hw_amp_vs_dist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0ec00416",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "import math\n",
    "import numpy as np\n",
    "import cv2\n",
    "import pandas as pd\n",
    "import pyautogui as pa\n",
    "\n",
    "from lib.auto_GUI.auto_GUI_base import AutoGUIBase\n",
    "from lib.auto_GUI.auto_PhotoZ import AutoPhotoZ\n",
    "\n",
    "from lib.file.ROI_reader import ROIFileReader\n",
    "from lib.analysis.laminar_dist import LaminarROI, Line\n",
    "from lib.analysis.align import ImageAlign\n",
    "from lib.utilities import *\n",
    "\n",
    "####################################################################################\n",
    "#    Build a hw vs dist scatter and amp vs dist scatter for all high-SNR px for all\n",
    "#       of Kate's data\n",
    "####################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5b23cb4f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_dir = \"C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/\"\n",
    "\n",
    "enable_photoZ_interaction = True\n",
    "initialize_photoZ = False\n",
    "snr_cutoff_signal = 5.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f78e4f4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OK'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up PhotoZ (open it manually)\n",
    "if enable_photoZ_interaction:\n",
    "    aPhz = AutoPhotoZ(data_dir=data_dir)\n",
    "    if initialize_photoZ:\n",
    "        aPhz.prepare_photoZ()\n",
    "pa.alert(\"Load your ROIs before continuing.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f3a5126f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-13_02-01-02_L4_intralaminar/02_01_02.zda\n",
      "\n",
      "\n",
      "Opening 02_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-28_03-01-08_aka_02-01-08_L23_intralaminar/02_01_08.zda\n",
      "\n",
      "\n",
      "Opening 02_01_08.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-28_04-01-01_aka_03-01-01_L4_interlaminar/03_01_01.zda\n",
      "\n",
      "\n",
      "Opening 03_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-28_06-01-01_aka_part2_03-01-01_L4_interlaminar/03_01_01.zda\n",
      "\n",
      "\n",
      "Opening 03_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-28_08-01-04_aka_part2_05-01-04_L4_interlaminar/05_01_04.zda\n",
      "\n",
      "\n",
      "Opening 05_01_04.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-28_10-01-01_aka_part2_07-01-01_L4_interlaminar/07_01_01.zda\n",
      "\n",
      "\n",
      "Opening 07_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-28_11-01-06_aka_part2_08-01-06_L4_interlaminar/08_01_06.zda\n",
      "\n",
      "\n",
      "Opening 08_01_06.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-29_04-01-01_L4_interlaminar/04_01_01.zda\n",
      "\n",
      "\n",
      "Opening 04_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-29_06-01-01_L4_interlaminar/06_01_01.zda\n",
      "\n",
      "\n",
      "Opening 06_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-29_07-01-03_L4_intralaminar/07_01_03.zda\n",
      "\n",
      "\n",
      "Opening 07_01_03.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2020-12-29_09-01-02_L4_interlaminar/09_01_02.zda\n",
      "\n",
      "\n",
      "Opening 09_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-01-11_09-01-01_L23_interlaminar/09_01_01.zda\n",
      "\n",
      "\n",
      "Opening 09_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-01-18_04-01-02_L23_interlaminar/04_01_02.zda\n",
      "\n",
      "\n",
      "Opening 04_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-01-18_05-01-03_L23_interlaminar/05_01_03.zda\n",
      "\n",
      "\n",
      "Opening 05_01_03.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-01-18_07-01-02_L23_interlaminar/07_01_02.zda\n",
      "\n",
      "\n",
      "Opening 07_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-01-19_05-01-01_L23_interlaminar/05_01_01.zda\n",
      "\n",
      "\n",
      "Opening 05_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-03-02_03-01-02_L5_interlaminar/03_01_02.zda\n",
      "\n",
      "\n",
      "Opening 03_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-03-02_05-01-02_L5_interlaminar/05_01_02.zda\n",
      "\n",
      "\n",
      "Opening 05_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-03-02_07-01-03_L5_interlaminar/07_01_03.zda\n",
      "\n",
      "\n",
      "Opening 07_01_03.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-03-02_08-01-01_L5_interlaminar_intralaminar/08_01_01.zda\n",
      "\n",
      "\n",
      "Opening 08_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-03-09_03-01-02_L5_interlaminar/03_01_02.zda\n",
      "\n",
      "\n",
      "Opening 03_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-03-09_05-01-02_L5_interlaminar/05_01_02.zda\n",
      "\n",
      "\n",
      "Opening 05_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-03-23_05-01-02_L5_interlaminar_intralaminar/05_01_02.zda\n",
      "\n",
      "\n",
      "Opening 05_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-03-23_06-01-01_L5_interlaminar/06_01_01.zda\n",
      "\n",
      "\n",
      "Opening 06_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-03-23_07-01-01_L5_interlaminar/07_01_01.zda\n",
      "\n",
      "\n",
      "Opening 07_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-04-13_03-01-02_L5_interlaminar_intralaminar/03_01_02.zda\n",
      "\n",
      "\n",
      "Opening 03_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-04-13_04-01-02_L23_interlaminar/04_01_02.zda\n",
      "\n",
      "\n",
      "Opening 04_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-04-13_05-01-01_L23_interlaminar_intralaminar/05_01_01.zda\n",
      "\n",
      "\n",
      "Opening 05_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-04-13_06-01-01_L23_interlaminar/06_01_01.zda\n",
      "\n",
      "\n",
      "Opening 06_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-04-13_07-01-02_L4_interlaminar_intralaminar/07_01_02.zda\n",
      "\n",
      "\n",
      "Opening 07_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-04-13_08-01-01_L23_interlaminar/08_01_01.zda\n",
      "\n",
      "\n",
      "Opening 08_01_01.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-25_01-01-04_L23_intralaminar/01_01_04.zda\n",
      "\n",
      "\n",
      "Opening 01_01_04.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-25_02-01-03_L23_interlaminar_intralaminar/02_01_03.zda\n",
      "\n",
      "\n",
      "Opening 02_01_03.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-25_03-01-05_L23_interlaminar/03_01_05.zda\n",
      "\n",
      "\n",
      "Opening 03_01_05.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-25_04-01-03_L23_interlaminar/04_01_03.zda\n",
      "\n",
      "\n",
      "Opening 04_01_03.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-25_05-01-04_L23_interlaminar/05_01_04.zda\n",
      "\n",
      "\n",
      "Opening 05_01_04.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-25_07-01-03_L4_intralaminar/07_01_03.zda\n",
      "\n",
      "\n",
      "Opening 07_01_03.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-25_08-01-04_L4_interlaminar_intralaminar/08_01_04.zda\n",
      "\n",
      "\n",
      "Opening 08_01_04.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-25_09-01-04_L23_interlaminar_intralaminar/09_01_04.zda\n",
      "\n",
      "\n",
      "Opening 09_01_04.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-26_02-01-04_L23_interlaminar/02_01_04.zda\n",
      "\n",
      "\n",
      "Opening 02_01_04.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-26_04-01-05_L23_interlaminar_intralaminar/04_01_05.zda\n",
      "\n",
      "\n",
      "Opening 04_01_05.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-26_05-01-03_L23_interlaminar_intralaminar/05_01_03.zda\n",
      "\n",
      "\n",
      "Opening 05_01_03.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-26_07-01-03_L4_interlaminar_intralaminar/07_01_03.zda\n",
      "\n",
      "\n",
      "Opening 07_01_03.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-31_05-01-04_L5_interlaminar/05_01_04.zda\n",
      "\n",
      "\n",
      "Opening 05_01_04.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-31_06-01-05_L23_interlaminar_intralaminar/06_01_05.zda\n",
      "\n",
      "\n",
      "Opening 06_01_05.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-31_07-01-05_L23_interlaminar_intralaminar/07_01_05.zda\n",
      "\n",
      "\n",
      "Opening 07_01_05.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-31_09-01-05_L23_interlaminar/09_01_05.zda\n",
      "\n",
      "\n",
      "Opening 09_01_05.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-05-31_10-01-05_L23_interlaminar/10_01_05.zda\n",
      "\n",
      "\n",
      "Opening 10_01_05.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-06-28_03-01-02_L5_interlaminar/03_01_02.zda\n",
      "\n",
      "\n",
      "Opening 03_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-06-28_04-01-02_L5_interlaminar/04_01_02.zda\n",
      "\n",
      "\n",
      "Opening 04_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-06-28_05-01-02_L5_interlaminar/05_01_02.zda\n",
      "\n",
      "\n",
      "Opening 05_01_02.zda\n",
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/Kate's/hw_and_amp_scatter/2021-06-28_06-01-01_L5_interlaminar/06_01_01.zda\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Opening 06_01_01.zda\n"
     ]
    }
   ],
   "source": [
    "# extract all hw and amp files \n",
    "for subdir, dirs, files in os.walk(data_dir):\n",
    "    for zda_file in files:\n",
    "        if zda_file.endswith('.zda'):\n",
    "            zda_filepath = subdir + \"/\" + zda_file\n",
    "            print(zda_filepath)\n",
    "            if enable_photoZ_interaction:\n",
    "                aPhz = AutoPhotoZ(data_dir=subdir)\n",
    "                # open the PhotoZ file\n",
    "                aPhz.select_PhotoZ()\n",
    "\n",
    "                print(\"\\n\\nOpening\", zda_file)\n",
    "                aPhz.open_zda_file(zda_filepath)\n",
    "\n",
    "                # save SNR values from PhotoZ\n",
    "                snr_filename = subdir + \"/\" + \"SNR.dat\"\n",
    "                if enable_photoZ_interaction:\n",
    "                    # aPhz.select_SNR_trace_value()\n",
    "                    aPhz.save_trace_values(snr_filename)\n",
    "\n",
    "                # save half-width values from PhotoZ\n",
    "                hw_filename = subdir + \"/\" + \"hw.dat\" \n",
    "                if enable_photoZ_interaction:\n",
    "                    aPhz.select_half_width_trace_value()\n",
    "                    aPhz.save_trace_values(hw_filename)\n",
    "\n",
    "                # save latency values from PhotoZ\n",
    "                amp_filename = subdir + \"/\" + \"amp.dat\"\n",
    "                if enable_photoZ_interaction:\n",
    "                    aPhz.select_maxamp_trace_value()\n",
    "                    aPhz.save_trace_values(amp_filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7145bca7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4160\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>Diode Number</th>\n",
       "      <th>Value</th>\n",
       "      <th>Diode Numberamp</th>\n",
       "      <th>Valueamp</th>\n",
       "      <th>electrode_dist</th>\n",
       "      <th>Diode Numberhw</th>\n",
       "      <th>Valuehw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5.16216</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003334</td>\n",
       "      <td>477.631657</td>\n",
       "      <td>2</td>\n",
       "      <td>3.34766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5.60960</td>\n",
       "      <td>3</td>\n",
       "      <td>0.002547</td>\n",
       "      <td>474.948418</td>\n",
       "      <td>3</td>\n",
       "      <td>3.30060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5.19279</td>\n",
       "      <td>4</td>\n",
       "      <td>0.002246</td>\n",
       "      <td>472.326158</td>\n",
       "      <td>4</td>\n",
       "      <td>7.36340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6.16472</td>\n",
       "      <td>8</td>\n",
       "      <td>0.002286</td>\n",
       "      <td>462.467296</td>\n",
       "      <td>8</td>\n",
       "      <td>3.15799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>8.59641</td>\n",
       "      <td>11</td>\n",
       "      <td>0.002398</td>\n",
       "      <td>455.763096</td>\n",
       "      <td>11</td>\n",
       "      <td>5.66684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>9.32633</td>\n",
       "      <td>23</td>\n",
       "      <td>0.002910</td>\n",
       "      <td>435.403261</td>\n",
       "      <td>23</td>\n",
       "      <td>3.96285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>5.98822</td>\n",
       "      <td>26</td>\n",
       "      <td>0.003021</td>\n",
       "      <td>432.041665</td>\n",
       "      <td>26</td>\n",
       "      <td>2.61267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>5.09460</td>\n",
       "      <td>38</td>\n",
       "      <td>0.002476</td>\n",
       "      <td>426.000000</td>\n",
       "      <td>38</td>\n",
       "      <td>6.30548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>10.49100</td>\n",
       "      <td>50</td>\n",
       "      <td>0.003857</td>\n",
       "      <td>432.041665</td>\n",
       "      <td>50</td>\n",
       "      <td>3.26058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>5.18741</td>\n",
       "      <td>51</td>\n",
       "      <td>0.003150</td>\n",
       "      <td>433.081978</td>\n",
       "      <td>51</td>\n",
       "      <td>3.40328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>5.45185</td>\n",
       "      <td>59</td>\n",
       "      <td>0.002512</td>\n",
       "      <td>444.243177</td>\n",
       "      <td>59</td>\n",
       "      <td>3.58069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>10.18130</td>\n",
       "      <td>60</td>\n",
       "      <td>0.003156</td>\n",
       "      <td>445.982062</td>\n",
       "      <td>60</td>\n",
       "      <td>3.40688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64</td>\n",
       "      <td>6.37100</td>\n",
       "      <td>64</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>453.665075</td>\n",
       "      <td>64</td>\n",
       "      <td>7.36022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>5.06671</td>\n",
       "      <td>73</td>\n",
       "      <td>0.001816</td>\n",
       "      <td>474.948418</td>\n",
       "      <td>73</td>\n",
       "      <td>7.00020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>7.00884</td>\n",
       "      <td>74</td>\n",
       "      <td>0.002632</td>\n",
       "      <td>477.631657</td>\n",
       "      <td>74</td>\n",
       "      <td>6.50891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6.22465</td>\n",
       "      <td>2</td>\n",
       "      <td>0.004445</td>\n",
       "      <td>379.473319</td>\n",
       "      <td>2</td>\n",
       "      <td>7.95571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>18.04580</td>\n",
       "      <td>3</td>\n",
       "      <td>0.008226</td>\n",
       "      <td>377.618855</td>\n",
       "      <td>3</td>\n",
       "      <td>4.24118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>26.92070</td>\n",
       "      <td>4</td>\n",
       "      <td>0.010575</td>\n",
       "      <td>375.851034</td>\n",
       "      <td>4</td>\n",
       "      <td>4.17384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>24.26950</td>\n",
       "      <td>5</td>\n",
       "      <td>0.010388</td>\n",
       "      <td>374.171084</td>\n",
       "      <td>5</td>\n",
       "      <td>4.22152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>13.15800</td>\n",
       "      <td>6</td>\n",
       "      <td>0.007873</td>\n",
       "      <td>372.580193</td>\n",
       "      <td>6</td>\n",
       "      <td>4.03199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>30.10610</td>\n",
       "      <td>8</td>\n",
       "      <td>0.006844</td>\n",
       "      <td>369.670123</td>\n",
       "      <td>8</td>\n",
       "      <td>4.59880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>8.55286</td>\n",
       "      <td>9</td>\n",
       "      <td>0.005962</td>\n",
       "      <td>368.353091</td>\n",
       "      <td>9</td>\n",
       "      <td>6.14126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>9.38304</td>\n",
       "      <td>10</td>\n",
       "      <td>0.006700</td>\n",
       "      <td>367.129405</td>\n",
       "      <td>10</td>\n",
       "      <td>4.10874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>55.78670</td>\n",
       "      <td>11</td>\n",
       "      <td>0.010733</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>4.39739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>6.05315</td>\n",
       "      <td>12</td>\n",
       "      <td>0.003402</td>\n",
       "      <td>364.965752</td>\n",
       "      <td>12</td>\n",
       "      <td>4.55072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>33.31080</td>\n",
       "      <td>14</td>\n",
       "      <td>0.009185</td>\n",
       "      <td>363.185903</td>\n",
       "      <td>14</td>\n",
       "      <td>5.28238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>29.68670</td>\n",
       "      <td>22</td>\n",
       "      <td>0.007511</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>4.62894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>25.39740</td>\n",
       "      <td>23</td>\n",
       "      <td>0.010067</td>\n",
       "      <td>360.049997</td>\n",
       "      <td>23</td>\n",
       "      <td>4.00692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>7.00183</td>\n",
       "      <td>26</td>\n",
       "      <td>0.005498</td>\n",
       "      <td>360.799113</td>\n",
       "      <td>26</td>\n",
       "      <td>6.80207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>11.56820</td>\n",
       "      <td>29</td>\n",
       "      <td>0.005778</td>\n",
       "      <td>362.441719</td>\n",
       "      <td>29</td>\n",
       "      <td>8.61069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>26.17090</td>\n",
       "      <td>30</td>\n",
       "      <td>0.008456</td>\n",
       "      <td>363.185903</td>\n",
       "      <td>30</td>\n",
       "      <td>7.33235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>15.00480</td>\n",
       "      <td>31</td>\n",
       "      <td>0.005405</td>\n",
       "      <td>364.027471</td>\n",
       "      <td>31</td>\n",
       "      <td>5.11752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>5.66901</td>\n",
       "      <td>36</td>\n",
       "      <td>0.005633</td>\n",
       "      <td>369.670123</td>\n",
       "      <td>36</td>\n",
       "      <td>8.20342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>5.82761</td>\n",
       "      <td>37</td>\n",
       "      <td>0.004048</td>\n",
       "      <td>371.079506</td>\n",
       "      <td>37</td>\n",
       "      <td>7.21087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>27.83670</td>\n",
       "      <td>38</td>\n",
       "      <td>0.012120</td>\n",
       "      <td>372.580193</td>\n",
       "      <td>38</td>\n",
       "      <td>4.04434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>11.87620</td>\n",
       "      <td>43</td>\n",
       "      <td>0.004914</td>\n",
       "      <td>381.413162</td>\n",
       "      <td>43</td>\n",
       "      <td>5.99762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>5.37842</td>\n",
       "      <td>45</td>\n",
       "      <td>0.003124</td>\n",
       "      <td>385.543772</td>\n",
       "      <td>45</td>\n",
       "      <td>5.57705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>18.18180</td>\n",
       "      <td>46</td>\n",
       "      <td>0.008236</td>\n",
       "      <td>387.731866</td>\n",
       "      <td>46</td>\n",
       "      <td>4.97282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>24.80020</td>\n",
       "      <td>47</td>\n",
       "      <td>0.008464</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>47</td>\n",
       "      <td>4.82965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>40.10550</td>\n",
       "      <td>50</td>\n",
       "      <td>0.012497</td>\n",
       "      <td>397.270689</td>\n",
       "      <td>50</td>\n",
       "      <td>3.65462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>9.41433</td>\n",
       "      <td>51</td>\n",
       "      <td>0.005166</td>\n",
       "      <td>399.844970</td>\n",
       "      <td>51</td>\n",
       "      <td>6.41611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>10.69630</td>\n",
       "      <td>52</td>\n",
       "      <td>0.005998</td>\n",
       "      <td>402.492236</td>\n",
       "      <td>52</td>\n",
       "      <td>8.66724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>10.39940</td>\n",
       "      <td>53</td>\n",
       "      <td>0.005199</td>\n",
       "      <td>405.211056</td>\n",
       "      <td>53</td>\n",
       "      <td>8.27346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>8.01103</td>\n",
       "      <td>54</td>\n",
       "      <td>0.004786</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>54</td>\n",
       "      <td>5.13426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>12.99730</td>\n",
       "      <td>56</td>\n",
       "      <td>0.007276</td>\n",
       "      <td>413.782552</td>\n",
       "      <td>56</td>\n",
       "      <td>8.47903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>5.64295</td>\n",
       "      <td>58</td>\n",
       "      <td>0.003509</td>\n",
       "      <td>419.828536</td>\n",
       "      <td>58</td>\n",
       "      <td>6.59843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>29.39130</td>\n",
       "      <td>59</td>\n",
       "      <td>0.011488</td>\n",
       "      <td>422.946805</td>\n",
       "      <td>59</td>\n",
       "      <td>3.98097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>17.59380</td>\n",
       "      <td>60</td>\n",
       "      <td>0.008523</td>\n",
       "      <td>426.126742</td>\n",
       "      <td>60</td>\n",
       "      <td>4.04126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>16.70660</td>\n",
       "      <td>61</td>\n",
       "      <td>0.005149</td>\n",
       "      <td>429.366976</td>\n",
       "      <td>61</td>\n",
       "      <td>4.19815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>8.09601</td>\n",
       "      <td>63</td>\n",
       "      <td>0.005963</td>\n",
       "      <td>436.022935</td>\n",
       "      <td>63</td>\n",
       "      <td>8.40219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64</td>\n",
       "      <td>30.58820</td>\n",
       "      <td>64</td>\n",
       "      <td>0.009770</td>\n",
       "      <td>439.436002</td>\n",
       "      <td>64</td>\n",
       "      <td>4.86472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>24.61870</td>\n",
       "      <td>66</td>\n",
       "      <td>0.009546</td>\n",
       "      <td>446.425806</td>\n",
       "      <td>66</td>\n",
       "      <td>4.27404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>5.42675</td>\n",
       "      <td>69</td>\n",
       "      <td>0.004668</td>\n",
       "      <td>457.300776</td>\n",
       "      <td>69</td>\n",
       "      <td>3.84779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>5.10000</td>\n",
       "      <td>70</td>\n",
       "      <td>0.004196</td>\n",
       "      <td>461.024945</td>\n",
       "      <td>70</td>\n",
       "      <td>8.16376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>5.25062</td>\n",
       "      <td>72</td>\n",
       "      <td>0.004568</td>\n",
       "      <td>468.614981</td>\n",
       "      <td>72</td>\n",
       "      <td>7.61778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>10.56960</td>\n",
       "      <td>73</td>\n",
       "      <td>0.005344</td>\n",
       "      <td>472.478571</td>\n",
       "      <td>73</td>\n",
       "      <td>9.09097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>10.39610</td>\n",
       "      <td>74</td>\n",
       "      <td>0.004903</td>\n",
       "      <td>476.386398</td>\n",
       "      <td>74</td>\n",
       "      <td>8.01913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>19.56820</td>\n",
       "      <td>75</td>\n",
       "      <td>0.007166</td>\n",
       "      <td>480.337381</td>\n",
       "      <td>75</td>\n",
       "      <td>5.30627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>8.70279</td>\n",
       "      <td>79</td>\n",
       "      <td>0.002507</td>\n",
       "      <td>496.552112</td>\n",
       "      <td>79</td>\n",
       "      <td>5.78988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Diode Number     Value  Diode Numberamp  Valueamp  electrode_dist  \\\n",
       "1              2   5.16216                2  0.003334      477.631657   \n",
       "2              3   5.60960                3  0.002547      474.948418   \n",
       "3              4   5.19279                4  0.002246      472.326158   \n",
       "7              8   6.16472                8  0.002286      462.467296   \n",
       "10            11   8.59641               11  0.002398      455.763096   \n",
       "22            23   9.32633               23  0.002910      435.403261   \n",
       "25            26   5.98822               26  0.003021      432.041665   \n",
       "37            38   5.09460               38  0.002476      426.000000   \n",
       "49            50  10.49100               50  0.003857      432.041665   \n",
       "50            51   5.18741               51  0.003150      433.081978   \n",
       "58            59   5.45185               59  0.002512      444.243177   \n",
       "59            60  10.18130               60  0.003156      445.982062   \n",
       "63            64   6.37100               64  0.002701      453.665075   \n",
       "72            73   5.06671               73  0.001816      474.948418   \n",
       "73            74   7.00884               74  0.002632      477.631657   \n",
       "1              2   6.22465                2  0.004445      379.473319   \n",
       "2              3  18.04580                3  0.008226      377.618855   \n",
       "3              4  26.92070                4  0.010575      375.851034   \n",
       "4              5  24.26950                5  0.010388      374.171084   \n",
       "5              6  13.15800                6  0.007873      372.580193   \n",
       "7              8  30.10610                8  0.006844      369.670123   \n",
       "8              9   8.55286                9  0.005962      368.353091   \n",
       "9             10   9.38304               10  0.006700      367.129405   \n",
       "10            11  55.78670               11  0.010733      366.000000   \n",
       "11            12   6.05315               12  0.003402      364.965752   \n",
       "13            14  33.31080               14  0.009185      363.185903   \n",
       "21            22  29.68670               22  0.007511      360.000000   \n",
       "22            23  25.39740               23  0.010067      360.049997   \n",
       "25            26   7.00183               26  0.005498      360.799113   \n",
       "28            29  11.56820               29  0.005778      362.441719   \n",
       "29            30  26.17090               30  0.008456      363.185903   \n",
       "30            31  15.00480               31  0.005405      364.027471   \n",
       "35            36   5.66901               36  0.005633      369.670123   \n",
       "36            37   5.82761               37  0.004048      371.079506   \n",
       "37            38  27.83670               38  0.012120      372.580193   \n",
       "42            43  11.87620               43  0.004914      381.413162   \n",
       "44            45   5.37842               45  0.003124      385.543772   \n",
       "45            46  18.18180               46  0.008236      387.731866   \n",
       "46            47  24.80020               47  0.008464      390.000000   \n",
       "49            50  40.10550               50  0.012497      397.270689   \n",
       "50            51   9.41433               51  0.005166      399.844970   \n",
       "51            52  10.69630               52  0.005998      402.492236   \n",
       "52            53  10.39940               53  0.005199      405.211056   \n",
       "53            54   8.01103               54  0.004786      408.000000   \n",
       "55            56  12.99730               56  0.007276      413.782552   \n",
       "57            58   5.64295               58  0.003509      419.828536   \n",
       "58            59  29.39130               59  0.011488      422.946805   \n",
       "59            60  17.59380               60  0.008523      426.126742   \n",
       "60            61  16.70660               61  0.005149      429.366976   \n",
       "62            63   8.09601               63  0.005963      436.022935   \n",
       "63            64  30.58820               64  0.009770      439.436002   \n",
       "65            66  24.61870               66  0.009546      446.425806   \n",
       "68            69   5.42675               69  0.004668      457.300776   \n",
       "69            70   5.10000               70  0.004196      461.024945   \n",
       "71            72   5.25062               72  0.004568      468.614981   \n",
       "72            73  10.56960               73  0.005344      472.478571   \n",
       "73            74  10.39610               74  0.004903      476.386398   \n",
       "74            75  19.56820               75  0.007166      480.337381   \n",
       "78            79   8.70279               79  0.002507      496.552112   \n",
       "\n",
       "    Diode Numberhw  Valuehw  \n",
       "1                2  3.34766  \n",
       "2                3  3.30060  \n",
       "3                4  7.36340  \n",
       "7                8  3.15799  \n",
       "10              11  5.66684  \n",
       "22              23  3.96285  \n",
       "25              26  2.61267  \n",
       "37              38  6.30548  \n",
       "49              50  3.26058  \n",
       "50              51  3.40328  \n",
       "58              59  3.58069  \n",
       "59              60  3.40688  \n",
       "63              64  7.36022  \n",
       "72              73  7.00020  \n",
       "73              74  6.50891  \n",
       "1                2  7.95571  \n",
       "2                3  4.24118  \n",
       "3                4  4.17384  \n",
       "4                5  4.22152  \n",
       "5                6  4.03199  \n",
       "7                8  4.59880  \n",
       "8                9  6.14126  \n",
       "9               10  4.10874  \n",
       "10              11  4.39739  \n",
       "11              12  4.55072  \n",
       "13              14  5.28238  \n",
       "21              22  4.62894  \n",
       "22              23  4.00692  \n",
       "25              26  6.80207  \n",
       "28              29  8.61069  \n",
       "29              30  7.33235  \n",
       "30              31  5.11752  \n",
       "35              36  8.20342  \n",
       "36              37  7.21087  \n",
       "37              38  4.04434  \n",
       "42              43  5.99762  \n",
       "44              45  5.57705  \n",
       "45              46  4.97282  \n",
       "46              47  4.82965  \n",
       "49              50  3.65462  \n",
       "50              51  6.41611  \n",
       "51              52  8.66724  \n",
       "52              53  8.27346  \n",
       "53              54  5.13426  \n",
       "55              56  8.47903  \n",
       "57              58  6.59843  \n",
       "58              59  3.98097  \n",
       "59              60  4.04126  \n",
       "60              61  4.19815  \n",
       "62              63  8.40219  \n",
       "63              64  4.86472  \n",
       "65              66  4.27404  \n",
       "68              69  3.84779  \n",
       "69              70  8.16376  \n",
       "71              72  7.61778  \n",
       "72              73  9.09097  \n",
       "73              74  8.01913  \n",
       "74              75  5.30627  \n",
       "78              79  5.78988  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for each subdir, take all hw/amp/electrode dist of points with SNR > cutoff\n",
    "def read_dat_trace_file(filename):\n",
    "    data_arr = pd.read_csv(filename,\n",
    "                                 sep='\\t',\n",
    "                                 header=None,\n",
    "                                 names=['Diode Number',  'Value'])\n",
    "    return data_arr\n",
    "\n",
    "def read_dat_arr_file(filename):\n",
    "    data_arr = pd.read_csv(filename,\n",
    "                                 sep='\\t',\n",
    "                                 header=None,\n",
    "                                 names=['Index',  'Value'])\n",
    "    data_arr = np.array(data_arr['Value']).reshape((80, 80))\n",
    "    return data_arr\n",
    "    \n",
    "concat_data = pd.DataFrame({})\n",
    "snr_dfs = []\n",
    "total_before_filter = 0\n",
    "for subdir, dirs, files in os.walk(data_dir):\n",
    "    if subdir.endswith(\"hw_and_amp_scatter/\"):\n",
    "        continue\n",
    "    snr_arr = None\n",
    "    for f in files:\n",
    "        if '_snr.txt' in f:\n",
    "            snr_arr = read_dat_arr_file(subdir + \"/\" + f)\n",
    "            \n",
    "    amp = read_dat_trace_file(subdir + \"/\" + 'amp.dat')\n",
    "    hw = read_dat_trace_file(subdir + \"/\" + 'hw.dat')\n",
    "    snr = read_dat_trace_file(subdir + \"/\" + 'SNR.dat')\n",
    "    \n",
    "    total_before_filter += len(amp['Value'])\n",
    "    \n",
    "    snr = snr.join(amp, rsuffix='amp')\n",
    "    snr = snr.join(hw, rsuffix='hw')\n",
    "    \n",
    "    # open stim point roi as a single integer (its diode number) in variable stim_pt\n",
    "    sp_file = subdir + \"/\" + 'electrode.dat'\n",
    "    with open(sp_file, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    stim_pt = int(lines[-1]) # last line is always electrode location\n",
    "    aux_obj = LaminarROI([stim_pt]).get_points()\n",
    "    stim_pt = aux_obj[0]  # should be a list of len 2, representing px location [x, y]\n",
    "    \n",
    "    snr = snr[pd.to_numeric(snr.Value, errors='coerce').notnull()]\n",
    "    snr = snr[snr['Value'] > snr_cutoff_signal]\n",
    "    \n",
    "    snr = snr[snr['Valuehw'] > 0]\n",
    "    snr = snr[snr['Valuehw'] < 15]\n",
    "    snr = snr[snr['Valueamp'] > 1/1000]\n",
    "    \n",
    "    \n",
    "    # Find distance from diode number to stim_pt\n",
    "    dist = []\n",
    "    for dn in snr['Diode Number']:\n",
    "        px = LaminarROI([dn]).get_points()[0]\n",
    "        d = Line(stim_pt, px).get_length()\n",
    "        dist.append(d * 6)  # convert px -> um\n",
    "    \n",
    "    snr.insert(4, 'electrode_dist', dist)\n",
    "    \n",
    "    snr_dfs.append(snr)\n",
    "    \n",
    "all_snr = pd.concat(snr_dfs)\n",
    "print(total_before_filter)\n",
    "all_snr = all_snr[all_snr['electrode_dist'] > 100]\n",
    "all_snr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2f61e22e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.55705579e-03 4.88139831e+00]\n",
      "r^2 = 0.0013426919848363614\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot: hw vs electrode dist\n",
    "x = all_snr['electrode_dist']\n",
    "plt.scatter(x, all_snr['Valuehw'])\n",
    "coef = np.polyfit(x, all_snr['Valuehw'], 1)\n",
    "print(coef)\n",
    "poly1d_fn = np.poly1d(coef) \n",
    "correlation = np.corrcoef(x, all_snr['Valuehw'])[0,1]\n",
    "\n",
    " # r\n",
    "print(\"r^2 =\", correlation**2)\n",
    "plt.plot(x, poly1d_fn(x), '--k', label='Slope:' + str(coef[1])[5])\n",
    "plt.xlabel(\"Electrode distance (um)\")\n",
    "plt.ylabel(\"Half width (um)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "9824adce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot: amp vs electrode dist\n",
    "plt.scatter(all_snr['electrode_dist'], all_snr['Valueamp'])\n",
    "coef = np.polyfit(x, all_snr['Valueamp'], 1)\n",
    "poly1d_fn = np.poly1d(coef) \n",
    "plt.plot(x, poly1d_fn(x), '--k', label='Slope:' + str(coef[1])[5])\n",
    "plt.xlabel(\"Electrode distance (um)\")\n",
    "plt.ylabel(\"Amplitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef366ed0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5da88afa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": 5
}