test_backend.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "# path = \"C:/Users/debji/Downloads/ch2_xsm_20211111_v1/xsm/data/2021/11/11/calibrated/ch2_xsm_20211111_v1_level2.lc\"\n",
    "# path = \"C:/Users/debji/Downloads/ch2_xsm_20211027_v1/xsm/data/2021/10/27/calibrated/ch2_xsm_20211027_v1_level2.lc\"\n",
    "# path = \"C:/Users/debji/Downloads/lightcurve_ascii_sample.txt\"\n",
    "path = \"C:/Users/debji/Downloads/ISRO final_testdata/test_data/ch2_xsm_20190930_v1_level2.lc\"\n",
    "from backend.main import get_analysis_data\n",
    "resp = get_analysis_data(path, \"fits\")\n",
    "# print(resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(resp[\"peaks\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x241d11a8e50>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(resp[\"graph_data\"])\n",
    "plt.plot(resp[\"peaks\"][0][\"peak_position\"], resp[\"peaks\"][0][\"peak_flux\"], \"ro\")\n",
    "x = resp[\"peaks\"][0][\"range\"][0]\n",
    "plt.plot(x, resp[\"graph_data\"][x], \"ro\")\n",
    "x = resp[\"peaks\"][0][\"range\"][1]\n",
    "plt.plot(x, resp[\"graph_data\"][x], \"ro\")\n",
    "# x = 100\n",
    "# plt.plot(x, resp[\"graph_data\"][x], \"ro\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'peak_position': 21675,\n",
       "  'range': [19059, 24354],\n",
       "  'rise_time': '2615.34998859364 ms',\n",
       "  'decay_time': '2679.4805727008315 ms',\n",
       "  'peak_flux': 13310.249456380208,\n",
       "  'duration': '5294.830561294471 ms'},\n",
       " {'peak_position': 106738,\n",
       "  'range': [104098, 109488],\n",
       "  'rise_time': '2639.7302180996485 ms',\n",
       "  'decay_time': '2750.780408140301 ms',\n",
       "  'peak_flux': 13310.249456380208,\n",
       "  'duration': '5390.5106262399495 ms'}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resp[\"peaks\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "avg = float(np.average(resp[\"graph_data\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "372.7851668288825"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg"
   ]
  }
 ],
 "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
}