map_gosat_L3_timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Python built-in library imports\n",
    "import os\n",
    "from collections import OrderedDict\n",
    "\n",
    "# pip installed library imports\n",
    "import h5py\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sci\n",
    "\n",
    "import mpl_toolkits\n",
    "from mpl_toolkits.basemap import Basemap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objective:\n",
    "\n",
    "1. To calculate global average per year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [],
   "source": [
    "utils = {\n",
    "    'map_title': 'Monthly Averaged Methane Column Mixing Ratio (20{})',\n",
    "    'data_dir': './data/SWIRL3CH4/all_data/SWIRL3CH4/',  \n",
    "    'data_loc': '/Users/mlimb/sio/methane/gosat/data/SWIRL3CH4/all_data/SWIRL3CH4/{}',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intialize `aliso_trend` dictionary\n",
    "\n",
    "Notes\n",
    "\n",
    "- OrderedDict has year [int] as keys from 2009 to 2017\n",
    "- aliso_trend[year] has an list of 12 elements\n",
    "- each element is initialized to null value of **-1** \n",
    "\n",
    "#### **<span style=\"color:orange; background-color: navy\">Question</span>**\n",
    "\n",
    "- Is **-1** a good choice of null value?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_years = range(2009, 2017+1)\n",
    "\n",
    "# initialize OrderedDict with year [int] mapping to empty list []\n",
    "# using -1 as init\n",
    "aliso_trend = OrderedDict((year,[-1]*12) for year in all_years)\n",
    "\n",
    "# manual chek\n",
    "# for k,v in aliso_trend.items():\n",
    "#     print k # should year year [int] 2009 to 2017\n",
    "#     print v # should be []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 586,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = utils['data_dir']\n",
    "data_loc = utils['data_loc']\n",
    "len(os.listdir(data_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = [f for f in os.listdir(data_dir) if f.endswith('.h5')]\n",
    "len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dictionary that maps month number to names (Long version, 3-letter abbreviated version,\n",
    "# month number as int).\n",
    "# all in Strings\n",
    "month_names = {\n",
    "    '01': ('January', 'JAN', 1),\n",
    "    '02': ('February', 'FEB', 2),\n",
    "    '03': ('March', 'MAR', 3),\n",
    "    '04': ('April', 'APR', 4),\n",
    "    '05': ('May', 'MAY', 5),\n",
    "    '06': ('June', 'JUN', 6),\n",
    "    '07': ('July', 'JUL', 7),\n",
    "    '08': ('August', 'AUG', 8),\n",
    "    '09': ('September', 'SEP', 9),\n",
    "    '10': ('October', 'OCT', 10),\n",
    "    '11': ('November', 'NOV', 11),\n",
    "    '12': ('December', 'DEC', 12)\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I manually confirmed all filenames.\n",
    "\n",
    "**All available data**<br />\n",
    "2009 June to 2017 February EXCEPT:\n",
    "\n",
    "Known missing data<br />\n",
    "- 2014 June and December \n",
    "- 2015 January and September \n",
    "\n",
    "Q. Why is there no data after 2017 FEB?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write out what to do\n",
    "\n",
    "I have a list of files that have global coverage, \n",
    "one monthly average per file\n",
    "\n",
    "From each of the 89 matrix, I only need a single value,\n",
    "the one nearest to the Aliso Canyon Oil Field\n",
    "\n",
    "    this is at: \n",
    "        [lon, lat] = -118.75, 33.75\n",
    "\n",
    "    which is same as location as:\n",
    "        ???\n",
    "    \n",
    "Given:\n",
    "- 2.5 degrees lat/lon spatial resolution\n",
    "- starting values hidden in the h5 file\n",
    "\n",
    "`get_aliso_value` (function): given the point, get value at that exact point for all years\n",
    "\n",
    "<hr >\n",
    "**Tasks:**\n",
    "\n",
    "1) Open files **<span style=\"color:black; background-color: yellow\">DONE</span>**\n",
    "\n",
    "- Loop through all files, open all, save netcdf of all **<span style=\"color:black; background-color: yellow\">DONE</span>**\n",
    "- Mask the files **<span style=\"color:black; background-color: yellow\">DONE</span>**\n",
    "\n",
    "2) Determine the location for the Aliso lon/lat combo **<span style=\"color:red; background-color: black\">Don't Panic</span>**\n",
    "\n",
    "- What is the lon/lat value nearest to the Aliso Canyon Oil Field? **<span style=\"color:black; background-color: yellow\">DONE</span>**\n",
    "\n",
    "        34.3122, -118.5645 (from USGS data)\n",
    "\n",
    "- What is the matrix [x,y] of the [lon, lat]? **<span style=\"color:black; background-color: yellow\">DONE</span>**\n",
    "\n",
    "        Latitude: lats[22][0] (33.75)\n",
    "        Longitude: lons[0][24] (-118.75)\n",
    "\n",
    "\n",
    "- Write a function that retrieves the value at [x,y] for each matrix **<span style=\"color:black; background-color: yellow\">DONE</span>**\n",
    "\n",
    "\n",
    "3) Write `get_aliso_value` function: **<span style=\"color:black; background-color: yellow\">DONE</span>**\n",
    "\n",
    "- Add 1 value to aliso_trend as looping over** \n",
    "\n",
    "Either \n",
    "- given the point, get value at that exact point for all years\n",
    "- OR given the global monthly data and one point, return the methane value at that point\n",
    "\n",
    "4) Create an OrderedDictionary of years to store all data per month by year **<span style=\"color:black; background-color: yellow\">DONE</span>**\n",
    "\n",
    "5) Manual confirmation that point is accurate** **<span style=\"color:black; background-color: lightgreen\">In Progress</span>**\n",
    "\n",
    "\n",
    "missing value (invalid) is `-9999.99`\n",
    "```\n",
    "aliso_trend = \n",
    "{\n",
    "    2009: [] # 12 values per yer\n",
    "    2010: [] \n",
    "    2011: []\n",
    "    2012\n",
    "    2013\n",
    "    2014\n",
    "    2015\n",
    "    2016\n",
    "    2017: []\n",
    "}\n",
    "```\n",
    "\n",
    "Need to be 100% correct:\n",
    "- correct matrix location for lon/lat pair\n",
    "\n",
    "Status Indicators:\n",
    "**<span style=\"color:black; background-color: yellow\">DONE</span>**\n",
    "**<span style=\"color:black; background-color: lightgreen\">In Progress</span>**\n",
    "**<span style=\"color:red; background-color: black\">Don't Panic</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to retrieve month-year from file name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "06 2009\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "200906"
      ]
     },
     "execution_count": 589,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = files[0]\n",
    "year_month = test[9:15]\n",
    "\n",
    "month = test[13:15]\n",
    "year = test[9:13]\n",
    "print month, year\n",
    "\n",
    "get_year_month = lambda fname: fname[9:15]\n",
    "get_year_month_tup = lambda year_month_str: (int(year_month_str[0:  4]), month_names.get(year_month_str[4:]))\n",
    "\n",
    "int(get_year_month(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GOSATTFTS2009070120090731_03C02SV0221.h5',\n",
       " 'GOSATTFTS2009080120090831_03C02SV0221.h5']"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check file string names\n",
    "files[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store h5 data as netCDF in `ncs` OrderedDict\n",
    "\n",
    "string year-month (ex. `200906`) is used a key\n",
    "\n",
    "### **<span style=\"color:yellow; background-color: darkblue\">Added empty string for 4 missing months here</span>**\n",
    "\n",
    "Having added empty list for invalid data is causing trouble for masked np array calculations\n",
    "\n",
    "TODO:\n",
    "**update [] to np.empty([72, 144], dtype=int) then mask it**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize empty OrderedDict\n",
    "ncs = OrderedDict()\n",
    "\n",
    "for fname in files:\n",
    "    year_month = get_year_month(fname)\n",
    "    ncs[year_month] = netCDF4.Dataset(data_loc.format(fname))\n",
    "    # manual insertion of missing data as empty arrays (Better here than later due to use\n",
    "    # or OrderedDicts that doesn't allow inserting element at specified locations)\n",
    "    if year_month == '201405':\n",
    "        ncs['201406'] = []\n",
    "    if year_month == '201411': #consecutive\n",
    "        ncs['201412'] = []\n",
    "        ncs['201501'] = []\n",
    "    if year_month == '201508':\n",
    "        ncs['201509'] = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Essentiall create 72 by 144 completely masked array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data = [-- -- --],\n",
       "             mask = [ True  True  True],\n",
       "       fill_value = 999999)"
      ]
     },
     "execution_count": 717,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.ma.array([1, 2, 3], mask = [1]*3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# initialize empty OrderedDict\n",
    "ncs2 = OrderedDict()\n",
    "for fname in files:\n",
    "    year_month = get_year_month(fname)\n",
    "    ncs2[year_month] = netCDF4.Dataset(data_loc.format(fname))\n",
    "    # manual insertion of missing data as empty arrays (Better here than later due to use\n",
    "    # or OrderedDicts that doesn't allow inserting element at specified locations)\n",
    "#     if year_month == '201405':\n",
    "#         ncs2['201406'] = nnp.empty([72, 144], dtype=float)\n",
    "#     if year_month == '201411': #consecutive\n",
    "#         ncs2['201412'] = np.empty([72, 144], dtype=float)\n",
    "#         ncs2['201501'] = np.empty([72, 144], dtype=float)\n",
    "#     if year_month == '201508':\n",
    "#         ncs2['201509'] = np.empty([72, 144], dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 855,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ncs2.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check that 4 missing date-month pairs were corrected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print (len(ncs.keys()) - len(files)) is 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ncs.keys() # manual check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check keys output\n",
    "# ncs.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<type 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF4 data model, file format HDF5):\n",
       "    dimensions(sizes): \n",
       "    variables(dimensions): \n",
       "    groups: Attribute, Data, Global"
      ]
     },
     "execution_count": 595,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncs['200910']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_ch4_masked_data(nc):\n",
    "    \"\"\"Return the *masked* monthly methane average column mixing ratio values\n",
    "    \n",
    "    *This is what should be used to generate figures as invalid values will be masked\n",
    "    and automatically NOT Plotted\n",
    "    \"\"\"\n",
    "    if nc == []:\n",
    "        return []\n",
    "    ch4 = nc.groups['Data']['mixingRatio']['XCH4']\n",
    "    masked_data = np.ma.masked_equal(ch4, value=ch4.invalidValue)\n",
    "#   print masked_data\n",
    "    return masked_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ncs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# init emtpy ordered dicts\n",
    "data = OrderedDict()\n",
    "\n",
    "def make_data_dict(_ncs, _data=OrderedDict()):  \n",
    "    \"\"\"Populate the given OrderedDict with given hdf5 data\n",
    "    Note: If no OrderedDict is given, create an empty one\n",
    "    \n",
    "    Result:\n",
    "        OrderedDict will have these keys: `masked_data` and `flat_data`\n",
    "        \n",
    "    * Currently, *masked_data* is used to generate all the maps\n",
    "    * flat_data is 1D version of 2D arrays and is used to generate histograms\n",
    "    \"\"\"\n",
    "    for key, _nc in _ncs.items():\n",
    "        _data[key] = {}\n",
    "        _data[key]['masked_data'] = get_ch4_masked_data(_nc) # used to make maps\n",
    "#         _data[key]['flat_data'] = _data[key]['data'].flatten() # used to make histograms\n",
    "    #return _data # commenting this line prevents output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_data_dict(ncs, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data2 = OrderedDict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_data_dict(ncs2, data2)\n",
    "# data2.keys() # missing four values but doesn't matter anyways for masked array op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24529727888695985"
      ]
     },
     "execution_count": 726,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a = data['200906']['masked_data']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## don't forget!\n",
    "\n",
    "data is at `data['200906']['masked_data']`\n",
    "\n",
    "or more generally `data['yyyymm']['masked_data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 601,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GET lat and lon matrices (in 'geolocation') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72, 144)"
      ]
     },
     "execution_count": 602,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo = ncs['200910']['Data']['geolocation']\n",
    "geo.variables.keys()\n",
    "lat =geo.variables.get('latitude')\n",
    "lon =geo.variables.get('longitude')\n",
    "lat.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Searching for numLatitude and intervalLengthOfLatitude (+ longitude equivalents) in nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'intervalLengthOfLatitude',\n",
       " u'intevalLengthOfLongitude',\n",
       " u'numLatitude',\n",
       " u'numLongitude']"
      ]
     },
     "execution_count": 603,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc = ncs['200910']\n",
    "ncattrs = nc.groups.get('Attribute')\n",
    "ncattrs.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 604,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa = [v for k, v in ncattrs.variables.iteritems()]\n",
    "v = aa[0]\n",
    "v[0]\n",
    "\n",
    "aa[3][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'unit', u'validRange', u'longName', u'invalidValue']"
      ]
     },
     "execution_count": 605,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll = nc.groups.get('Data')['geolocation']['latitude']\n",
    "ll.ncattrs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Covert lats and lons to numpy arrays (right now netCDF.Variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lats = np.array(lat)\n",
    "lons = np.array(lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 88.75,  88.75,  88.75, ...,  88.75,  88.75,  88.75],\n",
       "       [ 86.25,  86.25,  86.25, ...,  86.25,  86.25,  86.25],\n",
       "       [ 83.75,  83.75,  83.75, ...,  83.75,  83.75,  83.75],\n",
       "       ..., \n",
       "       [-83.75, -83.75, -83.75, ..., -83.75, -83.75, -83.75],\n",
       "       [-86.25, -86.25, -86.25, ..., -86.25, -86.25, -86.25],\n",
       "       [-88.75, -88.75, -88.75, ..., -88.75, -88.75, -88.75]], dtype=float32)"
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-178.75, -176.25, -173.75, ...,  173.75,  176.25,  178.75],\n",
       "       [-178.75, -176.25, -173.75, ...,  173.75,  176.25,  178.75],\n",
       "       [-178.75, -176.25, -173.75, ...,  173.75,  176.25,  178.75],\n",
       "       ..., \n",
       "       [-178.75, -176.25, -173.75, ...,  173.75,  176.25,  178.75],\n",
       "       [-178.75, -176.25, -173.75, ...,  173.75,  176.25,  178.75],\n",
       "       [-178.75, -176.25, -173.75, ...,  173.75,  176.25,  178.75]], dtype=float32)"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate and plot global average per year (eventually make this a fn)\n",
    "\n",
    "1. sum all month matrices for each year\n",
    "\n",
    "2. one gigantic matrix per year (need to keep track of valid and invalid numbers for division)\n",
    "3. average per year (make sure to divide by only valid_num) -> use numpy `mean()`\n",
    "4. plot timeseries\n",
    "\n",
    "### 2 and 3 -> numpy masked array took care of this. no need to keep track of invalid numbers\n",
    "\n",
    "Good reference: (https://stackoverflow.com/questions/26931318/averaging-multiple-masked-numpy-arrays-in-python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(2009, 1.7631361649328829),\n",
       "             (2010, 1.7680713253161526),\n",
       "             (2011, 1.7740664372109158),\n",
       "             (2012, 1.7792443956908857),\n",
       "             (2013, 1.7847130645941414),\n",
       "             (2014, 1.7943381471950925),\n",
       "             (2015, 1.8011343579254582),\n",
       "             (2016, 1.8088695560892187),\n",
       "             (2017, 1.8073336761597305)])"
      ]
     },
     "execution_count": 745,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Init OrderedDict with empty lists\n",
    "data_per_year = OrderedDict((year, []) for year in all_years)\n",
    "# print data_per_year\n",
    "\n",
    "# Init OrderedDict with invalid -1 \n",
    "global_annual_mean = OrderedDict((year, -1) for year in all_years)\n",
    "# print global_avg_per_year\n",
    "\n",
    "# all_years include up to 2017\n",
    "# years exclude 2017\n",
    "\n",
    "# Constructs data_per_year that maps\n",
    "# year (int) to a list of masked_data 2-d numpy arrays for all\n",
    "# available months in that year\n",
    "for key, _month_data in data2.iteritems():\n",
    "    (year, month_info) = get_year_month_tup(key)\n",
    "    (m_name, m_abbr, m_num) = month_info\n",
    "    data_per_year[year].append(_month_data['masked_data'])\n",
    "\n",
    "# NOW: Calculate mean of all masked 2-d numpy arrays per year\n",
    "# should result in single value per year\n",
    "# np.ma.array((a, b, c)).mean(axis=0)\n",
    "\n",
    "# I am the global annual means\n",
    "for year, data_ls in data_per_year.items():\n",
    "#     for d in data_ls:\n",
    "#         print d.shape # was causing error due to use of [] for missing months now excluded\n",
    "    annual_mean_matrix = np.ma.array((data_ls)).mean(axis=0)\n",
    "    global_annual_mean[year]= annual_mean_matrix.mean()\n",
    "\n",
    "global_annual_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the indices for latitude closest to 34.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.75"
      ]
     },
     "execution_count": 609,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lats[0,:] # -> 88.75 (top value) NH\n",
    "lats[71,:] # -88.75 (bottom value) SH\n",
    "\n",
    "lats[22][0] # closest\n",
    "lats[21][0], lats[23][0]\n",
    "\n",
    "lats[22][0] # choose row first (latitude) then all values in that row are equivalent, so pick first one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the indices for longitude closet to -118.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-118.75"
      ]
     },
     "execution_count": 610,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lons[:, 0][0]# -> -178.75\n",
    "lons[: -1][0] \n",
    "np.where(lons[: -1][0] == -118.75)\n",
    "\n",
    "lons[0][24] # choose any row (picked first), and all lon values should be different across\n",
    "# pick the right oen located at i=24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a function (lambda to retrieve the aliso_data from each data matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_aliso_data(_data):\n",
    "    # return -1 if masked_data is [] <-- these are missing months (4)\n",
    "    if _data['masked_data'] == []:\n",
    "        return -1\n",
    "    else:\n",
    "        return np.ma.getdata(_data['masked_data'][22][24]).item() or -1\n",
    "    # or statement exists so that -1 is returned in place of masked value of 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This step of retrieving Aliso specific data needs validation***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--\n",
      "-1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "masked"
      ]
     },
     "execution_count": 612,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = data['201506']\n",
    "d1['masked_data'].shape\n",
    "v = d1['masked_data'][22][24]\n",
    "print v\n",
    "d = np.ma.getdata(v)\n",
    "print 0.0 or -1\n",
    "# d.item()\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## some manual validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['201501', '201502', '201503', '201504', '201505', '201506', '201507', '201508', '201509', '201510', '201511', '201512']\n",
      "null\n",
      "1.81371\n",
      "1.81371\n",
      "1.81363\n",
      "1.81237\n",
      "--\n",
      "1.803\n",
      "--\n",
      "null\n",
      "--\n",
      "--\n",
      "--\n"
     ]
    }
   ],
   "source": [
    "ms = month_names.keys()\n",
    "\n",
    "#print ms # not ordered \n",
    "m2015 = sorted(['2015' + m for m in ms]) # sorted\n",
    "print m2015\n",
    "\n",
    "# for m in m2015:\n",
    "#     val = data.get(m)['masked_data']\n",
    "#     if val == []:\n",
    "#         print 'null'\n",
    "#     else:\n",
    "#         print val[22][24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B. where aliso data is retrieved and added to `aliso_trend`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, _month_data in data.iteritems():\n",
    "    (year, month_info) = get_year_month_tup(key)\n",
    "    (m_name, m_abbr, m_num) = month_info\n",
    "    \n",
    "    # this is either float value of methane concentration or -1 for invalid/missing data\n",
    "    aliso_monthly_ch4 = get_aliso_data(_month_data)\n",
    "    \n",
    "    # aliso_trend[year] is initialized to [-1, ... -1] that has twelve -1's\n",
    "    # append methane concentration\n",
    "    # the index location corresponds to month #\n",
    "    index = m_num - 1 # since Python is 0 indexed\n",
    "    aliso_trend[year][index] = aliso_monthly_ch4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8254609405994415"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aliso_yearly_trend = []\n",
    "aliso_trend\n",
    "\n",
    "def get_yearly_average(_year_data):\n",
    "    num_invalid = _year_data.count(-1)\n",
    "    num_valid = 12.0 - num_invalid\n",
    "    sum_valid_data = sum([month_data for month_data in _year_data if month_data != -1])\n",
    "    if num_valid == 0: # 2017 had 0 valid data, 2017 removed but keeping this here\n",
    "        return -1 # invalid data\n",
    "    else: # to avoid float division by 0 error (1 case)\n",
    "        year_average = sum_valid_data / num_valid\n",
    "        return year_average\n",
    "\n",
    "# 2017 is not removed\n",
    "get_yearly_average(aliso_trend[2017]) # this is a good test case that should return 0!\n",
    "get_yearly_average(aliso_trend[2016])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2017 does not have any valid data, just remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del aliso_trend[2017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2016 in aliso_trend.keys() and 2017 not in aliso_trend.keys() # True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, months_data in aliso_trend.items():\n",
    "    yearly_avg = get_yearly_average(months_data)\n",
    "    aliso_yearly_trend.append((k, yearly_avg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2009, 1.7755793843950545), (2010, 1.780842771132787), (2011, 1.7835861146450043), (2012, 1.7882656753063202), (2013, 1.7968865633010864), (2014, 1.8032719161775377), (2015, 1.8112823963165283), (2016, 1.8254609405994415)]\n"
     ]
    }
   ],
   "source": [
    "print aliso_yearly_trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def label_axes(_plt, x_lab, y_lab, title):\n",
    "    _plt.xlabel(x_lab,  fontsize=12)\n",
    "    _plt.ylabel(y_lab, fontsize=12)\n",
    "    _plt.title(title, fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a scatter plot for methane trend over 2009-2016, just for aliso\n",
    "\n",
    "#### Reference for legend:\n",
    "http://www.pybloggers.com/2015/09/my-first-time-using-matplotlib/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 769,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_annual_mean(_plt, _xs, _ys, _title, _xlab):\n",
    "    ylab = 'Methane Concentrations (ppmv)'\n",
    "    _plt.scatter(_xs, _ys, s=80, c='Lime', edgecolor='', alpha=0.8)\n",
    "    label_axes(_plt, _xlab, ylab, _title)\n",
    "    _plt.show()\n",
    "\n",
    "# reference\n",
    "# red_patch = mpatches.Patch(color='Purple', label='Near Aliso Canyon Oil Filed (33.75, -118.75)')\n",
    "# plt.legend(handles=[red_patch])\n",
    "plt.close()\n",
    "# plt.figure(figsize=(6, 6))\n",
    "# plot_annual_mean(plt, all_years, global_annual_mean.values(), 'GOSAT Global Annual Mean Methane Concentrations')\n",
    "# plt.savefig('gosat-yearly-average(2009-2017.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove 2017 data because it only has two months!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 775,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rhMolH8CuwFoRMalWIOmvwO04+TAzM2u7Kg67vJpv9WWvdCAWMzOzyqliz8fR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\nTj7MzMysU38DhXQeHelL+LkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1166a2d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "#del global_annual_mean[2017]\n",
    "global_annual_mean # no 2017\n",
    "global_annual_means = global_annual_mean.values()\n",
    "plot_annual_mean(plt, all_years[:-1], global_annual_mean.values(), 'GOSAT Global Annual Mean Methane Concentrations (2009-2016)'\n",
    "                , 'Years (2009 - 2016)')\n",
    "plt.savefig('gosat-yearly-average(2009-2016.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+wCRgV6Ab8KCZNQJ+Bbzp7vmlTcDdH3X3PHfPa95cj1LOJZdddhnPPPMMK1eu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\nm5mZVZALvJmZWQX9P3dj5g5TBf19AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11425c050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = [y[0] for y in aliso_yearly_trend]\n",
    "ys = [y[1] for y in aliso_yearly_trend]\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(xs, ys, s=80, c='Purple', edgecolor='', alpha=0.8)\n",
    "label_axes(plt, 'Years (2009 - 2016)', 'Methane Concentrations (ppmv)', 'GOSAT Yearly Average Methane Concentrations (2009-2016)')\n",
    "#plt.savefig('gosat-yearly-average-aliso.png')\n",
    "\n",
    "# reference\n",
    "red_patch = mpatches.Patch(color='Purple', label='Near Aliso Canyon Oil Filed (33.75, -118.75)')\n",
    "plt.legend(handles=[red_patch])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aliso_annual_means = ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 819,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_annual_anomaly(_plt, _xs, _ys, _title, _xlab, addpatch=False, _color='lightsalmon'):\n",
    "    print _color\n",
    "    ylab = 'Methane Concentrations Anomaly (ppmv)'\n",
    "    _plt.scatter(_xs, _ys, s=80, c=_color, edgecolor='', alpha=0.8)\n",
    "    label_axes(_plt, _xlab, ylab, _title)\n",
    "    if addpatch:\n",
    "        patch = mpatches.Patch(color=_color, label='Near Aliso Canyon Oil Filed (33.75, -118.75)')\n",
    "        plt.legend(handles=[patch])\n",
    "    _plt.show()\n",
    "\n",
    "aliso_annual_anomaly = [aliso_mean - global_mean for aliso_mean, global_mean in zip(aliso_annual_means, global_annual_means)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.012443219462171529,\n",
       " 0.012771445816634364,\n",
       " 0.009519677434088436,\n",
       " 0.0090212796154345298,\n",
       " 0.012173498706945063,\n",
       " 0.0089337689824451338,\n",
       " 0.010148038391070147,\n",
       " 0.016591384510222795]"
      ]
     },
     "execution_count": 791,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aliso_annual_anomaly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TpzN9+nR69+6ds94zzzzDM888Q//+/RkwYADvvfces2fP5sUXX2To0KG0aNGC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\n/wvL3guqo7hfZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176986d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "plot_annual_anomaly(plt, all_years[:-1], aliso_annual_anomaly, \n",
    "                 'GOSAT Yearly Average Methane Anomalies (2009-2016)', 'Years (2009-2016)', addpatch=True)\n",
    "plt.show()\n",
    "plt.savefig('gosat-year-average-methane-anomaly-2009-2016')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zJ7/61a+YO3cu3333HUDh+U4I3Zmp7sNzzz2XRx55hPXr1zNy5EhOPfVUpk6d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\nC62ZmVmFXGjNzMwq9P8zz5fAPhHP8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176f1210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aliso_annual_anomaly = [aliso_mean - global_mean for aliso_mean, global_mean in zip(aliso_annual_means, [the_global_mean]*8)]\n",
    "\n",
    "plt.close()\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "plot_annual_anomaly(plt, all_years[:-1], aliso_annual_anomaly, \n",
    "                 'GOSAT Yearly Average Methane Anomalies* (2009-2016)', 'Years (2009-2016)', addpatch=True)\n",
    "plt.show()\n",
    "plt.savefig('gosat-year-average-methane-anomaly-2009-2016*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use `aliso_trend` to plot anomalies per month-year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 887,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_monthly_anomaly(_plt, _xs, _ys, _title, _xlab, addpatch=False, _color='lightseagreen'):\n",
    "    ylab = 'Methane Concentrations Anomaly (ppmv)'\n",
    "    _plt.scatter(_xs, _ys, s=80, c=_color, edgecolor='', alpha=0.8)\n",
    "    label_axes(_plt, _xlab, ylab, _title)\n",
    "    plt.xticks(np.arange(min(_xs), max(_xs)+1, 3))\n",
    "    if addpatch:\n",
    "        patch = mpatches.Patch(color=_color, label='Near Aliso Canyon Oil Filed (33.75, -118.75)')\n",
    "        plt.legend(handles=[patch])\n",
    "    _plt.show()\n",
    "\n",
    "aliso_annual_anomaly = [aliso_mean - global_mean for aliso_mean, global_mean in zip(aliso_annual_means, global_annual_means)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The global mean 2009-2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.78419668112\n"
     ]
    }
   ],
   "source": [
    "the_global_mean = sum(global_annual_means)/len(global_annual_means)\n",
    "print the_global_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot aliso and global anomalies using the global_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 820,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lime\n"
     ]
    },
    {
     "data": {
      "image/png": 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sTTnJm5mZtalSJHlJA4qOwczMrN2UIskDUyX9RlKfnc5Q0ihJD0qaLOnQOtOX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\n/g8J2Xv+m+fc0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a0dc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "\n",
    "global_annual_mean_anomalies =[global_year_val - global_mean for global_year_val, global_mean in zip(global_annual_mean.values(), [the_global_mean]*8)]\n",
    "\n",
    "plot_annual_anomaly(plt, all_years[:-1], global_annual_mean_anomalies, 'GOSAT Global Annual Mean Methane Anomalies (2009-2016)'\n",
    "                , 'Years (2009 - 2016)', _color='Lime')\n",
    "plt.show()\n",
    "plt.savefig('gosat-annual-mean-methane-anomalies(2009-2016).png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 823,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2009\n",
      "12\n",
      "2010\n",
      "12\n",
      "2011\n",
      "12\n",
      "2012\n",
      "12\n",
      "2013\n",
      "12\n",
      "2014\n",
      "12\n",
      "2015\n",
      "12\n",
      "2016\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "for k, v in aliso_trend_masked.items():\n",
    "    print k\n",
    "    print len(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 851,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "[-- -- -- -- -- 0.014204724044167838 0.006873710364663443\n",
      " 0.00917421123441442 0.016923172683083854 0.015074594230019889\n",
      " 0.013069613189065299 0.011782510489785514]\n"
     ]
    }
   ],
   "source": [
    "aliso_trend_masked = OrderedDict()\n",
    "\n",
    "for k, v in aliso_trend.items():\n",
    "    aliso_trend_masked[k] = np.ma.masked_equal(v, -1)\n",
    "\n",
    "aliso_monthly_anomaly = [aliso_month_mean - global_mean for aliso_month_mean, global_mean in\n",
    "                        zip(aliso_trend_masked.values(), global_annual_means)]\n",
    "print len(aliso_monthly_anomaly) is 8\n",
    "print aliso_monthly_anomaly[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 871,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91\n"
     ]
    }
   ],
   "source": [
    "first_item = aliso_monthly_anomaly[0]\n",
    "# print first_item\n",
    "\n",
    "aliso_monthly_anomaly2 = first_item\n",
    "\n",
    "for month_mean in aliso_monthly_anomaly[1:]:\n",
    "    aliso_monthly_anomaly2 = np.ma.append(aliso_monthly_anomaly2, month_mean)\n",
    "    \n",
    "# for month_means in aliso_monthly_anomaly:\n",
    "#     print len(month_means)\n",
    "#     np.ma.append(all_aliso_monthly_anomalies, month_means)\n",
    "    \n",
    "print len(aliso_monthly_anomaly2[5:])\n",
    "# remove 2009 Jan to May (5 no data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot monthly anomalies! aliso - global\n",
    "\n",
    "    `aliso_monthly_anomaly2`: 12*8 long list of aliso anomalies for 8 years. masked array\n",
    "    `month_years`: string combo of 200906 to 201702 # 93 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 864,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 864,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7*12+7 + 2 # 7 years,AND 7 for 2009, 2 for 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 873,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91"
      ]
     },
     "execution_count": 873,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(month_years[:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 874,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91"
      ]
     },
     "execution_count": 874,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(aliso_monthly_anomaly2[5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 884,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aliso_monthly_anomaly2[5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 896,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UUjmh3t9BeSgICNFgN7UFhdlu0rho024sSimllFJqsmpoDjCttICS/L7rYPke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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c7ab10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "plt.figure(figsize=(18, 6))\n",
    "#def plot_monthly_anomaly(_plt, _xs, _ys, _title, _xlab, addpatch=False, _color='lightseagreen'):\n",
    "\n",
    "month_years = ncs.keys() # corrected missing data version\n",
    "\n",
    "dates = [i for i in range(len(aliso_monthly_anomaly2[5:]))]\n",
    "aliso_data = aliso_monthly_anomaly2[5:]\n",
    "plt.plot(dates, aliso_data)\n",
    "plt.axvline(x=77, c='Red')\n",
    "plt.axvline(x=78, c='Red')\n",
    "plt.axvline(x=79, c='Red')\n",
    "plt.axvline(x=80, c='Red')\n",
    "plt.axvline(x=81, c='Red')\n",
    "plot_monthly_anomaly(plt, dates, \n",
    "                     aliso_monthly_anomaly2[5:], \n",
    "                     'GOSAT Monthly Methane Anomalies for the Aliso Canyon Oil Field', \n",
    "                     'Month-Year (06/2009- 12/2016)')\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('gosat-monthly-anomaly-aliso-2009-2016')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###\n",
    "\n",
    "since \n",
    "begin 2009 jun\n",
    "\n",
    "end 2016 dec\n",
    "\n",
    "there are 91 dates\n",
    "\n",
    "2009 6-12  7\n",
    "\n",
    "2010 12    19\n",
    "\n",
    "2011 12    31\n",
    "\n",
    "2012 12    43\n",
    "\n",
    "2013 12    55\n",
    "\n",
    "2014 12    67\n",
    "\n",
    "2015 12 \n",
    "  - sept 9 76\n",
    "  -  oct    *77*\n",
    "  - dec    79\n",
    "    \n",
    "2016 12\n",
    "  - jan    80\n",
    "  - feb    *81*\n",
    "  - dec    91\n",
    "\n",
    "77-81 are aliso months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOW calculate seasonal means\n",
    "\n",
    "1. Sum all months -> one matrix per month\n",
    "\n",
    "2. Calculate average of each month\n",
    "\n",
    "3.\n"
   ]
  },
  {
   "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": 2
}