analyze_row_2_col_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7e740ad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import seaborn as sns\n",
    "sns.set_theme('notebook')\n",
    "import matplotlib as mpl\n",
    "import progressbar\n",
    "import pickle\n",
    "from scipy.signal import find_peaks, peak_prominences, peak_widths\n",
    "from datetime import datetime\n",
    "import warnings\n",
    "\n",
    "warnings.simplefilter('ignore')\n",
    "%run -i helper_functions.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5d22231-c992-450d-878a-da8c8e4dbff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Die_01 Not Found\n",
      "Die_07 Not Found\n",
      "Die_13 Not Found\n",
      "Die_15 Not Found\n",
      "Die_17 Not Found\n",
      "Die_25 Not Found\n",
      "Die_39 Not Found\n"
     ]
    }
   ],
   "source": [
    "data_dir = os.getcwd() + '/Full_Data/'\n",
    "data = []\n",
    "row_num = 2\n",
    "col_num = 2\n",
    "\n",
    "get_datafiles(data_dir, data, row_num, col_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "029e0505",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_VW(data[1], fit_order=8, prominence=0.5, fsr=15, ideal_peak_wl = 1515, zoom_span=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edac20a9-d1df-4cfb-811c-281a1e4a1d08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n"
     ]
    }
   ],
   "source": [
    "peak = []\n",
    "res = []\n",
    "\n",
    "# extract peak 1\n",
    "peak.append(extract_peaks(data[0], zoom_span=0.75, prominence=1, ideal_peak_wl=1470))\n",
    "peak.append(extract_peaks(data[1], zoom_span=0.75, ideal_peak_wl=1470))\n",
    "peak.append(extract_peaks(data[2], zoom_span=0.75, ideal_peak_wl=1470))\n",
    "peak.append(extract_peaks(data[3], zoom_span=0.75, ideal_peak_wl=1470))\n",
    "peak.append(extract_peaks(data[4], zoom_span=0.75, ideal_peak_wl=1470))\n",
    "peak.append(extract_peaks(data[5], zoom_span=0.75, ideal_peak_wl=1470))\n",
    "\n",
    "# extract peak 2\n",
    "peak.append(extract_peaks(data[0], zoom_span=0.75, ideal_peak_wl=1515))\n",
    "peak.append(extract_peaks(data[1], zoom_span=0.5, ideal_peak_wl=1515))\n",
    "peak.append(extract_peaks(data[2], zoom_span=0.75, ideal_peak_wl=1515))\n",
    "peak.append(extract_peaks(data[3], zoom_span=0.75, ideal_peak_wl=1515))\n",
    "peak.append(extract_peaks(data[4], zoom_span=0.75, ideal_peak_wl=1515))\n",
    "peak.append(extract_peaks(data[5], zoom_span=0.75, ideal_peak_wl=1515))\n",
    "\n",
    "# extract peak 3\n",
    "peak.append(extract_peaks(data[0], zoom_span=0.75, ideal_peak_wl=1550))\n",
    "peak.append(extract_peaks(data[1], zoom_span=0.75, ideal_peak_wl=1550))\n",
    "peak.append(extract_peaks(data[2], zoom_span=0.75, ideal_peak_wl=1550))\n",
    "peak.append(extract_peaks(data[3], zoom_span=0.75, ideal_peak_wl=1550))\n",
    "peak.append(extract_peaks(data[4], zoom_span=0.75, ideal_peak_wl=1550))\n",
    "peak.append(extract_peaks(data[5], zoom_span=0.75, ideal_peak_wl=1550))\n",
    "\n",
    "# extract peak 4\n",
    "peak.append(extract_peaks(data[0], zoom_span=0.5, ideal_peak_wl=1590, exclude=True))\n",
    "peak.append(extract_peaks(data[1], zoom_span=0.5, ideal_peak_wl=1590, exclude=True))\n",
    "peak.append(extract_peaks(data[2], zoom_span=0.5, ideal_peak_wl=1590, exclude=True))\n",
    "peak.append(extract_peaks(data[3], zoom_span=0.6, ideal_peak_wl=1590, exclude=True))\n",
    "peak.append(extract_peaks(data[4], zoom_span=0.5, ideal_peak_wl=1590, exclude=True))\n",
    "peak.append(extract_peaks(data[5], zoom_span=0.45, ideal_peak_wl=1590, exclude=True))\n",
    "\n",
    "peak = np.array(peak)\n",
    "\n",
    "# determine average resonances and scale to 0\n",
    "for i in range(0, len(peak), 6):\n",
    "    resonances = []\n",
    "    for j in range(6):\n",
    "        if (peak[i+j,0][0] != 0):\n",
    "            resonances.append([peak[i+j,0]])\n",
    "    res.append( int( np.nan_to_num( np.average(resonances) ) ) )\n",
    "\n",
    "for i in range(len(peak)):\n",
    "    peak[i,0] = peak[i,0] - min(peak[i,0])\n",
    "\n",
    "# test stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0f3d0668-4e4e-41d7-b682-2177295a87bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_resonances(data, peak, res, row_num, col_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08d6d8c0-08fd-4b6e-80ad-a4c1c2deb617",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty data\n",
      "Empty data\n",
      "Empty data\n",
      "Empty data\n"
     ]
    }
   ],
   "source": [
    "peak0 = []\n",
    "res0 = []\n",
    "\n",
    "peak0.append(extract_peaks(data[6], ideal_peak_wl=1475))\n",
    "peak0.append(extract_peaks(data[6], ideal_peak_wl=1515))\n",
    "peak0.append(extract_peaks(data[6], zoom_span=0.75, ideal_peak_wl=1550))\n",
    "peak0.append(extract_peaks(data[6], zoom_span=0.5, ideal_peak_wl=1590, exclude=True))\n",
    "\n",
    "peak0 = np.array(peak0)\n",
    "\n",
    "# determine average resonances and scale to 0\n",
    "for i in range(0, len(peak0), 1):\n",
    "    resonances = []\n",
    "    for j in range(1):\n",
    "        if (peak0[i+j,0][0] != 0):\n",
    "            resonances.append([peak0[i+j,0]])\n",
    "    res0.append( int( np.nan_to_num( np.average(resonances) ) ) )\n",
    "\n",
    "for i in range(len(peak0)):\n",
    "    peak0[i,0] = peak0[i,0] - min(peak0[i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "02e28532-a14c-47f9-8e01-522d32dd9ae2",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'voltages'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/jupyter/helper_functions.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_res\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpeak\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpeak0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpeak0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/jupyter/helper_functions.py\u001b[0m in \u001b[0;36mplot_res\u001b[0;34m(data, peak, ave_res, row_num, col_num, num_die, peak0, colors, dice)\u001b[0m\n\u001b[1;32m    448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    449\u001b[0m         \u001b[0;31m# plot data for comparison die\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 450\u001b[0;31m         \u001b[0mvoltages0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_die\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'voltages'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    451\u001b[0m         \u001b[0mv0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvoltages0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeak0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0mnum_die\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minf\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m         \u001b[0mr0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpeak0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0mnum_die\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeak0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0mnum_die\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minf\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'voltages'"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_res(data, peak, res, row_num, col_num, peak0=peak0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba697e5e-5f4e-4ff2-9aaa-21b828218073",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}