Anna-PV-PPR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from lib.utilities import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import norm, kstest, sem, ttest_ind\n",
    "\n",
    "import datetime\n",
    "\n",
    "# investigate probability of crossing controlling for distance to boundary\n",
    "# (and size of barrel?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Anna PV PPR/\"\n",
    "# data_dir = 'C:/Users/jjudg/Downloads/'\n",
    "plt.rcParams.update({'font.size': 13})\n",
    "#plt.rcParams['figure.figsize'] = [20, 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date 3\n",
      "date 5\n",
      "date 7\n",
      "date 3\n",
      "date 5\n",
      "date 7\n"
     ]
    },
    {
     "data": {
      "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>ROI_ID</th>\n",
       "      <th>Amp_1</th>\n",
       "      <th>Amp_2</th>\n",
       "      <th>SNR_1</th>\n",
       "      <th>SNR_2</th>\n",
       "      <th>nPixels</th>\n",
       "      <th>Barrels</th>\n",
       "      <th>Layers</th>\n",
       "      <th>PPR</th>\n",
       "      <th>Slice_Loc_Run</th>\n",
       "      <th>Stim_layer</th>\n",
       "      <th>IPI</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.001240</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>3.48335</td>\n",
       "      <td>1.91849</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.550762</td>\n",
       "      <td>03-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>2022-07-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.001375</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>6.49998</td>\n",
       "      <td>3.32801</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.512007</td>\n",
       "      <td>03-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>2022-07-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>7.30323</td>\n",
       "      <td>4.16637</td>\n",
       "      <td>5</td>\n",
       "      <td>Edge</td>\n",
       "      <td>4</td>\n",
       "      <td>0.570476</td>\n",
       "      <td>03-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>2022-07-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>4.53138</td>\n",
       "      <td>2.97294</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.656075</td>\n",
       "      <td>03-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>2022-07-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.001375</td>\n",
       "      <td>0.000819</td>\n",
       "      <td>4.93487</td>\n",
       "      <td>2.94083</td>\n",
       "      <td>1</td>\n",
       "      <td>Edge</td>\n",
       "      <td>4</td>\n",
       "      <td>0.595926</td>\n",
       "      <td>03-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>2022-07-26</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>67</td>\n",
       "      <td>0.006902</td>\n",
       "      <td>0.006908</td>\n",
       "      <td>15.87650</td>\n",
       "      <td>15.89030</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000866</td>\n",
       "      <td>05-02-07</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>2023-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>68</td>\n",
       "      <td>0.007524</td>\n",
       "      <td>0.006853</td>\n",
       "      <td>16.59150</td>\n",
       "      <td>15.11340</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.910916</td>\n",
       "      <td>05-02-07</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>2023-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>69</td>\n",
       "      <td>0.007424</td>\n",
       "      <td>0.007933</td>\n",
       "      <td>19.59830</td>\n",
       "      <td>20.94230</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.068574</td>\n",
       "      <td>05-02-07</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>2023-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>70</td>\n",
       "      <td>0.007239</td>\n",
       "      <td>0.006950</td>\n",
       "      <td>11.93390</td>\n",
       "      <td>11.45680</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.960016</td>\n",
       "      <td>05-02-07</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>2023-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>71</td>\n",
       "      <td>0.009182</td>\n",
       "      <td>0.007991</td>\n",
       "      <td>15.64580</td>\n",
       "      <td>13.61580</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.870251</td>\n",
       "      <td>05-02-07</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>2023-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1755 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ROI_ID     Amp_1     Amp_2     SNR_1     SNR_2  nPixels Barrels Layers  \\\n",
       "0          1  0.001240  0.000683   3.48335   1.91849        1       1      4   \n",
       "1          2  0.001375  0.000704   6.49998   3.32801        1       1      4   \n",
       "2          3  0.001445  0.000824   7.30323   4.16637        5    Edge      4   \n",
       "3          4  0.001114  0.000731   4.53138   2.97294        2       1      4   \n",
       "4          5  0.001375  0.000819   4.93487   2.94083        1    Edge      4   \n",
       "...      ...       ...       ...       ...       ...      ...     ...    ...   \n",
       "1754      67  0.006902  0.006908  15.87650  15.89030        2       0      4   \n",
       "1755      68  0.007524  0.006853  16.59150  15.11340        1       0      4   \n",
       "1756      69  0.007424  0.007933  19.59830  20.94230        1       0      4   \n",
       "1757      70  0.007239  0.006950  11.93390  11.45680        1       0      4   \n",
       "1758      71  0.009182  0.007991  15.64580  13.61580        1       0      4   \n",
       "\n",
       "           PPR Slice_Loc_Run  Stim_layer  IPI        date  \n",
       "0     0.550762      03-01-01           4   20  2022-07-26  \n",
       "1     0.512007      03-01-01           4   20  2022-07-26  \n",
       "2     0.570476      03-01-01           4   20  2022-07-26  \n",
       "3     0.656075      03-01-01           4   20  2022-07-26  \n",
       "4     0.595926      03-01-01           4   20  2022-07-26  \n",
       "...        ...           ...         ...  ...         ...  \n",
       "1754  1.000866      05-02-07           4  200  2023-06-23  \n",
       "1755  0.910916      05-02-07           4  200  2023-06-23  \n",
       "1756  1.068574      05-02-07           4  200  2023-06-23  \n",
       "1757  0.960016      05-02-07           4  200  2023-06-23  \n",
       "1758  0.870251      05-02-07           4  200  2023-06-23  \n",
       "\n",
       "[1755 rows x 13 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# work with latency data\n",
    "# Load data, not from Drive for Desktop since PhotoZ complains about loading zda from Drive\n",
    "csv_file = data_dir + 'PreLim_PV_STP_Layer4StimOnly_4_2_2024.csv'\n",
    "\n",
    "# load the all-data CSV dir\n",
    "df = pd.read_csv(csv_file)\n",
    "df = df.drop(columns=['Latency_1', 'Latency_2', 'Halfwidth_1', \n",
    "                      'Halfwidth_2', 'PeakTime_1', 'PeakTime_2',\n",
    "                      'Rise_1', 'Rise_2', 'Decay_1', 'Decay_2',\n",
    "                      'Stim_intensity', 'RLI', 'Cx', 'Trial_x_Time'])\n",
    "df = df[df[\"PPR\"] > 0]\n",
    "df = df[df[\"PPR\"] < 2]\n",
    "\n",
    "for bar in ['0', '1']:\n",
    "    df2 = df[df['Barrels'] == bar]\n",
    "    for date in df2['date'].unique():\n",
    "        slrs = []\n",
    "        for slr in df2[df2['date'] == date]['Slice_Loc_Run'].unique():\n",
    "            sl = slr[:2]\n",
    "            if slr not in slrs:\n",
    "                slrs.append(slr)\n",
    "        print('date', len(slrs))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   IPI     Amp_1\n",
      "0   10  0.005481\n",
      "1   20  0.003137\n",
      "2   50  0.003248\n",
      "3   75  0.004861\n",
      "4  100  0.003050\n",
      "5  150  0.005086\n",
      "6  200  0.004996\n",
      "   IPI     Amp_2\n",
      "0   10  0.004787\n",
      "1   20  0.002020\n",
      "2   50  0.002020\n",
      "3   75  0.003027\n",
      "4  100  0.002204\n",
      "5  150  0.002966\n",
      "6  200  0.003090\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col, amp in [['blue', 'Amp_1'], ['red', 'Amp_2']]:\n",
    "    pulse_df_std = df[['IPI', amp]].groupby(by=['IPI']).agg(np.std)\n",
    "    pulse_df = df[['IPI', amp]].groupby(by=['IPI']).mean()\n",
    "    pulse_df.reset_index(inplace=True)\n",
    "\n",
    "    print(pulse_df)\n",
    "    plt.errorbar(pulse_df['IPI'], # + i * 0.5 - 0.25, \n",
    "                 pulse_df[amp], \n",
    "                 pulse_df_std[amp], ls='none', \n",
    "                 capsize=3, alpha=0.4)\n",
    "    plt.scatter(pulse_df['IPI'], pulse_df[amp],\n",
    "                    s=30, color=col)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_val for IPI 10 : 5.031629374242374e-08\n",
      "p_val for IPI 20 : 3.518173955830885e-51\n",
      "p_val for IPI 50 : 2.7501980464648443e-56\n",
      "p_val for IPI 75 : 2.8453382981513855e-11\n",
      "p_val for IPI 100 : 2.9223744057975974e-57\n",
      "p_val for IPI 150 : 2.2426057291160036e-13\n",
      "p_val for IPI 200 : 1.3298672592258563e-11\n",
      "significance tests that PPR < 1 ['***', '***', '***', '***', '***', '***', '***']\n",
      "p_val for IPI 10 : 1.0670888104036437e-07\n",
      "p_val for IPI 20 : 8.377365485120737e-11\n",
      "p_val for IPI 50 : 6.307639243461494e-06\n",
      "p_val for IPI 75 : 1.205123424540656e-05\n",
      "p_val for IPI 100 : 0.00036219210797049545\n",
      "p_val for IPI 150 : 2.6166041238783184e-06\n",
      "p_val for IPI 200 : 3.604202108412528e-07\n",
      "significance tests that PPR(1) < PPR(0) ['***', '***', '***', '***', '***', '***', '***']\n"
     ]
    }
   ],
   "source": [
    "amp = 'PPR'\n",
    "\n",
    "pvals = []\n",
    "\n",
    "# significance tests that PPR < 1\n",
    "first_ipi_data = np.random.normal(loc=1.0, \n",
    "                                    scale=np.average(pulse_df_std['PPR']),\n",
    "                                    size=1000)\n",
    "for ipi in pulse_df['IPI']:\n",
    "    ipi_data = df2[df2['IPI'] == ipi]['PPR']\n",
    "    \n",
    "    # one-sided t-test if ipi_data is less than first_ipi_data\n",
    "    t_stat, p_val = ttest_ind(ipi_data, first_ipi_data, alternative='less', equal_var=False)\n",
    "    print(\"p_val for IPI\", ipi, \":\", p_val)\n",
    "    pvals.append(p_val)\n",
    "\n",
    "def get_sig_level(x):\n",
    "    if x < 0.001:\n",
    "        return '***'\n",
    "    if x < 0.01:\n",
    "        return '**'\n",
    "    if x < 0.05:\n",
    "        return '*'\n",
    "    \n",
    "\n",
    "significance_levels = [get_sig_level(x) for x in pvals]\n",
    "print('significance tests that PPR < 1', significance_levels)\n",
    "\n",
    "pvals = []\n",
    "df0 = df[df['Barrels'] == '0']\n",
    "df1 = df[df['Barrels'] == '1']\n",
    "\n",
    "# significance tests that barrel 1 < barrel 0\n",
    "for ipi in pulse_df['IPI']:\n",
    "    ipi_data0 = df0[df0['IPI'] == ipi]['PPR']\n",
    "    ipi_data1 = df1[df1['IPI'] == ipi]['PPR']\n",
    "    \n",
    "    # one-sided t-test\n",
    "    t_stat, p_val = ttest_ind(ipi_data1, ipi_data0, alternative='less', equal_var=False)\n",
    "    print(\"p_val for IPI\", ipi, \":\", p_val)\n",
    "    pvals.append(p_val)\n",
    "\n",
    "significance_levels = [get_sig_level(x) for x in pvals]\n",
    "print('significance tests that PPR(1) < PPR(0)', significance_levels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   IPI       PPR\n",
      "0   10  0.992089\n",
      "1   20  0.793934\n",
      "2   50  0.729818\n",
      "3   75  0.812945\n",
      "4  100  0.773361\n",
      "5  150  0.710368\n",
      "6  200  0.804792\n",
      "   IPI       PPR\n",
      "0   10  0.768019\n",
      "1   20  0.601703\n",
      "2   50  0.598177\n",
      "3   75  0.435752\n",
      "4  100  0.702284\n",
      "5  150  0.424639\n",
      "6  200  0.464633\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# plot PPR for each barrel\n",
    "for bar in ['0', '1']:\n",
    "    df2 = df[df['Barrels'] == bar]\n",
    "    pulse_df_std = df2[['IPI', amp]].groupby(by=['IPI']).agg(np.std)\n",
    "    pulse_df = df2[['IPI', amp]].groupby(by=['IPI']).mean()\n",
    "    pulse_df.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "    print(pulse_df)\n",
    "    plt.errorbar(pulse_df['IPI'], # + i * 0.5 - 0.25, \n",
    "                    pulse_df[amp], \n",
    "                    pulse_df_std[amp], ls='none', \n",
    "                    capsize=3, alpha=0.4)\n",
    "    plt.scatter(pulse_df['IPI'], pulse_df[amp],\n",
    "                    s=30, color=col)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TSM-to-ZDA",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}