diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_load_and_create_data-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
similarity index 100%
rename from figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_load_and_create_data-checkpoint.py
rename to figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_LOAD_DATA-checkpoint.py
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_VISUALIZATION-checkpoint.py
similarity index 94%
rename from figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
rename to figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_VISUALIZATION-checkpoint.py
index 1b89896babc39f435a88db08beebf53adad27d9c..b22bb9fd68ab69f79d77b40ec4498ef4b5ccc1b5 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_VISUALIZATION-checkpoint.py
@@ -17,6 +17,29 @@ from matplotlib import gridspec
icolor = myred
ecolor = myblue
+# label_spikes = M.simulation.label
+label = M.simulation.label
+
+from MAM2EBRAINS_LOAD_DATA import load_and_create_data
+A = load_and_create_data(M)
+
+def plot_instan_mean_firing_rate(M):
+ # load spike data and calculate instantaneous and mean firing rates
+ data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
+ tsteps, spikecount = np.unique(data[:,1], return_counts=True)
+ firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
+
+ # visualize calculate instantaneous and mean firing rates
+ ax = pl.subplot()
+ 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 set_boxplot_props(d):
for i in range(len(d['boxes'])):
if i % 2 == 0:
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/untitled-checkpoint.py
similarity index 100%
rename from figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize-checkpoint.py
rename to figures/MAM2EBRAINS/.ipynb_checkpoints/untitled-checkpoint.py
diff --git a/figures/MAM2EBRAINS/M2E_load_and_create_data.py b/figures/MAM2EBRAINS/M2E_LOAD_DATA.py
similarity index 100%
rename from figures/MAM2EBRAINS/M2E_load_and_create_data.py
rename to figures/MAM2EBRAINS/M2E_LOAD_DATA.py
diff --git a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py b/figures/MAM2EBRAINS/M2E_VISUALIZATION.py
similarity index 94%
rename from figures/MAM2EBRAINS/M2E_visualize_resting_state.py
rename to figures/MAM2EBRAINS/M2E_VISUALIZATION.py
index 1b89896babc39f435a88db08beebf53adad27d9c..b22bb9fd68ab69f79d77b40ec4498ef4b5ccc1b5 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
+++ b/figures/MAM2EBRAINS/M2E_VISUALIZATION.py
@@ -17,6 +17,29 @@ from matplotlib import gridspec
icolor = myred
ecolor = myblue
+# label_spikes = M.simulation.label
+label = M.simulation.label
+
+from MAM2EBRAINS_LOAD_DATA import load_and_create_data
+A = load_and_create_data(M)
+
+def plot_instan_mean_firing_rate(M):
+ # load spike data and calculate instantaneous and mean firing rates
+ data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
+ tsteps, spikecount = np.unique(data[:,1], return_counts=True)
+ firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
+
+ # visualize calculate instantaneous and mean firing rates
+ ax = pl.subplot()
+ 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 set_boxplot_props(d):
for i in range(len(d['boxes'])):
if i % 2 == 0:
diff --git a/figures/MAM2EBRAINS/M2E_visualize_instaneus_and_mean_rate.py b/figures/MAM2EBRAINS/M2E_visualize_instaneus_and_mean_rate.py
index 1d223e89cd4911327489812e8eb16008b38eae16..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_instaneus_and_mean_rate.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_instaneus_and_mean_rate.py
@@ -1,15 +0,0 @@
-def plot_instan_mean_firing_rate(M):
- # load spike data and calculate instantaneous and mean firing rates
- data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
- tsteps, spikecount = np.unique(data[:,1], return_counts=True)
- firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
-
- ax = pl.subplot()
- 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()
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/M2E_visualize.py b/figures/MAM2EBRAINS/untitled.py
similarity index 100%
rename from figures/MAM2EBRAINS/M2E_visualize.py
rename to figures/MAM2EBRAINS/untitled.py
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 8ae4670dd7132a62d18c5cd80bb1bf6a702cca5f..baef6de81efd308c240de5fdd9eb76f20ff46d8b 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -46,7 +46,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 37,
"id": "9d6cc7d9-3110-4d96-9f9a-9ec7dee6d145",
"metadata": {},
"outputs": [],
@@ -64,7 +64,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 38,
"id": "96517739",
"metadata": {
"tags": []
@@ -84,40 +84,31 @@
"\n",
"sys.path.append('./figures/MAM2EBRAINS')\n",
"\n",
- "# Ignore and don't display warning messages\n",
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")\n",
+ "# # Ignore and don't display warning messages\n",
+ "# import warnings\n",
+ "# warnings.filterwarnings(\"ignore\")\n",
"\n",
- "# Redirect stdout to a dummy stream\n",
- "original_stdout = sys.stdout\n",
- "sys.stdout = StringIO()"
+ "# # Redirect stdout to a dummy stream\n",
+ "# original_stdout = sys.stdout\n",
+ "# sys.stdout = StringIO()"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 39,
"id": "7e07b0d0",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already satisfied: nested_dict in /srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/._view/ugqhkplzbs5vx62qrzwub7ficqgepl3r/lib/python3.8/site-packages (1.61)\n",
- "Requirement already satisfied: dicthash in /srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/._view/ugqhkplzbs5vx62qrzwub7ficqgepl3r/lib/python3.8/site-packages (0.0.2)\n",
- "Requirement already satisfied: future in /srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/._view/ugqhkplzbs5vx62qrzwub7ficqgepl3r/lib/python3.8/site-packages (from dicthash) (0.18.2)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "!pip install nested_dict dicthash;"
+ "%%capture captured\n",
+ "!pip install nested_dict dicthash"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 40,
"id": "1d440c07-9b69-4e52-8573-26b13493bc5a",
"metadata": {
"tags": []
@@ -147,8 +138,7 @@
"table {float:left}\n",
"</style>\n",
"\"\"\"\n",
- "display(HTML(style))\n",
- "\n"
+ "display(HTML(style))"
]
},
{
@@ -209,7 +199,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 41,
"id": "60265d52",
"metadata": {},
"outputs": [],
@@ -246,7 +236,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 42,
"id": "6e4bed8d",
"metadata": {},
"outputs": [],
@@ -329,18 +319,10 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 43,
"id": "ab25f9f8",
"metadata": {},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Initializing network from dictionary.\n",
- "RAND_DATA_LABEL 3631\n"
- ]
- },
{
"name": "stderr",
"output_type": "stream",
@@ -348,37 +330,10 @@
"Error in library(\"aod\") : there is no package called ‘aod’\n",
"Execution halted\n"
]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
- "\n",
- "\n",
- "========================================\n",
- "Customized parameters\n",
- "--------------------\n",
- "{'K_scaling': 0.005,\n",
- " 'N_scaling': 0.005,\n",
- " 'connection_params': {'K_stable': 'K_stable.npy',\n",
- " 'av_indegree_V1': 3950.0,\n",
- " 'fac_nu_ext_5E': 1.125,\n",
- " 'fac_nu_ext_6E': 1.41666667,\n",
- " 'fac_nu_ext_TH': 1.2,\n",
- " 'g': -11.0,\n",
- " 'replace_non_simulated_areas': 'het_poisson_stat'},\n",
- " 'fullscale_rates': 'tests/fullscale_rates.json',\n",
- " 'input_params': {'rate_ext': 10.0},\n",
- " 'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}\n",
- "========================================\n",
- "Simulation label: 27d81076e6d6e9e591684be053078477\n",
- "Copied files.\n",
- "Initialized simulation class.\n"
- ]
}
],
"source": [
+ "%%capture captured\n",
"M = MultiAreaModel(network_params, \n",
" simulation=True,\n",
" sim_spec=sim_params,\n",
@@ -396,19 +351,10 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 44,
"id": "6a7ddf0e",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Iteration: 0\n",
- "Mean-field theory predicts an average firing rate of 29.588 spikes/s across all populations.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"p, r = M.theory.integrate_siegert()\n",
"print(\"Mean-field theory predicts an average \"\n",
@@ -433,7 +379,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 45,
"id": "6316ac24",
"metadata": {},
"outputs": [],
@@ -449,7 +395,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 46,
"id": "8408d463-557b-481b-afc1-5fbbbd67306d",
"metadata": {},
"outputs": [],
@@ -466,7 +412,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 47,
"id": "445a722a",
"metadata": {},
"outputs": [],
@@ -483,18 +429,10 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 48,
"id": "05512922-26e5-425f-90a4-0df7c2279ccf",
"metadata": {},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Initializing network from dictionary.\n",
- "RAND_DATA_LABEL 9405\n"
- ]
- },
{
"name": "stderr",
"output_type": "stream",
@@ -503,35 +441,6 @@
"Execution halted\n"
]
},
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
- "\n",
- "\n",
- "========================================\n",
- "Customized parameters\n",
- "--------------------\n",
- "{'K_scaling': 1,\n",
- " 'N_scaling': 1,\n",
- " 'connection_params': {'K_stable': 'K_stable.npy',\n",
- " 'av_indegree_V1': 3950.0,\n",
- " 'fac_nu_ext_5E': 1.125,\n",
- " 'fac_nu_ext_6E': 1.41666667,\n",
- " 'fac_nu_ext_TH': 1.2,\n",
- " 'g': -11.0,\n",
- " 'replace_non_simulated_areas': 'het_poisson_stat'},\n",
- " 'fullscale_rates': 'tests/fullscale_rates.json',\n",
- " 'input_params': {'rate_ext': 10.0},\n",
- " 'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}\n",
- "========================================\n",
- "Simulation label: 155470013b00dadc9c4a4af26ef5090e\n",
- "Copied files.\n",
- "Initialized simulation class.\n",
- "6.8556 6.052848304676828\n"
- ]
- },
{
"ename": "ValueError",
"evalue": "The number of FixedLocator locations (33), usually from a call to set_ticks, does not match the number of ticklabels (32).",
@@ -539,28 +448,17 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn [24], 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_interareal_connectivity\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m visualize_interareal_connectivity\n\u001b[0;32m----> 2\u001b[0m visualize_interareal_connectivity(M)\n",
+ "Cell \u001b[0;32mIn [48], 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_interareal_connectivity\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m visualize_interareal_connectivity\n\u001b[0;32m----> 2\u001b[0m visualize_interareal_connectivity(M)\n",
"File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py:236\u001b[0m, in \u001b[0;36mvisualize_interareal_connectivity\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m 233\u001b[0m X, Y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmeshgrid(x, y)\n\u001b[1;32m 235\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xticks([i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m0.5\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(area_list) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)])\n\u001b[0;32m--> 236\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_xticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43marea_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrotation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m90\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m6.\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 238\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_yticks([i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m0.5\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(area_list) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)])\n\u001b[1;32m 239\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_yticklabels(area_list[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6.\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-matplotlib-3.6.2-lhkot3cmeebfk5dp74dnubweq56upksc/lib/python3.8/site-packages/matplotlib/axes/_base.py:73\u001b[0m, in \u001b[0;36m_axis_method_wrapper.__set_name__.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, \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 \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mget_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;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",
"File \u001b[0;32m/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-matplotlib-3.6.2-lhkot3cmeebfk5dp74dnubweq56upksc/lib/python3.8/site-packages/matplotlib/axis.py:1968\u001b[0m, in \u001b[0;36mAxis._set_ticklabels\u001b[0;34m(self, labels, fontdict, minor, **kwargs)\u001b[0m\n\u001b[1;32m 1966\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fontdict \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 1967\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate(fontdict)\n\u001b[0;32m-> 1968\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_ticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mminor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mminor\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",
"File \u001b[0;32m/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-matplotlib-3.6.2-lhkot3cmeebfk5dp74dnubweq56upksc/lib/python3.8/site-packages/matplotlib/axis.py:1890\u001b[0m, in \u001b[0;36mAxis.set_ticklabels\u001b[0;34m(self, ticklabels, minor, **kwargs)\u001b[0m\n\u001b[1;32m 1886\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(locator, mticker\u001b[38;5;241m.\u001b[39mFixedLocator):\n\u001b[1;32m 1887\u001b[0m \u001b[38;5;66;03m# Passing [] as a list of ticklabels is often used as a way to\u001b[39;00m\n\u001b[1;32m 1888\u001b[0m \u001b[38;5;66;03m# remove all tick labels, so only error for > 0 ticklabels\u001b[39;00m\n\u001b[1;32m 1889\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(ticklabels) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ticklabels) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 1890\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1891\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe number of FixedLocator locations\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1892\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m), usually from a call to\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1893\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m set_ticks, does not match\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1894\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m the number of ticklabels (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(ticklabels)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m).\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1895\u001b[0m tickd \u001b[38;5;241m=\u001b[39m {loc: lab \u001b[38;5;28;01mfor\u001b[39;00m loc, lab \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs, ticklabels)}\n\u001b[1;32m 1896\u001b[0m func \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_with_dict, tickd)\n",
"\u001b[0;31mValueError\u001b[0m: The number of FixedLocator locations (33), usually from a call to set_ticks, does not match the number of ticklabels (32)."
]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 493.603x435.805 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
}
],
"source": [
+ "%%capture captured\n",
"from M2E_visualize_interareal_connectivity import visualize_interareal_connectivity\n",
"visualize_interareal_connectivity(M);"
]
@@ -732,13 +630,7 @@
]
}
],
- "source": [
- "label_spikes = M.simulation.label\n",
- "label = M.simulation.label\n",
- "\n",
- "from MAM2EBRAINS_LOAD_DATA import load_data\n",
- "A, tsteps, firing_rate = load_data(M)"
- ]
+ "source": []
},
{
"cell_type": "markdown",