plot_evolution_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-01-15T15:10:04.704167879Z",
     "start_time": "2024-01-15T15:10:04.700332976Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "from models import MN_neuron_sp\n",
    "import os\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import json\n",
    "from tbparse import SummaryReader\n",
    "from pathlib import Path\n",
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def get_event_files(path):\n",
    "    event_files = []\n",
    "    for file in glob.iglob(path + \"/**/events.out.tfevents*.0\", recursive=True):\n",
    "        print(file)\n",
    "        event_files.append(file)\n",
    "    return event_files"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T15:10:04.719366691Z",
     "start_time": "2024-01-15T15:10:04.707663424Z"
    }
   },
   "id": "c75cec3d0fe90c0f",
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def extract_data_from_tensorboard(path):\n",
    "    path_above = '/'.join(path.split('/')[:-1])\n",
    "    reader = SummaryReader(path_above)\n",
    "    df_scalar = reader.scalars\n",
    "    df_hparam = reader.hparams\n",
    "    if 'Braille' in path:\n",
    "        name = 'braille'\n",
    "    elif 'compressed' in path:\n",
    "        name = 'mnist_compressed'\n",
    "    elif 'mnist' in path:\n",
    "        name = 'mnist'\n",
    "    else:\n",
    "        raise ValueError('No name found')\n",
    "    \n",
    "    # print(df_hparam)\n",
    "    df_scalar['simulation'] = [name]*df_scalar.shape[0]\n",
    "    # print(df_hparam)\n",
    "    # print(path_above)\n",
    "    seed_loc = df_hparam['tag'] == 'seed'\n",
    "    \n",
    "    if (name == 'braille') and (float(df_hparam.loc[df_hparam['tag'] == 'no_train_weights']['value']) == 1.0):\n",
    "        # \n",
    "        # train_weight_loc = df_hparam['tag'] == 'no_train_weights'\n",
    "        # # print(df_hparam.loc[train_weight_loc]['value'])\n",
    "        # if float(df_hparam.loc[train_weight_loc]['value']) == 1.0:\n",
    "            df_scalar['trained'] = [False]*df_scalar.shape[0]\n",
    "    else:\n",
    "        df_scalar['trained'] = [True]*df_scalar.shape[0]\n",
    "    print(name,path_above.split('/')[-1],float(df_hparam.loc[seed_loc]['value']),df_scalar['trained'].unique(),df_scalar.shape[0])\n",
    "    # print(df_hparam.loc[seed_loc])\n",
    "    df_scalar['seed'] = [int(df_hparam.loc[seed_loc]['value'])]*df_scalar.shape[0]\n",
    "\n",
    "    # for tag in df_scalar['tag'].unique():\n",
    "    #     if len(df_scalar.loc[df_scalar['tag']==tag]['step']) != 300:\n",
    "    #         print(name,int(df_hparam.loc[seed_loc]['value']),tag,len(df_scalar.loc[df_scalar['tag']==tag]['step']))\n",
    "        \n",
    "    # print([df_hparam.loc[seed_loc]['value']]*df_scalar.shape[0])\n",
    "    df_hparam = pd.concat([df_hparam,pd.DataFrame({'tag':['name'],'value':[name]})], ignore_index=True)\n",
    "    # df_hparam.loc[len(df_hparam.index)-1]['tag'] =  'name'\n",
    "    # df_hparam.loc[len(df_hparam.index)-1]['value'] = name\n",
    "    df_hparam = pd.concat([df_hparam,pd.DataFrame({'tag':['path'],'value':[path_above]})], ignore_index=True)\n",
    "    # print(df_scalar)\n",
    "\n",
    "    return df_scalar, df_hparam"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T15:10:04.732159270Z",
     "start_time": "2024-01-15T15:10:04.720302492Z"
    }
   },
   "id": "4a51ee2c40aa3668",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_07-58-50_v100gpu9GR_MNIST_optim_seed-12/events.out.tfevents.1701845930.v100gpu9.661393.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_14-27-13_a100gpu2GR_MNIST_optim_seed-4/events.out.tfevents.1701782833.a100gpu2.1447842.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_03-37-56_v100gpu29GR_MNIST_optim_seed-10/events.out.tfevents.1701830276.v100gpu29.4061382.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_09-32-00_v100gpu12GR_MNIST_optim_seed-20/events.out.tfevents.1701851520.v100gpu12.4046862.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_16-37-34_v100gpu27GR_MNIST_optim_seed-7/events.out.tfevents.1701790654.v100gpu27.3350906.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_14-59-48_a100gpu2GR_MNIST_optim_seed-5/events.out.tfevents.1701784788.a100gpu2.1458614.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_14-10-08_a100gpu2GR_MNIST_optim_seed-2/events.out.tfevents.1701781808.a100gpu2.1446843.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec13_11-30-01_a100gpu1GR_MNIST_optim_seed-1/events.out.tfevents.1702463401.a100gpu1.94359.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-34-35_a100gpu1GR_MNIST_optim_seed-16/events.out.tfevents.1701848075.a100gpu1.1609174.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-32-40_v100gpu16GR_MNIST_optim_seed-15/events.out.tfevents.1701847960.v100gpu16.2588244.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_14-20-49_a100gpu2GR_MNIST_optim_seed-3/events.out.tfevents.1701782449.a100gpu2.1447590.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-26-08_a100gpu1GR_MNIST_optim_seed-14/events.out.tfevents.1701847568.a100gpu1.1608914.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_03-28-14_a100gpu1GR_MNIST_optim_seed-8/events.out.tfevents.1701829694.a100gpu1.1605167.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-43-05_a100gpu1GR_MNIST_optim_seed-17/events.out.tfevents.1701848585.a100gpu1.1609453.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_16-26-40_a100gpu2GR_MNIST_optim_seed-6/events.out.tfevents.1701790000.a100gpu2.1485866.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_09-05-56_v100gpu26GR_MNIST_optim_seed-0/events.out.tfevents.1701763556.v100gpu26.2956108.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-45-15_v100gpu11GR_MNIST_optim_seed-18/events.out.tfevents.1701848715.v100gpu11.1073052.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-51-28_a100gpu1GR_MNIST_optim_seed-19/events.out.tfevents.1701849088.a100gpu1.1610020.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-15-47_v100gpu14GR_MNIST_optim_seed-13/events.out.tfevents.1701846947.v100gpu14.2118834.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_03-31-25_a100gpu1GR_MNIST_optim_seed-9/events.out.tfevents.1701829885.a100gpu1.1605436.0\n",
      "Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_05-36-53_v100gpu26GR_MNIST_optim_seed-11/events.out.tfevents.1701837413.v100gpu26.3051641.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-32-46_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701433966.a100gpu1.840396.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Nov30_18-14-14_v100gpu3ALIF_WITH_GR_L1/events.out.tfevents.1701364454.v100gpu3.488645.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-42-16_v100gpu9ALIF_WITH_GR_L1/events.out.tfevents.1701434536.v100gpu9.462152.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_14-02-30_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701435750.a100gpu1.860637.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Nov30_18-14-12_v100gpu2ALIF_WITH_GR_L1/events.out.tfevents.1701364452.v100gpu2.4077031.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/ccc/events.out.tfevents.1701387619.a100gpu1.267457.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_00-40-19_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701387619.a100gpu1.267455.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Nov30_18-26-45_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701365205.a100gpu1.233231.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_22-04-37_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701464677.a100gpu1.1043235.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-44-55_v100gpu15ALIF_WITH_GR_L1/events.out.tfevents.1701434695.v100gpu15.3266997.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Nov30_19-22-53_v100gpu9ALIF_WITH_GR_L1/events.out.tfevents.1701368573.v100gpu9.203421.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-38-20_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701434300.a100gpu1.844293.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/bbb/events.out.tfevents.1701387619.a100gpu1.267456.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_03-26-44_v100gpu3ALIF_WITH_GR_L1/events.out.tfevents.1701397604.v100gpu3.524652.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_03-16-41_v100gpu2ALIF_WITH_GR_L1/events.out.tfevents.1701397001.v100gpu2.4112872.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_04-09-08_v100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701400148.v100gpu1.1876832.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec02_04-18-05_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701487085.a100gpu1.1189739.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Nov30_18-14-15_v100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701364455.v100gpu1.1840280.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_22-33-25_v100gpu9ALIF_WITH_GR_L1/events.out.tfevents.1701466405.v100gpu9.497943.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_23-41-29_a100gpu1ALIF_WITH_GR_L1_2/events.out.tfevents.1701470489.a100gpu1.1074954.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec02_04-30-28_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701487828.a100gpu1.1195662.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_07-25-40_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701411940.a100gpu1.625865.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_07-05-25_a100gpu1ALIF_WITH_GR_L1_2/events.out.tfevents.1701410725.a100gpu1.615309.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/aaa/events.out.tfevents.1701387619.a100gpu1.267493.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_22-16-58_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701465419.a100gpu1.1047801.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_00-57-26_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701388646.a100gpu1.277356.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_07-05-25_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701410725.a100gpu1.615308.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_21-56-22_v100gpu3ALIF_WITH_GR_L1/events.out.tfevents.1701464182.v100gpu3.755273.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_04-31-23_v100gpu9ALIF_WITH_GR_L1/events.out.tfevents.1701401483.v100gpu9.239431.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_11-00-52_v100gpu13ALIF_WITH_GR_L1/events.out.tfevents.1701424852.v100gpu13.363855.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-54-27_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701435267.a100gpu1.854994.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_21-37-26_v100gpu12ALIF_WITH_GR_L1/events.out.tfevents.1701463046.v100gpu12.3857430.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_07-07-26_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701410846.a100gpu1.617872.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec02_00-58-44_v100gpu10ALIF_WITH_GR_L1/events.out.tfevents.1701475124.v100gpu10.864573.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-38-20_a100gpu1ALIF_WITH_GR_L1_2/events.out.tfevents.1701434300.a100gpu1.844294.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_08-28-25_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701415705.a100gpu1.652483.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_23-41-29_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701470489.a100gpu1.1074953.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_22-23-16_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701465796.a100gpu1.1050541.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_09-08-53_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701418133.a100gpu1.676343.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_22-39-36_v100gpu15ALIF_WITH_GR_L1/events.out.tfevents.1701466776.v100gpu15.3302815.0\n",
      "Training Neuron Parameters/runs_gr_Braille_converging/Dec01_07-03-25_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701410605.a100gpu1.613603.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_11-02-38_urg02GR_MNIST_optim_seed-0/events.out.tfevents.1701684158.urg02.2590907.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_11-34-00_urg02GR_MNIST_optim_seed-1/events.out.tfevents.1701686040.urg02.2591927.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_18-42-18_urg04GR_MNIST_optim_seed-14/events.out.tfevents.1701711738.urg04.1015239.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_11-34-41_urg04GR_MNIST_optim_seed-4/events.out.tfevents.1701686081.urg04.1010926.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_21-45-16_urg03GR_MNIST_optim_seed-18/events.out.tfevents.1701722716.urg03.1760160.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_13-36-34_urg01GR_MNIST_optim_seed-5/events.out.tfevents.1701693394.urg01.2678101.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_16-40-09_urg03GR_MNIST_optim_seed-11/events.out.tfevents.1701704409.urg03.1758091.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_16-07-55_urg02GR_MNIST_optim_seed-8/events.out.tfevents.1701702475.urg02.2593819.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_15-28-53_urg01GR_MNIST_optim_seed-7/events.out.tfevents.1701700133.urg01.2679437.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_21-10-57_urg02GR_MNIST_optim_seed-16/events.out.tfevents.1701720657.urg02.2596775.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_16-40-09_urg03GR_MNIST_optim_seed-10/events.out.tfevents.1701704409.urg03.1757271.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_11-34-36_urg03GR_MNIST_optim_seed-3/events.out.tfevents.1701686076.urg03.1755157.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_16-38-36_urg02GR_MNIST_optim_seed-9/events.out.tfevents.1701704316.urg02.2594841.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_16-43-14_urg04GR_MNIST_optim_seed-12/events.out.tfevents.1701704594.urg04.1013889.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_20-37-15_urg01GR_MNIST_optim_seed-15/events.out.tfevents.1701718635.urg01.2682394.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_11-34-36_urg03GR_MNIST_optim_seed-2/events.out.tfevents.1701686076.urg03.1755156.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_21-41-08_urg02GR_MNIST_optim_seed-17/events.out.tfevents.1701722468.urg02.2597794.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_18-41-01_urg01GR_MNIST_optim_seed-13/events.out.tfevents.1701711661.urg01.2681060.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_13-36-36_urg04GR_MNIST_optim_seed-6/events.out.tfevents.1701693396.urg04.1012277.0\n",
      "Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_21-51-39_urg03GR_MNIST_optim_seed-19/events.out.tfevents.1701723099.urg03.1761093.0\n",
      "['Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_07-58-50_v100gpu9GR_MNIST_optim_seed-12/events.out.tfevents.1701845930.v100gpu9.661393.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_14-27-13_a100gpu2GR_MNIST_optim_seed-4/events.out.tfevents.1701782833.a100gpu2.1447842.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_03-37-56_v100gpu29GR_MNIST_optim_seed-10/events.out.tfevents.1701830276.v100gpu29.4061382.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_09-32-00_v100gpu12GR_MNIST_optim_seed-20/events.out.tfevents.1701851520.v100gpu12.4046862.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_16-37-34_v100gpu27GR_MNIST_optim_seed-7/events.out.tfevents.1701790654.v100gpu27.3350906.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_14-59-48_a100gpu2GR_MNIST_optim_seed-5/events.out.tfevents.1701784788.a100gpu2.1458614.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_14-10-08_a100gpu2GR_MNIST_optim_seed-2/events.out.tfevents.1701781808.a100gpu2.1446843.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec13_11-30-01_a100gpu1GR_MNIST_optim_seed-1/events.out.tfevents.1702463401.a100gpu1.94359.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-34-35_a100gpu1GR_MNIST_optim_seed-16/events.out.tfevents.1701848075.a100gpu1.1609174.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-32-40_v100gpu16GR_MNIST_optim_seed-15/events.out.tfevents.1701847960.v100gpu16.2588244.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_14-20-49_a100gpu2GR_MNIST_optim_seed-3/events.out.tfevents.1701782449.a100gpu2.1447590.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-26-08_a100gpu1GR_MNIST_optim_seed-14/events.out.tfevents.1701847568.a100gpu1.1608914.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_03-28-14_a100gpu1GR_MNIST_optim_seed-8/events.out.tfevents.1701829694.a100gpu1.1605167.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-43-05_a100gpu1GR_MNIST_optim_seed-17/events.out.tfevents.1701848585.a100gpu1.1609453.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_16-26-40_a100gpu2GR_MNIST_optim_seed-6/events.out.tfevents.1701790000.a100gpu2.1485866.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec05_09-05-56_v100gpu26GR_MNIST_optim_seed-0/events.out.tfevents.1701763556.v100gpu26.2956108.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-45-15_v100gpu11GR_MNIST_optim_seed-18/events.out.tfevents.1701848715.v100gpu11.1073052.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-51-28_a100gpu1GR_MNIST_optim_seed-19/events.out.tfevents.1701849088.a100gpu1.1610020.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_08-15-47_v100gpu14GR_MNIST_optim_seed-13/events.out.tfevents.1701846947.v100gpu14.2118834.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_03-31-25_a100gpu1GR_MNIST_optim_seed-9/events.out.tfevents.1701829885.a100gpu1.1605436.0', 'Training Neuron Parameters/runs_michele_mnist_compressed/Dec06_05-36-53_v100gpu26GR_MNIST_optim_seed-11/events.out.tfevents.1701837413.v100gpu26.3051641.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-32-46_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701433966.a100gpu1.840396.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Nov30_18-14-14_v100gpu3ALIF_WITH_GR_L1/events.out.tfevents.1701364454.v100gpu3.488645.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-42-16_v100gpu9ALIF_WITH_GR_L1/events.out.tfevents.1701434536.v100gpu9.462152.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_14-02-30_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701435750.a100gpu1.860637.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Nov30_18-14-12_v100gpu2ALIF_WITH_GR_L1/events.out.tfevents.1701364452.v100gpu2.4077031.0', 'Training Neuron Parameters/runs_gr_Braille_converging/ccc/events.out.tfevents.1701387619.a100gpu1.267457.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_00-40-19_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701387619.a100gpu1.267455.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Nov30_18-26-45_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701365205.a100gpu1.233231.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_22-04-37_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701464677.a100gpu1.1043235.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-44-55_v100gpu15ALIF_WITH_GR_L1/events.out.tfevents.1701434695.v100gpu15.3266997.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Nov30_19-22-53_v100gpu9ALIF_WITH_GR_L1/events.out.tfevents.1701368573.v100gpu9.203421.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-38-20_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701434300.a100gpu1.844293.0', 'Training Neuron Parameters/runs_gr_Braille_converging/bbb/events.out.tfevents.1701387619.a100gpu1.267456.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_03-26-44_v100gpu3ALIF_WITH_GR_L1/events.out.tfevents.1701397604.v100gpu3.524652.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_03-16-41_v100gpu2ALIF_WITH_GR_L1/events.out.tfevents.1701397001.v100gpu2.4112872.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_04-09-08_v100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701400148.v100gpu1.1876832.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec02_04-18-05_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701487085.a100gpu1.1189739.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Nov30_18-14-15_v100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701364455.v100gpu1.1840280.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_22-33-25_v100gpu9ALIF_WITH_GR_L1/events.out.tfevents.1701466405.v100gpu9.497943.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_23-41-29_a100gpu1ALIF_WITH_GR_L1_2/events.out.tfevents.1701470489.a100gpu1.1074954.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec02_04-30-28_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701487828.a100gpu1.1195662.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_07-25-40_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701411940.a100gpu1.625865.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_07-05-25_a100gpu1ALIF_WITH_GR_L1_2/events.out.tfevents.1701410725.a100gpu1.615309.0', 'Training Neuron Parameters/runs_gr_Braille_converging/aaa/events.out.tfevents.1701387619.a100gpu1.267493.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_22-16-58_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701465419.a100gpu1.1047801.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_00-57-26_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701388646.a100gpu1.277356.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_07-05-25_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701410725.a100gpu1.615308.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_21-56-22_v100gpu3ALIF_WITH_GR_L1/events.out.tfevents.1701464182.v100gpu3.755273.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_04-31-23_v100gpu9ALIF_WITH_GR_L1/events.out.tfevents.1701401483.v100gpu9.239431.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_11-00-52_v100gpu13ALIF_WITH_GR_L1/events.out.tfevents.1701424852.v100gpu13.363855.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-54-27_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701435267.a100gpu1.854994.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_21-37-26_v100gpu12ALIF_WITH_GR_L1/events.out.tfevents.1701463046.v100gpu12.3857430.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_07-07-26_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701410846.a100gpu1.617872.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec02_00-58-44_v100gpu10ALIF_WITH_GR_L1/events.out.tfevents.1701475124.v100gpu10.864573.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_13-38-20_a100gpu1ALIF_WITH_GR_L1_2/events.out.tfevents.1701434300.a100gpu1.844294.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_08-28-25_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701415705.a100gpu1.652483.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_23-41-29_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701470489.a100gpu1.1074953.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_22-23-16_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701465796.a100gpu1.1050541.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_09-08-53_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701418133.a100gpu1.676343.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_22-39-36_v100gpu15ALIF_WITH_GR_L1/events.out.tfevents.1701466776.v100gpu15.3302815.0', 'Training Neuron Parameters/runs_gr_Braille_converging/Dec01_07-03-25_a100gpu1ALIF_WITH_GR_L1/events.out.tfevents.1701410605.a100gpu1.613603.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_11-02-38_urg02GR_MNIST_optim_seed-0/events.out.tfevents.1701684158.urg02.2590907.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_11-34-00_urg02GR_MNIST_optim_seed-1/events.out.tfevents.1701686040.urg02.2591927.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_18-42-18_urg04GR_MNIST_optim_seed-14/events.out.tfevents.1701711738.urg04.1015239.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_11-34-41_urg04GR_MNIST_optim_seed-4/events.out.tfevents.1701686081.urg04.1010926.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_21-45-16_urg03GR_MNIST_optim_seed-18/events.out.tfevents.1701722716.urg03.1760160.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_13-36-34_urg01GR_MNIST_optim_seed-5/events.out.tfevents.1701693394.urg01.2678101.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_16-40-09_urg03GR_MNIST_optim_seed-11/events.out.tfevents.1701704409.urg03.1758091.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_16-07-55_urg02GR_MNIST_optim_seed-8/events.out.tfevents.1701702475.urg02.2593819.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_15-28-53_urg01GR_MNIST_optim_seed-7/events.out.tfevents.1701700133.urg01.2679437.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_21-10-57_urg02GR_MNIST_optim_seed-16/events.out.tfevents.1701720657.urg02.2596775.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_16-40-09_urg03GR_MNIST_optim_seed-10/events.out.tfevents.1701704409.urg03.1757271.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_11-34-36_urg03GR_MNIST_optim_seed-3/events.out.tfevents.1701686076.urg03.1755157.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_16-38-36_urg02GR_MNIST_optim_seed-9/events.out.tfevents.1701704316.urg02.2594841.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_16-43-14_urg04GR_MNIST_optim_seed-12/events.out.tfevents.1701704594.urg04.1013889.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_20-37-15_urg01GR_MNIST_optim_seed-15/events.out.tfevents.1701718635.urg01.2682394.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_11-34-36_urg03GR_MNIST_optim_seed-2/events.out.tfevents.1701686076.urg03.1755156.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_21-41-08_urg02GR_MNIST_optim_seed-17/events.out.tfevents.1701722468.urg02.2597794.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_18-41-01_urg01GR_MNIST_optim_seed-13/events.out.tfevents.1701711661.urg01.2681060.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_13-36-36_urg04GR_MNIST_optim_seed-6/events.out.tfevents.1701693396.urg04.1012277.0', 'Training Neuron Parameters/runs_fernando_mnist_converging/Dec04_21-51-39_urg03GR_MNIST_optim_seed-19/events.out.tfevents.1701723099.urg03.1761093.0']\n",
      "mnist_compressed Dec06_07-58-50_v100gpu9GR_MNIST_optim_seed-12 12.0 [ True] 6303\n",
      "mnist_compressed Dec05_14-27-13_a100gpu2GR_MNIST_optim_seed-4 4.0 [ True] 6303\n",
      "mnist_compressed Dec06_03-37-56_v100gpu29GR_MNIST_optim_seed-10 10.0 [ True] 6303\n",
      "mnist_compressed Dec06_09-32-00_v100gpu12GR_MNIST_optim_seed-20 20.0 [ True] 6303\n",
      "mnist_compressed Dec05_16-37-34_v100gpu27GR_MNIST_optim_seed-7 7.0 [ True] 6303\n",
      "mnist_compressed Dec05_14-59-48_a100gpu2GR_MNIST_optim_seed-5 5.0 [ True] 6303\n",
      "mnist_compressed Dec05_14-10-08_a100gpu2GR_MNIST_optim_seed-2 2.0 [ True] 6303\n",
      "mnist_compressed Dec13_11-30-01_a100gpu1GR_MNIST_optim_seed-1 1.0 [ True] 6303\n",
      "mnist_compressed Dec06_08-34-35_a100gpu1GR_MNIST_optim_seed-16 16.0 [ True] 6303\n",
      "mnist_compressed Dec06_08-32-40_v100gpu16GR_MNIST_optim_seed-15 15.0 [ True] 6303\n",
      "mnist_compressed Dec05_14-20-49_a100gpu2GR_MNIST_optim_seed-3 3.0 [ True] 6303\n",
      "mnist_compressed Dec06_08-26-08_a100gpu1GR_MNIST_optim_seed-14 14.0 [ True] 6303\n",
      "mnist_compressed Dec06_03-28-14_a100gpu1GR_MNIST_optim_seed-8 8.0 [ True] 6303\n",
      "mnist_compressed Dec06_08-43-05_a100gpu1GR_MNIST_optim_seed-17 17.0 [ True] 6303\n",
      "mnist_compressed Dec05_16-26-40_a100gpu2GR_MNIST_optim_seed-6 6.0 [ True] 6303\n",
      "mnist_compressed Dec05_09-05-56_v100gpu26GR_MNIST_optim_seed-0 0.0 [ True] 6303\n",
      "mnist_compressed Dec06_08-45-15_v100gpu11GR_MNIST_optim_seed-18 18.0 [ True] 6303\n",
      "mnist_compressed Dec06_08-51-28_a100gpu1GR_MNIST_optim_seed-19 19.0 [ True] 6303\n",
      "mnist_compressed Dec06_08-15-47_v100gpu14GR_MNIST_optim_seed-13 13.0 [ True] 6303\n",
      "mnist_compressed Dec06_03-31-25_a100gpu1GR_MNIST_optim_seed-9 9.0 [ True] 6303\n",
      "mnist_compressed Dec06_05-36-53_v100gpu26GR_MNIST_optim_seed-11 11.0 [ True] 6303\n",
      "braille Dec01_13-32-46_a100gpu1ALIF_WITH_GR_L1 2.0 [False] 3603\n",
      "braille Nov30_18-14-14_v100gpu3ALIF_WITH_GR_L1 0.0 [ True] 3595\n",
      "braille Dec01_13-42-16_v100gpu9ALIF_WITH_GR_L1 5.0 [False] 3603\n",
      "braille Dec01_14-02-30_a100gpu1ALIF_WITH_GR_L1 8.0 [False] 3603\n",
      "braille Nov30_18-14-12_v100gpu2ALIF_WITH_GR_L1 1.0 [ True] 3603\n",
      "braille ccc 7.0 [ True] 3603\n",
      "braille Dec01_00-40-19_a100gpu1ALIF_WITH_GR_L1 8.0 [ True] 3602\n",
      "braille Nov30_18-26-45_a100gpu1ALIF_WITH_GR_L1 3.0 [ True] 3603\n",
      "braille Dec01_22-04-37_a100gpu1ALIF_WITH_GR_L1 11.0 [False] 3603\n",
      "braille Dec01_13-44-55_v100gpu15ALIF_WITH_GR_L1 6.0 [False] 3603\n",
      "braille Nov30_19-22-53_v100gpu9ALIF_WITH_GR_L1 4.0 [ True] 3603\n",
      "braille Dec01_13-38-20_a100gpu1ALIF_WITH_GR_L1 3.0 [False] 3603\n",
      "braille bbb 5.0 [ True] 3603\n",
      "braille Dec01_03-26-44_v100gpu3ALIF_WITH_GR_L1 11.0 [ True] 3603\n",
      "braille Dec01_03-16-41_v100gpu2ALIF_WITH_GR_L1 10.0 [ True] 3603\n",
      "braille Dec01_04-09-08_v100gpu1ALIF_WITH_GR_L1 12.0 [ True] 3603\n",
      "braille Dec02_04-18-05_a100gpu1ALIF_WITH_GR_L1 19.0 [False] 3603\n",
      "braille Nov30_18-14-15_v100gpu1ALIF_WITH_GR_L1 2.0 [ True] 3603\n",
      "braille Dec01_22-33-25_v100gpu9ALIF_WITH_GR_L1 14.0 [False] 3603\n",
      "braille Dec01_23-41-29_a100gpu1ALIF_WITH_GR_L1_2 16.0 [False] 3603\n",
      "braille Dec02_04-30-28_a100gpu1ALIF_WITH_GR_L1 20.0 [False] 3603\n",
      "braille Dec01_07-25-40_a100gpu1ALIF_WITH_GR_L1 18.0 [ True] 3603\n",
      "braille Dec01_07-05-25_a100gpu1ALIF_WITH_GR_L1_2 15.0 [ True] 3603\n",
      "braille aaa 6.0 [ True] 3603\n",
      "braille Dec01_22-16-58_a100gpu1ALIF_WITH_GR_L1 12.0 [False] 3603\n",
      "braille Dec01_00-57-26_a100gpu1ALIF_WITH_GR_L1 9.0 [ True] 3603\n",
      "braille Dec01_07-05-25_a100gpu1ALIF_WITH_GR_L1 16.0 [ True] 3603\n",
      "braille Dec01_21-56-22_v100gpu3ALIF_WITH_GR_L1 10.0 [False] 3603\n",
      "braille Dec01_04-31-23_v100gpu9ALIF_WITH_GR_L1 13.0 [ True] 3603\n",
      "braille Dec01_11-00-52_v100gpu13ALIF_WITH_GR_L1 1.0 [False] 3603\n",
      "braille Dec01_13-54-27_a100gpu1ALIF_WITH_GR_L1 7.0 [False] 3603\n",
      "braille Dec01_21-37-26_v100gpu12ALIF_WITH_GR_L1 9.0 [False] 3603\n",
      "braille Dec01_07-07-26_a100gpu1ALIF_WITH_GR_L1 17.0 [ True] 3603\n",
      "braille Dec02_00-58-44_v100gpu10ALIF_WITH_GR_L1 18.0 [False] 3603\n",
      "braille Dec01_13-38-20_a100gpu1ALIF_WITH_GR_L1_2 4.0 [False] 3603\n",
      "braille Dec01_08-28-25_a100gpu1ALIF_WITH_GR_L1 19.0 [ True] 3603\n",
      "braille Dec01_23-41-29_a100gpu1ALIF_WITH_GR_L1 17.0 [False] 3603\n",
      "braille Dec01_22-23-16_a100gpu1ALIF_WITH_GR_L1 13.0 [False] 3603\n",
      "braille Dec01_09-08-53_a100gpu1ALIF_WITH_GR_L1 0.0 [False] 3603\n",
      "braille Dec01_22-39-36_v100gpu15ALIF_WITH_GR_L1 15.0 [False] 3603\n",
      "braille Dec01_07-03-25_a100gpu1ALIF_WITH_GR_L1 14.0 [ True] 3603\n",
      "mnist Dec04_11-02-38_urg02GR_MNIST_optim_seed-0 0.0 [ True] 6303\n",
      "mnist Dec04_11-34-00_urg02GR_MNIST_optim_seed-1 1.0 [ True] 6303\n",
      "mnist Dec04_18-42-18_urg04GR_MNIST_optim_seed-14 14.0 [ True] 6303\n",
      "mnist Dec04_11-34-41_urg04GR_MNIST_optim_seed-4 4.0 [ True] 6303\n",
      "mnist Dec04_21-45-16_urg03GR_MNIST_optim_seed-18 18.0 [ True] 6303\n",
      "mnist Dec04_13-36-34_urg01GR_MNIST_optim_seed-5 5.0 [ True] 6303\n",
      "mnist Dec04_16-40-09_urg03GR_MNIST_optim_seed-11 11.0 [ True] 6303\n",
      "mnist Dec04_16-07-55_urg02GR_MNIST_optim_seed-8 8.0 [ True] 6303\n",
      "mnist Dec04_15-28-53_urg01GR_MNIST_optim_seed-7 7.0 [ True] 6303\n",
      "mnist Dec04_21-10-57_urg02GR_MNIST_optim_seed-16 16.0 [ True] 6303\n",
      "mnist Dec04_16-40-09_urg03GR_MNIST_optim_seed-10 10.0 [ True] 6303\n",
      "mnist Dec04_11-34-36_urg03GR_MNIST_optim_seed-3 3.0 [ True] 6303\n",
      "mnist Dec04_16-38-36_urg02GR_MNIST_optim_seed-9 9.0 [ True] 6303\n",
      "mnist Dec04_16-43-14_urg04GR_MNIST_optim_seed-12 12.0 [ True] 6303\n",
      "mnist Dec04_20-37-15_urg01GR_MNIST_optim_seed-15 15.0 [ True] 6303\n",
      "mnist Dec04_11-34-36_urg03GR_MNIST_optim_seed-2 2.0 [ True] 6303\n",
      "mnist Dec04_21-41-08_urg02GR_MNIST_optim_seed-17 17.0 [ True] 6303\n",
      "mnist Dec04_18-41-01_urg01GR_MNIST_optim_seed-13 13.0 [ True] 6303\n",
      "mnist Dec04_13-36-36_urg04GR_MNIST_optim_seed-6 6.0 [ True] 6303\n",
      "mnist Dec04_21-51-39_urg03GR_MNIST_optim_seed-19 19.0 [ True] 6303\n"
     ]
    }
   ],
   "source": [
    "event_files = get_event_files(\"Training Neuron Parameters\")\n",
    "print(event_files)\n",
    "dfs = []\n",
    "for file in event_files:\n",
    "    \n",
    "    data = extract_data_from_tensorboard(file)\n",
    "    # print(data[0]['simulation'][0])\n",
    "    dfs.append(data)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T15:11:23.989510042Z",
     "start_time": "2024-01-15T15:10:04.734519060Z"
    }
   },
   "id": "92d839d676ce0f34",
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "299\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "299\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "299\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "299\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "299\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "299\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "299\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "\n",
    "dfs_scalar = pd.concat([df[0] for df in dfs] , ignore_index=True)\n",
    "conditions = np.array([dfs_scalar['tag'] == 'A1',\n",
    "              dfs_scalar['tag'] == 'A2',\n",
    "                dfs_scalar['tag'] == 'a',\n",
    "                dfs_scalar['tag'] == 'b',\n",
    "                dfs_scalar['tag'] == 'G',\n",
    "              dfs_scalar['tag'] == 'R1',\n",
    "                dfs_scalar['tag'] == 'R2',\n",
    "              ])\n",
    "conditions = conditions.any(axis=0)\n",
    "# print(conditions)\n",
    "dfs_scalar = dfs_scalar.loc[conditions]\n",
    "# print(dfs_scalar)\n",
    "for simulation in dfs_scalar['simulation'].unique():\n",
    "    loc_s = dfs_scalar['simulation'] == simulation\n",
    "    # print(np.where(loc_s))\n",
    "    for tag in dfs_scalar.loc[loc_s]['tag'].unique():\n",
    "        loc_t = dfs_scalar['tag'] == tag\n",
    "        # print(np.where(loc_t))\n",
    "\n",
    "        for seed in dfs_scalar.loc[loc_t & loc_s]['seed'].unique():\n",
    "            loc_seed = dfs_scalar['seed'] == seed\n",
    "\n",
    "            for train in dfs_scalar.loc[loc_t & loc_s & loc_seed]['trained'].unique():\n",
    "                loc_train = dfs_scalar['trained'] == train\n",
    "                # print(dfs_scalar.loc[loc_t & loc_s & loc])\n",
    "                # dfs_scalar.loc[loc_t & loc_s & loc,'value'] = dfs_scalar.loc[loc_t & loc_s & loc,'value']/dfs_scalar.loc[loc_t & loc_s & loc & dfs_scalar.loc[loc_t & loc_s & loc,'step'] == 299,'value']\n",
    "    \n",
    "                # print(np.where(loc_t & loc_s & loc & last_epoch))\n",
    "                # dfs_scalar.loc[loc_t & loc_s & loc, 'value'] = dfs_scalar.loc[loc_t & loc_s & loc, 'value'].rolling(20).mean()\n",
    "                print(len(dfs_scalar.loc[loc_t & loc_s & loc_seed & loc_train,'value']))\n",
    "                last_epoch = dfs_scalar.loc[loc_t & loc_s & loc_seed & loc_train]['step'] == dfs_scalar.loc[loc_t & loc_s  & loc_seed & loc_train]['step'].max()\n",
    "                try:\n",
    "                    mylist = dfs_scalar.loc[loc_t & loc_s  & loc_seed & loc_train,'value']/float(dfs_scalar.loc[loc_t & loc_s  & loc_seed & loc_train & last_epoch,'value'])\n",
    "                except TypeError:\n",
    "                    print(simulation,tag,seed,train)\n",
    "                    print(np.where(loc_t & loc_s  & loc_seed & loc_train& last_epoch))\n",
    "                    print(dfs_scalar.loc[loc_t & loc_s  & loc_seed & loc_train,'step'])\n",
    "\n",
    "                    raise TypeError\n",
    "                # print(last_epoch)\n",
    "                # print(mylist)\n",
    "    \n",
    "                dfs_scalar.loc[loc_t & loc_s  & loc_seed & loc_train,'value_norm'] = mylist\n",
    "            # print(dfs_scalar.l\n",
    "            # oc[dfs_scalar.loc[loc_t & loc_s & loc,'step'] == 299,'value'])\n",
    "            # print(loc)\n",
    "            # print(seed)\n",
    "            # # print(np.argmax(dfs_scalar.loc[loc,'step']))\n",
    "            # print(pd.argmax(dfs_scalar.loc[loc,'step']))\n",
    "            # print(step_max_where)\n",
    "            # print(np.where(step_max_where))\n",
    "            # print(dfs_scalar[step_max_where])\n",
    "            # print(dfs_scalar.loc[step_max_where,'value'])\n",
    "#             dfs_scalar.loc[loc,'value'] = dfs_scalar.loc[loc,'value']/dfs_scalar.loc[step_max_where,'value']\n",
    "#             \n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T15:11:59.702056296Z",
     "start_time": "2024-01-15T15:11:23.994313222Z"
    }
   },
   "id": "1379e74de100da6a",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x360 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfs_scalar_here = dfs_scalar[dfs_scalar['trained'] == True]#.loc[dfs_scalar['seed'] == 1]\n",
    "# sns.FacetGrid(dfs_scalar_here, col='tag', hue=\"simulation\", height=10, aspect=1.5,sharey='row',despine=True,legend_out=True,margin_titles=True).map(sns.lineplot, \"step\", \"value\")\n",
    "# dfs_scalar_here = dfs_scalar_here.loc[dfs_scalar_here['simulation'] == 'braille']\n",
    "## import line2d\n",
    "SMALL_SIZE = 8\n",
    "MEDIUM_SIZE = 12\n",
    "BIGGER_SIZE = 14\n",
    "plt.rc('font', family='georgia', size=SMALL_SIZE)\n",
    "# plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=BIGGER_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=MEDIUM_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=MEDIUM_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "# fig1 = plt.figure(figsize=(20,5))\n",
    "fig1,axis1 = plt.subplots(1,1,figsize=(10,5))\n",
    "\n",
    "from matplotlib.lines import Line2D\n",
    "p1 = Line2D([0], [0], color=sns.color_palette('Purples',10)[5], lw=4, label='A1')\n",
    "p2 = Line2D([0], [0], color=sns.color_palette('Greens',10)[5], lw=4, label='A2')\n",
    "# p4 = Line2D([0], [0], color='gray', lw=4, label='mnist_compressed',dashes=(2,2))\n",
    "# p5 = Line2D([0], [0], color='gray', lw=4, label='braille',dashes=(3,0))\n",
    "# p6 = Line2D([0], [0], color='gray', lw=4, label='mnist',dashes=(4,4))\n",
    "# p0, = plt.plot([0], marker='None',\n",
    "#            linestyle='None', label='dummy-tophead')\n",
    "# p3, = plt.plot([0],  marker='None',\n",
    "#            linestyle='None', label='dummy-empty')\n",
    "sns_plot = sns.lineplot(data=dfs_scalar_here.query(\"tag == 'A1'\"), x=\"step\", y=\"value\",style=\"simulation\",legend=False,color=sns.color_palette('Purples',10)[5],ax=axis1,zorder=0)\n",
    "axis2 = axis1.twinx()\n",
    "sns_plot = sns.lineplot(data=dfs_scalar_here.query(\"tag == 'A2'\"), x=\"step\", y=\"value\",style=\"simulation\",legend=True,color=sns.color_palette('Greens',10)[5],ax=axis2,zorder=0)\n",
    "## add title to legend\n",
    "# for line in sns_plot.get_lines():\n",
    "#     # lines.set_linestyle(\"--\")\n",
    "#     print(line.get_drawstyle())\n",
    "axis1.set_ylabel('A1 [Ampere]')\n",
    "axis2.set_ylabel('A2 [Ampere]')\n",
    "##anchor legend outside to the left\n",
    "# plt.legend(bbox_to_anchor=(-0.5, 1), loc=2, borderaxespad=0.)\n",
    "# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "other_legend = plt.legend([p1,p2],['A1','A2'],title='Parameters',fancybox=True,shadow=False,loc = 'lower left',framealpha=0.5)\n",
    "\n",
    "axis2.legend(title='Datasets',fancybox=True,shadow=False,loc = 'lower right',framealpha=0.5)\n",
    "plt.gca().add_artist(other_legend)\n",
    "# \n",
    "# axis2 = axis1.twinx()\n",
    "# sns.lineplot(data=dfs_scalar_here.query(\"tag == 'A2'\"), x=\"step\", y=\"value\", hue = 'tag', style=\"simulation\",legend=True,palette='Greens',ax=axis2)\n",
    "sns.despine(offset=10, trim=True,right=False)\n",
    "# .despine(offset=10, trim=True,left=True)\n",
    "# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "axis1.set_xlabel('Epoch')\n",
    "# plt.ylabel('Value')\n",
    "# plt.title('Parameter Evolution')\n",
    "plt.tight_layout()\n",
    "plt.title('MN Neuron Parameters Evolution')\n",
    "plt.savefig('trained_params.pdf',dpi=300)\n",
    "# plt.ylim(-5,6)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T15:13:18.263368403Z",
     "start_time": "2024-01-15T15:11:59.714195860Z"
    }
   },
   "id": "c544e9bf10b4d5d1",
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T15:13:18.263947525Z",
     "start_time": "2024-01-15T15:13:18.261517335Z"
    }
   },
   "id": "37044d23a018a5da",
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "os.mkdir('MN_params_new')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T16:00:00.537586578Z",
     "start_time": "2024-01-15T16:00:00.449313604Z"
    }
   },
   "id": "986622df1e962735",
   "execution_count": 32
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sim mnist_compressed\n",
      "seed 0\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 1\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 2\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 3\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 4\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 5\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 6\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 7\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 8\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 9\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 10\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 11\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 12\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 13\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 14\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 15\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 16\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 17\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 18\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 19\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "sim braille\n",
      "seed 0\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 1\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 2\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 3\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 4\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 5\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 6\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 7\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 8\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 9\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 10\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 11\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 12\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 13\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 14\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 15\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 16\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 17\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 18\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 19\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "sim mnist\n",
      "seed 0\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 1\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 2\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 3\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 4\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 5\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 6\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 7\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 8\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 9\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 10\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 11\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 12\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 13\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 14\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 15\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 16\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 17\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 18\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "seed 19\n",
      "epoch 0\n",
      "epoch 98\n",
      "epoch 197\n",
      "epoch 296\n",
      "we are here\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "dict_coll = []\n",
    "dfs_scalar = dfs_scalar.loc[dfs_scalar['trained'] == True]\n",
    "# fig1,axis1 = plt.subplots(2,1,figsize=(10,10))\n",
    "for simulation in dfs_scalar['simulation'].unique():\n",
    "    loc_s = dfs_scalar['simulation'] == simulation\n",
    "    print('sim',simulation)\n",
    "    for seed in range(20):\n",
    "        print('seed',seed)\n",
    "        loc_seed = dfs_scalar.loc[loc_s]['seed'] == seed\n",
    "        for epoch in [0,98,197,296]:\n",
    "            print('epoch',epoch)\n",
    "            dict_sim = {}\n",
    "            dict_param = {}\n",
    "\n",
    "            loc_epoch = dfs_scalar.loc[loc_s & loc_seed]['step'] == epoch\n",
    "            \n",
    "            for tag in dfs_scalar.loc[loc_s]['tag'].unique():\n",
    "                loc_t = dfs_scalar.loc[loc_s & loc_seed & loc_epoch]['tag'] == tag\n",
    "                dict_param[tag] = float(dfs_scalar.loc[loc_t & loc_s & loc_seed & loc_epoch,'value'])\n",
    "            dict_param['k1']=200\n",
    "            dict_param['k2']=20\n",
    "            l0 = MN_neuron_sp(\n",
    "                1,\n",
    "                {\"a\": 5, \"A1\": 0, \"A2\": 0},\n",
    "                dt=(1 / 100.0),\n",
    "                train=False,\n",
    "                a=dict_param[\"a\"],\n",
    "                A1=dict_param[\"A1\"],\n",
    "                A2=dict_param[\"A2\"],\n",
    "                b=dict_param[\"b\"],\n",
    "                G=dict_param[\"G\"],\n",
    "                k1=dict_param['k1'],\n",
    "                k2=dict_param['k2'],\n",
    "                R1=dict_param[\"R1\"],\n",
    "                R2=dict_param[\"R2\"],\n",
    "                C=1,\n",
    "            )\n",
    "            if epoch == 296:\n",
    "                json.dump(dict_param,open(f'MN_params_new/GR_{simulation}_w_'+str(seed)+'.json','w'))\n",
    "            l0_spk = []\n",
    "            l0_v = []\n",
    "            l0_thr = []\n",
    "            x_local = torch.zeros(1, 300)\n",
    "            x_local[0, 100:] = torch.linspace(0, 10, 200)\n",
    "            for t in range(x_local.shape[1]):\n",
    "                _ = l0(x_local[:, t])\n",
    "                # Get the spikes and voltages from the MN neuron encoder\n",
    "                l0_spk.append(l0.state.spk)\n",
    "                l0_v.append(l0.state.V)\n",
    "                l0_thr.append(l0.state.Thr)\n",
    "            dict_sim['t'] = (torch.arange(0,x_local.shape[1])*l0.dt).tolist()\n",
    "            l0_spk = torch.stack(l0_spk, dim=1)\n",
    "            dict_sim['l0_spk'] = l0_spk[0,:,0].tolist()\n",
    "            l0_v = torch.stack(l0_v, dim=1)\n",
    "            dict_sim['l0_v'] = l0_v[0,:,0].tolist()\n",
    "            l0_thr = torch.stack(l0_thr, dim=1)\n",
    "            dict_sim['l0_thr'] = l0_thr[0,:,0].tolist()\n",
    "            for tag in dfs_scalar.loc[loc_s]['tag'].unique():\n",
    "                loc_t = dfs_scalar.loc[loc_s & loc_seed & loc_epoch]['tag'] == tag\n",
    "                dict_sim[tag] = [float(dfs_scalar.loc[loc_t & loc_s & loc_seed & loc_epoch,'value'])]*x_local.shape[1]\n",
    "            dict_sim['seed'] = list([int(seed)]*x_local.shape[1])\n",
    "            dict_sim['simulation'] = [simulation]*x_local.shape[1]\n",
    "            dict_sim['epoch'] = list([int(epoch)]*x_local.shape[1])\n",
    "            # axis1[0].plot(dict_sim['t'],dict_sim['l0_v'])\n",
    "            # axis1[0].plot(dict_sim['t'],dict_sim['l0_thr'])\n",
    "            # spikes = dict_sim['l0_spk'] == 1\n",
    "            # axis1[1].scatter(dict_sim['t'][spikes],torch.ones_like(dict_sim['t'])[spikes],color='r',marker='.',s=10)\n",
    "            # axis1[1].set_xlim(0,3)\n",
    "            dict_coll.append(dict_sim)\n",
    "print('we are here')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T16:25:42.871776762Z",
     "start_time": "2024-01-15T16:04:41.257731629Z"
    }
   },
   "id": "625d4a7320d0da76",
   "execution_count": 35
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "dict_compressed = {}\n",
    "for key in dict_coll[0].keys():\n",
    "    dict_compressed[key] = []\n",
    "for dict in dict_coll:\n",
    "    for key in dict.keys():\n",
    "        dict_compressed[key] += dict[key]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T16:27:47.959967973Z",
     "start_time": "2024-01-15T16:27:47.880389956Z"
    }
   },
   "id": "eba76ab5dd43d1f3",
   "execution_count": 36
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dict_compressed)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T16:27:48.633603328Z",
     "start_time": "2024-01-15T16:27:48.379580791Z"
    }
   },
   "id": "6ae7cb30ca8ea2dc",
   "execution_count": 37
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x360 with 3 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2,axis2 = plt.subplots(1,3,figsize=(10,5),sharey=False)\n",
    "x_local = torch.zeros(1, 300)\n",
    "x_local[0, 100:] = torch.linspace(0, 10, 200)\n",
    "df_input = {}\n",
    "df_input['t'] = (torch.arange(0,x_local.shape[1])*l0.dt).tolist()\n",
    "df_input['input'] = x_local[0, :].tolist()\n",
    "sns.lineplot(data=pd.DataFrame(df_input), x=\"t\", y=\"input\",legend=False,ax=axis2[0],color='k')\n",
    "sim = df['simulation'][df['l0_spk'] == 1]\n",
    "t = df['t'][df['l0_spk'] == 1] \n",
    "epochs = df['epoch'][df['l0_spk'] == 1]\n",
    "seed = df['seed'][df['l0_spk'] == 1]\n",
    "my_dict = {'sim':sim.tolist(),'t':t.tolist(),'epochs':epochs.tolist(),'seed':seed.tolist()}\n",
    "spikes = pd.DataFrame(my_dict)\n",
    "# sns.scatterplot(spikes.query('epochs==296'),x='t',y='seed',hue='sim',marker='|',ax=axis2[0])\n",
    "sns.lineplot(data=df, x=\"t\", y=\"l0_v\", hue=\"epoch\", style=\"simulation\",palette='Reds',legend=True,ax=axis2[1])\n",
    "sns.lineplot(data=df, x=\"t\", y=\"l0_thr\", hue=\"epoch\", style=\"simulation\",palette='Blues',legend=True,ax=axis2[2])\n",
    "#remove spines\n",
    "sns.despine(offset=10, trim=True)\n",
    "#save figure\n",
    "axis2[0].set_xlabel('Time (s)')\n",
    "axis2[0].set_ylabel('Current (Ampere)')\n",
    "axis2[0].set_title('Input Current')\n",
    "axis2[1].set_xlabel('Time (s)')\n",
    "axis2[1].set_ylabel('Voltage (mV)')\n",
    "axis2[1].set_title('$V_{mem}$')\n",
    "axis2[2].set_xlabel('Time (s)')\n",
    "axis2[2].set_ylabel('Voltage (mV)')\n",
    "\n",
    "# axis2[1].set_ylabel('Threshold (mV)')\n",
    "axis2[2].set_title('$V_{thr}$')\n",
    "plt.tight_layout()\n",
    "plt.savefig('neuron_behaviour.pdf',dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T20:53:07.394072063Z",
     "start_time": "2024-01-15T20:48:01.290812602Z"
    }
   },
   "id": "6d24ead184f0deb0",
   "execution_count": 70
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='t', ylabel='l0_v'>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df.query('seed==0 & simulation == \"braille\" & epoch==197'), x=\"t\", y=\"l0_v\",hue='epoch',legend=True)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T15:29:00.662145848Z",
     "start_time": "2024-01-15T15:29:00.274887645Z"
    }
   },
   "id": "fe4101ba568122f",
   "execution_count": 26
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A1': -0.0009264695108868182, 'A2': -0.6338659524917603, 'G': 46.74788284301758, 'R1': 0.48022520542144775, 'R2': -1.1800005435943604, 'a': 3.318552017211914, 'b': 11.684232711791992}\n"
     ]
    }
   ],
   "source": [
    "dfs_scalar_here.query('seed==0 & simulation == \"braille\" & step==197')\n",
    "import json\n",
    "my_dict = {}\n",
    "for elements in dfs_scalar_here.query('seed==0 & simulation == \"braille\" & step==197').iterrows():\n",
    "    my_dict[elements[1]['tag']] = elements[1]['value']\n",
    "print(my_dict)\n",
    "json.dump(my_dict,open('GR_Braille_w_0.json','w'))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T15:31:54.017704929Z",
     "start_time": "2024-01-15T15:31:53.951352150Z"
    }
   },
   "id": "8316d388797cb27b",
   "execution_count": 31
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "        step tag      value        simulation  trained  seed  value_norm\n0          0  A1  -0.016873  mnist_compressed     True    12   20.990913\n1          1  A1  -0.017102  mnist_compressed     True    12   21.276197\n2          2  A1  -0.017492  mnist_compressed     True    12   21.761152\n3          3  A1  -0.016877  mnist_compressed     True    12   20.995322\n4          4  A1  -0.017150  mnist_compressed     True    12   21.334999\n...      ...  ..        ...               ...      ...   ...         ...\n405229   295   b  10.926833             mnist     True    19    0.998357\n405230   296   b  10.932067             mnist     True    19    0.998835\n405231   297   b  10.933944             mnist     True    19    0.999006\n405232   298   b  10.941789             mnist     True    19    0.999723\n405233   299   b  10.944820             mnist     True    19    1.000000\n\n[128093 rows x 7 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>step</th>\n      <th>tag</th>\n      <th>value</th>\n      <th>simulation</th>\n      <th>trained</th>\n      <th>seed</th>\n      <th>value_norm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>A1</td>\n      <td>-0.016873</td>\n      <td>mnist_compressed</td>\n      <td>True</td>\n      <td>12</td>\n      <td>20.990913</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>A1</td>\n      <td>-0.017102</td>\n      <td>mnist_compressed</td>\n      <td>True</td>\n      <td>12</td>\n      <td>21.276197</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>A1</td>\n      <td>-0.017492</td>\n      <td>mnist_compressed</td>\n      <td>True</td>\n      <td>12</td>\n      <td>21.761152</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>A1</td>\n      <td>-0.016877</td>\n      <td>mnist_compressed</td>\n      <td>True</td>\n      <td>12</td>\n      <td>20.995322</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>A1</td>\n      <td>-0.017150</td>\n      <td>mnist_compressed</td>\n      <td>True</td>\n      <td>12</td>\n      <td>21.334999</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>405229</th>\n      <td>295</td>\n      <td>b</td>\n      <td>10.926833</td>\n      <td>mnist</td>\n      <td>True</td>\n      <td>19</td>\n      <td>0.998357</td>\n    </tr>\n    <tr>\n      <th>405230</th>\n      <td>296</td>\n      <td>b</td>\n      <td>10.932067</td>\n      <td>mnist</td>\n      <td>True</td>\n      <td>19</td>\n      <td>0.998835</td>\n    </tr>\n    <tr>\n      <th>405231</th>\n      <td>297</td>\n      <td>b</td>\n      <td>10.933944</td>\n      <td>mnist</td>\n      <td>True</td>\n      <td>19</td>\n      <td>0.999006</td>\n    </tr>\n    <tr>\n      <th>405232</th>\n      <td>298</td>\n      <td>b</td>\n      <td>10.941789</td>\n      <td>mnist</td>\n      <td>True</td>\n      <td>19</td>\n      <td>0.999723</td>\n    </tr>\n    <tr>\n      <th>405233</th>\n      <td>299</td>\n      <td>b</td>\n      <td>10.944820</td>\n      <td>mnist</td>\n      <td>True</td>\n      <td>19</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>128093 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs_scalar_here"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T15:29:39.568994429Z",
     "start_time": "2024-01-15T15:29:39.492898264Z"
    }
   },
   "id": "adbc7b6c95819f6e",
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T17:08:18.405956873Z",
     "start_time": "2024-01-15T17:08:18.320977558Z"
    }
   },
   "id": "c0e169efbcca9d20",
   "execution_count": 44
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='t', ylabel='seed'>"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(data=spikes,col='sim')scatterplot(data=spikes,x='t',y='seed',hue='epochs',style='sim',legend=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T17:09:52.206443496Z",
     "start_time": "2024-01-15T17:09:47.397372235Z"
    }
   },
   "id": "9e71fe7de611c8f4",
   "execution_count": 49
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "(1.0, 2.0)"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.FacetGrid(data=spikes.query('epochs==296'),col='sim',legend_out=True).map(sns.scatterplot,'t','seed',hue='sim',marker='|')\n",
    "\n",
    "\n",
    "plt.xlim(1,2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-15T17:14:43.563020313Z",
     "start_time": "2024-01-15T17:14:42.365924326Z"
    }
   },
   "id": "e535902c1c75e584",
   "execution_count": 64
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "5a403f4d741b4e77"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}