{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74581d9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spike distance lib import failed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /home/smestern/miniconda3/envs/modelling/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      " [py.warnings]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:py.warnings:/home/smestern/miniconda3/envs/modelling/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "\n",
      "WARNING:root:Ax not installed, Ax optimizer unavailible\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from utils import *\n",
    "from spike_train_utils import *\n",
    "from scipy.signal import chirp\n",
    "from brian2 import *#BrianLogger.suppress_hierarchy('brian2.codegen')\n",
    "import os\n",
    "\n",
    "import joblib\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "file_list  = {\n",
    "   # \"Clustered\": \"/media/smestern/sgbackup/network_clust_w_shift/concat_res.csv\",\n",
    "   # \"All Fast\": \"/media/smestern/sgbackup/grid_all_fast_v5_w_shift/concat_res.csv\",\n",
    "   # \"Slow GABA\": \"/media/smestern/sgbackup/slow_gaba_w_shift/concat_res.csv\",\n",
    "   # \"FeedForward\": \"/media/smestern/sgbackup/network_ff_w_shift/concat_res.csv\",\n",
    "\n",
    "   \"full_data\": \"/media/smestern/sgbackup/aoi_paper_2/concat_res.csv\"\n",
    "}\n",
    "\n",
    "#load the reference frame\n",
    "invivo_ref = pd.read_excel(\"/home/smestern/Dropbox/PVN_MODELLING_WORK/CADEX_MODEL/output/VGAT DREADD Gi Statistics Notes.xlsx\", sheet_name=\"Firing Rate 5 min bin\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20f297b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Firing rate all VGATCRH_17-3_231102_ID68 VGATCRH_17-3_231102_ID523  \\\n",
      "0               0                     2.29                  0.923333   \n",
      "1               5                 2.183333                  0.896667   \n",
      "2              10                 2.553333                  0.993333   \n",
      "3              15                     4.88                  2.436667   \n",
      "\n",
      "  VGATCRH-23-1_240223_ID283 VGATCRH-23-1_240223_ID57  \\\n",
      "0                  1.876667                 2.283333   \n",
      "1                  1.796667                 2.213333   \n",
      "2                  2.293333                      3.4   \n",
      "3                  2.293333                      3.4   \n",
      "\n",
      "  VGATCRH-28-1_240228_ID391 VGATCRH-51-2_240815_ID71  \\\n",
      "0                      3.35                 5.046667   \n",
      "1                  6.476667                 7.283333   \n",
      "2                      7.86                    11.02   \n",
      "3                  7.673333                12.726667   \n",
      "\n",
      "  VGATCRH-51-2_240815_ID279 VGATCRH-51-2_240815_ID59  \n",
      "0                  1.103333                 2.966667  \n",
      "1                  2.033333                     3.64  \n",
      "2                  2.506667                     4.01  \n",
      "3                  5.413333                 4.206667  \n",
      "Found InVivo reference firing rates: 2.4800000000000004 pre, 5.378750000000005 post\n"
     ]
    }
   ],
   "source": [
    "invivo_ref_cv2 = invivo_ref.iloc[-5:,:]\n",
    "\n",
    "invivo_ref_fr = invivo_ref.iloc[:4,:]\n",
    "\n",
    "invivo_ref_cv2 = invivo_ref_cv2.reset_index(drop=True)\n",
    "invivo_pre_cv2 = invivo_ref_cv2.iloc[1:2, 1:].to_numpy().flatten()\n",
    "invivo_post_cv2 = invivo_ref_cv2.iloc[-1:, 1:].to_numpy().flatten()\n",
    "\n",
    "print(invivo_ref_fr)\n",
    "\n",
    "invivo_ref_fr = invivo_ref_fr.reset_index(drop=True)\n",
    "invivo_pre_fr = invivo_ref_fr.iloc[0:1, 1:].to_numpy().flatten()\n",
    "invivo_post_fr = invivo_ref_fr.iloc[-1:, 1:].to_numpy().flatten()\n",
    "\n",
    "print(f\"Found InVivo reference firing rates: {np.nanmean(invivo_pre_fr)} pre, {np.nanmean(invivo_post_fr)} post\")\n",
    "\n",
    "#save for loading later\n",
    "dict_invivo_ref = {\n",
    "    'invivo_pre_fr': invivo_pre_fr,\n",
    "    'invivo_post_fr': invivo_post_fr,\n",
    "    'invivo_pre_cv2': invivo_pre_cv2,\n",
    "    'invivo_post_cv2': invivo_post_cv2\n",
    "}\n",
    "df_invivo_ref = pd.DataFrame(dict_invivo_ref)\n",
    "df_invivo_ref.to_csv('./output/_ap2_invivo_ref.csv', index=False)\n",
    "\n",
    "dict_invivo_means = {\n",
    "    'invivo_pre_fr': np.nanmean(invivo_pre_fr),\n",
    "    'invivo_post_fr': np.nanmean(invivo_post_fr),\n",
    "    'invivo_pre_fr_std': np.nanstd(invivo_pre_fr),\n",
    "    'invivo_post_fr_std': np.nanstd(invivo_post_fr),\n",
    "    'invivo_pre_cv2': np.nanmean(invivo_pre_cv2),\n",
    "    'invivo_post_cv2': np.nanmean(invivo_post_cv2),\n",
    "    'invivo_pre_cv2_std': np.nanstd(invivo_pre_cv2),\n",
    "    'invivo_post_cv2_std': np.nanstd(invivo_post_cv2)\n",
    "}\n",
    "df_invivo_means = pd.DataFrame(dict_invivo_means, index=[0])\n",
    "df_invivo_means.to_csv('./output/_ap2_invivo_means.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9f0f597",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading dataframes from files...\n",
      "Network type: ['grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 'grid_all_fast_v5_w_shift', 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'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift', 'slow_gaba_w_shift']\n",
      "Loaded full_data data from /media/smestern/sgbackup/aoi_paper_2/concat_res.csv.\n"
     ]
    }
   ],
   "source": [
    "print(\"Loading dataframes from files...\")\n",
    "\n",
    "dataframes = {}\n",
    "for key, file in file_list.items():\n",
    "    if os.path.exists(file):\n",
    "        temp = pd.read_csv(file, index_col=0)\n",
    "        net_type = [os.path.basename(os.path.dirname(x)) for x in temp['path']]\n",
    "        print(f\"Network type: {net_type}\")\n",
    "        temp['Network_type'] = net_type\n",
    "\n",
    "        dist_baseline_cv2 = np.abs(temp['baseline_mean_cv2_bursters'] - np.nanmean(invivo_pre_cv2))\n",
    "        dist_post_cv2 = np.abs(temp['end_mean_cv2_bursters'] - np.nanmean(invivo_post_cv2))\n",
    "\n",
    "        temp['dist_baseline_cv2'] = dist_baseline_cv2\n",
    "        temp['dist_post_cv2'] = dist_post_cv2\n",
    "\n",
    "        dist_baseline_cv2_full = np.abs(temp['baseline_mean_cv2_overall'] - np.nanmean(invivo_pre_cv2))\n",
    "        dist_post_cv2_full = np.abs(temp['end_mean_cv2_overall'] - np.nanmean(invivo_post_cv2))\n",
    "\n",
    "        temp['dist_baseline_cv2_full'] = dist_baseline_cv2_full\n",
    "        temp['dist_post_cv2_full'] = dist_post_cv2_full\n",
    "\n",
    "        dist_baseline_fr = np.abs(temp['baseline_mean_fr_bursters'] - np.nanmean(invivo_pre_fr))\n",
    "        dist_post_fr = np.abs(temp['end_mean_fr_bursters'] - np.nanmean(invivo_post_fr))\n",
    "\n",
    "        temp['dist_baseline_fr'] = dist_baseline_fr\n",
    "        temp['dist_post_fr'] = dist_post_fr\n",
    "\n",
    "\n",
    "        dist_baseline_fr_full = np.abs(temp['baseline_mean_firing_rate'] - np.nanmean(invivo_pre_fr))\n",
    "        dist_post_fr_full = np.abs(temp['end_mean_firing_rate'] - np.nanmean(invivo_post_fr))\n",
    "        temp['dist_baseline_fr_full'] = dist_baseline_fr_full\n",
    "        temp['dist_post_fr_full'] = dist_post_fr_full\n",
    "\n",
    "        dataframes[key] = temp\n",
    "        print(f\"Loaded {key} data from {file}.\")\n",
    "    else:\n",
    "        print(f\"File {file} does not exist.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24450c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(dataframes.values(), ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb558cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"./output/concat_res_all.csv\")\n",
    "df_clean = df.dropna(how='any', subset=['baseline_mean_fr_bursters', 'end_mean_fr_bursters'])\n",
    "#drop rows with 'Network_type' as 'network_clust_w_shift'\n",
    "df_clean = df_clean[~df_clean['Network_type'].str.contains(\"network_clust_w_shift\")]\n",
    "df_clean.to_csv(\"./output/concat_res_all_clean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc9e8905",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "#paracoords = ['baseline_mean_fr_bursters', 'end_mean_fr_bursters', 'baseline_mean_cv2_bursters', 'end_mean_cv2_bursters']\n",
    "df_for_px = df.copy()\n",
    "# Convert 'Network_type' to a categorical type for better handling in Plotly\n",
    "df_for_px['Network_type'] = df_for_px['Network_type'].astype('category')\n",
    "df_for_px['Network_type_cat'] = df_for_px['Network_type'].cat.codes\n",
    "fig = px.parallel_coordinates(df_for_px,\n",
    "                        color='Network_type_cat',\n",
    "                        dimensions=['baseline_mean_fr_bursters', 'end_mean_fr_bursters',\n",
    "                                    'baseline_mean_cv2_bursters', 'end_mean_cv2_bursters',\n",
    "                                    'dist_baseline_fr', 'dist_post_fr',\n",
    "                                    'dist_baseline_cv2', 'dist_post_cv2', 'burst_error', 'Network_type_cat'],\n",
    "                        labels={\n",
    "                            'baseline_mean_fr_bursters': 'Baseline FR (Hz)',\n",
    "                            'end_mean_fr_bursters': 'End FR (Hz)',\n",
    "                            'baseline_mean_cv2_bursters': 'Baseline CV2',\n",
    "                            'end_mean_cv2_bursters': 'End CV2',\n",
    "                            'dist_baseline_fr': 'Dist. Baseline FR (Hz)',\n",
    "                            'dist_post_fr': 'Dist. Post FR (Hz)',\n",
    "                            'dist_baseline_cv2': 'Dist. Baseline CV2',\n",
    "                            'dist_post_cv2': 'Dist. Post CV2'\n",
    "                        },\n",
    "                        title='Parallel Coordinates Plot of Network Types',\n",
    "                        width=1200,\n",
    "                        height=800,\n",
    ")\n",
    "# Update the color scale to use the categorical co\n",
    "fig.update_layout(coloraxis_colorbar=dict(title='Network Type', tickvals=list(range(len(df_for_px['Network_type'].cat.categories))),\n",
    "                                           ticktext=df_for_px['Network_type'].cat.categories.tolist(),\n",
    "                                           lenmode='fraction', len=0.5, yanchor='top',\n",
    "                                           y=0.95, xanchor='left', x=0.01))\n",
    "fig.update_layout(title_x=0.5, title_y=0.95)\n",
    "fig.update_layout(margin=dict(l=50, r=50, t=50, b=50))\n",
    "fig.write_html(\"./output/parallel_coordinates_plot.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ffef086",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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