diff --git a/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py b/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..922e10c4f80d54bcd4b8f4b50fc97bfad33c09a7 100644
--- a/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py
+++ b/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py
@@ -0,0 +1,423 @@
+import json
+import numpy as np
+import os
+
+import sys
+sys.path.append('./figures/Schmidt2018_dyn')
+from helpers import original_data_path, population_labels
+from multiarea_model import MultiAreaModel
+from plotcolors import myred, myblue
+
+import matplotlib.pyplot as pl
+from matplotlib import gridspec
+from matplotlib import rc_file
+rc_file('./figures/Schmidt2018_dyn/plotstyle.rc')
+
+icolor = myred
+ecolor = myblue
+
+# Instantaneous and mean firing rate across all populations
+def plot_instan_mean_firing_rate(tsteps, rate, sim_params):
+ ax = pl.subplots()
+ ax.plot(tsteps, rate)
+ ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
+ ax.set_title('Instantaneous and mean firing rate across all populations')
+ ax.set_xlabel('time (ms)')
+ ax.set_ylabel('firing rate (spikes / s)')
+ ax.set_xlim(0, sim_params['t_sim'])
+ ax.set_ylim(0, 50)
+ ax.legend()
+
+def plot_raster_plot(A):
+ """
+ Create raster display of a single area with populations stacked onto each other. Excitatory neurons in blue, inhibitory neurons in red.
+
+ Parameters
+ ----------
+ area : string {area}
+ Area to be plotted.
+ frac_neurons : float, [0,1]
+ Fraction of cells to be considered.
+ t_min : float, optional
+ Minimal time in ms of spikes to be shown. Defaults to 0 ms.
+ t_max : float, optional
+ Minimal time in ms of spikes to be shown. Defaults to simulation time.
+ output : {'pdf', 'png', 'eps'}, optional
+ If given, the function stores the plot to a file of the given format.
+ """
+ t_min = 0.
+ t_max = 500.
+ areas = ['V1', 'V2', 'FEF']
+ frac_neurons = 1.
+ for area in areas:
+ A.single_dot_display(area, frac_neurons, t_min, t_max)
+
+def set_boxplot_props(d):
+ for i in range(len(d['boxes'])):
+ if i % 2 == 0:
+ d['boxes'][i].set_facecolor(icolor)
+ d['boxes'][i].set_color(icolor)
+ else:
+ d['boxes'][i].set_facecolor(ecolor)
+ d['boxes'][i].set_color(ecolor)
+ pl.setp(d['whiskers'], color='k')
+ pl.setp(d['fliers'], color='k', markerfacecolor='k', marker='+')
+ pl.setp(d['medians'], color='none')
+ pl.setp(d['caps'], color='k')
+ pl.setp(d['means'], marker='x', color='k',
+ markerfacecolor='k', markeredgecolor='k', markersize=3.)
+
+def plot_resting_state(A):
+ """
+ Figure layout
+ """
+ nrows = 4
+ ncols = 4
+ width = 7.0866
+ panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio
+
+ height = width / panel_wh_ratio * float(nrows) / ncols
+ pl.rcParams['figure.figsize'] = (width, height)
+
+
+ fig = pl.figure()
+ axes = {}
+
+ gs1 = gridspec.GridSpec(1, 3)
+ gs1.update(left=0.06, right=0.72, top=0.95, wspace=0.4, bottom=0.35)
+ axes['A'] = pl.subplot(gs1[:-1, :1])
+ axes['B'] = pl.subplot(gs1[:-1, 1:2])
+ axes['C'] = pl.subplot(gs1[:-1, 2:])
+
+ gs2 = gridspec.GridSpec(3, 1)
+ gs2.update(left=0.78, right=0.95, top=0.95, bottom=0.35)
+ axes['D'] = pl.subplot(gs2[:1, :1])
+ axes['E'] = pl.subplot(gs2[1:2, :1])
+ axes['F'] = pl.subplot(gs2[2:3, :1])
+
+
+ gs3 = gridspec.GridSpec(1, 1)
+ gs3.update(left=0.1, right=0.95, top=0.3, bottom=0.075)
+ axes['G'] = pl.subplot(gs3[:1, :1])
+
+ areas = ['V1', 'V2', 'FEF']
+
+ labels = ['A', 'B', 'C']
+ for area, label in zip(areas, labels):
+ label_pos = [-0.2, 1.01]
+ pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label + ': ' + area,
+ fontdict={'fontsize': 10, 'weight': 'bold',
+ 'horizontalalignment': 'left', 'verticalalignment':
+ 'bottom'}, transform=axes[label].transAxes)
+
+ label = 'G'
+ label_pos = [-0.1, 0.92]
+ pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
+ fontdict={'fontsize': 10, 'weight': 'bold',
+ 'horizontalalignment': 'left', 'verticalalignment':
+ 'bottom'}, transform=axes[label].transAxes)
+
+
+ labels = ['E', 'D', 'F']
+ for label in labels:
+ label_pos = [-0.2, 1.05]
+ pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
+ fontdict={'fontsize': 10, 'weight': 'bold',
+ 'horizontalalignment': 'left', 'verticalalignment':
+ 'bottom'}, transform=axes[label].transAxes)
+
+ labels = ['A', 'B', 'C', 'D', 'E', 'F']
+
+ for label in labels:
+ axes[label].spines['right'].set_color('none')
+ axes[label].spines['top'].set_color('none')
+ axes[label].yaxis.set_ticks_position("left")
+ axes[label].xaxis.set_ticks_position("bottom")
+
+ for label in ['A', 'B', 'C']:
+ axes[label].yaxis.set_ticks_position('none')
+
+
+ """
+ Load data
+ """
+ LOAD_ORIGINAL_DATA = True
+
+
+ if LOAD_ORIGINAL_DATA:
+ # use T=10500 simulation for spike raster plots
+ label_spikes = '3afaec94d650c637ef8419611c3f80b3cb3ff539'
+ # and T=100500 simulation for all other panels
+ label = '99c0024eacc275d13f719afd59357f7d12f02b77'
+ data_path = original_data_path
+ else:
+ from network_simulations import init_models
+ from config import data_path
+ models = init_models('Fig5')
+ label_spikes = models[0].simulation.label
+ label = models[1].simulation.label
+
+ """
+ Create MultiAreaModel instance to have access to data structures
+ """
+ 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)))
+ # stationary firing rates
+ fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
+ with open(fn, 'r') as f:
+ pop_rates = json.load(f)
+
+ # time series of firing rates
+ rate_time_series = {}
+ for area in areas:
+ fn = os.path.join(data_path, label,
+ 'Analysis',
+ 'rate_time_series_full',
+ 'rate_time_series_full_{}.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)
+
+ # local variance revised (LvR)
+ fn = os.path.join(data_path, label, 'Analysis', 'pop_LvR.json')
+ with open(fn, 'r') as f:
+ pop_LvR = json.load(f)
+
+ # correlation coefficients
+ fn = os.path.join(data_path, label, 'Analysis', 'corrcoeff.json')
+ with open(fn, 'r') as f:
+ corrcoeff = json.load(f)
+
+ """
+ Plotting
+ """
+ print("Raster plots")
+
+ t_min = 3000.
+ t_max = 3500.
+
+ icolor = myred
+ ecolor = myblue
+
+ frac_neurons = 0.03
+
+ for i, area in enumerate(areas):
+ ax = axes[labels[i]]
+
+ if area in spike_data:
+ n_pops = len(spike_data[area])
+ # Determine number of neurons that will be plotted for this area (for
+ # vertical offset)
+ offset = 0
+ n_to_plot = {}
+ for pop in M.structure[area]:
+ n_to_plot[pop] = int(M.N[area][pop] * frac_neurons)
+ offset = offset + n_to_plot[pop]
+ y_max = offset + 1
+ prev_pop = ''
+ yticks = []
+ yticklocs = []
+ for jj, pop in enumerate(M.structure[area]):
+ if pop[0:-1] != prev_pop:
+ prev_pop = pop[0:-1]
+ yticks.append('L' + population_labels[jj][0:-1])
+ yticklocs.append(offset - 0.5 * n_to_plot[pop])
+ ind = np.where(np.logical_and(
+ spike_data[area][pop][:, 1] <= t_max, spike_data[area][pop][:, 1] >= t_min))
+ pop_data = spike_data[area][pop][ind]
+ pop_neurons = np.unique(pop_data[:, 0])
+ neurons_to_ = np.arange(np.min(spike_data[area][pop][:, 0]), np.min(
+ spike_data[area][pop][:, 0]) + n_to_plot[pop], 1)
+
+ if pop.find('E') > (-1):
+ pcolor = ecolor
+ else:
+ pcolor = icolor
+
+ for kk in range(n_to_plot[pop]):
+ spike_times = pop_data[pop_data[:, 0] == neurons_to_[kk], 1]
+
+ _ = ax.plot(spike_times, np.zeros(len(spike_times)) +
+ offset - kk, '.', color=pcolor, markersize=1)
+ offset = offset - n_to_plot[pop]
+ y_min = offset
+ ax.set_xlim([t_min, t_max])
+ ax.set_ylim([y_min, y_max])
+ ax.set_yticklabels(yticks)
+ ax.set_yticks(yticklocs)
+ ax.set_xlabel('Time (s)', labelpad=-0.1)
+ ax.set_xticks([t_min, t_min + 250., t_max])
+ ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])
+
+
+ print("plotting Population rates")
+
+ rates = np.zeros((len(M.area_list), 8))
+ for i, area in enumerate(M.area_list):
+ for j, pop in enumerate(M.structure[area][::-1]):
+ rate = pop_rates[area][pop][0]
+ if rate == 0.0:
+ rate = 1e-5
+ if area == 'TH' and j > 3: # To account for missing layer 4 in TH
+ rates[i][j + 2] = rate
+ else:
+ rates[i][j] = rate
+
+
+ rates = np.transpose(rates)
+ masked_rates = np.ma.masked_where(rates < 1e-4, rates)
+
+ ax = axes['D']
+ d = ax.boxplot(np.transpose(rates), vert=False,
+ patch_artist=True, whis=1.5, showmeans=True)
+ set_boxplot_props(d)
+
+ ax.plot(np.mean(rates, axis=1), np.arange(
+ 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)
+ ax.set_yticklabels(population_labels[::-1], size=8)
+ ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))
+ ax.set_ylim((0., len(M.structure['V1']) + .5))
+
+ x_max = 220.
+ ax.set_xlim((-1., x_max))
+ ax.set_xlabel(r'Rate (spikes/s)', labelpad=-0.1)
+ ax.set_xticks([0., 50., 100.])
+
+ print("plotting Synchrony")
+
+ syn = np.zeros((len(M.area_list), 8))
+ for i, area in enumerate(M.area_list):
+ for j, pop in enumerate(M.structure[area][::-1]):
+ value = corrcoeff[area][pop]
+ if value == 0.0:
+ value = 1e-5
+ if area == 'TH' and j > 3: # To account for missing layer 4 in TH
+ syn[i][j + 2] = value
+ else:
+ syn[i][j] = value
+
+
+ syn = np.transpose(syn)
+ masked_syn = np.ma.masked_where(syn < 1e-4, syn)
+
+ ax = axes['E']
+ d = ax.boxplot(np.transpose(syn), vert=False,
+ patch_artist=True, whis=1.5, showmeans=True)
+ set_boxplot_props(d)
+
+ ax.plot(np.mean(syn, axis=1), np.arange(
+ 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)
+
+ ax.set_yticklabels(population_labels[::-1], size=8)
+ ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))
+ ax.set_ylim((0., len(M.structure['V1']) + .5))
+ ax.set_xticks(np.arange(0.0, 0.601, 0.2))
+ ax.set_xlabel('Correlation coefficient', labelpad=-0.1)
+
+
+ print("plotting Irregularity")
+
+ LvR = np.zeros((len(M.area_list), 8))
+ for i, area in enumerate(M.area_list):
+ for j, pop in enumerate(M.structure[area][::-1]):
+ value = pop_LvR[area][pop]
+ if value == 0.0:
+ value = 1e-5
+ if area == 'TH' and j > 3: # To account for missing layer 4 in TH
+ LvR[i][j + 2] = value
+ else:
+ LvR[i][j] = value
+
+ LvR = np.transpose(LvR)
+ masked_LvR = np.ma.masked_where(LvR < 1e-4, LvR)
+
+ ax = axes['F']
+ d = ax.boxplot(np.transpose(LvR), vert=False,
+ patch_artist=True, whis=1.5, showmeans=True)
+ set_boxplot_props(d)
+
+ ax.plot(np.mean(LvR, axis=1), np.arange(
+ 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)
+ ax.set_yticklabels(population_labels[::-1], size=8)
+ ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))
+ ax.set_ylim((0., len(M.structure['V1']) + .5))
+
+
+ x_max = 2.9
+ ax.set_xlim((0., x_max))
+ ax.set_xlabel('Irregularity', labelpad=-0.1)
+ ax.set_xticks([0., 1., 2.])
+
+ axes['G'].spines['right'].set_color('none')
+ axes['G'].spines['left'].set_color('none')
+ axes['G'].spines['top'].set_color('none')
+ axes['G'].spines['bottom'].set_color('none')
+ axes['G'].yaxis.set_ticks_position("none")
+ axes['G'].xaxis.set_ticks_position("none")
+ axes['G'].set_xticks([])
+ axes['G'].set_yticks([])
+
+
+ print("Plotting rate time series")
+ pos = axes['G'].get_position()
+ ax = []
+ h = pos.y1 - pos.y0
+ w = pos.x1 - pos.x0
+ ax.append(pl.axes([pos.x0, pos.y0, w, 0.28 * h]))
+ ax.append(pl.axes([pos.x0, pos.y0 + 0.33 * h, w, 0.28 * h]))
+ ax.append(pl.axes([pos.x0, pos.y0 + 0.67 * h, w, 0.28 * h]))
+
+ colors = ['0.5', '0.3', '0.0']
+
+ t_min = 500.
+ t_max = 10500.
+ time = np.arange(500., t_max)
+ for i, area in enumerate(areas[::-1]):
+ ax[i].spines['right'].set_color('none')
+ ax[i].spines['top'].set_color('none')
+ ax[i].yaxis.set_ticks_position("left")
+ ax[i].xaxis.set_ticks_position("none")
+
+ 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]
+ ax[i].plot(time, rate, color=colors[2], label=area)
+ ax[i].set_xlim((500., t_max))
+
+ ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)
+
+ if i > 0:
+ ax[i].spines['bottom'].set_color('none')
+ ax[i].set_xticks([])
+ ax[i].set_yticks([0., 30.])
+ else:
+ ax[i].set_xticks([1000., 5000., 10000.])
+ ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])
+ ax[i].set_yticks([0., 5.])
+ if i == 1:
+ ax[i].set_ylabel(r'Rate (spikes/s)')
+
+ ax[0].set_xlabel('Time (s)', labelpad=-0.05)
+
+ fig.subplots_adjust(left=0.05, right=0.95, top=0.95,
+ bottom=0.075, wspace=1., hspace=.5)
+
+ # pl.savefig('Fig5_ground_state.eps')
diff --git a/figures/MAM2EBRAINS.py b/figures/MAM2EBRAINS.py
index afa764b144fa4a2c657aae83a8df41c5266c156f..922e10c4f80d54bcd4b8f4b50fc97bfad33c09a7 100644
--- a/figures/MAM2EBRAINS.py
+++ b/figures/MAM2EBRAINS.py
@@ -18,7 +18,7 @@ ecolor = myblue
# Instantaneous and mean firing rate across all populations
def plot_instan_mean_firing_rate(tsteps, rate, sim_params):
- fig, ax = pl.subplots()
+ ax = pl.subplots()
ax.plot(tsteps, rate)
ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
ax.set_title('Instantaneous and mean firing rate across all populations')
@@ -67,7 +67,7 @@ def set_boxplot_props(d):
pl.setp(d['means'], marker='x', color='k',
markerfacecolor='k', markeredgecolor='k', markersize=3.)
-def plot_resting_state():
+def plot_resting_state(A):
"""
Figure layout
"""
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index f2e9007fec21dee5d8d4cea01af17625798f7aa9..63a36af655dc2d2d5017e0af28c636dbac3e093d 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -118,8 +118,8 @@
"from multiarea_model import Analysis\n",
"from config import base_path, data_path\n",
"import sys\n",
- "sys.path.append('./figures/MAM2EBRAINS')\n",
- "import plot_instan_mean_firing_rate"
+ "sys.path.append('./figures')\n",
+ "from MAM2EBRAINS import plot_instan_mean_firing_rate"
]
},
{
@@ -370,7 +370,7 @@
"output_type": "stream",
"text": [
"Initializing network from dictionary.\n",
- "RAND_DATA_LABEL 2610\n"
+ "RAND_DATA_LABEL 588\n"
]
},
{
@@ -546,72 +546,72 @@
"text": [
"Prepared simulation in 0.00 seconds.\n",
"Rank 0: created area V1 with 0 local nodes\n",
- "Memory after V1 : 1912.18 MB\n",
+ "Memory after V1 : 1911.94 MB\n",
"Rank 0: created area V2 with 0 local nodes\n",
- "Memory after V2 : 1938.88 MB\n",
+ "Memory after V2 : 1938.54 MB\n",
"Rank 0: created area VP with 0 local nodes\n",
- "Memory after VP : 1968.07 MB\n",
+ "Memory after VP : 1967.84 MB\n",
"Rank 0: created area V3 with 0 local nodes\n",
- "Memory after V3 : 1996.37 MB\n",
+ "Memory after V3 : 1996.10 MB\n",
"Rank 0: created area V3A with 0 local nodes\n",
- "Memory after V3A : 2016.15 MB\n",
+ "Memory after V3A : 2016.04 MB\n",
"Rank 0: created area MT with 0 local nodes\n",
- "Memory after MT : 2041.78 MB\n",
+ "Memory after MT : 2041.55 MB\n",
"Rank 0: created area V4t with 0 local nodes\n",
- "Memory after V4t : 2066.72 MB\n",
+ "Memory after V4t : 2066.49 MB\n",
"Rank 0: created area V4 with 0 local nodes\n",
- "Memory after V4 : 2093.75 MB\n",
+ "Memory after V4 : 2093.56 MB\n",
"Rank 0: created area VOT with 0 local nodes\n",
- "Memory after VOT : 2118.94 MB\n",
+ "Memory after VOT : 2118.79 MB\n",
"Rank 0: created area MSTd with 0 local nodes\n",
- "Memory after MSTd : 2140.53 MB\n",
+ "Memory after MSTd : 2140.26 MB\n",
"Rank 0: created area PIP with 0 local nodes\n",
- "Memory after PIP : 2161.88 MB\n",
+ "Memory after PIP : 2161.65 MB\n",
"Rank 0: created area PO with 0 local nodes\n",
- "Memory after PO : 2183.39 MB\n",
+ "Memory after PO : 2183.16 MB\n",
"Rank 0: created area DP with 0 local nodes\n",
- "Memory after DP : 2203.62 MB\n",
+ "Memory after DP : 2203.39 MB\n",
"Rank 0: created area MIP with 0 local nodes\n",
- "Memory after MIP : 2225.19 MB\n",
+ "Memory after MIP : 2224.92 MB\n",
"Rank 0: created area MDP with 0 local nodes\n",
- "Memory after MDP : 2246.55 MB\n",
+ "Memory after MDP : 2246.44 MB\n",
"Rank 0: created area VIP with 0 local nodes\n",
- "Memory after VIP : 2268.48 MB\n",
+ "Memory after VIP : 2268.37 MB\n",
"Rank 0: created area LIP with 0 local nodes\n",
- "Memory after LIP : 2292.55 MB\n",
+ "Memory after LIP : 2292.32 MB\n",
"Rank 0: created area PITv with 0 local nodes\n",
- "Memory after PITv : 2317.86 MB\n",
+ "Memory after PITv : 2317.62 MB\n",
"Rank 0: created area PITd with 0 local nodes\n",
- "Memory after PITd : 2343.10 MB\n",
+ "Memory after PITd : 2342.83 MB\n",
"Rank 0: created area MSTl with 0 local nodes\n",
- "Memory after MSTl : 2364.56 MB\n",
+ "Memory after MSTl : 2364.29 MB\n",
"Rank 0: created area CITv with 0 local nodes\n",
- "Memory after CITv : 2383.62 MB\n",
+ "Memory after CITv : 2383.51 MB\n",
"Rank 0: created area CITd with 0 local nodes\n",
- "Memory after CITd : 2403.07 MB\n",
+ "Memory after CITd : 2402.80 MB\n",
"Rank 0: created area FEF with 0 local nodes\n",
- "Memory after FEF : 2424.43 MB\n",
+ "Memory after FEF : 2424.31 MB\n",
"Rank 0: created area TF with 0 local nodes\n",
- "Memory after TF : 2440.11 MB\n",
+ "Memory after TF : 2439.88 MB\n",
"Rank 0: created area AITv with 0 local nodes\n",
- "Memory after AITv : 2462.82 MB\n",
+ "Memory after AITv : 2462.55 MB\n",
"Rank 0: created area FST with 0 local nodes\n",
- "Memory after FST : 2479.44 MB\n",
+ "Memory after FST : 2479.28 MB\n",
"Rank 0: created area 7a with 0 local nodes\n",
- "Memory after 7a : 2500.65 MB\n",
+ "Memory after 7a : 2500.42 MB\n",
"Rank 0: created area STPp with 0 local nodes\n",
- "Memory after STPp : 2519.37 MB\n",
+ "Memory after STPp : 2519.25 MB\n",
"Rank 0: created area STPa with 0 local nodes\n",
- "Memory after STPa : 2538.63 MB\n",
+ "Memory after STPa : 2538.40 MB\n",
"Rank 0: created area 46 with 0 local nodes\n",
- "Memory after 46 : 2553.96 MB\n",
+ "Memory after 46 : 2553.76 MB\n",
"Rank 0: created area AITd with 0 local nodes\n",
- "Memory after AITd : 2576.64 MB\n",
+ "Memory after AITd : 2576.32 MB\n",
"Rank 0: created area TH with 0 local nodes\n",
- "Memory after TH : 2589.26 MB\n",
- "Created areas and internal connections in 2.27 seconds.\n",
- "Created cortico-cortical connections in 23.31 seconds.\n",
- "Simulated network in 76.51 seconds.\n"
+ "Memory after TH : 2589.07 MB\n",
+ "Created areas and internal connections in 2.19 seconds.\n",
+ "Created cortico-cortical connections in 22.42 seconds.\n",
+ "Simulated network in 74.96 seconds.\n"
]
}
],
@@ -755,7 +755,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 15,
"id": "f5b58845-4d1a-430f-83f4-402fdf918aef",
"metadata": {
"tags": []
@@ -827,7 +827,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 18,
"id": "1870cf34-ee62-4614-bc25-c36bc9a7377c",
"metadata": {
"tags": []
@@ -874,7 +874,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 19,
"id": "50b7df89",
"metadata": {
"tags": []
@@ -921,7 +921,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 20,
"id": "d43b493c",
"metadata": {
"tags": []
@@ -963,7 +963,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 21,
"id": "401ece2d-47c8-4775-80ae-92a8e432520c",
"metadata": {
"tags": []
@@ -1018,7 +1018,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 22,
"id": "fa3ea20e-e456-4608-a711-e2c320bcaf91",
"metadata": {},
"outputs": [
@@ -1069,10 +1069,10 @@
"metadata": {},
"outputs": [],
"source": [
- "# visualization settings\n",
- "icolor = 'r'\n",
- "ecolor = 'b'\n",
- "population_labels = ['2/3E', '2/3I', '4E', '4I', '5E', '5I', '6E', '6I']"
+ "# # visualization settings\n",
+ "# icolor = 'r'\n",
+ "# ecolor = 'b'\n",
+ "# population_labels = ['2/3E', '2/3I', '4E', '4I', '5E', '5I', '6E', '6I']"
]
},
{
@@ -1087,35 +1087,54 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 25,
"id": "bea30fc8",
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f030935c9a0>"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "RuntimeError",
+ "evalue": "latex was not able to process the following string:\nb'lp'\n\nHere is the full report generated by latex:\nThis is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019/Debian) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(../540885da22f8d5147f0088a11e43701a.tex\nLaTeX2e <2020-02-02> patch level 2\nL3 programming layer <2020-02-14>\n(/usr/share/texlive/texmf-dist/tex/latex/base/article.cls\nDocument Class: article 2019/12/20 v1.4l Standard LaTeX document class\n(/usr/share/texlive/texmf-dist/tex/latex/base/size10.clo))\n(/usr/share/texlive/texmf-dist/tex/latex/type1cm/type1cm.sty)\n\n! LaTeX Error: File `type1ec.sty' not found.\n\nType X to quit or <RETURN> to proceed,\nor enter new name. (Default extension: sty)\n\nEnter file name: \n! Emergency stop.\n<read *> \n \nl.6 \\usepackage\n [utf8]{inputenc}^^M\nNo pages of output.\nTranscript written on 540885da22f8d5147f0088a11e43701a.log.\n\n\n",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mCalledProcessError\u001b[0m Traceback (most recent call last)",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/texmanager.py:233\u001b[0m, in \u001b[0;36mTexManager._run_checked_subprocess\u001b[0;34m(self, command, tex, cwd)\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 233\u001b[0m report \u001b[38;5;241m=\u001b[39m \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_output\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 234\u001b[0m \u001b[43m \u001b[49m\u001b[43mcommand\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcwd\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtexcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 235\u001b[0m \u001b[43m \u001b[49m\u001b[43mstderr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSTDOUT\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/._view/6axslmv6jvf4v2nte3uwlayg4vhsjoha/lib/python3.8/subprocess.py:415\u001b[0m, in \u001b[0;36mcheck_output\u001b[0;34m(timeout, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 413\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minput\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m empty\n\u001b[0;32m--> 415\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpopenargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstdout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mPIPE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 416\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mstdout\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/._view/6axslmv6jvf4v2nte3uwlayg4vhsjoha/lib/python3.8/subprocess.py:516\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check \u001b[38;5;129;01mand\u001b[39;00m retcode:\n\u001b[0;32m--> 516\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CalledProcessError(retcode, process\u001b[38;5;241m.\u001b[39margs,\n\u001b[1;32m 517\u001b[0m output\u001b[38;5;241m=\u001b[39mstdout, stderr\u001b[38;5;241m=\u001b[39mstderr)\n\u001b[1;32m 518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m CompletedProcess(process\u001b[38;5;241m.\u001b[39margs, retcode, stdout, stderr)\n",
+ "\u001b[0;31mCalledProcessError\u001b[0m: Command '['latex', '-interaction=nonstopmode', '--halt-on-error', '../540885da22f8d5147f0088a11e43701a.tex']' returned non-zero exit status 1.",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/IPython/core/formatters.py:339\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 337\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 338\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 339\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 340\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m 341\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/IPython/core/pylabtools.py:151\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m 149\u001b[0m FigureCanvasBase(fig)\n\u001b[0;32m--> 151\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 152\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/backend_bases.py:2295\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2289\u001b[0m renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m 2290\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m 2291\u001b[0m functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m 2292\u001b[0m print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m 2293\u001b[0m )\n\u001b[1;32m 2294\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2295\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2297\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m 2298\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/artist.py:73\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 73\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 75\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/artist.py:50\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 48\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 50\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/figure.py:2837\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 2834\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 2836\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 2837\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2838\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2840\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m 2841\u001b[0m sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/artist.py:50\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 48\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 50\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/axes/_base.py:3055\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3052\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m spine \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspines\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m 3053\u001b[0m artists\u001b[38;5;241m.\u001b[39mremove(spine)\n\u001b[0;32m-> 3055\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_title_position\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxison:\n\u001b[1;32m 3058\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis_list():\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/axes/_base.py:3005\u001b[0m, in \u001b[0;36m_AxesBase._update_title_position\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3003\u001b[0m _log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtop of Axes not in the figure, so title not moved\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3004\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m-> 3005\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtitle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_window_extent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mymin \u001b[38;5;241m<\u001b[39m top:\n\u001b[1;32m 3006\u001b[0m _, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransAxes\u001b[38;5;241m.\u001b[39minverted()\u001b[38;5;241m.\u001b[39mtransform((\u001b[38;5;241m0\u001b[39m, top))\n\u001b[1;32m 3007\u001b[0m title\u001b[38;5;241m.\u001b[39mset_position((x, y))\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/text.py:910\u001b[0m, in \u001b[0;36mText.get_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m 907\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCannot get window extent w/o renderer\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 909\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m--> 910\u001b[0m bbox, info, descent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_renderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 911\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_unitless_position()\n\u001b[1;32m 912\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\u001b[38;5;241m.\u001b[39mtransform((x, y))\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/text.py:309\u001b[0m, in \u001b[0;36mText._get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 306\u001b[0m ys \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 308\u001b[0m \u001b[38;5;66;03m# Full vertical extent of font, including ascenders and descenders:\u001b[39;00m\n\u001b[0;32m--> 309\u001b[0m _, lp_h, lp_d \u001b[38;5;241m=\u001b[39m \u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 310\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 311\u001b[0m \u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTeX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_usetex\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 312\u001b[0m min_dy \u001b[38;5;241m=\u001b[39m (lp_h \u001b[38;5;241m-\u001b[39m lp_d) \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_linespacing\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, line \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(lines):\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:259\u001b[0m, in \u001b[0;36mRendererAgg.get_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m 257\u001b[0m texmanager \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_texmanager()\n\u001b[1;32m 258\u001b[0m fontsize \u001b[38;5;241m=\u001b[39m prop\u001b[38;5;241m.\u001b[39mget_size_in_points()\n\u001b[0;32m--> 259\u001b[0m w, h, d \u001b[38;5;241m=\u001b[39m \u001b[43mtexmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 261\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m w, h, d\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/texmanager.py:335\u001b[0m, in \u001b[0;36mTexManager.get_text_width_height_descent\u001b[0;34m(self, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tex\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 334\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 335\u001b[0m dvifile \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_dvi\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 336\u001b[0m dpi_fraction \u001b[38;5;241m=\u001b[39m renderer\u001b[38;5;241m.\u001b[39mpoints_to_pixels(\u001b[38;5;241m1.\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m renderer \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 337\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m dviread\u001b[38;5;241m.\u001b[39mDvi(dvifile, \u001b[38;5;241m72\u001b[39m \u001b[38;5;241m*\u001b[39m dpi_fraction) \u001b[38;5;28;01mas\u001b[39;00m dvi:\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/texmanager.py:271\u001b[0m, in \u001b[0;36mTexManager.make_dvi\u001b[0;34m(self, tex, fontsize)\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;66;03m# Generate the dvi in a temporary directory to avoid race\u001b[39;00m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# conditions e.g. if multiple processes try to process the same tex\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# string at the same time. Having tmpdir be a subdirectory of the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[38;5;66;03m# the absolute path may contain characters (e.g. ~) that TeX does\u001b[39;00m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;66;03m# not support.)\u001b[39;00m\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m TemporaryDirectory(\u001b[38;5;28mdir\u001b[39m\u001b[38;5;241m=\u001b[39mPath(dvifile)\u001b[38;5;241m.\u001b[39mparent) \u001b[38;5;28;01mas\u001b[39;00m tmpdir:\n\u001b[0;32m--> 271\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_checked_subprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlatex\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m-interaction=nonstopmode\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m--halt-on-error\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mtexfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtmpdir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m (Path(tmpdir) \u001b[38;5;241m/\u001b[39m Path(dvifile)\u001b[38;5;241m.\u001b[39mname)\u001b[38;5;241m.\u001b[39mreplace(dvifile)\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dvifile\n",
+ "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/texmanager.py:241\u001b[0m, in \u001b[0;36mTexManager._run_checked_subprocess\u001b[0;34m(self, command, tex, cwd)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 238\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to process string with tex because \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m could not be \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 239\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfound\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(command[\u001b[38;5;241m0\u001b[39m])) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m 240\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m subprocess\u001b[38;5;241m.\u001b[39mCalledProcessError \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m--> 241\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 242\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{prog}\u001b[39;00m\u001b[38;5;124m was not able to process the following string:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{tex!r}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 244\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHere is the full report generated by \u001b[39m\u001b[38;5;132;01m{prog}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{exc}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 246\u001b[0m prog\u001b[38;5;241m=\u001b[39mcommand[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 247\u001b[0m tex\u001b[38;5;241m=\u001b[39mtex\u001b[38;5;241m.\u001b[39mencode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124municode_escape\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m 248\u001b[0m exc\u001b[38;5;241m=\u001b[39mexc\u001b[38;5;241m.\u001b[39moutput\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m))) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m 249\u001b[0m _log\u001b[38;5;241m.\u001b[39mdebug(report)\n\u001b[1;32m 250\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m report\n",
+ "\u001b[0;31mRuntimeError\u001b[0m: latex was not able to process the following string:\nb'lp'\n\nHere is the full report generated by latex:\nThis is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019/Debian) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(../540885da22f8d5147f0088a11e43701a.tex\nLaTeX2e <2020-02-02> patch level 2\nL3 programming layer <2020-02-14>\n(/usr/share/texlive/texmf-dist/tex/latex/base/article.cls\nDocument Class: article 2019/12/20 v1.4l Standard LaTeX document class\n(/usr/share/texlive/texmf-dist/tex/latex/base/size10.clo))\n(/usr/share/texlive/texmf-dist/tex/latex/type1cm/type1cm.sty)\n\n! LaTeX Error: File `type1ec.sty' not found.\n\nType X to quit or <RETURN> to proceed,\nor enter new name. (Default extension: sty)\n\nEnter file name: \n! Emergency stop.\n<read *> \n \nl.6 \\usepackage\n [utf8]{inputenc}^^M\nNo pages of output.\nTranscript written on 540885da22f8d5147f0088a11e43701a.log.\n\n\n"
+ ]
},
{
"data": {
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",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 1800x1113 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
"source": [
- "M2E.plot_instan_mean_firing _rate(tsteps, rate, sim_params)"
+ "plot_instan_mean_firing_rate(tsteps, rate, sim_params)"
]
},
{
@@ -1143,7 +1162,8 @@
},
"outputs": [],
"source": [
- "M2E.plot_resting_state(A)"
+ "from MAM2EBRAINS import plot_resting_state\n",
+ "plot_resting_state(A)"
]
},
{
@@ -1157,54 +1177,17 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"id": "1da18fee",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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",
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- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"M2E.plot_raster_plot(A)"
]
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"id": "7d5a5e32-bd12-4e91-a65d-91d279edc450",
"metadata": {},
"outputs": [],
@@ -1215,30 +1198,10 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
"id": "7947fea3-ba4c-4b1d-94fc-16614e4e4a11",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/tmp/ipykernel_12774/362875631.py:71: UserWarning:FixedFormatter should only be used together with FixedLocator\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 720x216 with 3 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# plotting raster plot of spiking activity for single area\n",
"from matplotlib import gridspec\n",
@@ -1330,7 +1293,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": null,
"id": "b069bc59-44ae-450a-b0a5-b073951e3604",
"metadata": {},
"outputs": [],
@@ -1345,34 +1308,10 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": null,
"id": "96baac8f-3a0e-4d69-92ac-1d7cb4aff0d8",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.axis.XTick at 0x7f03090ca1f0>,\n",
- " <matplotlib.axis.XTick at 0x7f03090ca1c0>]"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"def set_boxplot_props(d):\n",
" for i in range(len(d['boxes'])):\n",
@@ -1438,62 +1377,10 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"id": "721d1f03-df25-468d-8075-a807025a9c58",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0 V1\n",
- "1 V2\n",
- "2 VP\n",
- "3 V3\n",
- "4 PIP\n",
- "5 V3A\n",
- "6 MT\n",
- "7 V4t\n",
- "8 V4\n",
- "9 PO\n",
- "10 VOT\n",
- "11 DP\n",
- "12 MIP\n",
- "13 MDP\n",
- "14 MSTd\n",
- "15 VIP\n",
- "16 LIP\n",
- "17 PITv\n",
- "18 PITd\n",
- "19 AITv\n",
- "20 MSTl\n",
- "21 FST\n",
- "22 CITv\n",
- "23 CITd\n",
- "24 7a\n",
- "25 STPp\n",
- "26 STPa\n",
- "27 FEF\n",
- "28 46\n",
- "29 TF\n",
- "30 TH\n",
- "31 AITd\n",
- "['23E', '23I', '4E', '4I', '5E', '5I', '6E', '6I']\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"\"\"\"\n",
"Plot overview over time-averaged population rates encoded in colors\n",
@@ -1523,7 +1410,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": null,
"id": "a8e77836-4c37-4b78-b7c4-5e11bc67b4fa",
"metadata": {},
"outputs": [],
@@ -1540,33 +1427,10 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": null,
"id": "218367da-82ef-47b6-bf15-083ef3d43013",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 0, 'Correlation coefficient')"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# print(\"plotting Synchrony\")\n",
"\n",
@@ -1615,7 +1479,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": null,
"id": "65377033-f3c0-4f90-be13-70594cfda292",
"metadata": {},
"outputs": [],
@@ -1630,35 +1494,10 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": null,
"id": "d7480a9b",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.axis.XTick at 0x7f02ed742ca0>,\n",
- " <matplotlib.axis.XTick at 0x7f02ed742c70>,\n",
- " <matplotlib.axis.XTick at 0x7f02ed7423a0>]"
- ]
- },
- "execution_count": 64,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAECCAYAAAAb5qc/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAATWUlEQVR4nO3df4xlZ13H8c+H7Q+NlArdde6mBYcY2gRoLWEKxhbbwlB0DVQFmkq2KAIDJBp1F9QYk7kbyh+k7qLSmLoBDNBSBVtIoU1oZ7uLXZYUbpGotJQQobEuM05LUii2K61f/7jnLjPD3Jl75z7nPvc+9/1KTnLOvefH95yZ/eyZ7/3xOCIEACjTM3IXAACoDyEPAAUj5AGgYIQ8ABSMkAeAgp2Su4Dt27fH9PR07jIAYKzcd999j0TEjs3Wyx7y09PTarVaucsAgLFi+6Fe1qNdAwAFI+QBoGCEPAAUjJAHgIIR8gBQMEI+s0ZDsuubGo3cZwggJ0I+s6Wl8d4/gNFGyANAwYoJ+WazmeW4trMcd5RwDYDRtWnI23657WO277H9geqx02zfZPsK20dtf8n2+6rnLrP9kO0j1fS6uk9Ckvbt2zeMwwDAWOnlaw0ekvTKiHiyCvbzJZ0l6ZikwxFxpyTZPmy78z0KH4+Iv6in5Em0S9JsNb8g6Y6MtQAYJ5veyUfEYkQ8WS0+JelpSVdIuisifiRJtrdJWpT0/V4OanvOdst2a3l5eWuVq92isX2yXdCZH3brpnPcrUxSL62OWUl7q2l2k3V/orqB6uvtHACMqp578rYvkLQ9Iu6XdG5EfLN6fE7Sg5IejYgT1erXrGjXvGztviLiYETMRMTMjh2bfolaV81mUxGhzji1nflhh3znuFuZpLrH2N16bb2fA4BR1VPI236OpOslvbWaf7TzXEQclHSepHNsv6R6+OMRcVk1fTl10ZNnQdL+alrIXAuAcbJpT972KZJulPSeiFi0fZWkQ9Vzp0fEiYh42vYPJT0h6cxaK+5ifn4+x2GHZGUPfk+2KgCMn15eeH2jpIskvb/qv56Q9KbqubfYvrraz+GI+IbthtrtmkuqdT4cER9PXPdPyPUWStoVXANglG0a8hFxs6SbO8u2r4qIR6vnbpB0w5r1j0j6+bRlAgC2ou8PQ0XEJ+soBACQXjGfeB1XU1PjvX8Aoy37GK+TbnExdwUASsadPAAUjJAHgIIR8gBQMEIeAApGyANAwQh5ACgYIQ8ABSPkAaBghDwAFIyQB4CCEfIlazQkO+3UaOQ+KwB9IORLtrQ0HvsEUBtCHgAKRsgDQMEI+S7qHE6wGkYRXXB9gHT6CnnbL7d9zPY9tj9QPXaa7ZtsX2H7qO0v2X5f9dxltq+to/C67du3L3cJADCwfgcNeUjSKyPiySrYz5d0lqRjag/kfack2T5se0fiWpHYLkmz1fyCpDsy1gKgHn3dyUfEYkQ8WS0+JelpSVdIuisifiRJtrdJWpT0/W77sT1nu2W7tby8vLXKa9BsNmX7ZLugM19H66az71qnTWqYlbS3mmY3Wfdk3UOoHUA6W+rJ275A0vaIuF/SuRHxzerxOUkPSno0Ik502z4iDkbETETM7NgxOjf8zWZTEaGIkKST83WEfGfftU7Jq5ZiCLUDSKfvkLf9HEnXS3prNf9o57mIOCjpPEnn2H5JsipRiwVJ+6tpIXMtAOrRV0/e9imSbpT0nohYtH2VpEPVc6dHxImIeNr2DyU9IenM5BUPyfz8fO4SareyB78nWxUA6tTvC69vlHSRpPdXvdMTkt5UPfcW21dX+zwcEd+wPbafga/zLZS0JDbG9QHS8SD/oGxfFRGfHKSAmZmZaLVag+wC3dT1IiYhDGRn+76ImNlsvYE+DDVowKNmU1PjsU8Atem3XYNxsriYuwIAmfG1BgBQMEIeAApGyANAwQh5ACgYIQ8ABSPkAaBghDwAFIyQB4CCEfIAUDBCHgAKRsgDQMEI+YI0Gu0vnqxraoztF0cDk4uQL8jS0njvH0B6hDwAFCx7yB8/fjx3CUm4rgE6JkjKa1jnyF7AOBloZKiTO7H3SPotSbsl3SvpgeqpT1SDe2+0bZQw3Jvt7MPWDeP/mTpPMeU1HIWfB1CnXkeGGnjQENunS/rFFQ/dFRG7B90vUtslabaaX9DqYbwBlCpFu+Ztkj7azwa252y3bLeqZdke+z+xO+eRa5I2upWflbS3mmY3WG/DM6y5/sE0m81V+yrl9woYxKADeZ8q6aaIuMr2Uf1ku+ZARNy2yT5o1ySrYaNnD6gd8JK0X9KeLR2Ddg0wGobVrrlG0ifWPEa7ZiQtqB3unfmthTyA8TJoyJ8n6ULb75T0Ikmv7XcHO3fuHLAE9GZlD778gJ+fn89dAjASkry7RpK6tGtuj4jrNtpuZmYmWq1WkhpyGoX2AO+uASbH0N5d0xERl1SzU6n2OU4Ip8FxDYH0sn8YCgBQH0K+IFM1/w1V9/4BpJesXYP8FhdzVwBg1HAnDwAFI+QBoGCEPAAUjJAHgIIR8gBQMEIeAApGyANAwQh5ACgYIQ8ABSPkAaBghDwAFIyQHxeNRvsL44cxNRq5zxZAIoT8uFhaKvNYAGpFyANAwZKHvO09to/anrZ942brHz9+PHUJSXgYY+mhZ738PJrNZk+PAZMk2RivkmT7dEkHJf2C2uO9XhsRuzfZJkZx2LeRG2902P/pjNK5q7efx3rrjNzPEUik1zFeU9/Jv03SRxPvE33aJelANe3KXAuAvJKFvO1TJV0aEXf3sO6c7ZbtVrUs2yP3p3WnrpGY+qh7VtLeaprdynmP2rlv8FdMs9lctc56243q7xcwDMnaNbZ/T9L3IuIzto+Kdk1afbRrDqgd8JK0X9KerRxvlM5dtGuAtXpt16Qc4/U8SRfafqekF0l6bcJ9ow8Laod7Z35LIQ+gCMlCPiL+tDNf3cl/VtLLN9tu586dqUpA5Y4V85MU8PPz8z09BkySpO+u2YqZmZlotVpZaxgLE/7uGgCr5Xp3DQBghBDy42JqqsxjAahVyhdeUafFxdwVABhD3MkDQMEIeQAoGCEPAAUj5AGgYIQ8ABSMkAeAghHyAFAwQh4ACkbIA0DBCHkAKBghDwAFI+QnXKPR/hbjrUyNRu7qAWyGkJ9wS0t5tgUwHIQ8ABSMkB8jHvboUDUq6VyAUTZwyNuetr1k+4jtO9csH7E9l6LQUjWbzdwljJX1rhfXEOhu4DFebU9LujYidq+3vJlJH+PVtnr9GfSzbu/HH2z7rZaz1XNZb7s6rgsw6nod4zXVyFCX275H0q2SPp1on8hml6TZan5B0h0ZawEwiBQ9+e9KOlfS5Wonw7MkvXpFu+Z1azewPWe7Zbu1vLycoITx0mw2ZftkX7oz30vbobNuqkla71Z+VtLeappd5/mT1Qxw3MGv11avITBJBm7XrNqZ/S5Jz5b0Qto1vRnNds0BtQNekvZL2tN1e9o1QB69tmtSvPB6xorFiyUdGnSfyG1B7XDfX80DGFcpevKvsP1eSSckHZW0pKpdUz1/e0Rcl+A4RZqfn89dwjpW9uC738XnsN71Gs1rCIyGpO2arZj0dk1uud5dA2AwQ2vXAABGFyE/4aam8mwLYDhSvU8eY2pxMXcFAOrEnTwAFIyQB4CCEfIAUDBCHgAKRsgDQMEIeQAoGCEPAAUj5AGgYIQ8ABSMkAeAghHyAFAwQn5cNBrt7wUextRo5D5bAIkQ8uNiaanMYwGoFSEPAAVLFvK2p20v2T5i+85q+cYU+242myl2s2UedPikCZf7+qX8/cn9uwj0K9nwf7anJV0bEbvXW+6ml+H/bCvnMIW5j18VMdzjJTzf3Ncv5fFznwvQ0evwf6kHDbnc9j2SbpX06cT7Rp92SZqt5he0enhuAJMhZU/+u5LOlXS52tnyrG4r2p6z3bLdWl5eXnedZrMp2yf/1O/M5/pzuXP8bNMWap6VtLeaZjdZd9W5Jj7fHFL+/oza7yLQj2TtmlU7td8l6dmSXki7JlkRfW9yQO2Al6T9kvb0szHtmtr3BQyi13ZNyhdez1ixeLGkQ6n2ja1ZUDvc91fzACZPyp78K2y/V9IJSUclJXuz9fz8fKpdTZSVPfi+7uILk/L3h99FjJta2jX96KVdA431u2sApDf0dg0AYPQQ8gBQMEJ+XExNlXksALVK/WEo1GVxMXcFAMYQd/IAUDBCHgAKRsgDQMEIeQAoGCEPAAUj5AGgYIQ8ABSMkAeAghHyAFAwQh4ACkbID0Gj0f6m4LqnRiP3mQIYNYT8ECwlGz5lNI4DYHwQ8gBQMEIeAAqWJORtv9n2IdtHbF9se6maP2J7LsUx6uRhD603prhOwPgZ+PvkbZ8t6dKIeFW1PC3projYPei+AQCDSTFoyGskbbN9SNL9kv4qwT6hXZJmq/kFSXdkrAXAuEoR8lOSTouIV9l+v6QrJb3a9pHq+QMRcdvKDaoWzpwkPe95z0tQwuDqb0VEn+vPStpbze/vcRuLjgqAlVKE/GOSvlDN3y1pRpu0ayLioKSDkjQzM9Nv+tUior4yhhe8oRpPg548MIZSvPB6TNIF1fyF6v+WFetaUPsOfn81DwD9G/hOPiK+ZvuJqj3ziKQ/l3TPinbN7RFx3aDHmTwre/B7slUBYLylaNcoIt695qGpFPsdljpbNSXhOgHjhw9DAUDBCPkhmBrS3zXDOg6A8ZGkXYONLS7mrgDApOJOHgAKRsgDQMEIeQAoGCEPAAUj5AGgYIQ8ABSMkAeAghHyAFAwQh4ACkbIA0DBCHkAKBghP+oajfbQUsOcGo3cZw0gEUJ+1C0tTcYxAdSCkAeAghHyXTBodV6DXP9ms5muEGDMJQ1522+2fcj2EdsX274x5f6BXuzbty93CcDISDZoiO2zJV0aEa+qlqdT7Rv92SVptppf0OohwQFMlpR38q+RtK26k/+gpG3dVrQ9Z7tlu7W8vJywhLRs55+2UPespL3VNLvJuuue9wice7+azeaqbTvztG4w6VKG/JSk06o7+f+RdGW3FSPiYETMRMTMjh07EpaQVkTkn3Kc9wice7+azeaqbTvzhDwmXcqQf0zSF6r5uyX9dMJ9ow8LkvZX00LmWgDklXIg72OS3l7NXyhluQmFVvfg92SrIp/5+fncJQAjI9mdfER8TdITto9IukjSP6XaN9APWjTAj6W8k1dEvHvNQ7tT7n+YttIXRjpcfyANPgwFAAUj5Efd1NRkHBNALZK2a1CDxcXcFQAYY9zJA0DBCHkAKBghDwAFI+QBoGCEPAAUjJAHgIIR8gBQMEIeAApGyANAwQh5ACgYIQ8ABSPkAaBghDwAFIyQB4CCEfIAUDBCHgAKRsgDQMGce8Bk2z+Q9GDWIoDutkt6JHcRwDrOi4gzNltpFIb/ezAiZnIXAazHdovfT4wi261e1qNdAwAFI+QBoGCjEPIHcxcAbIDfT4yqnn43s7/wCgCozyjcyQMAakLIA0DBsoa87V+1/aDtb9n+s5y1ACvZ/ojt/7b977lrAVay/Vzbh20/YPvrtv9ww/Vz9eRtb5P0TUmvlvSwpK9I+u2IuD9LQcAKtn9F0uOSPhYRL85dD9Bhe6eknRHxVdtnSLpP0m90y86cd/Ivk/StiPiPiPhfSf8g6cqM9QAnRcQ/S/pe7jqAtSLiuxHx1Wr+B5IekHR2t/VzhvzZkv5zxfLD2qBQAMBqtqclvUTSvd3WyRnyXucx3s8JAD2w/UxJt0j6o4j4frf1cob8w5Keu2L5HEnHM9UCAGPD9qlqB/xNEXHrRuvmDPmvSHqB7efbPk3S1ZJuy1gPAIw825b0YUkPRMSBzdbPFvIR8ZSk35f0ebVfOPhkRHw9Vz3ASrZvlvQlSefZftj2W3PXBFQulnSNpFfa/lo17eq2Ml9rAAAF4xOvAFAwQh5jx/bjGY75u7av73ObGdt/U81fZvuX66kO6G4URoYCBmZ7W0Q83W05Qz2nRERLUmf0nsvU/gTtsVw1YTJxJ4+xVd0dH7b9CUn/ts7yNtvX2f6K7X+1/Y5qu2fY/tvqez8+Z/sO22+onvuO7e3V/IztI+sc97W277X9L7YXbE9VjzdtH7R9p6SPVfV8rvrAyjsl/XH1ItkrbH+7ehucbD+rOu6pQ7hsmDDcyWPcvUzSiyPi27YvW7M8J+mxiLjI9umSvlgF8EslTUs6X9LPqf3uro/0ccyjkn4pIsL22yT9iaS91XMvlXRJRDxR1aOI+I7tGyQ9HhF/KUnVfx6/Lukzar99+JaI+NGWrgCwAUIe4+7LEfHtLstXSLqgc5cu6UxJL5B0iaRPRcT/SVq0fbjPY54j6R+rL4o6TdLK498WEU/0sI8Pqf2fw2ckvUXS2/usAegJ7RqMux9usGxJfxARF1bT8yPiTq3/lRodT+nH/y5+qss6H5R0fUScL+kda9ZbW8+6IuKLkqZtXyppW0TwlcaoBSGPkn1e0rtW9L7Ptf0zardbXl/15qfUflG04ztqt1wk6fVd9numpP+q5n+nx1p+IOmMNY99TNLNkv6+x30AfSPkUbIPSbpf0lerwT/+Tu0W5S1qf3dS57F7JT1WbbNP0l/bvkdSt3fnNCV9qlrnkR5r+ayk3+y88Fo9dpOkZ6sd9EAt+MQrJpLtZ0bE47bPkvRlSRdHxOKQa3iDpCsj4pphHheThRdeMak+Z/tn1X7h9L0ZAv6Dkn5NUtfvHAFS4E4eAApGTx4ACkbIA0DBCHkAKBghDwAFI+QBoGD/D/k7Lt9knDSwAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# print(\"plotting Irregularity\")\n",
"\n",
@@ -1717,23 +1556,10 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": null,
"id": "0308d50a-1906-4860-9194-7f8664bd1f9d",
"metadata": {},
- "outputs": [
- {
- "ename": "FileNotFoundError",
- "evalue": "[Errno 2] No such file or directory: '/opt/app-root/src/MAM2EBRAINS/simulations/27d81076e6d6e9e591684be053078477/Analysis/rate_time_series_full/rate_time_series_full_V1.npy'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
- "Input \u001b[0;32mIn [66]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m area \u001b[38;5;129;01min\u001b[39;00m areas:\n\u001b[1;32m 6\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 7\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAnalysis\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrate_time_series_full\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 9\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrate_time_series_full_\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---> 10\u001b[0m rate_time_series[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",
- "File \u001b[0;32m/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/lib/npyio.py:417\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m 415\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 416\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 417\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 418\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 420\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_full/rate_time_series_full_V1.npy'"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# load data\n",
"\n",
@@ -1817,9 +1643,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "EBRAINS-23.02",
"language": "python",
- "name": "python3"
+ "name": "ebrains-23.02"
},
"language_info": {
"codemirror_mode": {
@@ -1831,7 +1657,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.10"
+ "version": "3.8.11"
}
},
"nbformat": 4,
diff --git a/multiarea_model/data_multiarea/viscortex_processed_data.json b/multiarea_model/data_multiarea/viscortex_processed_data.json
index c0a7fe7d01a7c0a87d71b4cab803401382eeb5f8..05a0cfc5c725a53cf7badf8dafd9e7fcd0c23d8b 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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