Bias_Correction_for_Precip.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bias correcting precipitation\n",
    "\n",
    "* Should we apply the BC using a (e.g.) 15-day rolling window? or all Januarys, Februarys etc ??\n",
    "* How should we account for missing observational data?\n",
    "* What quality control checks should we do on the station data before we use it?\n",
    "* How do we correct for elevation? e.g., regression using monthly sum precipitation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import optimize, spatial\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cv_diff(a, series, target_cv):\n",
    "    '''\n",
    "    Returns the difference between the coefficient of variation of the input \n",
    "        series and the target coefficient of variation. This value is then \n",
    "        minimised in order to find the power-correction coefficients needed\n",
    "    Input:\n",
    "        a : power-correction coefficient to be tested\n",
    "        series : time-series that needs to be corrected in order to match its \n",
    "            coefficient of variation with target_cv\n",
    "        target_cv : target coefficient of variation for the time-series\n",
    "    Output:\n",
    "        cv_diff : difference between the target coefficient of variation and \n",
    "            the coefficient of variation of the series after having been \n",
    "            raised to the power of a; this value should be minimised through \n",
    "            an iterative scheme\n",
    "    '''\n",
    "    series_new = np.power(series, a)\n",
    "    cv_new = np.std(series_new) / np.mean(series_new)\n",
    "    cv_diff = target_cv - cv_new\n",
    "    return cv_diff\n",
    "\n",
    "\n",
    "def correct_series_coeffs(series, target_mean, target_cv):\n",
    "    '''\n",
    "    Returns the power-correction coefficients that are needed in order to \n",
    "        correct the series and increase the rainfall variability. If the \n",
    "        optimization algorithm fails to converge, it will leave the \n",
    "        coefficient of variation unchanged and match the mean by setting \n",
    "        the b coefficient to 1\n",
    "    Input:\n",
    "        series - time-series that needs to be corrected in order to match its \n",
    "            coefficient of variation with target_cv\n",
    "        target_mean - target mean for the time-series\n",
    "        target_cv - target coefficient of variation for the time-series\n",
    "    Output:\n",
    "        a - linear coefficient that the entire series is multiplied by\n",
    "        b - coefficient that the entire series is raised to\n",
    "    '''\n",
    "    try:\n",
    "        b = optimize.newton(cv_diff, 1.0, args = (series, target_cv), \n",
    "                                tol = 1.48e-05, maxiter = 100)\n",
    "    except RuntimeError:\n",
    "        b = 1\n",
    "    series_corrected_cv = np.power(series, b)\n",
    "    a = target_mean / np.mean(series_corrected_cv)\n",
    "    return a, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_nearest_point(df, lon_label, lat_label, my_lon, my_lat):\n",
    "    df1 = df.groupby([lon_label, lat_label]).mean().reset_index()\n",
    "    xs = df1[lon_label].values\n",
    "    ys = df1[lat_label].values\n",
    "    tree = spatial.KDTree(list(zip(xs, ys)))\n",
    "    d, i = tree.query( [(my_lon, my_lat)], k=1)\n",
    "    df = df[ (df[lon_label] == xs[i][0]) & (df[lat_label] == ys[i][0]) ]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Example for a single station named 'Suni' for a period where we have a \n",
    "### complete set of daily data\n",
    "lon, lat   = 77.164200, 31.2303\n",
    "year_range = [2008, 2013]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>RAINC</th>\n",
       "      <th>RAINNC</th>\n",
       "      <th>year</th>\n",
       "      <th>doy</th>\n",
       "      <th>Total_Rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1636320</th>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>29.597431</td>\n",
       "      <td>75.117004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636321</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>29.597431</td>\n",
       "      <td>75.117004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636322</th>\n",
       "      <td>2008-01-03</td>\n",
       "      <td>29.597431</td>\n",
       "      <td>75.117004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636323</th>\n",
       "      <td>2008-01-04</td>\n",
       "      <td>29.597431</td>\n",
       "      <td>75.117004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636324</th>\n",
       "      <td>2008-01-05</td>\n",
       "      <td>29.597431</td>\n",
       "      <td>75.117004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time        lat        lon  RAINC  RAINNC  year  doy  \\\n",
       "1636320  2008-01-01  29.597431  75.117004    0.0     0.0  2008    1   \n",
       "1636321  2008-01-02  29.597431  75.117004    0.0     0.0  2008    2   \n",
       "1636322  2008-01-03  29.597431  75.117004    0.0     0.0  2008    3   \n",
       "1636323  2008-01-04  29.597431  75.117004    0.0     0.0  2008    4   \n",
       "1636324  2008-01-05  29.597431  75.117004    0.0     0.0  2008    5   \n",
       "\n",
       "         Total_Rain  \n",
       "1636320         0.0  \n",
       "1636321         0.0  \n",
       "1636322         0.0  \n",
       "1636323         0.0  \n",
       "1636324         0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data = pd.read_csv('../envkrig/Data/WRF_precip_1D_1980-2015.csv.gz')\n",
    "model_dates = pd.DatetimeIndex(model_data['Time'])\n",
    "model_data['year'] = model_dates.year\n",
    "model_data['doy']  = model_dates.dayofyear\n",
    "model_data = model_data[ (model_dates.year >= year_range[0]) & (model_dates.year <= year_range[1]) ]\n",
    "model_data['Total_Rain'] = model_data['RAINC'] + model_data['RAINNC']\n",
    "model_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected vs Actual ndays =  2191.5 2192\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>RAINC</th>\n",
       "      <th>RAINNC</th>\n",
       "      <th>year</th>\n",
       "      <th>doy</th>\n",
       "      <th>Total_Rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>31.00657</td>\n",
       "      <td>77.146666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>31.00657</td>\n",
       "      <td>77.146666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-01-03</td>\n",
       "      <td>31.00657</td>\n",
       "      <td>77.146666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000812</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-01-04</td>\n",
       "      <td>31.00657</td>\n",
       "      <td>77.146666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003708</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>0.003708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-01-05</td>\n",
       "      <td>31.00657</td>\n",
       "      <td>77.146666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.635166</td>\n",
       "      <td>2008</td>\n",
       "      <td>5</td>\n",
       "      <td>0.635166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time       lat        lon  RAINC    RAINNC  year  doy  Total_Rain\n",
       "0  2008-01-01  31.00657  77.146666    0.0  0.000000  2008    1    0.000000\n",
       "1  2008-01-02  31.00657  77.146666    0.0  0.000000  2008    2    0.000000\n",
       "2  2008-01-03  31.00657  77.146666    0.0  0.000812  2008    3    0.000812\n",
       "3  2008-01-04  31.00657  77.146666    0.0  0.003708  2008    4    0.003708\n",
       "4  2008-01-05  31.00657  77.146666    0.0  0.635166  2008    5    0.635166"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data = get_nearest_point(model_data, 'lon', 'lat', lon, lat)\n",
    "model_data = model_data.reset_index(drop=True)\n",
    "print( 'Expected vs Actual ndays = ', len(np.unique(model_data['year'].values))*365.25, len(model_data))\n",
    "model_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Station Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Station_Name</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>year</th>\n",
       "      <th>doy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975-03-05</td>\n",
       "      <td>SHIQUANHE, CH</td>\n",
       "      <td>80.083</td>\n",
       "      <td>32.5</td>\n",
       "      <td>4280.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975-06-22</td>\n",
       "      <td>SHIQUANHE, CH</td>\n",
       "      <td>80.083</td>\n",
       "      <td>32.5</td>\n",
       "      <td>4280.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1975-07-21</td>\n",
       "      <td>SHIQUANHE, CH</td>\n",
       "      <td>80.083</td>\n",
       "      <td>32.5</td>\n",
       "      <td>4280.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>1975</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1978-03-12</td>\n",
       "      <td>SHIQUANHE, CH</td>\n",
       "      <td>80.083</td>\n",
       "      <td>32.5</td>\n",
       "      <td>4280.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1979-06-05</td>\n",
       "      <td>SHIQUANHE, CH</td>\n",
       "      <td>80.083</td>\n",
       "      <td>32.5</td>\n",
       "      <td>4280.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1979</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Station_Name  Longitude  Latitude  Altitude  Rainfall  year  \\\n",
       "0  1975-03-05  SHIQUANHE, CH     80.083      32.5    4280.0       0.0  1975   \n",
       "1  1975-06-22  SHIQUANHE, CH     80.083      32.5    4280.0       1.0  1975   \n",
       "2  1975-07-21  SHIQUANHE, CH     80.083      32.5    4280.0       7.1  1975   \n",
       "3  1978-03-12  SHIQUANHE, CH     80.083      32.5    4280.0       1.0  1978   \n",
       "4  1979-06-05  SHIQUANHE, CH     80.083      32.5    4280.0       0.0  1979   \n",
       "\n",
       "   doy  \n",
       "0   64  \n",
       "1  173  \n",
       "2  202  \n",
       "3   71  \n",
       "4  156  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_data  = pd.read_csv('../envkrig/Data/rainfall_data.csv').drop(labels=['Unnamed: 0'], axis=1)\n",
    "obs_dates = pd.DatetimeIndex(obs_data.Date)\n",
    "obs_data['year'] = obs_dates.year\n",
    "obs_data['doy'] = obs_dates.dayofyear\n",
    "obs_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected vs Actual ndays =  2191.5 2192\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Station_Name</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>year</th>\n",
       "      <th>doy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>SUNI</td>\n",
       "      <td>77.108333</td>\n",
       "      <td>31.2375</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-02-01</td>\n",
       "      <td>SUNI</td>\n",
       "      <td>77.108333</td>\n",
       "      <td>31.2375</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>SUNI</td>\n",
       "      <td>77.108333</td>\n",
       "      <td>31.2375</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-04-01</td>\n",
       "      <td>SUNI</td>\n",
       "      <td>77.108333</td>\n",
       "      <td>31.2375</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-05-01</td>\n",
       "      <td>SUNI</td>\n",
       "      <td>77.108333</td>\n",
       "      <td>31.2375</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Station_Name  Longitude  Latitude  Altitude  Rainfall  year  doy\n",
       "0  2008-01-01         SUNI  77.108333   31.2375     655.0       0.0  2008    1\n",
       "1  2008-02-01         SUNI  77.108333   31.2375     655.0       0.0  2008   32\n",
       "2  2008-03-01         SUNI  77.108333   31.2375     655.0       0.0  2008   61\n",
       "3  2008-04-01         SUNI  77.108333   31.2375     655.0       0.0  2008   92\n",
       "4  2008-05-01         SUNI  77.108333   31.2375     655.0       0.0  2008  122"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_data = obs_data[ (obs_dates.year >= year_range[0]) & (obs_dates.year <= year_range[1]) ]\n",
    "obs_data = get_nearest_point(obs_data, 'Longitude', 'Latitude', lon, lat).reset_index(drop=True)\n",
    "print( 'Expected vs Actual ndays = ', len(np.unique(obs_data['year'].values))*365.25, len(obs_data))\n",
    "obs_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sanity check...\n",
    "if len(obs_data) != len(model_data):\n",
    "    print(len(obs_data), len(model_data))\n",
    "    raise ValueError('Timeseries are not the same length')\n",
    "    \n",
    "if all(obs_data.doy == model_data.doy):\n",
    "    raise ValueError('Day of year columns do not match')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply Bias Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9062582333942119 0.9424487226670006\n"
     ]
    }
   ],
   "source": [
    "target_mean = obs_data['Rainfall'].values.mean() # mean for closest station \n",
    "target_sd   = obs_data['Rainfall'].values.std()  # std for closest station\n",
    "target_cv   = target_sd / target_mean\n",
    "\n",
    "a_coeff, b_coeff   = correct_series_coeffs(model_data['Total_Rain'].values, target_mean, target_cv)\n",
    "print(a_coeff, b_coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_rainfall = a_coeff * (np.power(model_data['Total_Rain'].values, b_coeff))\n",
    "model_data['Corrected_Rain'] = corrected_rainfall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total_Rain     : 4.065324387598479 16.299922739654537\n",
      "Rainfall       : 3.0108576642335763 11.193726687195097\n",
      "Corrected_Rain : 3.0108576642335763 11.19372668723725\n"
     ]
    }
   ],
   "source": [
    "for v in [model_data['Total_Rain'], obs_data['Rainfall'], model_data['Corrected_Rain']]:\n",
    "    print(v.name.ljust(15)+':', v.values.mean(), v.values.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=[15,6])\n",
    "\n",
    "ax.plot(obs_data.index,   obs_data['Rainfall'],         'k-', alpha=0.5, label='Observations')\n",
    "ax.plot(model_data.index, model_data['Total_Rain'],     'r.', alpha=0.5, label='Raw Model Output')\n",
    "ax.plot(model_data.index, model_data['Corrected_Rain'], 'b.', alpha=0.5, label='Bias Corrected')\n",
    "ax.legend()\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('rainfall (mm/day)')\n",
    "\n",
    "### label xaxis with years\n",
    "ind = model_data['doy'].values == 1\n",
    "labels = model_data['year'].values[ind]\n",
    "ax.set_xticks(model_data.index[ind])\n",
    "ax.set_xticklabels(labels, fontdict=None, minor=False, visible=True, size='large')\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### difference between corrected and raw model output\n",
    "f, ax = plt.subplots(figsize=[15,6])\n",
    "ax.plot(model_data.index, model_data['Corrected_Rain']-model_data['Total_Rain'])\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('BC difference (mm/day)')\n",
    "\n",
    "### label xaxis with years\n",
    "ind = model_data['doy'].values == 1\n",
    "labels = model_data['year'].values[ind]\n",
    "ax.set_xticks(model_data.index[ind])\n",
    "ax.set_xticklabels(labels, fontdict=None, minor=False, visible=True, size='large')\n",
    "print('')"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}