{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ISM Delays: Tutorial 4\n",
    "\n",
    "This notebook will build on the previous tutorials, showing more features of the `PsrSigSim`. Details will be given for new features, while other features have been discussed in the previous tutorial notebook. This notebook shows the details of different delays and effects due to the interstellar medium (ISM) that can be added to the simulated data.\n",
    "\n",
    "We again simulate precision pulsar timing data with high signal-to-noise pulse profiles in order to clearly show the input pulse profile in the final simulated data product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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": [
    "## Setting up the Folded Signal\n",
    "\n",
    "Here we will again set up the folded signal class as in previous introductory tutorials. We will again simulate a 20 minute long observation total, with subintegrations of 1 minute. The other simulation parameters will be 64 frequency channels each 12.5 MHz wide (for 800 MHz bandwidth) observed with the Green Bank Telescope at L-band (1500 MHz center frequency).\n",
    "\n",
    "We will simulate a real pulsar, J1713+0747, as we have a premade profile for this pulsar. The period, dm, and other relavent pulsar parameters come from the NANOGrav 11-yr data release."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: specified sample rate 0.4481400437636761 MHz < Nyquist frequency 1600.0 MHz\n"
     ]
    }
   ],
   "source": [
    "# Define our signal variables.\n",
    "f0 = 1500 # center observing frequecy in MHz\n",
    "bw = 800.0 # observation MHz\n",
    "Nf = 64 # number of frequency channels\n",
    "# We define the pulse period early here so we can similarly define the frequency\n",
    "period = 0.00457 # pulsar period in seconds for J1713+0747\n",
    "f_samp = (1.0/period)*2048*10**-6 # sample rate of data in MHz (here 2048 samples across the pulse period\n",
    "sublen = 60.0 # subintegration length in seconds, or rate to dump data at\n",
    "# Now we define our signal\n",
    "signal_1713 = pss.signal.FilterBankSignal(fcent = f0, bandwidth = bw, Nsubband=Nf, sample_rate = f_samp,\n",
    "                                       sublen = sublen, fold = True) # fold is set to `True`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Pulsar and Profiles\n",
    "\n",
    "Now we will load the pulse profile as in Tutorial 3 and intilialize a single `Pulsar` object. We will also make the pulses now so that we can add different ISM effects to them later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we load the data array\n",
    "path = 'psrsigsim/data/J1713+0747_profile.npy'\n",
    "J1713_dataprof = np.load(path)\n",
    "\n",
    "# Now we define the data profile\n",
    "J1713_prof = pss.pulsar.DataProfile(J1713_dataprof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the values needed for the puslar\n",
    "Smean = 0.009 # The mean flux of the pulsar, J1713+0747 at 1400 MHz from the ATNF pulsar catatlog, here 0.009 Jy\n",
    "psr_name = \"J1713+0747\" # The name of our simulated pulsar\n",
    "\n",
    "# Now we define the pulsar with the scaled J1713+0747 profiles\n",
    "pulsar_J1713 = pss.pulsar.Pulsar(period, Smean, profiles=J1713_prof, name = psr_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the observation length\n",
    "obslen = 60.0*20 # seconds, 20 minutes in total\n",
    "# Make the pulses\n",
    "pulsar_J1713.make_pulses(signal_1713, tobs = obslen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Telescope\n",
    "\n",
    "We will set up the `telescope` object in the same way as in the previous tutorials. Since we can set these up in any order, we will do these first to better show the different `ISM` properties later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We intialize the telescope object as the Green Bank Telescope\n",
    "tscope = pss.telescope.telescope.GBT()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The ISM\n",
    "\n",
    "Here we will initialize the ISM class and show the various different delays that may be added to the simulated data that are due to the ISM or are specifically frequency dependent delay. In particular these include dispersion due to the ISM, delays due to pulse scatter broadening, and other frequency dependen, or \"FD\", parameters as defined Zhu et al. 2015 and Arzoumanian et al. 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the ISM object, note that this class takes no initial arguements\n",
    "ism_sim = pss.ism.ISM()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pulse Dispersion\n",
    "\n",
    "We first show how to add dispersion of pulsars due to the ISM. This has been shown in previous tutorials as well. To do this, we first define the dispersion measure, or DM, the number of free electrons along the line of sight. This follows a frequeny^-2 relation that can be found in the Handbook of Pulsar Astronomy, by Lorimer and Kramer, 2005. The DM we use here is the same as in the NANOGrav 11-yr par file for PSR J1713+0747. We show the pulses both before and after dispersion to show the effects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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61+rAcwdaoxBCWIRikL9drwbmXtsv7/VkSXcMuD4hhLDo2DEG00mvBuYVFZ5fZZ8QQhhxohFRZG26viO2dwJIukzS0eXHJL2vvE+YR61hyl0WlgKoNzvX3WHBsUgXsyRImvxby6aWUwzVcer8FrjOtOzbkSpmbmxVui0lVZrcU4DLJb2lVHbGkOoTQgiLTjMX2SBWtBwlVRqYR4BfA86W9AlJY8S0rBBCmOJ0oVjltpRUaWBk+0nbvwH8DPgG8OzhVitUVhRTXRqTGXPTgmN1zLgak7u6xhDXrgt7VTmrNkx2pbqUSRnSObBvbRc7PZ4+FJuhys3zYql9Ag5QgSrdlpIqEy03Ne/Yfl9e6vO/Dq9KIYSwuDgG+Tua8R2xfXHL9ldsnzjT8yRtlPSIpLtbyt8u6QeS7pH0p6XyiyRtyY+dUio/NZdtkXRhtV8rhBDmV3SRtes1k387nTtURFrnZv8Zjn058HHgytIxXw2cCbzU9i5Jh+byo4BzgBcDvwh8TdIL8tM+AbyWtJb0bZI22f5ehd9taWjpGmnO2G5eiW9vrIC6KeotM7bDaCl3lcJkdKGa3aaFKSQwTHiMncV42k/RPTYoSy1CrIquDYzt/Zr3Jd0x06TLDs+/SdKRLcW/C/yJ7V15n0dy+ZnA1bn8AUlbgGPzY1ts35/rcXXeNxqYEMKCka5OooFpVbXTcFBfbV4A/KqkWyR9Q9Kv5PJVwIOl/bbmsm7lbSStl3S7pNsn2DWg6oYQQjURptxuvrMpjwEHAscDvwJcK+l5dA57Np0bwI6Nne0NwAaA/XVQXOuHEOZV9DC26zUG8/rS5gEt29j+4hxebyvwRdsGbpVUAIfk8iNK+x1OSrRJj/JQYnuylbaEBTs9nsKUrfaZ/GG0dFpgrGVGvvIn4Lj2cPDYU2kWv6ZClcPcGVFEFFmbXlcwv1G6/42WbQNzaWD+BjgR+Mc8iL8MeJQUCv3Xkj5MGuRfA9xKurJZI2k18GNSIMB/mMPrhhDCUEUb3a7XIP95/RxY0lXACcAhkrYCFwMbgY05dHk3sC5fzdwj6VrS4P0e4HzbjXycC4AbSEsDbLR9Tz/1CiGEgYtB/o56dZH1nExp+8MzPH5ul4f+Y5f9LwEu6VB+PXB9r9cK08NRmwuP7Vd7hp218cmvVmoQC46NspqgMX3bduoak1ISVMH2Yh9+vudZQIckqHFezF1cwrTp1Wn4IVJjcDDwLGC/llsIIYRskNmUJf1+nox+t6SrJK2QtDpH4N4n6RpJy/K+y/P2lvz4kaXjdJzAPl96jcEcQxrz+HXgW8BVwI25SyuEEEJmoCgGc/UnaRXwDuAo28/k4YNzgNOBj9i+WtJfAm8FPpV/Pmb730o6B/gg8KZuE9ibww/zodd6MHfavtD20cBl5AmOkiJVfwghlJmpkLyZbtWMAfvk7PUrgYdJAVLX5cevAM7K98/M2+THT1LqM5+cwG77AaA8gX1ezBhXJ+k5wMuB/5sUTvxI72eEeVcKR3XLQlDNrLnpsTxGAxG0vwTJBkMDceSyn017zDXS+EucF3M2i1xkhzQnhefb+unH8Y9JQxQ/IjUsT5B6kR63vSfvVp50PjkhPT/+BGloo/JE9WHpNch/HvAmYAWpVXxjKbVLCCGEsupt86O213Z7UNKBpKuP1cDjwOeB03q8YreJ6t3KZ0XSQba3zfZ50HsM5jLgLlIregpwssrrStjRVRZCCAAMdDnk1wAP2P4ZgKQvAq8kTXgfy1cp5UnnzYnqW3OX2rOBbfSewD4bt0i6E/gM8HezGYfv1cC8eg4VCcPWacZ2qVtsMmuuYFwNxtVAY8045XK25QhHHWlFAfVaCl9X+fxIC9E9OHEw7BHN9ehmm+nBhVEtzqFpBte7+CPgeEkrgWeAk4Dbga8DbwCuBtYBX877b8rb38yP/4NtS+o2gX22XkBq9H4b+Jika4DLbf/LTE/sNdHyG3OoSAghLD0GDyiKzPYtkq4Dvk2aeH4HKc/i3wJXS/rjXHZZfsplwGdzFvptpMgxbHedwD7L+hjYDGzOS678L+D3JH0HuND2N7s9t9cYzAbb67s9XnWfEEJYGgZ3RZcXery4pfh+OkSB2d4JnN3lOB0nsM+GpINJcyL/E/BT4O2kq6ajSeNDq7s9t1cX2VmSdvZ6XaIbbf4VRft2vTZtxnbT9mIFdQq8RzmMsvS8iBYaLa3nRe5K7XRe1ChSEtQxUzQ/AWZ5OkT3WAej+y/1TeCzwFm2t5bKb8/zcbrq1cD8vxVe+J8q7BNCCKNvdBuYP7R9bblA0tm2P2/7g72e2GsM5opuj4UQQihpTrQcTRcC17aUXUTqHutpvhccCyGEkTRqvc6STiOlp1kl6dLSQ/uTggZmFA3MYqIcolxeSCr3tasl7FgFPKe+nR9PHDS53VxgKoyo8nlRDlMmhac735+gzoTr4HRexGJ0AzKgKLIF5CFSePQZpEwCTduB369ygBkbGEkvsX33nKoXQghLxKg11La/A3xH0udKKWpmpcoVzF/mtNCXA39t+/G5vFAIIYys1ijNESDpWttvBO6QpjWfIk2PeelMx5ixgbH97yStIc3ivF3SrcBnbG+ea8XDHLlIXWKtC0sVTuGoTM3UL+qwrfEsVmg3CIqx1D3W8VtW6/HCyFFhXEtJLctdp1M77J16jY6R7H9+Z/75urkeYMZsygC27wP+EHgP8O+BSyV9X9Lr5/rCIYQwUlzxtkjYfjjffRR40Pa/AsuBl1Exp1mVdP0vlfQR4F7SegS/YfuX8v2P9HjeRkmPSLq7VPY+ST+WdGe+nV56rOPKa5JOzWVbJF1Y5ZcKIYR5V1S8LT43ASvyQmg3AueRhkxmVOUK5uOknDgvs32+7W8D2H6IdFXTzeXAqR3KP2L76Hy7HqBl5bVTgU9KqkuqA58gpao+Cjg37xtCCAvH4BccW0hkewfweuBjtn+T9Hk8oyqD/KcDzzSTpEmqASts77D92W5Psn1TeW3oGUyuvAY8kJO2NXPubLF9f37tq/O+36t43NHTmhIEoKbJcFQVxkBtDxxUf4ofTRwMNaevEt3O7WIRXbeH7mYaS8unzrbGs1imPSlBY/6K6TRsG1m2+zBqUWQlkvQK4M2k5Zmh4hSXKlcwXwP2KW2vzGVzdYGk7+YutANzWbeV1yqvyCZpfXOFuAl29VG9EEKYgxEbgyl5J2nm/pdyhubnkZYOmFGVBmaF7aeaG/n+yjlVEz4FPJ+UhfNh4M9zed8rstneYHut7bXjLJ9j9UIIIZTZvsn2Gc28Y7bvt/2OKs+tcpnztKRjmmMvkn6ZtAjOXCr60+Z9SX8FfCVv9lp5bRArso2uPGO7levwkz3Ppk4Bu2uTzbKiO2w0tS5E17LtmpCNCmggVtZ2gZUyPLiZ6UGjl+9kHo1qF5mkFwDvBo6k1GbYPnGm51ZpYN4FfF5S84P9MOBNs68mSDqsFPr2m0AzwqzbymsC1khaDfyYFAjwH+by2iGEMDRmFFPFNH0e+Evg08xyxlyViZa3SXoR8ELSB/73bU/M9DxJVwEnAIdI2kpaPOcESUeT/hw/BP5Lfo2uK69JugC4AagDG23fM5tfMIQQ5sWIXsEAe2x/ai5PrJrs8leYujx6uSRsX9nrCbbP7VB8WYey5v4dV17LoczXV6zn6JtFV8gB9R083lgJy4rJbpBmwsOIFhoxnaILmyRUmCL/zffVbrZ7HxgrUhSZiJn8AzCqXWTA/5b0e8CXYCqCyva2mZ5YJdnlZ0kD83cydXlkoGcDE0IIS8roNjDr8s/yIpQGnjfTE6tcwawFjrJj9C+EELoa0U9I26vn+twqYcp3A/9mri8QQgijTq5+W2wkrZT0h5I25O01kiolwKxyBXMI8L2cRbnc/3bGnGob+tOpr70UetycyV/UYcJ1Jjw2LRy1Y197ZFNe/Gq1jguOlckpo3JNBbtdn4p6WoQfegvS6EaRfYa04Ngr8/ZWUmTZV7o+I6vSwLxvztUKIYQlYjFenVT0fNtvknQugO1n1LqEbhczdpHZ/gYppHg837+NlPwyhBBC0wBTxUg6QNJ1eVmUeyW9QtJBkjZLui//PDDvK0mX5ozz35V0TOk46/L+90la1/0Ve9otaZ9m7SU9H6rl46qSrv93gOuA/5mLVgF/M7d6hoEritTFVeJ62h5Xg31r6TxQM7GhaZ+tHbP7F7/W86BD+DqQFp9zLXWd1kytUTovwtwNfgzmo8Df234Raf2Ve4ELgRttryGlzW8uX3IaaXL6GmA9KSUXkg4izT88jpQ8+OJS/sfZeB/w98ARkj6XX/s9VZ5YZZD/fOBVwJMwufjYoXOoZAghjK4BXcFI2h/4NfK8Qdu781L1ZwJX5N2uAM7K988ErnRyM3CApMOAU4DNtrfZfgzYTOclVHr/WvZXSan6fwu4Clhre2DJLnfZ3t3ckDRGfN8JIYRpVFS7kbKb3F66rW851POAnwGfkXSHpE9L2hf4hWaqrfyz+UW/72z0PX8v6UbbP7f9t7a/YvtRSTdWeW6VQf5vSPoDYB9JrwV+D/jfs61kCCEEAB61vbbH42PAMcDbbd8i6aNMdYd10nc2+o4HlVaQMucfkrvWmsfbn5QzckZVrmAuJLWmd5Fyh11P75UswzB1SRXTnAfrmqBIC4493lhJTSlNTFHP/b8isuaOotbzohzO3jw3JDCs0EQamys0ueBYpIoZgMEN8m8Fttq+JW9fR2pwfpq7vsg/Hynt3ynrfK8s9VX8F1J48ovyz+bty6SVhmdUJYqssP1Xts+2/YZ8Pz6dQgihaYCD/LZ/Ajwo6YW56CRSIuBNTKVtWUf6oCeXvyVHkx0PPJG70G4ATpZ0YL4COTmXVfuV7I/mWfzvtv0826vz7WW2P17lGFVykT1Ah3bX9ox5aEIIYckY7NfutwOfk7QMuB84j3RBcK2ktwI/As7O+15PWtp+C7Aj74vtbZI+QJpaAvD+KgkqW9n+mKRX0r4ezIz5KKvmImtaQfqlDpplHcOgFEXHGduSJrPmmrTg2DI1uH/3oajuFKYspv4JIpvyaCt1nap0XqiAFdqTZvLXnM4J6HxeRIaH2RlgA2P7TqZ/9jad1GFfk6J9Ox1nI7Cxn7r0k/C4ynowP28p+gtJ/wz80SzrGUIII0lMRoiNojknPK7SRXZMabOWX2y/2b5QCCGMrEWayLKiZsLjh2fasVWVLrI/L93fQ0ob88bZvlAYoNaui2aXmT3ZxaECxrWHA+o78EQtdYWUe8XKX0aiK2Q0dMjIoGbEoDSZ7PJpj7NvbXdKzqge37wjw8PsjO7bNeeEx1W6yF7dX91CCGEJGN0G5n1zfWKVLrL/2utx2x+e64uHEMKoGNUuspzkeE6qTLRcC/wuU6kH3gYcRRqH6ToWI2mjpEck3d3hsXdLsqRD8vaws4GGEMJwDTCb8kIgabukJzvctkt6ssoxqi44dozt7flF3wd83vZ/nuF5lwMfpyWUTdIRwGtJcdxN5Wygx5GygR5Xyga6lvSn+ZakTTlx29LUaSZ/KYvuZNbcfCI/Z+xJtKwBjANp0anIpjyCmtmUO43NMXVeyDDhMRoIau6eUCTMjkcvisx238FcVa5gngvsLm3vJk246cn2TUCnST0fAf4709vyoWYDDSGEoRuxK5hBqHIF81ngVklfIr09v0mFCTadSDoD+LHt77QsiNZ3NtCckXQ9wApWzqV6IYQwZ6M6BtOPKlFkl0j6O+BXc9F5tu+Y7QtJWgm8l5QPp+3hTi/do7xTPTcAGwD210Gj+6dunclf1jI7v+EaO4tl6aGcKtw1zWoWvwujWvShLHi1Wvt5MbnIWPrp/HO362nBMUj/YeWnRZrBuYu3rk2VLjJIKZuftP1RYKuk1XN4recDq4HvSPohKbPntyX9G4aXDTSEEIavavfYEmuEqiyZfDFpecyLctE48L9m+0K277J9qO0jbR9JajyOyZlDh5INNIQQ5oMY+JLJI6HKFcxvAmcATwPYfogKqWIkXQV8E3ihpK05A2g315Myhm4B/oq0qBk582czG+htzDEbaAghDFs0MO2qDPLvtm0pvTV56c4Z2T53hsePLN0fajbQkdIMS+6S2kVyJ7wAABPPSURBVKWZNbcYgwnqbC9W4EYtLSw1h0v0GH9ZJIqifVtK2ZTtFEJrT/79J1yHQqm8NY1QmJsl1nhUUeUK5lpJ/5MUOvw7wNdIVxkhhBCaYgymTZUosg9Jei3wJPBC4I9sbx56zUIIYbFYgt1fVfRsYCTVgRtsv4Y0yTEscJMztou09voKTaBaXnCsTmkN9ugTGTnlrtPcldqcb9Y8L1xLXacAqpcWHIMIUe5XvH1tejYwthuSdkh6tu0n5qtSIYSw2IxaqphBqDLIvxO4S9JmciQZgO13DK1WIYSwyEQXWbsqDczf5ltYwMrRQiZ1hTRcY0VtIj1em5rNn58w9eRYcGw0lWb2N6MLAeqYGkVah6yZpNFEt2k/luAAfhVdGxhJz7X9I9tXzGeFQghhUYoGpk2vMOW/ad6R9IV5qEsIISxKMZO/s15dZOXr5ecNuyIhhLCYKdZVatPrCsZd7oe9qXXGdialLMnlMOWdHufJxgpcCDVyiHKnbvb4xxhtLdmUaxSTY3OpnJjJ368BJ7uUVJd0h6Sv5O3Vkm7JK/teI2lZLl+et7fkx48sHeOiXP4DSacM6ledjV4NzMuay2MCL53LcpkhhLBUDLiL7J3AvaXtDwIfsb0GeAxo5nZ8K/CY7X9LWszxgwCSjgLOAV5MWqTxk3le47zq2sDYrtve3/Z+tsfy/eb2/vNZyRBCWPAGdAUj6XDg14FP520BJwLX5V2uAM7K98/M2+THT8r7nwlcbXuX7QdIiYSP7ev3m4Oq68GEhaRHl5aKHK5sKKhRn/zq1DIRLEJSlxzZk5kcdhTLU1mRMjyE/s3iCuYQSbeXbutbDvUXpGXlm/+xBwOP296Tt8sr+06u+psffyLvX3k14GGqMg8mhBDCTKp3fz1qe22nByS9DnjE9rckndAs7vFqfa8GPEzRwIQQQr/MoFLFvAo4Q9LpwApgf9IVzQGSxvJVSnll3+aqv1sljQHPBraxQFYDji6yEELo06Dmwdi+yPbheb2sc4B/sP1m4OvAG/Ju64Av5/ub8jb58X/I62ttAs7JUWargTXArYP7jauJK5jFriignr8n2Djfdw32qz3DzmIc9tTATC46ZimiUkeJ8t+/PDZXFDA+NplCCCn93fPY3Lga0BBujs1FpHr/hpuN+j3A1ZL+GLgDuCyXXwZ8VtIW0pXLOakqvkfStcD3gD3A+bbnPSFUNDAhhDAAg56lb/sfgX/M9++nQxSY7Z3A2V2efwlwyWBrNTtD6yKTtFHSI5LuLpV9QNJ3Jd0p6auSfjGXS9KleVLQdyUdU3rOujy56D5J6zq9Vggh7FUDnmg5KoY5BnM5aYJP2Z/Zfqnto4GvAH+Uy08j9RGuAdYDnwKQdBBwMXAcqfW+WNKBQ6zzwpcXkuqmma5CDXg6h6JSmypL4cpFLC41StxhdLlWg8KTGR6m9oVx7aFOAYVwjemLjoU5a2Yrn+m2lAytgbF9E6lPsFxWzgCwL1Pt+ZnAlU5uJkVMHAacAmy2vc32Y6RVNVsbrRBC2OuigWk372Mwki4B3kKaEPTqXNxtUtCCmCwUQgg9megV6GDew5Rtv9f2EcDngAtycd+ThSStb86MnWDXYCobQggVRbr+dntzHsxfA/9Pvt9tUlDlyUK2N9hea3vtOMuHUN0FojWbci2HJZe+PVnCdairYLfrk33tiKmVCyNVzOipqev4XDnL9tPFclbWdsF4MZlCSHZ8A+9XDPK3mdcGRtKa0uYZwPfz/U3AW3I02fHAE7YfBm4ATpZ0YB7cPzmXhRDCghELjnU2tDEYSVcBJ5ASu20lRYOdLumFpCRu/wq8Le9+PXA6KePnDuA8ANvbJH0AuC3v937b0wIHQghhr7NjwbEOhtbA2D63Q/FlHcrIqQ3O7/LYRmDjAKs2ktRcVKrURTJOg4IajBWoOZM/jK7C02fzl84FFU49NDVYpkbqOm1oMnTdtVK3aY8w+NBDtC9tYiZ/CCEMwFLr/qoiGpgQQuiXiaXHO4hOk1HQPLGlqX5gw/ZinxQtRMskr4gWGk3lrq1ytGH+eyuXrdDE5IJjrtMe3RQflHMTUWRt4gomhBAGILrI2kUDE0IIAxBRZO2igQkhhH4twe6vKqKBGQUtYaWykeGA+tM83tgXnBaWmpzNL8U4zCiaFqJcmzY2B+Cc9WG36+xXf2Yys4PrpWNEhoc5SRMt43+qVTQwIYQwCEssU3IV0cCEEMIAxBVMuwhTXkxU7c9laXLW/vZiRX4u0/uJoytk9HSYge+cxLKc4WHf2i5+vudZaKyg1iDGDgYhVrTsKK5gQgihb5GLrJNoYEIIYRCii6xNNDAhhNAvL73lkKuIBmYxcTG5wNikooB6LrNxferxfbW79NypmcaWYkByiVDL4nKuwc8a+7PTy3ChyLA9SPE/1SZOrxBCGIQBDfJLOkLS1yXdK+keSe/M5QdJ2izpvvzzwFwuSZdK2iLpu5KOKR1rXd7/PknrBvwbzygamBBCGAAVRaVbBXuA/2b7l4DjgfMlHQVcCNxoew1wY94GOA1Yk2/rgU9BapBICz0eBxwLXNxslOZLNDCLTVF0zHZrezKbsmxUwCON/dKDclpUSs3NuJQfeUUxFbbczKbczPBQ28H+tWdQ3VNdpxG13h+TJlpWuc10KPth29/O97cD9wKrgDOBK/JuVwBn5ftnAlc6uRk4QNJhwCnAZtvbbD8GbAZO7ft3nYUYgwkhhD4Jz+aL2yGSbi9tb7C9oeNxpSOBlwO3AL9g+2FIjZCkQ/Nuq4AHS0/bmsu6lc+baGBCCGEQqjcwj9peO9NOkp4FfAF4l+0n1X1ydKcHul2Xzmv3xdC6yCRtlPSIpLtLZX8m6ft5IOpLkg4oPXZRHqT6gaRTSuWn5rItki5sfZ0lLyc11GRCw/xTae31FZqAIpXFehVLSDnasGUhugZip8fTeZG7TpvdpxEJ1YecNWHGWwWSxkmNy+dsfzEX/zR3fZF/PpLLtwJHlJ5+OPBQj/J5M8wxmMtp7+/bDLzE9kuBfwEuAsgDWOcAL87P+aSkuqQ68AnSINZRwLl53xBCWDgGOAaj9G3xMuBe2x8uPbQJaEaCrQO+XCp/S44mOx54Inel3QCcLOnAPLh/ci6bN0PrIrN9U+4/LJd9tbR5M/CGfP9M4Grbu4AHJG0hRT0AbLF9P4Ckq/O+3xtWvUMIYS4qRohV8SrgPwF3Sbozl/0B8CfAtZLeCvwIODs/dj1wOrAF2AGcB2B7m6QPALfl/d5ve9ugKlnF3hyD+W3gmnx/FanBaSoPRrUOUh3X6WCS1pNC9FjByoFWNIQQeqve/TXjkex/pntc30kd9jdwfpdjbQQ2DqRic7BXwpQlvZcU6/25ZlGH3WY1SGV7g+21tteOs3wwFV2oyllzy+GodFm2NaexKOrKWZU9/TgdsvCGRaY1w0MHllABdczTxfLJ00CR5qR/ZqBjMKNi3q9g8mzS1wEn5ZYXeg9G7dVBqhBCqCQa6TbzegUj6VTgPcAZtneUHtoEnCNpuaTVpBmpt5L6DtdIWi1pGSkQYNN81jmEEKpIE1lnvi0lQ7uCkXQVcAJpUtFWUsqCi4DlwOYcVnuz7bfZvkfStaTB+z3A+bYb+TgXkCIf6sBG2/cMq86LUu4acfPklXAOWV5Z28Vu19GyAgtqe6av0T6ZESDWsVj8Og0wl/6ukwuOCXa7zqrxx8CaHqIc+rPEGo8qhhlFdm6H4st67H8JcEmH8utJURIhhLAw2dCIPrJWMZM/hBAGIa5g2kQDE0IIgxANTJtoYBarmqDB5IJjraliAArXqKvAz9RTQfS1j7ZuY2lOEf+ysWCnx9lZjKfHIsP2YJgYy+wgGpgQQuib04qzYZpoYEIIoV8mBvk7iAXHFpvmjO1Ol+OlrLkqoKaC7Y19YMxzXnvdcdm/eLRmZJgMTZ5efkB9B08XOdtFXnDM3VPBh6piJn+buIIJIYRBWGKNRxXRwIQQQt+W3tVJFdHALHbdkhwKdhbj1HIWQxUtM7YrdokoEmGOBBWmkJBhR7GcA+o7UL1oPy/C3JjO2RSWuGhgQghhEOIKpk00MCGE0LdIFdNJNDAhhNAvg2MeTJtoYBab1n7ePJO/rDlju66COk6ztZv97HEVP7p6zOR3vTZ5XjzeWMnK2i68p3TeNM+PCFeeuwjpbxMNTAghDEKMwbSJBiaEEPplRxRZB9HALHblMOW84BgAgoZrPF0si26xpaKZALUDFZ48DVbWdrGt8Sw0VuBamskPaTZ/JL3sQ7x3baKBCSGEvhk3urTuS1g0MCGE0K9I199RNDAhhDAIEabcZmjZlCVtlPSIpLtLZWdLukdSIWlty/4XSdoi6QeSTimVn5rLtki6cFj1XTS6pYbJXFPqSy9SmPLBY0+lck31taeC+LY10vKAs/O4XHkhugmP0XANT9SmnRMx/jJ3JmUer3KrYlQ+94aZrv9y4NSWsruB1wM3lQslHQWcA7w4P+eTkuqS6sAngNOAo4Bz874hhLBwOC84VuU2g1H63BtaF5ntmyQd2VJ2LzC5vG/JmcDVtncBD0jaAhybH9ti+/78vKvzvt8bVr1DCGEuBjjIfywj8rm3UMZgVgE3l7a35jKAB1vKj+t0AEnrgfV5c9fXfN3dnfZbwA4BHu36qIECmKh4tLvg5I81N2Z5hd38P5n5y1bvOi88i62+MJvz4qkKR7sb3nhpc+OfZ1eT0T0v/q9+D7Cdx274mq87pOLuKyTdXtreYHtDaXsVFT/3FrqF0sB0yk9hOnfhdezEzH+gDQCSbre9ttN+C1XUefgWW30h6rxY2G4dDuhHt8/DRWehNDBbgSNK24cDD+X73cpDCGEU9fo8XFSGOcg/G5uAcyQtl7QaWAPcCtwGrJG0WtIyUiDApr1YzxBCGLaR+dwb2hWMpKuAE4BDJG0FLga2AR8DngP8raQ7bZ9i+x5J15IGsfYA59tu5ONcANwA1IGNtu+p8PIbZt5lwYk6D99iqy9EnZcc23vm+Lm34MgR+x5CCGEIFkoXWQghhBETDUwIIYShGLkGZjGkWJB0hKSvS7o3p855Zy4/SNJmSfflnwfu7bqW5ewKd0j6St5eLemWXN9r8oDkgiHpAEnXSfp+fq9fsZDfY0m/n8+HuyVdJWnFQnuPu6SA6vieKrk0/y9+V9Ixe6/mYW8YqQZmEaVY2AP8N9u/BBwPnJ/reSFwo+01wI3Meobk0L0TuLe0/UHgI7m+jwFv3Su16u6jwN/bfhHwMlLdF+R7LGkV8A5gre2XkAZ3z2HhvceX054Cqtt7ehopInQNaRL0p+apjmGBGKkGhlKKBdu7gWaKhQXF9sO2v53vbyd98K0i1fWKvNsVwFl7p4btJB0O/Drw6bwt4ETgurzLQqvv/sCvAZcB2N5t+3EW8HtMiurcR9IYsBJ4mAX2Htu+iRQNWtbtPT0TuNLJzcABkg6bn5qGhWDUGphOKRZWddl3Qcj52l4O3AL8gu2HITVCwKF7r2Zt/gL470wlCjkYeNz2nry90N7r5wE/Az6Tu/U+LWlfFuh7bPvHwIeAH5EalieAb7Gw3+Ombu/povt/DIM1ag3MokqxIOlZwBeAd9l+cm/XpxtJrwMesf2tcnGHXRfSez0GHAN8yvbLgadZIN1hneRxizOB1cAvAvuSuphaLaT3eCYL/RwJQzZqDcyiSbEgaZzUuHzO9hdz8U+bXQj55yN7q34tXgWcIemHpG7HE0lXNAfk7hxYeO/1VmCr7Vvy9nWkBmehvsevAR6w/TPbE8AXgVeysN/jpm7v6aL5fwzDMWoNzKJIsZDHLy4D7rX94dJDm4B1+f464MvzXbdObF9k+3DbR5Le03+w/Wbg68Ab8m4Lpr4Atn8CPCjphbnoJFKmiAX5HpO6xo6XtDKfH836Ltj3uKTbe7oJeEuOJjseeKLZlRaWhpGbyS/pdNK362aKhUv2cpXaSPp3wD8BdzE1pvEHpHGYa4Hnkj5wzrbdOqC6V0k6AXi37ddJeh7piuYg4A7gP+Y1fRYESUeTghKWAfcD55G+VC3I91jS/wDeRIoyvAP4z6QxiwXzHpdTQAE/JaWA+hs6vKe5ofw4KepsB3Ce7ds7HTeMppFrYEIIISwMo9ZFFkIIYYGIBiaEEMJQRAMTQghhKKKBCSGEMBTRwIQQQhiKaGBCCCEMRTQwYcGSdLCkO/PtJ5J+XNr+P0N4vd+S9DNJn+6xzz759XdLOmTQdQhhlIzNvEsIe4ftnwNHA0h6H/CU7Q8N+WWvsX1Bjzo9Axyd0+aEEHqIK5iwKEl6Kv88QdI3JF0r6V8k/YmkN0u6VdJdkp6f93uOpC9Iui3fXlXhNV6cj3NnXjBrzbB/rxBGSVzBhFHwMuCXSOuU3A982vaxSiuFvh14F2nxsY/Y/mdJzwVuyM/p5W3AR21/Lue2qw/tNwhhBEUDE0bBbc0kipL+P+Crufwu4NX5/muAo1J6LAD2l7RfXvCtm28C782LrX3R9n2Dr3oIoyu6yMIoKCd/LErbBVNfomrAK2wfnW+rZmhcsP3XwBnAM8ANkk4ccL1DGGnRwISl4qvA5OB9zrTcU84Wfb/tS0mp5186vOqFMHqigQlLxTuAtXmw/nuk8ZWZvAm4W9KdwIuAK4dZwRBGTaTrDyGT9FvA2l5hyqV9f5j3fXTY9QphsYormBCmPAOcVmWiJTDO1GJxIYQO4gomhBDCUMQVTAghhKGIBiaEEMJQRAMTQghhKKKBCSGEMBT/PzW37IRg1a5YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We first plot the first two pulses in frequency-time space to show the undispersed pulses\n",
    "time = np.linspace(0, obslen, len(signal_1713.data[0,:]))\n",
    "# And the 2-D plot\n",
    "plt.imshow(signal_1713.data[:,:4096], aspect = 'auto', interpolation='nearest', origin = 'lower', \\\n",
    "           extent = [min(time[:4096]), max(time[:4096]), signal_1713.dat_freq[0].value, signal_1713.dat_freq[-1].value])\n",
    "plt.ylabel(\"Frequency [MHz]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.colorbar(label = \"Intensity\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the dispersion measure\n",
    "dm =  15.921200 # pc cm^-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98% dispersed in 0.143 seconds."
     ]
    }
   ],
   "source": [
    "# Now disperse the pulses. Once this is done, the psrsigsim remember that the simulated data have been dispersed.\n",
    "ism_sim.disperse(signal_1713, dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": [
    "# Now we can plot the dispersed pulses\n",
    "plt.imshow(signal_1713.data[:,:4096], aspect = 'auto', interpolation='nearest', origin = 'lower', \\\n",
    "           extent = [min(time[:4096]), max(time[:4096]), signal_1713.dat_freq[0].value, signal_1713.dat_freq[-1].value])\n",
    "plt.ylabel(\"Frequency [MHz]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.colorbar(label = \"Intensity\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can clearly see the time delay as a function of observing frequency that has been added to the signal. However, addition effects can be added either separtely or in combination with dispersion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency Dependent Delays\n",
    "\n",
    "We can also add frequency dependent, or FD, delays to the simulated data. The formula for these FD parameters can be found in Zhu et al. 2015 and Arzoumanian et al. 2016. These delays are usually attributed to pulse profile evolution in frequency, but with the psrsigsim can also be directly injected into the simulated data without a frequency dependent pulse `Portait`.\n",
    "\n",
    "The input for these delays are a list of coefficients (in units of seconds) that are used to determine the FD delay as computed in log-frequency space. FD delays are referenced such that the delay due to FD parameters is 0 at observing frequencies of 1 GHz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can input any number of FD parameters as a list, but we will use the FD parameters in the NANOGrav 11-yr parfile\n",
    "FD_J1713 = [-5.68565522e-04, 5.41762131e-04, -3.34764893e-04, 1.35695342e-04, -2.87410591e-05] # seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As delays due to FD parameters are usually much smaller than those from dispersion, we will re-instantiate the pulse signal to better show the delays added from FD parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98% shifted in 0.113 seconds."
     ]
    }
   ],
   "source": [
    "# Re-make the pulses\n",
    "pulsar_J1713.make_pulses(signal_1713, tobs = obslen)\n",
    "\n",
    "# Now add the FD parameter delay, this takes two arguements, the signal and the list of FD parameters\n",
    "ism_sim.FD_shift(signal_1713, FD_J1713)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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": [
    "# Show the 2-D plot with the frequency dependent effects\n",
    "plt.imshow(signal_1713.data[:,:4096], aspect = 'auto', interpolation='nearest', origin = 'lower', \\\n",
    "           extent = [min(time[:4096]), max(time[:4096]), signal_1713.dat_freq[0].value, signal_1713.dat_freq[-1].value])\n",
    "plt.ylabel(\"Frequency [MHz]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.colorbar(label = \"Intensity\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shfit here is clearly visible at lower frequencies, though it is easy to see that the significance of this shift is much smaller than that from DM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scattering Broadening Delays\n",
    "\n",
    "We can also add delays due to pulse scatter broadening to the simulated data. We can do this two different ways, both of which will be demonstrated here. The first is by directly shifting the simulated profile in time by the appropriate scattering delay. The second is by convolving an exponential scattering tail, based on the input parameters, and then convolving it with the pulse profile. Both of these effects are frequency dependent, so the direct shifts are frequency dependent, and the exponential tails are similarly frequency dependent.\n",
    "\n",
    "Note that delays from scattering due to the ISM tend to be very small for low DM pulsars at low radio frequencies, so the scattering delay we will use here will be largely inflated so the effects are visible by-eye."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will first define the scattering timescale and reference frequency for the timescale\n",
    "tau_d = 1e-4 # seconds; note this is an unphysical number\n",
    "ref_freq = 1500.0 # MHz, reference frequency of the scatter timescale input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Direct Shifting in Time\n",
    "\n",
    "We start by showing how to directly shift the pulse profiles in time by the scattering timescale. We note that this does not add any pulse broadening, it simply shifts the peak of the pulse very slightly. Again, we remake the signal to better show the scatter broadening separately from the other ISM effects.\n",
    "\n",
    "We also note here that `convolve=False` and `pulsar=None` are default inputs, and are not necessary for a direct shift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98% scatter shifted in 0.115 seconds."
     ]
    }
   ],
   "source": [
    "# Re-make the pulses\n",
    "pulsar_J1713.make_pulses(signal_1713, tobs = obslen)\n",
    "\n",
    "# Now add the FD parameter delay, this takes two arguements, the signal and the list of FD parameters\n",
    "ism_sim.scatter_broaden(signal_1713, tau_d, ref_freq, convolve = False, pulsar = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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"
    },
    {
     "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": [
    "# Now we plot these profiles\n",
    "# Since we know there are 2048 bins per pulse period, we can index the appropriate amount\n",
    "plt.plot(time[:4096], signal_1713.data[0,:4096], label = signal_1713.dat_freq[0])\n",
    "plt.plot(time[:4096], signal_1713.data[-1,:4096], label = signal_1713.dat_freq[-1])\n",
    "plt.ylabel(\"Intensity\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "# Show the 2-D plot with the frequency dependent effects\n",
    "plt.imshow(signal_1713.data[:,:4096], aspect = 'auto', interpolation='nearest', origin = 'lower', \\\n",
    "           extent = [min(time[:4096]), max(time[:4096]), signal_1713.dat_freq[0].value, signal_1713.dat_freq[-1].value])\n",
    "plt.ylabel(\"Frequency [MHz]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.colorbar(label = \"Intensity\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the signal has been shifted in time as a function of frequency in both the profiles and in the 2-D power spectrum. But the input profiles themselves remain unchanged from the input profile, e.g. no eponential scattering convolution as been done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scattering Tail Convolution\n",
    "\n",
    "Here we show how to scatter broaden the profiles themselves in order to add pulse scatter broadening delays. The inputs necessary to do this are very similar to dierectly shifting it, with the addition of changing `convolve=True` and adding the `Pulsar` object as input. Because this acts directly on the profiles, this must be done before `make_pulses()` is run, and cannot be undone.\n",
    "\n",
    "We also note that the number of input profile channels must match the number of channels specified in the `Signal`. Here this is 64 channels, so we can reinstantiate the profile including the `Nchan=64` flag, and then make the pulsar again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we define the data profile\n",
    "J1713_prof = pss.pulsar.DataProfile(J1713_dataprof, Nchan=64)\n",
    "\n",
    "# Now we define the pulsar with the scaled J1713+0747 profiles\n",
    "pulsar_J1713 = pss.pulsar.Pulsar(period, Smean, profiles=J1713_prof, name = psr_name)\n",
    "\n",
    "# Now add the FD parameter delay, this takes two arguements, the signal and the list of FD parameters\n",
    "ism_sim.scatter_broaden(signal_1713, tau_d, ref_freq, convolve = True, pulsar = pulsar_J1713)\n",
    "\n",
    "# Re-make the pulses\n",
    "pulsar_J1713.make_pulses(signal_1713, tobs = obslen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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"
    },
    {
     "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": [
    "# Now we plot these profiles\n",
    "# Since we know there are 2048 bins per pulse period, we can index the appropriate amount\n",
    "plt.plot(time[:4096], signal_1713.data[0,:4096], label = signal_1713.dat_freq[0])\n",
    "plt.plot(time[:4096], signal_1713.data[-1,:4096], label = signal_1713.dat_freq[-1])\n",
    "plt.ylabel(\"Intensity\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "# Show the 2-D plot with the frequency dependent effects\n",
    "plt.imshow(signal_1713.data[:,:4096], aspect = 'auto', interpolation='nearest', origin = 'lower', \\\n",
    "           extent = [min(time[:4096]), max(time[:4096]), signal_1713.dat_freq[0].value, signal_1713.dat_freq[-1].value])\n",
    "plt.ylabel(\"Frequency [MHz]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.colorbar(label = \"Intensity\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see now that the scattering tails have been convolved with the actual profiles, and this shows up in both the individual pulse profiles, which clearly show a shift in the peak of the pulse, as well as in the power spectrum, where the profile is clearly getting scattered out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating the Scatter Broadened Pulsar\n",
    "\n",
    "Now we will finish by `observe`ing the pulsar and looking at the data with the added noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Observe with the telescope\n",
    "tscope.observe(signal_1713, pulsar_J1713, system=\"Lband_GUPPI\", noise=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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"
    },
    {
     "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": [
    "# Since we know there are 2048 bins per pulse period, we can index the appropriate amount\n",
    "plt.plot(time[:4096], signal_1713.data[0,:4096], label = signal_1713.dat_freq[0])\n",
    "plt.plot(time[:4096], signal_1713.data[-1,:4096], label = signal_1713.dat_freq[-1])\n",
    "plt.ylabel(\"Intensity\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "# And the 2-D plot\n",
    "plt.imshow(signal_1713.data[:,:4096], aspect = 'auto', interpolation='nearest', origin = 'lower', \\\n",
    "           extent = [min(time[:4096]), max(time[:4096]), signal_1713.dat_freq[0].value, signal_1713.dat_freq[-1].value])\n",
    "plt.ylabel(\"Frequency [MHz]\")\n",
    "plt.xlabel(\"Time [s]\")\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 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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
