From 373f11872023d4c3443b45602d31eb9a0dee5c33 Mon Sep 17 00:00:00 2001
From: didihou <didi.hou@rwth-aachen.de>
Date: Thu, 14 Sep 2023 13:45:27 +0200
Subject: [PATCH] /

---
 .../multi-area-model-checkpoint.ipynb         | 134 +++++++++-----
 ...lize_interareal_connectivity-checkpoint.py | 169 ++++++++++--------
 .../M2E_visualize_interareal_connectivity.py  | 169 ++++++++++--------
 multi-area-model.ipynb                        | 134 +++++++++-----
 .../viscortex_processed_data.json             |   2 +-
 5 files changed, 363 insertions(+), 245 deletions(-)

diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index fc4796d..91e5874 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -480,7 +480,7 @@
      "output_type": "stream",
      "text": [
       "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 6714\n"
+      "RAND_DATA_LABEL 3966\n"
      ]
     },
     {
@@ -516,7 +516,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x476.769 with 4 Axes>"
       ]
@@ -563,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "15778e9c",
    "metadata": {},
    "outputs": [
@@ -573,73 +573,87 @@
      "text": [
       "Prepared simulation in 0.00 seconds.\n",
       "\n",
-      "Sep 14 13:31:34 SimulationManager::set_status [Info]: \n",
+      "Sep 14 13:33:20 SimulationManager::set_status [Info]: \n",
       "    Temporal resolution changed from 0.1 to 0.1 ms.\n",
       "Rank 0: created area V1 with 0 local nodes\n",
-      "Memory after V1 : 1514.34 MB\n",
+      "Memory after V1 : 1515.46 MB\n",
       "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1541.01 MB\n",
+      "Memory after V2 : 1542.18 MB\n",
       "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1570.18 MB\n",
+      "Memory after VP : 1571.23 MB\n",
       "Rank 0: created area V3 with 0 local nodes\n",
-      "Memory after V3 : 1598.54 MB\n",
+      "Memory after V3 : 1599.63 MB\n",
       "Rank 0: created area V3A with 0 local nodes\n",
-      "Memory after V3A : 1618.44 MB\n",
+      "Memory after V3A : 1619.41 MB\n",
       "Rank 0: created area MT with 0 local nodes\n",
-      "Memory after MT : 1643.94 MB\n",
+      "Memory after MT : 1645.06 MB\n",
       "Rank 0: created area V4t with 0 local nodes\n",
-      "Memory after V4t : 1668.87 MB\n",
+      "Memory after V4t : 1669.95 MB\n",
       "Rank 0: created area V4 with 0 local nodes\n",
-      "Memory after V4 : 1695.88 MB\n",
+      "Memory after V4 : 1696.96 MB\n",
       "Rank 0: created area VOT with 0 local nodes\n",
-      "Memory after VOT : 1720.45 MB\n",
+      "Memory after VOT : 1721.51 MB\n",
       "Rank 0: created area MSTd with 0 local nodes\n",
-      "Memory after MSTd : 1740.63 MB\n",
+      "Memory after MSTd : 1741.65 MB\n",
       "Rank 0: created area PIP with 0 local nodes\n",
-      "Memory after PIP : 1762.02 MB\n",
+      "Memory after PIP : 1762.99 MB\n",
       "Rank 0: created area PO with 0 local nodes\n",
-      "Memory after PO : 1783.39 MB\n",
+      "Memory after PO : 1784.48 MB\n",
       "Rank 0: created area DP with 0 local nodes\n",
-      "Memory after DP : 1803.69 MB\n",
+      "Memory after DP : 1804.71 MB\n",
       "Rank 0: created area MIP with 0 local nodes\n",
-      "Memory after MIP : 1825.26 MB\n",
+      "Memory after MIP : 1826.27 MB\n",
       "Rank 0: created area MDP with 0 local nodes\n",
-      "Memory after MDP : 1846.60 MB\n",
+      "Memory after MDP : 1847.73 MB\n",
       "Rank 0: created area VIP with 0 local nodes\n",
-      "Memory after VIP : 1868.53 MB\n",
+      "Memory after VIP : 1869.55 MB\n",
       "Rank 0: created area LIP with 0 local nodes\n",
-      "Memory after LIP : 1892.50 MB\n",
+      "Memory after LIP : 1893.60 MB\n",
       "Rank 0: created area PITv with 0 local nodes\n",
-      "Memory after PITv : 1917.82 MB\n",
+      "Memory after PITv : 1918.89 MB\n",
       "Rank 0: created area PITd with 0 local nodes\n",
-      "Memory after PITd : 1943.00 MB\n",
+      "Memory after PITd : 1944.12 MB\n",
       "Rank 0: created area MSTl with 0 local nodes\n",
-      "Memory after MSTl : 1964.21 MB\n",
+      "Memory after MSTl : 1965.46 MB\n",
       "Rank 0: created area CITv with 0 local nodes\n",
-      "Memory after CITv : 1983.17 MB\n",
+      "Memory after CITv : 1984.64 MB\n",
       "Rank 0: created area CITd with 0 local nodes\n",
-      "Memory after CITd : 2002.50 MB\n",
+      "Memory after CITd : 2004.00 MB\n",
       "Rank 0: created area FEF with 0 local nodes\n",
-      "Memory after FEF : 2023.88 MB\n",
+      "Memory after FEF : 2025.49 MB\n",
       "Rank 0: created area TF with 0 local nodes\n",
-      "Memory after TF : 2039.51 MB\n",
+      "Memory after TF : 2041.12 MB\n",
       "Rank 0: created area AITv with 0 local nodes\n",
-      "Memory after AITv : 2062.22 MB\n",
+      "Memory after AITv : 2063.71 MB\n",
       "Rank 0: created area FST with 0 local nodes\n",
-      "Memory after FST : 2078.82 MB\n",
+      "Memory after FST : 2080.43 MB\n",
       "Rank 0: created area 7a with 0 local nodes\n",
-      "Memory after 7a : 2100.10 MB\n",
+      "Memory after 7a : 2101.60 MB\n",
       "Rank 0: created area STPp with 0 local nodes\n",
-      "Memory after STPp : 2118.84 MB\n",
+      "Memory after STPp : 2120.31 MB\n",
       "Rank 0: created area STPa with 0 local nodes\n",
-      "Memory after STPa : 2137.98 MB\n",
+      "Memory after STPa : 2139.55 MB\n",
       "Rank 0: created area 46 with 0 local nodes\n",
-      "Memory after 46 : 2153.30 MB\n",
+      "Memory after 46 : 2154.92 MB\n",
       "Rank 0: created area AITd with 0 local nodes\n",
-      "Memory after AITd : 2175.89 MB\n",
+      "Memory after AITd : 2177.21 MB\n",
       "Rank 0: created area TH with 0 local nodes\n",
-      "Memory after TH : 2188.59 MB\n",
-      "Created areas and internal connections in 2.25 seconds.\n"
+      "Memory after TH : 2189.67 MB\n",
+      "Created areas and internal connections in 2.27 seconds.\n",
+      "Created cortico-cortical connections in 22.61 seconds.\n",
+      "Simulated network in 85.16 seconds.\n",
+      "\n",
+      "Sep 14 13:33:45 NodeManager::prepare_nodes [Info]: \n",
+      "    Preparing 20780 nodes for simulation.\n",
+      "\n",
+      "Sep 14 13:33:45 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 13:35:10 SimulationManager::run [Info]: \n",
+      "    Simulation finished.\n"
      ]
     }
    ],
@@ -669,10 +683,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "c1d7aa61-e85a-4e6a-9e01-e018413a572b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading spikes\n"
+     ]
+    }
+   ],
    "source": [
     "# Instantiate an analysis class and load spike data\n",
     "A = Analysis(network=M, \n",
@@ -693,10 +715,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "bea30fc8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x476.769 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from M2E_visualize_instantaneous_and_mean_firing_rates import plot_instan_mean_firing_rate\n",
     "plot_instan_mean_firing_rate(M)"
@@ -723,12 +758,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "ae19bcc3",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "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)"
+     ]
+    }
+   ],
    "source": [
     "from M2E_visualize_resting_state import plot_resting_state\n",
     "plot_resting_state(M, A, data_path)"
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 80e228c..4932f57 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
@@ -26,7 +26,7 @@ def visualize_interareal_connectivity(M):
     """
     # nrows = 2
     nrows = 1
-    ncols = 2
+    ncols = 3
     # width = 6.8556
     width = 15
     panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2.  # golden ratio
@@ -36,18 +36,17 @@ def visualize_interareal_connectivity(M):
     pl.rcParams['figure.figsize'] = (width, height)
 
     fig = pl.figure()
-    fig.suptitle('Interareal connectivity for full-scale (left) and down-scale (right) multi-area model', fontsize=15, y=1.05)
+    fig.suptitle('Interareal connectivity for full-scale (left) and down-scale (right) multi-area model', fontsize=14, y=1.05)
     axes = {}
 
     # gs1 = gridspec.GridSpec(2, 2)
-    gs1 = gridspec.GridSpec(1, 2)
+    gs1 = gridspec.GridSpec(1, 3)
     # gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.1, hspace=0.3)
     gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.3, hspace=0.3)
     
-    # axes['A'] = pl.subplot(gs1[:1, :1])
-    # axes['B'] = pl.subplot(gs1[:1, 1:2])
-    axes['B'] = pl.subplot(gs1[:1, :1])
-    axes['D'] = pl.subplot(gs1[:1, 1:2])
+    axes['A'] = pl.subplot(gs1[:1, :1])
+    axes['B'] = pl.subplot(gs1[:1, 1:2])
+    axes['D'] = pl.subplot(gs1[:1, 2:])
 
     # pos = axes['A'].get_position()
     pos2 = axes['D'].get_position()
@@ -56,8 +55,8 @@ def visualize_interareal_connectivity(M):
     # print(pos.x1 - pos.x0 - 0.025)
 
     # labels = ['A', 'B', 'C', 'D']
-    labels = ['B', 'D']
-    labels_display = ['Full-scale model', 'Down-scale model']
+    labels = ['A','B', 'D']
+    labels_display = ['Binary connectivity from CoCoMac', 'Full-scale model', 'Down-scale model']
     # for label in labels:
     for i in range(len(labels)):
         label = labels[i]
@@ -75,96 +74,102 @@ def visualize_interareal_connectivity(M):
                            'horizontalalignment': 'left', 'verticalalignment': 
                            'bottom'}, transform=axes[label].transAxes)
 
-    # """
-    # Load data
-    # """
+    """
+    Load data
+    """
     # M = MultiAreaModel({})
+    M_full_scale = MultiAreaModel({})
 
-    # with open(os.path.join(datapath, 'viscortex_processed_data.json'), 'r') as f:
-    #     proc = json.load(f)
-    # with open(os.path.join(datapath, 'viscortex_raw_data.json'), 'r') as f:
-    #     raw = json.load(f)
+    with open(os.path.join(datapath, 'viscortex_processed_data.json'), 'r') as f:
+        proc = json.load(f)
+    with open(os.path.join(datapath, 'viscortex_raw_data.json'), 'r') as f:
+        raw = json.load(f)
 
-    # FLN_Data_FV91 = proc['FLN_Data_FV91']
+    FLN_Data_FV91 = proc['FLN_Data_FV91']
 
-    # cocomac_data = raw['cocomac_data']
-    # median_distance_data = raw['median_distance_data']
+    cocomac_data = raw['cocomac_data']
+    median_distance_data = raw['median_distance_data']
 
-    # cocomac = np.zeros((32, 32))
-    # conn_matrix = np.zeros((32, 32))
-    # for i, area1 in enumerate(area_list[::-1]):
-    #     for j, area2 in enumerate(area_list):
-    #         if M.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
-    #             cocomac[i][j] = 1.
-    #         if area2 in FLN_Data_FV91[area1]:
-    #             conn_matrix[i][j] = FLN_Data_FV91[area1][area2]
+    cocomac = np.zeros((32, 32))
+    conn_matrix = np.zeros((32, 32))
+    for i, area1 in enumerate(area_list[::-1]):
+        for j, area2 in enumerate(area_list):
+            # if M.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
+            if M_full_scale.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
+                cocomac[i][j] = 1.
+            if area2 in FLN_Data_FV91[area1]:
+                conn_matrix[i][j] = FLN_Data_FV91[area1][area2]
 
-    # """
-    # Panel A: CoCoMac Data
-    # """
-    # ax = axes['A']
-    # ax.yaxis.set_ticks_position("left")
-    # ax.xaxis.set_ticks_position("bottom")
+    """
+    Panel A: CoCoMac Data
+    """
+    ax = axes['A']
+    ax.yaxis.set_ticks_position("left")
+    ax.xaxis.set_ticks_position("bottom")
 
-    # ax.set_aspect(1. / ax.get_data_ratio())
-    # ax.yaxis.set_ticks_position("none")
-    # ax.xaxis.set_ticks_position("none")
+    ax.set_aspect(1. / ax.get_data_ratio())
+    ax.yaxis.set_ticks_position("none")
+    ax.xaxis.set_ticks_position("none")
 
-    # masked_matrix = np.ma.masked_values(cocomac, 0.0)
-    # cmap = pl.cm.binary
-    # cmap.set_bad('w', 1.0)
+    masked_matrix = np.ma.masked_values(cocomac, 0.0)
+    cmap = pl.cm.binary
+    cmap.set_bad('w', 1.0)
 
-    # x = np.arange(0, len(area_list) + 1)
-    # y = np.arange(0, len(area_list[::-1]) + 1)
-    # X, Y = np.meshgrid(x, y)
+    x = np.arange(0, len(area_list) + 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_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
     # ax.set_xticklabels(area_list, rotation=90, size=6.)
+    ax.set_xticklabels(area_list, rotation=90, size=12.)
 
     # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
     # ax.set_yticklabels(area_list[::-1], size=6.)
+    ax.set_yticklabels(area_list[::-1], size=12.)
 
-    # ax.set_ylabel('Target area')
-    # ax.set_xlabel('Source area')
+    ax.set_ylabel('Target area')
+    ax.set_xlabel('Source area')
 
-    # im = ax.pcolormesh(masked_matrix, cmap=cmap,
-    #                    edgecolors='None', vmin=0., vmax=1.)
+    im = ax.pcolormesh(masked_matrix, cmap=cmap,
+                       edgecolors='None', vmin=0., vmax=1.)
 
-    # t = FixedLocator([])
-    # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
-    # cbar.set_alpha(0.)
-    # cbar.remove()
+    t = FixedLocator([])
+    cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    cbar.set_alpha(0.)
+    cbar.remove()
 
-    # """
-    # Panel B: Data from Markov et al. (2014) "A weighted and directed
-    # interareal connectivity matrix for macaque cerebral cortex."
-    # Cerebral Cortex, 24(1), 17–36.
-    # """
-    # ax = axes['B']
-    # ax.set_aspect(1. / ax.get_data_ratio())
-    # ax.yaxis.set_ticks_position("none")
-    # ax.xaxis.set_ticks_position("none")
+#     """
+#     Panel B: Data from Markov et al. (2014) "A weighted and directed
+#     interareal connectivity matrix for macaque cerebral cortex."
+#     Cerebral Cortex, 24(1), 17–36.
+#     """
+#     ax = axes['B']
+#     ax.set_aspect(1. / ax.get_data_ratio())
+#     ax.yaxis.set_ticks_position("none")
+#     ax.xaxis.set_ticks_position("none")
 
-    # masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
-    # cmap = pl.get_cmap('inferno')
-    # cmap.set_bad('w', 1.0)
+#     masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
+#     cmap = pl.get_cmap('inferno')
+#     cmap.set_bad('w', 1.0)
 
-    # x = np.arange(0, len(area_list) + 1)
-    # y = np.arange(0, len(area_list[::-1]) + 1)
-    # X, Y = np.meshgrid(x, y)
+#     x = np.arange(0, len(area_list) + 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_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_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+#     ax.set_yticklabels(area_list[::-1], size=6.)
 
-    # im = ax.pcolormesh(masked_matrix, cmap=cmap,
-    #                    edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
+#     im = ax.pcolormesh(masked_matrix, cmap=cmap,
+#                        edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
 
-    # t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
-    # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
-    # cbar.set_alpha(0.)
+#     t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
+#     cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+#     cbar.set_alpha(0.)
 
     """
     Panel B: Interareal connectivity of full-scaling multi-area model
@@ -191,11 +196,13 @@ def visualize_interareal_connectivity(M):
 
     # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
     ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
-    ax.set_xticklabels(area_list, rotation=90, size=6.)
+    # ax.set_xticklabels(area_list, rotation=90, size=6.)
+    ax.set_xticklabels(area_list, rotation=90, size=12.)
 
     # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
     ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
-    ax.set_yticklabels(area_list[::-1], size=6.)
+    # ax.set_yticklabels(area_list[::-1], size=6.)
+    ax.set_yticklabels(area_list[::-1], size=12.)
 
     ax.set_ylabel('Target area')
     ax.set_xlabel('Source area')
@@ -316,11 +323,15 @@ def visualize_interareal_connectivity(M):
     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_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
     # ax.set_xticklabels(area_list, rotation=90, size=6.)
+    ax.set_xticklabels(area_list, rotation=90, size=12.)
 
-    ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
     # ax.set_yticklabels(area_list[::-1], size=6.)
+    ax.set_yticklabels(area_list[::-1], size=12.)
 
     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 80e228c..4932f57 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
@@ -26,7 +26,7 @@ def visualize_interareal_connectivity(M):
     """
     # nrows = 2
     nrows = 1
-    ncols = 2
+    ncols = 3
     # width = 6.8556
     width = 15
     panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2.  # golden ratio
@@ -36,18 +36,17 @@ def visualize_interareal_connectivity(M):
     pl.rcParams['figure.figsize'] = (width, height)
 
     fig = pl.figure()
-    fig.suptitle('Interareal connectivity for full-scale (left) and down-scale (right) multi-area model', fontsize=15, y=1.05)
+    fig.suptitle('Interareal connectivity for full-scale (left) and down-scale (right) multi-area model', fontsize=14, y=1.05)
     axes = {}
 
     # gs1 = gridspec.GridSpec(2, 2)
-    gs1 = gridspec.GridSpec(1, 2)
+    gs1 = gridspec.GridSpec(1, 3)
     # gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.1, hspace=0.3)
     gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.3, hspace=0.3)
     
-    # axes['A'] = pl.subplot(gs1[:1, :1])
-    # axes['B'] = pl.subplot(gs1[:1, 1:2])
-    axes['B'] = pl.subplot(gs1[:1, :1])
-    axes['D'] = pl.subplot(gs1[:1, 1:2])
+    axes['A'] = pl.subplot(gs1[:1, :1])
+    axes['B'] = pl.subplot(gs1[:1, 1:2])
+    axes['D'] = pl.subplot(gs1[:1, 2:])
 
     # pos = axes['A'].get_position()
     pos2 = axes['D'].get_position()
@@ -56,8 +55,8 @@ def visualize_interareal_connectivity(M):
     # print(pos.x1 - pos.x0 - 0.025)
 
     # labels = ['A', 'B', 'C', 'D']
-    labels = ['B', 'D']
-    labels_display = ['Full-scale model', 'Down-scale model']
+    labels = ['A','B', 'D']
+    labels_display = ['Binary connectivity from CoCoMac', 'Full-scale model', 'Down-scale model']
     # for label in labels:
     for i in range(len(labels)):
         label = labels[i]
@@ -75,96 +74,102 @@ def visualize_interareal_connectivity(M):
                            'horizontalalignment': 'left', 'verticalalignment': 
                            'bottom'}, transform=axes[label].transAxes)
 
-    # """
-    # Load data
-    # """
+    """
+    Load data
+    """
     # M = MultiAreaModel({})
+    M_full_scale = MultiAreaModel({})
 
-    # with open(os.path.join(datapath, 'viscortex_processed_data.json'), 'r') as f:
-    #     proc = json.load(f)
-    # with open(os.path.join(datapath, 'viscortex_raw_data.json'), 'r') as f:
-    #     raw = json.load(f)
+    with open(os.path.join(datapath, 'viscortex_processed_data.json'), 'r') as f:
+        proc = json.load(f)
+    with open(os.path.join(datapath, 'viscortex_raw_data.json'), 'r') as f:
+        raw = json.load(f)
 
-    # FLN_Data_FV91 = proc['FLN_Data_FV91']
+    FLN_Data_FV91 = proc['FLN_Data_FV91']
 
-    # cocomac_data = raw['cocomac_data']
-    # median_distance_data = raw['median_distance_data']
+    cocomac_data = raw['cocomac_data']
+    median_distance_data = raw['median_distance_data']
 
-    # cocomac = np.zeros((32, 32))
-    # conn_matrix = np.zeros((32, 32))
-    # for i, area1 in enumerate(area_list[::-1]):
-    #     for j, area2 in enumerate(area_list):
-    #         if M.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
-    #             cocomac[i][j] = 1.
-    #         if area2 in FLN_Data_FV91[area1]:
-    #             conn_matrix[i][j] = FLN_Data_FV91[area1][area2]
+    cocomac = np.zeros((32, 32))
+    conn_matrix = np.zeros((32, 32))
+    for i, area1 in enumerate(area_list[::-1]):
+        for j, area2 in enumerate(area_list):
+            # if M.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
+            if M_full_scale.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
+                cocomac[i][j] = 1.
+            if area2 in FLN_Data_FV91[area1]:
+                conn_matrix[i][j] = FLN_Data_FV91[area1][area2]
 
-    # """
-    # Panel A: CoCoMac Data
-    # """
-    # ax = axes['A']
-    # ax.yaxis.set_ticks_position("left")
-    # ax.xaxis.set_ticks_position("bottom")
+    """
+    Panel A: CoCoMac Data
+    """
+    ax = axes['A']
+    ax.yaxis.set_ticks_position("left")
+    ax.xaxis.set_ticks_position("bottom")
 
-    # ax.set_aspect(1. / ax.get_data_ratio())
-    # ax.yaxis.set_ticks_position("none")
-    # ax.xaxis.set_ticks_position("none")
+    ax.set_aspect(1. / ax.get_data_ratio())
+    ax.yaxis.set_ticks_position("none")
+    ax.xaxis.set_ticks_position("none")
 
-    # masked_matrix = np.ma.masked_values(cocomac, 0.0)
-    # cmap = pl.cm.binary
-    # cmap.set_bad('w', 1.0)
+    masked_matrix = np.ma.masked_values(cocomac, 0.0)
+    cmap = pl.cm.binary
+    cmap.set_bad('w', 1.0)
 
-    # x = np.arange(0, len(area_list) + 1)
-    # y = np.arange(0, len(area_list[::-1]) + 1)
-    # X, Y = np.meshgrid(x, y)
+    x = np.arange(0, len(area_list) + 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_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
     # ax.set_xticklabels(area_list, rotation=90, size=6.)
+    ax.set_xticklabels(area_list, rotation=90, size=12.)
 
     # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
     # ax.set_yticklabels(area_list[::-1], size=6.)
+    ax.set_yticklabels(area_list[::-1], size=12.)
 
-    # ax.set_ylabel('Target area')
-    # ax.set_xlabel('Source area')
+    ax.set_ylabel('Target area')
+    ax.set_xlabel('Source area')
 
-    # im = ax.pcolormesh(masked_matrix, cmap=cmap,
-    #                    edgecolors='None', vmin=0., vmax=1.)
+    im = ax.pcolormesh(masked_matrix, cmap=cmap,
+                       edgecolors='None', vmin=0., vmax=1.)
 
-    # t = FixedLocator([])
-    # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
-    # cbar.set_alpha(0.)
-    # cbar.remove()
+    t = FixedLocator([])
+    cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    cbar.set_alpha(0.)
+    cbar.remove()
 
-    # """
-    # Panel B: Data from Markov et al. (2014) "A weighted and directed
-    # interareal connectivity matrix for macaque cerebral cortex."
-    # Cerebral Cortex, 24(1), 17–36.
-    # """
-    # ax = axes['B']
-    # ax.set_aspect(1. / ax.get_data_ratio())
-    # ax.yaxis.set_ticks_position("none")
-    # ax.xaxis.set_ticks_position("none")
+#     """
+#     Panel B: Data from Markov et al. (2014) "A weighted and directed
+#     interareal connectivity matrix for macaque cerebral cortex."
+#     Cerebral Cortex, 24(1), 17–36.
+#     """
+#     ax = axes['B']
+#     ax.set_aspect(1. / ax.get_data_ratio())
+#     ax.yaxis.set_ticks_position("none")
+#     ax.xaxis.set_ticks_position("none")
 
-    # masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
-    # cmap = pl.get_cmap('inferno')
-    # cmap.set_bad('w', 1.0)
+#     masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
+#     cmap = pl.get_cmap('inferno')
+#     cmap.set_bad('w', 1.0)
 
-    # x = np.arange(0, len(area_list) + 1)
-    # y = np.arange(0, len(area_list[::-1]) + 1)
-    # X, Y = np.meshgrid(x, y)
+#     x = np.arange(0, len(area_list) + 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_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_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+#     ax.set_yticklabels(area_list[::-1], size=6.)
 
-    # im = ax.pcolormesh(masked_matrix, cmap=cmap,
-    #                    edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
+#     im = ax.pcolormesh(masked_matrix, cmap=cmap,
+#                        edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
 
-    # t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
-    # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
-    # cbar.set_alpha(0.)
+#     t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
+#     cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+#     cbar.set_alpha(0.)
 
     """
     Panel B: Interareal connectivity of full-scaling multi-area model
@@ -191,11 +196,13 @@ def visualize_interareal_connectivity(M):
 
     # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
     ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
-    ax.set_xticklabels(area_list, rotation=90, size=6.)
+    # ax.set_xticklabels(area_list, rotation=90, size=6.)
+    ax.set_xticklabels(area_list, rotation=90, size=12.)
 
     # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
     ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
-    ax.set_yticklabels(area_list[::-1], size=6.)
+    # ax.set_yticklabels(area_list[::-1], size=6.)
+    ax.set_yticklabels(area_list[::-1], size=12.)
 
     ax.set_ylabel('Target area')
     ax.set_xlabel('Source area')
@@ -316,11 +323,15 @@ def visualize_interareal_connectivity(M):
     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_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
     # ax.set_xticklabels(area_list, rotation=90, size=6.)
+    ax.set_xticklabels(area_list, rotation=90, size=12.)
 
-    ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
     # ax.set_yticklabels(area_list[::-1], size=6.)
+    ax.set_yticklabels(area_list[::-1], size=12.)
 
     ax.set_ylabel('Target area')
     ax.set_xlabel('Source area')
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index fc4796d..e6e9efe 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -480,7 +480,7 @@
      "output_type": "stream",
      "text": [
       "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 6714\n"
+      "RAND_DATA_LABEL 2812\n"
      ]
     },
     {
@@ -516,7 +516,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x476.769 with 4 Axes>"
       ]
@@ -563,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "15778e9c",
    "metadata": {},
    "outputs": [
@@ -573,73 +573,75 @@
      "text": [
       "Prepared simulation in 0.00 seconds.\n",
       "\n",
-      "Sep 14 13:31:34 SimulationManager::set_status [Info]: \n",
+      "Sep 14 13:36:30 SimulationManager::set_status [Info]: \n",
       "    Temporal resolution changed from 0.1 to 0.1 ms.\n",
       "Rank 0: created area V1 with 0 local nodes\n",
-      "Memory after V1 : 1514.34 MB\n",
+      "Memory after V1 : 1514.97 MB\n",
       "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1541.01 MB\n",
+      "Memory after V2 : 1541.68 MB\n",
       "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1570.18 MB\n",
+      "Memory after VP : 1570.82 MB\n",
       "Rank 0: created area V3 with 0 local nodes\n",
-      "Memory after V3 : 1598.54 MB\n",
+      "Memory after V3 : 1599.06 MB\n",
       "Rank 0: created area V3A with 0 local nodes\n",
-      "Memory after V3A : 1618.44 MB\n",
+      "Memory after V3A : 1619.04 MB\n",
       "Rank 0: created area MT with 0 local nodes\n",
-      "Memory after MT : 1643.94 MB\n",
+      "Memory after MT : 1644.58 MB\n",
       "Rank 0: created area V4t with 0 local nodes\n",
-      "Memory after V4t : 1668.87 MB\n",
+      "Memory after V4t : 1669.54 MB\n",
       "Rank 0: created area V4 with 0 local nodes\n",
-      "Memory after V4 : 1695.88 MB\n",
+      "Memory after V4 : 1696.47 MB\n",
       "Rank 0: created area VOT with 0 local nodes\n",
-      "Memory after VOT : 1720.45 MB\n",
+      "Memory after VOT : 1721.05 MB\n",
       "Rank 0: created area MSTd with 0 local nodes\n",
-      "Memory after MSTd : 1740.63 MB\n",
+      "Memory after MSTd : 1741.16 MB\n",
       "Rank 0: created area PIP with 0 local nodes\n",
-      "Memory after PIP : 1762.02 MB\n",
+      "Memory after PIP : 1762.50 MB\n",
       "Rank 0: created area PO with 0 local nodes\n",
-      "Memory after PO : 1783.39 MB\n",
+      "Memory after PO : 1783.95 MB\n",
       "Rank 0: created area DP with 0 local nodes\n",
-      "Memory after DP : 1803.69 MB\n",
+      "Memory after DP : 1804.21 MB\n",
       "Rank 0: created area MIP with 0 local nodes\n",
-      "Memory after MIP : 1825.26 MB\n",
+      "Memory after MIP : 1825.82 MB\n",
       "Rank 0: created area MDP with 0 local nodes\n",
-      "Memory after MDP : 1846.60 MB\n",
+      "Memory after MDP : 1847.16 MB\n",
       "Rank 0: created area VIP with 0 local nodes\n",
-      "Memory after VIP : 1868.53 MB\n",
+      "Memory after VIP : 1869.14 MB\n",
       "Rank 0: created area LIP with 0 local nodes\n",
-      "Memory after LIP : 1892.50 MB\n",
+      "Memory after LIP : 1893.07 MB\n",
       "Rank 0: created area PITv with 0 local nodes\n",
-      "Memory after PITv : 1917.82 MB\n",
+      "Memory after PITv : 1918.38 MB\n",
       "Rank 0: created area PITd with 0 local nodes\n",
-      "Memory after PITd : 1943.00 MB\n",
+      "Memory after PITd : 1943.52 MB\n",
       "Rank 0: created area MSTl with 0 local nodes\n",
-      "Memory after MSTl : 1964.21 MB\n",
+      "Memory after MSTl : 1965.05 MB\n",
       "Rank 0: created area CITv with 0 local nodes\n",
-      "Memory after CITv : 1983.17 MB\n",
+      "Memory after CITv : 1984.18 MB\n",
       "Rank 0: created area CITd with 0 local nodes\n",
-      "Memory after CITd : 2002.50 MB\n",
+      "Memory after CITd : 2003.47 MB\n",
       "Rank 0: created area FEF with 0 local nodes\n",
-      "Memory after FEF : 2023.88 MB\n",
+      "Memory after FEF : 2024.96 MB\n",
       "Rank 0: created area TF with 0 local nodes\n",
-      "Memory after TF : 2039.51 MB\n",
+      "Memory after TF : 2040.63 MB\n",
       "Rank 0: created area AITv with 0 local nodes\n",
-      "Memory after AITv : 2062.22 MB\n",
+      "Memory after AITv : 2063.18 MB\n",
       "Rank 0: created area FST with 0 local nodes\n",
-      "Memory after FST : 2078.82 MB\n",
+      "Memory after FST : 2079.90 MB\n",
       "Rank 0: created area 7a with 0 local nodes\n",
-      "Memory after 7a : 2100.10 MB\n",
+      "Memory after 7a : 2101.22 MB\n",
       "Rank 0: created area STPp with 0 local nodes\n",
-      "Memory after STPp : 2118.84 MB\n",
+      "Memory after STPp : 2119.93 MB\n",
       "Rank 0: created area STPa with 0 local nodes\n",
-      "Memory after STPa : 2137.98 MB\n",
+      "Memory after STPa : 2139.07 MB\n",
       "Rank 0: created area 46 with 0 local nodes\n",
-      "Memory after 46 : 2153.30 MB\n",
+      "Memory after 46 : 2154.39 MB\n",
       "Rank 0: created area AITd with 0 local nodes\n",
-      "Memory after AITd : 2175.89 MB\n",
+      "Memory after AITd : 2176.82 MB\n",
       "Rank 0: created area TH with 0 local nodes\n",
-      "Memory after TH : 2188.59 MB\n",
-      "Created areas and internal connections in 2.25 seconds.\n"
+      "Memory after TH : 2189.11 MB\n",
+      "Created areas and internal connections in 2.17 seconds.\n",
+      "Created cortico-cortical connections in 21.99 seconds.\n",
+      "Simulated network in 84.94 seconds.\n"
      ]
     }
    ],
@@ -669,10 +671,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "c1d7aa61-e85a-4e6a-9e01-e018413a572b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Sep 14 13:36:54 NodeManager::prepare_nodes [Info]: \n",
+      "loading spikes\n",
+      "    Preparing 20780 nodes for simulation.\n",
+      "\n",
+      "Sep 14 13:36:54 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 13:38:19 SimulationManager::run [Info]: \n",
+      "    Simulation finished.\n"
+     ]
+    }
+   ],
    "source": [
     "# Instantiate an analysis class and load spike data\n",
     "A = Analysis(network=M, \n",
@@ -693,10 +715,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "bea30fc8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x476.769 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from M2E_visualize_instantaneous_and_mean_firing_rates import plot_instan_mean_firing_rate\n",
     "plot_instan_mean_firing_rate(M)"
@@ -723,12 +758,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "ae19bcc3",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "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)"
+     ]
+    }
+   ],
    "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 9db1dca..8421316 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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