UBC_usage_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> **UBC Simulation** </center>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this project is to investigate the properties of a specific type of synapses in the cerebellum: between **mossy fibers (MF)** and **unipolar brush cells (UBCs)**. \n",
    "\n",
    "These synapses are interesting because they feature remarkably large dimensions, with numerous neurotransmitter release sites.\n",
    "They could potentially play a role in the transformation of the sensory signal encoding acceleration. More precisely, they could act like filters, leading to the temporal integration of the signal. This process would be a step to convert the sensory detection of acceleration into the cognitive perception of movement.\n",
    "\n",
    "The aim of this modeling is thus to assess the effects of the **morphology** of a synapse on the **signal transmission**.\n",
    "\n",
    " ---\n",
    " \n",
    "*All the modules and functions provided in the project are documented, and accessible through help().*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UBC_synapse importation:\n",
      "synapse successfully imported\n",
      "simulate successfully imported\n",
      "visualize successfully imported\n",
      "manage_data successfully imported\n",
      "patterns_release successfully imported\n",
      "Help on package UBC_synapse:\n",
      "\n",
      "NAME\n",
      "    UBC_synapse - Modeling of synapses between mossy fibers and unipolar brush cells (UBCs) in the cerebellum.\n",
      "\n",
      "DESCRIPTION\n",
      "    The goal of this modeling is to investigate the properties of a specific type of synapses in the cerebellum: between mossy fibers (MF) and unipolar brush cells (UBCs).\n",
      "    More precisely, it consists in assessing the effects of the morphology of a synapse on the signal transmission.\n",
      "    For this purpose, the package achieves the following tasks:\n",
      "        1. Modeling the morphology of synapses.\n",
      "        2. Simulating their behavior in response to a stimulation pattern (events of glutamate release).\n",
      "    \n",
      "    Usage\n",
      "    -------\n",
      "    The Jupyter notebook provided in the package is designed to guide the user in the exploration of the functionalities.\n",
      "    \n",
      "    Structure of the package\n",
      "    ------------------------\n",
      "    PCBS_UBC_synapse/\n",
      "    │\n",
      "    ├── README.md\n",
      "    ├── UBC_usage_demo.ipynb    <- notebook to guide the user into the project\n",
      "    ├── UBC_synapse/            <- source codes directory\n",
      "    │   └── __init__.py\n",
      "    │   └── synapse_model.py\n",
      "    │   └── synapse_morph.py\n",
      "    │   └── stimulation_pattern.py\n",
      "    │   └── stimulation_sim.py\n",
      "    │   └── manage_data.py\n",
      "    │   └── visuaize.py\n",
      "    │   └── c_code/\n",
      "    │       └── run_simulation.c\n",
      "    │       └── run_simulation.so\n",
      "    ├── UBC_data/               <- directory for storing the data generated by running the codes\n",
      "    │   └── synapses_register.csv\n",
      "    │   └── syn0/\n",
      "    │       └── responses_syn0_register.csv\n",
      "    │       └── resp0_syn0/\n",
      "    │           └── coords.csv\n",
      "    │           └── resgluxx-xx.csv\n",
      "    │           └── resAMPAtot.csv\n",
      "    │           └── resV.csv\n",
      "    ├── UBC_installation/       <- directory for the project installation setup\n",
      "    │   └── environment.yml     <- list of dependencies, file usable by conda\n",
      "    │   └── requirements.txt    <- list of dependencies, file usable by virtualenv\n",
      "    │   └── setup.py            <- configuration file\n",
      "    │   └── MANIFEST.in         <- specification of the non-.py files to be included in the project\n",
      "    ├── UBC_references/         <- directory for other documentation and resources\n",
      "    │   └── LICENSE.txt\n",
      "    │   └── AUTHORS.rst\n",
      "    │   └── fig1_cerebelllum_network.png\n",
      "    │   └── parameters.py\n",
      "    \n",
      "    Modules\n",
      "    -------\n",
      "    All the Python modules are located in the directory `UBC_synapse/`. The C library is contained in the sub-folder `c_codes/`.\n",
      "        * synapse_model - Class for building synapses objects. \n",
      "        * synapse_morph - Functions for building matrixes representing the morphology of a synapse.\n",
      "        * stimulation_pattern - Class for generating sequences of stimulation events, under which the synapses' behaviors will be observed.\n",
      "        * stim_sim - Functions for setting up a simulation sun, and interfacing with the C code. \n",
      "        * run_simulation (C code in `UBC_synapse/c_codes`) - Core computations for simulating the response of a synapse under a stimulation pattern.\n",
      "        * manage_data - Functions for saving and retrieving the results.\n",
      "        * visuaize - Functions for generating representations of the synapses' morphology and behaviors.\n",
      "    A more detailed documentation for individual modules is provided in each one, with their respective functions.\n",
      "    \n",
      "    Data\n",
      "    -------\n",
      "    The synapses and the data generated by running the package can be saved in the directory `UBC_data/`, thanks to the methods of the class Synapse.\n",
      "    The file `register_syn.csv` keeps track of the saved synapses: \n",
      "        * the different instances are classified according to their attributes,\n",
      "        * each saved synapse is referenced by an index (number).\n",
      "    Each saved synapse is associated to a sub-directory named `sny{index}`. One directory contains:\n",
      "        * a file `responses_syn{index}_register.csv`, classifying the stimulation trials with this synapse, and referencing them by an index (number),\n",
      "        * directories `resp{index}_syn{index}/`, one for each trial, containing the results.\n",
      "    For each stimulation trial, the results are made up of:\n",
      "        * `coords.csv`: locations of the points on the synapse where the glutamate concentrations were recorded,\n",
      "        * `resgluxx-xx.csv`: variations of the glutamate concentration at location xx-xx (row-column),\n",
      "        * `resAMPAtot.csv`: evolution of the activation state of the post-synaptic AMPA receptors,\n",
      "        * `resV.csv`: variations of the UBC's membrane potential.\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    manage_data\n",
      "    stimulation_pattern\n",
      "    stimulation_sim\n",
      "    synapse_model\n",
      "    synapse_morph\n",
      "    visualize\n",
      "\n",
      "DATA\n",
      "    __authors__ = 'esther-poniatowski'\n",
      "    __contact__ = 'esther.poniatowski@ens.fr'\n",
      "\n",
      "VERSION\n",
      "    1.0.0\n",
      "\n",
      "DATE\n",
      "    01/01/2020\n",
      "\n",
      "FILE\n",
      "    /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_synapse/__init__.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import UBC_synapse as UBC\n",
    "\n",
    "help(UBC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I - Determination of parameters\n",
    "---\n",
    "\n",
    "The main parameter assumed to influence the signal transmission is the **diameter of the glomerule** (synaptic surface). Analysig its effects requires to test the behavior of different synapses with various diameters, while keeping the other parameters fixed.\n",
    "A first step in building this model consists in determining appropriate ranges of parameters. Namely, the following parameters have to be set:\n",
    "- the range of the diameters: `dim_list`,\n",
    "- the number of sites for neurotransmitter release: `n_sites`,\n",
    "- the cumulative area of neurotransmitter realease: `area_tot_sites`.\n",
    "\n",
    "A first criteria to chose those values is their matching as much as possible the physiological parameters. Some of them have been measured experimentally:\n",
    "- diameters of the synapses: between 200 and 1000 µm,\n",
    "- cumulative release area: 12 - 40 µm² (given by Mugni).\n",
    "\n",
    "A second criteria for the parameters' values is their convenience for modeling constraints. The synapse is modelled by a circular matrix. The release sites are homogenousely distributed on this area, according to a hexagonal grid. These properties impose some geometric relations between the spatial parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Determination of `n_sites` and `dim_min`\n",
    "\n",
    "Let us chose `area_tot_sites = 25e-12 m²`, and try several values for `n_sites`.\n",
    "The function called below automatically computes an appropriate value for `dim`, in order to match the geometric constraints of the hexagonal grid.\n",
    "\n",
    "Different values for `n_sites` lead to different shapes of synapses. More precisely, the synapses differ regarding the spatial distribution of the release sites, and the individual sites' shape.\n",
    "From the results shown below, it seems acceptable to chose `n_sites = 37`.\n",
    "In that case, the minimal dimension for the glomerule is `dim_min = 43`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For bluiding a hexgrid, the closest possible value for n_sites is 37.\n",
      "Release area updated with this parameter: 2.5159999999999997e-11 m^2.\n",
      "Area of a single site: 3.3999999999999996e-06 m^2 (17 px).\n",
      "Diameter obtained for the glomerulus: 8.599999999999999e-06 m = 85.99999999999999 um (43 px).\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For bluiding a hexgrid, the closest possible value for n_sites is 55.\n",
      "Release area updated with this parameter: 2.6399999999999997e-11 m^2.\n",
      "Area of a single site: 2.4e-06 m^2 (12 px).\n",
      "Diameter obtained for the glomerulus: 8.599999999999999e-06 m = 85.99999999999999 um (43 px).\n"
     ]
    },
    {
     "data": {
      "image/png": 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bm5KSsr9Xr14JS5YsicjMzMz9xz/+0ea555675pZbbtmTnJxcvmnTptyAgAB88MEHoVOnTo1et27dnscff/xQZmZmy7fffvtHAJg0aVLULbfcUvLee+/tKyws9E9NTU0YMWJESevWrS/oXmzbtm0tN2/enBMfH3/u+++/D16xYkV4ZmZmblBQkN57770xr732WsSkSZOKqstfqsxLL710sEOHDlUOhwM33nhj940bN7aIjY09t3bt2rZ79+7N9vPzQ2FhoT8AjB8/vvPkyZOPDh48+Mzu3bsDBw8eHLd3794LRln/y1/+Evnwww8fnThx4ony8nLxTLzPPffcQfdm5Dlz5pwf2qisrEyuu+66+OHDh5e8++67bQcNGlQ8e/bsIw6HA6dPn76iyhqTHRE1K+7D+2RnZ++szT6cTqf87ne/i/72229b+fn54dixY4EHDhywAUBUVFRFWlpaGQDY7faygQMHlvj5+aFPnz6lM2fO7AQAJ06c8B89enTXffv2BYuIehv1AAA+//zz1uvWrWuTkZHREXB1Lp2fnx/Yp0+fC/rb7NWr19nqoX8+/vjj0Ozs7JDk5OQEACgvL/dr3779BRnmUmUWLVoU/tZbb7VzOBxy/PjxgK1btwb36dOnLCgoyDlmzJgut99+e3H1NcUNGza03r17d4vq/Z45c8b/5MmTfm3btj2fjG+44Yazc+bMuebAgQOBY8aMOdmzZ8+KS53bmoY2uv7668+OHz8+trKy0u+uu+46eeONN5Zd/i/1H6YmOxEZAuB/APgDWKiqszzWBwF4G0AKgCIAo1V1n4gEAFgIoI8R49uq+oKZsRKRNfk6vA/gGnrH6fxPpal6xAFPCxYsCC8qKrJt3759Z1BQkEZFRfUsKyvzA4DAwMDzHQ77+fkhODj4/NBAVVVVAgCPPfZY1M0333z6f//3f/fk5eUFDhw4sLu346gqVqxYkZ+cnHzJBBESEuJ020ZGjRpV9MorrxysqXxNZXJzcwP/+te/dsjKytoZGRlZNXLkyNjy8nK/gIAAbNmyZeeqVataL126tO2rr77a/ttvv92lqsjMzNzZqlWrGjtZnjBhwon+/fuf/de//hU2dOhQ+/z58/eNGDHi9KViq2looy+//DLv/fffD3vggQe6PvLII0fda6uXY9o1OxHxB/AKgKEAEgHcLSKJHsUeBHBSVbsBmAdgtrF8FIAgVe0JVyIcLyKxZsVKRNbl6/A+ABAbG1uxZcuWlgDw1VdfhRw8eDAIAMLCwqrOnj17fpvi4mL/du3aVQYFBenq1atDDx06dEUjiJeUlPhHR0efA4AFCxa0q17eunXrKvfYbrnllpK5c+d2qE7AGzZsaHHRzjwMGTKkZM2aNW0PHjxoA4CjR4/679q1K9CXMidPnvRv0aKFMzw8vKqgoMD2+eefhxnv18+ojRa/9tprBTt37gwBgH79+pXMnj37/I07X3/99UXx7dixIzAhIaHiySefPHbbbbed2rJlywVlwsLCqs6cOeNfPV/T0Ea7du0KjIqKqpwyZUrhvffeW/j999+HXO5cuDOzZpcGIF9V9wKAiCwFcAeAHW5l7gAw3ZheAeCvIiIAFEBLEbEBaAHgHABLDmDYGK/PrRs1rFbbDX5vTR1HcqHaxGV2TD7F4OVv3Kyv4/lHOOr80YNL8HV4HwC4//77Ty5ZsiQiPj4+8b/+67/OdunSpRwAOnbsWJWSknImLi6ux8CBA4unT59+ZOjQod2SkpISevToUdq1a9eLhvG5lMcee+zIQw891DUjI6Nj//79z3+3DR069PScOXOuiY+PT5wyZcrhWbNmHUpPT4+Jj49PVFWJjo6uuNQjEgCQkpJS/uSTTx4cNGiQ3el0IiAgQDMyMn602+3nLldm0KBBZ5OSkkrj4uJ6xMTEVKSkpJwBgFOnTvkPGzasW3UCmjlzZgEAvP766wUPPfRQjN1uT6yqqpK+ffuevvHGG390j2fx4sXh7733XoTNZtPIyMjKF1544ZD7+rS0tDKbzabdu3dPHDt2bOGTTz55zNvQRuvWrQvNyMjoaLPZNCQkpGrJkiU+D78EmDjEj4jcBWCIqj5kzN8HoK+qTnIrk22UOWDM7wHQF0AxgMUABgEIAfB7VX3dyzHSAaQDQExM55T9+7eb8l7MxGTnu6aa7LxpqsmOQ/xQY1fTED9mPnrgra3bM7PWVCYNQBWATgC6ApgiIj+5qKDq66qaqqqpkZERVxsvERFZlJnJ7gCAzm7z0QAO1VTGaLIMA3ACwFgAH6tqpaoeA7ABwBX9miQiIqpmZrL7DkCciHQVkUAAYwCs8iizCsA4Y/ouAJ+pq131RwADxaUlgOsB5JoYKxERWZhpN6ioqkNEJgFYB9ejB39X1RwReQZApqquAvAGgMUikg9XjW6MsfkrAN4EkA1XU+ebqrrNrFjrU2O8Rkf1z/Nz0FSv4RE1FaY+Z6eqawGs9Vj2lNt0OVyPGXhud8bbciIiotpg35hERGR57C6MiOpZSTKgdfjdIw6g9SWH+PH390+Ji4s7373UnXfeeeL5558/UlP5adOmdZw1a1aN669URkZGhHuflw3JlyGNrIjJjojqWV0mOt/2FxQU5KzuxNkXGRkZ13hLdk6nE6oKf39/b5s1Gg6HAzZb3ZzmyspKBAQENGgMdaHxRGJB9X0zSmN8GJwx+bhv9rJS74qKivxTUlISVq5cuTs5Obli+PDhXQcMGHB6z549QRUVFX7GkDRlL7744sGhQ4fG3XjjjaezsrJarVy5Mn/GjBkdt27d2rK8vNxv+PDhJ+fNm3cIAJYtWxY2bdq06PDwcEfPnj1L9+/fH+RZg9q1a1fguHHjYouKimwRERGOt99+e19cXNy5kSNHxgYHBzvz8/ODDx48GLRgwYIf3nrrrXZZWVkte/fuffb999/fBwD//Oc/Wz/zzDOdzp07J126dKlYunTpvrCwMGdUVFTPu+++u3D9+vWtx48ff2zhwoXt58yZU3DTTTeVVvcFevDgwQt63li/fn3I5MmTY8rLy/2Cg4Odb7311g/JyckVGRkZER999FFYRUWFX2lpqd+33367y327W2+99drDhw8HVlRU+E2YMOHoo48+Wgi4hu1JT08/+tlnn7V+8cUXD4SEhDgnT57cubS01K9t27aOJUuW7OvSpUvl3Llz27355puRlZWVEhsbW7FixYofQkNDLxjJoa7xmh0RWV518qp+/e1vf2sbERFRNW/evB/HjRvX9fXXX2976tQp25QpUwrnz59/sLomuGrVqh8AYN++fcG/+tWvinbu3LnDbrefe+mllw5mZ2fvzM3NzdmwYUPoxo0bW5SWlspvf/vbLh999NHurKysvKIi712iTZgwIWbs2LFFu3bt2jF69OiiiRMnnn8eubi42PbNN9/smjVrVsHo0aPj/vCHPxzdvXt3Tm5ubouvv/66xeHDh23PP//8NV9++eWuHTt27OzTp0/ps88+e378veDgYGdWVlZeenr6SV/OS/VQQzt37tzx9NNPH5w6dWp09brvv/++1bvvvvuDZ6IDgCVLluzLycnZuWXLlh0LFizocOTIEX8AKCsr80tKSirbtm1b7oABA84+8sgjMStXrtyTk5Ozc9y4cYWPPvpoFADcc889J7Ozs3fm5eXt6N69e1lGRkY7z2PUNdbsiMjyamrG/MUvflGyfPnytlOnTu2SlZWV421bALjmmmvODRo06Gz1vLdhcKqqqtC5c+eK6qF2xowZc2LhwoWRnvvavHlzy48++mgPAEycOPHEjBkzzieY22+//VT1cEARERGV7kMF7dmzJ2j//v2Be/bsCU5LS4sHgMrKSqnuvxJw9e15JeflUkMN9e/fv6RDhw5V3rabPXt2hw8//LANABw5ciQgJycnuGPHjmf9/f3xwAMPnARcHTjv3r27xcCBA+2Aqwk4MjKyEgCysrJaPPXUU1GnT5/2P3v2rP/NN99cfCVx1waTHRE1W1VVVdi1a1dwUFCQs7Cw0HbttddWeivnPoROTcPg1EU/w+7DAXkOFeRwOMTf31/79etXsnr1aq+dILs3BdpsNq2qcuWq0tJSr0MVXWqoIff37G7NmjWhX3zxRWhmZmZuaGioMy0trbvb8EbO6ut0qirdunUr27Jly0UdgqSnp3ddsWJF/g033FCWkZER8cUXX4Re/uxcHTZj1pHBz3510YvoavAzZb5nnnmmg91uL1+0aNHeBx98MLa6V3+bzaY1jWVX0zA4ycnJ5QUFBUF5eXmBALBs2bJwb9v37t377MKFC9sCrnHxUlNTz3gr582AAQPOZmZmtsrOzg4CgNOnT/tt27YtyFvZzp07V2zatKklACxZsqSttzI1DTV0KadOnfIPCwurCg0NdW7evDl469atLb2V69WrV/mJEydsn376aUvANTZgZmZmMACUlpb6xcTEVFZUVMjSpUu9nqe6xmRHRPVMLjkkjxn787xm9/DDD0dt27YtaPHixe3mz59fMGTIkDPXX3/96WnTpl0DAPfcc8/xhISExBEjRnT13NcNN9xQVj0Mzn333Rdb3YzYqlUrfemll/YPGTIkLiUlpXv79u0rQ0NDL2oGfPXVV39cvHhxO7vdnvjuu+9GzJ8/v8DXd9qpUyfHggUL9o0ZM+Yndrs9MSUlJX779u3B3spOmzbt6BtvvBHZu3fv+MLCQq+teI899tiR6dOnR/fp0ye+uhZ4OSNHjix2OBxit9sT//jHP3ZKTk4+661ccHCwLl26dM+0adOiu3fvntijR4/EL774opUR26G0tLSE/v372+Pi4q5oeKTaMm2In/qWmtpbMzM/b7DjN4Zf3XV1l2Fd3q3Y0PsyO6b61tB3aHKIn0srLi72CwsLczqdTtx///0xcXFx5U8//fSxho6rOWmIIX6IiJqVl19+uV18fHxiXFxcj5KSEv/Jkyc3iyTfFPAGFSKiOvL0008fY02ucWKyq6XG0GxJzU8THS3B6XQ6xc/PzxrXTKjRcjqdAsDrXaRsxiQis2UfP348zPgiIjKF0+mU48ePh8E1NNxFWLMjIlM5HI6Hjhw5svDIkSNJ4A9sMo8TQLbD4XjI20omOyIyVUpKyjEAIxo6Dmre+CuLiIgsj8mOiIgsj8mOiIgsj8mOiIgsj8mOiIgsj3dj+oAPkFNjxRHOiXzDmh0REVkekx0REVkekx0REVkekx0REVkekx0REVkekx0REVkekx0REVkekx0REVkekx0REVkee1Dxoj57TFk3alitthv83po6joRqozH+/Tw/v+xRhYg1OyIiagaY7IiIyPKY7IiIyPKY7IiIyPKY7IiIyPJMTXYiMkRE8kQkX0SmeVkfJCLLjPUbRSTWbV0vEflGRHJEZLuIBJsZKxERWZdpyU5E/AG8AmAogEQAd4tIokexBwGcVNVuAOYBmG1sawPwDoAJqtoDwAAAlWbFSkRE1mZmzS4NQL6q7lXVcwCWArjDo8wdABYZ0ysADBIRAXAbgG2quhUAVLVIVatMjJWIiCzMzIfKowAUuM0fANC3pjKq6hCRYgARAOwAVETWAYgEsFRV/+x5ABFJB5AOADExnWsVZG0fIG+MDxNT02Xm58nbZ5wPmlNzY2bNTrwsUx/L2AD0A3CP8e8vRGTQRQVVX1fVVFVNjYyMuNp4iYjIosxMdgcAuFe3ogEcqqmMcZ0uDMAJY/kXqlqoqqUA1gLoY2KsRERkYWYmu+8AxIlIVxEJBDAGwCqPMqsAjDOm7wLwmaoqgHUAeolIiJEEbwaww8RYiYjIwky7Zmdcg5sEV+LyB/B3Vc0RkWcAZKrqKgBvAFgsIvlw1ejGGNueFJGX4EqYCmCtqn5oVqxERGRtpo56oKpr4WqCdF/2lNt0OYBRNWz7DlyPHxAREV0V9qBCRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWZ/O1oIi0BdDZfRtV/d6MoIiIiOqST8lORJ4F8ACAPQDUWKwABpoTFhERUd28QHI6AAAL50lEQVTxtWb3SwDXquo5M4MhIiIyg6/JLhtAGwDHTIzFdIOf/aqhQyBqFLz9X1j3p34NEAlR/fA12b0AYLOIZAOoqF6oqiNMiYqIiKgO+ZrsFgGYDWA7AKd54RAREdU9X5NdoapmmBoJERGRSXxNdlki8gKAVbiwGbNJPXrg7ZoEr+NRc8Trc9Tc+Jrsehv/9jX+FQA/AdClziMiIiKqY74mu8895v0BdK3bUIiIiMzha7I74zYdAOAmAMvqPhwiIqK651OyU9W57vMi8mcA35kSERERUR2rbUfQbQEcqctAiIiIzOJr35jb8Z8+MQVALICi6uWq2suc8IiIiK6er9fshpkaBRERkYl8vWa33+xAiIiIzMLBW4mIyPKY7IiIyPJMTXYiMkRE8kQkX0SmeVkfJCLLjPUbRSTWY32MiJwRkUfNjJOIiKzNtGQnIv4AXgEwFEAigLtFJNGj2IMATqpqNwDz4BpZwd08AB+ZFSMRETUPZtbs0gDkq+peY4TzpQDu8ChzB1zDBwHACgCDREQAQER+DmAvgBwTYyQiombAzGQXBaDAbf6AscxrGVV1ACgGECEiLQE8BmDGpQ4gIukikikimcePF9VZ4EREZC1mJjvxskx9LDMDwDxVPeNl/X8Kqr6uqqmqmhoZGVHLMImIyOp8fai8Ng4A6Ow2Hw3gUA1lDoiIDUAYgBNwDSV0l9EHZxsAThEpV9W/mhgvERFZlJnJ7jsAcSLSFcBBAGMAjPUoswrAOADfALgLwGeqqgD6VxcQkekAzjDRERFRbZmW7FTVISKTAKyDa/y7v6tqjog8AyBTVVcBeAPAYhHJh6tGN8aseIiIqPkys2YHVV0LYK3HsqfcpssBjLrMPqabEhwRETUb7EGFiIgsj8mOiIgsj8mOiIgsj8mOiIgsz9QbVKxs8HtrGjoEshB+nojMxZodERFZHpMdERFZHpMdERFZXrO/ZrfuT/0uWjb42a8aIBIic3j7jBM1N6zZERGR5THZERGR5THZERGR5THZERGR5TX7G1QaGh8mbtr49yNqGlizIyIiy2OyIyIiy2OyIyIiy+M1Oy88H8LlQ+bUlPAhcqKLsWZHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESWxx5UfOCtRwr2qkKNAXtLIfINa3ZERGR5THZERGR5THZERGR5THZERGR5THZERGR5THZERGR5THZERGR5THZERGR5fKi8ljwf5uVD5lQf+BA5Ue2YWrMTkSEikici+SIyzcv6IBFZZqzfKCKxxvKfikiWiGw3/h1oZpxERGRtpiU7EfEH8AqAoQASAdwtIokexR4EcFJVuwGYB2C2sbwQwHBV7QlgHIDFZsVJRETWZ2bNLg1AvqruVdVzAJYCuMOjzB0AFhnTKwAMEhFR1c2qeshYngMgWESCTIyViIgszMxkFwWgwG3+gLHMaxlVdQAoBhDhUWYkgM2qWmFSnEREZHFm3qAiXpbplZQRkR5wNW3e5vUAIukA0gEgJqZz7aKsI41hZITB762p1+P5gjHVHm9GIao7ZtbsDgBwz0DRAA7VVEZEbADCAJww5qMB/AvA/aq6x9sBVPV1VU1V1dTISM8KIRERkYuZye47AHEi0lVEAgGMAbDKo8wquG5AAYC7AHymqioibQB8COBxVd1gYoxERNQMmJbsjGtwkwCsA7ATwHJVzRGRZ0RkhFHsDQARIpIPYDKA6scTJgHoBuBPIrLFeLU3K1YiIrI2Ux8qV9W1ANZ6LHvKbbocwCgv280EMNPM2OpDY7iOR00Dr88RmYvdhRERkeUx2RERkeUx2RERkeUx2RERkeWJqudz3k1Tampvzcz8vKHDuGK8YaV5aqo3pIi0yVLV1IaOg+hKsWZHRESWx2RHRESWx2RHRESWx2RHRESWZ2oPKnR57GXF+prqzShEVsKaHRERWR6THRERWR6THRERWR6v2TVCvlzj4XW9xoHX44iaBtbsiIjI8pjsiIjI8pjsiIjI8pjsiIjI8niDShPl640RvJGldnjjCZG1sGZHRESWx2RHRESWx2RHRESWx2t2FscH1C/G63FEzQ9rdkREZHlMdkREZHlMdkREZHlMdkREZHm8QYXq/YYNzxtieMMIEZmNNTsiIrI8JjsiIrI8JjsiIrI8JjsiIrI83qBC9Y43pBBRfWPNjoiILI/JjoiILI/JjoiILI/JjoiILI/JjoiILM/UZCciQ0QkT0TyRWSal/VBIrLMWL9RRGLd1j1uLM8TkcFmxklERNZmWrITEX8ArwAYCiARwN0ikuhR7EEAJ1W1G4B5AGYb2yYCGAOgB4AhAOYb+yMiIrpiZtbs0gDkq+peVT0HYCmAOzzK3AFgkTG9AsAgERFj+VJVrVDVHwDkG/sjIiK6YmY+VB4FoMBt/gCAvjWVUVWHiBQDiDCWf+uxbZTnAUQkHUC6MVsh0ia7bkKvd+0AFDZ0ELXAuOtXY4i7SwMfn6hWzEx24mWZ+ljGl22hqq8DeB0ARCRTVVOvNMjGoKnGzrjrV1ONm6gxMLMZ8wCAzm7z0QAO1VRGRGwAwgCc8HFbIiIin5iZ7L4DECciXUUkEK4bTlZ5lFkFYJwxfReAz1RVjeVjjLs1uwKIA7DJxFiJiMjCTGvGNK7BTQKwDoA/gL+rao6IPAMgU1VXAXgDwGIRyYerRjfG2DZHRJYD2AHAAeDXqlp1mUO+btZ7qQdNNXbGXb+aatxEDU5cFSkiIiLrYg8qRERkeUx2RERkeU0i2TXVbsdqG7eI/FREskRku/HvwKYQt9v6GBE5IyKP1lfMbse+ms9KLxH5RkRyjHMf3NjjFpEAEVlkxLtTRB6vr5iJmhRVbdQvuG5u2QPgJwACAWwFkOhR5mEArxnTYwAsM6YTjfJBALoa+/FvAnH3BtDJmE4CcLApnG+39e8DeA/Ao03os2IDsA1AsjEf0UQ+K2Ph6m0IAEIA7AMQW5/nnS++msKrKdTsmmq3Y7WOW1U3q2r1c4U5AIJFJKheor668w0R+TmAvXDFXd+uJvbbAGxT1a0AoKpFevk7gBtD3AqgpfGcagsA5wCU1E/YRE1HU0h23rod8+w67IJuxwC4dzt2uW3NcjVxuxsJYLOqVpgUp6daxy0iLQE8BmBGPcTpzdWcczsAFZF1IvK9iEyth3gvislwJXGvAHAWwGEAPwKYo6onzA6YqKkxs7uwumJ6t2MmuZq4XStFesA1EsRtdRjX5VxN3DMAzFPVM0ZFr75dTew2AP0AXAegFMD/iUiWqv5f3Ybo1dXEnQagCkAnAG0B/FtEPlXVvXUbIlHT1hRqdk2127GriRsiEg3gXwDuV9U9pkfrJSbDlcTdF8CfRWQfgN8B+KPRsUB9udrPyheqWqiqpQDWAuhjesQeMRmuJO6xAD5W1UpVPQZgAwD2n0nkoSkku6ba7Vit4xaRNgA+BPC4qm6op3ir1TpuVe2vqrGqGgvgZQDPq+pf6ytwXN1nZR2AXiISYiSTm+Hqwaexx/0jgIHi0hLA9QBy6yluoqajoe+Q8eUF4GcAdsF1x9oTxrJnAIwwpoPhuvsvH65k9hO3bZ8wtssDMLQpxA3gSbiuw2xxe7Vv7HF77GM66vluzDr4rNwL14012QD+3BTiBtDKWJ4DV3L+Q32fc774agovdhdGRESW1xSaMYmIiK4Kkx0REVkekx0REVkekx0REVkekx0REVkekx0REVkekx0REVkekx2ZSkRiRSTbbf5REZkuIp+LyDwR+dIYh+06EfmniOwWkZkNGTMRWU9T6AiarOucqt4kIr8FsBJAClz9Pe4RkXmqWtSw4RGRVbBmRw2puv/H7QByVPWwuoYy2osLO0YmIroqTHZUH9yHpwlwm64eo8/pNl09z1YHIqozTHZUH7qISKSI+AG4CYB/QwdERM0Lfz1TfSgC8DaAjgA+BXA/+EOLiOoRRz0gU4lILIA1qprUwKEQUTPGX9dERGR5rNkREZHlsWZHRESWx2RHRESWx2RHRESWx2RHRESWx2RHRESW9/8BWmNATikje/wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For bluiding a hexgrid, the closest possible value for n_sites is 61.\n",
      "Release area updated with this parameter: 2.6839999999999995e-11 m^2.\n",
      "Area of a single site: 2.2e-06 m^2 (11 px).\n",
      "Diameter obtained for the glomerulus: 8.599999999999999e-06 m = 85.99999999999999 um (43 px).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "area_tot_sites = 25e-12\n",
    "# Instanciation of synapses with different numbers of sites:\n",
    "for n_sites in [35, 50, 60] :\n",
    "    syn = UBC.Synapse(n_sites=n_sites, area_tot_sites=area_tot_sites, verbose=True)\n",
    "    syn.visualize_synapse()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Determination of a range of dimensions to test \n",
    "\n",
    "Let us now try the several values for `dim`, keeping `n_sites` fixed to `37`. \n",
    "\n",
    "The dimensions tested below, `[40, 80, 130]`, will be adjested by the function in order to yield more convenient final dimensions.\n",
    "\n",
    "From the results shown below, it seems that choosing `dim_list = [43, 79, 115]` enables to asses synapses with noticeable differences, which is the aim of the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of release sites: 37.\n",
      "Release area updated with this parameter: 2.5159999999999997e-11 m^2.\n",
      "Area of a single site: 3.3999999999999996e-06 m^2 (17 px).\n",
      "Diameter obtained for the glomerulus: 8.599999999999999e-06 m = 85.99999999999999 um (43 px).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of release sites: 37.\n",
      "Release area updated with this parameter: 2.5159999999999997e-11 m^2.\n",
      "Area of a single site: 3.3999999999999996e-06 m^2 (17 px).\n",
      "Diameter obtained for the glomerulus: 1.5799999999999998e-05 m = 157.99999999999997 um (79 px).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of release sites: 37.\n",
      "Release area updated with this parameter: 2.5159999999999997e-11 m^2.\n",
      "Area of a single site: 3.3999999999999996e-06 m^2 (17 px).\n",
      "Diameter obtained for the glomerulus: 2.3e-05 m = 230.0 um (115 px).\n"
     ]
    },
    {
     "data": {
      "image/png": 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6Kc1ms6WOGzeuDQAcOXIkqH///jelp6enpKenp/zjH/+4wfVzcnJywqo+x2azpe7YsSMU+M/QRk888URsTk5O4+Tk5NTnnnvuxuqGNzp06FBwVlZWUnJycmpiYmLa3//+98a1+w1gNyYR+YHaDvFTk9mzZxcNGjSoUdWQP+Xl5Vi1atW+6OjoyqNHjwZ169YteeTIkWcAoLCwMGzJkiUHMjMzD3Xq1Cll0aJFzXJycnb/9a9/bfriiy+26t279/6MjIxLGzdu3B0cHIwPP/wwYurUqW3WrFmz/7e//e2RnJycG959991vAWDSpEmxvXv3Prts2bKDJ0+eDMzKykoZMmTIWddnZG7fvv2GLVu25CcnJ1/evHlz2Pvvvx+dk5OzOzQ0VO+///64119/vdmkSZOKq9rX1OaVV1453KJFiwq73Y5bb7016euvv24UHx9/efXq1VEHDhzICwgIwMmTJwMB4OGHH247efLk4/379z+/d+/ekP79+yceOHDgeyOt/+lPf4qZOHHi8QkTJpy6dOmSuCbeF1988bBzN/KsWbPcDm/03nvvRfXt27dk5syZx+x2O86dO1frgo3JroHw2zvVhDeiNyznIX7y8vJ2Xcs2Kisr5Ve/+lWbDRs2NA4ICMB3330XUlRUFAQAsbGxZdnZ2aUAYLPZSvv06XM2ICAAXbp0ufjCCy+0BoBTp04FDh8+POHgwYNhIqLuRj4AgPXr1zdZs2ZN0zlz5rQEgLKyMtm3b19Ily5dvvfMzU6dOl2oGv7n73//e0ReXl54RkZGCgBcunQp4MYbb/xehqmpzYIFC6Lfeeed5na7XU6cOBG8bdu2sC5dupSGhoZWjhgxot1dd91VUnVO8csvv2yyd+/eRlXbPX/+fODp06cDoqKiriTj7t27X5g1a1aroqKikBEjRpzu2LFjWU3HtrrhjW655ZYLDz/8cHx5eXnAvffee/rWW2+t9SjzTHZEZHm1HeIHcAy/U1n5n6KpahRyV/PmzYsuLi4O2rFjx67Q0FCNjY3tWFpaGgAAISEhVx46HBAQgLCwsCvDA1VUVAgAPP7447F33HHHuU8++WR/QUFBSJ8+fZLcfY6q4v3339+XkZFRY4IIDw+vdFpHhg0bVvzqq68erq59dW12794d8r//+78tcnNzd8XExFQMHTo0/tKlSwHBwcHYunXrruXLlzdZvHhx1GuvvXbjhg0b9qgqcnJydjVu3LjaBy2PHz/+VM+ePS/83//9X+TAgQNtc+fOPThkyJBzNcVW3fBGn332WcEHH3wQOWbMmIRHH330uHO1WhMmO5N5U0XnTw9jdlXTvnvTfrPCM0dth/gBgPj4+LLVq1c3BYAvvvgi/PDhw6EAEBkZWXHhwoUr65SUlAQ2b968PDQ0VFesWBFx5MiRWl/sAgBnz54NbNOmzWUAmDdvXvOq+U2aNKlwjq13795nZ8+e3eKdd975NiAgAF9++WWj2267rcaKZsCAAWfvueeeDr/73e+Ox8bG2o8fPx5YUlISaLPZLl+tzenTpwMbNWpUGR0dXVFYWBi0fv36yDvuuONcSUlJwPnz5wOGDx9e0qtXr/M2m60jAPTo0ePszJkzb5w+ffpxwHE1qWvFtXPnzpCUlJSytLS07w4cOBC6devWRs7JLjIysuL8+fOBVe+rG97o2LFjQQkJCZenTJly8sKFCwGbN28OB+D5ZCciAwD8D4BAAPNVdYbL8skAxgKwAzgB4L9U9ZCxbDSAJ42mL6jqAjNjJaIGFNjMXu+3HtSgtkP8AMCDDz54etGiRc2Sk5NTb7755gvt2rW7BAAtW7asyMzMPJ+YmJjWp0+fkmefffbYwIEDO6Snp6ekpaVdTEhI+MFQPjV5/PHHj40dOzZhzpw5LXv27Hmlghk4cOC5WbNmtUpOTk6dMmXK0RkzZhwZN25cXHJycqqqSps2bcpqukUCADIzMy89+eSTh/v27WurrKxEcHCwzpkz51vnZFddm759+15IT0+/mJiYmBYXF1eWmZl5HgDOnDkTOGjQoA5Vle4LL7xQCABvvPFG4dixY+NsNltqRUWFdOvW7dytt976rXM8CxcujF62bFmzoKAgjYmJKX/ppZeOOC/Pzs4uDQoK0qSkpNSRI0eefPLJJ79zN7zRmjVrIubMmdMyKChIw8PDKxYtWlTrIZhMG+JHRAIB7AFwJ4AiAJsA3KeqO53a9AbwtapeFJEJAHqp6nARiQaQAyALgALIBZCpqqer+zxvHOLHm6o6gJVddbx1v72xuuMQP+TtPDHETzaAfap6QFUvA1gM4G7nBqq6TlWrrpDaAKCNMd0fwCeqespIcJ8AGGBirEREZGFmdmPGAih0el8EoFsN7R8C8HEN68a6riAi4wCMA4C4uLbXE2u98raKjnwTz98R1R8zKzt3VzC57TMVkfvh6LJ8uS7rquobqpqlqlkxMc2uOVAiIrI2Myu7IgDO5VYbAEdcG4lIPwBPALhDVcuc1u3lsu56U6KsR6zoyAys8Iiun5nJbhOARBFJAHAYwAgAI50biEhnAPMADFBV58f2rAHwexGpui/mRwB+a2Ks18XXkpy3XpBhNl/fbyY9omtnWrJTVbuITIIjcQUCeFtV80XkeQA5qrocjm7LxgCWiQgAfKuqQ1T1lIhMhyNhAsDzqmra07CJiMjaTLv1oKF54tYDX6voyFo8UeHV360HZzMArccv22IHmtQ4xE9gYGBmYmLilZud77nnnlO///3vj1XXftq0aS1nzJhR7fK6mjNnTjPnZ156Um2GNPJV1d16wCeoEJEH1Geiq932QkNDK6se4lwbc+bMaeUu2VVWVkJVERgY6G41r2G32xEUVD+Huby8HMHBwR6N4XpxiJ9rwGFZyBvw9/D6FRcXB8bHx6dv27YtFAAGDx6cMHv27OYTJ06MLSsrC0hOTk4dMmRIQkFBQUj79u3T7r///ri0tLTU/fv3h1Q3DM6SJUsiExIS0jIzM5PGjBnTtnfv3h1cP3fPnj0h3bt3t9lsttTu3bvb9u7dGwIAQ4cOjR81alRct27dbG3atOm4atWqxsOGDYtv37592tChQ+Or1v/b3/7W5Oabb05OTU1NGThwYPuSkpIAwDEw62OPPdYqMzMz6e23347Kzs5O+uyzz8IB4OjRo0GxsbEdXWNxN9QQ4KhEBw4c2L5Pnz4devbsaXNdr1+/fjelpaWldOjQIW3WrFlXHncWHh7e+Ve/+lXrTp06Jf/zn/9s/Pnnn4d37do1KS0tLaVHjx6Jhw4dCgaA2bNnN09PT09JSkpK7d+//011GcHgWjDZEZFfqEpeVa8333wzqlmzZhV/+MMfvh09enTCG2+8EXXmzJmgKVOmnJw7d+7hqkpw+fLl3wDAwYMHw37+858X79q1a6fNZrv8yiuvHM7Ly9u1e/fu/C+//DLi66+/bnTx4kX55S9/2e7jjz/em5ubW1Bc7P6RaOPHj48bOXJk8Z49e3YOHz68eMKECVeuXC8pKQn697//vWfGjBmFw4cPT/zNb35zfO/evfm7d+9u9NVXXzU6evRo0O9///tWn3322Z6dO3fu6tKly8Xp06dfGX8vLCysMjc3t2DcuHHVPnHKWdVQQ7t27dr5zDPPHJ46dWrVwz2wefPmxu+99943GzZs2OO63qJFiw7m5+fv2rp168558+a1OHbsWCAAlJaWBqSnp5du3759d69evS48+uijcR999NH+/Pz8XaNHjz752GOPxQLAqFGjTufl5e0qKCjYmZSUVDpnzpzmrp9Rn7yjviQiMll13Zg//elPzy5dujRq6tSp7XJzc/PdrQsArVq1uty3b98LVe/dDYNTUVGBtm3bllUNtTNixIhT8+fPj3Hd1pYtW274+OOP9wPAhAkTTj333HNXEsxdd911pmo4oGbNmpU7DxW0f//+0EOHDoXs378/LDs7OxkAysvLper5lYDj2Z51OS41DTXUs2fPsy1atKhwt97MmTNbrFq1qikAHDt2LDg/Pz+sZcuWFwIDAzFmzJjTALB9+/bQvXv3NurTp48NcHQBx8TElANAbm5uo6effjr23LlzgRcuXAi84447SuoSd10x2RGRX6uoqMCePXvCQkNDK0+ePBl00003lbtr5zyETnXD4NTHBX/OwwG5DhVkt9slMDBQe/TocXbFihVuH4IcERFxJc6goCCtqHDkqosXL7odqqimoYac99nZypUrIz799NOInJyc3REREZXZ2dlJTsMbVVadp1NV6dChQ+nWrVt3u25j3LhxCe+///6+7t27l86ZM6fZp59+GnH1o3PtmOzq6HrPkfjiA4nrAx9C7V597Hf/6V/w3rvr8Pzzz7ew2WyXXnzxxcMPPfRQfG5u7u7Q0FANCgrSsrIyCQ0N/UEGq24YnIyMjEuFhYWhBQUFIUlJSZeXLFkS7e4zO3fufGH+/PlRjzzyyKl58+ZFZ2VlnXfXzp1evXpdmDJlSlxeXl5oenp62blz5wK++eab4E6dOv1gvLu2bduWbdy48YbevXtfXLRoUZS77VU31FBNzpw5ExgZGVkRERFRuWXLlrBt27bd4K5dp06dLp06dSpo7dq1N/Tr1+9CWVmZ7NixIzQrK+vSxYsXA+Li4srLyspk8eLF0a1atXL7JaO+8JwdEXmA1Dgkjxnbcz1nN3HixNjt27eHLly4sPncuXMLBwwYcP6WW245N23atFYAMGrUqBMpKSmpQ4YMSXDdVvfu3UurhsF54IEH4qu6ERs3bqyvvPLKoQEDBiRmZmYm3XjjjeURERE/6AZ87bXXvl24cGFzm82W+t577zWbO3duoWub6rRu3do+b968gyNGjGhvs9lSMzMzk3fs2BHmru20adOOv/XWWzGdO3dOPnnypNvi5vHHHz/27LPPtunSpUtyVRV4NUOHDi2x2+1is9lSf/e737XOyMi44K5dWFiYLl68eP+0adPaJCUlpaalpaV++umnjY3YjmRnZ6f07NnTlpiYWKfhka4F77Orpfq66o2VnXv+uu/1vd9mV3gc4ufqSkpKAiIjIysrKyvx4IMPxiUmJl565plnvrv6mlQfeJ/dNeKl3eRL+Egxz/vjH//Y/L333mteXl4uaWlpFydPnuwXSd7bMdkREdWjZ5555jtWct6H5+yIqCFUVlZWur0akKi+GL9jbq8gZbIjooaQd+LEiUgmPDJLZWWlnDhxIhJAnrvl7Mashtnn6qx8QUZN/HW/gYbdd287d2e328ceO3Zs/rFjx9LBL9lkjkoAeXa7fay7hUx2RGS6zMzM7wAM8XQc5L9464ELXn1JVlRfFd613npA5GnsTiAiIstjsiMiIstjsiMiIsvjBSoGnqsjK/O2qzOJGhqTnR/h8ynds/J+E5EDuzGJiMjymOzALkzyH/xdJ3/FZEdERJbHZEdERJbHZEdERJbn11dj8vwF+SPehkD+iJUdERFZnl9Xdv7KX+8r89f9JiJWdkRE5AdY2fkRf65s/HnfichPkx0vTCHihSrkX9iNSURElsdkR0RElsdkR0REludX5+y84Vydvw41w+GF3POG/ea5O/IHrOyIiMjymOyIiMjyTE12IjJARApEZJ+ITHOz/HYR2SwidhG512VZhYhsNV7LzYyTiIiszbRzdiISCOBVAHcCKAKwSUSWq+pOp2bfAhgD4DE3myhV1ZvNio+IiPyHmReoZAPYp6oHAEBEFgO4G8CVZKeqB41llSbGQUREfs7MbsxYAIVO74uMebUVJiI5IrJBRH7iroGIjDPa5Jw4UXw9sRIRkYWZWdmJm3lah/XjVPWIiLQH8C8R2aGq+7+3MdU3ALwBAFlZnavdtjfccuDKGy459wR/3W/A+/edtyCQlZlZ2RUBaOv0vg2AI7VdWVWPGP8eALAeQOf6DI6IiPyHmZXdJgCJIpIA4DCAEQBG1mZFEYkCcFFVy0SkOYDbAPy3aZE2IG//dm8Wf91vwL/3nchbmFbZqaodwCQAawDsArBUVfNF5HkRGQIAItJVRIoADAMwT0TyjdVTAOSIyDYA6wDMcLmKk4iIqNZMfVyYqq4GsNpl3tNO05vg6N50Xe8rAB3NjI2IiPwHn6BCRESWx2RHRESWx2RHRETlfc3CAAANdklEQVSWZ+khfqq7v87bh1wxC4fZcY/77TJ/+he8144sh5UdERFZHpMdERFZHpMdERFZHpMdERFZHpMdERFZHpMdERFZnqVvPbgaK19yXhPut//x530nAljZERGRH/DLys5fv+X6634D/rvv/rrfRK5Y2RERkeUx2RERkeUx2RERkeUx2RERkeXV+gIVEYkC0NZ5HVXdbEZQVsan77vnr/tu5f0m8ia1SnYiMh3AGAD7AagxWwH0MScsIiKi+lPbyu5nAG5S1ctmBkNERGSG2p6zywPQ1MxAiIiIzFLbyu4lAFtEJA9AWdVMVR1iSlT1oLpRyono6qr+/3DEcrKK2ia7BQBmAtgBoNK8cIiIiOpfbZPdSVWdY2ok9WzNUz1Y3RFdI1Z0ZDW1TXa5IvISgOX4fjcmbz0gIiKvV9tk19n4t5vxrwBoD6BdvUfkJ/z1/ip/3W/Av/edyNNqm+zWu7wPBJBQv6EQERGZo7bJ7rzTdDCA2wEsqf9wrM9fv937634D/r3vRN6iVslOVWc7vxeR/wawyZSIiIiI6tm1Pgg6CsCx+gyEiIjILLV9NuYO/OeZmAIgHkBx1XxV7WROeERERNevtufsan5kPRERkRer7Tm7Q2YH0pD8dcgVDrPjHvebyPo4eCsREVkekx0REVkekx0REVmeqclORAaISIGI7BORaW6W3y4im0XELiL3uiwbLSJ7jddoM+MkIiJrMy3ZiUgggFcBDASQCuA+EUl1afYtgDEA/uqybjSAZ+B4Fmc2gGdEJMqsWImIyNrMrOyyAexT1QOqehnAYgB3OzdQ1YOquh0/HCOvP4BPVPWUqp4G8AmAASbGSkREFlbb++yuRSyAQqf3RfjPqAnXsm6sayMRGQdgHADExbWtc4D+euk199v/+PO+EwHmVnbiZp66mXfN66rqG6qapapZMTHN6hQcERH5DzMruyIAzuVWGwBH6rBuL5d119c1gKrRll1HLPfXb7n+ut+A/+77tew3RyknKzKzstsEIFFEEkQkBMAIOEY6r401AH4kIlHGhSk/MuYRERHVmWnJTlXtACbBkaR2AViqqvki8ryIDAEAEekqIkUAhgGYJyL5xrqnAEyHI2FuAvC8MY+IiKjOzOzGhKquBrDaZd7TTtOb4OiidLfu2wDeNjM+IiLyD3yCChERWR6THRERWZ6p3Zj0Q/465AqHF3LPyvtN5E1Y2RERkeX5RWVX3f12RPQfvL+OrIyVHRERWR6THRERWR6THRERWR6THRERWZ5fXKDijfz1knN/3W/Av/edyNNY2RERkeWJam2HmPNuWVmdNSdnfa3a8hYEov+oyy0HIk1zVTXLxHCITMHKjoiILI/JjoiILI/JjoiILM8vr8bk48OI+Hgw8i9+mez8FUcecM/K+01EDuzGJCIiy2OyIyIiy2OyIyIiy/Prc3a8UIX8ES9MIX/Eyo6IiCyPyY6IiCyPyY6IiCzPr8/ZVVnzVA+/Om/nr/eV+et+O+P5OvJXrOyIiMjyWNn5EX+ubPx534mIye4K3oZAVsbuS/J37MYkIiLLY7IjIiLLY7IjIiLL4zk7F2afu/PXoWY4vJB7Zu83z9URObCyIyIiy2NlVw1enUm+jBUd0fexsiMiIstjsiMiIstjsiMiIssz9ZydiAwA8D8AAgHMV9UZLstDAbwLIBNAMYDhqnpQROIB7AJQYDTdoKrjzYy1Ojx3R76E5+qI3DMt2YlIIIBXAdwJoAjAJhFZrqo7nZo9BOC0qnYQkREAZgIYbizbr6o3mxVfXdV30rPypfY18df9BszddyY5opqZ2Y2ZDWCfqh5Q1csAFgO426XN3QAWGNPvA+grImJiTERE5IdEVc3ZsMi9AAao6ljj/QMAuqnqJKc2eUabIuP9fgDdADQGkA9gD4CzAJ5U1c/dfMY4AOMAIC6ubeahQztM2RdX7NIkb9KQVZ1I01xVzWqwDySqJ2ZWdu4qNNfMWl2bowDiVLUzgMkA/ioiTX7QUPUNVc1S1ayYmGbXHTAREVmTmcmuCEBbp/dtABypro2IBAGIBHBKVctUtRgAVDUXwH4ANhNjJSIiCzMz2W0CkCgiCSISAmAEgOUubZYDGG1M3wvgX6qqIhJjXOACEWkPIBHAARNjJSIiCzPtakxVtYvIJABr4Lj14G1VzReR5wHkqOpyAG8BWCgi+wCcgiMhAsDtAJ4XETuACgDjVfWUWbHWFW9HIG/AKzCJas/U++xUdTWA1S7znnaavgRgmJv1PgDwgZmxERGR/+CDoK+Dr1V4HGbHPV/bb1Z0RHXHx4UREZHlsbKrB75W4ZFvYkVHdO2Y7OoRkx6ZgUmO6PqxG5OIiCyPlZ0JWOFRfWBFR1R/WNkREZHlsbIz0Zqnenhldedrl9rXF1/ab1Z1RPWLlR0REVmeaUP8NLSsrM6ak7Pe02FUyxsrPPI+3l7RcYgf8lWs7IiIyPJ4zq6B8ApNqom3V3REvo6VHRERWR4ruwbm+g2elZ5/YiVH1LCY7DzMX7o3/XnEBWdMckSewW5MIiKyPCY7L8Fv/NbHnzGR5zDZERGR5fGcnRfhxSvWwkqOyHuwsiMiIstjZefF/OVKTathRUfkfVjZERGR5bGy8wFWOpdnxfvpWMkReT9WdkREZHkc4sfH+XKV58v8tZrjED/kq9iN6eOs1MXpzfw1uRFZBbsxiYjI8ljZWYy7CoTVXt2wiiOyHlZ2RERkeazs/ADP69WMlRyR9bGyIyIiy2Nl54dqqmSsWvWxeiPyb6zsiIjI8ljZ0fdcrQLy1sqPlRsR1YSVHRERWR4rO6qT+qqgqipEVmRE1BCY7MgjmOSIqCGZ2o0pIgNEpEBE9onINDfLQ0VkibH8axGJd1r2W2N+gYj0NzNOIiKyNtOSnYgEAngVwEAAqQDuE5FUl2YPATitqh0A/AHATGPdVAAjAKQBGABgrrE9IiKiOjOzsssGsE9VD6jqZQCLAdzt0uZuAAuM6fcB9BURMeYvVtUyVf0GwD5je0RERHVm5jm7WACFTu+LAHSrro2q2kWkBEAzY/4Gl3VjXT9ARMYBGGe8LRNpmlc/oTeo5gBOejqIa+CLcftizIB3xd3O0wEQXQszk524mec6Umx1bWqzLlT1DQBvAICI5PjioJKMu+H4YsyA78ZN5E3M7MYsAtDW6X0bAEeqayMiQQAiAZyq5bpERES1Ymay2wQgUUQSRCQEjgtOlru0WQ5gtDF9L4B/qaoa80cYV2smAEgEsNHEWImIyMJM68Y0zsFNArAGQCCAt1U1X0SeB5CjqssBvAVgoYjsg6OiG2Gsmy8iSwHsBGAH8IiqVlzlI98wa19Mxrgbji/GDPhu3EReQxyFFBERkXXx2ZhERGR5THZERGR5PpHsfPWxY9cat4jEi0ipiGw1Xq97Ucy3i8hmEbGLyL0uy0aLyF7jNdp1XTNdZ9wVTsfa9SIqU9Ui7skislNEtovIP0WkndMyjx1vIp+jql79guPilv0A2gMIAbANQKpLm4kAXjemRwBYYkynGu1DASQY2wn0gbjjAeR56bGOB9AJwLsA7nWaHw3ggPFvlDEd5e1xG8vOe/Hvdm8A4cb0BKffEY8db7748sWXL1R2vvrYseuJ21OuGrOqHlTV7QAqXdbtD+ATVT2lqqcBfALHc00bwvXE7Um1iXudql403m6A455TwLPHm8jn+EKyc/fYMddHh33vsWMAnB87drV1zXI9cQNAgohsEZFPRaSn2cG6xmOoy/Hy9mNdkzARyRGRDSLyk/oNrUZ1jfshAB9f47pEfs0XxrMz/bFjJrmeuI8CiFPVYhHJBPChiKSp6tn6DrKW8Zi97vW63s+OU9UjItIewL9EZIeq7q+n2GpS67hF5H4AWQDuqOu6ROQblZ2vPnbsmuM2ul2LAUBVc+E4r2MzPeLrO17efqyrpapHjH8PAFgPoHN9BleDWsUtIv0APAFgiKqW1WVdInLwhWTnq48du+a4RSSmavw+o9pIhOMCBG+IuTprAPxIRKJEJArAj4x5DeGa4zbiDTWmmwO4DY4n9zSEq8YtIp0BzIMj0X3ntMiTx5vI93j6CpnavAD8GMAeOCqcJ4x5z8PxBwAAwgAsg+MClI0A2jut+4SxXgGAgb4QN4ChAPLhuDpvM4DBXhRzVziqigsAigHkO637X8a+7APwcy871m7jBnArgB3Gsd4B4CEvi3stgOMAthqv5d5wvPniy9defFwYERFZni90YxIREV0XJjsiIrI8JjsiIrI8JjsiIrI8JjsiIrI8JjsiIrI8JjsiIrI8JjsylTE2X57T+8dE5FkRWS8ifxCRz0Rkl4h0FZG/GWOzveDJmInIenzhQdBkXZdV9XYR+SWAjwBkwvFM0/0i8gc1ng9KRHS9WNmRJ1U9B3IHHI/vOqqOBx0fwPcfckxEdF2Y7KghOA9HE+w0XfUE/0qn6ar37HUgonrDZEcNoZ0xkkMAgNsBBHo6ICLyL/z2TA2hGMC7AFrC8RT/B8EvWkTUgDjqAZlKROIBrFTVdA+HQkR+jN+uiYjI8ljZERGR5bGyIyIiy2OyIyIiy2OyIyIiy2OyIyIiy2OyIyIiy/t/Z+hNq/z273cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final dimensions: [43, 79, 115]\n"
     ]
    }
   ],
   "source": [
    "n_sites = 37\n",
    "dim_list = [40, 80, 130]\n",
    "\n",
    "# Diameters that will be optimized by the function:\n",
    "final_dims = []\n",
    "\n",
    "# Instanciation of synapses with testing different diameters:\n",
    "for dim in dim_list :\n",
    "    syn = UBC.Synapse(n_sites=n_sites, dim=dim, area_tot_sites=area_tot_sites, verbose=True)\n",
    "    final_dims.append(syn.dim)\n",
    "    syn.visualize_synapse()\n",
    "\n",
    "print(\"Final dimensions: {}\".format(final_dims))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Generation of stimulation patterns\n",
    "---\n",
    "\n",
    "Now that several synapses have been selected, the next step is to investigate their behavior. For this purpose, they can be submitted to different **stimulation patterns**. \n",
    "\n",
    "A stimulation pattern is a sequence of spikes of the presynaptic neuron (mossy fiber). It describes, in time, the events of glutamate release. Different types of patterns can be tested."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "#### 1. \"Experimental stimulation\" patterns\n",
    "\n",
    "Those patterns consist in one or several spike events (burst), at a fixed frequency. \n",
    "\n",
    "This type of patterns is meant to validate the model, by comparing the shape of the computed responses to real neurons' responses recorded on under artifical stimulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "patt = UBC.Pattern(mode='stim', n_stim=5, dur=0.5, f_stim=150)\n",
    "patt.visualize_patt()\n",
    "\n",
    "patt = UBC.Pattern(mode='stim', n_stim=10, dur=0.5, f_stim=80)\n",
    "patt.visualize_patt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. \"Oscillatory\" patterns\n",
    "\n",
    "Those patterns consist in continuous firing, whose frequency follows a sinusoidal variation. \n",
    "\n",
    "This type of pattern is closer to the in vivo firings of the pre-synaptic neurons. It is the overall variation of firing frequency which constitutes the \"signal\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "patt = UBC.Pattern(mode='oscill', fenv=10)\n",
    "patt.visualize_patt()\n",
    "patt = UBC.Pattern(mode='oscill', fenv=5)\n",
    "patt.visualize_patt()\n",
    "patt = UBC.Pattern(mode='oscill', fenv=1)\n",
    "patt.visualize_patt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. Generating simulations data\n",
    "---\n",
    "\n",
    "Henceforth, let us take a look at the synapse behavior under a stimulation pattern. A **response** features several components:\n",
    "1. The evolution of the **neurotransmitter concentrations(glutamate)** in the synapse. Its variations can be recorder at different locations on the glomerule, to investigate local inhomogeneities (between release sites and non-release areas, between the synapse center and the periphery). The evolution of the glutamate concentration in one voxel is determined by diffusion equations (Fick's laws in discrete time).\n",
    "2. The activation state of the post-synaptic **AMPA receptor**. The AMPA receptor is a ionic channel, whose opening triggers the membrane depolarization. At each moment, the receptor takes one state among those three: open, closed, or desensitised. The probability of being in a state depends on the local concentration of glutamate. At each time, the overall activation in the population of AMPA receptors (at the whole synapse level) is measured by computing the proportion of AMPA receptors in the open state.\n",
    "3. The variations of the post-synaptic **membrane potential**. The depolarization of the UBC depends on the total current intering through its AMPA receptors. The variations of the membrane potential represent the signal transmitted in the post-synaptic neuron."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Stimulating a synapse with a pattern\n",
    "\n",
    "Below is provided an example of procedure in several steps.\n",
    "\n",
    "1. Instanciation of a dictionary of synapses of various diameters.\n",
    "2. Generation of a pattern.\n",
    "3. Running a simulation with this pattern on the synapses.\n",
    "4. Saving the results for future analyses.\n",
    "\n",
    "The project folder contains a directory `UBC_data/` meant to store the results of the computations. If this notebook is run for the first time, the folder is empty for now. During the process, it will be filled with nested sub-directories, corresponding to the different synapses and their responses. Registers will also be created, in order to keep track of all synapses and responses saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Synapses instanciated: dict_keys(['43', '79', '115'])\n"
     ]
    }
   ],
   "source": [
    "n_sites = 37\n",
    "dim_list = [43, 79, 130]\n",
    "# 1. Dictionary of synapses objects:\n",
    "syn = UBC.Synapse.instantiate(dim_list=dim_list, n_sites=n_sites) \n",
    "# 2. Pattern:\n",
    "patt = UBC.Pattern(mode='stim')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "For instance, running the first simulation with the smallest synapse (`syn['743']`) will instantiate a directory for registering all the future synapses. \n",
    "\n",
    "In this file, a line summarizes the properties of `syn['43']`, and attributes it an index (`0`). This index allows to refer to the synapse directory `syn0/`, containing the responses obtained with this synapse. \n",
    "\n",
    "Likewise, inside the directory `syn0/`, each response is registered under an index, reported in a file, which points to the sub-directory containing the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running the simulation. It may take some time.\n",
      "Simulation completed.\n",
      "Creating a register at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/synapses_register.csv.\n",
      "Creating a directory at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/syn0\n",
      "Creating a register at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/syn0/responses_syn0_register.csv.\n",
      "Creating a directory at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/syn0/resp0_syn0\n",
      "Saved: response at index 0 for synapse 0.\n"
     ]
    }
   ],
   "source": [
    "# 3. Simulation:\n",
    "syn['43'].simulate_stimulation(patt)\n",
    "# 4. Saving:\n",
    "syn['43'].save_response()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This procedure can be reproduced for `syn['79']` and `syn['115']`, leading to appending new lines in `register_syn.csv`. It also creates new sub-foders `syn1/` and `syn2/`, including the respective sub-directories for their responses.\n",
    "\n",
    "*Please wait about 2 minutes so that the computations could complete.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running the simulation. It may take some time.\n",
      "Simulation completed.\n",
      "Creating a directory at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/syn1\n",
      "Creating a register at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/syn1/responses_syn1_register.csv.\n",
      "Creating a directory at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/syn1/resp0_syn1\n",
      "Saved: response at index 0 for synapse 1.\n",
      "Running the simulation. It may take some time.\n",
      "Simulation completed.\n",
      "Creating a directory at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/syn2\n",
      "Creating a register at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/syn2/responses_syn2_register.csv.\n",
      "Creating a directory at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/syn2/resp0_syn2\n",
      "Saved: response at index 0 for synapse 2.\n",
      "Indexes of found synapses:[0, 1, 2]\n"
     ]
    },
    {
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       "   Index  dim  n_sites  area_tot_sites           res  border_intra  \\\n",
       "0      0   43       37    2.516000e-11  2.000000e-07             2   \n",
       "1      1   79       37    2.516000e-11  2.000000e-07             2   \n",
       "2      2  115       37    2.516000e-11  2.000000e-07             2   \n",
       "\n",
       "   border_extra  N_AMPA        G_AMPA  E_AMPA     V0          R             C  \n",
       "0             2       3  2.000000e-11       0 -0.075  652000000  1.700000e-11  \n",
       "1             2       3  2.000000e-11       0 -0.075  652000000  1.700000e-11  \n",
       "2             2       3  2.000000e-11       0 -0.075  652000000  1.700000e-11  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syn['79'].simulate_stimulation(patt)\n",
    "syn['79'].save_response()\n",
    "syn['115'].simulate_stimulation(patt)\n",
    "syn['115'].save_response()\n",
    "\n",
    "found, register_syns = UBC.Synapse.search_synapses()\n",
    "register_syns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to save various responses for the same synapse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running the simulation. It may take some time.\n",
      "Simulation completed.\n",
      "Creating a directory at /media/esther/Données/Travail/Stages/PCBS_UBC_synapse/UBC_data/syn0/resp1_syn0\n",
      "Saved: response at index 1 for synapse 0.\n",
      "Indexes of found responses for this synapse:[0, 1]\n"
     ]
    },
    {
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       "      <th>mode</th>\n",
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       "      <th>nit</th>\n",
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       "   Index  mode         tstep    dur    nit  start  n_stim  f_stim  fenv  fmin  \\\n",
       "0      0  stim  1.000000e-07  0.002  20000   1000       1     100   NaN   NaN   \n",
       "1      1  stim  1.000000e-07  0.005  50000   1000       2     500   NaN   NaN   \n",
       "\n",
       "   fmax  \n",
       "0   NaN  \n",
       "1   NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patt2 = UBC.Pattern(mode='stim', n_stim=2, dur=0.005, f_stim=500)\n",
    "syn['43'].simulate_stimulation(patt2)\n",
    "syn['43'].save_response()\n",
    "\n",
    "found, register_responses = syn['43'].search_responses()\n",
    "register_responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Loading previousely saved data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further analysis with multiple syapses, it could be useful to retrieve a previousely saved synapse and a particular response.\n",
    "\n",
    "The register of all synapses and the register of a synapse's responses can be filtered according to specific criteria.\n",
    "For instance, a synapse with a specific diameter of interest can be recovered. \n",
    "Then, a response featuring can one single stimulation spike can be looked for in the synapse register of responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indexes of found synapses:[0]\n",
      "<UBC_synapse.synapse_model.Synapse object at 0x7f6e84247890>\n",
      "Indexes of found responses for this synapse:[0]\n",
      "Response retrieved.\n"
     ]
    }
   ],
   "source": [
    "found_syn, _ = UBC.Synapse.search_synapses(dim=43)\n",
    "syn_backup = UBC.Synapse.retrieve_synapse(found_syn[0])\n",
    "print(syn_backup)\n",
    "\n",
    "found_resp, _ = syn_backup.search_responses(n_stim=1)\n",
    "syn_backup.retrieve_response(found_resp[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV. Visualizing the results\n",
    "\n",
    "Below are displayed the results for two synapses for a simple stimulation event (\"small\": diameter 43 px, \"large\": diameter 115 px):\n",
    "1. Evolution of the glutamate concentrations at four points of interest.\n",
    "2. Evolution of proportion of the opened AMPA receptors on the overall glomerule.\n",
    "3. Evolution of the membrane potential of the UBC.\n",
    "\n",
    "These time series highlight different behaviors according to the size of the synapse. In a larger synapse, the responses are more heterogeneous between locations (center vs. periphery). The AMPA opening reaches its maximum later, which results in a delayed signal transmission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response retrieved.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "syn['43'].retrieve_response(0)\n",
    "\n",
    "fig, axes = plt.subplots(3, 2, figsize=(15,15))\n",
    "\n",
    "axes[0, 0].set_title(\"Glutamate concentrations - Small synapse\")\n",
    "axes[0, 0].plot(syn['43'].resp['glus'][0], label=\"Center - Inside synaptic site\")\n",
    "axes[0, 0].plot(syn['43'].resp['glus'][1], label=\"Center - Outside synaptic site\")\n",
    "axes[0, 0].plot(syn['43'].resp['glus'][2], label=\"Periphery - Inside synaptic site\")\n",
    "axes[0, 0].plot(syn['43'].resp['glus'][3], label=\"Periphery - Outside synaptic site\")\n",
    "axes[0, 0].set_xlabel(\"Iterations\")\n",
    "axes[0, 0].legend()\n",
    "\n",
    "axes[1, 0].set_title(\"AMPA receptor opening - Small synapse\")\n",
    "axes[1, 0].plot(syn['43'].resp['AMPAtot'], label=\"AMPA activation\")\n",
    "axes[1, 0].set_xlabel(\"Iterations\")\n",
    "axes[1, 0].legend()\n",
    "\n",
    "axes[2, 0].set_title(\"UBC's membrane potential - Small synapse\")\n",
    "axes[2, 0].plot(syn['43'].resp['V'], label=\"UBC's membrane potential\", color='orange')\n",
    "axes[2, 0].set_xlabel(\"Iterations\")\n",
    "axes[2, 0].legend()\n",
    "\n",
    "axes[0, 1].set_title(\"Glutamate concentrations - Large synapse\")\n",
    "axes[0, 1].plot(syn['115'].resp['glus'][0], label=\"Center - Inside synaptic site\")\n",
    "axes[0, 1].plot(syn['115'].resp['glus'][1], label=\"Center - Outside synaptic site\")\n",
    "axes[0, 1].plot(syn['115'].resp['glus'][2], label=\"Periphery - Inside synaptic site\")\n",
    "axes[0, 1].plot(syn['115'].resp['glus'][3], label=\"Periphery - Outside synaptic site\")\n",
    "axes[0, 1].set_xlabel(\"Iterations\")\n",
    "axes[0, 1].legend()\n",
    "\n",
    "axes[1, 1].set_title(\"AMPA receptor opening - Large synapse\")\n",
    "axes[1, 1].plot(syn['115'].resp['AMPAtot'], label=\"AMPA activation\")\n",
    "axes[1, 1].set_xlabel(\"Iterations\")\n",
    "axes[1, 1].legend()\n",
    "\n",
    "axes[2, 1].set_title(\"UBC's membrane potential - Large synapse\")\n",
    "axes[2, 1].plot(syn['115'].resp['V'], label=\"UBC's membrane potential\", color='orange')\n",
    "axes[2, 1].set_xlabel(\"Iterations\")\n",
    "axes[2, 1].legend()\n",
    "\n",
    "plt.show()"
   ]
  }
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