diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
index 3552c0964d358d7acf0f94b7f024ebdea971419f..cdd991029ac3fe855b39519d996ae78b3f1d71c4 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
@@ -97,14 +97,14 @@ def load_and_create_data(M, A, raster_areas):
print("Computing rate time series done")
- # Compute time series of firing rates convolved with a kernel
- # data_path = sys.argv[1]
- print(data_path)
- label = M.simulation.label
- method = 'auto_kernel'
- for area in raster_areas:
- subprocess.run(['python3', './figures/Schmidt2018_dyn/compute_rate_time_series.py', data_path, label, area, method])
- print("Computing rate time series auto kernel done")
+ # # Compute time series of firing rates convolved with a kernel
+ # # data_path = sys.argv[1]
+ # print(data_path)
+ # label = M.simulation.label
+ # method = 'auto_kernel'
+ # for area in raster_areas:
+ # subprocess.run(['python3', './figures/Schmidt2018_dyn/compute_rate_time_series.py', data_path, label, area, method])
+ # print("Computing rate time series auto kernel done")
"""
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
index 450adaa03481114a495dcd12d8f07efaa8b1f7d7..b19ae9c826974b05369fa769eb9598d24b3dcd86 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
@@ -64,7 +64,7 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
load_areas=None)
# load data
- load_and_create_data(M, A)
+ load_and_create_data(M, A, raster_areas)
t_sim = M.simulation.params["t_sim"]
@@ -220,7 +220,7 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
'rate_time_series-{}.npy'.format(area))
rate_time_series[area] = np.load(fn)
- # time series of firing rates convolved with a kernel
+ # # time series of firing rates convolved with a kernel
# rate_time_series_auto_kernel = {}
# for area in areas:
# fn = os.path.join(data_path, label,
@@ -444,9 +444,8 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
binned_spikes = rate_time_series[area][np.where(
np.logical_and(time >= t_min, time < t_max))]
ax[i].plot(time, binned_spikes, color=colors[0], label=area)
- rate = rate_time_series_auto_kernel[area]
- # rate = rate_time_series[area]
- ax[i].plot(time, rate, color=colors[2], label=area)
+ # rate = rate_time_series_auto_kernel[area]
+ # ax[i].plot(time, rate, color=colors[2], label=area)
ax[i].set_xlim((t_min, t_max))
ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)
diff --git a/figures/MAM2EBRAINS/M2E_LOAD_DATA.py b/figures/MAM2EBRAINS/M2E_LOAD_DATA.py
index 3552c0964d358d7acf0f94b7f024ebdea971419f..cdd991029ac3fe855b39519d996ae78b3f1d71c4 100644
--- a/figures/MAM2EBRAINS/M2E_LOAD_DATA.py
+++ b/figures/MAM2EBRAINS/M2E_LOAD_DATA.py
@@ -97,14 +97,14 @@ def load_and_create_data(M, A, raster_areas):
print("Computing rate time series done")
- # Compute time series of firing rates convolved with a kernel
- # data_path = sys.argv[1]
- print(data_path)
- label = M.simulation.label
- method = 'auto_kernel'
- for area in raster_areas:
- subprocess.run(['python3', './figures/Schmidt2018_dyn/compute_rate_time_series.py', data_path, label, area, method])
- print("Computing rate time series auto kernel done")
+ # # Compute time series of firing rates convolved with a kernel
+ # # data_path = sys.argv[1]
+ # print(data_path)
+ # label = M.simulation.label
+ # method = 'auto_kernel'
+ # for area in raster_areas:
+ # subprocess.run(['python3', './figures/Schmidt2018_dyn/compute_rate_time_series.py', data_path, label, area, method])
+ # print("Computing rate time series auto kernel done")
"""
diff --git a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
index 9dda1f86a0365051c1130d3610a4e25c49b570a8..b19ae9c826974b05369fa769eb9598d24b3dcd86 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
@@ -220,14 +220,14 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
'rate_time_series-{}.npy'.format(area))
rate_time_series[area] = np.load(fn)
- # time series of firing rates convolved with a kernel
- rate_time_series_auto_kernel = {}
- for area in areas:
- fn = os.path.join(data_path, label,
- 'Analysis',
- 'rate_time_series_auto_kernel',
- 'rate_time_series_auto_kernel_{}.npy'.format(area))
- rate_time_series_auto_kernel[area] = np.load(fn)
+ # # time series of firing rates convolved with a kernel
+ # rate_time_series_auto_kernel = {}
+ # for area in areas:
+ # fn = os.path.join(data_path, label,
+ # 'Analysis',
+ # 'rate_time_series_auto_kernel',
+ # 'rate_time_series_auto_kernel_{}.npy'.format(area))
+ # rate_time_series_auto_kernel[area] = np.load(fn)
# local variance revised (LvR)
fn = os.path.join(data_path, label, 'Analysis', 'pop_LvR.json')
@@ -444,9 +444,8 @@ def plot_resting_state(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
binned_spikes = rate_time_series[area][np.where(
np.logical_and(time >= t_min, time < t_max))]
ax[i].plot(time, binned_spikes, color=colors[0], label=area)
- rate = rate_time_series_auto_kernel[area]
- # rate = rate_time_series[area]
- ax[i].plot(time, rate, color=colors[2], label=area)
+ # rate = rate_time_series_auto_kernel[area]
+ # ax[i].plot(time, rate, color=colors[2], label=area)
ax[i].set_xlim((t_min, t_max))
ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)
diff --git a/figures/Schmidt2018_dyn/compute_rate_time_series.py b/figures/Schmidt2018_dyn/compute_rate_time_series.py
index 2ec875575bd8c3fdf4ff7d54761a8045889d182e..77bc6d9cd78a9c23f34a83d9f5fb4b9973cb9978 100644
--- a/figures/Schmidt2018_dyn/compute_rate_time_series.py
+++ b/figures/Schmidt2018_dyn/compute_rate_time_series.py
@@ -51,8 +51,7 @@ except FileExistsError:
with open(os.path.join(data_path, label, 'custom_params_{}'.format(label)), 'r') as f:
sim_params = json.load(f)
-# T = sim_params['T']
-T = sim_params['t_sim']
+T = sim_params['T']
"""
Create MultiAreaModel instance to have access to data structures
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index a7d072190e81bdd9ec9fa7451ce4e89000f82e7b..24601edefb0eba5aa5907e1781442b1ba84a6121 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -378,7 +378,7 @@
"output_type": "stream",
"text": [
"Initializing network from dictionary.\n",
- "RAND_DATA_LABEL 7821\n"
+ "RAND_DATA_LABEL 7495\n"
]
},
{
@@ -533,7 +533,7 @@
"output_type": "stream",
"text": [
"Initializing network from dictionary.\n",
- "RAND_DATA_LABEL 4983\n"
+ "RAND_DATA_LABEL 1869\n"
]
},
{
@@ -615,72 +615,72 @@
"text": [
"Prepared simulation in 0.00 seconds.\n",
"Rank 0: created area V1 with 0 local nodes\n",
- "Memory after V1 : 1515.90 MB\n",
+ "Memory after V1 : 1515.14 MB\n",
"Rank 0: created area V2 with 0 local nodes\n",
- "Memory after V2 : 1542.60 MB\n",
+ "Memory after V2 : 1541.84 MB\n",
"Rank 0: created area VP with 0 local nodes\n",
- "Memory after VP : 1571.73 MB\n",
+ "Memory after VP : 1571.05 MB\n",
"Rank 0: created area V3 with 0 local nodes\n",
- "Memory after V3 : 1600.13 MB\n",
+ "Memory after V3 : 1599.30 MB\n",
"Rank 0: created area V3A with 0 local nodes\n",
- "Memory after V3A : 1620.03 MB\n",
+ "Memory after V3A : 1619.27 MB\n",
"Rank 0: created area MT with 0 local nodes\n",
- "Memory after MT : 1645.52 MB\n",
+ "Memory after MT : 1644.77 MB\n",
"Rank 0: created area V4t with 0 local nodes\n",
- "Memory after V4t : 1670.48 MB\n",
+ "Memory after V4t : 1669.73 MB\n",
"Rank 0: created area V4 with 0 local nodes\n",
- "Memory after V4 : 1697.54 MB\n",
+ "Memory after V4 : 1696.79 MB\n",
"Rank 0: created area VOT with 0 local nodes\n",
- "Memory after VOT : 1722.00 MB\n",
+ "Memory after VOT : 1721.25 MB\n",
"Rank 0: created area MSTd with 0 local nodes\n",
- "Memory after MSTd : 1742.01 MB\n",
+ "Memory after MSTd : 1741.25 MB\n",
"Rank 0: created area PIP with 0 local nodes\n",
- "Memory after PIP : 1763.47 MB\n",
+ "Memory after PIP : 1762.75 MB\n",
"Rank 0: created area PO with 0 local nodes\n",
- "Memory after PO : 1784.81 MB\n",
+ "Memory after PO : 1784.13 MB\n",
"Rank 0: created area DP with 0 local nodes\n",
- "Memory after DP : 1805.07 MB\n",
+ "Memory after DP : 1804.39 MB\n",
"Rank 0: created area MIP with 0 local nodes\n",
- "Memory after MIP : 1826.64 MB\n",
+ "Memory after MIP : 1825.91 MB\n",
"Rank 0: created area MDP with 0 local nodes\n",
- "Memory after MDP : 1848.14 MB\n",
+ "Memory after MDP : 1847.42 MB\n",
"Rank 0: created area VIP with 0 local nodes\n",
- "Memory after VIP : 1870.10 MB\n",
+ "Memory after VIP : 1869.27 MB\n",
"Rank 0: created area LIP with 0 local nodes\n",
- "Memory after LIP : 1894.04 MB\n",
+ "Memory after LIP : 1893.36 MB\n",
"Rank 0: created area PITv with 0 local nodes\n",
- "Memory after PITv : 1919.33 MB\n",
+ "Memory after PITv : 1918.56 MB\n",
"Rank 0: created area PITd with 0 local nodes\n",
- "Memory after PITd : 1944.53 MB\n",
+ "Memory after PITd : 1943.77 MB\n",
"Rank 0: created area MSTl with 0 local nodes\n",
- "Memory after MSTl : 1966.02 MB\n",
+ "Memory after MSTl : 1965.01 MB\n",
"Rank 0: created area CITv with 0 local nodes\n",
- "Memory after CITv : 1985.20 MB\n",
+ "Memory after CITv : 1983.81 MB\n",
"Rank 0: created area CITd with 0 local nodes\n",
- "Memory after CITd : 2004.47 MB\n",
+ "Memory after CITd : 2003.21 MB\n",
"Rank 0: created area FEF with 0 local nodes\n",
- "Memory after FEF : 2025.85 MB\n",
+ "Memory after FEF : 2024.55 MB\n",
"Rank 0: created area TF with 0 local nodes\n",
- "Memory after TF : 2041.52 MB\n",
+ "Memory after TF : 2040.23 MB\n",
"Rank 0: created area AITv with 0 local nodes\n",
- "Memory after AITv : 2064.24 MB\n",
+ "Memory after AITv : 2062.93 MB\n",
"Rank 0: created area FST with 0 local nodes\n",
- "Memory after FST : 2080.84 MB\n",
+ "Memory after FST : 2079.66 MB\n",
"Rank 0: created area 7a with 0 local nodes\n",
- "Memory after 7a : 2102.16 MB\n",
+ "Memory after 7a : 2100.74 MB\n",
"Rank 0: created area STPp with 0 local nodes\n",
- "Memory after STPp : 2120.87 MB\n",
+ "Memory after STPp : 2119.45 MB\n",
"Rank 0: created area STPa with 0 local nodes\n",
- "Memory after STPa : 2140.00 MB\n",
+ "Memory after STPa : 2138.70 MB\n",
"Rank 0: created area 46 with 0 local nodes\n",
- "Memory after 46 : 2155.36 MB\n",
+ "Memory after 46 : 2154.06 MB\n",
"Rank 0: created area AITd with 0 local nodes\n",
- "Memory after AITd : 2177.66 MB\n",
+ "Memory after AITd : 2176.65 MB\n",
"Rank 0: created area TH with 0 local nodes\n",
- "Memory after TH : 2190.14 MB\n",
- "Created areas and internal connections in 2.21 seconds.\n",
- "Created cortico-cortical connections in 22.73 seconds.\n",
- "Simulated network in 84.21 seconds.\n"
+ "Memory after TH : 2189.35 MB\n",
+ "Created areas and internal connections in 2.23 seconds.\n",
+ "Created cortico-cortical connections in 22.53 seconds.\n",
+ "Simulated network in 77.18 seconds.\n"
]
}
],
@@ -767,6 +767,7 @@
"Loading data from file\n",
"Loading data from file\n",
"Computing rate time series done\n",
+ "/opt/app-root/src/MAM2EBRAINS/simulations\n",
"\n",
" -- N E S T --\n",
" Copyright (C) 2004 The NEST Initiative\n",
@@ -863,26 +864,35 @@
"Computing rate time series auto kernel done\n",
"Loading data from file\n",
"Computing synchrony done\n",
- "Loading data from file\n",
- "Computing synaptic input done\n",
"pop_LvR\n",
- "pop_rates\n"
+ "pop_rates\n",
+ "synchrony\n"
]
},
{
- "ename": "AttributeError",
- "evalue": "'NoneType' object has no attribute 'items'",
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: '/opt/app-root/src/MAM2EBRAINS/simulations/27d81076e6d6e9e591684be053078477/Analysis/rate_time_series_auto_kernel/rate_time_series_auto_kernel_V4.npy'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn [15], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Choose 3 areas from the complete_area_list to show their sipking activity\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# By default, it's set as ['V1', 'V2', 'FEF']\u001b[39;00m\n\u001b[1;32m 3\u001b[0m raster_areas \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mV4\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mV1\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFEF\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 4\u001b[0m plot_resting_state(M, data_path, raster_areas)\n",
- "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_resting_state.py:67\u001b[0m, in \u001b[0;36mplot_resting_state\u001b[0;34m(M, data_path, raster_areas)\u001b[0m\n\u001b[1;32m 61\u001b[0m A \u001b[38;5;241m=\u001b[39m Analysis(network\u001b[38;5;241m=\u001b[39mM, \n\u001b[1;32m 62\u001b[0m simulation\u001b[38;5;241m=\u001b[39mM\u001b[38;5;241m.\u001b[39msimulation, \n\u001b[1;32m 63\u001b[0m data_list\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspikes\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 64\u001b[0m load_areas\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 66\u001b[0m \u001b[38;5;66;03m# load data\u001b[39;00m\n\u001b[0;32m---> 67\u001b[0m \u001b[43mload_and_create_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mM\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraster_areas\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m t_sim \u001b[38;5;241m=\u001b[39m M\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mparams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt_sim\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 71\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;124;03mFigure layout\u001b[39;00m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n",
- "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_LOAD_DATA.py:180\u001b[0m, in \u001b[0;36mload_and_create_data\u001b[0;34m(M, A, raster_areas)\u001b[0m\n\u001b[1;32m 177\u001b[0m A\u001b[38;5;241m.\u001b[39mcreate_synaptic_input()\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComputing synaptic input done\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 180\u001b[0m \u001b[43mA\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/MAM2EBRAINS/multiarea_model/analysis.py:973\u001b[0m, in \u001b[0;36mAnalysis.save\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 971\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m members[i][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m save_list_npy:\n\u001b[1;32m 972\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_dir \u001b[38;5;241m+\u001b[39m members[i][\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 973\u001b[0m \u001b[43mah\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_save_dict_to_npy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmembers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/MAM2EBRAINS/multiarea_model/analysis_helpers.py:288\u001b[0m, in \u001b[0;36m_save_dict_to_npy\u001b[0;34m(fp, data)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_save_dict_to_npy\u001b[39m(fp, data):\n\u001b[1;32m 277\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;124;03m Save data dictionary to binary numpy files\u001b[39;00m\n\u001b[1;32m 279\u001b[0m \u001b[38;5;124;03m by iteratively going through the dictionary.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[38;5;124;03m Dictionary containing the data\u001b[39;00m\n\u001b[1;32m 287\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 288\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, val \u001b[38;5;129;01min\u001b[39;00m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m():\n\u001b[1;32m 289\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mParameters\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 290\u001b[0m fp_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin((fp, key))\n",
- "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'items'"
+ "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_resting_state.py:230\u001b[0m, in \u001b[0;36mplot_resting_state\u001b[0;34m(M, data_path, raster_areas)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m area \u001b[38;5;129;01min\u001b[39;00m areas:\n\u001b[1;32m 226\u001b[0m fn \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(data_path, label,\n\u001b[1;32m 227\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAnalysis\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 228\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrate_time_series_auto_kernel\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 229\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrate_time_series_auto_kernel_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.npy\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(area))\n\u001b[0;32m--> 230\u001b[0m rate_time_series_auto_kernel[area] \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;66;03m# local variance revised (LvR)\u001b[39;00m\n\u001b[1;32m 233\u001b[0m fn \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(data_path, label, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAnalysis\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpop_LvR.json\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/lib/npyio.py:407\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m 405\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 407\u001b[0m fid \u001b[38;5;241m=\u001b[39m stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mos_fspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 408\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/opt/app-root/src/MAM2EBRAINS/simulations/27d81076e6d6e9e591684be053078477/Analysis/rate_time_series_auto_kernel/rate_time_series_auto_kernel_V4.npy'"
]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x762.831 with 7 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
diff --git a/multiarea_model/data_multiarea/viscortex_processed_data.json b/multiarea_model/data_multiarea/viscortex_processed_data.json
index b7692fa566555a9d07d87088f2a855cad07d5259..b4705ee484ba5d2e65039de05d790c484d569098 100644
--- a/multiarea_model/data_multiarea/viscortex_processed_data.json
+++ b/multiarea_model/data_multiarea/viscortex_processed_data.json
@@ -1 +1 @@
-{"cocomac_completed": {"V1": {"7a": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "IT": {"source_pattern": ["0", "0", 2, "0", "0", "3"], "target_pattern": [2.0, "0", "0", "0", "0", "0"]}, "LIP": {"source_pattern": ["0", "0", "0", "0", "0", "2"], "target_pattern": null}, "MT": {"source_pattern": ["0", "0", 2, "0", 2, 2], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "PIP": {"source_pattern": null, "target_pattern": null}, "PO": {"source_pattern": null, "target_pattern": null}, "STP": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "STPp": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "TF": {"source_pattern": ["0", "0", "0", "0", "0", 2], "target_pattern": [2.0, "0", "0", "0", "0", "0"]}, "TH": {"source_pattern": ["0", "0", "0", "0", "0", 2], "target_pattern": null}, "V1": {"source_pattern": null, "target_pattern": [2.0, 2.0, 2.0, "0", "3", "3"]}, "V2": {"source_pattern": ["0", 2, 2, "0", "3", "3"], "target_pattern": ["3", 2.0, 2.0, "0", 2.0, 2.0]}, "V3": {"source_pattern": ["0", 2, 2, "0", 2, "3"], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "V3A": {"source_pattern": ["0", "0", "0", "0", 2, 2], "target_pattern": null}, "V4": {"source_pattern": ["0", 2, 2, "0", "3", "3"], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "V4t": {"source_pattern": ["0", 2, 2, "0", "3", "3"], "target_pattern": null}, "VIP": {"source_pattern": null, "target_pattern": null}, "MSTd": {"target_pattern": null, "source_pattern": null}, "AITv": {"target_pattern": null, "source_pattern": null}, "PITd": {"target_pattern": null, "source_pattern": null}, "MSTl": {"target_pattern": null, "source_pattern": null}, "FST": {"target_pattern": null, "source_pattern": null}, "PITv": {"target_pattern": null, "source_pattern": null}, "46": {"target_pattern": null, "source_pattern": null}, "STPa": {"target_pattern": null, "source_pattern": null}, "DP": {"target_pattern": null, "source_pattern": null}, 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