{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "83f59ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "with open(\"../../../../out_4.json\") as f:\n",
    "    data = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c378430",
   "metadata": {},
   "source": [
    "## Dictionary entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f65235e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['DIT', 'NDIT', 'channel', 'configuration', 'instrument', 'sat_limit', 'saturated', 'snr_at_ref', 'spectra'])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['results'][0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0af65636",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['noise_frac_dark', 'noise_frac_ron', 'noise_frac_sky', 'noise_frac_source', 'noise_total', 'sky_counts', 'snr', 'source_counts', 'throughput_atmosphere', 'throughput_instrument', 'throughput_total', 'wave'])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['results'][0]['spectra'].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f2b0b2",
   "metadata": {},
   "source": [
    "## Check generic curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b3093ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "wav = data['results'][0]['spectra']['wave']\n",
    "dat = data['results'][0]['spectra']['sky_counts']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098bf38b",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ae332bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x122644af0>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(wav, dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95262907",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77160a7f",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}