From c004fcae6524ad38a55488c22db0b64cb8e09b59 Mon Sep 17 00:00:00 2001
From: didihou <didi.hou@rwth-aachen.de>
Date: Thu, 14 Sep 2023 12:33:34 +0200
Subject: [PATCH] /

---
 .../multi-area-model-checkpoint.ipynb             | 15 ++++++---------
 ...isualize_interareal_connectivity-checkpoint.py |  2 +-
 .../M2E_visualize_interareal_connectivity.py      |  2 +-
 multi-area-model.ipynb                            | 15 ++++++---------
 .../data_multiarea/viscortex_processed_data.json  |  2 +-
 5 files changed, 15 insertions(+), 21 deletions(-)

diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index fd54d27..3146605 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -492,23 +492,20 @@
      ]
     },
     {
-     "ename": "ValueError",
-     "evalue": "The number of FixedLocator locations (33), usually from a call to set_ticks, does not match the number of ticklabels (32).",
+     "ename": "UnboundLocalError",
+     "evalue": "local variable 'y' referenced before assignment",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;31mUnboundLocalError\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:239\u001b[0m, in \u001b[0;36mvisualize_interareal_connectivity\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;66;03m# ax.set_xticklabels(area_list, rotation=90, size=6.)\u001b[39;00m\n\u001b[1;32m    238\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[0;32m--> 239\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_yticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43marea_list\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[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    241\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTarget area\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    242\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSource area\u001b[39m\u001b[38;5;124m'\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)."
+      "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py:233\u001b[0m, in \u001b[0;36mvisualize_interareal_connectivity\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m    231\u001b[0m x \u001b[38;5;241m=\u001b[39m 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)\n\u001b[1;32m    232\u001b[0m \u001b[38;5;66;03m# y = np.arange(0, len(area_list[::-1]) + 1)\u001b[39;00m\n\u001b[0;32m--> 233\u001b[0m X, Y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmeshgrid(x, \u001b[43my\u001b[49m)\n\u001b[1;32m    235\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[1;32m    236\u001b[0m \u001b[38;5;66;03m# ax.set_xticklabels(area_list, rotation=90, size=6.)\u001b[39;00m\n",
+      "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'y' referenced before assignment"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 493.603x435.805 with 2 Axes>"
       ]
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 ed5161a..1eafc1e 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
@@ -229,7 +229,7 @@ def visualize_interareal_connectivity(M):
     cmap.set_bad('w', 1.0)
 
     x = np.arange(0, len(area_list) + 1)
-    # y = np.arange(0, len(area_list[::-1]) + 1)
+    y = np.arange(0, len(area_list[::-1]) + 1)
     X, Y = np.meshgrid(x, y)
 
     ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
diff --git a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
index ed5161a..1eafc1e 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
@@ -229,7 +229,7 @@ def visualize_interareal_connectivity(M):
     cmap.set_bad('w', 1.0)
 
     x = np.arange(0, len(area_list) + 1)
-    # y = np.arange(0, len(area_list[::-1]) + 1)
+    y = np.arange(0, len(area_list[::-1]) + 1)
     X, Y = np.meshgrid(x, y)
 
     ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index fd54d27..3146605 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -492,23 +492,20 @@
      ]
     },
     {
-     "ename": "ValueError",
-     "evalue": "The number of FixedLocator locations (33), usually from a call to set_ticks, does not match the number of ticklabels (32).",
+     "ename": "UnboundLocalError",
+     "evalue": "local variable 'y' referenced before assignment",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;31mUnboundLocalError\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:239\u001b[0m, in \u001b[0;36mvisualize_interareal_connectivity\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;66;03m# ax.set_xticklabels(area_list, rotation=90, size=6.)\u001b[39;00m\n\u001b[1;32m    238\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[0;32m--> 239\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_yticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43marea_list\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[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    241\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTarget area\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    242\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSource area\u001b[39m\u001b[38;5;124m'\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)."
+      "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py:233\u001b[0m, in \u001b[0;36mvisualize_interareal_connectivity\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m    231\u001b[0m x \u001b[38;5;241m=\u001b[39m 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)\n\u001b[1;32m    232\u001b[0m \u001b[38;5;66;03m# y = np.arange(0, len(area_list[::-1]) + 1)\u001b[39;00m\n\u001b[0;32m--> 233\u001b[0m X, Y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmeshgrid(x, \u001b[43my\u001b[49m)\n\u001b[1;32m    235\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[1;32m    236\u001b[0m \u001b[38;5;66;03m# ax.set_xticklabels(area_list, rotation=90, size=6.)\u001b[39;00m\n",
+      "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'y' referenced before assignment"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 493.603x435.805 with 2 Axes>"
       ]
diff --git a/multiarea_model/data_multiarea/viscortex_processed_data.json b/multiarea_model/data_multiarea/viscortex_processed_data.json
index e2ee906..ab8d118 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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\ No newline at end of file
-- 
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