diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
index f173b43d6dcaef12ad3ec03c68559c2c0109a8e3..3ad0c8fd188d94a8f1bed1b358fd0ff8c677b140 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
@@ -190,7 +190,7 @@ def visualize_interareal_connectivity(M):
X, Y = np.meshgrid(x, y)
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
- ax.set_xticklabels(area_list, rotation=90, size=6.)
+ # ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_yticklabels(area_list[::-1], size=6.)
@@ -315,7 +315,7 @@ def visualize_interareal_connectivity(M):
X, Y = np.meshgrid(x, y)
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
- ax.set_xticklabels(area_list, rotation=90, size=6.)
+ # ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_yticklabels(area_list[::-1], size=6.)
diff --git a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
index f173b43d6dcaef12ad3ec03c68559c2c0109a8e3..3ad0c8fd188d94a8f1bed1b358fd0ff8c677b140 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
@@ -190,7 +190,7 @@ def visualize_interareal_connectivity(M):
X, Y = np.meshgrid(x, y)
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
- ax.set_xticklabels(area_list, rotation=90, size=6.)
+ # ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_yticklabels(area_list[::-1], size=6.)
@@ -315,7 +315,7 @@ def visualize_interareal_connectivity(M):
X, Y = np.meshgrid(x, y)
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
- ax.set_xticklabels(area_list, rotation=90, size=6.)
+ # ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_yticklabels(area_list[::-1], size=6.)
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 678dfe91ca2aa6f758c979bcaddc3fa2d455b127..8cfbfbd25faa99d28c5e7ed33fa379bdf0e0b67e 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -476,16 +476,70 @@
"metadata": {},
"outputs": [
{
- "ename": "TypeError",
- "evalue": "__init__() missing 1 required positional argument: 'network_spec'",
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Initializing network from dictionary.\n",
+ "RAND_DATA_LABEL 7773\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning:Mean of empty slice.\n",
+ "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
+ "Error in library(\"aod\") : there is no package called ‘aod’\n",
+ "Execution halted\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
+ "\n",
+ "\n",
+ "========================================\n",
+ "Customized parameters\n",
+ "--------------------\n",
+ "{}\n",
+ "========================================\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "The number of FixedLocator locations (33), usually from a call to set_ticks, does not match the number of ticklabels (32).",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn [12], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# %%capture captured\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_interareal_connectivity\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m visualize_interareal_connectivity\n\u001b[0;32m----> 3\u001b[0m visualize_interareal_connectivity(M)\n",
- "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py:22\u001b[0m, in \u001b[0;36mvisualize_interareal_connectivity\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvisualize_interareal_connectivity\u001b[39m(M):\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# full-scale model\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m M_full_scale \u001b[38;5;241m=\u001b[39m \u001b[43mMultiAreaModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;124;03m Figure layout\u001b[39;00m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;66;03m# nrows = 2\u001b[39;00m\n",
- "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'network_spec'"
+ "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py:193\u001b[0m, in \u001b[0;36mvisualize_interareal_connectivity\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m 190\u001b[0m X, Y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmeshgrid(x, y)\n\u001b[1;32m 192\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xticks([i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m0.5\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(area_list) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)])\n\u001b[0;32m--> 193\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_xticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43marea_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrotation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m90\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m6.\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 195\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_yticks([i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m0.5\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(area_list) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)])\n\u001b[1;32m 196\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_yticklabels(area_list[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6.\u001b[39m)\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-matplotlib-3.6.2-lhkot3cmeebfk5dp74dnubweq56upksc/lib/python3.8/site-packages/matplotlib/axes/_base.py:73\u001b[0m, in \u001b[0;36m_axis_method_wrapper.__set_name__.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mget_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-matplotlib-3.6.2-lhkot3cmeebfk5dp74dnubweq56upksc/lib/python3.8/site-packages/matplotlib/axis.py:1968\u001b[0m, in \u001b[0;36mAxis._set_ticklabels\u001b[0;34m(self, labels, fontdict, minor, **kwargs)\u001b[0m\n\u001b[1;32m 1966\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fontdict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1967\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate(fontdict)\n\u001b[0;32m-> 1968\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_ticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mminor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mminor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-matplotlib-3.6.2-lhkot3cmeebfk5dp74dnubweq56upksc/lib/python3.8/site-packages/matplotlib/axis.py:1890\u001b[0m, in \u001b[0;36mAxis.set_ticklabels\u001b[0;34m(self, ticklabels, minor, **kwargs)\u001b[0m\n\u001b[1;32m 1886\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(locator, mticker\u001b[38;5;241m.\u001b[39mFixedLocator):\n\u001b[1;32m 1887\u001b[0m \u001b[38;5;66;03m# Passing [] as a list of ticklabels is often used as a way to\u001b[39;00m\n\u001b[1;32m 1888\u001b[0m \u001b[38;5;66;03m# remove all tick labels, so only error for > 0 ticklabels\u001b[39;00m\n\u001b[1;32m 1889\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(ticklabels) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ticklabels) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 1890\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1891\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe number of FixedLocator locations\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1892\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m), usually from a call to\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1893\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m set_ticks, does not match\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1894\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m the number of ticklabels (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(ticklabels)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m).\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1895\u001b[0m tickd \u001b[38;5;241m=\u001b[39m {loc: lab \u001b[38;5;28;01mfor\u001b[39;00m loc, lab \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs, ticklabels)}\n\u001b[1;32m 1896\u001b[0m func \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_with_dict, tickd)\n",
+ "\u001b[0;31mValueError\u001b[0m: The number of FixedLocator locations (33), usually from a call to set_ticks, does not match the number of ticklabels (32)."
]
+ },
+ {
+ "data": {
+ "image/png": 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dds30g5LlbO32l/OjLP/5PTG9r/59kvukt8cJc/JdMLU8+XlcCoqcM5VmenmWvabTtX6xwu9PrLrx8H+6by4tL/Xh9yfZPT0AfJf0A9kPJdmrLp9C9cEZQckLppYnX9tVoT+u5DO/V/oB7LRZ6y6YWp7eLy/nR+kjD5a7QOyFbfMLFS+V89PyZ3wn7GgnZPP35ZHD+mcleUL6tJXD0t/rpSl7K/ke/61c8T2/Ry7/Hv+V9ClSm9KnBx2U/lth1vZnuWBq+ZI56ya3dYXvlSFw8e1c/pm8wud7MLnu+ukjQV6YzV/fj9JHOm3LfhCudFw5G9jelq68flT6PP9pX5l4POvM/oPT77oweTHPu80pazr/+enDp39jQf0OSj/jde/W2ucnyvjZBXlm+U76sOcbL0jzjfR5tdNulH6WaHtbavv7ZfYP8jPS67xcvbfEa9Lnj++X/gPz3Gx+pm9LvDbJP6f3gXuk94Pps8Ir8Y30ee3TbpTL22jJtowuuTj9toSTpoMhyzkt/Yf8kguTZDgoeW6S51bVvulTBZ6R3r+PS/+BvOjihIekz3E/vPXri2Q4e7dc/c5PP5P90hnPLRo5cX76gfNytnb7y9k9V3xvf6r6nYbumz4c/riJ9Vtz0mbprPz0Z3vWZ31W3s3SDfPcJy/St3TQP729pQvzLr3O04fHd0kfRfHk1tr3qurTw7q7pF9jYktcFfrjSj7z30wfbTdtzxXk3VK7J/n+MMplR/hRtn0/Nc+xGS6KOVj6fn9wkn9prf30Oi5Vdd8525j1PX5yZt+GeulipQ9I/675raVgXFXddItqvuWu8H0+BOtukMs/k9/M5XeomjSZ74IM077ST+pMO3sb6wlXKoIVwLaYdfb/jPQfsOtaay/Zim1eK1c8I/bwWQlneHeSP0v/4ff5OWmWLtD10zKqX1F/XbYggNBa+0FVfSzJI6rq+XOmVHwsyR9U1XVbaxcOZf3KUNYHZ6TfVh9Jn499k9ba3OkNK6j3JUO6aw6jJxZ5R3rg5iHpwYrXTQ2TnbntzDj71Vr7YVW9On2++K3SR75sjaXXt39r7StJUlV7p19079it3OYsm9IPEifde0s2MPSLDcukOSvJM6vqUelz0pPe1x9cVbdprX16RrZrpQelJqfQPCTLf++/O/1M/WlbOE3ojPS7KvxMa23RCIut3f5yZ4zXJfnCgvw/k342c/Jzf930i/ptacDqrPSDksPTL7i35IGzk2/m1CSPrqrdJqaCTOfblH7A8uBsPk3uIUm+l+FODa21VlUfGtb/n/SRFhn+Pzo9IDl9cc2FroL9cZ5Tkzy8qvZemgpS/SKkN1qcbaZt7bvbalP6xYaT/DRAd/D85ElWODqktXZm+pScaZt9jw8H9QvveDLh3el94/Q2/xap10ry46k+sdLfCVvrY+l3hPqLie+4B6b34aXv86V+s8/SVJDqF9P8abBi+O3w0SS3bK399Q6uM+z0BCuAbfH5JIdX1W9k+IHdWju7qv4sySur3270bek/fG6WPuLhQVPztae9M/3q9H+Z/uPgsPQLZ67EO9OvxP3OqnpW+pnH66VfiPKarbUnp195/ftJXlJVz04fZXFseoBlSz0pybuSvK2qjk/yg/SRGxtaa29JP6v5B0nePtTnOukX3fxM+lzX7aq1dkH128f+03CW6f3p0/1ukX7hyqXpNMvVeynQ8ydV9Z706SdnzCnzx1X1xvTAwo0z484hU76WHlB55DDX+MettcmDo5elXyDzh9n66SQnpg8df1tVPSX9uiTHpp+JffFWbnOWNyZ5TFU9N33e9T2yzO0zV6qqXpx+tu6j6ReWu0f6hQWXrqb/ivSgzjuG9/yM9Due3KK19qT00S27pN/t4mXpI02ekCsOdZ52bPqF5d5aVSekt9ne6UGYE1tr752T70PpQ5h/KYsPdrd2+8v1m/XpQ89naq19t/otkp9SVd9LP3B+UnrbruiWoxPb+smw73hO9ds/fiD97gC/sDhnkn5ngMcmeUtV/WP6tSmePLy2pe1fNrynLx6u7/DO9OkZf5DkL6YCiO9Pn9ZyRmttaaj5B9LvVnJR+t1VttmVuD/O8/Ikf5X+Pj0t/eD4aeln8y9blHFaa+2SqvpKkodU1WfTR2R9emLKxvqhvjvKG5M8tqr+J8mX0++Gs1yfX+7ztpx3DmVuTO83j00PGK7EPyb57fQ7e/xL+nfzjdI/Ax9srb162P7jqup56ddp+bUhz470N+mjRd9UVS9K/+3wrCRvb619ZEiz1G/eOnwOrpU+QmR6FNCfJ3l3VV2WfqHyC9Ov9XXfJH/ZWtuRwSvYqbhmBbAtXph+Zv2E9DMKRyVJa+016Wcdb5c+tP8N6Qexn8jlZ2zmeXH6Le/+ZMh30/RbgC1rOMvywKE+j0sPXLw4/UD8g0Oab6Wfsdwr/XoLj0s/ON54hQ0uX977038075Z+u9HXpP+g2jQ8f276j/qL06/c/4L0A4l7b+kFKLegTs9Ofx8OTX99r04/4/SBiTQL6z2k/fv09+BjWf4A/6T0QMXZWeZM7nCg9XvpFxF7X3q/mXx+Q/qP0ze01q5wS8GVGM6k3is96PKy9NuifjX9yvZzpwpsRTlvTZ/H/6D0A4Kbpven7eEj6aM2Xp4+VPgBSX6vtfamoeyL08+Ovjn9rilvS/8BfPbw/GeSPCr9YqpvSf8MPTgzbtM49Zq+kD73+6L0+eZvSz9I+1EWfEaGfJ9N73c7Yvtz+01V/XL68PzlLnD5sPRh6q9I8k/pAcNXLJNnnuel3zbx6GE710lv/4WGs/SHpc/Lf336fvG309tjMt1L0gMOD0h//45I8mettWdObXLp8/b+Ges+th2nFlwp++OC/BelT135Yfr+8djh9VyQPrplSx2d/p6/K73v3iRJqmqP9D693YPXE56W/j38N+mB3E+mf0fOtdx+egX+KL0fvmAo67O54q3L55V9Xvp7/vn0qUfvSL+Lzs+mX0Q2rbVT0gNlv5k+ZeRu6dMfd5jW2unp/emG6fuav0n/fn3QRJqL0gPWP0j/Xnxq+mjPr05t64Ppn6c90+948+b0/nVW1v46SjAqtWWj6gBgx6l+L/vTk9yrtfbu5dIzHlX1+CSPaa3detnE27fcv0vyK621e61muYzb9u6PVbV/+nSNo1prW3Ph31nb/P30ESa32MJpLgBXCYIVAKy5qrpB+gXtnp5+5uo2frzvXKrfFvfLSR7eWnvXKpb51fTpZe9djTLZOWxrf6yqJ6ePDPlq+hD9J6ef3b9Vm7gd9TbUr9IDs89urZ24rdsDuDJyzQoAxuD/Sx8u/PkkvyNQsfMZLhz3yCTXXsVi90vy1wIVTNsO/bGlD+O/Sfq0kw8kecL2CFQM9kryqvRpAADMYGQFAAAAMCousAkAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAMBoVNW6qmrD38VVdVZVvaqq9l/rum2Lidf1lhHU5QlDXY5cJt1o6gzAVY9gBQAwRv+T5Ogk70nysCQfrqobrm2VAIDVIlgBAIzR2a21E1trj0zykiR7Jfn9JKmqO1fVx6rq+1W1saqOGtb/7TAS4JZVddDw+InDc+dV1QeHx2dW1Q+q6llVdX5VnVpVe82qRFX9/jC640dV9bWq+rNh/S2q6pSquqCqvlNVxwzrXzssX1xV/1tVD5iz3b2r6vVD2rOr6plVdYXfZVV15PA6Thy2d05VHTqMNvlBVb2pqnZd1C7Dc08Y2uC0JL80VcZBVfWRId8XquqILXurAGD7E6wAAMbubcP/21bVDZKcnOSmSZ6Q5JwkL66qg5O8f0h3UJI7Lj2uqlsmuUGSD0xsc7ckN0zy1iTrk/xeVV2tqvZY+hvSPTvJd9JHebwwyaVDcODNSe6d5J+S/GWSHw7pT03y50mePCy/oqquOeM1/dtE/pOTPDHJHy5og4OTvCjJHknekuSCJB9McniS+y1ql6q6bZK/T/LNJC9Ocq+ljVbVzw3b2z3JM5KcmeSVVXW7BXUBgB1u17WuAADAMmr439IDEddP8retteOq6ktJ3pHk0CRPT/KTiTRvHx4fNOSfDFZcluQPktwuyW8nWZdkvyRfmSr3i0luluRuSU5L8qokt0xyiySva6099aeJq3ZJckCSI5JcY2I765JcPJHuOsP2KslTJ9LdJ8nz57TBK1pr/1JVT0ofZfL4JL8z5Nk/yaUL2mXTsI3nttZeVlX7JvmrYd1BSX5u+PvbifIOTvKGOXUBgB1OsAIAGLtfH/5/emJdm07UWvteVX06lwcrjk7yxvRgxGVJPjSR/IettYur6tJheZf0kQf3ntrswUl+M8ntk/xdkt/KMB1lhnsneWSSdyd53lD+fZNcMxPBigmfSh8FseS7c7ab9JEUSfLjoe6XVNVPJuq+5ArtMkPNePyKJK+cWH/mCrYDADuMYAUAMEY3Ge5Wcbf0AMA3kxyfPoLgO0keU1VnpY8uSJJThv/vT/LH6cGJ9yX5ZJJ7Jvlka21RMCCttYuTvGtq9fPSR1R8IskDk9wkyRlJvpDkN6rq2CTfSnJJkrOHPLulj6a405xyvl9V70ty1yR3SfL1JHdO8vn0aSRb4yOZ3y7nD48fP4z+eNREvg8Pzx8ylL1rkvulj1L56lbWBQC2mWtWAABj9Mu5/PoK/57koNbat1pr305y/yRfS/KPGS682Vr77yHfB9JHC3y2tfb99IP4pfVbY/ckT0tyXJILkzy+tXbpUId3JXlc+vSJ3dKnXZyUfgHLB6ZPQ5nnt9OnWRyT5DlJfj7Jx7eyjlnULq21TyX5v8O6Y5K8cyLf+enBiY1Jnpl+/Y2LYmQFAGusWlvJaEEAAACA1WFkBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAGusqk6oqnOq6rNznq+q+ueq2lhVn66q2692HQFgNQlWAACsvROTHLLg+UOT3Hz4OyrJi1ahTgCwZgQrAADWWGvt/UnOX5Dk8CSvaN1Hk+xeVTdendoBwOoTrAAAGL+9k5w1sbxpWAcAV0q7rnUFAABYVs1Y12YmrDoqfapIrn3ta9/hVre61Y6sFwDMddppp53XWttza/IKVgAAjN+mJPtOLO+T5OxZCVtrxyc5PknWr1/fNmzYsONrBwAzVNVXtzavaSAAAON3cpJHDHcFuWOS77bWvrHWlQKAHcXICgCANVZVr05y9yR7VNWmJE9NcvUkaa0dl+SUJIcl2ZjkoiSPWpuaAsDqEKwAAFhjrbUjlnm+JXnsKlUHANacaSAAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAACNQVYdU1RlVtbGqnjTj+Z+tqjdX1aeq6vSqetRa1BMAVoNgBQDAGquqXZK8IMmhSQ5IckRVHTCV7LFJ/re1dtskd0/yD1V1jVWtKACsEsEKAIC1d2CSja21L7fWLklyUpLDp9K0JNetqkpynSTnJ7l0dasJAKtDsAIAYO3tneSsieVNw7pJz0/yC0nOTvKZJH/SWrtsdaoHAKtLsAIAYO3VjHVtavnXk3wyyU2S3C7J86vqelfYUNVRVbWhqjace+6527ueALAqBCsAANbepiT7Tizvkz6CYtKjkryhdRuTfCXJraY31Fo7vrW2vrW2fs8999xhFQaAHUmwAgBg7Z2a5OZVtf9w0cyHJjl5Ks3XktwzSarqRklumeTLq1pLAFglu651BQAArupaa5dW1TFJ3p5klyQntNZOr6qjh+ePS/L0JCdW1WfSp408sbV23ppVGgB2IMEKAIARaK2dkuSUqXXHTTw+O8l9VrteALAWTAMBAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAABiBqjqkqs6oqo1V9aQ5ae5eVZ+sqtOr6n2rXUcAWC27rnUFAACu6qpqlyQvSHLvJJuSnFpVJ7fW/ncize5JXpjkkNba16rqhmtSWQBYBUZWAACsvQOTbGytfbm1dkmSk5IcPpXmYUne0Fr7WpK01s5Z5ToCwKoRrAAAWHt7JzlrYnnTsG7SLZJcv6reW1WnVdUjVq12ALDKTAMBAFh7NWNdm1reNckdktwzybWSfKSqPtpa+8JmG6o6KslRSbLffvvtgKoCwI5nZAUAwNrblGTfieV9kpw9I81/tdZ+0Fo7L8n7k9x2ekOtteNba+tba+v33HPPHVZhANiRBCsAANbeqUluXlX7V9U1kjw0yclTaf4zyV2qateq2i3Jryb53CrXEwBWhWkgAABrrLV2aVUdk+TtSXZJckJr7fSqOnp4/rjW2ueq6r+SfDrJZUle2lr77NrVGgB2nGptejokAABXBuvXr28bNmxY62oAcBVVVae11tZvTV7TQAAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGJWFwYqqWldVbervguU2WlXvHdLuUVV3Hx4/f7vVenFd37Ijy1lhXZ4w1OXIZdKNps4AAAAwFruuMN3/JHn28PiSHVQXAAAAgBVPAzk3ybuGv3cnyTAi4LPD4wcNy8duaQWq6ver6qyq+lFVfa2q/mxYf4uqOqWqLqiq71TVMcP61w7LF1fV/1bVA+Zsd++qev2Q9uyqemZVXeH1VtWRQ91PHLZ3TlUdWlWvqqofVNWbqmrXIe2dq+pjVfX9qtpYVUdNbOcJVXVeVZ2W5Jemyjioqj4y5PtCVR2xpe0EAAAAVxUrDVbcJz1gcW6S/9zawqrqasPUkD2qao9h9bOTfCfJ0UlemOTSITjw5iT3TvJPSf4yyQ+H9Kcm+fMkTx6WX1FV15xR3L9N5D85yROT/OGC6h2c5EVJ9kjyliQXJPlgksOT3K+qbjBs56ZJnpDknCQvrqqDq+q2Sf4+yTeTvDjJvSZe888N29s9yTOSnJnklVV1uwV1AQAAgKuslU4D+ViSvxoef2cbytsvyVcmlivJF5PcLMndkpyW5FVJbpnkFkle11p76k8TV+2S5IAkRyS5xsR21iW5eCLddYbtVZKnTqS7T5J51854RWvtX6rqSUn2SvL4JL8z5Nk/yaVJrp/kb1trx1XVl5K8I8mhSTYN23hua+1lVbVvLm+vg5L83PD3txPlHZzkDXPqAgAAAFdZKw1WnNdae9fUussm8u++wu18M320w6SDk/xmktsn+bskv5Xk9+fkv3eSR6ZPRXle+miM+ya5ZiaCFRM+lT4KYsl3F9TtguH/j5P8sLV2SVX9ZFi3y0S6tmAbS2rG41ckeeXE+jNXsB0AAAC4yllpsGKWM5PsX1UPS3LMSjK01i5Ov+7FpOelj6j4RJIHJrlJkjOSfCHJbwzXwfhW+oU9zx7y7JY+muJOc8r5flW9L8ldk9wlydeT3DnJ59OnkWyNj6SPKnlMVZ2VPuoiSX43ybeHx48fRn88aiLfh5Ocn+SQoexdk9wvydOTfHUr6wIAAABXWiu9ZsUsT0zyvSR/k2TDNmxn9yRPS3JckguTPL61dmmS+6cHNh6XPn1it/RpFyelX8DygUnevmC7v50+zeKYJM9J8vNJPr61lWytfXuo09eS/GP6VJHnJLln+qiO/zusOybJOyfynZ8enNiY5Jnp19+4KEZWAAAAwEzV2kpmNTBPVa1L8pbW2q3Xui4AAJPWr1/fNmzYlnNKALD1quq01tr6rcm7LSMrAAAAALa7bblmBVtojz32aOvWrVvragBwFXXaaaed11rbc63rAQCwHMGKVbRu3boYignAWqkqF3YGAHYKpoEAAAAAoyJYsQ2q6tXptzS9ZVVtqqrHrHWdAAAAYGdnGsg2aK0dsdZ1AAAAgCsbIysAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEK7ZBVR1SVWdU1caqetJa1wcAAACuDAQrtlJV7ZLkBUkOTXJAkiOq6oC1rRUAAADs/AQrtt6BSTa21r7cWrskyUlJDl/jOgEAAMBOT7Bi6+2d5KyJ5U3DOgAAAGAb7LrWFdiJ1Yx1bVGG00477byq+uoOqg8ALOema10BAICVEKzYepuS7DuxvE+SsxdlaK3tuUNrBAAAAFcCpoFsvVOT3Lyq9q+qayR5aJKT17hOAAAAsNMzsmIrtdYurapjkrw9yS5JTmitnb7G1QIAAICdnmDFNmitnZLklLWuBwAAAFyZmAYCAAAAjIpgBQAAADAqpoGsoqo6JMk/pV/j4qWttWdOPX/3JP+Z5CvDqsvS7zJyTmvt1jO2N53+DUlenuQVSfYa8h/fWvunZfKdnOSQJD+T3ide11p76grKekaSDUm+3lq73wrSPzrJhUl+kuTS1tr6FeT55yQvTXLr9FvDPrq19pEFed6X5G4Tm71Zkqe01p63TDkXJvndoYzPJHlUa+3iZfJ8N8nvpd/G9iWTZUzkecfw/KVJ/q619tdV9XNJXpNkXZIzkzyktfadiXwnJLlfkt2TnDG013WTXJzkF5Ic2FrbMCP9OUmuM7yWGw15vpjkS8PruWCZMm6Y5Lz0fnNOkiNba2cvk+fS1tr6qnpCkr9Psmdr7bwVlNOSnDsk+4thStXCcpL8a5Jjhsdvba39+TJ59kvytSHJ7kkuaK3dbpk8Vx/a75pDOX/YWvv4gna+evpn9Cvp7+XDW2vfmyhj3/TP452Gdj0vyTeT3Cdz+sBEnr2S/PyQ55ws7gPz8uyV5IIkl2SqHyzIM7MfTKW/bPh/9sT7c1Km+sAyZczsAwvyzO0DC/LM7QML8izqA4vyfC+9X5yZiX5QVddM8v70/esvJPlOkm8Mr/+8TPWBqfS7Jtk7yVnDtvca1k+///PKmPv+T6qqM7Ng3wwAsCZaa/5W4S89QPGl9IPnayT5VJIDptLcPclbJpbvmuT2ST47Z5ubpR/W3TjJ7YfH103yhRWUU0muMzy+epKPJbnjCsr60yT/Pr1+Qfozk+yxoI1m5fnXJL87PL5Gkt2XyzPV5t9MctNlXv/e6Qeb1xqW/yP9AG1Rnlsn+WyS3dIPKN6V5OYz8nx4+j1M8uwkTxoePynJs6byLb3vlyy1V/oByC2TvDfJ+jnpP7vUxukHw7sOzz9rhWVcb+L5P05y3HJ5hvX7pl9o9qvT7++cco5N8oQF/WBWnnsMbfwzw/INV1K3ief/IT1otVw570hy6PD4sCTvXaadT01yt+H5Ryd5+qzP45D+phk+j4v6QDb/DH81ycYhz6I+MC/P3H6wIM/MfpAr7lt+nOTXFvWBBWXM7QML8sztA/PyLOoDC8pZ1Afm5ZnbD7L5/vXMJKclueO8PpAr7o9/lOTXl3n/55WxcD8wkf/MLNg3+9u5/+5whzs0AFgrSTa0rfwOMw1k9RyYZGNr7cuttUvSz0IevihDa+39Sc7fkkJaa99orX1ieHxhks+lH4wvytNaa98fFq8+/LVFeapqnyT3TR/1sENU1fXSDxBfNtTzkjbjrOAC90zypdbaV1eQdtck16qqXdMDEGcvk/4Xkny0tXZRa+3S9BEdD5iR7vxc8T08PD0Ik+H/b0w+Oet9b619rrV2xqyKzEn/jqFeSfLR9LP/y+X53sTitTPVBxb0x+cm+fPp9MvkmWtOnj9I8szW2o+GNOestJyqqiQPSfLqFeRpSa43PP7ZTPSDOelvmX5GO0nemeQ3p8r46ecxyQ9y+edxbh+YytPSR8fsvUwfmJdnbj9YkGdmP5ixb/lx+sF7MqcPzCtj1mtYQZ65fWC5cmb1gQV5FvWBeXnm9oOp/Wul72ta5vSBGfvjpdWL3v+ZZSy3HwAAGDPBitWzNJR3yabM/tF+UFV9qqreVlW/uILtzk1fVeuS/HL6SImF+apql6r6ZPqQ5ne21hbmSXJC+sHJZVtQt5bkHVV1WlUdtVyeJPdKHyb+8qr6n6p6aVVdewva4KGZOkCdU87uSZ6TPlz8G0m+21p7xzJ5Lk5y16q6QVXtln4Gdt9ZedLvGHPTibrdqLX2jaQf/KQPiZ9nufaaNquNH53kbSspo6qeUVVnJXl4kqesIM/z06cBfWoFdXtHVZ2W5A5JjqmqT1fVCVV1/RXkOSjJXarqY1X1vqr6lZW8nmH5Lkm+1Vr74grK+VCSvx/a4DlJnjwnfUs/A3+1JM8f1j04s/vAUvr3Jjk0PdC10j6wS3rQ7dlb0Afm5VnUDzbLs1w/GPYt10jy/6pqY5Lrr6AP/LSMrLwPTOZZaR+Y9fqX6wOT5ay0D0zmOTfJ/Yf1V+gHE/vXfdOnaL0wyb7z+sDU/viHSZ653H5gVhlb8P6vZN8MALC6tnZIhr8t+0v/AfvSieXfSfIvU2mul8uH8h6WftZuXeZPA7lC+onnrpM+FPiBW5hv9yT/neTWC/Icmz73O5kzDWPOa7nJsHzD9Gkwd10mz9fS54z/6rDun3LFYfYzX0v6gdR56QeFy9XtS0nek2TP9DOZb0ry2yt4PY9J8on0M6rHJXnurDy5fE76Ut0umEr3nRl1XJfk89PtlRlDwCfSf3ZGG78kyRuT1ErLGJafnORpy+S5afpogaUh82dm9hSM6XJOH/rN1dKve3LCCvL8MMnr0s8aH5g+badW2GYvSvJncz5D03nOTfJXw/JDkrxrmXb+tfS5/mckeWqSb88p5/+kfx6PHOp14Qr6wHWSfDrJA1fSB5bJ85cL+sHMPPP6QS7ftzwmfRTSaenXebnrgj4wXcZK+sB0npX0gXmvf1EfmM6zsA/MyfP5JB8f2mJRP7hV+v71runXh7jrxHOz+sDu6cGTW0+8nv+Z9/7PKGPZ93/Is3Df7G/n/jMNBIC1FNNAdgqbsvnZtn0yNdWgtfa9Ngzlbf1ic1dPMves86z0VbVHVV09yeuTvKq19oaV5huWL0g/GDpkXp70i7hdp6q+lj6d5eCq+rcVvJZLhuVz0n84H7hMnkpydrt8lMfr0uf/r+S1HJrkE621b63g9V93KOfc1tqP0y+e+WsreD3/2Vq7fWvtrunTA744L0+S70/U7VtVdeMkGf5vNqVhwqWL2muWNlwQc8jz5ST3Tr/Y37xpPfPK+PdMTWmYzpPLh8q/arhA3z5JPlFVey1TzmvTD7YuSw+mzHtdk3m+nuTcYZ/38fQRPXus4PXcMf2A8jVzypjOs3QRywz1nFm3iXb+cPq1EF6SPornS9Nph8/jC9I/jycO9frhoj4w8Rk+sbX2hpX0gQV5/iD9wqBX6AcrKGezfjC1b3lZ+kUm9x3+3pQZfWBOGQv7wJw8C/vAgtcytw/MybOwD8zJc1KS/2it3SFz+kGStNY+n75/PTD9Ipj3HLY5cz8w7I/fkeSQiddzvel0C8pY+P5P5Jncb6xoXwMAsKMJVqyeU5PcvKr2r6prpE9ROHkyQVXtNcytTlUdmP7+fGfeBuek/3b6NR4+11r7xxXm2zXDAVtVXSt9+sXn5+VJ/zF7dvqZ9YcmeU9r7beXKWOX9AvFZZjKcZ/0M9SL8rQkX62qWw5J7pnkf1fYBkdkzhSQGXkuS/LLVbXbsP6e6dcWWK6cqw3L+6UfDL16Xp4k15qo28lJHjmsf2T6XUamLaWf216zX1pdd3hwePpF+Z7YWrtoTvrpMu4/Ucb9M9UHpvOkB0M+leRhrbV16QG527fWvrlMOfedKOcBc17XdJ7KcJBWVbfI5SNnFuW5T3pQ6fOttU2zm+AKeS6beH0HZyoANZhs53VDOacn+av0ETabJUy/HsHG1to/TtTrfZnTB4Y8LxvKfsnU65nZBxbkeUh64O3+0/1gQZ6Z/WAi/eeSvLiqrtta+0yS/dM/lw/LVB9YUMbcPrAgz9w+sEybzewDC/LM7QML8hyW5LNVdbVM9YOq2rOqdq+qa1fVnun716+k7w+XAtg/7QNL6YfHN0j/HH9+4vX8IFMWlDH3/Z/Ie+2J/rzSfQ0AwA5X80+4sr1V1WFJnpd+4H5Ca+0ZVXV0krTWjquqY9LPgl2aPuT5wvThv3sk+Vb68OKrL0j/p+k/sj+QPix76XoSf5F+C795+V4w5N1lyP8frd9ic1Hd/rS19uHqt+d8Qmvtfsukf06S/zfUZ9ck/76C1/+nSS5Kv4jnNdIPjh+V5LeWyfPJ9OuD3Ky19t2h7Zcr59eH7V6aPsz6d4eyFuV5VpIbpF9o8E9ba++eUc4z0ofK75J+YPXk9DPQ/5HLb6v44Nba+VV1k+G1fjc9YLLnsO1z08+U3m1Yd0GST7bWfn3IsyH9QG6P9ADPt3L5sPkzhzb/aGvt6GXKWAqMXZZ+p4OjW2tfXybPC1trzxja+Mz0s+XnLZPnnFx+kcEzk/x+a+0by+R5cZJbJLld+gidJ7TW3rNc3ZLcfHjtkwePi/K8Nf16Crumn13/w9baaQvaedf0z+T56SNyntxaaxNl/G365/Gnt8Ed3vs/zfw+8Pr00QBnpAcEM7TZBzO/D8zLc530z9C3h3WT/WBenguHx9P94PD0vvuZ4XXvn36Nlx/m8s/zmdm8D8wr4/zM7wPz8rws8/vAvDzHZ34fmJfn7ZnfB+bl+cxQt+SK/eA1w/twrSHPd4f3443p1+GY7gP3HLbx1fRRbD+b3teuPzy+Zq74/s8rY6/Mf/9f2lo7rKpuNtQlmdg3hyuN9evXtw0bNiyfEAB2gKo6rW3lbdEFKwAArqQEKwBYS9sSrDANBAAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAABGoKoOqaozqmpjVT1pQbpfqaqfVNWDVrN+ALCaBCsAANZYVe2S5AVJDk1yQJIjquqAOemeleTtq1tDAFhdghUAAGvvwCQbW2tfbq1dkuSkJIfPSPdHSV6f5JzVrBwArDbBCgCAtbd3krMmljcN636qqvZO8oAkx61ivQBgTQhWAACsvZqxrk0tPy/JE1trP1m4oaqjqmpDVW0499xzt1f9AGBV7brWFQAAIJuS7DuxvE+Ss6fSrE9yUlUlyR5JDquqS1trb5pM1Fo7PsnxSbJ+/frpgAcA7BQEKwAA1t6pSW5eVfsn+XqShyZ52GSC1tr+S4+r6sQkb5kOVADAlYVgBQDAGmutXVpVx6Tf5WOXJCe01k6vqqOH512nAoCrFMEKAIARaK2dkuSUqXUzgxSttSNXo04AsFZcYBMAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAAAAYFcEKAAAAYFQEKwAAAIBREawAAAAARkWwAgAAABgVwQoAAABgVAQrAAAAgFERrAAAAABGRbACAAAAGBXBCgAAAGBUBCsAAACAURGsAAAAAEZFsAIAYASq6pCqOqOqNlbVk2Y8//Cq+vTw9+Gquu1a1BMAVoNgBQDAGquqXZK8IMmhSQ5IckRVHTCV7CtJ7tZau02Spyc5fnVrCQCrR7ACAGDtHZhkY2vty621S5KclOTwyQSttQ+31r4zLH40yT6rXEcAWDWCFQAAa2/vJGdNLG8a1s3zmCRv26E1AoA1tOtaVwAAgNSMdW1mwqp7pAcr7jzn+aOSHJUk++233/aqHwCsKiMrAADW3qYk+04s75Pk7OlEVXWbJC9Ncnhr7duzNtRaO761tr61tn7PPffcIZUFgB1NsAIAYO2dmuTmVbV/VV0jyUOTnDyZoKr2S/KGJL/TWvvCGtQRAFaNaSAAAGustXZpVR2T5O1JdklyQmvt9Ko6enj+uCRPSXKDJC+sqiS5tLW2fq3qDAA7UrU2czokAAA7ufXr17cNGzasdTUAuIqqqtO2NrBuGggAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKgIVgAAAACjIlgBAAAAjIpgBQAAADAqghUAAADAqAhWAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAwAhU1SFVdUZVbayqJ814vqrqn4fnP11Vt1+LegLAahCsAABYY1W1S5IXJDk0yQFJjqiqA6aSHZrk5sPfUUletKqVBIBVJFgBALD2DkyysbX25dbaJUlOSnL4VJrDk7yidR9NsntV3Xi1KwoAq0GwAgBg7e2d5KyJ5U3Dui1NAwBXCruudQUAAEjNWNe2Ik2q6qj0aSJJ8qOq+uw21o3L7ZHkvLWuxJWEtty+tOf2oy23r1tubUbBCgCAtbcpyb4Ty/skOXsr0qS1dnyS45Okqja01tZv36pedWnP7Udbbl/ac/vRlttXVW3Y2rymgQAArL1Tk9y8qvavqmskeWiSk6fSnJzkEcNdQe6Y5LuttW+sdkUBYDUYWQEAsMZaa5dW1TFJ3p5klyQntNZOr6qjh+ePS3JKksOSbExyUZJHrVV9AWBHE6wAABiB1top6QGJyXXHTTxuSR67hZs9fjtUjctpz+1HW25f2nP70Zbb11a3Z/XvPQAAAIBxcM0KAAAAYFQEKwAAdnJVdUhVnVFVG6vqSTOer6r65+H5T1fV7deinjuDFbTlw4c2/HRVfbiqbrsW9dxZLNeeE+l+pap+UlUPWs367UxW0pZVdfeq+mRVnV5V71vtOu5MVvBZ/9mqenNVfWpoT9cJmqOqTqiqc+bdKntrv4MEKwAAdmJVtUuSFyQ5NMkBSY6oqgOmkh2a5ObD31FJXrSqldxJrLAtv5Lkbq212yR5esxvn2uF7bmU7lnpF5hlhpW0ZVXtnuSFSe7fWvvFJA9e7XruLFbYNx+b5H9ba7dNcvck/zDcrYkrOjHJIQue36rvIMEKAICd24FJNrbWvtxauyTJSUkOn0pzeJJXtO6jSXavqhuvdkV3Asu2ZWvtw6217wyLH02yzyrXcWeykr6ZJH+U5PVJzlnNyu1kVtKWD0vyhtba15KktaY951tJe7Yk162qSnKdJOcnuXR1q7lzaK29P7195tmq7yDBCgCAndveSc6aWN40rNvSNGx5Oz0mydt2aI12bsu2Z1XtneQBSY4Li6ykb94iyfWr6r1VdVpVPWLVarfzWUl7Pj/JLyQ5O8lnkvxJa+2y1anelc5WfQe5dSkAwM6tZqybvt3bStKwBe1UVfdID1bceYfWaOe2kvZ8XpInttZ+0k9gM8dK2nLXJHdIcs8k10rykar6aGvtCzu6cjuhlbTnryf5ZJKDk/x8kndW1Qdaa9/bwXW7Mtqq7yDBCgCAndumJPtOLO+TfiZwS9OwwnaqqtskeWmSQ1tr316luu2MVtKe65OcNAQq9khyWFVd2lp706rUcOex0s/5ea21HyT5QVW9P8ltkwhWXNFK2vNRSZ7ZWmtJNlbVV5LcKsnHV6eKVypb9R1kGggAwM7t1CQ3r6r9h4u/PTTJyVNpTk7yiOGK7HdM8t3W2jdWu6I7gWXbsqr2S/KGJL/jjPWylm3P1tr+rbV1rbV1SV6X5A8FKmZayef8P5Pcpap2rardkvxqks+tcj13Fitpz6+lj1JJVd0oyS2TfHlVa3nlsVXfQUZWAADsxFprl1bVMel3UtglyQmttdOr6ujh+eOSnJLksCQbk1yUfsaQKStsy6ckuUGSFw6jAS5tra1fqzqP2QrbkxVYSVu21j5XVf+V5NNJLkvy0tbazFtJXtWtsG8+PcmJVfWZ9GkMT2ytnbdmlR6xqnp1+h1T9qiqTUmemuTqybZ9B1Uf1QIAAAAwDqaBAAAAAKMiWAEAAACMimAFAAAAMCqCFQAAAMCoCFYAAAAAoyJYAQAAAIyKYAUAAAAwKoIVAAAAwKj8/5j5qJ6jsPUeAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 1080x476.769 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
@@ -499,7 +553,7 @@
"id": "bae85d86-157c-47a2-9826-860b410a440e",
"metadata": {},
"source": [
- "Above figure in paper: <br>\n",
+ "Comparable figure in our publication: <br>\n",
"Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ <br>\n",
"Multi-scale account of the network structure of macaque visual cortex <br>\n",
"Brain Structure and Function (2018), 223: 1409 [https://doi.org/10.1007/s00429-017-1554-4](https://doi.org/10.1007/s00429-017-1554-4) <br> \n",
diff --git a/multiarea_model/data_multiarea/viscortex_processed_data.json b/multiarea_model/data_multiarea/viscortex_processed_data.json
index d53318895e1641bc1ce6b69311cdb8b58b7dcad6..cad996fffceaf6f4b361bb3fd244426bb5cef78b 100644
--- a/multiarea_model/data_multiarea/viscortex_processed_data.json
+++ b/multiarea_model/data_multiarea/viscortex_processed_data.json
@@ -1 +1 @@
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"3"], "target_pattern": ["3", 2.0, 2.0, "0", 2.0, 2.0]}, "V3": {"source_pattern": ["0", 2, 2, "0", 2, "3"], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "V3A": {"source_pattern": ["0", "0", "0", "0", 2, 2], "target_pattern": null}, "V4": {"source_pattern": ["0", 2, 2, "0", "3", "3"], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "V4t": {"source_pattern": ["0", 2, 2, "0", "3", "3"], "target_pattern": null}, "VIP": {"source_pattern": null, "target_pattern": null}, "STPa": {"target_pattern": null, "source_pattern": null}, "VOT": {"target_pattern": null, "source_pattern": null}, "MSTl": {"target_pattern": null, "source_pattern": null}, "46": {"target_pattern": null, "source_pattern": null}, "CITd": {"target_pattern": null, "source_pattern": null}, "FST": {"target_pattern": null, "source_pattern": null}, "AITd": {"target_pattern": null, "source_pattern": null}, "AITv": {"target_pattern": null, "source_pattern": null}, "MSTd": {"target_pattern": null, "source_pattern": null}, 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