first commit with npy files using lfs

.gitattributes

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+*.psd filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# log folders
+sbi-logs/
+
+# Files
+*.npz
+
+# Images
+*.png
+*.svg
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+notebooks/tmp/

Analysis/Example.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1f52d2e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5ab04fd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import os\n",
+    "from matplotlib import rcParams\n",
+    "import h5py\n",
+    "import hashlib\n",
+    "import time\n",
+    "from typing import List\n",
+    "import aux_analysis as aux\n",
+    "from sklearn import manifold\n",
+    "from sklearn import decomposition\n",
+    "from synapsbi.utils import apply_n_conditions, load_and_merge\n",
+    "from sbi.analysis import pairplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f6ae1552",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "retrieved 354300/354300 simulations\n",
+      "retrieved 10/10 simulations\n"
+     ]
+    }
+   ],
+   "source": [
+    "save_dir = \"../data_synapsesbi/bg_IF_EEEIIEII_6pPol/\"\n",
+    "\n",
+    "# load all the rules simulated in the paper (pi0 -> pi3)\n",
+    "dataset_all = load_and_merge(save_dir,\n",
+    "                         (\"bg_IF_EEEIIEII_6pPol_all.npy\",))\n",
+    "\n",
+    "#load the new rules simulated in the tutorial\n",
+    "dataset_tuto = load_and_merge(save_dir,\n",
+    "                         (\"pi3_r5to10Hz_metrics.npy\",))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "076e9773",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In the original dataset:\n",
+      "60004 simulations out of 354300 are plausible, aka 16.935930002822467 %\n",
+      "52474 simulations out of 354300 have rates between 5 and 10Hz, aka 14.810612475303413 %\n",
+      "6959 simulations out of 354300 are plausible and have rates between 5 and 10Hz, aka 1.9641546711826137 %\n",
+      "\n",
+      "For the newly sampled rules:\n",
+      "4 simulations out of 10 are plausible, aka 40.0 %\n",
+      "7 simulations out of 10 have rates between 5 and 10Hz, aka 70.0 %\n",
+      "3 simulations out of 10 are plausible and have rates between 5 and 10Hz, aka 30.0 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set of conditions for plausibility from the paper, if you are using MLP rules, remove the weif and wiif conditions\n",
+    "cond_r = (\"rate\", 1, 50)\n",
+    "cond_cv = (\"cv_isi\", 0.7, 1000)\n",
+    "cond_sf = (\"spatial_Fano\", 0.5, 2.5)\n",
+    "cond_tf = (\"temporal_Fano\", 0.5, 2.5)\n",
+    "cond_ac = (\"auto_cov\", 0, 0.1)\n",
+    "cond_fft = (\"fft\", 0, 1)\n",
+    "cond_wb = (\"w_blow\", 0, 0.1)\n",
+    "cond_srt = (\"std_rate_temporal\", 0, 0.5)\n",
+    "cond_srs = (\"std_rate_spatial\", 0, 5)\n",
+    "cond_scv = (\"std_cv\", 0, 0.2)\n",
+    "cond_wc = (\"w_creep\", 0, 0.05)\n",
+    "cond_ri = (\"rate_i\", 1, 50)\n",
+    "cond_weef =(\"weef\", 0 ,0.5)\n",
+    "cond_weif =(\"weif\", 0 ,0.5)\n",
+    "cond_wief =(\"wief\", 0 ,5)\n",
+    "cond_wiif =(\"wiif\", 0 ,5)\n",
+    "\n",
+    "cond_plausible = (cond_r,cond_ri,\n",
+    "            cond_wb,cond_wc,cond_weef,cond_weif, cond_wief, cond_wiif,\n",
+    "            cond_ac,cond_cv,cond_fft,cond_srt,cond_srs,cond_sf,cond_tf)\n",
+    "\n",
+    "cond_r5to10 = ((\"rate\", 5, 10),)\n",
+    "\n",
+    "cond_plpausible_r5to10 = ((\"rate\", 5, 10),cond_ri,\n",
+    "            cond_wb,cond_wc,cond_weef,cond_weif, cond_wief, cond_wiif,\n",
+    "            cond_ac,cond_cv,cond_fft,cond_srt,cond_srs,cond_sf,cond_tf)\n",
+    "\n",
+    "cond_plausible_all = apply_n_conditions(dataset_all, cond_plausible)\n",
+    "cond_r5to10_all = apply_n_conditions(dataset_all, cond_r5to10)\n",
+    "cond_plpausible_r5to10_all = apply_n_conditions(dataset_all, cond_plpausible_r5to10)\n",
+    "\n",
+    "cond_plausible_tuto = apply_n_conditions(dataset_tuto, cond_plausible)\n",
+    "cond_r5to10_tuto = apply_n_conditions(dataset_tuto, cond_r5to10)\n",
+    "cond_plpausible_r5to10_tuto = apply_n_conditions(dataset_tuto, cond_plpausible_r5to10)\n",
+    "\n",
+    "print(\"In the original dataset:\")\n",
+    "print(np.sum(cond_plausible_all), \"simulations out of\", len(dataset_all), \"are plausible, aka\", np.sum(cond_plausible_all)/len(dataset_all)*100,\"%\")\n",
+    "print(np.sum(cond_r5to10_all), \"simulations out of\", len(dataset_all), \"have rates between 5 and 10Hz, aka\", np.sum(cond_r5to10_all)/len(dataset_all)*100,\"%\")\n",
+    "print(np.sum(cond_plpausible_r5to10_all), \"simulations out of\", len(dataset_all), \"are plausible and have rates between 5 and 10Hz, aka\", np.sum(cond_plpausible_r5to10_all)/len(dataset_all)*100,\"%\")\n",
+    "\n",
+    "print(\"\\nFor the newly sampled rules:\")\n",
+    "print(np.sum(cond_plausible_tuto), \"simulations out of\", len(dataset_tuto), \"are plausible, aka\", np.sum(cond_plausible_tuto)/len(dataset_tuto)*100,\"%\")\n",
+    "print(np.sum(cond_r5to10_tuto), \"simulations out of\", len(dataset_tuto), \"have rates between 5 and 10Hz, aka\", np.sum(cond_r5to10_tuto)/len(dataset_tuto)*100,\"%\")\n",
+    "print(np.sum(cond_plpausible_r5to10_tuto), \"simulations out of\", len(dataset_tuto), \"are plausible and have rates between 5 and 10Hz, aka\", np.sum(cond_plpausible_r5to10_tuto)/len(dataset_tuto)*100,\"%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70df64dc",
+   "metadata": {},
+   "source": [
+    "Congratulations, you are done with the tutorial, thank you for going through it :)\n",
+    "\n",
+    "Obviously to get more reliable percentages for the \"new\" posterior, we would need much more than 10 samples, but this require many more spiking network simulations.\n",
+    "\n",
+    "You can check the analysis carried out in the paper, as well as the figures in the other notebooks of this folder.\n",
+    "\n",
+    "Don't hesitate to contact us if you have any further questions."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}