FLUXNET_coupling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using FLUXNET2015 data to compute terrestrial leg of coupling index and compare against CESM models \n",
    "<b>Author:</b> Meg D. Fowler <br>\n",
    "<b>Date:</b> 15 Oct 2020 <br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Information on using FLUXNET data, including how to download various sites, can be found at: https://fluxnet.org </b>\n",
    "\n",
    "For metadata of location, data is saved here: https://fluxnet.org/sites/site-list-and-pages/, and has been saved into a .csv file manually.\n",
    "\n",
    "A note on the QC flags based on quick-start guide from above: <br>\n",
    "* QC flags are available as the varName_QC. For daily and coarser data, the flag \"indicates the percentage of measured (_QC=0) or good quality gap-filled (*_QC=1) records aggregated from finer temporal resolutions.\" <br><br>\n",
    "\n",
    "It's also important to note that the FLUXNET data is provided in local standard time (so it ignores the presence of Daylight Savings Time). And although the order of columns is *typically* similar between sites, there is no guarantee that it is constant across all sites. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "\n",
    "# Plotting utils \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.ticker as ticker \n",
    "import matplotlib.patches as patches\n",
    "import cartopy\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.util\n",
    "\n",
    "# Analysis\n",
    "import numpy\n",
    "import os\n",
    "import glob\n",
    "import numpy as np \n",
    "import xarray as xr\n",
    "import datetime\n",
    "from datetime import date, timedelta\n",
    "import pandas as pd \n",
    "import pickle\n",
    "import comet as cm\n",
    "from comet.metrics import utils as gf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Begin process of reading in FLUXNET data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, define directory where FLUXNET data is saved:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mainDir   = '/Users/mdfowler/Documents/Analysis/Coupling_initial/data/FLUXNET2015/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within that directory, you should have some kind of site list saved as a .csv (explanation above). So let's read that in first "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available site characteristics (metadata) include: \n",
      "\n",
      " ['SITE_ID' 'SITE_NAME' 'FLUXNET2015' 'FLUXNET-CH4' 'LOCATION_LAT'\n",
      " 'LOCATION_LONG' 'LOCATION_ELEV' 'IGBP' 'MAT' 'MAP'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "siteInfoFile = mainDir+'SiteList.csv'\n",
    "\n",
    "# Now read that CSV file into a pandas dataframe \n",
    "siteInfoDF = pd.read_csv(siteInfoFile)\n",
    "\n",
    "# Print out information available:\n",
    "print('Available site characteristics (metadata) include: \\n\\n', siteInfoDF.columns.values, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's also a downloaded BIF file with lots of metadata you can download with the FLUXNET dataset. This is all in one file and can be a bit clunky, but does allow us to get the depth of soil moisture being used! So let's go ahead and read that in here too. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadataFile = mainDir+'FLX_AA-Flx_BIF_ALL_20200501/FLX_AA-Flx_BIF_DD_20200501.xlsx'\n",
    "\n",
    "# Read in that file \n",
    "metadataDF = pd.read_excel(metadataFile)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Now we want to get a list of all the files containing useful data.</b> <br><br>\n",
    "In this case, I've downloaded the SUBSET data (all sites, but only a selection of available variables). For variable names and definitions, see: https://fluxnet.org/data/fluxnet2015-dataset/subset-data-product/ <br>\n",
    "For this case, I'd like to get daily data, so the appropriate file suffix is *SUBSET_DD*, where the \"DD\" implies daily data. In the SUBSET data, you can also access weekly (\"WW\"), monthly (\"MM\"), and yearly (\"YY\"), as well as hourly data (\"HH\"). <br><br>\n",
    "The \"glob\" library will list the full paths to each file matching this string: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFiles = glob.glob(mainDir + 'FLX_*/*SUBSET_DD*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Let's use a function to read in data from each file, saving only the variables we'll need to compute the terrestrial leg of the coupling index - soil water in the top layer of soil, and sensible heat flux. </b> <br>\n",
    "Note, however, that the function is designed to be flexible. Any set of variables can be requested with this function. If any of those variables are not found to exist, none are returned (default behavior, but can likely be changed as needed). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --------------------------------------------------------\n",
    "# Function to read requested variables from FLUXNET file. \n",
    "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
    "# \n",
    "# Inputs:  fileName --> Full path to FLUXNET data file \n",
    "#          varNames --> An array of variable names to be \n",
    "#                       retrieved from said data file. \n",
    "#                       NOTE: If you wish to retrieve *all* \n",
    "#                       variables, pass the string 'ALL'. \n",
    "#  \n",
    "# Outputs: fluxnetID --> ID string used to identify station\n",
    "#          fluxnetDS --> An x-array dataset containing the \n",
    "#                        requested variables.\n",
    "#                        Missing values will be set to NaN. \n",
    "# \n",
    "# --------------------------------------------------------\n",
    "\n",
    "def readFLUXNET_var(fileName, varNames): \n",
    "    # Get ID of station \n",
    "    startID   = fileName.find('FLX_')\n",
    "    fluxnetID = fileName[startID+4:startID+10]\n",
    "    \n",
    "    # print('Reading in site - ', fluxnetID)\n",
    "    \n",
    "    # Read in CSV file containing data \n",
    "    dataDF = pd.read_csv(fileName)\n",
    "    \n",
    "    # Return ALL variables from dataDF if requested\n",
    "    if varNames=='ALL':\n",
    "        fluxnetDF = dataDF\n",
    "        \n",
    "        # Set any value that's missing to NaN (not -999)\n",
    "        fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
    "\n",
    "        \n",
    "        # If time has been requested, reformat to pandas make index\n",
    "        fluxnetDF['TIMESTAMP'] = pd.to_datetime(fluxnetDF['TIMESTAMP'].values, format='%Y%m%d')\n",
    "        fluxnetDF = fluxnetDF.set_index(['TIMESTAMP'])\n",
    "        \n",
    "        # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
    "        fluxnetDS = fluxnetDF.to_xarray()\n",
    "            \n",
    "    # Reduce returned DF to contain only variables of interest  \n",
    "    else:\n",
    "        \n",
    "        # Check that requested variables are available in specific file\n",
    "        errCount = 0                       # Initialize flag for error \n",
    "        colNames = dataDF.columns.values   # Available variables in file \n",
    "        \n",
    "        for iVar in range(len(varNames)):  # Check each variable individually\n",
    "            if (varNames[iVar] in colNames)==False:\n",
    "                # Turn on print statement for more verbose output\n",
    "                # print('** ERROR: %13s   not contained in file for %8s  **' %(varNames[iVar], fluxnetID))\n",
    "                \n",
    "                # If any variable is not conatined in file, return a NaN \n",
    "                fluxnetDS = -999\n",
    "                errCount  = errCount+1\n",
    "                \n",
    "        # If all the variables *are* available, isolate those in DF and return that\n",
    "        if errCount == 0: \n",
    "            fluxnetDF = dataDF[varNames]\n",
    "            \n",
    "            # Set any value that's missing to NaN (not -999)\n",
    "            fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
    "            \n",
    "            # If time has been requested, reformat to pandas make index\n",
    "            if ('TIMESTAMP' in varNames)==True:\n",
    "                fluxnetDF['TIMESTAMP'] = pd.to_datetime(fluxnetDF['TIMESTAMP'].values, format='%Y%m%d')\n",
    "                fluxnetDF = fluxnetDF.set_index(['TIMESTAMP'])\n",
    "        \n",
    "            # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
    "            fluxnetDS = fluxnetDF.to_xarray()\n",
    "    \n",
    "    return(fluxnetID, fluxnetDS)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Compute terrestrial leg of coupling index at each FLUXNET site"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this calculation, we'll use the package built into the CoMeT libaray to compute the coupling index between top level soil moisture and sensible heat flux as observed at each FLUXNET site having data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model data has soil moisture in units of kg/m2, so we'll need to convert the volumetric version reported as part of the FLUXNET data (in %) to the same unit. That means multiplying by the density of water (assumed constant at 1000 kg/m3) and the depth of the observation. <br>\n",
    "The depth of soil moisture is only reported in the metadata BIF file. So let's go through each site and if terraCI_fluxnet was computed - what was the depth of soil moisture used? It's reported in [m] so we've been using a depth of 0.10 (10 cm) in CESM analysis. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for station:   US-ORv\n",
      "No data for station:   US-WPT\n",
      "No data for station:   DE-RuR\n",
      "No data for station:   RU-Cok\n",
      "No data for station:   RU-Che\n",
      "No data for station:   AR-Vir\n",
      "No data for station:   US-Ne2\n",
      "No data for station:   US-Wi1\n",
      "No data for station:   US-Wi2\n",
      "No data for station:   IT-La2\n",
      "No data for station:   US-Wi9\n",
      "No data for station:   US-Wi6\n",
      "No data for station:   US-Tw4\n",
      "No data for station:   DE-Zrk\n",
      "No data for station:   GL-NuF\n",
      "No data for station:   NL-Hor\n",
      "No data for station:   US-Los\n",
      "No data for station:   DE-Spw\n",
      "No data for station:   US-Atq\n",
      "No data for station:   US-Myb\n",
      "No data for station:   GL-ZaF\n",
      "No data for station:   CG-Tch\n",
      "No data for station:   DE-Akm\n",
      "No data for station:   US-Wi5\n",
      "No data for station:   ES-Ln2\n",
      "No data for station:   US-Ivo\n",
      "No data for station:   CZ-wet\n",
      "No data for station:   US-GBT\n",
      "No data for station:   AU-Cum\n",
      "No data for station:   SJ-Adv\n",
      "No data for station:   US-Wi0\n",
      "No data for station:   US-Ne3\n",
      "No data for station:   BR-Sa1\n",
      "No data for station:   DE-RuS\n",
      "No data for station:   US-Wi7\n",
      "No data for station:   US-Wi4\n",
      "No data for station:   FI-Let\n",
      "No data for station:   US-Wi8\n",
      "No data for station:   US-Prr\n",
      "No data for station:   US-Twt\n",
      "No data for station:   CA-Obs\n",
      "No data for station:   FR-Pue\n",
      "No data for station:   BE-Bra\n",
      "No data for station:   US-Wi3\n",
      "No data for station:   US-Cop\n",
      "No data for station:   FI-Lom\n",
      "No data for station:   DE-SfN\n",
      "No data for station:   CA-Man\n",
      "No data for station:   US-Tw1\n",
      "No data for station:   FR-Fon\n",
      "No data for station:   US-Ha1\n"
     ]
    }
   ],
   "source": [
    "# Set up a few empty arrays to save data into \n",
    "terraCI_fluxnet   = np.full([len(dataFiles), 4], np.nan)\n",
    "terraCI_fluxnetConverted   = np.full([len(dataFiles), 4], np.nan)   # CI using kg/m2 soil water content \n",
    "startTime_fluxnet = np.zeros(len(dataFiles), dtype='datetime64[s]')\n",
    "endTime_fluxnet   = np.zeros(len(dataFiles), dtype='datetime64[s]')\n",
    "lat_fluxnet       = np.full([len(dataFiles)], np.nan)\n",
    "lon_fluxnet       = np.full([len(dataFiles)], np.nan)\n",
    "SWCdepth          = np.full([len(dataFiles)], np.nan)\n",
    "\n",
    "stationID           = []\n",
    "stationID_converted = []\n",
    "\n",
    "allStationID        = []\n",
    "    \n",
    "# Variables I want returned:\n",
    "varNames = ['TIMESTAMP','H_F_MDS','SWC_F_MDS_1','SWC_F_MDS_1_QC']\n",
    "\n",
    "# Loop over each station (data file)\n",
    "for iStation in range(len(dataFiles)):\n",
    "    \n",
    "    # Read in data \n",
    "    # ----------------------------------------------------------\n",
    "    fluxnetID,fluxnetDS = readFLUXNET_var(dataFiles[iStation], varNames)\n",
    "    \n",
    "    # Save lat and lon for this station \n",
    "    # ----------------------------------------------------------\n",
    "    indStation            = np.where(fluxnetID==siteInfoDF['SITE_ID'])\n",
    "    lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
    "    lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
    "    allStationID.append(fluxnetID)\n",
    "    \n",
    "    # Check that there was data saved for this particular site: \n",
    "    # ----------------------------------------------------------\n",
    "    if (type(fluxnetDS)==int):\n",
    "        print('No data for station: %8s' % fluxnetID)\n",
    "    \n",
    "    elif ( (np.all(np.isnan(fluxnetDS['H_F_MDS']))==True) | (np.all(np.isnan(fluxnetDS['SWC_F_MDS_1']))==True) ):\n",
    "        print('No data for station: %8s' % fluxnetID)\n",
    "        \n",
    "    # If data is present: \n",
    "    # ----------------------------------------------------------\n",
    "    else:        \n",
    "        # Only consider where data is actually present for selected vars\n",
    "        iReal      = np.where((np.isfinite(fluxnetDS['SWC_F_MDS_1'])==True) & \n",
    "                              (np.isfinite(fluxnetDS['H_F_MDS'])==True))[0]\n",
    "        fluxnetDS  = fluxnetDS.isel(TIMESTAMP=iReal)\n",
    "        \n",
    "        stationID.append(fluxnetID)\n",
    "        \n",
    "        # Convert units from volumetric (%) to mass (kg/m2)\n",
    "        # -------------------------------------------------\n",
    "        #   Step 1: Convert from % to fraction\n",
    "        fracSM = (fluxnetDS['SWC_F_MDS_1'].values)/100.0\n",
    "        \n",
    "        #    Step 2: Need to use depth of obs in conversion \n",
    "        metaData_station    = metadataDF[metadataDF.SITE_ID==fluxnetID]\n",
    "        iSWC                = np.where(metaData_station.DATAVALUE=='SWC_F_MDS_1')[0]\n",
    "        #  Some locations (5) have two depths \n",
    "        if len(iSWC)>1:             \n",
    "            for iDepth in range(len(iSWC)): \n",
    "                SWC_DF   = metaData_station[iSWC[iDepth]:iSWC[iDepth]+4]\n",
    "                \n",
    "                depth    = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
    "                depthDay = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_DATE'].DATAVALUE.values).astype(int)\n",
    "                depthDay = int(str(depthDay[0])[:8])  # Some weird ones have time attached; don't want that\n",
    "                depthDay = pd.to_datetime(depthDay, format='%Y%m%d')\n",
    "        \n",
    "                # Keep deepest level as the depth for station \n",
    "                if iDepth==0:\n",
    "                    SWCdepth[iStation] = depth \n",
    "                    convertSM = fracSM*1000.0*np.abs(depth)\n",
    "                else: \n",
    "                    # Use date as break point for getting kg/m2 SWC\n",
    "                    #  Eq: SWC_kgm2 = SWC_vol [m3/m3] * 1000 [kg/m3] * depth [m]\n",
    "                    dateArr = pd.DatetimeIndex(fluxnetDS.TIMESTAMP.values)\n",
    "                    iTime   = int(np.where(dateArr==depthDay)[0])\n",
    "                    convertSM[iTime::] = (fracSM[iTime::])*1000.0*np.abs(depth)\n",
    "                    \n",
    "                    # Keep deepest level as the depth for station \n",
    "                    if depth<SWCdepth[iStation]: \n",
    "                        SWCdepth[iStation] = depth\n",
    "                        \n",
    "            stationID_converted.append(fluxnetID)\n",
    "                \n",
    "        # If station only has one level recorded, things are a bit easier:             \n",
    "        else: \n",
    "            SWC_DF             = metaData_station[iSWC[0]:iSWC[0]+4]\n",
    "            SWCdepth[iStation] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE.values).astype(float)\n",
    "            convertSM          = fracSM*1000.0*np.abs(SWCdepth[iStation])\n",
    "            \n",
    "            stationID_converted.append(fluxnetID)\n",
    "\n",
    "        # Save converted soil moisture to dataset \n",
    "        fluxnetDS['SWC_F_MDS_1_convert'] = (('TIMESTAMP'), convertSM)\n",
    "        \n",
    "        \n",
    "        # Save first and last time used for computing CI \n",
    "        # ----------------------------------------------\n",
    "        startTime_fluxnet[iStation] = fluxnetDS['TIMESTAMP'].values[0]\n",
    "        endTime_fluxnet[iStation]   = fluxnetDS['TIMESTAMP'].values[-1]\n",
    "                \n",
    "        # Compute terrestrial coupling metric\n",
    "        # -----------------------------------\n",
    "        terraLeg = cm.metrics.CouplingIndex()\n",
    "        terraLeg = terraLeg.compute(fluxnetDS, xname    ='SWC_F_MDS_1', \n",
    "                                               yname    ='H_F_MDS',\n",
    "                                               averaging='season')\n",
    "        terraLeg2 = cm.metrics.CouplingIndex()\n",
    "        terraLeg2 = terraLeg2.compute(fluxnetDS,xname   ='SWC_F_MDS_1_convert', \n",
    "                                               yname    ='H_F_MDS',\n",
    "                                               averaging='season')\n",
    "        \n",
    "        # If there's less than one full year of data, don't use station\n",
    "        #   (i.e., as long as all 4 seasons are defined, save values)\n",
    "        if len(terraLeg.season)==4:\n",
    "            terraCI_fluxnet[iStation,:]          = terraLeg.SWC_F_MDS_1_H_F_MDS_CI.values\n",
    "            terraCI_fluxnetConverted[iStation,:] = terraLeg2.SWC_F_MDS_1_convert_H_F_MDS_CI.values\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'd like to print/save a few summary statistics from the Fluxnet data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of FLUXNET stations with CI calculated: 153\n"
     ]
    }
   ],
   "source": [
    "print('Number of FLUXNET stations with CI calculated: %i' % len(np.where(np.isfinite(terraCI_fluxnet[:,1])==True)[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of FLUXNET stations with CI calculated: 150\n"
     ]
    }
   ],
   "source": [
    "# There should be three less valid stations for converted, becasue three stations list their depths as 0.0 meters. \n",
    "# These will be removed below \n",
    "print('Number of FLUXNET stations with CI calculated: %i' % len(np.where(np.isfinite(terraCI_fluxnetConverted[:,1])==True)[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum number of months used for JJA mean CI: 1 \n",
      "Maximum number of months used for JJA mean CI: 57 \n"
     ]
    }
   ],
   "source": [
    "# How many months go into each calculation of CI for JJA? \n",
    "nMonths = np.full([len(dataFiles)], np.nan)\n",
    "\n",
    "for iSt in range(len(dataFiles)):\n",
    "    if np.isfinite(terraCI_fluxnet[iSt,1]):\n",
    "        dateRange      = pd.date_range(start=startTime_fluxnet[iSt],end=endTime_fluxnet[iSt],freq='M')\n",
    "        nMonths[iSt]   = len(np.where((dateRange.month>=6) & (dateRange.month<=8))[0])\n",
    "        \n",
    "print('Minimum number of months used for JJA mean CI: %i ' % int(np.nanmin(nMonths)) )\n",
    "print('Maximum number of months used for JJA mean CI: %i ' % int(np.nanmax(nMonths)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Check which stations to eliminate based on nmonths and depth of measurement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below was used before I was looking at depth in the original reading-in of data, so it shouldn't be necessary. But leaving it here as legacy just in case... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two measurements for IT-CA3\n",
      "      depth 1: -0.150000\n",
      "      depth 2: -0.050000\n",
      "Two measurements for IT-BCi\n",
      "      depth 1: -0.100000\n",
      "      depth 2: -0.035000\n",
      "Two measurements for BE-Vie\n",
      "      depth 1: -0.020000\n",
      "      depth 2: -0.050000\n",
      "Two measurements for NL-Loo\n",
      "      depth 1: -0.100000\n",
      "      depth 2: -0.030000\n",
      "Two measurements for IT-CA2\n",
      "      depth 1: -0.150000\n",
      "      depth 2: -0.050000\n"
     ]
    }
   ],
   "source": [
    "# # Empty array to store depth \n",
    "# SWCdepth = np.full([len(dataFiles)], np.nan)\n",
    "\n",
    "# for iSt in range(len(dataFiles)):\n",
    "    \n",
    "#     # If JJA coupling index was computed (there was data)...\n",
    "#     if np.isfinite(terraCI_fluxnet[iSt,1])==True:\n",
    "        \n",
    "#         # Get station ID \n",
    "#         fluxnetID,fluxnetDS = readFLUXNET_var(dataFiles[iSt], varNames)\n",
    "        \n",
    "#         # Get metadata for said station \n",
    "#         metaData_station = metadataDF[metadataDF.SITE_ID==fluxnetID]\n",
    "        \n",
    "#         # Get index where SWC info starts \n",
    "#         iSWC = np.where(metaData_station.DATAVALUE=='SWC_F_MDS_1')[0]\n",
    "        \n",
    "#         # Some locations (5) have two measurements recorded \n",
    "#         if len(iSWC)>1: \n",
    "#             print('Two measurements for %s' % fluxnetID)\n",
    "            \n",
    "#             for iDepth in range(len(iSWC)): \n",
    "#                 SWC_DF   = metaData_station[iSWC[iDepth]:iSWC[iDepth]+4]\n",
    "#                 depth    = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE).astype(float)\n",
    "#                 # Manual check of depths... \n",
    "#                 print('      depth %i: %f' % ((iDepth+1), depth))\n",
    "                \n",
    "#                 # Keep deepest level as the depth for station \n",
    "#                 if iDepth==0:\n",
    "#                     SWCdepth[iSt] = depth \n",
    "#                 else: \n",
    "#                     if depth<SWCdepth[iSt]: \n",
    "#                         SWCdepth[iSt] = depth \n",
    "                        \n",
    "#         # If staiton only has one level recorded, things are a bit easier:             \n",
    "#         else: \n",
    "#             SWC_DF        = metaData_station[iSWC[0]:iSWC[0]+4]\n",
    "#             SWCdepth[iSt] = np.asarray(SWC_DF[SWC_DF.VARIABLE=='VAR_INFO_HEIGHT'].DATAVALUE).astype(float)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's limit usage to depths less than 20 cm (arbitrary, but I don't want us using non-surface soil moisture for this application). This will eliminate 11 stations.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of FLUXNET stations to use with reasonable depths of SWC: 139\n",
      "Number of FLUXNET stations to use with reasonable depths of SWC_converted: 139\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-0f85a5487fa2>:2: RuntimeWarning: invalid value encountered in less\n",
      "  iLimit = np.where((SWCdepth==0.0) | (SWCdepth<-0.2))[0]\n"
     ]
    }
   ],
   "source": [
    "# Get stations with SWC from below 20 cm (or equal to zero)\n",
    "iLimit = np.where((SWCdepth==0.0) | (SWCdepth<-0.2))[0]\n",
    "\n",
    "# Set the terrestrial leg of CI to missing so we don't consider those \n",
    "terraCI_fluxnet[iLimit,:]          = np.nan\n",
    "terraCI_fluxnetConverted[iLimit,:] = np.nan\n",
    "\n",
    "print('Number of FLUXNET stations to use with reasonable depths of SWC: %i' % len(np.where(np.isfinite(terraCI_fluxnetConverted[:,1])==True)[0]))\n",
    "print('Number of FLUXNET stations to use with reasonable depths of SWC_converted: %i' % len(np.where(np.isfinite(terraCI_fluxnet[:,1])==True)[0]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It would also be good to put some time limits on this. So let's say the observations need to have at least 9 months of data for JJA means (3-years). Otherwise, set terraCI to np.nan again so we don't use it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of FLUXNET stations to use with 3+ years of JJA data: 115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-e6f76751fb74>:2: RuntimeWarning: invalid value encountered in less\n",
      "  iLimit = np.where(nMonths<9)[0]\n"
     ]
    }
   ],
   "source": [
    "# Get stations with less than 9 months used for JJA terrestrial CI \n",
    "iLimit = np.where(nMonths<9)[0]\n",
    "\n",
    "# Set to missing so we don't consider stations with less than three years of data going into the average \n",
    "terraCI_fluxnet[iLimit,:] = np.nan\n",
    "terraCI_fluxnetConverted[iLimit,:] = np.nan\n",
    "\n",
    "print('Number of FLUXNET stations to use with 3+ years of JJA data: %i' % len(np.where(np.isfinite(terraCI_fluxnetConverted[:,1])==True)[0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Compare terrestrial leg of the coupling index to what's been found in our CESM2 simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to read in the coupling index that's been computed previously for different versions of CESM. These in particular are based on daily data for 10-11cm soil moisture and sensible heat flux. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define directory where files are saved \n",
    "dataDir = '/Users/mdfowler/Documents/Analysis/Coupling_initial/Coupling_CAM6CLM5/processed_data/'\n",
    "\n",
    "# Filenames for CI(SM_10cm, SHFLX)\n",
    "CESM2_file = dataDir+'TerraLeg_coupling_shf_vs_soilm_1979-2006.nc'  \n",
    "CESM1_file = dataDir+'TerraLeg_coupling_shf_vs_soilm_CESM1.nc'\n",
    "CLM45_file = dataDir+'TerraLeg_coupling_shf_vs_soilm_CESM2-CLM45.nc'\n",
    "\n",
    "# Read those files in\n",
    "terraLeg_CESM2 = xr.open_dataset(CESM2_file, decode_times=False)\n",
    "terraLeg_CESM1 = xr.open_dataset(CESM1_file, decode_times=False)\n",
    "terraLeg_CLM45 = xr.open_dataset(CLM45_file, decode_times=False)\n",
    "\n",
    "# Save just CI to its own array \n",
    "terraCI_CESM2 = terraLeg_CESM2.mrsos_hfss_CI.values\n",
    "terraCI_CESM1 = terraLeg_CESM1.soilM_10_SHFLX_CI.values\n",
    "terraCI_CLM45 = terraLeg_CLM45.soilM_10_SHFLX_CI.values\n",
    "\n",
    "# Save lat/lon for model output\n",
    "latModel = terraLeg_CESM2.lat.values\n",
    "lonModel = terraLeg_CESM2.lon.values\n",
    "season   = terraLeg_CESM2.season.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Map comparison "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Let's try plotting a map of model CI, with circles of filled colors representing the flux tower estimates at a certain location.</b> <br>\n",
    "Whenever I make these plots, I use a function that creates a custom colormap to match what's been used in previous studies.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_cmap(colors, position=None, bit=False):\n",
    "    '''\n",
    "    make_cmap takes a list of tuples which contain RGB values. The RGB\n",
    "    values may either be in 8-bit [0 to 255] (in which bit must be set to\n",
    "    True when called) or arithmetic [0 to 1] (default). make_cmap returns\n",
    "    a cmap with equally spaced colors.\n",
    "    Arrange your tuples so that the first color is the lowest value for the\n",
    "    colorbar and the last is the highest.\n",
    "    position contains values from 0 to 1 to dictate the location of each color.\n",
    "    '''\n",
    "    \n",
    "    import matplotlib as mpl\n",
    "    import numpy as np\n",
    "    \n",
    "    bit_rgb = np.linspace(0,1,256)\n",
    "    if position == None:\n",
    "        position = np.linspace(0,1,len(colors))\n",
    "    else:\n",
    "        if len(position) != len(colors):\n",
    "            sys.exit(\"position length must be the same as colors\")\n",
    "        elif position[0] != 0 or position[-1] != 1:\n",
    "            sys.exit(\"position must start with 0 and end with 1\")\n",
    "    \n",
    "    if bit:\n",
    "        for i in range(len(colors)):\n",
    "            colors[i] = (bit_rgb[colors[i][0]],\n",
    "                         bit_rgb[colors[i][1]],\n",
    "                         bit_rgb[colors[i][2]])\n",
    "    \n",
    "    cdict = {'red':[], 'green':[], 'blue':[]}\n",
    "    for pos, color in zip(position, colors):\n",
    "        cdict['red'].append((pos, color[0], color[0]))\n",
    "        cdict['green'].append((pos, color[1], color[1]))\n",
    "        cdict['blue'].append((pos, color[2], color[2]))\n",
    "\n",
    "    cmap = mpl.colors.LinearSegmentedColormap('my_colormap',cdict,256)\n",
    "    return cmap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create a list of RGB tuples for terrestrial leg (SM, SHFLX)\n",
    "colorsList_SMvSHF = [(124,135,181), \n",
    "                     (107,109,161),\n",
    "                     (51,82,120),\n",
    "                     (49,114,127),\n",
    "                     (97,181,89),\n",
    "                     (200,218,102),\n",
    "                     (255,242,116),\n",
    "                     (238,164,58)] # This example uses the 8-bit RGB\n",
    "\n",
    "my_cmap_SMvSHF = make_cmap(colorsList_SMvSHF, bit=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a <i>very</i> good chance that this is *not* the most efficient way to map data to the color map I've created above. But I do at least know that it works. <br><br>\n",
    "So first, let's create a function that maps terrestrial CI to the color ranges defined above. <br>\n",
    "We'll also need to make the data cyclic for when we map it with cartopy. This is a safety step; otherwise, you can end up with a white stripe of 'missing' data at the dateline just because the plotting function doesn't realize the lons wrap all the way around. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_terraCI_colors(data):\n",
    "    # Empty array the same size as input data, to save color \n",
    "    colorsTerraLeg = np.copy(data)\n",
    "    colorsTerraLeg.fill(np.nan)\n",
    "    \n",
    "    # Use color bounds of previous work for easy comparison   \n",
    "    colorsTerraLeg[np.where(data<-20)]                  = 1\n",
    "    colorsTerraLeg[np.where((data>-20) & (data<=-15))]  = 2\n",
    "    colorsTerraLeg[np.where((data>-15) & (data<=-10))]  = 3\n",
    "    colorsTerraLeg[np.where((data>-10) & (data<=-5))]   = 4\n",
    "    colorsTerraLeg[np.where((data>-5)  & (data<=-2))]   = 5\n",
    "    colorsTerraLeg[np.where((data>-2)  & (data<=-1))]   = 6\n",
    "    colorsTerraLeg[np.where((data>-1)  & (data<=0))]    = 7\n",
    "    colorsTerraLeg[np.where(data>0)]                    = 8\n",
    "    \n",
    "    return(colorsTerraLeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-32ce97a1d946>:7: RuntimeWarning: invalid value encountered in less\n",
      "  colorsTerraLeg[np.where(data<-20)]                  = 1\n",
      "<ipython-input-17-32ce97a1d946>:8: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-20) & (data<=-15))]  = 2\n",
      "<ipython-input-17-32ce97a1d946>:8: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-20) & (data<=-15))]  = 2\n",
      "<ipython-input-17-32ce97a1d946>:9: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-15) & (data<=-10))]  = 3\n",
      "<ipython-input-17-32ce97a1d946>:9: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-15) & (data<=-10))]  = 3\n",
      "<ipython-input-17-32ce97a1d946>:10: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-10) & (data<=-5))]   = 4\n",
      "<ipython-input-17-32ce97a1d946>:10: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-10) & (data<=-5))]   = 4\n",
      "<ipython-input-17-32ce97a1d946>:11: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-5)  & (data<=-2))]   = 5\n",
      "<ipython-input-17-32ce97a1d946>:11: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-5)  & (data<=-2))]   = 5\n",
      "<ipython-input-17-32ce97a1d946>:12: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-2)  & (data<=-1))]   = 6\n",
      "<ipython-input-17-32ce97a1d946>:12: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-2)  & (data<=-1))]   = 6\n",
      "<ipython-input-17-32ce97a1d946>:13: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-1)  & (data<=0))]    = 7\n",
      "<ipython-input-17-32ce97a1d946>:13: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-1)  & (data<=0))]    = 7\n",
      "<ipython-input-17-32ce97a1d946>:14: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where(data>0)]                    = 8\n"
     ]
    }
   ],
   "source": [
    "# Make data cylic for JJA\n",
    "TerraLegCESM2_JJA_cyclic, lon_cyclic = cartopy.util.add_cyclic_point(terraCI_CESM2[1,:,:], lonModel)\n",
    "TerraLegCESM1_JJA_cyclic, lon_cyclic = cartopy.util.add_cyclic_point(terraCI_CESM1[1,:,:], lonModel)\n",
    "TerraLegCLM45_JJA_cyclic, lon_cyclic = cartopy.util.add_cyclic_point(terraCI_CLM45[1,:,:], lonModel)\n",
    "\n",
    "# Now get \"colorsTerraLeg\" (the color aliased version of TerraCI arrays)\n",
    "colorsTerraLeg_CESM2   = map_terraCI_colors(TerraLegCESM2_JJA_cyclic)\n",
    "colorsTerraLeg_CESM1   = map_terraCI_colors(TerraLegCESM1_JJA_cyclic)\n",
    "colorsTerraLeg_CLM45   = map_terraCI_colors(TerraLegCLM45_JJA_cyclic)\n",
    "#colorsTerraLeg_FLUXNET = map_terraCI_colors(terraCI_fluxnet[:,1])\n",
    "colorsTerraLeg_FLUXNET = map_terraCI_colors(terraCI_fluxnetConverted[:,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Plot JJA Terrestrial leg \n",
    "\n",
    "fig=plt.figure(figsize=(11, 8), facecolor='w', edgecolor='k')\n",
    "\n",
    "my_fontsize = 14\n",
    "\n",
    "## --- Set axes and color sections --- \n",
    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
    "ax.set_extent([-180,180,-60,90], crs=ccrs.PlateCarree())   #Set to global ot match previous version\n",
    "bounds = [1,2,3,4,5,6,7,8,9]\n",
    "norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)\n",
    "\n",
    "## --- Create Plot ---\n",
    "contour_plot = ax.pcolormesh(lon_cyclic,latModel,colorsTerraLeg_CESM2,norm=norm,\n",
    "                             cmap=my_cmap_SMvSHF,\n",
    "                             vmin=1,vmax=9)\n",
    "\n",
    "# Colorbar\n",
    "ax_position = ax.get_position()\n",
    "cbar_ax = fig.add_axes([ax_position.x0, ax_position.y0-0.1, ax_position.width, 0.06])\n",
    "cbar = plt.colorbar(contour_plot, orientation='horizontal',ticks=range(13),cax=cbar_ax)\n",
    "cbar.set_label('Coupling Index (SM, SHF) [W/m2]',fontsize=my_fontsize)\n",
    "cbar.ax.set_xticklabels(['<-20','-20','-15','-10','-5','-2','-1','0','>0'],fontsize=my_fontsize)  # vertically oriented colorbar\n",
    "\n",
    "#Additional options \n",
    "ax.set_title('CESM2 Terrestrial Coupling Index (SM, SHF): JJA\\n FLUXNET-based estimates in circles',fontsize=16)\n",
    "ax.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "# ----------- Add FluxNet measurements ------------ \n",
    "for iStation in range(len(dataFiles)):\n",
    "    if (np.isfinite(colorsTerraLeg_FLUXNET[iStation])==True):\n",
    "    \n",
    "        colorChoice = colorsList_SMvSHF[int(colorsTerraLeg_FLUXNET[iStation]-1)]\n",
    "\n",
    "        ax.plot(lon_fluxnet[iStation],lat_fluxnet[iStation], 'o',\n",
    "                markerfacecolor=colorChoice,\n",
    "                markeredgecolor='k',\n",
    "                markersize=7,\n",
    "                transform=ccrs.PlateCarree())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Plot JJA Terrestrial leg \n",
    "\n",
    "fig=plt.figure(figsize=(11, 8), facecolor='w', edgecolor='k')\n",
    "\n",
    "my_fontsize = 14\n",
    "\n",
    "## --- Set axes and color sections --- \n",
    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
    "ax.set_extent([-180,180,-60,90], crs=ccrs.PlateCarree())   #Set to global ot match previous version\n",
    "bounds = [1,2,3,4,5,6,7,8,9]\n",
    "norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)\n",
    "\n",
    "## --- Create Plot ---\n",
    "contour_plot = ax.pcolormesh(lon_cyclic,latModel,colorsTerraLeg_CESM1,norm=norm,\n",
    "                             cmap=my_cmap_SMvSHF,\n",
    "                             vmin=1,vmax=9)\n",
    "\n",
    "# Colorbar\n",
    "ax_position = ax.get_position()\n",
    "cbar_ax = fig.add_axes([ax_position.x0, ax_position.y0-0.1, ax_position.width, 0.06])\n",
    "cbar = plt.colorbar(contour_plot, orientation='horizontal',ticks=range(13),cax=cbar_ax)\n",
    "cbar.set_label('Coupling Index (SM, SHF) [W/m2]',fontsize=my_fontsize)\n",
    "cbar.ax.set_xticklabels(['<-20','-20','-15','-10','-5','-2','-1','0','>0'],fontsize=my_fontsize)  # vertically oriented colorbar\n",
    "\n",
    "#Additional options \n",
    "ax.set_title('CESM1 Terrestrial Coupling Index (SM, SHF): JJA\\n FLUXNET-based estimates in circles',fontsize=16)\n",
    "ax.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "# ----------- Add FluxNet measurements ------------ \n",
    "for iStation in range(len(dataFiles)):\n",
    "    if (np.isfinite(colorsTerraLeg_FLUXNET[iStation])==True):\n",
    "    \n",
    "        colorChoice = colorsList_SMvSHF[int(colorsTerraLeg_FLUXNET[iStation]-1)]\n",
    "\n",
    "        ax.plot(lon_fluxnet[iStation],lat_fluxnet[iStation], 'o',\n",
    "                markerfacecolor=colorChoice,\n",
    "                markeredgecolor='k',\n",
    "                markersize=7,\n",
    "                transform=ccrs.PlateCarree())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Plot JJA Terrestrial leg \n",
    "\n",
    "fig=plt.figure(figsize=(11, 8), facecolor='w', edgecolor='k')\n",
    "\n",
    "my_fontsize = 14\n",
    "\n",
    "## --- Set axes and color sections --- \n",
    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
    "ax.set_extent([-180,180,-60,90], crs=ccrs.PlateCarree())   #Set to global ot match previous version\n",
    "bounds = [1,2,3,4,5,6,7,8,9]\n",
    "norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)\n",
    "\n",
    "## --- Create Plot ---\n",
    "contour_plot = ax.pcolormesh(lon_cyclic,latModel,colorsTerraLeg_CLM45,norm=norm,\n",
    "                             cmap=my_cmap_SMvSHF,\n",
    "                             vmin=1,vmax=9)\n",
    "\n",
    "# Colorbar\n",
    "ax_position = ax.get_position()\n",
    "cbar_ax = fig.add_axes([ax_position.x0, ax_position.y0-0.1, ax_position.width, 0.06])\n",
    "cbar = plt.colorbar(contour_plot, orientation='horizontal',ticks=range(13),cax=cbar_ax)\n",
    "cbar.set_label('Coupling Index (SM, SHF) [W/m2]',fontsize=my_fontsize)\n",
    "cbar.ax.set_xticklabels(['<-20','-20','-15','-10','-5','-2','-1','0','>0'],fontsize=my_fontsize)  # vertically oriented colorbar\n",
    "\n",
    "#Additional options \n",
    "ax.set_title('CLM45+CAM6 Terrestrial Coupling Index (SM, SHF): JJA\\n FLUXNET-based estimates in circles',fontsize=16)\n",
    "ax.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "# ----------- Add FluxNet measurements ------------ \n",
    "for iStation in range(len(dataFiles)):\n",
    "    if (np.isfinite(colorsTerraLeg_FLUXNET[iStation])==True):\n",
    "    \n",
    "        colorChoice = colorsList_SMvSHF[int(colorsTerraLeg_FLUXNET[iStation]-1)]\n",
    "\n",
    "        ax.plot(lon_fluxnet[iStation],lat_fluxnet[iStation], 'o',\n",
    "                markerfacecolor=colorChoice,\n",
    "                markeredgecolor='k',\n",
    "                markersize=7,\n",
    "                transform=ccrs.PlateCarree())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 RMSE of JJA coupling index "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compute the RMSE over all points that are found to have valid coupling data. <br><br>\n",
    "So for each FLUXNET station, we need to: <br>\n",
    "(1) check that it has CI data; <br>\n",
    "(2) get the lat/lon of the model gridcell that contains the tower; and <br>\n",
    "(3) take the difference between modeled and observed terrestrial CI. <br> <br>\n",
    "From there, we square the differences at each site, sum them all together, and divide by the number of sites used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define empty arrays to save differences in \n",
    "terraCI_CESM2diff = np.full([len(dataFiles)], np.nan)\n",
    "terraCI_CESM1diff = np.full([len(dataFiles)], np.nan)\n",
    "terraCI_CLM45diff = np.full([len(dataFiles)], np.nan)\n",
    "\n",
    "# Count number of stations used...\n",
    "nValidStation = 0\n",
    "\n",
    "# Consider each station individually \n",
    "for iStation in range(len(dataFiles)):\n",
    "    \n",
    "    # Isolate station CI for JJA\n",
    "    #fluxnetCI = terraCI_fluxnet[iStation,1]\n",
    "    fluxnetCI = terraCI_fluxnetConverted[iStation,1]\n",
    "\n",
    "    # Check that there's data here (and at least 3 months went into JJA avg CI)\n",
    "    # ------------------------------------------------------------------------\n",
    "    if ( (np.isnan(fluxnetCI)==False) ): \n",
    "        \n",
    "        # Get model gridcells that match fluxnet site \n",
    "        iLat        = np.where( (latModel>=(lat_fluxnet[iStation]-0.4712)) & \n",
    "                                (latModel<=(lat_fluxnet[iStation]+0.4712)) )[0]\n",
    "         # Convert longitude of point into 0-360 range \n",
    "        lonSel_flux = (lon_fluxnet[iStation] + 360) % 360                \n",
    "        iLon        = np.where( (lonModel>=(lonSel_flux-0.625)) & \n",
    "                                (lonModel<=(lonSel_flux+0.625)) )[0]\n",
    "        \n",
    "        gridValue_CESM2 = terraCI_CESM2[1,iLat,iLon]\n",
    "        gridValue_CESM1 = terraCI_CESM1[1,iLat,iLon]\n",
    "        gridValue_CLM45 = terraCI_CLM45[1,iLat,iLon]\n",
    "        \n",
    "        # Compare to fluxnet-based estimate\n",
    "        terraCI_CESM2diff[iStation] = gridValue_CESM2 - fluxnetCI\n",
    "        terraCI_CESM1diff[iStation] = gridValue_CESM1 - fluxnetCI\n",
    "        terraCI_CLM45diff[iStation] = gridValue_CLM45 - fluxnetCI\n",
    "\n",
    "        # Increment counter for number of stations used \n",
    "        nValidStation =  nValidStation+1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations considered: 115 \n",
      "\n",
      "RMSE between model terrestrial CI and fluxnet-based observations:\n",
      "     CESM2 RMSE:    7.873\n",
      "     CESM1 RMSE:    8.378\n",
      "     CLM45 RMSE:   10.577\n",
      "\n",
      " Mean absolute bias (|model - observations|)\n",
      "     CESM2 Bias:    5.900\n",
      "     CESM1 Bias:    6.679\n",
      "     CLM45 Bias:    8.699\n",
      "\n",
      " Mean Bias (model - observations):\n",
      "     CESM2 Bias:   -1.744\n",
      "     CESM1 Bias:   -3.275\n",
      "     CLM45 Bias:   -6.578\n"
     ]
    }
   ],
   "source": [
    "# Compute RMSE per model version \n",
    "RMSE_CESM2 = np.sqrt(np.nansum(terraCI_CESM2diff**2) / nValidStation)\n",
    "RMSE_CESM1 = np.sqrt(np.nansum(terraCI_CESM1diff**2) / nValidStation)\n",
    "RMSE_CLM45 = np.sqrt(np.nansum(terraCI_CLM45diff**2) / nValidStation)\n",
    "\n",
    "# Print results of RMSE analysis \n",
    "print('Number of stations considered: %i \\n' % nValidStation)\n",
    "print('RMSE between model terrestrial CI and fluxnet-based observations:')\n",
    "print('     CESM2 RMSE: %8.3f' % RMSE_CESM2)\n",
    "print('     CESM1 RMSE: %8.3f' % RMSE_CESM1)\n",
    "print('     CLM45 RMSE: %8.3f' % RMSE_CLM45)\n",
    "\n",
    "print('\\n Mean absolute bias (|model - observations|)')\n",
    "print('     CESM2 Bias: %8.3f' % np.nanmean(np.abs(terraCI_CESM2diff)))\n",
    "print('     CESM1 Bias: %8.3f' % np.nanmean(np.abs(terraCI_CESM1diff)))\n",
    "print('     CLM45 Bias: %8.3f' % np.nanmean(np.abs(terraCI_CLM45diff)))\n",
    "\n",
    "print('\\n Mean Bias (model - observations):')\n",
    "print('     CESM2 Bias: %8.3f' % np.nanmean(terraCI_CESM2diff))\n",
    "print('     CESM1 Bias: %8.3f' % np.nanmean(terraCI_CESM1diff))\n",
    "print('     CLM45 Bias: %8.3f' % np.nanmean(terraCI_CLM45diff))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check for any major outliers?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM2 RMSE (including outliers): 7.873\n",
      "CESM2 RMSE (excluding outliers): 6.511\n",
      "Number of points removed:  4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-9aea31d68ee3>:20: RuntimeWarning: invalid value encountered in less_equal\n",
      "  iExclude = np.where( (terraCI_CESM2diff<=pct1) | (terraCI_CESM2diff>=pct99) )[0]\n",
      "<ipython-input-31-9aea31d68ee3>:20: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  iExclude = np.where( (terraCI_CESM2diff<=pct1) | (terraCI_CESM2diff>=pct99) )[0]\n",
      "<ipython-input-31-9aea31d68ee3>:24: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  ikeep = np.where( (terraCI_CESM2diff>= pct1) & (terraCI_CESM2diff<=pct99))[0]\n",
      "<ipython-input-31-9aea31d68ee3>:24: RuntimeWarning: invalid value encountered in less_equal\n",
      "  ikeep = np.where( (terraCI_CESM2diff>= pct1) & (terraCI_CESM2diff<=pct99))[0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------\n",
    "# CESM2 simulation... \n",
    "# -----------------------\n",
    "\n",
    "# Plot difference in CI at each station as a dot \n",
    "plt.plot(np.arange(205), terraCI_CESM2diff,'*')\n",
    "plt.xlabel('Station index',fontsize=12)\n",
    "plt.ylabel('CI difference',fontsize=12)\n",
    "plt.title('Terrestrial CI difference: \\nCESM2 - FLUXNET', fontsize=14)\n",
    "\n",
    "# Get percentiles too and plot as horizontal lines\n",
    "pct1  = np.nanpercentile(terraCI_CESM2diff, 1)\n",
    "pct99 = np.nanpercentile(terraCI_CESM2diff, 99)\n",
    "plt.axhline(pct1,  color='k')\n",
    "plt.axhline(pct99, color='k')\n",
    "plt.ylim([-40,30])\n",
    "plt.legend(['Station difference','1st/99th percentile'],loc='upper left')\n",
    "\n",
    "# Print RMSE with vs. without points outside this boundary...\n",
    "iExclude = np.where( (terraCI_CESM2diff<=pct1) | (terraCI_CESM2diff>=pct99) )[0]\n",
    "indexes  = np.arange(205)\n",
    "plt.plot(indexes[iExclude], terraCI_CESM2diff[iExclude],'r*')\n",
    "\n",
    "ikeep = np.where( (terraCI_CESM2diff>= pct1) & (terraCI_CESM2diff<=pct99))[0]\n",
    "RMSE_CESM2exclude = np.sqrt(np.nansum(terraCI_CESM2diff[ikeep]**2) / len(ikeep) )\n",
    "\n",
    "\n",
    "print('CESM2 RMSE (including outliers): %.3f' % RMSE_CESM2)\n",
    "print('CESM2 RMSE (excluding outliers): %.3f' % RMSE_CESM2exclude)\n",
    "print('Number of points removed: ', nValidStation - len(ikeep))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CESM1 RMSE (including outliers): 8.378\n",
      "CESM1 RMSE (excluding outliers): 7.407\n",
      "Number of points removed:  4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-33-9cdc027de0ab>:20: RuntimeWarning: invalid value encountered in less_equal\n",
      "  iExclude = np.where( (terraCI_CESM1diff<=pct1) | (terraCI_CESM1diff>=pct99) )[0]\n",
      "<ipython-input-33-9cdc027de0ab>:20: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  iExclude = np.where( (terraCI_CESM1diff<=pct1) | (terraCI_CESM1diff>=pct99) )[0]\n",
      "<ipython-input-33-9cdc027de0ab>:24: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  ikeep = np.where( (terraCI_CESM1diff>= pct1) & (terraCI_CESM1diff<=pct99))[0]\n",
      "<ipython-input-33-9cdc027de0ab>:24: RuntimeWarning: invalid value encountered in less_equal\n",
      "  ikeep = np.where( (terraCI_CESM1diff>= pct1) & (terraCI_CESM1diff<=pct99))[0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------\n",
    "# CESM1 simulation... \n",
    "# -----------------------\n",
    "\n",
    "# Plot difference in CI at each station as a dot \n",
    "plt.plot(np.arange(205), terraCI_CESM1diff,'*')\n",
    "plt.xlabel('Station index',fontsize=12)\n",
    "plt.ylabel('CI difference',fontsize=12)\n",
    "plt.title('Terrestrial CI difference: \\nCESM1 - FLUXNET', fontsize=14)\n",
    "\n",
    "# Get percentiles too and plot as horizontal lines\n",
    "pct1  = np.nanpercentile(terraCI_CESM1diff, 1)\n",
    "pct99 = np.nanpercentile(terraCI_CESM1diff, 99)\n",
    "plt.axhline(pct1,  color='k')\n",
    "plt.axhline(pct99, color='k')\n",
    "plt.ylim([-40,30])\n",
    "plt.legend(['Station difference','1st/99th percentile'],loc='upper left')\n",
    "\n",
    "# Print RMSE with vs. without points outside this boundary...\n",
    "iExclude = np.where( (terraCI_CESM1diff<=pct1) | (terraCI_CESM1diff>=pct99) )[0]\n",
    "indexes  = np.arange(205)\n",
    "plt.plot(indexes[iExclude], terraCI_CESM1diff[iExclude],'r*')\n",
    "\n",
    "ikeep = np.where( (terraCI_CESM1diff>= pct1) & (terraCI_CESM1diff<=pct99))[0]\n",
    "RMSE_CESM1exclude = np.sqrt(np.nansum(terraCI_CESM1diff[ikeep]**2) / len(ikeep) )\n",
    "\n",
    "print('CESM1 RMSE (including outliers): %.3f' % RMSE_CESM1)\n",
    "print('CESM1 RMSE (excluding outliers): %.3f' % RMSE_CESM1exclude)\n",
    "print('Number of points removed: ', nValidStation - len(ikeep))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLM45 RMSE (including outliers): 10.577\n",
      "CLM45 RMSE (excluding outliers): 9.725\n",
      "Number of points removed:  4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-34-c344a6b2203a>:20: RuntimeWarning: invalid value encountered in less_equal\n",
      "  iExclude = np.where( (terraCI_CLM45diff<=pct1) | (terraCI_CLM45diff>=pct99) )[0]\n",
      "<ipython-input-34-c344a6b2203a>:20: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  iExclude = np.where( (terraCI_CLM45diff<=pct1) | (terraCI_CLM45diff>=pct99) )[0]\n",
      "<ipython-input-34-c344a6b2203a>:24: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  ikeep = np.where( (terraCI_CLM45diff>= pct1) & (terraCI_CLM45diff<=pct99))[0]\n",
      "<ipython-input-34-c344a6b2203a>:24: RuntimeWarning: invalid value encountered in less_equal\n",
      "  ikeep = np.where( (terraCI_CLM45diff>= pct1) & (terraCI_CLM45diff<=pct99))[0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------\n",
    "# CESM1 simulation... \n",
    "# -----------------------\n",
    "\n",
    "# Plot difference in CI at each station as a dot \n",
    "plt.plot(np.arange(205), terraCI_CLM45diff,'*')\n",
    "plt.xlabel('Station index',fontsize=12)\n",
    "plt.ylabel('CI difference',fontsize=12)\n",
    "plt.title('Terrestrial CI difference: \\nCLM4.5 - FLUXNET', fontsize=14)\n",
    "\n",
    "# Get percentiles too and plot as horizontal lines\n",
    "pct1  = np.nanpercentile(terraCI_CLM45diff, 1)\n",
    "pct99 = np.nanpercentile(terraCI_CLM45diff, 99)\n",
    "plt.axhline(pct1,  color='k')\n",
    "plt.axhline(pct99, color='k')\n",
    "plt.ylim([-40,30])\n",
    "plt.legend(['Station difference','1st/99th percentile'],loc='upper left')\n",
    "\n",
    "# Print RMSE with vs. without points outside this boundary...\n",
    "iExclude = np.where( (terraCI_CLM45diff<=pct1) | (terraCI_CLM45diff>=pct99) )[0]\n",
    "indexes  = np.arange(205)\n",
    "plt.plot(indexes[iExclude], terraCI_CLM45diff[iExclude],'r*')\n",
    "\n",
    "ikeep = np.where( (terraCI_CLM45diff>= pct1) & (terraCI_CLM45diff<=pct99))[0]\n",
    "RMSE_CLMexclude = np.sqrt(np.nansum(terraCI_CLM45diff[ikeep]**2) / len(ikeep) )\n",
    "\n",
    "print('CLM45 RMSE (including outliers): %.3f' % RMSE_CLM45)\n",
    "print('CLM45 RMSE (excluding outliers): %.3f' % RMSE_CLMexclude)\n",
    "print('Number of points removed: ', nValidStation - len(ikeep))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What station is a strong negative outlier in all three sims?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-26.97641744 -30.92463442] [-17.39996166 -29.15043349] [-17.6964566  -31.77732546]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb6ab217550>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What station is a strong negative outlier in all three sims? \n",
    "print(terraCI_CESM2diff[20:22], terraCI_CESM1diff[20:22], terraCI_CLM45diff[20:22])\n",
    "\n",
    "# Index = 21... Where is that? \n",
    "\n",
    "# --- Plot JJA Terrestrial leg \n",
    "\n",
    "fig=plt.figure(figsize=(11, 8), facecolor='w', edgecolor='k')\n",
    "\n",
    "my_fontsize = 14\n",
    "\n",
    "## --- Set axes and color sections --- \n",
    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
    "ax.set_extent([-40,100,-40,30], crs=ccrs.PlateCarree())   #Set to global ot match previous version\n",
    "bounds = [1,2,3,4,5,6,7,8,9]\n",
    "norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)\n",
    "\n",
    "## --- Create Plot ---\n",
    "contour_plot = ax.pcolormesh(lon_cyclic,latModel,colorsTerraLeg_CESM2,norm=norm,\n",
    "                             cmap=my_cmap_SMvSHF,\n",
    "                             vmin=1,vmax=9)\n",
    "\n",
    "# Colorbar\n",
    "ax_position = ax.get_position()\n",
    "cbar_ax = fig.add_axes([ax_position.x0, ax_position.y0-0.1, ax_position.width, 0.06])\n",
    "cbar = plt.colorbar(contour_plot, orientation='horizontal',ticks=range(13),cax=cbar_ax)\n",
    "cbar.set_label('Coupling Index (SM, SHF) [W/m2]',fontsize=my_fontsize)\n",
    "cbar.ax.set_xticklabels(['<-20','-20','-15','-10','-5','-2','-1','0','>0'],fontsize=my_fontsize)  # vertically oriented colorbar\n",
    "\n",
    "#Additional options \n",
    "ax.set_title('CESM2 Terrestrial Coupling Index (SM, SHF): JJA\\n FLUXNET-based estimates in circles',fontsize=16)\n",
    "ax.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "# ----------- Add FluxNet measurements ------------ \n",
    "iStation = 21\n",
    "    \n",
    "colorChoice = colorsList_SMvSHF[int(colorsTerraLeg_FLUXNET[iStation]-1)]\n",
    "\n",
    "ax.plot(lon_fluxnet[iStation],lat_fluxnet[iStation], 'o',\n",
    "        markerfacecolor=colorChoice,\n",
    "        markeredgecolor='k',\n",
    "        markersize=7,\n",
    "        transform=ccrs.PlateCarree())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wonder if that's because of SH or SM... would need to look at timeseries of each variable.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Compare to land-only simulations with various versions of CLM\n",
    "These all use SP (satellite phenology) and historical forcing, without any other options turned on. Climatology is from the Global Soil Wetness Program (GSWP3V1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up data directory and filename structures \n",
    "dataDir = '/Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/moreCLMversions/'\n",
    "\n",
    "clm4case  = 'clm40sp_clm50d001_1deg_GSWP3V1_hist.clm2.h5.'\n",
    "clm45case = 'clm45sp_clm50d001_1deg_GSWP3V1_hist.clm2.h5.'\n",
    "clm5case  = 'clm50sp_clm50dev001_1deg_GSWP3V1_hist.clm2.h6.'\n",
    "\n",
    "varNames = ['FSH.','SOILWATER_10CM.']\n",
    "\n",
    "timeNames = ['19890101-19981231.nc', '19990101-20081231.nc', '20090101-20141231.nc']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File names... \n",
      "CLM4: /Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/moreCLMversions/clm40sp_clm50d001_1deg_GSWP3V1_hist.clm2.h5.FSH.19890101-19981231.nc \n",
      "CLM4.5: /Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/moreCLMversions/clm45sp_clm50d001_1deg_GSWP3V1_hist.clm2.h5.FSH.19890101-19981231.nc \n",
      "CLM5: /Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/moreCLMversions/clm50sp_clm50dev001_1deg_GSWP3V1_hist.clm2.h6.FSH.19890101-19981231.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-36-77cdf956614b>:14: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  clm4_ds['time'] = clm4_ds.indexes['time'].to_datetimeindex()\n",
      "<ipython-input-36-77cdf956614b>:17: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  clm45_ds['time'] = clm45_ds.indexes['time'].to_datetimeindex()\n",
      "<ipython-input-36-77cdf956614b>:20: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  clm5_ds['time'] = clm5_ds.indexes['time'].to_datetimeindex()\n",
      "<ipython-input-36-77cdf956614b>:28: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  clm4SM_ds['time']         = clm4SM_ds.indexes['time'].to_datetimeindex()\n",
      "<ipython-input-36-77cdf956614b>:32: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  clm45SM_ds['time']         = clm45SM_ds.indexes['time'].to_datetimeindex()\n",
      "<ipython-input-36-77cdf956614b>:36: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  clm5SM_ds['time']         = clm5SM_ds.indexes['time'].to_datetimeindex()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with file 1 of 3\n",
      "File names... \n",
      "CLM4: /Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/moreCLMversions/clm40sp_clm50d001_1deg_GSWP3V1_hist.clm2.h5.FSH.19990101-20081231.nc \n",
      "CLM4.5: /Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/moreCLMversions/clm45sp_clm50d001_1deg_GSWP3V1_hist.clm2.h5.FSH.19990101-20081231.nc \n",
      "CLM5: /Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/moreCLMversions/clm50sp_clm50dev001_1deg_GSWP3V1_hist.clm2.h6.FSH.19990101-20081231.nc\n",
      "Done with file 2 of 3\n",
      "File names... \n",
      "CLM4: /Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/moreCLMversions/clm40sp_clm50d001_1deg_GSWP3V1_hist.clm2.h5.FSH.20090101-20141231.nc \n",
      "CLM4.5: /Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/moreCLMversions/clm45sp_clm50d001_1deg_GSWP3V1_hist.clm2.h5.FSH.20090101-20141231.nc \n",
      "CLM5: /Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/moreCLMversions/clm50sp_clm50dev001_1deg_GSWP3V1_hist.clm2.h6.FSH.20090101-20141231.nc\n",
      "Done with file 3 of 3\n"
     ]
    }
   ],
   "source": [
    "# ---------------------------------------------------------\n",
    "# Read in files and get time in usable format for each one \n",
    "# ---------------------------------------------------------\n",
    "\n",
    "for iTime in range(len(timeNames)):  \n",
    "        # Read in SHFLX first...\n",
    "        clm4file  = dataDir+clm4case+ 'FSH.'+timeNames[iTime]\n",
    "        clm45file = dataDir+clm45case+'FSH.'+timeNames[iTime]\n",
    "        clm5file  = dataDir+clm5case+ 'FSH.'+timeNames[iTime]\n",
    "        \n",
    "        #print('File names... \\nCLM4: %s \\nCLM4.5: %s \\nCLM5: %s' % (clm4file, clm45file,clm5file))\n",
    "\n",
    "        with xr.open_dataset(clm4file, decode_times=True) as clm4_ds: \n",
    "            clm4_ds['time'] = clm4_ds.indexes['time'].to_datetimeindex()\n",
    "        \n",
    "        with xr.open_dataset(clm45file, decode_times=True) as clm45_ds:\n",
    "            clm45_ds['time'] = clm45_ds.indexes['time'].to_datetimeindex()\n",
    "          \n",
    "        with xr.open_dataset(clm5file, decode_times=True) as clm5_ds:\n",
    "            clm5_ds['time'] = clm5_ds.indexes['time'].to_datetimeindex()\n",
    "            \n",
    "        # Now open the soil water files and add that DS to this one... \n",
    "        clm4fileSM  = dataDir+clm4case+ 'SOILWATER_10CM.'+timeNames[iTime]\n",
    "        clm45fileSM = dataDir+clm45case+'SOILWATER_10CM.'+timeNames[iTime]\n",
    "        clm5fileSM  = dataDir+clm5case+ 'SOILWATER_10CM.'+timeNames[iTime]\n",
    "        \n",
    "        with xr.open_dataset(clm4fileSM, decode_times=True) as clm4SM_ds: \n",
    "            clm4SM_ds['time']         = clm4SM_ds.indexes['time'].to_datetimeindex()\n",
    "            clm4_ds['SOILWATER_10CM'] = (('time','lat','lon'), clm4SM_ds.SOILWATER_10CM)\n",
    "        \n",
    "        with xr.open_dataset(clm45fileSM, decode_times=True) as clm45SM_ds:\n",
    "            clm45SM_ds['time']         = clm45SM_ds.indexes['time'].to_datetimeindex()\n",
    "            clm45_ds['SOILWATER_10CM'] = (('time','lat','lon'), clm45SM_ds.SOILWATER_10CM)\n",
    "          \n",
    "        with xr.open_dataset(clm5fileSM, decode_times=True) as clm5SM_ds:\n",
    "            clm5SM_ds['time']         = clm5SM_ds.indexes['time'].to_datetimeindex()\n",
    "            clm5_ds['SOILWATER_10CM'] = (('time','lat','lon'), clm5SM_ds.SOILWATER_10CM)\n",
    "            \n",
    "        # Now concatenate everything together.     \n",
    "        if iTime==0: \n",
    "            CLM4ds  = clm4_ds \n",
    "            CLM45ds = clm45_ds\n",
    "            CLM5ds  = clm5_ds\n",
    "        else: \n",
    "            CLM4ds  = xr.concat([CLM4ds,  clm4_ds],   dim=\"time\")\n",
    "            CLM45ds = xr.concat([CLM45ds, clm45_ds],  dim=\"time\")\n",
    "            CLM5ds  = xr.concat([CLM5ds,  clm5_ds],   dim=\"time\")\n",
    "            \n",
    "        print('Done with file %i of %i' % (iTime+1, len(timeNames)))\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should we limit time period? It starts much later, in 1989 rather than 1979, so maybe not. Run out to 2014 instead of 2006 to get at least similar number of years, if not quite the same period... \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with land only CLM4   CI calculation\n",
      "Done with land only CLM4.5 CI calculation\n",
      "Done with land only CLM5   CI calculation\n"
     ]
    }
   ],
   "source": [
    "# --- Compute coupling index between soil moisture and sensible heat flux (terrestrial leg) --- # \n",
    "\n",
    "terraLeg_lndOnly_CLM4  = cm.metrics.CouplingIndex()\n",
    "terraLeg_lndOnly_CLM4  = terraLeg_lndOnly_CLM4.compute(CLM4ds, xname='SOILWATER_10CM',yname='FSH',averaging='season')\n",
    "print('Done with land only CLM4   CI calculation')\n",
    "\n",
    "terraLeg_lndOnly_CLM45 = cm.metrics.CouplingIndex()\n",
    "terraLeg_lndOnly_CLM45 = terraLeg_lndOnly_CLM45.compute(CLM45ds, xname='SOILWATER_10CM',yname='FSH',averaging='season')\n",
    "print('Done with land only CLM4.5 CI calculation')\n",
    "\n",
    "terraLeg_lndOnly_CLM5  = cm.metrics.CouplingIndex()\n",
    "terraLeg_lndOnly_CLM5  = terraLeg_lndOnly_CLM5.compute(CLM5ds, xname='SOILWATER_10CM',yname='FSH',averaging='season')\n",
    "print('Done with land only CLM5   CI calculation')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-32ce97a1d946>:7: RuntimeWarning: invalid value encountered in less\n",
      "  colorsTerraLeg[np.where(data<-20)]                  = 1\n",
      "<ipython-input-17-32ce97a1d946>:8: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-20) & (data<=-15))]  = 2\n",
      "<ipython-input-17-32ce97a1d946>:8: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-20) & (data<=-15))]  = 2\n",
      "<ipython-input-17-32ce97a1d946>:9: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-15) & (data<=-10))]  = 3\n",
      "<ipython-input-17-32ce97a1d946>:9: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-15) & (data<=-10))]  = 3\n",
      "<ipython-input-17-32ce97a1d946>:10: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-10) & (data<=-5))]   = 4\n",
      "<ipython-input-17-32ce97a1d946>:10: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-10) & (data<=-5))]   = 4\n",
      "<ipython-input-17-32ce97a1d946>:11: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-5)  & (data<=-2))]   = 5\n",
      "<ipython-input-17-32ce97a1d946>:11: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-5)  & (data<=-2))]   = 5\n",
      "<ipython-input-17-32ce97a1d946>:12: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-2)  & (data<=-1))]   = 6\n",
      "<ipython-input-17-32ce97a1d946>:12: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-2)  & (data<=-1))]   = 6\n",
      "<ipython-input-17-32ce97a1d946>:13: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-1)  & (data<=0))]    = 7\n",
      "<ipython-input-17-32ce97a1d946>:13: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-1)  & (data<=0))]    = 7\n",
      "<ipython-input-17-32ce97a1d946>:14: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where(data>0)]                    = 8\n"
     ]
    }
   ],
   "source": [
    "# Pull out JJA value \n",
    "terraLeg_lndOnlyCLM4_JJA  = terraLeg_lndOnly_CLM4.SOILWATER_10CM_FSH_CI.values[1,:,:]\n",
    "terraLeg_lndOnlyCLM45_JJA = terraLeg_lndOnly_CLM45.SOILWATER_10CM_FSH_CI.values[1,:,:]\n",
    "terraLeg_lndOnlyCLM5_JJA  = terraLeg_lndOnly_CLM5.SOILWATER_10CM_FSH_CI.values[1,:,:]\n",
    "\n",
    "# Make data cylic for JJA\n",
    "terraLeg_lndOnlyCLM4_JJAcyclic,  lon_cyclic = cartopy.util.add_cyclic_point(terraLeg_lndOnlyCLM4_JJA,  CLM4ds.lon.values)\n",
    "terraLeg_lndOnlyCLM45_JJAcyclic, lon_cyclic = cartopy.util.add_cyclic_point(terraLeg_lndOnlyCLM45_JJA, CLM45ds.lon.values)\n",
    "terraLeg_lndOnlyCLM5_JJAcyclic,  lon_cyclic = cartopy.util.add_cyclic_point(terraLeg_lndOnlyCLM5_JJA,  CLM5ds.lon.values)\n",
    "\n",
    "# Now get \"colorsTerraLeg\" (the color aliased version of TerraCI arrays)\n",
    "colorsTerraLeg_lndOnlyCLM4    = map_terraCI_colors(terraLeg_lndOnlyCLM4_JJAcyclic)\n",
    "colorsTerraLeg_lndOnlyCLM45   = map_terraCI_colors(terraLeg_lndOnlyCLM45_JJAcyclic)\n",
    "colorsTerraLeg_lndOnlyCLM5    = map_terraCI_colors(terraLeg_lndOnlyCLM5_JJAcyclic)\n",
    "\n",
    "# Also pull out lat/lon\n",
    "lat_lndOnly = CLM5ds.lat.values\n",
    "lon_lndOnly = CLM5ds.lon.values\n",
    "lon         = lon_cyclic\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RMSE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define empty arrays to save differences in \n",
    "terraCI_lndOnlyDiff_CLM4  = np.full([len(dataFiles)], np.nan)\n",
    "terraCI_lndOnlyDiff_CLM45 = np.full([len(dataFiles)], np.nan)\n",
    "terraCI_lndOnlyDiff_CLM5  = np.full([len(dataFiles)], np.nan)\n",
    "\n",
    "# Count number of stations used...\n",
    "nValidStation_lnd = 0\n",
    "\n",
    "# Consider each station individually \n",
    "for iStation in range(len(dataFiles)):\n",
    "    \n",
    "    # Isolate station CI for JJA\n",
    "    fluxnetCI = terraCI_fluxnetConverted[iStation,1]\n",
    "\n",
    "    # Check that there's data here (and at least 3 months went into JJA avg CI)\n",
    "    # ------------------------------------------------------------------------\n",
    "    if ( (np.isnan(fluxnetCI)==False) ): \n",
    "        \n",
    "        # Get model gridcells that match fluxnet site \n",
    "        iLat        = np.where( (lat_lndOnly>=(lat_fluxnet[iStation]-0.4712)) & \n",
    "                                (lat_lndOnly<=(lat_fluxnet[iStation]+0.4712)) )[0]\n",
    "         # Convert longitude of point into 0-360 range \n",
    "        lonSel_flux = (lon_fluxnet[iStation] + 360) % 360                \n",
    "        iLon        = np.where( (lon_lndOnly>=(lonSel_flux-0.625)) & \n",
    "                                (lon_lndOnly<=(lonSel_flux+0.625)) )[0]\n",
    "        \n",
    "        gridValue_lndOnly_CLM4 = terraLeg_lndOnlyCLM4_JJA[iLat,iLon]\n",
    "        gridValue_lndOnly_CLM45= terraLeg_lndOnlyCLM45_JJA[iLat,iLon]\n",
    "        gridValue_lndOnly_CLM5 = terraLeg_lndOnlyCLM5_JJA[iLat,iLon]\n",
    "\n",
    "        # Compare to fluxnet-based estimate\n",
    "        terraCI_lndOnlyDiff_CLM4[iStation]  = gridValue_lndOnly_CLM4  - fluxnetCI\n",
    "        terraCI_lndOnlyDiff_CLM45[iStation] = gridValue_lndOnly_CLM45 - fluxnetCI\n",
    "        terraCI_lndOnlyDiff_CLM5[iStation]  = gridValue_lndOnly_CLM5  - fluxnetCI\n",
    "\n",
    "        # Increment counter for number of stations used \n",
    "        nValidStation_lnd =  nValidStation_lnd+1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations considered: 115 \n",
      "\n",
      "RMSE between model terrestrial CI and fluxnet-based observations:\n",
      "     CESM2 RMSE:    7.873\n",
      "     CESM1 RMSE:    8.378\n",
      "     CLM45 RMSE:   10.577\n",
      "\n",
      "     LandOnly-CLM4   RMSE:  8.733\n",
      "     LandOnly-CLM4.5 RMSE:  8.589\n",
      "     LandOnly-CLM5   RMSE:  7.572\n",
      "\n",
      " Mean absolute bias (|model - observations|)\n",
      "     CESM2 Bias:    5.900\n",
      "     CESM1 Bias:    6.679\n",
      "     CLM45 Bias:    8.699\n",
      "\n",
      "     LandOnly-CLM4   RMSE:  6.139\n",
      "     LandOnly-CLM4.5 RMSE:  5.858\n",
      "     LandOnly-CLM5   RMSE:  5.551\n"
     ]
    }
   ],
   "source": [
    "# Print full table...\n",
    "# Compute RMSE per model version \n",
    "RMSE_lndCLM4  = np.sqrt(np.nansum(terraCI_lndOnlyDiff_CLM4**2) / nValidStation)\n",
    "RMSE_lndCLM45 = np.sqrt(np.nansum(terraCI_lndOnlyDiff_CLM45**2) / nValidStation)\n",
    "RMSE_lndCLM5  = np.sqrt(np.nansum(terraCI_lndOnlyDiff_CLM5**2) / nValidStation)\n",
    "\n",
    "# Print results of RMSE analysis \n",
    "print('Number of stations considered: %i \\n' % nValidStation)\n",
    "print('RMSE between model terrestrial CI and fluxnet-based observations:')\n",
    "print('     CESM2 RMSE: %8.3f' % RMSE_CESM2)\n",
    "print('     CESM1 RMSE: %8.3f' % RMSE_CESM1)\n",
    "print('     CLM45 RMSE: %8.3f' % RMSE_CLM45)\n",
    "print('')\n",
    "print('     LandOnly-CLM4   RMSE: %6.3f' % RMSE_lndCLM4)\n",
    "print('     LandOnly-CLM4.5 RMSE: %6.3f' % RMSE_lndCLM45)\n",
    "print('     LandOnly-CLM5   RMSE: %6.3f' % RMSE_lndCLM5)\n",
    "\n",
    "\n",
    "# Print mean absolute bias: \n",
    "print('\\n Mean absolute bias (|model - observations|)')\n",
    "print('     CESM2 Bias: %8.3f' % np.nanmean(np.abs(terraCI_CESM2diff)))\n",
    "print('     CESM1 Bias: %8.3f' % np.nanmean(np.abs(terraCI_CESM1diff)))\n",
    "print('     CLM45 Bias: %8.3f' % np.nanmean(np.abs(terraCI_CLM45diff)))\n",
    "print('')\n",
    "print('     LandOnly-CLM4   RMSE: %6.3f' % np.nanmean(np.abs(terraCI_lndOnlyDiff_CLM4)))\n",
    "print('     LandOnly-CLM4.5 RMSE: %6.3f' % np.nanmean(np.abs(terraCI_lndOnlyDiff_CLM45)))\n",
    "print('     LandOnly-CLM5   RMSE: %6.3f' % np.nanmean(np.abs(terraCI_lndOnlyDiff_CLM5)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Maps**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. It's really weird that CAM6+CLM4.5 has the *worst* performance. Investigate further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import csv\n",
    "\n",
    "# with open('terraCIvalues.csv',mode='w') as file:\n",
    "#     fileWriter = csv.writer(file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
    "#     fileWriter.writerow(['stationID', 'FLUXNET','CAM6+CLM5','CAM6+CLM4.5','CAM5+CLM4','CLM5','CLM4.5','CLM4','CESM2diff','CLM45diff','CESM1diff'])\n",
    "    \n",
    "#     for iSt in range(len(dataFiles)): \n",
    "#         fluxCI = terraCI_fluxnetConverted[iSt,1]\n",
    "    \n",
    "#         if np.isfinite(terraCI_CESM2diff[iSt])==True:\n",
    "#             # Didn't save station-grid's values of each model CI, but did save difference.\n",
    "#             #   Diff = terraCI_CESM2[stationLoc] - fluxCI \n",
    "#             CESM2_CI = terraCI_CESM2diff[iSt] + fluxCI\n",
    "#             CLM45_CI = terraCI_CLM45diff[iSt] + fluxCI\n",
    "#             CESM1_CI = terraCI_CESM1diff[iSt] + fluxCI \n",
    "\n",
    "#             lndCLM5_CI  = terraCI_lndOnlyDiff_CLM5[iSt]  + fluxCI \n",
    "#             lndCLM45_CI = terraCI_lndOnlyDiff_CLM45[iSt] + fluxCI\n",
    "#             lndCLM4_CI  = terraCI_lndOnlyDiff_CLM4[iSt]  + fluxCI\n",
    "\n",
    "#             line = np.array([allStationID[iSt], fluxCI, CESM2_CI, CLM45_CI, CESM1_CI,lndCLM5_CI, lndCLM45_CI, lndCLM4_CI,terraCI_CESM2diff[iSt], terraCI_CLM45diff[iSt],terraCI_CESM1diff[iSt]])\n",
    "\n",
    "#             fileWriter.writerow(line.astype(str))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                TERRESTRIAL COUPLING INDEX                  \n",
      "\n",
      "Station      FLUXNET      CAM6+CLM5    CAM6+CLM4.5    CAM5+CLM4    CLM5     CLM4.5    CLM4\n",
      "-------     ---------    -----------   -----------    ---------    ----     ------    ----\n",
      "CA-NS3       0.905          0.944        -3.999        -4.641      3.894    1.943   -5.093\n",
      "IT-SRo     -13.322         -3.636        -5.379        -3.825    -12.612  -20.348  -15.374\n",
      "CN-Du2     -13.884         -7.752       -17.132       -12.401     -6.004  -13.585  -17.281\n",
      "US-NR1       3.817         -7.002       -19.082        -6.360      0.781  -13.722  -11.841\n",
      "ES-LJu      -7.524         -3.464        -3.984        -3.079      1.828   -8.400   -8.391\n",
      "CA-TP2      -1.174         -1.222        -3.642        -0.687     -1.204   -3.625    1.668\n",
      "CA-TP1     -12.075         -1.222        -3.642        -0.687     -1.204   -3.625    1.668\n",
      "IT-Lav      -9.111         -5.556       -15.414        -3.698     -1.655   -8.847    0.271\n",
      "US-WCr       1.685          1.394        -4.865        -6.245      0.667   -2.852   -2.698\n",
      "AU-Fog      -2.925         -1.443       -10.239        -8.476     -8.021   -8.477   -9.413\n",
      "GL-ZaH      -3.875         -0.389        -0.550        -2.811     -8.709   -0.848   -9.888\n",
      "CA-SF2      -2.317         -4.089       -12.991        -8.319      2.548   -4.505   -2.397\n",
      "JP-SMF      -0.487        -27.463       -18.183       -17.887     -9.155   -4.939  -10.494\n",
      "ZM-Mon      22.497         -8.428        -9.281        -6.654     -8.136   -7.127   -7.934\n",
      "CN-Cng      -8.075         -9.115       -14.208       -10.834     -3.867   -8.410  -17.273\n",
      "IT-Cp2       7.916         -7.890        -8.000        -6.271    -16.400  -19.916  -17.231\n",
      "US-IB2      -1.591         -9.883       -13.747        -8.499     -0.054   -3.336   -1.753\n",
      "DE-Lkb      -9.422         -7.030       -10.403        -5.753      1.445   -1.966   -0.536\n",
      "RU-Ha1      -4.525         -2.885       -13.315        -6.920     -2.446   -2.907   -1.981\n",
      "AU-Tum      -3.965         -0.700        -6.591        -3.056      2.637   -2.000   -3.186\n",
      "CN-Qia      -7.197        -18.847       -19.727       -23.017     -7.143   -4.119   -9.137\n",
      "US-GLE       5.775         -4.481       -20.607        -8.294     -0.687  -16.905  -12.292\n",
      "AU-How      -6.617         -1.443       -10.239        -8.476     -8.021   -8.477   -9.413\n",
      "CN-HaM      -0.904         -9.510       -11.851       -13.889     -2.261   -7.211  -10.603\n",
      "US-ARM      -8.001        -21.190       -26.921       -23.066     -4.111   -9.990  -16.739\n",
      "DK-Sor      -2.409         -0.501        -1.291        -0.957     -6.891   -7.080   -6.366\n",
      "IT-PT1     -12.046        -13.254       -20.274       -14.474    -16.229  -19.962  -12.325\n",
      "IT-CA3     -17.481         -7.890        -8.000        -6.271    -16.400  -19.916  -17.231\n",
      "US-SRC     -18.916        -15.368       -20.016       -13.908    -15.920  -28.013  -29.471\n",
      "IT-Ro1     -15.308         -7.890        -8.000        -6.271    -16.400  -19.916  -17.231\n",
      "US-SRG     -25.756        -15.368       -20.016       -13.908    -15.920  -28.013  -29.471\n",
      "AU-DaP      -1.997         -9.461       -11.365        -6.945     -8.502   -5.488   -5.132\n",
      "AU-ASM      -3.875         -4.772       -14.357        -8.411     -4.803   -9.540  -11.299\n",
      "MY-PSO      -4.259         -8.801        -6.531        -2.256     -3.013   -0.148    1.955\n",
      "FI-Hyy      -7.447         -5.767        -9.439       -11.492      0.719   -1.916  -10.775\n",
      "US-Wkg     -19.231        -17.078       -25.096       -17.514    -16.305  -27.471  -29.355\n",
      "AU-Wom      -1.761         -1.094        -3.979        -2.521      1.538    0.039   -0.568\n",
      "AU-Dry      -4.446         -9.002       -10.280        -5.619    -11.995   -4.588   -4.816\n",
      "DE-Tha      -5.454         -7.924       -14.305        -7.531     -2.228   -6.802   -3.573\n",
      "IT-Ren     -12.694         -3.391       -10.534        -7.908      2.897   -1.336   -4.145\n",
      "DE-Obe      -1.027         -6.371       -11.428        -5.967      1.394   -2.604   -0.686\n",
      "US-UMB      -0.881          0.641        -3.806        -2.936     -0.286   -3.237   -1.262\n",
      "US-Me2      -0.730          2.638       -20.099       -12.296     -5.834  -16.735  -14.512\n",
      "CH-Oe2      -2.451         -7.383       -10.817        -5.590      4.114    1.333   -0.507\n",
      "IT-Ro2      -8.955         -7.890        -8.000        -6.271    -16.400  -19.916  -17.231\n",
      "FI-Sod      -6.821         -1.187        -5.725        -9.797      1.611   -1.944   -3.183\n",
      "CA-Qfo      -7.966         -0.183        -7.454        -9.042      2.045   -1.816   -6.134\n",
      "RU-Fyo      -2.365         -6.245        -8.825        -6.195      1.541   -2.432   -2.034\n",
      "CA-TPD      -4.620         -1.222        -3.642        -0.687     -1.204   -3.625    1.668\n",
      "US-PFa      -1.117          1.394        -4.865        -6.245      0.667   -2.852   -2.698\n",
      "IT-Col      -0.137        -16.023       -19.548       -14.799    -13.108  -24.221  -17.655\n",
      "BE-Lon      -6.882         -8.532       -17.358       -10.222      5.284   -2.200   -0.669\n",
      "IT-BCi      -7.778        -14.696       -14.918       -13.370    -19.867  -26.154  -20.432\n",
      "CZ-BK2      -1.335         -5.280       -10.399        -6.348      3.625    0.356   -0.028\n",
      "CA-TP3      -7.230         -1.222        -3.642        -0.687     -1.204   -3.625    1.668\n",
      "IT-Tor       2.059         -5.799       -17.066        -8.033      3.511   -5.249   -3.454\n",
      "IT-CA1     -30.061         -7.890        -8.000        -6.271    -16.400  -19.916  -17.231\n",
      "AU-Cpr      -2.982         -1.565        -3.626        -3.948      1.388   -2.424   -3.260\n",
      "GF-Guy      -6.107         -4.344        -3.089        -1.120     -5.339   -6.984   -0.716\n",
      "DK-Eng      -9.590         -8.776       -16.368       -11.938     -5.520   -7.052   -5.789\n",
      "US-AR2     -12.039        -15.085       -22.456       -22.294     -2.595  -13.479  -23.473\n",
      "SD-Dem      -1.452         -9.636       -23.024       -16.691     -9.133  -22.339  -26.713\n",
      "BE-Vie       1.783         -8.154       -15.087        -7.708      3.469    0.544    1.856\n",
      "DE-Lnf     -17.530         -7.537       -12.722        -6.892      1.376   -1.053    0.518\n",
      "GH-Ank       1.606         -8.838        -9.984        -2.781     -1.785   -2.812    1.077\n",
      "CA-NS7       7.761          0.751        -6.090        -7.241      3.456   -4.238  -11.457\n",
      "AU-Wac       3.160         -1.953        -4.632        -2.719      1.499   -1.201   -2.008\n",
      "CA-NS6       0.878          0.944        -3.999        -4.641      3.894    1.943   -5.093\n",
      "AU-Whr      -4.881         -1.888        -3.728        -2.534      0.159   -1.132   -1.093\n",
      "US-MMS      -4.938         -9.479       -13.388        -6.016     -0.767   -7.667   -7.118\n",
      "IT-MBo      -0.396         -5.556       -15.414        -3.698     -1.655   -8.847    0.271\n",
      "CA-NS2       2.905          0.944        -3.999        -4.641      3.894    1.943   -5.093\n",
      "US-KS2     -12.689         -9.493       -16.836       -14.382     -7.243  -11.203  -12.960\n",
      "CA-SF3      -0.757         -4.089       -12.991        -8.319      2.548   -4.505   -2.397\n",
      "CN-Ha2      -3.973         -9.510       -11.851       -13.889     -2.261   -7.211  -10.603\n",
      "US-CRT       0.399         -5.355        -9.549        -4.838      2.301   -5.052   -0.881\n",
      "ES-Amo       8.104         -3.464        -3.984        -3.079      1.828   -8.400   -8.391\n",
      "CN-Din      -6.768        -19.279       -18.858       -16.725     -5.466   -3.563   -9.771\n",
      "US-Whs     -23.046        -17.078       -25.096       -17.514    -16.305  -27.471  -29.355\n",
      "DE-Geb       0.554         -7.249       -11.292        -6.360     -0.464   -2.222    1.629\n",
      "FR-LBr     -12.360         -0.608        -1.745        -1.066     -9.908  -19.967  -16.160\n",
      "US-AR1     -13.851        -15.085       -22.456       -22.294     -2.595  -13.479  -23.473\n",
      "CN-Cha      -5.663        -12.500       -16.502       -17.905     -2.516   -7.222   -9.978\n",
      "US-UMd      -6.077          0.641        -3.806        -2.936     -0.286   -3.237   -1.262\n",
      "AU-Gin      -0.441         -1.731        -5.891        -4.728      2.563    2.058    0.786\n",
      "AU-Rig      -2.253         -1.888        -3.728        -2.534      0.159   -1.132   -1.093\n",
      "CH-Lae      -6.075         -6.229        -8.766        -5.934      3.539    1.941   -1.240\n",
      "DE-Hai      -1.861         -7.537       -12.722        -6.892      1.376   -1.053    0.518\n",
      "US-SRM     -27.013        -15.368       -20.016       -13.908    -15.920  -28.013  -29.471\n",
      "CZ-BK1      -9.673         -5.280       -10.399        -6.348      3.625    0.356   -0.028\n",
      "CA-Oas      -2.820        -10.340       -19.959       -11.362      1.621   -7.087   -4.884\n",
      "SN-Dhr     -14.901        -17.233       -29.951       -28.857    -17.328  -20.748  -24.718\n",
      "AT-Neu       0.848         -3.391       -10.534        -7.908      2.897   -1.336   -4.145\n",
      "DE-Gri      -2.311         -7.924       -14.305        -7.531     -2.228   -6.802   -3.573\n",
      "US-Me6      -4.693          2.638       -20.099       -12.296     -5.834  -16.735  -14.512\n",
      "US-Sta      -6.459         -4.481       -20.607        -8.294     -0.687  -16.905  -12.292\n",
      "US-Syv       2.168          1.336        -4.613        -2.947      3.641   -0.538    1.084\n",
      "AU-DaS      -5.062         -8.664       -10.724        -6.227     -7.466   -5.284   -5.751\n",
      "PA-SPs       0.722         -0.906        -4.547        -3.433     -3.040   -7.120   -7.388\n",
      "CH-Dav      -1.134         -3.222       -10.147        -6.164      3.891   -0.970   -3.929\n",
      "CH-Fru      -2.766         -3.161        -8.468        -7.644      5.535    2.380   -4.506\n",
      "US-Goo      -4.482        -10.036       -13.971       -12.879     -0.612   -3.599   -4.589\n",
      "DE-Kli      -2.892         -7.924       -14.305        -7.531     -2.228   -6.802   -3.573\n",
      "IT-Cpz      -8.899         -7.890        -8.000        -6.271    -16.400  -19.916  -17.231\n",
      "CH-Cha      -1.386         -6.229        -8.766        -5.934      3.539    1.941   -1.240\n",
      "CH-Oe1      -4.137         -7.383       -10.817        -5.590      4.114    1.333   -0.507\n",
      "US-Blo       3.928         -6.088        -5.629        -7.264     -4.922  -13.875  -11.819\n",
      "FR-Gri      -8.251        -14.295       -19.117       -14.545     -4.159   -9.516   -5.713\n",
      "CA-TP4     -16.918         -1.222        -3.642        -0.687     -1.204   -3.625    1.668\n",
      "BR-Sa3      -2.870        -13.868        -9.705        -7.759     -5.863  -10.190  -15.776\n",
      "AU-Emr      -7.589         -9.046        -6.922       -10.678     -6.189  -10.232  -11.558\n",
      "US-Oho      -1.204         -5.355        -9.549        -4.838      2.301   -5.052   -0.881\n",
      "NL-Loo      -0.429         -7.797       -16.170       -11.303      0.937   -5.482   -7.468\n",
      "DE-Seh      -0.076         -8.154       -15.087        -7.708      3.469    0.544    1.856\n",
      "IT-CA2       8.306         -7.890        -8.000        -6.271    -16.400  -19.916  -17.231\n"
     ]
    }
   ],
   "source": [
    "print('                                TERRESTRIAL COUPLING INDEX                  ')\n",
    "print()\n",
    "print('Station      FLUXNET      CAM6+CLM5    CAM6+CLM4.5    CAM5+CLM4    CLM5     CLM4.5    CLM4')\n",
    "print('-------     ---------    -----------   -----------    ---------    ----     ------    ----')\n",
    "\n",
    "for iSt in range(len(dataFiles)): \n",
    "    fluxCI = terraCI_fluxnetConverted[iSt,1]\n",
    "    \n",
    "    if np.isfinite(terraCI_CESM2diff[iSt])==True:\n",
    "        # Didn't save station-grid's values of each model CI, but did save difference.\n",
    "        #   Diff = terraCI_CESM2[stationLoc] - fluxCI \n",
    "        CESM2_CI = terraCI_CESM2diff[iSt] + fluxCI\n",
    "        CLM45_CI = terraCI_CLM45diff[iSt] + fluxCI\n",
    "        CESM1_CI = terraCI_CESM1diff[iSt] + fluxCI \n",
    "        \n",
    "        lndCLM5_CI  = terraCI_lndOnlyDiff_CLM5[iSt]  + fluxCI \n",
    "        lndCLM45_CI = terraCI_lndOnlyDiff_CLM45[iSt] + fluxCI\n",
    "        lndCLM4_CI  = terraCI_lndOnlyDiff_CLM4[iSt]  + fluxCI\n",
    "        \n",
    "        \n",
    "        print('%s %11.3f %14.3f %13.3f %13.3f %10.3f %8.3f %8.3f' % (allStationID[iSt], fluxCI,\n",
    "                                                                       CESM2_CI, CLM45_CI, CESM1_CI, \n",
    "                                                                       lndCLM5_CI, lndCLM45_CI, lndCLM4_CI) )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations with CAM6+CLM4.5 as worst:  67 ( of  115  stations)\n",
      "\n",
      " Station    CESM2diff    CLM45diff    CESM1diff    lndCLM5diff    lndCLM45diff    lndCLM4diff \n",
      "---------  -----------  -----------   ---------    -----------    ------------    ------------\n",
      "IT-Lav         3.555       -6.302        5.414        7.457          0.264           9.382\n",
      "ZM-Mon       -30.925      -31.777      -29.150      -30.632        -29.624         -30.431\n",
      "CN-Cng        -1.040       -6.132       -2.759        4.208         -0.335          -9.198\n",
      "RU-Ha1         1.641       -8.790       -2.395        2.079          1.619           2.545\n",
      "US-ARM       -13.189      -18.920      -15.065        3.890         -1.989          -8.738\n",
      "AU-Dry        -4.556       -5.834       -1.173       -7.549         -0.142          -0.370\n",
      "DE-Tha        -2.471       -8.851       -2.078        3.226         -1.349           1.880\n",
      "RU-Fyo        -3.880       -6.460       -3.830        3.906         -0.066           0.332\n",
      "BE-Lon        -1.650      -10.476       -3.340       12.166          4.682           6.213\n",
      "DK-Eng         0.814       -6.778       -2.348        4.070          2.537           3.801\n",
      "US-AR2        -3.046      -10.417      -10.255        9.444         -1.440         -11.434\n",
      "US-AR1        -1.234       -8.605       -8.443       11.256          0.373          -9.622\n",
      "AU-Rig         0.365       -1.476       -0.281        2.412          1.120           1.160\n",
      "DE-Hai        -5.676      -10.861       -5.031        3.237          0.808           2.379\n",
      "AU-DaS        -3.602       -5.661       -1.164       -2.404         -0.222          -0.689\n",
      "CH-Dav        -2.088       -9.014       -5.030        5.025          0.163          -2.796\n",
      "FR-Gri        -6.044      -10.866       -6.294        4.092         -1.265           2.537\n",
      "DE-Seh        -8.078      -15.010       -7.632        3.546          0.620           1.933\n",
      "\n",
      "Number of stations where lndCLM45 is not the worst but coupled CLM45 is:  18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-142-a3009d80bb33>:1: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  iCLM45bad = np.where((np.abs(terraCI_CLM45diff)>=np.abs(terraCI_CESM2diff)) &\n",
      "<ipython-input-142-a3009d80bb33>:2: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  (np.abs(terraCI_CLM45diff)>=np.abs(terraCI_CESM1diff)))[0]\n"
     ]
    }
   ],
   "source": [
    "iCLM45bad = np.where((np.abs(terraCI_CLM45diff)>=np.abs(terraCI_CESM2diff)) & \n",
    "                     (np.abs(terraCI_CLM45diff)>=np.abs(terraCI_CESM1diff)))[0]\n",
    "\n",
    "print('Number of stations with CAM6+CLM4.5 as worst: ', len(iCLM45bad), '( of ', nValidStation,' stations)')\n",
    "print()\n",
    "print(' Station    CESM2diff    CLM45diff    CESM1diff    lndCLM5diff    lndCLM45diff    lndCLM4diff ')\n",
    "print('---------  -----------  -----------   ---------    -----------    ------------    ------------')\n",
    "\n",
    "indCoupledProblem = []\n",
    "for iStation in range(len(iCLM45bad)):\n",
    "    iSt = iCLM45bad[iStation]\n",
    "    \n",
    "    if ( (np.abs(terraCI_lndOnlyDiff_CLM45[iSt])<=np.abs(terraCI_lndOnlyDiff_CLM5[iSt])) & \n",
    "         (np.abs(terraCI_lndOnlyDiff_CLM45[iSt])<=np.abs(terraCI_lndOnlyDiff_CLM4[iSt])) ):\n",
    "        \n",
    "        indCoupledProblem.append(iSt)\n",
    "    \n",
    "        print('%s %13.3f %12.3f %12.3f %12.3f %14.3f %15.3f' % (allStationID[iSt], \n",
    "                                                                terraCI_CESM2diff[iSt],\n",
    "                                                                terraCI_CLM45diff[iSt],\n",
    "                                                                terraCI_CESM1diff[iSt], \n",
    "                                                                terraCI_lndOnlyDiff_CLM5[iSt],\n",
    "                                                                terraCI_lndOnlyDiff_CLM45[iSt],\n",
    "                                                                terraCI_lndOnlyDiff_CLM4[iSt]))\n",
    "    \n",
    "print('\\nNumber of stations where lndCLM45 is not the worst but coupled CLM45 is: ', len(indCoupledProblem))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-26.97641744 -30.92463442] [-17.39996166 -29.15043349] [-17.6964566  -31.77732546]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What station is a strong negative outlier in all three sims? \n",
    "print(terraCI_CESM2diff[20:22], terraCI_CESM1diff[20:22], terraCI_CLM45diff[20:22])\n",
    "\n",
    "# Index = 21... Where is that? \n",
    "\n",
    "# --- Plot JJA Terrestrial leg \n",
    "\n",
    "fig=plt.figure(figsize=(11, 8), facecolor='w', edgecolor='k')\n",
    "\n",
    "my_fontsize = 14\n",
    "\n",
    "## --- Set axes and color sections --- \n",
    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
    "ax.set_extent([-180,180,-60,90], crs=ccrs.PlateCarree())   #Set to global ot match previous version\n",
    "bounds = [1,2,3,4,5,6,7,8,9]\n",
    "norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)\n",
    "\n",
    "## --- Create Plot ---\n",
    "# contour_plot = ax.pcolormesh(lon_cyclic,latModel,colorsTerraLeg_CESM2,norm=norm,\n",
    "#                              cmap=my_cmap_SMvSHF,\n",
    "#                              vmin=1,vmax=9)\n",
    "\n",
    "# # Colorbar\n",
    "# ax_position = ax.get_position()\n",
    "# cbar_ax = fig.add_axes([ax_position.x0, ax_position.y0-0.1, ax_position.width, 0.06])\n",
    "# cbar = plt.colorbar(contour_plot, orientation='horizontal',ticks=range(13),cax=cbar_ax)\n",
    "# cbar.set_label('Coupling Index (SM, SHF) [W/m2]',fontsize=my_fontsize)\n",
    "# cbar.ax.set_xticklabels(['<-20','-20','-15','-10','-5','-2','-1','0','>0'],fontsize=my_fontsize)  # vertically oriented colorbar\n",
    "\n",
    "# #Additional options \n",
    "# ax.set_title('CESM2 Terrestrial Coupling Index (SM, SHF): JJA\\n FLUXNET-based estimates in circles',fontsize=16)\n",
    "ax.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "# ----------- Add FluxNet measurements ------------ \n",
    "for iPoint in range(len(indCoupledProblem)):\n",
    "    iStation = indCoupledProblem[iPoint]\n",
    "    \n",
    "    colorChoice = colorsList_SMvSHF[int(colorsTerraLeg_FLUXNET[iStation]-1)]\n",
    "\n",
    "    ax.plot(lon_fluxnet[iStation],lat_fluxnet[iStation], 'o',\n",
    "            markerfacecolor=colorChoice,\n",
    "            markeredgecolor='k',\n",
    "            markersize=7,\n",
    "            transform=ccrs.PlateCarree())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ( old) 4. Also compare to a land-only simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up data directory and filename structure\n",
    "dataDir   = '/Users/mdfowler/Documents/Analysis/Coupling_initial/data/LandOnlySim/'\n",
    "fileStart = 'clm50_release-clm5.0.20_1deg_GSWP3V1_isofix2_hist.clm2.h5.'\n",
    "fileEnd   = '_terraLegVars.nc'\n",
    "timeName  = ['1990-1999', '2000-2014']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-dd42fb171612>:4: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  lndOnlyDF1['time'] = lndOnlyDF1.indexes['time'].to_datetimeindex()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File 1 finished reading in...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-dd42fb171612>:9: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  lndOnlyDF2['time'] = lndOnlyDF2.indexes['time'].to_datetimeindex()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File 2 finished reading in...\n"
     ]
    }
   ],
   "source": [
    "# Read in files and get time as usable format \n",
    "file1 = dataDir+fileStart+timeName[0]+fileEnd \n",
    "lndOnlyDF1 = xr.open_dataset(file1, decode_times=True)\n",
    "lndOnlyDF1['time'] = lndOnlyDF1.indexes['time'].to_datetimeindex()\n",
    "print('File 1 finished reading in...')\n",
    "\n",
    "file2 = dataDir+fileStart+timeName[1]+fileEnd \n",
    "lndOnlyDF2 = xr.open_dataset(file2, decode_times=True) \n",
    "lndOnlyDF2['time'] = lndOnlyDF2.indexes['time'].to_datetimeindex()\n",
    "print('File 2 finished reading in...')\n",
    "\n",
    "# Concat in one array \n",
    "flux_full  = xr.concat([lndOnlyDF1, lndOnlyDF2], dim=\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Compute coupling index between soil moisture and sensible heat flux (terrestrial leg) --- # \n",
    "\n",
    "# Compute coupling index \n",
    "terraLeg_lndOnly = cm.metrics.CouplingIndex()\n",
    "terraLeg_lndOnly = terraLeg_lndOnly.compute(flux_full, xname='SOILWATER_10CM',yname='FSH',averaging='season')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-35-32ce97a1d946>:7: RuntimeWarning: invalid value encountered in less\n",
      "  colorsTerraLeg[np.where(data<-20)]                  = 1\n",
      "<ipython-input-35-32ce97a1d946>:8: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-20) & (data<=-15))]  = 2\n",
      "<ipython-input-35-32ce97a1d946>:8: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-20) & (data<=-15))]  = 2\n",
      "<ipython-input-35-32ce97a1d946>:9: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-15) & (data<=-10))]  = 3\n",
      "<ipython-input-35-32ce97a1d946>:9: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-15) & (data<=-10))]  = 3\n",
      "<ipython-input-35-32ce97a1d946>:10: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-10) & (data<=-5))]   = 4\n",
      "<ipython-input-35-32ce97a1d946>:10: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-10) & (data<=-5))]   = 4\n",
      "<ipython-input-35-32ce97a1d946>:11: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-5)  & (data<=-2))]   = 5\n",
      "<ipython-input-35-32ce97a1d946>:11: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-5)  & (data<=-2))]   = 5\n",
      "<ipython-input-35-32ce97a1d946>:12: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-2)  & (data<=-1))]   = 6\n",
      "<ipython-input-35-32ce97a1d946>:12: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-2)  & (data<=-1))]   = 6\n",
      "<ipython-input-35-32ce97a1d946>:13: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where((data>-1)  & (data<=0))]    = 7\n",
      "<ipython-input-35-32ce97a1d946>:13: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg[np.where((data>-1)  & (data<=0))]    = 7\n",
      "<ipython-input-35-32ce97a1d946>:14: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg[np.where(data>0)]                    = 8\n"
     ]
    }
   ],
   "source": [
    "# Pull out JJA value \n",
    "terraLeg_lndOnly_JJA = terraLeg_lndOnly.SOILWATER_10CM_FSH_CI.values[1,:,:]\n",
    "\n",
    "# Make data cylic for JJA\n",
    "terraLeg_lndOnly_JJA_cyclic, lon_cyclic = cartopy.util.add_cyclic_point(terraLeg_lndOnly_JJA, flux_full.lon.values)\n",
    "\n",
    "# Now get \"colorsTerraLeg\" (the color aliased version of TerraCI arrays)\n",
    "colorsTerraLeg_lndOnly   = map_terraCI_colors(terraLeg_lndOnly_JJA_cyclic)\n",
    "\n",
    "# Also pull out lat/lon\n",
    "lat_lndOnly = flux_full.lat.values\n",
    "lon_lndOnly = flux_full.lon.values\n",
    "lon = lon_cyclic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Plot JJA Terrestrial leg \n",
    "\n",
    "fig=plt.figure(figsize=(11, 8), facecolor='w', edgecolor='k')\n",
    "\n",
    "my_fontsize = 14\n",
    "\n",
    "## --- Set axes and color sections --- \n",
    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
    "ax.set_extent([-180,180,-60,90], crs=ccrs.PlateCarree())   #Set to global ot match previous version\n",
    "bounds = [1,2,3,4,5,6,7,8,9]\n",
    "norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)\n",
    "\n",
    "## --- Create Plot ---\n",
    "contour_plot = ax.pcolormesh(lon_cyclic,lat_lndOnly,colorsTerraLeg_lndOnly,norm=norm,\n",
    "                             cmap=my_cmap_SMvSHF,\n",
    "                             vmin=1,vmax=9)\n",
    "\n",
    "# Colorbar\n",
    "ax_position = ax.get_position()\n",
    "cbar_ax = fig.add_axes([ax_position.x0, ax_position.y0-0.1, ax_position.width, 0.06])\n",
    "cbar = plt.colorbar(contour_plot, orientation='horizontal',ticks=range(13),cax=cbar_ax)\n",
    "cbar.set_label('Coupling Index (SM, SHF) [W/m2]',fontsize=my_fontsize)\n",
    "cbar.ax.set_xticklabels(['<-20','-20','-15','-10','-5','-2','-1','0','>0'],fontsize=my_fontsize)  # vertically oriented colorbar\n",
    "\n",
    "#Additional options \n",
    "ax.set_title('CESM2 Land-Only Terrestrial Coupling Index (SM, SHF): JJA\\n FLUXNET-based estimates in circles',fontsize=16)\n",
    "ax.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "# ----------- Add FluxNet measurements ------------ \n",
    "for iStation in range(len(dataFiles)):\n",
    "    if (np.isfinite(colorsTerraLeg_FLUXNET[iStation])==True):\n",
    "    \n",
    "        colorChoice = colorsList_SMvSHF[int(colorsTerraLeg_FLUXNET[iStation]-1)]\n",
    "\n",
    "        ax.plot(lon_fluxnet[iStation],lat_fluxnet[iStation], 'o',\n",
    "                markerfacecolor=colorChoice,\n",
    "                markeredgecolor='k',\n",
    "                markersize=7,\n",
    "                transform=ccrs.PlateCarree())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RMSE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define empty arrays to save differences in \n",
    "terraCI_lndOnlyDiff = np.full([len(dataFiles)], np.nan)\n",
    "\n",
    "# Count number of stations used...\n",
    "nValidStation_lnd = 0\n",
    "\n",
    "# Consider each station individually \n",
    "for iStation in range(len(dataFiles)):\n",
    "    \n",
    "    # Isolate station CI for JJA\n",
    "    #fluxnetCI = terraCI_fluxnet[iStation,1]\n",
    "    fluxnetCI = terraCI_fluxnetConverted[iStation,1]\n",
    "\n",
    "    # Check that there's data here (and at least 3 months went into JJA avg CI)\n",
    "    # ------------------------------------------------------------------------\n",
    "    if ( (np.isnan(fluxnetCI)==False) ): \n",
    "        \n",
    "        # Get model gridcells that match fluxnet site \n",
    "        iLat        = np.where( (lat_lndOnly>=(lat_fluxnet[iStation]-0.4712)) & \n",
    "                                (lat_lndOnly<=(lat_fluxnet[iStation]+0.4712)) )[0]\n",
    "         # Convert longitude of point into 0-360 range \n",
    "        lonSel_flux = (lon_fluxnet[iStation] + 360) % 360                \n",
    "        iLon        = np.where( (lon_lndOnly>=(lonSel_flux-0.625)) & \n",
    "                                (lon_lndOnly<=(lonSel_flux+0.625)) )[0]\n",
    "        \n",
    "        gridValue_lndOnly= terraLeg_lndOnly_JJA[iLat,iLon]\n",
    "\n",
    "        # Compare to fluxnet-based estimate\n",
    "        terraCI_lndOnlyDiff[iStation] = gridValue_lndOnly - fluxnetCI\n",
    "\n",
    "        # Increment counter for number of stations used \n",
    "        nValidStation_lnd =  nValidStation_lnd+1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations considered: 115 \n",
      "\n",
      "RMSE between model terrestrial CI and fluxnet-based observations:\n",
      "     CESM2 RMSE:    7.873\n",
      "     CESM1 RMSE:    8.378\n",
      "     CLM45 RMSE:   10.577\n",
      "     LndOnly RMSE:  7.439\n",
      "\n",
      " Mean Bias (model - observations):\n",
      "     CESM2 Bias:   -1.744\n",
      "     CESM1 Bias:   -3.275\n",
      "     CLM45 Bias:   -6.578\n",
      "     LndOnly Bias:  1.053\n"
     ]
    }
   ],
   "source": [
    "# Print full table...\n",
    "# Compute RMSE per model version \n",
    "RMSE_CESM2 = np.sqrt(np.nansum(terraCI_CESM2diff**2) / nValidStation)\n",
    "RMSE_CESM1 = np.sqrt(np.nansum(terraCI_CESM1diff**2) / nValidStation)\n",
    "RMSE_CLM45 = np.sqrt(np.nansum(terraCI_CLM45diff**2) / nValidStation)\n",
    "RMSE_LndOnly = np.sqrt(np.nansum(terraCI_lndOnlyDiff**2) / nValidStation_lnd)\n",
    "\n",
    "# Print results of RMSE analysis \n",
    "print('Number of stations considered: %i \\n' % nValidStation)\n",
    "print('RMSE between model terrestrial CI and fluxnet-based observations:')\n",
    "print('     CESM2 RMSE: %8.3f' % RMSE_CESM2)\n",
    "print('     CESM1 RMSE: %8.3f' % RMSE_CESM1)\n",
    "print('     CLM45 RMSE: %8.3f' % RMSE_CLM45)\n",
    "print('     LndOnly RMSE: %6.3f' % RMSE_LndOnly)\n",
    "\n",
    "print('\\n Mean Bias (model - observations):')\n",
    "print('     CESM2 Bias: %8.3f' % np.nanmean(terraCI_CESM2diff))\n",
    "print('     CESM1 Bias: %8.3f' % np.nanmean(terraCI_CESM1diff))\n",
    "print('     CLM45 Bias: %8.3f' % np.nanmean(terraCI_CLM45diff))\n",
    "print('     LndOnly Bias: %6.3f' % np.nanmean(terraCI_lndOnlyDiff))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing/debug section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CA-NS3 \n",
      "\n",
      "['TIMESTAMP' 'TA_F' 'TA_F_QC' 'SW_IN_POT' 'SW_IN_F' 'SW_IN_F_QC' 'LW_IN_F'\n",
      " 'LW_IN_F_QC' 'VPD_F' 'VPD_F_QC' 'PA_F' 'PA_F_QC' 'P_F' 'P_F_QC' 'WS_F'\n",
      " 'WS_F_QC' 'USTAR' 'USTAR_QC' 'NETRAD' 'NETRAD_QC' 'PPFD_IN' 'PPFD_IN_QC'\n",
      " 'PPFD_OUT' 'PPFD_OUT_QC' 'SW_OUT' 'SW_OUT_QC' 'CO2_F_MDS' 'CO2_F_MDS_QC'\n",
      " 'TS_F_MDS_1' 'TS_F_MDS_1_QC' 'SWC_F_MDS_1' 'SWC_F_MDS_1_QC' 'G_F_MDS'\n",
      " 'G_F_MDS_QC' 'LE_F_MDS' 'LE_F_MDS_QC' 'LE_CORR' 'LE_CORR_25' 'LE_CORR_75'\n",
      " 'LE_RANDUNC' 'H_F_MDS' 'H_F_MDS_QC' 'H_CORR' 'H_CORR_25' 'H_CORR_75'\n",
      " 'H_RANDUNC' 'NEE_VUT_REF' 'NEE_VUT_REF_QC' 'NEE_VUT_REF_RANDUNC'\n",
      " 'NEE_VUT_25' 'NEE_VUT_50' 'NEE_VUT_75' 'NEE_VUT_25_QC' 'NEE_VUT_50_QC'\n",
      " 'NEE_VUT_75_QC' 'RECO_NT_VUT_REF' 'RECO_NT_VUT_25' 'RECO_NT_VUT_50'\n",
      " 'RECO_NT_VUT_75' 'GPP_NT_VUT_REF' 'GPP_NT_VUT_25' 'GPP_NT_VUT_50'\n",
      " 'GPP_NT_VUT_75' 'RECO_DT_VUT_REF' 'RECO_DT_VUT_25' 'RECO_DT_VUT_50'\n",
      " 'RECO_DT_VUT_75' 'GPP_DT_VUT_REF' 'GPP_DT_VUT_25' 'GPP_DT_VUT_50'\n",
      " 'GPP_DT_VUT_75' 'RECO_SR' 'RECO_SR_N'] \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (TIMESTAMP: 1126)\n",
       "Coordinates:\n",
       "  * TIMESTAMP       (TIMESTAMP) datetime64[ns] 2002-09-08 ... 2005-10-07\n",
       "Data variables:\n",
       "    H_F_MDS         (TIMESTAMP) float64 20.24 39.42 13.91 ... 15.59 23.15 -9.814\n",
       "    SWC_F_MDS_1     (TIMESTAMP) float64 15.44 15.33 15.32 ... 27.22 27.09 26.94\n",
       "    SWC_F_MDS_1_QC  (TIMESTAMP) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-abd48694-7008-4098-87b7-48d849eb7012' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-abd48694-7008-4098-87b7-48d849eb7012' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>TIMESTAMP</span>: 1126</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-03ee7f31-c896-49dc-8b95-dea2032ba566' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03ee7f31-c896-49dc-8b95-dea2032ba566' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>TIMESTAMP</span></div><div class='xr-var-dims'>(TIMESTAMP)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2002-09-08 ... 2005-10-07</div><input id='attrs-d747cdd5-4c1d-4e2d-ae41-019c97974a87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d747cdd5-4c1d-4e2d-ae41-019c97974a87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf205ebd-5fc1-432b-8e16-703d3124f39d' class='xr-var-data-in' type='checkbox'><label for='data-cf205ebd-5fc1-432b-8e16-703d3124f39d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-09-08T00:00:00.000000000&#x27;, &#x27;2002-09-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-09-10T00:00:00.000000000&#x27;, ..., &#x27;2005-10-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-10-06T00:00:00.000000000&#x27;, &#x27;2005-10-07T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9720780c-a93a-42fd-a016-97be6a74061e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9720780c-a93a-42fd-a016-97be6a74061e' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>H_F_MDS</span></div><div class='xr-var-dims'>(TIMESTAMP)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.24 39.42 13.91 ... 23.15 -9.814</div><input id='attrs-f22c95b8-9eb2-466b-8901-1a7b1a5ed884' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f22c95b8-9eb2-466b-8901-1a7b1a5ed884' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-134e8fd0-2a19-45f6-baeb-82e2dc70a98a' class='xr-var-data-in' type='checkbox'><label for='data-134e8fd0-2a19-45f6-baeb-82e2dc70a98a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([20.2363 , 39.4217 , 13.9141 , ..., 15.5909 , 23.1528 , -9.81413])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWC_F_MDS_1</span></div><div class='xr-var-dims'>(TIMESTAMP)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.44 15.33 15.32 ... 27.09 26.94</div><input id='attrs-b5ca97a0-106b-493d-8801-a005bc3452f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5ca97a0-106b-493d-8801-a005bc3452f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62c7171c-8018-4c6d-9588-064cdfb2232d' class='xr-var-data-in' type='checkbox'><label for='data-62c7171c-8018-4c6d-9588-064cdfb2232d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15.436, 15.331, 15.325, ..., 27.218, 27.094, 26.938])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWC_F_MDS_1_QC</span></div><div class='xr-var-dims'>(TIMESTAMP)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ea56aba8-e0a3-432a-8e14-40b13078d29b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea56aba8-e0a3-432a-8e14-40b13078d29b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a682270c-ea13-4df1-888b-0084dd90ec48' class='xr-var-data-in' type='checkbox'><label for='data-a682270c-ea13-4df1-888b-0084dd90ec48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff1252ec-9f84-4b18-8934-9d40b4cd9633' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ff1252ec-9f84-4b18-8934-9d40b4cd9633' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (TIMESTAMP: 1126)\n",
       "Coordinates:\n",
       "  * TIMESTAMP       (TIMESTAMP) datetime64[ns] 2002-09-08 ... 2005-10-07\n",
       "Data variables:\n",
       "    H_F_MDS         (TIMESTAMP) float64 20.24 39.42 13.91 ... 15.59 23.15 -9.814\n",
       "    SWC_F_MDS_1     (TIMESTAMP) float64 15.44 15.33 15.32 ... 27.22 27.09 26.94\n",
       "    SWC_F_MDS_1_QC  (TIMESTAMP) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing section \n",
    "fileName = dataFiles[0]\n",
    "\n",
    "# Get ID of station \n",
    "startID = fileName.find('FLX_')\n",
    "fluxnetID = fileName[startID+4:startID+10]\n",
    "print(fluxnetID, '\\n')\n",
    "\n",
    "dataDF   = pd.read_csv(fileName)\n",
    "\n",
    "varNames = ['TIMESTAMP','H_F_MDS','SWC_F_MDS_1','SWC_F_MDS_1_QC']\n",
    "\n",
    "# Check that requested variables are available in specific file\n",
    "colNames = dataDF.columns.values\n",
    "print(colNames, '\\n')\n",
    "\n",
    "for iVar in range(len(varNames)):\n",
    "    if (varNames[iVar] in colNames)==False:\n",
    "        print('** ERROR: %13s   not contained in file for %8s  **' % \n",
    "              (varNames[iVar], fluxnetID))\n",
    "\n",
    "        testDF = np.nan\n",
    "        \n",
    "testDF               = dataDF[varNames]\n",
    "testDF = testDF.replace(-9999, np.nan)\n",
    "\n",
    "\n",
    "testDF['TIMESTAMP'] = pd.to_datetime(testDF['TIMESTAMP'].values, format='%Y%m%d')\n",
    "testDF = testDF.set_index(['TIMESTAMP'])\n",
    "testDS = testDF.to_xarray()\n",
    "testDS\n",
    "\n",
    "iReal      = np.where((np.isfinite(testDS['SWC_F_MDS_1'])==True) & \n",
    "                     (np.isfinite(testDS['H_F_MDS'])==True))[0]\n",
    "testDS  = testDS.isel(TIMESTAMP=iReal)\n",
    "        \n",
    "\n",
    "testDS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}