NK_X_corr_ns60.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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAGOJYQAAxhLDAACMJYYBABhLDAMAMJYYBgBgLDEMAMBYYhgAgLHEMAAAY4lhAADGEsMA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\nAACM9X8B9O7SYl1hOAYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85d46bced0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8604922290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85d215ff10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85d20ada50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85d210f990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AdloqhqvqPlkP4V/s7jcvbv5sVV24uP/CJHfszIgAALAzlvk0iUry80lu7O6fPuGuq5Nc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\nGACAscQwAABjiWEAAMb6f9wTl1UXpKkAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85d21abf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85d20a0050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85d1cf9590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85d1ccf150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85d1a6f0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hAADGEsMAAIwlhgEAGEsMAwAwlhgGAGAsMQwAwFhiGACAscQwAABjiWEAAMYSwwAAjCWG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\nTsp8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85d1bfaf10>"
      ]
     },
     "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  60\n",
      "File : https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns60/ns60_061008_v1.03.ascii\n",
      "{0: array([ 0.89358921]), 1: array([ 0.94154385]), 2: array([ 1.]), 3: array([ 0.80901217]), 4: array([ 0.58260469]), 5: array([ 0.51722767]), 6: array([ 0.45829559]), 7: array([ 0.42376277]), 8: array([ 0.55254468]), 9: array([ 0.54852483]), 10: array([ 0.51567984])}\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);\n",
    "\n",
    "#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[60][signal])[:,2]\n",
    "    ar2 = np.asarray(output_data[60][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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch2_0.ascii [ 0.89358921] 0.58\n",
      "ch2_1.ascii [ 0.94154385] 0.60\n",
      "ch2_2.ascii [ 1.] 1.00\n",
      "ch2_3.ascii [ 0.80901217] 0.05\n",
      "ch2_4.ascii [ 0.58260469] 0.09\n",
      "ch2_5.ascii [ 0.51722767] 0.18\n",
      "ch2_6.ascii [ 0.45829559] 0.13\n",
      "ch2_7.ascii [ 0.42376277] 0.12\n",
      "ch2_8.ascii [ 0.55254468] 0.28\n",
      "ch2_9.ascii [ 0.54852483] 0.18\n",
      "ch2_10.ascii [ 0.51567984] 0.21\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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================\n",
      "Loading data for satellite  60\n",
      "File : https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns60/ns60_090524_v1.03.ascii\n",
      "{0: array([ 0.6594]), 1: array([ 0.78430742]), 2: array([ 1.]), 3: array([ 0.93972232]), 4: array([ 0.90521799]), 5: array([ 0.79535507]), 6: array([ 0.78106313]), 7: array([ 0.84611588]), 8: array([ 0.84898872]), 9: array([ 0.852014]), 10: array([ 0.84382275])}\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);\n",
    "\n",
    "#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[60][signal])[:,2]\n",
    "    ar2 = np.asarray(output_data[60][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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch2_0.ascii [ 0.6594] 0.04\n",
      "ch2_1.ascii [ 0.78430742] 0.02\n",
      "ch2_2.ascii [ 1.] 1.00\n",
      "ch2_3.ascii [ 0.93972232] 0.58\n",
      "ch2_4.ascii [ 0.90521799] 0.76\n",
      "ch2_5.ascii [ 0.79535507] 0.57\n",
      "ch2_6.ascii [ 0.78106313] 0.71\n",
      "ch2_7.ascii [ 0.84611588] 0.85\n",
      "ch2_8.ascii [ 0.84898872] 0.82\n",
      "ch2_9.ascii [ 0.852014] 0.84\n",
      "ch2_10.ascii [ 0.84382275] 0.85\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================\n",
      "Loading data for satellite  60\n",
      "File : https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns60/ns60_130210_v1.03.ascii\n",
      "{0: array([ 0.88336327]), 1: array([ 0.97164362]), 2: array([ 1.]), 3: array([ 0.95310482]), 4: array([ 0.58560819]), 5: array([ 0.49249522]), 6: array([ 0.55871001]), 7: array([ 0.59650274]), 8: array([ 0.512302]), 9: array([ 0.43558046]), 10: array([ 0.43687271])}\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);\n",
    "\n",
    "#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[60][signal])[:,2]\n",
    "    ar2 = np.asarray(output_data[60][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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch2_0.ascii [ 0.88336327] 0.37\n",
      "ch2_1.ascii [ 0.97164362] 0.60\n",
      "ch2_2.ascii [ 1.] 1.00\n",
      "ch2_3.ascii [ 0.95310482] 0.58\n",
      "ch2_4.ascii [ 0.58560819] 0.09\n",
      "ch2_5.ascii [ 0.49249522] 0.13\n",
      "ch2_6.ascii [ 0.55871001] 0.23\n",
      "ch2_7.ascii [ 0.59650274] 0.31\n",
      "ch2_8.ascii [ 0.512302] 0.22\n",
      "ch2_9.ascii [ 0.43558046] 0.05\n",
      "ch2_10.ascii [ 0.43687271] 0.05\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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================\n",
      "Loading data for satellite  60\n",
      "File : https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns60/ns60_160103_v1.03.ascii\n",
      "{0: array([ 0.99314933]), 1: array([ 0.99729651]), 2: array([ 1.]), 3: array([ 0.98827454]), 4: array([ 0.96586856]), 5: array([ 0.86352748]), 6: array([ 0.85929706]), 7: array([ 0.8457972]), 8: array([ 0.85031547]), 9: array([ 0.85689577]), 10: array([ 0.8501666])}\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);\n",
    "\n",
    "#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[60][signal])[:,2]\n",
    "    ar2 = np.asarray(output_data[60][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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch2_0.ascii [ 0.99314933] 1.00\n",
      "ch2_1.ascii [ 0.99729651] 1.00\n",
      "ch2_2.ascii [ 1.] 1.00\n",
      "ch2_3.ascii [ 0.98827454] 1.00\n",
      "ch2_4.ascii [ 0.96586856] 0.97\n",
      "ch2_5.ascii [ 0.86352748] 0.89\n",
      "ch2_6.ascii [ 0.85929706] 0.82\n",
      "ch2_7.ascii [ 0.8457972] 0.85\n",
      "ch2_8.ascii [ 0.85031547] 0.82\n",
      "ch2_9.ascii [ 0.85689577] 0.84\n",
      "ch2_10.ascii [ 0.8501666] 0.93\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================\n",
      "Loading data for satellite  60\n",
      "File : https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/data/ns60/ns60_160904_v1.03.ascii\n",
      "{0: array([ 0.99497016]), 1: array([ 0.99848189]), 2: array([ 1.]), 3: array([ 0.99619655]), 4: array([ 0.9760615]), 5: array([ 0.8457109]), 6: array([ 0.82430511]), 7: array([ 0.78219247]), 8: array([ 0.76600174]), 9: array([ 0.81461769]), 10: array([ 0.82483713])}\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);\n",
    "\n",
    "#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[60][signal])[:,2]\n",
    "    ar2 = np.asarray(output_data[60][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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch2_0.ascii [ 0.99497016] 1.00\n",
      "ch2_1.ascii [ 0.99848189] 1.00\n",
      "ch2_2.ascii [ 1.] 1.00\n",
      "ch2_3.ascii [ 0.99619655] 1.00\n",
      "ch2_4.ascii [ 0.9760615] 0.97\n",
      "ch2_5.ascii [ 0.8457109] 0.76\n",
      "ch2_6.ascii [ 0.82430511] 0.82\n",
      "ch2_7.ascii [ 0.78219247] 0.75\n",
      "ch2_8.ascii [ 0.76600174] 0.75\n",
      "ch2_9.ascii [ 0.81461769] 0.73\n",
      "ch2_10.ascii [ 0.82483713] 0.85\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"
   ]
  }
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