diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index 254f0da96b1160b7394c97e556e2dea26139b38e..7addec20de6a64199821d9029ccd96d136653126 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -58,7 +58,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "06e7f341-c226-4782-a913-4868027d7d06",
"metadata": {},
"outputs": [],
@@ -76,12 +76,34 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "96517739",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " -- N E S T --\n",
+ " Copyright (C) 2004 The NEST Initiative\n",
+ "\n",
+ " Version: 3.4\n",
+ " Built: May 17 2023 20:48:31\n",
+ "\n",
+ " This program is provided AS IS and comes with\n",
+ " NO WARRANTY. See the file LICENSE for details.\n",
+ "\n",
+ " Problems or suggestions?\n",
+ " Visit https://www.nest-simulator.org\n",
+ "\n",
+ " Type 'nest.help()' to find out more about NEST.\n",
+ "\n"
+ ]
+ }
+ ],
"source": [
"# Import dependencies\n",
"%matplotlib inline\n",
@@ -99,22 +121,48 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "7e07b0d0",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: nested_dict in /srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/._view/6axslmv6jvf4v2nte3uwlayg4vhsjoha/lib/python3.8/site-packages (1.61)\n",
+ "Requirement already satisfied: dicthash in /srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/._view/6axslmv6jvf4v2nte3uwlayg4vhsjoha/lib/python3.8/site-packages (0.0.2)\n",
+ "Requirement already satisfied: future in /srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/._view/6axslmv6jvf4v2nte3uwlayg4vhsjoha/lib/python3.8/site-packages (from dicthash) (0.18.2)\n"
+ ]
+ }
+ ],
"source": [
"!pip install nested_dict dicthash"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "1d440c07-9b69-4e52-8573-26b13493bc5a",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "<style>\n",
+ "table {float:left}\n",
+ "</style>\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Jupyter notebook display format setting\n",
"style = \"\"\"\n",
@@ -192,7 +240,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "60265d52",
"metadata": {},
"outputs": [],
@@ -221,7 +269,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "6e4bed8d",
"metadata": {},
"outputs": [],
@@ -304,7 +352,6 @@
"cell_type": "markdown",
"id": "1fd58841",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@@ -313,10 +360,70 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "ab25f9f8",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Initializing network from dictionary.\n",
+ "RAND_DATA_LABEL 4294\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/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/core/fromnumeric.py:3440: RuntimeWarning:Mean of empty slice.\n",
+ "/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/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
+ "Error in library(\"aod\") : there is no package called ‘aod’\n",
+ "Execution halted\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
+ "\n",
+ "\n",
+ "========================================\n",
+ "Customized parameters\n",
+ "--------------------\n",
+ "{'K_scaling': 0.005,\n",
+ " 'N_scaling': 0.005,\n",
+ " 'connection_params': {'K_stable': 'K_stable.npy',\n",
+ " 'av_indegree_V1': 3950.0,\n",
+ " 'fac_nu_ext_5E': 1.125,\n",
+ " 'fac_nu_ext_6E': 1.41666667,\n",
+ " 'fac_nu_ext_TH': 1.2,\n",
+ " 'g': -11.0,\n",
+ " 'replace_non_simulated_areas': 'het_poisson_stat'},\n",
+ " 'fullscale_rates': 'tests/fullscale_rates.json',\n",
+ " 'input_params': {'rate_ext': 10.0},\n",
+ " 'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}\n",
+ "========================================\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulation label: 27d81076e6d6e9e591684be053078477\n",
+ "Copied files.\n",
+ "Initialized simulation class.\n"
+ ]
+ }
+ ],
"source": [
"M = MultiAreaModel(network_params, \n",
" simulation=True,\n",
@@ -335,10 +442,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "6a7ddf0e",
"metadata": {},
- "outputs": [],
+ "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"
+ ]
+ }
+ ],
"source": [
"p, r = M.theory.integrate_siegert()\n",
"print(\"Mean-field theory predicts an average \"\n",
@@ -371,7 +487,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "6316ac24",
"metadata": {},
"outputs": [],
@@ -391,7 +507,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "445a722a",
"metadata": {},
"outputs": [],
@@ -409,14 +525,6 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "04894f5e-35ec-4b22-8891-bd7ba86098e9",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "0c1cad59-81d0-4e24-ac33-13c4ca8c6dec",
@@ -427,10 +535,85 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "15778e9c",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prepared simulation in 0.00 seconds.\n",
+ "Rank 0: created area V1 with 0 local nodes\n",
+ "Memory after V1 : 1911.26 MB\n",
+ "Rank 0: created area V2 with 0 local nodes\n",
+ "Memory after V2 : 1937.84 MB\n",
+ "Rank 0: created area VP with 0 local nodes\n",
+ "Memory after VP : 1967.04 MB\n",
+ "Rank 0: created area V3 with 0 local nodes\n",
+ "Memory after V3 : 1995.29 MB\n",
+ "Rank 0: created area V3A with 0 local nodes\n",
+ "Memory after V3A : 2015.20 MB\n",
+ "Rank 0: created area MT with 0 local nodes\n",
+ "Memory after MT : 2040.86 MB\n",
+ "Rank 0: created area V4t with 0 local nodes\n",
+ "Memory after V4t : 2065.80 MB\n",
+ "Rank 0: created area V4 with 0 local nodes\n",
+ "Memory after V4 : 2092.75 MB\n",
+ "Rank 0: created area VOT with 0 local nodes\n",
+ "Memory after VOT : 2118.06 MB\n",
+ "Rank 0: created area MSTd with 0 local nodes\n",
+ "Memory after MSTd : 2139.53 MB\n",
+ "Rank 0: created area PIP with 0 local nodes\n",
+ "Memory after PIP : 2160.92 MB\n",
+ "Rank 0: created area PO with 0 local nodes\n",
+ "Memory after PO : 2182.43 MB\n",
+ "Rank 0: created area DP with 0 local nodes\n",
+ "Memory after DP : 2202.58 MB\n",
+ "Rank 0: created area MIP with 0 local nodes\n",
+ "Memory after MIP : 2224.23 MB\n",
+ "Rank 0: created area MDP with 0 local nodes\n",
+ "Memory after MDP : 2245.59 MB\n",
+ "Rank 0: created area VIP with 0 local nodes\n",
+ "Memory after VIP : 2267.52 MB\n",
+ "Rank 0: created area LIP with 0 local nodes\n",
+ "Memory after LIP : 2291.56 MB\n",
+ "Rank 0: created area PITv with 0 local nodes\n",
+ "Memory after PITv : 2316.88 MB\n",
+ "Rank 0: created area PITd with 0 local nodes\n",
+ "Memory after PITd : 2342.02 MB\n",
+ "Rank 0: created area MSTl with 0 local nodes\n",
+ "Memory after MSTl : 2363.48 MB\n",
+ "Rank 0: created area CITv with 0 local nodes\n",
+ "Memory after CITv : 2382.79 MB\n",
+ "Rank 0: created area CITd with 0 local nodes\n",
+ "Memory after CITd : 2402.08 MB\n",
+ "Rank 0: created area FEF with 0 local nodes\n",
+ "Memory after FEF : 2423.46 MB\n",
+ "Rank 0: created area TF with 0 local nodes\n",
+ "Memory after TF : 2439.15 MB\n",
+ "Rank 0: created area AITv with 0 local nodes\n",
+ "Memory after AITv : 2461.86 MB\n",
+ "Rank 0: created area FST with 0 local nodes\n",
+ "Memory after FST : 2478.59 MB\n",
+ "Rank 0: created area 7a with 0 local nodes\n",
+ "Memory after 7a : 2499.80 MB\n",
+ "Rank 0: created area STPp with 0 local nodes\n",
+ "Memory after STPp : 2518.41 MB\n",
+ "Rank 0: created area STPa with 0 local nodes\n",
+ "Memory after STPa : 2537.55 MB\n",
+ "Rank 0: created area 46 with 0 local nodes\n",
+ "Memory after 46 : 2553.03 MB\n",
+ "Rank 0: created area AITd with 0 local nodes\n",
+ "Memory after AITd : 2575.56 MB\n",
+ "Rank 0: created area TH with 0 local nodes\n",
+ "Memory after TH : 2588.26 MB\n",
+ "Created areas and internal connections in 2.22 seconds.\n",
+ "Created cortico-cortical connections in 22.70 seconds.\n",
+ "Simulated network in 70.32 seconds.\n"
+ ]
+ }
+ ],
"source": [
"# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.\n",
"M.simulation.simulate()"
@@ -473,7 +656,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "dc3b1820",
"metadata": {},
"outputs": [],
@@ -507,7 +690,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "e7eb052e",
"metadata": {},
"outputs": [],
@@ -530,7 +713,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "902f2800",
"metadata": {},
"outputs": [],
@@ -572,7 +755,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "f5b58845-4d1a-430f-83f4-402fdf918aef",
"metadata": {
"tags": []
@@ -585,12 +768,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"id": "6607a73d-1c74-4848-9603-081ad0e7cae8",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "loading spikes\n"
+ ]
+ }
+ ],
"source": [
"\"\"\"\n",
"Analysis class.\n",
@@ -621,7 +812,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 25,
"id": "c471e9c8-b1e4-43e4-a6a1-443b8b8963be",
"metadata": {
"tags": []
@@ -636,12 +827,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 26,
"id": "1870cf34-ee62-4614-bc25-c36bc9a7377c",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Stored data have been computed with different parameters\n",
+ "Computing population rates\n"
+ ]
+ }
+ ],
"source": [
"\"\"\"\n",
"Calculate time-averaged population rates and store them in member pop_rates.\n",
@@ -673,12 +873,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 27,
"id": "50b7df89",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Computing synchrony\n"
+ ]
+ }
+ ],
"source": [
"\"\"\"\n",
"Calculate synchrony as the coefficient of variation of the population rate\n",
@@ -710,12 +918,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 28,
"id": "d43b493c",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Computing population LvR\n"
+ ]
+ }
+ ],
"source": [
"\"\"\"\n",
"Calculate poulation-averaged LvR (see Shinomoto et al. 2009) and\n",
@@ -742,12 +958,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 29,
"id": "401ece2d-47c8-4775-80ae-92a8e432520c",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Computing rate time series\n"
+ ]
+ },
+ {
+ "ename": "TypeError",
+ "evalue": "'float' object cannot be interpreted as an integer",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [29]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03mCalculate time series of population- and area-averaged firing rates.\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03mUses ah.pop_rate_time_series.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;124;03m - 'rect_time_window' : width of the moving rectangular function\u001b[39;00m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m \u001b[43mA\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_rate_time_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/MAM2EBRAINS/multiarea_model/analysis.py:446\u001b[0m, in \u001b[0;36mAnalysis.create_rate_time_series\u001b[0;34m(self, **keywords)\u001b[0m\n\u001b[1;32m 439\u001b[0m time_series \u001b[38;5;241m=\u001b[39m ah\u001b[38;5;241m.\u001b[39mpop_rate_time_series(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspike_data[area][pop],\n\u001b[1;32m 440\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnetwork\u001b[38;5;241m.\u001b[39mN[area][pop],\n\u001b[1;32m 441\u001b[0m params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt_min\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 442\u001b[0m params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt_max\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 443\u001b[0m params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 444\u001b[0m kernel\u001b[38;5;241m=\u001b[39mparams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkernel\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 445\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 446\u001b[0m time_series \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnan\u001b[38;5;241m*\u001b[39m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_max\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_min\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 447\u001b[0m d_pops[area][pop] \u001b[38;5;241m=\u001b[39m time_series\n\u001b[1;32m 449\u001b[0m total_spikes \u001b[38;5;241m=\u001b[39m ah\u001b[38;5;241m.\u001b[39marea_spike_train(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspike_data[area])\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/core/numeric.py:204\u001b[0m, in \u001b[0;36mones\u001b[0;34m(shape, dtype, order, like)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m like \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 202\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _ones_with_like(shape, dtype\u001b[38;5;241m=\u001b[39mdtype, order\u001b[38;5;241m=\u001b[39morder, like\u001b[38;5;241m=\u001b[39mlike)\n\u001b[0;32m--> 204\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[43mempty\u001b[49m\u001b[43m(\u001b[49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 205\u001b[0m multiarray\u001b[38;5;241m.\u001b[39mcopyto(a, \u001b[38;5;241m1\u001b[39m, casting\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsafe\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n",
+ "\u001b[0;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
+ ]
+ }
+ ],
"source": [
"\"\"\"\n",
"Calculate time series of population- and area-averaged firing rates.\n",
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index f92008e4cec6dba4d5b28f4b1e7547808fbcb746..7addec20de6a64199821d9029ccd96d136653126 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -768,7 +768,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 24,
"id": "6607a73d-1c74-4848-9603-081ad0e7cae8",
"metadata": {
"tags": []
@@ -812,7 +812,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 25,
"id": "c471e9c8-b1e4-43e4-a6a1-443b8b8963be",
"metadata": {
"tags": []
@@ -827,7 +827,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 26,
"id": "1870cf34-ee62-4614-bc25-c36bc9a7377c",
"metadata": {
"tags": []
@@ -873,7 +873,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 27,
"id": "50b7df89",
"metadata": {
"tags": []
@@ -918,7 +918,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 28,
"id": "d43b493c",
"metadata": {
"tags": []
@@ -958,7 +958,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 29,
"id": "401ece2d-47c8-4775-80ae-92a8e432520c",
"metadata": {
"tags": []
@@ -978,7 +978,7 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "Input \u001b[0;32mIn [21]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03mCalculate time series of population- and area-averaged firing rates.\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03mUses ah.pop_rate_time_series.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;124;03m - 'rect_time_window' : width of the moving rectangular function\u001b[39;00m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m \u001b[43mA\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_rate_time_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+ "Input \u001b[0;32mIn [29]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03mCalculate time series of population- and area-averaged firing rates.\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03mUses ah.pop_rate_time_series.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;124;03m - 'rect_time_window' : width of the moving rectangular function\u001b[39;00m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m \u001b[43mA\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_rate_time_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/MAM2EBRAINS/multiarea_model/analysis.py:446\u001b[0m, in \u001b[0;36mAnalysis.create_rate_time_series\u001b[0;34m(self, **keywords)\u001b[0m\n\u001b[1;32m 439\u001b[0m time_series \u001b[38;5;241m=\u001b[39m ah\u001b[38;5;241m.\u001b[39mpop_rate_time_series(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspike_data[area][pop],\n\u001b[1;32m 440\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnetwork\u001b[38;5;241m.\u001b[39mN[area][pop],\n\u001b[1;32m 441\u001b[0m params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt_min\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 442\u001b[0m params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt_max\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 443\u001b[0m params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 444\u001b[0m kernel\u001b[38;5;241m=\u001b[39mparams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkernel\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 445\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 446\u001b[0m time_series \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnan\u001b[38;5;241m*\u001b[39m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_max\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_min\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 447\u001b[0m d_pops[area][pop] \u001b[38;5;241m=\u001b[39m time_series\n\u001b[1;32m 449\u001b[0m total_spikes \u001b[38;5;241m=\u001b[39m ah\u001b[38;5;241m.\u001b[39marea_spike_train(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspike_data[area])\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/core/numeric.py:204\u001b[0m, in \u001b[0;36mones\u001b[0;34m(shape, dtype, order, like)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m like \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 202\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _ones_with_like(shape, dtype\u001b[38;5;241m=\u001b[39mdtype, order\u001b[38;5;241m=\u001b[39morder, like\u001b[38;5;241m=\u001b[39mlike)\n\u001b[0;32m--> 204\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[43mempty\u001b[49m\u001b[43m(\u001b[49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 205\u001b[0m multiarray\u001b[38;5;241m.\u001b[39mcopyto(a, \u001b[38;5;241m1\u001b[39m, casting\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsafe\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n",
"\u001b[0;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"