diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
index 7e32bac7ece0e1f4c06ee4f0bdec608e25736171..241c3256b704867be05179a45f2ee381dff11c55 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
@@ -4,34 +4,7 @@ import matplotlib.pyplot as plt
from multiarea_model import Analysis
-def load_and_create_data(M):
- """
- Analysis class.
- An instance of the analysis class for the given network and simulation.
- Can be created as a member class of a multiarea_model instance or standalone.
-
- Parameters
- ----------
- network : MultiAreaModel
- An instance of the multiarea_model class that specifies
- the network to be analyzed.
- simulation : Simulation
- An instance of the simulation class that specifies
- the simulation to be analyzed.
- data_list : list of strings {'spikes', vm'}, optional
- Specifies which type of data is to load. Defaults to ['spikes'].
- load_areas : list of strings with area names, optional
- Specifies the areas for which data is to be loaded.
- Default value is None and leads to loading of data for all
- simulated areas.
- """
- # Instantiate an analysis class and load spike data
- # A = Analysis(network=M,
- # simulation=M.simulation,
- # data_list=['spikes'],
- # load_areas=None)
-
-
+def load_and_create_data(M, A):
"""
Calculate time-averaged population rates and store them in member pop_rates.
If the rates had previously been stored with the same
@@ -57,8 +30,8 @@ def load_and_create_data(M):
If set to 'complete', all populations the respective areas
are included. Defaults to 'complete'.
"""
- # A.create_pop_rates()
- subprocess.run(['python3', './figures/Schmidt2018_dyn/compute_pop_rates.py'])
+ A.create_pop_rates()
+ # subprocess.run(['python3', './figures/Schmidt2018_dyn/compute_pop_rates.py'])
# subprocess.run(['Rscript', '--vanilla', 'compute_bold_signal.R', fn, out_fn])
print("Computing population rates done")
@@ -83,7 +56,7 @@ def load_and_create_data(M):
If set to 'complete', all populations the respective areas
are included. Defaults to 'complete'.
"""
- # A.create_pop_LvR()
+ A.create_pop_LvR()
print("Computing population LvR done")
@@ -119,55 +92,55 @@ def load_and_create_data(M):
- 'alpha_time_window' : time constant of the alpha function
- 'rect_time_window' : width of the moving rectangular function
"""
- # A.create_rate_time_series()
+ A.create_rate_time_series()
print("Computing rate time series done")
-# """
-# Calculate synaptic input of populations and areas using the spike data.
-# Uses function ah.pop_synaptic_input.
-# If the synaptic inputs have previously been stored with the
-# same parameters, they are loaded from file.
+ """
+ Calculate synaptic input of populations and areas using the spike data.
+ Uses function ah.pop_synaptic_input.
+ If the synaptic inputs have previously been stored with the
+ same parameters, they are loaded from file.
-# Parameters
-# ----------
-# t_min : float, optional
-# Minimal time in ms of the simulation to take into account
-# for the calculation. Defaults to 500 ms.
-# t_max : float, optional
-# Maximal time in ms of the simulation to take into account
-# for the calculation. Defaults to the simulation time.
-# areas : list, optional
-# Which areas to include in the calculcation.
-# Defaults to all loaded areas.
-# pops : list or {'complete'}, optional
-# Which populations to include in the calculation.
-# If set to 'complete', all populations the respective areas
-# are included. Defaults to 'complete'.
-# kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional
-# Convolution kernel for the calculation of the underlying firing rates.
-# Defaults to 'binned' which corresponds to a simple histogram.
-# resolution: float, optional
-# Width of the convolution kernel. Specifically it correponds to:
-# - 'binned' : bin width of the histogram
-# - 'gauss_time_window' : sigma
-# - 'alpha_time_window' : time constant of the alpha function
-# - 'rect_time_window' : width of the moving rectangular function
-# """
-# A.create_synaptic_input()
-# print("Computing synaptic input done")
+ Parameters
+ ----------
+ t_min : float, optional
+ Minimal time in ms of the simulation to take into account
+ for the calculation. Defaults to 500 ms.
+ t_max : float, optional
+ Maximal time in ms of the simulation to take into account
+ for the calculation. Defaults to the simulation time.
+ areas : list, optional
+ Which areas to include in the calculcation.
+ Defaults to all loaded areas.
+ pops : list or {'complete'}, optional
+ Which populations to include in the calculation.
+ If set to 'complete', all populations the respective areas
+ are included. Defaults to 'complete'.
+ kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional
+ Convolution kernel for the calculation of the underlying firing rates.
+ Defaults to 'binned' which corresponds to a simple histogram.
+ resolution: float, optional
+ Width of the convolution kernel. Specifically it correponds to:
+ - 'binned' : bin width of the histogram
+ - 'gauss_time_window' : sigma
+ - 'alpha_time_window' : time constant of the alpha function
+ - 'rect_time_window' : width of the moving rectangular function
+ """
+ A.create_synaptic_input()
+ print("Computing synaptic input done")
- # A.save()
+ A.save()
- # """
- # Compute BOLD signal for a given area from the time series of
- # population-averaged spike rates of a given simulation using the
- # neuRosim package of R (see Schmidt et al. 2018 for more details).
- # """
- # try:
- # 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?")
+ """
+ Compute BOLD signal for a given area from the time series of
+ population-averaged spike rates of a given simulation using the
+ neuRosim package of R (see Schmidt et al. 2018 for more details).
+ """
+ try:
+ 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
+ return A
\ No newline at end of file
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 d1970f682c8e4fb7047b9f024c37ba18ce27a018..d6da1ef94e4a74805a035f295948b82230a456b4 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
@@ -35,8 +35,34 @@ def set_boxplot_props(d):
markerfacecolor='k', markeredgecolor='k', markersize=3.)
def plot_resting_state(M, data_path):
+ """
+ Analysis class.
+ An instance of the analysis class for the given network and simulation.
+ Can be created as a member class of a multiarea_model instance or standalone.
+
+ Parameters
+ ----------
+ network : MultiAreaModel
+ An instance of the multiarea_model class that specifies
+ the network to be analyzed.
+ simulation : Simulation
+ An instance of the simulation class that specifies
+ the simulation to be analyzed.
+ data_list : list of strings {'spikes', vm'}, optional
+ Specifies which type of data is to load. Defaults to ['spikes'].
+ load_areas : list of strings with area names, optional
+ Specifies the areas for which data is to be loaded.
+ Default value is None and leads to loading of data for all
+ simulated areas.
+ """
+ # Instantiate an analysis class and load spike data
+ A = Analysis(network=M,
+ simulation=M.simulation,
+ data_list=['spikes'],
+ load_areas=None)
+
# load data
- A = load_and_create_data(M)
+ load_and_create_data(M, A)
label_spikes = M.simulation.label
label = M.simulation.label
@@ -154,16 +180,16 @@ def plot_resting_state(M, data_path):
# M = MultiAreaModel({})
# spike data
- spike_data = {}
- for area in areas:
- spike_data[area] = {}
- for pop in M.structure[area]:
- spike_data[area][pop] = np.load(os.path.join(data_path,
- label_spikes,
- 'recordings',
- '{}-spikes-{}-{}.npy'.format(label_spikes,
- area, pop)))
- # spike_data = A.spike_data
+ # spike_data = {}
+ # for area in areas:
+ # spike_data[area] = {}
+ # for pop in M.structure[area]:
+ # spike_data[area][pop] = np.load(os.path.join(data_path,
+ # label_spikes,
+ # 'recordings',
+ # '{}-spikes-{}-{}.npy'.format(label_spikes,
+ # area, pop)))
+ spike_data = A.spike_data
# stationary firing rates
fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
diff --git a/figures/MAM2EBRAINS/M2E_LOAD_DATA.py b/figures/MAM2EBRAINS/M2E_LOAD_DATA.py
index 7e32bac7ece0e1f4c06ee4f0bdec608e25736171..241c3256b704867be05179a45f2ee381dff11c55 100644
--- a/figures/MAM2EBRAINS/M2E_LOAD_DATA.py
+++ b/figures/MAM2EBRAINS/M2E_LOAD_DATA.py
@@ -4,34 +4,7 @@ import matplotlib.pyplot as plt
from multiarea_model import Analysis
-def load_and_create_data(M):
- """
- Analysis class.
- An instance of the analysis class for the given network and simulation.
- Can be created as a member class of a multiarea_model instance or standalone.
-
- Parameters
- ----------
- network : MultiAreaModel
- An instance of the multiarea_model class that specifies
- the network to be analyzed.
- simulation : Simulation
- An instance of the simulation class that specifies
- the simulation to be analyzed.
- data_list : list of strings {'spikes', vm'}, optional
- Specifies which type of data is to load. Defaults to ['spikes'].
- load_areas : list of strings with area names, optional
- Specifies the areas for which data is to be loaded.
- Default value is None and leads to loading of data for all
- simulated areas.
- """
- # Instantiate an analysis class and load spike data
- # A = Analysis(network=M,
- # simulation=M.simulation,
- # data_list=['spikes'],
- # load_areas=None)
-
-
+def load_and_create_data(M, A):
"""
Calculate time-averaged population rates and store them in member pop_rates.
If the rates had previously been stored with the same
@@ -57,8 +30,8 @@ def load_and_create_data(M):
If set to 'complete', all populations the respective areas
are included. Defaults to 'complete'.
"""
- # A.create_pop_rates()
- subprocess.run(['python3', './figures/Schmidt2018_dyn/compute_pop_rates.py'])
+ A.create_pop_rates()
+ # subprocess.run(['python3', './figures/Schmidt2018_dyn/compute_pop_rates.py'])
# subprocess.run(['Rscript', '--vanilla', 'compute_bold_signal.R', fn, out_fn])
print("Computing population rates done")
@@ -83,7 +56,7 @@ def load_and_create_data(M):
If set to 'complete', all populations the respective areas
are included. Defaults to 'complete'.
"""
- # A.create_pop_LvR()
+ A.create_pop_LvR()
print("Computing population LvR done")
@@ -119,55 +92,55 @@ def load_and_create_data(M):
- 'alpha_time_window' : time constant of the alpha function
- 'rect_time_window' : width of the moving rectangular function
"""
- # A.create_rate_time_series()
+ A.create_rate_time_series()
print("Computing rate time series done")
-# """
-# Calculate synaptic input of populations and areas using the spike data.
-# Uses function ah.pop_synaptic_input.
-# If the synaptic inputs have previously been stored with the
-# same parameters, they are loaded from file.
+ """
+ Calculate synaptic input of populations and areas using the spike data.
+ Uses function ah.pop_synaptic_input.
+ If the synaptic inputs have previously been stored with the
+ same parameters, they are loaded from file.
-# Parameters
-# ----------
-# t_min : float, optional
-# Minimal time in ms of the simulation to take into account
-# for the calculation. Defaults to 500 ms.
-# t_max : float, optional
-# Maximal time in ms of the simulation to take into account
-# for the calculation. Defaults to the simulation time.
-# areas : list, optional
-# Which areas to include in the calculcation.
-# Defaults to all loaded areas.
-# pops : list or {'complete'}, optional
-# Which populations to include in the calculation.
-# If set to 'complete', all populations the respective areas
-# are included. Defaults to 'complete'.
-# kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional
-# Convolution kernel for the calculation of the underlying firing rates.
-# Defaults to 'binned' which corresponds to a simple histogram.
-# resolution: float, optional
-# Width of the convolution kernel. Specifically it correponds to:
-# - 'binned' : bin width of the histogram
-# - 'gauss_time_window' : sigma
-# - 'alpha_time_window' : time constant of the alpha function
-# - 'rect_time_window' : width of the moving rectangular function
-# """
-# A.create_synaptic_input()
-# print("Computing synaptic input done")
+ Parameters
+ ----------
+ t_min : float, optional
+ Minimal time in ms of the simulation to take into account
+ for the calculation. Defaults to 500 ms.
+ t_max : float, optional
+ Maximal time in ms of the simulation to take into account
+ for the calculation. Defaults to the simulation time.
+ areas : list, optional
+ Which areas to include in the calculcation.
+ Defaults to all loaded areas.
+ pops : list or {'complete'}, optional
+ Which populations to include in the calculation.
+ If set to 'complete', all populations the respective areas
+ are included. Defaults to 'complete'.
+ kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional
+ Convolution kernel for the calculation of the underlying firing rates.
+ Defaults to 'binned' which corresponds to a simple histogram.
+ resolution: float, optional
+ Width of the convolution kernel. Specifically it correponds to:
+ - 'binned' : bin width of the histogram
+ - 'gauss_time_window' : sigma
+ - 'alpha_time_window' : time constant of the alpha function
+ - 'rect_time_window' : width of the moving rectangular function
+ """
+ A.create_synaptic_input()
+ print("Computing synaptic input done")
- # A.save()
+ A.save()
- # """
- # Compute BOLD signal for a given area from the time series of
- # population-averaged spike rates of a given simulation using the
- # neuRosim package of R (see Schmidt et al. 2018 for more details).
- # """
- # try:
- # 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?")
+ """
+ Compute BOLD signal for a given area from the time series of
+ population-averaged spike rates of a given simulation using the
+ neuRosim package of R (see Schmidt et al. 2018 for more details).
+ """
+ try:
+ 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
+ return A
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
index d1970f682c8e4fb7047b9f024c37ba18ce27a018..d6da1ef94e4a74805a035f295948b82230a456b4 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
@@ -35,8 +35,34 @@ def set_boxplot_props(d):
markerfacecolor='k', markeredgecolor='k', markersize=3.)
def plot_resting_state(M, data_path):
+ """
+ Analysis class.
+ An instance of the analysis class for the given network and simulation.
+ Can be created as a member class of a multiarea_model instance or standalone.
+
+ Parameters
+ ----------
+ network : MultiAreaModel
+ An instance of the multiarea_model class that specifies
+ the network to be analyzed.
+ simulation : Simulation
+ An instance of the simulation class that specifies
+ the simulation to be analyzed.
+ data_list : list of strings {'spikes', vm'}, optional
+ Specifies which type of data is to load. Defaults to ['spikes'].
+ load_areas : list of strings with area names, optional
+ Specifies the areas for which data is to be loaded.
+ Default value is None and leads to loading of data for all
+ simulated areas.
+ """
+ # Instantiate an analysis class and load spike data
+ A = Analysis(network=M,
+ simulation=M.simulation,
+ data_list=['spikes'],
+ load_areas=None)
+
# load data
- A = load_and_create_data(M)
+ load_and_create_data(M, A)
label_spikes = M.simulation.label
label = M.simulation.label
@@ -154,16 +180,16 @@ def plot_resting_state(M, data_path):
# M = MultiAreaModel({})
# spike data
- spike_data = {}
- for area in areas:
- spike_data[area] = {}
- for pop in M.structure[area]:
- spike_data[area][pop] = np.load(os.path.join(data_path,
- label_spikes,
- 'recordings',
- '{}-spikes-{}-{}.npy'.format(label_spikes,
- area, pop)))
- # spike_data = A.spike_data
+ # spike_data = {}
+ # for area in areas:
+ # spike_data[area] = {}
+ # for pop in M.structure[area]:
+ # spike_data[area][pop] = np.load(os.path.join(data_path,
+ # label_spikes,
+ # 'recordings',
+ # '{}-spikes-{}-{}.npy'.format(label_spikes,
+ # area, pop)))
+ spike_data = A.spike_data
# stationary firing rates
fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index f9c882e161bcf23f2fd5e3c001ddee158735a87f..ba9073d59d189e9ff3458ef0496241a4fe4cb079 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -570,7 +570,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 18,
"id": "ae19bcc3",
"metadata": {
"tags": []
@@ -583,7 +583,7 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn [16], line 2\u001b[0m\n\u001b[1;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[0;32m----> 2\u001b[0m plot_resting_state(M, data_path)\n",
+ "Cell \u001b[0;32mIn [18], line 2\u001b[0m\n\u001b[1;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[0;32m----> 2\u001b[0m plot_resting_state(M, data_path)\n",
"File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_resting_state.py:161\u001b[0m, in \u001b[0;36mplot_resting_state\u001b[0;34m(M, data_path)\u001b[0m\n\u001b[1;32m 159\u001b[0m spike_data[area] \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 160\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m pop \u001b[38;5;129;01min\u001b[39;00m M\u001b[38;5;241m.\u001b[39mstructure[area]:\n\u001b[0;32m--> 161\u001b[0m spike_data[area][pop] \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[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 162\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabel_spikes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 163\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrecordings\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m-spikes-\u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m-\u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m.npy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel_spikes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 165\u001b[0m \u001b[43m \u001b[49m\u001b[43marea\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpop\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;66;03m# spike_data = A.spike_data\u001b[39;00m\n\u001b[1;32m 167\u001b[0m \n\u001b[1;32m 168\u001b[0m \u001b[38;5;66;03m# stationary firing rates\u001b[39;00m\n\u001b[1;32m 169\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_rates.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:430\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m.\u001b[39mopen_memmap(file, mode\u001b[38;5;241m=\u001b[39mmmap_mode)\n\u001b[1;32m 429\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_pickle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_pickle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 431\u001b[0m \u001b[43m \u001b[49m\u001b[43mpickle_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpickle_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 433\u001b[0m \u001b[38;5;66;03m# Try a pickle\u001b[39;00m\n\u001b[1;32m 434\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_pickle:\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/format.py:742\u001b[0m, in \u001b[0;36mread_array\u001b[0;34m(fp, allow_pickle, pickle_kwargs)\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mhasobject:\n\u001b[1;32m 740\u001b[0m \u001b[38;5;66;03m# The array contained Python objects. We need to unpickle the data.\u001b[39;00m\n\u001b[1;32m 741\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_pickle:\n\u001b[0;32m--> 742\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mObject arrays cannot be loaded when \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 743\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_pickle=False\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pickle_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 745\u001b[0m pickle_kwargs \u001b[38;5;241m=\u001b[39m {}\n",