diff --git a/.github/workflows/ebrains-push.yml b/.github/workflows/ebrains-push.yml
new file mode 100644
index 0000000000000000000000000000000000000000..a7e08d550336e76fe56c6b516755c8667f67a6b9
--- /dev/null
+++ b/.github/workflows/ebrains-push.yml
@@ -0,0 +1,31 @@
+name: Mirror to EBRAINS
+
+on:
+ push:
+ branches: [ master ]
+
+jobs:
+ sync_to_ebrains:
+ runs-on: ubuntu-latest
+ if: ${{ github.repository_owner == 'INM-6' }}
+ steps:
+ - name: Harden Runner
+ uses: step-security/harden-runner@63c24ba6bd7ba022e95695ff85de572c04a18142 # v2.7.0
+ with:
+ egress-policy: audit
+ disable-telemetry: true
+
+ - name: sycnmaster
+ uses: wei/git-sync@55c6b63b4f21607da0e9877ca9b4d11a29fc6d83
+ with:
+ source_repo: "INM-6/multi-area-model"
+ source_branch: "master"
+ destination_repo: "https://ghpusher:${{ secrets.EBRAINS_GITLAB_ACCESS_TOKEN }}@gitlab.ebrains.eu/IAS-6/multi-area-model.git"
+ destination_branch: "master"
+ - name: synctags
+ uses: wei/git-sync@55c6b63b4f21607da0e9877ca9b4d11a29fc6d83
+ with:
+ source_repo: "INM-6/multi-area-model"
+ source_branch: "refs/tags/*"
+ destination_repo: "https://ghpusher:${{ secrets.EBRAINS_GITLAB_ACCESS_TOKEN }}@gitlab.ebrains.eu/IAS-6/multi-area-model.git"
+ destination_branch: "refs/tags/*"
diff --git a/M2E_pop_rates_ref.npy b/M2E_pop_rates_ref.npy
new file mode 100644
index 0000000000000000000000000000000000000000..0dd7a7d493ff7dee24ab55f916107fb5b8566a51
Binary files /dev/null and b/M2E_pop_rates_ref.npy differ
diff --git a/M2E_statistical_test.ipynb b/M2E_statistical_test.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..705867b91f2d5f67c0ea98eebf4b867e31dc64fc
--- /dev/null
+++ b/M2E_statistical_test.ipynb
@@ -0,0 +1,168 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0bc96f39-b94f-40d5-be5c-a0d9ebdf8180",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import os\n",
+ "import sys\n",
+ "import json\n",
+ "\n",
+ "sys.path.append('./figures/MAM2EBRAINS')\n",
+ "\n",
+ "from multiarea_model import MultiAreaModel\n",
+ "from multiarea_model import Analysis\n",
+ "from M2E_compute_pop_rates import compute_pop_rates\n",
+ "\n",
+ "base_path = os.path.abspath(\".\")\n",
+ "data_path = os.path.abspath(\"simulations\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c907631c-4611-469a-941c-ddb31ef753c8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set the threshold of matching rate of mean firing rate of all populations\n",
+ "# if smaller than this value, raise error so that the test won't pass and something is wrong and needs to be checked manually\n",
+ "match_rate_threshold = 0.95"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "97fbab67-7282-4800-a6d4-78bb3a36640a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "Down-scaled model.\n",
+ "Neurons and indegrees are both scaled down to 10 %.\n",
+ "Can usually be simulated on a local machine.\n",
+ "\n",
+ "Warning: This will not yield reasonable dynamical results from the\n",
+ "network and is only meant to demonstrate the simulation workflow.\n",
+ "\"\"\"\n",
+ "d = {}\n",
+ "conn_params = {'replace_non_simulated_areas': 'het_poisson_stat',\n",
+ " 'cc_weights_factor': 1.9, # run model in Ground State\n",
+ " 'cc_weights_I_factor': 2.0}\n",
+ "network_params = {'N_scaling': 0.006,\n",
+ " 'K_scaling': 0.006,\n",
+ " 'fullscale_rates': os.path.join(base_path, 'tests/fullscale_rates.json')}\n",
+ "\n",
+ "sim_params = {'t_sim': 2000.,\n",
+ " 'num_processes': 1,\n",
+ " 'local_num_threads': 1}\n",
+ "\n",
+ "M = MultiAreaModel(network_params, simulation=True,\n",
+ " sim_spec=sim_params,\n",
+ " theory=True)\n",
+ "\n",
+ "M.simulation.simulate()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6730fe7b-e8bf-4aff-9497-bc614f44febd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create an instance of Analysis to load data\n",
+ "A = Analysis(M, M.simulation, data_list=['spikes'], load_areas=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1c907303-d2ad-4f17-83df-8b0e21749a1d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create pop_rates, load stationary firing rates, and calculate mean firing rate for all populations\n",
+ "label = M.simulation.label\n",
+ "complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd',\n",
+ " 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd',\n",
+ " 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp',\n",
+ " 'STPa', '46', 'AITd', 'TH']\n",
+ "\n",
+ "area_list = complete_area_list\n",
+ "\n",
+ "compute_pop_rates(M, data_path, label) # Compute pop_rates\n",
+ "\n",
+ "fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json') # Load stationary firing rates\n",
+ "with open(fn, 'r') as f:\n",
+ " pop_rates = json.load(f)\n",
+ "\n",
+ "# Process spike data and calculate mean firing rate for all populations, save them in a dict\n",
+ "rates = np.zeros((len(area_list), 8))\n",
+ "for i, area in enumerate(area_list):\n",
+ " for j, pop in enumerate(M.structure[area][::-1]):\n",
+ " rate = pop_rates[area][pop]\n",
+ " if rate == 0.0:\n",
+ " rate = 1e-5\n",
+ " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
+ " rates[i][j + 2] = rate\n",
+ " else:\n",
+ " rates[i][j] = rate\n",
+ "\n",
+ "rates = np.transpose(rates)\n",
+ "rates = np.array(rates)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "34d46019-38f2-4b5d-af09-575906b1d4d2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compare the calculated mean firing rate with the reference values saved in M2E_reference_MFRs.json and calculate the percentage of matching\n",
+ "# Load the array\n",
+ "pop_rates_ref = np.load('M2E_pop_rates_ref.npy')\n",
+ "\n",
+ "# Count the percentage of cells that values match (the same)\n",
+ "comparison_result = pop_rates_ref == rates\n",
+ "# print(comparison_result)\n",
+ "same_cells = np.sum(comparison_result)\n",
+ "# print(same_cells)\n",
+ "total_cells = np.prod(pop_rates_ref.shape)\n",
+ "# print(total_cells)\n",
+ "match_rate = same_cells / total_cells\n",
+ "# print(match_rate)\n",
+ "\n",
+ "# Judge if the matching rate is lower than threshold set and raise error if yes\n",
+ "if match_rate < match_rate_threshold:\n",
+ " raise TestError(\"Mismatch of mean firing rates over populations between the latest simulation and saved reference values, there may be problems of recent updates of dependencies, please take a check!\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "EBRAINS-23.09",
+ "language": "python",
+ "name": "ebrains-23.09"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}