test_peak_finder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"C:/Users/debji/Downloads/ch2_xsm_20211111_v1/xsm/data/2021/11/11/calibrated/ch2_xsm_20211111_v1_level2.lc\"\n",
    "from backend.file_handler.handle_types import read_fits\n",
    "file_response = read_fits(path)\n",
    "if file_response[\"good\"]:\n",
    "    matrix = file_response[\"data\"]\n",
    "else:\n",
    "    print(file_response[\"message\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from backend.core.curve_completion import complete_curve\n",
    "complete = complete_curve(matrix)\n",
    "\n",
    "from backend.core.smoothen_curve import moving_average\n",
    "smooth = moving_average(complete, 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2427fd3a850>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(smooth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.signal import find_peaks, peak_widths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x=((np.max(smooth)-np.average(smooth))/3+np.average(smooth))\n",
    "peaks, p_data = find_peaks(smooth, x, width = 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14931, 15728], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([35995.31890902,   273.10861992]),\n",
       " array([223.67209853, 519.39881003]),\n",
       " array([   37.60777467, 15583.11948987]),\n",
       " array([36032.92668369, 15856.2281098 ]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widths = peak_widths(smooth, peaks, 0.97)\n",
    "widths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranges = [\n",
    "    {\n",
    "        \"range\": [\n",
    "            widths[2][i],\n",
    "            widths[3][i]\n",
    "        ],\n",
    "        \"rise_time\": peaks[i] - widths[2][i],\n",
    "        \"decay_time\": widths[3][i] - peaks[i],\n",
    "        \"peak_flux\": smooth[peaks[i]],\n",
    "        \"duration\": widths[0][i]\n",
    "    }\n",
    "    for i in range(len(peaks))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'range': [37.60777467144239, 36032.92668369149],\n",
       "  'rise_time': 14893.392225328558,\n",
       "  'decay_time': 21101.92668369149,\n",
       "  'peak_flux': 786.863818359375,\n",
       "  'duration': 35995.31890902005},\n",
       " {'range': [15583.119489872093, 15856.228109795407],\n",
       "  'rise_time': 144.88051012790675,\n",
       "  'decay_time': 128.22810979540736,\n",
       "  'peak_flux': 523.1142868437929,\n",
       "  'duration': 273.1086199233141}]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "9413ecba12c8d30d71729b19a0b593a81253692ec7bc6aed5a642efcc0099d68"
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 ('slf')",
   "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": "3.8.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}