NK_X_corr_ns54.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gps_particle_data\n",
    "import wget\n",
    "import os\n",
    "import numpy as np \n",
    "import time\n",
    "import json\n",
    "import sys\n",
    "import math\n",
    "from datetime import datetime, timedelta, date\n",
    "from itertools import compress\n",
    "from random import randint\n",
    "import urllib2\n",
    "import aacgmv2\n",
    "import scipy\n",
    "import matplotlib\n",
    "from matplotlib import gridspec\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GEsMAAIwlhgEAGEsMAwAwlhgGAGAsMQwAwFhiGACAscQwAABjiWEAAMYSwwAAjCWGAQAY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\nwwAAjCWGAQAYSwwDADCWGAYAYKz/B0rIGYxDdH2NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f916f371ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5fUmuXv1IAACwM9yBDgCAscQwAABjiWEAAMYSwwAAjCWGAQAYSwwDADCWGAYAYCwxDADA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\nY4lhAADGEsMAAIwlhgEAGEsMAwAw1v8DI7qT15Tm4FIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9143dc9f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9143d2d490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'ch2_3.ascii'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-addff67bd9e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'ch2_'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.ascii'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mcor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/filip/.local/lib/python2.7/site-packages/numpy/lib/npyio.pyc\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)\u001b[0m\n\u001b[1;32m    894\u001b[0m                 \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbz2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    895\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 896\u001b[0;31m                 \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'U'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    897\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    898\u001b[0m                 \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'ch2_3.ascii'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9143a829d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(11):\n",
    "    fname = 'ch2_'+str(i)+'.ascii'\n",
    "    fig = plt.figure(figsize=(12, 9))\n",
    "    cor = np.loadtxt(fname)\n",
    "    plt.hist(cor, bins = np.arange(min(cor), max(cor)+0.1, 0.05))\n",
    "    plt.title(fname)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================\n",
      "Loading data for satellite  56\n",
      "File : https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns56/ns56_061008_v1.03.ascii\n"
     ]
    }
   ],
   "source": [
    "#Test 9th Oct 2006\n",
    "\n",
    "start_date = datetime(2006,10,8,0,0,0);\n",
    "end_date = datetime(2006,10,9,0,0,0);\n",
    "ms = gps_particle_data.meta_search();\n",
    "ms.load_data(start_date,end_date);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: array([ 0.83148883]), 1: array([ 0.94619019]), 2: array([ 1.]), 3: array([ 0.79996203]), 4: array([ 0.55877969]), 5: array([ 0.49685049]), 6: array([ 0.4309518]), 7: array([ 0.4236888]), 8: array([ 0.57866459]), 9: array([ 0.54628556]), 10: array([ 0.50906531])}\n"
     ]
    }
   ],
   "source": [
    "#Print cross-cor coef between channel 2 and all other channels.\n",
    "dcor = {}\n",
    "signal = 'rate_electron_measured'\n",
    "output_data = ms.get_all_data_by_satellite()\n",
    "for i in range(11):\n",
    "    a0 = np.asarray(output_data[54][signal])[:,2]\n",
    "    ar2 = np.asarray(output_data[54][signal])[:,i]\n",
    "    cor = np.correlate(a0, ar2)/(math.sqrt(np.sum(a0**2)*np.sum(ar2**2)))\n",
    "    dcor.update({i:cor})\n",
    "print dcor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch2_0.ascii [ 0.83148883] 0.37\n",
      "ch2_1.ascii [ 0.94619019] 0.72\n",
      "ch2_2.ascii [ 1.] 1.00\n",
      "ch2_3.ascii [ 0.79996203] 0.03\n",
      "ch2_4.ascii [ 0.55877969] 0.08\n",
      "ch2_5.ascii [ 0.49685049] 0.14\n",
      "ch2_6.ascii [ 0.4309518] 0.17\n",
      "ch2_7.ascii [ 0.4236888] 0.15\n",
      "ch2_8.ascii [ 0.57866459] 0.24\n",
      "ch2_9.ascii [ 0.54628556] 0.21\n",
      "ch2_10.ascii [ 0.50906531] 0.20\n"
     ]
    }
   ],
   "source": [
    "#To find porbability of a give X-correlation parameter to land in a given or lower bin.\n",
    "\n",
    "xv = dcor.values()\n",
    "for i in range(len(xv)):\n",
    "    sum_bins_up_to = []\n",
    "    fname = 'ch2_'+str(i)+'.ascii'\n",
    "    cor = np.loadtxt(fname)\n",
    "    sort = np.histogram(cor, bins = np.arange(min(cor), max(cor)+0.1, 0.05))\n",
    "    for j in range(len(sort[1])):\n",
    "        if sort[1][j] <= xv[i] <= sort[1][j+1]:\n",
    "            for k in np.arange(j+1):\n",
    "                sum_bins_up_to.append(sort[0][k])\n",
    "            tot_sum_up_to = np.sum(sum_bins_up_to)\n",
    "            pvalue = float(tot_sum_up_to)/float(np.sum(sort[0]))\n",
    "            print fname, xv[i], '%.2f' % (pvalue)\n",
    "#prints file name, cross-cor value and corresponding p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================\n",
      "Loading data for satellite  54\n",
      "File : https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns54/ns54_090524_v1.03.ascii\n"
     ]
    }
   ],
   "source": [
    "#Test 25th May 2009\n",
    "\n",
    "start_date = datetime(2009,5,20,0,0,0);\n",
    "end_date = datetime(2009,5,26,0,0,0);\n",
    "ms = gps_particle_data.meta_search();\n",
    "ms.load_data(start_date,end_date);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: array([ 0.90564443]), 1: array([ 0.9698943]), 2: array([ 1.]), 3: array([ 0.51876266]), 4: array([ 0.28480329]), 5: array([ 0.22985944]), 6: array([ 0.2348699]), 7: array([ 0.26582402]), 8: array([ 0.2645961]), 9: array([ 0.26655484]), 10: array([ 0.26925032])}\n"
     ]
    }
   ],
   "source": [
    "dcor = {}\n",
    "signal = 'rate_electron_measured'\n",
    "output_data = ms.get_all_data_by_satellite()\n",
    "for i in range(11):\n",
    "    a0 = np.asarray(output_data[54][signal])[:,2]\n",
    "    ar2 = np.asarray(output_data[54][signal])[:,i]\n",
    "    cor = np.correlate(a0, ar2)/(math.sqrt(np.sum(a0**2)*np.sum(ar2**2)))\n",
    "    dcor.update({i:cor})\n",
    "print dcor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch2_0.ascii [ 0.90564443] 0.49\n",
      "ch2_1.ascii [ 0.9698943] 0.72\n",
      "ch2_2.ascii [ 1.] 1.00\n",
      "ch2_3.ascii [ 0.51876266] 0.01\n",
      "ch2_5.ascii [ 0.22985944] 0.04\n",
      "ch2_6.ascii [ 0.2348699] 0.04\n",
      "ch2_7.ascii [ 0.26582402] 0.07\n",
      "ch2_8.ascii [ 0.2645961] 0.05\n",
      "ch2_9.ascii [ 0.26655484] 0.03\n",
      "ch2_10.ascii [ 0.26925032] 0.03\n"
     ]
    }
   ],
   "source": [
    "xv = dcor.values()\n",
    "for i in range(len(xv)):\n",
    "    sum_bins_up_to = []\n",
    "    fname = 'ch2_'+str(i)+'.ascii'\n",
    "    cor = np.loadtxt(fname)\n",
    "    sort = np.histogram(cor, bins = np.arange(min(cor), max(cor)+0.1, 0.05))\n",
    "    for j in range(len(sort[1])):\n",
    "        if sort[1][j] <= xv[i] <= sort[1][j+1]:\n",
    "            for k in np.arange(j+1):\n",
    "                sum_bins_up_to.append(sort[0][k])\n",
    "            tot_sum_up_to = np.sum(sum_bins_up_to)\n",
    "            pvalue = float(tot_sum_up_to)/float(np.sum(sort[0]))\n",
    "            print fname, xv[i], '%.2f' % (pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kKQMM5pIkSVIGGMwlSZKkDDCYS5IkSRlgMJckSZIywGAuSZIkZYDBXJIkScoA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fae981e1bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fname = 'ch2_3.ascii'\n",
    "fig = plt.figure(figsize=(12, 9))\n",
    "ax = fig.add_subplot(111)\n",
    "cor = np.loadtxt(fname)\n",
    "ax.hist(cor, bins = np.arange(min(cor), max(cor)+0.1, 0.05), label = 'No tests')\n",
    "ax.hist(xv[3], np.arange(min(cor), max(cor)+0.1, 0.05), color = 'red', label = 'Test: 25th May 2009')\n",
    "#plt.title(fname)\n",
    "plt.xlabel('Cross-correlation coefficient', fontsize = 30)\n",
    "plt.ylabel('Frequency', fontsize = 30)\n",
    "plt.legend(fontsize = 'xx-large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================\n",
      "Loading data for satellite  54\n",
      "File : https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns54/ns54_130210_v1.03.ascii\n"
     ]
    }
   ],
   "source": [
    "#Test 12th Feb 2013\n",
    "\n",
    "start_date = datetime(2013,2,9,0,0,0);\n",
    "end_date = datetime(2013,2,13,0,0,0);\n",
    "ms = gps_particle_data.meta_search();\n",
    "ms.load_data(start_date,end_date);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: array([ 0.86554345]), 1: array([ 0.97030944]), 2: array([ 1.]), 3: array([ 0.95936102]), 4: array([ 0.60858687]), 5: array([ 0.44541832]), 6: array([ 0.29237644]), 7: array([ 0.22931216]), 8: array([ 0.26515978]), 9: array([ 0.3520484]), 10: array([ 0.40900525])}\n"
     ]
    }
   ],
   "source": [
    "dcor = {}\n",
    "signal = 'rate_electron_measured'\n",
    "output_data = ms.get_all_data_by_satellite()\n",
    "for i in range(11):\n",
    "    a0 = np.asarray(output_data[54][signal])[:,2]\n",
    "    ar2 = np.asarray(output_data[54][signal])[:,i]\n",
    "    cor = np.correlate(a0, ar2)/(math.sqrt(np.sum(a0**2)*np.sum(ar2**2)))\n",
    "    dcor.update({i:cor})\n",
    "print dcor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch2_0.ascii [ 0.86554345] 0.49\n",
      "ch2_1.ascii [ 0.97030944] 0.72\n",
      "ch2_2.ascii [ 1.] 1.00\n",
      "ch2_3.ascii [ 0.95936102] 0.63\n",
      "ch2_4.ascii [ 0.60858687] 0.12\n",
      "ch2_5.ascii [ 0.44541832] 0.10\n",
      "ch2_6.ascii [ 0.29237644] 0.06\n",
      "ch2_7.ascii [ 0.22931216] 0.05\n",
      "ch2_8.ascii [ 0.26515978] 0.05\n",
      "ch2_9.ascii [ 0.3520484] 0.04\n",
      "ch2_10.ascii [ 0.40900525] 0.08\n"
     ]
    }
   ],
   "source": [
    "xv = dcor.values()\n",
    "for i in range(len(xv)):\n",
    "    sum_bins_up_to = []\n",
    "    fname = 'ch2_'+str(i)+'.ascii'\n",
    "    cor = np.loadtxt(fname)\n",
    "    sort = np.histogram(cor, bins = np.arange(min(cor), max(cor)+0.1, 0.05))\n",
    "    for j in range(len(sort[1])):\n",
    "        if sort[1][j] <= xv[i] <= sort[1][j+1]:\n",
    "            for k in np.arange(j+1):\n",
    "                sum_bins_up_to.append(sort[0][k])\n",
    "            tot_sum_up_to = np.sum(sum_bins_up_to)\n",
    "            pvalue = float(tot_sum_up_to)/float(np.sum(sort[0]))\n",
    "            print fname, xv[i], '%.2f' % (pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================\n",
      "Loading data for satellite  54\n",
      "File : https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns54/ns54_160103_v1.03.ascii\n"
     ]
    }
   ],
   "source": [
    "#Test 6th Jan 2016\n",
    "\n",
    "start_date = datetime(2016,1,2,0,0,0);\n",
    "end_date = datetime(2016,1,7,0,0,0);\n",
    "ms = gps_particle_data.meta_search();\n",
    "ms.load_data(start_date,end_date);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: array([ 0.9921995]), 1: array([ 0.99709324]), 2: array([ 1.]), 3: array([ 0.98807985]), 4: array([ 0.96474178]), 5: array([ 0.95080567]), 6: array([ 0.93242719]), 7: array([ 0.9167545]), 8: array([ 0.9298768]), 9: array([ 0.93921355]), 10: array([ 0.93808342])}\n"
     ]
    }
   ],
   "source": [
    "dcor = {}\n",
    "signal = 'rate_electron_measured'\n",
    "output_data = ms.get_all_data_by_satellite()\n",
    "for i in range(11):\n",
    "    a0 = np.asarray(output_data[54][signal])[:,2]\n",
    "    ar2 = np.asarray(output_data[54][signal])[:,i]\n",
    "    cor = np.correlate(a0, ar2)/(math.sqrt(np.sum(a0**2)*np.sum(ar2**2)))\n",
    "    dcor.update({i:cor})\n",
    "print dcor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch2_0.ascii [ 0.9921995] 1.00\n",
      "ch2_1.ascii [ 0.99709324] 1.00\n",
      "ch2_2.ascii [ 1.] 1.00\n",
      "ch2_3.ascii [ 0.98807985] 1.00\n",
      "ch2_4.ascii [ 0.96474178] 1.00\n",
      "ch2_5.ascii [ 0.95080567] 0.95\n",
      "ch2_6.ascii [ 0.93242719] 0.96\n",
      "ch2_7.ascii [ 0.9167545] 0.99\n",
      "ch2_8.ascii [ 0.9298768] 1.00\n",
      "ch2_9.ascii [ 0.93921355] 1.00\n",
      "ch2_10.ascii [ 0.93808342] 0.99\n"
     ]
    }
   ],
   "source": [
    "xv = dcor.values()\n",
    "for i in range(len(xv)):\n",
    "    sum_bins_up_to = []\n",
    "    fname = 'ch2_'+str(i)+'.ascii'\n",
    "    cor = np.loadtxt(fname)\n",
    "    sort = np.histogram(cor, bins = np.arange(min(cor), max(cor)+0.1, 0.05))\n",
    "    for j in range(len(sort[1])):\n",
    "        if sort[1][j] <= xv[i] <= sort[1][j+1]:\n",
    "            for k in np.arange(j+1):\n",
    "                sum_bins_up_to.append(sort[0][k])\n",
    "            tot_sum_up_to = np.sum(sum_bins_up_to)\n",
    "            pvalue = float(tot_sum_up_to)/float(np.sum(sort[0]))\n",
    "            print fname, xv[i], '%.2f' % (pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================\n",
      "Loading data for satellite  54\n",
      "File : https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns54/ns54_160904_v1.03.ascii\n"
     ]
    }
   ],
   "source": [
    "#Test 9th Sept 2016\n",
    "\n",
    "start_date = datetime(2016,9,4,0,0,0);\n",
    "end_date = datetime(2016,9,10,0,0,0);\n",
    "ms = gps_particle_data.meta_search();\n",
    "ms.load_data(start_date,end_date);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: array([ 0.99509029]), 1: array([ 0.99840777]), 2: array([ 1.]), 3: array([ 0.99635832]), 4: array([ 0.97559366]), 5: array([ 0.95880331]), 6: array([ 0.92702036]), 7: array([ 0.8872584]), 8: array([ 0.89495642]), 9: array([ 0.93346193]), 10: array([ 0.92548569])}\n"
     ]
    }
   ],
   "source": [
    "dcor = {}\n",
    "signal = 'rate_electron_measured'\n",
    "output_data = ms.get_all_data_by_satellite()\n",
    "for i in range(11):\n",
    "    a0 = np.asarray(output_data[54][signal])[:,2]\n",
    "    ar2 = np.asarray(output_data[54][signal])[:,i]\n",
    "    cor = np.correlate(a0, ar2)/(math.sqrt(np.sum(a0**2)*np.sum(ar2**2)))\n",
    "    dcor.update({i:cor})\n",
    "print dcor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch2_0.ascii [ 0.99509029] 1.00\n",
      "ch2_1.ascii [ 0.99840777] 1.00\n",
      "ch2_2.ascii [ 1.] 1.00\n",
      "ch2_3.ascii [ 0.99635832] 1.00\n",
      "ch2_4.ascii [ 0.97559366] 1.00\n",
      "ch2_5.ascii [ 0.95880331] 0.95\n",
      "ch2_6.ascii [ 0.92702036] 0.96\n",
      "ch2_7.ascii [ 0.8872584] 0.90\n",
      "ch2_8.ascii [ 0.89495642] 0.94\n",
      "ch2_9.ascii [ 0.93346193] 1.00\n",
      "ch2_10.ascii [ 0.92548569] 0.99\n"
     ]
    }
   ],
   "source": [
    "xv = dcor.values()\n",
    "for i in range(len(xv)):\n",
    "    sum_bins_up_to = []\n",
    "    fname = 'ch2_'+str(i)+'.ascii'\n",
    "    cor = np.loadtxt(fname)\n",
    "    sort = np.histogram(cor, bins = np.arange(min(cor), max(cor)+0.1, 0.05))\n",
    "    for j in range(len(sort[1])):\n",
    "        if sort[1][j] <= xv[i] <= sort[1][j+1]:\n",
    "            for k in np.arange(j+1):\n",
    "                sum_bins_up_to.append(sort[0][k])\n",
    "            tot_sum_up_to = np.sum(sum_bins_up_to)\n",
    "            pvalue = float(tot_sum_up_to)/float(np.sum(sort[0]))\n",
    "            print fname, xv[i], '%.2f' % (pvalue)"
   ]
  }
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