{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create synthetic spectrums\n",
    "**************************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import apollinaire as apn\n",
    "from apollinaire.simulate import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "initialise ()\n",
    "\n",
    "nday = 3000\n",
    "# resolution\n",
    "t = nday * 86400.\n",
    "dt = 20.\n",
    "ptday = int (86400./dt)\n",
    "\n",
    "n = int (t / dt)\n",
    "\n",
    "wdw = np.zeros (n)\n",
    "wdw = np.reshape (wdw, (nday, ptday))\n",
    "wdw[:,:ptday//2] = 1\n",
    "wdw = np.ravel (wdw)\n",
    "dc = np.count_nonzero (wdw) / wdw.size\n",
    "\n",
    "nu = np.fft.rfftfreq (n, d=dt)\n",
    "\n",
    "param_1 = [[2, 3025e-6, 0.9e-6],    \n",
    "           [10, 3035e-6, 0.9e-6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_ideal = gen_tf_multmode (param_1, nu, chi2=False)\n",
    "ideal = apn.psd.tf_to_psd (tf_ideal, 1.) + 1. \n",
    "\n",
    "tf = gen_tf_multmode (param_1, nu, chi2=True)\n",
    "psd = apn.psd.tf_to_psd (tf, 1.) + 1.\n",
    "\n",
    "tf_ideal_wdw = gen_tf_multmode (param_1, nu, chi2=False, conv=True, window=wdw)\n",
    "ideal_wdw = apn.psd.tf_to_psd (tf_ideal_wdw, 1.) / dc + 1.   \n",
    "\n",
    "tf_wdw = gen_tf_multmode (param_1, nu, chi2=True, conv=True, window=wdw)\n",
    "psd_wdw = apn.psd.tf_to_psd (tf_wdw, 1.) / dc + 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2975.0, 3075.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ion ()\n",
    "\n",
    "fig = plt.figure (figsize=(12,6))\n",
    "ax1 = fig.add_subplot (121)\n",
    "ax2 = fig.add_subplot (122)\n",
    "\n",
    "ax1.plot (nu*1e6, psd, color='black')\n",
    "ax1.plot (nu*1e6, ideal, color='red')\n",
    "ax2.plot (nu*1e6, psd_wdw, color='black')\n",
    "ax2.plot (nu*1e6, ideal_wdw, color='red')\n",
    "ax1.set_xlim (3020, 3040)\n",
    "ax2.set_xlim (2975, 3075)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
