diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index 2d6bd6c75e69a699143517625fe5c9836be899ea..c2f117f530cb52611b3246cd1f466adf351fa43c 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -118,6 +118,17 @@
       " Type 'nest.help()' to find out more about NEST.\n",
       "\n"
      ]
+    },
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'M2E_visualize_interareal_connectivity'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [2], line 12\u001b[0m\n\u001b[1;32m     10\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     11\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 Analysis\n\u001b[0;32m---> 12\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[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_instantaneous_and_mean_firing_rates\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_instan_mean_firing_rate\n\u001b[1;32m     14\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[0;31mModuleNotFoundError\u001b[0m: No module named 'M2E_visualize_interareal_connectivity'"
+     ]
     }
    ],
    "source": [
@@ -142,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "7e07b0d0",
    "metadata": {
     "tags": []
@@ -155,28 +166,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "1d440c07-9b69-4e52-8573-26b13493bc5a",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "table {float:left}\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Jupyter notebook display format setting\n",
     "style = \"\"\"\n",
@@ -247,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "60265d52",
    "metadata": {},
    "outputs": [],
@@ -284,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "6e4bed8d",
    "metadata": {},
    "outputs": [],
@@ -367,70 +362,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "ab25f9f8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 5245\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning:Mean of empty slice.\n",
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{'K_scaling': 0.005,\n",
-      " 'N_scaling': 0.005,\n",
-      " 'connection_params': {'K_stable': 'K_stable.npy',\n",
-      "                       'av_indegree_V1': 3950.0,\n",
-      "                       'fac_nu_ext_5E': 1.125,\n",
-      "                       'fac_nu_ext_6E': 1.41666667,\n",
-      "                       'fac_nu_ext_TH': 1.2,\n",
-      "                       'g': -11.0,\n",
-      "                       'replace_non_simulated_areas': 'het_poisson_stat'},\n",
-      " 'fullscale_rates': 'tests/fullscale_rates.json',\n",
-      " 'input_params': {'rate_ext': 10.0},\n",
-      " 'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Simulation label: 27d81076e6d6e9e591684be053078477\n",
-      "Copied files.\n",
-      "Initialized simulation class.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# %%capture captured\n",
     "M = MultiAreaModel(network_params, \n",
@@ -450,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "6a7ddf0e",
    "metadata": {},
    "outputs": [],
@@ -478,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "6316ac24",
    "metadata": {},
    "outputs": [],
@@ -494,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "8408d463-557b-481b-afc1-5fbbbd67306d",
    "metadata": {},
    "outputs": [],
@@ -511,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "445a722a",
    "metadata": {},
    "outputs": [],
@@ -528,101 +463,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "05512922-26e5-425f-90a4-0df7c2279ccf",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 4160\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning:Mean of empty slice.\n",
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 5810\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning:Mean of empty slice.\n",
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1080x317.846 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "visualize_interareal_connectivity(M)"
    ]
@@ -657,100 +501,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "15778e9c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prepared simulation in 0.00 seconds.\n",
-      "\n",
-      "Sep 14 14:11:17 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 : 1518.50 MB\n",
-      "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1545.09 MB\n",
-      "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1574.23 MB\n",
-      "Rank 0: created area V3 with 0 local nodes\n",
-      "Memory after V3 : 1602.59 MB\n",
-      "Rank 0: created area V3A with 0 local nodes\n",
-      "Memory after V3A : 1622.52 MB\n",
-      "Rank 0: created area MT with 0 local nodes\n",
-      "Memory after MT : 1647.44 MB\n",
-      "Rank 0: created area V4t with 0 local nodes\n",
-      "Memory after V4t : 1671.26 MB\n",
-      "Rank 0: created area V4 with 0 local nodes\n",
-      "Memory after V4 : 1698.32 MB\n",
-      "Rank 0: created area VOT with 0 local nodes\n",
-      "Memory after VOT : 1723.66 MB\n",
-      "Rank 0: created area MSTd with 0 local nodes\n",
-      "Memory after MSTd : 1745.11 MB\n",
-      "Rank 0: created area PIP with 0 local nodes\n",
-      "Memory after PIP : 1766.46 MB\n",
-      "Rank 0: created area PO with 0 local nodes\n",
-      "Memory after PO : 1787.95 MB\n",
-      "Rank 0: created area DP with 0 local nodes\n",
-      "Memory after DP : 1808.21 MB\n",
-      "Rank 0: created area MIP with 0 local nodes\n",
-      "Memory after MIP : 1829.66 MB\n",
-      "Rank 0: created area MDP with 0 local nodes\n",
-      "Memory after MDP : 1851.20 MB\n",
-      "Rank 0: created area VIP with 0 local nodes\n",
-      "Memory after VIP : 1873.14 MB\n",
-      "Rank 0: created area LIP with 0 local nodes\n",
-      "Memory after LIP : 1897.10 MB\n",
-      "Rank 0: created area PITv with 0 local nodes\n",
-      "Memory after PITv : 1922.34 MB\n",
-      "Rank 0: created area PITd with 0 local nodes\n",
-      "Memory after PITd : 1947.55 MB\n",
-      "Rank 0: created area MSTl with 0 local nodes\n",
-      "Memory after MSTl : 1969.04 MB\n",
-      "Rank 0: created area CITv with 0 local nodes\n",
-      "Memory after CITv : 1988.21 MB\n",
-      "Rank 0: created area CITd with 0 local nodes\n",
-      "Memory after CITd : 2007.42 MB\n",
-      "Rank 0: created area FEF with 0 local nodes\n",
-      "Memory after FEF : 2028.92 MB\n",
-      "Rank 0: created area TF with 0 local nodes\n",
-      "Memory after TF : 2044.55 MB\n",
-      "Rank 0: created area AITv with 0 local nodes\n",
-      "Memory after AITv : 2067.25 MB\n",
-      "Rank 0: created area FST with 0 local nodes\n",
-      "Memory after FST : 2083.86 MB\n",
-      "Rank 0: created area 7a with 0 local nodes\n",
-      "Memory after 7a : 2105.18 MB\n",
-      "Rank 0: created area STPp with 0 local nodes\n",
-      "Memory after STPp : 2123.89 MB\n",
-      "Rank 0: created area STPa with 0 local nodes\n",
-      "Memory after STPa : 2143.02 MB\n",
-      "Rank 0: created area 46 with 0 local nodes\n",
-      "Memory after 46 : 2158.38 MB\n",
-      "Rank 0: created area AITd with 0 local nodes\n",
-      "Memory after AITd : 2180.93 MB\n",
-      "Rank 0: created area TH with 0 local nodes\n",
-      "Memory after TH : 2193.63 MB\n",
-      "Created areas and internal connections in 2.24 seconds.\n",
-      "Created cortico-cortical connections in 22.28 seconds.\n",
-      "Simulated network in 90.03 seconds.\n",
-      "\n",
-      "Sep 14 14:11:42 NodeManager::prepare_nodes [Info]: \n",
-      "    Preparing 20780 nodes for simulation.\n",
-      "\n",
-      "Sep 14 14:11:42 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 14:13:12 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",
@@ -777,18 +531,10 @@
   },
   {
    "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",
@@ -809,23 +555,10 @@
   },
   {
    "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 1080x317.846 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_instan_mean_firing_rate(M)"
    ]
@@ -851,25 +584,12 @@
   },
   {
    "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": [
     "plot_resting_state(M, A, data_path)"
    ]
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
index 22874b12b2440ce044cc5eee4c9a690f2a11f6c2..6311270496010f2419ccce6833987fb322b0f4db 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
@@ -129,7 +129,7 @@ def load_and_create_data(M, A):
         - 'alpha_time_window' : time constant of the alpha function
         - 'rect_time_window' : width of the moving rectangular function
     """
-    A.create_synaptic_input()
+    # A.create_synaptic_input()
     # print("Computing synaptic input done")
     
     A.save()
@@ -143,6 +143,4 @@ def load_and_create_data(M, A):
     #     subprocess.run(['python3', './Schmidt2018_dyn/compute_bold_signal.py'])
     #     # subprocess.run(['Rscript', '--vanilla', 'compute_bold_signal.R', fn, out_fn])
     # except FileNotFoundError:
-    #     raise FileNotFoundError("Executing R failed. Did you install R?")
-    
-    return A
\ No newline at end of file
+    #     raise FileNotFoundError("Executing R failed. Did you install R?")
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/M2E_LOAD_DATA.py b/figures/MAM2EBRAINS/M2E_LOAD_DATA.py
index 22874b12b2440ce044cc5eee4c9a690f2a11f6c2..6311270496010f2419ccce6833987fb322b0f4db 100644
--- a/figures/MAM2EBRAINS/M2E_LOAD_DATA.py
+++ b/figures/MAM2EBRAINS/M2E_LOAD_DATA.py
@@ -129,7 +129,7 @@ def load_and_create_data(M, A):
         - 'alpha_time_window' : time constant of the alpha function
         - 'rect_time_window' : width of the moving rectangular function
     """
-    A.create_synaptic_input()
+    # A.create_synaptic_input()
     # print("Computing synaptic input done")
     
     A.save()
@@ -143,6 +143,4 @@ def load_and_create_data(M, A):
     #     subprocess.run(['python3', './Schmidt2018_dyn/compute_bold_signal.py'])
     #     # subprocess.run(['Rscript', '--vanilla', 'compute_bold_signal.R', fn, out_fn])
     # except FileNotFoundError:
-    #     raise FileNotFoundError("Executing R failed. Did you install R?")
-    
-    return A
\ No newline at end of file
+    #     raise FileNotFoundError("Executing R failed. Did you install R?")
\ No newline at end of file
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 2d6bd6c75e69a699143517625fe5c9836be899ea..c2f117f530cb52611b3246cd1f466adf351fa43c 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -118,6 +118,17 @@
       " Type 'nest.help()' to find out more about NEST.\n",
       "\n"
      ]
+    },
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'M2E_visualize_interareal_connectivity'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [2], line 12\u001b[0m\n\u001b[1;32m     10\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     11\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 Analysis\n\u001b[0;32m---> 12\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[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_instantaneous_and_mean_firing_rates\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_instan_mean_firing_rate\n\u001b[1;32m     14\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[0;31mModuleNotFoundError\u001b[0m: No module named 'M2E_visualize_interareal_connectivity'"
+     ]
     }
    ],
    "source": [
@@ -142,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "7e07b0d0",
    "metadata": {
     "tags": []
@@ -155,28 +166,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "1d440c07-9b69-4e52-8573-26b13493bc5a",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "table {float:left}\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Jupyter notebook display format setting\n",
     "style = \"\"\"\n",
@@ -247,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "60265d52",
    "metadata": {},
    "outputs": [],
@@ -284,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "6e4bed8d",
    "metadata": {},
    "outputs": [],
@@ -367,70 +362,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "ab25f9f8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 5245\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning:Mean of empty slice.\n",
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{'K_scaling': 0.005,\n",
-      " 'N_scaling': 0.005,\n",
-      " 'connection_params': {'K_stable': 'K_stable.npy',\n",
-      "                       'av_indegree_V1': 3950.0,\n",
-      "                       'fac_nu_ext_5E': 1.125,\n",
-      "                       'fac_nu_ext_6E': 1.41666667,\n",
-      "                       'fac_nu_ext_TH': 1.2,\n",
-      "                       'g': -11.0,\n",
-      "                       'replace_non_simulated_areas': 'het_poisson_stat'},\n",
-      " 'fullscale_rates': 'tests/fullscale_rates.json',\n",
-      " 'input_params': {'rate_ext': 10.0},\n",
-      " 'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Simulation label: 27d81076e6d6e9e591684be053078477\n",
-      "Copied files.\n",
-      "Initialized simulation class.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# %%capture captured\n",
     "M = MultiAreaModel(network_params, \n",
@@ -450,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "6a7ddf0e",
    "metadata": {},
    "outputs": [],
@@ -478,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "6316ac24",
    "metadata": {},
    "outputs": [],
@@ -494,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "8408d463-557b-481b-afc1-5fbbbd67306d",
    "metadata": {},
    "outputs": [],
@@ -511,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "445a722a",
    "metadata": {},
    "outputs": [],
@@ -528,101 +463,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "05512922-26e5-425f-90a4-0df7c2279ccf",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 4160\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning:Mean of empty slice.\n",
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 5810\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning:Mean of empty slice.\n",
-      "/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1080x317.846 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "visualize_interareal_connectivity(M)"
    ]
@@ -657,100 +501,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "15778e9c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prepared simulation in 0.00 seconds.\n",
-      "\n",
-      "Sep 14 14:11:17 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 : 1518.50 MB\n",
-      "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1545.09 MB\n",
-      "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1574.23 MB\n",
-      "Rank 0: created area V3 with 0 local nodes\n",
-      "Memory after V3 : 1602.59 MB\n",
-      "Rank 0: created area V3A with 0 local nodes\n",
-      "Memory after V3A : 1622.52 MB\n",
-      "Rank 0: created area MT with 0 local nodes\n",
-      "Memory after MT : 1647.44 MB\n",
-      "Rank 0: created area V4t with 0 local nodes\n",
-      "Memory after V4t : 1671.26 MB\n",
-      "Rank 0: created area V4 with 0 local nodes\n",
-      "Memory after V4 : 1698.32 MB\n",
-      "Rank 0: created area VOT with 0 local nodes\n",
-      "Memory after VOT : 1723.66 MB\n",
-      "Rank 0: created area MSTd with 0 local nodes\n",
-      "Memory after MSTd : 1745.11 MB\n",
-      "Rank 0: created area PIP with 0 local nodes\n",
-      "Memory after PIP : 1766.46 MB\n",
-      "Rank 0: created area PO with 0 local nodes\n",
-      "Memory after PO : 1787.95 MB\n",
-      "Rank 0: created area DP with 0 local nodes\n",
-      "Memory after DP : 1808.21 MB\n",
-      "Rank 0: created area MIP with 0 local nodes\n",
-      "Memory after MIP : 1829.66 MB\n",
-      "Rank 0: created area MDP with 0 local nodes\n",
-      "Memory after MDP : 1851.20 MB\n",
-      "Rank 0: created area VIP with 0 local nodes\n",
-      "Memory after VIP : 1873.14 MB\n",
-      "Rank 0: created area LIP with 0 local nodes\n",
-      "Memory after LIP : 1897.10 MB\n",
-      "Rank 0: created area PITv with 0 local nodes\n",
-      "Memory after PITv : 1922.34 MB\n",
-      "Rank 0: created area PITd with 0 local nodes\n",
-      "Memory after PITd : 1947.55 MB\n",
-      "Rank 0: created area MSTl with 0 local nodes\n",
-      "Memory after MSTl : 1969.04 MB\n",
-      "Rank 0: created area CITv with 0 local nodes\n",
-      "Memory after CITv : 1988.21 MB\n",
-      "Rank 0: created area CITd with 0 local nodes\n",
-      "Memory after CITd : 2007.42 MB\n",
-      "Rank 0: created area FEF with 0 local nodes\n",
-      "Memory after FEF : 2028.92 MB\n",
-      "Rank 0: created area TF with 0 local nodes\n",
-      "Memory after TF : 2044.55 MB\n",
-      "Rank 0: created area AITv with 0 local nodes\n",
-      "Memory after AITv : 2067.25 MB\n",
-      "Rank 0: created area FST with 0 local nodes\n",
-      "Memory after FST : 2083.86 MB\n",
-      "Rank 0: created area 7a with 0 local nodes\n",
-      "Memory after 7a : 2105.18 MB\n",
-      "Rank 0: created area STPp with 0 local nodes\n",
-      "Memory after STPp : 2123.89 MB\n",
-      "Rank 0: created area STPa with 0 local nodes\n",
-      "Memory after STPa : 2143.02 MB\n",
-      "Rank 0: created area 46 with 0 local nodes\n",
-      "Memory after 46 : 2158.38 MB\n",
-      "Rank 0: created area AITd with 0 local nodes\n",
-      "Memory after AITd : 2180.93 MB\n",
-      "Rank 0: created area TH with 0 local nodes\n",
-      "Memory after TH : 2193.63 MB\n",
-      "Created areas and internal connections in 2.24 seconds.\n",
-      "Created cortico-cortical connections in 22.28 seconds.\n",
-      "Simulated network in 90.03 seconds.\n",
-      "\n",
-      "Sep 14 14:11:42 NodeManager::prepare_nodes [Info]: \n",
-      "    Preparing 20780 nodes for simulation.\n",
-      "\n",
-      "Sep 14 14:11:42 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 14:13:12 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",
@@ -777,18 +531,10 @@
   },
   {
    "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",
@@ -809,23 +555,10 @@
   },
   {
    "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 1080x317.846 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_instan_mean_firing_rate(M)"
    ]
@@ -851,25 +584,12 @@
   },
   {
    "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": [
     "plot_resting_state(M, A, data_path)"
    ]