{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get some 40m data using NDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Library Imports and Python parameter settings\n",
    "%matplotlib inline\n",
    "from __future__ import division\n",
    "import nds2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.signal as sig\n",
    "import scipy.constants as const\n",
    "from astropy.time import Time\n",
    "\n",
    "# Update the matplotlib configuration parameters:\n",
    "plt.rcParams.update({'font.size': 20,\n",
    "                     'font.family': 'serif',\n",
    "                     'figure.figsize': (10, 8),\n",
    "                     'axes.grid': True,\n",
    "                     'grid.color': '#555555'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some references\n",
    "* https://www.lsc-group.phys.uwm.edu/daswg/projects/nds-client/doc/manual/ch04.html\n",
    "* http://wiki.scipy.org/NumPy_for_Matlab_Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Setup connection to the NDS\n",
    "conn = nds2.connection('nds40.ligo.caltech.edu', 31200)\n",
    "\n",
    "# Setup start and stop times\n",
    "times = '2017-01-12 19:45:00'\n",
    "t = Time(times, format='iso', scale='utc')\n",
    "t_start = int(t.gps)\n",
    "dur = 400\n",
    "\n",
    "# channel names\n",
    "#channels = ['C1:LSC-TRY_OUT_DQ', 'C1:ALS-BEATY_FINE_PHASE_OUT_DQ']\n",
    "#channels = ['C1:IOO-MC_F_DQ']\n",
    "channels = ['C1:SUS-BS_OL_SUM_OUT16']\n",
    "#\n",
    "data = conn.fetch(t_start, t_start + dur, channels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(16, 9))\n",
    "#plt.loglog(aligo[:,0], sqrt(aligo[:,1]), color='Indigo', ls='--', alpha=0.65, lw=4)\n",
    "fs = data[0].channel.sample_rate\n",
    "y = data[0].data\n",
    "tt = np.arange(0,1/fs*np.size(data[0].data),1/fs)\n",
    "plt.plot(tt, y, linestyle='-', lw=2, alpha=0.9, color='BlueViolet',\n",
    "             label=channels[0])\n",
    "\n",
    "plt.xlabel(r'Time [s]', fontsize=18)\n",
    "plt.ylabel(r'Frequency', fontsize=18)\n",
    "\n",
    "#plt.title(\"IR Transmission during ALS Scan\");\n",
    "plt.grid(b=True, which=\"major\", color='gray', alpha=0.33, linestyle='-' , lw=4)\n",
    "plt.grid(b=True, which=\"minor\", color='gray', alpha=0.22, linestyle='--', lw=1)\n",
    "plt.axis('tight')\n",
    "#plt.xlim((2,12))\n",
    "#plt.ylim((1e-3, 1))\n",
    "\n",
    "leg = plt.legend(loc='best', fancybox=True, fontsize=14)\n",
    "leg.get_frame().set_alpha(0.75)\n",
    "\n",
    "#plt.savefig(\"TRY.pdf\", bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "channels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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