#### Flavor X (Stand-in for Mu and Tau Neutrinos)\n",
+    "\n",
+    "Note that in this model, the mu and tau antineutrino flux is identical to the neutrino flux so we won't bother plotting it here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee70cfa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if healpy_available:\n",
+    "    Lavg = np.average(L_nux, axis=1)\n",
+    "    dL_over_L = (L_nux - Lavg[:,np.newaxis]) / Lavg[:,np.newaxis]\n",
+    "\n",
+    "    fig, axes = plt.subplots(2,1, figsize=(10,8),\n",
+    "                        gridspec_kw = {'height_ratios':[3,1], 'hspace':0},\n",
+    "                        sharex=True, tight_layout=True)\n",
+    "\n",
+    "    ax = axes[0]\n",
+    "    ax.plot(cmodel.time, L_nux, color='gray', alpha=0.05)\n",
+    "    ax.plot(cmodel.time, Lavg, color='r', ls='--')\n",
+    "    ax.set(xlim=cmodel.time[0::len(cmodel.time)-1].value,\n",
+    "            ylabel=r'$L_{\\nu_X}(t,\\theta,\\varphi)$ [$10^{52}$ erg s$^{-1}$]',\n",
+    "            ylim=(0, 1.1*np.max(L_nux).value),\n",
+    "            title=r'$L_{\\nu_X}(t,\\theta,\\varphi)$')\n",
+    "    ax.grid(ls=':')\n",
+    "\n",
+    "    ax = axes[1]\n",
+    "    ax.plot(cmodel.time, dL_over_L, color='gray', alpha=0.1)\n",
+    "    ax.plot(cmodel.time, np.zeros(cmodel.time.shape), ls='--', color='r')\n",
+    "    ax.set(xlabel='time [s]',\n",
+    "            ylabel=r'$(L_\\nu - \\bar{L}_\\nu)/\\bar{L}_\\nu$',\n",
+    "            ylim=(-0.3, 0.3))\n",
+    "    ax.grid(ls=':');"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.5 ('snews')",
+   "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": "3.12.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/nb/ccsn/Fornax_2021.ipynb b/doc/nb/ccsn/Fornax_2021.ipynb
new file mode 100644
index 00000000..b4697f04
--- /dev/null
+++ b/doc/nb/ccsn/Fornax_2021.ipynb
@@ -0,0 +1,331 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FORNAX 2D Neutrino Spectra\n",
+    "\n",
+    "Neutrino spectra from the long-time run 2D (axisymmetric) models produced by Burrows and Vartanyan, \n",
+    "[Nature **589**:29-39, 2021](https://www.nature.com/articles/s41586-020-03059-w).\n",
+    "\n",
+    "Data taken from the HDF5 files available for download at [the Princeton group website](https://www.astro.princeton.edu/~burrows/nu-emissions.2d/data/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from snewpy.neutrino import Flavor\n",
+    "from snewpy.models.ccsn import Fornax_2021\n",
+    "\n",
+    "from astropy import units as u\n",
+    "from glob import glob\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4799f82",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Fornax_2021` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd8215d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Fornax_2021.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96bfc909",
+   "metadata": {},
+   "source": [
+    "We’ll initialise some of these progenitors and plot the luminosity of different neutrino flavors for two of them. (Note that the `Fornax_2021` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.) If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c10c3261",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = {}\n",
+    "for m in Fornax_2021.param['progenitor_mass'].value[::2]:\n",
+    "    # Initialise every second progenitor\n",
+    "    models[m] = Fornax_2021(progenitor_mass=m*u.solMass)\n",
+    "\n",
+    "models[12]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56eb4178",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, model in enumerate([models[12], models[20]]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':',\n",
+    "                lw=2)\n",
+    "    ax.set(xlim=(-0.05, 1.0),\n",
+    "           xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "           title=r'{} $M_\\odot$'.format(model.metadata['Progenitor mass'].value))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
+    "\n",
+    "axes[0].set(ylabel=r'luminosity [foe s$^{-1}$]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Spectra of All Flavors vs. Time for the $12M_{\\odot}$ Model\n",
+    "\n",
+    "#### Use Default Linear Interpolation in Flux Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = models[12]  # Use the 12 solar mass model\n",
+    "\n",
+    "times = np.arange(-0.2, 3.8, 0.2) * u.s\n",
+    "E = np.arange(0, 101, 1) * u.MeV\n",
+    "\n",
+    "fig, axes = plt.subplots(5,4, figsize=(15,12), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "linestyles = ['-', '--', '-.', ':']\n",
+    "\n",
+    "spectra = model.get_initial_spectra(times, E)\n",
+    "\n",
+    "for i, ax in enumerate(axes.flatten()):\n",
+    "    for line, flavor in zip(linestyles, Flavor):\n",
+    "        ax.plot(E, spectra[flavor][i], lw=3, ls=line, label=flavor.to_tex())\n",
+    "    ax.set(xlim=(0,100))\n",
+    "    ax.set_title('$t$ = {:g}'.format(times[i]), fontsize=16)\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
+    "\n",
+    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
+    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Use Nearest-Bin \"Interpolation\" in Flux Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "times = np.arange(-0.2, 3.8, 0.2) * u.s\n",
+    "E = np.arange(0, 101, 1) * u.MeV\n",
+    "\n",
+    "fig, axes = plt.subplots(5,4, figsize=(15,12), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "linestyles = ['-', '--', '-.', ':']\n",
+    "\n",
+    "spectra = model.get_initial_spectra(times, E, interpolation='nearest')\n",
+    "\n",
+    "for i, ax in enumerate(axes.flatten()):\n",
+    "    for line, flavor in zip(linestyles, Flavor):\n",
+    "        ax.plot(E, spectra[flavor][i], lw=3, ls=line, label=flavor.to_tex())\n",
+    "    ax.set(xlim=(0,100))\n",
+    "    ax.set_title('$t$ = {:g}'.format(times[i]), fontsize=16)\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
+    "\n",
+    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
+    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Progenitor Mass Dependence\n",
+    "\n",
+    "### Luminosity vs. Time for a Selected List of Progenitor Masses\n",
+    "\n",
+    "Plot $L_{\\nu_{e}}(t)$ for a selection of progenitor masses to observe the dependence of the emission on mass."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(3,1, figsize=(10,13), sharex=True, sharey=True,\n",
+    "                         gridspec_kw = {'hspace':0.02})\n",
+    "\n",
+    "colors0 = mpl.cm.viridis(np.linspace(0.1,0.9, len(models)))\n",
+    "colors1 = mpl.cm.inferno(np.linspace(0.1,0.9, len(models)))\n",
+    "colors2 = mpl.cm.cividis(np.linspace(0.1,0.9, len(models)))\n",
+    "\n",
+    "linestyles = ['-', '--', '-.', ':']\n",
+    "\n",
+    "for i, model in enumerate(models.values()):\n",
+    "    ax = axes[0]\n",
+    "    flavor = Flavor.NU_E\n",
+    "    ax.plot(model.time, model.luminosity[flavor], lw=2, color=colors0[i], ls=linestyles[i%4],\n",
+    "            label='${0.value:g}$ {0.unit:latex}'.format(model.progenitor_mass))\n",
+    "    ax.set(xscale='log',\n",
+    "           xlim=(1e-3, 4),\n",
+    "           yscale='log',\n",
+    "           ylim=(0.4e52, 9e53),\n",
+    "           ylabel=r'$L_{\\nu_e}(t)$ [erg s$^{-1}$]')\n",
+    "    ax.grid(ls=':', which='both')\n",
+    "    ax.legend(ncol=3, fontsize=12, title=r'$\\nu_e$');\n",
+    "    \n",
+    "    ax = axes[1]\n",
+    "    flavor = Flavor.NU_E_BAR\n",
+    "    ax.plot(model.time, model.luminosity[flavor], lw=2, color=colors1[i], ls=linestyles[i%4],\n",
+    "        label='${0.value:g}$ {0.unit:latex}'.format(model.progenitor_mass))\n",
+    "    ax.set(ylabel=r'$L_{\\bar{\\nu}_e}(t)$ [erg s$^{-1}$]')\n",
+    "    ax.grid(ls=':', which='both')\n",
+    "    ax.legend(ncol=3, fontsize=12, title=r'$\\bar{\\nu}_e$');\n",
+    "    \n",
+    "    ax = axes[2]\n",
+    "    flavor = Flavor.NU_X\n",
+    "    ax.plot(model.time, model.luminosity[flavor], lw=2, color=colors2[i], ls=linestyles[i%4],\n",
+    "        label='${0.value:g}$ {0.unit:latex}'.format(model.progenitor_mass))\n",
+    "    ax.set(xlabel='time [s]',\n",
+    "           ylabel=r'$L_{\\nu_X}(t)$ [erg s$^{-1}$]')\n",
+    "    ax.grid(ls=':', which='both')\n",
+    "    ax.legend(ncol=3, fontsize=12, title=r'$\\nu_X$');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Progenitor Dependence of Spectra at 70 ms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Use Default Linear Interpolation in Flux Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = 70*u.ms\n",
+    "E = np.arange(0, 101, 1) * u.MeV\n",
+    "\n",
+    "fig, axes = plt.subplots(2,4, figsize=(16,6), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "linestyles = ['-', '--', '-.', ':']\n",
+    "\n",
+    "for model, ax in zip(models.values(), axes.flatten()):\n",
+    "    spectra = model.get_initial_spectra(t, E)\n",
+    "    for line, flavor in zip(linestyles, Flavor):\n",
+    "        ax.plot(E, spectra[flavor][0], lw=3, ls=line, label=flavor.to_tex())\n",
+    "    ax.set(xlim=(0,100))\n",
+    "    ax.set_title('${0.value:g}$ {0.unit:latex}'.format(model.progenitor_mass))\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
+    "    ax.grid(ls=':')\n",
+    "\n",
+    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
+    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Use Nearest-Bin \"Interpolation\" in Flux Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = 70*u.ms\n",
+    "E = np.arange(0, 101, 1) * u.MeV\n",
+    "\n",
+    "fig, axes = plt.subplots(2,4, figsize=(16,6), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "linestyles = ['-', '--', '-.', ':']\n",
+    "\n",
+    "for model, ax in zip(models.values(), axes.flatten()):\n",
+    "    spectra = model.get_initial_spectra(t, E, interpolation='nearest')\n",
+    "    for line, flavor in zip(linestyles, Flavor):\n",
+    "        ax.plot(E, spectra[flavor][0], lw=3, ls=line, label=flavor.to_tex())\n",
+    "    ax.set(xlim=(0,100))\n",
+    "    ax.set_title('${0.value:g}$ {0.unit:latex}'.format(model.progenitor_mass))\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
+    "    ax.grid(ls=':')\n",
+    "\n",
+    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
+    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.5 ('snews')",
+   "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": "3.12.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/nb/ccsn/Fornax_2022.ipynb b/doc/nb/ccsn/Fornax_2022.ipynb
new file mode 100644
index 00000000..ea4754ef
--- /dev/null
+++ b/doc/nb/ccsn/Fornax_2022.ipynb
@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1e8dbfcc",
+   "metadata": {},
+   "source": [
+    "# FORNAX 2D Neutrino Spectra\n",
+    "\n",
+    "Neutrino spectra from the 2D long-duraction models produced for 100 progenitors (1/3 form black holes).\n",
+    "\n",
+    "Data take from the [Princeton group webpage](https://www.astro.princeton.edu/~burrows/nu-emissions.2d.large/) and converted to HDF5 format for use in SNEWPY."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3dcba05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from snewpy.neutrino import Flavor\n",
+    "from snewpy.models.ccsn import Fornax_2022\n",
+    "\n",
+    "from astropy import units as u\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69e2e682",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mpl.rc('font', size=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2c2ac5d",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, see what progenitors are avaialble for the `Fornax_2022` model. Use the `param` property to view all physics parameters and their possible values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e311770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Fornax_2022.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e5f0052",
+   "metadata": {},
+   "source": [
+    "Initialize some progenitors and plot the luminosity of different neutrino flavors for two of them. Note that the `Fornax_2022` set of models do not distinguish between $\\nu_x$ and $\\bar{\\nu}_x$ so both have the same luminosity. If this is the first time you are using a progenitor model, `snewpy` will download the data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0750b09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = {}\n",
+    "for m in Fornax_2022.param['progenitor_mass'][::19]:\n",
+    "    # Initialize every 20th progenitor.\n",
+    "    print(m)\n",
+    "    models[m] = Fornax_2022(progenitor_mass=m)\n",
+    "    \n",
+    "models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d7c2a2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models[23.43*u.solMass].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ded8b91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(3, 2, figsize=(12, 15), sharex=True, sharey=True, tight_layout=True)\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for i, (key, model) in enumerate(models.items()):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':',\n",
+    "                lw=2)\n",
+    "        \n",
+    "    modtitle = '{} $M_\\odot$'.format(model.metadata['Progenitor mass'].value)\n",
+    "    if model.metadata['Black hole']:\n",
+    "        modtitle += ' (BH)'\n",
+    "        \n",
+    "    ax.set(xlim=(-0.05, 1.5),\n",
+    "           xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "           title=modtitle)\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
+    "\n",
+    "axes[0].set(ylabel=r'luminosity [foe s$^{-1}$]');\n",
+    "# axes[5].set_axis_off();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78d70089",
+   "metadata": {},
+   "source": [
+    "## Spectra of All Flavors vs. Time for the 19.02 $M_\\odot$ Model\n",
+    "\n",
+    "### Use Default Linear Interpolation in Flux Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a301327",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = models[23.43*u.solMass]\n",
+    "\n",
+    "times = np.arange(-0.2, 3.8, 0.2) * u.s\n",
+    "E = np.arange(0, 101, 1) * u.MeV\n",
+    "\n",
+    "fig, axes = plt.subplots(5,4, figsize=(15,12), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "linestyles = ['-', '--', '-.', ':']\n",
+    "\n",
+    "spectra = model.get_initial_spectra(times, E)\n",
+    "\n",
+    "for i, ax in enumerate(axes.flatten()):\n",
+    "    for line, flavor in zip(linestyles, Flavor):\n",
+    "        ax.plot(E, spectra[flavor][i], lw=3, ls=line, label=flavor.to_tex())\n",
+    "    ax.set(xlim=(0,100))\n",
+    "    ax.set_title('$t$ = {:g}'.format(times[i]), fontsize=16)\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
+    "\n",
+    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
+    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27ef0892",
+   "metadata": {},
+   "source": [
+    "### Use Nearest-Bin \"Interpolation\" in Flux Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cea3d5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "times = np.arange(-0.2, 3.8, 0.2) * u.s\n",
+    "E = np.arange(0, 101, 1) * u.MeV\n",
+    "\n",
+    "fig, axes = plt.subplots(5,4, figsize=(15,12), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "linestyles = ['-', '--', '-.', ':']\n",
+    "\n",
+    "spectra = model.get_initial_spectra(times, E, interpolation='nearest')\n",
+    "\n",
+    "for i, ax in enumerate(axes.flatten()):\n",
+    "    for line, flavor in zip(linestyles, Flavor):\n",
+    "        ax.plot(E, spectra[flavor][i], lw=3, ls=line, label=flavor.to_tex())\n",
+    "    ax.set(xlim=(0,100))\n",
+    "    ax.set_title('$t$ = {:g}'.format(times[i]), fontsize=16)\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
+    "\n",
+    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
+    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9dc1934",
+   "metadata": {},
+   "source": [
+    "## Progenitor Mass Dependence\n",
+    "\n",
+    "### Luminosity vs. Time for a Selected List of Progenitor Masses\n",
+    "\n",
+    "Plot $L_{\\nu_e}(t)$ for a selection of progenitor masses to observe the dependence of the emission on mass."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab022bc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(3,1, figsize=(10,13), sharex=True, sharey=True,\n",
+    "                         gridspec_kw = {'hspace':0.02})\n",
+    "\n",
+    "colors0 = mpl.cm.viridis(np.linspace(0.1,0.9, len(models)))\n",
+    "colors1 = mpl.cm.inferno(np.linspace(0.1,0.9, len(models)))\n",
+    "colors2 = mpl.cm.cividis(np.linspace(0.1,0.9, len(models)))\n",
+    "\n",
+    "linestyles = ['-', '--', '-.', ':']\n",
+    "\n",
+    "for i, model in enumerate(models.values()):\n",
+    "    ax = axes[0]\n",
+    "    flavor = Flavor.NU_E\n",
+    "    ax.plot(model.time, model.luminosity[flavor], lw=2, color=colors0[i], ls=linestyles[i%4],\n",
+    "            label='${0.value:g}$ {0.unit:latex}{1}'.format(model.progenitor_mass, ' (BH)' if 'bh' in model.progenitor else ''))\n",
+    "    ax.set(xscale='log',\n",
+    "           xlim=(1e-3, 4),\n",
+    "           yscale='log',\n",
+    "           ylim=(0.4e52, 9e53),\n",
+    "           ylabel=r'$L_{\\nu_e}(t)$ [erg s$^{-1}$]')\n",
+    "    ax.grid(ls=':', which='both')\n",
+    "    ax.legend(ncol=3, fontsize=12, title=r'$\\nu_e$');\n",
+    "    \n",
+    "    ax = axes[1]\n",
+    "    flavor = Flavor.NU_E_BAR\n",
+    "    ax.plot(model.time, model.luminosity[flavor], lw=2, color=colors1[i], ls=linestyles[i%4],\n",
+    "        label='${0.value:g}$ {0.unit:latex}{1}'.format(model.progenitor_mass, ' (BH)' if 'bh' in model.progenitor else ''))\n",
+    "    ax.set(ylabel=r'$L_{\\bar{\\nu}_e}(t)$ [erg s$^{-1}$]')\n",
+    "    ax.grid(ls=':', which='both')\n",
+    "    ax.legend(ncol=3, fontsize=12, title=r'$\\bar{\\nu}_e$');\n",
+    "    \n",
+    "    ax = axes[2]\n",
+    "    flavor = Flavor.NU_X\n",
+    "    ax.plot(model.time, model.luminosity[flavor], lw=2, color=colors2[i], ls=linestyles[i%4],\n",
+    "        label='${0.value:g}$ {0.unit:latex}{1}'.format(model.progenitor_mass, ' (BH)' if 'bh' in model.progenitor else ''))\n",
+    "    ax.set(xlabel='time [s]',\n",
+    "           ylabel=r'$L_{\\nu_X}(t)$ [erg s$^{-1}$]')\n",
+    "    ax.grid(ls=':', which='both')\n",
+    "    ax.legend(ncol=3, fontsize=12, title=r'$\\nu_X$');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89a17c23",
+   "metadata": {},
+   "source": [
+    "## Progenitor Dependence of Spectra at 70 ms\n",
+    "\n",
+    "### Use Default Linear Interpolation in Flux Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f0eba54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = 70*u.ms\n",
+    "E = np.arange(0, 101, 1) * u.MeV\n",
+    "\n",
+    "fig, axes = plt.subplots(2,3, figsize=(12,6), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "linestyles = ['-', '--', '-.', ':']\n",
+    "\n",
+    "for model, ax in zip(models.values(), axes.flatten()):\n",
+    "    spectra = model.get_initial_spectra(t, E)\n",
+    "    for line, flavor in zip(linestyles, Flavor):\n",
+    "        ax.plot(E, spectra[flavor][0], lw=3, ls=line, label=flavor.to_tex())\n",
+    "    ax.set(xlim=(0,100))\n",
+    "    ax.set_title('${0.value:g}$ {0.unit:latex}{1}'.format(model.progenitor_mass, ' (BH)' if 'bh' in model.progenitor else ''))\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
+    "    ax.grid(ls=':')\n",
+    "\n",
+    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
+    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "456450c5",
+   "metadata": {},
+   "source": [
+    "### Use Nearest-Bin \"Interpolation\" in Flux Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52dca852",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = 70*u.ms\n",
+    "E = np.arange(0, 101, 1) * u.MeV\n",
+    "\n",
+    "fig, axes = plt.subplots(2,3, figsize=(12,6), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "linestyles = ['-', '--', '-.', ':']\n",
+    "\n",
+    "for model, ax in zip(models.values(), axes.flatten()):\n",
+    "    spectra = model.get_initial_spectra(t, E, interpolation='nearest')\n",
+    "    for line, flavor in zip(linestyles, Flavor):\n",
+    "        ax.plot(E, spectra[flavor][0], lw=3, ls=line, label=flavor.to_tex())\n",
+    "    ax.set(xlim=(0,100))\n",
+    "    ax.set_title('${0.value:g}$ {0.unit:latex}{1}'.format(model.progenitor_mass, ' (BH)' if 'bh' in model.progenitor else ''))\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=12)\n",
+    "    ax.grid(ls=':')\n",
+    "\n",
+    "fig.text(0.5, 0., 'energy [MeV]', ha='center')\n",
+    "fig.text(0., 0.5, f'flux [{spectra[Flavor.NU_E].unit}]', va='center', rotation='vertical');"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/nb/ccsn/Kuroda_2020.ipynb b/doc/nb/ccsn/Kuroda_2020.ipynb
new file mode 100644
index 00000000..fdff4f7a
--- /dev/null
+++ b/doc/nb/ccsn/Kuroda_2020.ipynb
@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Kuroda 2020 Models\n",
+    "\n",
+    "Models from Takami Kuroda. The paper describing the simulations is \"Impact of magnetic field on neutrino-matter interactions in core-collapse supernova\" arXiv:2009.07733.\n",
+    "Included with SNEWPY with permission of the authors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import Kuroda_2020\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Kuroda_2020` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Kuroda_2020.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quite a lot of choice there! However, for this model, not all combinations of these parameters are valid. We can use the `get_param_combinations` function to get a list of all valid combinations or to filter it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This will print a tuple of all combinations:\n",
+    "Kuroda_2020.get_param_combinations()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll pick one of these progenitors and initialise it. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Kuroda_2020(**Kuroda_2020.get_param_combinations()[1])\n",
+    "# This is equivalent to:\n",
+    "# model = Kuroda_2020(rotational_velocity=1*u.rad/u.s, magnetic_field_exponent=12)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `Kuroda_2020` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, figsize=(8,6), tight_layout=False)\n",
+    "\n",
+    "for flavor in Flavor:\n",
+    "    ax.plot(model.time, model.luminosity[flavor]/1e51, # Report luminosity in units foe/s\n",
+    "            label=flavor.to_tex(),\n",
+    "            color = 'C0' if flavor.is_electron else 'C1',\n",
+    "            ls = '-' if flavor.is_neutrino else ':',\n",
+    "            lw = 2 )\n",
+    "\n",
+    "ax.set(xlim=(0.0, 0.35),\n",
+    "       xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "       ylabel=r'luminosity [foe s$^{-1}$]')\n",
+    "ax.grid()\n",
+    "ax.legend(loc='upper right', ncol=2, fontsize=18);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 50*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [erg$^{-1}$ s$^{-1}$]')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.5 ('snews')",
+   "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": "3.9.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/ccsn/Mori_2023.ipynb b/doc/nb/ccsn/Mori_2023.ipynb
new file mode 100644
index 00000000..05d80e2e
--- /dev/null
+++ b/doc/nb/ccsn/Mori_2023.ipynb
@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "04bf2ca6",
+   "metadata": {},
+   "source": [
+    "# Mori 2023 2D Models with Axionlike Production\n",
+    "\n",
+    "Neutrino spectra from a set of 2D simulations with axionlike particle production. The models are described in the paper [Multi-messenger signals of heavy axionlike particles in core-collapse supernovae: two-dimensional simulations](https://arxiv.org/abs/2304.11360) by K. Mori, T. Takiwaki, K. Kotake and S. Horiuchi, Phys. Rev. D 108:063027, 2023.\n",
+    "\n",
+    "The following models are supported:\n",
+    "\n",
+    "| Model | Axion mass [MeV] | Coupling g10 | tpb,2000 [ms] | Ediag [1e51 erg] | MPNS [Msun] |\n",
+    "| ----- | ---------------- | ------------ | ------------- | ---------------- | ---------- |\n",
+    "| Standard | − | 0 | 390 | 0.40 | 1.78 |\n",
+    "| (100, 2) | 100 | 2 | 385 | 0.37 | 1.77 |\n",
+    "| (100, 4) | 100 | 4 | 362 | 0.34 | 1.76 |\n",
+    "| (100, 10) | 100 | 10 | 395 | 0.36 | 1.77 |\n",
+    "| (100, 12) | 100 | 12 | 357 | 0.43 | 1.77 |\n",
+    "| (100, 14) | 100 | 14 | 360 | 0.44 | 1.77 |\n",
+    "| (100, 16) | 100 | 16 | 367 | 0.51 | 1.77 |\n",
+    "| (100, 20) | 100 | 20 | 330 | 1.10 | 1.74 |\n",
+    "| (200, 2) | 200 | 2 | 374 | 0.45 | 1.77 |\n",
+    "| (200, 4) | 200 | 4 | 376 | 0.45 | 1.76 |\n",
+    "| (200, 6) | 200 | 6 | 333 | 0.54 | 1.75 |\n",
+    "| (200, 8) | 200 | 8 | 323 | 0.94 | 1.74 |\n",
+    "| (200, 10) | 200 | 10 | 319 | 1.61 | 1.73 |\n",
+    "| (200, 20) | 200 | 20 | 248 | 3.87 | 1.62 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29ceae1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from snewpy.neutrino import Flavor\n",
+    "from snewpy.models.ccsn import Mori_2023\n",
+    "\n",
+    "from astropy import units as u\n",
+    "from glob import glob\n",
+    "\n",
+    "from scipy.interpolate import PchipInterpolator\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b7566ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mpl.rc('font', size=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66d13225",
+   "metadata": {},
+   "source": [
+    "## Initialize the 2D models\n",
+    "\n",
+    "Use the `param` property of the model class to see the available parameters. Models are initialized using the `axion_mass` and `axion_coupling` parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f62e690f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Mori_2023.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55479166",
+   "metadata": {},
+   "source": [
+    "The model with `axion_mass=0` and `axion_coupling=0` is a standard simulation with no ALP production."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "649f5ae4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_std = Mori_2023(axion_mass=0, axion_coupling=0)\n",
+    "model_std.metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f15de7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize a handful of axion models.\n",
+    "models = {}\n",
+    "for (am, ac) in ((100*u.MeV, 2e-10/u.GeV), (200*u.MeV,2e-10/u.GeV), (100*u.MeV,10e-10/u.GeV), (100*u.MeV,20e-10/u.GeV), (200*u.MeV,10e-10/u.GeV), (200*u.MeV,20e-10/u.GeV)):\n",
+    "    models[(am,ac)] = Mori_2023(axion_mass=am, axion_coupling=ac)\n",
+    "\n",
+    "models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cab5a9c",
+   "metadata": {},
+   "source": [
+    "## Plot Model Luminosities\n",
+    "\n",
+    "Compare axion model luminosity to the standard 2D simulation.\n",
+    "\n",
+    "Higher mass models with stronger coupling constants should produce a decrease in neutrino luminosity at all flavors relative to the reference simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a398333",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for (m,c), model in models.items():\n",
+    "    \n",
+    "    fig, axes = plt.subplots(2, 4, figsize=(28, 8), sharex=True,\n",
+    "                         gridspec_kw={'height_ratios':[3.2,1], 'hspace':0})\n",
+    "    \n",
+    "    Lmin,  Lmax  = 1e99, -1e99\n",
+    "    dLmin, dLmax = 1e99, -1e99\n",
+    "    \n",
+    "    for j, (flavor) in enumerate([Flavor.NU_E, Flavor.NU_E_BAR, Flavor.NU_X, Flavor.NU_X_BAR]):\n",
+    "        ax = axes[0][j]\n",
+    "        \n",
+    "        ax.plot(model_std.time, model_std.luminosity[flavor]/1e51, 'k', label='$20M_\\odot$ reference')  # Report luminosity in [foe/s]\n",
+    "        Lmin = np.minimum(Lmin, np.min(model_std.luminosity[flavor].to_value('1e51 erg/s')))\n",
+    "        Lmax = np.maximum(Lmax, np.max(model_std.luminosity[flavor].to_value('1e51 erg/s')))\n",
+    "        \n",
+    "        modlabel = f\"{flavor.to_tex()}: $m_a=${m.to_string(format='latex_inline')}\\n     $g_{{a\\gamma}}=${c.to_string(format='latex_inline')}\"\n",
+    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "                label=modlabel,\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':',\n",
+    "                lw=2)\n",
+    "        if j==0:\n",
+    "            ax.set(ylabel=r'luminosity [$10^{51}$ erg s$^{-1}$]')\n",
+    "        \n",
+    "        ax.legend(fontsize=12)\n",
+    "        ax.set(xlim=(model_std.time[0].to_value('s'), model_std.time[-1].to_value('s')))\n",
+    "        \n",
+    "        ax = axes[1][j]\n",
+    "        tmin = np.maximum(model.time[0], model_std.time[0]).to_value('s')\n",
+    "        tmax = np.minimum(model.time[-1], model_std.time[-1]).to_value('s')\n",
+    "        times = np.arange(tmin, tmax, 0.001)*u.s\n",
+    "\n",
+    "        Lstd = PchipInterpolator(model_std.time, model_std.luminosity[flavor].to_value('1e51 erg/s'))\n",
+    "        Lstd_t = Lstd(times)\n",
+    "        select = Lstd_t != 0\n",
+    "        \n",
+    "        Lmod = PchipInterpolator(model.time, model.luminosity[flavor].to_value('1e51 erg/s'))\n",
+    "        Lmod_t = Lmod(times)\n",
+    "        dL = (Lmod_t[select] - Lstd_t[select]) / Lstd_t[select]\n",
+    "        \n",
+    "        dLmin = np.minimum(dLmin, np.min(dL))\n",
+    "        dLmax = np.maximum(dLmax, np.max(dL))\n",
+    "\n",
+    "        ax.plot(times[select], dL)\n",
+    "        if j==0:\n",
+    "            ax.set(xlabel='time [s]',\n",
+    "                   ylabel=r'$\\Delta L_\\nu/L_\\nu$')\n",
+    "            \n",
+    "    for j in range(4):\n",
+    "        axes[0][j].set(ylim=(Lmin, 1.1*Lmax))\n",
+    "        axes[1][j].set(ylim=(dLmin, dLmax))\n",
+    "        \n",
+    "    fig.suptitle(f\"Axionlike model: $m_a=${m.to_string(format='latex_inline')}, $g_{{a\\gamma}}=${c.to_string(format='latex_inline')}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/nb/ccsn/Nakazato_2013.ipynb b/doc/nb/ccsn/Nakazato_2013.ipynb
new file mode 100644
index 00000000..1fe131c3
--- /dev/null
+++ b/doc/nb/ccsn/Nakazato_2013.ipynb
@@ -0,0 +1,211 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nakazato 2013 Models\n",
+    "\n",
+    "This notebook includes an example of how to read the .fits files located in same directory as this notebook. These files contain neutrino luminosity, mean energy, and energy spectrum pinch parameter (alpha) as functions of time for the CCSN neutrino flavors. These were obtained using models from Nakazato et al., 2013 and 2015. Data are public and taken from their [website](http://asphwww.ph.noda.tus.ac.jp/snn/).\n",
+    "\n",
+    "The citation for use of the database is: *Supernova Neutrino Light Curves and Spectra for Various Progenitor Stars: From Core Collapse to Proto-neutron Star Cooling*, K. Nakazato, K. Sumiyoshi, H. Suzuki, T. Totani, H. Umeda, and S. Yamada, [Astrophys. J. Supp. 205 (2013) 2](http://dx.doi.org/10.1088/0067-0049/205/1/2), [arXiv:1210.6841](http://arxiv.org/abs/1210.6841).\n",
+    "\n",
+    "If the BH model with LS220 EOS is used, the citation is: *Spectrum of the Supernova Relic Neutrino Background and Metallicity Evolution of Galaxies*, K. Nakazato, E. Mochida, Y. Niino, and H. Suzuki, [Astrophys. J. 804 (2015) 75](http://dx.doi.org/10.1088/0004-637X/804/1/75), [arXiv:1503.01236](http://arxiv.org/abs/1503.01236).\n",
+    "\n",
+    "For the BH model with Togashi EOS, the citation is: *Numerical Study of Stellar Core Collapse and Neutrino Emission Using the Nuclear Equation of State Obtained by the Variational Method*, K. Nakazato, K. Sumiyoshi, and H. Togashi, [Publ. Astron. Soc. Jpn. 73 (2021) 639-651](https://doi.org/10.1093/pasj/psab026), [arXiv:2103.14386](http://arxiv.org/abs/2103.14386).\n",
+    "\n",
+    "These examples use code taken from IceCube's fast supernova monte carlo, [ASTERIA](https://github.com/IceCubeOpenSource/ASTERIA). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import Nakazato_2013\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Nakazato_2013` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Nakazato_2013.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quite a lot of choice there! However, for the Nakazato model, not all combinations of these parameters are valid. We can use the `get_param_combinations` function to get a list of all valid combinations or to filter it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This will print a long tuple of combinations:\n",
+    "#Nakazato_2013.get_param_combinations()\n",
+    "\n",
+    "# To get a more manageable list, let’s filter it to show only the\n",
+    "# available progenitors with a metallicity of z=0.004:\n",
+    "highz_models = list(params for params in Nakazato_2013.get_param_combinations() if params['metallicity'] == 0.02)\n",
+    "print(\"Progenitors with metallicity z=0.02:\", *highz_models, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll pick one of these progenitors and initialise it. If this is the first time you’re using this progenitor, snewpy will automatically download the required data file for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_params = highz_models[3]\n",
+    "model = Nakazato_2013(**model_params)\n",
+    "# This is equivalent to:\n",
+    "#model = Nakazato_2013(progenitor_mass=20*u.solMass, revival_time=100*u.ms, metallicity=0.004, eos='shen')\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `Nakazato_2013` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, figsize=(8,6), tight_layout=False)\n",
+    "\n",
+    "for flavor in Flavor:\n",
+    "    ax.plot(model.time, model.luminosity[flavor]/1e51, # Report luminosity in units foe/s\n",
+    "            label=flavor.to_tex(),\n",
+    "            color = 'C0' if flavor.is_electron else 'C1',\n",
+    "            ls = '-' if flavor.is_neutrino else ':',\n",
+    "            lw = 2 )\n",
+    "\n",
+    "ax.set(xlim=(-0.05, 0.5),\n",
+    "       xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "       ylabel=r'luminosity [foe s$^{-1}$]')\n",
+    "ax.grid()\n",
+    "ax.legend(loc='upper right', ncol=2, fontsize=18);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 100*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [erg$^{-1}$ s$^{-1}$]');"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/ccsn/OConnor_2013.ipynb b/doc/nb/ccsn/OConnor_2013.ipynb
new file mode 100644
index 00000000..643d4931
--- /dev/null
+++ b/doc/nb/ccsn/OConnor_2013.ipynb
@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# O'Connor 2013 Models\n",
+    "\n",
+    "Data from O'Connor & Ott 2013, 32 progenitors (Woosley and Heger 2007) and 2 EOS (LS220 and HShen) for 500 ms post bounce in spherical symmetry (no explosions)\n",
+    "  \n",
+    "Reference: O'Connor and Ott ApJ 762 126 2013\n",
+    "- [doi:10.1088/0004-637X/762/2/126](https://doi.org/10.1088/0004-637X/762/2/126)\n",
+    "- [arXiv:1207.1100](https://arxiv.org/abs/1207.1100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import OConnor_2013\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `OConnor_2013` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "OConnor_2013.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quite a lot of choice there! Let’s initialise all of these progenitors and compare their $\\nu_e$ luminosities. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = {}\n",
+    "for mass in OConnor_2013.param['progenitor_mass']:\n",
+    "    models[int(mass.value)] = OConnor_2013(progenitor_mass=mass, eos='LS220')\n",
+    "\n",
+    "for model in models.values():\n",
+    "    plt.plot(model.time, model.luminosity[Flavor.NU_E]/1e51, 'C0', lw=1)\n",
+    "\n",
+    "plt.xlabel(r'$t$ [s]')\n",
+    "plt.ylabel(r'luminosity [foe s$^{-1}$]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for two of these progenitors. (Note that the `OConnor_2013` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both flavors have the same luminosity.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, model in enumerate([models[12], models[20]]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':',\n",
+    "                lw=2)\n",
+    "    ax.set(xlim=(-0.05, 0.51),\n",
+    "           xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "           title=r'{}: {} $M_\\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
+    "\n",
+    "axes[0].set(ylabel=r'luminosity [foe s$^{-1}$]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 400*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [erg$^{-1}$ s$^{-1}$]')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/ccsn/OConnor_2015.ipynb b/doc/nb/ccsn/OConnor_2015.ipynb
new file mode 100644
index 00000000..dd6a9d3c
--- /dev/null
+++ b/doc/nb/ccsn/OConnor_2015.ipynb
@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# O'Connor 2015 Models\n",
+    "\n",
+    "Data from O'Connor 2015, black hole forming simulations of a 40 solar mass progenitor from Woosley and Heger 2007 and the LS220 EOS.\n",
+    "  \n",
+    "Reference: O'Connor ApJS 219 24 2015\n",
+    "- doi:10.1088/0067-0049/219/2/24\n",
+    "- arXiv:1411.7058"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import OConnor_2015\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `OConnor_2015` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "OConnor_2015.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll initialise this progenitor. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = OConnor_2015(progenitor_mass=40*u.solMass)\n",
+    "\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `OConnor_2015` simulation didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, figsize=(8, 6), tight_layout=False)\n",
+    "\n",
+    "for flavor in Flavor:\n",
+    "    ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "            label=flavor.to_tex(),\n",
+    "            color='C0' if flavor.is_electron else 'C1',\n",
+    "            ls='-' if flavor.is_neutrino else ':',\n",
+    "            lw=2)\n",
+    "ax.set(xlim=(0, 0.55),\n",
+    "        xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "        title=r'{}: {} $M_\\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))\n",
+    "ax.grid()\n",
+    "ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
+    "ax.set(ylabel=r'luminosity [foe s$^{-1}$]');\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 400*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [erg$^{-1}$ s$^{-1}$]')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.5 ('snews')",
+   "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": "3.9.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/ccsn/Sukhbold_2015.ipynb b/doc/nb/ccsn/Sukhbold_2015.ipynb
new file mode 100644
index 00000000..e8bd6587
--- /dev/null
+++ b/doc/nb/ccsn/Sukhbold_2015.ipynb
@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sukhbold 2015 Models\n",
+    "\n",
+    "CSN neutrino models from the MPA Garching CCSN archive based on the paper by Sukhbold et al., 2015. The archive is available on\n",
+    "[their website](https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/SEWBJ_2015/index.html), but the data are private and available only upon request.\n",
+    "Note these are the results using the PROMETHEUS-VERTEX code https://ui.adsabs.harvard.edu/abs/2002A%26A...396..361R/abstract.\n",
+    "The four models are also described in Appendix C of this paper https://arxiv.org/abs/2010.04728\n",
+    "\n",
+    "The citation is: *Core-Collapse Supernovae from 9 to 120 Solar Masses Based on Neutrino-powered Explosions*, Tuguldur Sukhbold, T. Ertl, S. E. Woosley, Justin M. Brown, H.-T. Janka, [Astrophys. J. 821 (2016)\n",
+    "38](http://dx.doi.org/10.3847/0004-637X/821/1/38), [arXiv:1510.04643](http://arxiv.org/abs/1510.04643)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import Sukhbold_2015\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Sukhbold_2015` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Sukhbold_2015.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll initialise two of these progenitors. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mls220 = Sukhbold_2015(progenitor_mass=27*u.solMass, eos='LS220')\n",
+    "msfho = Sukhbold_2015(progenitor_mass=27*u.solMass, eos='SFHo')\n",
+    "\n",
+    "mls220"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `Sukhbold_2015` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, model in enumerate([mls220, msfho]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':',\n",
+    "                lw=2)\n",
+    "    ax.set(xlim=(-0.05, 0.65),\n",
+    "           xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "           title=r'{}: {} $M_\\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
+    "\n",
+    "axes[0].set(ylabel=r'luminosity [foe s$^{-1}$]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 50*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [erg$^{-1}$ s$^{-1}$]')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.5 ('snews')",
+   "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": "3.9.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/ccsn/Tamborra_2014.ipynb b/doc/nb/ccsn/Tamborra_2014.ipynb
new file mode 100644
index 00000000..3d5c0e8d
--- /dev/null
+++ b/doc/nb/ccsn/Tamborra_2014.ipynb
@@ -0,0 +1,188 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tamborra 2014 Models\n",
+    "\n",
+    "Data from Tamborra et al., [Phys. Rev. D90:045032, 2014](http://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Tamborra2014/). Models (s20.0c and s27.0c) taken from the Garching Supernova archive (https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Tamborra2014/) with permission for use in SNEWS2.0.\n",
+    "\n",
+    "Reference: I. Tamborra et al., *Neutrino emission characteristics and detection opportunities based on three-dimensional supernova simulations*, Phys. Rev D90:045032, 2014.\n",
+    "- [doi:10.1103/PhysRevD.90.045032](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.90.045032)\n",
+    "- [arXiv:1406.0006](https://arxiv.org/abs/1406.0006)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import Tamborra_2014\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Tamborra_2014` model. We can use the `param` property to view all physics parameters and their possible values. However, for this model, not all combinations of these parameters are valid, so we use the `get_param_combinations` function to get a list of all valid combinations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tamborra_2014.param\n",
+    "Tamborra_2014.get_param_combinations()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll initialise two of these progenitors. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m20 = Tamborra_2014(progenitor_mass=20*u.solMass, direction=1)\n",
+    "m27 = Tamborra_2014(progenitor_mass=27*u.solMass, direction=1)\n",
+    "\n",
+    "m20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `Tamborra_2014` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, model in enumerate([m20, m27]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':',\n",
+    "                lw=2)\n",
+    "    ax.set(xlim=(0.0, 0.35),\n",
+    "           xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "           title=r'{}: {} $M_\\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
+    "\n",
+    "axes[0].set(ylabel=r'luminosity [foe s$^{-1}$]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 50*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [erg$^{-1}$ s$^{-1}$]');"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.5 ('snews')",
+   "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": "3.9.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/ccsn/Walk_2018.ipynb b/doc/nb/ccsn/Walk_2018.ipynb
new file mode 100644
index 00000000..9ad5fe82
--- /dev/null
+++ b/doc/nb/ccsn/Walk_2018.ipynb
@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Walk 2018 Models\n",
+    "\n",
+    "Models from L. Walk et al., [Phys. Rev. D 98:123001, 2018](https://arxiv.org/abs/1807.02366) (s15.0c) taken from the Garching Supernova archive (https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Walk2018/) with permission for use in SNEWPY.\n",
+    "\n",
+    "Reference: L. Walk et al., *Identifying rotation in SASI-dominated core-collapse supernovae with a neutrino gyroscope*, Phys. Rev. D 98:123001, 2018\n",
+    "- [doi:10.1103/PhysRevD.98.123001](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.98.123001)\n",
+    "- [arXiv:1807.02366](https://arxiv.org/abs/1807.02366)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import Walk_2018\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Walk_2018` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Walk_2018.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll initialise this progenitor. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Walk_2018(progenitor_mass=15*u.solMass, rotation='fast', direction=1)\n",
+    "\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `Sukhbold_2015` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, figsize=(8, 6), tight_layout=False)\n",
+    "\n",
+    "for flavor in Flavor:\n",
+    "    ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "            label=flavor.to_tex(),\n",
+    "            color='C0' if flavor.is_electron else 'C1',\n",
+    "            ls='-' if flavor.is_neutrino else ':',\n",
+    "            lw=2)\n",
+    "ax.set(xlim=(0, 0.35),\n",
+    "        xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "        title=r'{}: {} $M_\\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))\n",
+    "ax.grid()\n",
+    "ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
+    "ax.set(ylabel=r'luminosity [foe s$^{-1}$]');\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 50*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [s$^{-1}$]')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.5 ('snews')",
+   "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": "3.9.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/ccsn/Walk_2019.ipynb b/doc/nb/ccsn/Walk_2019.ipynb
new file mode 100644
index 00000000..309172ab
--- /dev/null
+++ b/doc/nb/ccsn/Walk_2019.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Walk 2019 Models\n",
+    "\n",
+    "Models from L. Walk et al., [Phys. Rev. D 101:123013, 2019](https://arxiv.org/abs/1910.12971) (s15.0c) taken from the Garching Supernova archive (https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Walk2019/) with permission for use in SNEWPY.\n",
+    "\n",
+    "Reference: L. Walk et al., *Neutrino emission characteristics of black hole formation in three-dimensional simulations of stellar collapse*, Phys. Rev. D 101:123013, 2019\n",
+    "- [doi:10.1103/PhysRevD.101.123013](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.101.123013)\n",
+    "- [arXiv:1910.12971](https://arxiv.org/abs/1910.12971)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import Walk_2019\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Walk_2019` model. We can use the `param` property to view all physics parameters and their possible values. However, for this model, not all combinations of these parameters are valid, so we use the `get_param_combinations` function to get a list of all valid combinations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Walk_2019.param\n",
+    "Walk_2019.get_param_combinations()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll initialise this progenitor. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Walk_2019(progenitor_mass=40*u.solMass, direction=1)\n",
+    "\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `Walk_2019` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 1, figsize=(12, 5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for flavor in Flavor:\n",
+    "    axes.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "            label=flavor.to_tex(),\n",
+    "            color='C0' if flavor.is_electron else 'C1',\n",
+    "            ls='-' if flavor.is_neutrino else ':',\n",
+    "            lw=2)\n",
+    "axes.set(xlim=(0, 0.6),\n",
+    "        xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "        title=r'{}: {} $M_\\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))\n",
+    "axes.grid()\n",
+    "axes.legend(loc='upper right', ncol=2, fontsize=18)\n",
+    "axes.set(ylabel=r'luminosity [foe s$^{-1}$]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 50*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [s$^{-1}$]')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.5 ('snews')",
+   "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": "3.9.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/ccsn/Warren_2020.ipynb b/doc/nb/ccsn/Warren_2020.ipynb
new file mode 100644
index 00000000..0b57582f
--- /dev/null
+++ b/doc/nb/ccsn/Warren_2020.ipynb
@@ -0,0 +1,219 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Warren 2020 Models\n",
+    "\n",
+    "Data from \"Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: multi-messenger signals\" by Warren, MacKenzie; Couch, Sean; O'Connor, Evan; Morozova, Viktoriya.\n",
+    "\n",
+    "1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27.  Run with SFHo EOS, M1 with 12 energy groups.\n",
+    "\n",
+    "For more information on these simulations, see Warren, Couch, O'Connor, & Morozova ([arXiv:1912.03328](https://arxiv.org/abs/1912.03328)) and Couch, Warren, & O'Connor (2020).\n",
+    "\n",
+    "Includes the multi-messenger data from the STIR simulations. The filename indicates the turbulent mixing parameter a and progenitor mass m of the simulation.  Columns are time [s], shock radius [cm], explosion energy [ergs], electron neutrino mean energy [MeV], electron neutrino rms energy [MeV], electron neutrino luminosity [10^51 ergs/s], electron antineutrino mean energy [MeV], electron antineutrino rms energy [MeV], electron antineutrino luminosity [10^51 ergs/s], x neutrino mean energy [MeV], x neutrino rms energy [MeV], x neutrino luminosity [10^51 ergs/s], gravitational wave frequency from eigenmode analysis of the protoneutron star structure [Hz].  Note that the x neutrino luminosity is for one neutrino flavor - to get the total mu/tau neutrino and antineutrino luminosities requires multiplying this number by 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import Warren_2020\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Warren_2020` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Warren_2020.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quite a lot of choice there! Let’s use the `get_param_combinations` function to get a list of all valid combinations and filter it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This will print a long tuple of combinations:\n",
+    "#Warren_2020.get_param_combinations()\n",
+    "\n",
+    "# To get a more manageable list, let’s filter it to show only the\n",
+    "# available progenitors with integer masses and mixing of 1.23:\n",
+    "filtered_models = list(params for params in Warren_2020.get_param_combinations()\n",
+    "                              if params['turbmixing_param'] == 1.23\n",
+    "                              and params['progenitor_mass'].value.is_integer()\n",
+    "                              and 10 <= params['progenitor_mass'].value <= 25)\n",
+    "print(\"Filtered list of progenitors:\\n\", *filtered_models, sep='\\n')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll pick one of these progenitors and initialise it. If this is the first time you’re using this progenitor, snewpy will automatically download the required data file for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m10 = Warren_2020(**filtered_models[0])\n",
+    "m25 = Warren_2020(**filtered_models[-1])\n",
+    "# This is equivalent to:\n",
+    "#m10 = Warren_2020(progenitor_mass=10*u.solMass, turbmixing_param=1.23)\n",
+    "#m25 = Warren_2020(progenitor_mass=25*u.solMass, turbmixing_param=1.23)\n",
+    "\n",
+    "m10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `Warren_2020` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, model in enumerate([m10, m25]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':',\n",
+    "                lw=2)\n",
+    "    ax.set(xlim=(-0.05, 0.65),\n",
+    "           xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "           title=r'{}: {} $M_\\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=18)\n",
+    "\n",
+    "axes[0].set(ylabel=r'luminosity [foe s$^{-1}$]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 50*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [erg$^{-1}$ s$^{-1}$]')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/ccsn/Zha_2021.ipynb b/doc/nb/ccsn/Zha_2021.ipynb
new file mode 100644
index 00000000..5d8ef05c
--- /dev/null
+++ b/doc/nb/ccsn/Zha_2021.ipynb
@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Zha et al. 2021 Models\n",
+    "\n",
+    "Data from Zha et al. 2021, hadron-quark phase transition simulations of a variety of progenitors from Sukhbold et al. 2018 and the STOS EOS with B=145MeV.\n",
+    "  \n",
+    "Reference: Zha et al. ApJ 911 74 2021\n",
+    "- [doi:10.3847/1538-4357/abec4c](https://doi.org/10.3847/1538-4357/abec4c)\n",
+    "- [arXiv:2103.02268](https://arxiv.org/abs/2103.02268)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units as u \n",
+    "\n",
+    "from snewpy.neutrino import Flavor, MassHierarchy\n",
+    "from snewpy.models.ccsn import Zha_2021\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence\n",
+    "\n",
+    "mpl.rc('font', size=16)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Models\n",
+    "\n",
+    "To start, let’s see what progenitors are available for the `Zha_2021` model. We can use the `param` property to view all physics parameters and their possible values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Zha_2021.param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll initialise two of these progenitors. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m19 = Zha_2021(progenitor_mass=19*u.solMass)\n",
+    "m19_89 = Zha_2021(progenitor_mass=19.89*u.solMass)\n",
+    "\n",
+    "m19"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the `Zha_2021` simulations didn’t distinguish between $\\nu_x$ and $\\bar{\\nu}_x$, so both have the same luminosity.) We’ll also add zoomed-in plots to see the phase transition better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 10), tight_layout=True)\n",
+    "\n",
+    "for i, model in enumerate([m19, m19_89]):\n",
+    "    for j in range(2):\n",
+    "        ax = axes[j, i]\n",
+    "        for flavor in Flavor:\n",
+    "            ax.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s\n",
+    "                    label=flavor.to_tex(),\n",
+    "                    color='C0' if flavor.is_electron else 'C1',\n",
+    "                    ls='-' if flavor.is_neutrino else ':',\n",
+    "                    lw=2)\n",
+    "        ax.set(xlim=(-0.05, 0.9) if j==0 else (0.605, 0.665),\n",
+    "               ylim=(0, 600) if j==0 else (0, 125),\n",
+    "               xlabel=r'$t-t_{\\rm bounce}$ [s]',\n",
+    "               ylabel=r'luminosity [foe s$^{-1}$]',\n",
+    "               title=r'{}: {} $M_\\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))\n",
+    "        ax.grid()\n",
+    "        ax.legend(loc='upper right', ncol=2, fontsize=18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial and Oscillated Spectra\n",
+    "\n",
+    "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.\n",
+    "\n",
+    "### Adiabatic MSW Flavor Transformation: Normal mass ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adiabatic MSW effect. NMO is used by default.\n",
+    "xform_nmo = AdiabaticMSW()\n",
+    "\n",
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,100,201) * u.MeV\n",
+    "t = 400*u.ms\n",
+    "\n",
+    "ispec = model.get_initial_spectra(t, E)\n",
+    "ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n",
+    "\n",
+    "for i, spec in enumerate([ispec, ospec_nmo]):\n",
+    "    ax = axes[i]\n",
+    "    for flavor in Flavor:\n",
+    "        ax.plot(E, spec[flavor],\n",
+    "                label=flavor.to_tex(),\n",
+    "                color='C0' if flavor.is_electron else 'C1',\n",
+    "                ls='-' if flavor.is_neutrino else ':', lw=2,\n",
+    "                alpha=0.7)\n",
+    "\n",
+    "    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n",
+    "           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))\n",
+    "    ax.grid()\n",
+    "    ax.legend(loc='upper right', ncol=2, fontsize=16)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "ax.set(ylabel=r'flux [erg$^{-1}$ s$^{-1}$]')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2528887d751495e023d57d695389d9a04f4c4d2e5866aaf6dc03a1ed45c573e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/nb/presn/Kato_2017.ipynb b/doc/nb/presn/Kato_2017.ipynb
new file mode 100644
index 00000000..bb57f166
--- /dev/null
+++ b/doc/nb/presn/Kato_2017.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3434b585",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from astropy import units as u\n",
+    "\n",
+    "# import the snewpy modules\n",
+    "from snewpy.models import presn\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, MassHierarchy\n",
+    "from snewpy.neutrino import Flavor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62b73c22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the plot parameters\n",
+    "plt.rc('grid', ls=':')\n",
+    "plt.rc('axes', grid=True)\n",
+    "plt.rc('legend', fontsize=12, loc='upper right')\n",
+    "\n",
+    "# define drawing styles for each flavor\n",
+    "styles = {f: dict(color='C0' if f.is_electron else 'C1',\n",
+    "                  ls='-' if f.is_neutrino else ':',\n",
+    "                  label=f.to_tex()) for f in Flavor}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "212af44c-6498-4a2c-b1e2-4bc98a1e4645",
+   "metadata": {},
+   "source": [
+    "## Initialize the model and calculate the flux"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e56a06b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# See what progenitors are available …\n",
+    "print(f\"Available progenitors:\\n{presn.Kato_2017.param}\")\n",
+    "\n",
+    "# … and select one of them to initialise the model\n",
+    "model = presn.Kato_2017(progenitor_mass=15*u.Msun)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b629c538-005d-43c5-b244-bafc9dcb78dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0, 25, 201) * u.MeV\n",
+    "t = np.geomspace(-2*u.day, -1*u.s, 101)\n",
+    "distance = 1*u.kpc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d0a2218-5955-4d20-89ee-f6de8181bb4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flux = model.get_flux(t, E,\n",
+    "                      distance=distance,\n",
+    "                      flavor_xform=NoTransformation())\n",
+    "flux"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88e8ce95-6705-4282-82c6-ca797234ca2f",
+   "metadata": {},
+   "source": [
+    "## Plotting the integral neutrino fluence and rates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "522e70c0-e6a3-4d5f-9a80-4f970c23d69b",
+   "metadata": {},
+   "source": [
+    "### Integral neutrino fluence vs. Energy for the last hour before collapse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2be68b2-bb67-470e-bc01-74e4e48df195",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#integrate the flux over the last hour before the collapse\n",
+    "fluence = flux.integrate('time', limits=[-1, 0]<<u.hour)\n",
+    "#get the relevant arrays for plotting\n",
+    "x = fluence.energy\n",
+    "y = fluence.array.squeeze()\n",
+    "#plot each flavor\n",
+    "for flv in fluence.flavor:\n",
+    "    plt.plot(x,y[flv], **styles[flv])\n",
+    "#add the legend\n",
+    "plt.legend(ncol=2, loc='lower center')\n",
+    "#adjust the scales\n",
+    "plt.autoscale(tight=True,axis='x')\n",
+    "plt.yscale('log')\n",
+    "plt.ylim(1)\n",
+    "#define the labels\n",
+    "xlabel = 'Neutrino energy'\n",
+    "ylabel = 'Neutrino fluence'\n",
+    "plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "plt.ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "#add the plot title\n",
+    "plt.title('Neutrinos in the last 1 hour before collapse')\n",
+    "#add the model parameters\n",
+    "plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "             xy=(0.98,0.98),\n",
+    "             xycoords='axes fraction',\n",
+    "             va='top', ha='right'\n",
+    "            )\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ffa39ae",
+   "metadata": {},
+   "source": [
+    "### Integral neutrino rate vs. time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ce19434-8e82-4876-8bd0-ac695df339fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#integrate the flux over all energy bins\n",
+    "rate = flux.integrate('energy')\n",
+    "#get the relevant arrays for plotting\n",
+    "x = rate.time\n",
+    "y = rate.array.squeeze()\n",
+    "#we can also convert to a more convenient unit\n",
+    "y = y.to('1/(hour*m**2)')\n",
+    "#plot each flavor\n",
+    "for flv in rate.flavor:\n",
+    "    plt.plot(x,y[flv], **styles[flv])\n",
+    "#add the legend\n",
+    "plt.legend(ncol=2, loc='lower center')\n",
+    "#adjust the scales\n",
+    "plt.autoscale(tight=True,axis='x')\n",
+    "plt.yscale('log')\n",
+    "#plt.ylim(1)\n",
+    "#define the labels\n",
+    "xlabel = 'Time before collapse'\n",
+    "ylabel = 'Neutrino rate'\n",
+    "plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "plt.ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "#add the plot title\n",
+    "plt.title('Integral neutrino rate')\n",
+    "#add the model parameters\n",
+    "plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "             xy=(0.02,0.98),\n",
+    "             xycoords='axes fraction',\n",
+    "             va='top', ha='left'\n",
+    "            )\n",
+    "plt.xscale('symlog')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9769409-4520-4e7e-a6ea-79941d260b76",
+   "metadata": {},
+   "source": [
+    "## Plot energy spectrum with neutrino oscillations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41d18d10-5cc9-4e24-bd7c-3fe991c17b97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,25,201) * u.MeV\n",
+    "t = -1*u.s\n",
+    "\n",
+    "xforms = [NoTransformation(), AdiabaticMSW(mh=MassHierarchy.NORMAL), AdiabaticMSW(mh=MassHierarchy.INVERTED)]\n",
+    "labels = ['No transformation', 'AdiabaticMSW NMO', 'AdiabaticMSW IMO']\n",
+    "xform_styles = ['-','--',':']\n",
+    "flavor_colors = ['C0','C1','C2','C3']\n",
+    "\n",
+    "#calculate the flux for different transformations\n",
+    "fluxes  = {xform: model.get_flux(t, E, distance, flavor_xform=xform) for xform in xforms}\n",
+    "#make a big figure\n",
+    "fig, axes = plt.subplots(1,4,figsize=(16,4), sharex=True, sharey=True)\n",
+    "\n",
+    "for flv, ax in zip(Flavor,axes):\n",
+    "    plt.sca(ax)\n",
+    "    for xform, line_style, label in zip(xforms,xform_styles, labels):\n",
+    "        flux = fluxes[xform]\n",
+    "        #get the relevant arrays for plotting\n",
+    "        x = flux.energy\n",
+    "        y = flux.array[flv].squeeze()\n",
+    "        #plot each flavor\n",
+    "        plt.plot(x,y, \n",
+    "                 ls=line_style, \n",
+    "                 color=flavor_colors[flv], \n",
+    "                 label=f'{Flavor(flv).to_tex()} {label}'\n",
+    "                )\n",
+    "        \n",
+    "    #add the legend\n",
+    "    plt.legend(ncol=1, loc='lower left', fontsize=10)\n",
+    "    \n",
+    "    #define the labels\n",
+    "    xlabel = 'Neutrino energy'\n",
+    "    ylabel = 'Neutrino flux'\n",
+    "    plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "    #add the plot title\n",
+    "\n",
+    "    #add the model parameters\n",
+    "    plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "                 xy=(0.98,0.98),\n",
+    "                 xycoords='axes fraction',\n",
+    "                 va='top', ha='right'\n",
+    "                )\n",
+    "    axes[0].set_ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "    #adjust the scales\n",
+    "    plt.autoscale(tight=True,axis='x')\n",
+    "    plt.autoscale(tight=False,axis='y')\n",
+    "    plt.yscale('log')\n",
+    "    plt.ylim(1)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/nb/presn/Odrzywolek_2010.ipynb b/doc/nb/presn/Odrzywolek_2010.ipynb
new file mode 100644
index 00000000..156b104d
--- /dev/null
+++ b/doc/nb/presn/Odrzywolek_2010.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3434b585",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from astropy import units as u\n",
+    "\n",
+    "#import the snewpy modules\n",
+    "from snewpy.models import presn\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, MassHierarchy\n",
+    "from snewpy.neutrino import Flavor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62b73c22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the plot parameters\n",
+    "plt.rc('grid', ls=':')\n",
+    "plt.rc('axes', grid=True)\n",
+    "plt.rc('legend', fontsize=12, loc='upper right')\n",
+    "\n",
+    "# define drawing styles for each flavor\n",
+    "styles = {f: dict(color='C0' if f.is_electron else 'C1',\n",
+    "                  ls='-' if f.is_neutrino else ':',\n",
+    "                  label=f.to_tex()) for f in Flavor}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0855f1d-17f6-43f5-b4ab-e5d6c109953b",
+   "metadata": {},
+   "source": [
+    "## Initialize the model and calculate the flux"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e56a06b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# See what progenitors are available …\n",
+    "print(f\"Available progenitors:\\n{presn.Odrzywolek_2010.param}\")\n",
+    "\n",
+    "# … and select one of them to initialise the model\n",
+    "model = presn.Odrzywolek_2010(progenitor_mass=15*u.Msun)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b629c538-005d-43c5-b244-bafc9dcb78dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0, 25, 201) * u.MeV\n",
+    "t = np.geomspace(-2*u.day, -1*u.s, 101)\n",
+    "distance = 1*u.kpc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d0a2218-5955-4d20-89ee-f6de8181bb4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flux = model.get_flux(t, E,\n",
+    "                      distance=distance,\n",
+    "                      flavor_xform=NoTransformation())\n",
+    "flux"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b721815e-78eb-47bf-9e55-d2c3b0e28e27",
+   "metadata": {},
+   "source": [
+    "## Plotting the integral neutrino fluence and rates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53fdc99a-0614-4cce-941e-502a668cb63d",
+   "metadata": {},
+   "source": [
+    "### Integral neutrino fluence vs. Energy for the last hour before collapse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2be68b2-bb67-470e-bc01-74e4e48df195",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#integrate the flux over the last hour before the collapse\n",
+    "fluence = flux.integrate('time', limits=[-1, 0]<<u.hour)\n",
+    "#get the relevant arrays for plotting\n",
+    "x = fluence.energy\n",
+    "y = fluence.array.squeeze()\n",
+    "#plot each flavor\n",
+    "for flv in fluence.flavor:\n",
+    "    plt.plot(x,y[flv], **styles[flv])\n",
+    "#add the legend\n",
+    "plt.legend(ncol=2, loc='lower center')\n",
+    "#adjust the scales\n",
+    "plt.autoscale(tight=True,axis='x')\n",
+    "plt.yscale('log')\n",
+    "plt.ylim(1)\n",
+    "#define the labels\n",
+    "xlabel = 'Neutrino energy'\n",
+    "ylabel = 'Neutrino fluence'\n",
+    "plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "plt.ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "#add the plot title\n",
+    "plt.title('Neutrinos in the last 1 hour before collapse')\n",
+    "#add the model parameters\n",
+    "plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "             xy=(0.98,0.98),\n",
+    "             xycoords='axes fraction',\n",
+    "             va='top', ha='right'\n",
+    "            )\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68eaad48",
+   "metadata": {},
+   "source": [
+    "### Integral neutrino rate vs. time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ce19434-8e82-4876-8bd0-ac695df339fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#integrate the flux over all energy bins\n",
+    "rate = flux.integrate('energy')\n",
+    "#get the relevant arrays for plotting\n",
+    "x = rate.time\n",
+    "y = rate.array.squeeze()\n",
+    "#we can also convert to a more convenient unit\n",
+    "y = y.to('1/(hour*m**2)')\n",
+    "#plot each flavor\n",
+    "for flv in rate.flavor:\n",
+    "    plt.plot(x,y[flv], **styles[flv])\n",
+    "#add the legend\n",
+    "plt.legend(ncol=2, loc='lower center')\n",
+    "#adjust the scales\n",
+    "plt.autoscale(tight=True,axis='x')\n",
+    "plt.yscale('log')\n",
+    "#plt.ylim(1)\n",
+    "#define the labels\n",
+    "xlabel = 'Time before collapse'\n",
+    "ylabel = 'Neutrino rate'\n",
+    "plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "plt.ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "#add the plot title\n",
+    "plt.title('Integral neutrino rate')\n",
+    "#add the model parameters\n",
+    "plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "             xy=(0.02,0.98),\n",
+    "             xycoords='axes fraction',\n",
+    "             va='top', ha='left'\n",
+    "            )\n",
+    "plt.xscale('symlog')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e192c197-e17f-4c83-8db0-1dbcbeceb207",
+   "metadata": {},
+   "source": [
+    "## Plot energy spectrum with neutrino oscillations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41d18d10-5cc9-4e24-bd7c-3fe991c17b97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,25,201) * u.MeV\n",
+    "t = -1*u.s\n",
+    "\n",
+    "xforms = [NoTransformation(), AdiabaticMSW(mh=MassHierarchy.NORMAL), AdiabaticMSW(mh=MassHierarchy.INVERTED)]\n",
+    "labels = ['No transformation', 'AdiabaticMSW NMO', 'AdiabaticMSW IMO']\n",
+    "xform_styles = ['-','--',':']\n",
+    "flavor_colors = ['C0','C1','C2','C3']\n",
+    "\n",
+    "#calculate the flux for different transformations\n",
+    "fluxes  = {xform: model.get_flux(t, E, distance, flavor_xform=xform) for xform in xforms}\n",
+    "#make a big figure\n",
+    "fig, axes = plt.subplots(1,4,figsize=(16,4), sharex=True, sharey=True)\n",
+    "\n",
+    "for flv, ax in zip(Flavor,axes):\n",
+    "    plt.sca(ax)\n",
+    "    for xform, line_style, label in zip(xforms,xform_styles, labels):\n",
+    "        flux = fluxes[xform]\n",
+    "        #get the relevant arrays for plotting\n",
+    "        x = flux.energy\n",
+    "        y = flux.array[flv].squeeze()\n",
+    "        #plot each flavor\n",
+    "        plt.plot(x,y, \n",
+    "                 ls=line_style, \n",
+    "                 color=flavor_colors[flv], \n",
+    "                 label=f'{Flavor(flv).to_tex()} {label}'\n",
+    "                )\n",
+    "        \n",
+    "    #add the legend\n",
+    "    plt.legend(ncol=1, loc='lower left', fontsize=10)\n",
+    "    \n",
+    "    #define the labels\n",
+    "    xlabel = 'Neutrino energy'\n",
+    "    ylabel = 'Neutrino flux'\n",
+    "    plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "    #add the plot title\n",
+    "\n",
+    "    #add the model parameters\n",
+    "    plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "                 xy=(0.98,0.98),\n",
+    "                 xycoords='axes fraction',\n",
+    "                 va='top', ha='right'\n",
+    "                )\n",
+    "    axes[0].set_ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "    #adjust the scales\n",
+    "    plt.autoscale(tight=True,axis='x')\n",
+    "    plt.autoscale(tight=False,axis='y')\n",
+    "    plt.yscale('log')\n",
+    "    plt.ylim(1)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/nb/presn/Patton_2017.ipynb b/doc/nb/presn/Patton_2017.ipynb
new file mode 100644
index 00000000..9437858c
--- /dev/null
+++ b/doc/nb/presn/Patton_2017.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83d2d170-a5b3-44d9-ad05-762448b08c80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from astropy import units as u\n",
+    "\n",
+    "#import the snewpy modules\n",
+    "from snewpy.models import presn\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, MassHierarchy\n",
+    "from snewpy.neutrino import Flavor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2ad86ec-ff7e-4ef6-818e-f244ac8112f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the plot parameters\n",
+    "plt.rc('grid', ls=':')\n",
+    "plt.rc('axes', grid=True)\n",
+    "plt.rc('legend', fontsize=12, loc='upper right')\n",
+    "\n",
+    "# define drawing styles for each flavor\n",
+    "styles = {f: dict(color='C0' if f.is_electron else 'C1',\n",
+    "                  ls='-' if f.is_neutrino else ':',\n",
+    "                  label=f.to_tex()) for f in Flavor}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f1b6439-6702-417a-a9b6-0ca0d5798f47",
+   "metadata": {},
+   "source": [
+    "## Initialize the model and calculate the flux"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffff2732-a87a-401c-b85d-a0f0d5598657",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# See what progenitors are available …\n",
+    "print(f\"Available progenitors:\\n{presn.Patton_2017.param}\")\n",
+    "\n",
+    "# … and select one of them to initialise the model\n",
+    "model = presn.Patton_2017(progenitor_mass=15*u.Msun)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d8e7b15-0a7e-49f3-b6bd-9463e8b371a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0, 25, 201) * u.MeV\n",
+    "t = np.geomspace(-2*u.day, -1*u.s, 101)\n",
+    "distance = 1*u.kpc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efb466d7-1b4c-4949-a983-5134fba3f35b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flux = model.get_flux(t, E,\n",
+    "                      distance=distance,\n",
+    "                      flavor_xform=NoTransformation())\n",
+    "flux"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f06fc5ed-e39f-4f91-a8c6-09a671a7f6d0",
+   "metadata": {},
+   "source": [
+    "## Plotting the integral neutrino fluence and rates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34cd5637-201f-493c-9aa8-10a653d4158b",
+   "metadata": {},
+   "source": [
+    "### Integral neutrino fluence vs. Energy for the last hour before collapse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f8f5b53-257d-43b7-b933-415738e8b4a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#integrate the flux over the last hour before the collapse\n",
+    "fluence = flux.integrate('time', limits=[-1, 0]<<u.hour)\n",
+    "#get the relevant arrays for plotting\n",
+    "x = fluence.energy\n",
+    "y = fluence.array.squeeze()\n",
+    "#plot each flavor\n",
+    "for flv in fluence.flavor:\n",
+    "    plt.plot(x,y[flv], **styles[flv])\n",
+    "#add the legend\n",
+    "plt.legend(ncol=2, loc='lower center')\n",
+    "#adjust the scales\n",
+    "plt.autoscale(tight=True,axis='x')\n",
+    "plt.yscale('log')\n",
+    "plt.ylim(1)\n",
+    "#define the labels\n",
+    "xlabel = 'Neutrino energy'\n",
+    "ylabel = 'Neutrino fluence'\n",
+    "plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "plt.ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "#add the plot title\n",
+    "plt.title('Neutrinos in the last 1 hour before collapse')\n",
+    "#add the model parameters\n",
+    "plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "             xy=(0.98,0.98),\n",
+    "             xycoords='axes fraction',\n",
+    "             va='top', ha='right'\n",
+    "            )\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8347961e",
+   "metadata": {},
+   "source": [
+    "### Integral neutrino rate vs. time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dba0da41-6061-4c7a-b1fc-4b73f29ea960",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#integrate the flux over all energy bins\n",
+    "rate = flux.integrate('energy')\n",
+    "#get the relevant arrays for plotting\n",
+    "x = rate.time\n",
+    "y = rate.array.squeeze()\n",
+    "#we can also convert to a more convenient unit\n",
+    "y = y.to('1/(hour*m**2)')\n",
+    "#plot each flavor\n",
+    "for flv in rate.flavor:\n",
+    "    plt.plot(x,y[flv], **styles[flv])\n",
+    "#add the legend\n",
+    "plt.legend(ncol=2, loc='lower center')\n",
+    "#adjust the scales\n",
+    "plt.autoscale(tight=True,axis='x')\n",
+    "plt.yscale('log')\n",
+    "#plt.ylim(1)\n",
+    "#define the labels\n",
+    "xlabel = 'Time before collapse'\n",
+    "ylabel = 'Neutrino rate'\n",
+    "plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "plt.ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "#add the plot title\n",
+    "plt.title('Integral neutrino rate')\n",
+    "#add the model parameters\n",
+    "plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "             xy=(0.02,0.98),\n",
+    "             xycoords='axes fraction',\n",
+    "             va='top', ha='left'\n",
+    "            )\n",
+    "plt.xscale('symlog')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dfec9e5-2819-4993-b352-819224b65279",
+   "metadata": {},
+   "source": [
+    "## Plot energy spectrum with neutrino oscillations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94e4605b-fcee-4d3a-8344-caa4100e3ee1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,25,201) * u.MeV\n",
+    "t = -1*u.s\n",
+    "\n",
+    "xforms = [NoTransformation(), AdiabaticMSW(mh=MassHierarchy.NORMAL), AdiabaticMSW(mh=MassHierarchy.INVERTED)]\n",
+    "labels = ['No transformation', 'AdiabaticMSW NMO', 'AdiabaticMSW IMO']\n",
+    "xform_styles = ['-','--',':']\n",
+    "flavor_colors = ['C0','C1','C2','C3']\n",
+    "\n",
+    "#calculate the flux for different transformations\n",
+    "fluxes  = {xform: model.get_flux(t, E, distance, flavor_xform=xform) for xform in xforms}\n",
+    "#make a big figure\n",
+    "fig, axes = plt.subplots(1,4,figsize=(16,4), sharex=True, sharey=True)\n",
+    "\n",
+    "for flv, ax in zip(Flavor,axes):\n",
+    "    plt.sca(ax)\n",
+    "    for xform, line_style, label in zip(xforms,xform_styles, labels):\n",
+    "        flux = fluxes[xform]\n",
+    "        #get the relevant arrays for plotting\n",
+    "        x = flux.energy\n",
+    "        y = flux.array[flv].squeeze()\n",
+    "        #plot each flavor\n",
+    "        plt.plot(x,y, \n",
+    "                 ls=line_style, \n",
+    "                 color=flavor_colors[flv], \n",
+    "                 label=f'{Flavor(flv).to_tex()} {label}'\n",
+    "                )\n",
+    "        \n",
+    "    #add the legend\n",
+    "    plt.legend(ncol=1, loc='lower left', fontsize=10)\n",
+    "    \n",
+    "    #define the labels\n",
+    "    xlabel = 'Neutrino energy'\n",
+    "    ylabel = 'Neutrino flux'\n",
+    "    plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "    #add the plot title\n",
+    "\n",
+    "    #add the model parameters\n",
+    "    plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "                 xy=(0.98,0.98),\n",
+    "                 xycoords='axes fraction',\n",
+    "                 va='top', ha='right'\n",
+    "                )\n",
+    "    axes[0].set_ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "    #adjust the scales\n",
+    "    plt.autoscale(tight=True,axis='x')\n",
+    "    plt.autoscale(tight=False,axis='y')\n",
+    "    plt.yscale('log')\n",
+    "    plt.ylim(1)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/nb/presn/Yoshida_2016.ipynb b/doc/nb/presn/Yoshida_2016.ipynb
new file mode 100644
index 00000000..127a6dd5
--- /dev/null
+++ b/doc/nb/presn/Yoshida_2016.ipynb
@@ -0,0 +1,287 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83d2d170-a5b3-44d9-ad05-762448b08c80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from astropy import units as u\n",
+    "\n",
+    "#import the snewpy modules\n",
+    "from snewpy.models import presn\n",
+    "from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, MassHierarchy\n",
+    "from snewpy.neutrino import Flavor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2ad86ec-ff7e-4ef6-818e-f244ac8112f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the plot parameters\n",
+    "plt.rc('grid', ls=':')\n",
+    "plt.rc('axes', grid=True)\n",
+    "plt.rc('legend', fontsize=12, loc='upper right')\n",
+    "\n",
+    "# define drawing styles for each flavor\n",
+    "styles = {f: dict(color='C0' if f.is_electron else 'C1',\n",
+    "                  ls='-' if f.is_neutrino else ':',\n",
+    "                  label=f.to_tex()) for f in Flavor}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f1b6439-6702-417a-a9b6-0ca0d5798f47",
+   "metadata": {},
+   "source": [
+    "## Initialize the model and calculate the flux"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffff2732-a87a-401c-b85d-a0f0d5598657",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# See what progenitors are available …\n",
+    "print(f\"Available progenitors:\\n{presn.Yoshida_2016.param}\")\n",
+    "\n",
+    "# … and select one of them to initialise the model\n",
+    "model = presn.Yoshida_2016(progenitor_mass=15*u.Msun)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d8e7b15-0a7e-49f3-b6bd-9463e8b371a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0, 25, 201) * u.MeV\n",
+    "t = np.geomspace(-2*u.day, -1*u.s, 101)\n",
+    "distance = 1*u.kpc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efb466d7-1b4c-4949-a983-5134fba3f35b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flux = model.get_flux(t, E,\n",
+    "                      distance=distance,\n",
+    "                      flavor_xform=NoTransformation())\n",
+    "flux"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f06fc5ed-e39f-4f91-a8c6-09a671a7f6d0",
+   "metadata": {},
+   "source": [
+    "## Plotting the integral neutrino fluence and rates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34cd5637-201f-493c-9aa8-10a653d4158b",
+   "metadata": {},
+   "source": [
+    "### Integral neutrino fluence vs. Energy for the last hour before collapse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f8f5b53-257d-43b7-b933-415738e8b4a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#integrate the flux over the last hour before the collapse\n",
+    "fluence = flux.integrate('time', limits=[-1, 0]<<u.hour)\n",
+    "#get the relevant arrays for plotting\n",
+    "x = fluence.energy\n",
+    "y = fluence.array.squeeze()\n",
+    "#plot each flavor\n",
+    "for flv in fluence.flavor:\n",
+    "    plt.plot(x,y[flv], **styles[flv])\n",
+    "#add the legend\n",
+    "plt.legend(ncol=2, loc='lower center')\n",
+    "#adjust the scales\n",
+    "plt.autoscale(tight=True,axis='x')\n",
+    "plt.yscale('log')\n",
+    "plt.ylim(1)\n",
+    "#define the labels\n",
+    "xlabel = 'Neutrino energy'\n",
+    "ylabel = 'Neutrino fluence'\n",
+    "plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "plt.ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "#add the plot title\n",
+    "plt.title('Neutrinos in the last 1 hour before collapse')\n",
+    "#add the model parameters\n",
+    "plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "             xy=(0.98,0.98),\n",
+    "             xycoords='axes fraction',\n",
+    "             va='top', ha='right'\n",
+    "            )\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8347961e",
+   "metadata": {},
+   "source": [
+    "### Integral neutrino rate vs. time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dba0da41-6061-4c7a-b1fc-4b73f29ea960",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#integrate the flux over all energy bins\n",
+    "rate = flux.integrate('energy')\n",
+    "#get the relevant arrays for plotting\n",
+    "x = rate.time\n",
+    "y = rate.array.squeeze()\n",
+    "#we can also convert to a more convenient unit\n",
+    "y = y.to('1/(hour*m**2)')\n",
+    "#plot each flavor\n",
+    "for flv in rate.flavor:\n",
+    "    plt.plot(x,y[flv], **styles[flv])\n",
+    "#add the legend\n",
+    "plt.legend(ncol=2, loc='lower center')\n",
+    "#adjust the scales\n",
+    "plt.autoscale(tight=True,axis='x')\n",
+    "plt.yscale('log')\n",
+    "#plt.ylim(1)\n",
+    "#define the labels\n",
+    "xlabel = 'Time before collapse'\n",
+    "ylabel = 'Neutrino rate'\n",
+    "plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "plt.ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "#add the plot title\n",
+    "plt.title('Integral neutrino rate')\n",
+    "#add the model parameters\n",
+    "plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "             xy=(0.02,0.98),\n",
+    "             xycoords='axes fraction',\n",
+    "             va='top', ha='left'\n",
+    "            )\n",
+    "plt.xscale('symlog')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dfec9e5-2819-4993-b352-819224b65279",
+   "metadata": {},
+   "source": [
+    "## Plot energy spectrum with neutrino oscillations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94e4605b-fcee-4d3a-8344-caa4100e3ee1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Energy array and time to compute spectra.\n",
+    "# Note that any convenient units can be used and the calculation will remain internally consistent.\n",
+    "E = np.linspace(0,25,201) * u.MeV\n",
+    "t = -1*u.s\n",
+    "\n",
+    "xforms = [NoTransformation(), AdiabaticMSW(mh=MassHierarchy.NORMAL), AdiabaticMSW(mh=MassHierarchy.INVERTED)]\n",
+    "labels = ['No transformation', 'AdiabaticMSW NMO', 'AdiabaticMSW IMO']\n",
+    "xform_styles = ['-','--',':']\n",
+    "flavor_colors = ['C0','C1','C2','C3']\n",
+    "\n",
+    "#calculate the flux for different transformations\n",
+    "fluxes  = {xform: model.get_flux(t, E, distance, flavor_xform=xform) for xform in xforms}\n",
+    "#make a big figure\n",
+    "fig, axes = plt.subplots(1,4,figsize=(16,4), sharex=True, sharey=True)\n",
+    "\n",
+    "for flv, ax in zip(Flavor,axes):\n",
+    "    plt.sca(ax)\n",
+    "    for xform, line_style, label in zip(xforms,xform_styles, labels):\n",
+    "        flux = fluxes[xform]\n",
+    "        #get the relevant arrays for plotting\n",
+    "        x = flux.energy\n",
+    "        y = flux.array[flv].squeeze()\n",
+    "        #plot each flavor\n",
+    "        plt.plot(x,y, \n",
+    "                 ls=line_style, \n",
+    "                 color=flavor_colors[flv], \n",
+    "                 label=f'{Flavor(flv).to_tex()} {label}'\n",
+    "                )\n",
+    "        \n",
+    "    #add the legend\n",
+    "    plt.legend(ncol=1, loc='lower left', fontsize=10)\n",
+    "    \n",
+    "    #define the labels\n",
+    "    xlabel = 'Neutrino energy'\n",
+    "    ylabel = 'Neutrino flux'\n",
+    "    plt.xlabel(f'{xlabel} [{x.unit._repr_latex_()}]')\n",
+    "    #add the plot title\n",
+    "\n",
+    "    #add the model parameters\n",
+    "    plt.annotate(str(model) + f'\\ndistance : {distance}',\n",
+    "                 xy=(0.98,0.98),\n",
+    "                 xycoords='axes fraction',\n",
+    "                 va='top', ha='right'\n",
+    "                )\n",
+    "    axes[0].set_ylabel(f'{ylabel} [{y.unit._repr_latex_()}]')\n",
+    "    #adjust the scales\n",
+    "    plt.autoscale(tight=True,axis='x')\n",
+    "    plt.autoscale(tight=False,axis='y')\n",
+    "    plt.yscale('log')\n",
+    "    plt.ylim(1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d12483ed-e5f7-48a0-99a7-9f7f1bfdd289",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/source/gettingstarted.rst b/doc/source/gettingstarted.rst
index 7c281fd8..e97dd6b3 100644
--- a/doc/source/gettingstarted.rst
+++ b/doc/source/gettingstarted.rst
@@ -18,18 +18,18 @@ Download Supernova Models
 
 SNEWPY includes a large number of supernova models from different simulation groups.
 Since these models have a size of several 100 MB, they are not included in the initial install.
-Instead, SNEWPY automatically loads these files the first time you use a model. By default,
-they are downloaded to a hidden directory given by ``snewpy.model_path``.
-
-Alternatively, you can run the following command to bulk download model files to the current directory:
+Instead, SNEWPY automatically loads these files the first time you use a model. 
+Alternatively, you can run the following command to bulk download model files:
 
 .. code-block:: console
 
    $ python -c 'import snewpy; snewpy.get_models()'
 
+Files are downloaded to a hidden directory given by ``snewpy.model_path``.
+
 .. note::
 
-   Each model includes a README file with more information, usually including a reference to the corresponding publication
+   The documentation for each model includes more information, including a reference to the corresponding publication
    (e.g. DOI or arXiv identifier). If you use one of these models, please always cite the appropriate reference.
 
 
@@ -81,7 +81,7 @@ This will generate the following figure:
 .. image:: luminosity-comparison.*
 
 
-The SNEWPY repository contains many Jupyter notebooks in ``models/<model-name>/`` or ``doc/nb/`` with sample code
+The SNEWPY repository contains many Jupyter notebooks in ``doc/nb/`` with sample code
 showing different models or how to apply flavor transformations to the neutrino fluxes.
 
 More advanced usage of SNEWPY requires SNOwGLoBES and is described in the following section.
diff --git a/doc/source/models.rst b/doc/source/models.rst
index 9e095937..debb1da5 100644
--- a/doc/source/models.rst
+++ b/doc/source/models.rst
@@ -9,12 +9,23 @@ Base Class for Supernova Models
 Derived Models
 --------------
 
+Core-Collapse Supernova Models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 .. automodule:: snewpy.models.ccsn
    :members:
-   :exclude-members: SNOwGLoBES
+   :exclude-members: get_param_combinations, SNOwGLoBES, Analytic3Species
+
+Presupernova Models
+~~~~~~~~~~~~~~~~~~~
+.. automodule:: snewpy.models.presn
+   :members:
+   :exclude-members: get_param_combinations
 
 Other Models
 ------------
+.. autoclass:: snewpy.models.ccsn.Analytic3Species
+   :members:
 
 .. autoclass:: snewpy.models.ccsn.SNOwGLoBES
    :members:
diff --git a/python/snewpy/__init__.py b/python/snewpy/__init__.py
index c962d197..367a9522 100644
--- a/python/snewpy/__init__.py
+++ b/python/snewpy/__init__.py
@@ -8,7 +8,7 @@
 """
 
 from ._version import __version__
-from pathlib import Path
+from sys import exit
 import os
 
 try:
@@ -21,7 +21,7 @@
 base_path = os.sep.join(src_path.split(os.sep)[:-2])
 model_path = os.path.join(get_cache_dir(), 'snewpy/models')
 
-def get_models(models=None, download_dir='SNEWPY_models'):
+def get_models(models=None, download_dir=None):
     """Download model files from the snewpy repository.
 
     Parameters
@@ -29,40 +29,37 @@ def get_models(models=None, download_dir='SNEWPY_models'):
     models : list or str
         Models to download. Can be 'all', name of a single model or list of model names.
     download_dir : str
-        Local directory to download model files to.
+        [Deprecated, do not use.]
     """
     from concurrent.futures import ThreadPoolExecutor, as_completed
-    from ._model_urls import model_urls
-    from ._model_downloader import _download as download
+    from warnings import warn
+    from .models.registry_model import all_models
 
-    for model in list(model_urls):
-        if model_urls[model] == []:
-            del model_urls[model]
-            continue
+    if download_dir is not None:
+        warn("The `download_dir` argument to `get_models` is deprecated and will be removed soon.", FutureWarning, stacklevel=2)
+
+    all_models = {m.__name__: m for m in all_models}
+    all_model_names = sorted(all_models.keys())
 
     if models == "all":
-        models = model_urls.keys()
+        models = all_model_names
     elif isinstance(models, str):
         models = [models]
-    elif models == None:
+    elif models is None:
         # Select model(s) to download
-        print(f"Available models in this version of SNEWPY: {list(model_urls.keys())}")
-        if not model_urls:
-            print("Error: `get_models()` only works after installing SNEWPY via `pip install snewpy`. "
-                  "If you have cloned the git repo, model files are available in the `models/` folder.")
-            return False
+        print(f"Available models in SNEWPY v{__version__}: {all_model_names}")
 
         selected = input("\nType a model name, 'all' to download all models or <Enter> to cancel: ").strip()
         if selected == "all":
-            models = model_urls.keys()
+            models = all_model_names
         elif selected == "":
             exit()
-        elif selected in model_urls.keys():
-            models = [selected]
+        elif selected in all_model_names:
+            models = {selected}
             while True:
                 selected = input("\nType another model name or <Enter> if you have selected all models you want to download: ").strip()
-                if selected in model_urls.keys():
-                    models.append(selected)
+                if selected in all_model_names:
+                    models.add(selected)
                 elif selected == "":
                     break
                 else:
@@ -73,25 +70,12 @@ def get_models(models=None, download_dir='SNEWPY_models'):
 
         print(f"\nYou have selected the models: {models}\n")
 
-    # Download model files
-    if not os.path.isdir(download_dir):
-        print(f"Creating directory '{download_dir}' ...")
-        os.makedirs(download_dir)
-
     pool = ThreadPoolExecutor(max_workers=8)
     results = []
+    print(f"Downloading files for {models} to '{model_path}' ...")
     for model in models:
-        model_dir = download_dir + '/' + model
-        print(f"Downloading files for '{model}' into '{model_dir}' ...")
-
-        for url in model_urls[model]:
-            local_file = model_dir + url.split(model, maxsplit=1)[1]
-            if os.path.exists(local_file) and local_file.find('README') == -1 and local_file.find('.ipynb') == -1:
-                print(f"File '{local_file}' already exists. Skipping download.")
-            else:
-                if not os.path.isdir(os.path.dirname(local_file)):
-                    os.makedirs(os.path.dirname(local_file))
-                results.append(pool.submit(download, src=url, dest=Path(local_file)))
+        for progenitor in all_models[model].get_param_combinations():
+            results.append(pool.submit(all_models[model], **progenitor))
 
     exceptions = []
     for result in as_completed(results):
@@ -102,29 +86,3 @@ def get_models(models=None, download_dir='SNEWPY_models'):
         print("Please check your internet connection and try again later. If this persists, please report it at https://github.com/SNEWS2/snewpy/issues")
         exit(1)
     pool.shutdown(wait=False)
-
-
-def _get_model_urls():
-    """List URLs of model files for the current release.
-
-    When building a snewpy release, generate a dictionary of available models
-    and the URLs at which the respective files are located. Users can then use
-    get_models() to interactively select which model(s) to download.
-    """
-
-    repo_dir = os.path.normpath(os.path.dirname(os.path.abspath(__file__)) + '/../../')
-    url_base = 'https://github.com/SNEWS2/snewpy/raw/v' + __version__
-
-    with open(os.path.dirname(os.path.abspath(__file__)) + '/_model_urls.py', 'w') as f:
-        f.write('model_urls = {\n')
-        for model in sorted(os.listdir(repo_dir + '/models')):
-            urls = []
-            for root, dirs, files in os.walk(repo_dir + '/models/' + model):
-                for file in files:
-                    urls.append(f'{url_base}{root[len(repo_dir):]}/{file}')
-
-            f.write(f'    "{model}": [\n')
-            for url in sorted(urls):
-                f.write(f'        "{url}",\n')
-            f.write('    ],\n')
-        f.write('}\n')
diff --git a/python/snewpy/_model_downloader.py b/python/snewpy/_model_downloader.py
index dea01a01..bb7096f3 100644
--- a/python/snewpy/_model_downloader.py
+++ b/python/snewpy/_model_downloader.py
@@ -198,6 +198,24 @@ def get_file(self, config_path: str, filename: str) -> Path:
 registry = ModelRegistry()
 
 
+class LocalFileLoader:
+    @classmethod
+    def request_file(cls, filename:str)->Path:
+        "Require that the provided filename exists locally"
+        path = Path(filename).absolute()
+        if not path.exists():
+            raise FileNotFoundError(path)
+        return path
+
+class RegistryFileLoader(LocalFileLoader):
+    _registry = registry
+    
+    @classmethod
+    def request_file(cls, filename:str)->Path:
+        "Request file from the model registry"
+        return cls._registry.get_file(cls._config_path, filename)
+        
+        
 def get_model_data(model: str, filename: str) -> Path:
     """Get the requested data file from the models file repository
 
diff --git a/python/snewpy/_model_urls.py b/python/snewpy/_model_urls.py
deleted file mode 100644
index a80525a5..00000000
--- a/python/snewpy/_model_urls.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Dictionary of models and URLs where model files are available to download.
-When preparing a snewpy release, this dictionary should be automatically
-generated using _get_model_urls() in __init__.py. Do NOT edit this manually.
-"""
-
-model_urls = {}
\ No newline at end of file
diff --git a/python/snewpy/_version.py b/python/snewpy/_version.py
index 8e3c933c..51ed7c48 100644
--- a/python/snewpy/_version.py
+++ b/python/snewpy/_version.py
@@ -1 +1 @@
-__version__ = '1.4.1'
+__version__ = '1.5.1'
diff --git a/python/snewpy/flux.py b/python/snewpy/flux.py
index da994962..0fb33037 100644
--- a/python/snewpy/flux.py
+++ b/python/snewpy/flux.py
@@ -60,8 +60,6 @@
 from scipy.integrate import cumulative_trapezoid
 from scipy.interpolate import interp1d
 from enum import IntEnum
-
-from copy import copy
 from functools import wraps
 
 #list of units which will be used as units for decomposition inside the Container
@@ -285,7 +283,7 @@ def integrate(self, axis:Union[Axes,str], limits:np.ndarray=None)->'Container':
         yc = cumulative_trapezoid(self.array, x=ax, axis=axis, initial=0)
         #get first and last value to use as the fill values
         yc_limits = (yc.take(0,axis=axis), yc.take(-1,axis=axis)) 
-        #this will make the _intergal constant if it gets out of bounds, 
+        #this will make the _integral constant if it gets out of bounds,
         # i.e. effectively the flux outside of bounds is zero
         _integral = interp1d(x=ax, y=yc, fill_value=yc_limits, axis=axis, bounds_error=False)
         array = np.diff(_integral(limits),axis=axis) << (self.array.unit*ax.unit)
@@ -293,8 +291,7 @@ def integrate(self, axis:Union[Axes,str], limits:np.ndarray=None)->'Container':
         axes[axis] = limits
         #choose the proper class
         return Container(array, *axes, integrable_axes=self._integrable_axes.difference({axis}))
-        return result
-        
+
     def integrate_or_sum(self, axis:Union[Axes,str])->'Container':
         if self.can_integrate(axis):
             return self.integrate(axis)
diff --git a/python/snewpy/models/__init__.py b/python/snewpy/models/__init__.py
index 57f1c877..b8569918 100644
--- a/python/snewpy/models/__init__.py
+++ b/python/snewpy/models/__init__.py
@@ -1,7 +1,5 @@
-import logging
 from warnings import warn
 
-from snewpy import get_models, model_path
 from . import ccsn, presn
 
 
@@ -12,18 +10,13 @@ def __getattr__(name):
     raise AttributeError(f"module {__name__} has no attribute {name}")
 
 
-def _init_model(model_name, download=True, download_dir=model_path, **user_param):
+def _init_model(model_name, **user_param):
     """Attempts to retrieve instantiated SNEWPY model using model class name and model parameters.
-    If a model name is valid, but is not found and `download`=True, this function will attempt to download the model
 
     Parameters
     ----------
     model_name : str
         Name of SNEWPY model to import, must exactly match the name of the corresponding model class
-    download : bool
-        Switch for attempting to download model data if the first load attempt failed due to a missing file.
-    download_dir : str
-        Local directory to download model files to.
     user_param : varies
         User-requested model parameters used to initialize the model, if one is found.
         Error checking is performed during model initialization
@@ -57,13 +50,4 @@ def _init_model(model_name, download=True, download_dir=model_path, **user_param
     else:
         raise ValueError(f"Unable to find model with name '{model_name}' in snewpy.models.ccsn or snewpy.models.presn")
 
-    try:
-        return getattr(module, model_name)(**user_param)
-    except FileNotFoundError as e:
-        logger = logging.getLogger()
-        logger.warning(f"Unable to find model {model_name} in {download_dir}")
-        if not download:
-            raise e
-        logger.warning(f"Attempting to download model...")
-        get_models(model_name, download_dir)
-        return getattr(module, model_name)(**user_param)
+    return getattr(module, model_name)(**user_param)
diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py
index 90f7e454..49e13a7a 100644
--- a/python/snewpy/models/base.py
+++ b/python/snewpy/models/base.py
@@ -5,17 +5,18 @@
 
 import numpy as np
 from astropy import units as u
-from astropy.table import Table, join
+from astropy.table import Table
 from astropy.units import UnitTypeError, get_physical_type
 from astropy.units.quantity import Quantity
 from scipy.special import loggamma
-from snewpy import _model_downloader
+from snewpy._model_downloader import LocalFileLoader
 
 from snewpy.neutrino import Flavor
 from snewpy.flavor_transformation import NoTransformation
 from functools import wraps
 
 from snewpy.flux import Flux
+from pathlib import Path
 
 def _wrap_init(init, check):
     @wraps(init)
@@ -23,9 +24,8 @@ def _wrapper(self, *arg, **kwargs):
         init(self, *arg, **kwargs)
         check(self)
     return _wrapper
-
-
-class SupernovaModel(ABC):
+    
+class SupernovaModel(ABC, LocalFileLoader):
     """Base class defining an interface to a supernova model."""
 
     def __init_subclass__(cls, **kwargs):
@@ -297,79 +297,20 @@ def get_initial_spectra(self, t, E, flavors=Flavor):
             L  = np.expand_dims(L, axis=1)
             Ea = np.expand_dims(Ea,axis=1)
             a  = np.expand_dims(a, axis=1)
+
             # For numerical stability, evaluate log PDF and then exponentiate.
-            result = \
-              np.exp(np.log(L) - (2+a)*np.log(Ea) + (1+a)*np.log(1+a)
-                    - loggamma(1+a) + a*np.log(E) - (1+a)*(E/Ea)) / (u.erg * u.s)
+            # Suppress div-by-zero and other warnings that do not matter here.
+            with np.errstate(divide='ignore', invalid='ignore'):
+                result = \
+                  np.exp(np.log(L) - (2+a)*np.log(Ea) + (1+a)*np.log(1+a)
+                        - loggamma(1+a) + a*np.log(E) - (1+a)*(E/Ea)) / (u.erg * u.s)
+
             #remove bad values
             result[np.isnan(result)] = 0
             result[:, E[0]==0] = 0
+
             #remove unnecessary dimensions, if E or t was scalar:
             result = np.squeeze(result)
             initialspectra[flavor] = result
-        return initialspectra
 
-
-class _GarchingArchiveModel(PinchedModel):
-    """Subclass that reads models in the format used in the
-    `Garching Supernova Archive <https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/>`_."""
-    def __init__(self, filename, eos='LS220', metadata={}):
-        """Model Initialization.
-
-        Parameters
-        ----------
-        filename : str
-            Absolute or relative path to file with model data, we add nue/nuebar/nux.  This argument will be deprecated.
-        eos: str
-            Equation of state. Valid value is 'LS220'. This argument will be deprecated.
-
-        Other Parameters
-        ----------------
-        progenitor_mass: astropy.units.Quantity
-            Mass of model progenitor in units Msun. Valid values are {progenitor_mass}.
-        Raises
-        ------
-        FileNotFoundError
-            If a file for the chosen model parameters cannot be found
-        ValueError
-            If a combination of parameters is invalid when loading from parameters
-        """
-        if not metadata:
-            metadata = {
-                'Progenitor mass': float(os.path.basename(filename).split('s')[1].split('c')[0]) * u.Msun,
-                'EOS': eos,
-            }
-
-        # Read through the several ASCII files for the chosen simulation and
-        # merge the data into one giant table.
-        mergtab = None
-        for flavor in Flavor:
-            _flav = Flavor.NU_X if flavor == Flavor.NU_X_BAR else flavor
-            _sfx = _flav.name.replace('_', '').lower()
-            _filename = '{}_{}_{}'.format(filename, eos, _sfx)
-            _lname = 'L_{}'.format(flavor.name)
-            _ename = 'E_{}'.format(flavor.name)
-            _e2name = 'E2_{}'.format(flavor.name)
-            _aname = 'ALPHA_{}'.format(flavor.name)
-
-            # Open the requested filename using the model downloader.
-            datafile = _model_downloader.get_model_data(self.__class__.__name__, _filename)
-
-            simtab = Table.read(datafile,
-                                names=['TIME', _lname, _ename, _e2name],
-                                format='ascii')
-            simtab['TIME'].unit = 's'
-            simtab[_lname].unit = '1e51 erg/s'
-            simtab[_aname] = (2*simtab[_ename]**2 - simtab[_e2name]) / (simtab[_e2name] - simtab[_ename]**2)
-            simtab[_ename].unit = 'MeV'
-            del simtab[_e2name]
-
-            if mergtab is None:
-                mergtab = simtab
-            else:
-                mergtab = join(mergtab, simtab, keys='TIME', join_type='left')
-                mergtab[_lname].fill_value = 0.
-                mergtab[_ename].fill_value = 0.
-                mergtab[_aname].fill_value = 0.
-        simtab = mergtab.filled()
-        super().__init__(simtab, metadata)
+        return initialspectra
diff --git a/python/snewpy/models/ccsn.py b/python/snewpy/models/ccsn.py
index 8dea432a..2d40ba7c 100644
--- a/python/snewpy/models/ccsn.py
+++ b/python/snewpy/models/ccsn.py
@@ -36,7 +36,7 @@ class method to get a list of all valid combinations and filter it:
 from astropy import units as u
 from astropy.table import Table
 
-from snewpy.models import loaders
+from snewpy.models import ccsn_loaders as loaders
 from .base import PinchedModel
 
 from snewpy.models.registry_model import RegistryModel, Parameter
@@ -44,29 +44,6 @@ class method to get a list of all valid combinations and filter it:
 from snewpy.models.registry_model import all_models
 from textwrap import dedent
 
-class Analytic3Species(PinchedModel):
-    """An analytical model calculating spectra given total luminosity,
-    average energy, and rms or pinch, for each species.
-    """
-
-    param = "There are no input files available for this class. Use `doc/scripts/Analytic.py` in the SNEWPY GitHub repo to create a custom input file."
-
-    def get_param_combinations(cls):
-        print(cls.param)
-        return []
-
-    def __init__(self, filename):
-        """
-        Parameters
-        ----------
-        filename : str
-            Absolute or relative path to file with model data.
-        """
-
-        simtab = Table.read(filename,format='ascii')
-        self.filename = filename
-        super().__init__(simtab, metadata={})
-
 @legacy_filename_initialization
 @RegistryModel(
     progenitor_mass = [13, 20, 30, 50] * u.Msun,
@@ -222,6 +199,8 @@ def __init__(self, eos:str, progenitor_mass:u.Quantity):
         return super().__init__(filename=filename, metadata=self.metadata)
 
 class OConnor_2013(legacy_filename_initialization(_OConnor_2013_new)):
+    _config_path='ccsn.OConnor_2013'
+    
     @deprecated('base', 'mass')
     def __init__(self, base=None, mass=None, eos='LS220', *, progenitor_mass=None):
         """
@@ -312,11 +291,9 @@ def _metadata_from_filename(self, filename:str)->dict:
 
     def __init__(self, *, progenitor_mass, turbmixing_param):
         if progenitor_mass.value.is_integer() and progenitor_mass.value <= 30.:
-            fname = os.path.join(f'stir_a{turbmixing_param:3.2f}',
-                                 f'stir_multimessenger_a{turbmixing_param:3.2f}_m{progenitor_mass.value:.1f}.h5')
+            fname = f'stir_a{turbmixing_param:3.2f}/stir_multimessenger_a{turbmixing_param:3.2f}_m{progenitor_mass.value:.1f}.h5'
         else:
-            fname = os.path.join(f'stir_a{turbmixing_param:3.2f}',
-                                 f'stir_multimessenger_a{turbmixing_param:3.2f}_m{progenitor_mass.value:g}.h5')
+            fname = f'stir_a{turbmixing_param:3.2f}/stir_multimessenger_a{turbmixing_param:3.2f}_m{progenitor_mass.value:g}.h5'
         return super().__init__(fname, self.metadata)
 
 @deprecated('eos','mass')
@@ -476,8 +453,10 @@ def __init__(self, axion_mass:u.Quantity, axion_coupling:u.Quantity):
                  (200, 8):  1.74,
                  (200, 10): 1.73,
                  (200, 20): 1.62 }
-        am = int(axion_mass.to_value('MeV'))
-        ac = int(axion_coupling.to_value('1e-10/GeV'))
+
+        am = int(axion_mass.to_value('MeV')) if isinstance(axion_mass, u.quantity.Quantity) else int(axion_mass)
+        ac = int(axion_coupling.to_value('1e-10/GeV')) if isinstance(axion_coupling, u.quantity.Quantity) else int(axion_coupling)
+
         pns_mass = mpns[(am,ac)]
 
         # Set the metadata.
@@ -562,3 +541,25 @@ def get_fluence(self, t):
 
         return fluence
 
+class Analytic3Species(PinchedModel):
+    """An analytical model calculating spectra given total luminosity,
+    average energy, and rms or pinch, for each species.
+    """
+
+    param = "There are no input files available for this class. Use `doc/scripts/Analytic.py` in the SNEWPY GitHub repo to create a custom input file."
+
+    def get_param_combinations(cls):
+        print(cls.param)
+        return []
+
+    def __init__(self, filename):
+        """
+        Parameters
+        ----------
+        filename : str
+            Absolute or relative path to file with model data.
+        """
+
+        simtab = Table.read(filename,format='ascii')
+        self.filename = filename
+        super().__init__(simtab, metadata={})
diff --git a/python/snewpy/models/loaders.py b/python/snewpy/models/ccsn_loaders.py
similarity index 85%
rename from python/snewpy/models/loaders.py
rename to python/snewpy/models/ccsn_loaders.py
index 04af4193..73136a7b 100644
--- a/python/snewpy/models/loaders.py
+++ b/python/snewpy/models/ccsn_loaders.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 """
-The submodule ``snewpy.models.loaders`` contains classes to load core-collapse
+The submodule ``snewpy.models.ccsn_loaders`` contains classes to load core-collapse
 supernova models from files stored on disk.
 """
 
@@ -11,7 +11,7 @@
 import tarfile
 
 from astropy import units as u
-from astropy.table import Table
+from astropy.table import Table, join
 from astropy.io import ascii, fits
 import h5py
 import numpy as np
@@ -22,10 +22,73 @@
 except ImportError:
     pass
 
-from snewpy.models.base import PinchedModel, _GarchingArchiveModel, SupernovaModel
+from snewpy.models.base import PinchedModel, SupernovaModel
 from snewpy.neutrino import Flavor
 from snewpy import _model_downloader
 
+class GarchingArchiveModel(PinchedModel):
+    """Subclass that reads models in the format used in the
+    `Garching Supernova Archive <https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/>`_."""
+    def __init__(self, filename, eos='LS220', metadata={}):
+        """Model Initialization.
+
+        Parameters
+        ----------
+        filename : str
+            Absolute or relative path to file with model data, we add nue/nuebar/nux.  This argument will be deprecated.
+        eos: str
+            Equation of state. Valid value is 'LS220'. This argument will be deprecated.
+
+        Other Parameters
+        ----------------
+        progenitor_mass: astropy.units.Quantity
+            Mass of model progenitor in units Msun. Valid values are {progenitor_mass}.
+        Raises
+        ------
+        FileNotFoundError
+            If a file for the chosen model parameters cannot be found
+        ValueError
+            If a combination of parameters is invalid when loading from parameters
+        """
+        # Read through the several ASCII files for the chosen simulation and
+        # merge the data into one giant table.
+        mergtab = None
+        for flavor in Flavor:
+            _flav = Flavor.NU_X if flavor == Flavor.NU_X_BAR else flavor
+            _sfx = _flav.name.replace('_', '').lower()
+            _filename = '{}_{}_{}'.format(filename, eos, _sfx)
+            _lname = 'L_{}'.format(flavor.name)
+            _ename = 'E_{}'.format(flavor.name)
+            _e2name = 'E2_{}'.format(flavor.name)
+            _aname = 'ALPHA_{}'.format(flavor.name)
+
+            # Open the requested filename using the model downloader.
+            datafile = self.request_file(_filename)
+
+            simtab = Table.read(datafile,
+                                names=['TIME', _lname, _ename, _e2name],
+                                format='ascii')
+            simtab['TIME'].unit = 's'
+            simtab[_lname].unit = '1e51 erg/s'
+            simtab[_aname] = (2*simtab[_ename]**2 - simtab[_e2name]) / (simtab[_e2name] - simtab[_ename]**2)
+            simtab[_ename].unit = 'MeV'
+            del simtab[_e2name]
+
+            if mergtab is None:
+                mergtab = simtab
+            else:
+                mergtab = join(mergtab, simtab, keys='TIME', join_type='left')
+                mergtab[_lname].fill_value = 0.
+                mergtab[_ename].fill_value = 0.
+                mergtab[_aname].fill_value = 0.
+        simtab = mergtab.filled()
+        if not metadata:
+            metadata = {
+                'Progenitor mass': float(os.path.basename(filename).split('s')[1].split('c')[0]) * u.Msun,
+                'EOS': eos,
+            }
+        super().__init__(simtab, metadata)
+        
 class Nakazato_2013(PinchedModel):
     def __init__(self, filename, metadata={}):
         """Model initialization.
@@ -41,7 +104,7 @@ def __init__(self, filename, metadata={}):
             If a file for the chosen model parameters cannot be found
         """
         # Open the requested filename using the model downloader.
-        datafile = _model_downloader.get_model_data(self.__class__.__name__, filename)
+        datafile = self.request_file(filename)
         # Read FITS table using the astropy reader.
         simtab = Table.read(datafile)
 
@@ -53,19 +116,19 @@ class Sukhbold_2015(Nakazato_2013):
     pass
 
 
-class Tamborra_2014(_GarchingArchiveModel):
+class Tamborra_2014(GarchingArchiveModel):
     pass
 
 
-class Bollig_2016(_GarchingArchiveModel):
+class Bollig_2016(GarchingArchiveModel):
     pass
 
 
-class Walk_2018(_GarchingArchiveModel):
+class Walk_2018(GarchingArchiveModel):
     pass
 
 
-class Walk_2019(_GarchingArchiveModel):
+class Walk_2019(GarchingArchiveModel):
     pass
 
 
@@ -80,7 +143,7 @@ def __init__(self, filename, metadata={}):
         filename : str
             Absolute or relative path to FITS file with model data.
         """
-        datafile = _model_downloader.get_model_data(self.__class__.__name__, filename)
+        datafile = self.request_file(filename)
         # Open luminosity file.
         with tarfile.open(datafile) as tf:
             # Extract luminosity data.
@@ -113,7 +176,7 @@ def __init__(self, filename, metadata={}):
             Absolute or relative path to FITS file with model data.
         """
 
-        datafile = _model_downloader.get_model_data(self.__class__.__name__, filename)
+        datafile = self.request_file(filename)
         simtab = Table.read(datafile,
                             names=['TIME', 'L_NU_E', 'L_NU_E_BAR', 'L_NU_X',
                                     'E_NU_E', 'E_NU_E_BAR', 'E_NU_X',
@@ -157,7 +220,7 @@ def __init__(self, filename, metadata={}):
             Absolute or relative path to file prefix, we add nue/nuebar/nux
         """
         # Open the requested filename using the model downloader.
-        datafile = _model_downloader.get_model_data(self.__class__.__name__, filename)
+        datafile = self.request_file(filename)
 
         # Open luminosity file.
         # Read data from HDF5 files, then store.
@@ -204,7 +267,7 @@ def __init__(self, filename, metadata={}):
         """
 
         # Open the requested filename using the model downloader.
-        datafile = _model_downloader.get_model_data(self.__class__.__name__, filename)
+        datafile = self.request_file(filename)
         # Read ASCII data.
         simtab = Table.read(datafile, format='ascii')
 
@@ -335,7 +398,7 @@ def __init__(self, filename, metadata={}, cache_flux=False):
                                 Flavor.NU_X_BAR: 'nu2'}
 
             # Open the requested filename using the model downloader.
-            datafile = _model_downloader.get_model_data(self.__class__.__name__, filename)
+            datafile = self.request_file(filename)
             # Open HDF5 data file.
             self._h5file = h5py.File(datafile, 'r')
 
@@ -597,12 +660,12 @@ def __init__(self, filename, metadata={}):
         filename : str
             Absolute or relative path to HDF5 file with model data.
         """
+
+        # Open the requested filename using the model downloader.
+        datafile = self.request_file(filename)
         # Set up model metadata.
         self.progenitor_mass = float(filename.split('/')[-1].split('_')[2][:-1]) * u.Msun
         self.metadata = metadata
-
-        # Open the requested filename using the model downloader.
-        datafile = _model_downloader.get_model_data(self.__class__.__name__, filename)
         # Open HDF5 data file.
         _h5file = h5py.File(datafile, 'r')
 
@@ -659,8 +722,8 @@ def get_initial_spectra(self, t, E, flavors=Flavor, interpolation='linear'):
 
         # Make sure the input time uses the same units as the model time grid.
         # Convert input time to a time index.
-        t = t.to(self.time.unit)
-        j = (np.abs(t - self.time)).argmin()
+        t = u.Quantity(t.to(self.time.unit), ndmin=1)
+        j = np.array(list(np.abs(_t - self.time).argmin() for _t in t))
 
         for flavor in flavors:
             # Energy bin centers (in MeV)
@@ -675,27 +738,29 @@ def get_initial_spectra(self, t, E, flavors=Flavor, interpolation='linear'):
             # Linear interpolation in flux.
             if interpolation.lower() == 'linear':
                 # Pad log(E) array with values where flux is fixed to zero.
-                _logEbins = np.insert(_logE, 0, np.log10(np.finfo(float).eps * E.unit/u.MeV))
-                _logEbins = np.append(_logEbins, _logE[-1] + _dlogE[-1])
+                _logEbins = np.insert(_logE, 0, np.log10(np.finfo(float).eps * E.unit/u.MeV), axis=1)
+                _logEbins = np.append(_logEbins, np.expand_dims(_logE[:,-1] + _dlogE[:,-1], 1), axis=1)
 
                 # Luminosity spectrum _dLdE is in units of 1e50 erg/s/MeV.
                 # Pad with values where flux is fixed to zero, then divide by E to get number luminosity
-                _dNLdE = np.asarray([0.] + [self._dLdE[flavor]['g{}'.format(i)][j] for i in range(12)] + [0.])
-                initialspectra[flavor] = (np.interp(logE, _logEbins, _dNLdE) / E *
-                                          factor * 1e50 * u.erg/u.s/u.MeV).to('1 / (erg s)')
+                _dNLdE = np.asarray([np.zeros(j.shape)] + [self._dLdE[flavor]['g{}'.format(i)][j] for i in range(12)] + [np.zeros(j.shape)]).T
+                interp_values = np.array([np.interp(logE, __logEbins, __dNLdE)
+                                          for __logEbins, __dNLdE in zip(_logEbins, _dNLdE)])
+                initialspectra[flavor] = (interp_values / E * factor * 1e50 * u.erg/u.s/u.MeV).to('1 / (erg s)')
 
             elif interpolation.lower() == 'nearest':
                 # Find edges of energy bins and identify which energy bin (each entry of) E falls into
-                _logEbinEdges = _logE - _dlogE[0] / 2
-                _logEbinEdges = np.concatenate((_logEbinEdges, [_logE[-1] + _dlogE[-1] / 2]))
+                _logEbinEdges = _logE - _dlogE[0,0] / 2
+                _logEbinEdges = np.append(_logEbinEdges, np.expand_dims(_logE[:,-1] + _dlogE[:,-1]/2, 1), axis=1)
                 _EbinEdges = 10**_logEbinEdges
-                idx = np.searchsorted(_EbinEdges, E) - 1
-                select = (idx > 0) & (idx < len(_E))
+                idx = np.array([np.searchsorted(edges, E) - 1 for edges in _EbinEdges])
+                select = np.array([(_idx > 0) & (_idx < len(__E)) for _idx, __E in zip(idx, _E)])
 
                 # Divide luminosity spectrum by energy at bin center to get number luminosity spectrum
-                _dNLdE = np.zeros(len(E))
-                _dNLdE[np.where(select)] = np.asarray(
-                    [self._dLdE[flavor]['g{}'.format(i)][j] / _E[i] for i in idx[select]])
+                _dNLdE = np.zeros([len(j), len(np.atleast_1d(E))])
+                for i in range(len(j)):
+                    _dNLdE[i][np.where(select[i])] = np.asarray([self._dLdE[flavor]['g{}'.format(ebin_idx)][j[i]] / _E[i][ebin_idx]
+                                                                 for ebin_idx in idx[i][select[i]]])
                 initialspectra[flavor] = ((_dNLdE << 1/u.MeV) * factor * 1e50 * u.erg/u.s/u.MeV).to('1 / (erg s)')
 
             else:
@@ -712,14 +777,14 @@ def __init__(self, filename, metadata={}):
         filename : str
             Absolute or relative path to HDF5 file with model data.
         """
+        # Open the requested filename using the model downloader.
+        datafile = self.request_file(filename)
         # Set up model metadata.
         self.progenitor = os.path.splitext(os.path.basename(filename))[0].split('_')[2]
         self.progenitor_mass = float(self.progenitor[:-3])*u.Msun if self.progenitor.endswith('bh') else float(self.progenitor)*u.Msun
 
         self.metadata = metadata
 
-        # Open the requested filename using the model downloader.
-        datafile = _model_downloader.get_model_data(self.__class__.__name__, filename)
         # Open HDF5 data file.
         _h5file = h5py.File(datafile, 'r')
 
@@ -759,17 +824,11 @@ def __init__(self, filename, metadata={}):
         filename : str
             Absolute or relative path to file prefix.
         """
-        # Set up model metadata
-        self.axion_mass = metadata['Axion mass']
-        self.axion_coupling = metadata['Axion coupling']
-        self.progenitor_mass = metadata['Progenitor mass']
-        self.pns_mass = metadata['PNS mass']
+        # Open the requested filename using the model downloader.
+        datafile = self.request_file(filename)
 
         self.metadata = metadata
 
-        # Open the requested filename using the model downloader.
-        datafile = _model_downloader.get_model_data(self.__class__.__name__, filename)
-
         # Read ASCII data.
         simtab = Table.read(datafile, format='ascii')
 
diff --git a/python/snewpy/models/model_files.yml b/python/snewpy/models/model_files.yml
index 6440ee22..d00848ef 100644
--- a/python/snewpy/models/model_files.yml
+++ b/python/snewpy/models/model_files.yml
@@ -5,50 +5,65 @@
 config:
     - &snewpy "https://github.com/SNEWS2/snewpy/raw/v{snewpy_version}/models/{model}/{filename}"
     - &ccsn_repository "https://github.com/SNEWS2/snewpy-models-ccsn/raw/v0.2/models/{model}/{filename}"
+    - &presn_repository "https://github.com/SNEWS2/snewpy-models-presn/raw/v0.2/models/{model}/{filename}"
+    
 models:
+    ccsn:
+        Bollig_2016:
+            repository: *ccsn_repository
+    
+        Fornax_2019: 
+            repository: *ccsn_repository
+    
+        Fornax_2021: 
+            repository: *ccsn_repository
+    
+        Fornax_2022: 
+            repository: *ccsn_repository
+    
+        Kuroda_2020: 
+            repository: *ccsn_repository
+    
+        Nakazato_2013: 
+            repository: *ccsn_repository
+    
+        OConnor_2013: 
+            repository: *ccsn_repository
+    
+        OConnor_2015: 
+            repository: *ccsn_repository
+     
+        Sukhbold_2015: 
+            repository: *ccsn_repository
+     
+        Tamborra_2014: 
+            repository: *ccsn_repository
+     
+        Walk_2018: 
+            repository: *ccsn_repository
+     
+        Walk_2019: 
+            repository: *ccsn_repository
+    
+        Warren_2020:
+            repository: *ccsn_repository
+    
+        Zha_2021: 
+            repository: *ccsn_repository
+    
+        Mori_2023: 
+            repository: *ccsn_repository
+            
+    presn:
+    
+        Odrzywolek_2010:
+            repository: *presn_repository
+            
+        Patton_2017:
+            repository: *presn_repository
+            
+        Kato_2017:
+            repository: *presn_repository
 
-    Bollig_2016:
-        repository: *ccsn_repository
-
-    Fornax_2019: 
-        repository: *ccsn_repository
-
-    Fornax_2021: 
-        repository: *ccsn_repository
-
-    Fornax_2022: 
-        repository: *ccsn_repository
-
-    Kuroda_2020: 
-        repository: *ccsn_repository
-
-    Nakazato_2013: 
-        repository: *ccsn_repository
-
-    OConnor_2013: 
-        repository: *ccsn_repository
-
-    OConnor_2015: 
-        repository: *ccsn_repository
- 
-    Sukhbold_2015: 
-        repository: *ccsn_repository
- 
-    Tamborra_2014: 
-        repository: *ccsn_repository
- 
-    Walk_2018: 
-        repository: *ccsn_repository
- 
-    Walk_2019: 
-        repository: *ccsn_repository
-
-    Warren_2020:
-        repository: *ccsn_repository
-
-    Zha_2021: 
-        repository: *ccsn_repository
-
-    Mori_2023: 
-        repository: *ccsn_repository
-
+        Yoshida_2016:
+            repository: *presn_repository
diff --git a/python/snewpy/models/presn.py b/python/snewpy/models/presn.py
index 6e339562..7290e17b 100644
--- a/python/snewpy/models/presn.py
+++ b/python/snewpy/models/presn.py
@@ -1,142 +1,61 @@
-import numpy as np
-import pandas as pd
-from scipy.interpolate import interp1d
+# -*- coding: utf-8 -*-
+"""
+The submodule ``snewpy.models.presn`` contains models of presupernova neutrino fluxes,
+derived from the :class:`SupernovaModel` base class.
+"""
+import snewpy.models.presn_loaders as loaders
+from snewpy.models.registry_model import RegistryModel
 from astropy import units as u
-from .base import SupernovaModel
-from snewpy.neutrino import Flavor
-from pathlib import Path
 
-def _interp_T(t0, v0, dt=1e-3, dv=1e-10, axis=0):
-    "dilog interpolation"
-    lt0 = np.log(t0 + dt)
-    lv0 = np.log(v0 + dv)
-    lv1 = interp1d(lt0, lv0, axis=axis, fill_value=0, copy=False, bounds_error=False)
-    return lambda t1: np.exp(lv1(np.log(t1 + dt)))
-
-
-def _interp_E(e0, v0, axis=1):
-    "linear interpolation"
-    return interp1d(e0, v0, axis=axis, fill_value=0, copy=False, bounds_error=False)
-
-
-def _interp_TE(times, energies, array, ax_t=1, ax_e=2):
-    def _f(t, E):
-        a_t = _interp_T(times, array, axis=ax_t)(t)
-        a_te = _interp_E(energies, a_t, axis=ax_e)(E)
-        return a_te
-
-    return _f
-
-class Odrzywolek_2010(SupernovaModel):
-    """Set up a presupernova model, based on 
-    [A. Odrzywolek and A. Heger, Acta Phys. Polon. B 41 (2010) 1611.]
+@RegistryModel(
+    progenitor_mass = [15, 25]<<u.Msun
+)
+class Odrzywolek_2010(loaders.Odrzywolek_2010):
+    """Presupernova model based on
+    `A.Odrzywolek, Alexander Heger, Acta Phys.Polon.B 41 (2010) <https://inspirehep.net/literature/870759>`_
+    
+    Dataset available on `Odrzywolek’s website <http://th.if.uj.edu.pl/~odrzywolek/psns/index.html>`_
     """
-
-    def __init__(self, fname):
-        df = pd.read_csv(
-            fname,
-            delim_whitespace=True,
-            skiprows=1,
-            usecols=[1,6,7,8],
-            names=["time","a","alpha","b"],
-            index_col="time",
-        )
-        # interpolated in time
-        self.df_t = _interp_T(df.index, df)
-        self.factor = {}
-        for f in Flavor:
-            if f.is_electron:
-                self.factor[f] = 1.0
-            else:
-                # nuX/nuE ratio from Odrzywolek paper: (arXiv:astro-ph/0311012)
-                self.factor[f] = 0.36
-        self.filename = Path(fname).stem
-        self.progenitor_mass = float(self.filename.split('_')[0][1:]) * u.Msun
-        time = -df.index.to_numpy() << u.s
-        metadata = {'Progenitor Mass': self.progenitor_mass}
-        super().__init__(time, metadata)
-
-    def get_initial_spectra(self, t, E, flavors=Flavor):
-        # negative t for time before SN
-        t = -t.to_value("s")
-        E = E.to_value("MeV")
-        df = self.df_t(t)
-        a, alpha, b = df.T
-        Enu = np.expand_dims(E, 1)
-        a = np.expand_dims(a, 0)
-        alpha = np.expand_dims(alpha, 0)
-        b = np.expand_dims(b, 0)
-        fluence = a * Enu ** alpha * np.exp(-b * Enu) / (u.MeV * u.s)
-        result = {f: fluence.T * self.factor[f] for f in flavors}
-        return result
-
-
-class Patton_2017(SupernovaModel):
-    """Set up a presupernova model based on 
-    [Kelly M. Patton et al 2017 ApJ 851 6, 
-     https://doi.org/10.5281/zenodo.2598709]
+    def __init__(self, progenitor_mass:u.Quantity):
+        filename=f"s{progenitor_mass.to_value('Msun'):.0f}_nuebar_data.txt"
+        super().__init__(filename)
+    
+@RegistryModel(
+    progenitor_mass = [15, 30]<<u.Msun
+)
+class Patton_2017(loaders.Patton_2017):
+    """Presupernova model based on
+    `Kelly M. Patton et al. 2017 ApJ 851 6 <https://iopscience.iop.org/article/10.3847/1538-4357/aa95c4>`_
+    
+    Dataset available on Zenodo (`DOI:10.5281/zenodo.2626645 <http://doi.org/10.5281/zenodo.2626645>`_)
     """
-    def __init__(self, fname):
-        df = pd.read_csv(
-            fname,
-            comment="#",
-            delim_whitespace=True,
-            names=["time","Enu",Flavor.NU_E,Flavor.NU_E_BAR,Flavor.NU_X,Flavor.NU_X_BAR],
-            usecols=range(6),
-        )
-
-        df = df.set_index(["time", "Enu"])
-        times = df.index.levels[0].to_numpy()
-        energies = df.index.levels[1].to_numpy()
-        df = df.unstack("Enu")
-        # make a 3d array
-        self.array = np.stack([df[f] for f in Flavor], axis=0)
-        self.interpolated = _interp_TE(
-            times, energies, self.array, ax_t=1, ax_e=2
-        )
-        self.filename = Path(fname).stem
-        self.progenitor_mass = float(self.filename.split('_')[-1].rstrip('SolarMass')) * u.Msun
-        metadata = {'Progenitor Mass': self.progenitor_mass}
-        super().__init__(times << u.hour, metadata)
-
-    def get_initial_spectra(self, t, E, flavors=Flavor):
-        t = np.array(-t.to_value("hour"), ndmin=1)
-        E = np.array(E.to_value("MeV"), ndmin=1)
-        flux = self.interpolated(t, E) / (u.MeV * u.s)
-        return {f: flux[f] for f in flavors}
-
-class Kato_2017(SupernovaModel):
-    """Set up a presupernova model based on 
-    [Chinami Kato et al 2017 ApJ 848 48]
+    def __init__(self, progenitor_mass:u.Quantity):
+        filename=f"totalLuminosity_{progenitor_mass.to_value('Msun'):.0f}SolarMass.dat"
+        super().__init__(filename)
+        
+@RegistryModel(
+    progenitor_mass = [12, 15]<<u.Msun
+)
+class Kato_2017(loaders.Kato_2017):
+    """Presupernova model based on
+    `Chinami Kato et al. 2017 ApJ 848 48 <https://iopscience.iop.org/article/10.3847/1538-4357/aa8b72>`_
+    
+    Dataset available on `Zenodo <https://zenodo.org/records/3768052>`__
     """
-    def __init__(self, path):
-        fluxes = {}
-        #reading the time steps values:
-        times, step = np.loadtxt(f"{path}/total_nue/lightcurve_nue_all.dat", usecols=[0, 3]).T
-
-        file_base = {Flavor.NU_E: 'total_nue/spe_all',
-                 Flavor.NU_E_BAR:'total_nueb/spe_all',
-                 Flavor.NU_X:    'total_nux/spe_sum_mu_nu',
-                 Flavor.NU_X_BAR:'total_nux/spe_sum_mu'
-                 }
-        for flv,file_base in file_base.items():
-            d2NdEdT = []
-            for s in step:
-                energies, dNdE = np.loadtxt(f"{path}/{file_base}{s:05.0f}.dat").T
-                d2NdEdT += [dNdE]
-            fluxes[flv] = np.stack(d2NdEdT)
-        self.array = np.stack([fluxes[f] for f in Flavor], axis=0)
-        self.interpolated = _interp_TE(
-            times, energies, self.array, ax_t=1, ax_e=2
-        )
-        self.filename = Path(path).stem
-        self.progenitor_mass = float(self.filename.strip('m')) * u.Msun
-        metadata = {'Progenitor Mass': self.progenitor_mass}
-        super().__init__(times << u.s, metadata)
-
-    def get_initial_spectra(self, t, E, flavors=Flavor):
-        t = np.array(-t.to_value("s"), ndmin=1)
-        E = np.array(E.to_value("MeV"), ndmin=1)
-        flux = self.interpolated(t, E) / (u.MeV * u.s)
-        return {f: flux[f] for f in flavors}
-
+    def __init__(self, progenitor_mass:u.Quantity):
+        path=f"pre_collapse/m{progenitor_mass.to_value('Msun'):.0f}"
+        super().__init__(path)
+
+
+@RegistryModel(
+    progenitor_mass = [12, 15, 20]<<u.Msun
+)
+class Yoshida_2016(loaders.Yoshida_2016):
+    """Presupernova model based on
+    `Yoshida et al. (2016), PRD 93, 123012 <https://doi.org/10.1103/PhysRevD.93.123012>`_
+    
+    Dataset available on `Zenodo <https://zenodo.org/records/3778014>`__
+    """
+    def __init__(self, progenitor_mass:u.Quantity):
+        path=f"t_spc_m{progenitor_mass.to_value('Msun'):.0f}_1.2.txt"
+        super().__init__(path)
diff --git a/python/snewpy/models/presn_loaders.py b/python/snewpy/models/presn_loaders.py
new file mode 100644
index 00000000..bbcafc22
--- /dev/null
+++ b/python/snewpy/models/presn_loaders.py
@@ -0,0 +1,173 @@
+# -*- coding: utf-8 -*-
+"""
+The submodule ``snewpy.models.presn_loaders`` contains classes to load pre-supernova
+models from files stored on disk.
+"""
+
+import numpy as np
+import pandas as pd
+from scipy.interpolate import interp1d
+from astropy import units as u
+from snewpy.models.base import SupernovaModel
+from snewpy.neutrino import Flavor
+from pathlib import Path
+
+def _interp_T(t0, v0, dt=1e-3, dv=1e-10, axis=0):
+    "dilog interpolation"
+    lt0 = np.log(t0 + dt)
+    lv0 = np.log(v0 + dv)
+    lv1 = interp1d(lt0, lv0, axis=axis, fill_value=0, copy=False, bounds_error=False)
+    return lambda t1: np.exp(lv1(np.log(t1 + dt)))
+
+
+def _interp_E(e0, v0, axis=1):
+    "linear interpolation"
+    return interp1d(e0, v0, axis=axis, fill_value=0, copy=False, bounds_error=False)
+
+
+def _interp_TE(times, energies, array, ax_t=1, ax_e=2):
+    def _f(t, E):
+        a_t = _interp_T(times, array, axis=ax_t)(t)
+        a_te = _interp_E(energies, a_t, axis=ax_e)(E)
+        return a_te
+
+    return _f
+
+class Odrzywolek_2010(SupernovaModel):
+    """Set up a presupernova model, based on 
+    [A. Odrzywolek and A. Heger, Acta Phys. Polon. B 41 (2010) 1611.]
+    """
+
+    def __init__(self, filename:str):
+        df = pd.read_csv(
+            self.request_file(filename),
+            sep='\s+',
+            skiprows=1,
+            usecols=[1,6,7,8],
+            names=["time","a","alpha","b"],
+            index_col="time",
+        )
+        # interpolated in time
+        self.df_t = _interp_T(df.index, df)
+        self.factor = {}
+        for f in Flavor:
+            if f.is_electron:
+                self.factor[f] = 1.0
+            else:
+                # nuX/nuE ratio from Odrzywolek paper: (arXiv:astro-ph/0311012)
+                self.factor[f] = 0.19
+        time = -df.index.to_numpy() << u.s
+        super().__init__(time, self.metadata)
+
+    def get_initial_spectra(self, t, E, flavors=Flavor):
+        # negative t for time before SN
+        t = -t.to_value("s")
+        E = E.to_value("MeV")
+        df = self.df_t(t)
+        a, alpha, b = df.T
+        Enu = np.expand_dims(E, 1)
+        a = np.expand_dims(a, 0)
+        alpha = np.expand_dims(alpha, 0)
+        b = np.expand_dims(b, 0)
+        fluence = a * Enu ** alpha * np.exp(-b * Enu) / (u.MeV * u.s)
+        result = {f: fluence.T * self.factor[f] for f in flavors}
+        return result
+
+
+class Patton_2017(SupernovaModel):
+    """Set up a presupernova model based on 
+    [Kelly M. Patton et al 2017 ApJ 851 6, 
+     https://doi.org/10.5281/zenodo.2598709]
+    """
+    def __init__(self, filename:str):
+        df = pd.read_csv(
+            self.request_file(filename),
+            comment="#",
+            sep='\s+',
+            names=["time","Enu",Flavor.NU_E,Flavor.NU_E_BAR,Flavor.NU_X,Flavor.NU_X_BAR],
+            usecols=range(6),
+        )
+
+        df = df.set_index(["time", "Enu"])
+        times = df.index.levels[0].to_numpy()
+        energies = df.index.levels[1].to_numpy()
+        df = df.unstack("Enu")
+        # make a 3d array
+        self.array = np.stack([df[f] for f in Flavor], axis=0)
+        self.interpolated = _interp_TE(
+            times, energies, self.array, ax_t=1, ax_e=2
+        )
+        super().__init__(times << u.hour, self.metadata)
+
+    def get_initial_spectra(self, t, E, flavors=Flavor):
+        t = np.array(-t.to_value("hour"), ndmin=1)
+        E = np.array(E.to_value("MeV"), ndmin=1)
+        flux = self.interpolated(t, E) / (u.MeV * u.s)
+        return {f: flux[f] for f in flavors}
+
+class Kato_2017(SupernovaModel):
+    """Set up a presupernova model based on 
+    [Chinami Kato et al 2017 ApJ 848 48]
+    """
+    def __init__(self, path):
+        fluxes = {}
+        #reading the time steps values:
+        times, step = np.loadtxt(self.request_file(f"{path}/total_nue/lightcurve_nue_all.dat"), usecols=[0, 3]).T
+
+        file_base = {Flavor.NU_E: 'total_nue/spe_all',
+                 Flavor.NU_E_BAR:'total_nueb/spe_all',
+                 Flavor.NU_X:    'total_nux/spe_sum_mu_nu',
+                 Flavor.NU_X_BAR:'total_nux/spe_sum_mu'
+                 }
+        for flv,file_base in file_base.items():
+            d2NdEdT = []
+            for s in step:
+                energies, dNdE = np.loadtxt(
+                    self.request_file(f"{path}/{file_base}{s:05.0f}.dat")
+                ).T
+                d2NdEdT += [dNdE]
+            fluxes[flv] = np.stack(d2NdEdT)
+        self.array = np.stack([fluxes[f] for f in Flavor], axis=0)
+        self.interpolated = _interp_TE(
+            times, energies, self.array, ax_t=1, ax_e=2
+        )
+        super().__init__(times << u.s, self.metadata)
+
+    def get_initial_spectra(self, t, E, flavors=Flavor):
+        t = np.array(-t.to_value("s"), ndmin=1)
+        E = np.array(E.to_value("MeV"), ndmin=1)
+        flux = self.interpolated(t, E) / (u.MeV * u.s)
+        return {f: flux[f] for f in flavors}
+
+class Yoshida_2016(SupernovaModel):
+    """Set up a presupernova model based on 
+    [Yoshida et al. (2016), PRD 93, 123012.]
+    """
+    def __init__(self, filename):
+        with open(self.request_file(filename)) as f:
+            data = []
+            T = []
+            while (line := f.readline()):
+                if not line: break
+                T += [float(line.split()[1])]
+                data += [[np.loadtxt(f, max_rows=100).flatten() for i in range(4)]]
+        times = np.array(T)
+        energies = np.concatenate([
+                np.linspace(0,10,1001)[1:],
+                np.linspace(10,20,501)[1:]
+            ])
+        dNdEdT = np.stack(data, axis=1)
+        #rearrange flavors from NU_E, NU_E_BAR,_NU_X, NU_X_BAR to current 
+        indices = np.argsort([Flavor.NU_E,Flavor.NU_E_BAR, Flavor.NU_X, Flavor.NU_X_BAR]) 
+        dNdEdT = dNdEdT.take(indices, axis=0)
+        
+        self.interpolated = _interp_TE(
+            times, energies, dNdEdT, ax_t=1, ax_e=2
+        )
+        super().__init__(times << u.s, self.metadata)
+
+    def get_initial_spectra(self, t, E, flavors=Flavor):
+        t = np.array(-t.to_value("s"), ndmin=1)
+        E = np.array(E.to_value("MeV"), ndmin=1)
+        flux = self.interpolated(t, E) / (u.MeV * u.s)
+        return {f: flux[f] for f in flavors}
diff --git a/python/snewpy/models/registry_model.py b/python/snewpy/models/registry_model.py
index 88f1b736..5e578acf 100644
--- a/python/snewpy/models/registry_model.py
+++ b/python/snewpy/models/registry_model.py
@@ -17,6 +17,7 @@
 from warnings import warn
 import numpy as np
 import re
+from snewpy._model_downloader import RegistryFileLoader
 
 from astropy import table
 from tqdm.auto import tqdm
@@ -393,7 +394,7 @@ def RegistryModel(_param_validator=None, **params):
     """
     pset:ParameterSet = ParameterSet(param_validator=_param_validator, **params)
     def _wrap(base_class):
-        class c(base_class):
+        class c(base_class, RegistryFileLoader):
             parameters:ParameterSet = pset
             @classmethod
             def _generate_docstring(cls)->str:
@@ -450,6 +451,10 @@ def get_param_combinations(cls)->tuple:
         c.__qualname__ = base_class.__qualname__
         c.__name__ = base_class.__name__
         c.__module__ = base_class.__module__
+        #set the configuration path
+        if not hasattr(c, '_config_path'):
+            module_name = c.__module__.split(".")[-1]
+            c._config_path = f'{module_name}.{c.__name__}'
         #register the model in the list
         global all_models
         all_models.add(c)
@@ -468,7 +473,7 @@ def __init__(self, filename:str=None, *args, **kwargs):
                 if not hasattr(self,'metadata'):
                     self.metadata = {}
                 if hasattr(self,'_metadata_from_filename'):
-                    self.metadata.update(*self._metadata_from_filename(filename))
+                    self.metadata.update(self._metadata_from_filename(filename))
                 self._loader_class.__init__(self, filename=os.path.abspath(filename), metadata=self.metadata)
             else:
                 super().__init__(*args, **kwargs)
@@ -499,4 +504,4 @@ def __init__(self, filename:str=None, *args, **kwargs):
     global all_models
     all_models.remove(c)
     all_models.add(c1)
-    return c1
\ No newline at end of file
+    return c1
diff --git a/python/snewpy/rate_calculator.py b/python/snewpy/rate_calculator.py
index cb2edd1c..9b1bc275 100644
--- a/python/snewpy/rate_calculator.py
+++ b/python/snewpy/rate_calculator.py
@@ -13,7 +13,6 @@
 from astropy import units as u
 from warnings import warn
 from typing import Dict
-from dataclasses import dataclass
 from typing import Callable
 import scipy.stats as st
 
diff --git a/python/snewpy/snowglobes_interface.py b/python/snewpy/snowglobes_interface.py
index 7f194c5c..2e09753b 100644
--- a/python/snewpy/snowglobes_interface.py
+++ b/python/snewpy/snowglobes_interface.py
@@ -36,7 +36,11 @@ def guess_material(detector):
         mat = 'lead'
     elif detector.startswith('scint'):
         mat = 'scint'
-    else: 
+    elif detector.startswith(('ds20','argo')):
+        mat = 'argon_coh'
+    elif detector.startswith('xe'):
+        mat = 'xenon_coh'
+    else:
         raise ValueError(f'Please provide material for {detector}')
 
     if '_he' in detector:
@@ -46,8 +50,8 @@ def guess_material(detector):
 class SnowglobesData:
     def __init__(self, base_dir:Path='', detectors:str="all", detector_effects=True):
         """Interface to SNOwGLoBES data
-        
-        On construction the code will read: 
+
+        On construction the code will read:
 
         * detectors from `<base_dir>/detector_configurations.dat`,
         * channels from `<base_dir>/channels/channel_*.dat`
@@ -62,7 +66,7 @@ def __init__(self, base_dir:Path='', detectors:str="all", detector_effects=True)
         base_dir:         Path or None
             Path to the directory where the cross-section, detector, and channel files are located
             If empty, try to get it from ``$SNOWGLOBES`` environment var
-            
+
         detectors: str
             Name of detector. If ``"all"``, will use all detectors supported by SNOwGLoBES.
 
@@ -83,7 +87,7 @@ def __init__(self, base_dir:Path='', detectors:str="all", detector_effects=True)
             self._load_smearing_matrices(self.base_dir/'smear')
 
     def _load_detectors(self, path:Path, detectors:str):
-        df = pd.read_table(path,names=['name','mass','factor'], delim_whitespace=True, comment='#')
+        df = pd.read_table(path, names=['name', 'mass', 'factor'], sep='\s+', comment='#')
         df['tgt_mass']=df.mass*df.factor
 
         if detectors == 'all':
@@ -95,7 +99,7 @@ def _load_detectors(self, path:Path, detectors:str):
         self.detectors = df.set_index('name').T.filter(items=detectors)
         logger.info(f'read masses for detectors {list(self.detectors)}')
         logger.debug(f'detectors: {self.detectors}')
-       
+
     def _load_channels(self, chan_dir):
         self.channels = {}
         self.binning = {}
@@ -108,17 +112,19 @@ def _load_channels(self, chan_dir):
             if l.startswith('%'):
                 # Format 1: explicit binning, same for all channels
                 tokens = l.split()[1:]
-                df = pd.read_table(f, delim_whitespace=True, index_col=1, comment='%', names=['name','n','parity','flavor','weight'])
+                df = pd.read_table(f, sep='\s+', index_col=1, comment='%', names=['name','n','parity','flavor','weight'])
             elif l.startswith('SN_nu'):
                 # Format 2: explicit binning, differs per channel
                 tokens = l.split()[6:]
-                df = pd.read_table(f, delim_whitespace=True, index_col=1, comment='%', names=['name','n','parity','flavor','weight'], usecols=range(1,6))
-                # drop coherent scattering channels, which have different binning
-                df = df[df["name"].str.startswith('coh_') == False]
+                df = pd.read_table(f, sep='\s+', index_col=1, comment='%', names=['name','n','parity','flavor','weight'], usecols=range(1,6))              
+                # drop coherent scattering channels if material is argon_400bins, mixing channels which have different binning
+                if material=='argon_400bins':
+                    df = df[df["name"].str.contains('_coh') == False]
+
             else:
                 # Format 3: no binning specified, use SNOwGLoBES default values
                 tokens = '% 200 0.0005 0.100 200 0.0005 0.100'.split()[1:]
-                df = pd.read_table(f, delim_whitespace=True, index_col=1, comment='%', names=['name','n','parity','flavor','weight'])
+                df = pd.read_table(f, sep='\s+', index_col=1, comment='%', names=['name','n','parity','flavor','weight'])
 
             self.channels[material] = df
 
@@ -136,13 +142,13 @@ def _load_efficiency_vectors(self, path):
         for detector in self.detectors:
             res_det = {}
             for file in (path/str(detector)).glob(f'effic_*_{detector}.dat'):
-                channel =  file.stem[len('effic_'):-len(detector)-1]
+                channel = file.stem[len('effic_'):-len(detector)-1]
                 logger.debug(f'Reading file ({detector},{channel}): {file}')
                 with open(file) as f:
                     effs = np.fromiter(f.readlines()[0].split("{")[-1].split("}")[0].split(","), float)
                     res_det[channel]= effs
             result[detector]=res_det
-        self.efficiencies = result 
+        self.efficiencies = result
         logger.info(f'read efficiencies for detectors: {list(self.efficiencies.keys())}')
         logger.debug(f'efficiencies: {self.efficiencies}')
 
@@ -162,7 +168,7 @@ def _load_smearing_matrices(self, path):
                         matrix[i, int(elements[0]+0.1):int(elements[1]+0.1)+1] = elements[2:]
                     res_det[channel]= matrix
             result[detector]=res_det
-        self.smearings = result 
+        self.smearings = result
         logger.info(f'read smearing matrices for detectors: {list(self.smearings.keys())}')
         logger.debug(f'smearing matrices: {self.smearings}')
 
@@ -189,9 +195,9 @@ def __init__(self, detectors:str="all", detector_effects=True, base_dir:Path='')
         base_dir:         Path or None
             Path to the directory where the cross-section, detector, and channel files are located
             If empty, try to get it from ``$SNOWGLOBES`` environment var
-        """    
+        """
         super().__init__(base_dir, detectors=detectors, detector_effects = detector_effects)
-        
+
     def compute_rates(self, detector:str, material:str, fluxes:np.ndarray, flux_energies:np.ndarray):
         """ Calculate the rates for the given neutrino fluxes interacting in the given detector.
 
@@ -215,14 +221,14 @@ def compute_rates(self, detector:str, material:str, fluxes:np.ndarray, flux_ener
             Table with Energy (GeV) as index values,
             and number of events for each energy bin, for all interaction channels.
             Columns are hierarchical: (is_weighted, channel),
-            so one can easily access the desired final table. 
+            so one can easily access the desired final table.
         """
         TargetMass = self.detectors[detector].tgt_mass
         data = {}
 
         #load the binning for the smearing
         binning= self.binning[material]
-        #calculate bin centers 
+        #calculate bin centers
         energies_t = 0.5*(binning['e_true'][1:]+ binning['e_true'][:-1])
         energies_s= 0.5*(binning['e_smear'][1:]+binning['e_smear'][:-1])
         binsize = np.diff(binning['e_true'])
@@ -235,7 +241,7 @@ def compute_rates(self, detector:str, material:str, fluxes:np.ndarray, flux_ener
             flux = fluxes[flavor]
             # Cross-section in 10^-38 cm^2
             xsecs = np.interp(np.log(energies_t)/np.log(10), xsec[:, 0], xsec[:, 1+flavor], left=0, right=0) * energies_t
-            # Fluence (flux integrated over time bin) in cm^-2 
+            # Fluence (flux integrated over time bin) in cm^-2
             # (must be divided by 0.2 MeV to compensate the multiplication in generate_time_series)
             fluxs = np.interp(energies_t, flux_energies, flux, left=0, right=0)/2e-4
             # Rate computation
@@ -259,7 +265,7 @@ def compute_rates(self, detector:str, material:str, fluxes:np.ndarray, flux_ener
         df.index.rename('E', inplace=True)
         df.columns.rename(['channel','is_smeared','is_weighted'], inplace=True)
         return df.reorder_levels([2,1,0], axis='columns')
-       
+
     def run(self, flux_files, detector:str, material:str=None):
         """ Compute expected rates for given configuration,
         collect the resulting data and return it in `pandas.DataFrame`
@@ -277,10 +283,10 @@ def run(self, flux_files, detector:str, material:str=None):
         --------
         list(pd.DataFrame or Exception)
             List with the data table for each flux_file, keeping the order.
-            Each table containing Energy (GeV) as index values, 
+            Each table containing Energy (GeV) as index values,
             and number of events for each energy bin, for all interaction channels.
             Columns are hierarchical: (is_weighted, channel),
-            so one can easily access the desired final table. 
+            so one can easily access the desired final table.
             If run failed with exception, this exception will be returned (not raised).
 
         Raises
diff --git a/python/snewpy/test/_rate_crosscheck_table.py b/python/snewpy/test/_rate_crosscheck_table.py
index fb264077..4d75b829 100644
--- a/python/snewpy/test/_rate_crosscheck_table.py
+++ b/python/snewpy/test/_rate_crosscheck_table.py
@@ -1,27 +1,27 @@
 #generated by "make_rate_table.py" on 2023-06-02 14:05:49.504799
 rate_table= \
-{('Bollig_2016', 's11.2c', 'AdiabaticMSW_IMO'): {'ar40kt': 2680.934960405018,
+{('Bollig_2016', 's11.2c', 'AdiabaticMSW_IMO'): {'ar40kt': 2509.8697955822163,
                                                  'halo1': 3.7370536263087892,
                                                  'halo2': 47.30447628238974,
                                                  'icecube': 332587.17107218195,
                                                  'novaFD': 1955.8019842893325,
                                                  'novaND': 41.91004252048569,
                                                  'wc100kt30prct': 12723.21134007678},
- ('Bollig_2016', 's11.2c', 'AdiabaticMSW_NMO'): {'ar40kt': 2860.015571948873,
+ ('Bollig_2016', 's11.2c', 'AdiabaticMSW_NMO'): {'ar40kt': 2696.396378383758,
                                                  'halo1': 4.261668358305819,
                                                  'halo2': 53.94516909247873,
                                                  'icecube': 320766.2999953658,
                                                  'novaFD': 1947.9479073661355,
                                                  'novaND': 41.741740872131466,
                                                  'wc100kt30prct': 12645.788228369147},
- ('Bollig_2016', 's27.0c', 'AdiabaticMSW_IMO'): {'ar40kt': 5465.621983231249,
+ ('Bollig_2016', 's27.0c', 'AdiabaticMSW_IMO'): {'ar40kt': 5192.001233928208,
                                                  'halo1': 8.180298088573792,
                                                  'halo2': 103.54807707055436,
                                                  'icecube': 659449.9649827218,
                                                  'novaFD': 3638.7825760092305,
                                                  'novaND': 77.97391234305493,
                                                  'wc100kt30prct': 23596.070817047243},
- ('Bollig_2016', 's27.0c', 'AdiabaticMSW_NMO'): {'ar40kt': 5785.497035954946,
+ ('Bollig_2016', 's27.0c', 'AdiabaticMSW_NMO'): {'ar40kt': 5520.903869068218,
                                                  'halo1': 9.105675711861647,
                                                  'halo2': 115.2617178716664,
                                                  'icecube': 661517.4858901916,
diff --git a/python/snewpy/test/test_02_models.py b/python/snewpy/test/test_02_models.py
index 2b0afc5d..c66387c2 100644
--- a/python/snewpy/test/test_02_models.py
+++ b/python/snewpy/test/test_02_models.py
@@ -4,11 +4,10 @@
 import unittest
 
 from snewpy.neutrino import Flavor
-from snewpy.models.loaders import Nakazato_2013, Tamborra_2014, OConnor_2013, OConnor_2015, \
+from snewpy.models.ccsn_loaders import Nakazato_2013, Tamborra_2014, OConnor_2013, OConnor_2015, \
                                   Sukhbold_2015, Bollig_2016, Walk_2018, \
                                   Walk_2019, Fornax_2019, Warren_2020, \
                                   Kuroda_2020, Fornax_2021, Zha_2021
-from snewpy._model_urls import model_urls
 from astropy import units as u
 from snewpy import model_path
 import os
@@ -16,12 +15,6 @@
 
 class TestModels(unittest.TestCase):
 
-    def test_model_urls(self):
-        """Test that snewpy._model_urls.model_urls is empty. This should be populated if snewpy is downloaded from PyPI.
-        This serves as a guard against accidentally committing/merging a populated model_urls to main.
-        """
-        self.assertFalse(model_urls)
-
     def test_Nakazato_2013(self):
         """
         Instantiate a set of 'Nakazato 2013' models
@@ -76,7 +69,8 @@ def test_OConnor_2013(self):
                     'Progenitor mass': mass * u.Msun,
                     'EOS': eos,
                 }
-                model = OConnor_2013(filename=f'{eos}_timeseries.tar.gz', metadata=metadata)
+                mfile = f'OConnor_2013/{eos}_timeseries.tar.gz'
+                model = OConnor_2013(filename=os.path.join(model_path,mfile), metadata=metadata)
 
                 self.assertEqual(model.metadata['EOS'], eos)
                 self.assertEqual(model.metadata['Progenitor mass'].value, mass)
@@ -95,8 +89,8 @@ def test_OConnor_2015(self):
         """
         Instantiate a set of "O'Connor 2015" models
         """
-        mfile = 'M1_neutrinos.dat'
-        model = OConnor_2015(mfile)
+        mfile = 'OConnor_2015/M1_neutrinos.dat'
+        model = OConnor_2015(filename=os.path.join(model_path,mfile))
 
         # Check that times are in proper units.
         t = model.get_time()
@@ -237,13 +231,13 @@ def test_Warren_2020(self):
 
         for mixing in [1.23, 1.25, 1.27]:
             for mass in masses:
-                mfile = f'stir_a{mixing}/stir_multimessenger_a{mixing}_m{mass}.h5'
+                mfile = f'Warren_2020/stir_a{mixing}/stir_multimessenger_a{mixing}_m{mass}.h5'
                 metadata = {
                     'Progenitor mass': float(mass) * u.Msun,
                     'Turb. mixing param.': mixing,
                     'EOS': 'SFHo',
                 }
-                model = Warren_2020(mfile, metadata)
+                model = Warren_2020(os.path.join(model_path, mfile), metadata)
 
                 # Check that times are in proper units.
                 t = model.get_time()
diff --git a/python/snewpy/test/test_flux_container.py b/python/snewpy/test/test_flux_container.py
index 35f1c6e1..72e1dc7a 100644
--- a/python/snewpy/test/test_flux_container.py
+++ b/python/snewpy/test/test_flux_container.py
@@ -1,3 +1,6 @@
+import os
+from tempfile import TemporaryDirectory
+
 from hypothesis import given, assume, note
 from hypothesis import strategies as st
 from hypothesis.extra.numpy import arrays, array_shapes
@@ -7,6 +10,7 @@
 import numpy as np
 import astropy.units as u
 import pytest
+
 #define strategies to generate float values
 def float_values(shape=array_shapes(max_dims=1),**kwargs):
     return arrays(float, shape=shape, 
@@ -111,7 +115,8 @@ def test_integration_over_axes(f:Container, axis):
 
 @given(f=random_flux_containers())
 def test_save_and_load(f):
-    fname = '/tmp/flux.npz'
-    f.save(fname)
-    f1 = Container.load(fname)
-    assert f1==f
\ No newline at end of file
+    with TemporaryDirectory() as tmpdir:
+        fname = os.path.join(tmpdir, 'flux.npz')
+        f.save(fname)
+        f1 = Container.load(fname)
+        assert f1 == f
\ No newline at end of file
diff --git a/python/snewpy/test/test_presn_rates.py b/python/snewpy/test/test_presn_rates.py
new file mode 100644
index 00000000..d614078e
--- /dev/null
+++ b/python/snewpy/test/test_presn_rates.py
@@ -0,0 +1,25 @@
+import astropy.units as u
+import numpy as np
+from snewpy.models import presn
+from snewpy.flavor_transformation import AdiabaticMSW, MassHierarchy
+from snewpy.rate_calculator import RateCalculator
+import pytest
+
+pytestmark=pytest.mark.snowglobes
+
+rc = RateCalculator()
+
+distance = 200*u.pc
+ #SNOwGLoBES detector for water Cerenkov
+T = np.geomspace(-1*u.hour, -1*u.min,1000)
+E = np.linspace(0,20,100)*u.MeV
+
+@pytest.mark.parametrize('model_class',[presn.Odrzywolek_2010, presn.Kato_2017, presn.Patton_2017, presn.Yoshida_2016])
+@pytest.mark.parametrize('transformation',[AdiabaticMSW(mh=mh) for mh in MassHierarchy])
+@pytest.mark.parametrize('detector', ["wc100kt30prct"])
+def test_presn_rate(model_class, transformation, detector):
+    model = model_class(progenitor_mass=15*u.Msun)
+    flux = model.get_flux(T, E, distance=distance, flavor_xform=transformation)
+    rate = rc.run(flux, detector='scint20kt', detector_effects=False)['ibd']
+    ibd_events = rate.integrate_or_sum('time').integrate_or_sum('energy').array.squeeze()
+    assert 10<ibd_events<1000
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index f83fe62a..5d5864e1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy >= 1.16
+numpy
 scipy
 astropy >=4.3
 pandas
@@ -7,5 +7,5 @@ matplotlib
 h5py
 requests
 pyyaml
-snowglobes_data == 1.3.1
+snowglobes_data == 1.3.2
 importlib_resources; python_version < "3.9"