MCS_Atmosphere_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import time\n",
    "import glob\n",
    "from astropy.time import Time\n",
    "import os\n",
    "import fnmatch\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getMCSprofiles(filename):\n",
    "    '''Function to select specific text data from the MCS orbiter and convert it into an array\n",
    "    Input:\n",
    "    filename = the location of the file\n",
    "    Output:\n",
    "    x: data converted to data frames'''\n",
    "    # The header contains column names and description\n",
    "    header_string = ' ### Retrieved profiles:'\n",
    "    header_lines = 4\n",
    "    # Open the file for reading\n",
    "    f = open(filename)\n",
    "    lines = f.readlines()\n",
    "    # Strip newline character from each line\n",
    "    lines = [line.replace(\"\\n\",\"\") for line in lines]\n",
    "    # Read through all lines to find header string\n",
    "    nlines = len(lines)\n",
    "    i = 0\n",
    "    for line in lines:\n",
    "        i += 1\n",
    "        if re.match(header_string, line):\n",
    "            idx = i + header_lines-1\n",
    "    # Extract only the lines containing profile data\n",
    "    data = lines[idx:nlines-1]\n",
    "    # Convert to floats\n",
    "    nrows = len(data)-1\n",
    "    y = np.array(data[0].split(), dtype=np.float)\n",
    "    ncols = len(y)\n",
    "    x = np.array([])\n",
    "    for j in np.arange(0,nrows):\n",
    "        x = np.append(x, np.array(data[j].split(), dtype=np.float))\n",
    "    x = np.reshape(x, [nrows,ncols])\n",
    "    columns = ['Pressure', 'Temperature', 'Temp_Err',\n",
    "        'Dust','Dust_Err', 'H2Oice','H2Oice_Err', 'H2Ovap', 'H2Ovap_Err', 'CO2ice', 'CO2ice_Err', 'Altitude', 'Latitude', 'Longitude'  ]\n",
    "    values = x\n",
    "    df = pd.DataFrame(x,columns=columns)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getMCSdata(filename, data_string):\n",
    "    '''Function to select specific text data from the MCS orbiter and convert it into an array\n",
    "    Input:\n",
    "    filename = the location of the file\n",
    "    data_string = specific data to be looked at\n",
    "    Output:\n",
    "    x: data converted to floats'''\n",
    "    # The header contains column names and description\n",
    "    header_string = ' ### Header data:'\n",
    "    header_lines = 1\n",
    "    foot_string = ' ### RMS data:'\n",
    "    # Open the file for reading\n",
    "    f = open(filename)\n",
    "    lines = f.readlines()\n",
    "    # Strip newline character from each line\n",
    "    lines = [line.replace(\"\\n\",\"\") for line in lines]\n",
    "    foot_lines = lines.index(foot_string)\n",
    "    nlines = len(lines)\n",
    "    i = 0\n",
    "    # Restrict the text to only the data needed\n",
    "    for line in lines:\n",
    "        i += 1\n",
    "        if re.match(header_string, line):\n",
    "            idx = i + header_lines - 1 \n",
    "        if re.match(foot_string, line):\n",
    "            idy = foot_lines - nlines\n",
    "    #store data into a list\n",
    "    data = lines[idx:idy]\n",
    "    #get rid of leading whitespaces\n",
    "    x =[]\n",
    "    for i in data:\n",
    "        x.append(i.lstrip())\n",
    "    #get rid of trailing whitespaces\n",
    "    y = []\n",
    "    for i in x:\n",
    "        y.append(i.rstrip())\n",
    "    #split the data into two columns\n",
    "    z =[]\n",
    "    for i in y:\n",
    "        z.append(i.split('='))\n",
    "    #convert data to arrays\n",
    "    col1 = np.array(z)\n",
    "    #create column of strings\n",
    "    a = col1[:,0]\n",
    "    #convert array to list\n",
    "    b = a.tolist()\n",
    "    #strip column of strings of whitespaces\n",
    "    c=[]\n",
    "    for i in b:\n",
    "        c.append(i.strip())\n",
    "    #index for the matching data_string\n",
    "    colindex = c.index(data_string)\n",
    "    #create column of floats\n",
    "    d = col1[:,1]\n",
    "    #convert array to list\n",
    "    e = d.tolist()\n",
    "    #convert list of strings to floats\n",
    "    f = [float(i) for i in e]\n",
    "    #find the float matching data_string index\n",
    "    result = f[colindex]\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getAllMCSdata(data_string):\n",
    "    '''Function that uses the getMCSdata function to retrieve specific data from multiple data files\n",
    "    Input:\n",
    "    data_string = specific data to be looked at\n",
    "    Output:\n",
    "    x: a list of data converted to floats corresponding to the data files '''\n",
    "    path = '../WORK/MCS_atm/060924160000/mcs_ret_v*.out'\n",
    "    files = sorted(glob.glob(path))\n",
    "    x = []\n",
    "    for filename in files:\n",
    "        x.append(getMCSdata(filename, data_string))\n",
    "    return x    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getAllMCSprofiles():\n",
    "    '''Function that compiles all text files from MCS into a single list bys using the getMCSprofiles function\n",
    "    Input:\n",
    "    None\n",
    "    Output:\n",
    "    x = list containing all the text files retrieved from the MCS'''\n",
    "    path = '../WORK/MCS_atm/060924160000/mcs_ret_v*.out'\n",
    "    files = sorted(glob.glob(path))\n",
    "    x = []\n",
    "    for filename in files:\n",
    "        x.append(getMCSprofiles(filename))\n",
    "    return x  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getMCSSum(parameter):\n",
    "    '''Function that uses the getAllMCSprofiles and getMCSprofiles functions to obtain specific column data \n",
    "    from each text file and sum it into a single float. The function returns a list of floats that  depends on \n",
    "    the amount of text files.\n",
    "    Input:\n",
    "    parameter = string that matches a column from a text file\n",
    "    Output:\n",
    "    y = list of floats corresponding to the specific string from the data frame generated by the getMCSprofile function'''\n",
    "    DFlist = getAllMCSprofiles()\n",
    "    x = []\n",
    "    for i in range(len(DFlist)):\n",
    "         x.append(getAllMCSprofiles()[i][parameter])\n",
    "    x = np.array(x)\n",
    "    y = []\n",
    "    for j in x:\n",
    "        y.append(sum(j[j != -9999.000]))\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "x = (getMCSSum('H2Oice'))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47310590744\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "198"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0=time.time()\n",
    "ayou = getAllMCSdata('Scene latitude')\n",
    "t1=time.time()\n",
    "print t1-t0\n",
    "print ayou\n",
    "len(ayou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00579690933228\n",
      "0.0034\n"
     ]
    }
   ],
   "source": [
    "t0=time.time()\n",
    "a = getMCSdata('../WORK/mcs_ret_v4.3.11.00001.out','H2Oice_843_nad_od')\n",
    "t1=time.time()\n",
    "print t1-t0\n",
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00514101982117\n",
      "      Pressure  Temperature  Temp_Err          Dust     Dust_Err  \\\n",
      "16  254.285800      166.367     0.730 -9.999000e+03 -9999.000000   \n",
      "17  224.406500      167.106     0.532  2.411100e-07     0.000003   \n",
      "18  198.038000      168.412     0.527  9.749200e-08     0.000001   \n",
      "19  174.767900      170.159     0.655  1.547400e-08     0.000001   \n",
      "20  154.232100      171.874     0.656  1.332500e-09     0.000001   \n",
      "21  136.109400      173.075     0.623  1.000000e-09     0.000001   \n",
      "22  120.116100      173.542     0.646  1.000000e-09     0.000001   \n",
      "23  106.002100      173.395     0.605  1.000000e-09     0.000001   \n",
      "24   93.546530      172.883     0.332  1.000000e-09     0.000001   \n",
      "25   82.554520      172.217     0.232 -9.999000e+03 -9999.000000   \n",
      "26   72.854110      171.515     0.220 -9.999000e+03 -9999.000000   \n",
      "27   64.293530      170.787     0.267 -9.999000e+03 -9999.000000   \n",
      "28   56.738840      169.896     0.309 -9.999000e+03 -9999.000000   \n",
      "29   50.071850      168.679     0.340 -9.999000e+03 -9999.000000   \n",
      "30   44.188260      167.063     0.389 -9.999000e+03 -9999.000000   \n",
      "31   38.995990      165.341     0.477 -9.999000e+03 -9999.000000   \n",
      "32   34.413850      164.245     0.557 -9.999000e+03 -9999.000000   \n",
      "33   30.370110      164.354     0.624 -9.999000e+03 -9999.000000   \n",
      "34   26.801530      165.841     0.672 -9.999000e+03 -9999.000000   \n",
      "35   23.652270      168.350     0.704 -9.999000e+03 -9999.000000   \n",
      "36   20.873050      170.931     0.729 -9.999000e+03 -9999.000000   \n",
      "37   18.420400      172.697     0.762 -9.999000e+03 -9999.000000   \n",
      "38   16.255950      173.242     0.791 -9.999000e+03 -9999.000000   \n",
      "39   14.345830      172.647     0.499 -9.999000e+03 -9999.000000   \n",
      "40   12.660150      171.134     0.355 -9.999000e+03 -9999.000000   \n",
      "41   11.172540      168.777     0.342 -9.999000e+03 -9999.000000   \n",
      "42    9.859732      165.802     0.469 -9.999000e+03 -9999.000000   \n",
      "43    8.701183      162.636     0.611 -9.999000e+03 -9999.000000   \n",
      "44    7.678767      159.786     0.712 -9.999000e+03 -9999.000000   \n",
      "45    6.776488      157.457     0.826 -9.999000e+03 -9999.000000   \n",
      "46    5.980229      155.626     0.797 -9.999000e+03 -9999.000000   \n",
      "47    5.277534      154.182     0.582 -9.999000e+03 -9999.000000   \n",
      "48    4.657407      153.081     0.499 -9.999000e+03 -9999.000000   \n",
      "49    4.110147      152.311     0.553 -9.999000e+03 -9999.000000   \n",
      "50    3.627193      151.826     0.614 -9.999000e+03 -9999.000000   \n",
      "51    3.200986      151.540     0.544 -9.999000e+03 -9999.000000   \n",
      "52    2.824860      151.374     0.485 -9.999000e+03 -9999.000000   \n",
      "53    2.492931      151.289     0.553 -9.999000e+03 -9999.000000   \n",
      "54    2.200004      151.246     0.677 -9.999000e+03 -9999.000000   \n",
      "55    1.941496      151.166     0.700 -9.999000e+03 -9999.000000   \n",
      "56    1.713364      150.969     0.696 -9.999000e+03 -9999.000000   \n",
      "57    1.512039      150.606     0.817 -9.999000e+03 -9999.000000   \n",
      "58    1.334370      150.007     1.045 -9.999000e+03 -9999.000000   \n",
      "59    1.177577      149.006     1.216 -9.999000e+03 -9999.000000   \n",
      "60    1.039208      147.422     1.384 -9.999000e+03 -9999.000000   \n",
      "61    0.917098      145.221     1.738 -9.999000e+03 -9999.000000   \n",
      "62    0.809336      142.543     2.156 -9.999000e+03 -9999.000000   \n",
      "63    0.714237      139.633     2.230 -9.999000e+03 -9999.000000   \n",
      "64    0.630312      136.676     2.386 -9.999000e+03 -9999.000000   \n",
      "65    0.556248      133.756     2.872 -9.999000e+03 -9999.000000   \n",
      "66    0.490887      130.975     4.143 -9.999000e+03 -9999.000000   \n",
      "67    0.433206      128.614     5.019 -9.999000e+03 -9999.000000   \n",
      "68    0.382303      126.951     5.708 -9.999000e+03 -9999.000000   \n",
      "69    0.337382      126.101     6.443 -9.999000e+03 -9999.000000   \n",
      "70    0.297738      126.029     7.278 -9.999000e+03 -9999.000000   \n",
      "71    0.262753      126.578     7.864 -9.999000e+03 -9999.000000   \n",
      "72    0.231879      127.464     7.023 -9.999000e+03 -9999.000000   \n",
      "\n",
      "          H2Oice   H2Oice_Err  H2Ovap  H2Ovap_Err  CO2ice  CO2ice_Err  \\\n",
      "16 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "17  7.469800e-04     0.000044 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "18  6.553200e-04     0.000033 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "19  1.964300e-04     0.000031 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "20  8.676900e-06     0.000008 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "21  1.244800e-08     0.000001 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "22  1.000000e-09     0.000001 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "23  1.000000e-09     0.000001 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "24  1.064000e-09     0.000001 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "25  1.267800e-09     0.000001 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "26 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "27 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "28 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "29 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "30 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "31 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "32 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "33 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "34 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "35 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "36 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "37 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "38 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "39 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "40 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "41 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "42 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "43 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "44 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "45 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "46 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "47 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "48 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "49 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "50 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "51 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "52 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "53 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "54 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "55 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "56 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "57 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "58 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "59 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "60 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "61 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "62 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "63 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "64 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "65 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "66 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "67 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "68 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "69 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "70 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "71 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "72 -9.999000e+03 -9999.000000 -9999.0     -9999.0 -9999.0     -9999.0   \n",
      "\n",
      "    Altitude  Latitude  Longitude  \n",
      "16     5.086   -42.620    -62.774  \n",
      "17     6.159   -42.668    -62.780  \n",
      "18     7.238   -42.716    -62.785  \n",
      "19     8.329   -42.764    -62.791  \n",
      "20     9.431   -42.813    -62.797  \n",
      "21    10.543   -42.863    -62.803  \n",
      "22    11.662   -42.913    -62.810  \n",
      "23    12.783   -42.963    -62.816  \n",
      "24    13.902   -43.014    -62.822  \n",
      "25    15.018   -43.064    -62.828  \n",
      "26    16.130   -43.114    -62.834  \n",
      "27    17.239   -43.165    -62.841  \n",
      "28    18.343   -43.215    -62.847  \n",
      "29    19.441   -43.265    -62.853  \n",
      "30    20.530   -43.314    -62.859  \n",
      "31    21.609   -43.364    -62.865  \n",
      "32    22.680   -43.413    -62.872  \n",
      "33    23.748   -43.462    -62.878  \n",
      "34    24.822   -43.511    -62.884  \n",
      "35    25.910   -43.562    -62.890  \n",
      "36    27.015   -43.613    -62.897  \n",
      "37    28.135   -43.665    -62.903  \n",
      "38    29.263   -43.717    -62.910  \n",
      "39    30.392   -43.770    -62.916  \n",
      "40    31.515   -43.822    -62.923  \n",
      "41    32.625   -43.874    -62.930  \n",
      "42    33.718   -43.926    -62.936  \n",
      "43    34.792   -43.976    -62.943  \n",
      "44    35.847   -44.026    -62.949  \n",
      "45    36.886   -44.076    -62.955  \n",
      "46    37.912   -44.124    -62.961  \n",
      "47    38.928   -44.173    -62.968  \n",
      "48    39.937   -44.221    -62.974  \n",
      "49    40.940   -44.269    -62.980  \n",
      "50    41.939   -44.317    -62.986  \n",
      "51    42.936   -44.364    -62.992  \n",
      "52    43.932   -44.412    -62.999  \n",
      "53    44.929   -44.461    -63.005  \n",
      "54    45.925   -44.509    -63.011  \n",
      "55    46.921   -44.557    -63.017  \n",
      "56    47.917   -44.606    -63.024  \n",
      "57    48.912   -44.654    -63.030  \n",
      "58    49.904   -44.703    -63.036  \n",
      "59    50.892   -44.751    -63.043  \n",
      "60    51.871   -44.799    -63.049  \n",
      "61    52.839   -44.847    -63.055  \n",
      "62    53.791   -44.894    -63.061  \n",
      "63    54.725   -44.940    -63.067  \n",
      "64    55.641   -44.985    -63.073  \n",
      "65    56.538   -45.030    -63.079  \n",
      "66    57.415   -45.074    -63.085  \n",
      "67    58.275   -45.117    -63.091  \n",
      "68    59.122   -45.159    -63.096  \n",
      "69    59.963   -45.201    -63.102  \n",
      "70    60.801   -45.243    -63.108  \n",
      "71    61.640   -45.286    -63.113  \n",
      "72    62.484   -45.328    -63.119  \n"
     ]
    }
   ],
   "source": [
    "t0=time.time()\n",
    "DF = getMCSprofiles('mcs_ret_v4.3.11.00001.out')\n",
    "t1=time.time()\n",
    "print t1-t0\n",
    "e = DF[16:73]\n",
    "print e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "useful = DF.loc[13:91]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = np.array(useful['Temperature'])\n",
    "a = np.array(useful['Altitude'])\n",
    "p = np.array(useful['Pressure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NT+CsPCcYFuQkEhkW6udKjb+NdBA8DrQA/4dzTP8zQJyqXvdRCx0OCwJjBtbb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\n1MqqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105e37b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, a)\n",
    "plt.title('Temperature vs. Altitude')\n",
    "plt.xlabel('Temperature (K)')\n",
    "plt.ylabel('Altitude (km)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VB57bykPPb6ezu5eK0hIWzpnI+XMnc+7cyZw5u4HKstJCl2tmGRwElhed3ft5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105c1ff90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(p, a)\n",
    "plt.title('Pressure vs. Altitude')\n",
    "plt.xlabel('Pressure (Pa)')\n",
    "plt.ylabel('Altitude (km)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def datetoMY(string):\n",
    "        x = string[0]+string[1]\n",
    "        year =  '20{}'.format(x)\n",
    "        month = string[2]+string[3]\n",
    "        day = string[4]+string[5]\n",
    "        hour = string[6]+string[7]\n",
    "        minute = string[8]+string[9]\n",
    "        y = string[10]+string[11]\n",
    "        second = '{}.000'.format(y)\n",
    "        date = '{}-{}-{} {}:{}:{}'.format(year,month,day,hour,minute,second)\n",
    "        time = Time(date, scale='utc')\n",
    "        time = time.jd\n",
    "        if 2453757.2 <= time < 2454444.2:\n",
    "            return 'MY28'\n",
    "        elif 2454444.2 <= time < 2455131.1:\n",
    "            return 'MY29'\n",
    "        elif 2455131.1 <= time < 2455818.1:\n",
    "            return 'MY30'\n",
    "        elif 2455818.1 <= time < 2456505.1:\n",
    "            return 'MY31'\n",
    "        elif 2456505.1 <= time < 2457192.0:\n",
    "            return 'MY32'\n",
    "        elif 2457192.0 <= time < 2457879.0:\n",
    "            return 'MY33' \n",
    "        elif 2457879.0 <= time < 2458566.0:\n",
    "            return 'MY34'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def MYtodate(MY):\n",
    "    if MY == '28':\n",
    "        x = np.linspace(2453757.2,2454444.19999999)\n",
    "    elif MY == '29':\n",
    "        x = np.linspace(2454444.2,2455131.09999999)\n",
    "    elif MY == '30':\n",
    "        x = np.linspace(2455131.1,2455818.09999999)\n",
    "    elif MY == '31':\n",
    "        x = np.linspace(2455818.1,2456505.09999999)\n",
    "    elif MY == '32':\n",
    "        x = np.linspace(2456505.1,2457191.99999999)\n",
    "    elif MY == '33':\n",
    "        x = np.linspace(2457192.0,2457878.99999999)\n",
    "    elif MY == '34':\n",
    "        x = np.linspace(2457879.0,2458565.99999999)\n",
    "    y = Time(x, format='jd')\n",
    "    y = y.iso\n",
    "    y_i = y[0]\n",
    "    y_f = y[-1]\n",
    "    z_i = y_i[2]+y_i[3]+y_i[5]+y_i[6]+y_i[8]+y_i[9]+y_i[11]+y_i[12]+'0000'\n",
    "    z_f = y_f[2]+y_f[3]+y_f[5]+y_f[6]+y_f[8]+y_f[9]+y_f[11]+y_f[12]+'0000'\n",
    "    return (z_i),(z_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('060121160000', '071209160000')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = MYtodate('28')\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def openFiles(MY):\n",
    "    path = '../WORK/MCS_atm/'\n",
    "    a = MYtodate(MY)\n",
    "    files1 = sorted(glob.glob(path+'{}/*'.format(a[0])))\n",
    "    files2 = sorted(glob.glob(path+'{}/*'.format(a[-1])))\n",
    "    allfiles = (files1,files2)\n",
    "    return allfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], [])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openFiles('28')"
   ]
  },
  {
   "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": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}