From 7a1d2e6a6c862cb70a4159b36e9ebab004d5f09a Mon Sep 17 00:00:00 2001
From: didihou <didi.hou@rwth-aachen.de>
Date: Thu, 14 Sep 2023 12:52:53 +0200
Subject: [PATCH] /

---
 ...lize_interareal_connectivity-checkpoint.py | 11 ++-
 .../M2E_visualize_interareal_connectivity.py  | 11 ++-
 multi-area-model.ipynb                        | 89 ++++---------------
 .../viscortex_processed_data.json             |  2 +-
 4 files changed, 36 insertions(+), 77 deletions(-)

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 b3ac01b..faf1a6e 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
@@ -87,6 +87,7 @@ def visualize_interareal_connectivity(M):
     pl.rcParams['figure.figsize'] = (width, height)
 
     fig = pl.figure()
+    fig.suptitle('Your Title Here', fontsize=16)
     axes = {}
 
     # gs1 = gridspec.GridSpec(2, 2)
@@ -106,8 +107,12 @@ def visualize_interareal_connectivity(M):
     # print(pos.x1 - pos.x0 - 0.025)
 
     # labels = ['A', 'B', 'C', 'D']
-    labels = ['Full-scale model', 'Down scale model']
-    for label in labels:
+    labels = ['B', 'D']
+    labels_display = ['Full-scale model', 'Down-scale model']
+    # for label in labels:
+    for i in range(labels):
+        label = labels[i]
+        label_display = labels_display[i]
         if label in ['C']:
             label_pos = [-0.045, 1.18]
         else:
@@ -116,7 +121,7 @@ def visualize_interareal_connectivity(M):
         #         fontdict={'fontsize': 10, 'weight': 'bold',
         #                   'horizontalalignment': 'left', 'verticalalignment':
         #                   'bottom'}, transform=axes[label].transAxes)
-        pl.text(label_pos[0], label_pos[1], label,
+        pl.text(label_pos[0], label_pos[1], label_display,
                  fontdict={'fontsize': 10, 'weight': 'bold', 
                            'horizontalalignment': 'left', 'verticalalignment': 
                            'bottom'}, transform=axes[label].transAxes)
diff --git a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
index b3ac01b..faf1a6e 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
@@ -87,6 +87,7 @@ def visualize_interareal_connectivity(M):
     pl.rcParams['figure.figsize'] = (width, height)
 
     fig = pl.figure()
+    fig.suptitle('Your Title Here', fontsize=16)
     axes = {}
 
     # gs1 = gridspec.GridSpec(2, 2)
@@ -106,8 +107,12 @@ def visualize_interareal_connectivity(M):
     # print(pos.x1 - pos.x0 - 0.025)
 
     # labels = ['A', 'B', 'C', 'D']
-    labels = ['Full-scale model', 'Down scale model']
-    for label in labels:
+    labels = ['B', 'D']
+    labels_display = ['Full-scale model', 'Down-scale model']
+    # for label in labels:
+    for i in range(labels):
+        label = labels[i]
+        label_display = labels_display[i]
         if label in ['C']:
             label_pos = [-0.045, 1.18]
         else:
@@ -116,7 +121,7 @@ def visualize_interareal_connectivity(M):
         #         fontdict={'fontsize': 10, 'weight': 'bold',
         #                   'horizontalalignment': 'left', 'verticalalignment':
         #                   'bottom'}, transform=axes[label].transAxes)
-        pl.text(label_pos[0], label_pos[1], label,
+        pl.text(label_pos[0], label_pos[1], label_display,
                  fontdict={'fontsize': 10, 'weight': 'bold', 
                            'horizontalalignment': 'left', 'verticalalignment': 
                            'bottom'}, transform=axes[label].transAxes)
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index c52c647..3279c09 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -476,17 +476,22 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 4.414528491070678\n"
+     "ename": "KeyError",
+     "evalue": "'Full-scale model'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\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:122\u001b[0m, in \u001b[0;36mvisualize_interareal_connectivity\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m    114\u001b[0m         label_pos \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.2\u001b[39m, \u001b[38;5;241m1.04\u001b[39m]\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;66;03m# pl.text(label_pos[0], label_pos[1], r'\\bfseries{}' + label,\u001b[39;00m\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;66;03m#         fontdict={'fontsize': 10, 'weight': 'bold',\u001b[39;00m\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;66;03m#                   'horizontalalignment': 'left', 'verticalalignment':\u001b[39;00m\n\u001b[1;32m    118\u001b[0m     \u001b[38;5;66;03m#                   'bottom'}, transform=axes[label].transAxes)\u001b[39;00m\n\u001b[1;32m    119\u001b[0m     pl\u001b[38;5;241m.\u001b[39mtext(label_pos[\u001b[38;5;241m0\u001b[39m], label_pos[\u001b[38;5;241m1\u001b[39m], label,\n\u001b[1;32m    120\u001b[0m              fontdict\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfontsize\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbold\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m    121\u001b[0m                        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhorizontalalignment\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mverticalalignment\u001b[39m\u001b[38;5;124m'\u001b[39m: \n\u001b[0;32m--> 122\u001b[0m                        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbottom\u001b[39m\u001b[38;5;124m'\u001b[39m}, transform\u001b[38;5;241m=\u001b[39m\u001b[43maxes\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mtransAxes)\n\u001b[1;32m    124\u001b[0m \u001b[38;5;66;03m# \"\"\"\u001b[39;00m\n\u001b[1;32m    125\u001b[0m \u001b[38;5;66;03m# Load data\u001b[39;00m\n\u001b[1;32m    126\u001b[0m \u001b[38;5;66;03m# \"\"\"\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;66;03m# cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)\u001b[39;00m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;66;03m# cbar.set_alpha(0.)\u001b[39;00m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;124;03mPanel B: Interareal connectivity of full-scaling multi-area model\u001b[39;00m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'Full-scale model'"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 720x317.846 with 4 Axes>"
+       "<Figure size 1080x476.769 with 2 Axes>"
       ]
      },
      "metadata": {
@@ -531,32 +536,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "15778e9c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Sep 14 12:43:41 SimulationManager::set_status [Info]: \n",
-      "    Temporal resolution changed from 0.1 to 0.1 ms.\n",
-      "\n",
-      "Sep 14 12:44:06 NodeManager::prepare_nodes [Info]: \n",
-      "    Preparing 20780 nodes for simulation.\n",
-      "\n",
-      "Sep 14 12:44:06 SimulationManager::start_updating_ [Info]: \n",
-      "    Number of local nodes: 20780\n",
-      "    Simulation time (ms): 2000\n",
-      "    Number of OpenMP threads: 1\n",
-      "    Number of MPI processes: 1\n",
-      "\n",
-      "Sep 14 12:45:30 SimulationManager::run [Info]: \n",
-      "    Simulation finished.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%capture captured\n",
     "# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.\n",
@@ -583,18 +566,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "c1d7aa61-e85a-4e6a-9e01-e018413a572b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loading spikes\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Instantiate an analysis class and load spike data\n",
     "A = Analysis(network=M, \n",
@@ -615,23 +590,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "bea30fc8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x317.846 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from M2E_visualize_instantaneous_and_mean_firing_rates import plot_instan_mean_firing_rate\n",
     "plot_instan_mean_firing_rate(M)"
@@ -655,25 +617,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "ae19bcc3",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'original_data_path' from 'helpers' (/opt/app-root/src/MAM2EBRAINS/./figures/Schmidt2018/helpers.py)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_resting_state\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_resting_state\n\u001b[1;32m      2\u001b[0m plot_resting_state(M, A, data_path)\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_resting_state.py:8\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m      6\u001b[0m sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./figures/Schmidt2018_dyn\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhelpers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m original_data_path, population_labels\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmultiarea_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MultiAreaModel\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplotcolors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m myred, myblue\n",
-      "\u001b[0;31mImportError\u001b[0m: cannot import name 'original_data_path' from 'helpers' (/opt/app-root/src/MAM2EBRAINS/./figures/Schmidt2018/helpers.py)"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from M2E_visualize_resting_state import plot_resting_state\n",
     "plot_resting_state(M, A, data_path)"
diff --git a/multiarea_model/data_multiarea/viscortex_processed_data.json b/multiarea_model/data_multiarea/viscortex_processed_data.json
index 3e1921c..7f28f07 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", 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\ No newline at end of file
-- 
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