From c851bc4a752580605f390295a009c1f5566f949e Mon Sep 17 00:00:00 2001
From: didihou <didi.hou@rwth-aachen.de>
Date: Thu, 14 Sep 2023 12:31:36 +0200
Subject: [PATCH] /

---
 .ipynb_checkpoints/multi-area-model-checkpoint.ipynb | 12 ++++++------
 ...E_visualize_interareal_connectivity-checkpoint.py |  6 +++---
 .../M2E_visualize_interareal_connectivity.py         |  6 +++---
 multi-area-model.ipynb                               | 12 ++++++------
 .../data_multiarea/viscortex_processed_data.json     |  2 +-
 5 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index 1fbd35a..fd54d27 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -228,15 +228,15 @@
    "metadata": {},
    "source": [
     "1. `scale_down_to` <br>\n",
-    "`scale_down_to` is the down-scaling factor which defines the the ratio of the full scale multi-area model being down-scaled to a model with fewer neurons and indegrees so as to be simulated on machines with lower computational ability and the simulation results can be obtained within relative shorter period of time. <br> Its deafualt value if `1.` meaning full scale simulation. <br> In the pre-set downscale version, it's set as `0.005`, where the numer of neurons and indegrees are both scaled down to 0.5% of its full scale amount, where the model can usually be simulated on a local machine. <br> **Warning**: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow <br> \n",
+    "scale_down_to is the down-scaling factor which defines the the ratio of the full scale multi-area model being down-scaled to a model with fewer neurons and indegrees so as to be simulated on machines with lower computational ability and the simulation results can be obtained within relative shorter period of time. <br> Its deafualt value if `1.` meaning full scale simulation. <br> In the pre-set downscale version, it's set as `0.005`, where the numer of neurons and indegrees are both scaled down to 0.5% of its full scale amount, where the model can usually be simulated on a local machine. <br> **Warning**: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow <br> \n",
     "2. `cc_weights_factor` <br>\n",
     "This scaling factor controls the cortico-cortical synaptic strength. <br> By default it's set as `1.0`, where the inter-area synaptic strength is the same as the intra-areal. <br> **Important**: This factor changes the network activity from ground state to metastable state. <br>\n",
     "3. `areas_simulated` <br>\n",
     "This parameter specifies the cortical areas included in the simulation process. Its default value is `complete_area_list` meaning all the areas in the complete_area_list will be actually simulated. <br>\n",
-    "complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH'] <br>\n",
+    "complete_area_list = `['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']` <br>\n",
     "The value assigned to simulation_areas can be any sublist of the compete_area_list specifying areas a user want to include in his/her simulation. <br>\n",
     "4. `replace_non_simulated_areas` <br>\n",
-    "The paramter `replace_non_simulated_areas` defines how non-simulated areas will be replaced. <br> It's set as `None` by default when the parameter areas_simulated is set as full_area_list where all areas will be simulated so that no areas need to be replaced. <br> Other options are: `'hom_poisson_stat'`, `'het_poisson_stat'`, and `'het_current_nonstat'`. `'hom_poisson_stat'` is a manually set parameter which can be tuned. When it's set as 'het_poisson_stat' or 'het_current_nonstat', the data to replace the cortico-cortical input is loaded from 'replace_cc_input_source' which is the firing rates of our full scale simulation results. The differenc between 'het_poisson_stat' and 'het_current_nonstat' is that 'het_poisson_stat' is the mean of the time-series firing rate so that it's static, yet 'het_current_nonstat' is time-varying specific current, which is varying by time. "
+    "The paramter replace_non_simulated_areas defines how non-simulated areas will be replaced. <br> It's set as `None` by default when the parameter areas_simulated is set as full_area_list where all areas will be simulated so that no areas need to be replaced. <br> Other options are: `'hom_poisson_stat'`, `'het_poisson_stat'`, and `'het_current_nonstat'`. `'hom_poisson_stat'` is a manually set parameter which can be tuned. When it's set as 'het_poisson_stat' or 'het_current_nonstat', the data to replace the cortico-cortical input is loaded from 'replace_cc_input_source' which is the firing rates of our full scale simulation results. The differenc between 'het_poisson_stat' and 'het_current_nonstat' is that 'het_poisson_stat' is the mean of the time-series firing rate so that it's static, yet 'het_current_nonstat' is time-varying specific current, which is varying by time. "
    ]
   },
   {
@@ -380,7 +380,7 @@
     "                   simulation=True,\n",
     "                   sim_spec=sim_params,\n",
     "                   theory=True,\n",
-    "                   theory_spec=theory_params);"
+    "                   theory_spec=theory_params)"
    ]
   },
   {
@@ -499,7 +499,7 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\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:236\u001b[0m, in \u001b[0;36mvisualize_interareal_connectivity\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m    233\u001b[0m X, Y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmeshgrid(x, y)\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[0;32m--> 236\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    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[1;32m    239\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~/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",
@@ -508,7 +508,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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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 6988458..ed5161a 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
@@ -229,14 +229,14 @@ 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)])
     # 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.)
+    # ax.set_yticklabels(area_list[::-1], size=6.)
 
     ax.set_ylabel('Target area')
     ax.set_xlabel('Source area')
@@ -361,7 +361,7 @@ def visualize_interareal_connectivity(M):
     # 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.)
+    # ax.set_yticklabels(area_list[::-1], size=6.)
 
     ax.set_ylabel('Target area')
     ax.set_xlabel('Source area')
diff --git a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
index 6988458..ed5161a 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
@@ -229,14 +229,14 @@ 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)])
     # 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.)
+    # ax.set_yticklabels(area_list[::-1], size=6.)
 
     ax.set_ylabel('Target area')
     ax.set_xlabel('Source area')
@@ -361,7 +361,7 @@ def visualize_interareal_connectivity(M):
     # 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.)
+    # ax.set_yticklabels(area_list[::-1], size=6.)
 
     ax.set_ylabel('Target area')
     ax.set_xlabel('Source area')
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 1fbd35a..fd54d27 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -228,15 +228,15 @@
    "metadata": {},
    "source": [
     "1. `scale_down_to` <br>\n",
-    "`scale_down_to` is the down-scaling factor which defines the the ratio of the full scale multi-area model being down-scaled to a model with fewer neurons and indegrees so as to be simulated on machines with lower computational ability and the simulation results can be obtained within relative shorter period of time. <br> Its deafualt value if `1.` meaning full scale simulation. <br> In the pre-set downscale version, it's set as `0.005`, where the numer of neurons and indegrees are both scaled down to 0.5% of its full scale amount, where the model can usually be simulated on a local machine. <br> **Warning**: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow <br> \n",
+    "scale_down_to is the down-scaling factor which defines the the ratio of the full scale multi-area model being down-scaled to a model with fewer neurons and indegrees so as to be simulated on machines with lower computational ability and the simulation results can be obtained within relative shorter period of time. <br> Its deafualt value if `1.` meaning full scale simulation. <br> In the pre-set downscale version, it's set as `0.005`, where the numer of neurons and indegrees are both scaled down to 0.5% of its full scale amount, where the model can usually be simulated on a local machine. <br> **Warning**: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow <br> \n",
     "2. `cc_weights_factor` <br>\n",
     "This scaling factor controls the cortico-cortical synaptic strength. <br> By default it's set as `1.0`, where the inter-area synaptic strength is the same as the intra-areal. <br> **Important**: This factor changes the network activity from ground state to metastable state. <br>\n",
     "3. `areas_simulated` <br>\n",
     "This parameter specifies the cortical areas included in the simulation process. Its default value is `complete_area_list` meaning all the areas in the complete_area_list will be actually simulated. <br>\n",
-    "complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH'] <br>\n",
+    "complete_area_list = `['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']` <br>\n",
     "The value assigned to simulation_areas can be any sublist of the compete_area_list specifying areas a user want to include in his/her simulation. <br>\n",
     "4. `replace_non_simulated_areas` <br>\n",
-    "The paramter `replace_non_simulated_areas` defines how non-simulated areas will be replaced. <br> It's set as `None` by default when the parameter areas_simulated is set as full_area_list where all areas will be simulated so that no areas need to be replaced. <br> Other options are: `'hom_poisson_stat'`, `'het_poisson_stat'`, and `'het_current_nonstat'`. `'hom_poisson_stat'` is a manually set parameter which can be tuned. When it's set as 'het_poisson_stat' or 'het_current_nonstat', the data to replace the cortico-cortical input is loaded from 'replace_cc_input_source' which is the firing rates of our full scale simulation results. The differenc between 'het_poisson_stat' and 'het_current_nonstat' is that 'het_poisson_stat' is the mean of the time-series firing rate so that it's static, yet 'het_current_nonstat' is time-varying specific current, which is varying by time. "
+    "The paramter replace_non_simulated_areas defines how non-simulated areas will be replaced. <br> It's set as `None` by default when the parameter areas_simulated is set as full_area_list where all areas will be simulated so that no areas need to be replaced. <br> Other options are: `'hom_poisson_stat'`, `'het_poisson_stat'`, and `'het_current_nonstat'`. `'hom_poisson_stat'` is a manually set parameter which can be tuned. When it's set as 'het_poisson_stat' or 'het_current_nonstat', the data to replace the cortico-cortical input is loaded from 'replace_cc_input_source' which is the firing rates of our full scale simulation results. The differenc between 'het_poisson_stat' and 'het_current_nonstat' is that 'het_poisson_stat' is the mean of the time-series firing rate so that it's static, yet 'het_current_nonstat' is time-varying specific current, which is varying by time. "
    ]
   },
   {
@@ -380,7 +380,7 @@
     "                   simulation=True,\n",
     "                   sim_spec=sim_params,\n",
     "                   theory=True,\n",
-    "                   theory_spec=theory_params);"
+    "                   theory_spec=theory_params)"
    ]
   },
   {
@@ -499,7 +499,7 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\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:236\u001b[0m, in \u001b[0;36mvisualize_interareal_connectivity\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m    233\u001b[0m X, Y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmeshgrid(x, y)\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[0;32m--> 236\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    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[1;32m    239\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~/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",
@@ -508,7 +508,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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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 e11bfe2..e2ee906 100644
--- a/multiarea_model/data_multiarea/viscortex_processed_data.json
+++ b/multiarea_model/data_multiarea/viscortex_processed_data.json
@@ -1 +1 @@
-{"cocomac_completed": {"V1": {"7a": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "IT": {"source_pattern": ["0", "0", 2, "0", "0", "3"], "target_pattern": [2.0, "0", "0", "0", "0", "0"]}, "LIP": {"source_pattern": ["0", "0", "0", "0", "0", "2"], "target_pattern": null}, "MT": {"source_pattern": ["0", "0", 2, "0", 2, 2], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "PIP": {"source_pattern": null, "target_pattern": null}, "PO": {"source_pattern": null, "target_pattern": null}, "STP": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "STPp": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "TF": {"source_pattern": ["0", "0", "0", "0", "0", 2], "target_pattern": [2.0, "0", "0", "0", "0", "0"]}, "TH": {"source_pattern": ["0", "0", "0", "0", "0", 2], "target_pattern": null}, "V1": {"source_pattern": null, "target_pattern": [2.0, 2.0, 2.0, "0", "3", "3"]}, "V2": {"source_pattern": ["0", 2, 2, "0", "3", "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}, "MSTd": {"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}, "FEF": {"target_pattern": null, "source_pattern": null}, "PITd": {"target_pattern": null, "source_pattern": null}, "VOT": {"target_pattern": null, "source_pattern": null}, "VP": {"target_pattern": null, "source_pattern": null}, "PITv": {"target_pattern": null, "source_pattern": null}, 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\ No newline at end of file
-- 
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