{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started: Introductory Tutorial 1\n",
    "\n",
    "This notebook introduces the basic features of the pulsar signal simulator, and leading the user through the steps of how to simulate a pulsar signal from start to finish.\n",
    "\n",
    "The `PsrSigSim` can be run in a jupyter notebook or python script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import some useful packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# import the pulsar signal simulator\n",
    "import psrsigsim as pss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Signal\n",
    "\n",
    "The first thing we need to do in order to simulate a pulsar is to initialize our signal. This will be done for a filterbank-style signal class. This type of signal needs parameters first though. One needs to enter the number of frequency channels the signal should be recorded with, what the bandwidth of the signal is, what the center frequency of the signal is, and how quickly it should record the data, or the sampling rate. To make single pulses, we also need to set the 'fold' flag to False (the default is True).\n",
    "\n",
    "For this example, we will simulate single pulses from a 350 MHz observation from the Green Bank Telescope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define our signal variables.\n",
    "f0 = 820 # center observing frequecy in MHz\n",
    "bw = 200.0 # observation MHz\n",
    "Nf = 128 # number of frequency channels\n",
    "f_samp = 0.001526 # sample rate of data in MHz (here 0.6554 ms for size purposes)\n",
    "# Now we define our signal\n",
    "signal_1 = pss.signal.FilterBankSignal(fcent = f0, bandwidth = bw, Nsubband=Nf, fold = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Pulsar\n",
    "\n",
    "Next we define a pulsar object. The pulsar needs a pulse shape though. There are a number of ways to define this in the pulsar signal simulator, but here we will make a simple, predefined Gaussian profile. The Guassian needs three parameters, an amplitude, a width (or sigma), and a peak, the center of the Gaussian in phase space (e.g. 0-1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We define the Guassian profile\n",
    "gauss_prof = pss.pulsar.GaussProfile(peak = 0.5, width = 0.05, amp = 1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining the profile just tells the simulator how to make the pulses. If we want to see what they look like, we need to initialize the profile, and then we can give it a number of phase bins and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want to use 2048 phase bins and just one frequency channel for this test.\n",
    "gauss_prof.init_profiles(2048, Nchan = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2048)\n"
     ]
    }
   ],
   "source": [
    "# We can look at the shape of the profile array to make sure it matches with what we expect\n",
    "print(np.shape(gauss_prof.profiles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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3KXid/QnKS0tYEfBUv3NVlMZYVVup4ZASCIW7FLzOgQQt9VWUlAQ/1e9crfVxTUEggVC4S8EL4zDItLbGOKf7R4IuQ4qQwl0Kmrtzpi8RmmkH5lrfVE3PpXESE1NBlyJFRuEuBW0gMcmlsanAb4o9n/bGagBOXVTXjOSXwl0K2smLyS6P9U3VAVdydekPndN96pqR/Moo3M1sl5kdM7MOM/vcNdp91MzczHZmr0SR+Z1KhXt7SMM9XddJhbvk2YLhbmYx4EngI8A24GEz23aVdsuA3wReznaRIvM53TdCiYVnqt+5aipKaaqp4LS6ZSTPMjlyvwPocPcT7j4BPA3cf5V2vwd8CRjLYn0i13SyL0FLfZzy0vD2MK5viuvIXfIuk9+ItUDnrOddqWVXmNltQKu7P5vF2kQWdOriSGi7ZNLWNVarz13ybsmHO2ZWAnwV+K0M2j5qZvvMbF9vb+9SNy1Fzt2T4R7SkTJpGg4pQcgk3LuB1lnPW1LL0pYB24Efmtkp4E5g99VOqrr7U+6+0913Njc3L75qEaBvZILh8akrww3DKj1iRsMhJZ8yCfe9wGYzW29m5cBDwO70i+4+5O5N7t7u7u3AS8B97r4vJxWLpJwK+TDItPSHj7pmJJ8WDHd3nwI+DTwHHAGecfdDZvZFM7sv1wWKzOdkyIdBpmk4pAShNJNG7r4H2DNn2ePztH3/0ssSWdipvhFiJUZLat70sNJwSAlCeMePiSzg1MUErfVVlMXC/2Pc3qjhkJJf4f+tEJnHyQIYBpnW3qThkJJfCncpSO7Oqb6R0I+USWtvjGs4pOSVwl0KUu/lcRIT06Ef45525aTqRR29S34o3KUgnewtjJEyaRubawA43qtwl/xQuEtB6ui9DMCmFTUBV5KZ9U3VlBh0XLgcdClSJBTuUpA6LlymqizGmrpwD4NMqyyL0doQ53ivwl3yQ+EuBanjwmU2rqgO5U2x57OpuYbjOnKXPFG4S0E60TtypR+7UGxcUcOJiyNMz3jQpUgRULhLwRkZn6J7cJRNBRbum5prmJiaoWtAV6pK7incpeCcSI04KZSTqWkbU/XqpKrkg8JdCk5H7zBQeOG+6cpwSIW75J7CXQrO8QvJCcPWFcjVqWl18TKaaip05C55oXCXgtNx4TLrGsJ939T5bFpRrXCXvCi83w4peh29l6/0Xxeajc01HO8dwV0jZiS3FO5SUCanZzh1caTg+tvTNq2oYWh0kouXJ4IuRSJO4S4F5XTfCFMzXnDDINPSH0pvXhgOuBKJOoW7FJSj55OheMOqZQFXsjjpuo+dV7hLbincpaAcOz9MrMQKtlumuaaCxupyjp5TuEtuKdyloBw5N8yGpmoqy2JBl7IoZsaNq5dx9PyloEuRiFO4S0E5ev5SwXbJpN24qpZjPcOaY0ZySuEuBWN4bJKugVG2rq4NupQluXHVMsYmZ3RPVckphbsUjDd6kv3UNxb4kXv6w+moTqpKDincpWAcSZ2EvLHAj9w3raihxODoOfW7S+4o3KVgHD1/iWWVpaypqwy6lCWpLIuxoblGR+6SUwp3KRjHzg9z46plmBXO3Zfmc8OqZQp3ySmFuxSEmRnn6LlhblxV2F0yaVtXLeNMf4LL41NBlyIRpXCXgnCqb4Th8SluWlsXdClZkT6pevis+t0lNxTuUhBe7x4CYHtEwv2mluR+HOgaDLgSiSqFuxSE17uGqCgtYfPKwpx2YK4VyypZXVd55UNLJNsU7lIQDnQPsW1NLWWx6PzI7mip40CXwl1yI6PfFDPbZWbHzKzDzD53ldc/a2aHzeyAmX3fzNZlv1QpVjMzzqHuocj0t6ftaFnOyYsjDI1OBl2KRNCC4W5mMeBJ4CPANuBhM9s2p9mrwE533wF8E/j9bBcqxevExRFGJqYjGO7J/TmorhnJgUyO3O8AOtz9hLtPAE8D989u4O4/cPdE6ulLQEt2y5Ri9np38qTjjpblAVeSXekPq/06qSo5kEm4rwU6Zz3vSi2bzyPAt6/2gpk9amb7zGxfb29v5lVKUdvfOURlWQkbm6uDLiWrlsfLWdcY50Cnjtwl+7J6dsrMPg7sBL58tdfd/Sl33+nuO5ubm7O5aYmwV04PcHPLckojdDI1bUfLcg2HlJzI5LelG2id9bwltewtzOwe4PPAfe4+np3ypNiNjE9x+NwldrbXB11KTtzSupyzQ2OcGxoNuhSJmEzCfS+w2czWm1k58BCwe3YDM7sV+FOSwX4h+2VKsdrfOcj0jLNzXUPQpeTEHe3J/dp7aiDgSiRqFgx3d58CPg08BxwBnnH3Q2b2RTO7L9Xsy0AN8A0ze83Mds+zOpHrsu90MvRua4vmkfvW1cuoLo+x92R/0KVIxJRm0sjd9wB75ix7fNbje7JclwiQDPctK2uoi5cFXUpOlMZKuG1dPXtPKdwlu6J3hkoiY3rGefX0AO+MaJdM2u3tDRzrGdbFTJJVCncJrWPnhxken2Lnumh2yaTtbK/HHf75tPrdJXsU7hJa/3T8IgB3bWwMuJLcurW1ntIS42X1u0sWKdwltF7suMiG5mrWLK8KupScqiqPcWvb8isfZiLZoHCXUJqYmuHlk/383KamoEvJi5/f3Mzr3UP0j0wEXYpEhMJdQum1zkESE9O8p0jC/b1bmnGHn3To6F2yQ+EuofSTjouUGNy5Idr97Wk3ra1jebyMF97QnEuSHQp3CaUfHbvAza3LqauK5vj2uWIlxns2NfHjN3tx96DLkQhQuEvo9FwaY3/XEPdsXRl0KXn13s1N9Fwa51jPcNClSAQo3CV0vnu4B4APbiuucP/ADSswg+8c6gm6FIkAhbuEzveO9LCuMc7mFdG4GXamVtRW8s62er598HzQpUgEKNwlVC6PT/FPHX18cOtKzCzocvJu1/ZVHDl3idN9I0GXIgVO4S6h8t3D55mYnuHD21cFXUogPvyO5H7/o47eZYkU7hIqf//qWVrqq3hnRKf4XUhrQ5yb1tbxDwfOBl2KFDiFu4TGhUtj/OTNXh64ZS0lJcXXJZP24K1rOdh9iaPnLwVdihQwhbuExu79Z5lxeODWa91/PfoeuHUtZTHjG/u6gi5FCpjCXULB3Xl6byc3t9SxqchGyczVUF3O3Teu5FuvdjM5PRN0OVKgFO4SCj893kfHhcv827vagy4lFH759lb6RiY0LFIWTeEuofD1fzpFQ3U5/3rH6qBLCYX3bWlmQ1M1f/7jE5qOQBZF4S6BO957me8d6eGh21upLIsFXU4olJQYj/z8eg50DfEz3cRDFkHhLoH7o+c7qCiN8e9+bn3QpYTKR29roaG6nCd/eDzoUqQAKdwlUMd7L/P/Xuvm43e20VRTEXQ5oVJZFuOx923ghTd6dZcmuW4KdwnUE88eoaosxqPv3Rh0KaH0q3e1s6auki99+6j63uW6KNwlMM8f7eH5oxf4zXs207xMR+1XU1kW47c+dAP7u4Z4em9n0OVIAVG4SyCGEpN8/u8PsrG5mk+8W33t1/LgrWu5a0MjTzx7hLODo0GXIwVC4S555+787rdep3d4nP/+y7dQXqofw2spKTG+9NEdTM84n33mNV3YJBnRb5Xk3R//8DjPHjjHZz+0hR0ty4MupyC0NcZ54sHtvHSinyeePRJ0OVIASoMuQIrL3+49w5efO8b9t6zhU+/TSdTr8W9ua+HQ2Uv8xU9O0lhdzmfu3hx0SRJiCnfJC3fnz398kif2HOG9W5r50kd3FOXNOJbqd+/dykBigq989w1GJqb57Q/fQKyIZ9CU+SncJecGExN8/lsHefbAOXa9YxX/4+FbqCjVlaiLESsxvvyLN1NZFuNPfnScQ2eH+P1f3MHquqqgS5OQyajP3cx2mdkxM+sws89d5fUKM/vb1Osvm1l7tguVwpOYmOJ/v3iS9//BD/nHg+f5nV038se/cpuCfYliJcYTD2znvz14Ez872c/dX/kRf/T8mwyNTgZdmoSILXRhhJnFgDeADwJdwF7gYXc/PKvNrwM73P0xM3sIeNDdf/la6925c6fv27dvqfVLyExOz7D3VD/fOdTD37/azdDoJHdtaOS/3LeNG1fVBl1e5HT2J/iv/3CI7x25QHV5jF+4eQ0fuWk171rfoHl6IsrMXnH3nQu1y6Rb5g6gw91PpFb8NHA/cHhWm/uBL6QefxP4IzMz1yV1keLujE5Oc3l8ipHxaQYTE5wbGuPs4ChdA6O83j3EobNDjE3OUFFawj1bV/KJ97Szc129+tdzpLUhzp//2u0c7B7iay+e5B/2n+XpvZ2UxYxta+p4x5pa1jXEWdcYp6mmguXxcpbHy6itLKMsZvq+RFgm4b4WmH1pXBfwrvnauPuUmQ0BjUDWJ8R4Zm8nT/34BKltveU1n+fJ3E+Y2e97+2uz3+dXXX6151db99z1z7fut792re1co6Z56rjmPl6j3ewFM+4kJqfn3e/q8hjb1tTy8B1tvGt9I+/d0kS8XKd08mX72jq++rFbGHtwmhc7LvKzU/28enqQPa+fYzAxf3dNeayE8tLUV6yEEuNK4JulvrDUv6SWW/Jxapk+IK7fb969mV+4eU1Ot5HX3z4zexR4FKCtrW1R66ivLueGlctmrXTONt66vfmaMfvn8e2vzfO+t21rVrtrrm/h97z9+TXazfue66np6r+Q863PDOLlMaorSpNf5TFqK8tYs7yKtcurqK0q1S95CFSWxbh760ru3rryyrKhxCRn+hP0JyYYTEwwmJhkeGySiakZJqY99e80E1MzzHjyw99xUv/h/i+HIj53mf42X5S6qrKcbyOTcO8GWmc9b0ktu1qbLjMrBeqAvrkrcvengKcg2ee+mII/uG0lH9y2cuGGIgJAXbyMm+J1QZcheZbJaJm9wGYzW29m5cBDwO45bXYDv5Z6/IvA8+pvFxEJzoJH7qk+9E8DzwEx4GvufsjMvgjsc/fdwF8Af2VmHUA/yQ8AEREJSEZ97u6+B9gzZ9njsx6PAb+U3dJERGSxNHGYiEgEKdxFRCJI4S4iEkEKdxGRCFK4i4hE0IITh+Vsw2a9wOlFvr2JHExtEHLa5+KgfS4OS9nnde7evFCjwMJ9KcxsXyazokWJ9rk4aJ+LQz72Wd0yIiIRpHAXEYmgQg33p4IuIADa5+KgfS4OOd/nguxzFxGRayvUI3cREbmGUId7Md6YO4N9/qyZHTazA2b2fTNbF0Sd2bTQPs9q91EzczMr+JEVmeyzmX0s9b0+ZGZ/ne8asy2Dn+02M/uBmb2a+vm+N4g6s8XMvmZmF8zs4Dyvm5n9Yer/xwEzuy2rBbh7KL9ITi98HNgAlAP7gW1z2vw68Cepxw8Bfxt03XnY5w8A8dTjTxXDPqfaLQNeAF4CdgZddx6+z5uBV4H61PMVQdedh31+CvhU6vE24FTQdS9xn98L3AYcnOf1e4Fvk7xR2p3Ay9ncfpiP3K/cmNvdJ4D0jblnux/4P6nH3wTutsK+19uC++zuP3D3ROrpSyTvjFXIMvk+A/we8CVgLJ/F5Ugm+/xJ4El3HwBw9wt5rjHbMtlnB2pTj+uAs3msL+vc/QWS97eYz/3AX3rSS8ByM1udre2HOdyvdmPutfO1cfcpIH1j7kKVyT7P9gjJT/5CtuA+p/5cbXX3Z/NZWA5l8n3eAmwxsxfN7CUz25W36nIjk33+AvBxM+sief+Iz+SntMBc7+/7ddHt6QuUmX0c2Am8L+hacsnMSoCvAp8IuJR8KyXZNfN+kn+dvWBmN7n7YKBV5dbDwNfd/StmdhfJu7ttd/eZoAsrRGE+cr+eG3NzrRtzF5BM9hkzuwf4PHCfu4/nqbZcWWiflwHbgR+a2SmSfZO7C/ykaibf5y5gt7tPuvtJ4A2SYV+oMtnnR4BnANz9p0AlyTlYoiqj3/fFCnO4F+ONuRfcZzO7FfhTksFe6P2wsMA+u/uQuze5e7u7t5M8z3Cfu+8LptysyORn+1skj9oxsyaS3TQn8llklmWyz2eAuwHMbCvJcO/Na5X5tRv41dSomTuBIXc/l7W1B31GeYGzzfeSPGI5Dnw+teyLJH+5IfnN/wbQAfwM2BB0zXnY5+8BPcBrqa/dQdec632e0/aHFPiuZqaBAAACGklEQVRomQy/z0ayO+ow8DrwUNA152GftwEvkhxJ8xrwoaBrXuL+/g1wDpgk+ZfYI8BjwGOzvsdPpv5/vJ7tn2tdoSoiEkFh7pYREZFFUriLiESQwl1EJIIU7iIiEaRwFxGJIIW7RJqZTZvZa2Z20My+YWZxM2ufb6Y+kahQuEvUjbr7Le6+HZggOc5YJPIU7lJMfgxsSj2OmdmfpeZK/46ZVQGY2SfNbK+Z7Tez/2tm8dTyX0od/e83sxdSy2Jm9uVU+wNm9u+D2S2Rt1O4S1FIzT30EZJXAkJynpYn3f0dwCDw0dTyv3P32939ZuAIyasKAR4HPpxafl9q2SMkLxm/Hbgd+KSZrc/93ogsTOEuUVdlZq8B+0jOXfIXqeUn3f211ONXgPbU4+1m9mMzex34FeAdqeUvAl83s0+SvPEEwIdIzg3yGvAyyemmC3lyL4kQTfkrUTfq7rfMXpC6n8vs2TSngarU468DD7j7fjP7BKnJu9z9MTN7F/CvgFfM7J0k5wb5jLs/l8sdEFkMHbmLvNUy4JyZlZE8cgfAzDa6+8vu/jjJmQpbgeeAT6XaYmZbzKw6iKJF5tKRu8hb/WeSXSy9qX+XpZZ/2cw2kzxa/z7JmQsPkOzO+efU7R17gQfyXbDI1WhWSBGRCFK3jIhIBCncRUQiSOEuIhJBCncRkQhSuIuIRJDCXUQkghTuIiIRpHAXEYmg/w/aylBmzbbaBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And then we can plot the array to see what the profile looks like\n",
    "plt.plot(np.linspace(0,1,2048), gauss_prof.profiles[0])\n",
    "plt.xlabel(\"Phase\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can define the pulsar object itself. Out pulsar needs a period (s), a mean flux (Jy), a profile, which we've defined above, and a name (e.g. JXXXX+XXXX). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the values needed for the puslar\n",
    "period = 1.0 # pulse period of our simulated pulsar, here one second\n",
    "Smean = 10.0 # The mean flux of the pulsar, here 10.0 Jy (note that this is very bright for a pulsar)\n",
    "psr_name = \"J0000+0000\" # The name of our simulated pulsar\n",
    "# Now we define the pulsar\n",
    "pulsar_1 = pss.pulsar.Pulsar(period, Smean, profiles=gauss_prof, name = psr_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The ISM\n",
    "\n",
    "Now we define the interstellar medium (ISM) properties that will affect our pulsar signal as it 'travels' from the pulsar to our telescope. The main property here is the dispersion measure, DM, which is the number of electrons along the line of sight from us to the pulsar. These electrons will delay the pulsed emission from the pulsar, causing lower radio frequencies to arrive at the telescope later than higher radio frequencies. Here we will just define the ISM object and the DM we would like the pulsar to have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the dispersion measure\n",
    "dm = 40.0 # pc cm^-3\n",
    "# And define the ISM object, note that this class takes no initial arguements\n",
    "ism_1 = pss.ism.ISM()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Telescope\n",
    "\n",
    "The last thing we need to define is the telescope object. While you can define a telescope with any properties that you like with the pulsar signal simulator, it also comes with two pre-defined telescopes: The Arecibo Telescope and the Green Bank Telescope 9GBT). We will set up the GBT as our telescope. The telescope class when set up from a predefined telescope needs no additional input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tscope = pss.telescope.telescope.GBT()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating the Signal\n",
    "\n",
    "Now we have everything set up to actually simulate our signal, though there is one extra value we need to define: the simulated observation length (s). For size and time purposes, we will only simulate 2 seconds of observing, which amounts to just two pulse periods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the observation length\n",
    "obslen = 2.0 # seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can make the pulses! This is done using the make_pulses() function of the `pulsar` object we made before. It takes just the signal object, and the observation length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pulsar_1.make_pulses(signal_1, tobs = obslen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we disperse our pulses, or propagate them through the interstellar medium. We can do that easily using the disperse() function of the ISM object. This again takes the signal object, as well as the DM value defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98% dispersed in 1.399 seconds."
     ]
    }
   ],
   "source": [
    "ism_1.disperse(signal_1, dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to observe the signal with our telescope. This will add radiometer noise from the telescope receiver and backend to the signal. This is done using the observe() function of the telescope object, which takes the signal, the pulsar, the system name (for the GBT telescope this is either '820_GUPPI' or 'Lband_GUPPI'), and make sure that the noise variable is set to 'True'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: AstropyDeprecationWarning: The truth value of a Quantity is ambiguous. In the future this will raise a ValueError. [astropy.units.quantity]\n"
     ]
    }
   ],
   "source": [
    "tscope.observe(signal_1, pulsar_1, system=\"820_GUPPI\", noise=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at the Results\n",
    "\n",
    "And that's all that needs to be done to simulate a signal! If you want to view the simulated signal, you can access the full data array through `signal_1.data`. Two ways to look at the data are to just plot an individual frequency channel (a phase plot), or make a 2-D of the power as a function of the pulse phase and frequency channel (a filterbank plot), both of which are demonstrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the phases of the pulse\n",
    "phases = np.linspace(0, obslen/period, len(signal_1.data[0,:]))\n",
    "# Plot just the pulses in the first frequency channels\n",
    "plt.plot(phases, signal_1.data[0,:], label = signal_1.dat_freq[0])\n",
    "plt.ylabel(\"Intensity\")\n",
    "plt.xlabel(\"Phase\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the 2-D plot of intensity v. frequency and pulse phase. You can see the slight dispersive sweep here.\n",
    "plt.imshow(signal_1.data, aspect = 'auto', interpolation='nearest', origin = 'lower', \\\n",
    "           extent = [min(phases), max(phases), signal_1.dat_freq[0].value, signal_1.dat_freq[-1].value])\n",
    "plt.ylabel(\"Frequency [MHz]\")\n",
    "plt.xlabel(\"Phase\")\n",
    "plt.colorbar(label = \"Intensity\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (pss)",
   "language": "python",
   "name": "pss"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
