animate_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import lichen.lichen as lch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from datetime import datetime,timedelta,date\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.ticker as ticker\n",
    "from cogent_utilities import sec2days\n",
    "\n",
    "import sys\n",
    "#import seaborn as sn\n",
    "\n",
    "#%matplotlib notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#from plotly.widgets import GraphWidget\n",
    "\n",
    "#from IPython.html import widgets \n",
    "#from ipywidgets import widgets\n",
    "#from IPython.display import display, clear_output\n",
    "#import plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9318\n"
     ]
    }
   ],
   "source": [
    "vals = np.loadtxt('data/LE.txt')\n",
    "\n",
    "seconds = vals[:,0]\n",
    "org_days = sec2days(seconds)\n",
    "org_energies = vals[:,1]\n",
    "org_risetimes = vals[:,2]\n",
    "\n",
    "elo = 0.5\n",
    "ehi = 3.3\n",
    "enbins = 70\n",
    "ewidth = (ehi-elo)/enbins\n",
    "\n",
    "dlo = 0.0\n",
    "dhi = 1240.\n",
    "dnbins = 62\n",
    "dwidth = (dhi-dlo)/dnbins\n",
    "\n",
    "rlo = 0.0\n",
    "rhi = 5.0\n",
    "rnbins = 120\n",
    "rwidth = (rhi-rlo)/rnbins\n",
    "\n",
    "index = np.ones(len(org_days)).astype(bool)\n",
    "\n",
    "org_index = index.copy()\n",
    "\n",
    "print len(org_days[org_energies<ehi])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "edata = []\n",
    "ddata = []\n",
    "rtdata = []\n",
    "\n",
    "# Sliding window\n",
    "ndays = 30\n",
    "nslices = int(dhi)\n",
    "tag = \"sliding\"\n",
    "\n",
    "for i in range(0,nslices):\n",
    "    daylo = i\n",
    "    dayhi = ndays + i\n",
    "\n",
    "    #print daylo\n",
    "    #print dayhi\n",
    "\n",
    "    index = org_days>daylo\n",
    "    index *= org_days<dayhi\n",
    "    index *= org_energies>elo\n",
    "    index *= org_energies<ehi\n",
    "\n",
    "    days = org_days[index]\n",
    "    energies = org_energies[index]\n",
    "    risetimes = org_risetimes[index]\n",
    "\n",
    "    edata.append(energies)\n",
    "    ddata.append(days)\n",
    "    rtdata.append(risetimes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def plot_data(x):\n",
    "    \n",
    "    # Sliding window\n",
    "    \n",
    "    days = ddata[x]\n",
    "    energies = edata[x]\n",
    "    risetimes = rtdata[x]\n",
    "\n",
    "    fig = plt.figure(figsize=(16,12))\n",
    "    ax0 = fig.add_subplot(3,1,1)\n",
    "    ax1 = fig.add_subplot(3,1,2)\n",
    "    ax2 = fig.add_subplot(3,1,3)\n",
    "\n",
    "    #plt.plot(energies)\n",
    "    lch.hist_err(energies,bins=enbins,linewidth=2,range=(elo,ehi),axes=ax0)\n",
    "    #plt.hist(energies,bins=enbins,range=(elo,ehi))\n",
    "    #name = \"# interactions/ %0.2f keVee\" % (ewidth)\n",
    "    #plt.ylabel(name,fontsize=14)\n",
    "    #plt.xlabel('Recoil energy (keVee)',fontsize=18)\n",
    "    plt.ylim(0,25) # This might have to be changed for sliding or discrete\n",
    "    #plt.xlim(elo,ehi)\n",
    "    #plt.tight_layout()\n",
    "    lch.hist_err(days,bins=dnbins,linewidth=2,range=(dlo,dhi),axes=ax1)\n",
    "    \n",
    "    lch.hist_err(risetimes,bins=rnbins,linewidth=2,range=(rlo,rhi),axes=ax2)\n",
    "    ax2.set_ylim(0,25)\n",
    "    ax2.set_xlim(rlo,rhi)\n",
    "\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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2dd7WEa3z/tR3gOURXgHOo7uf9kpOJzmZU6duPWTc5tQzUi0jWuf9M2/t67ytI1rn/anv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7a4bd1f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(plot_data,x=(0,1238,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rlo\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rhi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}